Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This Non-Final rejection is in reply to the application filed 4/8/2025.
Claims 1-20 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation “the first visualization” and “the second visualization. There is insufficient antecedent basis for each of these limitations in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-7 are directed to a process (an act, or series of acts or steps); claims 8-14 are directed to a system and claims 15-20 are directed to a non-transitory computer readable storage medium. Thus claims 1-20 fall within one of the four statutory categories.
Step 2A-Prong 1:
Claim 1 recites in part, “…training with training data, a machine learning model to receive as input a plurality of attributes associated with an end-user of an online service and to generate as output a score for each attribute, the score for an attribute indicating the predictive power of the attribute when used as a term of a query for use in selecting candidate job postings to recommend to the end-user, the training data comprising a plurality of attributes i) obtained for an end-user and job posting pair for which the end-user has previously applied for a job, associated with the job posting, and ii) having at least "k" attributes for the end-user matching corresponding attributes of the job posting; subsequent to training the machine learning model: receiving a request to generate a plurality of job posting recommendations for a first end-user of the online service; responsive to receiving the request, obtaining a plurality of attributes associated with the first end-user; providing the plurality of attributes associated with the first end-user as input to a query rewriter, the query rewriter including the trained machine learning model; applying, by the query rewriter, a loss function to the trained machine learning model to optimize the trained machine learning model to generate the score for the plurality of attributes based on a similarity that two or more attributes to be shared in common between the first end-user and one of the plurality of job postings; generating, by the trained machine learning model, for each attribute the score indicating the predictive power of the attribute when used as a term of a query for use in selecting candidate job postings to recommend to the end-user; deriving using the query rewriter a query for fetching candidate job postings, the query i) including a term for each attribute in the plurality of attributes for which the machine learning model generated a corresponding score that exceeds a predetermined threshold, and ii) when executed against a plurality of job postings, fetches as candidate job postings those job postings in the plurality of job postings that have at least "k" attributes matching attributes expressed in the terms of the query; executing the query to fetch a plurality of candidate job postings; processing the plurality of candidate job postings to derive a ranking score for each job posting; and based at least in part on the ranking scores of the plurality of candidate job postings, selecting a subset of the plurality of job postings for presentation as recommendations to the first end-user.”
The underlined limitations above demonstrate independent claim 1 is directed toward the abstract idea for receiving and matching attributes of a user to a query, generating corresponding attribute scores and presenting a subset of ranked job postings as recommendations to a user based on a similarity between a user and a job posting having two or more common attributes in a computing environment.
Applicant’s specification discusses a recommendation system for online job postings with a candidate selection technique using machine learning to rank and select attributes of a user to be used with a query for selecting the candidate job postings. The specification also discloses ranking attributes of an end-user so that a query can be generated to return relevant content items (job postings) for recommendation. (¶1- ¶4, ¶14). Claim 1 is considered an abstract idea because the (underlined) limitations as claimed, pertains to certain methods of organizing human activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) whereby attributes of a user are provided and matched to a query, a loss function is applied to generate a corresponding score for the plurality of attributes based on a similarity, and a subset of job postings are presented to a user as recommendations based on the ranking scores of the job postings, which is directed to managing interactions between people including following rules or instructions. With the exception of generic computing components, the limitations are merely using computing components as a tool to perform the abstract idea. The limitations are also directed to mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) since the claim limitations require applying by a query writer a loss function to generate a corresponding score for the plurality of attributes based on a similarity between a user and a job posting having two or more common attributes, and deriving a ranking score for each job posting; hence directed to mathematical concepts. Therefore, the claim recites an abstract idea--see MPEP 2106.04(II).
Step 2A-Prong 2: This judicial exception is not integrated into a practical application because the additional elements “a machine learning model, ””query rewriter”, “loss function” [claim 1]; “processor”, “computer-readable instructions”, “memory storage device storing computer-readable instructions”, “system” [claim 8]; “non-transitory computer-readable storage medium”, “processing circuit” [claim 15] for (receiving, generating, obtaining, providing, deriving, executing, processing and selecting) data gathering and analysis and merely to provide instructions for managing information, and to implement the abstract idea recited above utilizing “a machine learning model, ””query rewriter”, “loss function” [claim 1]; “processor”, “computer-readable instructions”, “memory storage device storing computer-readable instructions”, “system” [claim 8]; “non-transitory computer-readable storage medium”, “processing circuit” [claim 15] as a tool to perform the abstract idea, and generally links the abstract idea to a particular technological environment. See MPEP 2106.05 (f-h).
Independent claim 1 fails to operate “a machine learning model, ””query rewriter”, “loss function” [claim 1]; “processor”, “computer-readable instructions”, “memory storage device storing computer-readable instructions”, “system” [claim 8]; “non-transitory computer-readable storage medium”, “processing circuit” [claim 15] (which is merely a nominal recitation of a standard computer technology, database and hardware/software components) in any exceptional manner, and there is no evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself, or improvement in any specific computer technology other than utilizing ordinary computational tools to automate and perform the abstract idea for receiving and matching attributes of a user to a query, generating corresponding attribute scores and presenting a subset of ranked job postings as recommendations to a user based on a similarity between a user and a job posting having two or more common attributes in a computing environment —see MPEP 2106.05(a). Accordingly, applicant has not shown an improvement or practical application under the guidance of MPEP section 2106.04(d) or 2106.05(a). Applicant’s limitations as recited above do nothing more than supplement the abstract idea using generic computer components performing generic computer functions (receiving, generating, obtaining, providing, applying, deriving, executing, processing and selecting) such that it amounts to no more than mere instruction to apply the exception using a generic computer component-see MPEP 2106.05(f) and linking the use of the judicial exception to a particular technological environment as discussed in MPEP 2106.05(h). Independent claims 8 and 15 recite substantially similar limitations as independent claim 1 and therefore also recite the same abstract idea.
Dependent claims 2-7, 9-14 and 16-20 fail to cure the deficiencies of the above noted independent claim from which they depend and are therefore rejected under the same grounds. The dependent claims further recite the abstract idea without imposing any meaningful limits on practicing the abstract idea. Dependent claims 2, 9 and 16 recite in part, “wherein the loss function comprises”; claims 3 and 10 recite in part, “deriving the query as a ..”; claims 4 and 11 recite in part, “assigning to the query a query threshold score”; claims 5, 12 and 19 recite in part, “wherein the threshold score is”; claims 6, 13 and 20 recite in part, “wherein the value of k is set to”; claims 7 and 14 recite in part, “wherein obtaining”; claim 17 recites in part, “ wherein the query for fetching candidate job postings comprises”; claim 18 recites in part, “wherein the processing circuit is further configured to”, which is still directed toward the abstract idea identified previously and are no more than mere instructions to apply the exception using a computer or with computing components. Therefore, the abstract idea fails to integrate into any practical application. Thus, under Step 2A-Prong Two the claims are directed to an abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above, with respect to integration of the abstract idea into a practical application, the additional element “a machine learning model”, “query rewriter”, “loss function” [claim 1]; “processor”, “computer-readable instructions”, “memory storage device storing computer-readable instructions”, “system” [claim 8]; “non-transitory computer-readable storage medium”, “processing circuit” [claim 15] amounts to no more than mere instructions to apply the exception using a generic computer component and linking the use of the judicial exception to a computing environment which does not integrate a judicial exception into a practical application nor provide an inventive concept (significantly more than the abstract idea). In this case, the “a machine learning model, “query rewriter”, “loss function” [claim 1]; “processor”, “computer-readable instructions”, “memory storage device storing computer-readable instructions”, “system” [claim 8]; “non-transitory computer-readable storage medium”, “processing circuit” [claim 15] are generically used to further process and store received data- see ¶51: computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically, FIG. 7 shows a diagrammatic representation of the machine 900 in the example form of a computer system, within which instructions 916 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 900 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 916 may cause the machine 900 to execute any one of the methods or algorithms described herein… The machine 900 may comprise, but not be limited to, a server computer, a client computer, a PC, a tablet computer, a laptop computer… “.
Further applicant’s query re-writer, applying a loss function to the machine learning model is merely used as a tool to further process received data based on rules logic- see applicant’s disclosure, ¶9: “a machine learning model for use in ranking attributes to be used in a query of a candidate selection technique”; ¶14: “machine learning model is optimized using a loss function that expresses similarity between an end-user and a content item as a match between at least "kc" attributes”; ¶17: “a pre-trained machine learned model receives as input the attributes of the end-user, and then outputs a score for each attribute”; ¶24: “after the machine learned model 310 has generated scores for each of the attributes of the end-user, the query processor 312 of the query rewriter 308 derives a query 314 including a term for each attribute having a score that exceeds a predetermined threshold. The query 314 is derived as a weighted-OR query, with individual weighting factors being assigned to each term, and a threshold score for the query”; ¶29: “the query rewriter selects those attributes that have scores exceeding some predetermined threshold, for use in a weighted OR query for selecting the candidate content items (e.g., job postings)”, and amounts to no more than applying the judicial exception using generic computing components, and linking the use of the judicial exception to a computing environment. Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, even when considered as a whole, the claims do not transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea for receiving and matching attributes of a user to a query, generating corresponding attribute scores and presenting a subset of ranked job postings as recommendations to a user based on a similarity between a user and a job posting having two or more common attributes in a computing environment. Therefore, claims 1-20 are directed to non-statutory subject matter and are rejected under 35 USC 101. See 2019 PEG and MPEP 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Grady Smith et al., US Patent Application Publication No US 2017/0236081 A1.
With respect to claim 1,
Grady Smith discloses,
A method for helping to assess an employment level or a level of progression of an employee within an organization, the method comprising (¶6: “Embodiments of the invention may be used to access, track, and analyze various types of organizational interactions (primarily interactions that involve participation or communication) to [0007] (1) develop a visual representation of the structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making within the organization; and [0008] (2) use the results of evaluating and analyzing interaction data and/or the visual representation to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, or employee development”; ¶9: “provides a more accurate and realistic view of how information and communications move within an organization. It may also be used to provide insight into the strength of certain relationships, the degree of involvement of certain people or groups in implementing policies or in making decisions, or the relative importance of certain communication channels (formal or informal)”; ¶11: “evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”)
receiving a technical skill structure of an organization, wherein the technical skill structure comprises different employment levels and/or different levels of progression in the organization, (¶10: “typically based on the management or reporting hierarchy, with employees or groups (represented by nodes) being connected by reporting lines to create a tree-like representation of the organization, with the nodes at one level being placed into a lower or higher hierarchy than the nodes at an adjacent level. While such types of organizational representations/structures provide an indication of reporting lines and/or decision-making authority”; ¶11: “it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; ¶67: “The Hierarchy of roles within the organization; ¶68: “Department/Group Structures (the identification and purpose/task/goals of such structures)”; ¶92: “focusing efforts on the people most likely to be in possession of needed information or skill sets, instead of moving through a conventional organizational chart in an effort to find the correct person for a task. This results from using the inventive system and methods to more efficiently and accurately identify those people and interactions that represent greater knowledge or involvement with certain information or tasks”; ¶202: “One or more of the visualizations (such as FIG. 6(a) or 6(b)) may be rendered as a hub-spoke model, where the employee at the hub is the employee of current focus and the thickness of spokes represented the amount of influence/interaction with other employees along the circumference”; ¶203: “Note that there are a number of layout options or factors that can be emphasized, and that are available to a user when viewing an interaction-based organization chart that is generated by an embodiment of the inventive system and methods. As examples, these options may include: [0204] Show reporting lines: The traditional lines of the reporting structure are added to the graph (this would be an overlay of FIG. 5 on another representation, with the lines of FIG. 5 perhaps displayed in a different color, etc.); [0206] Hierarchy-biased view: An algorithm arranges the organization members such that those higher up in the organization structure appear higher in the chart, thereby maintaining the ‘top-down’ view of the organization; [0207] User-focused view: A specific member/employee is defined as central to the chart (typically the current user), and the organization is arranged around/below them, thereby more readily indicating key influencers for that member; [0208] Influencer-weighted view: Members with heavier/thicker Lines of Influence, either with the user of focus (if available), or in the organization in total, would appear larger/bolder in the chart; [0209] Minimum Threshold: Lines of Influence below a certain threshold may be discarded and not represented (i.e., they do not influence the illustration/layout), or are utilized but not shown in order to reduce clutter in the chart”)
wherein the organization comprises a plurality of employees that are assigned to the different employment levels and/or the different levels of progression (¶11: “it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; ¶65: “A process or method for comparing a conventional or current organizational chart or arrangement (such as one based on role or reporting structure) with the visualization/representation of an organization's information and process flows (as appropriately filtered or analyzed) in order to identify differences between the actual (or most effective) and the expected (or desired) flow of information, interactions, or decision making responsibility within the organization. This may be of assistance in identifying a preferred reporting arrangement, a more effective information distribution channel, a possible explanation for why a policy was or was not successfully implemented”; ¶66: “The employee interaction related data or information that may be accessed and processed as part of implementing an embodiment of the inventive system and methods may include (but is not required to include, nor are other sources or types of data excluded from consideration) information regarding: [0067] The Hierarchy of roles within the organization; [0068] Department/Group Structures (the identification and purpose/task/goals of such structures); [0069] Performance Metrics (individual and group)’; ¶77: “Degrees of separation of a person from a specific level or levels of management”; ¶203: “Show reporting lines: The traditional lines of the reporting structure are added to the graph (this would be an overlay of FIG. 5 on another representation, with the lines of FIG. 5 perhaps displayed in a different color, etc.); [0205] Show influences: The various Lines of Influence are shown on the graph, color-coded by type (e.g., mode of communication), allowing for analysis of how the organization communicates; [0206] Hierarchy-biased view: An algorithm arranges the organization members such that those higher up in the organization structure appear higher in the chart, thereby maintaining the ‘top-down’ view of the organization; [0207] User-focused view: A specific member/employee is defined as central to the chart (typically the current user), and the organization is arranged around/below them, thereby more readily indicating key influencers for that member; [0208] Influencer-weighted view: Members with heavier/thicker Lines of Influence, either with the user of focus (if available), or in the organization in total, would appear larger/bolder in the chart; [0209] Minimum Threshold: Lines of Influence below a certain threshold may be discarded and not represented (i.e., they do not influence the illustration/layout), or are utilized but not shown in order to reduce clutter in the chart”)
receiving interaction data representing interactions between the employees, wherein the interactions occur while the employees perform one or more duties for the organization; (¶14: “the invention is directed to a method for assisting in making organizational decisions, where the method includes: [0015] identifying one or more sources of information regarding interactions between a first employee and one or more other employees of an organization; [0016] accessing the one or more sources of information and identifying data for further analysis and evaluation; [0017] processing at least some of the identified data to determine one or more characteristics of the interactions between the first employee and the one or more other employees”; ¶18: “applying a data analysis, modeling, or decision process to the determined characteristics to identify an employee or employees that are most likely to have, or be associated with, a desired characteristic or would be expected to be in possession of a specific item of information, wherein such an employee or employees are those that either attended a meeting where certain projects or tasks were discussed, interacted with one or more persons who attended the meetings, or was made aware of aspects of a project or task of interest to the user”; ¶66: “The employee interaction related data or information that may be accessed and processed as part of implementing an embodiment of the inventive system and methods may include (but is not required to include, nor are other sources or types of data excluded from consideration) information regarding:¶70: “Attendance rate, types of interactions participated in, types declined by a person or group member”; ¶75: “Company events invited to, events attended, and the nature of an event”)
determining an interaction intensity between the employees during a first time period based upon the interaction data (Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶47: “access, track, and analyze various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to [0048] (1) develop a visual representation of the operational or functional (as opposed to strictly title or hierarchical position based) structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization; ¶61: “ A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc.; [0063] A process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information)”; ¶63: “process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow. An example of such a visualization is a network model of employees/nodes and one or more types of connecting attributes between the nodes. These connecting attributes may include aspects such as communication/information flows, relative involvement in a project, the number of contacts/interactions, the relative value of an interaction to a specified decision process, an indication of regular contacts, contacts in a specified group, reporting relationships, position in a role-based hierarchy, etc.”; ¶148-¶155; ¶148: “layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶149: “a user may identify keywords, topics, categories, specific events, date ranges, employee IDs, etc, that are of interest (as suggested by step or stage 402). These may be used by the system to narrow down the set of all communications/interaction data to those items that are expected to be most relevant to identifying/determining the information flow of interest… the user or system (by default) may specify the potential sources of data or information of interest. These may include email, text messages, phone calls, meeting invitations, calendaring related data, HR records, etc. (as suggested by step or stage 404)”; ¶150: “, the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization, as suggested by step or stage 408. This may be done with the aid of a constructed visualization (network model, org chart, “tree” model, etc.) of the organization, as modified by one or more filters or weighting mechanisms. Such a comparison/display may provide insight into the flow of information; the flow of information over time, the development of consensus, the implementation of a policy, the formation of a decision, etc”; ¶155: “The multiple types of interaction data may be accessed and processed (using suitable filters, decision processes, thresholds, criteria, rules, etc.) and provided as inputs to one or more analytical processes that can evaluate the data and produce a model of the interactions and relationships that the data represents. These analytical processes may include machine learning techniques, collaborative or other types of filtering, neural networks, network modeling, optimization, pattern recognition, statistical modeling, etc.”; ¶180: Time & Date of interaction to monitor interactions over time, and if desired, give more weight to recent interactions, to filter interactions to use in analysis based on when they occurred;“ ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions, such as the ones mentioned above (shared meetings, email correspondence, formal recognition, mentions in version control, chat room mentions, etc.). In some embodiments, this data is then considered with the existing static information about the reporting hierarchy”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc.”; ¶188: “The following approach can be used to calculate a Member-to-Member Interaction Influence Factor (IIF): [0189] The IIF is the sum of all weights for a set of interactions between one member and another”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links. Note that in some embodiments, a more insightful/useful organizational chart can be constructed by optimizing the layout of the organization to cause the strongest lines of influence to be the shortest lines on the new layout”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization. This may be used to identify “choke points” in a process, to identify those that influence a decision maker, to determine actual information flows and to modify them to be more effective, etc”)
Applicant’s disclosure teaches at ¶24: “The system and method may first determine a calculation rule to represent interactions between an employee with the rest of the organization. This calculation may take into consideration the digital activity of the employee related to the day-to-day collaboration with other employees. As an example, the intensity of the collaboration with other employees may be represented by the distance”.
Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner interprets at least the various techniques for generating organizational charts and/or visualizations of different types of interactions between members of an organization for identifying influence strength/factors, flow of information, and calculating distance as teaching applicant’s interaction intensity.
Although Grady Smith does not distinctly describe verbatim the wording of applicant’s claim limitation (determining an interaction intensity between the employees during a first time period based upon the interaction data). Grady Smith teaches a method/system for an interaction-weighted visualization of an organization or group, with the relationships between members being based on, or weighted by, the amount, type, subject matter, degree, or significance of interactions between them and the flow of communications between members. Grady Smith discloses at Fig 6a, an “Interaction Based Chart”- Lines represent interactions between people; and techniques for tracking and analyzing various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to develop a visual representation of the operational or functional structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization. Grady Smith further discloses that the visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship which may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow including but not limited to a particular date range for interaction analysis, additional weight can be assigned to more recent interactions and that different types of interactions may be weighted differently, Grady Smith also teaches a process or method for implementing one or more of statistical, machine learning based, rule based, filtering, or other form of data analysis on the interaction and participant information, and for assisting in making decisions relevant to an organization (e.g., generating recommendations, generating probabilities of success, assigning a “cost” or “value” to a possible decision, etc.) based on that data analysis. Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the time-based order of interactions techniques for generating an interaction-weighted visualization of an organization with the relationships between members as taught by Grady Smith to track, and analyze various types/characteristics of organizational interactions between members based on or weighted by the amount, type, degree, or significance of interactions between them and the flow of communications between members, etc to show how the interaction(s) evolve over time. The known time progression analysis techniques for generating an interaction-weighted visualization of an organization with the relationships between members of Grady Smith would have predictably resulted in providing time progression analysis insight into the strength of certain relationships, the degree of involvement of certain people or groups in implementing policies or in making decisions, and/or the relative importance of certain communication channels to assist in making decisions for purposes of organizational planning, employee or project management, hence, creating a more efficient flow of communications, task assignment, and/or employee development (Fig. 4, Fig 5, FIG. 6(a) through FIG. 6(i); ¶6-¶11, ¶14-¶19, ¶41, ¶48, ¶65-¶68, ¶155, ¶182, ¶186-¶188, ¶197, ¶211, ¶259, ¶265, ¶273).
With respect to claim 2,
Grady Smith discloses all of the above limitations, Grady Smith further discloses,
wherein the employees each have a technical skill level, and wherein at least some of the employees are assigned to one of the employment levels and/or the levels of progression based upon their respective technical skill levels (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge”; ¶65: “A process or method for comparing a conventional or current organizational chart or arrangement (such as one based on role or reporting structure) with the visualization/representation of an organization's information and process flows (as appropriately filtered or analyzed) in order to identify differences between the actual (or most effective) and the expected (or desired) flow of information, interactions, or decision making responsibility within the organization”; [0067] The Hierarchy of roles within the organization; [0068] Department/Group Structures (the identification and purpose/task/goals of such structures); ¶77: “Degrees of separation of a person from a specific level or levels of management”; Fig 6a, Fig 6b, ¶202: “it may be helpful to know the most effective influencers on a project team or in a group in order to conduct a meeting or engage in communications with the right person or set of people. This information can also help in determining the advancement/promotion opportunities for the employee at the hub”; ¶203: “Note that there are a number of layout options or factors that can be emphasized, and that are available to a user when viewing an interaction-based organization chart that is generated by an embodiment of the inventive system and methods. As examples, these options may include: [0204] Show reporting lines: The traditional lines of the reporting structure are added to the graph (this would be an overlay of FIG. 5 on another representation, with the lines of FIG. 5 perhaps displayed in a different color, etc.); [0206] Hierarchy-biased view: An algorithm arranges the organization members such that those higher up in the organization structure appear higher in the chart, thereby maintaining the ‘top-down’ view of the organization; [0207] User-focused view: A specific member/employee is defined as central to the chart (typically the current user), and the organization is arranged around/below them, thereby more readily indicating key influencers for that member; [0208] Influencer-weighted view: Members with heavier/thicker Lines of Influence, either with the user of focus (if available), or in the organization in total, would appear larger/bolder in the chart; [0209] Minimum Threshold: Lines of Influence below a certain threshold may be discarded and not represented (i.e., they do not influence the illustration/layout), or are utilized but not shown in order to reduce clutter in the chart”)
With respect to claim 3,
Grady Smith discloses all of the above limitations, Grady Smith further discloses,
wherein the employees comprise at least a first employee and a second employee, and wherein the second employee is assigned to a different employment level or a different level of progression than the first employee (Fig 5, ¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge”; ¶65: “A process or method for comparing a conventional or current organizational chart or arrangement (such as one based on role or reporting structure) with the visualization/representation of an organization's information and process flows (as appropriately filtered or analyzed) in order to identify differences between the actual (or most effective) and the expected (or desired) flow of information, interactions, or decision making responsibility within the organization. This may be of assistance in identifying a preferred reporting arrangement, a more effective information distribution channel, a possible explanation for why a policy was or was not successfully implemented, etc.”; ¶66: “The Hierarchy of roles within the organization; [0068] Department/Group Structures (the identification and purpose/task/goals of such structures)”; [0067] The Hierarchy of roles within the organization; [0068] Department/Group Structures (the identification and purpose/task/goals of such structures); ¶77: “Degrees of separation of a person from a specific level or levels of management; ¶221: “allows an organization to proactively identify candidates for promotion, and proactively identify potential succession gaps”; ¶249: “Interaction analysis can be used to assist in making hiring or promotion decisions)
With respect to claim 4,
Grady Smith discloses all of the above limitations, Grady Smith further discloses,
wherein the interactions comprise in-person interactions and/or digital interactions, wherein the in-person interactions comprise meetings (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge”; ¶12: “By tracking certain attributes of interactions (e.g., the topic of a meeting, the time/date, those invited, those choosing to attend, other interactions of those invited, and any related records), and applying suitable filters to an interaction-based organizational structure, a set of maps or models of the information or process flow within the organization can be created”; ¶18: “applying a data analysis, modeling, or decision process to the determined characteristics to identify an employee or employees that are most likely to have, or be associated with, a desired characteristic or would be expected to be in possession of a specific item of information, wherein such an employee or employees are those that either attended a meeting where certain projects or tasks were discussed, interacted with one or more persons who attended the meetings, or was made aware of aspects of a project or task of interest to the user”; ¶70: “Attendance rate, types of interactions participated in”; ¶177: “Interaction Type. This can be email/message, event, recognition, record notes, version control, communications/mentions in other systems, chat rooms, etc”)
wherein the digital interactions comprise emails and/or typed chats (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge”; ¶318: External systems for chat (i.e., Hipchat, Slack), for issue tracking (JIRA), or for version control (GitHub, BitBucket) typically have an API that would be available to a suitably configured data acquisition engine. Collecting data regarding emails may be accomplished by using a plugin on a mail server (e.g., some type of modification to an email header to redirect messages to a processing module)”; ¶177: “Interaction Type. This can be email/message, event, recognition, record notes, version control, communications/mentions in other systems, chat rooms, etc;”)
With respect to claim 5,
Grady Smith discloses all of the above limitations, Grady Smith further discloses,
wherein the interaction intensity between the first employee and the second employee during the first time period is represented by a first distance between the first and second employees (Fig 6(a)-Fig 6(i); Abstract: “a tree structure with nodes representing employees being connected by branches. The size, color, or number of branches may indicate characteristics of the interactions between the connected nodes (e.g., the frequency, importance, or topic of the interactions, etc.)”; ¶12: “each individual interaction is part of a larger process flow for the organization. By tracking certain attributes of interactions (e.g., the topic of a meeting, the time/date, those invited, those choosing to attend, other interactions of those invited, and any related records), and applying suitable filters to an interaction-based organizational structure, a set of maps or models of the information or process flow within the organization can be created”; ¶55: “a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.)”; ¶61: “storage sub-system configured for use in acquiring and processing interaction and participant information for an organization… email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶148: “layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure… this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶174: “Interactions instances are related to a user/participant and may include one or more of the following information/details”; ¶180: “email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions, such as the ones mentioned above (shared meetings, email correspondence, formal recognition, mentions in version control, chat room mentions, etc.). In some embodiments, this data is then considered with the existing static information about the reporting hierarchy”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc.”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links. Note that in some embodiments, a more insightful/useful organizational chart can be constructed by optimizing the layout of the organization to cause the strongest lines of influence to be the shortest lines on the new layout”; ¶201: graph optimization algorithms may be used to minimize an overall metric (such as “value”, “cost”, weighted distance, etc.) of the graph based on the value of the sum of the weight multiplied by the length of all interaction lines, while maintaining non-collision between the individuals on the graph. See, for example, FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶215: “Member Interaction Profile” can be created for each employee/node/member, which includes their IIF metric(s) as determined based on each possible pairing with another member in the organization; this can be represented in the form of a multi-factor vector. A member's “Total Influence Factor” may be represented by the magnitude of this vector. The cosine similarity between two Interaction Profile vectors is a measure of the similarity of the interaction histories of any two members with regards to their interactions with other members of the organization (note that the process may subtract out the components representing interactions between the two members being compared)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “ FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”)
wherein the interaction intensity between the first and second employees during a second time period is represented by a second distance between the first and second employees. (Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶47: “access, track, and analyze various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to [0048] (1) develop a visual representation of the operational or functional (as opposed to strictly title or hierarchical position based) structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization; Fig 6c, Fig 6i; ¶55: “a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.)”; ¶62: “A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶148-¶155; ¶148: “The layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶201: “graph optimization algorithms may be used to minimize an overall metric (such as “value”, “cost”, weighted distance, etc.) of the graph based on the value of the sum of the weight multiplied by the length of all interaction lines, while maintaining non-collision between the individuals on the graph. See, for example, FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “ FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization. This may be used to identify “choke points” in a process, to identify those that influence a decision maker, to determine actual information flows and to modify them to be more effective, etc”)
Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Grady Smith discloses graph optimization algorithms for illustrating interactions between employees, and that given two interaction profiles of employees, using data processing and interaction vector analysis techniques the distance between each profile can be calculated. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the graph optimization algorithms, interaction vector analysis and/or data processing techniques for generating an interaction-weighted visualization of an organization as taught by Grady Smith to calculate the distance between profiles. The known graph optimization algorithms, time progression analysis, interaction vector analysis and data processing techniques for generating an interaction-weighted visualization of an organization to calculate distance between individuals/employees as taught by Grady Smith would have predictably resulted in generating an effective measure of the similarity and/or significance of interactions between members (Figs 6(a)- 6(i); ¶12, ¶55, ¶61, ¶148, ¶155, ¶197, ¶201, ¶211, ¶215, ¶222, ¶259, ¶265, ¶273).
With respect to claim 9,
Grady Smith discloses all of the above limitations, Grady Smith further discloses,
further comprising generating a visualization of the interaction intensity during the first time period (Fig 6(a)- Fig 6(i); ¶61: “A process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow”; ¶63: “The indication may be based on a metric related to interactions/information flow. An example of such a visualization is a network model of employees/nodes and one or more types of connecting attributes between the nodes. These connecting attributes may include aspects such as communication/information flows, relative involvement in a project, the number of contacts/interactions, the relative value of an interaction to a specified decision process, an indication of regular contacts, contacts in a specified group, reporting relationships, position in a role-based hierarchy, etc”; ¶184-¶197; ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc”; ¶192: “Adjustment for date of interaction to give more weight to more recent interactions”; ¶196; “The categories of interactions to be considered and the type and participation weightings may be input to the system and adjusted”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links”; ¶201: “FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶214: “The inventive system and methods may be used to generate a representation, and in some cases a characterization, of the interactions between multiple employees/nodes in an organization. As part of generating this representation a method for calculating a metric, termed a “Member-to-Member Interaction Influence Factor (IIF)”; ¶215: “Member Interaction Profile” can be created for each employee/node/member, which includes their IIF metric(s) as determined based on each possible pairing with another member in the organization; this can be represented in the form of a multi-factor vector. A member's “Total Influence Factor” may be represented by the magnitude of this vector. The cosine similarity between two Interaction Profile vectors is a measure of the similarity of the interaction histories of any two members with regards to their interactions with other members of the organization (note that the process may subtract out the components representing interactions between the two members being compared). Note also that other forms of metrics may be suitable, depending upon the type of data and the use case (such as ranking by most frequent or common interactions, filtering or application of a threshold value, etc.))
With respect to claim 10,
Grady Smith discloses all of the above limitations, Grady Smith further discloses,
further comprising performing an action in response to the interaction intensity, wherein the action comprises updating the technical skill structure to promote or demote the first employee to one of the different employment levels or the different levels of progression (¶202-¶211; ¶227; ¶231; ¶Fig 6a, Fig 6b, ¶202: “it may be helpful to know the most effective influencers on a project team or in a group in order to conduct a meeting or engage in communications with the right person or set of people. This information can also help in determining the advancement/promotion opportunities for the employee at the hub”; ¶220: “Interaction Analysis for Succession Planning allows an organization to compare the interaction profiles of an employee (Employee A) in a particular position with the profile of another employee (Employee B). This produces a new dimension/metric with which to evaluate successors to a role if Employee A were to vacate their position, and can provide a higher degree of confidence in Employee B's likelihood of being successful in a role; [0221] Note that because this does not rely on subjective measures, the analysis/evaluation can be performed continuously for multiple combinations of Employee A and Employee B. This allows an organization to proactively identify candidates for promotion, and proactively identify potential succession gaps; [0222] Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶249: “Interaction analysis can be used to assist in making hiring or promotion decisions”)
Claims 6-8 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Grady Smith in view of Kim et al., US Patent Application Publication No US 2020/0042928 A1.
With respect to claim 6,
Grady Smith discloses all of the above limitations, Grady Smith does not distinctly describe the following limitations, but Kim however as shown discloses,
wherein the first distance is based upon a number of the emails sent from the first employee to the second employee, a number of the typed chats sent from the first employee to the second employee, a number of hours of the meetings where the first and second employees are both present, or a combination thereof (Fig 1, Fig 3, Figs 6- 12; Abstract: “Metadata is extracted from the captured communication data and the extracted communication metadata is analyzed by (i) calculating time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise adjacency matrix based on the attention time each participant spent communicating with each other participant”; ¶7: metadata may be extracted from the communication data and aggregated into a communications file and associated or linked with respective users (i.e., participants) in a participant file. This results in (1) a participant file which lists all the users whose communication and interaction data was measured or extracted, and (2) a communications file comprising all the extracted communication metadata organized by participant“; ¶27: “FIG. 11 depicts an example visualization illustrating the amount of time different teams of participants spend communicating on email, chats, meetings and calls, according to embodiments of the present disclosure”; ¶52: “ the Participant Analytics Platform 250 analyzes the extracted communication metadata and collected sensor data in order to develop an objective model of the communication distribution in the organization. In some embodiments, the analysis may include: (i) calculating amount of time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise directional weighted adjacency matrix (stored in computer memory as a logical data structure) based on the attention time each participant spent communicating with each other participant and other correspondents that may not part of the organization”; ¶53: “ the Participant Analytics Platform 250 may use the pairwise directional weighted adjacency matrix to create and display a visualization illustrating the communication distribution of the participants (or groups of participants) throughout the organization”; ¶60: “According to some embodiments, the DGGT 225 may utilize application programing interface (API) calls to extract the various metadata fields from the one or more communication servers (230 and 235) capturing the underlying digital communication data”; ¶68: “FIG. 6 is a communication and activity graph for an example workday for a given participant. In some embodiments, the Participant Analytics Platform 250 may analyze the metadata extracted from the digital communication data and the sensor data to create the graphs illustrated in FIG. 6”; ¶75: “the amount of time spent by a participant on an instant message chat session is determined as a function of number of involved messages, and in particular by the total number of messages multiplied by an amount of time estimated for each message”; Fig 7, ¶77: “the Participant Analytics Platform 250 determines a single prioritized activity and interaction time block (or time series represented in the system 250 and method 700) 705 for the participant according to a defined order of priority for the concurrent activities and interactions of the participant. In some embodiments, the order of priority of the activities may be (1) meetings, (2) calls, (3) emails, and (4) other communication types”; ¶78: “FIG. 8 illustrates an example method 800 of building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating with each other participant, according to some embodiments”; ¶87: “the Participant Analytics Platform 250 may determine various metrics that are objectively representative of human interaction and activity within an organization. For example, the Participant Analytics Platform 250 may create a Participant Pairwise Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization”; ¶93: “each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums”; ¶95: “Below is an example code for an adjacency matrix generated based only on emails for an organization that has 11 people. The nodes are the active participants and the edges represent the communications in the format (from, to, attention minutes). According to some embodiments, the Participant Analytics Platform 250 converts the analytics payload into an API payload and then to a dashboard visualization such as FIG. 9. ¶96: “FIG. 9 depicts an example visualization of communication distribution between participants throughout an organization according to embodiments of the present disclosure. In some embodiments, the visualization depicted in FIG. 9 may be generated by the collaboration and delivery module of the Participant Analytics Platform 250. The nodes in the edge weighted graph represent participants and the edges represent communications and interactions between the participants. The larger the size of the node the greater the amount communication and interactions the respective participant had with other participants. Further, the distance between any two nodes represents that amount of communication and interaction between those respective participants; the shorter the distance the more communication and interaction”; ¶101: “the Participant Analytics Platform 250 may determine the total time that a participant spends on each communication medium over a given period of time (e.g., day, week, month, etc.) In some embodiments, the Participant Analytics Platform 250 may calculate the amount of time spent communicating on each medium”; ¶102: “ the analysis returns a single dictionary that has info on the amounts of time spent on all communication media (time spent on email, time spent on chat, time spent on meetings, time spent on calls (not meetings), time spent on face-to-face interactions, and estimated time not involved in communication during normal work hours) for each of during work, during after-work, overall”; ¶110: the Participant Analytics Platform 250 calculates the number of attention minutes each participant spends with managers, non-managers, and external persons, as well as whether the attention minutes were spent during work hours or after work hours. For example, the following metrics may be determined”)
Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Grady Smith discloses graph optimization algorithms for illustrating interactions between employees, and that given two interaction profiles of employees, using data processing and interaction vector analysis techniques the distance between each profile can be calculated.
Although Kim does not distinctly describe verbatim the wording of applicant’s claim limitation (first distance is based upon a number of the emails sent from the first employee to the second employee, a number of the typed chats sent from the first employee to the second employee, and a number of hours of the meetings where the first and second employees are both present), Kim teaches a Participant Analytics Platform for determining various metrics that are objectively representative of human interaction and activity within an organization via a Participant Pairwise (weighted) Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization. Kim also teaches that each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums and providing a visualization of communication distribution between participants throughout an organization. The visualization of communication distribution between participants may be generated via the collaboration and delivery module of the Participant Analysis Platform via a weighted graph representing communications, interactions and distance between participants. Kim also discloses that the Participant Analytics Platform can determine time series activity and interactions of a participant for concurrent activities as well as building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating (meetings, calls, emails, and other communication types (chat, face-to face interactions) with each other participant. Applicant’s disclosure generically teaches, ¶37: “The coefficients may provide more or less weight to one channel over the other ones”. Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner contends that the techniques for building a weighted adjacency matrix based on a value(s) as taught by Kim as teaching applicant’s number(s) and is a form of linearization. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim to identify the distance between participants and the communications and interactions the respective participant had with other participants based on a specific weight for the associated communication medium (email, meetings, calls, chats, face-to-face interaction) whereby a visualization of communication distribution between participants can be represented via a dashboard. The known techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim would have predictably resulted in determining various metrics that are objectively representative of interaction and communication activity within an organization including distance between respective participants for the purposes of identifying the amount of communications and interactions a respective participant had with other participants via a weighted graph. Therefore, one of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to combine the method/system for interaction-weighted visualization of Grady Smith with the techniques for measuring communication distribution throughout an organization as taught by Kim since it allows for improving data processing and analysis of interaction metadata to accurately and objectively measure and represent communication distribution (via distance) throughout an organization based participants’ interactions via a weighted graph (Fig 1, Fig 3, Figs 6- Fig 12; ¶7, ¶27, ¶53, ¶60, ¶75, ¶77, ¶78, ¶87, ¶93, ¶95, ¶96, ¶101, ¶102, ¶110)
With respect to claim 7,
Grady Smith and Kim disclose all of the above limitations, Grady Smith further discloses,
further comprising comparing the interaction intensity during the first time period to the interaction intensity during the second time period, wherein an action is performed based upon or in response to the comparison (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶47: “access, track, and analyze various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to [0048] (1) develop a visual representation of the operational or functional (as opposed to strictly title or hierarchical position based) structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization; ¶55: “In some organizations or groups, a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.).”; ¶61: “ A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc.; ¶62: “A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶124-¶136; ¶129: Using the outcome of the data analysis, modeling, or decision process (and if desired, by comparing a model of the actual or inferred interactions and/or information flow within an organization to an existing, assumed, or proposed organizational model (such as one based on role, reporting structure, seniority, etc.)), identifying one or more indicators of suggested organizational actions or potential concerns, such as: [0130] resignation of a key employee; [0131] an increased employee churn rate; [0132] a possible reason for a lack of operational effectiveness or efficiency; [0133] factors associated with a successful task or project completion; [0134] indicators of under recognized influencers within the organization; [0135] an employee most likely to have specific information or an understanding of a task or project (which may be valuable in the situation in which the primary contact for that information or task is not available); [0136] potentially more effective communication channels within the organization; or [0137] training or development opportunities for employees that the organization may wish to encourage“; ¶148-¶155; ¶148: “layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶149: “a user may identify keywords, topics, categories, specific events, date ranges, employee IDs, etc, that are of interest (as suggested by step or stage 402). These may be used by the system to narrow down the set of all communications/interaction data to those items that are expected to be most relevant to identifying/determining the information flow of interest… the user or system (by default) may specify the potential sources of data or information of interest. These may include email, text messages, phone calls, meeting invitations, calendaring related data, HR records, etc. (as suggested by step or stage 404)”; ¶150: “The identified/filtered data may then be processed to determine one or more of correlations, associations, or other relationships between the data input to a model or process (such as employees and the related interaction data) and an event or goal of interest (such as a decision being made, a policy being implemented, etc.)… the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization… Such a comparison/display may provide insight into the flow of information; the flow of information over time, the development of consensus, the implementation of a policy, the formation of a decision, etc”; Fig 6a, Fig 6b, ¶155: “The multiple types of interaction data may be accessed and processed (using suitable filters, decision processes, thresholds, criteria, rules, etc.) and provided as inputs to one or more analytical processes that can evaluate the data and produce a model of the interactions and relationships that the data represents. These analytical processes may include machine learning techniques, collaborative or other types of filtering, neural networks, network modeling, optimization, pattern recognition, statistical modeling, etc.”; ¶180: Time & Date of interaction to monitor interactions over time, and if desired, give more weight to recent interactions, to filter interactions to use in analysis based on when they occurred;“ ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc.”; ¶188: “The following approach can be used to calculate a Member-to-Member Interaction Influence Factor (IIF): [0189] The IIF is the sum of all weights for a set of interactions between one member and another”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links”; ¶202: “it may be helpful to know the most effective influencers on a project team or in a group in order to conduct a meeting or engage in communications with the right person or set of people. This information can also help in determining the advancement/promotion opportunities for the employee at the hub”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶221: “allows an organization to proactively identify candidates for promotion, and proactively identify potential succession gaps”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶249: “Interaction analysis can be used to assist in making hiring or promotion decisions”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “ FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc” ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization”; ¶305: “an interaction-based weighting might be applied to a standard organizational metric (such as revenue, profit, head count, etc.) to provide new insights and value into the reasons for (and ways to improve) bottom line financial results; and [0306] 4. The use of real-time business data or metrics may enable a dynamic visualization or representation that indicates what business issues trigger communications, and hence may provide a way to “learn” (e.g., based on machine learning to determine which factors are strongly correlated with a certain type of interaction) what business data combinations may indicate a possible problem before that problem becomes noticed and acted upon”; ¶308: “results of evaluating and analyzing interaction data and/or the visual representation may be used to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, or employee development. As additional examples, the following describe possible situations in which valuable insight(s) can be obtained from use of the inventive system and methods: [0309] Specific information obtained from the inventive interaction—weighted display and/or data analysis may be used to initiate specific organizational programs, tasks, or changes in staffing”)
Applicant’s disclosure teaches at ¶24: “The system and method may first determine a calculation rule to represent interactions between an employee with the rest of the organization. This calculation may take into consideration the digital activity of the employee related to the day-to-day collaboration with other employees. As an example, the intensity of the collaboration with other employees may be represented by the distance”.
Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner interprets at least the various techniques for generating organizational charts and/or visualizations of different types of interactions between members of an organization for identifying influence strength/factors, flow of information, and calculating distance as teaching applicant’s interaction intensity.
With respect to claim 8,
Grady Smith and Kim disclose all of the above limitations, Grady Smith further discloses,
wherein comparing the interaction intensity comprises comparing the first distance to the second distance (Fig 5, Fig 6A, Fig 6b; Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶55: “In some organizations or groups, a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.).”; ¶61: “A process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. ¶148: “The layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶150: “the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization, as suggested by step or stage 408. This may be done with the aid of a constructed visualization (network model, org chart, “tree” model, etc.) of the organization, as modified by one or more filters or weighting mechanisms”; ¶160: “A process or method for generating a visualization/representation of an organization's information and process flows based-on/weighted-by one or more characteristics of the interactions”; ¶162: “used to identify differences (qualitative and/or quantitative) between the expected flow of information, interactions, or decision making responsibility within the organization and the actual or effective flow”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc”; ¶188: “The IIF is the sum of all weights for a set of interactions between one member and another”; ¶194: “Note that the IIF between two members (i.e., IIF from member A to B, versus IIF from member B to A) may be different due to the participation level; for example, if sending an email has more weight than receiving an email, or if assigning a task gives more weight to the assignor than the assignee. An overall direction of influence between two members can be established by comparing their relative factors. Identifying employees with net outward influence could be used to determine candidates for promotion, management, or as champions for ideas and projects”; Fig 6a- Fig 6i; ¶200: “When considering a particular member/employee rather than the organization as a whole, the visualization can provide information useful to evaluating a member's performance, or to helping their manager understand their strengths and weakness as they interact with the team. A graph showing in what ways and how much a member interacted with other teammates, including whether the interactions appeared positive or negative, would help a manager anticipate problems, or capitalize on strong sources of decision making or collaboration”; ¶201: “FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶256: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods; and then used in making decisions or evaluating the operation of an organization”; ¶259: “the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization”; ¶305: “an interaction-based weighting might be applied to a standard organizational metric (such as revenue, profit, head count, etc.) to provide new insights and value into the reasons for (and ways to improve) bottom line financial results; and [0306] 4. The use of real-time business data or metrics may enable a dynamic visualization or representation that indicates what business issues trigger communications, and hence may provide a way to “learn” (e.g., based on machine learning to determine which factors are strongly correlated with a certain type of interaction) what business data combinations may indicate a possible problem before that problem becomes noticed and acted upon”; ¶308: “results of evaluating and analyzing interaction data and/or the visual representation may be used to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, or employee development. As additional examples, the following describe possible situations in which valuable insight(s) can be obtained from use of the inventive system and methods: [0309] Specific information obtained from the inventive interaction—weighted display and/or data analysis may be used to initiate specific organizational programs, tasks, or changes in staffing”)
Although Grady Smith does not distinctly describe verbatim the wording of applicant’s claim limitation (comparing the interaction intensity comprises comparing the first distance in the first visualization to the second distance in the second visualization). Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Grady Smith discloses graph optimization algorithms for illustrating interactions between employees, and that given two interaction profiles of employees, using data processing and interaction vector analysis techniques the distance between each profile can be calculated. Grady Smith also teaches that the interaction weighted display and data analysis provides a dynamic visualization that indicates what business issues trigger communications, hence providing a way to learn/determine which factors are strongly correlated with a certain type of interaction and to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, or employee development.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the techniques for generating an interaction-weighted visualization of an organization as taught by Grady Smith to visualize and identify differences (qualitative and/or quantitative) between the expected flow of information, interactions, or decision-making responsibility within the organization and the actual or effective flow between members. The known techniques for generating an interaction-weighted visualization of an organization of Grady Smith would have predictably resulted in an interaction-based comparison/display to assist in calculating distance between members in an organization for the purposes determining which factors are strongly correlated with a certain type of interaction and to assist in making organizational decisions (Figs 5, 6(a)-6(i); ¶55, ¶60-¶63; ¶148, ¶150, ¶160, ¶162, ¶187, ¶200, ¶201, ¶211, ¶222, ¶256, ¶259, ¶273, ¶305, ¶306, ¶308, ¶309).
With respect to claim 11,
Grady Smith discloses,
A computing system, comprising: one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising: (¶20: “The invention is directed to a data processing system, where the system includes: ¶21: “a data storage element; ¶22: “a processor programmed with a set of instructions, wherein when executed by the processor, the instructions cause the system to”; ¶28: “the invention is directed to one or more non-transitory computer-readable medium on which are included a set of computer-executable instructions, which when executed by a suitably programmed electronic processing element implement a method for assisting in making organizational decisions”)
receiving a technical skill structure of an organization, wherein the technical skill structure comprises different employment levels, wherein the organization comprises a plurality of employees including at least a first employee and a second employee, wherein the employees each have a technical skill level, wherein at least some of the employees are assigned to one of the employment levels based upon their respective technical skill levels (¶10: “typically based on the management or reporting hierarchy, with employees or groups (represented by nodes) being connected by reporting lines to create a tree-like representation of the organization, with the nodes at one level being placed into a lower or higher hierarchy than the nodes at an adjacent level. While such types of organizational representations/structures provide an indication of reporting lines and/or decision-making authority”; ¶11: “it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; ¶65: “A process or method for comparing a conventional or current organizational chart or arrangement (such as one based on role or reporting structure) with the visualization/representation of an organization's information and process flows (as appropriately filtered or analyzed) in order to identify differences between the actual (or most effective) and the expected (or desired) flow of information, interactions, or decision making responsibility within the organization”; ¶67: “The Hierarchy of roles within the organization; ¶68: “Department/Group Structures (the identification and purpose/task/goals of such structures)”; ¶77: “Degrees of separation of a person from a specific level or levels of management”; ¶92: “focusing efforts on the people most likely to be in possession of needed information or skill sets, instead of moving through a conventional organizational chart in an effort to find the correct person for a task. This results from using the inventive system and methods to more efficiently and accurately identify those people and interactions that represent greater knowledge or involvement with certain information or tasks”; ¶203: “Note that there are a number of layout options or factors that can be emphasized, and that are available to a user when viewing an interaction-based organization chart that is generated by an embodiment of the inventive system and methods. As examples, these options may include: [0204] Show reporting lines: The traditional lines of the reporting structure are added to the graph (this would be an overlay of FIG. 5 on another representation, with the lines of FIG. 5 perhaps displayed in a different color, etc.); [0206] Hierarchy-biased view: An algorithm arranges the organization members such that those higher up in the organization structure appear higher in the chart, thereby maintaining the ‘top-down’ view of the organization; [0207] User-focused view: A specific member/employee is defined as central to the chart (typically the current user), and the organization is arranged around/below them, thereby more readily indicating key influencers for that member; [0208] Influencer-weighted view: Members with heavier/thicker Lines of Influence, either with the user of focus (if available), or in the organization in total, would appear larger/bolder in the chart; [0209] Minimum Threshold: Lines of Influence below a certain threshold may be discarded and not represented (i.e., they do not influence the illustration/layout), or are utilized but not shown in order to reduce clutter in the chart”)
receiving interaction data representing interactions between the employees, wherein the interactions occur while the employees perform one or more duties for the organization (¶14: “the invention is directed to a method for assisting in making organizational decisions, where the method includes: [0015] identifying one or more sources of information regarding interactions between a first employee and one or more other employees of an organization; [0016] accessing the one or more sources of information and identifying data for further analysis and evaluation; [0017] processing at least some of the identified data to determine one or more characteristics of the interactions between the first employee and the one or more other employees”; ¶18: “applying a data analysis, modeling, or decision process to the determined characteristics to identify an employee or employees that are most likely to have, or be associated with, a desired characteristic or would be expected to be in possession of a specific item of information, wherein such an employee or employees are those that either attended a meeting where certain projects or tasks were discussed, interacted with one or more persons who attended the meetings, or was made aware of aspects of a project or task of interest to the user”; ¶51: “evaluating and analyzing interactions such as emails, meetings, attendance at events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge… the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; ¶66: “The employee interaction related data or information that may be accessed and processed as part of implementing an embodiment of the inventive system and methods may include (but is not required to include, nor are other sources or types of data excluded from consideration) information regarding:¶70: “Attendance rate, types of interactions participated in, types declined by a person or group member”; ¶75: “Company events invited to, events attended, and the nature of an event”)
wherein the interactions comprise in-person interactions and/or digital interactions, wherein the in-person interactions comprise meetings (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge”; ¶12: “By tracking certain attributes of interactions (e.g., the topic of a meeting, the time/date, those invited, those choosing to attend, other interactions of those invited, and any related records), and applying suitable filters to an interaction-based organizational structure, a set of maps or models of the information or process flow within the organization can be created”; ¶18: “applying a data analysis, modeling, or decision process to the determined characteristics to identify an employee or employees that are most likely to have, or be associated with, a desired characteristic or would be expected to be in possession of a specific item of information, wherein such an employee or employees are those that either attended a meeting where certain projects or tasks were discussed, interacted with one or more persons who attended the meetings, or was made aware of aspects of a project or task of interest to the user”; ¶70: “Attendance rate, types of interactions participated in”; ¶177: “Interaction Type. This can be email/message, event, recognition, record notes, version control, communications/mentions in other systems, chat rooms, etc”)
wherein the digital interactions comprise emails and/or typed chats (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge”; ¶318: External systems for chat (i.e., Hipchat, Slack), for issue tracking (JIRA), or for version control (GitHub, BitBucket) typically have an API that would be available to a suitably configured data acquisition engine. Collecting data regarding emails may be accomplished by using a plugin on a mail server (e.g., some type of modification to an email header to redirect messages to a processing module)”; ¶177: “Interaction Type. This can be email/message, event, recognition, record notes, version control, communications, in other systems, chat rooms, etc;”)
wherein the interactions occur during a first time period and a second time period (Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶180: “Time & Date of interaction to monitor interactions over time, and if desired, give more weight to recent interactions, to filter interactions to use in analysis based on when they occurred”; ¶187: “When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc.”; ¶188: “The following approach can be used to calculate a Member-to-Member Interaction Influence Factor (IIF): [0189] The IIF is the sum of all weights for a set of interactions between one member and another”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions”; ¶265: “the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization”)
determining an interaction intensity between the employees during the first time period based upon the interaction data (Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶47: “access, track, and analyze various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to [0048] (1) develop a visual representation of the operational or functional (as opposed to strictly title or hierarchical position based) structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization; ¶61: “ A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc.; ¶63: “process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow. An example of such a visualization is a network model of employees/nodes and one or more types of connecting attributes between the nodes. These connecting attributes may include aspects such as communication/information flows, relative involvement in a project, the number of contacts/interactions, the relative value of an interaction to a specified decision process, an indication of regular contacts, contacts in a specified group, reporting relationships, position in a role-based hierarchy, etc.”; ¶148-¶155; ¶148: “layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶149: “a user may identify keywords, topics, categories, specific events, date ranges, employee IDs, etc, that are of interest (as suggested by step or stage 402). These may be used by the system to narrow down the set of all communications/interaction data to those items that are expected to be most relevant to identifying/determining the information flow of interest… the user or system (by default) may specify the potential sources of data or information of interest. These may include email, text messages, phone calls, meeting invitations, calendaring related data, HR records, etc. (as suggested by step or stage 404)”; ¶150: “, the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization, as suggested by step or stage 408. This may be done with the aid of a constructed visualization (network model, org chart, “tree” model, etc.) of the organization, as modified by one or more filters or weighting mechanisms. Such a comparison/display may provide insight into the flow of information; the flow of information over time, the development of consensus, the implementation of a policy, the formation of a decision, etc”; ¶155: “The multiple types of interaction data may be accessed and processed (using suitable filters, decision processes, thresholds, criteria, rules, etc.) and provided as inputs to one or more analytical processes that can evaluate the data and produce a model of the interactions and relationships that the data represents. These analytical processes may include machine learning techniques, collaborative or other types of filtering, neural networks, network modeling, optimization, pattern recognition, statistical modeling, etc.”; ¶180: Time & Date of interaction to monitor interactions over time, and if desired, give more weight to recent interactions, to filter interactions to use in analysis based on when they occurred;“ ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions, such as the ones mentioned above (shared meetings, email correspondence, formal recognition, mentions in version control, chat room mentions, etc.). In some embodiments, this data is then considered with the existing static information about the reporting hierarchy”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc.”; ¶188: “The following approach can be used to calculate a Member-to-Member Interaction Influence Factor (IIF): [0189] The IIF is the sum of all weights for a set of interactions between one member and another”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links. Note that in some embodiments, a more insightful/useful organizational chart can be constructed by optimizing the layout of the organization to cause the strongest lines of influence to be the shortest lines on the new layout”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization. This may be used to identify “choke points” in a process, to identify those that influence a decision maker, to determine actual information flows and to modify them to be more effective, etc”)
Applicant’s disclosure teaches at ¶24: “The system and method may first determine a calculation rule to represent interactions between an employee with the rest of the organization. This calculation may take into consideration the digital activity of the employee related to the day-to-day collaboration with other employees. As an example, the intensity of the collaboration with other employees may be represented by the distance”.
Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner interprets at least the various techniques for generating organizational charts and/or visualizations of different types of interactions between members of an organization for identifying influence strength/factors, flow of information, and calculating distance as teaching applicant’s interaction intensity.
Although Grady Smith does not distinctly describe verbatim the wording of applicant’s claim limitation (determining an interaction intensity between the employees during a first time period based upon the interaction data), Grady Smith teaches a method/system for an interaction-weighted visualization of an organization or group, with the relationships between members being based on, or weighted by, the amount, type, subject matter, degree, or significance of interactions between them and the flow of communications between members. Grady Smith discloses at Fig 6a, an “Interaction Based Chart”- Lines represent interactions between people; and techniques for tracking and analyzing various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to develop a visual representation of the operational or functional structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization. Grady Smith further discloses that the visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship which may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow including but not limited to a particular date range for interaction analysis, additional weight can be assigned to more recent interactions and that different types of interactions may be weighted differently, Grady Smith also teaches a process or method for implementing one or more of statistical, machine learning based, rule based, filtering, or other form of data analysis on the interaction and participant information, and for assisting in making decisions relevant to an organization (e.g., generating recommendations, generating probabilities of success, assigning a “cost” or “value” to a possible decision, etc.) based on that data analysis. Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the time-based order of interactions techniques for generating an interaction-weighted visualization of an organization as taught by Grady Smith to track, and analyze various types/characteristics of organizational interactions between members based on or weighted by the amount, type, degree, or significance of interactions between them and the flow of communications between members, etc to show how the interaction(s) evolve over time. The known time progression analysis techniques for generating an interaction-weighted visualization of an organization of Grady Smith would have predictably resulted in providing time progression analysis insight into the strength of certain relationships, the degree of involvement of certain people or groups in implementing policies or in making decisions, and/or the relative importance of certain communication channels to assist in making decisions for purposes of organizational planning, employee or project management, hence, creating a more efficient flow of communications, task assignment, and/or employee development (Fig. 4, Fig 5, FIG. 6(a) through FIG. 6(i); ¶6-¶11, ¶14-¶19, ¶41, ¶48, ¶65-¶68, ¶155, ¶182, ¶186-¶188, ¶197, ¶211, ¶259, ¶265, ¶273).
wherein the interaction intensity between the first and second employees during the first time period is represented by a first distance between the first and second employees (Abstract: “a tree structure with nodes representing employees being connected by branches. The size, color, or number of branches may indicate characteristics of the interactions between the connected nodes (e.g., the frequency, importance, or topic of the interactions, etc.)”; ¶12: “each individual interaction is part of a larger process flow for the organization. By tracking certain attributes of interactions (e.g., the topic of a meeting, the time/date, those invited, those choosing to attend, other interactions of those invited, and any related records), and applying suitable filters to an interaction-based organizational structure, a set of maps or models of the information or process flow within the organization can be created”; ¶55: “a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.)”; ¶61: “storage sub-system configured for use in acquiring and processing interaction and participant information for an organization… email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶148: “layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure… this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶174: “Interactions instances are related to a user/participant and may include one or more of the following information/details”; ¶180: “email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions, such as the ones mentioned above (shared meetings, email correspondence, formal recognition, mentions in version control, chat room mentions, etc.). In some embodiments, this data is then considered with the existing static information about the reporting hierarchy”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc.”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links. Note that in some embodiments, a more insightful/useful organizational chart can be constructed by optimizing the layout of the organization to cause the strongest lines of influence to be the shortest lines on the new layout”; ¶201: “graph optimization algorithms may be used to minimize an overall metric (such as “value”, “cost”, weighted distance, etc.) of the graph based on the value of the sum of the weight multiplied by the length of all interaction lines, while maintaining non-collision between the individuals on the graph. See, for example, FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶215: “Member Interaction Profile” can be created for each employee/node/member, which includes their IIF metric(s) as determined based on each possible pairing with another member in the organization; this can be represented in the form of a multi-factor vector. A member's “Total Influence Factor” may be represented by the magnitude of this vector. The cosine similarity between two Interaction Profile vectors is a measure of the similarity of the interaction histories of any two members with regards to their interactions with other members of the organization (note that the process may subtract out the components representing interactions between the two members being compared)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “ FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization. This may be used to identify “choke points” in a process, to identify those that influence a decision maker, to determine actual information flows and to modify them to be more effective, etc”)
determining the interaction intensity between the employees during the second time period based upon the interaction data, wherein the interaction intensity between the first and second employees during the second time period is represented by a second distance between the first and second employees (Fig 6c, Fig 6i; ¶55: ““a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.)”; Fig 5, Fig 6A, Fig 6b, ¶63: “This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow. An example of such a visualization is a network model of employees/nodes and one or more types of connecting attributes between the nodes. These connecting attributes may include aspects such as communication/information flows, relative involvement in a project, the number of contacts/interactions, the relative value of an interaction to a specified decision process, an indication of regular contacts, contacts in a specified group, reporting relationships, position in a role-based hierarchy, etc.”; ¶148: “layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure… this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶201: “graph optimization algorithms may be used to minimize an overall metric (such as “value”, “cost”, weighted distance, etc.) of the graph based on the value of the sum of the weight multiplied by the length of all interaction lines, while maintaining non-collision between the individuals on the graph. See, for example, FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶215: “Member Interaction Profile” can be created for each employee/node/member, which includes their IIF metric(s) as determined based on each possible pairing with another member in the organization; this can be represented in the form of a multi-factor vector. A member's “Total Influence Factor” may be represented by the magnitude of this vector. The cosine similarity between two Interaction Profile vectors is a measure of the similarity of the interaction histories of any two members with regards to their interactions with other members of the organization (note that the process may subtract out the components representing interactions between the two members being compared)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “ FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization. This may be used to identify “choke points” in a process, to identify those that influence a decision maker, to determine actual information flows and to modify them to be more effective, etc”)
comparing the interaction intensity during the first time period to the interaction intensity during the second time period (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶47: “access, track, and analyze various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to [0048] (1) develop a visual representation of the operational or functional (as opposed to strictly title or hierarchical position based) structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization; ¶55: “In some organizations or groups, a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.).”; ¶61: “ A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc.; ¶62: “A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶148-¶155; ¶148: “layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶149: “a user may identify keywords, topics, categories, specific events, date ranges, employee IDs, etc, that are of interest (as suggested by step or stage 402). These may be used by the system to narrow down the set of all communications/interaction data to those items that are expected to be most relevant to identifying/determining the information flow of interest… the user or system (by default) may specify the potential sources of data or information of interest. These may include email, text messages, phone calls, meeting invitations, calendaring related data, HR records, etc. (as suggested by step or stage 404)”; ¶150: “The identified/filtered data may then be processed to determine one or more of correlations, associations, or other relationships between the data input to a model or process (such as employees and the related interaction data) and an event or goal of interest (such as a decision being made, a policy being implemented, etc.)… the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization… Such a comparison/display may provide insight into the flow of information; the flow of information over time, the development of consensus, the implementation of a policy, the formation of a decision, etc”; Fig 6a, Fig 6b, ¶155: “The multiple types of interaction data may be accessed and processed (using suitable filters, decision processes, thresholds, criteria, rules, etc.) and provided as inputs to one or more analytical processes that can evaluate the data and produce a model of the interactions and relationships that the data represents. These analytical processes may include machine learning techniques, collaborative or other types of filtering, neural networks, network modeling, optimization, pattern recognition, statistical modeling, etc.”; ¶180: Time & Date of interaction to monitor interactions over time, and if desired, give more weight to recent interactions, to filter interactions to use in analysis based on when they occurred;“ ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc.”; ¶188: “The following approach can be used to calculate a Member-to-Member Interaction Influence Factor (IIF): [0189] The IIF is the sum of all weights for a set of interactions between one member and another”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “ FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”)
Applicant’s disclosure teaches at ¶24: “The system and method may first determine a calculation rule to represent interactions between an employee with the rest of the organization. This calculation may take into consideration the digital activity of the employee related to the day-to-day collaboration with other employees. As an example, the intensity of the collaboration with other employees may be represented by the distance”.
Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner interprets at least the various techniques for generating organizational charts and/or visualizations of different types of interactions between members of an organization for identifying influence strength/factors, flow of information, and calculating distance as teaching applicant’s interaction intensity.
wherein comparing the interaction intensity comprises comparing the first distance in the first visualization to the second distance in the second visualization (Fig 5, Fig 6A, Fig 6b; Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶55: “In some organizations or groups, a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.).”; ¶61: “A process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. ¶148: “The layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶150: “the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization, as suggested by step or stage 408. This may be done with the aid of a constructed visualization (network model, org chart, “tree” model, etc.) of the organization, as modified by one or more filters or weighting mechanisms”; ¶160: “A process or method for generating a visualization/representation of an organization's information and process flows based-on/weighted-by one or more characteristics of the interactions”; ¶162: “used to identify differences (qualitative and/or quantitative) between the expected flow of information, interactions, or decision making responsibility within the organization and the actual or effective flow”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc”; ¶188: “The IIF is the sum of all weights for a set of interactions between one member and another”; ¶194: “Note that the IIF between two members (i.e., IIF from member A to B, versus IIF from member B to A) may be different due to the participation level; for example, if sending an email has more weight than receiving an email, or if assigning a task gives more weight to the assignor than the assignee. An overall direction of influence between two members can be established by comparing their relative factors. Identifying employees with net outward influence could be used to determine candidates for promotion, management, or as champions for ideas and projects”; Fig 6a- Fig 6i; ¶200: “When considering a particular member/employee rather than the organization as a whole, the visualization can provide information useful to evaluating a member's performance, or to helping their manager understand their strengths and weakness as they interact with the team. A graph showing in what ways and how much a member interacted with other teammates, including whether the interactions appeared positive or negative, would help a manager anticipate problems, or capitalize on strong sources of decision making or collaboration”; ¶201: “FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶256: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods; and then used in making decisions or evaluating the operation of an organization”; ¶259: “the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization”; ¶305: “an interaction-based weighting might be applied to a standard organizational metric (such as revenue, profit, head count, etc.) to provide new insights and value into the reasons for (and ways to improve) bottom line financial results; and [0306] 4. The use of real-time business data or metrics may enable a dynamic visualization or representation that indicates what business issues trigger communications, and hence may provide a way to “learn” (e.g., based on machine learning to determine which factors are strongly correlated with a certain type of interaction) what business data combinations may indicate a possible problem before that problem becomes noticed and acted upon”; ¶308: “results of evaluating and analyzing interaction data and/or the visual representation may be used to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, or employee development. As additional examples, the following describe possible situations in which valuable insight(s) can be obtained from use of the inventive system and methods: [0309] Specific information obtained from the inventive interaction—weighted display and/or data analysis may be used to initiate specific organizational programs, tasks, or changes in staffing”)
Although Grady Smith does not distinctly describe verbatim the wording of applicant’s claim limitation (comparing the interaction intensity comprises comparing the first distance in the first visualization to the second distance in the second visualization). Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Grady Smith discloses graph optimization algorithms for illustrating interactions between employees, and that given two interaction profiles of employees, using data processing and interaction vector analysis techniques the distance between each profile can be calculated. Grady Smith also teaches that the interaction weighted display and data analysis provides a dynamic visualization that indicates what business issues trigger communications, hence providing a way to learn/determine which factors are strongly correlated with a certain type of interaction and to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, or employee development.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the techniques for generating an interaction-weighted visualization of an organization as taught by Grady Smith to visualize and identify differences (qualitative and/or quantitative) between the expected flow of information, interactions, or decision-making responsibility within the organization and the actual or effective flow between members. The known techniques for generating an interaction-weighted visualization of an organization of Grady Smith would have predictably resulted in an interaction-based comparison/display to assist in calculating distance between members in an organization for the purposes determining which factors are strongly correlated with a certain type of interaction and to assist in making organizational decisions (Figs 5, 6(a)-6(i); ¶55, ¶60-¶63; ¶148, ¶150, ¶160, ¶162, ¶187, ¶200, ¶201, ¶211, ¶222, ¶256, ¶259, ¶273, ¶305, ¶306, ¶308, ¶309).
performing an action in response to the interaction intensity between the employees during the second time period, the comparison, or a combination thereof (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; ¶62: “A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶124-¶136; ¶129: Using the outcome of the data analysis, modeling, or decision process (and if desired, by comparing a model of the actual or inferred interactions and/or information flow within an organization to an existing, assumed, or proposed organizational model (such as one based on role, reporting structure, seniority, etc.)), identifying one or more indicators of suggested organizational actions or potential concerns, such as: [0130] resignation of a key employee; [0131] an increased employee churn rate; [0132] a possible reason for a lack of operational effectiveness or efficiency; [0133] factors associated with a successful task or project completion; [0134] indicators of under recognized influencers within the organization; [0135] an employee most likely to have specific information or an understanding of a task or project (which may be valuable in the situation in which the primary contact for that information or task is not available); [0136] potentially more effective communication channels within the organization; or [0137] training or development opportunities for employees that the organization may wish to encourage“; ¶150: “The identified/filtered data may then be processed to determine one or more of correlations, associations, or other relationships between the data input to a model or process (such as employees and the related interaction data) and an event or goal of interest (such as a decision being made, a policy being implemented, etc.). This may include one or more of statistical, machine learning (supervised or unsupervised), rule-based, or other suitable modeling and data mining methods, as suggested by step or stage 406. If applicable to the situation being examined, the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization… Such a comparison/display may provide insight into the flow of information; the flow of information over time, the development of consensus, the implementation of a policy, the formation of a decision, etc”; Fig 6a, Fig 6b, ¶202: “it may be helpful to know the most effective influencers on a project team or in a group in order to conduct a meeting or engage in communications with the right person or set of people. This information can also help in determining the advancement/promotion opportunities for the employee at the hub”; ¶221: “allows an organization to proactively identify candidates for promotion, and proactively identify potential succession gaps”; [0222] Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶249: “Interaction analysis can be used to assist in making hiring or promotion decisions”; ¶259: “the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization”; ¶305: “an interaction-based weighting might be applied to a standard organizational metric (such as revenue, profit, head count, etc.) to provide new insights and value into the reasons for (and ways to improve) bottom line financial results; and [0306] 4. The use of real-time business data or metrics may enable a dynamic visualization or representation that indicates what business issues trigger communications, and hence may provide a way to “learn” (e.g., based on machine learning to determine which factors are strongly correlated with a certain type of interaction) what business data combinations may indicate a possible problem before that problem becomes noticed and acted upon”; ¶308: “results of evaluating and analyzing interaction data and/or the visual representation may be used to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, or employee development. As additional examples, the following describe possible situations in which valuable insight(s) can be obtained from use of the inventive system and methods: [0309] Specific information obtained from the inventive interaction—weighted display and/or data analysis may be used to initiate specific organizational programs, tasks, or changes in staffing”)
Grady Smith discloses all of the above limitations, Grady Smith does not distinctly describe the following limitations, but Kim however as shown discloses,
wherein the first distance is based upon a number of the emails sent from the first employee to the second employee, a number of the typed chats sent from the first employee to the second employee, and a number of hours of the meetings where the first and second employees are both present (Fig 1, Fig 3, Figs 6- 12; Abstract: “Metadata is extracted from the captured communication data and the extracted communication metadata is analyzed by (i) calculating time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise adjacency matrix based on the attention time each participant spent communicating with each other participant”; ¶7: metadata may be extracted from the communication data and aggregated into a communications file and associated or linked with respective users (i.e., participants) in a participant file. This results in (1) a participant file which lists all the users whose communication and interaction data was measured or extracted, and (2) a communications file comprising all the extracted communication metadata organized by participant“; ¶27: “FIG. 11 depicts an example visualization illustrating the amount of time different teams of participants spend communicating on email, chats, meetings and calls, according to embodiments of the present disclosure”; ¶52: “ the Participant Analytics Platform 250 analyzes the extracted communication metadata and collected sensor data in order to develop an objective model of the communication distribution in the organization. In some embodiments, the analysis may include: (i) calculating amount of time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise directional weighted adjacency matrix (stored in computer memory as a logical data structure) based on the attention time each participant spent communicating with each other participant and other correspondents that may not part of the organization”; ¶53: “ the Participant Analytics Platform 250 may use the pairwise directional weighted adjacency matrix to create and display a visualization illustrating the communication distribution of the participants (or groups of participants) throughout the organization”; ¶60: “According to some embodiments, the DGGT 225 may utilize application programing interface (API) calls to extract the various metadata fields from the one or more communication servers (230 and 235) capturing the underlying digital communication data”; ¶68: “FIG. 6 is a communication and activity graph for an example workday for a given participant. In some embodiments, the Participant Analytics Platform 250 may analyze the metadata extracted from the digital communication data and the sensor data to create the graphs illustrated in FIG. 6”; ¶75: “the amount of time spent by a participant on an instant message chat session is determined as a function of number of involved messages, and in particular by the total number of messages multiplied by an amount of time estimated for each message”; Fig 7, ¶77: “the Participant Analytics Platform 250 determines a single prioritized activity and interaction time block (or time series represented in the system 250 and method 700) 705 for the participant according to a defined order of priority for the concurrent activities and interactions of the participant. In some embodiments, the order of priority of the activities may be (1) meetings, (2) calls, (3) emails, and (4) other communication types”; ¶78: “FIG. 8 illustrates an example method 800 of building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating with each other participant, according to some embodiments”; ¶87: “the Participant Analytics Platform 250 may determine various metrics that are objectively representative of human interaction and activity within an organization. For example, the Participant Analytics Platform 250 may create a Participant Pairwise Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization”; ¶93: “each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums”; ¶95: “Below is an example code for an adjacency matrix generated based only on emails for an organization that has 11 people. The nodes are the active participants and the edges represent the communications in the format (from, to, attention minutes). According to some embodiments, the Participant Analytics Platform 250 converts the analytics payload into an API payload and then to a dashboard visualization such as FIG. 9. ¶96: “FIG. 9 depicts an example visualization of communication distribution between participants throughout an organization according to embodiments of the present disclosure. In some embodiments, the visualization depicted in FIG. 9 may be generated by the collaboration and delivery module of the Participant Analytics Platform 250. The nodes in the edge weighted graph represent participants and the edges represent communications and interactions between the participants. The larger the size of the node the greater the amount communication and interactions the respective participant had with other participants. Further, the distance between any two nodes represents that amount of communication and interaction between those respective participants; the shorter the distance the more communication and interaction”; ¶101: “the Participant Analytics Platform 250 may determine the total time that a participant spends on each communication medium over a given period of time (e.g., day, week, month, etc.) In some embodiments, the Participant Analytics Platform 250 may calculate the amount of time spent communicating on each medium”; ¶102: “ the analysis returns a single dictionary that has info on the amounts of time spent on all communication media (time spent on email, time spent on chat, time spent on meetings, time spent on calls (not meetings), time spent on face-to-face interactions, and estimated time not involved in communication during normal work hours) for each of during work, during after-work, overall”; ¶110: the Participant Analytics Platform 250 calculates the number of attention minutes each participant spends with managers, non-managers, and external persons, as well as whether the attention minutes were spent during work hours or after work hours. For example, the following metrics may be determined”)
Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Grady Smith discloses graph optimization algorithms for illustrating interactions between employees, and that given two interaction profiles of employees, using data processing and interaction vector analysis techniques the distance between each profile can be calculated.
Although Kim does not distinctly describe verbatim the wording of applicant’s claim limitation (first distance is based upon a number of the emails sent from the first employee to the second employee, a number of the typed chats sent from the first employee to the second employee, and a number of hours of the meetings where the first and second employees are both present), Kim teaches a Participant Analytics Platform for determining various metrics that are objectively representative of human interaction and activity within an organization via a Participant Pairwise (weighted) Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization. Kim also teaches that each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums and providing a visualization of communication distribution between participants throughout an organization. The visualization of communication distribution between participants may be generated via the collaboration and delivery module of the Participant Analysis Platform via a weighted graph representing communications, interactions and distance between participants. Kim also discloses that the Participant Analytics Platform can determine time series activity and interactions of a participant for concurrent activities as well as building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating (meetings, calls, emails, and other communication types (chat, face-to face interactions) with each other participant. Applicant’s disclosure generically teaches, ¶37: “The coefficients may provide more or less weight to one channel over the other ones”. Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner contends that the techniques for building a weighted adjacency matrix based on a value(s) as taught by Kim as teaching applicant’s number(s) and is a form of linearization. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim to identify the distance between participants and the communications and interactions the respective participant had with other participants based on a specific weight for the associated communication medium (email, meetings, calls, chats, face-to-face interaction) whereby a visualization of communication distribution between participants can be represented via a dashboard. The known techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim would have predictably resulted in determining various metrics that are objectively representative of interaction and communication activity within an organization including distance between respective participants for the purposes of identifying the amount of communications and interactions a respective participant had with other participants via a weighted graph. Therefore, one of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to combine the method/system for interaction-weighted visualization of Grady Smith with the techniques for measuring communication distribution throughout an organization as taught by Kim since it allows for improving data processing and analysis of interaction metadata to accurately and objectively measure and represent communication distribution (via distance) throughout an organization based participants’ interactions via a weighted graph (Fig 1, Fig 3, Figs 6- Fig 12; ¶7, ¶27, ¶53, ¶60, ¶75, ¶77, ¶78, ¶87, ¶93, ¶95, ¶96, ¶101, ¶102, ¶110)
With respect to claim 12,
Grady Smith and Kim disclose all of the above limitations, Grady Smith further discloses,
wherein the operations further comprise: generating a first visualization of the interaction intensity during the first time period, wherein the first visualization shows the first distance between the first and second employees (Fig 6(a)- Fig 6(i); ¶55: “In some organizations or groups, a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.)”; ¶61: “A process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow”; ¶63: “The indication may be based on a metric related to interactions/information flow. An example of such a visualization is a network model of employees/nodes and one or more types of connecting attributes between the nodes. These connecting attributes may include aspects such as communication/information flows, relative involvement in a project, the number of contacts/interactions, the relative value of an interaction to a specified decision process, an indication of regular contacts, contacts in a specified group, reporting relationships, position in a role-based hierarchy, etc”; ¶148: “The layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶184-¶197; ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc”; ¶192: “Adjustment for date of interaction to give more weight to more recent interactions”; ¶196; “The categories of interactions to be considered and the type and participation weightings may be input to the system and adjusted”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links”; ¶201: “graph optimization algorithms may be used to minimize an overall metric (such as “value”, “cost”, weighted distance, etc.) of the graph based on the value of the sum of the weight multiplied by the length of all interaction lines.. FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶214: “The inventive system and methods may be used to generate a representation, and in some cases a characterization, of the interactions between multiple employees/nodes in an organization. As part of generating this representation a method for calculating a metric, termed a “Member-to-Member Interaction Influence Factor (IIF)”; ¶215: a “Member Interaction Profile” can be created for each employee/node/member, which includes their IIF metric(s) as determined based on each possible pairing with another member in the organization; this can be represented in the form of a multi-factor vector. A member's “Total Influence Factor” may be represented by the magnitude of this vector. The cosine similarity between two Interaction Profile vectors is a measure of the similarity of the interaction histories of any two members with regards to their interactions with other members of the organization (note that the process may subtract out the components representing interactions between the two members being compared). Note also that other forms of metrics may be suitable, depending upon the type of data and the use case (such as ranking by most frequent or common interactions, filtering or application of a threshold value, etc.); ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”)
wherein the technical skill levels of the employees are represented by indicators in the first visualization (Fig 5, Fig 6A, Fig 6b, Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶66: “The employee interaction related data or information that may be accessed and processed as part of implementing an embodiment of the inventive system and methods may include (but is not required to include, nor are other sources or types of data excluded from consideration) information regarding: [0067] The Hierarchy of roles within the organization; [0068] Department/Group Structures (the identification and purpose/task/goals of such structures); ¶77: “Degrees of separation of a person from a specific level or levels of management; ¶220: “Interaction Analysis for Succession Planning allows an organization to compare the interaction profiles of an employee (Employee A) in a particular position with the profile of another employee (Employee B). This produces a new dimension/metric with which to evaluate successors to a role if Employee A were to vacate their position, and can provide a higher degree of confidence in Employee B's likelihood of being successful in a role”; ¶221: “analysis/evaluation can be performed continuously for multiple combinations of Employee A and Employee B. This allows an organization to proactively identify candidates for promotion, and proactively identify potential succession gaps”)
generating a second visualization of the interaction intensity during the second time period, wherein comparing the interaction intensity comprises comparing the indicators in the first visualization to the indicators in the second visualization (Fig 5, Fig 6A, Fig 6b; Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶55: “In some organizations or groups, a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.).”; ¶61: “A process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. ¶148: “The layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶150: “the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization, as suggested by step or stage 408. This may be done with the aid of a constructed visualization (network model, org chart, “tree” model, etc.) of the organization, as modified by one or more filters or weighting mechanisms”; ¶160: “A process or method for generating a visualization/representation of an organization's information and process flows based-on/weighted-by one or more characteristics of the interactions”; ¶162: “used to identify differences (qualitative and/or quantitative) between the expected flow of information, interactions, or decision making responsibility within the organization and the actual or effective flow”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc”; ¶188: “The IIF is the sum of all weights for a set of interactions between one member and another”; ¶194: “Note that the IIF between two members (i.e., IIF from member A to B, versus IIF from member B to A) may be different due to the participation level; for example, if sending an email has more weight than receiving an email, or if assigning a task gives more weight to the assignor than the assignee. An overall direction of influence between two members can be established by comparing their relative factors. Identifying employees with net outward influence could be used to determine candidates for promotion, management, or as champions for ideas and projects”; Fig 6a- Fig 6i; ¶200: “When considering a particular member/employee rather than the organization as a whole, the visualization can provide information useful to evaluating a member's performance, or to helping their manager understand their strengths and weakness as they interact with the team. A graph showing in what ways and how much a member interacted with other teammates, including whether the interactions appeared positive or negative, would help a manager anticipate problems, or capitalize on strong sources of decision making or collaboration”; ¶201: “FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶256: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods; and then used in making decisions or evaluating the operation of an organization”; ¶259: “the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization”; ¶305: “an interaction-based weighting might be applied to a standard organizational metric (such as revenue, profit, head count, etc.) to provide new insights and value into the reasons for (and ways to improve) bottom line financial results; and [0306] 4. The use of real-time business data or metrics may enable a dynamic visualization or representation that indicates what business issues trigger communications, and hence may provide a way to “learn” (e.g., based on machine learning to determine which factors are strongly correlated with a certain type of interaction) what business data combinations may indicate a possible problem before that problem becomes noticed and acted upon”; ¶308: “results of evaluating and analyzing interaction data and/or the visual representation may be used to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, or employee development. As additional examples, the following describe possible situations in which valuable insight(s) can be obtained from use of the inventive system and methods: [0309] Specific information obtained from the inventive interaction—weighted display and/or data analysis may be used to initiate specific organizational programs, tasks, or changes in staffing”)
Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Grady Smith discloses graph optimization algorithms for illustrating interactions between employees, and that given two interaction profiles of employees, using data processing and interaction vector analysis techniques the distance between each profile can be calculated. Grady Smith also teaches that the interaction weighted display and data analysis provides a dynamic visualization that indicates what business issues trigger communications, hence providing a way to learn/determine which factors are strongly correlated with a certain type of interaction and to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, or employee development.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the techniques for generating an interaction-weighted visualization of an organization as taught by Grady Smith to visualize and identify differences (qualitative and/or quantitative) between the expected flow of information, interactions, or decision-making responsibility within the organization and the actual or effective flow between members. The known techniques for generating an interaction-weighted visualization of an organization of Grady Smith would have predictably resulted in an interaction-based comparison/display to assist in calculating distance between members in an organization for the purposes determining which factors are strongly correlated with a certain type of interaction and to assist in making organizational decisions (Figs 5, 6(a)-6(i); ¶55, ¶60-¶63; ¶148, ¶150, ¶160, ¶162, ¶187, ¶200, ¶201, ¶211, ¶222, ¶256, ¶259, ¶273, ¶305, ¶306, ¶308, ¶309).
With respect to claim 13,
Grady Smith discloses all of the above limitations, Grady Smith does not distinctly describe the following limitations, but Kim however as shown discloses,
wherein the first distance is also based upon a first coefficient corresponding to the number of the emails, a second coefficient corresponding to the number of the typed chats, and a third coefficient corresponding to the number of hours of the meetings (Fig 1, Fig 3, Figs 6- 12; Abstract: “Metadata is extracted from the captured communication data and the extracted communication metadata is analyzed by (i) calculating time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise adjacency matrix based on the attention time each participant spent communicating with each other participant”; ¶7: metadata may be extracted from the communication data and aggregated into a communications file and associated or linked with respective users (i.e., participants) in a participant file. This results in (1) a participant file which lists all the users whose communication and interaction data was measured or extracted, and (2) a communications file comprising all the extracted communication metadata organized by participant“; ¶27: “FIG. 11 depicts an example visualization illustrating the amount of time different teams of participants spend communicating on email, chats, meetings and calls, according to embodiments of the present disclosure”; ¶52: “ the Participant Analytics Platform 250 analyzes the extracted communication metadata and collected sensor data in order to develop an objective model of the communication distribution in the organization. In some embodiments, the analysis may include: (i) calculating amount of time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise directional weighted adjacency matrix (stored in computer memory as a logical data structure) based on the attention time each participant spent communicating with each other participant and other correspondents that may not part of the organization”; ¶53: “ the Participant Analytics Platform 250 may use the pairwise directional weighted adjacency matrix to create and display a visualization illustrating the communication distribution of the participants (or groups of participants) throughout the organization”; ¶60: “According to some embodiments, the DGGT 225 may utilize application programing interface (API) calls to extract the various metadata fields from the one or more communication servers (230 and 235) capturing the underlying digital communication data”; ¶68: “FIG. 6 is a communication and activity graph for an example workday for a given participant. In some embodiments, the Participant Analytics Platform 250 may analyze the metadata extracted from the digital communication data and the sensor data to create the graphs illustrated in FIG. 6”; ¶75: “the amount of time spent by a participant on an instant message chat session is determined as a function of number of involved messages, and in particular by the total number of messages multiplied by an amount of time estimated for each message”; Fig 7, ¶77: “the Participant Analytics Platform 250 determines a single prioritized activity and interaction time block (or time series represented in the system 250 and method 700) 705 for the participant according to a defined order of priority for the concurrent activities and interactions of the participant. In some embodiments, the order of priority of the activities may be (1) meetings, (2) calls, (3) emails, and (4) other communication types”; ¶78: “FIG. 8 illustrates an example method 800 of building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating with each other participant, according to some embodiments”; ¶87: “the Participant Analytics Platform 250 may determine various metrics that are objectively representative of human interaction and activity within an organization. For example, the Participant Analytics Platform 250 may create a Participant Pairwise Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization”; ¶93: “each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums”; ¶95: “Below is an example code for an adjacency matrix generated based only on emails for an organization that has 11 people. The nodes are the active participants and the edges represent the communications in the format (from, to, attention minutes). According to some embodiments, the Participant Analytics Platform 250 converts the analytics payload into an API payload and then to a dashboard visualization such as FIG. 9. ¶96: “FIG. 9 depicts an example visualization of communication distribution between participants throughout an organization according to embodiments of the present disclosure. In some embodiments, the visualization depicted in FIG. 9 may be generated by the collaboration and delivery module of the Participant Analytics Platform 250. The nodes in the edge weighted graph represent participants and the edges represent communications and interactions between the participants. The larger the size of the node the greater the amount communication and interactions the respective participant had with other participants. Further, the distance between any two nodes represents that amount of communication and interaction between those respective participants; the shorter the distance the more communication and interaction”; ¶101: “the Participant Analytics Platform 250 may determine the total time that a participant spends on each communication medium over a given period of time (e.g., day, week, month, etc.) In some embodiments, the Participant Analytics Platform 250 may calculate the amount of time spent communicating on each medium”; ¶102: “ the analysis returns a single dictionary that has info on the amounts of time spent on all communication media (time spent on email, time spent on chat, time spent on meetings, time spent on calls (not meetings), time spent on face-to-face interactions, and estimated time not involved in communication during normal work hours) for each of during work, during after-work, overall”; ¶110: the Participant Analytics Platform 250 calculates the number of attention minutes each participant spends with managers, non-managers, and external persons, as well as whether the attention minutes were spent during work hours or after work hours. For example, the following metrics may be determined”)
Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Grady Smith discloses graph optimization algorithms for illustrating interactions between employees, and that given two interaction profiles of employees, using data processing and interaction vector analysis techniques the distance between each profile can be calculated.
Although Kim does not distinctly describe verbatim the wording of applicant’s claim limitation (first distance is based upon a number of the emails sent from the first employee to the second employee, a number of the typed chats sent from the first employee to the second employee, and a number of hours of the meetings), Kim teaches a Participant Analytics Platform for determining various metrics that are objectively representative of human interaction and activity within an organization via a Participant Pairwise (weighted) Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization. Kim also teaches that each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums and providing a visualization of communication distribution between participants throughout an organization. The visualization of communication distribution between participants may be generated via the collaboration and delivery module of the Participant Analysis Platform via a weighted graph representing communications, interactions and distance between participants. Kim also discloses that the Participant Analytics Platform can determine time series activity and interactions of a participant for concurrent activities as well as building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating (meetings, calls, emails, and other communication types (chat, face-to face interactions) with each other participant. Applicant’s disclosure generically teaches, ¶37: “The coefficients may provide more or less weight to one channel over the other ones”. Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner contends that the techniques for building a weighted adjacency matrix based on a value(s) as taught by Kim as teaching applicant’s number(s) and is a form of linearization. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim to identify the distance between participants and the communications and interactions the respective participant had with other participants based on a specific weight for the associated communication medium (email, meetings, calls, chats, face-to-face interaction) whereby a visualization of communication distribution between participants can be represented via a dashboard. The known techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim would have predictably resulted in determining various metrics that are objectively representative of interaction and communication activity within an organization including distance between respective participants for the purposes of identifying the amount of communications and interactions a respective participant had with other participants via a weighted graph. Therefore, one of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to combine the method/system for interaction-weighted visualization of Grady Smith with the techniques for measuring communication distribution throughout an organization as taught by Kim since it allows for improving data processing and analysis of interaction metadata to accurately and objectively measure and represent communication distribution (via distance) throughout an organization based participants’ interactions via a weighted graph (Fig 1, Fig 3, Figs 6- Fig 12; ¶7, ¶27, ¶53, ¶60, ¶75, ¶77, ¶78, ¶87, ¶93, ¶95, ¶96, ¶101, ¶102, ¶110).
With respect to claim 14,
Grady Smith and Kim disclose all of the above limitations, Kim further discloses,
wherein the second coefficient is greater than the first coefficient, and wherein the third coefficient is between the first and second coefficients (Fig 1, Fig 3, Figs 6-8, Fig 11, Fig 12; ¶27: “FIG. 11 depicts an example visualization illustrating the amount of time different teams of participants spend communicating on email, chats, meetings and calls, according to embodiments of the present disclosure”; ¶60: “According to some embodiments, the DGGT 225 may utilize application programing interface (API) calls to extract the various metadata fields from the one or more communication servers (230 and 235) capturing the underlying digital communication data”; ¶68: “FIG. 6 is a communication and activity graph for an example workday for a given participant. In some embodiments, the Participant Analytics Platform 250 may analyze the metadata extracted from the digital communication data and the sensor data to create the graphs illustrated in FIG. 6”; ¶75: “the amount of time spent by a participant on an instant message chat session is determined as a function of number of involved messages, and in particular by the total number of messages multiplied by an amount of time estimated for each message”;¶78: “FIG. 8 illustrates an example method 800 of building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating with each other participant, according to some embodiments”; ¶87: “the Participant Analytics Platform 250 may determine various metrics that are objectively representative of human interaction and activity within an organization. For example, the Participant Analytics Platform 250 may create a Participant Pairwise Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization”; ¶93: “each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums”; ¶101: “the Participant Analytics Platform 250 may determine the total time that a participant spends on each communication medium over a given period of time (e.g., day, week, month, etc.) In some embodiments, the Participant Analytics Platform 250 may calculate the amount of time spent communicating on each medium”; ¶102: “the analysis returns a single dictionary that has info on the amounts of time spent on all communication media (time spent on email, time spent on chat, time spent on meetings, time spent on calls (not meetings), time spent on face-to-face interactions, and estimated time not involved in communication during normal work hours) for each of during work, during after-work, overall)
Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Grady Smith discloses graph optimization algorithms for illustrating interactions between employees, and that given two interaction profiles of employees, using data processing and interaction vector analysis techniques the distance between each profile can be calculated.
Although Kim does not distinctly describe verbatim the wording of applicant’s claim limitation (wherein the second coefficient is greater than the first coefficient, and wherein the third coefficient is between the first and second coefficients), Kim teaches a Participant Analytics Platform for determining various metrics that are objectively representative of activity within an organization via a Participant Pairwise (weighted) Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization. Kim also teaches that each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums and providing a visualization of communication distribution between participants throughout an organization. The visualization of communication distribution between participants may be generated via the collaboration and delivery module of the Participant Analysis Platform via a weighted graph representing communications, interactions and distance between participants. Kim also discloses that the Participant Analytics Platform can determine time series activity and interactions of a participant for concurrent activities as well as building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating (meetings, calls, emails, and other communication types (chat, face-to face interactions) with each other participant.
Applicant’s disclosure generically teaches, ¶37: “The coefficients may provide more or less weight to one channel over the other ones”. Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner contends that the techniques for building a weighted adjacency matrix based on a value(s) as taught by Kim as teaching applicant’s (coefficient) number(s). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim to identify the distance between participants and the communications and interactions the respective participant had with other participants based on a specific weight for the associated communication medium (email, meetings, calls, chats, face-to-face interaction) whereby a visualization of communication distribution between participants can be represented via a dashboard. The known techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim would have predictably resulted in determining various metrics that are objectively representative of interaction and communication activity within an organization including distance between respective participants for the purposes of identifying the amount of communications and interactions a respective participant had with other participants via a weighted graph. Therefore, one of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to combine the method/system for interaction-weighted visualization of Grady Smith with the techniques for measuring communication distribution throughout an organization as taught by Kim since it allows for improving data processing and analysis of interaction metadata to accurately and objectively measure and represent communication distribution (via distance) throughout an organization based participants’ interactions via a weighted graph (Fig 1, Fig 3, Figs 6- Fig 12; ¶7, ¶27, ¶53, ¶60, ¶75, ¶77, ¶78, ¶87, ¶93, ¶95, ¶96, ¶101, ¶102, ¶110)
With respect to claim 15,
Grady Smith and Kim disclose all of the above limitations, Kim further discloses,
wherein the number of the emails, the number of the typed chats, and the number of hours of the meetings are normalized based upon a distribution of the interaction data. (Fig 1, Fig 3, Figs 6-8, Fig 11, Fig 12; ¶27: “FIG. 11 depicts an example visualization illustrating the amount of time different teams of participants spend communicating on email, chats, meetings and calls, according to embodiments of the present disclosure”; ¶60: “According to some embodiments, the DGGT 225 may utilize application programing interface (API) calls to extract the various metadata fields from the one or more communication servers (230 and 235) capturing the underlying digital communication data”; ¶68: “FIG. 6 is a communication and activity graph for an example workday for a given participant. In some embodiments, the Participant Analytics Platform 250 may analyze the metadata extracted from the digital communication data and the sensor data to create the graphs illustrated in FIG. 6”; ¶75: “the amount of time spent by a participant on an instant message chat session is determined as a function of number of involved messages, and in particular by the total number of messages multiplied by an amount of time estimated for each message”;¶78: “FIG. 8 illustrates an example method 800 of building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating with each other participant, according to some embodiments”; ¶87: “the Participant Analytics Platform 250 may determine various metrics that are objectively representative of human interaction and activity within an organization. For example, the Participant Analytics Platform 250 may create a Participant Pairwise Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization”; ¶93: “each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums”; ¶101: “the Participant Analytics Platform 250 may determine the total time that a participant spends on each communication medium over a given period of time (e.g., day, week, month, etc.) In some embodiments, the Participant Analytics Platform 250 may calculate the amount of time spent communicating on each medium”; ¶102: “ the analysis returns a single dictionary that has info on the amounts of time spent on all communication media (time spent on email, time spent on chat, time spent on meetings, time spent on calls (not meetings), time spent on face-to-face interactions, and estimated time not involved in communication during normal work hours) for each of during work, during after-work, overall”; ¶236: “Preparing a pairwise attention minute matrix of the same participant group, normalized to be between 0-1.1 is the maximum amount of communication a pair can have”)
Kim discloses a method and system for capturing and analyzing communication metadata and sensor data of an organization. The extracted communication metadata and sensor data is analyzed by: (i) calculating time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating with each other participant. Kim further discloses that the pairwise directional weighted adjacency matrix may be utilized to create and display a visualization illustrating the communication distribution of the participants throughout the organization. Kim teaches that the Participant Analytics Platform may determine various metrics that are objectively representative of human interaction and activity within an organization and that each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums”.
Applicant’s disclosure generically teaches, ¶5: “X, Y, and Z are normalized based upon a distribution of the interaction data”; ¶37: “The variables X, Y, and Z may be normalized based upon a distribution of the interaction data to obtain a spread distribution…. The coefficients may provide more or less weight to one channel over the other ones”. Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner contends that the techniques for normalizing an adjacency matrix for determining a spread distribution and maximum number as taught by Kim as teaching applicant’s normalization.
Claims 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Grady Smith, Kim, in further view of Rivette et al., US Patent Application Publication No US 2003/0046307 A1.
With respect to claim 16,
Grady Smith discloses,
A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising: (¶28: “the invention is directed to one or more non-transitory computer-readable medium on which are included a set of computer-executable instructions, which when executed by a suitably programmed electronic processing element implement a method for assisting in making organizational decisions”)
receiving a technical skill structure of an organization, wherein the technical skill structure comprises different employment levels, wherein the organization comprises a plurality of employees including at least a first employee, a second employee, and other employees, wherein the employees each have a technical skill level, wherein at least some of the employees are assigned to one of the employment levels based upon their respective technical skill levels, and wherein the other employees are assigned to a higher employment level than the first employee (¶10: “typically based on the management or reporting hierarchy, with employees or groups (represented by nodes) being connected by reporting lines to create a tree-like representation of the organization, with the nodes at one level being placed into a lower or higher hierarchy than the nodes at an adjacent level. While such types of organizational representations/structures provide an indication of reporting lines and/or decision-making authority”; ¶11: “it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; ¶65: “A process or method for comparing a conventional or current organizational chart or arrangement (such as one based on role or reporting structure) with the visualization/representation of an organization's information and process flows (as appropriately filtered or analyzed) in order to identify differences between the actual (or most effective) and the expected (or desired) flow of information, interactions, or decision making responsibility within the organization”; ¶67: “The Hierarchy of roles within the organization; ¶68: “Department/Group Structures (the identification and purpose/task/goals of such structures)”; ¶77: “Degrees of separation of a person from a specific level or levels of management”; ¶92: “focusing efforts on the people most likely to be in possession of needed information or skill sets, instead of moving through a conventional organizational chart in an effort to find the correct person for a task. This results from using the inventive system and methods to more efficiently and accurately identify those people and interactions that represent greater knowledge or involvement with certain information or tasks”; ¶203: “Note that there are a number of layout options or factors that can be emphasized, and that are available to a user when viewing an interaction-based organization chart that is generated by an embodiment of the inventive system and methods. As examples, these options may include: [0204] Show reporting lines: The traditional lines of the reporting structure are added to the graph (this would be an overlay of FIG. 5 on another representation, with the lines of FIG. 5 perhaps displayed in a different color, etc.); [0206] Hierarchy-biased view: An algorithm arranges the organization members such that those higher up in the organization structure appear higher in the chart, thereby maintaining the ‘top-down’ view of the organization; [0207] User-focused view: A specific member/employee is defined as central to the chart (typically the current user), and the organization is arranged around/below them, thereby more readily indicating key influencers for that member; [0208] Influencer-weighted view: Members with heavier/thicker Lines of Influence, either with the user of focus (if available), or in the organization in total, would appear larger/bolder in the chart; [0209] Minimum Threshold: Lines of Influence below a certain threshold may be discarded and not represented (i.e., they do not influence the illustration/layout), or are utilized but not shown in order to reduce clutter in the chart”)
receiving interaction data representing interactions between the employees, wherein the interactions occur while the employees perform one or more duties for the organization (¶14: “the invention is directed to a method for assisting in making organizational decisions, where the method includes: [0015] identifying one or more sources of information regarding interactions between a first employee and one or more other employees of an organization; [0016] accessing the one or more sources of information and identifying data for further analysis and evaluation; [0017] processing at least some of the identified data to determine one or more characteristics of the interactions between the first employee and the one or more other employees”; ¶18: “applying a data analysis, modeling, or decision process to the determined characteristics to identify an employee or employees that are most likely to have, or be associated with, a desired characteristic or would be expected to be in possession of a specific item of information, wherein such an employee or employees are those that either attended a meeting where certain projects or tasks were discussed, interacted with one or more persons who attended the meetings, or was made aware of aspects of a project or task of interest to the user”; ¶51: “evaluating and analyzing interactions such as emails, meetings, attendance at events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge… the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; ¶66: “The employee interaction related data or information that may be accessed and processed as part of implementing an embodiment of the inventive system and methods may include (but is not required to include, nor are other sources or types of data excluded from consideration) information regarding:¶70: “Attendance rate, types of interactions participated in, types declined by a person or group member”; ¶75: “Company events invited to, events attended, and the nature of an event”)
wherein the interactions comprise in-person interactions and digital interactions, wherein the in-person interactions comprise meetings (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge”; ¶12: “By tracking certain attributes of interactions (e.g., the topic of a meeting, the time/date, those invited, those choosing to attend, other interactions of those invited, and any related records), and applying suitable filters to an interaction-based organizational structure, a set of maps or models of the information or process flow within the organization can be created”; ¶18: “applying a data analysis, modeling, or decision process to the determined characteristics to identify an employee or employees that are most likely to have, or be associated with, a desired characteristic or would be expected to be in possession of a specific item of information, wherein such an employee or employees are those that either attended a meeting where certain projects or tasks were discussed, interacted with one or more persons who attended the meetings, or was made aware of aspects of a project or task of interest to the user”; ¶70: “Attendance rate, types of interactions participated in”; ¶177: “Interaction Type. This can be email/message, event, recognition, record notes, version control, communications/mentions in other systems, chat rooms, etc”)
wherein the digital interactions comprise emails and typed chats, ¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge”; ¶318: External systems for chat (i.e., Hipchat, Slack), for issue tracking (JIRA), or for version control (GitHub, BitBucket) typically have an API that would be available to a suitably configured data acquisition engine. Collecting data regarding emails may be accomplished by using a plugin on a mail server (e.g., some type of modification to an email header to redirect messages to a processing module)”; ¶177: “Interaction Type. This can be email/message, event, recognition, record notes, version control, communications, in other systems, chat rooms, etc;”)
wherein the interactions occur during a first time period and a second time period ((Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶180: “Time & Date of interaction to monitor interactions over time, and if desired, give more weight to recent interactions, to filter interactions to use in analysis based on when they occurred”; ¶187: “When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc.”; ¶188: “The following approach can be used to calculate a Member-to-Member Interaction Influence Factor (IIF): [0189] The IIF is the sum of all weights for a set of interactions between one member and another”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions”; ¶265: “the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization”)
determining an interaction intensity between the employees during the first time period based upon the interaction data Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶47: “access, track, and analyze various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to [0048] (1) develop a visual representation of the operational or functional (as opposed to strictly title or hierarchical position based) structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization; ¶61: “ A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc.; ¶63: “process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow. An example of such a visualization is a network model of employees/nodes and one or more types of connecting attributes between the nodes. These connecting attributes may include aspects such as communication/information flows, relative involvement in a project, the number of contacts/interactions, the relative value of an interaction to a specified decision process, an indication of regular contacts, contacts in a specified group, reporting relationships, position in a role-based hierarchy, etc.”; ¶148-¶155; ¶148: “layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶149: “a user may identify keywords, topics, categories, specific events, date ranges, employee IDs, etc, that are of interest (as suggested by step or stage 402). These may be used by the system to narrow down the set of all communications/interaction data to those items that are expected to be most relevant to identifying/determining the information flow of interest… the user or system (by default) may specify the potential sources of data or information of interest. These may include email, text messages, phone calls, meeting invitations, calendaring related data, HR records, etc. (as suggested by step or stage 404)”; ¶150: “, the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization, as suggested by step or stage 408. This may be done with the aid of a constructed visualization (network model, org chart, “tree” model, etc.) of the organization, as modified by one or more filters or weighting mechanisms. Such a comparison/display may provide insight into the flow of information; the flow of information over time, the development of consensus, the implementation of a policy, the formation of a decision, etc”; ¶155: “The multiple types of interaction data may be accessed and processed (using suitable filters, decision processes, thresholds, criteria, rules, etc.) and provided as inputs to one or more analytical processes that can evaluate the data and produce a model of the interactions and relationships that the data represents. These analytical processes may include machine learning techniques, collaborative or other types of filtering, neural networks, network modeling, optimization, pattern recognition, statistical modeling, etc.”; ¶180: Time & Date of interaction to monitor interactions over time, and if desired, give more weight to recent interactions, to filter interactions to use in analysis based on when they occurred;“ ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions, such as the ones mentioned above (shared meetings, email correspondence, formal recognition, mentions in version control, chat room mentions, etc.). In some embodiments, this data is then considered with the existing static information about the reporting hierarchy”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc.”; ¶188: “The following approach can be used to calculate a Member-to-Member Interaction Influence Factor (IIF): [0189] The IIF is the sum of all weights for a set of interactions between one member and another”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links. Note that in some embodiments, a more insightful/useful organizational chart can be constructed by optimizing the layout of the organization to cause the strongest lines of influence to be the shortest lines on the new layout”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization. This may be used to identify “choke points” in a process, to identify those that influence a decision maker, to determine actual information flows and to modify them to be more effective, etc”)
Applicant’s disclosure teaches at ¶24: “The system and method may first determine a calculation rule to represent interactions between an employee with the rest of the organization. This calculation may take into consideration the digital activity of the employee related to the day-to-day collaboration with other employees. As an example, the intensity of the collaboration with other employees may be represented by the distance”.
Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner interprets at least the various techniques for generating organizational charts and/or visualizations of different types of interactions between members of an organization for identifying influence strength/factors, flow of information, and calculating distance as teaching applicant’s interaction intensity.
Although Grady Smith does not distinctly describe verbatim the wording of applicant’s claim limitation (determining an interaction intensity between the employees during a first time period based upon the interaction data), Grady Smith teaches a method/system for an interaction-weighted visualization of an organization or group, with the relationships between members being based on, or weighted by, the amount, type, subject matter, degree, or significance of interactions between them and the flow of communications between members. Grady Smith discloses at Fig 6a, an “Interaction Based Chart”- Lines represent interactions between people; and techniques for tracking and analyzing various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to develop a visual representation of the operational or functional structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization. Grady Smith further discloses that the visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship which may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow including but not limited to a particular date range for interaction analysis, additional weight can be assigned to more recent interactions and that different types of interactions may be weighted differently, Grady Smith also teaches a process or method for implementing one or more of statistical, machine learning based, rule based, filtering, or other form of data analysis on the interaction and participant information, and for assisting in making decisions relevant to an organization (e.g., generating recommendations, generating probabilities of success, assigning a “cost” or “value” to a possible decision, etc.) based on that data analysis. Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the time-based order of interactions techniques for generating an interaction-weighted visualization of an organization as taught by Grady Smith to track, and analyze various types/characteristics of organizational interactions between members based on or weighted by the amount, type, degree, or significance of interactions between them and the flow of communications between members, etc to show how the interaction(s) evolve over time. The known time progression analysis techniques for generating an interaction-weighted visualization of an organization of Grady Smith would have predictably resulted in providing time progression analysis insight into the strength of certain relationships, the degree of involvement of certain people or groups in implementing policies or in making decisions, and/or the relative importance of certain communication channels to assist in making decisions for purposes of organizational planning, employee or project management, hence creating a more efficient flow of communications, task assignment, and/or employee development (Fig. 4, Fig 5, FIG. 6(a) through FIG. 6(i); ¶6-¶11, ¶14-¶19, ¶41, ¶48, ¶65-¶68, ¶155, ¶182, ¶186-¶188, ¶197, ¶211, ¶259, ¶265, ¶273).
wherein the interaction intensity between the first and second employees during the first time period is represented by a first distance between the first and second employees (¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links. Note that in some embodiments, a more insightful/useful organizational chart can be constructed by optimizing the layout of the organization to cause the strongest lines of influence to be the shortest lines on the new layout”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”)
Applicant’s disclosure teaches at ¶24: “The system and method may first determine a calculation rule to represent interactions between an employee with the rest of the organization. This calculation may take into consideration the digital activity of the employee related to the day-to-day collaboration with other employees. As an example, the intensity of the collaboration with other employees may be represented by the distance”.
Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner interprets at least the various techniques for generating organizational charts and/or visualizations of different types of interactions between members of an organization for identifying influence strength/factors, flow of information, and calculating distance as teaching applicant’s interaction intensity.
Although Grady Smith does not distinctly describe verbatim the wording of applicant’s claim limitation (determining an interaction intensity between the employees during a first time period based upon the interaction data). Grady Smith teaches a method/system for an interaction-weighted visualization of an organization or group, with the relationships between members being based on, or weighted by, the amount, type, subject matter, degree, or significance of interactions between them and the flow of communications between members. Grady Smith discloses at Fig 6a, an “Interaction Based Chart”- Lines represent interactions between people; and techniques for tracking and analyzing various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to develop a visual representation of the operational or functional structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization. Grady Smith further discloses that the visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship which may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow including but not limited to a particular date range for interaction analysis, additional weight can be assigned to more recent interactions and that different types of interactions may be weighted differently, Grady Smith also teaches a process or method for implementing one or more of statistical, machine learning based, rule based, filtering, or other form of data analysis on the interaction and participant information, and for assisting in making decisions relevant to an organization (e.g., generating recommendations, generating probabilities of success, assigning a “cost” or “value” to a possible decision, etc.) based on that data analysis. Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to use the techniques for generating an interaction-weighted visualization of an organization with the relationships between members as taught by Grady Smith to access, track, and analyze various types/characteristics of organizational interactions between members based on or weighted by the amount, type, degree, or significance of interactions between them and the flow of communications between members, etc to show how the interaction(s) evolve over time. The known techniques for generating an interaction-weighted visualization of an organization with the relationships between members as taught by Grady Smith would have predictably resulted in providing insight into the strength of certain relationships, the degree of involvement of certain people or groups in implementing policies or in making decisions, or the relative importance of certain communication channels to assist in making decisions for purposes of one or more of organizational planning, employee or project management, creating a more efficient flow of communications, task assignment, and/or employee development (Fig. 4, Fig 5, FIG. 6(a) through FIG. 6(i); ¶6-¶11, ¶14-¶19, ¶41, ¶48, ¶65-¶68, ¶155, ¶182, ¶186-¶188, ¶197, ¶211, ¶222)
generating a first visualization of the interaction intensity during the first time period, wherein the first visualization shows the first distance between the first and second employees (Fig 5, Fig 6A, Fig 6b, Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶61: “A process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow”; ¶63: “The indication may be based on a metric related to interactions/information flow. An example of such a visualization is a network model of employees/nodes and one or more types of connecting attributes between the nodes. These connecting attributes may include aspects such as communication/information flows, relative involvement in a project, the number of contacts/interactions, the relative value of an interaction to a specified decision process, an indication of regular contacts, contacts in a specified group, reporting relationships, position in a role-based hierarchy, etc”; ¶184-¶197; ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc”; ¶192: “Adjustment for date of interaction to give more weight to more recent interactions”; ¶196; “The categories of interactions to be considered and the type and participation weightings may be input to the system and adjusted”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links”; ¶201: “FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶214: “The inventive system and methods may be used to generate a representation, and in some cases a characterization, of the interactions between multiple employees/nodes in an organization. As part of generating this representation a method for calculating a metric, termed a “Member-to-Member Interaction Influence Factor (IIF)”; ¶215: a “Member Interaction Profile” can be created for each employee/node/member, which includes their IIF metric(s) as determined based on each possible pairing with another member in the organization; this can be represented in the form of a multi-factor vector. A member's “Total Influence Factor” may be represented by the magnitude of this vector. The cosine similarity between two Interaction Profile vectors is a measure of the similarity of the interaction histories of any two members with regards to their interactions with other members of the organization (note that the process may subtract out the components representing interactions between the two members being compared). Note also that other forms of metrics may be suitable, depending upon the type of data and the use case (such as ranking by most frequent or common interactions, filtering or application of a threshold value, etc.) “; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”)
wherein the technical skill levels of the employees are represented by indicators in the first visualization (Fig 5, Fig 6A, Fig 6b, Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶66: “The employee interaction related data or information that may be accessed and processed as part of implementing an embodiment of the inventive system and methods may include (but is not required to include, nor are other sources or types of data excluded from consideration) information regarding: [0067] The Hierarchy of roles within the organization; [0068] Department/Group Structures (the identification and purpose/task/goals of such structures); ¶77: “Degrees of separation of a person from a specific level or levels of management; ¶220: “] Interaction Analysis for Succession Planning allows an organization to compare the interaction profiles of an employee (Employee A) in a particular position with the profile of another employee (Employee B). This produces a new dimension/metric with which to evaluate successors to a role if Employee A were to vacate their position, and can provide a higher degree of confidence in Employee B's likelihood of being successful in a role”; ¶221: “analysis/evaluation can be performed continuously for multiple combinations of Employee A and Employee B. This allows an organization to proactively identify candidates for promotion, and proactively identify potential succession gaps”)
determining the interaction intensity between the employees during the second time period based upon the interaction data, wherein the interaction intensity between the first and second employees during the second time period is represented by a second distance between the first and second employees, (¶55: “a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.)”; Fig 5, Fig 6A, Fig 6b, ¶63: “This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow. An example of such a visualization is a network model of employees/nodes and one or more types of connecting attributes between the nodes. These connecting attributes may include aspects such as communication/information flows, relative involvement in a project, the number of contacts/interactions, the relative value of an interaction to a specified decision process, an indication of regular contacts, contacts in a specified group, reporting relationships, position in a role-based hierarchy, etc.”; ¶148: “layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure… this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶201: “graph optimization algorithms may be used to minimize an overall metric (such as “value”, “cost”, weighted distance, etc.) of the graph based on the value of the sum of the weight multiplied by the length of all interaction lines, while maintaining non-collision between the individuals on the graph. See, for example, FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions; ¶265: “ FIG. 6(i)—in this representation, the order of occurrence of the interactions is indicated by a numerical sequence—this provides insight into how the communications and interactions occurred over time and may suggest how best to investigate the cause of an issue, how the solution to an issue was developed, etc”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization. This may be used to identify “choke points” in a process, to identify those that influence a decision maker, to determine actual information flows and to modify them to be more effective, etc”)
generating a second visualization of the interaction intensity during the second time period (Fig 5, Fig 6A, Fig 6b, Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶61: “A process or method for generating a visualization/representation of an organization's communications, information and process flows (based on and using the interaction and participant information). The visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship. This indication may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow”; ¶63: “The indication may be based on a metric related to interactions/information flow. An example of such a visualization is a network model of employees/nodes and one or more types of connecting attributes between the nodes. These connecting attributes may include aspects such as communication/information flows, relative involvement in a project, the number of contacts/interactions, the relative value of an interaction to a specified decision process, an indication of regular contacts, contacts in a specified group, reporting relationships, position in a role-based hierarchy, etc”; ¶148: “The layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶184-¶197; ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc”; ¶192: “Adjustment for date of interaction to give more weight to more recent interactions”; ¶196; “The categories of interactions to be considered and the type and participation weightings may be input to the system and adjusted”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links”; ¶201: “FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶214: “The inventive system and methods may be used to generate a representation, and in some cases a characterization, of the interactions between multiple employees/nodes in an organization. As part of generating this representation a method for calculating a metric, termed a “Member-to-Member Interaction Influence Factor (IIF)”; ¶215: a “Member Interaction Profile” can be created for each employee/node/member, which includes their IIF metric(s) as determined based on each possible pairing with another member in the organization; this can be represented in the form of a multi-factor vector. A member's “Total Influence Factor” may be represented by the magnitude of this vector. The cosine similarity between two Interaction Profile vectors is a measure of the similarity of the interaction histories of any two members with regards to their interactions with other members of the organization (note that the process may subtract out the components representing interactions between the two members being compared). Note also that other forms of metrics may be suitable, depending upon the type of data and the use case (such as ranking by most frequent or common interactions, filtering or application of a threshold value, etc.) “)
comparing the interaction intensity during the first time period to the interaction intensity during the second time period, wherein comparing the interaction intensity comprises comparing the first distance in the first visualization to the second distance in the second visualization (¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; ¶55: “In some organizations or groups, a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.).”; ¶62: “A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶124-¶136; ¶129: Using the outcome of the data analysis, modeling, or decision process (and if desired, by comparing a model of the actual or inferred interactions and/or information flow within an organization to an existing, assumed, or proposed organizational model (such as one based on role, reporting structure, seniority, etc.)), identifying one or more indicators of suggested organizational actions or potential concerns, such as: [0130] resignation of a key employee; [0131] an increased employee churn rate; [0132] a possible reason for a lack of operational effectiveness or efficiency; [0133] factors associated with a successful task or project completion; [0134] indicators of under recognized influencers within the organization; [0135] an employee most likely to have specific information or an understanding of a task or project (which may be valuable in the situation in which the primary contact for that information or task is not available); [0136] potentially more effective communication channels within the organization; or [0137] training or development opportunities for employees that the organization may wish to encourage“; ¶150: “The identified/filtered data may then be processed to determine one or more of correlations, associations, or other relationships between the data input to a model or process (such as employees and the related interaction data) and an event or goal of interest (such as a decision being made, a policy being implemented, etc.). This may include one or more of statistical, machine learning (supervised or unsupervised), rule-based, or other suitable modeling and data mining methods, as suggested by step or stage 406. If applicable to the situation being examined, the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization… Such a comparison/display may provide insight into the flow of information; the flow of information over time, the development of consensus, the implementation of a policy, the formation of a decision, etc”; Fig 6a, Fig 6b, ¶202: “it may be helpful to know the most effective influencers on a project team or in a group in order to conduct a meeting or engage in communications with the right person or set of people. This information can also help in determining the advancement/promotion opportunities for the employee at the hub”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶223: “By performing a vector multiplication between this new vector of “similarities” and a separate weighting vector (which specifies how important each characteristic is), the inventive method can arrive at a single numeric value/metric representing an Interaction Network Fit for the network of employees in the organization (similar to the dot product of two vectors). This value provides a strong indication about how well Employee B fits into the network that Employee A operates in”)
wherein comparing the interaction intensity comprises comparing the indicators in the first visualization to the indicators in the second visualization (Fig 5, Fig 6A, Fig 6b; Fig 6a, “Interaction Based Chart”- Lines represent interactions between people; -Can be filtered to only show lines for #interactions above certain threshold, within particular date range, for a particular topic”; ¶11: “by evaluating and analyzing interactions such as emails, meetings, events, and other business related-interactions, it is possible to generate an organizational representation/structure where individuals or groups are connected by shared experiences and/or knowledge. Further, the type and frequency of interactions can be used to further define the strength or weights of connections and provide insight into certain operational aspects of the organization”; 41: “FIG. 6(a) through FIG. 6(i) are diagrams illustrating forms of organizational charts or visualizations that may be generated by an embodiment of the inventive system and methods, and then used in making decisions or evaluating the operation of an organization; ¶55: “In some organizations or groups, a network model of people and interactions may be developed and used for analysis of the operations of the organization or group. In such models, individuals or groups may be indicated as a “node” with nodes being separated by “paths” or links. The paths or links can be chosen to represent an attribute (such as a type or level of interactions). Based on the attribute represented by a link, a metric may be developed that can be used to characterize the relationship between two nodes (such as a measure of “distance”, a magnitude of the difference in the value of a parameter between two nodes, etc.).”;¶62: “A data acquisition, processing, and storage sub-system configured for use in acquiring and processing interaction and participant information for an organization, from sources including (but not limited to, or required to include) email, voice mail, call records, text messages, calendaring or event information, task records, time entry records, etc”; ¶148: “The layout of FIG. 6(b) has been constructed to emphasize the degree, number or significance of interactions between individuals or groups. This is suggested by the thickness or strength of a connection between two nodes in the figure. In some representations, this type of figure may be optimized to show shorter distances between heavily interacting members. This embodiment/layout of the chart may be used to identify and emphasize the team structure and suggest the most involved members or participants”; ¶150: “The identified/filtered data may then be processed to determine one or more of correlations, associations, or other relationships between the data input to a model or process (such as employees and the related interaction data) and an event or goal of interest (such as a decision being made, a policy being implemented, etc.). This may include one or more of statistical, machine learning (supervised or unsupervised), rule-based, or other suitable modeling and data mining methods, as suggested by step or stage 406. If applicable to the situation being examined, the results of the processing or modeling may be compared to the expected or intended relationships and/or data flow within an organization… Such a comparison/display may provide insight into the flow of information; the flow of information over time, the development of consensus, the implementation of a policy, the formation of a decision, etc”; Fig 6a, Fig 6b, ¶184-¶197; ¶180: “Time & Date of interaction to monitor interactions over time, and if desired, give more weight to recent interactions, to filter interactions to use in analysis based on when they occurred;“ ¶186: “calculating Member-to-Member Interaction Influence Factors (IIF) and using them to construct representative “Lines of Influence” between members of the organization. The IIF values may be dynamically determined based on interactions”; ¶187: “the structure/visualization may include further information based on one or more of the quantity, type, participation level or date of interactions between members (or other indicia or metrics that may be derived from the available interaction data). Further, different types of interactions may be weighted differently, thereby giving more significance or assumed influence to a shared meeting as opposed to a chat room mention, for example. Additional weight can be given based on the level of participation in each interaction, if that information is known. When given a particular date range for interaction analysis, additional weight can be assigned to more recent interactions, or those associated with the implementation of a particular policy, etc”; ¶192: “Adjustment for date of interaction to give more weight to more recent interactions”; ¶196; “The categories of interactions to be considered and the type and participation weightings may be input to the system and adjusted”; ¶197: “The influence strength between two members is derived in a similar manner, thereby producing a weighted mesh of interactions between all organization members. Influence strength may be a measure of the total amount of interaction between any pair of nodes, or the magnitude of influence between two nodes. The influence strength of each pair can be compared to find pairs with stronger or weaker links”; ¶201: “FIG. 6(a) which illustrates interactions between employees/nodes and FIG. 6(b) which illustrates a modification of FIG. 6(a) that emphasizes the relative amount of interactions between different nodes. As mentioned, the length of a connection between two employees may be shortened (suggesting a greater closeness and amount of interactions) to indicate a greater number and/or significance of interactions between two employees”; ¶211: “Topic Filtered View with Time/Date Details (a Process Flow Map): In addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic. Starting with the interaction-based chart as filtered for a certain topic, a time progression analysis can be applied to show how the interaction lines evolve over time, thereby producing an illustration of a time-evolving process flow. Note that the actual time-based evolution of a process may be different from the documented process flow—comparison between the two can provide suggestions for either improving the actual process or for modifying the documented process to make it more accurate. An example of this form of map or representation is shown in FIG. 6(i)”; ¶222: “Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶259: “FIG. 6(c)—in this representation, the number of interactions that a person/node participates in is indicated by a label associated with the icon for the person—note that based on application of the appropriate filters or thresholds, this may represent the total number of interactions the person engaged in with all others over a specific period of time; the total number related to a specific topic, the total number satisfying a specific rule or condition, etc. This number can be used as the basis for determining the person's relative total influence or another measure of the significance of their interactions”; ¶273: “Mapping process flows, information distribution, or decision processes using appropriate filter(s) (e.g., process, topic, participants, keywords, etc.) and adding data regarding the time/date and/or length of an interaction to obtain insight into the development of a process flow, task, or decision process as a function of time through an organization”)
Although Grady Smith does not distinctly describe verbatim the wording of applicant’s claim limitations, it teaches a method/system for an interaction-weighted visualization of an organization or group, with the relationships between members being based on, or weighted by, the amount, type, subject matter, degree, or significance of interactions between them and the flow of communications between members. Grady Smith discloses at Fig 6a, an “Interaction Based Chart”- Lines represent interactions between people; and techniques for tracking and analyzing various types of organizational interactions (where those interactions are those which primarily involve participation or communication) in order to develop a visual representation of the operational or functional structure of an organization, based at least in part on the interactions, communications, and processes within the organization, where the representation may be used to more efficiently and accurately determine the flow of information and decision making processes within the organization. Grady Smith further discloses that the visualization or representation may include an indication of the relative strength or importance of a particular flow or relationship which may be based on the number of interactions, the frequency of interactions, the category or subject matter of the interactions, or another characteristic of the interactions. The indication may be based on a metric related to interactions/information flow including but not limited to a particular date range for interaction analysis, additional weight can be assigned to more recent interactions and that different types of interactions may be weighted differently, Grady Smith also teaches a process or method for implementing one or more of statistical, machine learning based, rule based, filtering, or other form of data analysis on the interaction and participant information, and for assisting in making decisions relevant to an organization (e.g., generating recommendations, generating probabilities of success, assigning a “cost” or “value” to a possible decision, etc.) based on that data analysis. Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the techniques for generating an interaction-weighted visualization of an organization as taught by Grady Smith to access, track, and analyze various types/characteristics of organizational interactions between members based on or weighted by the amount, type, degree, or significance of interactions between them and the flow of communications between members, etc to show how the interaction(s) evolve over time. The known techniques for generating an interaction-weighted visualization of an organization of Grady Smith would have predictably resulted in providing visualization and time progression analysis insight into the strength of certain relationships, the degree of involvement of certain people or groups in implementing policies or in making decisions, or the relative importance of certain communication channels to assist in making decisions for purposes of one or more of organizational planning, employee or project management, hence, creating a more efficient flow of communications, task assignment, and/or employee development (Fig. 4, Fig 5, FIG. 6(a) through FIG. 6(i); ¶6-¶11, ¶14-¶19, ¶41, ¶48, ¶65-¶68, ¶155, ¶182, ¶186-¶188, ¶197, ¶211, ¶259, 265, ¶273).
Grady Smith discloses all of the above limitations, Grady Smith does not distinctly describe the following limitations but Kim however as shown discloses,
wherein the first distance is determined using: D = a*X + b*Y + c*Z where D is the first distance, X is a number of the emails sent from the first employee to the second employee, Y is a number of the typed chats sent from the first employee to the second employee, Z is a number of hours of the meetings where the first and second employees are both present, a is a first coefficient, b is a second coefficient that is greater than the first coefficient, and c is a third coefficient that is between the first and second coefficients (Fig 1, Fig 3, Figs 6-8, Fig 11, Fig 12; Abstract: “Metadata is extracted from the captured communication data and the extracted communication metadata is analyzed by (i) calculating time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise adjacency matrix based on the attention time each participant spent communicating with each other participant”; ¶9: “the extracted communication metadata and sensor data is analyzed by: (i) calculating time spent on each activity or interaction (e.g., email, meeting, call, chat, conversation) for each participant; (ii) prioritizing activities and interactions for each participant; and (iii) building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating with each other participant. According to some embodiments, the pairwise directional weighted adjacency matrix may be utilized to create and display a visualization illustrating the communication distribution of the participants throughout the organization”; ¶27: “FIG. 11 depicts an example visualization illustrating the amount of time different teams of participants spend communicating on email, chats, meetings and calls, according to embodiments of the present disclosure”; ¶53: “the Participant Analytics Platform 250 may use the pairwise directional weighted adjacency matrix to create and display a visualization illustrating the communication distribution of the participants (or groups of participants) throughout the organization”; ¶60: “According to some embodiments, the DGGT 225 may utilize application programing interface (API) calls to extract the various metadata fields from the one or more communication servers (230 and 235) capturing the underlying digital communication data”; ¶68: “FIG. 6 is a communication and activity graph for an example workday for a given participant. In some embodiments, the Participant Analytics Platform 250 may analyze the metadata extracted from the digital communication data and the sensor data to create the graphs illustrated in FIG. 6”; ¶75: “the amount of time spent by a participant on an instant message chat session is determined as a function of number of involved messages, and in particular by the total number of messages multiplied by an amount of time estimated for each message”; ¶78: “FIG. 8 illustrates an example method 800 of building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating with each other participant, according to some embodiments”; ¶87: “the Participant Analytics Platform 250 may determine various metrics that are objectively representative of human interaction and activity within an organization. For example, the Participant Analytics Platform 250 may create a Participant Pairwise Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization”; ¶93: “each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums”; ¶96: “FIG. 9 depicts an example visualization of communication distribution between participants throughout an organization according to embodiments of the present disclosure. In some embodiments, the visualization depicted in FIG. 9 may be generated by the collaboration and delivery module of the Participant Analytics Platform 250. The nodes in the edge weighted graph represent participants and the edges represent communications and interactions between the participants. The larger the size of the node the greater the amount communication and interactions the respective participant had with other participants. Further, the distance between any two nodes represents that amount of communication and interaction between those respective participants; the shorter the distance the more communication and interaction”; ¶101: “the Participant Analytics Platform 250 may determine the total time that a participant spends on each communication medium over a given period of time (e.g., day, week, month, etc.) In some embodiments, the Participant Analytics Platform 250 may calculate the amount of time spent communicating on each medium”; ¶102: “ the analysis returns a single dictionary that has info on the amounts of time spent on all communication media (time spent on email, time spent on chat, time spent on meetings, time spent on calls (not meetings), time spent on face-to-face interactions, and estimated time not involved in communication during normal work hours) for each of during work, during after-work, overall “;¶110: , the Participant Analytics Platform 250 calculates the number of attention minutes each participant spends with managers, non-managers, and external persons, as well as whether the attention minutes were spent during work hours or after work hours“)
wherein X, Y, and Z are normalized based upon a distribution of the interaction data (¶234-¶236; Figs 6- Fig 10; ¶236: “Preparing a pairwise attention minute matrix of the same participant group, normalized to be between 0-1.1 is the maximum amount of communication a pair can have”)
Applicant’s disclosure teaches at ¶24: “The system and method may first determine a calculation rule to represent interactions between an employee with the rest of the organization. This calculation may take into consideration the digital activity of the employee related to the day-to-day collaboration with other employees. As an example, the intensity of the collaboration with other employees may be represented by the distance”; and ¶36: “The interaction intensity between the first and second employees during the first time period may be represented by a first distance between the first and second employees. The first distance may be determined using: D = a*X + b*Y + c*Z, where D is the first distance, X is a number of the emails sent from the first employee to the second employee, Y is a number of the typed chats sent from the first employee to the second employee, Z is a number of hours of the meetings where the first and second employees are both present, a is a first coefficient, b is a second coefficient that is greater than the first coefficient, and c is a third coefficient that is between the first and second coefficients”.
Giving the broadest reasonable interpretation of the claim limitation in light of the specification, applicant’s technique for calculating distance D = a*X + b*Y + c*Z, where D is the first distance, X is a number of the emails sent from the first employee to the second employee, Y is a number of the typed chats sent from the first employee to the second employee, Z is a number of hours of the meetings where the first and second employees are both present, a is a first coefficient, b is a second coefficient that is greater than the first coefficient, and c is a third coefficient that is between the first and second coefficients”, is simply a linear equation, comprising three (weighted) coefficients (a, b, and c) and three variables (X, Y, Z). Further, the equation of a plane in three-dimensional space, where a, b and c are weighted coefficients, represents a form of linearization and the equation of a plane in three-dimensional space. Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner contends that the techniques for building a weighted adjacency matrix as taught by Kim is also a form of linearization.
Grady Smith discusses that in addition to a topic filter, adding time/date details of interactions allows establishing the time-based order of interactions related to a certain topic to show how the interaction lines evolve over time, thereby producing a time progression analysis. Grady Smith discloses graph optimization algorithms for illustrating interactions between employees, and that given two interaction profiles of employees, using data processing and interaction vector analysis techniques the distance between each profile can be calculated.
Although Kim does not distinctly describe verbatim the wording of applicant’s claim limitation, Kim teaches a Participant Analytics Platform for determining various metrics that are objectively representative of activity within an organization via a Participant Pairwise (weighted) Interaction Adjacency Matrix to visualize and determine the communication distribution of individuals per day throughout the organization. Kim also teaches that each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums and providing a visualization of communication distribution between participants throughout an organization. The visualization of communication distribution between participants may be generated via the collaboration and delivery module of the Participant Analysis Platform via a weighted graph representing communications, interactions and distance between participants. Kim also discloses that the Participant Analytics Platform can determine time series activity and interactions of a participant for concurrent activities as well as building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating (meetings, calls, emails, and other communication types (chat, face-to face interactions) with each other participant. Kim also discloses that the amount of time spent by a participant on an instant message chat session is determined as a function of number of involved messages, and in particular by the total number of messages multiplied by an amount of time estimated for each message.
A person of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to combine the techniques for measuring communication distribution throughout an organization as taught by Kim with the method/system for interaction-weighted visualization of Grady Smith to achieve the claimed invention with a reasonable expectation of success in doing so, for the purposes of objectively representing activity within an organization by visualizing, calculating distance and measuring the weighted communication distribution of members via the Participant Analytics Platform.
Grady Smith and Kim are directed to the same field of endeavor since they are related to maintaining, processing and visualizing data related to an organization and its employees in a computing environment. Therefore, one of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to modify the method/system for interaction-weighted visualization of Grady Smith with the techniques for measuring communication distribution throughout an organization as taught by Kim since it allows for improving data processing and analysis technology in order to enable computer systems to accurately and objectively measure communication distribution throughout an organization based on large amounts of data (¶234-¶236; Figs 6- Fig 10)
Grady Smith and Kim disclose all of the above limitations, the combination of Grady Smith and Kim does not distinctly describe the following limitations, but Rivette however as shown discloses,
receiving patent data representing a number of patent applications or patents where each of the employees is listed as an inventor (Abstract: “The processing automatically performed by the system relates to (but is not limited to) patent mapping, document mapping, patent citation (both forward and backward), patent aging, patent bracketing/clustering (both forward and backward), inventor patent count, inventor employment information, patent claim tree analysis, and finance. Other functions and capabilities are also covered, including the ability to utilize hyperbolic trees to visualize data generated by the system, method, and computer program product”; ¶69: “FIGS. 74-77 are examples of inventor patent count display formats”; ¶1041: “The inventor patent count module 1012 in the enterprise server 314 operates to analyze patent inventor information to identify the top inventors for an operator-specified group. Top inventors are defined herein as being persons who most frequently are named as inventors on the patents in the group”; ¶1046: “the inventor patent count module 1012 extracts additional pertinent information from the databases 316, such as each person's employment status from the employee table 1243, the relevance of the identified patents from the corp_patent_xref table 1233, and the corporate levels of the corporate entities that own the identified patents from the corporate_entity table 1230.)
Rivette teaches a method/system for patent-centric and group-oriented data processing. Rivette further teaches maintaining databases of patents and non-patent information to a corporate entity whereby the groups may be product based, person based, corporate entity based, or user-defined.
Grady Smith, Kim and Rivette are directed to the same field of endeavor since they are related to maintaining, processing and visualizing data related to an organization and its employees in a computing environment. Therefore, one of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to modify the method/system for interaction-weighted visualization of Grady Smith with the techniques for measuring weighted communication distribution throughout an organization of Kim and the method/system for patent-centric and group-oriented data processing as taught by Rivette since it allows for maintaining databases of patent information of interest to a corporate entity for data presentation and processing whereby inventor patent count, and inventor employment information can be visualized (Abstract, ¶3, ¶25, ¶69, ¶1041, ¶1046, FIGS. 74-77 ).
Grady Smith further describes,
and performing an action in response to the patent data, the interaction intensity between the employees during the second time period, the second visualization, and the comparison (¶129: Using the outcome of the data analysis, modeling, or decision process (and if desired, by comparing a model of the actual or inferred interactions and/or information flow within an organization to an existing, assumed, or proposed organizational model (such as one based on role, reporting structure, seniority, etc.)), identifying one or more indicators of suggested organizational actions or potential concerns, such as: [0130] resignation of a key employee; [0131] an increased employee churn rate; [0132] a possible reason for a lack of operational effectiveness or efficiency; [0133] factors associated with a successful task or project completion; [0134] indicators of under recognized influencers within the organization; [0135] an employee most likely to have specific information or an understanding of a task or project (which may be valuable in the situation in which the primary contact for that information or task is not available); [0136] potentially more effective communication channels within the organization; or [0137] training or development opportunities for employees that the organization may wish to encourage“; ¶249: “Interaction analysis can be used to assist in making hiring or promotion decisions”
With respect to claim 17,
Grady Smith, Rivette and Kim discloses all of the above limitations, Kim further discloses,
wherein a is from about 1.1 to about 1.5, wherein b is from about 1.8 to about 2.2, and wherein c is from about 1.4 to about 1.8.( (Fig 1, Fig 3, Figs 6-8, Fig 11, Fig 12; ¶27: “FIG. 11 depicts an example visualization illustrating the amount of time different teams of participants spend communicating on email, chats, meetings and calls, according to embodiments of the present disclosure”; ¶60: “According to some embodiments, the DGGT 225 may utilize application programing interface (API) calls to extract the various metadata fields from the one or more communication servers (230 and 235) capturing the underlying digital communication data”; ¶68: “FIG. 6 is a communication and activity graph for an example workday for a given participant. In some embodiments, the Participant Analytics Platform 250 may analyze the metadata extracted from the digital communication data and the sensor data to create the graphs illustrated in FIG. 6”; ¶75: “the amount of time spent by a participant on an instant message chat session is determined as a function of number of involved messages, and in particular by the total number of messages multiplied by an amount of time estimated for each message”;¶78: “FIG. 8 illustrates an example method 800 of building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating with each other participant, according to some embodiments”; ¶87: “the Participant Analytics Platform 250 may determine various metrics that are objectively representative of human interaction and activity within an organization. For example, the Participant Analytics Platform 250 may create a Participant Pairwise Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization”; ¶93: “each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums”)
Kim teaches a Participant Analytics Platform for determining various metrics that are objectively representative of activity within an organization via a Participant Pairwise (weighted) Interaction Adjacency Matrix to determine the communication distribution of individuals per day throughout the organization. Kim also teaches that each communication medium (email, meetings, calls, chats, face-to-face interaction, etc.) may be used to build a separate adjacency matrix or all aggregated in a single adjacency matrix for total attention time spent for all communication mediums and providing a visualization of communication distribution between participants throughout an organization. The visualization of communication distribution between participants may be generated via the collaboration and delivery module of the Participant Analysis Platform via a weighted graph representing communications, interactions and distance between participants. Kim also discloses that the Participant Analytics Platform can determine time series activity and interactions of a participant for concurrent activities as well as building a pairwise directional weighted adjacency matrix based on the attention time each participant spent communicating (meetings, calls, emails, and other communication types (chat, face-to face interactions) with each other participant.
Applicant’s disclosure generically teaches, ¶37: “The coefficients may provide more or less weight to one channel over the other ones”. Giving the broadest reasonable interpretation of the claim limitation in light of the specification, Examiner contends that the techniques for building a weighted adjacency matrix based on a value(s) as taught by Kim as teaching applicant’s a, b, c (coefficient) values. Hence, applicant’s coefficient values (numbers) are merely weights defined for the respective interaction data/factor (i.e. chat, email, meeting) used to calculate a first distance. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim to identify the distance between participants and the communications and interactions the respective participant had with other participants based on a specific weight (value) for the associated communication medium (email, meetings, calls, chats, face-to-face interaction) whereby a visualization of communication distribution between participants can be represented via a dashboard. The known techniques for measuring and visualizing communication distribution throughout an organization via the Participant Analytics Platform as taught by Kim would have predictably resulted in determining and providing various metrics that are objectively representative of interaction and communication activity within an organization including distance between respective participants for the purposes of identifying the amount of communications and interactions a respective participant had with other participants via a weighted graph. Therefore, one of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to combine the method/system for interaction-weighted visualization of Grady Smith with the techniques for measuring communication distribution throughout an organization as taught by Kim since it allows for improving data processing and analysis of interaction metadata to accurately and objectively measure and represent communication distribution (via distance) throughout an organization based participants’ interactions via a weighted graph (Fig 1, Fig 3, Figs 6- Fig 12; ¶7, ¶27, ¶53, ¶60, ¶75, ¶77, ¶78, ¶87, ¶93, ¶95, ¶96, ¶101, ¶102, ¶110)
With respect to claim 18,
Grady Smith, Rivette and Kim discloses all of the above limitations, Grady Smith further discloses,
wherein the action comprises generating or transmitting a signal or notification that recommends, instructs, or causes the technical skill structure to be updated (¶198: “since the interaction-weighted org-chart is generated and updated automatically from real-time data, it can be assumed to be up-to-date and reflect presently existing or current relationships, patterns of communication, etc”; ¶249: “Interaction analysis can be used to assist in making hiring or promotion decisions”; ¶129: Using the outcome of the data analysis, modeling, or decision process (and if desired, by comparing a model of the actual or inferred interactions and/or information flow within an organization to an existing, assumed, or proposed organizational model (such as one based on role, reporting structure, seniority, etc.)), identifying one or more indicators of suggested organizational actions or potential concerns, such as: [0130] resignation of a key employee; [0131] an increased employee churn rate; [0132] a possible reason for a lack of operational effectiveness or efficiency; [0133] factors associated with a successful task or project completion; [0134] indicators of under recognized influencers within the organization; [0135] an employee most likely to have specific information or an understanding of a task or project (which may be valuable in the situation in which the primary contact for that information or task is not available); [0136] potentially more effective communication channels within the organization; or [0137] training or development opportunities for employees that the organization may wish to encourage“)
With respect to claim 19,
Grady Smith, Rivette and Kim discloses all of the above limitations, Grady Smith further discloses,
wherein the technical skill structure is updated to promote or demote the first employee to a different employment level (¶172: “Finding patterns between interaction behavior and performance/productivity used to recommend employee or group development, promotions, etc”; ¶194: “An overall direction of influence between two members can be established by comparing their relative factors. Identifying employees with net outward influence could be used to determine candidates for promotion, management, or as champions for ideas and projects; and [0195] The suggested formula may be modified to include additional weights based on a hierarchy level or other attributes that relate one person to another within the organization (tenure, scope of management responsibilities, etc.)”; ¶198: “since the interaction-weighted org-chart is generated and updated automatically from real-time data, it can be assumed to be up-to-date and reflect presently existing or current relationships, patterns of communication, etc”; ¶202: “One or more of the visualizations (such as FIG. 6(a) or 6(b)) may be rendered as a hub-spoke model, where the employee at the hub is the employee of current focus and the thickness of spokes represented the amount of influence/interaction with other employees along the circumference… can also help in determining the advancement/promotion opportunities for the employee at the hub”; ¶221: “This allows an organization to proactively identify candidates for promotion, and proactively identify potential succession gaps; [0222] Given two interaction Profiles (one for Employee A and one for Employee B, or for group A and Group B), the inventive methods can be used to calculate the distance between each profile, thereby generating an effective measure of the similarity in interactions between the two”; ¶249: “Interaction analysis can be used to assist in making hiring or promotion decisions. Traditional factors for hiring/promoting may include performance, education, previous experience, and the hiring managers “feeling” about the candidate. Interaction activity is an additional factor to consider that can provide a measure of the amount and significance of an individual's previous interactions within the organization”)
With respect to claim 20,
Grady Smith, Rivette and Kim discloses all of the above limitations, Grady Smith further discloses,
wherein promoting or demoting the first employee comprises automatically adjusting an ability of the first employee to enter a building or access technical information stored in a database (¶254: “Interaction analysis can provide insights and alternative suggestions for facilities organization and planning. Measuring the quantity of interactions between individuals and groups and comparing this across the organization indicates which parties communicate more often and participate in similar events. Instead of seating being based on functional groups, the inventive interaction analysis may recommend placing groups closer based on shared experiences. This could translate into an increase in productivity and savings for the organization, since it facilitates communication between members and groups that have a proven interest in interacting; [0255] For example, instead of placing the administrative functions of Legal, Finance; and HR together based on a cost-center perspective, interaction analysis may show that the Legal department has more interactions with Sales & Marketing, or that HR has more interaction with IT. The inventive analysis can also be used to determine the seating arrangement for individual participants (e.g., for one particular lawyer it may be beneficial to sit near legal)”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY L EVANS whose telephone number is (571)270-3929. The examiner can normally be reached M-F 730a-5p. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached on (571)272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIMBERLY L EVANS/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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