DETAILED ACTION
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 .
The following FINAL Office Action is in response to Applicant’s communication filed 06/04/2025.
Status of Claim(s)
Claim(s) 1-2, 4-9, 11-15, and 17-21 is/are currently pending and are rejected as follows.
Response to Arguments – 103 Rejection
Applicant’s arguments and amendments in regards to the previously applied 103 rejection have been fully considered but are not deemed persuasive.
Applicant argues that the prior art of Rogynskyy fails to read on Applicant’s limitations, specifically the identification of inconsistencies between an engagement score and engagement plan and determining a specific action to reduce the inconsistencies, and further fails to send a signal to implement the specific action. Applicant also claims that the art of Rogynskyy fails to disclose “further training the reinforcement learning model based on the engagement score for the user”.
Examiner does not find Applicant’s arguments persuasive for the following reasons. First the art of Rogynskyy recites the use of a performance metric, and similarly can compare that performance metric with an expected level of performance. These are deemed equivalent to the engagement score and engagement plan as disclosed in Applicant’s claims under broadest reasonable interpretation and in view of Applicant’s specification. The art of Rogynskyy then continues to observe a difference in expected performance, and comparative performance to diagnose and determine an ‘inconsistency’ in terms of the initial node (which as explained by Rogynskyy is equivalent to a user or individual) and the expected performance of the node (Rogynskyy: Paragraph 139 and 412) . Rogynskyy then, can use this information and determine actions and activities that can help to cause the performance to meet the expected performance. (Rogynskyy: Paragraph 426, 428, and 430) which was deemed to read on Applicant’s claimed limitations. With regards to Applicant’s argument, Rogynskyy in Paragraph 235 explains how the models can be updated as electronic activities are matched, therefore reading on the idea of constantly re-training the model as new data becomes relevant. This is further supported in Paragraph 236 where it is discussed how the model can be updated based on indications of acceptance, rejection, or remapping of the electronic activities to the relevant records for determining engagement. Therefore the previously applied prior art continues to read on Applicant’s claim even as amended. Further citations and elaborations are given in the amended prior art rejection below.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1-2, 4-5, 7-9, 11-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rogynskyy (US 2019/0364131 Al) in view of Ackermann (US 2022/0138233 Al)
Claim(s) 1 –
Rogynskyy discloses the following:
defining, using a reinforcement learning model trained to increase a reward specific to an overall strategy of an entity, an engagement plan for a user, the engagement plan for the user specific to past engagement of the user with the entity; (Rogynskyy: Paragraph 240, "In addition, the matching strategies can be designed or created to identify candidate record objects using other types of data included in the node graph generation system, or one or more systems of record, among others. In some embodiments, the matching strategies can be generated by analyzing how one or more electronic activities are matched to one or more record objects, including using machine learning techniques to generate matching strategies in a supervised or unsupervised learning environments."; Paragraph 426, "The node graph generation system 200 can identify metrics for each member node profile based on the electronic activities. The node graph generation system can correlate the metrics with desired performance outcomes or results, including but not limited to closed sales, recruited candidates, or renewed contracts to identify which metrics are correlated with desired performance outcomes. Based on identifying the desired metrics that result in desired outcomes, the node graph generation system 200 can set one or more goals for member nodes, as well as help track those goals to increase the likelihood that the member node achieves the desired performance outcome, thereby improving the likelihood that the member node achieves the desired performance outcome."; Paragraph 427, "The node graph generation system 200 can, for example, provide these recommendations or target goals to one or more member nodes or one or more group nodes based on historical matching electronic activities to desired performance outcomes. The node graph generation system 200 ( or one or more component thereof) can match electronic activities to desired performance outcomes stored or indicated in one or more systems of record.")
receiving engagement data from a plurality of digital artifacts associated with the user; (Rogynskyy: Paragraph 428, "The node graph generation system 200 can include a performance module designed and constructed to determine a performance metric or performance level of a member node based on electronic activities. To generate a recommendation, the node graph generation system 200 (via a performance module 280 and recommendation engine 275) can identify member node performance as compared to a member node's past performance or as compared to the performance of other member nodes that have a similar role or otherwise share similar characteristics. The node graph generation system 200 (e.g., via a member node performance module) can determine a performance of a member node. For example, the node graph generation system 200 can identify electronic activities associated with multiple member nodes that are linked to a group node in a group node graph. The node graph generation system 200 can then identify a system of record associated with the group node. The system of record can include account record objects, lead record objects, opportunity record objects, deal record objects or other types of record objects. The system of record can include stages for any business process, such as opportunities with stages, recruiting of candidate with interview stages, renewing contract with renewal stages, etc. In an illustrative example, an opportunity record object can include multiple sequential stages for the opportunity, such as a first stage, second stage, third stage, and a fourth stage, where the first stage indicates an initial stage and the fourth stage indicates a final or completion stage for the opportunity. The node graph generation system 200 can correlate electronic activities with the opportunity record objects as well as the stages of the opportunity record objects. The node graph generation system 200 can determine metrics based on electronic activities that are associated with an opportunity advancing stages or not advancing stages. For example, the node graph generation system 200 can correlate that, on average: 5 emails and 1 in-person meeting occurred in a time interval for an opportunity before it moved from a first stage to a second stage; 10 emails and 2 in-person meetings occurred during a time interval for an opportunity to move from a second stage to a third stage; 15 emails and 3 inperson meetings occurred during a time interval for an opportunity to move from a third stage to a fourth stage; and 20 emails and 4 in-person meetings occurred during a time interval for an opportunity to move from a third stage to a fourth or final stage. By determining metrics that are correlated with advancing an opportunity from one stage to another based on electronic activities correlated with stages in opportunity record objects stored in a system of record, the node graph generation system 200 ( or component or module thereof) can predict or forecast metrics that, when met, are likely to result in the desired performance outcome. The node graph generation system 200 can determine which metrics of electronic activities have the highest correlation to successful outcomes in order to generate goals.")
assigning a focus area score for each digital artifact from the plurality of digital artifacts based on the context associated with each term associated with that digital artifact, the focus area score for each digital artifact from the plurality of digital artifacts indicating a level of association of that digital artifact with each focus area from a plurality of focus areas; (Rogynskyy: Paragraph 428, "The node graph generation system 200 can include a performance module designed and constructed to determine a performance metric or performance level of a member node based on electronic activities. To generate a recommendation, the node graph generation system 200 (via a performance module 280 and recommendation engine 275) can identify member node performance as compared to a member node's past performance or as compared to the performance of other member nodes that have a similar role or otherwise share similar characteristics. The node graph generation system 200 (e.g., via a member node performance module) can determine a performance of a member node. For example, the node graph generation system 200 can identify electronic activities associated with multiple member nodes that are linked to a group node in a group node graph. The node graph generation system 200 can then identify a system of record associated with the group node. The system of record can include account record objects, lead record objects, opportunity record objects, deal record objects or other types of record objects. The system of record can include stages for any business process, such as opportunities with stages, recruiting of candidate with interview stages, renewing contract with renewal stages, etc. In an illustrative example, an opportunity record object can include multiple sequential stages for the opportunity, such as a first stage, second stage, third stage, and a fourth stage, where the first stage indicates an initial stage and the fourth stage indicates a final or completion stage for the opportunity. The node graph generation system 200 can correlate electronic activities with the opportunity record objects as well as the stages of the opportunity record objects. The node graph generation system 200 can determine metrics based on electronic activities that are associated with an opportunity advancing stages or not advancing stages. For example, the node graph generation system 200 can correlate that, on average: 5 emails and 1 inperson meeting occurred in a time interval for an opportunity before it moved from a first stage to a second stage; 10 emails and 2 in-person meetings occurred during a time interval for an opportunity to move from a second stage to a third stage; 15 emails and 3 in-person meetings occurred during a time interval for an opportunity to move from a third stage to a fourth stage; and 20 emails and 4 inperson meetings occurred during a time interval for an opportunity to move from a third stage to a fourth or final stage. By determining metrics that are correlated with advancing an opportunity from one stage to another based on electronic activities correlated with stages in opportunity record objects stored in a system of record, the node graph generation system 200 ( or component or module thereof) can predict or forecast metrics that, when met, are likely to result in the desired performance outcome. The node graph generation system 200 can determine which metrics of electronic activities have the highest correlation to successful outcomes in order to generate goals."; Paragraph 521, "In some such embodiments, the data processing system 9300 can determine the shadow record object with which the electronic activity most closely matches (or has the highest match score) and cause the electronic activity to match the corresponding record object in the system of record 9360.")
calculating, for each focus area from the plurality of focus areas and based on the level of association of each digital artifact with that focus area, an engagement score for the user; (Rogynskyy: Paragraph 428, "The node graph generation system 200 can include a performance module designed and constructed to determine a performance metric or performance level of a member node based on electronic activities. To generate a recommendation, the node graph generation system 200 (via a performance module 280 and recommendation engine 275) can identify member node performance as compared to a member node's past performance or as compared to the performance of other member nodes that have a similar role or otherwise share similar characteristics. The node graph generation system 200 (e.g., via a member node performance module) can determine a performance of a member node. For example, the node graph generation system 200 can identify electronic activities associated with multiple member nodes that are linked to a group node in a group node graph. The node graph generation system 200 can then identify a system of record associated with the group node. The system of record can include account record objects, lead record objects, opportunity record objects, deal record objects or other types of record objects. The system of record can include stages for any business process, such as opportunities with stages, recruiting of candidate with interview stages, renewing contract with renewal stages, etc. In an illustrative example, an opportunity record object can include multiple sequential stages for the opportunity, such as a first stage, second stage, third stage, and a fourth stage, where the first stage indicates an initial stage and the fourth stage indicates a final or completion stage for the opportunity. The node graph generation system 200 can correlate electronic activities with the opportunity record objects as well as the stages of the opportunity record objects. The node graph generation system 200 can determine metrics based on electronic activities that are associated with an opportunity advancing stages or not advancing stages. For example, the node graph generation system 200 can correlate that, on average: 5 emails and 1 inperson meeting occurred in a time interval for an opportunity before it moved from a first stage to a second stage; 10 emails and 2 in-person meetings occurred during a time interval for an opportunity to move from a second stage to a third stage; 15 emails and 3 in-person meetings occurred during a time interval for an opportunity to move from a third stage to a fourth stage; and 20 emails and 4 inperson meetings occurred during a time interval for an opportunity to move from a third stage to a fourth or final stage. By determining metrics that are correlated with advancing an opportunity from one stage to another based on electronic activities correlated with stages in opportunity record objects stored in a system of record, the node graph generation system 200 ( or component or module thereof) can predict or forecast metrics that, when met, are likely to result in the desired performance outcome. The node graph generation system 200 can determine which metrics of electronic activities have the highest correlation to successful outcomes in order to generate goals."; Paragraph 521, "In some such embodiments, the data processing system 9300 can determine the shadow record object with which the electronic activity most closely matches (or has the highest match score) and cause the electronic activity to match the corresponding record object in the system of record 9360.")
comparing the engagement score for the user to the engagement plan for the user to identify inconsistencies between the engagement score and the engagement plan; (Rogynskyy: Paragraph 430, "The node graph generation system 200 (e.g., via recommendation engine 275) can identify a temporal aspect to the metrics associated with the member node. The node graph generation system 200 can determine when member node first joined the group node or was first linked to the group node (e.g., a job start date or beginning date), and how the member node's performance and behavior metrics evolved over time. This initial time interval can be referred to as a ramp-up period (e.g., when an employee first joins a company and then gets up to speed or ramps up). The node graph generation system 200 can identify metrics associated with a successful ramp-up period based on identifying member nodes that are associated with desired performance outcomes based on reaching desired stages in an opportunity record object (i.e. by analyzing how successful employees had ramped in the past). Thus, by analyzing electronic activities and a corresponding system of record to determine data driven metrics associated with desired performance outcomes determined by linking activities with record objects describing process stages (e.g., an opportunity record) in the system of record, the node graph generation system 200 can generate or identify goals to set for member nodes that are in a ramp-up period or other time interval, such as during a performance improvement plan (a plan, set up by employee's manager to bring the employee to optimal performance after a period of poor performance). The node graph generation system 200 can further reevaluate the member node's metrics to update the goals or set new goals by comparing current metrics (e.g., actual actions or performance) associated with the member node's current electronic activities with the desired metrics (e.g., planned actions or performance) for electronic activities correlated with the desired performance outcome or result.")
defining, based on the inconsistencies, a specific action for the user to reduce the inconsistencies; and (Rogynskyy: Paragraph 431, "For instance, a high performing employee may be involved in electronic activities that are linked to opportunity record objects that advance from one of the stages to another stage much quicker than another employee with the same role. Similarly, a high performing employee may be involved in electronic activities that are linked to a greater number of opportunity record objects that advance from one of the stages to another stage than another employee with the same role. as such, by tracking the opportunity record objects with which an employee is linked, a performance of the employee can be determined and the employee's metrics can be used to set certain benchmarks that can then be used to determine a performance of another employee with a similar role or generate a ramp up schedule based on the employee's metrics. For example, the node graph generation system 200 can determine that when a member node completes 25 calls in a week, reaches out to 10 companies in a week, has 5 in-person meetings in a week, and then writes 100 emails in the same week, then the member node should be able to complete a number of deals or advance a desired number of stages in one or more deals or otherwise achieve an expected performance outcome after a certain time (e.g., a time delay between input activities and outcome results). The metric can refer to or include an attribute of an activity, such as an amount of the activity. The metric can be a binary value that indicates a yes or no, such as "did you have a meeting with 10 people", with a value of 1 or 0 indicated yes or no, respectively. In some cases, the metric can be a count, a ratio, a time value, or a percentage value, based on any combination/formula, calculated from any number of data points in the member node graph or system of records. The metrics can vary in granularity based on the data the node graph generation system 200 can analyze via electronic activities or one or more systems of record. Based on previous or historical activity, the node graph generation system 200 can predict, forecast or estimate what activity should occur to achieve a desired outcome, and propose or set goals for a member node or group node accordingly. The node graph generation system 200 (e.g., via the electronic activity linking engine) can correlate the electronic activities with the stages or desired outcomes as stored or determined in the system of record or an opportunity record object thereof. The electronic activity linking engine can match, correlate, link or otherwise associate electronic activities with outcomes (e.g., advancing stages, won, lost, etc.) stored in the system of record."; Paragraph 437, "The performance improvement plan can be based on human input received from a manager member node. Thus, the node graph generation system 200 (e.g., via recommendation engine 275) can generate a customized or tailored performance improvement plan that is based on a similar member node whose activity levels and goal attainment indicates that the similar member node successfully completed a performance improvement plan and is now a high performing member node. The node graph generation system 200 can generate this customized or tailored performance improvement plan using human input from a manager that is deemed, by the recommendation engine 275, to be a high performing manager.")
sending a signal to a compute device of the user to implement the specific action. (Rogynskyy: Paragraph 431, "For instance, a high performing employee may be involved in electronic activities that are linked to opportunity record objects that advance from one of the stages to another stage much quicker than another employee with the same role. Similarly, a high performing employee may be involved in electronic activities that are linked to a greater number of opportunity record objects that advance from one of the stages to another stage than another employee with the same role. as such, by tracking the opportunity record objects with which an employee is linked, a performance of the employee can be determined and the employee's metrics can be used to set certain benchmarks that can then be used to determine a performance of another employee with a similar role or generate a ramp up schedule based on the employee's metrics. For example, the node graph generation system 200 can determine that when a member node completes 25 calls in a week, reaches out to 10 companies in a week, has 5 in-person meetings in a week, and then writes 100 emails in the same week, then the member node should be able to complete a number of deals or advance a desired number of stages in one or more deals or otherwise achieve an expected performance outcome after a certain time (e.g., a time delay between input activities and outcome results). The metric can refer to or include an attribute of an activity, such as an amount of the activity. The metric can be a binary value that indicates a yes or no, such as "did you have a meeting with 10 people", with a value of 1 or 0 indicated yes or no, respectively. In some cases, the metric can be a count, a ratio, a time value, or a percentage value, based on any combination/formula, calculated from any number of data points in the member node graph or system of records. The metrics can vary in granularity based on the data the node graph generation system 200 can analyze via electronic activities or one or more systems of record. Based on previous or historical activity, the node graph generation system 200 can predict, forecast or estimate what activity should occur to achieve a desired outcome, and propose or set goals for a member node or group node accordingly. The node graph generation system 200 (e.g., via the electronic activity linking engine) can correlate the electronic activities with the stages or desired outcomes as stored or determined in the system of record or an opportunity record object thereof. The electronic activity linking engine can match, correlate, link or otherwise associate electronic activities with outcomes (e.g., advancing stages, won, lost, etc.) stored in the system of record."; Paragraph 437, "The performance improvement plan can be based on human input received from a manager member node. Thus, the node graph generation system 200 (e.g., via recommendation engine 275) can generate a customized or tailored performance improvement plan that is based on a similar member node whose activity levels and goal attainment indicates that the similar member node successfully completed a performance improvement plan and is now a high performing member node. The node graph generation system 200 can generate this customized or tailored performance improvement plan using human input from a manager that is deemed, by the recommendation engine 275, to be a high performing manager."; Paragraph 449, "The system 200 can be configured to automatically assign or generate a recommendation to assign a business process or associated record object to an employee of a company associated with the business process. Perhaps, more generally, the system 200 can automatically match or generate a recommendation to match or pair an employee of a company and a record object of a system of record of the company.")
further training the reinforcement learning model based on the engagement score of the user (Rogynskyy: Paragraph 235, “In some embodiments, the record object identification engine 315 can use matching models 340 trained with machine learning to match, for example, the electronic activity to a record object based on a similarity of the text in and the sender of the electronic activity with the text in and sender of an electronic activity previously matched to a given electronic activity. In some embodiments, the matching model 340 can be updated as electronic activities are matched to record objects. For example, a matching model 340 can include one or more rules to use when matching an electronic activity to a record object. If a user matches an electronic activity to a record object other than the record object to which the electronic activity linking engine 250 matched the electronic activity, record object identification engine 315 can update the matching model 340 to alter or remove the rule that led to the incorrect matching.”; Paragraph 236, “In some embodiments, once an electronic activity is matched with a record object, a user can accept or reject the linking. Additionally, the user can change or remap the linking between the electronic activity and the record object. An indication of the acceptance, rejection, or remapping can be used to update the machine learning model or reorder the matching strategies as discussed in relation to FIGS. 11 and 12. The updated model can be used in the future linking of electronic activity to nodes and the nodes to record objects by the record object identification engine 315. To train the machine learning models, the system can scan one or more systems of record that include manually matched electronic activity and record objects. The previous manually matched data can be used as a training set for the machine learning models.”; Paragraph 436, “The node graph generation system 200 (e.g., via recommendation engine 275) can provide the target goal or recommendation to the member node, or a manager member node that may then propagate the target goal to employee member nodes. A manager member node can refer or correspond to a person whose role is a manager of employees or a team of people. The member node profile can include a field that denominates a role of the member, such as manager or employee. The member node profile can further include a field that denominates who the manager is, such as a “managed by” field. In some embodiments, the recommendation engine 275 can include or interface with a machine learning engine that obtains feedback from a manager member node and adjusts the recommendations or target goals accordingly. For example, the node graph generation system 200 can identify manager member nodes that are linked to employee member nodes that are performing with a desired outcome based on a system of record. The node graph generation system 200 can further identify that when new employee member nodes are linked or join the network of the manager member node, the new employee member node ramps up in a desired time interval and to a desired performance level. The node graph generation system 200 can receive human input from a manager corresponding to a manager member node. Based on the human input, the node graph generation system 200 can determine that the manager member node sets goals that are effective or successful in improving the performance of the employee member nodes. The node graph generation system 200 can receive, via the manager member node or one or more employee member nodes, the target goals and input these target goals into a machine learning engine or otherwise compare the input target goals with automatically generated target goals to tune or update the generation of target goals. Thus, the node graph generation system 200 (or recommendation engine 275) can receive human input from high performing managers in order to update the recommendation engine 275 and improve the generation of recommendations or goals for member nodes.”)
Rogynskyy does not explicitly disclose the specific natural language processing model or terms being specific to a user, however, in analogous art of data analysis and efficiency, Ackermann discloses the following:
providing the engagement data from each digital artifact from the plurality of digital artifacts as an input to a term frequency - inverse document frequency (TFIDF) natural language processing (NLP) model specific to the user to identify a context associated with each term from a plurality of terms in the engagement data; (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection. In various embodiments, disambiguation is performed on based on name similarity in order to reduce duplication. Various implementations make sure of character-based, and term frequency and inverse document frequency (TF-IDF) based similarity scores for disambiguation to group similar entities. The described systems and methods provide support for instances where variants for the same entity have relatively large string distances."; Paragraph 21, "Some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity."; Paragraph 22, "The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm.")
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy with the teachings of Ackermann in order to improve the finding of relevant information through large amounts of data as disclosed by Ackermann (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection.")
Claim(s) 2 –
Rogynskyy in view of Ackermann disclose the limitations of claim 1
Rogynskyy further discloses the following:
wherein the plurality of digital artifacts includes at least one of an email message, a calendar appointment, a document, a text message or a report. (Rogynskyy: Paragraph 61, "In a particular use case, sales representatives of an organization may be involved in electronic activities, such as emails, phone calls, meetings, among others that can be tracked and captured by the system via ingestion from one or more data sources of the organization or other organizations. The system can extract information from the electronic activities that may be associated with deals or opportunities the sales representatives are working on.")
Claim(s) 4 –
Rogynskyy in view of Ackermann disclose the limitations of claim 1
Rogynskyy does not explicitly disclose the following, however, in analogous art of data analysis, Ackermann teaches the following:
wherein the TF-IDF NLP model is trained using a corpus specific to the user. (Ackermann: Paragraph 16, "For example, knowledge manager 100 may receive input from the computer network environment 102, computer network 106, a knowledge base 108 which can include a corpus of electronic documents 110 or other data, a content creator 112, content users, and other possible sources of input. In various embodiments, the other possible sources of input can include location information. In one embodiment, some or all of the inputs to knowledge manager 100 may be routed through the computer network 106. The various computing devices on the computer network environment 102 may include access points for content creators and content users. Some of the computing devices may include devices for a database storing the corpus of data. The knowledge manager information handling system computing device 104 further includes
search/discovery engine 114."; Paragraph 42, "In certain instances, unstructured document corpus(es) 306 contains additional noise (e.g., typos), rather than an exact match to query name constituents, and only an approximate match is needed. In certain implementations, an approach is to retrieve names that match query name constituents within some edit distance threshold, for example a maximum edit distance of "2". The result at this step 406 can a large set of entity names that match the query name constituents. In such a set, distinct entities may be represented under multiple different names"; Paragraph 50, "In these instances, name variants are merged if the name variants occur in the same document with another name variant. The procedure assumes that documents mention an entity more than once and use distinct names for distinct individuals. If present, "withindocument coreference" can be leveraged.")
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy with the teachings of Ackermann in order to improve the finding of relevant information through large amounts of data as disclosed by Ackermann (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection.")
Claim(s) 5 –
Rogynskyy in view of Ackermann disclose the limitations of claim 1
Rogynskyy further discloses the following:
receiving, from the user, feedback regarding the focus area score for each digital artifact from the plurality of digital artifacts; and training the ... model based on the feedback. (Rogynskyy: Paragraph 339, "The system 200 can tune or improve the machine learning techniques based on feedback. For example, upon applying the machine learning techniques to electronic activities, the system 200 can provide the filter decision to an administrator or other user of system 200. The user can input whether the filter decision was correct or incorrect. If the filter decision was correct, the machine learning filter can maintain the weights or rules used to make the filter decision, or increase weights used to make the filter decision. If the filter decision was incorrect, the system 200 can modify the features, weights or criteria in an attempt to correct the filter decision. Similarly, the system 200 can use user input to modify features, weights, or criteria for other types of tagging or filtering, including, for example, natural language processing, rules, linking, or other logic flows that can be improved, enhanced or otherwise benefit from user input. In some embodiments, the machine can be configured to update the weights based on feedback without any intervention of a user or administrator.")
Rogynskyy does not explicitly disclose the following, however, in analogous art of data analysis, Ackermann teaches the following:
... TF-IDF NLP ... (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection. In various embodiments, disambiguation is performed on based on name similarity in order to reduce duplication. Various implementations make sure of character-based, and term frequency and inverse document frequency (TF-IDF) based similarity scores for disambiguation to group similar entities. The described systems and methods provide support for instances where variants for the same entity have relatively large string distances.")
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy with the teachings of Ackermann in order to improve the finding of relevant information through large amounts of data as disclosed by Ackermann (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection.")
Claim(s) 7 –
Rogynskyy in view of Ackermann disclose the limitations of claim 1
Rogynskyy further discloses the following:
receiving an indication of an updated overall strategy of the entity; (Rogynskyy: Paragraph 61, "The system can further receive information from the one or more systems and records to determine the results associated with the sales representative's efforts and perform analytics to generate recommendations to assist the sales representatives achieve their goals and eventually improve their performance as sales representatives as well as provide company management with recommendations about improving the performance of the overall business."; Paragraph 436, "The node graph generation system 200 (e.g., via recommendation engine 275) can provide the target goal or recommendation to the member node, or a manager member node that may then propagate the target goal to employee member nodes. A manager member node can refer or correspond to a person whose role is a manager of employees or a team of people. The member node profile can include a field that denominates a role of the member, such as manager or employee. The member node profile can further include a field that denominates who the manager is, such as a "managed by" field. In some embodiments, the recommendation engine 275 can include or interface with a machine learning engine that obtains feedback from a manager member node and adjusts the recommendations or target goals accordingly. For example, the node graph generation system 200 can identify manager member nodes that are linked to employee member nodes that are performing with a desired outcome based on a system of record. The node graph generation system 200 can further identify that when new employee member nodes are linked or join the network of the manager member node, the new employee member node ramps up in a desired time interval and to a desired performance level. The node graph generation system 200 can receive human input from a manager corresponding to a manager member node. Based on the human input, the node graph generation system 200 can determine that the manager member node sets goals that are effective or successful in improving the performance of the employee member nodes. The node graph generation system 200 can receive, via the manager member node or one or more employee member nodes, the target goals and input these target goals into a machine learning engine or otherwise compare the input target goals with automatically generated target goals to tune or update the generation of target goals. Thus, the node graph generation system 200 ( or recommendation engine 275) can receive human input from high performing managers in order to update the recommendation engine 275 and improve the generation of recommendations or goals for member nodes."; Paragraph 449, "The system 200 can be configured to automatically assign at least one employee of a company to one or more record objects or provide recommendations to the company (for instance, the data source provider) to assign the at least one employee to the one or more record objects. The system 200 can be configured to automatically assign or generate a recommendation to assign a business process or associated record object to an employee of a company associated with the business process. Perhaps, more generally, the system 200 can automatically match or generate a recommendation to match or pair an employee of a company and a record object of a system of record of the company.")
further training the reinforcement learning model based on the updated overall strategy of the entity to define an updated reinforcement learning model; and (Rogynskyy: Paragraph 456, "The system 200 can be configured to assign different weights to different factors used for matching leads and employees. In some embodiments, the system can enable each company to establish its own rules or policies for recommending matches between leads and employees. In some embodiments, the system 200 can be configured to train a machine learning model to match leads and salespersons based on analyzing a salesperson's matches with leads in the past as well as analyzing the lead's matches with other salespersons in the past."; Paragraph 710, "The characteristics can be stored in the node profile as field-value pairs. The engagement profile generation policy can include policies, rules, heuristics, or machine learning models (e.g., bias and weights of a trained machine learning model) for determining the engagement profile based on the electronic activities.")
defining, using the updated reinforcement learning model, an updated engagement plan for the user such that the updated engagement plan supports the updated overall strategy of the entity. (Rogynskyy: Paragraph 61, "The system can further receive information from the one or more systems and records to determine the results associated with the sales representative's efforts and perform analytics to generate recommendations to assist the sales representatives achieve their goals and eventually improve their performance as sales representatives as well as provide company management with recommendations about improving the performance of the overall business."; Paragraph 436, "The node graph generation system 200 (e.g., via recommendation engine 275) can provide the target goal or recommendation to the member node, or a manager member node that may then propagate the target goal to employee member nodes. A manager member node can refer or correspond to a person whose role is a manager of employees or a team of people. The member node profile can include a field that denominates a role of the member, such as manager or employee. The member node profile can further include a field that denominates who the manager is, such as a "managed by" field. In some embodiments, the recommendation engine 275 can include or interface with a machine learning engine that obtains feedback from a manager member node and adjusts the recommendations or target goals accordingly. For example, the node graph generation system 200 can identify manager member nodes that are linked to employee member nodes that are performing with a desired outcome based on a system of record. The node graph generation system 200 can further identify that when new employee member nodes are linked or join the network of the manager member node, the new employee member node ramps up in a desired time interval and to a desired performance level. The node graph generation system 200 can receive human input from a manager corresponding to a manager member node. Based on the human input, the node graph generation system 200 can determine that the manager member node sets goals that are effective or successful in improving the performance of the employee member nodes. The node graph generation system 200 can receive, via the manager member node or one or more employee member nodes, the target goals and input these target goals into a machine learning engine or otherwise compare the input target goals with automatically generated target goals to tune or update the generation of target goals. Thus, the node graph generation system 200 ( or recommendation engine 275) can receive human input from high performing managers in order to update the recommendation engine 275 and improve the generation of recommendations or goals for member nodes."; Paragraph 449, "The system 200 can be configured to automatically assign at least one employee of a company to one or more record objects or provide recommendations to the company (for instance, the data source provider) to assign the at least one employee to the one or more record objects. The system 200 can be configured to automatically assign or generate a recommendation to assign a business process or associated record object to an employee of a company associated with the business process. Perhaps, more generally, the system 200 can automatically match or generate a recommendation to match or pair an employee of a company and a record object of a system of record of the company.")
Claim(s) 8 –
Rogynskyy in view of Ackermann disclose the limitations of claim 1
Rogynskyy does not explicitly disclose the following, however, in analogous art of data analysis, Ackermann discloses the limitations below:
wherein the TF-IDF NLP model uses word embedding to identify related terms of the plurality of terms in the engagement data, the context of each term from the plurality of terms being defined using each term from the plurality of terms and the related terms of that term. (Ackermann: Paragraph 18, "Semantic content is content based on the relation between signifiers, such as words, phrases, signs, and symbols, and what they stand for, their denotation, or connotation. In other words, semantic content is content that interprets an expression, such as by using Natural Language Processing (NLP), such that knowledge manager 100 can be considered as a NLP system, which in certain implementations performs the methods described herein. In one embodiment, the process sends well-formed questions (e.g., natural language questions, etc.) to the knowledge manager. Knowledge manager 100 may interpret the question and provide a response to the content user containing one or more answers to the question. In some embodiments, knowledge manager 100 may provide a response to users in a ranked list of answers. In various embodiments, the one or more answers take into account location information."; Paragraph 31, "In various embodiments, the system memory 236 includes a natural language processing (NLP) system 252 which can include code for implementing the processes described herein. Furthermore, system memory 236 can be configured with entity and relationship extraction engine 254 and entity name generator 256. As further described herein, the entity and relationship extraction engine 254 extracts a set of entities and a set of relationships between these entities using an automated entity and relationship extraction method. The extraction can be performed on a corpus( es) of unstructured documents as described herein. When names of entities are based on such an automated named entity extraction method, the same entity may be recorded under different names. Name variants may be stored in an entity store. As described herein, from the extracted set(s) of entities and set(s) of relationships, the entity name generator 256 is used in generating a list of name candidates that are potential completions to a user provided partial name. The name variants present in the entity store are disambiguated on query time to compile a smaller and more focused list of name candidates.")
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy with the teachings of Ackermann in order to improve the finding of relevant information through large amounts of data as disclosed by Ackermann (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection.")
Claim(s) 9 and 15 –
Rogynskyy discloses the following:
A non-transitory processor-readable medium storing code representing instructions to be executed by one or more processors, the instructions comprising code to cause the one or more processors to: (Rogynskyy: Paragraph 729, "The permanent storage device can be a non-volatile read-and-write memory device that can store instructions and data even when module 3002 is powered down. The term "storage medium" as used herein includes any medium in which data can be stored indefinitely (subject to overwriting, electrical disturbance, power loss, or the like) and does not include carrier waves and transitory electronic signals propagating wirelessly or over wired connections.")
define, using a reinforcement learning model trained to increase a reward specific to an overall strategy of an entity, an engagement plan for each user from a plurality of users associated with the entity, the engagement plan for each user from the plurality of users defined to collectively support the overall strategy; (Rogynskyy: Paragraph 240, "In addition, the matching strategies can be designed or created to identify candidate record objects using other types of data included in the node graph generation system, or one or more systems of record, among others. In some embodiments, the matching strategies can be generated by analyzing how one or more electronic activities are matched to one or more record objects, including using machine learning techniques to generate matching strategies in a supervised or unsupervised learning environments."; Paragraph 426, "The node graph generation system 200 can identify metrics for each member node profile based on the electronic activities. The node graph generation system can correlate the metrics with desired performance outcomes or results, including but not limited to closed sales, recruited candidates, or renewed contracts to identify which metrics are correlated with desired performance outcomes. Based on identifying the desired metrics that result in desired outcomes, the node graph generation system 200 can set one or more goals for member nodes, as well as help track those goals to increase the likelihood that the member node achieves the desired performance outcome, thereby improving the likelihood that the member node achieves the desired performance outcome."; Paragraph 427, "The node graph generation system 200 can, for example, provide these recommendations or target goals to one or more member nodes or one or more group nodes based on historical matching electronic activities to desired performance outcomes. The node graph generation system 200 ( or one or more component thereof) can match electronic activities to desired performance outcomes stored or indicated in one or more systems of record.")
receive engagement data from a plurality of digital artifacts associated with each user from the plurality of users; (Rogynskyy: Paragraph 428, "The node graph generation system 200 can include a performance module designed and constructed to determine a performance metric or performance level of a member node based on electronic activities. To generate a recommendation, the node graph generation system 200 (via a performance module 280 and recommendation engine 275) can identify member node performance as compared to a member node's past performance or as compared to the performance of other member nodes that have a similar role or otherwise share similar characteristics. The node graph generation system 200 (e.g., via a member node performance module) can determine a performance of a member node. For example, the node graph generation system 200 can identify electronic activities associated with multiple member nodes that are linked to a group node in a group node graph. The node graph generation system 200 can then identify a system of record associated with the group node. The system of record can include account record objects, lead record objects, opportunity record objects, deal record objects or other types of record objects. The system of record can include stages for any business process, such as opportunities with stages, recruiting of candidate with interview stages, renewing contract with renewal stages, etc. In an illustrative example, an opportunity record object can include multiple sequential stages for the opportunity, such as a first stage, second stage, third stage, and a fourth stage, where the first stage indicates an initial stage and the fourth stage indicates a final or completion stage for the opportunity. The node graph generation system 200 can correlate electronic activities with the opportunity record objects as well as the stages of the opportunity record objects. The node graph generation system 200 can determine metrics based on electronic activities that are associated with an opportunity advancing stages or not advancing stages. For example, the node graph generation system 200 can correlate that, on average: 5 emails and 1 inperson meeting occurred in a time interval for an opportunity before it moved from a first stage to a second stage; 10 emails and 2 in-person meetings occurred during a time interval for an opportunity to move from a second stage to a third stage; 15 emails and 3 in-person meetings occurred during a time interval for an opportunity to move from a third stage to a fourth stage; and 20 emails and 4 inperson meetings occurred during a time interval for an opportunity to move from a third stage to a fourth or final stage. By determining metrics that are correlated with advancing an opportunity from one stage to another based on electronic activities correlated with stages in opportunity record objects stored in a system of record, the node graph generation system 200 ( or component or module thereof) can predict or forecast metrics that, when met, are likely to result in the desired performance outcome. The node graph generation system 200 can determine which metrics of electronic activities have the highest correlation to successful outcomes in order to generate goals.")
assign for each user from the plurality of users a focus area score for each digital artifact from the plurality of digital artifacts associated with that user and based on the context associated with each term associated with that digital artifact for that user, the focus area score for each digital artifact from the plurality of digital artifacts indicating a level of association of that digital artifact with each focus area from a plurality of focus areas; (Rogynskyy: Paragraph 428, "The node graph generation system 200 can include a performance module designed and constructed to determine a performance metric or performance level of a member node based on electronic activities. To generate a recommendation, the node graph generation system 200 (via a performance module 280 and recommendation engine 275) can identify member node performance as compared to a member node's past performance or as compared to the performance of other member nodes that have a similar role or otherwise share similar characteristics. The node graph generation system 200 (e.g., via a member node performance module) can determine a performance of a member node. For example, the node graph generation system 200 can identify electronic activities associated with multiple member nodes that are linked to a group node in a group node graph. The node graph generation system 200 can then identify a system of record associated with the group node. The system of record can include account record objects, lead record objects, opportunity record objects, deal record objects or other types of record objects. The system of record can include stages for any business process, such as opportunities with stages, recruiting of candidate with interview stages, renewing contract with renewal stages, etc. In an illustrative example, an opportunity record object can include multiple sequential stages for the opportunity, such as a first stage, second stage, third stage, and a fourth stage, where the first stage indicates an initial stage and the fourth stage indicates a final or completion stage for the opportunity. The node graph generation system 200 can correlate electronic activities with the opportunity record objects as well as the stages of the opportunity record objects. The node graph generation system 200 can determine metrics based on electronic activities that are associated with an opportunity advancing stages or not advancing stages. For example, the node graph generation system 200 can correlate that, on average: 5 emails and 1 inperson meeting occurred in a time interval for an opportunity before it moved from a first stage to a second stage; 10 emails and 2 in-person meetings occurred during a time interval for an opportunity to move from a second stage to a third stage; 15 emails and 3 in-person meetings occurred during a time interval for an opportunity to move from a third stage to a fourth stage; and 20 emails and 4 inperson meetings occurred during a time interval for an opportunity to move from a third stage to a fourth or final stage. By determining metrics that are correlated with advancing an opportunity from one stage to another based on electronic activities correlated with stages in opportunity record objects stored in a system of record, the node graph generation system 200 ( or component or module thereof) can predict or forecast metrics that, when met, are likely to result in the desired performance outcome. The node graph generation system 200 can determine which metrics of electronic activities have the highest correlation to successful outcomes in order to generate goals."; Paragraph 521, "In some such embodiments, the data processing system 9300 can determine the shadow record object with which the electronic activity most closely matches (or has the highest match score) and cause the electronic activity to match the corresponding record object in the system of record 9360.")
calculate, for each focus area from the plurality of focus areas and based on the level of association of each digital artifact with that focus area for that user, an engagement score for each user from the plurality of users; (Rogynskyy: Paragraph 428, "The node graph generation system 200 can include a performance module designed and constructed to determine a performance metric or performance level of a member node based on electronic activities. To generate a recommendation, the node graph generation system 200 (via a performance module 280 and recommendation engine 275) can identify member node performance as compared to a member node's past performance or as compared to the performance of other member nodes that have a similar role or otherwise share similar characteristics. The node graph generation system 200 (e.g., via a member node performance module) can determine a performance of a member node. For example, the node graph generation system 200 can identify electronic activities associated with multiple member nodes that are linked to a group node in a group node graph. The node graph generation system 200 can then identify a system of record associated with the group node. The system of record can include account record objects, lead record objects, opportunity record objects, deal record objects or other types of record objects. The system of record can include stages for any business process, such as opportunities with stages, recruiting of candidate with interview stages, renewing contract with renewal stages, etc. In an illustrative example, an opportunity record object can include multiple sequential stages for the opportunity, such as a first stage, second stage, third stage, and a fourth stage, where the first stage indicates an initial stage and the fourth stage indicates a final or completion stage for the opportunity. The node graph generation system 200 can correlate electronic activities with the opportunity record objects as well as the stages of the opportunity record objects. The node graph generation system 200 can determine metrics based on electronic activities that are associated with an opportunity advancing stages or not advancing stages. For example, the node graph generation system 200 can correlate that, on average: 5 emails and 1 in-person meeting occurred in a time interval for an opportunity before it moved from a first stage to a second stage; 10 emails and 2 in-person meetings occurred during a time interval for an opportunity to move from a second stage to a third stage; 15 emails and 3 inperson meetings occurred during a time interval for an opportunity to move from a third stage to a fourth stage; and 20 emails and 4 in-person meetings occurred during a time interval for an opportunity to move from a third stage to a fourth or final stage. By determining metrics that are correlated with advancing an opportunity from one stage to another based on electronic activities correlated with stages in opportunity record objects stored in a system of record, the node graph generation system 200 ( or component or module thereof) can predict or forecast metrics that, when met, are likely to result in the desired performance outcome. The node graph generation system 200 can determine which metrics of electronic activities have the highest correlation to successful outcomes in order to generate goals."; Paragraph 521, "In some such embodiments, the data processing system 9300 can determine the shadow record object with which the electronic activity most closely matches (or has the highest match score) and cause the electronic activity to match the corresponding record object in the system of record 9360.")
compare the engagement score for each user from the plurality of users to the engagement plan for that user to identify inconsistencies between the engagement score for that user and the engagement plan for that user; (Rogynskyy: Paragraph 430, "The node graph generation system 200 (e.g., via recommendation engine 275) can identify a temporal aspect to the metrics associated with the member node. The node graph generation system 200 can determine when member node first joined the group node or was first linked to the group node ( e.g., a job start date or beginning date), and how the member node's performance and behavior metrics evolved over time. This initial time interval can be referred to as a rampup period (e.g., when an employee first joins a company and then gets up to speed or ramps up). The node graph generation system 200 can identify metrics associated with a successful ramp-up period based on identifying member nodes that are associated with desired performance outcomes based on reaching desired stages in an opportunity record object (i.e. by analyzing how successful employees had ramped in the past). Thus, by analyzing electronic activities and a corresponding system of record to determine data driven metrics associated with desired performance outcomes determined by linking activities with record objects describing process stages (e.g., an opportunity record) in the system of record, the node graph generation system 200 can generate or identify goals to set for member nodes that are in a ramp-up period or other time interval, such as during a performance improvement plan (a plan, set up by employee's manager to bring the employee to optimal performance after a period of poor performance). The node graph generation system 200 can further reevaluate the member node's metrics to update the goals or set new goals by comparing current metrics (e.g., actual actions or performance) associated with the member node's current electronic activities with the desired metrics (e.g., planned actions or performance) for electronic activities correlated with the desired performance outcome or result.")
further train the reinforcement learning model based on the engagement score for each user from the plurality of users and the inconsistencies between the engagement score for each user from the plurality of users and the engagement plan for that user to define an updated reinforcement learning model; (Rogynskyy: Paragraph 456, "The system 200 can be configured to assign different weights to different factors used for matching leads and employees. In some embodiments, the system can enable each company to establish its own rules or policies for recommending matches between leads and employees. In some embodiments, the system 200 can be configured to train a machine learning model to match leads and salespersons based on analyzing a salesperson's matches with leads in the past as well as analyzing the lead's matches with other salespersons in the past."; Paragraph 710, "The characteristics can be stored in the node profile as field-value pairs. The engagement profile generation policy can include policies, rules, heuristics, or machine learning models (e.g., bias and weights of a trained machine learning model) for determining the engagement profile based on the electronic activities.")
and redefine, using the updated reinforcement learning model, the engagement plan for each user from the plurality of users associated with the entity. (Rogynskyy: Paragraph 431, "For instance, a high performing employee may be involved in electronic activities that are linked to opportunity record objects that advance from one of the stages to another stage much quicker than another employee with the same role. Similarly, a high performing employee may be involved in electronic activities that are linked to a greater number of opportunity record objects that advance from one of the stages to another stage than another employee with the same role. as such, by tracking the opportunity record objects with which an employee is linked, a performance of the employee can be determined and the employee's metrics can be used to set certain benchmarks that can then be used to determine a performance of another employee with a similar role or generate a ramp up schedule based on the employee's metrics. For example, the node graph generation system 200 can determine that when a member node completes 25 calls in a week, reaches out to 10 companies in a week, has 5 in-person meetings in a week, and then writes 100 emails in the same week, then the member node should be able to complete a number of deals or advance a desired number of stages in one or more deals or otherwise achieve an expected performance outcome after a certain time (e.g., a time delay between input activities and outcome results). The metric can refer to or include an attribute of an activity, such as an amount of the activity. The metric can be a binary value that indicates a yes or no, such as "did you have a meeting with 10 people", with a value of 1 or 0 indicated yes or no, respectively. In some cases, the metric can be a count, a ratio, a time value, or a percentage value, based on any combination/formula, calculated from any number of data points in the member node graph or system of records. The metrics can vary in granularity based on the data the node graph generation system 200 can analyze via electronic activities or one or more systems of record. Based on previous or historical activity, the node graph generation system 200 can predict, forecast or estimate what activity should occur to achieve a desired outcome, and propose or set goals for a member node or group node accordingly. The node graph generation system 200 (e.g., via the electronic activity linking engine) can correlate the electronic activities with the stages or desired outcomes as stored or determined in the system of record or an opportunity record object thereof. The electronic activity linking engine can match, correlate, link or otherwise associate electronic activities with outcomes (e.g., advancing stages, won, lost, etc.) stored in the system of record." Paragraph 437, "The performance improvement plan can be based on human input received from a manager member node. Thus, the node graph generation system 200 (e.g., via recommendation engine 275) can generate a customized or tailored performance improvement plan that is based on a similar member node whose activity levels and goal attainment indicates that the similar member node successfully completed a performance improvement plan and is now a high performing member node. The node graph generation system 200 can generate this customized or tailored performance improvement plan using human input from a manager that is deemed, by the recommendation engine 275, to be a high performing manager.")
define, based on the inconsistencies between the engagement score for a user from the plurality of users and the engagement plan for the user, a specific action for the user to reduce the inconsistencies between the engagement score for the user and the engagement plan for the user; and (Rogynskyy: Paragraph 431, "For instance, a high performing employee may be involved in electronic activities that are linked to opportunity record objects that advance from one of the stages to another stage much quicker than another employee with the same role. Similarly, a high performing employee may be involved in electronic activities that are linked to a greater number of opportunity record objects that advance from one of the stages to another stage than another employee with the same role. as such, by tracking the opportunity record objects with which an employee is linked, a performance of the employee can be determined and the employee's metrics can be used to set certain benchmarks that can then be used to determine a performance of another employee with a similar role or generate a ramp up schedule based on the employee's metrics. For example, the node graph generation system 200 can determine that when a member node completes 25 calls in a week, reaches out to 10 companies in a week, has 5 in-person meetings in a week, and then writes 100 emails in the same week, then the member node should be able to complete a number of deals or advance a desired number of stages in one or more deals or otherwise achieve an expected performance outcome after a certain time (e.g., a time delay between input activities and outcome results). The metric can refer to or include an attribute of an activity, such as an amount of the activity. The metric can be a binary value that indicates a yes or no, such as "did you have a meeting with 10 people", with a value of 1 or 0 indicated yes or no, respectively. In some cases, the metric can be a count, a ratio, a time value, or a percentage value, based on any combination/formula, calculated from any number of data points in the member node graph or system of records. The metrics can vary in granularity based on the data the node graph generation system 200 can analyze via electronic activities or one or more systems of record. Based on previous or historical activity, the node graph generation system 200 can predict, forecast or estimate what activity should occur to achieve a desired outcome, and propose or set goals for a member node or group node accordingly. The node graph generation system 200 (e.g., via the electronic activity linking engine) can correlate the electronic activities with the stages or desired outcomes as stored or determined in the system of record or an opportunity record object thereof. The electronic activity linking engine can match, correlate, link or otherwise associate electronic activities with outcomes (e.g., advancing stages, won, lost, etc.) stored in the system of record."; Paragraph 437, "The performance improvement plan can be based on human input received from a manager member node. Thus, the node graph generation system 200 (e.g., via recommendation engine 275) can generate a customized or tailored performance improvement plan that is based on a similar member node whose activity levels and goal attainment indicates that the similar member node successfully completed a performance improvement plan and is now a high performing member node. The node graph generation system 200 can generate this customized or tailored performance improvement plan using human input from a manager that is deemed, by the recommendation engine 275, to be a high performing manager.")
send a signal to a compute device of the user to implement the specific action. (Rogynskyy: Paragraph 431, "For instance, a high performing employee may be involved in electronic activities that are linked to opportunity record objects that advance from one of the stages to another stage much quicker than another employee with the same role. Similarly, a high performing employee may be involved in electronic activities that are linked to a greater number of opportunity record objects that advance from one of the stages to another stage than another employee with the same role. as such, by tracking the opportunity record objects with which an employee is linked, a performance of the employee can be determined and the employee's metrics can be used to set certain benchmarks that can then be used to determine a performance of another employee with a similar role or generate a ramp up schedule based on the employee's metrics. For example, the node graph generation system 200 can determine that when a member node completes 25 calls in a week, reaches out to 10 companies in a week, has 5 in-person meetings in a week, and then writes 100 emails in the same week, then the member node should be able to complete a number of deals or advance a desired number of stages in one or more deals or otherwise achieve an expected performance outcome after a certain time (e.g., a time delay between input activities and outcome results). The metric can refer to or include an attribute of an activity, such as an amount of the activity. The metric can be a binary value that indicates a yes or no, such as "did you have a meeting with 10 people", with a value of 1 or 0 indicated yes or no, respectively. In some cases, the metric can be a count, a ratio, a time value, or a percentage value, based on any combination/formula, calculated from any number of data points in the member node graph or system of records. The metrics can vary in granularity based on the data the node graph generation system 200 can analyze via electronic activities or one or more systems of record. Based on previous or historical activity, the node graph generation system 200 can predict, forecast or estimate what activity should occur to achieve a desired outcome, and propose or set goals for a member node or group node accordingly. The node graph generation system 200 (e.g., via the electronic activity linking engine) can correlate the electronic activities with the stages or desired outcomes as stored or determined in the system of record or an opportunity record object thereof. The electronic activity linking engine can match, correlate, link or otherwise associate electronic activities with outcomes (e.g., advancing stages, won, lost, etc.) stored in the system of record."; Paragraph 437, "The performance improvement plan can be based on human input received from a manager member node. Thus, the node graph generation system 200 (e.g., via recommendation engine 275) can generate a customized or tailored performance improvement plan that is based on a similar member node whose activity levels and goal attainment indicates that the similar member node successfully completed a performance improvement plan and is now a high performing member node. The node graph generation system 200 can generate this customized or tailored performance improvement plan using human input from a manager that is deemed, by the recommendation engine 275, to be a high performing manager."; Paragraph 449, "The system 200 can be configured to automatically assign or generate a recommendation to assign a business process or associated record object to an employee of a company associated with the business process. Perhaps, more generally, the system 200 can automatically match or generate a recommendation to match or pair an employee of a company and a record object of a system of record of the company.")
Rogynskyy does not explicitly disclose the processing of specific terms being with regards to a user, however, in analogous art of data analysis and efficiency, Ackermann discloses the following:
provide the engagement data associated with each user from the plurality of users to a different machine learning model from a plurality of machine learning models to identify a context associated with each term from a plurality of terms in the engagement data for that user, each machine learning model from the plurality of machine learning models being specific to a different user from the plurality of users; (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection. In various embodiments, disambiguation is performed on based on name similarity in order to reduce duplication. Various implementations make sure of character-based, and term frequency and inverse document frequency (TF-IDF) based similarity scores for disambiguation to group similar entities. The described systems and methods provide support for instances where variants for the same entity have relatively large string distances."; Paragraph 21, "Some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity."; Paragraph 22, "The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm.")
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy with the teachings of Ackermann in order to improve the finding of relevant information through large amounts of data as disclosed by Ackermann (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection.")
Claim(s) 11 –
Rogynskyy and Ackermann disclose the limitations of claim 9
Rogynskyy does not explicitly disclose the following, however, in analogous art of data analysis Ackermann discloses the following:
wherein each machine learning model from the plurality of machine learning models is a term frequency - inverse document frequency (TF-IDF) natural language processing (NLP) model specific to a user from the plurality of users. (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection. In various embodiments, disambiguation is performed on based on name similarity in order to reduce duplication. Various implementations make sure of character-based, and term frequency and inverse document frequency (TF-IDF) based similarity scores for disambiguation to group similar entities. The described systems and methods provide support for instances where variants for the same entity have relatively large string distances."; Paragraph 21, "Some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity."; Paragraph 22, "The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm.")
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy with the teachings of Ackermann in order to improve the finding of relevant information through large amounts of data as disclosed by Ackermann (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection.")
Claim(s) 12 and 20 –
Rogynskyy in view of Ackermann disclose the limitations of claims 9 and 15
Rogynskyy further discloses the following:
wherein the plurality of digital artifacts includes at least one of an email message, a calendar appointment, a document, a text message or a report. (Rogynskyy: Paragraph 61, "In a particular use case, sales representatives of an organization may be involved in electronic activities, such as emails, phone calls, meetings, among others that can be tracked and captured by the system via ingestion from one or more data sources of the organization or other organizations. The system can extract information from the electronic activities that may be associated with deals or opportunities the sales representatives are working on.")
Claim(s) 13 –
Rogynskyy in view of Ackermann disclose the limitations of claim 9
Rogynskyy further discloses the following:
receive, from a user from the plurality of users, feedback regarding the focus area score for each digital artifact from the plurality of digital artifacts associated with the user; and train the machine learning model from the plurality of machine learning models and associated with the user based on the feedback. (Rogynskyy: Paragraph 339, "The system 200 can tune or improve the machine learning techniques based on feedback. For example, upon applying the machine learning techniques to electronic activities, the system 200 can provide the filter decision to an administrator or other user of system 200. The user can input whether the filter decision was correct or incorrect. If the filter decision was correct, the machine learning filter can maintain the weights or rules used to make the filter decision, or increase weights used to make the filter decision. If the filter decision was incorrect, the system 200 can modify the features, weights or criteria in an attempt to correct the filter decision. Similarly, the system 200 can use user input to modify features, weights, or criteria for other types of tagging or filtering, including, for example, natural language processing, rules, linking, or other logic flows that can be improved, enhanced or otherwise benefit from user input. In some embodiments, the machine can be configured to update the weights based on feedback without any intervention of a user or administrator.")
Claim(s) 14 –
Rogynskyy and Ackermann disclose the limitations of claim 9
Rogynskyy does not explicitly disclose the following, however, in analogous art of data analysis Ackermann discloses the following:
wherein each machine learning model from the plurality of machine learning models is a term frequency - inverse document frequency (TF-IDF) natural language processing (NLP) model specific to a user from the plurality of users. (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection. In various embodiments, disambiguation is performed on based on name similarity in order to reduce duplication. Various implementations make sure of character-based, and term frequency and inverse document frequency (TF-IDF) based similarity scores for disambiguation to group similar entities. The described systems and methods provide support for instances where variants for the same entity have relatively large string distances."; Paragraph 21, "Some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity."; Paragraph 22, "The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm.")
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy with the teachings of Ackermann in order to improve the finding of relevant information through large amounts of data as disclosed by Ackermann (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection.")
Claim(s) 17 –
Rogynskyy in view of Ackermann disclose the limitations of claim 15
Rogynskyy further discloses the following:
wherein the engagement plan for the user is specific to past engagement of the user with the entity. (Rogynskyy: Paragraph 240, "In addition, the matching strategies can be designed or created to identify candidate record objects using other types of data included in the node graph generation system, or one or more systems of record, among others. In some embodiments, the matching strategies can be generated by analyzing how one or more electronic activities are matched to one or more record objects, including using machine learning techniques to generate matching strategies in a supervised or unsupervised learning environments."; Paragraph 426, "The node graph generation system 200 can identify metrics for each member node profile based on the electronic activities. The node graph generation system can correlate the metrics with desired performance outcomes or results, including but not limited to closed sales, recruited candidates, or renewed contracts to identify which metrics are correlated with desired performance outcomes. Based on identifying the desired metrics that result in desired outcomes, the node graph generation system 200 can set one or more goals for member nodes, as well as help track those goals to increase the likelihood that the member node achieves the desired performance outcome, thereby improving the likelihood that the member node achieves the desired performance outcome."; Paragraph 427, "The node graph generation system 200 can, for example, provide these recommendations or target goals to one or more member nodes or one or more group nodes based on historical matching electronic activities to desired performance outcomes. The node graph generation system 200 ( or one or more component thereof) can match electronic activities to desired performance outcomes stored or indicated in one or more systems of record.")
Claim(s) 18 –
Rogynskyy in view of Ackermann disclose the limitations of claim 15
Rogynskyy further discloses the following:
wherein the first machine learning model is a reinforcement learning model. (Rogynskyy: Paragraph 240, "In addition, the matching strategies can be designed or created to identify candidate record objects using other types of data included in the node graph generation system, or one or more systems of record, among others. In some embodiments, the matching strategies can be generated by analyzing how one or more electronic activities are matched to one or more record objects, including using machine learning techniques to generate matching strategies in a supervised or unsupervised learning environments."; Paragraph 426, "The node graph generation system 200 can identify metrics for each member node profile based on the electronic activities. The node graph generation system can correlate the metrics with desired performance outcomes or results, including but not limited to closed sales, recruited candidates, or renewed contracts to identify which metrics are correlated with desired performance outcomes. Based on identifying the desired metrics that result in desired outcomes, the node graph generation system 200 can set one or more goals for member nodes, as well as help track those goals to increase the likelihood that the member node achieves the desired performance outcome, thereby improving the likelihood that the member node achieves the desired performance outcome."; Paragraph 427, "The node graph generation system 200 can, for example, provide these recommendations or target goals to one or more member nodes or one or more group nodes based on historical matching electronic activities to desired performance outcomes. The node graph generation system 200 (or one or more component thereof) can match electronic activities to desired performance outcomes stored or indicated in one or more systems of record.")
Claim(s) 19 –
Rogynskyy in view of Ackermann disclose the limitations of claim 15
Rogynskyy does not explicitly disclose the following, however, in analogous art of data analysis Ackermann discloses the following:
wherein the second machine learning model is a term frequency - inverse document frequency (TF-IDF) natural language processing (NLP) model. (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection. In various embodiments, disambiguation is performed on based on name similarity in order to reduce duplication. Various implementations make sure of character-based, and term frequency and inverse document frequency (TF-IDF) based similarity scores for disambiguation to group similar entities. The described systems and methods provide support for instances where variants for the same entity have relatively large string distances."; Paragraph 21, "Some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity."; Paragraph 22, "The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm.")
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy with the teachings of Ackermann in order to improve the finding of relevant information through large amounts of data as disclosed by Ackermann (Ackermann: Paragraph 14, "The present application relates generally to improving searching for and retrieving results for entities which are represented multiple times which can be due to noisy data collection.")
Claim(s) 6 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rogynskyy (US 2019/0364131 Al) in view of Ackermann (US 2022/0138233 Al) and
Camenares (US 2021/0406839 Al)
Claim(s) 6 –
Rogynskyy in view of Ackermann disclose the limitations of claim 1
Rogynskyy in view of Ackermann do not explicitly disclose the following, however, in analogous art of increasing efficiency Camenares teaches the limitation below:
wherein the user is a first user, the sending the signal includes sending the signal to the compute device such that a meeting is automatically scheduled between the first user and a second user based on an availability of the first user and the second user. (Camenares: Paragraph 15, "The system can generate meeting statistics based on the parsed electronic transcripts of the respective meetings, and using those meeting statistics generate meeting profiles for the respective meetings, compare the meeting profiles, and identify redundancies found in the respective meeting profiles. The system can then modify scheduled meetings to reduce the redundancies. For example, the system can send out updated electronic invitations to meeting invitees, where the updated electronic invitations cancel invitations to redundant personnel identified by the system. Similarly, the updated electronic invitations can change topics, subjects, speakers, or other aspects of a scheduled meeting based upon the evaluations."; Paragraph 29, "In alternative configurations, the system can auto-fill, or make suggestions for, a new meeting using the identified redundancies. Modification of a scheduled meeting can involve updating a meeting invitation distributed across a network and stored in multiple distinct computer systems using an electronic update to the meeting. The electronic update can, for example, only modify those aspects of the scheduled meeting which are being changed.")
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. Camenares discloses a method for automated collaboration. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy in view of Ackermann with the teachings of Camenares in order to improve the efficient use of resources by reducing redundancy, as disclosed by Camenares (Camenares: Paragraph 1, "The present disclosure relates to a computerized meeting system, and more specifically to reducing meeting redundancy by quantifying past meetings, identifying points of redundancy, and modifying future meetings based on the points of redundancy.")
Claim(s) 21 –
Rogynskyy in view of Ackermann disclose the limitations of claim 1
Rogynskyy in view of Ackermann do not explicitly disclose the following, however, in analogous art of increasing efficiency Camenares teaches the limitation below:
wherein the user is a first user, and the sending the signal to the compute device of the first user to implement the specific action includes sending the signal to the compute device of the first user to cause automatic scheduling of a meeting between the first user and a second user different from the user. (Camenares: Paragraph 15, "The system can generate meeting statistics based on the parsed electronic transcripts of the respective meetings, and using those meeting statistics generate meeting profiles for the respective meetings, compare the meeting profiles, and identify redundancies found in the respective meeting profiles. The system can then modify scheduled meetings to reduce the redundancies. For example, the system can send out updated electronic invitations to meeting invitees, where the updated electronic invitations cancel invitations to redundant personnel identified by the system. Similarly, the updated electronic invitations can change topics, subjects, speakers, or other aspects of a scheduled meeting based upon the evaluations."; Paragraph 27, “The meeting statistics and the results of the natural language processing together can be used to create a meeting profile 112 for each meeting 102, 104, the meeting profile containing information about meeting. The meeting profile can be thought of as a meeting summary generated using the quantitative data gleaned through the natural language processing and the statistical analysis. Exemplary data which can be contained in the meeting profile can include the meeting statistics as well as information about the meeting, such as who participated, when the meeting occurred, the meeting's overall duration, time of day of the meeting, etc.”; Paragraph 29, "In alternative configurations, the system can auto-fill, or make suggestions for, a new meeting using the identified redundancies. Modification of a scheduled meeting can involve updating a meeting invitation distributed across a network and stored in multiple distinct computer systems using an electronic update to the meeting. The electronic update can, for example, only modify those aspects of the scheduled meeting which are being changed."; Paragraph 39, “The system compares, via the processor, the first statistical profile to the second statistical profile, resulting in a statistical comparison (522), and modifies via the processor, a previously scheduled third meeting based on the statistical comparison, the modifying of the previously scheduled third meeting comprising transmitting electronic meeting updates to a plurality of devices associated with personnel invited to the previously scheduled third meeting. (524).”)
Rogynskyy discloses a method for determining engagement plans and focus and engagement scores for various uses based on digital artifacts. Ackermann discloses a method for determining the values of various terms using a specific type of natural language processing. Camenares discloses a method for automated collaboration. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Rogynskyy in view of Ackermann with the teachings of Camenares in order to improve the efficient use of resources by reducing redundancy, as disclosed by Camenares (Camenares: Paragraph 1, "The present disclosure relates to a computerized meeting system, and more specifically to reducing meeting redundancy by quantifying past meetings, identifying points of redundancy, and modifying future meetings based on the points of redundancy.")
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip N Warner whose telephone number is (571)270-7407. The examiner can normally be reached Monday-Friday 7am-4:00pm.
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/Philip N Warner/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624