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 .
Status of Application
This office action is in response to the most recent filings filed by applicants on 12/16/24.
No claims are amended
No claims are cancelled
No claims are added
Claims 1-20 are pending
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent Claims 1 and 9 contains the abbreviated term ML, which stands for [0004] Embodiments described herein include systems and methods for processing behavioral assessments, including machine learning (ML) systems and methods for analyzing or improving enterprise tactics or behavioral assessments, is not clearly defined in the specification per MPEP. Please see MPEP 2111.01 I. Under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the relevant time. The ordinary and customary meaning of a term may be evidenced by a variety of sources, including the words of the claims themselves, the specification, drawings, and prior art. However, the best source for determining the meaning of a claim term is the specification - the greatest clarity is obtained when the specification serves as a glossary for the claim terms. The words of the claim must be given their plain meaning unless the plain meaning is inconsistent with the specification. In re Zletz, 893 F.2d 319, 321, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (discussed below); Chef America, Inc. v. Lamb-Weston, Inc., 358 F.3d 1371, 1372, 69 USPQ2d 1857 (Fed. Cir. 2004) (Ordinary, simple English words whose meaning is clear and unquestionable, absent any indication that their use in a particular context changes their meaning, are construed to mean exactly what they say. Thus, "heating the resulting batter-coated dough to a temperature in the range of about 400oF to 850oF" required heating the dough, rather than the air inside an oven, to the specified temperature.).
The presumption that a term is given its ordinary and customary meaning may be rebutted by the applicant by clearly setting forth a different definition of the term in the specification. In re Morris, 127 F.3d 1048, 1054, 44 USPQ2d 1023, 1028 (Fed. Cir. 1997) (the USPTO looks to the ordinary use of the claim terms taking into account definitions or other "enlightenment" contained in the written description); But c.f. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1369, 70 USPQ2d 1827, 1834 (Fed. Cir. 2004) ("We have cautioned against reading limitations into a claim from the preferred embodiment described in the specification, even if it is the only embodiment described, absent clear disclaimer in the specification."). When the specification sets a clear path to the claim language, the scope of the claims is more easily determined and the public notice function of the claims is best served.
Abbreviations used in the above claims 1-17 should be avoided because there is no plain meaning associated with the term, as such the term creates ambiguity. The description of the term is not a clear definition per MPEP. “Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment." Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). (See MPEP 2111.01 II).
Similarly, dependent claims 2-8 and 10-17 include all the limitations from the independent claims 1 and 9 respectively and as such also contain the abbreviated terms.
Similarly, dependent claim 11 includes abbreviations like CRM, HCM, HRIS, etc. which are abbreviated terms and the above applies to them as well. In this claim limitation, in fact, applicants have recited the terms ambiguously so it is unclear whether the CRM stands for Customer Relationship Management or it stands for something different. For instance, “data from a Customer Relationship Management, CRM, platform; one or more customer feedback”. Here the claim is not defining Customer Relationship Management as CRM, instead it is simply recited as a separate term in the limitation divided by a comma, so it could be reasonably interpreted as “Customer Relationship Management, CRM, platform, one or more customer feedback” are four different terms in the limitation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 9-17 and 18-20 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-8 is/are directed to a system which is a statutory category.
Step 2A Prong 1: Identify the Abstract Idea(s)
The Alice framework, steps 2A-Prong One (part 1 of Mayo Test), here, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP 2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Independent claims 1, 9 and 18, with respect to the Step 2A, Prong One, when “taken as a whole” the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, Independent Method Claim 9 is directed to an abstract idea, as evidenced by claim limitations “obtaining a dataset of identified enterprise metrics, wherein the dataset of identified enterprise metrics comprises one or more personality assessments; training using the dataset of identified enterprise metrics thereby obtaining a trained model; and storing the trained model.”
Independent Method Claim 18 is directed to an abstract idea, as evidenced by claim limitations “inputting a dataset of team composition metrics, the model being trained using one or more personality assessments; and obtaining a dataset of team composition tactics labeled.”
In the originally submitted specification, [0003] Currently various behavioral assessment services are available that allow users to take a behavioral test and compare their personal results against other tests they have taken. Additionally, the tests are oftentimes administered by employers such that the results may be used to better understand the people that the employer utilizes. As an example, DISCTM, Myers-BriggsTM, BirkmanTM, EnneagramTM, CliftonStrengthsTM, CaliperTM, Profile XTTM, and/or other assessments may be taken by individuals and analyzed by employers. While these behavioral assessments are often beneficial, each behavioral assessment provides different results and employers have no access to that data once the particular behavioral assessment is complete. As such, a need exists in the industry.
In light of the specification, the claim limitations discussed above belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing behavioral test assessment results for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claim 1 is/are recites substantially similar limitations to independent claims 9 and 18 and is/are rejected under 2A for similar reasons to claims 9 and 18 above.
Step 2A Prong 2: Additional Elements That Integrate the Judicial Exception into a Practical Application
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: Claims 1 and 9: “A computer implemented method for training a machine learning model for optimizing one or more enterprise outcomes, comprising: a computing device that stores logic that, when executed by a processor of the computing device, causes the system to perform at least the following: a ML, ML model, ML”; Claim 18: “A computer implemented method for obtaining optimized team composition, comprising: into a trained model, by the trained model” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea with no significantly more elements.
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 9 and 16 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
The additional elements of a “machine learning model, ML”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer.
Similarly dependent claims 2-8, 10-17 and 19-20 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 14 recite “wherein the dataset of identified enterprise metrics comprise one or more of: text based coaching strategies e.g. for managing through change, dealing with change, leading team members with different styles, identifying the source of strategies to overcome conflict, motivation and persuasion strategies, communication and collaboration concepts that will be most effective for the team assembled” and dependent claims 17 recite “further comprising calculating a relationship map that indicates relationships among the respective persons of the team based on relationship criteria, wherein the relationship criteria may reside along a continuum between conflict and agreement”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 11 recites “wherein the dataset of identified enterprise metrics comprises one or more of: data from a Customer Relationship Management, CRM, platform; one or more customer feedback; one or more personality assessments; one or more sales outcomes; one or more profitability metrics; one or more retention metrics; a first behavioral assessment assessing a first behavioral characteristic of a first person; a second behavioral assessment assessing a second behavioral characteristic of the first person; a behavioral parameter for the first person based at least in part on the first behavioral characteristic and the second behavioral characteristic; a comparison of the behavioral parameter against corresponding behavioral parameters for respective persons for a team to determine how the first person would work with the respective persons; a desired dynamic for the team; a hypothetical scenario dataset, wherein the hypothetical scenario dataset describes how the first person would work with the respective persons of the team; an impact of including the first person in the team, based on the desired dynamic for the team and the hypothetical scenario dataset; data from a Human Capital Management, HCM, platform; data from a Human Resource Information System, HRIS, platform”. In this claim, “Customer Relationship Management, CRM, platform”, “Human Capital Management, HCM, platform”; “Human Resource Information System, HRIS, platform” is an additional element, but it is still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-8, 10-17 and 19-20 are also directed to the abstract idea identified above.
Step 2B: Determine Whether Any Element, Or Combination, Amount to “Significantly More” Than the Abstract Idea Itself
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of Claims 1 and 9: “A computer implemented method for training a machine learning model for optimizing one or more enterprise outcomes, comprising: a computing device that stores logic that, when executed by a processor of the computing device, causes the system to perform at least the following: a ML, ML model, ML”; Claim 18: “A computer implemented method for obtaining optimized team composition, comprising: into a trained model, by the trained model” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0099]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 9 and 18 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2B for similar reasons to claim 1 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 1 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-8, 10-17 and 19-20 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 9 and 18. As a result, Examiner asserts that dependent claims, such as dependent claims 2-8, 10-17 and 19-20 are also directed to the abstract idea identified above.
Further, Examiner notes that the addition limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 2017/0185942 A1) Hickson et al., and further in view of (US 2014/0358606 A1) Hull.
As per claims 1 and 9: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
A system for training a machine learning model for optimizing one or more team outcomes: a computing device that stores logic that, when executed by a processor of the computing device, causes the system to perform at least the following (Reference Hickson shows: [0010] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for generation of optimal team configuration recommendations are provided. The methods and systems described herein are directed to a system that methodically obtains information about a project and evaluates the employees using objective analyses. Characteristics of potential team members (i.e., skill sets, personality traits availability, etc.) and the requirements for a designated collaborative project are objectively determined by the systems and methods described herein. With both the pool of potential team members and the nature of the project objectively defined, one or more optimal team configuration recommendations may be made based on a training dataset of past projects and the teams assigned as well as characteristics of the employees and requirements of the project. [0031] The present disclosure may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. [0035] Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions):
Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
A computer implemented method for training a machine learning model for optimizing one or more enterprise outcomes, comprising (Reference Hickson shows: [0010] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for generation of optimal team configuration recommendations are provided. The methods and systems described herein are directed to a system that methodically obtains information about a project and evaluates the employees using objective analyses. Characteristics of potential team members (i.e., skill sets, personality traits availability, etc.) and the requirements for a designated collaborative project are objectively determined by the systems and methods described herein. With both the pool of potential team members and the nature of the project objectively defined, one or more optimal team configuration recommendations may be made based on a training dataset of past projects and the teams assigned as well as characteristics of the employees and requirements of the project. [0031] The present disclosure may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. [0035] Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions):
Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
(from claim 9): obtaining a dataset of identified enterprise metrics, wherein the dataset of identified enterprise metrics comprises one or more personality assessments
(from claim 1): obtain a dataset of identified team metrics
(claim 9): Reference Hickson shows “obtaining a dataset of identified enterprise …, wherein the dataset of identified enterprise metrics comprises one or more personality assessments”: [0010] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for generation of optimal team configuration recommendations are provided. The methods and systems described herein are directed to a system that methodically obtains information about a project and evaluates the employees using objective analyses. Characteristics of potential team members (i.e., skill sets, personality traits availability, etc.) and the requirements for a designated collaborative project are objectively determined by the systems and methods described herein. With both the pool of potential team members and the nature of the project objectively defined, one or more optimal team configuration recommendations may be made based on a training dataset of past projects and the teams assigned as well as characteristics of the employees and requirements of the project.
(claim 1): Reference Hickson shows “obtain a dataset of identified team …”: [0010] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for generation of optimal team configuration recommendations are provided. The methods and systems described herein are directed to a system that methodically obtains information about a project and evaluates the employees using objective analyses. Characteristics of potential team members (i.e., skill sets, personality traits availability, etc.) and the requirements for a designated collaborative project are objectively determined by the systems and methods described herein. With both the pool of potential team members and the nature of the project objectively defined, one or more optimal team configuration recommendations may be made based on a training dataset of past projects and the teams assigned as well as characteristics of the employees and requirements of the project. [0028] In some embodiments, an optimal team configuration assignment is then made from an available pool of individuals based on a match between employee profiles and the project profile. In some embodiments, the optimal team configuration assignment may be based on analysis of employee and project profiles with respect to a machine learning mechanism using provided training datasets. In some embodiments, the cumulative weighted compatibility of team members with one another may be maximized and weighted by closeness of position in the company as well as experience.
In both of the above instances, Reference Hickson does not explicitly show the term “metrics”. It is reasonably understood that the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028] reads on “metrics” discussed in the claim. However, Reference Hickson does not provide detail on how the matching is carried about.
Reference Hull shows the above limitation at least in [0081] With the gathered information, the system can correlate employees with different predetermined criteria, such as skills, reputation, productivity, experience, job/position titles they have held, job performance, products or projects they have worked on, communication skills, other employees with whom they have worked, availability (e.g., expected completion date of a current project), etc. Such correlation may entail rating how well an employee matches the specified criteria in an over-all sense. Individual values, scores or ratings may or may not be generated for each criterion, but at least one final rating or ranking will be generated for the employee for a given set of criteria. [0082] Regarding the organization's collaboration tool, the system may automatically and selectively analyze specific types of data determined by the criteria for which the employee is being rated. For example, if the criteria include things like "communication" or "camaraderie" or "ability to work in a team", then the employee's communications with other employees may be reviewed--possibly including electronic mail notes, instant messages, etc. If the criteria instead include things like "timeliness" or "productivity," his communications may be bypassed in favor of notes or posts regarding completed tasks, a schedule according to which he was supposed to complete tasks (and information indicating whether or not he did) and/or other relevant data. With employees' skills generally being an important criterion, the system will capture explicitly identified skills (e.g., explicit skill endorsements from colleagues) and/or implicit skills (e.g., from presentations or papers generated by the employee). [0083] In operation 204, for each of the predetermined criteria, and for multiple values of a given criterion where applicable, the system calculates a match or relevance between that criteria/value and each of multiple (or all) employees. A criterion such as skills, for example, could have myriad values--such as "enterprise systems," "C++ programming," "user interface design" and so on, and alphanumerical values, positive/negative values or other values may be assigned to an employee for each one to indicate how much of that experience she has. [0013] To enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee. The data may be processed by filtering out irrelevant information, weighting some data (e.g., data of a particular application) more or less than other data, using data from one application to bolster or disprove other data, and so on. [0057] In embodiments of the invention, data described above are analyzed to determine how well each candidate employee matches some or all criteria. In some implementations, employees may be explicitly rated with regard to various criteria based on their current jobs or roles (i.e., without regard to any new team or role for which they may be considered). In these implementations a recommendation system or apparatus such as system 130 of FIG. 1 (e.g., within collaboration tool 138) may support evaluation of employees according to some or all criteria once, multiple times or on a recurring basis, and record those evaluations for later reference when a new role is defined. In other implementations, however, existing records within some or all of the repositories depicted in FIG. 1, and/or others, are analyzed to derive ratings of individual employees and/or existing teams. [0068] For example, software engineers may use Jira or some other third-party bug tracking software. Collaboration tool 138 may record the creation of a bug report within an external Jira data repository, and also the resolution of the bug. The number of bugs resolved by an individual or a team may be used as a measure of productivity, especially when used to compare similar individuals or teams (e.g., those having the same job descriptions--such as software engineer). As another example, productivity of a salesperson may be rated based on information extracted from CRM database 112 (e.g., salesforce.com). [0069] A productivity rating may also or instead involve identifying a number of client presentations made (e.g., by a sales or marketing team), or a number of other resources generated, which may be aggregated to generate a numerical rating. Illustratively, if a team of 5 sales associates makes 2 client presentations in one week, the team may earn a rating of 10; by way of comparison, another team of 4 sales associates that makes 3 presentations in two weeks may yield a rating of 6 (e.g., (4*3)/2). Alternatively, ratings could be generated as described above for skills, wherein multiple teams' or individuals' tasks are aggregated and used to create a histogram, with the different values mapped to a bell curve to assign a rating.
Reference Hickson and Reference Hull are analogous prior art to the claimed invention because the references generally relate to field of making personnel recommendations. Further, said references are part of the same classification, i.e., G06Q10/063112. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Hull, particularly the ability to identify enterprise/ team metrics (Hull: [0081]-[0083], [0068]-[0069]), in the disclosure of Reference Hickson, particularly in the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028], in order to provide for a system that enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee as taught by Reference Hull (see at least in [0013]), where upon the execution of the method and system of Reference Hull for promoting internal mobility within an organization and, in particular, for recommending employees and/or teams of employees of the organization for a new project or for another role within the organization (Abstract) so that the process of making personnel recommendations can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar making personnel recommendations field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Hickson in view of Reference Hull, the results of the combination were predictable (MPEP 2143 A));
Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
training a ML model using the dataset of identified enterprise metrics thereby obtaining a trained ML model (Reference Hickson shows the above at least in [0028] In some embodiments, an optimal team configuration assignment is then made from an available pool of individuals based on a match between employee profiles and the project profile. In some embodiments, the optimal team configuration assignment may be based on analysis of employee and project profiles with respect to a machine learning mechanisms using provided training datasets. In some embodiments, the cumulative weighted compatibility of team members with one another may be maximized and weighted by closeness of position in the company as well as experience. [0030] At block 320, the recommendation server 220 may transmit the optimal team configuration recommendation(s). In some embodiments, the data management module 225 may transmit the one or more optimal team configuration recommendations (and their corresponding rankings, if relevant) to the project device 210. In some embodiments, the data management module 225 may add the team configuration recommendations to a training dataset. The training dataset is used to train the machine learning algorithms used to generate future team configuration recommendations. The training dataset may include the profile of past projects and the employee profiles of the team assigned to that project, and a measure of success (i.e., a score) determined for that project.
In the claim limitations above, it has already been established in the previous claim limitations that “metrics” is not shown by Reference Hickson in view of Reference Hull. Rationales to modify the References, particularly, Reference Hickson and Reference Hull are listed above and reincorporated herein); and
Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
storing the trained ML model.
(Reference Hickson shows the above at least in [0028] In some embodiments, an optimal team configuration assignment is then made from an available pool of individuals based on a match between employee profiles and the project profile. In some embodiments, the optimal team configuration assignment may be based on analysis of employee and project profiles with respect to a machine learning mechanisms using provided training datasets. In some embodiments, the cumulative weighted compatibility of team members with one another may be maximized and weighted by closeness of position in the company as well as experience. [0030] At block 320, the recommendation server 220 may transmit the optimal team configuration recommendation(s). In some embodiments, the data management module 225 may transmit the one or more optimal team configuration recommendations (and their corresponding rankings, if relevant) to the project device 210. In some embodiments, the data management module 225 may add the team configuration recommendations to a training dataset. The training dataset is used to train the machine learning algorithms used to generate future team configuration recommendations. The training dataset may include the profile of past projects and the employee profiles of the team assigned to that project, and a measure of success (i.e., a score) determined for that project.)
As per claims 2 and 10: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
further comprising; inputting a dataset of enterprise metrics into the trained model resulting in a dataset of optimized enterprise metrics.
(Reference Hickson shows the above at least in [0028] In some embodiments, an optimal team configuration assignment is then made from an available pool of individuals based on a match between employee profiles and the project profile. In some embodiments, the optimal team configuration assignment may be based on analysis of employee and project profiles with respect to a machine learning mechanisms using provided training datasets. In some embodiments, the cumulative weighted compatibility of team members with one another may be maximized and weighted by closeness of position in the company as well as experience. [0030] At block 320, the recommendation server 220 may transmit the optimal team configuration recommendation(s). In some embodiments, the data management module 225 may transmit the one or more optimal team configuration recommendations (and their corresponding rankings, if relevant) to the project device 210. In some embodiments, the data management module 225 may add the team configuration recommendations to a training dataset. The training dataset is used to train the machine learning algorithms used to generate future team configuration recommendations. The training dataset may include the profile of past projects and the employee profiles of the team assigned to that project, and a measure of success (i.e., a score) determined for that project.
In the claim limitations above, it has already been established in the previous claim limitations that “metrics” is not shown by Reference Hickson in view of Reference Hull. Rationales to modify the References, particularly, Reference Hickson and Reference Hull are listed above and reincorporated herein).
As per claims 3 and 11: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
wherein the dataset of identified enterprise metrics comprises one or more of:
data from a Customer Relationship Management, CRM, platform (Reference Hull: [0026] CRM (Customer Relationship Management) database 112 is used by the organization that operates system 130 to maintain contact and connections with clients/customers, facilitate sales, track leads, collaborate among different parts of the organization, monitor business processes and/or for other purposes. CRM database 112 may be provided by a third party, such as Salesforce.com, SugarCRM.TM., Zoho.TM. and so on. [0066] If the organization lacks specific data regarding employees' levels of skill, and such skill levels are desired to aid in filling an organizational role, such levels may be extrapolated from other information. For example, an employee's tenure in a position requiring or using a skill may be determined, an employee's title may indicate a degree of experience (e.g., Senior Sales Associate instead of simply Sales Associate), the length of time that has elapsed since an employee was first endorsed with a skill may be determined, etc.)
In the claim limitations above, it has already been established in the previous claim limitations that “metrics” is not shown by Reference Hickson in view of Reference Hull. Rationales to modify the References, particularly, Reference Hickson and Reference Hull are listed above and reincorporated herein;
one or more customer feedback;
one or more personality assessments;
one or more sales outcomes (Hickson: [0018]: Project parameters may be project requirements (e.g., number of employees required, timelines, budgets, etc.). In some embodiments, the project management module 215 may be a web-based interface accessibly by the project device 210. [0022]: Project parameters may be project requirements (e.g., number of employees required, timelines, budgets, desired work product, requirements analyses, etc.). [0024] At block 315, the recommendation server 220 may analyze data and project parameters to generate one or more optimal team configuration recommendations. In some embodiments, the data and project parameters may be analyzed using cognitive analysis. In some embodiments, cognitive analysis may include a cognitive model of a user, which may describe the way a user filters and processes stimulation from their environment. In some embodiments, the cognitive analysis may include a contextual model of a user, which describes how a user is predicted to act within a given context; in this case a set of proposed interactions.);
one or more profitability metrics;
one or more retention metrics;
a first behavioral assessment assessing a first behavioral characteristic of a first person;
a second behavioral assessment assessing a second behavioral characteristic of the first person;
a behavioral parameter for the first person based at least in part on the first behavioral characteristic and the second behavioral characteristic;
a comparison of the behavioral parameter against corresponding behavioral parameters for respective persons for a team to determine how the first person would work with the respective persons;
a desired dynamic for the team;
a hypothetical scenario dataset, wherein the hypothetical scenario dataset describes how the first person would work with the respective persons of the team;
an impact of including the first person in the team, based on the desired dynamic for the team and the hypothetical scenario dataset;
data from a Human Capital Management, HCM, platform; data from a Human Resource Information System, HRIS, platform (Hickson: [0018]: Project parameters may be project requirements (e.g., number of employees required, timelines, budgets, etc.). In some embodiments, the project management module 215 may be a web-based interface accessibly by the project device 210. [0022]: Project parameters may be project requirements (e.g., number of employees required, timelines, budgets, desired work product, requirements analyses, etc.). [0024] At block 315, the recommendation server 220 may analyze data and project parameters to generate one or more optimal team configuration recommendations. In some embodiments, the data and project parameters may be analyzed using cognitive analysis. In some embodiments, cognitive analysis may include a cognitive model of a user, which may describe the way a user filters and processes stimulation from their environment. In some embodiments, the cognitive analysis may include a contextual model of a user, which describes how a user is predicted to act within a given context; in this case a set of proposed interactions.).
As per claims 4 and 12: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
wherein the behavioral parameter includes a score for at least one of the following regarding the first person: personality, culture, strength, skills and competences, and a role of the first person (Reference Hickson: [0011] Data associated with employees may be collected from different sources. For example, data may be obtained through social media, any available communications data of the employee (including email, calendar, to-do lists, etc.), the skills, availability, workloads, personality traits, productivity etc. of each individual. Project data may be obtained through statements of work, project plans, project descriptions, requirements, project communications, etc. the project requirements, time requirements, and lists of roles and their potential skills, team characteristics, etc. Employee data and project data and requirements may then be analyzed and used to generate optimal team configuration recommendation. The method of analysis may be cognitive analysis, which may include a cognitive model of a user, which may describe the way a user filters and processes stimulation from their environment. In some embodiments, the cognitive analysis may include a contextual model of a user, which describes how a user is predicted to act within a given context; in this case a set of proposed interactions. [0030] At block 320, the recommendation server 220 may transmit the optimal team configuration recommendation(s). In some embodiments, the data management module 225 may transmit the one or more optimal team configuration recommendations (and their corresponding rankings, if relevant) to the project device 210. In some embodiments, the data management module 225 may add the team configuration recommendations to a training dataset. The training dataset is used to train the machine learning algorithms used to generate future team configuration recommendations. The training dataset may include the profile of past projects and the employee profiles of the team assigned to that project, and a measure of success (i.e., a score) determined for that project. [0025] In one example embodiment, the skill sets needed for each team position may be determined. The closeness of contact between each team position (e.g. strength of interactions) for a new project may be predicted from a training set of past employee data (e.g., number of emails between individuals, etc.), or estimated in the project management module by a user. The system may maximize the cumulative weighted compatibility of team members with one another based on the aforementioned information to generate optimal team configuration recommendations.).
As per claim 13: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
further comprising;
providing to a user an option to define desired behavioral characteristics of a team that includes the first person (Reference Hickson: [0011] Data associated with employees may be collected from different sources. For example, data may be obtained through social media, any available communications data of the employee (including email, calendar, to-do lists, etc.), the skills, availability, workloads, personality traits, productivity etc. of each individual. Project data may be obtained through statements of work, project plans, project descriptions, requirements, project communications, etc. the project requirements, time requirements, and lists of roles and their potential skills, team characteristics, etc. Employee data and project data and requirements may then be analyzed and used to generate optimal team configuration recommendation. The method of analysis may be cognitive analysis, which may include a cognitive model of a user, which may describe the way a user filters and processes stimulation from their environment. In some embodiments, the cognitive analysis may include a contextual model of a user, which describes how a user is predicted to act within a given context; in this case a set of proposed interactions.);
Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
inputting a dataset of enterprise metrics into the trained model, wherein the dataset of enterprise metrics comprises one or more behavioral characteristics (Reference Hickson: [0011] Data associated with employees may be collected from different sources. For example, data may be obtained through social media, any available communications data of the employee (including email, calendar, to-do lists, etc.), the skills, availability, workloads, personality traits, productivity etc. of each individual. Project data may be obtained through statements of work, project plans, project descriptions, requirements, project communications, etc. the project requirements, time requirements, and lists of roles and their potential skills, team characteristics, etc. Employee data and project data and requirements may then be analyzed and used to generate optimal team configuration recommendation. The method of analysis may be cognitive analysis, which may include a cognitive model of a user, which may describe the way a user filters and processes stimulation from their environment. In some embodiments, the cognitive analysis may include a contextual model of a user, which describes how a user is predicted to act within a given context; in this case a set of proposed interactions.); and
Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
obtaining a dataset of optimized desired behavioral characteristics from the trained model.
Reference Hickson shows [0010] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for generation of optimal team configuration recommendations are provided. The methods and systems described herein are directed to a system that methodically obtains information about a project and evaluates the employees using objective analyses. Characteristics of potential team members (i.e., skill sets, personality traits availability, etc.) and the requirements for a designated collaborative project are objectively determined by the systems and methods described herein. With both the pool of potential team members and the nature of the project objectively defined, one or more optimal team configuration recommendations may be made based on a training dataset of past projects and the teams assigned as well as characteristics of the employees and requirements of the project.
As per claims 5 and 14: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
wherein the dataset of identified enterprise metrics comprises one or more of:
text based coaching strategies e.g. for managing through change, dealing with change, leading team members with different styles, identifying the source of strategies to overcome conflict, motivation and persuasion strategies, communication and collaboration concepts that will be most effective for the team assembled (Reference Hickson shows: Such information may be provided to the recommendation server 220 for use in the analysis but may not be revealed to anyone else in the company without proper credentials. In some embodiments, the data may be implicit data obtained through monitoring or scanning systems of the company or explicit information, such as preferences provided by the employee or evaluations provided by a supervisor or manager. [0022] Now referring to FIG. 3, is a flow diagram of a method 300 for generating optimal team configuration recommendations in accordance with an exemplary embodiment is shown. At block 305, a recommendation server 220 may receive project data and parameters associated with a project. In some embodiments, the project data and parameters associated with the project may be obtained by the project management module 215 of the project device 210. The data may be obtained from a user, such as a project manager, through an interface presented to the user. In some embodiments, the project management module 215 may receive the data and may transmit the data to the data management module 225 of the recommendation server 220 over a network connection. Project data may be descriptive data associated with the project, which may include but is not limited to, statements of work, project descriptions, project communications, project plans, and the like. Project parameters may be project requirements (e.g., number of employees required, timelines, budgets, desired work product, requirements analyses, etc.
Reference Hull shows “change” and “conflict”. Hull shows “change” at least in [0087] In some embodiments of the invention, a rating for an employee regarding a given criterion begins at some median, average or other default value, and changes as data showing that the employee does (or does not) exhibit that criterion. Different criteria will be expressed differently and will therefore have different initial values. [0089] As the system obtains and analyzes an employee's activities within the organization's collaboration tool, and reviews any external profiles or repositories that may be relevant, the default value for a criterion will be adjusted accordingly. Therefore, criteria relating to specific skills or knowledge of a given employee may change to indicate that the system has (or has not) found evidence that the employee possesses those skills or that knowledge. Hull shows “conflict” in [0098] Similarly, identifying one position in a team as being the most (or least) important may also affect the employees' rankings. For example, if the product manager position is most important, relevance ratings of candidates for the other positions may be affected by whether they have or have not worked with the top-rated candidate(s) for the product manager position. In other words, for each employee suggested for the product manager position, a different set of employees may be suggested for other positions. Illustratively, this may occur based on who has worked well with each employee suggested for the product manager role. Those candidates for engineer and designer roles who worked with a particular product manager before, on successful projects, without any indication of conflict (or conversely, with explicit indications of a successful working relationship), may be ranked higher for their candidate roles.
Reference Hickson and Reference Hull are analogous prior art to the claimed invention because the references generally relate to field of making personnel recommendations. Further, said references are part of the same classification, i.e., G06Q10/063112. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Hull, particularly the ability to identify enterprise/ team metrics (Hull: [0081]-[0083], [0068]-[0069]), in the disclosure of Reference Hickson, particularly in the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028], in order to provide for a system that enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee as taught by Reference Hull (see at least in [0013]), where upon the execution of the method and system of Reference Hull for promoting internal mobility within an organization and, in particular, for recommending employees and/or teams of employees of the organization for a new project or for another role within the organization (Abstract) so that the process of making personnel recommendations can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar making personnel recommendations field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Hickson in view of Reference Hull, the results of the combination were predictable (MPEP 2143 A)).
As per claims 6 and 15: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
further comprising:
calculating a behavioral parameter for the first behavioral assessment of a team that includes the first person.
(Reference Hickson shows: Such information may be provided to the recommendation server 220 for use in the analysis but may not be revealed to anyone else in the company without proper credentials. In some embodiments, the data may be implicit data obtained through monitoring or scanning systems of the company or explicit information, such as preferences provided by the employee or evaluations provided by a supervisor or manager. [0022] Now referring to FIG. 3, is a flow diagram of a method 300 for generating optimal team configuration recommendations in accordance with an exemplary embodiment is shown. At block 305, a recommendation server 220 may receive project data and parameters associated with a project. In some embodiments, the project data and parameters associated with the project may be obtained by the project management module 215 of the project device 210. The data may be obtained from a user, such as a project manager, through an interface presented to the user. In some embodiments, the project management module 215 may receive the data and may transmit the data to the data management module 225 of the recommendation server 220 over a network connection. Project data may be descriptive data associated with the project, which may include but is not limited to, statements of work, project descriptions, project communications, project plans, and the like. Project parameters may be project requirements (e.g., number of employees required, timelines, budgets, desired work product, requirements analyses, etc.
Reference Hull shows “change” and “conflict”. Hull shows “change” at least in [0087] In some embodiments of the invention, a rating for an employee regarding a given criterion begins at some median, average or other default value, and changes as data showing that the employee does (or does not) exhibit that criterion. Different criteria will be expressed differently and will therefore have different initial values. [0089] As the system obtains and analyzes an employee's activities within the organization's collaboration tool, and reviews any external profiles or repositories that may be relevant, the default value for a criterion will be adjusted accordingly. Therefore, criteria relating to specific skills or knowledge of a given employee may change to indicate that the system has (or has not) found evidence that the employee possesses those skills or that knowledge. Hull shows “conflict” in [0098] Similarly, identifying one position in a team as being the most (or least) important may also affect the employees' rankings. For example, if the product manager position is most important, relevance ratings of candidates for the other positions may be affected by whether they have or have not worked with the top-rated candidate(s) for the product manager position. In other words, for each employee suggested for the product manager position, a different set of employees may be suggested for other positions. Illustratively, this may occur based on who has worked well with each employee suggested for the product manager role. Those candidates for engineer and designer roles who worked with a particular product manager before, on successful projects, without any indication of conflict (or conversely, with explicit indications of a successful working relationship), may be ranked higher for their candidate roles.
Reference Hickson and Reference Hull are analogous prior art to the claimed invention because the references generally relate to field of making personnel recommendations. Further, said references are part of the same classification, i.e., G06Q10/063112. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Hull, particularly the ability to identify enterprise/ team metrics (Hull: [0081]-[0083], [0068]-[0069]), in the disclosure of Reference Hickson, particularly in the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028], in order to provide for a system that enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee as taught by Reference Hull (see at least in [0013]), where upon the execution of the method and system of Reference Hull for promoting internal mobility within an organization and, in particular, for recommending employees and/or teams of employees of the organization for a new project or for another role within the organization (Abstract) so that the process of making personnel recommendations can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar making personnel recommendations field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Hickson in view of Reference Hull, the results of the combination were predictable (MPEP 2143 A)).
As per claims 7 and 16: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
further comprising creating team roles for the team, wherein the team roles are based on the corresponding behavioral parameters for the respective persons of the team.
(Reference Hickson shows: Such information may be provided to the recommendation server 220 for use in the analysis but may not be revealed to anyone else in the company without proper credentials. In some embodiments, the data may be implicit data obtained through monitoring or scanning systems of the company or explicit information, such as preferences provided by the employee or evaluations provided by a supervisor or manager. [0022] Now referring to FIG. 3, is a flow diagram of a method 300 for generating optimal team configuration recommendations in accordance with an exemplary embodiment is shown. At block 305, a recommendation server 220 may receive project data and parameters associated with a project. In some embodiments, the project data and parameters associated with the project may be obtained by the project management module 215 of the project device 210. The data may be obtained from a user, such as a project manager, through an interface presented to the user. In some embodiments, the project management module 215 may receive the data and may transmit the data to the data management module 225 of the recommendation server 220 over a network connection. Project data may be descriptive data associated with the project, which may include but is not limited to, statements of work, project descriptions, project communications, project plans, and the like. Project parameters may be project requirements (e.g., number of employees required, timelines, budgets, desired work product, requirements analyses, etc.
Reference Hull shows “change” and “conflict”. Hull shows “change” at least in [0087] In some embodiments of the invention, a rating for an employee regarding a given criterion begins at some median, average or other default value, and changes as data showing that the employee does (or does not) exhibit that criterion. Different criteria will be expressed differently and will therefore have different initial values. [0089] As the system obtains and analyzes an employee's activities within the organization's collaboration tool, and reviews any external profiles or repositories that may be relevant, the default value for a criterion will be adjusted accordingly. Therefore, criteria relating to specific skills or knowledge of a given employee may change to indicate that the system has (or has not) found evidence that the employee possesses those skills or that knowledge. Hull shows “conflict” in [0098] Similarly, identifying one position in a team as being the most (or least) important may also affect the employees' rankings. For example, if the product manager position is most important, relevance ratings of candidates for the other positions may be affected by whether they have or have not worked with the top-rated candidate(s) for the product manager position. In other words, for each employee suggested for the product manager position, a different set of employees may be suggested for other positions. Illustratively, this may occur based on who has worked well with each employee suggested for the product manager role. Those candidates for engineer and designer roles who worked with a particular product manager before, on successful projects, without any indication of conflict (or conversely, with explicit indications of a successful working relationship), may be ranked higher for their candidate roles.
Reference Hickson and Reference Hull are analogous prior art to the claimed invention because the references generally relate to field of making personnel recommendations. Further, said references are part of the same classification, i.e., G06Q10/063112. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Hull, particularly the ability to identify enterprise/ team metrics (Hull: [0081]-[0083], [0068]-[0069]), in the disclosure of Reference Hickson, particularly in the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028], in order to provide for a system that enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee as taught by Reference Hull (see at least in [0013]), where upon the execution of the method and system of Reference Hull for promoting internal mobility within an organization and, in particular, for recommending employees and/or teams of employees of the organization for a new project or for another role within the organization (Abstract) so that the process of making personnel recommendations can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar making personnel recommendations field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Hickson in view of Reference Hull, the results of the combination were predictable (MPEP 2143 A)).
As per claims 8 and 17: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
further comprising calculating a relationship map that indicates relationships among the respective persons of the team based on relationship criteria, wherein the relationship criteria may reside along a continuum between conflict and agreement.
(Reference Hickson shows: Such information may be provided to the recommendation server 220 for use in the analysis but may not be revealed to anyone else in the company without proper credentials. In some embodiments, the data may be implicit data obtained through monitoring or scanning systems of the company or explicit information, such as preferences provided by the employee or evaluations provided by a supervisor or manager. [0022] Now referring to FIG. 3, is a flow diagram of a method 300 for generating optimal team configuration recommendations in accordance with an exemplary embodiment is shown. At block 305, a recommendation server 220 may receive project data and parameters associated with a project. In some embodiments, the project data and parameters associated with the project may be obtained by the project management module 215 of the project device 210. The data may be obtained from a user, such as a project manager, through an interface presented to the user. In some embodiments, the project management module 215 may receive the data and may transmit the data to the data management module 225 of the recommendation server 220 over a network connection. Project data may be descriptive data associated with the project, which may include but is not limited to, statements of work, project descriptions, project communications, project plans, and the like. Project parameters may be project requirements (e.g., number of employees required, timelines, budgets, desired work product, requirements analyses, etc.
Reference Hull shows “change” and “conflict”. Hull shows “change” at least in [0087] In some embodiments of the invention, a rating for an employee regarding a given criterion begins at some median, average or other default value, and changes as data showing that the employee does (or does not) exhibit that criterion. Different criteria will be expressed differently and will therefore have different initial values. [0089] As the system obtains and analyzes an employee's activities within the organization's collaboration tool, and reviews any external profiles or repositories that may be relevant, the default value for a criterion will be adjusted accordingly. Therefore, criteria relating to specific skills or knowledge of a given employee may change to indicate that the system has (or has not) found evidence that the employee possesses those skills or that knowledge. Hull shows “conflict” in [0098] Similarly, identifying one position in a team as being the most (or least) important may also affect the employees' rankings. For example, if the product manager position is most important, relevance ratings of candidates for the other positions may be affected by whether they have or have not worked with the top-rated candidate(s) for the product manager position. In other words, for each employee suggested for the product manager position, a different set of employees may be suggested for other positions. Illustratively, this may occur based on who has worked well with each employee suggested for the product manager role. Those candidates for engineer and designer roles who worked with a particular product manager before, on successful projects, without any indication of conflict (or conversely, with explicit indications of a successful working relationship), may be ranked higher for their candidate roles.
Reference Hickson and Reference Hull are analogous prior art to the claimed invention because the references generally relate to field of making personnel recommendations. Further, said references are part of the same classification, i.e., G06Q10/063112. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Hull, particularly the ability to identify enterprise/ team metrics (Hull: [0081]-[0083], [0068]-[0069]), in the disclosure of Reference Hickson, particularly in the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028], in order to provide for a system that enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee as taught by Reference Hull (see at least in [0013]), where upon the execution of the method and system of Reference Hull for promoting internal mobility within an organization and, in particular, for recommending employees and/or teams of employees of the organization for a new project or for another role within the organization (Abstract) so that the process of making personnel recommendations can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar making personnel recommendations field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Hickson in view of Reference Hull, the results of the combination were predictable (MPEP 2143 A)).
As per claim 18: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
A computer implemented method for obtaining optimized team composition, comprising:
Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
inputting a dataset of team composition metrics into a trained model, the model being trained using one or more personality assessments
[0010] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for generation of optimal team configuration recommendations are provided. The methods and systems described herein are directed to a system that methodically obtains information about a project and evaluates the employees using objective analyses. Characteristics of potential team members (i.e., skill sets, personality traits availability, etc.) and the requirements for a designated collaborative project are objectively determined by the systems and methods described herein. With both the pool of potential team members and the nature of the project objectively defined, one or more optimal team configuration recommendations may be made based on a training dataset of past projects and the teams assigned as well as characteristics of the employees and requirements of the project. [0028] In some embodiments, an optimal team configuration assignment is then made from an available pool of individuals based on a match between employee profiles and the project profile. In some embodiments, the optimal team configuration assignment may be based on analysis of employee and project profiles with respect to a machine learning mechanism using provided training datasets. In some embodiments, the cumulative weighted compatibility of team members with one another may be maximized and weighted by closeness of position in the company as well as experience.
In both of the above instances, Reference Hickson does not explicitly show the term “metrics”. It is reasonably understood that the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028] reads on “metrics” discussed in the claim. However, Reference Hickson does not provide detail on how the matching is carried about.
Reference Hull shows the above limitation at least in [0081] With the gathered information, the system can correlate employees with different predetermined criteria, such as skills, reputation, productivity, experience, job/position titles they have held, job performance, products or projects they have worked on, communication skills, other employees with whom they have worked, availability (e.g., expected completion date of a current project), etc. Such correlation may entail rating how well an employee matches the specified criteria in an over-all sense. Individual values, scores or ratings may or may not be generated for each criterion, but at least one final rating or ranking will be generated for the employee for a given set of criteria. [0082] Regarding the organization's collaboration tool, the system may automatically and selectively analyze specific types of data determined by the criteria for which the employee is being rated. For example, if the criteria include things like "communication" or "camaraderie" or "ability to work in a team", then the employee's communications with other employees may be reviewed--possibly including electronic mail notes, instant messages, etc. If the criteria instead include things like "timeliness" or "productivity," his communications may be bypassed in favor of notes or posts regarding completed tasks, a schedule according to which he was supposed to complete tasks (and information indicating whether or not he did) and/or other relevant data. With employees' skills generally being an important criterion, the system will capture explicitly identified skills (e.g., explicit skill endorsements from colleagues) and/or implicit skills (e.g., from presentations or papers generated by the employee). [0083] In operation 204, for each of the predetermined criteria, and for multiple values of a given criterion where applicable, the system calculates a match or relevance between that criteria/value and each of multiple (or all) employees. A criterion such as skills, for example, could have myriad values--such as "enterprise systems," "C++ programming," "user interface design" and so on, and alphanumerical values, positive/negative values or other values may be assigned to an employee for each one to indicate how much of that experience she has. [0013] To enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee. The data may be processed by filtering out irrelevant information, weighting some data (e.g., data of a particular application) more or less than other data, using data from one application to bolster or disprove other data, and so on. [0057] In embodiments of the invention, data described above are analyzed to determine how well each candidate employee matches some or all criteria. In some implementations, employees may be explicitly rated with regard to various criteria based on their current jobs or roles (i.e., without regard to any new team or role for which they may be considered). In these implementations a recommendation system or apparatus such as system 130 of FIG. 1 (e.g., within collaboration tool 138) may support evaluation of employees according to some or all criteria once, multiple times or on a recurring basis, and record those evaluations for later reference when a new role is defined. In other implementations, however, existing records within some or all of the repositories depicted in FIG. 1, and/or others, are analyzed to derive ratings of individual employees and/or existing teams. [0068] For example, software engineers may use Jira or some other third-party bug tracking software. Collaboration tool 138 may record the creation of a bug report within an external Jira data repository, and also the resolution of the bug. The number of bugs resolved by an individual or a team may be used as a measure of productivity, especially when used to compare similar individuals or teams (e.g., those having the same job descriptions--such as software engineer). As another example, productivity of a salesperson may be rated based on information extracted from CRM database 112 (e.g., salesforce.com). [0069] A productivity rating may also or instead involve identifying a number of client presentations made (e.g., by a sales or marketing team), or a number of other resources generated, which may be aggregated to generate a numerical rating. Illustratively, if a team of 5 sales associates makes 2 client presentations in one week, the team may earn a rating of 10; by way of comparison, another team of 4 sales associates that makes 3 presentations in two weeks may yield a rating of 6 (e.g., (4*3)/2). Alternatively, ratings could be generated as described above for skills, wherein multiple teams' or individuals' tasks are aggregated and used to create a histogram, with the different values mapped to a bell curve to assign a rating.
Reference Hickson and Reference Hull are analogous prior art to the claimed invention because the references generally relate to field of making personnel recommendations. Further, said references are part of the same classification, i.e., G06Q10/063112. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Hull, particularly the ability to identify enterprise/ team metrics (Hull: [0081]-[0083], [0068]-[0069]), in the disclosure of Reference Hickson, particularly in the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028], in order to provide for a system that enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee as taught by Reference Hull (see at least in [0013]), where upon the execution of the method and system of Reference Hull for promoting internal mobility within an organization and, in particular, for recommending employees and/or teams of employees of the organization for a new project or for another role within the organization (Abstract) so that the process of making personnel recommendations can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar making personnel recommendations field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Hickson in view of Reference Hull, the results of the combination were predictable (MPEP 2143 A)); and
Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
obtaining a dataset of team composition tactics labeled by the trained model.
Reference Hickson shows: [0010] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for generation of optimal team configuration recommendations are provided. The methods and systems described herein are directed to a system that methodically obtains information about a project and evaluates the employees using objective analyses. Characteristics of potential team members (i.e., skill sets, personality traits availability, etc.) and the requirements for a designated collaborative project are objectively determined by the systems and methods described herein. With both the pool of potential team members and the nature of the project objectively defined, one or more optimal team configuration recommendations may be made based on a training dataset of past projects and the teams assigned as well as characteristics of the employees and requirements of the project. [0028] In some embodiments, an optimal team configuration assignment is then made from an available pool of individuals based on a match between employee profiles and the project profile. In some embodiments, the optimal team configuration assignment may be based on analysis of employee and project profiles with respect to a machine learning mechanism using provided training datasets. In some embodiments, the cumulative weighted compatibility of team members with one another may be maximized and weighted by closeness of position in the company as well as experience.
In both of the above instances, Reference Hickson does not explicitly show the term “metrics”. It is reasonably understood that the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028] reads on “metrics” discussed in the claim. However, Reference Hickson does not provide detail on how the matching is carried about.
Reference Hull shows the above limitation at least in [0081] With the gathered information, the system can correlate employees with different predetermined criteria, such as skills, reputation, productivity, experience, job/position titles they have held, job performance, products or projects they have worked on, communication skills, other employees with whom they have worked, availability (e.g., expected completion date of a current project), etc. Such correlation may entail rating how well an employee matches the specified criteria in an over-all sense. Individual values, scores or ratings may or may not be generated for each criterion, but at least one final rating or ranking will be generated for the employee for a given set of criteria. [0082] Regarding the organization's collaboration tool, the system may automatically and selectively analyze specific types of data determined by the criteria for which the employee is being rated. For example, if the criteria include things like "communication" or "camaraderie" or "ability to work in a team", then the employee's communications with other employees may be reviewed--possibly including electronic mail notes, instant messages, etc. If the criteria instead include things like "timeliness" or "productivity," his communications may be bypassed in favor of notes or posts regarding completed tasks, a schedule according to which he was supposed to complete tasks (and information indicating whether or not he did) and/or other relevant data. With employees' skills generally being an important criterion, the system will capture explicitly identified skills (e.g., explicit skill endorsements from colleagues) and/or implicit skills (e.g., from presentations or papers generated by the employee). [0083] In operation 204, for each of the predetermined criteria, and for multiple values of a given criterion where applicable, the system calculates a match or relevance between that criteria/value and each of multiple (or all) employees. A criterion such as skills, for example, could have myriad values--such as "enterprise systems," "C++ programming," "user interface design" and so on, and alphanumerical values, positive/negative values or other values may be assigned to an employee for each one to indicate how much of that experience she has. [0013] To enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee. The data may be processed by filtering out irrelevant information, weighting some data (e.g., data of a particular application) more or less than other data, using data from one application to bolster or disprove other data, and so on. [0057] In embodiments of the invention, data described above are analyzed to determine how well each candidate employee matches some or all criteria. In some implementations, employees may be explicitly rated with regard to various criteria based on their current jobs or roles (i.e., without regard to any new team or role for which they may be considered). In these implementations a recommendation system or apparatus such as system 130 of FIG. 1 (e.g., within collaboration tool 138) may support evaluation of employees according to some or all criteria once, multiple times or on a recurring basis, and record those evaluations for later reference when a new role is defined. In other implementations, however, existing records within some or all of the repositories depicted in FIG. 1, and/or others, are analyzed to derive ratings of individual employees and/or existing teams. [0068] For example, software engineers may use Jira or some other third-party bug tracking software. Collaboration tool 138 may record the creation of a bug report within an external Jira data repository, and also the resolution of the bug. The number of bugs resolved by an individual or a team may be used as a measure of productivity, especially when used to compare similar individuals or teams (e.g., those having the same job descriptions--such as software engineer). As another example, productivity of a salesperson may be rated based on information extracted from CRM database 112 (e.g., salesforce.com). [0069] A productivity rating may also or instead involve identifying a number of client presentations made (e.g., by a sales or marketing team), or a number of other resources generated, which may be aggregated to generate a numerical rating. Illustratively, if a team of 5 sales associates makes 2 client presentations in one week, the team may earn a rating of 10; by way of comparison, another team of 4 sales associates that makes 3 presentations in two weeks may yield a rating of 6 (e.g., (4*3)/2). Alternatively, ratings could be generated as described above for skills, wherein multiple teams' or individuals' tasks are aggregated and used to create a histogram, with the different values mapped to a bell curve to assign a rating.
Reference Hickson and Reference Hull are analogous prior art to the claimed invention because the references generally relate to field of making personnel recommendations. Further, said references are part of the same classification, i.e., G06Q10/063112. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Hull, particularly the ability to identify enterprise/ team metrics (Hull: [0081]-[0083], [0068]-[0069]), in the disclosure of Reference Hickson, particularly in the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028], in order to provide for a system that enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee as taught by Reference Hull (see at least in [0013]), where upon the execution of the method and system of Reference Hull for promoting internal mobility within an organization and, in particular, for recommending employees and/or teams of employees of the organization for a new project or for another role within the organization (Abstract) so that the process of making personnel recommendations can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar making personnel recommendations field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Hickson in view of Reference Hull, the results of the combination were predictable (MPEP 2143 A)).
As per claim 19: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
wherein the logic further causes the system to provide an actionable insight on at least one person (Reference Hickson shows: [0010] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for generation of optimal team configuration recommendations are provided. The methods and systems described herein are directed to a system that methodically obtains information about a project and evaluates the employees using objective analyses. Characteristics of potential team members (i.e., skill sets, personality traits availability, etc.) and the requirements for a designated collaborative project are objectively determined by the systems and methods described herein. With both the pool of potential team members and the nature of the project objectively defined, one or more optimal team configuration recommendations may be made based on a training dataset of past projects and the teams assigned as well as characteristics of the employees and requirements of the project.
In both of the above instances, Reference Hickson does not explicitly show the term “metrics”. It is reasonably understood that the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028] reads on “metrics” discussed in the claim. However, Reference Hickson does not provide detail on how the matching is carried about.
Reference Hull shows the above limitation at least in [0081] With the gathered information, the system can correlate employees with different predetermined criteria, such as skills, reputation, productivity, experience, job/position titles they have held, job performance, products or projects they have worked on, communication skills, other employees with whom they have worked, availability (e.g., expected completion date of a current project), etc. Such correlation may entail rating how well an employee matches the specified criteria in an over-all sense. Individual values, scores or ratings may or may not be generated for each criterion, but at least one final rating or ranking will be generated for the employee for a given set of criteria. [0082] Regarding the organization's collaboration tool, the system may automatically and selectively analyze specific types of data determined by the criteria for which the employee is being rated. For example, if the criteria include things like "communication" or "camaraderie" or "ability to work in a team", then the employee's communications with other employees may be reviewed--possibly including electronic mail notes, instant messages, etc. If the criteria instead include things like "timeliness" or "productivity," his communications may be bypassed in favor of notes or posts regarding completed tasks, a schedule according to which he was supposed to complete tasks (and information indicating whether or not he did) and/or other relevant data. With employees' skills generally being an important criterion, the system will capture explicitly identified skills (e.g., explicit skill endorsements from colleagues) and/or implicit skills (e.g., from presentations or papers generated by the employee). [0083] In operation 204, for each of the predetermined criteria, and for multiple values of a given criterion where applicable, the system calculates a match or relevance between that criteria/value and each of multiple (or all) employees. A criterion such as skills, for example, could have myriad values--such as "enterprise systems," "C++ programming," "user interface design" and so on, and alphanumerical values, positive/negative values or other values may be assigned to an employee for each one to indicate how much of that experience she has. [0013] To enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee. The data may be processed by filtering out irrelevant information, weighting some data (e.g., data of a particular application) more or less than other data, using data from one application to bolster or disprove other data, and so on. [0057] In embodiments of the invention, data described above are analyzed to determine how well each candidate employee matches some or all criteria. In some implementations, employees may be explicitly rated with regard to various criteria based on their current jobs or roles (i.e., without regard to any new team or role for which they may be considered). In these implementations a recommendation system or apparatus such as system 130 of FIG. 1 (e.g., within collaboration tool 138) may support evaluation of employees according to some or all criteria once, multiple times or on a recurring basis, and record those evaluations for later reference when a new role is defined. In other implementations, however, existing records within some or all of the repositories depicted in FIG. 1, and/or others, are analyzed to derive ratings of individual employees and/or existing teams. [0068] For example, software engineers may use Jira or some other third-party bug tracking software. Collaboration tool 138 may record the creation of a bug report within an external Jira data repository, and also the resolution of the bug. The number of bugs resolved by an individual or a team may be used as a measure of productivity, especially when used to compare similar individuals or teams (e.g., those having the same job descriptions--such as software engineer). As another example, productivity of a salesperson may be rated based on information extracted from CRM database 112 (e.g., salesforce.com). [0069] A productivity rating may also or instead involve identifying a number of client presentations made (e.g., by a sales or marketing team), or a number of other resources generated, which may be aggregated to generate a numerical rating. Illustratively, if a team of 5 sales associates makes 2 client presentations in one week, the team may earn a rating of 10; by way of comparison, another team of 4 sales associates that makes 3 presentations in two weeks may yield a rating of 6 (e.g., (4*3)/2). Alternatively, ratings could be generated as described above for skills, wherein multiple teams' or individuals' tasks are aggregated and used to create a histogram, with the different values mapped to a bell curve to assign a rating.
Reference Hull shows “actions” and “insights” [0018] In these embodiments, recommendation system 130 includes (or at least uses) employee work profiles 132, criteria 134 and relevance ratings 136. Based on criteria 134, system 130 evaluates employees based on their profiles 132 and/or other data described below, and produces relevance ratings 136. Employee work profiles 132 may alternatively be titled "internal profiles" or "work-related profiles" because, as discussed below, they may primarily encompass information and characteristics of employees that are specific to, or at least related to, the organization and the employees' work-related activities and attributes. For example, actions and transactions of employees (and/or teams) involving repositories 112, 114, 116, 118, 120, 122 may be posted to or available through their work profiles 132. By way of contrast, external profiles 116 may encompass personal characteristics of employees and/or information not internal to the organization, and may be available to people outside the organization. [0023] Thus, data assembled by collaboration tool 138 allows insight into the skills, experience and expertise of individual employees, and also into how well specific groups of individuals (e.g., existing teams) communicate and work together. As but one example, the collaboration tools may record the completion of 100 tasks by a given team within a short period of time, whereas other teams have completed at most 60 tasks within the same period of time. This may allow system 130 to assign a higher "productivity" score for the individuals of the given team, at least when working together.
Reference Hickson and Reference Hull are analogous prior art to the claimed invention because the references generally relate to field of making personnel recommendations. Further, said references are part of the same classification, i.e., G06Q10/063112. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Hull, particularly the ability to identify enterprise/ team metrics (Hull: [0081]-[0083], [0068]-[0069]), in the disclosure of Reference Hickson, particularly in the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028], in order to provide for a system that enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee as taught by Reference Hull (see at least in [0013]), where upon the execution of the method and system of Reference Hull for promoting internal mobility within an organization and, in particular, for recommending employees and/or teams of employees of the organization for a new project or for another role within the organization (Abstract) so that the process of making personnel recommendations can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar making personnel recommendations field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Hickson in view of Reference Hull, the results of the combination were predictable (MPEP 2143 A)).
As per claim 20: Regarding the claim limitations below, Reference Hickson in view of Reference Hull shows:
further comprising providing an option to define desired behavioral characteristics of a team that includes a first person.
(Reference Hickson shows: [0010] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for generation of optimal team configuration recommendations are provided. The methods and systems described herein are directed to a system that methodically obtains information about a project and evaluates the employees using objective analyses. Characteristics of potential team members (i.e., skill sets, personality traits availability, etc.) and the requirements for a designated collaborative project are objectively determined by the systems and methods described herein. With both the pool of potential team members and the nature of the project objectively defined, one or more optimal team configuration recommendations may be made based on a training dataset of past projects and the teams assigned as well as characteristics of the employees and requirements of the project.
In both of the above instances, Reference Hickson does not explicitly show the term “metrics”. It is reasonably understood that the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028] reads on “metrics” discussed in the claim. However, Reference Hickson does not provide detail on how the matching is carried about.
Reference Hull shows the above limitation at least in [0081] With the gathered information, the system can correlate employees with different predetermined criteria, such as skills, reputation, productivity, experience, job/position titles they have held, job performance, products or projects they have worked on, communication skills, other employees with whom they have worked, availability (e.g., expected completion date of a current project), etc. Such correlation may entail rating how well an employee matches the specified criteria in an over-all sense. Individual values, scores or ratings may or may not be generated for each criterion, but at least one final rating or ranking will be generated for the employee for a given set of criteria. [0082] Regarding the organization's collaboration tool, the system may automatically and selectively analyze specific types of data determined by the criteria for which the employee is being rated. For example, if the criteria include things like "communication" or "camaraderie" or "ability to work in a team", then the employee's communications with other employees may be reviewed--possibly including electronic mail notes, instant messages, etc. If the criteria instead include things like "timeliness" or "productivity," his communications may be bypassed in favor of notes or posts regarding completed tasks, a schedule according to which he was supposed to complete tasks (and information indicating whether or not he did) and/or other relevant data. With employees' skills generally being an important criterion, the system will capture explicitly identified skills (e.g., explicit skill endorsements from colleagues) and/or implicit skills (e.g., from presentations or papers generated by the employee). [0083] In operation 204, for each of the predetermined criteria, and for multiple values of a given criterion where applicable, the system calculates a match or relevance between that criteria/value and each of multiple (or all) employees. A criterion such as skills, for example, could have myriad values--such as "enterprise systems," "C++ programming," "user interface design" and so on, and alphanumerical values, positive/negative values or other values may be assigned to an employee for each one to indicate how much of that experience she has. [0013] To enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee. The data may be processed by filtering out irrelevant information, weighting some data (e.g., data of a particular application) more or less than other data, using data from one application to bolster or disprove other data, and so on. [0057] In embodiments of the invention, data described above are analyzed to determine how well each candidate employee matches some or all criteria. In some implementations, employees may be explicitly rated with regard to various criteria based on their current jobs or roles (i.e., without regard to any new team or role for which they may be considered). In these implementations a recommendation system or apparatus such as system 130 of FIG. 1 (e.g., within collaboration tool 138) may support evaluation of employees according to some or all criteria once, multiple times or on a recurring basis, and record those evaluations for later reference when a new role is defined. In other implementations, however, existing records within some or all of the repositories depicted in FIG. 1, and/or others, are analyzed to derive ratings of individual employees and/or existing teams. [0068] For example, software engineers may use Jira or some other third-party bug tracking software. Collaboration tool 138 may record the creation of a bug report within an external Jira data repository, and also the resolution of the bug. The number of bugs resolved by an individual or a team may be used as a measure of productivity, especially when used to compare similar individuals or teams (e.g., those having the same job descriptions--such as software engineer). As another example, productivity of a salesperson may be rated based on information extracted from CRM database 112 (e.g., salesforce.com). [0069] A productivity rating may also or instead involve identifying a number of client presentations made (e.g., by a sales or marketing team), or a number of other resources generated, which may be aggregated to generate a numerical rating. Illustratively, if a team of 5 sales associates makes 2 client presentations in one week, the team may earn a rating of 10; by way of comparison, another team of 4 sales associates that makes 3 presentations in two weeks may yield a rating of 6 (e.g., (4*3)/2). Alternatively, ratings could be generated as described above for skills, wherein multiple teams' or individuals' tasks are aggregated and used to create a histogram, with the different values mapped to a bell curve to assign a rating.
Reference Hull shows “actions” and “insights” [0018] In these embodiments, recommendation system 130 includes (or at least uses) employee work profiles 132, criteria 134 and relevance ratings 136. Based on criteria 134, system 130 evaluates employees based on their profiles 132 and/or other data described below, and produces relevance ratings 136. Employee work profiles 132 may alternatively be titled "internal profiles" or "work-related profiles" because, as discussed below, they may primarily encompass information and characteristics of employees that are specific to, or at least related to, the organization and the employees' work-related activities and attributes. For example, actions and transactions of employees (and/or teams) involving repositories 112, 114, 116, 118, 120, 122 may be posted to or available through their work profiles 132. By way of contrast, external profiles 116 may encompass personal characteristics of employees and/or information not internal to the organization, and may be available to people outside the organization. [0023] Thus, data assembled by collaboration tool 138 allows insight into the skills, experience and expertise of individual employees, and also into how well specific groups of individuals (e.g., existing teams) communicate and work together. As but one example, the collaboration tools may record the completion of 100 tasks by a given team within a short period of time, whereas other teams have completed at most 60 tasks within the same period of time. This may allow system 130 to assign a higher "productivity" score for the individuals of the given team, at least when working together.
Reference Hickson and Reference Hull are analogous prior art to the claimed invention because the references generally relate to field of making personnel recommendations. Further, said references are part of the same classification, i.e., G06Q10/063112. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Hull, particularly the ability to identify enterprise/ team metrics (Hull: [0081]-[0083], [0068]-[0069]), in the disclosure of Reference Hickson, particularly in the characteristics of potential team members discussed above in [0010] and matching employee and project profiles in [0028], in order to provide for a system that enable an organization to find employees that would be suitable members of a team, or to find an employee that matches particular criteria for some other purpose, data from multiple applications and/or services are analyzed to obtain an organization-specific profile of each candidate employee that reveals whether, and how well, those criteria match the employee as taught by Reference Hull (see at least in [0013]), where upon the execution of the method and system of Reference Hull for promoting internal mobility within an organization and, in particular, for recommending employees and/or teams of employees of the organization for a new project or for another role within the organization (Abstract) so that the process of making personnel recommendations can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar making personnel recommendations field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Hickson in view of Reference Hull, the results of the combination were predictable (MPEP 2143 A)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
S. A. Licorish and S. G. MacDonell, "What affects team behavior? Preliminary linguistic analysis of communications in the Jazz repository," 2012 5th International Workshop on Co-operative and Human Aspects of Software Engineering (CHASE), Zurich, Switzerland, 2012, pp. 83-89, doi: 10.1109/CHASE.2012.6223029.keywords: {Pragmatics;Software;Programming;Context;Project management;Data mining;Humans;software development;team behaviors;linguistic analysis;communication;Jazz},
This reference discloses there is a growing belief that understanding and addressing the human processes employed during software development is likely to provide substantially more value to industry than yet more recommendations for the implementation of various methods and tools. To this end, considerable research effort has been dedicated to studying human issues as represented in software artifacts, due to its relatively unobtrusive nature. We have followed this line of research and have conducted a preliminary study of team behaviors using data mining techniques and linguistic analysis. Our data source, the IBM Rational Jazz repository, was mined and data from three different project areas were extracted. Communications in these projects were then analyzed using the LIWC linguistic analysis tool. We found that although there are some variations in language use among teams working on project areas dedicated to different software outcomes, project type and the mix of (and number of) individuals involved did not affect team behaviors as evident in their communications. These assessments are initial conjectures, however; we plan further exploratory analysis to validate these results. We explain these findings and discuss their implications for software engineering practice.
Foreign Reference:
(EP 3846097 A1) Yoon. SYSTEM FOR CREATING EMPLOYMENT THROUGH WORKING TYPE CONFIGURED BY COLLECTION OF COLLECTIVE INTELLIGENCE, AND METHOD THEREFOR
This reference discloses the present invention relates to a system and a method of creating employment through a work type established by collective intelligence convergence. The present invention discloses a system and a method of creating employment through a work type of a general office work established by collective intelligence convergence; the system including: an operating computer which makes an adjustment for creating employment in relation to a four-team two-shift work type of a company; a collective intelligence converging system which converges collective intelligence for an opinion collection item of introduction of the four-team two-shift work type and an opinion collection item of an employment expansion rate by a request of the operating computer, and provides the operating computer with a converged opinion and a numerical value related to the converged opinion; and a numerical value determining system which reevaluates and adjusts unreasonable cost of the company by the request of the operating computer, in which the operating computer is operated to improve productivity of the company while increasing employment by increasing daily operating hours of the company compared to an existing work type through a work type that distributes workforce by operating the work in two shifts or a work type that distributes workforce into a weekday team (Monday, Tuesday, Wednesday, and Thursday) and a weekend team (Friday, Saturday, and Sunday) in the introduction of the four-team two-shift work type of the company.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANCY PRASAD whose telephone number is (571)270-3265. The examiner can normally be reached M-F: 8:00 AM - 4:30 PM EST.
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/N.N.P/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624