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 the Claims
Claims 2, 3, 5-17 are all the claims pending in the application.
Claim 2 is amended.
Claims 2, 3, 5-17 are rejected.
The following is a Final Office Action in response to amendments and remarks filed Dec. 2, 2025.
Response to Arguments
Regarding the claim objections, the objections are withdrawn in light of the amendments to the claims.
Regarding the 103 rejections, the rejections are maintained for the following reasons. First, Applicant asserts the rejections should be withdrawn because the personality characteristics described in Banerjee are not equivalent to the behavioral tendencies in the present claims because the personality characteristics in Banerjee are assessed in sets of three where as the behavioral tendencies in the present claims are assessed separately and independently. Examiner respectfully does not find this assertion persuasive because the features upon which applicant relies (i.e., separate and independent assessment) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). That is, nothing in the claims precludes relationships between the assessed personality traits.
Further, Examiner notes the behavioral tendencies in the present claims are not assessed completely separately because the claims explicitly recite summing the parameter values of behavioral tendencies to create a ranking of the candidate. Thus, the behavioral tendencies in the present claims are not assessed completely separately because they are combined.
Second, Applicant asserts the prior art does not teach a complementary personality parameter because Banerjee teaches using existing employees as a benchmark where as the complimentary personality parameter does not require using existing employes as benchmarks. Examiner respectfully does not find this assertion persuasive because the claims explicitly state the complimentary personality parameter is based on an individual already engage in the metier undertaking which would include existing employees.
Further, Examiner notes the purpose of the complementary parameter does not further limit the scope of the claim because it is only the intended use of the variable, see MPEP 2103.I.C. That is, Examiner does not find a patentable distinction between Banerjee’s comparison of existing employees and candidates for benchmarking purposes and the complementary parameter’s comparison of existing employees and candidates.
Third, Applicant asserts Banerjee cannot be combined with Carnicelli as done in the previous Office Action because the calculations in Banerjee cannot be incorporated into the calculations in Carnicelli. Examiner respectfully does not find this assertion persuasive because the calculations performed in Carnicelli are a weighted sum of scores of measures, ¶[0082]. Examiner finds no reason these measures could not be based on comparisons of the candidates to existing employees (i.e., the subtraction taught by Banerjee). Thus, Examiner does not find the combination of references would render Carnicelli unfit for its intended purpose.
Accordingly the 103 rejections are maintained, please see below for the new rejections of the claims as amended.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
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) 2, 3, 5-11, and 13-16, is/are rejected under 35 U.S.C. 103 as being unpatentable over Carnicelli et al, US Pub. No. 2020/0279226, herein referred to as “Carnicelli” in view of Borje et al, US Pub. No. 2019/0163668 herein referred to as “Borje”, further in view of Banerjee, US Pub. No. 2012/0185777, herein referred to as “Banerjee”, further in view of Riggs et al, US Pub. No. 2023/0072902, herein referred to as “Riggs”.
Regarding claim 2, Carnicelli teaches:
prompting said candidate individual for a plurality of metier specific behavioral tendency data of said candidate individual with a metier-methodized behavioral tendency self-assessment query for said candidate individual (provides questionnaire to applicant regarding cultural fit, management style fit, personality fit, ¶¶[0045], [0081]-[0082]);
prompting said candidate individual for a plurality of metier-specific objective data of said candidate individual with a metier-methodized objective data self-assessment query for said candidate individual (provides interface for uploading resumes, ¶¶[0040], [0081]-[0082]);
creating a set of mitier-specific numerical input data for said candidate individual form said mitier-specific behavioral tendency data of said candidate individual and said metier-specific objective data of said candidate individual by assigning a numerical value to each self-assessment query response of said candidate individual to each said self-assessment query for said candidate individual (maps personality trats across five dimensions, ¶¶[0045], [0053] and Fig. 3 and constructs a vector in n-dimensional space from a candidate applicant's resume, ¶[0049] and Fig. 2; see also ¶[0082] discussing numerical values that represent candidate personality traits and skills),
numericizing said set of metier-specific numerical input data for said candidate individual in an n-dimensional space wherein each dimension of said n-dimensional space represents a metier trait metric corresponding to each said behavioral tendency of said candidate individual and each said objective datum for said candidate individual inquired of by said self-assessment queries for said candidate individual (maps personality trats across five dimensions, ¶¶[0045], [0053] and Fig. 3 and constructs a vector in n-dimensional space from a candidate applicant's resume, ¶[0049] and Fig. 2);
inputting said numericized n- dimensional spaced metier-specific numerical input data for said candidate individual into a computer-implemented metier-applied complementary-personalities data valuation framework having a number of computer-implemented metier-trait-metric complementary personality parameters n corresponding to each dimension n of said n-dimensional space, comprising (analyzes vectors, e.g. ¶¶[0049]-[0050] and Figs. 2 and 3; see also ¶¶[0083]-[0084] discussing processors; and e.g., ¶¶[0051], [0053] discussing culture fit and personality):
performing a computer-implemented metier-trait-metric-parameter-specific mathematical operation with each said computer-implemented metier-trait-metric complementary personality parameter on at least one numerical value of said numericized n-dimensional spaced metier-specific numerical input data for said computer-implemented metier-trait-metric complementary personality parameter to create a computer-generated interim numerical data set for said computer-implemented metier-trait-metric complementary personality parameter (maps personality trats across five dimensions, ¶¶[0045], [0053] and Fig. 3 and constructs a vector in n-dimensional space from a candidate applicant's resume, ¶[0049] and Fig. 2; see also ¶[0082] discussing numerical values that represent candidate personality traits and skills);
applying a computer-implemented metier-trait-metric-parameter-specific mathematical calculation with each said computer-implemented metier-trait-metric complementary personality parameter to said computer-generated interim numerical data set for said computer-implemented metier-trait-metric complementary personality parameter to create a computer-generated interim metier-trait-metric parameter value for each said computer-implemented metier-trait-metric complementary personality parameter, comprising (maps personality trats across five dimensions, ¶¶[0045], [0053] and Fig. 3 and constructs a vector in n-dimensional space from a candidate applicant's resume, ¶[0049] and Fig. 2; see also ¶[0082] discussing numerical values that represent candidate personality traits and skills):
multiplying said computer-generated interim metier-trait-metric parameter value for each said computer-implemented metier-trait-metric complementary personality parameter by a computer-generated metier-trait-metric parameter weight for said computer-implemented metier-trait-metric complementary personality parameter to create a computer-generated weighted metier-trait-metric parameter value for each said computer-implemented metier-trait-metric complementary personality parameter (various categories of personality traits may be emphasized or de-emphasized (i.e., may be give greater or lesser weight), ¶¶[0043], [0046]),
taking the sum of the computer-generated weighted metier-trait-metric parameter values for all said computer -implemented metier-trait-metric complementary personality parameters of said computer-implemented metier-applied complementary-personalities data valuation framework divided by the sum of the computer-generated metier-trait-metric parameter weights for all said computer-implemented metier-trait-metric complementary personality parameters of said computer-implemented metier-applied complementary-personalities data valuation framework (generates difference metric as a weighted sum of scores for each of the one or more measures, ¶[0082])
to create a computer-generated metier-applied individual rank score for said candidate individual (determines overall match score for applicant, ¶¶[0043], [0047], [0050], [0054]; see also ¶[0041] discussing ranking candidates).
comparing said computer-generated metier-applied individual rank score for said candidate individual to at least one computer-generated metier-applied individual rank score for another candidate individual to create a computer generated metier-applied recommendation for said candidate individual (ranks candidates based on scores, ¶[0041]; see also Fig. 24 showing ranked list of candidates).
However Carnicelli does not teach but Borje does teach:
inputting said numericized n- dimensional spaced metier-specific numerical input data for said candidate individual into a computer-implemented machine- learning metier-applied complementary-personalities data valuation framework (inputs candidate dimensions into model ¶¶[0098]-[0100]; see also e.g., ¶[0023] discussing using machine learning for matching candidates and jobs)
wherein each said computer- generated métier-trait-metric parameter weight for each said computer- implemented métier-trait-metric complementary personality parameter is established as a computer- implemented métier-trait-metric-parameter-specific relative parameter weight value for said computer-implemented métier-trait-metric complementary personality parameter divided by the sum of the computer-implemented métier-trait-metric-parameter-specific relative parameter weight values for all computer-implemented métier-trait- metric complementary personality parameters of said computer-implemented métier-applied complementary personalities data valuation framework (sum of weights equal to 1, ¶¶[0144]-[0147]. Please note, the sum of the weights equaling 1 is within the scope of this limitation because dividing the weights by the sum of the weights results in a sum of weights being 1).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the hiring and recruitment of Carnicelli with the weighting of Borje because Carnicelli suggests doing so, see MPEP 243.I.G. That is, Carnicelli teaches applying a normalization factor, ¶¶[0052], [0055]. One of ordinary skill would have recognized normalizing would involve adjusting the weights to sum to 1 as in Borje.
However the combination of Carnicelli and Borje does not teach but Banerjee does teach:
prompting said candidate individual for a plurality of metier specific behavioral tendency data of said candidate individual comprising (users provide information on personality characteristics, ¶[0035], for themselves, ¶[0033]; see also e.g. ¶[0002] discussing hiring)
data pertaining to the trait of assertiveness (aggressive Tbl. 2);
data pertaining to the trait of compassion (selfless, Tbl3 3);
data pertaining to the trait of creativity (creative in ideas, Tbl. 1);
data pertaining to the trait of deliberation (conscientious, Tbl. 2);
data pertaining to the trait of ethicalness (too honest, highly principled, Tbl. 3);
data pertaining to the trait of open-mindedness (curious, open to new ideas, Tbl. 1);
data pertaining to the trait of passion (passionate about key interests, Tbl. 2);
data pertaining to the trait of perseverance (persistent, highly resilient, Tbl.2);
data pertaining to the trait of pragmatism (analytical, Tbl. 1);
data pertaining to the trait of skepticism (prefers “tried and tested” approach, Tbl. 1);
and data pertaining to the trait of wisdom (detail oriented, steady, methodical, Tbl. 1);
wherein said metier-specific numerical input data for said candidate individual comprises: an assertiveness behavioral tendency score; a compassion behavioral tendency score; a creativity behavioral tendency score; a deliberation behavioral tendency score; an ethicalness behavioral tendency score; an open-mindedness behavioral tendency score; a passion behavioral tendency score; a perseverance behavioral tendency score; a pragmatism behavioral tendency score; a skepticism behavioral tendency score; and a wisdom behavioral tendency score (uses numerical score ranging between various values for traits, e.g. ¶¶[0039], [0045]);
a behavioral-tendency-similarity-for-assertiveness parameter; a behavioral-tendency-similarity-for-compassion parameter; a behavioral-tendency-similarity-for-creativity parameter; a behavioral-tendency-similarity-for-deliberation parameter; a behavioral-tendency-similarity-for-ethicalness parameter; a behavioral-tendency-similarity-for-open-mindedness parameter; a behavioral-tendency-similarity-for-passion parameter; a behavioral-tendency-similarity-for-perseverance parameter; a behavioral-tendency-similarity-for-pragmatism parameter; a behavioral-tendency-similarity-for-skepticism parameter; a behavioral-tendency-similarity-for-wisdom parameter (uses numerical score ranging between various values for traits, e.g. ¶¶[0039], [0045]);
for said behavioral-tendency-similarity-for-assertiveness parameter, subtracting said assertiveness behavioral tendency score for said candidate individual from an assertiveness behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; for said behavioral-tendency-similarity-for-compassion parameter, subtracting said compassion behavioral tendency score for said candidate individual from a compassion behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; for said behavioral-tendency-similarity-for-creativity parameter, subtracting said creativity behavioral tendency score for said candidate individual from a creativity behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; for said behavioral-tendency-similarity-for-deliberation parameter, subtracting said deliberation behavioral tendency score for said candidate individual from a deliberation behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; for said behavioral-tendency-similarity-for-ethicalness parameter, subtracting said ethicalness behavioral tendency score for said candidate individual from an ethicalness behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; for said behavioral-tendency-similarity-for-open-mindedness parameter, subtracting said open-mindedness behavioral tendency score for said candidate individual from an open-mindedness behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; for said behavioral-tendency-similarity-for-passion parameter, subtracting said passion behavioral tendency score for said candidate individual from a passion behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; for said behavioral-tendency-similarity-for-perseverance parameter, subtracting said perseverance behavioral tendency score for said candidate individual from a perseverance behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; for said behavioral-tendency-similarity-for-pragmatism parameter, subtracting said pragmatism behavioral tendency score for said candidate individual from a pragmatism behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; for said behavioral-tendency-similarity-for-skepticism parameter, subtracting said skepticism behavioral tendency score for said candidate individual from a skepticism behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result; and for said behavioral-tendency-similarity-for-wisdom parameter, subtracting said wisdom behavioral tendency score for said candidate individual from a wisdom behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result (determines absolute difference between the values for the personality characteristics for individuals, ¶[0045]; see also ¶[0052] noting existing employees are used to create the benchmark for candidates).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the hiring and recruitment of Carnicelli and Borje with the various personality traits and individual comparisons of Banerjee because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Carnicelli teaches assessing cultural fit and personalities, e.g., Fig. 3. One of ordinary skill would have understood assessing cultural fit would likely be improved by comparing the personalities of the candidates, e.g. as taught by Banerjee and accordingly would have modified Carnicelli and Borje to compare personality traits as taught by Banerjee.
However the combination of Carnicelli, Borje and Banerjee does not teach but Riggs does teach:
data pertaining to the trait of urgency (behavioral assessment of candidates includes measurements of behavioral trait patience, ¶[0030]);
an urgency behavioral tendency score (determines scores of cognitive traits of candidates, ¶[0033])
a behavioral-tendency-similarity-for-urgency parameter (determines scores of cognitive traits of candidates, ¶[0033]);
receiving feedback reference data pertaining to said computer-generated metier-applied recommendation for said candidate individual; automatically updating said computer-implemented machine-learning metier-applied complementary-personalities data valuation framework with said feedback reference data to improve the efficacy of creating a computer-generated metier-applied recommendation for a subsequent candidate individual (uses success or failure of individuals hired based on the job target may be used to retrain system as feedback into the machine learning algorithm to evaluate the accuracy, ¶[0035]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the hiring and recruitment of Carnicelli, Borje and Banerjee with the retraining the machine learning based on success or failure of hired individuals because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the assessments of candidates in Carnicelli, Borje and Banerjee would likely be improved by retraining the model based on the successful or failure of the recommended candidates that were previously recommended, e.g., as taught by Riggs.
However the combination of Carnicelli, Borje, Banerjee and Riggs does not explicitly teach:
for said behavioral-tendency-similarity-for-urgency parameter, subtracting said urgency behavioral tendency score for said candidate individual from an urgency behavioral tendency score for an individual already engaged in the metier undertaking for which said candidate individual is a candidate and taking the absolute value of the result
Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to take the absolute value of the result subtracting said urgency behavioral tendency score for said candidate individual from an urgency behavioral tendency score for an individual already engaged in the metier undertaking in light of the combination of Carnicelli, Borje, Banerjee and Riggs because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Banerjee teaches the mathematical function for the various personality traits, ¶[0045], and Riggs teaches evaluating the patience personality trait, ¶[0030]. One skilled in the art would infer that the combination of Carnicelli, Borje, Banerjee and Riggs would perform the mathematical function for other personality traits analyzed including the patience personality trait taught by Riggs.
Regarding claim 3, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 2 and Carnicelli further teaches:
the step of fulfilling a vacant metier role for a particular metier application utilizing said computer-generated metier-applied recommendation (business decides whether or not to hire the candidate, ¶[0048]),
wherein said métier application comprises a métier application selected from métier networking, métier referrals, métier team-building, métier associations membership, métier leadership development, and métier vendors (business decides whether or not to hire the candidate, ¶[0048]. Please note, hiring is a part of team building because hiring leads to team building).
Regarding claim 5, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 2 and Carnicelli further teaches:
wherein said métier-specific objective data comprises métier-specific objective data selected from first name data, last name data, nickname data, vocation data, avocation data, location data, personal photo data, pronouns data, birthdate data, race data, ethnicity data, skills data, certifications data, training data, endorsements data, recommendations data, mentorship roles data, and any combination of the foregoing (job seeker provides name, physical address, ¶[0039], and system obtains data on skills, ¶¶[0049]-[0050] and Figs. 2 and 3).
Regarding claim 6, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 2 and does not explicitly teach:
wherein said metier comprises the legal profession and wherein said metier-specific objective data comprises metier-specific objective data selected from law firm data, field of law data, legal specialty area data, state licensing data, practice geography data, law school data, legal education data, year of bar admission data, desired case type data, recent case overview data, recent case results data, and any combination of the foregoing.
Nevertheless, it would have been obvious, at the time of filing, for the combination of Carnicelli, Borje and Banerjee to include the legal profession and data relevant to the legal profession because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Carnicelli, Borje and Banerjee all relate to hiring and there is no reason the teachings would not be applicate to hiring attorneys. Further, Borje using past employers and educational history data, ¶[0100]. Thus, it would have been obvious, in light of the combination of Carnicelli, Borje and Banerjee to use information like law firm data and law school data.
Regarding claim 7, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 2 and Carnicelli further teaches:
wherein said step of performing a computer-implemented métier-trait-metric-parameter- specific mathematical operation comprises the step of performing a computer- implemented métier-trait-metric-parameter-specific mathematical operation selected from the steps of eliminating one or more data pairs of data paired métier- relevant numerical input data, time-based eliminating one or more data pairs of data paired métier-relevant numerical input data, age-of-data eliminating one or more data pairs of data paired métier-relevant numerical input data, individually differentiated data pair weighting of data paired métier-relevant numerical input data, and any combination of the foregoing (applies time-weighting to linearly or exponentially assigning weights to the set of history items, ¶[0082]; e.g., most recent past employer has twice the weight of the second most recent, ¶[0144]).
Regarding claim 8, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 2 and Carnicelli further teaches:
wherein said computer-implemented métier-trait-metric-parameter-specific mathematical operation comprises an axiom-implementing computer-implemented métier-trait- metric-parameter-specific mathematical operation (utilizes the five factor model for assessing personalities, ¶[0045]).
Regarding claim 9, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 2 and Carnicelli further teaches:
wherein said step of applying a computer-implemented métier-trait-metric-parameter- specific mathematical calculation comprises the step of applying a computer- implemented métier-trait-metric-parameter-specific mathematical calculation selected from the steps of averaging at least some numerical values of data paired métier-relevant numerical input data, averaging at least some measured métier- metric numerical values of data paired métier-relevant numerical input data, finding a maximum of measured métier-metric numerical values of data paired métier- relevant numerical input data, and any combination of the foregoing (system generates a match score that is a weighted average of the scores, ¶[0043]).
Regarding claim 10, the combination of Carnicelli, Borje and Banerjee teaches all the limitations of claim 2 and Carnicelli further teaches:
wherein said computer-implemented métier-trait-metric-parameter-specific mathematical calculation comprises an axiom-implementing computer-implemented métier-trait- metric-parameter-specific mathematical calculation (system generates a match score that is a weighted average of the scores, ¶[0043]).
Regarding claim 11, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 2 and Carnicelli further teaches:
wherein said step of comparing said computer-generated métier-applied individual rank score for said candidate individual to at least one computer-generated métier-applied individual rank score for another candidate individual to create a computer-generated métier- applied recommendation for said candidate individual comprises the step of maximizing the aggregate effectiveness of a group of individuals (determines overall match score for applicant, ¶¶[0043], [0047], [0050], [0054]; see also ¶[0041] discussing ranking candidates. Please note, assessing candidates to find the most suitable maximizes the aggregate effectiveness of a group because hiring more suitable candidate improves the group overall).
Regarding claim 13, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 2 and Borje further teaches:
wherein said step of comparing said computer-generated métier-applied individual rank score for said candidate individual to at least one computer-generated métier-applied individual rank score for another candidate individual to create a computer-generated métier- applied recommendation for said candidate individual comprises the step of computer-implemented iteratively recasting said computer-generated métier-applied individual rank score for said candidate individual (applies multiple iterations when building model, ¶[0107]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the hiring and recruitment of Carnicelli with the iterative process of Borje because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized the process in Carnicelli would likely be improve by iteratively refining the scoring model and accordingly would have modified Carnicelli to iteratively refining the process, e.g. as taught by Borje.
Regarding claim 14, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 13 and Borje further teaches:
wherein said step of computer-implemented iteratively recasting said computer- generated métier-applied individual rank score for said candidate individual comprises a step selected from computer-implemented iteratively recasting said computer-generated métier-applied individual rank score for said candidate individual in response to computer- implemented iteratively developed networking reference data, computer- implemented iteratively recasting said computer-generated métier-applied individual rank score for said candidate individual in response to computer-implemented iteratively developed team-building reference data, computer-implemented iteratively recasting said computer-generated métier-applied individual rank score for said candidate individual in response to computer-implemented iteratively developed referrals reference data, computer-implemented iteratively recasting said computer- generated métier-applied individual rank score for said candidate individual in response to computer-implemented iteratively developed associations membership reference data, and computer-implemented iteratively recasting said computer-generated métier-applied individual rank score for said candidate individual in response to computer- implemented iteratively developed mentorship reference data (applies multiple iterations when building model, ¶[0107]; see also ¶[0087] discussing scoring and suggesting candidates).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the hiring and recruitment of Carnicelli with the iterative process of Borje because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized the process in Carnicelli would likely be improve by iteratively refining the scoring model and accordingly would have modified Carnicelli to iteratively refining the process, e.g. as taught by Borje.
Regarding claim 15, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 13 and Borje further teaches:
wherein said step of computer-implemented iteratively recasting said computer- generated métier-applied individual rank score for said candidate individual comprises the steps of:- computer-generating antecedent iterative reference data; - utilizing said computer-generated antecedent iterative reference data in a step of computer-implemented applicatively revaluing at least some said métier specific numerical input data for said candidate individual; and - computer-generating subsequent iterative reference data as a result of said step of computer-implemented applicatively revaluing at least some said métier- specific numerical input data for said candidate individual (ranks candidates based on feedback from client to refine ranking of candidates, ¶[0022]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the hiring and recruitment of Carnicelli with the feedback of Borje because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized the process in Carnicelli would likely be improve by obtaining feedback on the scoring model and accordingly would have modified Carnicelli to receive feedback to refine the process, e.g. as taught by Borje.
Regarding claim 16, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 15 and Borje further teaches:
wherein said computer-generated subsequent iterative reference data comprises computer-generated antecedent iterative reference data for a next succeeding iteration (ranks candidates based on feedback from client to refine ranking of candidates, ¶[0022]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the hiring and recruitment of Carnicelli with the feedback of Borje because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized the process in Carnicelli would likely be improve by obtaining feedback on the scoring model and accordingly would have modified Carnicelli to receive feedback to refine the process, e.g. as taught by Borje.
Claim(s) 12 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Carnicelli, Borje, Banerjee and Riggs, further in view of Ravi et al, US Pub. No. 2020/0184424, herein referred to as “Ravi”.
Regarding claim 12, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 1 and does not teach but Ravi does teach:
wherein said step of maximizing the aggregate effectiveness of a group of individuals comprises the step of taking the sum of the highest computer-generated meter-applied individual rank scores for a plurality of candidates individuals (determines an overall team score, ¶[0025]; see also Fig. 2 showing optimization process).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the hiring and recruitment of Carnicelli, Borje, Banerjee and Riggs with the team based metrics of Ravi because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized the users in Carnicelli and Borje would likely be interested recruiting candidates that improve the overall team and accordingly would have modified Carnicelli, Borje, Banerjee and Riggs to apply team metrics as taught by Ravi.
Regarding claim 17, the combination of Carnicelli, Borje, Banerjee and Riggs teaches all the limitations of claim 15 and does not teach but Ravi does teach:
wherein said step of utilizing said computer-generated antecedent iterative reference data in a step of computer-implemented applicatively revaluing at least some said métier-specific numerical input data for said candidate individual comprises the step of computer-implemented adjusting at least one numerical value of said métier- specific numerical input data in response to said computer-generated antecedent iterative reference data (determines current metric of team, ¶[0025], and recalculates after including candidate, ¶[0026] and Fig. 2).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the hiring and recruitment of Carnicelli, Borje and Banerjee with the team based metrics of Ravi because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized the users in Carnicelli and Borje would likely be interested recruiting candidates that improve the overall team and accordingly would have modified Carnicelli and Borje to apply team metrics as taught by Ravi.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626