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 .
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 27-46 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention. Applying Step 1 to the claims it is determined that: claims 27-40 are directed to a process; and claims 41-46 are directed to a machine. Therefore, we proceed to Step 2.
Independent Claim 27
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
Independent claim 27 recites an abstract idea in the limitations (emphasized):
…automatically analyzing, via at least one processor connected to a user interface and/or computer readable memory, the qualifications and capabilities of the one or more employment candidates in a manner to match the one or more employment candidates to specific job roles by utilizing a benchmarking process,
wherein the benchmarking process includes creation of at least one benchmark which includes obtaining feedback from subject matter experts in order to validate the at least one benchmark,
wherein the benchmarking process includes creating the at least one benchmark utilizing a reverse psychometric model, and
wherein the reverse psychometric model includes: initially providing meta inputs including at least one of stakeholder input and job description data;
subsequently generating modeling inputs based on the meta inputs, the modeling inputs including strawman initial parameters in response to the meta input being stakeholder input, the modeling inputs further including psychometric data and raw performance data in response to the meta input being job description data;
and subsequently model training machine learning classification algorithms based on at least one of the strawman initial parameters, the psychometric data, and the raw performance data.
These limitations recite an abstract idea because these limitations commercial or legal interactions (i.e., advertising, marketing or sales activities or behaviors; business relations). These limitations encompass commercial or legal interactions (i.e., advertising, marketing or sales activities or behaviors; business relations) because these limitations essentially encompass steps in the recruiting or hiring process. That is, these limitations essentially encompass creating a benchmark for comparing potential employees against, based on the various claimed inputs, and assessing the potential employees. Claim 27 recites an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of claim 27 do not integrate the abstract idea into a practical application. Claim 27 recites the additional elements (emphasized):
…automatically analyzing, via at least one processor connected to a user interface and/or computer readable memory, the qualifications and capabilities of the one or more employment candidates in a manner to match the one or more employment candidates to specific job roles by utilizing a benchmarking process,
wherein the benchmarking process includes creation of at least one benchmark which includes obtaining feedback from subject matter experts in order to validate the at least one benchmark,
wherein the benchmarking process includes creating the at least one benchmark utilizing a reverse psychometric model, and
wherein the reverse psychometric model includes: initially providing meta inputs including at least one of stakeholder input and job description data;
subsequently generating modeling inputs based on the meta inputs, the modeling inputs including strawman initial parameters in response to the meta input being stakeholder input, the modeling inputs further including psychometric data and raw performance data in response to the meta input being job description data;
and subsequently model training machine learning classification algorithms based on at least one of the strawman initial parameters, the psychometric data, and the raw performance data.
These limitations do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of the analyzing being performed automatically via at least one processor connected to a user interface and/or computer readable memory, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Second, the additional elements of training the machine learning classification algorithms based on the various inputs, when considered individually or in combination, do not integrate the abstract idea into a practical application because training the machine learning algorithms is claimed sufficiently broadly and generally such that it amounts to no more than mere instructions to apply the exception. Claims 27 is directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
Independent claim 27 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 27 is not patent eligible.
Independent Claim 35
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
Independent claim 35 recites an abstract idea in the limitations (emphasized):
… automatically determining, via at least one processor connected to a user interface and/or computer readable memory, one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates; and
automatically analyzing, via the at least one processor, the qualifications and capabilities of the one or more employment candidates in a manner to match the one or more employment candidates to specific job roles by utilizing psychometric data generated by the one or more psychometric tools and a benchmarking process,
wherein the benchmarking process includes creation of at least one benchmark which includes the psychometric data and obtaining feedback from subject matter experts in order to validate the at least one benchmark, wherein the feedback from subject matter experts includes input data obtained regarding ideal personality traits from sources including at least one source external to the one or more employment candidates and external to the hiring entity for comparison with the one or more personality traits of the one or more employment candidates, wherein the feedback and the outcome data are recorded in a feedback and outcome data database, and wherein the analyzing via the at least one processor is configured to utilize the feedback and outcome data database prior to hiring the one or more employment candidates, and
wherein the analysis of the qualifications and capabilities of the one or more employment candidates includes comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process to arrive at an overall comparative fit score.
These limitations recite an abstract idea because these limitations commercial or legal interactions (i.e., advertising, marketing or sales activities or behaviors; business relations). These limitations encompass commercial or legal interactions (i.e., advertising, marketing or sales activities or behaviors; business relations) because these limitations essentially encompass steps in the recruiting or hiring process. That is, these limitations essentially encompass creating a benchmark for comparing potential employees against, based on the various claimed inputs, and assessing the potential employees. Claim 35 recites an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of claim 35 do not integrate the abstract idea into a practical application. Claim 35 recites the additional elements (emphasized):
…automatically determining, via at least one processor connected to a user interface and/or computer readable memory, one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates; and
automatically analyzing, via the at least one processor, the qualifications and capabilities of the one or more employment candidates in a manner to match the one or more employment candidates to specific job roles by utilizing psychometric data generated by the one or more psychometric tools and a benchmarking process,
wherein the benchmarking process includes creation of at least one benchmark which includes the psychometric data and obtaining feedback from subject matter experts in order to validate the at least one benchmark, wherein the feedback from subject matter experts includes input data obtained regarding ideal personality traits from sources including at least one source external to the one or more employment candidates and external to the hiring entity for comparison with the one or more personality traits of the one or more employment candidates, wherein the feedback and the outcome data are recorded in a feedback and outcome data database, and wherein the analyzing via the at least one processor is configured to utilize the feedback and outcome data database prior to hiring the one or more employment candidates, and
wherein the analysis of the qualifications and capabilities of the one or more employment candidates includes comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process to arrive at an overall comparative fit score.
These limitations do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of the determining and analyzing being performed automatically via at least one processor connected to a user interface and/or computer readable memory, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Second, the additional elements of the feedback and outcome data being stored, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of storing data (i.e. storing user input), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application). Claims 35 is directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
Independent claim 35 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 35 is not patent eligible.
Independent Claim 41
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
Independent claim 41 recites an abstract idea in the limitations (emphasized):
… a non-transitory computer-readable storage medium with instructions which, when executed by a computer, include:
assigning a first fit score to the one or more employment candidates at a first hiring stage of a plurality of hiring stages, the first fit score being associated with a first set of qualifications and capabilities of the one or more employment candidates, the assigning of the first fit score including:
automatically determining one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates;
automatically analyzing the qualifications and capabilities of the one or more employment candidates in a manner to match the one or more employment candidates to specific job roles by utilizing psychometric data generated by the one or more psychometric tools and a benchmarking process, wherein the benchmarking process includes creation of at least one benchmark which includes the psychometric data and obtaining feedback from subject matter experts in order to validate the at least one benchmark; and
comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process to arrive at the first fit score;
assigning a second fit score to the one or more employment candidates at a second hiring stage of the plurality of hiring stages, the second fit score being associated with a second set of qualifications and capabilities of the one or more employment candidates including at least one qualification or capability different from the first set of qualifications and capabilities; and
aggregating the first fit score and the second fit score to arrive at an overall comparative fit score of the one or more employment candidates.
These limitations recite an abstract idea because these limitations commercial or legal interactions (i.e., advertising, marketing or sales activities or behaviors; business relations). These limitations encompass commercial or legal interactions (i.e., advertising, marketing or sales activities or behaviors; business relations) because these limitations essentially encompass steps in the recruiting or hiring process. That is, these limitations essentially encompass creating a benchmark for comparing potential employees against, based on the various claimed inputs, and assessing the potential employees. Claim 41 recites an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of claim 41 do not integrate the abstract idea into a practical application. Claim 41 recites the additional elements (emphasized):
… a non-transitory computer-readable storage medium with instructions which, when executed by a computer, include:
assigning a first fit score to the one or more employment candidates at a first hiring stage of a plurality of hiring stages, the first fit score being associated with a first set of qualifications and capabilities of the one or more employment candidates, the assigning of the first fit score including:
automatically determining one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates;
automatically analyzing the qualifications and capabilities of the one or more employment candidates in a manner to match the one or more employment candidates to specific job roles by utilizing psychometric data generated by the one or more psychometric tools and a benchmarking process, wherein the benchmarking process includes creation of at least one benchmark which includes the psychometric data and obtaining feedback from subject matter experts in order to validate the at least one benchmark; and
comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process to arrive at the first fit score;
assigning a second fit score to the one or more employment candidates at a second hiring stage of the plurality of hiring stages, the second fit score being associated with a second set of qualifications and capabilities of the one or more employment candidates including at least one qualification or capability different from the first set of qualifications and capabilities; and
aggregating the first fit score and the second fit score to arrive at an overall comparative fit score of the one or more employment candidates.
These limitations do not integrate the abstract idea into a practical application because the additional elements of the non-transitory computer-readable storage medium and the determining and analyzing being performed automatically, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Claims 41 is directed to an abstract idea.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
Independent claim 41 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 41 is not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract idea for the following reasons.
Claims 28-32 and 40 recite the same abstract idea as the independent claims because claims 28-32 encompass including psychometric data in various aspects of the process. Using psychometric data is a part of the recruiting and hiring process.
Claims 28-32 and 40 further recite the additional elements of performing various steps automatically and recording data in a database. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass generic use of a computer and a generic computer function of storing data (i.e. storing user input), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claims 33 and 34 recite the same abstract idea as the independent claims because receiving input from external and internal experts is a part of the recruiting or hiring process (i.e., determining preferred traits in job applicants.
Claims 36-39 recite the same abstract idea as the independent claims because receiving input from external and internal experts is a part of the recruiting or hiring process (i.e., determining preferred traits in job applicants.
Claims 42 and 43 recites the additional elements of receiving user input and displaying data on a dashboard. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving and displaying data, see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claims 44 and 45 recite the same abstract idea as the independent claims because receiving scores for candidates, ranking the candidates, and aggregating scores is a part of the recruiting and hiring process.
Claim 46 recites the same abstract idea as the independent claims because monitoring performance of an employee is a part of the recruiting and hiring process (i.e., determining if the employee is actually qualified.
Claim 46 further recites the additional elements of using a neural network. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the neural network is claimed sufficiently broadly and generally such that it amounts to no more than mere instructions to apply the exception.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 35-40 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mondal et al, US Pub. No. 2015/0161567, herein referred to as “Mondal”.
Regarding claim 35, Mondal teaches:
automatically determining, via at least one processor connected to a user interface and/or computer readable memory, one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates (provides online test to determine candidates personality, emotional intelligence, skills, etc. ¶¶[0048], [0071] Fig. 3A; see also e.g. Figs. 6A-6D discussing the psychometric test; see also e.g., ¶¶[0024]-[0027] and Fig. 2 discussing user interface and memory);
and automatically analyzing, via the at least one processor, the qualifications and capabilities of the one or more employment candidates (generates candidate profile based on test, ¶[0071]; see also Fig. 9 showing candidate profile)
in a manner to match the one or more employment candidates to specific job roles by utilizing psychometric data generated by the one or more psychometric tools and a benchmarking process (compares profile of job candidate to the Ideal Candidate profile for a role, ¶¶[0040], [0072] and Fig. 3A, ref. char. 340. Please note, the generation of the Ideal Candidate profile teaches the claimed benchmarking process),
wherein the benchmarking process includes creation of at least one benchmark which includes the psychometric data (generates Ideal Candidate profile for each role based on psychometric testing, ¶[0069] and Fig. 3A ref. char. 328)
and obtaining feedback from subject matter experts in order to validate the at least one benchmark (Ideal candidate profile is reassessed based on using hired candidates' data as feedback, ¶[0078] and Fig. 3B. Please note the candidate being hired would teach feedback from subject matter experts because the hiring managers hiring the candidates are subject matter experts and their hiring the candidates would be the feedback. Further, Examiner finds that the limitations specifying the feedback being from subject matter experts does not substantially further limit the scope of the claim because who the information is collected from does not functionally alter or relate to the system and merely labeling the user does not patentably distinguish the claimed invention, see MPEP 2111.05),
wherein the feedback from subject matter experts includes input data obtained regarding ideal personality traits from sources including at least one source external to the one or more employment candidates and external to the hiring entity for comparison with the one or more personality traits of the one or more employment candidates (Examiner finds that the limitations specifying the feedback being from sources external to the candidate and hiring entity does not substantially further limit the scope of the claim because who the information is collected from does not functionally alter or relate to the system and merely labeling the user does not patentably distinguish the claimed invention, see MPEP 2111.05),
wherein the feedback and the outcome data are recorded in a feedback and outcome data database (stores data including performance data in database, ¶¶[0030], [0045]),
and wherein the analyzing via the at least one processor is configured to utilize the feedback and outcome data database prior to hiring the one or more employment candidates (process is iterative, ¶[0078] and Fig. 3B. Thus, the system uses the feedback and outcome data prior to hiring a candidate),
and wherein the analysis of the qualifications and capabilities of the one or more employment candidates includes comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process (compares profile of job candidate to the Ideal Candidate profile for a role, ¶¶[0040], [0072] and Fig. 3A, ref. char. 340)
to arrive at an overall comparative fit score (determines match percentage based on comparison, ¶¶[0072], [0076]).
Regarding claim 36, Mondal teaches all the limitations of claim 35 and further teaches:
wherein the at least one source external to the one or more employment candidates and external to the hiring entity is unassociated with the one or more employment candidates and the hiring entity (Examiner finds that the limitations specifying the feedback being from sources external to the candidate and hiring entity does not substantially further limit the scope of the claim because who the information is collected from does not functionally alter or relate to the system and merely labeling the user does not patentably distinguish the claimed invention, see MPEP 2111.05).
Regarding claim 37, Mondal teaches all the limitations of claim 36 and further teaches:
wherein the at least one source external to the one or more employment candidates and external to the hiring entity is an individual trained and experienced in an industry related to the hiring entity (Examiner finds that the limitations specifying the feedback being from sources external to the candidate and hiring entity does not substantially further limit the scope of the claim because who the information is collected from does not functionally alter or relate to the system and merely labeling the user does not patentably distinguish the claimed invention, see MPEP 2111.05).
Regarding claim 38, Mondal teaches all the limitations of claim 37 and further teaches:
wherein the individual includes at least one of trained business consultants and client employees providing input on at least one personality trait of the one or more personality traits that determines candidate success in the specific job roles in order to create at least one provisional benchmark score for the at least one personality trait of the one or more personality traits (Examiner finds that the limitations specifying the feedback being from sources external to the candidate and hiring entity does not substantially further limit the scope of the claim because who the information is collected from does not functionally alter or relate to the system and merely labeling the user does not patentably distinguish the claimed invention, see MPEP 2111.05).
Regarding claim 39, Mondal teaches all the limitations of claim 38 and further teaches:
wherein the obtaining of feedback from subject matter experts is provided by performing at least one of a reactive external build (Ideal Candidate profile is reassessed based on using hired candidates as feedback, ¶[0078] and Fig. 3B. Please note the candidate being hired would teach feedback from subject matter experts because the hiring managers hiring the candidates are subject matter experts and their hiring the candidates would be the feedback. Further please note the ¶[0040] Specification as filed notes the reactive internal builds are based on personal that examine a particular job. Thus the hiring managers hiring candidates would be performing a reactive internal build because they would be examining the jobs (i.e. the open positions); see also ¶[0044] discussing the hiring manger examining roles).
which includes the input data obtained regarding ideal personality traits from sources including at least one source external to the one or more employment candidates and external to the hiring entity for comparison with the one or more personality traits of the one or more employment candidates (Examiner finds that the limitations specifying the feedback being from sources external to the candidate and hiring entity does not substantially further limit the scope of the claim because who the information is collected from does not functionally alter or relate to the system and merely labeling the user does not patentably distinguish the claimed invention, see MPEP 2111.05),
and performing statistical comparison (statistical analysis, ¶¶[0050], [0071] and Figs. 3A and 3B),
and wherein, after the obtaining of feedback from subject matter experts and the statistical comparison, the creation of the at least one benchmark further includes validating the at least one provisional benchmark score based on a predetermined minimum amount of iterations of the obtaining of feedback from subject matter experts and the statistical comparison in order to determine at least one validated benchmark score (Ideal Candidate profile is reassessed iteratively based on using hired candidates as feedback, ¶[0078] and Fig. 3B).
Regarding claim 40, Mondal teaches all the limitations of claim 35 and further teaches:
wherein the one or more psychometric tools is configured to generate at least one objective quantitative representation of the one or more personality traits (determines match percentage based on comparison, ¶¶[0072], [0076]).
wherein the at least one objective quantitative representation is recorded in a trait database, wherein the determining via the at least one processor is configured to utilize the trait database (stores data, ¶[0030]).
Claim Rejections - 35 USC § 103
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) 27-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mondal in view of Gangadhar et al, US Pub. No. 2020/0014607, herein referred to as “Gangadhar”.
Regarding claim 27, Mondal teaches:
automatically analyzing, via at least one processor connected to a user interface and/or computer readable memory, the qualifications and capabilities of the one or more employment candidates (provides online test to determine candidates personality, emotional intelligence, skills, etc. ¶¶[0048], [0071] Fig. 3A to generate candidate profile, ¶[0071]; see also e.g. Figs. 6A-6D discussing the psychometric test and Fig. 9 showing candidate profile; see also e.g., ¶¶[0024]-[0027] and Fig. 2 discussing user interface and processor),
in a manner to match the one or more employment candidates to specific job roles by utilizing a benchmarking process (compares profile of job candidate to the Ideal Candidate profile for a role, ¶¶[0040], [0072] and Fig. 3A, ref. char. 340. Please note, the generation of the Ideal Candidate profile teaches the claimed benchmarking process)
wherein the benchmarking process includes creation of at least one benchmark (generates Ideal Candidate profile for each role, ¶[0069] and Fig. 3A ref. char. 328)
which includes obtaining feedback from subject matter experts in order to validate the at least one benchmark (Ideal candidate profile is reassessed based on using hired candidates' data as feedback, ¶[0078] and Fig. 3B. Please note the candidate being hired would teach feedback from subject matter experts because the hiring managers hiring the candidates are subject matter experts and their hiring the candidates would be the feedback. Further, Examiner finds that the limitations specifying the feedback being from subject matter experts does not substantially further limit the scope of the claim because who the information is collected from does not functionally alter or relate to the system and merely labeling the user does not patentably distinguish the claimed invention, see MPEP 2111.05),
wherein the benchmarking process includes creating the at least one benchmark utilizing a reverse psychometric model, and wherein the reverse psychometric model includes: initially providing meta inputs including job description data (imports data on rolls, ¶[0044]);
subsequently generating modeling inputs based on the meta inputs, including psychometric data and raw performance data in response to the meta input being job description data (determines Ideal candidate profile based on psychometric test results and performance data, ¶[0069], and uses machine leaning techniques on performance data when generating ideal candidate profile , ¶[0053]);
and subsequently model training machine learning classification algorithms based on at least one of the strawman initial parameters, the psychometric data, and the raw performance data (uses top performers to generate Ideal candidate profile (i.e. as training data), ¶¶[0047]-[0048]; see also ¶[0053] discussing training using top performers).
However Mondal does not teach but Gangadhar does teach:
and wherein the reverse psychometric model includes: initially providing meta inputs including at least one of stakeholder input (uses expert input to initialize a machine learning knowledge base, ¶[0073])
subsequently generating modeling inputs based on the meta inputs, the modeling inputs including strawman initial parameters in response to the meta input being stakeholder input (uses expert input to initialize a machine learning knowledge base, ¶[0073]).
Further, it would have been obvious at the time of filing to combine the psychometric assessment of candidates of Mondal with the initialization of Gangadhar 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 machine learning used in Mondal would likely benefit from initializing its parameters, e.g., as taught by Gangadhar, and accordingly would have modified Mondal to incorporate the initialization of Gangadhar.
Regarding claim 28, the combination of Mondal and Gangadhar teaches all the limitations of claim 27 and Mondal further teaches:
automatically determining, via the at least one processor, one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates (candidate takes a psychometric test, Fig. 3A, ref. char. 332 and ¶[0071] to generate candidate profile, ¶[0071]; see also e.g. ¶[0048] and Figs. 6A-6D discussing the psychometric test and Fig. 9 showing candidate profile).
Regarding claim 29, the combination of Mondal and Gangadhar teaches all the limitations of claim 28 and Mondal further teaches:
wherein the one or more psychometric tools is configured to generate at least one objective quantitative representation of the one or more personality traits (determines match percentage based on comparison, ¶¶[0072], [0076]).
wherein the at least one objective quantitative representation is recorded in a trait database, wherein the determining via the at least one processor is configured to utilize the trait database (stores data, ¶[0030]).
Regarding claim 30, the combination of Mondal and Gangadhar teaches all the limitations of claim 29 and Mondal further teaches:
wherein the automatic analyzing of the qualifications and capabilities of the one or more employment candidates further utilizes psychometric data generated by the one or more psychometric tools candidates (candidate takes a psychometric test, Fig. 3A, ref. char. 332 and ¶[0071] to generate candidate profile, ¶[0071]; see also e.g. ¶[0048] and Figs. 6A-6D discussing the psychometric test and Fig. 9 showing candidate profile).
Regarding claim 31, the combination of Mondal and Gangadhar teaches all the limitations of claim 30 and Mondal further teaches:
wherein the benchmarking process includes the creation of the at least one benchmark which further includes the psychometric data (generates Ideal Candidate profile based on employee psychometric data, ¶[0069] and Fig. 3A ref. char. 328).
Regarding claim 32, the combination of Mondal and Gangadhar teaches all the limitations of claim 31 and Mondal further teaches:
wherein the analysis of the qualifications and capabilities of the one or more employment candidates includes comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process (compares profile of job candidate to the Ideal Candidate profile for a role, ¶¶[0040], [0072] and Fig. 3A, ref. char. 340)
to arrive at an overall comparative fit score (determines match percentage based on comparison, ¶¶[0072], [0076]).
Regarding claim 33, the combination of Mondal and Gangadhar teaches all the limitations of claim 27 and Mondal further teaches:
wherein the obtaining of feedback from subject matter experts is provided by performing at least one of a reactive external build and a reactive internal build and performing statistical comparison (Ideal Candidate profile is reassessed based on using hired candidates as feedback, ¶[0078] and Fig. 3B. Please note the candidate being hired would teach feedback from subject matter experts because the hiring managers hiring the candidates are subject matter experts and their hiring the candidates would be the feedback. Further please note the ¶[0040] Specification as filed notes the reactive internal builds are based on personal that examine a particular job. Thus the hiring managers hiring candidates would be performing a reactive internal build because they would be examining the jobs (i.e. the open positions); see also ¶[0044] discussing the hiring manger examining roles).
Regarding claim 34, the combination of Mondal and Gangadhar teaches all the limitations of claim 33 and Mondal further teaches:
wherein the performance of the reactive external build includes at least one of (i) utilizing a psychometric benchmark instrument to receive input from stakeholders regarding at least one personality trait of the one or more personality traits in the specific job role in order to create at least one provisional benchmark score for the at least one personality trait, and (ii) at least one of trained business consultants and client employees providing input on at least one personality trait of the one or more personality traits that determines candidate success in the specific job roles in order to create at least one provisional benchmark score for the at least one personality trait of the one or more personality traits (Ideal candidate profile is reassessed based on using hired candidates' data as feedback, ¶[0078] and Fig. 3B. Please note, Examiner finds that the limitations specifying the feedback being from stakeholders or trained business consultants and client employees does not substantially further limit the scope of the claim because who the information is collected from does not functionally alter or relate to the system and merely labeling the user does not patentably distinguish the claimed invention, see MPEP 2111.05, see MPEP 2111.05).
Claim(s) 41-46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mondal in view of Zhang et al, US Pub. No. 2017/0323268, herein referred to as “Zhang”.
Regarding claim 41, Mondal teaches:
a non-transitory computer-readable storage medium with instructions which, when executed by a computer, include (memory, ¶¶[0026]-[0027] and Fig. 2):
the first fit score being associated with a first set of qualifications and capabilities of the one or more employment candidates, the assigning of the first fit score including (determines match percentage based on comparison of job candidate to the Ideal Candidate profile, ¶¶[0072], [0076]):
automatically determining one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates (provides online test to determine candidates personality, emotional intelligence, skills, etc. ¶¶[0048], [0071] Fig. 3A; see also e.g. Figs. 6A-6D discussing the psychometric test);
automatically analyzing the qualifications and capabilities of the one or more employment candidates (generates candidate profile based on test, ¶[0071]; see also Fig. 9 showing candidate profile)
in a manner to match the one or more employment candidates to specific job roles by utilizing psychometric data generated by the one or more psychometric tools and a benchmarking process (compares profile of job candidate to the Ideal Candidate profile for a role, ¶¶[0040], [0072] and Fig. 3A, ref. char. 340. Please note, the generation of the Ideal Candidate profile teaches the claimed benchmarking process),
wherein the benchmarking process includes creation of at least one benchmark which includes the psychometric data (generates Ideal Candidate profile for each role based on psychometric testing, ¶[0069] and Fig. 3A ref. char. 328)
and obtaining feedback from subject matter experts in order to validate the at least one benchmark (Ideal candidate profile is reassessed based on using hired candidates' data as feedback, ¶[0078] and Fig. 3B. Please note the candidate being hired would teach feedback from subject matter experts because the hiring managers hiring the candidates are subject matter experts and their hiring the candidates would be the feedback. Further, Examiner finds that the limitations specifying the feedback being from subject matter experts does not substantially further limit the scope of the claim because who the information is collected from does not functionally alter or relate to the system and merely labeling the user does not patentably distinguish the claimed invention, see MPEP 2111.05);
and comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process (compares profile of job candidate to the Ideal Candidate profile for a role, ¶¶[0040], [0072] and Fig. 3A, ref. char. 340)
to arrive at the first fit score (determines match percentage based on comparison, ¶¶[0072], [0076]);
the second fit score being associated with a second set of qualifications and capabilities of the one or more employment candidates including at least one qualification or capability different from the first set of qualifications and capabilities (produces results for various different domains, ¶[0048]).
However Mondal does not teach but Zhang does teach:
assigning a first fit score to the one or more employment candidates at a first hiring stage of a plurality of hiring stages, assigning a second fit score to the one or more employment candidates at a second hiring stage of the plurality of hiring stages (assigns multiple scores in various stages, e.g., ¶¶[0032], [0036], [0051], [0054]),
and aggregating the first fit score and the second fit score to arrive at an overall comparative fit score of the one or more employment candidates (combines scores from stages e.g., ¶¶[0036], [0044], [0054]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the psychometric assessment of candidates of Mondal with the multi-stage analysis of Zhang 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 assessment in Mondal would likely be improved by considering other factors, e.g., by employing multiple different models, e.g., as taught by Zhang.
Regarding claim 42, the combination of Mondal and Zhang teaches all the limitations of claim 41 and Mondal further teaches:
a user dashboard operably connected to the non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including further instructions which, when executed by the computer, include displaying, on the user dashboard, a first hiring stage section including the first hiring stage, the first hiring stage section including a first ranking of the first fit score of the one or more employment candidates for the first set of qualifications and capabilities of the one or more employment candidates (displays ranked candidates, ¶[0070] and Fig. 8).
However Mondal does not explicitly teach:
and displaying a second hiring stage section separate from the first hiring stage section and including the second hiring stage, the second hiring stage section including a second ranking of the second fit score of the one or more employment candidates for the second set of qualifications and capabilities of the one or more employment candidates.
Nevertheless, it would have been obvious before the effective filing date of the claimed invention to display a second ranking as claimed because duplication of parts is obvious unless a new and unexpected result is produced, see MPEP 2144.04.VI.B. That is, Mondal teaches displaying a ranking of the candidates and Examiner finds no reason displaying a second ranking of candidates would produce new and unexpected results.
Regarding claim 43, the combination of Mondal and Zhang teaches all the limitations of claim 42 and Zhang further teaches:
receiving user input for adding hiring stages, the user input for adding hiring stages including instructions to add additional hiring stages of the plurality of hiring stages to the user dashboard in addition to the first and second hiring stages, and further include displaying the additional hiring stages on the user dashboard in additional hiring stage sections separate from the first and second hiring stage sections (provides further stages of models for scoring and ranking, ¶¶[0043]-[0044]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the psychometric assessment of candidates of Mondal with the multi-stage analysis of Zhang 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 assessment in Mondal would likely be improved by considering other factors, e.g., by employing multiple different models, e.g., as taught by Zhang.
Regarding claim 44, the combination of Monda