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 21-25 and 27-41 are all the claims pending in the application.
Claims 21, 24, 34, 37 and 39 are amended.
Claim 26 is cancelled.
Claim 41 is new.
Claims 21-25 and 27-41 are rejected.
The following is a Final Office Action in response to amendments and remarks filed Dec. 9, 2025.
Response to Arguments
Regarding the 101 rejections, the rejections are withdrawn in light of the amendments to the claims because Examiner finds the claims reflect a practical application. That is, under Step 2A Prong 1, Examiner finds the claims recite an abstract idea (i.e., teaching) because the claims essentially encompass providing coaching or advice to someone about how to interview for a job. Under Step 2A Prong 2, Examiner finds the additional elements of retraining the machine learning based on the feedback, as claimed, reflects an improvement because the additional elements improve the automated interview process.
Regarding the 103 rejections, the rejections are withdrawn in light of the amendments to the claims because the cited references do not teach all the newly amended limitations. Please see below for the new rejections of the claims as amended. Please note, the scope of claims 21, 34, and 39 differ now and accordingly different rejections have been made to the claims.
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) 34, 38, and 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al, US Pub. No. 2023/0088444, herein referred to as “Wong”, in view of Pande et al, US Pub. No. 2023/0368146, herein referred to as “Pande”, further in view of Asokan et al, US Pub. No. 2021/0312399, herein referred to as “Asokan”.
Regarding claim 34, Wong teaches:
a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions, including functions for (memory, processor and instructions, e.g., ¶[0044] and Fig. 6):
launching a mock interview application from a job post (conducts interview, ¶[0040]; see also e.g., ¶[0034] noting interview is created from job description);
analyzing job post data corresponding to the job post (selects list of questions for interview based on the job description, ¶[0034]),
the job post data including a plurality of competencies and a plurality of employer specified requirements (job description identifies skills desired is based on job requirement form, ¶[0032]);
determining one or more interview questions corresponding to the plurality of competencies and the plurality of employer specified requirements (selects list of questions for interview based on the job description based on attributes including category, subcategory, skill level and requirements from the job description ¶¶[0034]-[0035]);
displaying the one or more interview questions on a user interface of an interviewee device (conducts interview with candidate, ¶[0035], and user interface presents questions to candidate, ¶[0033]);
receiving, via the user interface, interview video data for each of the one or more interview questions from an interviewee, wherein the interview video data includes visual data and audio data (interview is an online video interview, ¶¶[0030], [0039] and includes audio data, ¶[0035]; see also ¶[0031] discussing user interface);
processing the interview video data to determine interview feedback data and future interview recommendation data, wherein the future interview recommendation data includes at least one recommendation for improving the interview feedback data (generates feedback report with recommendations for the candidate, ¶¶[0041]-[0042]);
storing the each of the one or more interview questions in at least one database (stores interview questions, ¶¶[0034]-[0035]).
However Wong does not explicitly teach but Pande does teach:
on a job website (system includes web browser, ¶[0074]);
storing the interview video data (digitally records candidate mock interviews, ¶[0033])
and displaying the interview feedback data and the future interview recommendation data on the user interface of the interviewee device (generates and displays feedback/results summary for user, ¶¶[0041]-[0042]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong with the mock interview feedback of Pande because use of a known technique to improve similar devices (methods, or products) in the same way is obvious, see MPEP 2143.I.C. That is, one of ordinary skill would have recognized the feedback report provided by Wong would likely be improved by additional feedback, e.g., on the candidate’s body language as taught by Pande and accordingly would have modified the feedback of Wong to also include the feedback of Pande.
However the combination of Wong and Pande does not tech but Asokan does teach:
receiving, by the one or more processors, feedback from an employer corresponding to the plurality of individual scores, the external feedback indicates an accuracy for at least one of the plurality of individual scores; and retraining, by the one or more processors, the machine-learning model based on the feedback (system monitors interview sessions from an interviewer/recruiter to retrain machine learning models, ¶[0038]; see also e.g., Abstract discussing scoring).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the customizable interview feedback of Wong and Pande with the retaining of Asokan because Asokan explicitly suggests doing so to refine the machine learning model, ¶[0038]; see also MPEP 2143.I.G.
Regarding claim 38, the combination of Wong, Pande and Asokan teaches all the limitations of claim 34 and Wong further teaches:
wherein the interview video data is provided in any language (audio answers from the candidate are converted to text, ¶[0042]).
Regarding claim 39, Wong teaches:
A non-transitory computer-readable medium containing instructions for analyzing video data and providing feedback, the instructions comprising (memory and instructions, e.g., ¶[0044] and Fig. 6):
analyzing, by the one or more processors, job post data corresponding to the job post (selects list of questions for interview based on the job description, ¶[0034])
the job post data including a plurality of competencies and a plurality of employer specified requirements (job description identifies skills desired is based on job requirement form, ¶[0032]);
determining, by the one or more processors, one or more interview questions corresponding to the plurality of competencies and the plurality of employer specified requirements (selects list of questions for interview based on the job description based on attributes including category, subcategory, skill level and requirements from the job description ¶¶[0034]-[0035]);
displaying, by the one or more processors, the one or more interview questions on a user interface of an interviewee device (conducts interview with candidate, ¶[0035], and user interface presents questions to candidate, ¶[0033]);
receiving, by the one or more processors, via the user interface, interview video data for each of the one or more interview questions from the interviewee, wherein the interview video data includes visual data and audio data (interview is an online video interview, ¶¶[0030], [0039] and includes audio data, ¶[0035]; see also ¶[0031] discussing user interface);
processing, by the one or more processors, the interview video data to determine interview feedback data and future interview recommendation data, wherein the future interview recommendation data includes at least one recommendation for improving the interview feedback data (generates feedback report with recommendations for the candidate, ¶¶[0041]-[0042]).
However Wong does not explicitly teach but Pande does teach:
on a job website (system includes web browser, ¶[0074]);
and displaying, by the one or more processors, the interview feedback data and the future interview recommendation data on the user interface of the interviewee device (generates and displays feedback/results summary for user, ¶¶[0041]-[0042]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong with the mock interview feedback of Pande because use of a known technique to improve similar devices (methods, or products) in the same way is obvious, see MPEP 2143.I.C. That is, one of ordinary skill would have recognized the feedback report provided by Wong would likely be improved by additional feedback, e.g., on the candidate’s body language as taught by Pande and accordingly would have modified the feedback of Wong to also include the feedback of Pande.
However the combination of Wong and Pande does not tech but Asokan does teach:
receiving, by the one or more processors, feedback from an employer corresponding to the plurality of individual scores, the external feedback indicates an accuracy for at least one of the plurality of individual scores; and retraining, by the one or more processors, the machine-learning model based on the feedback (system monitors interview sessions from an interviewer/recruiter to retrain machine learning models, ¶[0038]; see also e.g., Abstract discussing scoring).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the customizable interview feedback of Wong and Pande with the retaining of Asokan because Asokan explicitly suggests doing so to refine the machine learning model, ¶[0038]; see also MPEP 2143.I.G.
Claim(s) 35-37, 40 and 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Wong, Pande and Asokan further in view of Moni et al, US Pub. No. 2013/0052631, herein referred to as “Moni”.
Regarding claim 35, the combination of Wong, Pande and Asokan teaches all the limitations of claim 34 and Wong further teaches:
receiving, by the one or more processors, user profile data from a database, wherein the user profile data includes user personal data, user resume data (receives candidate data including general data and resume, ¶¶[0033]),
However the combination of Wong, Pande and Asokan does not teach but Moni does teach:
and user accessibility data, and wherein the user accessibility data includes an accessibility requirement that includes at least one of: a visibility requirement, a language requirement, or an audio requirement (user provides language preference, ¶[0081]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong, Pande and Asokan with the customization of Moni 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 of Wong and Pande would likely have individual preferences and would prefer a system that provided customizable options, e.g. as taught by Moni.
Regarding claim 36, the combination of Wong, Pande and Asokan and Mandi teaches all the limitations of claim 35 and Mandi further teaches:
wherein the displaying the one or more interview questions is in accordance with the user accessibility data on the user interface of the interviewee device (problems are displayed in choose language, ¶[0081]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong, Pande and Asokan with the customization of Moni 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 of Wong and Pande would likely have individual preferences and would prefer a system that provided customizable options, e.g. as taught by Moni.
Regarding claim 37, the combination of Wong, Pande and Asokan and Mandi teaches all the limitations of claim 35 and Wong further teaches:
wherein processing the interview video data to determine the interview feedback data and the future interview recommendation data includes: receiving, by a machine-learning model, input data including the interview video data and the corresponding one or more interview questions, the plurality of competencies, the plurality of employer specified requirements, and the job post data (uses recurrent neural network to select questions and analyze the interview, e.g., ¶¶[0031], [0042], [0045]; see also ¶¶[0036], [0048] discussing other aspects of the machine learning);
and determining, by the machine-learning model, the interview feedback data and the future interview recommendation data corresponding to the input data (uses recurrent neural generate feedback report, e.g., ¶¶[0031], [0042], [0045]),
wherein the interview feedback data includes a plurality of individual scores for the interview video data for each of the one or more interview questions (determines correctness of answers, ¶[0035]; see also ¶[0050])
and an overall score for all of the interview video data (ranks candidates, ¶¶[0048], [0062] and Fig. 14).
Regarding claim 40, the combination of Wong, Pande and Asokan teaches all the limitations of claim 39 and Wong further teaches:
and updating, by the one or more processors, the one or more interview questions to correspond to the selected one or more interview question difficulty levels (presents questions based on selected difficulty, ¶[0078]).
However the combination of Wong and Pande does not teach but Moni does teach:
displaying, by the one or more processors, one or more interview question difficulty levels on the user interface; in response to the displaying, receiving, by the one or more processors, a user selection of one of the one or more interview question difficulty levels (users select questions difficulty, ¶¶[0061], [0083]);
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong and Pande with the customization of Moni 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 of Wong and Pande would likely have individual preferences and would prefer a system that provided customizable options, e.g. as taught by Moni.
Regarding claim 41, the combination of Wong, Pande and Asokan teaches all the limitations of claim 39 and Wong further teaches:
receiving, by the one or more processors, user profile data from a database, wherein the user profile data includes user personal data, user resume data (receives candidate data including general data and resume, ¶¶[0033]),
However the combination of Wong, Pande and Asokan does not teach but Moni does teach:
and user accessibility data, and wherein the user accessibility data includes an accessibility requirement that includes at least one of: a visibility requirement, a language requirement, or an audio requirement (user provides language preference, ¶[0081]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong, Pande and Asokan with the customization of Moni 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 of Wong and Pande would likely have individual preferences and would prefer a system that provided customizable options, e.g. as taught by Moni.
Claim(s) 21 and 29-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong, in view of Pande, further in view of Asokan, further in view of Wu et al. US Pub. No. 2023/0086724, herein referred to as “Wu”.
Regarding claim 21 Wong teaches:
launching, by the one or more processors, a mock interview application from the selected job post (conducts interview, ¶[0040]; see also e.g., ¶¶[0037], [0044] and Fig. 1 discussing software and processor for interviewing; and ¶[0034] noting interview is created from job description);
analyzing, by the one or more processors, job post data corresponding to the job post (selects list of questions for interview based on the job description, ¶[0034])
the job post data including a plurality of competencies and a plurality of employer specified requirements (job description identifies skills desired is based on job requirement form, ¶[0032]);
determining, by the one or more processors, one or more interview questions corresponding to the plurality of competencies and the plurality of employer specified requirements (selects list of questions for interview based on the job description based on attributes including category, subcategory, skill level and requirements from the job description ¶¶[0034]-[0035]);
displaying, by the one or more processors, the one or more interview questions on a user interface of an interviewee device (conducts interview with candidate, ¶[0035], and user interface presents questions to candidate, ¶[0033]);
receiving, by the one or more processors, via the user interface, interview video data for each of the one or more interview questions from the interviewee, wherein the interview video data includes visual data and audio data (interview is an online video interview, ¶¶[0030], [0039] and includes audio data, ¶[0035]; see also ¶[0031] discussing user interface);
processing, by the one or more processors, the interview video data to determine interview feedback data and future interview recommendation data, wherein the future interview recommendation data includes at least one recommendation for improving the interview feedback data (generates feedback report with recommendations for the candidate, ¶¶[0041]-[0042]),
and wherein the interview feedback data is based on content (uses natural language processing to analyze the text transcribed from the speech audio of the candidate, ¶[0035]),
and relevance (assesses the correctness of the answer, ¶[0035]).
However Wong does not teach but Pande does teach:
wherein the interview video data includes visual data and audio data (analyzes video and audio from interview, ¶¶[0034]-[0036] and Fig. 2)
and wherein the interview feedback data is based on content (analyzes videos of candidate providing mock interview, ¶[0031], including content of interview, ¶¶[0032], [0035], [0036] and Fig. 3),
clarity (analyzes clarity of candidate’s speech, ¶[0035]),
structure (performs sentence-level analysis off what an interviewee speaks, ¶[0036]),
and depth of the interview video data (determines which concepts are being spoken about in an interview and which component students are missing out, ¶[0036]);
and displaying, by the one or more processors, the interview feedback data and the future interview recommendation data on the user interface of the interviewee device (generates and displays feedback/results summary for user, ¶¶[0041]-[0042]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong with the mock interview feedback of Pande because use of a known technique to improve similar devices (methods, or products) in the same way is obvious, see MPEP 2143.I.C. That is, one of ordinary skill would have recognized the feedback report provided by Wong would likely be improved by additional feedback, e.g., on the candidate’s body language as taught by Pande and accordingly would have modified the feedback of Wong to also include the feedback of Pande.
However the combination of Wong and Pande does not tech but Asokan does teach:
receiving, by the one or more processors, feedback from an employer corresponding to the plurality of individual scores, the external feedback indicates an accuracy for at least one of the plurality of individual scores; and retraining, by the one or more processors, the machine-learning model based on the feedback (system monitors interview sessions from an interviewer/recruiter to retrain machine learning models, ¶[0038]; see also e.g., Abstract discussing scoring).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the customizable interview feedback of Wong and Pande with the retaining of Asokan because Asokan explicitly suggests doing so to refine the machine learning model, ¶[0038]; see also MPEP 2143.I.G.
However the combination of Wong, Pande and Asokan does not teach but Wu does teach:
receiving, by one or more processors, a user selection of a job post displayed on a job website (users view job postings, ¶[0028]; see also ¶[0025] noting user interfaces are web pages and Fig. 5);
launching, by one or more processors and in response to the user selection, an application directly from the selected job post (user uses selectable user interface element to trigger another GUI, ¶[0045] and Fig. 5).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the customizable interview feedback of Wong, Pande and Asokan 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 users would likely want to see the mock interview application created from job description of Wong connected to the job description and accordingly would have modified Wong, Pande and Asokan to launch the mock interview application based on a trigger in the job posting, e.g. as show in Wu).
Regarding claim 29, the combination of Wong, Pande, Asokan and Wu teaches all the limitations of claim 21 and Wong further teaches:
wherein the interview video data is provided in any language (audio answers from the candidate are converted to text, ¶[0042]).
Regarding claim 30, the combination of Wong, Pande, Asokan and Wu teaches all the limitations of claim 21 and Wong further teaches:
wherein at least one database stores the one or more interview questions (stores interview questions, ¶¶[0034]-[0035]).
Regarding claim 31, the combination of Wong, Pande, Asokan and Wu teaches all the limitations of claim 30 and Wong further teaches:
wherein determining the one or more interview questions corresponding to the plurality of competencies and the plurality of employer specified requirements further comprises: querying, by the one or more processors, the at least one database for the one or more interview questions based on the plurality of competencies; and in response to the querying, receiving, by the one or more processors, the one or more interview questions from the at least one database (prepares set of interview by selecting questions from question bank based on skills from job description, ¶¶[0034]-[0035]).
Regarding claim 32, the combination of Wong, Pande, Asokan and Wu teaches all the limitations of claim 30 and Wong further teaches:
wherein determining the one or more interview questions corresponding to the plurality of competencies and the plurality of employer specified requirements further comprises: querying, by the one or more processors, the at least one database for the one or more interview questions based on the plurality of employer specified requirements; and in response to the querying, receiving, by the one or more processors, the one or more interview questions from the at least one database (prepares set of interview by selecting questions from question bank based on requirements from job description, ¶¶[0034]-[0035]).
Regarding claim 33, the combination of Wong, Pande, Asokan and Wu teaches all the limitations of claim 21 and Wong further teaches:
storing, by the one or more processors, each of the one or more interview questions in at least one database (stores interview questions, ¶¶[0034]-[0035]).
However Wong does not explicitly teach but Pande does teach:
storing the interview video data (digitally records candidate mock interviews, ¶[0033]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong with the storage of the interview data taught by Pande 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 it would be beneficial to store the interviews in Wong so the interviews could be analyzed later.
Claim(s) 22-25, 27 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Wong, Pande, Asokan and Wu further in view of Moni et al, US Pub. No. 2013/0052631, herein referred to as “Moni”.
Regarding claim 22, the combination of Wong, Pande, Asokan and Wu teaches all the limitations of claim 21 and Wong further teaches:
receiving, by the one or more processors, user profile data from a database, wherein the user profile data includes user personal data, user resume data (receives candidate data including general data and resume, ¶¶[0033]),
However the combination of Wong, Pande, Asokan and Wu does not teach but Moni does teach:
and user accessibility data, and wherein the user accessibility data includes an accessibility requirement that includes at least one of: a visibility requirement, a language requirement, or an audio requirement (user provides language preference, ¶[0081]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong, Pande, Asokan and Wu with the customization of Moni 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 of Wong and Pande would likely have individual preferences and would prefer a system that provided customizable options, e.g. as taught by Moni.
Regarding claim 23, the combination of Wong, Pande, Asokan and Wu teaches all the limitations of claim 22 and Mandi further teaches:
wherein the displaying the one or more interview questions is in accordance with the user accessibility data on the user interface of the interviewee device (problems are displayed in choose language, ¶[0081]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong, Pande, Asokan and Wu with the customization of Moni 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 of Wong and Pande would likely have individual preferences and would prefer a system that provided customizable options, e.g. as taught by Moni.
Regarding claim 24, the combination of Wong, Pande, Asokan and Wu teaches all the limitations of claim 22 and Wong further teaches:
wherein processing the interview video data to determine the interview feedback data and the future interview recommendation data includes: receiving, by the machine-learning model, input data including the interview video data and the corresponding one or more interview questions, the plurality of competencies, the plurality of employer specified requirements, and the job post data (uses recurrent neural network to select questions and analyze the interview, e.g., ¶¶[0031], [0042], [0045]; see also ¶¶[0036], [0048] discussing other aspects of the machine learning);
and determining, by the machine-learning model, the interview feedback data and the future interview recommendation data corresponding to the input data (uses recurrent neural generate feedback report, e.g., ¶¶[0031], [0042], [0045]),
wherein the interview feedback data includes the plurality of individual scores for the interview video data for each of the one or more interview questions (determines correctness of answers, ¶[0035]; see also ¶[0050])
and an overall score for all of the interview video data (ranks candidates, ¶¶[0048], [0062] and Fig. 14).
Regarding claim 25, the combination of Wong, Pande, Asokan, Wu and Mandi teaches all the limitations of claim 24 and Wong further teaches:
and updating, by the one or more processors, the one or more interview questions to correspond to the selected one or more interview question difficulty levels (presents questions based on selected difficulty, ¶[0078]).
However the combination of Wong, Pande, Asokan and Wu does not teach but Moni does teach:
displaying, by the one or more processors, one or more interview question difficulty levels on the user interface; in response to the displaying, receiving, by the one or more processors, a user selection of one of the one or more interview question difficulty levels (users select questions difficulty, ¶¶[0061], [0083]);
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the interview feedback of Wong, Pande, Asokan and Wu with the customization of Moni 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 of Wong and Pande would likely have individual preferences and would prefer a system that provided customizable options, e.g. as taught by Moni.
Regarding claim 27, the combination of Wong, Pande, Asokan, Wu and Mandi teaches all the limitations of claim 24 and Wong further teaches:
analyzing, by the one or more processors, via the machine-learning model, the job post data and the plurality of competencies; and based on the plurality of competencies, determining, by the one or more processors, via the machine-learning model, at least one additional competency (extracts skills based on job description using recurrent neural network, ¶[0032]).
Regarding claim 28, the combination of Wong, Pande, Asokan and Wu and Mandi teaches all the limitations of claim 24 and Wong further teaches:
analyzing, by the one or more processors, via the machine-learning model, the user resume data; and generating, by the one or more processors, via the machine-learning model, one or more resume interview questions based on the user resume data, wherein the one or more interview questions include the one or more resume interview questions (selects questions based on candidate body of knowledge, ¶[0036], which is based on candidate resumes, ¶[0031] and Fig. 1).
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.
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/BRENDAN S O'SHEA/
Examiner, Art Unit 3626