DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The following is a final office action.
Claims [1-2, 7, 9-10, 15, and 17] are currently pending and have been examined.
Claims 1, 9, and 17 are currently amended see REMARKS June 24, 2025.
Claims 3, 11, and 18 are newly cancelled see REMARKS June 24, 2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 7, 9-10, 15, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception that is an abstract idea without a practical application or significantly more.
Step 1: Claims 1-2 and 7 recite a computer implemented method (i.e. a series of steps), claims 9-10 and 15 recite a system, and claim 17 recites a non-transitory storage medium, and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 1, 9, and 17 recite: A method for providing assistance to interviewers, the method comprising: initiating, an interview session associated with interviewing a plurality of candidates for a job description conducted by one or more interviewers by performing the steps of: (i) identifying, a skill graph from an open domain knowledge graph by mapping curriculum with each skill required for the job description, wherein the skill graph annotates textual constructs associated with the interview session, wherein the curriculum comprises one or more concepts, (ii) constructing, a candidate profile of each candidate and an interviewer profile of each interviewer, wherein the candidate profile is constructed by extracting concepts and its expertise level from resume, wherein the interviewer profile is constructed from prior interviewer actions, and (iii) generating using a resource corpus, a question bank comprising technical long form questions related to the curriculum;
wherein the question bank generation comprises: generating the technical long form questions for the question bank using a template approach to assess candidate's expertise on (i) a concept understanding, (ii) properties of the concept, and (iii) an ability to apply concept knowledge for next question, wherein a difficulty level is assigned for each question; and extracting, a multi-span answer for each question from the resource corpus;
extracting, reference answers for the technical long form questions, associated skills, and the resource corpus for the skill and retrieves only articles that include an answer to the technical long form questions, obtains documents returned for the technical long form questions and processes to identify a ranked list of relevant answer spans and for identifying answers from the obtained documents, wherein the relevant answer spans are post-processed by selecting entire sentence(s) which contains document reader answer span to ensure that extracted answer include complete sentences;
selecting, a set of qualified candidates resume to be interviewed for the job description, wherein each candidate resume includes at least one concept associated with the skill graph;
recommending, each interviewer prior initiating the interview, a set of question and reference answer pairs from the question bank relevant to each candidate profile, wherein recommending the set of question and reference answer pairs comprises: determining, a weight for each concept in the skill graph based on the total number of questions associated with the concept in the question bank, wherein the weight for each concept is determined using an unsupervised sampling-based variation;
recommending the interviewer, the set of technical questions by (i) sampling concepts according to their weights with its difficulty level, and (iii) choosing technical questions for selective concepts and difficulty levels, wherein distribution over the difficulty levels is adjusted according to one or more prior assessments; and updating, each concept weights based on interviewer's feedback and collaborative filtering;
recommending, each interviewer at every interview step, the set of question and reference answer pairs from the question bank relevant to each candidate profile and their interview history, wherein each question provided by the interviewer to the candidate is based on at least one of (i) the recommended question answer pairs, and (ii) the interviewer's formulated question;
wherein recommends each interviewer based on dynamic understanding of the interview context by updating (i) a coverage of skills for previously asked question, (ii) the weight of concepts in previously asked question, (iii) assessment of skills and concepts based on previously asked question, and (iv) difficulty level of the question and candidate's response, wherein the recommended set of question and reference answer pairs are updated based on the interviewer’s feedback;
providing, dynamic recommendation updates to each interviewer, at each step of the interview, during the interview by: updating a question plan including the skills, concepts, and the difficulty levels for a next question and one or more specific questions, wherein the question plan at each step of the interview is updated by sampling one or more concepts and the skills from a posterior distribution based on a current completion of the skills and concepts, sampling a difficulty level from a posterior distribution based on a selected concept and a current assessment of the selected concept, and sampling a question from a uniform distribution over previously unused questions for the selected concept and the difficulty level, and updating the assessments of the skills, the concepts, and the current completion
and determining, an assessment score for the candidate provided answer using the reference answer extracted from the resource corpus and recommending next question to the candidate in response to the previous answer, wherein the assessment score for each candidate’s answer is assessed, having a feature concept alignment in the skill graph, and surface form similarity between the candidate’s answer and the reference answer;
generating, a summary of each candidate interview using the transcript of the interview based on (i) total number of questions asked by the interviewer, (ii) the coverage of relevant skills and a combined summary of multiple interviews, (i) distribution of number of questions asked, (ii) distribution of number of skills covered, and (iii) distribution of number of questions asked, wherein generating the summary further comprises: creating, using interview history, detailed transcripts for individual interviews containing individual question, candidate answers, assessments, their associated skills, and concepts, and generating the summary using the assessments and completion estimate at the end of the interview, the summary of the interview comprising an overall assessment, skill-wise summaries, mentioning of a number of questions asked, concepts covered, and skill-level assessment, wherein the overall summary provides insightful statistics for the interviewer across interviews by using their histories, assessments, and completion estimates.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure recites managing personal behavior or relationships or interactions between people. The claims recite a series of steps for conducting a job interview by determining a series of interview questions based on open domain knowledge graphs, determining candidate and interviewer information, suggesting interview questions to an interviewer, and scoring a job candidate’s responses. These steps are typically performed by a job recruiter that conducts interviews and are merely a series of instructions to evaluate a job candidate. Therefore, the claims recite an abstract idea.
Alternatively, the claims recite a mental process. The claims recite a method of obtaining position and candidate information and conducting a job interview for a position. The claims therefore, recite a mental process as a person is capable of identifying skills related to a job based on subject matter, determining candidate and job information, writing interview questions, and recommending interview questions, and determining an assessment score for a candidate in their mind or with the use of simple tools such as a pen and paper. Additionally, the claims recite steps and procedures that are similar to concepts the courts have identified as a mental process such as observations, evaluations, judgements, and opinions. Therefore, the claims recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: One or more hardware processors, a resume database, an interview assistant bot, a document retriever and a document reader, wherein the document retriever reduces search space for the document reader, wherein a multitask model is built of the document reader and fine-tuned on manually created question-answer pairs from technical domains, and while fine-tuning, hyperparameters are updated, a pre-interview question recommender executed via the one or more hardware processors, an in-interview question recommender executed via the one or more hardware processors, a regression model, capturing candidate speech through a candidate interface in the IA bot that is invisible to the candidate using an interviewer's device, and transcribed automatically to obtain a transcript of the candidate speech, and an interviewer interface of the IA bot.
Claim 9: A system comprising: a memory; one or more communication interfaces; and one or more hardware processors coupled to the memory, a resume database, an interview assistant bot, a document retriever and a document reader, wherein the document retriever reduces search space for the document reader, wherein a multitask model is built of the document reader and fine-tuned on manually created question-answer pairs from technical domains, and while fine-tuning, hyperparameters are updated, a pre-interview question recommender executed via the one or more hardware processors, an in-interview question recommender executed via the one or more hardware processors, a regression model, capturing candidate speech through a candidate interface in the IA bot that is invisible to the candidate using an interviewer's device, and transcribed automatically to obtain a transcript of the candidate speech, and an interviewer interface of the IA bot.
Claim 17: One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors perform actions, resume database, an interview assistant bot, a document retriever and a document reader, wherein the document retriever reduces search space for the document reader, wherein a multitask model is built of the document reader and fine-tuned on manually created question-answer pairs from technical domains, and while fine-tuning, hyperparameters are updated, a pre-interview question recommender executed via the one or more hardware processors, an in-interview question recommender executed via the one or more hardware processors, a regression model, capturing candidate speech through a candidate interface in the IA bot that is invisible to the candidate using an interviewer's device, and transcribed automatically to obtain a transcript of the candidate speech, and an interviewer interface of the IA bot.
The additional elements of generic computer elements to receive and process information as well as perform the abstract idea of recommending interview questions and determining an assessment score for a candidate are directed to merely “apply it” as they do not recite an improvement to a technology or technical field. Merely using generic computer elements such as a processor and memory to perform the actions of retrieving and processing information as well as using a database to store information and a generic “bot” to analyze information and generate an output such as suggested interview questions are directed to merely applying these additional elements to perform the abstract idea. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The additional elements do not recite an improvement to a technology or technical field but merely utilize the generic computer elements to perform the abstract idea of providing assistance to an interviewer by recommending interview questions and determining an assessment score for candidate provided answers. Therefore, the additional elements do not direct the claims to significantly more.
The dependent claims 2, 7, 10, and 15 further narrow the abstract idea of recommending questions to an interviewer conducting an interview, as recited in the independent claims 1, 9, and 17 and are therefore directed towards the same abstract idea.
The dependent claims do not recite any further additional elements than those disclosed above.
Therefore, claims 1-2, 7, 9-10, 15, and 17 are rejected under 35 U.S.C. 101.
Response to Arguments
Applicant’s arguments, see REMARKS, filed June 24, 2025, with respect to the rejections of claims 1-2, 7, 9-10, 15, and 17 under U.S.C. 101 have been fully considered but are not persuasive.
The applicant argues that the additional elements are directed to a practical application as they recite an improvement in the functioning of a computer as interlinking two logical modules that aids in reducing search space of one module by another module. The applicant argues that a document retriever reduces the search space for the reader module by only retrieving articles that are likely to contain an answer to a given question. Additionally, the applicant argues that the claims are directed to a practical application as they claim building a multitask module and further fine-tuning the multitask module and updating hyper parameters The applicant further argues that the claims are directed to a practical application as they recite capturing candidate speech through a candidate interface and transcribing automatically to obtain a transcript of the candidate speech. However, the examiner respectfully disagrees as the additional elements of “extracting, via one or more hardware processors, reference answers for the technical long form questions, associated skills, and the resource corpus for the skill with a document retriever and a document reader” are directed to merely “apply it” or applying generic computer elements to perform the abstract idea of generating technical long form questions and answer pairs for a question bank. Merely using generic computer hardware such as processors and generic elements such as a document retriever and reader to obtain and extract information from a plurality of document in a resource corpus is not an improvement to the functioning of a computer but merely applying generic computer elements to perform the abstract idea of retrieving ideal documents and reading the documents to obtain information for generation long form questions for an interview question bank. The examiner further finds that the multitask model build of the document reader is directed to merely “apply it” or applying a generic computer model to perform the abstract idea of creating question answer pairs. Merely using a generic computer element to identify sentences in documents that span answers to a question is not an improvement to a technology or technical field. While fine-tuning or training a model using manually created training data is not an improvement to a model but a standard practice for building a model for performing a function. Additionally, the additional element of capturing candidate speech through a candidate interface with an IA bot and transcribing to obtain a transcript of the candidate’s speech is directed to merely “apply it.” As merely reciting an IA bot to capture and transcribe candidate speech is not an improvement to a technology or technical field but merely using a generic computer element to perform the abstract idea of creating a summary of an interview by transcribing a candidate’s speech. As the additional elements are directed to merely applying generic computer elements for performing basic functions to automate the processes of generating interview question and answer pairs from a plurality of documents and creating an interview summary by transcribing speech from an interview, the examiner finds that the claims are not directed to a practical application.
The applicant further argues that the claim amount to significantly more as they recite using an interviewer’s device to capture candidate speech and transcribe automatically to obtain a transcript of the candidate’s speech. As the claims recite a system for creating a transcript of a candidate’s speech that an interviewer can directly assess and/or modify. However, the examiner respectfully disagrees as each of these elements recite an abstract idea of generating a summary of each candidate interview based on total number of questions asked by the interviewer, the coverage of relevant skills and a combined summary of multiple interviews, distribution of number of questions asked, distribution of number of skills covered, and distribution of questions asked. As merely creating a summary of an interview including statistical analysis of questions asked and answers given and transcribing the speech of the interviewee are not an improvement to a technology or technical field but are an abstract idea as the claims recite a mental process. A person such as a recruiter is capable of mentally, or using simple tool such as pen and paper, conducting or monitoring an interview and generating an interview summary including a transcript of the candidate’s speech. Furthermore, the additional elements are directed to merely “apply it” or applying a generic IA bot and candidate interface for performing the abstract idea of generating a summary based on received information. Therefore, the claims do not amount to significantly more.
The examiner maintains the current 101 rejection.
Applicant argues that claims 2-3, 7, 10-11, 15, and 18 are allowable as being dependent on claims 1, 9, and 17 and therefore are rejected under the same rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
McMahon (US 2023/0214778) Dynamic automated real-time management of virtual interviews between job providers and qualified job seekers.
Gold (US 2010/0114791) Automated interview systems and methods.
Olivier (US 2014/0156550) Systems and methods for conducting an interview.
Monasor (US 2019/0188645) Generation of automated job interview questionnaires adapted to candidate experience.
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 COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached on 5712726782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/COREY RUSS/Examiner, Art Unit 3629
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