DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claims 1-25 are currently pending and have been addressed below.
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 .
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 12/18/2025 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 121 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed parent applications, Application No. 17/166,740 filed on 11/04/2017 and Application No. 19/000,305 filed on 02/03/2021, fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The specification of the instant application contains new matter in at least Paragraphs 0022, 0069, 0074, and 0081. Claims 1, 15, and 25 include limitations not supported in the parent applications, including the use of a large language model (LLM) to evaluate an interview. Claims 2-14 and 16-24 are directed to further describing how the adaptive questionnaire of the LLM generates different questions based on a previous response which were not described in the parent applications 17/166,740 and 19/000,305 to which the instant application claim priority. Because the claims are not supported under 35 U.S.C. 112 first paragraph, the priority date for the claim limitations of the instant application will be the effective filing date of the instant application which is 12/23/2024.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to an apparatus which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A talent management comprising: creating a data set associated with a plurality of positions and training a model using the data set; receiving a request associated with an open position from one or more candidates; interviewing a candidate selected from the one or more candidates using an adaptive questionnaire, wherein the adaptive questionnaire dynamically adjusts a next question based on a previous response to a previous question; evaluating a candidate based on real-time benchmarking of the candidate, wherein the real-time benchmarking compares the candidate's performance to one or more position metrics; and stack-ranking the candidate compared to other candidates. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “managing personal behavior.” In this case, evaluating an interview based on responses is considered a social activity. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: an input-output interface; an artificial intelligence engine; a processor; a memory; a large language model; and a neural network algorithm.
The input-output interface is merely used to receive input data for an analysis and output and evaluation score for each candidate (Paragraphs 0039-0041). The artificial intelligence engine is merely used to create the candidates’ skills models (Paragraph 0055). The processor is used to perform the various exemplary methods (Paragraph 0041). The memory is merely used to store instructions (Paragraph 0041). The large language model is merely used to dynamically adjust the difficulty of subsequent questions based on performance metrics associated with the candidate’s answers. For example, questions may be made progressively harder as the automated interview progresses. Alternatively, questions may be generated based on the previous response(s) from the candidate (Paragraph 0074). The neural network algorithm is merely used to generate granular, multi-dimensional performance insights (Paragraph 0023). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “input-output interface,” “artificial intelligence engine,” “processor,” “memory,” “large language model,” and “neural network algorithm” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Further, the interface is considered “field of use” since it’s just used to receive information for an analysis, but the technology is not improved (MPEP 2106.05h). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of evaluating an interview. The specification shows that the input-output interface is merely used to receive input data for an analysis and output and evaluation score for each candidate (Paragraphs 0039-0041). The artificial intelligence engine is merely used to create the candidates’ skills models (Paragraph 0055). The processor is used to perform the various exemplary methods (Paragraph 0041). The memory is merely used to store instructions (Paragraph 0041). The large language model is merely used to dynamically adjust the difficulty of subsequent questions based on performance metrics associated with the candidate’s answers. For example, questions may be made progressively harder as the automated interview progresses. Alternatively, questions may be generated based on the previous response(s) from the candidate (Paragraph 0074). The neural network algorithm is merely used to generate granular, multi-dimensional performance insights (Paragraph 0023). In this case, the claim does not provide any details about how the large language model or the neural network operates (e.g., how the questions or scores are generated). See 2024 AI Guidance, example 47, claim 2. Further, the step of “real-time benchmarking” is considered a well-understood, routine, and conventional function since it's just “performing repetitive calculations” and “receiving or transmitting data over a network” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 15 is directed to a system at step 1, which is a statutory category. Claim 15 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 15 further recites: “proctoring system” – which is merely used to monitor candidate behavior, detect anomalies, and prevent fraudulent activities (Paragraph 0022). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). In this case, the claim does not provide any details about how the proctoring system operates (e.g., how the system is used to monitor candidate’s behavior). Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 25 is directed to a system at step 1, which is a statutory category. Claim 25 recites similar limitations as claim 15 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 25 further recites: “wherein proctoring is enhanced through AI-driven computer vision and natural language processing (NLP) models” – which are merely used to monitor candidate behavior, detect anomalies, and prevent fraudulent activities (Paragraph 0022). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). In this case, the claim does not provide any details about how the proctoring system operates (e.g., how the computer vision or NLP is used to monitor candidate’s behavior). Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claim 2 is not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above - such as wherein the interviewing step is initiated by the candidate and performed autonomously. Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Also, mere automation of a manual process may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05(a)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 3-4 and 18 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above - such as wherein the adaptive questionnaire increases the difficulty of questions as the interviewing step progresses; and wherein the adaptive questionnaire provides follow-up questions based on strengths or weaknesses of the candidate. Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In this case, the claims do not provide any details about how the large language model operates (e.g., how the questions are generated). See 2024 AI Guidance, example 47, claim 2. Further, the step of “adaptive questions” is considered a well-understood, routing, and conventional function since it's just “performing repetitive calculations” and “receiving or transmitting data over a network” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 5-9 and 19-21 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above - such as: wherein the real-time benchmarking comprises timing or latency of responses by the candidate; wherein the real-time benchmarking comprises comparing responses by the candidate to dynamic scoring parameters; wherein the dynamic scoring parameters are based on speed and precision scores relating to the interviewing step; and wherein the real-time benchmarking comprises developing a candidate skills model which is used as inputs to compare to the dynamic scoring parameters. Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Also, the step of “dynamic scoring” is considered a well-understood, routing, and conventional function since it's just “performing repetitive calculations” and “receiving or transmitting data over a network” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 10-11 and 16-17 are directed to additional elements such as: artificial intelligence driven computer vision; and a natural language model (NLP). The artificial intelligence driven computer vision is merely used to perform facial recognition and gaze tracking (Paragraphs 0022 & 0077). The NLP is merely used to perform audio-based anomaly detection (Paragraphs 0022 & 0077). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In this case, the claims do not provide any details about how the computer vision or NLP operates (e.g., how the behavior is analyzed). See 2024 AI Guidance, example 47, claim 2. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 12-13 and 22-23 are not directed to any additional claim elements. Rather, these claims offer further descriptive functions of elements found in the independent claims and addressed above - such as: wherein the stack-ranking step is performed by a neural networks algorithm; and wherein the stack-ranking step is performed using weighted scoring matrices. Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In this case, the claims do not provide any details about how the neural networks algorithm operates (e.g., adjusting weights is merely a mathematical calculation). See 2024 AI Guidance, example 47, claim 2. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 14 and 24 are directed to additional elements such as: a heatmap. The heatmap is merely used to provide detailed insights of the distribution and proficiency of relevant skills (Paragraph 0079). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4, 6-9, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson (US 2025/0232262 A1), in view of Castro Vilabella et al. (US 2026/0024054 A1).
Regarding claim 1, Jackson discloses a talent management system comprising (Paragraph 0004, The present disclosure generally relates to systems and methods for automated talent acquisition and management using artificial intelligence and machine learning to ingest data, generate job descriptions, source candidates, screen resumes, conduct interviews, match candidates to jobs, and manage payment of employees and contractors):
an input-output interface (Paragraph 0076, FIG. 2 shows an AI recruitment system 200 and illustrates a comprehensive, standalone Artificial Intelligence Recruiter (AIR) Pipeline system. The AI recruitment system automates and optimizes the recruitment process using advanced data processing and machine learning techniques. The pipeline consists of several components and processes including Domain Model Messaging Layer, Data Flow and Webhooks, Structured AIR Microservice Layer, AI/ML Processing Components, Matching and Scoring System, Call Transcription and Scoring, Conversational Summarization Service, Final Processing and Output, Data Storage and Management, and Integration and Scalability; Paragraph 0077, The pipeline begins with a domain model messaging layer 220, which serves as the input interface for the system);
and an artificial intelligence engine having a processor coupled to the input-output interface, wherein the processor is further coupled to a memory, the memory having stored thereon executable instructions that when executed by the processor cause the processor to effectuate operations comprising (Paragraph 0079, As shown in FIG. 3, a talent recruitment system 300 includes a computing system 308 and a recruitment application 310 running on the computing system 308, the recruitment application 310 including instructions or software 320 and a knowledge base 330. The talent recruitment system 300 includes a first AI module 340 for generating a job description wherein the first AI module 340 leverages a Large Language Model 390 trained on a proprietary dataset of successful job descriptions; Paragraph 0087, When such methods and processes are implemented, the state of the storage machine may be changed to hold different data. For example, the storage machine may include memory devices such as various hard disk drives, CD, or DVD devices. The logic machine may execute machine-readable instructions via one or more physical information and/or logic processing devices. For example, the logic machine may be configured to execute instructions to perform tasks for a computer program. The logic machine may include one or more processors to execute the machine-readable instructions):
creating a data set associated with a plurality of positions and training a large language model using the data set (Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring. The system may include use of managers with a detailed and nuanced profile of each candidate. The data-driven insights may include detailed analytics on various aspects of the recruitment process, including the applicant's skills, work history, and alignment with the client's role requirements. It collects and analyzes data such as applicant Skills: including specific technical and soft skills of candidates, derived from their resumes, profiles, and interview responses. The data-driven insights may include work history including the applicant's previous roles, duration of employment, and career progression, providing insights into their experience and suitability for the position. The data-driven insights may include client requirements including specific qualifications, skills, and experience required by the client for the role, ensuring that candidates are matched with positions that align with their expertise. The insights may help organizations refine their hiring strategies, optimize role descriptions, and make more informed decisions, ultimately improving the overall efficiency and effectiveness of the recruitment process);
receiving a request associated with an open position from one or more candidates (Paragraph 0077, The pipeline begins with a domain model messaging layer 220, which serves as the input interface for the system. The domain model messaging layer 220 comprises several data entities including, Application 214a, Candidate 214b, Job 214c, and Attachment 214d. A module 210 is provided for storage 212 for each of the Application 214a, Candidate 214b, Job 214c, and Attachment 214d entities These entities represent the core information units processed by the system, containing details about job applications, candidate profiles, job descriptions, and supplementary documents. The system employs webhooks and payload requests to initiate and manage data flow. The application created webhook 216 triggers the pipeline when a new application is submitted. A job request payload handles job-related data transmission. A structured AIR microservice layer 250 is a central component of the system, responsible for structured processing of the input data. The structured AIR microservice layer includes several microservices that handle specific aspects of the recruitment process. The system uses AI algorithms to assess the compatibility between candidates and job openings);
interviewing a candidate selected from the one or more candidates using an adaptive questionnaire, wherein the adaptive questionnaire dynamically adjusts a next question based on a previous response to a previous question (Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring. The system may include use of managers with a detailed and nuanced profile of each candidate. The data-driven insights may include detailed analytics on various aspects of the recruitment process, including the applicant's skills, work history, and alignment with the client's role requirements. It collects and analyzes data such as applicant Skills: including specific technical and soft skills of candidates, derived from their resumes, profiles, and interview responses. The data-driven insights may include work history including the applicant's previous roles, duration of employment, and career progression, providing insights into their experience and suitability for the position. The data-driven insights may include client requirements including specific qualifications, skills, and experience required by the client for the role, ensuring that candidates are matched with positions that align with their expertise. The insights may help organizations refine their hiring strategies, optimize role descriptions, and make more informed decisions, ultimately improving the overall efficiency and effectiveness of the recruitment process);
evaluating a candidate based on real-time benchmarking of the candidate, wherein the real-time benchmarking compares the candidate's performance to one or more position metrics … (Paragraph 0058, Another integral component of the system is an AI agent that is configured to screen resumes, profiles, conduct live interviews, and build candidate scorecards. This AI agent operates by analyzing the resumes and profiles of the sourced candidates, conducting live interviews using natural language processing techniques, and building scorecards that evaluate the candidates based on various criteria. The criteria can include, but are not limited to, the candidate's skills, experience, qualifications, and alignment with the company's culture and values. This comprehensive screening process allows the AI agent to identify candidates who are not just qualified for the job, but also a good fit for the company; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; Paragraph 0086, FIG. 7 is a slide 700 showing the step of interviewing the talent using the system. The system conducts the first interview using a proprietary set of questions or a set of questions by the user. The system fills out the scorecard and presents the results. The system automatically advances candidates and schedules the next set of interviews. The hiring manager reviews the candidate scorecards and video interviews asynchronously. The hiring manager edits and shares recorded interviews and scorecards);
and stack-ranking the candidate compared to other candidates (Paragraph 0059, Furthermore, this AI agent is not just a passive screener. It is designed with the capability to provide real-time feedback to the candidates during the live interviews. This real-time feedback can include, but is not limited to, clarifications on the candidate's responses, suggestions for improvement, and immediate evaluation of the candidate's performance; Paragraph 0061, Furthermore, this AI matching system is not just a passive matcher. It is designed with the capability to provide a ranking of the candidates based on the match. This ranking process is facilitated by advanced machine learning algorithms that evaluate the match between the candidates and the job based on the comprehensive set of parameters. The AI matching system can generate a ranking of the candidates, with the top-ranked candidates being those who are the closest match to the job. This ranking provides the company with a prioritized list of candidates, thereby assisting the company in making informed hiring decisions. This ranking capability of the AI matching system contributes to the system's overall effectiveness and efficiency in talent acquisition and management; Paragraph 0086, The AI sourcing agent sources candidates for a new job and sources candidates from multiple platforms and to screen resumes, profiles, conduct live interviews, and build candidate scorecards is further configured to provide real-time feedback to the candidates. The AI matching system is configured to match candidates to jobs and to provide a ranking of the candidates based on the match).
Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard), Jackson does not specifically disclose wherein the evaluation is performed using a neural network algorithm.
However, Castro Vilabella et al. discloses evaluating a candidate based on real-time benchmarking of the candidate, wherein the real-time benchmarking compares the candidate's performance to one or more position metrics using neural network algorithms (Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0080, It should be noted that the process steps and/or operations and instructions of the present invention can be embodied in software, firmware, and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by real-time network operating systems; see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation is performed using a neural network algorithm of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, which is dependent of claim 1, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 1. Jackson further discloses wherein the interviewing step is initiated by the candidate and performed autonomously (Paragraph 0073, An AI-powered interviewing system 100 is a component of the AI Recruiter (AIR) platform. The system 100 processes various inputs to conduct dynamic, context-aware interviews. Input components are included in the system which integrates multiple data sources and parameters including call metadata 112 which stores information about the interview call, chat history 114 which maintains a record of the conversation, new response from talent 116 which captures the latest input from the interviewee, job requirement 118 which contains specific criteria for the position, talent background 120 which includes the candidate's professional and educational history, interview questions 122 which include a repository of potential questions to ask, and interview setup 124 which includes parameters defining the structure and goals of the interview; Paragraph 0074, The system utilizes an embedding model 140 to identify previously asked question, log the remaining time for the entire interview and track time allocation for each question. The embedding model 140 ensures efficient time management and prevents redundancy in questioning; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role).
Regarding claim 4, which is dependent of claim 1, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 1. Jackson further discloses wherein the adaptive questionnaire provides follow-up questions based on strengths or weaknesses of the candidate (Paragraph 0068, Unlike conventional interview methods, the system actively understands and interacts with the applicant in real-time. The LLM processes the applicant's responses, assesses their relevance and depth, and formulates follow-up questions on the spot. This dynamic interaction allows the system to delve deeper into specific areas of the candidate's expertise, ensuring a comprehensive evaluation of their capabilities. The line of questioning is continuously adjusted based on the applicant's answers, enabling a thorough assessment of their skills, experience, and fit for the role; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; It can be noted that the claim language is written in alternative form. The limitation taught by Jackson is based on strengths of the candidate such as area of expertise).
Regarding claim 6, which is dependent of claim 1, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 1. Jackson further discloses wherein the real-time benchmarking comprises accuracy or relevance of responses by the candidate (Paragraph 0068, Unlike conventional interview methods, the system actively understands and interacts with the applicant in real-time. The LLM processes the applicant's responses, assesses their relevance and depth, and formulates follow-up questions on the spot. This dynamic interaction allows the system to delve deeper into specific areas of the candidate's expertise, ensuring a comprehensive evaluation of their capabilities. The line of questioning is continuously adjusted based on the applicant's answers, enabling a thorough assessment of their skills, experience, and fit for the role; It can be noted that the claim language is written in alternative form. The limitation taught by Jackson is based on relevance of responses by the candidate).
Regarding claim 7, which is dependent of claim 1, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 1. Jackson further discloses wherein the real-time benchmarking comprises comparing responses by the candidate to … scoring parameters (Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; Paragraph 0086, FIG. 7 is a slide 700 showing the step of interviewing the talent using the system. The system conducts the first interview using a proprietary set of questions or a set of questions by the user. The system fills out the scorecard and presents the results. The system automatically advances candidates and schedules the next set of interviews. The hiring manager reviews the candidate scorecards and video interviews asynchronously. The hiring manager edits and shares recorded interviews and scorecards).
Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard), Jackson does not specifically disclose wherein the score is dynamic (e.g., score parameters change based on feedback of the user).
However, Castro Vilabella et al. discloses wherein the real-time benchmarking comprises comparing responses by the candidate to dynamic scoring parameters (Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0080, It should be noted that the process steps and/or operations and instructions of the present invention can be embodied in software, firmware, and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by real-time network operating systems; see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9; Examiner interprets “adjusting weight based on feedback” as the “dynamic scoring parameters”).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes dynamic scoring parameters (e.g., retraining) of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8, which is dependent of claim 7, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 7. Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard), Jackson does not specifically disclose wherein the score is dynamic (e.g., score parameters change based on feedback of the user).
However, Castro Vilabella et al. discloses wherein the dynamic scoring parameters are based on speed and precision scores relating to the interviewing step (Paragraph 0042, Present invention provides an advanced AI-driven solution for optimizing recruitment processes and maximizing the person-job fit accuracy and robustness. The invention integrates multiple data sources and processes to evaluate candidates more precisely, enhancing the accuracy and efficiency of candidate selection; Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0080, It should be noted that the process steps and/or operations and instructions of the present invention can be embodied in software, firmware, and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by real-time network operating systems; It can be noted that the claim language is written in alternative form. The limitation taught by Castro Vilabella et al. is based on precision scores relating to the interviewing step; see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9; Examiner interprets “adjusting weight based on feedback” as the “dynamic scoring parameters”).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes dynamic scoring parameters (e.g., retraining) of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9, which is dependent of claim 8, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 8. Jackson further discloses wherein the real-time benchmarking comprises developing a candidate skills model … (Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; Paragraph 0086, FIG. 7 is a slide 700 showing the step of interviewing the talent using the system. The system conducts the first interview using a proprietary set of questions or a set of questions by the user. The system fills out the scorecard and presents the results. The system automatically advances candidates and schedules the next set of interviews. The hiring manager reviews the candidate scorecards and video interviews asynchronously. The hiring manager edits and shares recorded interviews and scorecards).
Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard), Jackson does not specifically disclose wherein the score is dynamic (e.g., score parameters change based on feedback of the user).
However, Castro Vilabella et al. discloses wherein the real-time benchmarking comprises developing a candidate skills model which is used as inputs to compare to the dynamic scoring parameters (Paragraph 0042, Present invention provides an advanced AI-driven solution for optimizing recruitment processes and maximizing the person-job fit accuracy and robustness. The invention integrates multiple data sources and processes to evaluate candidates more precisely, enhancing the accuracy and efficiency of candidate selection; Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0080, It should be noted that the process steps and/or operations and instructions of the present invention can be embodied in software, firmware, and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by real-time network operating systems; see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9; Examiner interprets “adjusting weight based on feedback” as the “dynamic scoring parameters”).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes dynamic scoring parameters (e.g., retraining) of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 12, which is dependent of claim 1, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 1. Jackson further discloses wherein the stack-ranking step is performed by a [artificial intelligence] (Paragraph 0059, Furthermore, this AI agent is not just a passive screener. It is designed with the capability to provide real-time feedback to the candidates during the live interviews. This real-time feedback can include, but is not limited to, clarifications on the candidate's responses, suggestions for improvement, and immediate evaluation of the candidate's performance; Paragraph 0061, Furthermore, this AI matching system is not just a passive matcher. It is designed with the capability to provide a ranking of the candidates based on the match. This ranking process is facilitated by advanced machine learning algorithms that evaluate the match between the candidates and the job based on the comprehensive set of parameters. The AI matching system can generate a ranking of the candidates, with the top-ranked candidates being those who are the closest match to the job. This ranking provides the company with a prioritized list of candidates, thereby assisting the company in making informed hiring decisions. This ranking capability of the AI matching system contributes to the system's overall effectiveness and efficiency in talent acquisition and management; Paragraph 0086, The AI sourcing agent sources candidates for a new job and sources candidates from multiple platforms and to screen resumes, profiles, conduct live interviews, and build candidate scorecards is further configured to provide real-time feedback to the candidates. The AI matching system is configured to match candidates to jobs and to provide a ranking of the candidates based on the match).
Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard and generating a ranking), Jackson does not specifically disclose wherein the evaluation is performed using a neural network algorithm.
However, Castro Vilabella et al. discloses wherein the stack-ranking step is performed by a neural networks (Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0063, Finally, the candidates are ranked (see FIG. 2) based on the fit percentage (107); see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation is performed using a neural network algorithm of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 13, which is dependent of claim 12, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 12. Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard and generating a ranking), Jackson does not specifically disclose wherein the ranking step is performed using weighted scoring matrices.
However, Castro Vilabella et al. discloses wherein the stack-ranking step is performed using weighted scoring matrices (Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0063, Finally, the candidates are ranked (see FIG. 2) based on the fit percentage (107); see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation is performed using a neural network algorithm of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson (US 2025/0232262 A1), in view of Castro Vilabella et al. (US 2026/0024054 A1), in further view of Light (US 2025/0335876 A1).
Regarding claim 3, which is dependent of claim 2, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 2. Jackson further discloses wherein the adaptive questionnaire [continuously adjust the line] of questions as the interviewing step progresses (Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring).
Although Jackson discloses an adaptive questionnaire that dynamically adjusts a next question based on a previous response to a previous question (see Paragraph 0084), the combination of Jackson and Castro Vilabella et al. does not specifically disclose wherein the adaptive questionnaire increases the difficulty of questions as the interviewing step progresses.
However, Light discloses wherein the adaptive questionnaire increases the difficulty of questions as the interviewing step progresses (Paragraph 0106, The personality assessment module 224 utilizes a dynamic question engine that adapts the assessment in real-time based on the candidate's previous responses, ensuring a personalized and relevant evaluation experience. This engine may employ conditional logic to present different sets of questions or follow-up prompts that delve deeper into certain personality facets, thereby refining the accuracy of the personality type determination; Paragraph 0113, Additionally, the video interview module 228 may provide a customization interface for recruiters, enabling them to adjust the question parameters or add specific questions they wish to include in the interview. This level of customization ensures that the interview process remains flexible and responsive to the unique needs of each hiring scenario; Paragraph 0214, Video Interviews: These assessments capture both verbal and nonverbal interactions as candidates respond to interview questions. The platform analyzes the video to assess communication skills, professionalism, and other soft skills that are difficult to gauge through written tests; see provisional application # 63/639,825, filed on 04/29/24, Paragraphs 0077, 0084, & 0179).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate, wherein the questions are dynamically adjusted based on a previous response (e.g., by using an AI agent) of the invention of Jackson to further incorporate wherein the level of difficulty is increased of the invention of Light because doing so would allow the system to present different sets of questions or follow-up prompts that delve deeper into certain facets (see Light, Paragraphs 0106). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 5, which is dependent of claim 1, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 1. Jackson further discloses wherein the real-time benchmarking comprises [relevance and depth] of responses by the candidate (Paragraph 0068, Unlike conventional interview methods, the system actively understands and interacts with the applicant in real-time. The LLM processes the applicant's responses, assesses their relevance and depth, and formulates follow-up questions on the spot. This dynamic interaction allows the system to delve deeper into specific areas of the candidate's expertise, ensuring a comprehensive evaluation of their capabilities. The line of questioning is continuously adjusted based on the applicant's answers, enabling a thorough assessment of their skills, experience, and fit for the role).
Although Jackson discloses evaluating a candidate based on real-time benchmarking of the candidate (e.g., assessment of their skills based on the applicant answers), Jackson does not specifically disclose wherein the real-time benchmarking comprises timing or latency of responses by the candidate.
However, Light discloses wherein the real-time benchmarking comprises timing or latency of responses by the candidate (Paragraph 0205, The insights from the nonverbal AI analysis add a layer of depth to the report. For example, the report might include a section detailing the candidate's response times, keystroke dynamics, and mouse movement patterns, interpreted to reflect their confidence and proficiency in each skill area; It can be noted that the claim language is written in alternative form. The limitation taught by Light is based on timing; see provisional application # 63/639,825, filed on 04/29/24, Paragraph 0174).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate, wherein the real-time benchmarking comprises relevance and depth of responses by the candidate of the invention of Jackson to further incorporate wherein the real-time benchmarking comprises timing or latency of responses by the candidate of the invention of Light because doing so would allow the system to analyze proficiency in each area based on candidate’s response times (see Light, Paragraphs 0205). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Jackson (US 2025/0232262 A1), in view of Castro Vilabella et al. (US 2026/0024054 A1), in further view of Edwards (US 12476965 B2).
Regarding claim 10, which is dependent of claim 1, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 1. Although Jackson discloses a candidate profile and a live video interview (Paragraph 0077, Candidate Profile; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews), Jackson does not specifically disclose how the candidate is authenticated.
However, Edwards wherein the executable instructions further comprise proctoring of the candidate interview, wherein the proctoring step comprises artificial intelligence driven computer vision to authenticate the candidate through facial recognition, wherein the facial recognition is performed based on a third-party document having a photograph of the candidate (Column 6, lines 47-67, As described herein, an entity-issued user-specific identification document refers to a physical item that is issued by a specific entity, such as a merchant or service, for a specific user. An illustrative example is an identification (ID) card used to identify a user with a merchant. The entity-issued user-specific identification document may be used to authenticate that user with that entity; Column 11, lines 1-22, In some examples, the trained computer vision model (e.g., computer vision model 212) comprises a facial recognition component, a character recognition component, a logo recognition component, or other components. For example, the facial recognition component of the trained computer vision model (e.g., computer vision model 212) may be trained to compute a facial recognition confidence score indicating a likelihood that at least a portion of the image depicts a face of the user. As depicted in FIG. 4, the captured image depicting candidate entity-issued user-specific identification document 410 may include facial image 412. The facial recognition component of the trained computer vision model may compare facial image 412 to a reference facial image of the user (i.e., an authorized user of the authorized user account attempting to be accessed). As mentioned previously, some examples include one or more identification features being stored in association with each authorized user account, as depicted in FIG. 2A. In one or more examples, an embedding representing a reference facial image of the user (e.g., a known valid image of the authorized user's face) may be stored within using one or more of identification features A-N).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify how the candidate is authenticated of the invention of Edwards because doing so would allow the system to use a user specific identification document to authenticate the user (see Edwards, Column 11, lines 1-22). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Jackson (US 2025/0232262 A1), in view of Castro Vilabella et al. (US 2026/0024054 A1), in further view of Makkar et al. (US 2024/0161045 A1).
Regarding claim 11, which is dependent of claim 1, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 1. Although Jackson discloses a candidate profile and a live video interview (Paragraph 0077, Candidate Profile; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews), Jackson does not specifically disclose how the system is used to detect anomalies associated with audible contents of the interview.
However, Makkar et al. discloses wherein the executable instructions further comprise proctoring of the candidate interview, wherein the proctoring step comprises using NLP models to detect anomalies associated with audible contents of the interview (Paragraph 0014, Determine, by applying a speech recognition and natural language processing (NLP) engine to the audio track, attributes associated with audio segments being tagged to the interviewer, wherein the attributes associated with an audio segment comprise timing parameters associated with the audio segment and a text content of the audio segment. Execute a rule-based analysis engine based on the attributes associated with audio segments being tagged to the interviewer to determine whether the interviewer conducts the interview in compliance with predetermined rules. Responsive to determining that the interviewer does not conduct the interview in compliance with the predetermined rules, the intelligent system may generate a notice to a user regarding the non-compliance; Paragraph 0026, At 120, processing device 102 may determine, by applying a speech recognition and natural language processing (NLP) engine to the audio track, attributes associated with audio segments being tagged to the interviewer, wherein the attributes associated with an audio segment comprise timing parameters associated with the audio segment and a text content of the audio segment. After the audio is segmented and tagged per speaker using the speaker identification model, it may be forwarded to the NLP engine for analysis of the text per speaker and determination of attributes based on the text).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes proctoring of the candidate interview using a natural language processing engine of the invention of Makkar et al. because doing so would allow the system to determine whether the interviewer conducts the interview in compliance with predetermined rules (see Makkar et al., Paragraph 0014). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Jackson (US 2025/0232262 A1), in view of Castro Vilabella et al. (US 2026/0024054 A1), in further view of Hingne et al. (US 2022/0253788 A1).
Regarding claim 14, which is dependent of claim 13, the combination of Jackson and Castro Vilabella et al. discloses all the limitations in claim 13. Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard and generating a ranking), Jackson does not specifically disclose wherein the scorecard is displayed as a heatmap.
However, Hingne et al. discloses wherein the stack-ranking step creates heatmaps based on relevant skills of the candidate (Paragraph 0089, For an agent, an agent skill profile may be created as a unique view that shows the details of the skills, skill distributions, and changes in skills over time based on the agent's experience. This profile view further keeps a count of the contacts, calls, and other interactions that the agent handled in a particular time period. Further, the profile view provides a heat map of the agent experience with different skills as compared between those different skills, jobs, agents (e.g., peer agents within a group, organization, or the like), or any combination thereof. The processing job to determine the agent skill profile may also be run once a month and may reference back to previous months; Paragraph 0099, The comparison may therefore correspond to a ranking of the agent's BAV score to the BAV score of the other agents (e.g., a score comparison, a percentile that the agent may fall in, a numeric or tiered ranking, or the like). The comparison may also compare each KPI of the agent to the other agents so that individual skills may be compared. This may include a heatmap or other visualization for comparison of agent skills).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation is displayed as a heatmap of the invention of Hingne et al. because doing so would allow the system to provide a heat map of the agent/applicant experience with different skills as compared between those different skills, jobs, agents (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Hingne et al, Paragraph 0089). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 15-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson (US 2025/0232262 A1), in view of Light (US 2025/0335876 A1).
Regarding claim 15, Jackson discloses a talent management system comprising (Paragraph 0004, The present disclosure generally relates to systems and methods for automated talent acquisition and management using artificial intelligence and machine learning to ingest data, generate job descriptions, source candidates, screen resumes, conduct interviews, match candidates to jobs, and manage payment of employees and contractors):
a trained large language model (LLM)-based interview system having audio and video inputs and audio outputs, wherein the interview system is configured to interact with a candidate using adaptive testing, the interview system comprising; a dynamic questionnaire system configured to adaptively test the candidate, wherein the dynamic questionnaire is configured to ask a series of questions in which one or more questions are based on answers supplied by the candidate (Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring. The system may include use of managers with a detailed and nuanced profile of each candidate. The data-driven insights may include detailed analytics on various aspects of the recruitment process, including the applicant's skills, work history, and alignment with the client's role requirements. It collects and analyzes data such as applicant Skills: including specific technical and soft skills of candidates, derived from their resumes, profiles, and interview responses. The data-driven insights may include work history including the applicant's previous roles, duration of employment, and career progression, providing insights into their experience and suitability for the position. The data-driven insights may include client requirements including specific qualifications, skills, and experience required by the client for the role, ensuring that candidates are matched with positions that align with their expertise. The insights may help organizations refine their hiring strategies, optimize role descriptions, and make more informed decisions, ultimately improving the overall efficiency and effectiveness of the recruitment process);
a proctoring system configured to monitor the [interview] (Paragraph 0058, Another integral component of the system is an AI agent that is configured to screen resumes, profiles, conduct live interviews, and build candidate scorecards. This AI agent operates by analyzing the resumes and profiles of the sourced candidates, conducting live interviews using natural language processing techniques, and building scorecards that evaluate the candidates based on various criteria. The criteria can include, but are not limited to, the candidate's skills, experience, qualifications, and alignment with the company's culture and values. This comprehensive screening process allows the AI agent to identify candidates who are not just qualified for the job, but also a good fit for the company);
a real-time benchmarking system configured to compare candidate performance data collected from the interview system (Paragraph 0058, Another integral component of the system is an AI agent that is configured to screen resumes, profiles, conduct live interviews, and build candidate scorecards. This AI agent operates by analyzing the resumes and profiles of the sourced candidates, conducting live interviews using natural language processing techniques, and building scorecards that evaluate the candidates based on various criteria. The criteria can include, but are not limited to, the candidate's skills, experience, qualifications, and alignment with the company's culture and values. This comprehensive screening process allows the AI agent to identify candidates who are not just qualified for the job, but also a good fit for the company; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; Paragraph 0086, FIG. 7 is a slide 700 showing the step of interviewing the talent using the system. The system conducts the first interview using a proprietary set of questions or a set of questions by the user. The system fills out the scorecard and presents the results. The system automatically advances candidates and schedules the next set of interviews. The hiring manager reviews the candidate scorecards and video interviews asynchronously. The hiring manager edits and shares recorded interviews and scorecards);
and a stack-ranking system configured to rank the candidate with respect to other candidates (Paragraph 0059, Furthermore, this AI agent is not just a passive screener. It is designed with the capability to provide real-time feedback to the candidates during the live interviews. This real-time feedback can include, but is not limited to, clarifications on the candidate's responses, suggestions for improvement, and immediate evaluation of the candidate's performance; Paragraph 0061, Furthermore, this AI matching system is not just a passive matcher. It is designed with the capability to provide a ranking of the candidates based on the match. This ranking process is facilitated by advanced machine learning algorithms that evaluate the match between the candidates and the job based on the comprehensive set of parameters. The AI matching system can generate a ranking of the candidates, with the top-ranked candidates being those who are the closest match to the job. This ranking provides the company with a prioritized list of candidates, thereby assisting the company in making informed hiring decisions. This ranking capability of the AI matching system contributes to the system's overall effectiveness and efficiency in talent acquisition and management; Paragraph 0086, The AI sourcing agent sources candidates for a new job and sources candidates from multiple platforms and to screen resumes, profiles, conduct live interviews, and build candidate scorecards is further configured to provide real-time feedback to the candidates. The AI matching system is configured to match candidates to jobs and to provide a ranking of the candidates based on the match).
Although Jackson discloses a proctoring system configured to monitor the interview (Paragraph 0077, Candidate Profile; Paragraph 0058, conducting live interviews using natural language processing techniques; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews), Jackson does not specifically disclose how the system is configured to monitor the behavior of the candidate.
However, Light discloses a proctoring system configured to monitor the behavior of the candidate (Paragraph 0025, Moreover, the evaluation of nonverbal communication during interviews is an area that has not been fully explored or integrated into digital recruitment solutions. Nonverbal cues, such as body language, facial expressions, and tone of voice, can provide valuable insights into a candidate's personality, confidence, and overall demeanor. However, the subjective nature of interpreting these cues and the lack of standardized methods for analysis present challenges in their consistent application within the hiring process; Paragraph 0123, The nonverbal analysis engine 220 employs facial recognition technology to detect and analyze the candidate's facial expressions, or to verify the candidate's identity is consistent during re-assessments. By referencing a database of facial expression markers, the nonverbal analysis engine 220 can discern subtle emotional cues that may not be overtly apparent. Similarly, gesture recognition algorithms interpret the candidate's body language, providing insights into their level of confidence and engagement).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes monitoring the behavior of the candidate of the invention of Light because doing so would allow the system to provide valuable insights into a candidate's personality, confidence, and overall demeanor (see Light, Paragraph 0025). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 16, which is dependent of claim 15, the combination of Jackson and Light discloses all the limitations in claim 15. Although Jackson discloses a system configured to monitor a live interview (see Jackson, Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews), Jackson does not specifically disclose using computer vision for facial recognition and NLP models for audio-based anomaly detection.
However, Light further discloses wherein the proctoring system monitors the behavior of the candidate using computer vision for facial recognition and NLP models for audio-based anomaly detection (Paragraph 0123, The nonverbal analysis engine 220 employs facial recognition technology to detect and analyze the candidate's facial expressions, or to verify the candidate's identity is consistent during re-assessments; Paragraph 0167, The video interview module captures a wide array of visual and auditory data from the candidate during the interview process. This data includes high-resolution video footage and clear audio recordings that are meticulously synchronized to ensure accurate temporal alignment. The rich visual data encompasses a spectrum of candidate behaviors, such as facial expressions, hand gestures, posture shifts, and eye movements, while the audio recordings capture variations in speech patterns, tone, pitch, and cadence; Paragraph 0168, To augment the training data, the video recordings are first processed to extract relevant features. This involves applying computer vision techniques to identify and track facial landmarks, gesture recognition algorithms to catalog hand movements, and speech processing methods to analyze vocal attributes. Each extracted feature is then annotated with descriptive labels that provide context for the machine learning algorithms. For example, a smile may be labeled with its intensity and duration, a hand gesture may be categorized by its type and frequency, and a speech segment may be annotated with prosodic features; Paragraph 0194, For free response questions, the platform 102 incorporates natural language processing (NLP) techniques to analyze the candidate's answers; see provisional application # 63/639,825, filed on 04/29/24, Paragraphs 0094, 0137-0138, & 0163).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes monitoring the behavior of the candidate of the invention of Light because doing so would allow the system to provide valuable insights into a candidate's personality, confidence, and overall demeanor (see Light, Paragraph 0025). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 18, which is dependent of claim 15, the combination of Jackson and Light discloses all the limitations in claim 15. Jackson further discloses wherein the follow-up questions are further based strengths or weaknesses of the candidate (Paragraph 0068, Unlike conventional interview methods, the system actively understands and interacts with the applicant in real-time. The LLM processes the applicant's responses, assesses their relevance and depth, and formulates follow-up questions on the spot. This dynamic interaction allows the system to delve deeper into specific areas of the candidate's expertise, ensuring a comprehensive evaluation of their capabilities. The line of questioning is continuously adjusted based on the applicant's answers, enabling a thorough assessment of their skills, experience, and fit for the role; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; It can be noted that the claim language is written in alternative form. The limitation taught by Jackson is based on strengths of the candidate such as area of expertise).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Jackson (US 2025/0232262 A1), in view of Light (US 2025/0335876 A1), in further view of Edwards (US 12476965 B2).
Regarding claim 17, which is dependent of claim 16, the combination of Jackson and Light discloses all the limitations in claim 16. Although Jackson discloses a candidate profile and a live video interview (Paragraph 0077, Candidate Profile; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews) and Light discloses a computer vision for facial recognition (Paragraph 0123, The nonverbal analysis engine 220 employs facial recognition technology to detect and analyze the candidate's facial expressions, or to verify the candidate's identity is consistent during re-assessments), the combination of Jackson and Light does not specifically disclose wherein facial recognition is performed based on a third-party document having a photograph of the candidate.
However, Edwards discloses wherein facial recognition is performed based on a third-party document having a photograph of the candidate (Column 6, lines 47-67, As described herein, an entity-issued user-specific identification document refers to a physical item that is issued by a specific entity, such as a merchant or service, for a specific user. An illustrative example is an identification (ID) card used to identify a user with a merchant. The entity-issued user-specific identification document may be used to authenticate that user with that entity; Column 11, lines 1-22, In some examples, the trained computer vision model (e.g., computer vision model 212) comprises a facial recognition component, a character recognition component, a logo recognition component, or other components. For example, the facial recognition component of the trained computer vision model (e.g., computer vision model 212) may be trained to compute a facial recognition confidence score indicating a likelihood that at least a portion of the image depicts a face of the user. As depicted in FIG. 4, the captured image depicting candidate entity-issued user-specific identification document 410 may include facial image 412. The facial recognition component of the trained computer vision model may compare facial image 412 to a reference facial image of the user (i.e., an authorized user of the authorized user account attempting to be accessed). As mentioned previously, some examples include one or more identification features being stored in association with each authorized user account, as depicted in FIG. 2A. In one or more examples, an embedding representing a reference facial image of the user (e.g., a known valid image of the authorized user's face) may be stored within using one or more of identification features A-N).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson and Light to further specify how the candidate is authenticated of the invention of Edwards because doing so would allow the system to use a user specific identification document to authenticate the user (see Edwards, Column 11, lines 1-22). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 19-23, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson (US 2025/0232262 A1), in view of Light (US 2025/0335876 A1), in further view of Castro Vilabella et al. (US 2026/0024054 A1).
Regarding claim 19, which is dependent of claim 15, the combination of Jackson and Light discloses all the limitations in claim 15. Jackson further discloses wherein the real-time benchmarking system comprises comparing responses by the candidate to … scoring parameters (Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; Paragraph 0086, FIG. 7 is a slide 700 showing the step of interviewing the talent using the system. The system conducts the first interview using a proprietary set of questions or a set of questions by the user. The system fills out the scorecard and presents the results. The system automatically advances candidates and schedules the next set of interviews. The hiring manager reviews the candidate scorecards and video interviews asynchronously. The hiring manager edits and shares recorded interviews and scorecards).
Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard), Jackson does not specifically disclose wherein the score is dynamic (e.g., score parameters change based on feedback of the user).
However, Castro Vilabella et al. discloses wherein the real-time benchmarking comprises comparing responses by the candidate to dynamic scoring parameters (Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0080, It should be noted that the process steps and/or operations and instructions of the present invention can be embodied in software, firmware, and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by real-time network operating systems; see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9; Examiner interprets “adjusting weight based on feedback” as the “dynamic scoring parameters”).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes dynamic scoring parameters (e.g., retraining) of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 20, which is dependent of claim 19, the combination of Jackson, Light, and Castro Vilabella et al. discloses all the limitations in claim 19. Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard), Jackson does not specifically disclose wherein the score is dynamic (e.g., score parameters change based on feedback of the user).
However, Castro Vilabella et al. further discloses wherein the dynamic scoring parameters are based on speed and precision scores relating to the interview system (Paragraph 0042, Present invention provides an advanced AI-driven solution for optimizing recruitment processes and maximizing the person-job fit accuracy and robustness. The invention integrates multiple data sources and processes to evaluate candidates more precisely, enhancing the accuracy and efficiency of candidate selection; Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0080, It should be noted that the process steps and/or operations and instructions of the present invention can be embodied in software, firmware, and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by real-time network operating systems; It can be noted that the claim language is written in alternative form. The limitation taught by Castro Vilabella et al. is based on precision scores relating to the interviewing step; see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9; Examiner interprets “adjusting weight based on feedback” as the “dynamic scoring parameters”).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes dynamic scoring parameters (e.g., retraining) of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 21, which is dependent of claim 20, the combination of Jackson, Light, and Castro Vilabella et al. discloses all the limitations in claim 20. Jackson further discloses wherein the real-time benchmarking comprises developing a candidate skills model … (Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; Paragraph 0086, FIG. 7 is a slide 700 showing the step of interviewing the talent using the system. The system conducts the first interview using a proprietary set of questions or a set of questions by the user. The system fills out the scorecard and presents the results. The system automatically advances candidates and schedules the next set of interviews. The hiring manager reviews the candidate scorecards and video interviews asynchronously. The hiring manager edits and shares recorded interviews and scorecards).
Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard), Jackson does not specifically disclose wherein the score is dynamic (e.g., score parameters change based on feedback of the user).
However, Castro Vilabella et al. further discloses wherein the real-time benchmarking comprises developing a candidate skills model which is used to compare to the dynamic scoring parameters (Paragraph 0042, Present invention provides an advanced AI-driven solution for optimizing recruitment processes and maximizing the person-job fit accuracy and robustness. The invention integrates multiple data sources and processes to evaluate candidates more precisely, enhancing the accuracy and efficiency of candidate selection; Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0080, It should be noted that the process steps and/or operations and instructions of the present invention can be embodied in software, firmware, and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by real-time network operating systems; see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9; Examiner interprets “adjusting weight based on feedback” as the “dynamic scoring parameters”).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes dynamic scoring parameters (e.g., retraining) of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 22, which is dependent of claim 15, the combination of Jackson and Light discloses all the limitations in claim 15. Jackson further discloses wherein the stack-ranking using a [artificial intelligence] (Paragraph 0059, Furthermore, this AI agent is not just a passive screener. It is designed with the capability to provide real-time feedback to the candidates during the live interviews. This real-time feedback can include, but is not limited to, clarifications on the candidate's responses, suggestions for improvement, and immediate evaluation of the candidate's performance; Paragraph 0061, Furthermore, this AI matching system is not just a passive matcher. It is designed with the capability to provide a ranking of the candidates based on the match. This ranking process is facilitated by advanced machine learning algorithms that evaluate the match between the candidates and the job based on the comprehensive set of parameters. The AI matching system can generate a ranking of the candidates, with the top-ranked candidates being those who are the closest match to the job. This ranking provides the company with a prioritized list of candidates, thereby assisting the company in making informed hiring decisions. This ranking capability of the AI matching system contributes to the system's overall effectiveness and efficiency in talent acquisition and management; Paragraph 0086, The AI sourcing agent sources candidates for a new job and sources candidates from multiple platforms and to screen resumes, profiles, conduct live interviews, and build candidate scorecards is further configured to provide real-time feedback to the candidates. The AI matching system is configured to match candidates to jobs and to provide a ranking of the candidates based on the match).
Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard and generating a ranking), Jackson does not specifically disclose wherein the evaluation is performed using a neural network algorithm.
However, Castro Vilabella et al. discloses wherein the stack-ranking system using a neural networks algorithm (Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0063, Finally, the candidates are ranked (see FIG. 2) based on the fit percentage (107); see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation is performed using a neural network algorithm of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 23, which is dependent of claim 22, the combination of Jackson, Light, and Castro Vilabella et al. discloses all the limitations in claim 22. Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard and generating a ranking), Jackson does not specifically disclose wherein the stack-ranking system further uses a weighted scoring matrix in conjunction with the neural networks algorithm.
However, Castro Vilabella et al. discloses wherein the stack-ranking system further uses a weighted scoring matrix in conjunction with the neural networks algorithm (Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0063, Finally, the candidates are ranked (see FIG. 2) based on the fit percentage (107); see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation is performed using a neural network algorithm of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 25, Jackson discloses a talent management system comprising (Paragraph 0004, The present disclosure generally relates to systems and methods for automated talent acquisition and management using artificial intelligence and machine learning to ingest data, generate job descriptions, source candidates, screen resumes, conduct interviews, match candidates to jobs, and manage payment of employees and contractors):
a trained large language model (LLM)-based interview system having audio and video inputs and audio outputs, wherein the interview system is configured to interact with a candidate using adaptive testing, the interview system comprising; a dynamic questionnaire system configured to adaptively test the candidate, wherein the dynamic questionnaire is configured to ask a series of questions in which one or more questions are based on answers supplied by the candidate and further based on strengths and weaknesses of the candidate (Paragraph 0068, Unlike conventional interview methods, the system actively understands and interacts with the applicant in real-time. The LLM processes the applicant's responses, assesses their relevance and depth, and formulates follow-up questions on the spot. This dynamic interaction allows the system to delve deeper into specific areas of the candidate's expertise, ensuring a comprehensive evaluation of their capabilities. The line of questioning is continuously adjusted based on the applicant's answers, enabling a thorough assessment of their skills, experience, and fit for the role; Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; It can be noted that the claim language is written in alternative form. The limitation taught by Jackson is based on strengths of the candidate such as area of expertise);
a proctoring system configured to monitor the [interview] (Paragraph 0058, Another integral component of the system is an AI agent that is configured to screen resumes, profiles, conduct live interviews, and build candidate scorecards. This AI agent operates by analyzing the resumes and profiles of the sourced candidates, conducting live interviews using natural language processing techniques, and building scorecards that evaluate the candidates based on various criteria. The criteria can include, but are not limited to, the candidate's skills, experience, qualifications, and alignment with the company's culture and values. This comprehensive screening process allows the AI agent to identify candidates who are not just qualified for the job, but also a good fit for the company);
a real-time benchmarking system configured to compare candidate performance data collected from the interview system, wherein the real-time benchmarking system comprises developing a candidate skills model which is used to compare to … scoring parameters (Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews, the system including an interview module using a marketplace large language model trained with proprietary data from the a marketplace using the system, the system actively understanding and interacting with the applicant in real-time, wherein the marketplace large language model processes responses of the applicant, assesses relevance and depth, and formulates follow-up questions dynamically, wherein the interaction between the applicant and the interview module allows the system to focus on specific areas of expertise of the candidate, ensuring a comprehensive evaluation of the applicant capabilities and wherein the line of questioning is continuously adjusted based on the applicant answers, enabling a thorough assessment of their skills, experience, and fit for the role. The system may ensure every candidate is assessed comprehensively, reducing the risk of overlooking critical competencies and providing hiring; Paragraph 0086, FIG. 7 is a slide 700 showing the step of interviewing the talent using the system. The system conducts the first interview using a proprietary set of questions or a set of questions by the user. The system fills out the scorecard and presents the results. The system automatically advances candidates and schedules the next set of interviews. The hiring manager reviews the candidate scorecards and video interviews asynchronously. The hiring manager edits and shares recorded interviews and scorecards);
and a stack-ranking system configured to rank the candidate with respect to other candidates, … (Paragraph 0059, Furthermore, this AI agent is not just a passive screener. It is designed with the capability to provide real-time feedback to the candidates during the live interviews. This real-time feedback can include, but is not limited to, clarifications on the candidate's responses, suggestions for improvement, and immediate evaluation of the candidate's performance; Paragraph 0061, Furthermore, this AI matching system is not just a passive matcher. It is designed with the capability to provide a ranking of the candidates based on the match. This ranking process is facilitated by advanced machine learning algorithms that evaluate the match between the candidates and the job based on the comprehensive set of parameters. The AI matching system can generate a ranking of the candidates, with the top-ranked candidates being those who are the closest match to the job. This ranking provides the company with a prioritized list of candidates, thereby assisting the company in making informed hiring decisions. This ranking capability of the AI matching system contributes to the system's overall effectiveness and efficiency in talent acquisition and management; Paragraph 0086, The AI sourcing agent sources candidates for a new job and sources candidates from multiple platforms and to screen resumes, profiles, conduct live interviews, and build candidate scorecards is further configured to provide real-time feedback to the candidates. The AI matching system is configured to match candidates to jobs and to provide a ranking of the candidates based on the match).
Although Jackson discloses a system configured to monitor a live interview (see Jackson, Paragraph 0084, The system may perform synchronous live video interviews or live audio interviews), Jackson does not specifically disclose using computer vision for facial recognition and NLP models for audio-based anomaly detection.
However, Light discloses a proctoring system configured to monitor the behavior of the candidate wherein the proctoring system monitors the behavior of the candidate using computer vision for facial recognition and NLP models for audio-based anomaly detection (Paragraph 0123, The nonverbal analysis engine 220 employs facial recognition technology to detect and analyze the candidate's facial expressions, or to verify the candidate's identity is consistent during re-assessments; Paragraph 0167, The video interview module captures a wide array of visual and auditory data from the candidate during the interview process. This data includes high-resolution video footage and clear audio recordings that are meticulously synchronized to ensure accurate temporal alignment. The rich visual data encompasses a spectrum of candidate behaviors, such as facial expressions, hand gestures, posture shifts, and eye movements, while the audio recordings capture variations in speech patterns, tone, pitch, and cadence; Paragraph 0168, To augment the training data, the video recordings are first processed to extract relevant features. This involves applying computer vision techniques to identify and track facial landmarks, gesture recognition algorithms to catalog hand movements, and speech processing methods to analyze vocal attributes. Each extracted feature is then annotated with descriptive labels that provide context for the machine learning algorithms. For example, a smile may be labeled with its intensity and duration, a hand gesture may be categorized by its type and frequency, and a speech segment may be annotated with prosodic features; Paragraph 0194, For free response questions, the platform 102 incorporates natural language processing (NLP) techniques to analyze the candidate's answers; see provisional application # 63/639,825, filed on 04/29/24, Paragraphs 0094, 0137-0138, & 0163);
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation includes monitoring the behavior of the candidate of the invention of Light because doing so would allow the system to provide valuable insights into a candidate's personality, confidence, and overall demeanor (see Light, Paragraph 0025). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard), the combination of Jackson and Light does not specifically disclose wherein the score is dynamic (e.g., score parameters change based on feedback of the user).
However, Castro Vilabella et al. discloses a real-time benchmarking system configured to compare candidate performance data collected from the interview system, wherein the real-time benchmarking system comprises developing a candidate skills model which is used to compare to dynamic scoring parameters ((Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0080, It should be noted that the process steps and/or operations and instructions of the present invention can be embodied in software, firmware, and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by real-time network operating systems; see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9; Examiner interprets “adjusting weight based on feedback” as the “dynamic scoring parameters”);
and a stack-ranking system configured to rank the candidate with respect to other candidates, wherein the stack-ranking system uses a weighted scoring matrix in conjunction with a neural networks algorithm (Paragraph 0044, FIG. 1 schematically shows the different sections in which the present invention can be divided to evaluate candidates of a recruitment process for a job position. As seen in the figure, each section or module performs a distinct function within the evaluation process, ranging from data collection and processing to comparison and determination of the candidate's suitability for the job position; Paragraph 0045, First of all, data from at least one first interview of the recruitment process is collected (101); Paragraph 0061, Once the similarity measures across various domains are calculated, they are aggregated and weighted based on the hiring manager's defined priorities. Additionally, if desired, the hiring manager will be able to prioritize relevant skills or information (hard skills, soft skills, salary, experience, education . . . ), and those domains will thus have more weight for the final decision; Paragraph 0062, An aggregation layer (106) is then used to convert the similarity measures into a single weighted score that indicates the (Person-Job) fit percentage (107) using an aggregation model. This phase involves assigning weights to different similarity scores according to the importance of various domains, such as hard skills, soft skills, etc., specified by the hiring managers. The weighted scores are then summed up to produce the overall fit percentage (107). This aggregated score reflects the comprehensive evaluation of a candidate across all relevant aspects, ensuring a more accurate person-job fit. The aggregation model may comprise a neural network, including a multi-layer perceptron, MLP, which starting from initial weights assigned by the hiring managers is further retrained using reinforcement learning with human feedback, that is, learning from the human feedback to the results of the model; Paragraph 0063, Finally, the candidates are ranked (see FIG. 2) based on the fit percentage (107); see provisional application # 19/272,362, filed on 07/18/24, Pages 7-9).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation is performed using a neural network algorithm of the invention of Castro Vilabella et al. because doing so would allow the system to provide a comprehensive evaluation of a candidate across all relevant aspects (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Castro Vilabella et al., Paragraphs 0061-0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Jackson (US 2025/0232262 A1), in view of Light (US 2025/0335876 A1), in further view of Castro Vilabella et al. (US 2026/0024054 A1) and Hingne et al. (US 2022/0253788 A1).
Regarding claim 24, which is dependent of claim 23, the combination of Jackson, Light, and Castro Vilabella et al. discloses all the limitations in claim 23. Although Jackson discloses an artificial intelligent algorithm for evaluating a candidate based on real-time benchmarking of the candidate (Paragraphs 0084 & 0086, assessment of candidate skills by filing out a scorecard and generating a ranking), Jackson does not specifically disclose wherein the scorecard is displayed as a heatmap.
However, Hingne et al. discloses wherein the stack-ranking system creates heatmaps based on relevant skills of the candidate (Paragraph 0089, For an agent, an agent skill profile may be created as a unique view that shows the details of the skills, skill distributions, and changes in skills over time based on the agent's experience. This profile view further keeps a count of the contacts, calls, and other interactions that the agent handled in a particular time period. Further, the profile view provides a heat map of the agent experience with different skills as compared between those different skills, jobs, agents (e.g., peer agents within a group, organization, or the like), or any combination thereof. The processing job to determine the agent skill profile may also be run once a month and may reference back to previous months; Paragraph 0099, The comparison may therefore correspond to a ranking of the agent's BAV score to the BAV score of the other agents (e.g., a score comparison, a percentile that the agent may fall in, a numeric or tiered ranking, or the like). The comparison may also compare each KPI of the agent to the other agents so that individual skills may be compared. This may include a heatmap or other visualization for comparison of agent skills).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the talent management system used for evaluating a candidate based on real-time benchmarking of the candidate (e.g., by using an AI agent) of the invention of Jackson to further specify wherein the evaluation is displayed as a heatmap of the invention of Hingne et al. because doing so would allow the system to provide a heat map of the agent/applicant experience with different skills as compared between those different skills, jobs, agents (e.g., hard skills, soft skills, etc.), ensuring a more accurate person-job fit (see Hingne et al, Paragraph 0089). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Barril et al. (US 2024/0020645 A) – discloses a score and/or weight of the response can then be used to evaluate the candidate. For example, the BARS generator and assessment model 105 can be configured to combine the score of responses to each question the candidate was asked during the interview to determine a final score for the candidate. For instance, the BARS generator and assessment model 105 can determine the final score by adding the score given to each response the candidate gave during the interview. Additionally or alternatively, the BARS generator and assessment model 105 can be configured to weigh the scores given to each response based on the skill set for the job position. For instance, for a customer-facing position, the scores given to responses about customer service can be given a higher weight in comparison to scores given to responses about managing a team (see at least Paragraph 0040).
Mishra (US 2025/0328869 A1) – discloses a candidate resume analysis module 132 (also “resume analyzer module”) that extracts work history and other experiences of a candidate, via text data of their resume, and crafts questions to ask the candidate 101 based on that extracted data. The system 100 may include an interactive interview agent module 134 (“interactive interview module”) that may be functionally coupled to the curated question library of the system database 120, the job benchmark analysis module 131 and the candidate resume analysis module 132. Using these and data generated by these modules, the interactive interview agent module 134 conducts a live interview with the candidate 101 using a smart phone app or portal-based user interface module running on a client device 400. This interactive interview agent module 134 may adjust the sequencing of the questions or modify the list of questions dynamically based on answers provided by the candidate 101 to earlier questions and based on the skill levels required for the job. The interactive interview agent module 134 also stores the audio and video transcripts of the interview. The system 100 may include a transcript analyzer assessment module 135 (also “transcript analyzer module”) that may be functionally coupled to the interactive interview agent module 134 and the transcript audio and video files created by it. Using Large Language Models (LLMs), the transcript analyzer assessment module 135 reviews the contents of the transcript audio and video files (transcripts) for both the verbatim responses as well as the tone of the candidate 101. It scores the candidate 101 against each of the required skills (see at least Paragraphs 0036-0037).
Park (KR 102729801 B1) – discloses a computer device that provides interview answer consulting information, it can input a type of specific question and a user answer into a pre-learned neural network model to obtain a completed answer and evaluation information related to the user answer. Then, the computing device (100) can provide the completed answer and evaluation information obtained from the neural network model as interview answer consulting information to the user terminal (200). That is, the interview answer consulting information can further include evaluation information. In the present invention, the evaluation information may include scores and expected passing rates determined based on job fit, company fit, answer length, structure fit, and sentence completeness (see at least Page 3).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJORIE PUJOLS-CRUZ whose telephone number is (571)272-4668. The examiner can normally be reached Mon-Thru 7:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia H Munson can be reached at (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARJORIE PUJOLS-CRUZ/Examiner, Art Unit 3624