DETAILED ACTION
This Non-Final Office Action is in response to the arguments and amendments filed May 21, 2026.
Claims 1-20 are Originals.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 1-3, 7-11, 13, 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jose et al [US2022,017,2147A1] hereafter Jose, in view of Pinel et al [US2020,0314,458A1], hereafter Pinel, in further view of Dangi et al [US2021,015,8302A1] hereafter Dangi.
As per claim 1, 9 and 17 (Similar scope and language)
Jose teaches;
A method for evaluating candidates through Artificial Intelligence (AI) models, the method comprising: receiving, by a computing device, input data comprising video data and audio data corresponding to an interview of a candidate, wherein the video data comprises a plurality of frames;
{[0027] In an embodiment, the computing system 112 is configured to receive the one or more interviews captured by the one or more image capturing devices 108 and the one or more microphones 110. The computing system 112 extracts audio and video data from the received one or more interviews between the interviewer and the candidate. Further, the computing system 112 also identifies one or more key segments from a plurality of segments.}
Jose discloses generating parameters;
and generating, by the computing device, a score corresponding to each of the set of predefined parameters of the candidate using the first self-learning AI model and the second self-learning AI model based on the comparison.
{[0038] The score card generation module 218 is configured to generate a score card associated with the interviewer including one or more interviewer profile parameters based on the determined one or more attributes and predefined criteria by using the interview optimization-based AI model.
{[0045] At step 310, a score card associated with the interviewer including one or more interviewer profile parameters is generated based on the determined one or more attributes and predefined criteria by using the interview optimization-based AI mode.
Jose does not explicitly disclose the self-learning AI model, comparing an information to reach a threshold, however; Pinel discloses;
extracting in near real-time, by the computing device, a set of video features from each of the plurality of frames of the video data using a first self-learning AI model, and a set of audio features from the audio data using a second self-learning AI model, wherein the set of video features and the set of audio features correspond to a set of predefined parameters;
[0046] Using the operations described above, the computing device(s) 112 are able to efficiently (e.g., using relative few processing resources) and reliably detect and classify events represented in multimedia content 110. In some implementations, the computing device(s) 112 can perform the event classification in real-time or near-real time. For example, the multimedia data 110 can include or correspond to a compressed multimedia stream having a first frame rate. In this example, a first segment of the compressed multimedia stream can be decoded (or decoded and decompressed) during a first time period to generate an audio signal (e.g., the audio data 118) and a video signal (e.g., the video data 116) for a multimedia output device (e.g., the output device 156). In this example, the trained event classifier 152 can generate output (e.g., the event label(s) 154) indicating a classification assigned to a particular event detected in the first segment of the compressed multimedia stream during the first time period and the audio signal, the video signal, and the output indicating the classification assigned to the particular event can be concurrently provided to the multimedia output device at the first frame rate for output to a user.
Pinel discloses;
comparing, by the computing device, the set of video features and the set of audio features with self-adjusting threshold values corresponding to the set of predefined parameters;
{[0019] If the multimedia data 110 uses such a media compression technique, the media pre-processor 114 includes circuitry to enable decompression of the multimedia data 110 by, for example, buffering some frames of video content from the media data, comparing some compressed frames to other frames to reproduce uncompressed versions of the compressed frames, etc. The media pre-processor 114 can also, or in the alternative, perform audio decompression if audio content of the multimedia data 110 is compressed.}
Motivation: It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of evaluating candidates as disclosed by Jose with the inclusion of analyzing data in real-time as taught by Pinel. Jose consisted references teaching receiving interviews, extracting audio and videos in real time using Artificial intelligence, determining attributes associated to the interviews and generating score cards associated with the interview parameter. Pinel also included analyzing in real time and comparing the information to reach a threshold, extracting in real time using a first and second self-supervised module.
The combination of Jose and Pinel does not disclose the screening method; however, Dangi discloses the following;
a score corresponding to each of the set of predefined parameters of the candidate using the first self-learning AI model and the second self-learning AI model based on the comparison.
{[0063] As set forth above, AI/ML engine 27 may assist with the screening and selection of candidate profiles. The AI/ML engine 27 may include an algorithm that parses resumes to extract information relevant to the position requisition. Alternatively, the AI/ML engine may access and assess parsed resume data received through an API. The AI/ML engine 27 may also access database 10 to access data that may include similar position requisitions and data associated with successful candidates that filled such similar position, such as educational background, years of experience, industry classifications and other data. The algorithm may be seeded with such data and continually learn which candidate profiles tend to be most successful. The AI/ML engine 27, after selecting such candidate profiles, may then receive additional inputs such as those described above with reference to FIG. 1e , to form a candidate skills model 50 for each candidate profile. The AI/ML engine 27 may then use predictive analytics models, such as forecast models, to predict which candidates will likely be most successful and to rank those candidate profiles based on a numerical score value. Other predictive analytics models may also be used, including, for example, a classification model in which the AI/ML engine 27 separates the data into categories that are applicable to previously successful candidates and then compares the data associated with the current selection of candidate profiles to the data classifications to determine whether a particular candidate is likely to be successful. Clustering models and others may also be used by the AI/ML engine 27.
Motivation: It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of evaluating candidates as disclosed by Jose and Pinel with the inclusion of a screening method as taught by Dangi.
The combination of Jose and Pinel, references receiving interviews, extracting audio and videos in real time using Artificial intelligence, determining attributes associated to the interviews and generating score cards associated with the interview parameter and comparing the information to reach a threshold. Dangi added to the references by screening the candidates.
As per claim 2, 10 and 18 (Similar scope and language)
Jose discloses;
The method of claim 1, further comprising generating a report for the candidate, wherein the report comprises the set of predefined parameters and the score corresponding to each of the set of predefined parameters.
{[0006] Furthermore, the plurality of modules include a score card generation module configured to generate a score card associated with the interviewer including one or more interviewer profile parameters based on the determined one or more attributes and predefined criteria by using the interview optimization-based AI model.
[0007] Also, the method includes generating a score card associated with the interviewer including one or more interviewer profile parameters based on the determined one or more attributes and predefined criteria by using the interview optimization-based AI model.
[0027] The computing system 112 generates a score card associated with the interviewer including one or more interviewer profile parameters based on the determined one or more attributes and predefined criteria by using the interview optimization-based AI model.}
As per claim 3, 11 and 19 (Similar scope and language)
Jose discloses;
The method of claim 1, wherein the set of predefined parameters comprises soft skill attributes, communication skill attributes, body language attributes, and knowledge attributes.
{[0034] The one or more key segments are sections of the plurality of segments in which relevant topics are discussed, such as qualification, experience, soft skills of the candidate and the like.
[0035] Body language and communication effectiveness is analyzed.}
As per claim 7 and 15 (Similar scope and language)
Jose discloses;
The method of claim 1, further comprising: extracting the video data from the input data; and extracting the audio data from the input data.
{[0027] The computing system 112 extracts audio and video data from the received one or more interviews between the interviewer and the candidate. Further, the computing system 112 also identifies one or more key segments from a plurality of segments. The plurality of segments are identified from the extracted audio data corresponding to the interviewer and the candidate. The computing system 112 determines one or more sentiment parameters for the interviewer and the candidate by analyzing the extracted video data. The one or more sentiment parameters include emotion, attitude, thought of the interviewer and the candidate and the like. Furthermore, the computing system 112 determines one or more attributes associated with the one or more interviews based on the extracted audio data, the extracted video data, the one or more key segments, the one or more sentiment parameters, job description, resume of the candidate or any combination thereof by using an interview optimization based Artificial Intelligence (AI) model.
Claim(s) 5, 8, 13, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jose et al, in view of Pinel et al, in view of Dangi et al; in further view of Olshansky et al [US2022,009,2548A1], hereafter Olshansky.
As per claim 5 and 13 (Similar scope and language)
Jose does not disclose receiving of data in real-time; however, Olshansky discloses the following;
The method of claim 1, further comprising receiving in real-time, the input data from a camera.
{[0015] In an embodiment, a method of connecting an employer with a candidate, is provided. The method can include receiving, at a system server, criteria data from the employer regarding a job opening, wherein the criteria data from the employer includes minimum attributes and real-time connection attributes. The method can include receiving, at the system server, background data from the candidate. The method can include recording audio data and video data of the candidate in a video interview of the candidate in a booth with a first camera, a second camera, and a microphone. The method can include recording behavioral data of the candidate with at least one depth sensor disposed in the booth. The method can include analyzing prior to an end of the video interview, at the system server, the audio data of the candidate with speech-to-text analysis to identify textual interview data, wherein candidate data includes the textual interview data and the background data. The method can include analyzing prior to the end of the video interview, at the system server, the behavioral data of the candidate to identify behavioral interview data, wherein the candidate data further includes the behavioral data.
Motivation: It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of evaluating candidates as disclosed by Jose, Pinel, and Dangi with the inclusion of receiving data in real-time as taught by Olshansky. Olshansky also included using cameras and microphone to receive data in real time, to enable analyzing the candidate behavioral data.
As per claim 8 and 16 (Similar scope and language)
The combination of Jose, Pinel, Dangi does not disclose the storing of video and audio data separately; however, Olshansky discloses the following;
The method of claim 1, further comprising: storing the video data in a video repository; and storing the audio data in an audio repository.
{[0085] To save storage space, audio and video compression formats can be utilized when storing data 862. These can include, but are not limited to, H.264, AVC, MPEG-4 Video, MP3, AAC, ALAC, and Windows Media Audio. Note that many of the video formats encode both visual and audio data. To the extent the microphones 220 are integrated into the cameras, the received audio and video data from a single integrated device can be stored as a single file. However, in some embodiments, audio data is stored separately the video data.
Motivation: It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of evaluating candidates as disclosed by Jose, Dangi and Mowbray with the inclusion of storing the video and audio data separately as taught by Olshansky. The combination of Jose and Dangi, references receiving interviews, extracting audio and videos in real time using Artificial intelligence, determining attributes associated to the interviews and generating score cards associated with the interview parameter, comparing the information to reach a threshold and screening the candidates. Olshansky also included a storage medium, to enable storing the audio and video formats in separate repository.
Claim(s) 4, 12, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jose et al, in view of Pinel et al, in view of Dangi et al; in further view of Mowbray et al [US2024,021,1888], hereafter Mowbray.
As per claim 4, 12 and 20 (Similar scope and language)
The combination of Jose, Pinel and Dangi does not disclose the Natural Language Processing (NLP) model; however, Mowbray discloses the following;
The method of claim 1, wherein the second self-learning AI model is a Natural Language Processing (NLP) model.
{[0024] In embodiments, the employment matching system 102 can provide one or more job matching and/or searching algorithms to identify potential employment opportunities for candidates, e.g., the user 104. The employment matching system 102 can utilize one or more weighted scoring algorithms for determining matches for potential employment opportunities, as described below. The employment matching system 102 can utilize one or more trained machine learning algorithms. The one or more trained machine learning algorithms can utilize natural language processing (NLP) techniques to match jobs to candidates.
Motivation: It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of evaluating candidates as disclosed by Jose, Pinel and Dangi with the inclusion of a Natural Language Processing (NLP) model as taught by Mowbray. The combination of Jose, Pinel and Dangi, references receiving interviews, extracting audio and videos in real time using Artificial intelligence, determining attributes associated to the interviews and generating score cards associated with the interview parameter, comparing the information to reach a threshold and screening the candidates. Mowbray included an employment matching system using a Natural Language Processing (NLP) model.
Claim(s) 6, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jose et al, in view of Pinel et al, in view of Dangi et al; in view of Olshansky et al, in further view of Mowbray et al [US2024,021,1888], hereafter Mowbray.
As per claim 6 and 14 (Similar scope and language)
The combination of Jose, Pinel, Olshansky and Dangi does not disclose the training of the AI system; however, Mowbray discloses the following;
The method of claim 5, further comprising training the first self-learning AI model and the second self-learning AI model using the input data received in real-time.
{[0024] In embodiments, the employment matching system 102 can provide one or more job matching and/or searching algorithms to identify potential employment opportunities for candidates, e.g., the user 104. The employment matching system 102 can utilize one or more weighted scoring algorithms for determining matches for potential employment opportunities, as described below. The employment matching system 102 can utilize one or more trained machine learning algorithms. The one or more trained machine learning algorithms can utilize natural language processing (NLP) techniques to match jobs to candidates. For example, various data points (e.g. job title and job description) embeddings are generated and then a cosine similarity is used to score the similarity between user and job profiles. Jobs with the highest scores will be recommended to the candidates. The one or more machine learning algorithms can utilize self-learning processes to tune the one or more machine learning algorithms to specific users, specific employers, specific job fields, specific demographics, and the like.
Motivation: It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of evaluating candidates as disclosed by Jose Olshansky and Dangi with the inclusion of training the AI system as taught by Mowbray. The combination of Jose, Olshansky and Dangi, references receiving interviews, extracting audio and videos in real time using Artificial intelligence, determining attributes associated to the interviews and generating score cards associated with the interview parameter, comparing the information to reach a threshold and screening the candidates. Mowbray included a trained machine learning algorithms using a Natural Language Processing (NLP) model to achieve matching the employment system.
Response to Argument
Applicant’s arguments, see response, filed May 21, 2026, with respect to the rejection(s) of claim(s) 1-20 under 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art reference(s), Pinel et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Zhang et al; [CN111770299B], discloses a real-time face abstract service method and a real-time face abstract service system for an intelligent video conference terminal, and belongs to the technical field of face recognition. The invention includes initializing a model; acquiring a video frame; carrying out face detection on the preprocessed frame image by using a face detection model.
H. Solieman and E. A. Pustozerov, "The Detection of Depression Using Multimodal Models Based on Text and Voice Quality Features," 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), St. Petersburg, Moscow, Russia, 2021, pp. 1843-1848.
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/VICTOR ESONU/
Examiner, Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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