DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1, 10 and 19 have been amended and are hereby entered.
Claims 7 – 9 and 16 – 18 were cancelled.
Claims 1 – 6, 10 - 15 and 19 - 20 are pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicant's arguments filed September 9, 2025 have been fully considered but they are not persuasive.
Regarding the applicant's arguments against the 101 rejection of pending claims on pages 8 -9: Applicant’s arguments directed to Step 2B was considered. However, these arguments are not persuasive and the examiner respectfully disagrees for the following reasons:
For Step 2B starting in p. 14: The Applicant argues that pending claim 1 is directed to an improvement to a technological field and that it “defines a technique for detecting whether an interviewee has used external resources during an interview” which is “exclusively applicable to the use of computers to conduct the virtual interviews”. However, the Examiner finds this argument unpersuasive and respectfully disagrees. Because as the Applicant asserted, the claims are reciting the use of “computers” to perform the claimed functions to “conduct virtual interviews” which due to the high level of generality that the computer is recited, their corresponding claim limitations are still considered to be invoking “computers merely as a tool” to perform the business process in which is merely adding the words “apply it” to the judicial exception (see MPEP 2106.05(a)(I) and 2106.05(f)(2) & (3)). Therefore, these limitations and their additional elements, individually and in combination, are not “significantly more” as these are recited in a high level of generality that cannot provide an inventive concept at Step 2B, and are not integrating the abstract idea into a practical application. (see MPEP 2106.05).
Thus, due to these reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC § 103 for the pending claims on pages 9 – 13: Applicant’s arguments regarding these amended limitation steps in the pending claims are not persuasive. Because Applicant’s arguments from pp. 11 – 12 from Remarks regarding the new limitations related to “using the video data to identify mouse exits and a mismatch between eye and cursor movements” did not rely on any reference applied in the previous prior rejection of record for any teaching or matter specifically challenged in the argument. Rather, Wang reference still teaches the use and processing of video data to “identify a pattern of interaction of the interview with a computing device that is used by the interviewee” to further indicate that the “interviewee was using external resources during the interview”, as claimed and as further referenced in 35 USC § 103 rejection maintained herein. Therefore, the Examiner respectfully disagrees, and maintains 35 USC § 103 rejection for these pending claims.
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 - 6, 10 - 15 and 19 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of this claimed invention recited in the claims begins in view of independent claim 10, the most representative claim of the independent claims set 1, 10 and 19, as follows:
At Step 1: Claim 1 falls under statutory category of a process. While claims 10 and 19 are considered to be machines.
At, Step 2A Prong 1: Claim 10 (representative of claims 1 and 19) recites an abstract idea, which is defined by the following underlined elements (e.g. functional steps) while omitting any hardware components (e.g. represented as “…”):
receiving a recording of a virtual interview, the recording including an audio of the virtual interview and a video of the virtual interview;
retrieving a profile image of a candidate;
determining an appearance of an interviewee in the video of the virtual interview;
processing the recording to identify a pattern of interaction of the interview…that is used by the interviewee to conduct the virtual interview, wherein identifying the pattern of the interaction includes detecting mouse exits performed by the interviewee and a mismatch between eye movements of the interviewee and a mouse cursor of the interviewee;
responsive to a determination that the appearance of the interviewee and profile image do not match, including an indication that the candidate did not participate in the virtual interview in an assessment of authenticity of the candidate;
responsive to a determination that there is a mismatch between the eye movements of the interviewee and a mouse cursor of the interviewee, and that there are mouse exits present in the pattern of interaction, including in the assessment of authenticity, an[[d]] indication that the interviewee was using external resources during the interview; and
sending a report of the assessment of the authenticity of the candidate to a computing device.
Generally, and as disclosed in the specification in ¶0053 from Applicant specifications, this invention “may analyze the images of the video stream provided by the interviewee’s client to determine whether the appearance of the interviewee participating in the interview (e.g., appearance of interviewee 412 in the images) is the same as the appearance of a candidate who applied for the position with the organization” for detect candidate fraud detection. However, the abstract idea(s) of a method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) is recited in claim 10 in the form of “commercial or legal interactions”. Specifically, the steps of “determining an appearance of an interviewee” and “processing the recording to identify a pattern of interaction of the interview… detecting mouse exits performed by the interviewee and a mouse cursor of the interviewee” that responsive to the determination, the subsequent step includes “…an indication that the candidate did not participate in the virtual interview” and “…indication that the interviewee was using external resources during the interview” falls under the abstract idea of mental processes, which can be practically be performed in the human mind (See MPEP 2106.04(a)(2), subsection III). Because determining interviewee’s appearance and interaction patterns by detecting mouse exits from the interviewee encompass observation, evaluation and judgement and/or opinion. While steps directed to “…including an indication that the candidate did not participate in the virtual interview” in an “assessment of authenticity of the candidate” and an “…indication that the interviewee was using external resources during the interview” is directed to a final evaluation and/or judgement.
Step 2A Prong 2: For independent claims 1, 10 and 19, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of a computing device (from claim 1, 10 and 19); one or more non-transitory machine-readable mediums and one or more processors (from claims 10 and 19). These additional elements, individually and in combination, and while considering the claims as a whole, are merely are used as a tool to perform the abstract idea (See MPEP 2106.05 (f)). Specifically, these steps of “determining an appearance of an interviewee…”, “processing the recording to identify a pattern of interaction of the interview…”, “responsive to the determination…including an indication that the candidate did not participate in the virtual interview” and an “…indication that the interviewee was using external resources during the interview” are recited as being performed by the computer. The computer used is recited at a high level of generality that is being used as a tool to perform the generic computer functions for sending a report of “the assessment of the authenticity of the candidate” based on the interviewee’s appearance determination.
As for the step of “receiving a recording of a virtual interview…”, “retrieving a profile image of a candidate” and “sending a report of the assessment of the authenticity of the candidate” in the representative claim 10 is really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)). Thus, in these limitation steps, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer.
Therefore, this analysis is indicative of the fact that even when viewed in combination, the claims’ additional elements do not integrate the abstract idea or judicial exception into a practical application.
Step 2B: For independent claims 1, 10 and 19, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: a computing device (from claim 1, 10 and 19); one or more non-transitory machine-readable mediums and one or more processors (from claims 10 and 19) are not sufficient to amount significantly more than the judicial exception. These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract idea(s), which cannot provide an inventive concept. Also, the recitation of a computer to perform the claim limitations (e.g. claim steps a – e) amounts to no more than mere instructions to apply the exception using a generic computer component. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 2-6, 11-15 and 19 - 20, the same analysis is incorporated. Due to their dependency to the independent claims analyzed, these claims cover or fall under the same abstract idea(s) of a method of organizing human activity and mental processes. They describe additional limitations steps of:
Claims 2-6, 11-15 and 19 - 20: further describes the abstract idea of the candidate fraud detection method and how user’s appearance, image, speech pattern and body language information is stored and determined based on video and audio from the virtual interview, determining past candidate attendance and whether the candidate is participating in an independent manner to determine if is the same candidate interviewed and indicate that the candidate does not appear to be the same and/or participate independently, if these data don’t match or differ. Thus, being directed to the abstract idea group of “commercial or legal interactions” and “managing personal behavior or relationships or interactions between people” as it encompasses business relations and monitoring candidates’ social activities as well as covering concepts performed in the human mind or with a pen and paper, including observation, evaluation, judgment, and opinion.
Step 2A Prong 2 and Step 2B: For dependent claims 2 – 4 and 11 – 13, these claims recite the additional elements of: a knowledge repository. This additional element recited is invoking computers merely used as a tool to perform or “apply” the abstract idea(s) to the existing process for storing information about the appearance of the interviewee. Thus, amounting to no more than mere instructions to “apply” the exception using a generic computer component (MPEP 2106 .05(f) and (f)(2)). Accordingly, for the same reasons stated above, these additional element(s) claimed cannot provide an inventive concept at Step 2B.
Finally, the additional elements previously mentioned above, are nothing more than descriptive language about the elements that define the abstract idea, and these claims remain rejected under 101 as well.
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.
Claims 1 - 6, 10 - 15 and 19 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tagra (U.S. Pub No. 20210385212 A1) in view of Wang (WO Pub No. 2017152425 A1) in further view of Shea (U.S. Pub No. 20230033257 A1).
Regarding claims 1, 10 and 19:
This independent claim set is represented by claim 10.
Tagra teaches:
one or more non-transitory machine-readable mediums configured to store instructions; and one or more processors configured to execute the instructions stored on the one or more non-transitory machine-readable mediums, wherein execution of the instructions causes the one or more processors to carry out a process comprising: (See Fig. 1 (102); Fig. 2 (102 and 202). Refer to ¶0027 wherein a system with multiple “microprocessors” or “microcomputers” that among “other capabilities, the at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 206” which further includes “any computer-readable medium or computer program product known in the art” (see ¶0029).)
receiving a recording of a virtual interview, the recording including an audio of the virtual interview and a video of the virtual interview; (In ¶0047; Fig. 3 (302 – 304); Fig. 4 (310): teaches that “at block 302, the data of the communication between one or more participants is received by the receiving module 212” wherein the data is in “audio-video mode” and is received as “offline data”.)
retrieving a profile image of a candidate; (In ¶0052; Fig. 3 (302 – 304); Fig. 4 (310): teaches that the system “may receive data of the communication between one or more participants” wherein the data includes “profile data of the interviewee over social media” which is directed to retrieving a candidate’s profile image since profile image is usually included in the profile data. Refer to ¶0043 – 44 for more details.)
determining an appearance of an interviewee in the video of the virtual interview; (In ¶0048 – 49; Fig. 3 (306); Fig. 4 (312); Fig. 5 (318); Fig. 6 (324 – 326); Fig. 7 (328 – 330): teaches that “at block 306, comparing is done by the processor 202 through the comparison module 216” wherein the comparing is done with “the one or more features with predefined threshold features stored in a data repository 238” such as “a lip movement, facial expression, eye contact, voice tone, body language, formal conversational cues and informal conversational cues”. While the “predefined threshold features are generated, based on historical data of pre-recorded interviews”.)
responsive to a determination that the appearance of the interviewee and profile image do not match, including an indication that the candidate did not participate in the virtual interview in an assessment of authenticity of the candidate; (In ¶0054, see also ¶0056; Fig. 3 (308); Fig. 4 (314): teaches that the system “generates one or more authenticity attributes by using one or more trained Artificial Intelligence (AI) models 312, along with results of the comparing, wherein each of the one or more authenticity attributes generates a recommendation output 314, providing the authenticity of the communication” wherein the outputs are “at least one of a profile verification score, a deception detection probability, and an impersonation detection percentage” (see Fig. 4), these are all examples of indications that the candidate did not participate given the Applicant’s disclosure at ¶0054 and ¶0087.)
Tagra teaches the processing of data directed to the usage of the AI models with “results of the comparing” for generating “one or more authenticity attributes” that includes at least an indication that the interviewee was using an external resource such as indications related to lip synching, in accordance to ¶0057 from Applicant specs (see ¶0058 and Fig 3 (302 – 308); Tagra). Also, Tagra teaches comparison of “eye position” based on “threshold features” that are recorded data from “historical in-person interviewee sessions” (see ¶0041; Tagra). Tagra does not explicitly teach the ability of specifically identifying interaction patterns with a computing device used by the interviewee including the detection of mouse exits and eye movement mismatches to indicate that interviewee was using external resources during the interview. However, Wang (see Wang translated document) teaches:
processing the recording to identify a pattern of interaction of the interview with a computing device that is used by the interviewee to conduct the virtual interview, wherein identifying the pattern of the interaction includes detecting mouse exits performed by the interviewee and a mismatch between eye movements of the interviewee and a mouse cursor of the interviewee; (In p.15 ¶5: teaches in Example 15 that the system can detect when a candidate can “copy the examination questions or drag to another display, ask others to coordinate” the answers of an examination questions of an interview (see p. 1, ¶2 from the Background Technique section) by getting the “number of display devices connected to the answering terminal” at S501, such as “acquiring the video interface of the answering terminal” which is directed to detecting mouse exits performed by the interviewee. Also, refer to Example 1 in p. 3, ¶3 wherein the acquired first image video image information in real time from a candidate that includes whether the user “blink” and “facial features” extracted that are compared and matched to a threshold to verify if the person did not cheat based on “specific design requirements” that one skilled in the art “can set” which is directed to mismatch between eye movements of the interviewee and a mouse cursor of the interviewee.)
responsive to a determination that there is a mismatch between the eye movements of the interviewee and a mouse cursor of the interviewee, and that there are mouse exits present in the pattern of interaction, including in the assessment of authenticity, an[[d]] indication that the interviewee was using external resources during the interview; (In p.15 ¶7: teaches in Example 15 that the system, that after getting “number of display devices connected to the answering terminal” by “acquiring the video interface of the answering terminal or the access of the USB port Information” can compare the “size of the information” or “quantity information” determinations done to the “interface information of all display devices” in order to indicate or “further confirm whether the candidates use a number of display terminals to cheat, access to display device interface information”. Refer to Example XIV (pp.14) for candidates using other program or apps to type keywords for querying and/or consulting purposes.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Tagra to provide the ability of specifically identifying interaction patterns with a computing device used by the interviewee including the detection of mouse exits and eye movement mismatches to indicate that interviewee was using external resources during the interview, as taught by Wang “in order to further confirm whether the candidates use a number of display terminals to cheat, access to display device interface information” (p.15 ¶12; Wang).
Neither Tagra or Wang explicitly teach the ability of specifically sending a report the assessment of the authenticity of the candidate to a computing device. However, Shea teaches:
sending a report of the assessment of the authenticity of the candidate to a computing device. (In ¶0076; Fig. 4 (460): teaches that the system transmits “to a human resource system, the aggregated feedback to enable a hiring recommendation regarding whether to offer the position associated with the interview session to the candidate (block 460).”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Tagra with Wang to provide the ability of specifically sending a report the assessment of the authenticity of the candidate to a computing device, as taught by Shea in order to “enable a hiring recommendation regarding whether to offer the position associated with the interview session to the candidate” (¶0076; Shea).
Regarding claims 2 and 11:
The combination of Tagra, Wang and Shea, as shown in the rejection above, discloses the limitations of claims 1 and 10, respectively.
Tagra further teaches:
further comprising, by the computing device, storing information about the appearance of the interviewee in a knowledge repository. (In ¶0035; Fig. 2 (230, 238 and 240); Fig. 4 (238); Figs. 5 – 8 (322): teaches that the “threshold features” related to interviewee’s appearance are “stored in the feature repository”, see also ¶0048, defining “threshold features” as “a lip movement, facial expression, eye contact, voice tone, body language, formal conversational cues and informal conversational cues”.)
Regarding claims 3 and 12:
The combination of Tagra, Wang and Shea, as shown in the rejection above, discloses the limitations of claims 1 and 10, respectively.
Tagra further teaches:
further comprising, by the computing device: determining a speech pattern of the interviewee from the audio of the virtual interview; and (In ¶0038; Fig. 2 (230, 238 and 240); Fig. 4 (238); Figs. 5 – 8 (322): teaches that the system uses “one or more trained AI models” that comprises “at least one of a behaviour model created using image input features, a semantic model created by using speech and textual data, an audio-video synchronization model created by using mage and speech input features”. Such behaviour model is used to “generate authenticity attributes based on predictions on each of an eye contact, smile, laugh, shoulder positioning, hand gestures, and facial analysis” which is directed to speech patterns as disclosed in ¶0072 from applicant specs. Refer to ¶0058 for more details of “speech analytics” used by the system of this prior art.)
storing information about the speech pattern of the interviewee in a knowledge repository. (In ¶0035; Fig. 2 (230, 238 and 240); Fig. 4 (238); Figs. 5 – 8 (322): teaches that the “threshold features” related to interviewee’s appearance are “stored in the feature repository”, see also ¶0048, defining “threshold features” as “a lip movement, facial expression, eye contact, voice tone, body language, formal conversational cues and informal conversational cues”.)
Regarding claims 4 and 13:
The combination of Tagra, Wang and Shea, as shown in the rejection above, discloses the limitations of claims 1 and 10, respectively.
Tagra further teaches:
further comprising, by the computing device: determining a body language of the interviewee from the video of the virtual interview; and (In ¶0038; Fig. 2 (230, 238 and 240); Fig. 4 (238); Figs. 5 – 8 (322): teaches that the system uses “one or more trained AI models” that comprises “at least one of a behaviour model created using image input features, a semantic model created by using speech and textual data, an audio-video synchronization model created by using mage and speech input features”. Such behaviour model is used to “generate authenticity attributes based on predictions on each of an eye contact, smile, laugh, shoulder positioning, hand gestures, and facial analysis”, in accordance to ¶0056 and ¶0072 from applicant specs. Refer to ¶0034 – 36 and ¶0041 for more details of “body language” features extracted and compared by the system.)
storing information about the body language of the interviewee in a knowledge repository. (In ¶0035; Fig. 2 (230, 238 and 240); Fig. 4 (238); Figs. 5 – 8 (322): teaches that the “threshold features” related to interviewee’s appearance are “stored in the feature repository”, see also ¶0048, defining “threshold features” as “a lip movement, facial expression, eye contact, voice tone, body language, formal conversational cues and informal conversational cues”.)
Regarding claims 5, 14 and 20:
The combination of Tagra, Wang and Shea, as shown in the rejection above, discloses the limitations of claims 1, 10 and 19, respectively.
Tagra further teaches:
further comprising, by the computing device: retrieving information about speech pattern and body language of the interviewee from prior interviews; (In ¶0056 – 57; Fig. 4 (310); Fig. 5 (316): teaches that the received “data of the communication” such as “a video mode” and “an audio-video mode” is “pre-processed 320 and stored as a structured data 322” to be further used (e.g. retrieved) and compared with “predefined threshold features stored in a data repository 238” such as “voice tone” and “body language”. See Fig. 4 wherein “data collection 310” includes “speech features”, “lip motion check” and “facial landmarks check” and in Fig. 5 the received data includes “face capture”, “lip features” and “body movement” that is processed into “structured data 322”.)
determining a speech pattern of the interviewee from the audio of the virtual interview; determining a body language of the interviewee from the video of the virtual interview; (In ¶0058; Fig. 6 (322 and 324); Fig. 7 (322 and 328): teaches that the “structured data 322” is further used (see Fig. 6) to generate “one or more authenticity attributes by using one or more trained Artificial Intelligence (AI) models 324, along with results of the comparing” that applies “video analytics, image analytics and speech analytics” (directed to determining the speech patterns and body language from the structured data) and produces “the audio, video lip synchronisation and facial expression recommendation output”. Similarly, a “behaviour model” is used with the structured data to generate “authenticity attributes 330 based on predictions on each of an eye contact, smile, laugh, shoulder positioning, hand gestures, and facial analysis by AI and deep learning techniques” (see ¶0059 and Fig. 7). Refer to Fig. 8 and ¶0060 for more details of the generation of more “authenticity attributes”.)
determining whether a same candidate appears in successive interviews based on a comparison of the information about the speech pattern and the body language of the interviewee from the prior interviews and the information about the speech pattern and the body language of the interviewee from the audio and the video of the virtual interview; and responsive to a determination that the same candidate does not appear in the successive interviews, including an indication that the same candidate does not appear in the successive interviews in the assessment of authenticity of the candidate. (In ¶0060; Fig. 4 (238 and 314); Fig. 8 (336): teaches that the system uses the “one or more authenticity attributes” that generates a “recommendation output” that can include “a deception detection probability, and an impersonation detection percentage” which were derived from the comparison results that included the generated “data of the communication” (e.g. video-audio data) and “predefined threshold features” (e.g. “voice tone” and “body language” data) which are “based on historical data of pre-recorded interviews” for a candidate (see ¶0057 – 58) and based on “disclosure analysis and check on facts in profile of a user” (see ¶0051). Thus, the system considers (e.g. determines) the candidate past interviews and their frequency to determine. whether a same candidate appears in successive interviews based on speech patterns and body language comparisons of prior and actual interviews.)
Regarding claims 6 and 15:
The combination of Tagra, Wang and Shea, as shown in the rejection above, discloses the limitations of claims 1 and 10, respectively.
Tagra do not explicitly teach the ability of specifically determining if the interviewee is participating independently during the interview to indicate such result in the assessment of authenticity of the candidate. However, Wang further teaches:
further comprising, by the computing device: determining whether the interviewee is participating in the virtual interview independently; and (In p.5 ¶4: teaches in Example II that the system, that can also be used for “network recruitment” and interviews (see p.1 ¶3), take “image information” that is “analyzed and judged to confirm whether the candidate is taking the examination for himself; or the confirmation method may be set to obtain the image information of the candidate through the second video image information, and the image information is matched with the standard image provided by the candidate”. Refer to Example XIV and Example 15 (pp.14 – 15) for candidates using other program or apps to type keywords or connecting with other users for querying and/or consulting purposes.)
responsive to a determination that the interviewee is not participating in the virtual interview independently, including an indication that the candidate did not participate in the virtual interview independently in the assessment of authenticity of the candidate. (In p.5 ¶4: teaches that when the “match degree is less than the preset threshold, then the examination is for the non-person to take the examination”, meaning that the examination was taken by the wrong person. Thus, “the technical staff of the field can also use other ways to according to the second Video image information to confirm whether the candidates to participate in the examination” which this “match degree” and other ways of confirmation are an example of indications that the candidate did not participate in the virtual interview independently, given the Applicant’s disclosure in ¶0057 and ¶0089 – 90.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Tagra to provide the ability of specifically determining if the interviewee is participating independently during the interview to indicate such result in the assessment of authenticity of the candidate, as taught by Wang in order to “quickly and accurately confirm whether or not the candidate has acted in an act of fraud, and thus effectively Fairness of the examination, fairness, and effectively improve the efficiency of the network interview” (p.5 ¶5; Wang).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zadeh (U.S. Pub No. 20200184278 A1) is pertinent because it is about “new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI. (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object)” for “fraud detection (e.g., for cryptocurrency)” and “social behavior”
Chen (U.S. Pub No. 20190332757 A1) is pertinent because it “relate to user authentication of a computing device. In particular, embodiments relate to photoplethysmogram-based authentication of a user of a computing device.”
Saha (U.S. Pub No. 20230095952 A1) is pertinent because it “relates to the automatic conduct of an interview over a telecommunication network with candidates to an open job position.”
Xiao (U.S. Pub No. 20160034851 A1) is pertinent because it “relate[s] to a virtual interview application that may be installed at a device for creating automated virtual interviews for participation by multiple interviewees who are being considered by an organization for placement in a certain position within the organization.”
Hazan (U.S. Pub No. 20170213190 A1) is pertinent because it is about “methods and systems that perform an interview of an interviewee and provide a score for that interviewee based on numerous characteristics of the interviewee from the interview”
Hazarika (U.S. Pub No. 20190130361 A1) is pertinent because it “relate[s] to human talent management systems and methods thereof, and more particularly, to improved method for unbiased Artificial Intelligence (AI)-, Machine Learning (ML)-based pre-assessment, screening, testing, interview, selection, background verification, hiring and on-boarding of the potential candidates, and combinations thereof, in cloud computing based human talent management system, thereby facilitating at least one of minimizing and reducing the at least one of time-to-hire, cost-to-hire, labor-to-hire, and a combination thereof.”
Taylor (U.S. Pub No. 20170206504 A1) is pertinent because it is about “Methods and systems for bias detection to improve the reviewing and assessment of digital interviews and other digitally-capture evaluation processes”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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