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 .
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- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Is the claimed invention a statutory category of invention?
Claims 1, 10 and 20 are directed to a method / system for replacing instructor based on instructor action (Step 1, Yes).
Step 2A, Prong 1: Does the claim recite an abstract idea?
The limitation of steps: … activating at least one instructor avatar (IA) in a virtual reality (VR) education session; capturing, from the session, a sampling, the sampling being one of (i) a pattern of an action of the IA in the session, and (ii) a sample of content being presented in the session; analyzing whether the sampling from the session corresponds to a valid human instructor assigned to the session; detecting, responsive to the analyzing, that the IA has been compromised in the session; ejecting the IA from the session while maintaining a continuity of the session; and replacing, in the session, the IA with a replacement IA such that the replacement IA continues a planned lesson in the session as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (claims 10 and 20). The claimed method akin to mental process of observations, evaluations, and judgements (monitoring an instructor pattern to detect whether the instructor is an impostor). The mere nominal recitation of one or more data processors performing these steps does not take the claim limitation outside of the mental processes grouping. Thus, the claim recites a mental process (Step 2A, Prong 1: yes).
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Per the 2019 Revised Patent Subject Matter Eligibility Guidance, if a claim as a whole integrates the recited judicial exception into a practical application of that exception, a claim is not "directed to" a judicial exception. Alternatively, a claim that does not integrate a recited judicial exception into a practical application is directed to the exception. Evaluating whether a claim integrates an abstract idea into a practical application is performed by a) identifying whether there are any additional elements recited in the claim beyond the abstract idea, and b) evaluating those additional elements individual and in combination to determine whether they integrate the abstract idea into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. Exemplary considerations indicative that an additional element (or combination of elements) may have or has not been integrated into a practical application are set forth in the 2019 PEG.
With respect to the instant claims, claim 1 does not require any statutory product; nor tied to any statutory product. Claims 10 and 20 recite the additional elements of: A computer system comprising a processor and one or more computer readable storage media. It is particularly noted that the use of processor "as a tool" to perform an abstract method are indicated in the 2019 PEG as examples that an additional element has not been integrated into a practical application. Even in combination, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits, such as an improvement to a computing system, on practicing the abstract idea (STEP 2A, Prong 2: NO).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
claim 1 does not require any statutory product; nor tied to any statutory product. Claims 10 and 20 recite the additional elements of: A computer system comprising a processor and one or more computer readable storage media set forth above for Step 2A, Prong 2. Regarding these limitations: Applicant's specification describes these features in generic manner "… one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry 120 may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry 120 may implement multiple processor threads and/or multiple processor cores. Cache 121 is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set 110” in the Applicant’s specification, para. [0045]). There is no indication in the Specification that Applicants have achieved an advancement or improvement in computer for verifying identity of a user. Dependent claims 2-9 and 11 – 19 inherit the deficiencies of their respective parent claims through their dependencies and do not recite additional limitations sufficient to direct the claims to more than the claimed abstract idea, and are thus rejected for the same reasons.
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.
Claims 1, 9, 10, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hancock et al. (US 2019/0066029 A1) in view of Wang et al. (US 2025/0030567 A1) and Henchy (US 2024/0379017 A1).
Re claims 1, 10, 20:
Hancock teaches 1. A computer-implemented method (Hancock, Abstract; fig. 4, “computer”) comprising:
activating session (Hancock, [0044], “track worker proficiency regarding various physical tasks”);
capturing, from the session, a sampling, the sampling being one of (i) a pattern of an action of the IA in the session, and (ii) a sample of content being presented in the session (Hancock, [0006], “tracked worker skill proficiency data”; [0042], “the system 10 can be provided with one or more historical databases 56, Worker profile databases 54, candidate profile or competency databases 62, wherein these databases can receive and track user and candidate action”; [0044], “that sensors can be utilized to track worker proficiency regarding various physical tasks, which physical tasks can be described by certain motions and compared to optimal motions for carrying out a particular task utilizing a particular tool having a particular feature set so as to determine a proficiency in a particular tool competency”);
analyzing whether the sampling from the session corresponds to a valid human instructor assigned to the session (Hancock, [0006], “tracked worker skill proficiency data”; [0042], “the system 10 can be provided with one or more historical databases 56, Worker profile databases 54, candidate profile or competency databases 62, wherein these databases can receive and track user and candidate action”; [0044], “that sensors can be utilized to track worker proficiency regarding various physical tasks, which physical tasks can be described by certain motions and compared to optimal motions for carrying out a particular task utilizing a particular tool having a particular feature set so as to determine a proficiency in a particular tool competency”);
analyzing whether the sampling from the session corresponds to a valid human instructor assigned to the session (Hancock, fig. 1, “Determine Worker Proficiency Deficiency”; [0046], “determine one or more deficiencies of one or more candidates between associated candidate skill proficiencies and the one or more necessary competencies”; [0047]; [0056], “how long the worker or candidate required to complete the assessment, and deviations from an optimal or most efficient path to completion”; a worker has a deficiencies in skills);
detecting, responsive to the analyzing, that the IA has been compromised in the session (Hancock, fig. 1, “Determine Worker Proficiency Deficiency”; [0046], “determine one or more deficiencies of one or more candidates between associated candidate skill proficiencies and the one or more necessary competencies”; [0047]; [0056], “how long the worker or candidate required to complete the assessment, and deviations from an optimal or most efficient path to completion”; a worker has a work deficiency (skill compromised));
replacing, in the session, the IA with a replacement IA such that the replacement IA continues a planned lesson in the session (Hancock, [0032], “this particular employee may be unfamiliar with the fluid dynamic analysis, while another employee might be familiar with the fluid dynamic modeling, while a third employee might be familiar with both. As such, the system as contemplated herein can match or give a probabilistic score representing the likelihood of the particular worker's ability to perform all the tasks in a proficient manner”; [0035], “two workers can claim to know how to use and use well Microsoft's Excel software. Whereas in practice worker 1 can successfully utilize the various regression tools, while worker 2 is proficient with creating and generating pivot tables. This example illustrates that for a given tool, and especially one that has hundreds if not thousands of functions, workers can be proficient at certain tasks utilizing particular features of the tool, while not being able to do other tasks”).
Hancock teaches replacing one worker with another worker based on skill proficiency associated with a task. Hancock does not explicitly disclose activating session at least one instructor avatar (IA) in a virtual reality (VR) education session (session); … ejecting the IA from the session while maintaining a continuity of the session; and replacing, in the session, the IA with a replacement IA such that the replacement IA continues a planned lesson in the session; replacing, in the session, the IA with a replacement IA such that the replacement IA continues a planned lesson in the session.
Wang et al. (US 2025/0030567 A1) user replacement techniques that can be performed to facilitate automatic stage user replacement for one or more stage users of an online video conference. Wang teaches … ejecting the IA from the session while maintaining a continuity of the session; and replacing, in the session, the IA with a replacement IA such that the replacement IA continues a planned lesson in the session; replacing, in the session, the IA with a replacement IA such that the replacement IA continues a planned lesson in the session (Wang, [0029], “the stage manipulator needs to remove the previous (current) presenter from the stage”; [0032], “provide the user interface screen 200 through which operations can be performed by the stage manipulator to facilitate stage user replacement for a stage display area 202 of the user interface screen”; [0048], “if the next set of stage users (#2) as configured for the stage user list 230 comes in turn, following the first set of stage users, the stage manipulator can trigger stage user replacement for the stage display area 202 and perform automatic synchronization for the user interfaces provided for the other user devices of the other participants by performing one user (selection)”). Therefore, in view of Wang, 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 method/system/computer described in Hancock, by removing / replace presenter (worker) as taught by Wang, in order to assign the order of presentation by removing and replacing presenters during a presentation session.
Henchy (US 2024/0379017 A1) teaches systems and techniques are provided for extended reality learning systems for hybrid on-demand and live learning systems (Henchy, Abstract). Henchy teaches activating session at least one instructor avatar (IA) in a virtual reality (VR) education session (session) (Henchy, [0164]; [0205]; [0211], “the XR classroom environment 1200 can depict an instructor 1202 of the class associated with the XR classroom environment 1200. The depiction of the instructor 1202 can include, for example and without limitation, an avatar associated with the instructor, a video feed of the instructor, a 3D model or rendering of the instructor or representing the instructor, an image of the
instructor, or any other visual representation”). The substitution of one known element (providing an instructor (and instructor avatar) as a worker in Henchy) for another (Office worker, engineer … as taught by Hancock) would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention since the substitution of the type of workers would have yielded predictable results, namely, provide an online learning system includes interactive features where students can interact with instructors such as, for example and without limitation, a chat, a messaging tool, a voice and/or video conferencing tool, a communication widget, etc. (Henchy, [0027]).
10. A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising:
activating at least one instructor avatar (IA) in a virtual reality (VR) education session (session);
capturing, from the session, a sampling, the sampling being one of (i) a pattern of an action of the IA in the session, and (ii) a sample of content being presented in the session;
analyzing whether the sampling from the session corresponds to a valid human instructor assigned to the session;
detecting, responsive to the analyzing, that the IA has been compromised in the session;
ejecting the IA from the session while maintaining a continuity of the session; and
replacing, in the session, the IA with a replacement IA such that the replacement IA continues a planned lesson in the session (See claim 1 citation above).
20. A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising:
activating at least one instructor avatar (IA) in a virtual reality (VR) education session (session);
capturing, from the session, a sampling, the sampling being one of (i) a pattern of an action of the IA in the session, and (ii) a sample of content being presented in the session;
analyzing whether the sampling from the session corresponds to a valid human instructor assigned to the session;
detecting, responsive to the analyzing, that the IA has been compromised in the session;
ejecting the IA from the session while maintaining a continuity of the session; and
replacing, in the session, the IA with a replacement IA such that the replacement IA continues a planned lesson in the session (See claim 1 citation above).
Re claim 9:
9. The computer-implemented method of 1, further comprising: activating a plurality of student avatars in the session, the session further comprising the planned lesson, wherein the IA is configured to deliver a learning experience in the session by representing a presentation of the content by the valid human instructor, and wherein the content corresponds to the planned lesson (Henchy, [0164]; [0205]; [0211], “the XR classroom environment 1200 can depict an instructor 1202 of the class associated with the XR classroom environment 1200. The depiction of the instructor 1202 can include, for example and without limitation, an avatar associated with the instructor, a video feed of the instructor, a 3D model or rendering of the instructor or representing the instructor, an image of the instructor, or any other visual representation”).
Re claim 18:
18. The computer program product of claim 10, wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system (Henchy, [0164]; [0205]; [0211]).
Claims 2, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Hancock et al. (US 2019/0066029 A1), Wang et al. (US 2025/0030567 A1) and Henchy (US 2024/0379017 A1) as applied to claims 1, 10 above, and further in view of Martin et al. (US 20240144935 A1).
Re claim 2, 11:
Hancock teaches a method for recognizing a deficient competency is not necessarily authoritative regarding whether a particular worker or candidate possesses a said competency or skill (Hancock, Abstract). Hancock does not explicitly disclose the pattern of the action comprises a speech sample being output by the IA into the session.
Martin teaches an invention related to acoustic characteristics of a user voice are analyzed by a first machine learning model of a processor. Martin teaches 2. The computer-implemented method of 1, further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a speech sample being output by the IA into the session; inputting the speech sample in a pre-trained speech analysis model, wherein the speech analysis model is pre-trained using a repository of speech snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the speech analysis model a determination that the speech sample fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting. 11. The computer program product of 10, the operations further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a speech sample being output by the IA into the session; inputting the speech sample in a pre-trained speech analysis model, wherein the speech analysis model is pre-trained using a repository of speech snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the speech analysis model a determination that the speech sample fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting (Martin, [0012], “authenticate voice signals for any scenarios or activities involving voice (e.g., telephone or other calls, communications, audio messages, speech and/or voice recognition systems, voice responsive systems for performing actions, online or other meetings or communication sessions, etc.”; [0015], “voice authentication module 150 of the authentication server system authenticates or verifies a user as a legitimate or authorized user based on voice signals”; [0009], “The user is authenticated with respect to an authorized user by the processor based on analysis of the acoustic characteristics and the linguistic patterns of the user voice by the first and second machine learning models”). Therefore, in view of Martin, 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 method/system described in Hancock, by providing voice authentication as taught by Martin, in rode to prevents malicious activities using voice impersonation based on social engineering by training machine learning (ML) models including an acoustic model and a grammatical-linguistic model. These machine learning (ML) models analyze various aspects of audio message impersonation or spoofing (acoustic and linguistic aspects), and predictions determined by these models are combined to produce a probability score that assesses authenticity (Martin, [0011]).
Claims 3 - 4, 12 – 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hancock et al. (US 2019/0066029 A1), Wang et al. (US 2025/0030567 A1) and Henchy (US 2024/0379017 A1) as applied to claims 1, 10 above, and further in view of Jaini et al. (US 2025/0265318 A1).
Re claims 3 - 4, 12 - 13:
Hancock teaches a method for recognizing a deficient competency is not necessarily authoritative regarding whether a particular worker or candidate possesses a said competency or skill (Hancock, Abstract). Hancock does not explicitly disclose the pattern of the action comprises a sample of a behavior being produced by the IA in the session; nor disclose the pattern of the action comprises a sample of an interaction between the IA and another avatar in the session.
Jaini et al. (US 2025/0265318 A1) teaches artificial intelligence based on authentication systems and more particularly to a system that dynamically authenticates users by employing artificial intelligence to extract and analyze behavioral and/or biometric data in Extended Reality (XR) environments (Jaini, Abstract). Jaini teaches 3. The computer-implemented method of 1, further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a sample of a behavior being produced by the IA in the session; inputting the sample of the behavior in a pre-trained behavior analysis model, wherein the behavior analysis model is pre-trained using a repository of behavior snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the behavior analysis model a determination that the sample of the behavior fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting. 12. The computer program product of 10, the operations further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a sample of a behavior being produced by the IA in the session; inputting the sample of the behavior in a pre-trained behavior analysis model, wherein the behavior analysis model is pre-trained using a repository of behavior snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the behavior analysis model a determination that the sample of the behavior fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting.
4. The computer-implemented method of 1, further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a sample of an interaction between the IA and another avatar in the session; inputting the sample of the interaction in a pre-trained behavior analysis model, wherein the behavior analysis model is pre-trained using a repository of interaction snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the behavior analysis model a determination that the sample of the interaction fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting. 13. The computer program product of 10, the operations further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a sample of an interaction between the IA and another avatar in the session; inputting the sample of the interaction in a pre-trained behavior analysis model, wherein the behavior analysis model is pre-trained using a repository of interaction snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the behavior analysis model a determination that the sample of the interaction fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting (Jaini, Abstract; [0026], “Avatar behavior metadata plays a crucial role in securely authorizing and interacting with human behavior”; [0027], “syngeneic AI Mimic human / avatar behavior meta data can include user body language/actions style, speech patterns, movements, typing speed, keystroke dynamics etc. Avatar behavior metadata carries significant value as it allows systems and processes to better understand and interact with both human behavior and avatar behavior. By imitating human behavior and utilizing metadata references, the disclosures contained herein gain deeper insights and improve human/Avatar interactions and consequent authentications”; [0028], “” [0031]; [0113]; [0131]). Therefore, in view of Jaini, 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 method / system described in Hancock, by analyzing the avatar motion as taught by Jaini, the systems and processes that blend advanced AI, sensory engineering, and user behavior analysis, to offer unprecedented levels of security in the authentication space (Jaini, [0022]).
Claims 5 – 6 and 14 - 15 are rejected under 35 U.S.C. 103 as being unpatentable over Hancock et al. (US 2019/0066029 A1), Wang et al. (US 2025/0030567 A1) and Henchy (US 2024/0379017 A1) as applied to claims 1, 10 above, and further in view of Richey et al. (US 2024/0005807 A1).
Re claims 5 - 6, 14 - 15:
Hancock teaches a method for recognizing a deficient competency is not necessarily authoritative regarding whether a particular worker or candidate possesses a said competency or skill (Hancock, Abstract). Hancock does not explicitly disclose authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a sample of a sequence of presentations by the IA in the session; nor disclose a sample of a timing alignment of a sequence of presentations with the content presented by the IA in the session.
Richey et al. (US 2024/0005807 A1) teaches apparatus, methods, and computer program products that can learn, identify, and launch operations in a digital learning environment (Richey, Abstract). Richey further teaches 5. The computer-implemented method of 1, further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a sample of a sequence of presentations by the IA in the session; inputting the sample of the sequence of presentations in a pre-trained sequence analysis model, wherein the sequence analysis model is pre-trained using a repository of presentation sequence snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the sequence analysis model a determination that the sample of the sequence of presentations fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting. 14. The computer program product of 10, the operations further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a sample of a sequence of presentations by the IA in the session; inputting the sample of the sequence of presentations in a pre-trained sequence analysis model, wherein the sequence analysis model is pre-trained using a repository of presentation sequence snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the sequence analysis model a determination that the sample of the sequence of presentations fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting. 6. The computer-implemented method of 1, further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a sample of a timing alignment of a sequence of presentations with the content presented by the IA in the session; inputting the sample of the timing alignment in a pre-trained sequence analysis model, wherein the sequence analysis model is pre-trained using a repository of presentation sequence snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the sequence analysis model a determination that the sample of the timing alignment fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting. 15. The computer program product of 10, the operations further comprising: authenticating, as a part of the analyzing, the pattern of the action, wherein the pattern of the action comprises a sample of a timing alignment of a sequence of presentations with the content presented by the IA in the session; inputting the sample of the timing alignment in a pre-trained sequence analysis model, wherein the sequence analysis model is pre-trained using a repository of presentation sequence snippets from a set of human instructors, the set of human instructors comprising the valid human instructor; and outputting from the sequence analysis model a determination that the sample of the timing alignment fails to correspond to the valid human instructor within a specified tolerance, wherein the determination forms a basis for the detecting (Richey, Abstract; [0127], “automatically learn, identify, and launch a presentation, lesson, and/or lesson plan, including the operations of the presentation, lesson, and/or lesson plan in their sequential order”; [0103], ”the particular learned and/or known presentation, lesson, and/or lesson plan for presentation to the attendee(s) (e.g., via their respective attendee computing devices 104) in the learned order, sequence, flow, and/or sequential order”; [0090], “digital learning environment. The machine learning algorithm(s) may include any suitable machine learning algorithm(s) that is/are known or developed in the future capable of learning a presentation conducted in a digital learning environment”). Therefore, in view of Richey, 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 method/system described in Hancock, by providing order of lesson as taught by Richey, to provide and/or make one or more suggestions for modifying a presentation, lesson, and/or lesson plan to a moderator and/or moderator computing device (Richey, [0114]).
Claims 7 - 8 and 16 - 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hancock et al. (US 2019/0066029 A1), Wang et al. (US 2025/0030567 A1) and Henchy (US 2024/0379017 A1) as applied to claims 1, 10 above, and further in view of Douglass (US 2024/0395156 A1).
Re claims 7 - 8, 16 – 17:
Hancock teaches a method for recognizing a deficient competency is not necessarily authoritative regarding whether a particular worker or candidate possesses a said competency or skill (Hancock, Abstract). Hancock does not explicitly disclose authenticating, as a part of the analyzing, the sample of the content, wherein the sample of the content comprises a teaching material being output by the IA into the session; nor disclose the sample of the content comprises a teaching material being output by the IA.
Douglass teaches methods and systems for managing teaching contents (e.g., in the form of content artifacts). Douglass teaches 7. The computer-implemented method of 1, further comprising: authenticating, as a part of the analyzing, the sample of the content, wherein the sample of the content comprises a teaching material being output by the IA into the session; determining that the teaching material fails to correspond to a pre-authenticated content within a specified tolerance; and outputting from the speech analysis model a determination that the sample of the content fails to correspond to the valid human instructor, wherein the determination forms a basis for the detecting. 16. The computer program product of 10, the operations further comprising: authenticating, as a part of the analyzing, the sample of the content, wherein the sample of the content comprises a teaching material being output by the IA into the session; determining that the teaching material fails to correspond to a pre-authenticated content within a specified tolerance; and outputting from the speech analysis model a determination that the sample of the content fails to correspond to the valid human instructor, wherein the determination forms a basis for the detecting. 8. The computer-implemented method of 1, further comprising: authenticating, as a part of the analyzing, the sample of the content, wherein the sample of the content comprises a teaching material being output by the IA into the session; determining that a source of the teaching material fails to correspond to a pre-authenticated source of a pre-authenticated content; and outputting a determination that the sample of the content fails to correspond to the valid human instructor, wherein the determination forms a basis for the detecting. 17. The computer program product of 10, the operations further comprising: authenticating, as a part of the analyzing, the sample of the content, wherein the sample of the content comprises a teaching material being output by the IA into the session; determining that a source of the teaching material fails to correspond to a pre-authenticated source of a pre-authenticated content; and outputting a determination that the sample of the content fails to correspond to the valid human instructor, wherein the determination forms a basis for the detecting (Douglass, [0023], “the teaching content management module 111 can categorize the proposed content submission as “approved” or “verified” and add that teaching content to the teaching content storage 115 for future uses”; [0024] – [0025]; [0035], “he content contributor 207 is also authorized by the system 100 to view content ranking/rating, such as the number of positive comments (“likes”) by students/educators, the number of utilizations of a teaching content, etc.”; [0050], “the foregoing QA processes can be handled by an AI module”). Therefore, in view of Douglass, 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 method/system described in Hancock, by verifying learning content using AI model as taught by Douglass, since the system use previous teaching contents and user/reviewer comments/feedback to generate, by ML and AI methods/ schemes, teaching contents that are suitable to achieve the learning objective (Douglass, [0019].
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Hancock et al. (US 2019/0066029 A1), Wang et al. (US 2025/0030567 A1) and Henchy (US 2024/0379017 A1) as applied to claim 10 above, and further in view of Asfaw et al. (US 2019/0347954 A1).
Re claim 19:
Hancock does not explicitly disclose a cost for providing training to the worker. Asfaw et al. (US 2019/0347954 A1) teaches a system and method of interaction with online educational programs provides a user with the ability to teach or enroll in classes remotely (Asfaw, Abstract). Asfaw further teaches 19. The computer program product of claim 10, wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising: program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use (Asfaw, [0007]; [0041], “provides a flexible way of paying the teacher”). Therefore, in view of Asfaw, 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 method/system described in Hancock, by paying the teacher as taught by Asfaw, in order to provide monetary gain to compensate the teacher’s effort.
Conclusion
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/JACK YIP/Primary Examiner, Art Unit 3715
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