DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to claims filed on 2/5/2026 in relation to application 18/372,745.
Claims 1-20 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Patent Application Publication Number US 20240364771 A1 (18/140,835) to Wächter. in view of US Patent Number US 11989524 B2 Huang and further in view of Patent Application Publication Number US 20240290331 A1 to Lu et al. (Lu).
Claim 1. Wächter teaches a method comprising:
establishing a virtual communication session between a host client device and a plurality of participant client devices (Fig.1);
receiving a request from a host client device to generate an interaction tool associated with a virtual communication session (Para 0003, 0004 receiving a scaffolding of communication interventions tools and promoting interaction tool among the participants within a video conferencing platform; Fig.1 elements 104, 108, 110 );
accessing virtual communication data associated with the virtual communication session (Fig.3 elements 306,310, 314; Para 0029 accessing devices in virtual environment session with virtual communication activities for associated engagement metrics);
executing a first generative artificial intelligence (Al) model to generate a list of questions at least based on the virtual communication data and the request ( Fig.8; diversity profile of participant may be calibrated based on one or more of a communication threshold or a communication practice i.e. a key point data that could be generated and supported from a generative artificial intelligence (Al) model as in paragraph 0100; Para 0066 virtual communication is adaptable to request -0067and is based on changes in participant dynamics for various conference phases representing list of questions-and-answer).
Wächter does not identify any type of specific request, wherein the first generative Al model is pre-trained with a set of labeled questions and corresponding content data. Huang, however, teaches request, wherein the first generative Al model is pre-trained with a set of labeled questions and corresponding content data (Fig.1 question answering technology relates to the two technologies is a technology that, given a text and a query, finds the related part from the text for the query, then extracts or generates an answer and provides it to the user. Theis could be a machine reading comprehension system and an example of using such a question answering technology; a dialogue corpus generating unit configured to generate a language learning dialogue corpus from reading comprehension data comprising a passage and an exercise). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate requests when a first generative Al model is pre-trained with a set of labeled questions and corresponding content data., as taught by Huang, into the virtual communication session of Wächter, so that model pre-training could be done efficiently.
Wächter in combination with Huang does not explicitly providing interaction tool comprising multiple interactive graphical user interface (GUI) elements corresponding to the list of questions to the plurality of participant client devices. Lu, however, teaches multiple interactive graphical user interface (GUI) elements corresponding to the list of questions to the plurality of participant client devices (Fig.1-11 Graphical user interfaces (GUIs); Para 0035 a prompt or text input to a generative language model may include an instruction or request that the model generate a list of questions. Specifically, the instruction may request that the model generate some number of questions that have not yet been asked by a meeting participant). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate providing interaction tool comprising multiple interactive graphical user interface (GUI) elements corresponding to the list of questions to the plurality of participant client devices., as taught by LU, into the virtual communication session of Wächter as modified by Huang, so that GUI elements could easily display and response a generated question lists for model pre-training done efficiently.
Wächter in combination providing the interaction tool comprising the list of questions to the plurality of participant client devices (Para 0022, 0023 interactions collect information plurality of participant client devices during the registration process that may use smart devices based on self-assessment questionnaires );
receiving a plurality of responses to the list of questions via the interaction tool from the plurality of participant client devices ( Para 0021 display a response received from a list of activities like completing a list of questions via interaction tool that may be completed by the user; Fig.2 elements 112,114; Para 0006 dialogue cues identified elements that include interactive multimedia tools set as a key point that support group dynamics, peer leadership, transformative interactive tools at dialogue cues or key points before, during and after the video conferencing);
generating an updated request by analyzing the plurality of responses ( Para 0058 update generated form insights gained from the conference with intervention dataset and refine the system for future video conferences); and
providing an updated list of questions based on the updated request (Fig.5 element 318 intervention module; Para 0071 -0077 updates that may comprise of questions with intervention criteria that prompt the activation of the corresponding intervention module or tool. The criteria may include an updating factors such as participant behavior, conference dynamics, or other contextual information gathered during the pre-conference, intra-conference, or post-conference stages; Para 0089 question updates on deepening interactions).
Claim 2. Wächter teaches the method of claim 1, wherein the virtual communication session is an online chat session, and wherein the virtual communication data comprises multiple chat messages in the online chat session (Para 0051 text chat sessions).
Claim 3. Wächter teaches the method of claim 1, wherein the virtual communication session is a virtual conference, and wherein the virtual communication data comprises a transcript for the virtual conference, or shared documents during the virtual conference (Para 0034,0039 shared function may contain document and transcript that are distributed).
Claim 4. Wächter teaches the method of claim 1, wherein the virtual communication session is an email thread, and wherein the virtual communication data comprises a sequence of emails (Para 0095 sequence of message that may include emails).
Claim 5. Wächter teaches the method of claim 1, further comprising: accessing metadata related to the virtual communication session (Para 0028 accessing engagement metadata) , wherein the metadata comprises one or more of a title of the virtual communication session, a start and end time of the virtual communication session, a description of the virtual communication session, an agenda of the virtual communication session, or participant data associated with the virtual communication session (Para 0028 virtual communication session engagement levels data; Para 0060 agenda and time schedule for a start and end time of the virtual communication session and other participant information) ; and identifying a set of key point data from the virtual communication data based on the metadata and the request using the machine learning model (Fig.3 element 318 intervention data set; Para 0006,0032 intervention key point data set as dialogue cues where participants can promote intentional pausing to reflect, growing capacities to integrate, translating insights into intentional perspective change and harnessing transformative learning towards new/refined behaviors and actions as supported by intervention data sets architecture of the system; Para 0053 triggering certain communication cues before, during, after the video-conferencing based on the dialogue contributions) and
generating the list of questions further based on the set of Kev point data (Fig.2 elements 112,114; Para 0006 dialogue cues identified elements that include interactive multimedia tools set as a key point that support group dynamics, peer leadership, transformative learning, and community building) from the virtual communication data based on the request using a machine learning model (Para 0007 Interactive tools based AI-empowered communication steps).
Claim 6. Wächter teaches the method of claim 1, wherein the request comprises a type of the interaction tool, a subject area for the list of questions to be generated (Para 0022 self-assessment questionnaires), and a style of answers to the list of questions to be generated ( Para 0067 answer sessions to follow the list of questions ).
Claim 7. Wächter teaches the method of claim 6, wherein the type of the interaction tool comprises survey, poll, or quiz (Para 0124 surveys).
Claim 8 The method of claim 1, further comprising generating answers corresponding to the list of questions based on the virtual communication data and the request using a second generative AI model (model (Fig.4 element 420 intervention engine is adaptive to dynamics of specific stage as in Para 0067 i.e. supported by staged related secondary AI network model).
Claim 9. Wächter teaches the method of claim 1, further comprising: receiving a selection of multiple questions out of the list of questions; generating the interaction tool comprising the multiple questions; transmitting the interaction tool to the plurality of participant client devices; and receiving one or more responses to the multiple questions via the interaction tool to the plurality of participant client device (Para 0034 personalized recommendations; Para 0054 multiple participant client for multiple specified questions).
Claim 10. Wächter teaches the method of claim 9, further comprising: analyzing the one or more responses to generate analytics data; and providing the analytics data to the host client device associated with the virtual communication session (Para 0024, 0094 utilizing generated analytic data in virtual communication session to tailor interventions to the unique needs of each conference and its participants.).
Clam 11 A system comprising: a communications interface; a non-transitory computer-readable medium; and one or more processors communicatively coupled to the communications interface and the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non-transitory computer-readable medium to: receive a request to generate an interaction tool associated with a virtual communication session; access virtual communication data associated with the virtual communication session(Para 039 Memory storage include a computer-readable medium for interactive tools)
establishing a virtual communication session between a host client device and a plurality of participant client devices (Fig.1)
execute a first generative artificial intelligence (Al) model to generate a list of questions at least based on the virtual communication data and the request ( Fig.8; diversity profile of participant may be calibrated based on one or more of a communication threshold or a communication practice i.e. a key point data that could be generated and supported from a generative artificial intelligence (Al) model as in paragraph 0100; Para 0066 virtual communication is adaptable to request -0067and is based on changes in participant dynamics for various conference phases representing list of questions-and-answer).
Wächter does not identify any type of specific request, wherein the first generative Al model is pre-trained with a set of labeled questions and corresponding content data. Huang, however, teaches request, wherein the first generative Al model is pre-trained with a set of labeled questions and corresponding content data (Fig.1 question answering technology relates to the two technologies is a technology that, given a text and a query, finds the related part from the text for the query, then extracts or generates an answer and provides it to the user. Theis could be a machine reading comprehension system and an example of using such a question answering technology; a dialogue corpus generating unit configured to generate a language learning dialogue corpus from reading comprehension data comprising a passage and an exercise). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate requests when a first generative Al model is pre-trained with a set of labeled questions and corresponding content data., as taught by Huang, into the virtual communication session of Wächter, so that model pre-training could be done efficiently.
provide the interaction tool comprising the list of questions to the plurality of client devices (Para 0022, 0023 interactions collect information plurality of participant client devices during the registration process that may use smart devices based on self-assessment questionnaires );
receive a plurality of responses to the list of questions via the interaction tool from the plurality of client devices ( Para 0021 display a response received from a list of activities like completing a list of questions via interaction tool that may be completed by the user; Fig.2 elements 112,114; Para 0006 dialogue cues identified elements that include interactive multimedia tools set as a key point that support group dynamics, peer leadership, transformative interactive tools at dialogue cues or key points before, during and after the video conferencing );
generate an updated request by analyzing the plurality of responses ( Para 0058 update generated form insights gained from the conference with intervention dataset and refine the system for future video conferences); and
generate an updated list of questions based on the updated request (Fig.5 element 318 intervention module; Para 0071 -0077 updates that may comprise of questions could be from intervention criteria that prompt the activation of the corresponding intervention module. The criteria depend on updating factors such as participant behavior, conference dynamics, or other contextual information gathered during the pre-conference, intra-conference, or post-conference stages; Para 0089 question updates on deepening interactions).
Claim 12. Wächter teaches the system of claim 11, wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to: access metadata related to the virtual communication session, wherein the metadata comprises one or more of a title of the virtual communication session, a start and end time of the virtual communication session, a description of the virtual communication session, an agenda of the virtual communication session, or participant data associated with the virtual communication session (Para 0028 virtual communication session engagement levels data; Para 0060 agenda and time schedule a start and end time of the virtual communication session and other participant information); and
identify the set of key point data from the virtual communication data based on the metadata and the request using the machine learning model (Para 0032 intervention key point data set as supported by architecture of the system) and
generate the list of questions further based on the set of Kev point data (Fig.2 elements 112,114; Para 0006 dialogue cues identified elements that include interactive multimedia tools set as a key point that support group dynamics, peer leadership, transformative learning, and community building) from the virtual communication data based on the request using a machine learning model (Para 0007 Interactive tools based AI-empowered communication steps.
Claim 13. Wächter teaches the system of claim 11, wherein the request comprises a type of the interaction tool, a subject area for the list of questions to be generated, and a style of answers to the list of questions to be generated (Para 0067 answer sessions that follow a list of questions); and wherein the type of the interaction tool comprises survey, poll, or quiz (Para 0067 answer sessions to follow the list of questions ).
Claim 14. Wächter teaches the system of claim 11, wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to: generate answers corresponding to the list of questions based on the virtual communication data and the request using a second generative AI model (Fig.4 element 420 intervention engine is adaptive to dynamics of specific stage as in Para 0067 i.e. supported by staged related secondary AI network model).
Claim 15. Wächter teaches the system of claim 11, wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to: receive a selection of multiple questions out of the list of questions; generate the interaction tool comprising the multiple questions; transmit the interaction tool to a plurality of client devices; and receive one or more responses to the multiple questions via the interaction tool from the plurality of client device (Para 0058, 0087 multiple user response according to respective prompts according multiple questions and client devices connected).
Claim16. Wächter teaches the system of claim 15, wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to: analyze the one or more responses to generate analytics data; and provide the analytics data to a host client device associated with the virtual communication session (Para 0024, 0094 utilizing generated analytic data in virtual communication session to tailor interventions to the unique needs of each conference and its participants) .
Claim 17. Wächter teaches a non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors (Para 0039) to:
receive a request to generate an interaction tool associated with a virtual communication session (Para 0003, 0004 receiving a scaffolding of communication interventions tools and promoting interaction tool among the participants within a video conferencing platform) ; access virtual communication data associated with the virtual communication session ( Para 0029 accessing devices in virtual environment sessions) ; identify a set of key point data from the virtual communication data (Para 0006 dialogue cues identified that include interactive multimedia tools set as a key point that support group dynamics, peer leadership, transformative learning, and community building) based on the request using a machine learning model Para 0007 Interactive tools based AI-empowered communication steps);
generate a list of questions based on the set of key point data and the request using a first generative artificial intelligence (AI) model (Para 0022 questionnaires request generated using AI interactive tools at dialogue cues or key points before, during and after the video conferencing); and
provide the interaction tool based on the list of questions ( Para 0022, 0023 interactions using smart devices based on self-assessment questionnaires) .
Wächter does not identify any type of specific request, wherein the first generative Al model is pre-trained with a set of labeled questions and corresponding content data. Huang, however, teaches request, wherein the first generative Al model is pre-trained with a set of labeled questions and corresponding content data (Fig.1 question answering technology relates to the two technologies is a technology that, given a text and a query, finds the related part from the text for the query, then extracts or generates an answer and provides it to the user. Theis could be a machine reading comprehension system and an example of using such a question answering technology; a dialogue corpus generating unit configured to generate a language learning dialogue corpus from reading comprehension data comprising a passage and an exercise). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate requests when a first generative Al model is pre-trained with a set of labeled questions and corresponding content data., as taught by Huang, into the virtual communication session of Wächter, so that model pre-training could be done efficiently.
Wächter in combination with Huang does not explicitly providing interaction tool comprising multiple interactive graphical user interface (GUI) elements corresponding to the list of questions to the plurality of participant client devices. Lu, however, teaches multiple interactive graphical user interface (GUI) elements corresponding to the list of questions to the plurality of participant client devices (Fig.1-11 Graphical user interfaces (GUIs); Para 0035 a prompt or text input to a generative language model may include an instruction or request that the model generate a list of questions. Specifically, the instruction may request that the model generate some number of questions that have not yet been asked by a meeting participant). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate providing interaction tool comprising multiple interactive graphical user interface (GUI) elements corresponding to the list of questions to the plurality of participant client devices., as taught by LU, into the virtual communication session of Wächter as modified by Huang, so that GUI elements could easily display and response a generated question lists for model pre-training done efficiently.
Claim 18. Wächter teaches the non-transitory computer-readable medium of claim 17, further comprising processor-executable instructions configured to cause one or more processors to: generate answers corresponding to the list of questions based on the virtual communication data and the request using a second generative AI model (Fig.4 element 420 intervention engine is adaptive to dynamics of specific stage as in Para 0067 i.e. supported by staged related secondary AI network model).
Claim 19. Wächter teaches the non-transitory computer-readable medium of claim 17, further comprising processor-executable instructions configured to cause one or more processors to: receive a selection of multiple questions out of the list of questions; generate the interaction tool comprising the multiple questions; transmit the interaction tool to a plurality of client devices; and receive one or more responses to the multiple questions via the interaction tool from the plurality of client device (Para 0058, 0087 multiple user responses according to respective prompts according questions or client devices ).
Claim 20. Wächter teaches the non-transitory computer-readable medium of claim 19, further comprising processor-executable instructions configured to cause one or more processors to: analyze the one or more responses to generate analytics data; and provide the analytics data to a host client device associated with the virtual communication session (Para 0024, 0094 utilizing generated analytic data in virtual communication session to tailor interventions to the unique needs of each conference and its participants.) .
Response to Arguments/Remarks
Applicant's arguments/amendments filed on February 5, 2026 have been considered.
Upon further consideration, a new ground(s) of rejection is made as necessitated by amendments changing the scope of the claims and arguments presented on 9/12/2025.
Some of examiner’s response may cite a different portions of an applied reference but do not go further and merely elaborates upon, what is taught in the previously cited portion of a reference. Thus the rejection not constituting a new ground of rejection.
35USC103
Applicant on Pages 8-9 filed on 2/5/2026 teaches a the language learning dialogue corpus in prior art Huang is data corpus, is used to train a dialogue model or in other words, the language learning dialogue corpus that is requested from the dialogue corpus generating unit are training data that is used to train a dialogue model. While, in contrast, the interaction tool in amended claims, which includes multiple interactive GUI elements corresponding to a list of questions generated based on virtual communication data, is a tool that is to be used for interaction with participant client devices associated with the virtual communication session, as specified in amended claim 1. Further the prior art combination does not "generate a list of questions at least based on the virtual communication data and the request," as also specified in claim 1.
Examiner agree that though the Office Action explains that "changes in participant dynamics" in art Wachter is equivalent to a list of questions and answers, it appears not to address the claim feature of multiple interactive graphical user interface (GUI) elements corresponding to "generate a list of questions." In addition, the language learning dialogue corpus generated in prior art Huang is a data structure including multiple components appears to be not equivalent to "a list of questions" generated by an Al model and used to generate an interaction tool, as claimed.
Upon further consideration, a new ground(s) of rejection is provided above as necessitated by amendments changing the scope of the claims.
Following traversals/Remark are retained as a summarized from prior comments so
as to address apriority varied interpretations. This is also answering proactively
some of the new questions that may arise because of current arguments:
Applicant's arguments/amendments filed on September 12, 2025 have been considered.
Upon further consideration, a new ground(s) of rejection is made as necessitated by amendments changing the scope of the claims and arguments presented on 9/12/2025.
Previous 35USC101
Applicant in arguments/remarks on 9/12/2025 indicated that the instant model may rely on mathematical principles during its development or operation. However, the claims at issue as a whole are directed to the use of a generative artificial intelligence (Al) model to generate a list of questions based on the virtual communication data and the request. This is different from an abstract mathematical calculation. Examiner agreed certain steps of executing and/or using a generative Al model is could not always be directed to a mathematical concept.
The systems and methods at issue here apparently providing a specific technological solution for updated interaction tools utilized between a host client device and participant client devices, including generating questions for an interaction tool, receiving responses via the interaction tool, and analyzing the responses to generate an updated request for generating updated questions. The interaction tool operates in a technological environment and requires the use of computer hardware and software to achieve its intended functionality. The claims do not involve management of human interactions or activities in an abstract sense. Hence claims at issue appears to be not recite steps corresponding to conventional human activities or seek to replicate them in a computer context to manage or organize user activity
The specification of the instant application explicitly describes a recent improvement provided to the virtual conferencing technologies:
Examiner respectfully traverses and finds that the communication host is basically utilizing certain specific well known interaction tools like such as surveys, polls, or quizzes, to collect information or evaluate the effect of the virtual communication before, during, or after the virtual communication. Questions and answers can always be easily generated based on communication data, communication metadata, and the prompt by the involvement on persons skilled in art. Response from users can be then be analyzed and the host adjust the prompt to update the interaction tool or take other actions based on the insight of the experts in the field. Moreover, the first generative AI model in amended claim 1 is apparently not a well-known, generic component. Instead, it is pre-trained with a set of labeled questions and corresponding content data. In other words, the first generative AI model is customized or fine-tuned for question generation, not any generic machine learning model can be used for generating questions that can meet specific user requirements.
35USC101 rejection is thus withdrawn.
Previous 35USC102
Applicant of Page 12 of argument/remarks asserted that the prior art Wachter does not receive a request for generating an interaction tool. It does not generate the questionnaires, but merely mentions "self-assessment questionnaires" based on pre-existing interactive tool by software applications. The module in the standardized education tool, which can help users assess and improve their communication skill.
The prior art Watcher indicated "machine learning" can be used for analyzing data or modifying the video conference experience for participant. Wachter, paragraphs [0024] and [0081]. It is not teaching "executing a first generative AI model to generate a list of questions at least based on the virtual communication data and the request," as specified in amended claim 1. Further, Wachter does not disclose any generative AI model pre-trained with a set of labeled questions and corresponding content data," as specified in amended claim 1.
Examiner conducted search and found in art of record other prior art specifically directed to prior art teaching generation of various phases or segments of the conferencing activities.
The summary paragraph 0003 clearly articulates that the interaction tools are automatically generated since the intervention module challenges scaled video conferences and harness automatically the potential of both human communication and technology by incorporating a scaffolding of communication interventions as designed to generate a highly effective and personal communication experience for participants that is easily scalable in diverse virtual settings. The patentability rejection is revised.
Conclusion
US 20170063744 A1 Banerjee et al. ; Generating Poll Information from a Chat Session.
US 20240282443 A1 BRYANT et al.; Conducting remote health testing and diagnostics of patients in multi-session proctored examination platform for medical diagnostic test establishing first electronic video conference session
WO 2024182148 A1 Lu xiao yan et al.; NETWORK-BASED COMMUNICATION SESSION COPILOT
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.
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/S.Z/Examiner, Art Unit 3715
February 28, 2026
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715