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 dated 7/13/2026 in relation to application 18/372,745 filed on 9/26/2023.
Claims 1-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/13/2026 has been entered.
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. The claimed invention is a computer system (11-16) and to a process (claim 1-10) and computer readable medium (17-20). Thus fall within one of the four statutory categories (Step 1: YES).
Claims 1, 11, 17 are directed to establishing a virtual communication session between a host client device and a plurality of participant client devices, receiving a request from the host client device to generate an interaction tool associated with the virtual communication session and accessing virtual communication data associated with the virtual communication session. The actions of communication, receiving, providing interaction tools, list of questions, analyzing the plurality of responses, displaying outcomes, accessing virtual session converting, judging for response falls within the “Certain Method of Organizing Human Activity” groupings of abstract ideas subject to the 2019 Revised Patent Subject Matter Eligibility Guidance. The operations also include feature identified as “Mental Processes” while citing some specific known specific instances of managing interactions between tutor and students in a virtual communication sessions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, and/or a certain method of managing interactions between people but for the recitation of generic computer components, then it falls within the “Mental Processes” and “Certain Method of Organizing Human Activity” groupings of abstract ideas, respectively. The analysis of virtual communication skills and operation by users with a verified algorithm could be use of existing mathematical relationships, formulas. Hence are mathematical concepts. Accordingly, the claims recite one or more groupings of abstract idea(s). (Step 2A: Prong 1 YES).
The steps in the recited claims that are highlighted are a well-understood, routine, and conventional activities known in art.
Fig.1-3 of the instant specification depict touchable object movements for a hardware/ software in a standard network environment with generic use of AI model to implement the process claimed here. They are disclosed in their specification in a manner that indicates that those features are well-known, routine, and conventional. They are not dealing with actual improvements to, e.g., AR/VR, machine learning, etc.
As a further example in case of Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, the activities of storing and retrieving of information in a memory of consumer electronic for a field of use purposes are recognized to be computer functions well-understood, routine, and conventional, when they are claimed in a merely generic manner. Further, there found to be no additional elements here in the claim recitation that improves the functioning of a computer itself to overcome the abstract idea rejection (Step 2B: No).
Claims 2-10,12-16,18-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, taking the claimed elements individually yields no difference from taking them in combination because each element simply performs its respective function as discussed above. In other words, these claims merely apply an abstract idea to a programmable processor or computer and do not improve the performance of the process or computer itself or provide a technical solution to a problem in a technical field. They do not effect a transformation of a particular article to a different state or thing, the underlying computing elements remain the same. Instead, the additional features merely amount to an instruction to apply the abstract idea using generic, functional, and conventional components well-known in the art. Mere instructions to apply an exception using the generic computer components cannot provide an inventive concept. Therefore, for these reasons, claims 1-20 are found to be not patent-eligible under 35 USC 101.
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 Patent Application Publication Number US 20080254419 A1 Cohen.
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 (Fig.5 element 318 implementation of intervention dataset may store information regarding predefined interventions or adjustments to be made during the conference like receiving a request to intervene and generate a interaction tool etc.; Para 0003, 0004, 0056, 007 receiving a scaffolding of communication interventions tools and promoting interaction tool among the participants within a video conferencing platform and may include module phase may include question and answer session as in Para 0067; 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 model to generate a list of questions at least based on the virtual communication data ( Para 0100 Intelligence Enhanced Video Conferencing i.e. a key point data that could be generated and supported from an artificial intelligence nudges, possibly utilizing various machine learning techniques as in paragraph 0100; virtual communication is adaptable to request based on changes in participant dynamics for various conference phases representing list of questions-and-answer as in Para 0066,0067).
Wächter does not explicitly teach executing a first generative Al model to generate a list of questions at least based on the virtual communication data and a related request. Cohen, in the same field of interactive training session, teaches a first generative Al model pre-trained with a set of labeled questions and corresponding content data based on the virtual communication data and the corresponding request (Para 0208 artificial intelligence model with respect to the question and answer flows from a corresponding content data guiding a facilitator execution that may include relevant requests therefrom; Fig.1A-1M question answering technology relates a text and a query i.e. finds a related part of a segment from text for a query and/or request, then extracts or generates an answer to provide as a challenge question to a user. This could be a machine reading comprehension system for example a using such a question answering technology; Fig.4A-1 elements 416A selected challenge as request to a learner in element 420A; option to launch challenges or query requests). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a first generative Al model to be pre-trained with a set of labeled questions and corresponding content data and request., as taught by Cohen, into a virtual communication session of Wächter, so that model pre-training could be approached to significantly reduce training costs and to provide for a relatively much larger pool of facilitator and tutors.
Wächter provides graphical interaction tool for participant client devices (Fig.3 client interactive tools with intervention data set arrangement; Para 0125 Trained peers adding external media; Para 0057-0059 intra-conference module 326 may facilitate the implementation of intervention dataset to cause or address adjustments that arise during the conference; video conference system may enable communication and collaboration for all participants) but does not indicate explicitly plurality of participant client devices, causing the list of questions to be displayed within a respective GUI having the multiple interactive GUI elements on each of the plurality of participant client devices;
Cohen, however, teaches multiple interactive graphical user interface (GUI) elements corresponding to the list of questions to the plurality of participant client devices (Fig.1-3 Graphical user interfaces (GUIs) corresponding to questions in training presentations; Para 0210 easy access for alignment between the pre-study segment and questions is provided without multi-step navigation; questions for the trainee are optionally formulated in a focused manner, with purposely limited scope per question a prompt or text input to a generative language model may include an instruction i.e. 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; Para 0258 can include several participants conversing e.g., in a social setting, such as at a party or dinner, at a business meeting, etc. where a communication platform receives a request to generate an interaction tool associated with a virtual communication session). 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 Cohen, into the virtual communication session of Wächter, so that GUI elements could easily display a generated question lists and responses from interactions of plurality of participant client devices
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; Cohen para 0210);
Wächter in combination teaches 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 explicitly teach executing a first generative Al model to generate a list of questions at least based on the virtual communication data and a request. Cohen, in the same field of interactive training session, teaches a first generative Al model pre-trained with a set of labeled questions and corresponding content data based on the virtual communication data and the request (Para 0208 artificial intelligence model with respect to the question and answer flows from a corresponding content data guiding a facilitator execution; Fig.1A-1M question answering technology relates a text and a query i.e. finds a related part of a segment from text for a query and request, then extracts or generates an answer to provide as a challenge to the user. This could be a machine reading comprehension system and an example of using such a question answering technology; Fig.4A-1 elements 416A selected challenge as request to a learner in element 420A; option to launch challenges or query requests). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a first generative Al model to be pre-trained with a set of labeled questions and corresponding content data and request., as taught by Cohen, into a virtual communication session of Wächter, so that model pre-training could be approached to significantly reduce training costs and to provide for a relatively much larger pool of facilitator and tutors.
Wächter provides graphical interaction tool for participant client devices (Fig.3 client interactive tools with intervention data set arrangement; Para 0125 Trained peers adding external media; Para 0057-0059 intra-conference module 326 may facilitate the implementation of intervention dataset to cause or address adjustments that arise during the conference; video conference system may enable communication and collaboration for all participants) but does not indicate explicitly plurality of participant client devices, causing the list of questions to be displayed within a respective GUI having the multiple interactive GUI elements on each of the plurality of participant client devices;
Cohen, however, teaches multiple interactive graphical user interface (GUI) elements corresponding to the list of questions to the plurality of participant client devices (Fig.1-3 Graphical user interfaces (GUIs) corresponding to questions in training presentations; Para 0210 easy access for alignment between the pre-study segment and questions is provided without multi-step navigation; questions for the trainee are optionally formulated in a focused manner, with purposely limited scope per question a prompt or text input to a generative language model may include an instruction i.e. 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; Para 0258 can include several participants conversing e.g., in a social setting, such as at a party or dinner, at a business meeting, etc.). 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 Cohen, into the virtual communication session of Wächter, so that GUI elements could easily display a generated question lists and responses from interactions of plurality of participant client devices
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 in combination 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 explicitly teach executing a first generative Al model to generate a list of questions at least based on the virtual communication data and a request. Cohen, in the same field of interactive training session, teaches a first generative Al model pre-trained with a set of labeled questions and corresponding content data based on the virtual communication data and the request (Para 0208 artificial intelligence model with respect to the question and answer flows from a corresponding content data guiding a facilitator execution; Fig.1A-1M question answering technology relates a text and a query i.e. finds a related part of a segment from text for a query and request, then extracts or generates an answer to provide as a challenge to the user. This could be a machine reading comprehension system and an example of using such a question answering technology; Fig.4A-1 elements 416A selected challenge as request to a learner in element 420A; option to launch challenges or query requests). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a first generative Al model to be pre-trained with a set of labeled questions and corresponding content data and request., as taught by Cohen, into a virtual communication session of Wächter, so that model pre-training could be approached to significantly reduce training costs and to provide for a relatively much larger pool of facilitator and tutors.
Wächter provides graphical interaction tool for participant client devices (Fig.3 client interactive tools with intervention data set arrangement; Para 0125 Trained peers adding external media; Para 0057-0059 intra-conference module 326 may facilitate the implementation of intervention dataset to cause or address adjustments that arise during the conference; video conference system may enable communication and collaboration for all participants) but does not indicate explicitly plurality of participant client devices, causing the list of questions to be displayed within a respective GUI having the multiple interactive GUI elements on each of the plurality of participant client devices;
Cohen, however, teaches multiple interactive graphical user interface (GUI) elements corresponding to the list of questions to the plurality of participant client devices (Fig.1-3 Graphical user interfaces (GUIs) corresponding to questions in training presentations; Para 0210 easy access for alignment between the pre-study segment and questions is provided without multi-step navigation; questions for the trainee are optionally formulated in a focused manner, with purposely limited scope per question a prompt or text input to a generative language model may include an instruction i.e. 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; Para 0258 can include several participants conversing e.g., in a social setting, such as at a party or dinner, at a business meeting, etc.). 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 Cohen, into the virtual communication session of Wächter, so that GUI elements could easily display a generated question lists and responses from interactions of plurality of participant client devices
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 July 13, 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 7/13/2026.
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 those statement not constituting a new ground of rejection.
35USC101
Examiner has included 35USC101 rejection for non-final in this continuation of examination based on recent guidance and court cases. The independent claim of the instant case is characterized to be directed to training/employing a machine learning model in a particular technological environment (“Generic AI model”) and thereby abstract under the CAFC’s decision in Recentive Analytics. To the extent that claim, e.g., a computing system comprising one or more processors, employing OCR, employing machine learning models, these are all well-known, routine, and conventional devices and/or software techniques as evidence by the limited disclosure in Applicant’s specification (cite spec sections) in regard how to make and/or use these devices and thereby do not constitute “significantly more” than the claimed abstract idea(s).
Applicant in previous 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 agree 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 a well-known, generic application of machine model. It may be 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.
35USC103
Applicant on Pages 8-10 argument/remarks filed on 7/13/2026 argue that neither FIG. 1 nor the entirety of secondary art Huang discloses any request from a host client device during a virtual communication session, let alone a request to generate an interaction tool as specified in claim 1 at issue. Moreover, There is no sense made to combine two unrelated claim elements from different steps together in one analysis. The Office Action's analysis was merely focused on generating language learning dialogue corpus. The Office Action does not address the "request" in the second step of claim 1 at issue at all in the analysis. However, 0081
the language learning dialogue corpus is not equivalent to an interaction tool as specified in amended claim 1. Even assuming arguendo there is a request in the language learning dialogue corpus process, it is a request for generating a language learning dialogue corpus, which is different from a request to generate an interaction tool.
Examiner agree that two unrelated claim elements from different steps may get together in one analysis. However it is traversed that a generation of interaction tool is available in art during virtual communication session (Wachter:0056-0067 module phase may also include question and answer session .
The prior art Watcher indicated "machine learning" and can be used for analyzing data or modifying the video conference experience for participant. Please see reference Wachter paragraphs [0024] and [0081]. But it is not teaching "executing a first generative AI model in details to generate a list of questions at least based on the virtual communication data and the request," as specified in amended claim 1. However the art Cohen teaches artificial intelligence nudges with possible utilizing various machine learning techniques. Requests could be presented (Fig.4A element 416A Launch challenges on selected segments as a request for questions).
Applicant further alleged that the other prior art is a user interface of a network-based communication application engaged in a network-based communication session. Lu, paragraph [0005]. In contrast, the GUI tool in claim 1 is a separate component provided via a GUI associated with the virtual communication session. Further, none of the cited references disclose or make obvious "causing the list of questions to be displayed within a respective GUI having the multiple interactive GUI elements on each of the plurality of participant client devices," as specified in amended claim. The list of questions in amended claim 1 are provided to or displayed on each of the plurality of participant client devices associated with the virtual communication session.
Examiner respectfully traverses and cites the GUI tool is widely used in user interface of a network-based communication application. Another prior art Cohen is cited to clarify the references make obvious "causing the list of questions to be displayed within a respective GUI having the multiple interactive GUI elements on each of the plurality of participant client devices," as specified in amended claims. In an instant paragraphs 0211-0213, a larger scope of training is optionally achieved through the combining of multiple questions that may cause the list of questions to be displayed and modules. without the answers being presented on the training system display until the trainee answers the questions. The display within a respective GUI having the multiple interactive GUI elements on each of the plurality of participant client devices such as in from figures 1b to Figure 1E challenges having multiple interactive GUI elements.
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:
Previous 35USC102 Argument and Response
Applicant of Page 12 of argument/remarks September 12, 2025 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SADARUZ ZAMAN whose telephone number is (571)270-3137. The examiner can normally be reached M-F 9am to 5pm CST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached on (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.Z/Examiner, Art Unit 3715
August 8, 2026
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
Prosecution Insights