Response to an Amendment
This office action is a response to a communication made on 10/30/2025.
Claims 1-5, 7-12, and 14-20 are currently amended.
Claims 13 is canceled.
Claims 21 is new.
Claims 1-12 and 14-21 are pending for this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/28/2025, 11/28/2025 and 02/04/2026 were filed before the mailing date of the final action on 02/04/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 8 and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments, see remarks on page 7-9, filed 10/30/2025, with respect to the rejection(s) of claim(s) 1, 8 and 15 under 103 have been considered and regarding the amended feature “execute a second AI model on the at least one vector and the metadata to identify a topic associated with a positive sentiment which was brought up during the at least one communication session and which was not addressed” are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Somech et al. (US 2019/0005024) in view of Ye et al. (CN 115757793A), and further in view of Sheikh et al. (US 2023/0368284).
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.
Claim(s) 1-4, 6-11, 13-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Somech et al. (US 2019/0005024), hereinafter “Somech” in view of Ye et al. (CN 115757793A), hereinafter “Ye”, and further in view of Sheikh et al. (US 2023/0368284), hereinafter “Sheikh”. Sheikh cited in applicant IDS filed 04/07/2024. AN English translation has been added with this application.
With respect to claim 1, Somech discloses an apparatus comprising:
a memory (Fig.6, memory 612); and
a processor coupled to the memory (Fig. 6, one or more processors 614), the processor configured to:
conduct at least one communication session between a host and a source device through a software application of the host (¶0033, ¶0053, and ¶0117 teaches the terms “communication session” and “CS” may be used interchangeably to broadly refer to any session where two or more computing devices (i.e. host and source device) are employed to exchange information and/or data between two or more users…a plurality of user interactions conducted using one or more user devices, activity by the user on or in connection to one or more user devices… user-activity monitor 296 comprises one or more applications (i.e. software application of the host) or services that analyze information detected via one or more user devices used by the user and/or cloud-based services associated with the user),
record content discussed during the at least one communication session (¶0030 and ¶0111, teaches generate a summary (i.e. record content) of a multi-person conversation carried out via a CS…CS summary engine 260 is generally responsible for generating the summary of the CS (i.e. record content discussed in communication session) and providing the summary to the user),
execute at least one artificial intelligence (AI) model on the recorded content to identify topics discussed and sentiments with respect to the topics (¶0020, ¶0026, ¶0044 and ¶0092 teaches data models (i.e. AI) are employed to determine a context of the CS, as well as a relevance of various portions of the content based on the determined context. Portions of the content that are likely to be relevant to the user, based on the user's activities and interests, are identified. The embodiments generate and provide the user with a summary of the conversation (i.e. content recorded) carried out via the CS based on the context of the CS and the relevance of the content… ML methodologies to learn topics of interests of the user… Content-style features may additionally encode one or more emotions of the speaker, e.g., anger, surprise, satisfaction, happiness, and other emotions… the content-substance features may indicate topics being conversed about, as well as the intentioned meaning and the sentiments of the conversation),
convert the recorded content into a vector and label the vector with metadata that identifies the topics and the sentiments (¶0020, ¶0040, ¶0044, ¶0082, ¶0092, ¶0102 and ¶0119 teaches provide the user with a summary of the conversation (i.e. content recorded) carried out via the CS based on the context of the CS and the relevance of the content…symbolic content may be automatically transformed (i.e. convert) into textual content via an identification of various concepts associated with the symbolic content metadata and/or other identifying data associated with the symbolic content may be employed in such natural language models… Content-style features may additionally encode one or more emotions of the speaker, e.g., anger, surprise, satisfaction, happiness, and other emotions…the ML data models may include vector machines… the content-substance features may indicate topics being conversed about, as well as the intentioned meaning and the sentiments of the conversation…a relevance for content may include one or more probabilities, wherein the one or more probabilities correlate with the likelihood that the content is relevant to the user. The one or more probabilities may be structured as a normalized scalar, vector…this information may be used for determining a label or identification of the device (e.g., a device ID) so that user interaction with the device may be recognized from user data by user-activity monitor 296).
However, Somech remain silent on execute a second AI model on the at least one vector and the metadata to identify a topic associated with a positive sentiment which was brought up during the at least one communication session and which was not addressed.
Ye discloses execute a second AI model on the at least one vector and the metadata to identify a topic associated with a positive sentiment which was brought up during the at least one communication session and which was not addressed (page-1, II. 26-27 and II. 53-55, teaches the combination of AI (i.e. second AI model) and Natural language processing (NLP) has been gradually applied in the field of sentiment analysis…by performing a vector clustering operation on the conversational text knowledge vector, page-6, II. 40-46, teaches each conversation text record included in the emotion analysis auxiliary data set carries at least one group of online user conversation texts belonging to the same topic comment statement. the artificial intelligence cloud platform can use the obtained sentiment analysis auxiliary data set and the online user conversation text collected in the current period to determine which part (i.e. which was not addressed) of the online user conversation text in the online user conversation text acquired in real time, and integrate it with the sentiment analysis auxiliary data set Which part of the conversation text records contained meets the set corresponding conditions (for example, it can be understood that there is a corresponding relationship), so as to determine the first conversation text record in the sentiment analysis auxiliary data set, and the second online user conversation text obtained in real time, page-7, II. 25-29, teaches the artificial intelligence cloud platform can determine the corresponding associated text of each topic comment sentence to be processed Sentiment polarity labels (i.e. metadata) within the infoset. For example, the emotional polarity label may include: a second emotional polarity label and a first emotional polarity label, wherein, for example, the first emotional polarity label may be a positive emotional label, page-11, II. 17-20, teaches the artificial intelligence cloud platform can mine the first conversation text knowledge vector of each prominent conversation text, and mine the second conversation text knowledge vector of each online user conversation text acquired in real time, and determine the first conversation text knowledge vector and the vector commonality value between the second conversational text knowledge vectors).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Somech’s topics of a conversation of communication sessions, content exchange in the communication session and relevant content with execute a second AI model on the at least one vector and the metadata to identify a topic associated with a positive sentiment which was brought up during the at least one communication session and which was not addressed of Ye, in order to find positively received topics that were never addressed or followed up on (Ye).
However, Somech in view of Ye remain silent on generate content about the topic, output the content about the content about the topic via the software application during an active communication session with the source device.
Sheikh discloses generate content about the topic (¶0067, ¶0080 and ¶0083 teaches “objective” as used herein refers to a desired outcome or goal that the client-agent device (client-AA) aims to achieve based on the service request (i.e. topic) received therethrough. Optionally, the objective defines the purpose or intent behind the service request. In this regard, the objective is generated by the client-agent device (client-AA)… when there is no previous task that contains sufficient contextual information, or if the Large Language Model (Internal-LLM) does not have the necessary data (i.e. topic), the client-agent device (client-AA) needs to refer to the external Large Language Model (External-LLM)… the service request to find information about a specific topic is received by the client-agent device (client-AA). The ML-Model AA, acting as a language model, receives the objective and processes the objective using its natural language processing capabilities), and
output the content about the content about the topic via the software application during an active communication session with the source device (¶0018, ¶0080 teaches when generating the objective associated with the service request (i.e. topic), the software application is configured to interact with the client at each step to confirm on the objective and metadata associated with the service request... The client-agent device (client-AA), as the initial point of interaction with the user, generates the objective based on the service request (i.e. content of the topic)).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Somech’s topics of a conversation of communication sessions, content exchange in the communication session and relevant content with output the content about the content about the topic via the software application during an active communication session with the source device of Sheikh, in order to keep the conversation dynamic and engaging (Sheikh, ¶0066 and ¶0080).
For claim 8, it is a method claim corresponding to the apparatus of claim 1. Therefore claim 8 is rejected under the same ground as claim 1.
For claim 15, it is a computer-readable storage medium claim corresponding to the method of claim 1. Therefore claim 6 is rejected under the same ground as claim 1.
With respect to claims 2, 9 and 16, Somech in view of Ye, and further in view of Sheikh discloses the apparatus, the method and the computer readable storage medium of claims 1, 8 and 15, wherein the processor is further configured to determine a mood with respect to the topic based on the execution of the at least one AI model (Somech, ¶0026 and ¶0044, teaches ML methodologies to learn topics of interests of the user…content-style features may encode the speaking style of the speaker. Content-style features may additionally encode one or more emotions (i.e. mood) of the speaker, e.g., anger, surprise, satisfaction, happiness, and other emotions, Sheikh, ¶0068, ¶0080, ¶0083, teaches the vector database employs embeddings that are generated by the Large Language Models (i.e. second AI model) and have a large number of attributes or features…when there is no previous task that contains sufficient contextual information, or if the Large Language Model (Internal-LLM) does not have the necessary data (i.e. topic that has not been discussed in previous session), the client-agent device (client-AA) needs to refer to the external Large Language Model (External-LLM)), and generate the content about the topic based on the mood (Somech, ¶0026, ¶0044, ¶0056, ML methodologies to learn topics of interests of the user…content-style features may encode the speaking style of the speaker. Content-style features may additionally encode one or more emotions (i.e. mood) of the speaker, e.g., anger, surprise, satisfaction, happiness, and other emotions…generate the various data models that enable identifying and summarizing likely relevant content of a CS, user data is received from one or more data sources).
With respect to claims 3, 10 and 17, Somech in view of Ye, and further in view of Sheikh discloses the apparatus, the method and the computer readable storage medium of claims 1, 8 and 15, wherein the processor is configured to identify the topic that has not been addressed based on at least one query within the content which was not answered (Ye, page-6, II. 40-46, teaches each conversation text record included in the emotion analysis auxiliary data set carries at least one group of online user conversation texts belonging to the same topic comment statement. the artificial intelligence cloud platform can use the obtained sentiment analysis auxiliary data set and the online user conversation text collected in the current period to determine which part (i.e. which was not addressed) of the online user conversation text in the online user conversation text acquired in real time, and integrate it with the sentiment analysis auxiliary data set Which part of the conversation text records contained meets the set corresponding conditions (for example, it can be understood that there is a corresponding relationship), so as to determine the first conversation text record in the sentiment analysis auxiliary data set, and the second online user conversation text obtained in real time, Sheikh, ¶0080, ¶0083 and ¶0090, ¶0109 teaches when there is no previous task that contains sufficient contextual information, or if the Large Language Model (Internal-LLM) does not have the necessary data (i.e. topic that has not been discussed in previous session), the client-agent device (client-AA) needs to refer to the external Large Language Model (External-LLM)… the search and discovery component would query the registry database to find autonomous agents that have the necessary components related to flight booking, such as flight search skills, flight booking protocols, and connections to airline reservation systems…SearchTask[query; context], which searches for tasks in the vector database using natural language query to find tasks which can provide missing information given the context).
With respect to claims 4,11 and 18, Somech in view of Ye, and further in view of Sheikh discloses the apparatus, the method and the computer readable storage medium of claims 1, 8 and 15, wherein the processor is further configured to identify a mood with respect to a different topic based on the execution of the at least one AI model (Somech, ¶0041, ¶0080, teaches Various responses may be generated to represent likely stylistic choices of the user, such as emotions (i.e. mood), sentiments, and the like. That is, a stylistic response may be automatically generated to include likely stylistic choices of the user, such as encoding emotions and sentiments in the response…a data model may include a set or list of topics that are weighted with respect to a determined level of interest) , and remove the different topic from a call script for a future communication session with the source device based on the mood (Somech, ¶0041, teaches various responses may be generated to represent likely stylistic choices of the user, such as emotions, sentiments (i.e. based on the mood), and the like, Sheikh, ¶0106, ¶0130, teaches the at least one task is defined by a JavaScript Object Notation (JSON) object... the user may remove the task T1 (i.e. different topic) from amongst the plurality of tasks T1, T2, . . . , Tn that are to be executed).
With respect to claims 6 and 20, Somech in view of Ye, and further in view of Sheikh discloses the apparatus, the method and the computer readable storage medium of claims 1, 8 and 15, wherein the processor is configured to generate a custom instruction for discussion based on the execution of the second AI model and output a display of the custom instruction via a graphical user interface of the software application during the active communication session (Somech, ¶0021, ¶0079, ¶0081, teaches Additional ML data models are generated and/or trained to identify relevant content for the user and generate the summary for the user. That is, a data model may be customized to a particular user… a data model receives input (i.e. instruction) data, such as but not limited to information derived from the content of a CS, and generates output data, based on the input data. For instance, a data model may generate a list of one or more topics that the discussion encoded in the content is directed towards. Additional output data generated by a data model may include a score or weight associated with each of the topics, where the score indicates a measure of the dominance of significance of the topic within the conversations…an ML data model may be customized (i.e. custom) to a particular user based on training the ML model with data that is specific to the user, Sheikh, ¶0062 teaches graphical user interface associated with client agent device).
With respect to claims 7 and 14, Somech in view of Ye, and further in view of Sheikh discloses the apparatus, the method and the computer readable storage medium of claims 1, 8 and 15, wherein the processor is configured to retrieve historical behavior associated with the source device from a data store, and execute the at least one AI model on the historical behavior to identify the topic (Sheikh, ¶0071, ¶0080, ¶0103, teaches the software application uses data processing algorithms to compare the metadata associated with at least one service that is requested in the service request (current) to the metadata from the previous service requests stored in the system…identifying relevant historical data (i.e. historical behavior) that can assist in generating the objective for at least one service that is requested in the service request…the creation of new agent is treated as a block of a blockchain network, and after validation by the ML model and/or LLM and/or by existing agents using association with a task…when there is no previous task that contains sufficient contextual information, or if the Large Language Model (Internal-LLM) does not have the necessary data (i.e. topic that has not been discussed in previous session), the client-agent device (client-AA) needs to refer to the external Large Language Model (External-LLM…an agent (i.e. source device) holding the transaction information (i.e. transaction history) such as metadata (i.e. identify the topic) associated with the task).
With respect to claim 21, Somech in view of Ye, and further in view of Sheikh discloses the apparatus of claim 1, wherein the at least one AI model comprises a multi-head attention mechanism (Sheikh, ¶0063, teaches a digital twin of a user, a digital representation of a user, an artificial intelligence model (AI-model) based on a Large Language Model (LLM) (i.e. a multi-head attention mechanism) and the processor is configured to identify the topics discussed based on a first attention head of an AI model (Somech, ¶0008, teaches the content-substance features indicate at least topics discussed in the conversation, ¶0043, teaches a content-substance feature may indicate one or more topics and/or keywords associated with the content line. A content-substance feature may also indicate a sentiment of the speaker. That is, a sentiment content-substance feature may encode an identified and/or categorized opinion expressed in the content) and identify the sentiments with respect to the topics based on a second attention head of the AI model (Ye, page-1, II. 26-27 teaches the combination of AI (i.e. second AI model) and Natural language processing (NLP) has been gradually applied in the field of sentiment analysis, page-7, II. 49-51, teaches the topic comment statement to be processed can improve the accuracy and credibility of the determined topic comment statement to be processed, and contribute to the efficient and rational sentiment analysis of the topic comment statement).
Claim(s) 5, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Somech in view of Ye in view of Sheikh, and further in view of Mukherjee et al. (US 8849812), hereinafter “Mukherjee”.
With respect to claims 5, 12 and 19, Somech in view of Ye, and further in view of Sheikh discloses the apparatus, the method and the computer readable storage medium of claims 1, 8 and 15, output the graphical user interface via the software application during the active communication session (Somech, ¶0018, ¶0033, ¶0080, ¶0109 teaches when generating the objective associated with the service request (i.e. topic), the software application is configured to interact with the client at each step to confirm on the objective and metadata associated with the service request... A CS may include the exchange of textual, audible, and/or visual content. Visual content may include image, graphical, and/or video content…The client-agent device (client-AA), as the initial point of interaction with the user, generates the objective based on the service request (i.e. content of the topic)… When a user is simultaneously participating in multiple CSs distributed across multiple windows or user interfaces (i.e. GUI)).
Sheikh ¶0080, teaches when there is no previous task that contains sufficient contextual information, or if the Large Language Model (Internal-LLM) does not have the necessary data (i.e. topic that has not been discussed in previous session), the client-agent device (client-AA) needs to refer to the external Large Language Model (External-LLM). However, Somech in view of Ye, and further in view of Sheikh remain silent on wherein the processor is configured to generate a graphical user interface with a clickable link associated with the topic which when clicked on registers the source device with a service corresponding to the topic.
Mukherjee discloses wherein the processor is configured to generate a graphical user interface with a clickable link associated with the topic which when clicked on registers the source device with a service corresponding to the topic (Fig. 5 step 502, Col-7, II. 44-47, Col-8, II. 8-11, Col-9, II. 28-30 teaches generating con tent for topics based on user demand further includes deter mining that the web site does not include existing web page content for the topic…generating content for topics based on user demand further includes adding a link on the web site for the topic, in which the link is associated with the landing page, and in which a web crawler can crawl the link…the pointing device 106 can be a mouse, stylus, track ball, or tablet, and is useful for interacting with a graphical user interface).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Somech’s topics of a conversation of communication sessions, content exchange in the communication session and relevant content in view of Ye’s, and further in view of Sheikh’s topic previous session with generate a graphical user interface with a clickable link associated with the topic which when clicked on registers the source device with a service corresponding to the topic that has not been discussed of Mukherjee, in order to see a new topic suggestion with a direct action button or link (Mukherjee).
Conclusion
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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/G.M/Examiner, Art Unit 2458
/UMAR CHEEMA/Supervisory Patent Examiner, Art Unit 2458