CTFR 18/620,832 CTFR 96826 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Amendment The Amendment filed April 29, 2026, has been entered. Claims 1 – 5, 7 – 12, 14 – 19 and 21 – 23 are pending in the application. Applicant’s amendments to the Specification have overcome each and every objection previously set forth in the Non-Final Office Action mailed February 18, 2026. Response to Arguments Applicant’s arguments, filed April 29, 2026, with respect to claims 1 – 5, 7 – 12, 14 – 19 and 21 – 23 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. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 3, 7 – 8, 10, 14 – 15, 17, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Taylert et al. (US Patent No. 11,960,514), hereinafter Taylert, in view of Penta et al. (US Patent No. 12,373,506), hereinafter Penta, and Qin (US Patent Application Publication No. 2024/0346256) . Regarding claim 1, Taylert discloses an apparatus comprising: a memory; and a processor coupled to the memory (Column 22, lines 26-30, "A machine implementing the techniques herein comprises a hardware processor, and non-transitory computer memory holding computer program instructions that are executed by the processor to perform the above-described methods."), the processor configured to: receive interaction content from an interaction session of a source device (Column 14, lines 23-40, "With the above as background, the following describes the interactive conversation (e.g., live-chat) assistance method and system of this disclosure. As a shorthand, and without intending to be limiting, this functionality is referred to as “suggested replies.” As will be seen, the approach leverages a generative AI (e.g., a language model such as OpenAI ChatGPT) to facilitate generation of a reply to an utterance that is received by the system during the interactive conversation, typically a conversation between a user and an agent. While the following description focuses on the user-live agent use case, this is not a limitation. The interactive conversation may also involve some automated process as a participant. Typically, and in the live agent use case, the utterance is a written query or question that the user enters into a conversational chat interface as previously described, although this is not a limitation, as the utterance may be received in the system in other words (orally, email, text message, an input form, or the like)."; Column 19, lines 19-22, "An individual end user typically accesses the system using a user application executing on a computing device (e.g., mobile phone, tablet, laptop or desktop computer, Internet-connected appliance, etc.)."; An interactive conversation between a user and an agent reads on interaction content from an interaction session.), identify contextual attributes of one or more of the interaction content and the interaction session (Column 17, lines 2-8, "When user input is received, the generative-chat-application first checks the conversation history cache to see if there is any relevant conversation history. If so, the conversation history is passed to the response generation module along with the current user input and the context found from semantic-search to generate a contextually-relevant response."; Finding context from a semantic-search reads on identifying contextual attributes.), match the interaction content to a subset of vectors within a vector storage [based on labels previously assigned to the subset of vectors] (Column 18, lines 27-29, "As noted, the semantic search receives the utterance from the generative chat application and retrieves the relevant context, in this scenario from the vector database 1514."; Retrieving relevant context from a vector database reads on matching the interaction content to a subset of vectors within a vector storage.), input the prompt to a machine learning (ML) model [prior to input of the interaction content to the ML model] to generate an augmented ML model [conditioned to generate responses based on the subset of vectors] (Column 15, lines 52-57, "The semantic-Search API returns the relevant context (when context can be found), and the prompt is then enriched to include it. The resulting enriched prompt is then passed through to a text completion endpoint associated with an external generative AI text completion endpoint, such as OpenAI ChatGPT-3."; Providing an enriched prompt to generative AI reads on augmenting a machine learning model.), and generate a response based on execution of the augmented ML model on the interaction content and output the response to the source device during the interaction session (Column 15, lines 51-63, "The semantic-Search API returns the relevant context (when context can be found), and the prompt is then enriched to include it. The resulting enriched prompt is then passed through to a text completion endpoint associated with an external generative AI text completion endpoint, such as OpenAI ChatGPT-3. This particular language model is not intended to be limiting, as other large language models may be used for this purpose. Upon receiving a response from the generative AI text completion endpoint, the suggested replies API returns it, e.g., to a front-end application tool that is managing the conversation (namely, the interaction between the user and the live-agent)."). Taylert does not specifically disclose: match the interaction content to a subset of vectors within a vector storage based on labels previously assigned to the subset of vectors. Penta teaches: match the interaction content to a subset of vectors within a vector storage based on labels previously assigned to the subset of vectors (Column 3, lines 45-52, "The personalized retrieval-augmented generation system can determine a data context for generating a personalized response by comparing a query embedding with vectorized segments of content items. For instance, the personalized retrieval-augmented generation system determines a data context specific to the entity by comparing the query embedding with a plurality of vectorized segments of content items associated with the entity stored in a database."; Column 18, lines 13-33, "Indeed, the metadata database 406 can store metadata for content items and/or for vectorized segments of content items, where different metadata are associated with or labeled as corresponding to the items or segments. In some instances, metadata can reflect information associated with or generated by an entity. For example, metadata can reflect relationships 410, location(s) 412, and/or the timing 414 associated with content items associated with the entity. For instance, the personalized retrieval-augmented generation system 106 can store metadata about the time of receiving email and who received the email in the metadata database 406. To further illustrate, the personalized retrieval-augmented generation system 106 can receive a query asking for details about an upcoming flight to Nashville. While generated a personalized response about the flight to Nashville, the personalized retrieval-augmented generation system 106 can access metadata regarding the time and date of the upcoming flight to Nashville and utilize the metadata to select relevant information (e.g., vectorized segments of content items) to include in the data context along with a vectorized segment of a flight receipt."; Utilize metadata to select vectorized segments of content items, where the metadata is labeled as corresponding to the items, reads on matching the interaction content to a subset of vectors within a vector storage based on labels previously assigned to the subset of vectors.). Penta is considered to be analogous to the claimed invention because it is in the same field of machine learning conversational response systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Taylert to incorporate the teachings of Penta to utilize metadata to select vectorized segments of content items, where the metadata is labeled as corresponding to the items. Doing so would allow for improving the accuracy of generating responses to queries utilizing retrieval-augmented generation systems or large language models (Penta; Column 4, lines 15-20). Taylert in view of Penta does not specifically disclose: generate a prompt comprising the subset of vectors and instructions to use the subset of vectors for response generation during the interaction session, input the prompt to a machine learning (ML) model prior to input of the interaction content to the ML model to generate an augmented ML model conditioned to generate responses based on the subset of vectors. Qin teaches: generate a prompt comprising the subset of vectors and instructions to use the subset of vectors for response generation during the interaction session (Paragraph 0019, lines 1-15, "In one implementation, a query may be used to retrieve pieces of augmentation information that may be included in a prompt to the LLM. For instance, a query string may be encoded into a first feature vector that is compared to a plurality of second feature vectors to determine a subset of the second feature vectors that satisfy a predetermined condition (e.g., threshold similarity). Augmentation information corresponding to the determined subset of second feature vectors may be retrieved and included in an augmented prompt to the LLM. In embodiments, the augmented prompt may include the original query, contextual information for answering the query, the retrieved augmentation information, and/or a request to answer the original query based on the contextual information and/or the retrieved augmentation information."; The augmented prompt including contextual information for answering the query and a request to answer the original query based on the contextual information reads on a prompt comprising the subset of vectors and instructions to use the subset of vectors for response generation during the interaction session.), input the prompt to a machine learning (ML) model prior to input of the interaction content to the ML model to generate an augmented ML model conditioned to generate responses based on the subset of vectors (Paragraph 0019, lines 1-19, "In one implementation, a query may be used to retrieve pieces of augmentation information that may be included in a prompt to the LLM. For instance, a query string may be encoded into a first feature vector that is compared to a plurality of second feature vectors to determine a subset of the second feature vectors that satisfy a predetermined condition (e.g., threshold similarity). Augmentation information corresponding to the determined subset of second feature vectors may be retrieved and included in an augmented prompt to the LLM. In embodiments, the augmented prompt may include the original query, contextual information for answering the query, the retrieved augmentation information, and/or a request to answer the original query based on the contextual information and/or the retrieved augmentation information. When presented with the retrieved augmentation information, the LLM prioritizes the retrieved augmentation information over the information present in its training data when generating a response to the query."; Paragraph 0053, lines 1-2, "In step 310, an augmented prompt is provided to a large language model.”; Paragraph 0054, lines 1-7, "In step 312, a response generated by the large language model is received. For instance, GUI manager 108 may receive from response generator 110 a response 238 generated by LLM 214. As discussed above, LLM 214 may process augmented prompt 236 to generate a response 238 based on contextual information 215 using augmentation information 232.”; Providing an augmented prompt to a large language model (LLM), where the augmented prompt includes contextual information and augmentation information for answering a query, followed by the LLM generating a response based on contextual information using augmentation information, reads on inputting the prompt to a machine learning (ML) model prior to inputting the interaction content to the ML model to generate an augmented ML model conditioned to generate responses based on the subset of vectors. The LLM generating a response to a query based on contextual information and retrieved augmentation information demonstrates that the contextual information and retrieved augmentation information are input to the LLM prior to the query being input to the LLM, even though the original query is included in the augmented prompt.). Qin is considered to be analogous to the claimed invention because it is in the same field of machine learning conversational response systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Taylert in view of Penta to incorporate the teachings of Qin to provide an augmented prompt to a large language model (LLM), where the augmented prompt includes contextual information and augmentation information for answering a query, followed by the LLM generating a response based on contextual information using augmentation information. Doing so would allow for generating answers more relevant to users (Qin; Paragraph 0019, line 19-24). Regarding claim 3, Taylert in view of Penta and Qin discloses the apparatus as claimed in claim 1. Taylert further discloses: wherein the interaction content comprises chat content from a chat conversation, and the processor is configured to generate a chat response for the chat conversation and output the chat response via a chat window of the chat conversation (Column 8, lines 25-36, "In a typical user case, the software application 402 executes in association with a website 408, although the chatbot functionality may be utilized by multiple distinct websites operated by separate and independent content providers. As such, the computing platform provides the chatbot functionality in a multi-tenant operating environment, although this is not a requirement. The user provides input to the chatbot as speech, as one or more physical actions (e.g., selecting a button or link, entering data in a field, etc.), or as some combination of speech and physical action. In this example, the chatbot 402 is an AI-based conversational bot."; Column 8, lines 51-52, "In the context of a chatbot, the response typically is provided in a chat window."). Regarding claim 7, Taylert in view of Penta and Qin discloses the apparatus as claimed in claim 1. Taylert further discloses: wherein the processor is configured to convert the interaction content into a vector via execution of a second ML model on the interaction content, and match the vector to the subset of vectors (Column 16, lines 25-27, "One model that the Semantic-Search API may use to embed text is a transformer"; Column 18, lines 27-29, "As noted, the semantic search receives the utterance from the generative chat application and retrieves the relevant context, in this scenario from the vector database 1514."; Embedding text using a transformer reads on converting the interaction content into a vector via execution of a machine learning model, and retrieving relevant context from a vector database reads on matching the vector to the subset of vectors.). Regarding claim 8, arguments analogous to claim 1 are applicable. Regarding claim 10, arguments analogous to claim 3 are applicable. Regarding claim 14, arguments analogous to claim 7 are applicable. Regarding claim 15, arguments analogous to claim 1 are applicable. In addition, Taylert discloses a non-transitory computer-readable storage medium comprising instructions stored therein (Column 22, lines 26-30, "A machine implementing the techniques herein comprises a hardware processor, and non-transitory computer memory holding computer program instructions that are executed by the processor to perform the above-described methods.") which when executed by a processor cause the processor to perform the steps of claim 1. Regarding claim 17, arguments analogous to claim 3 are applicable. Regarding claim 21, Taylert in view of Penta and Qin discloses the apparatus as claimed in claim 1. Qin further teaches: wherein the processor is configured to generate a description of the contextual attributes identified for the interaction session and a task to generate the response based on the contextual attributes and the subset of vectors (Paragraph 0046, lines 1-18, "Prompt generator 212 may generate a prompt for LLM 214 based on one or more of query 216, first feature vector 226, one or more of second feature vectors 228, indications 230, and/or augmentation information 232. For example, prompt generator 212 may generate an augmented prompt 236 that includes the original query, contextual information (e.g., the current webpage, the product or service of the current webpage, temporal information, location information, etc.), content information (e.g., the retrieved augmentation information 232), and a request to answer the original query based on the provided contextual information using the included content information. For example, prompt generator 212 may employ natural language processing (NLP) techniques to generate an augmented prompt 236 that requests LLM 214 to respond to query 216 based on contextual information using augmentation information 232. In embodiments, augmented prompt 236 may include, identify and/or link to augmentation information 232."; Generating an augmented prompt that includes the original query, contextual information, content information, and a request to answer the original query based on the provided contextual information using the included content information reads on generating a task to generate the response based on the contextual attributes and the subset of vectors, and generating an augmented prompt that identifies augmentation information or provides a link to augmentation information reads on generating a description of the contextual attributes identified for the interaction session.). Qin is considered to be analogous to the claimed invention because it is in the same field of machine learning conversational response systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Taylert in view of Penta and Qin to further incorporate the teachings of Qin to generate an augmented prompt that includes the original query, contextual information, content information, and a request to answer the original query based on the provided contextual information using the included content information, and identifies augmentation information or provides a link to augmentation information. Doing so would allow for generating answers more relevant to users (Qin; Paragraph 0019, line 19-24) . 07-21-aia AIA Claim s 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Taylert in view of Penta and Qin, and further in view of Rodriquez et al. (US Patent No. 11,366,857), hereinafter Rodriquez . Regarding claim 2, Taylert in view of Penta and Qin discloses the apparatus as claimed in claim 1, but does not specifically disclose: wherein the processor is configured to identify a policy of an organization discussed during the interaction session and match the interaction content to the subset of vectors based on labels of the subset of vectors including a label of the policy. Rodriquez teaches: wherein the processor is configured to identify a policy of an organization discussed during the interaction session and match the interaction content to the subset of vectors based on labels of the subset of vectors including a label of the policy (Column 5, lines 21-28, "The policies storage 103c stores policies (or conditions, criteria, etc.) of an organization that can be used to align transcripts and HCl actions, cluster transcripts, label feature vectors, and train models. For example, it may be a particular company's corporate policy to require certain information to authenticate users prior to some transactions (e.g., a bank may require detailed identification information to authenticate a caller)."; Column 7, lines 3-12, "In several embodiments, the transcripts module 135 tags the transcripts and/or the mapped vectors based on, for example, metadata associated with the transcript (stored in the metadata storage 103j), policies (stored in the policies storage 103c), communication medium/source via which the transcript was received (for example, chat, phone, email, web page, mobile application, smart device, IoT device, social media, etc.), and so on. The transcripts module 135 can store the vector representations of the transcripts and their tags in the transcripts storage 103a."; Tagging a transcript based on policies stored in a policies storage reads on identifying a policy of an organization discussed during the interaction session, and using stored policies to align transcripts and label feature vectors reads on matching the interaction content to the subset of vectors.). Rodriquez is considered to be analogous to the claimed invention because it is in the same field of machine learning conversational response systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Taylert in view of Penta and Qin to incorporate the teachings of Rodriquez to tag a transcript based on policies stored in a policies storage and use stored policies to align transcripts and label feature vectors. Doing so would allow for automating tasks that otherwise would require human interaction while simultaneously surfacing best practices among human agents (Rodriquez; Column 3, lines 5-22). Regarding claim 9, arguments analogous to claim 2 are applicable. Regarding claim 16, arguments analogous to claim 2 are applicable . 07-21-aia AIA Claim s 4 – 5, 11 – 12 and 18 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Taylert in view of Penta and Qin, and further in view of Guo et al. (US Patent No. 9,535,960), hereinafter Guo . Regarding claim 4, Taylert in view of Penta and Qin discloses the apparatus as claimed in claim 1, but does not specifically disclose: wherein the processor is configured to retrieve device data from the source device and identify the contextual attributes based on execution of at least one additional ML model on the interaction content and the device data. Guo teaches: wherein the processor is configured to retrieve device data from the source device and identify the contextual attributes based on execution of at least one additional ML model on the interaction content and the device data (Column 1, lines 29-34, "A search engine is described herein that retrieves information based, in part, on a context in which a query has been submitted. The search engine operates by using a deep learning model to project context information (associated with the context) into a context concept vector in a semantic space."; Column 1, lines 51-57, "According to one illustrative aspect, the context information may describe text in proximity to the query within a source document, demographic information regarding the user who has submitted the query, the time at which the query was submitted, the location at which the query was submitted, the prior search-related behavior of the user who has submitted the query, etc., or any combination thereof."; Column 5, lines 36-42, "Alternatively, or in addition, the context information may describe the location at which a user has submitted a query. The search engine 112 may determine the location of the user based on any position-determination mechanisms, such as satellite-based mechanisms (e.g., GPS mechanisms), triangulation mechanisms, dead-reckoning mechanisms, and so on. Alternatively, or in addition, the context information may describe the time at which a user has submitted a query."; Retrieving information based on a context in which a query has been submitted, including the location of the user and the time at which the query was submitted, reads on retrieving device data from the source device, and using a deep learning model to project context information into a context concept vector reads on identifying contextual attributes based on execution of a machine learning model.). Guo is considered to be analogous to the claimed invention because it is in the same field of machine learning conversational response systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Taylert in view of Penta and Qin to incorporate the teachings of Guo to retrieve information based on a context in which a query has been submitted, including the location of the user and the time at which the query was submitted, and use a deep learning model to project context information into a context concept vector. Doing so would allow for providing more useful search results to a user who has submitted a query (Guo; Column 1, lines 38-50). Regarding claim 5, Taylert in view of Penta and Qin discloses the apparatus as claimed in claim 1, but does not specifically disclose: wherein the processor is configured to identify a geographic location associated with the interaction content and match the interaction content to the subset of vectors based on labels of the subset of vectors including a label of the geographic location. Guo teaches: wherein the processor is configured to identify a geographic location associated with the interaction content and match the interaction content to the subset of vectors based on labels of the subset of vectors including a label of the geographic location (Column 1, lines 29-34, "A search engine is described herein that retrieves information based, in part, on a context in which a query has been submitted. The search engine operates by using a deep learning model to project context information (associated with the context) into a context concept vector in a semantic space."; Column 5, lines 36-42, "Alternatively, or in addition, the context information may describe the location at which a user has submitted a query. The search engine 112 may determine the location of the user based on any position-determination mechanisms, such as satellite-based mechanisms (e.g., GPS mechanisms), triangulation mechanisms, dead-reckoning mechanisms, and so on. Alternatively, or in addition, the context information may describe the time at which a user has submitted a query."; Column 6, lines 35-41, "Each transformation module uses an instantiation of the model 106 to map an input vector into an output concept vector. The input vector represents a particular linguistic item, such as a query, context, document, etc. The concept vector is expressed in a semantic space and reveals semantic information regarding the corresponding linguistic item from which it was derived."; Retrieving information based on a context in which a query has been submitted including the location of the user reads on identifying a geographic location associated with the interaction content, and projecting context information into a context concept vector in a semantic space reads on matching the interaction content to the subset of vectors based on labels of the subset of vectors including a label of the geographic location.). Guo is considered to be analogous to the claimed invention because it is in the same field of machine learning conversational response systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Taylert in view of Penta and Qin to incorporate the teachings of Guo to retrieve information based on a context in which a query has been submitted including the location of the user, and project context information into a context concept vector in a semantic space. Doing so would allow for providing more useful search results to a user who has submitted a query (Guo; Column 1, lines 38-50). Regarding claim 11, arguments analogous to claim 4 are applicable. Regarding claim 12, arguments analogous to claim 5 are applicable. Regarding claim 18, arguments analogous to claim 4 are applicable. Regarding claim 19, arguments analogous to claim 5 are applicable . 07-21-aia AIA Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Taylert in view of Penta and Qin, and further in view of Azarmi (US Patent Application Publication No. 2025/0086190) . Regarding claim 22, Taylert in view of Penta and Qin discloses the apparatus as claimed in claim 1, but does not specifically disclose: wherein the processor is configured to assign a weight to at least one vector of the subset of vectors based on feedback associated with previous responses, and condition the augmented ML model based on the assigned weight. Azarmi teaches: wherein the processor is configured to assign a weight to at least one vector of the subset of vectors based on feedback associated with previous responses, and condition the augmented ML model based on the assigned weight (Paragraph 0019, lines 1-13, "The disclosure discussed herein provides a system that implements a technical solution for computing an overall effectiveness value (e.g., a significance of context (SoC) value) of context data that is used in a retrieval augmented generation (RAG) system for generating model responses by one or more language models and for executing one or more computer actions based on the overall effectiveness value and/or one or more attribute scores (e.g., a relevance score, a timeliness score, a continuity score, and/or an accuracy score) that are used to compute the overall effectiveness value to increase the overall effectiveness score, thereby increasing the quality of model responses generated by a language model."; Paragraph 0021, lines 1-18, "According to the techniques discussed herein, the RAG system includes a SoC engine that computes or receives a relevance score, a timeliness score, a continuity score, and/or an accuracy score. A relevance score is a value that represents a level of semantic similarity between the user query and the retrieved context data. A timeliness score may represent a level of freshness of the retrieved context data (e.g., data with an older timestamp may have a lower timeliness score). A continuity score may represent a level of logical consistency (e.g., a higher score indicates a seamless flow of information). An accuracy score may represent a level of reliability or truthfulness of data. The SoC engine includes an algorithm that computes the SoC value based on the relevance score, the timeliness score, the continuity score, and/or the accuracy score. In some examples, the algorithm is a weighted algorithm that applies weights to the relevance score, the timeliness score, the continuity score, and/or the accuracy score."; Paragraph 0065, lines 1-4, "In some examples, as shown in FIG. 1B, the user interface 175 displays one or more feedback controls 133 to enable the user to submit feedback about the model response 130."; Paragraph 0065, lines 17-22, "In some examples, the feedback controls 133 include UI controls that enable the user to indicate a level of accuracy, relevance, continuity, and/or timeliness (e.g., controls that correspond to the relevance score 121, the timeliness score 123, the continuity score 125, and/or the accuracy score 127)."; Computing a significance of context value for context data based on a relevance score, a timeliness score, a continuity score, and a accuracy score, where the scores correspond to user submitted feedback about a response indicating a level of accuracy, relevance, continuity, and timeliness, and where weights are applied to the scores, reads on assigning a weight to at least one vector of the subset of vectors based on feedback associated with previous responses, and using the significance of context value to increase the quality of model responses generated by a language model reads on conditioning the augmented ML model based on the assigned weight.). Azarmi is considered to be analogous to the claimed invention because it is in the same field of machine learning conversational response systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Taylert in view of Penta and Qin to incorporate the teachings of Azarmi to compute a significance of context value for context data based on a relevance score, a timeliness score, a continuity score, and a accuracy score, where the scores correspond to user submitted feedback about a response indicating a level of accuracy, relevance, continuity, and timeliness, and use the significance of context value to increase the quality of model responses generated by a language model. Doing so would allow for increasing the quality of responses delivered by a language model (Azarmi; Paragraph 0003, lines 1-14) . 07-21-aia AIA Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Taylert in view of Penta and Qin, and further in view of Coudert et al. (US Patent No. 12,585,879), hereinafter Coudert . Regarding claim 23, Taylert in view of Penta and Qin discloses the apparatus as claimed in claim 1, but does not specifically disclose: wherein the processor is further configured to identify the contextual attributes based on execution of a plurality of artificial intelligence (AI) models, wherein the plurality of AI models are configured to identify a plurality of different contextual attributes, respectively, from the interaction session. Coudert teaches: wherein the processor is further configured to identify the contextual attributes based on execution of a plurality of artificial intelligence (AI) models, wherein the plurality of AI models are configured to identify a plurality of different contextual attributes, respectively, from the interaction session (Column 6, lines 41-56, "Context identification module 307 is representative of a module that employs natural language processing and machine learning algorithms to extract relevant information from a natural language input, such as temporal information (e.g., reference to a specific data/time, reference to a date/time range, etc.), location information, (e.g., asset location, event location, etc.), etc. Context identification module 307 also employs natural language processing and machine learning algorithms to derive an intention and/or purpose of the input (e.g., sales inquiry, maintenance inquiry, domain specific inquiry, etc.) and to derive the context of the input, such as a domain context (e.g., information about the domains of a network; template of a domain that maps relationships between the content of the domain with the assets of the domain; hierarchical structure of systems, data, assets, etc. of a domain; etc.) etc."; A context identification module employing machine learning algorithms to extract relevant information from a natural language input, such as temporal information and location information, and derive an intention and purpose of the input, reads on identifying the contextual attributes based on execution of a plurality of artificial intelligence (AI) models, where the AI models are configured to identify a plurality of different contextual attributes from the interaction session.). Coudert is considered to be analogous to the claimed invention because it is in the same field of machine learning conversational response systems. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Taylert in view of Penta and Qin to incorporate the teachings of Coudert to use machine learning algorithms to extract relevant information from a natural language input, such as temporal information and location information, and derive an intention and purpose of the input. Doing so would allow for generating responses that are contextually appropriate (Coudert; Column 2, lines 39-54) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Penta et al. (US Patent No. 12,373,506) teaches a method for generating personal responses through retrieval-augmented generation by providing data context to a large language model and generating a personalized response informed by the data context. Titus et al. (US Patent Application Publication No. 2025/0217341) teaches a method for implementing a large language model that receives an initial prompt that includes a query related to metadata and context for the query. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to James Boggs whose telephone number is (571)272-2968. The examiner can normally be reached M-F 8:00 AM - 5:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JAMES BOGGS/Examiner, Art Unit 2657 Application/Control Number: 18/620,832 Page 2 Art Unit: 2657 Application/Control Number: 18/620,832 Page 3 Art Unit: 2657 Application/Control Number: 18/620,832 Page 4 Art Unit: 2657 Application/Control Number: 18/620,832 Page 5 Art Unit: 2657 Application/Control Number: 18/620,832 Page 6 Art Unit: 2657 Application/Control Number: 18/620,832 Page 7 Art Unit: 2657 Application/Control Number: 18/620,832 Page 8 Art Unit: 2657 Application/Control Number: 18/620,832 Page 9 Art Unit: 2657 Application/Control Number: 18/620,832 Page 10 Art Unit: 2657 Application/Control Number: 18/620,832 Page 11 Art Unit: 2657 Application/Control Number: 18/620,832 Page 12 Art Unit: 2657 Application/Control Number: 18/620,832 Page 13 Art Unit: 2657 Application/Control Number: 18/620,832 Page 14 Art Unit: 2657 Application/Control Number: 18/620,832 Page 15 Art Unit: 2657 Application/Control Number: 18/620,832 Page 16 Art Unit: 2657 Application/Control Number: 18/620,832 Page 17 Art Unit: 2657 Application/Control Number: 18/620,832 Page 18 Art Unit: 2657 Application/Control Number: 18/620,832 Page 19 Art Unit: 2657 Application/Control Number: 18/620,832 Page 20 Art Unit: 2657 Application/Control Number: 18/620,832 Page 21 Art Unit: 2657 Application/Control Number: 18/620,832 Page 22 Art Unit: 2657 Application/Control Number: 18/620,832 Page 23 Art Unit: 2657 Application/Control Number: 18/620,832 Page 24 Art Unit: 2657
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