Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is sent in response to Applicant’s communication received on
11/13/2024 for the application number 18946714. The office hereby acknowledges receipt of the
following placed of record in the file: Specification, Abstract, Oath/Declaration and claims.
Status of the claims
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 3/27/2025, 8/06/2025, 3/10/2026 were filed before the mailing date of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 2, 3, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as
explained below.
Claim 1 recites a computerized system comprising:
Accessing a query from an artificial intelligence (AI) agent;
Based on the query, accessing a dense context corresponding to the query;
Generating the dense context using a dense context generation service and enterprise data;
Generating a programmatically generated concise representation of data that provides context for language models to generate responses to queries;
Using a dense context integrator and the dense context, generating an updated query of the query;
Integrating the dense context with the query to generate the updated query;
Using a contextual response generation model and the updated query, generating a response; and
Communicating the response as a response to the query.
Step (a) comprises a mental process. This step can be performed by a human as a person
can receive a query or request from another person
Step (b) comprises a mental process. This step can be performed by a human as a person can access, recall, or retrieve contextual information relevant to a received query.
Step (c) comprises a mental process. This step can be performed by a human as a person can generate context using organizational knowledge, records, experience, or enterprise information.
Step (d) comprises a mental process. This step can be performed by a human as a person can summarize information in to a concise representation that provides context for responding to a query.
Step (e) comprises a mental process. This step can be performed by a human as a person can combine contextual information with a received query to reformulate or refine a query.
Step (f) comprises a mental process. This step can be performed by a human as a person can integrate contextual information with a query to create an updated query.
Step (g) comprises a mental process. This step can be performed by a human as a person can evaluate an updated query and generate a response based on the information available.
Step (h) comprises a mental process. This step can be performed by a human as a person communicate the generated response to another person.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any
statutory category. See MPEP 2106.03. The claim recites at least system. Thus, the claim is a
machine, which is one of the statutory categories of invention. (Step 1: YES). Step 2A, Prong
One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the
judicial exception is “set forth” or “described” in the claim. As discussed above, the broadest
reasonable interpretation of steps (a)-(h) recites a mental process.
Specifically, step (a) can be performed by a human as a person can receive a query or request from another person.
Step (b) can be performed by a human as a person can access, retrieve, or recall contextual information relevant to the received query.
Step (c) can be performed by a human as a person can generate context using information obtained from organizational records, enterprise knowledge, or prior experience.
Step (d) can be performed by a human as a person can create a concise summary or representation of information that provides context for answering a query.
Step (e) can be performed by a human as a person can combine contextual information with a received query to formulate an updated query.
Step (f) can be performed by a human as a person can integrate contextual information with a query to refine or modify the query.
Step (g) can be performed by a human as a person can evaluate the updated query and generate a response based on the available information.
Step (h) can be performed by a human as a person can communicate the generated response to another person.
Hence the claim encompasses mental processes practically performed in the human mind
by observation, evaluation, judgement, and opinion. See MPEP 2106.04(a)(2), subsection III.
(Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a
whole integrates the recited judicial exception into a practical application of the exception or
whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial
exception, and (2) evaluating those additional elements individually and in combination to
determine whether the claim as a whole integrates the exception into a practical application. See
MPEP 2106.04(d).
The claim recited additional elements including an artificial intelligence (AI) agent, a dense context generation service, enterprise data, a dense context integrator, a contextual response generation model, one or more computer processors, and computer memory. However, these elements are recited at a high level of generality and perform generic computer functions, such as receiving data, processing data, generating content, and providing output. The use of these elements to access a query, retrieve contextual information, generate a dense context, integrate the dense context with the query, generate a response, and communicate the response merely automates the mental processes described above using generic computer components. Such implementation does not impose any meaningful limit on the judicial exception. Accordingly, these elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES)
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole
amounts to significantly more than the recited exception i.e., whether any additional element, or
combination of additional elements, adds an inventive concept to the claim. As discussed with
respect to Step 2A, Prong Two, artificial intelligence (AI) agent, a dense context generation service, enterprise data, a dense context integrator, a contextual response generation model, one or more computer processors, and computer memory comprise additional elements that perform well-understood, routine, and conventional activities in the field such as
receiving data, processing data, generating content, and providing output. See MPEP 2106.05(g).
As known in the art these elements are well understood, routine, and conventional functions of a
computing device. Even when considered in combination these additional elements merely
implement the abstract idea using generic computer components and perform insignificant extra -
solutional activity, which does not provide an inventive concept. The claim is not patent eligible.
Claim 2 recites a mental process as a human can access information associated with multiple enterprises and generate a summary based on that information.
Claim 3 recites a mental process as a human can transform a query into one or more additional queries.
Claim 6 recites a mental process as a human can receive a query, formulate a response, and communicate the response to another person.
Claim 7 recites a mental process as a human can access enterprise information, generate contextual information from that enterprise information and use the contextual information to formulate a response.
Claim 8 & 15 recite substantially the same limitations as claim 1, but in different
statutory categories (computer readable medium & method). Accordingly, they are directed to the same abstract idea as claim 1.
Claim 9 & 16 recite substantially the same limitations as claim 2. Accordingly, they are
directed to the same abstract idea.
Claim 10 recites a mental process as a human can integrate contextual information with a query to formulate an updated query.
Claim 13 recites a mental process as a human can consider organizational information and user-specific information when formulating a response.
Claim 14 recites a mental process as a human can generate information for presentation and provide a response that includes contextual information.
Claim 17 recites a mental process as a human can receive a query, access contextual information, formulate an updated query, generate a response, and communicate the response.
Claim 18 recites substantially the same limitations as claim 10. Accordingly, it is
directed to the same abstract idea.
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 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 non-obviousness.
Claims 1, 2, 6, 7, 8, 9, 14, 15, 16, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Schirmer et al. (US 20250384019 A1) in view of Saikia et al. (US 20250094735 A1).
Regarding claim 1, Schirmer teaches A computerized system comprising: one or more computer processors; and computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations, the operations comprising (Fig. 6 shows the whole system which is computerized): based on the query, accessing a dense context corresponding to the query (Para 0037, “At S532, a RAG retriever platform may receive a user prompt from a user and perform vector embedding on the prompt at S534.” And para 0037 “the RAG pre-processing platform may retrieve structured data associated with a second document from the knowledge base and create a summary and metadata about the structured data”, and fig. 4, where the pre-processing comprises accessing the dense context), wherein the dense context is generated using a dense context generation service and enterprise data (Para 0037, “At S18, the RAG pre-processing platform may retrieve structured data associated with a second document from the knowledge base and create a summary and metadata about the structured data at S520.” Where the enterprise data is the structured data from the organizations internal knowledge base as in para 0002, “RAG can extend LLM capabilities to specific domains or an organization’s internal knowledge base without retraining the model.”). the dense context is a programmatically-generated concise representation of data that provides context (the summary) for language models to generate responses to queries (Para 0037, “At S18, the RAG pre-processing platform may retrieve structured data associated with a second document from the knowledge base and create a summary and metadata about the structured data at S520” and para 0043, “generate a context-aware response 1190 to be provided to the user 1110” ); using a dense context integrator (Corresponds to the RAG pre-processing platform, which receives retrieved data, generates context, and integrates the context with the query, see para 0030) and the dense context, generating an updated query of the query (Para 0030), wherein the dense context integrator supports integrating the dense context with the query to generate the updated query (Para 0030); using a contextual response generation model and the updated query, generating a response (Para 0043, “A LLM 1170 may then receive the user prompt 1120, unstructured data 1140, and structured data 1160 along with an RAG prompt 1180 as inputs and generate a context-aware response 1190 to be provided to the user 1110”); and communicating the response as a response to the query (Para 0043).
Schirmer does not teach accessing a query from an artificial intelligence (AI) agent.
However, Saikia teaches accessing a query from an artificial intelligence (AI) agent. (Para 0090, “Pre-processing subsystem 210 receives an utterance “A” 202 from a user” where “the intent classifier 242 will determine a skill bot to route the utterance to for handling.”, See fig. 3).
It would have been obvious to a person of ordinary skill in the art to modify Schirmer before the effective filing date in such a way as to incorporate the teachings of Saikia in order to provide route received user queries to an appropriate artificial intelligence agent for processing, improving system flexibility (Para 105).
Regarding claim 2, Schirmer teaches wherein the dense context generation service is integrated into an enterprise computing environment associated with a plurality of enterprise data sources comprising the enterprise data (Para 0033, “In some cases, the RAG framework 450 may process information associated with a number of different enterprises”), wherein the dense context generation service is a contextual summary service that programmatically transforms the enterprise data into the dense context (Para 0037, “At S18, the RAG pre-processing platform may retrieve structured data associated with a second document from the knowledge base and create a summary and metadata about the structured data at S520.”).
Regarding claim 6, Schirmer teaches communicating the query from a client associated with an artificial intelligence (AI) agent (Para 0043, “A LLM 1170 may then receive the user prompt 1120, unstructured data 1140, and structured data 1160 along with an RAG prompt 1180 as inputs and generate a context-aware response”); based on communicating the query, receiving the response associated with the query (Para 0043); and causing display of the response (Para 0043, “generate a context-aware response 1190 to be provided to the user 1110”).
Regarding claim 7, Schirmer teaches accessing the enterprise data at a dense context generation service (Para 0031, “In particular, an RAG framework 450 may access information about a plurality of structured and unstructured documents from a knowledge base.”); using the enterprise data and a plurality of dense context generation models of the dense context generation service (Para 0026, “Pre-production, documents 320 from a knowledge base 310 are provided to an embedding model 330”, and para 0037, “the RAG pre-processing platform creates the summary and metadata (e.g., a source path of the second document, hierarchy information, information about related documents, etc.) using a summary LLM and a summary prompt.” Where together the embedding model and the LLM comprise a plurality of dense models); and communicating the dense context to support generating the response to the query. (Para 0043, “A LLM 1170 may then receive the user prompt 1120, unstructured data 1140, and structured data 1160 along with an RAG prompt 1180 as inputs and generate a context-aware response”).
Claim 8 & 15 are analogous to claim 1 in that they recite substantially the same limitations. They are therefore rejected for the same reasons set forth above.
Claim 9 & 16 are analogous to claim 2 in that they recite substantially the same limitations. They are therefore rejected for the same reasons set forth above.
Regarding claim 14, Schirmer teaches wherein an interface of the client is configured to generate one or more interface elements associated with the response (Para 0041, “a context-aware response may be output to the user via the immersive virtual experience at S730 (e.g., the user IO area 840).”), wherein the response comprises one or more segments of the dense context. (Para 0043, “A LLM 1170 may then receive the user prompt 1120, unstructured data 1140, and structured data 1160 along with an RAG prompt 1180 as inputs and generate a context-aware response”).
Regarding claim 17, Schirmer using a dense context integrator and the dense context, generating the updated query of the query (Para 0038, “At S532, a RAG retriever platform may receive a user prompt from a user and perform vector embedding on the prompt at S534.” And para 0030 “This information is provided as context 362 in the reader architecture 360 which processes and aggregates document contents for use in an LLM prompt 364”); using a contextual response generation model and the updated query, generating the response (Para 0043, “A LLM 1170 may then receive the user prompt 1120, unstructured data 1140, and structured data 1160 along with an RAG prompt 1180 as inputs and generate a context-aware response 1190 to be provided to the user 1110”); and communicating the response as a response to the query (Para 0043).
Schirmer does not teach accessing a query from an artificial intelligence (AI) agent.
However, Saikia teaches accessing a query from an artificial intelligence (AI) agent. (Para 0090, “Pre-processing subsystem 210 receives an utterance “A” 202 from a user” where “the intent classifier 242 will determine a skill bot to route the utterance to for handling.”, See fig. 3).
It would have been obvious to a person of ordinary skill in the art to modify Schirmer before the effective filing date in such a way as to incorporate the teachings of Saikia in order to provide route received user queries to an appropriate artificial intelligence agent for processing, improving system flexibility (Para 105).
Claims 3, 10, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Schirmer (US 20250384019 A1) and Saikia (US 20250094735 A1) as above in claims 1, 2, 6, 7, 8, 9, 14, 15, 16, 17 and further in view of Narula (US 20130198217 A1)
Regarding claim 3, Schirmer does not teach wherein the dense context integrator is configured to transform the updated query into one or more additional queries.
However, Narula teaches wherein the dense context integrator is configured to transform the updated query into one or more additional queries. (Para 0015, “transforming the search query into a different query and generating one or more new queries from the search query.”)
It would have been obvious to a person of ordinary skill in the art to modify Schirmer before the effective filing date in such a way as to incorporate the teachings of Narula in order to improve retrieval of relevant context information through generation of additional queries (Para 0037).
Regarding claim 10, Schirmer teaches wherein the dense context integrator supports integrating dense context with the query to generate the updated query (Para 0030, “The LLM prompt 364 is then created based on the original user query and the additional relevant context 362”).
Schirmer does not teach wherein the dense context integrator is configured to transform the updated query into one or more additional queries.
However, Narula teaches wherein the dense context integrator is configured to transform the updated query into one or more additional queries. (Para 0015, “transforming the search query into a different query and generating one or more new queries from the search query.”).
It would have been obvious to a person of ordinary skill in the art to modify Schirmer before the effective filing date in such a way as to incorporate the teachings of Narula in order to improve retrieval of relevant context information through generation of additional queries (Para 0037).
Claim 18 is analogous to claim 10 in that it recites substantially the same limitations. It is therefore rejected for the same reasons set forth above.
Claims 4, 5, 11, 12, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Schirmer (US 20250384019 A1) and Saikia (US 20250094735 A1) as above in claims 1, 2, 6, 7, 8, 9, 14, 15, 16, 17 and further in view of Chaudhary et al. ("Developing a Llama-Based Chatbot for CI/CD Question Answering: A Case Study at Ericsson", In Journal of IEEE International Conference on Software Maintenance and Evolution, October 06, 2024, pp. 707-718.)
Regarding claim 4, Schirmer teaches wherein generating the response is further based on a Retrieval-Augmented Generation (RAG) model (Para 0002, “RAG can extend LLM capabilities to specific domains or an organization’s internal knowledge base without retraining the model.”), and communicates the query and the RAG data to the contextual response generation model to cause generation of the response. (Para 0030, “This information is provided as context 362 in the reader architecture 360 which processes and aggregates document contents for use in an LLM prompt 364”).
Schirmer does not teach the RAG model uses at least the updated query to retrieve RAG data including one or more documents from a RAG document repository.
However, Chaudhary teaches the RAG model uses at least the updated query to retrieve RAG data including one or more documents from a RAG document repository (Pg. 11, Left column, “In our design, the retriever uses the enhanced query obtained from query rewriting (Step 1) to retrieve relevant context items from the domain-specific corpus”).
It would have been obvious to a person of ordinary skill in the art to modify Schirmer before the effective filing date in such a way as to incorporate the teachings of Chaudhary in order to improve retrieval of relevant context data by updating the query to the RAG model specifically (Pg. 711 Left Col.).
Regarding claim 5, Schirmer does not teach wherein the contextual response generation model is integrated in an enterprise computing environment to employ two or more of the following: the query, the updated query, a transformed updated query, RAG data, and the dense context to generate the response.
However, Chaudhary teaches wherein the contextual response generation model is integrated in an enterprise computing environment to employ two or more of the following (Pg. 709 Right col. “The chatbot takes as input a collection of documents– in the context of our industry collaboration, a collection of Ericsson documents related to CI/CD” where the collection of documents comprises the enterprise data): the query, the updated query, a transformed updated query, RAG data, and the dense context to generate the response. (Page 712. Left col. “This step takes as input the enhanced query generated from Step 1 and the relevant items retrieved in Step 2, and formulates a question prompt for the LLM”).
It would have been obvious to a person of ordinary skill in the art to modify Schirmer before the effective filing date in such a way as to incorporate the teachings of Chaudhary in order to allow for the use of this system in order to improve generation within an enterprise environment (Page 709, Right col.).
Claim 11 & 19 are analogous to claim 4 in that they recite substantially the same limitations. They are therefore rejected for the same reasons set forth above.
Claim 12 & 20 are analogous to claim 5 in that they recite substantially the same limitations. They are therefore rejected for the same reasons set forth above.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Schirmer (US 20250384019 A1) and Saikia (US 20250094735 A1) as above in claims 1, 2, 6, 7, 8, 9, 14, 15, 16, 17 and further in view of Heine et al. (US 12625860 B2)
Schirmer does not teach wherein the AI agents supports a combination mode, an enterprise context mode, and a user context mode based at least in part on a hierarchical structure associated with the dense context.
However, Heine teaches wherein the AI agents supports a combination mode, an enterprise context mode, and a user context mode based at least in part on a hierarchical structure associated with the dense context. (Col. 30, Ln 19-25: “The Web Application Interface is further designed to enforce multi-level user authentication… It also manages user roles and permissions, making sure that users only have access to the functionalities and data that are pertinent to their roles within the organization.”, and Fig. 3, the hierarchy contains organizational information and user specific role information, by using both simultaneously the system inherently supports a combination mode).
It would have been obvious to a person of ordinary skill in the art to modify Schirmer before the effective filing date in such a way as to incorporate the teachings of Heine in order to gain contextual information more efficiently and improve generation of responses. (Col. 30).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ALAN FOSTER JR. whose telephone number is (571)272-8874. The examiner can normally be reached M - Th 8:00am - 6:00pm.
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/MICHAEL A FOSTER JR/ Examiner, Art Unit 2654
/Richa Sonifrank/ Primary Examiner, Art Unit 2654
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