Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
DETAILED ACTION
This action is in response to application filed 11/11/2025.
Claim 1-15, 17, 19-20 is pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/29/2025 has been placed in record and considered by the examiner.
Response to Arguments
Applicant’s arguments 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
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2,4-6, 10, 17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chlipala et al. (US 2025/0315223 A1 – Priority date 04/09/2024) in view of Xu et al. (US 2025/0086402 A1) in further view of Schrader et al. (US 2025/0110786 A1 – Priority date 01/05/2024).
Regarding claim 1, Chlipala discloses a non-transitory computer-readable media storing computer instructions which when executed by one or more processors of a device cause the device to: receive a natural language input defining an intent for a service to be instantiated in a network, the natural language input describing properties of the service including inputs to the service, functionality of the service, outputs of the service ([0008]: For each application component of the list of application components; (ii) receiving a natural language input from a user, the natural language input indicating a user-specified functionality for an application (e.g., the natural language input is associated with a plurality of user requested application features. [0025]: the system obtains information about a set of pre-generated components for application generation, including obtaining input and output type information for each component of the set of pre-generated components), and visual interfaces of the service ([0084]: The data associated with the one or more user inputs is analyzed to generate one or more application components (e.g., visually presented data analytics, images, interactive agents, etc.);
process the natural language input, using a large language model (LLM), to translate the intent into a communication service capable of being orchestrated in a network ([0134]: developing applications using a large language model (LLM). The method includes: (i) receiving user input describing a desired application functionality; (ii) engineering prompts (e.g. translate) for the LLM that include user input and descriptions of available components; (iii) querying the LLM to select components and generate code snippets based on the prompts. [0154]-[0155]: the natural language input is translated to a set of two or more prompts that are provided to the generative AI component).
However, Chlipala does not disclose decompose, by the LLM, the communication service into network services and network resources for fulfillment of the intent; orchestrate the communication service in the network by orchestrating the network services and network resources in the network.
In an analogous art, Xu discloses decompose, by the LLM, the communication service into network services and network resources for fulfillment of the intent; orchestrate the communication service in the network by orchestrating the network services and network resources in the network (fig. 8, [0041]: A natural language input (also referred to herein as a natural language prompt, a user prompt, a user input) from a user (via a user device) may be input to a flow integration layer 315 hosted in a functional domain 305-b. [0042]: In decomposing the natural language input, the LLM service 325 may generate (e.g., using a generator component) high-level representations of a topology (e.g., elements and connectors), a resource list, and properties of a corresponding process flow. The topology may represent the structure of the flow as a graph and may include elements, which may be represented as nodes in the graph, and connectors, which may be represented as edges in the graph).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala to comprise “decompose, by the LLM, the communication service into network services and network resources for fulfillment of the intent; orchestrate the communication service in the network by orchestrating the network services and network resources in the network” taught by Xu.
One of ordinary skilled in the art would have been motivated because it would have enabled to decompose the natural language input into a set of multiple elements and a corresponding set of multiple connectors (Xu, [0016]).
However, Chlipala-Xu does not disclose produce, by the LLM, requirements for ensuring that fulfillment of the intent continues over time; and monitor the requirements of the intent to ensure that fulfillment of the intent continues over time.
In an analogous art, Schrader discloses produce, by the LLM, requirements for ensuring that fulfillment of the intent continues over time; and monitor the requirements of the intent to ensure that fulfillment of the intent continues over time ([0079]: initial and subsequent prompts from the user and/or steps taken by agents utilized to fulfill objectives of the user can be stored by the system using the interaction data object and/or data objects associated with the interaction data object for later evaluation. The system may utilize the interaction data object and/or additional data objects to automatically generate evaluation reports for various purposes, such as evaluating the performances of agents. [0086]: The LLM 130a and various modules of the agent system 102, such as the agent service 106, may also communicate with one or more data processing services 120 in the course of fulfilling a user input. The data processing services 120 may include any quantity of services (or “plug-ins”) and any available type of service).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala-Xu to comprise “produce, by the LLM, requirements for ensuring that fulfillment of the intent continues over time; and monitor the requirements of the intent to ensure that fulfillment of the intent continues over time” taught by Schrader.
One of ordinary skilled in the art would have been motivated because it would have enabled to automatically generate evaluation reports for various purposes, such as evaluating the performances of agents (Schrader, [0079]).
Regarding claim 2, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1, wherein the intent is translated to the communication service by the LLM (Chlipala, [0134]: developing applications using a large language model (LLM). The method includes: (i) receiving user input describing a desired application functionality; (ii) engineering prompts (e.g. translate) for the LLM that include user input and descriptions of available components; (iii) querying the LLM to select components and generate code snippets based on the prompts); translating the intent into technical features, and determining a communication service that will fulfill the technical features as the communication service capable of being orchestrated in a network (Chlipala, [0074]: users can describe what they want to accomplish to the intermediary component in plain language, without needing to use technical or programming terms. The intermediary component may then communicate with the AI component (LLM), using engineered prompts and a dynamic decision tree to extract technical specifications from the user's request).
Regarding claim 4, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1, wherein the communication service is comprised of at least one existing service (Chlipala, [0056]: One or more prompt generation modules 318 for generating, modifying, deleting, and/or updating user applications in response to queries, requests, modifications, and/or other user commands).
Regarding claim 5, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1, wherein the communication service is comprised of at least one new service (Chlipala, [0056]: One or more prompt generation modules 318 for generating, modifying, deleting, and/or updating user applications in response to queries, requests, modifications, and/or other user commands).
Regarding claim 6, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 5, wherein the new service is designed by the LLM (Chlipala, [0134]: developing applications using a large language model (LLM). The method includes: (i) receiving user input describing a desired application functionality; (ii) engineering prompts for the LLM that include user input and descriptions of available components; (iii) querying the LLM to select components and generate code snippets based on the prompts).
Regarding claim 10, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1, wherein the LLM is trained on communication service definitions included in at least one of: historical data, or synthetically generated data (Xu, [0052]: where the LLM may be trained on first metadata corresponding to a second process flow that is created by a user. That is, the LLM may be trained or fine-tuned based on a previous process flow that was manually created by the user). The same rationale applies as in claim 1.
Regarding claim 17, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1, wherein the network services and network resources are orchestrated in the network for providing the communication service (Xu, [0016], [0042]: to decompose the natural language input into a set of multiple elements and a corresponding set of multiple connectors. In decomposing the natural language input, the LLM service 325 may generate (e.g., using a generator component) high-level representations of a topology (e.g., elements and connectors), a resource list, and properties of a corresponding process flow. The topology may represent the structure of the flow as a graph and may include elements, which may be represented as nodes in the graph, and connectors, which may be represented as edges in the graph). The same rationale applies as in claim 16.
Regarding claims 19 and 20; the claims are interpreted and rejected for the same reason as set forth in claim 1.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Chlipala in view of Xu in view of Schrader, as applied to claim 1, in further view of Dandoush (“Large Language Models Meet Network Slicing Management and Orchestration”).
Regarding claim 3, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 2.
However, Chlipala-Xu-Schrader does not disclose wherein the technical features include technical requirements, capabilities, constraints, and policies.
In an analogous art, Dandoush discloses wherein the technical features include technical requirements (pg. 5, left column, [0004]: A LLM model as shown in Figure 4 can translate this high level request to a more concrete service profile, identifying more technical requirements), capabilities, constraints, and policies (pg. 5, [0004]: if the required constraints cannot be met due to capacity limitation on some portion of the slice, the agent can negotiate (e.g., by sending a specific prompt) with the corresponding agent located at the access network level to negotiate a relaxed version of the requirements that can be allocated and initiated if the user can approve them. Leveraging their natural language understanding capabilities, LLMs ensure precise communication between users and the network slice provider related agent. Pg. 7, left column, [0001]: deploying one or more network slices along with specific optimization goals for each of the network slice).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala-Xu-Schrader to comprise “wherein the technical features include technical requirements, capabilities, constraints, and policies” taught by Dandoush.
One of ordinary skilled in the art would have been motivated because it would have enabled to translate high level request to a more concrete service profile, identifying more technical requirements (Dandoush, pg. 5, left column, [0004]).
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Chlipala in view of Xu in view of Schrader, as applied to claim 1, in view of Manohar et al. (US 12,223,456 B1).
Regarding claim 7, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1.
However, Chlipala-Xu-Schrader does not disclose wherein Chain of Thought is used with the LLM.
In an analogous art, Manohar discloses wherein Chain of Thought is used with the LLM (column 28, 57-63: the large language model (i.e., trained first artificial intelligence (AI) model) is fine-tuned with the determined one or more semantics and structure using one or more techniques including at least one of: few shots learning, chain of thoughts, tree of thoughts, ReACT, symbolic reasoning, self-consistency, automatic reasoning, and tool use).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala-Xu-Schrader to comprise “wherein Chain of Thought is used with the LLM” taught by Manohar.
One of ordinary skilled in the art would have been motivated because it would have enabled the trained artificial intelligence (AI) model to be fine-tuned with the determined one or more semantics and structure of the one or more natural language texts specific to the one or more service level objectives (SLOs) and associated metrics (Manohar, column 19, 4-8).
Regarding claim 8, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1.
However, Chlipala-Xu-Schrader does not disclose wherein Tree of Thought is used with the LLM.
In an analogous art, Manohar discloses wherein Tree of Thought is used with the LLM (column 28, 57-63: the large language model (i.e., trained first artificial intelligence (AI) model) is fine-tuned with the determined one or more semantics and structure using one or more techniques including at least one of: few shots learning, chain of thoughts, tree of thoughts, ReACT, symbolic reasoning, self-consistency, automatic reasoning, and tool use).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala-Xu-Schrader to comprise “wherein Tree of Thought is used with the LLM” taught by Manohar.
One of ordinary skilled in the art would have been motivated because it would have enabled the trained artificial intelligence (AI) model to be fine-tuned with the determined one or more semantics and structure of the one or more natural language texts specific to the one or more service level objectives (SLOs) and associated metrics (Manohar, column 19, 4-8).
Claims 9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Chlipala in view of Xu in view of Schrader, as applied to claim 1, in view of Penta et al. (US 2025/0238470 A1 – Priority date 01/23/2024).
Regarding claim 9, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1.
However, Chlipala-Xu-Schrader does not disclose wherein Retrieval-Augmented Generation (RAG) is used with the LLM.
In an analogous art, Penta discloses wherein Retrieval-Augmented Generation (RAG) is used with the LLM ([0088]: based on the intent of the query 502, the personalized retrieval-augmented generation (RAG) system 106 can select a transformer-based large language model to generate the personalized response 512 with the relevant SQL language. In some cases, the personalized retrieval-augmented generation system 106 can store previous queries generated by the entity).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala-Xu-Schrader to comprise “wherein Retrieval-Augmented Generation (RAG) is used with the LLM.” taught by Penta.
One of ordinary skilled in the art would have been motivated because it would have enabled the personalized retrieval-augmented generation system fine-tuning large language models to specific data contexts relating to user accounts, data stored for user accounts, and/or specific software applications (Penta, [0018]).
Regarding claim 12, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1.
However, Chlipala-Xu-Schrader does not disclose wherein the LLM uses a vector database that stores embeddings generated from descriptors of services currently defined in an orchestration system.
In an analogous art, Penta discloses wherein the LLM uses a vector database that stores embeddings generated from descriptors of services currently defined in an orchestration system ([0028]: a vector database can store embeddings for many types of content items, such as but not limited to, video embeddings, image embeddings, or code embeddings. In some cases, a vector database can ingest information, data, and/or metadata from other sources. [0065]: the personalized retrieval-augmented generation system 106 can generate embeddings for content items and store the content item embeddings in one or more vector databases. As described in more detail below, in one or more embodiments, the personalized retrieval-augmented generation system 106 can compare the embeddings 312a-b (e.g., query embeddings) with vectorized segments and/or content embeddings of content items associated with the entity stored in one or more vector databases).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala-Xu-Schrader to comprise “wherein the LLM uses a vector database that stores embeddings generated from descriptors of services currently defined in an orchestration system” taught by Penta.
One of ordinary skilled in the art would have been motivated because it would have enabled the personalized retrieval-augmented generation system fine-tuning large language models to specific data contexts relating to user accounts, data stored for user accounts, and/or specific software applications (Penta, [0018]).
Claims 11, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Chlipala in view of Xu in view of Schrader, as applied to claim 1, in view of Blau et al. (US 2022/0217045 A1).
Regarding claim 11, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 10.
However, Chlipala-Xu-Schrader does not disclose wherein the communication service definitions are virtual service representations.
In an analogous art, Blau discloses wherein the communication service definitions are virtual service representations ([0165]: The node 15 receives from the orchestration node, a request for an operation related to virtual network function operation. The request may relate to adding, changing or removing virtual resources. The orchestration node may be a NFV-O).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala-Xu-Schrader to comprise “wherein the communication service definitions are virtual service representations” taught by Blau.
One of ordinary skilled in the art would have been motivated because it would have enabled for handling a lifecycle management request for a VNF (Blau, [0060]).
Regarding claim 14, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1.
However, Chlipala-Xu-Schrader does not disclose wherein orchestrating the communication service in the network includes initiating a life-cycle-management process for the communication service.
In an analogous art, Blau discloses wherein orchestrating the communication service in the network includes initiating a life-cycle-management process for the communication service ([0201]-[0206]: method for handling a lifecycle management operation, the method comprising at a management node: receiving (S200) a request for an operation from an orchestration node; initiating (S240) a script for mapping of at least one parameter; and invoking (S260) an orchestration command…The method of embodiment 2, wherein the request relates to adding, changing or removing virtual resources).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala-Xu-Schrader to comprise “wherein orchestrating the communication service in the network includes initiating a life-cycle-management process for the communication service” taught by Blau.
One of ordinary skilled in the art would have been motivated because it would have enabled for handling a lifecycle management request for a VNF (Blau, [0060]).
Regarding claim 15, Chlipala-Xu-Schrader-Blau discloses the non-transitory computer-readable media of claim 14, wherein the life-cycle-management process is performed to fulfill the intent (Blau, [0203]: handling a lifecycle management operation, the node adapted to: receive a request for an operation from an orchestration node; initiate a script for mapping of at least one parameter; and invoke an orchestration command). The same rationale applies as in claim 14.
Claims 13 are rejected under 35 U.S.C. 103 as being unpatentable over Chlipala in view of Xu in view of Schrader, as applied to claim 1, in view of de Witte et al. (herein after Witte, US 2024/0281457 A1 ).
Regarding claim 13, Chlipala-Xu-Schrader discloses the non-transitory computer-readable media of claim 1.
However, Chlipala-Xu-Schrader does not disclose wherein the LLM is aware of existing services and resources in an orchestration system, including their properties, relationships, capabilities, requirements, and policies.
In an analogous art, Witte discloses wherein the LLM is aware of existing services and resources in an orchestration system, including their properties, relationships, capabilities, requirements, and policies ([0092]: the predictive intent data field is the estimated output of the LLM based on the analysis of the captured content. This predictive intent data field can be generated based on recognition of relationships between user interaction data elements, including the LLM hosting data sets of relationships. The volume of relationships within the LLM can relate to the accuracy of the predictive intent. Wherein further embodiments can include iterative or feedback elements allowing the LLM to additionally learn and improve the accuracy of its predictive intent data generation operations. [0152]: The predicted intent and generating an inference request can require extra processing capabilities, for capturing contextual information, as well as burdens on storage requirements. Therefore, varying embodiments can include local storage and execution, if resources are available, and/or network and/or cloud-based. One embodiment may include a load balancing operation to determine the local processing abilities, as well as network load. One embodiment may include a cost service available with different load option).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Chlipala-Xu-Schrader to comprise “wherein the LLM is aware of existing services and resources in an orchestration system, including their properties, relationships, capabilities, requirements, and policies” taught by Witte.
One of ordinary skilled in the art would have been motivated because it would have enabled to predictive intent prompts associated with a plurality of engines based on the system dynamically tracking and reviewing content capture of prior user experiences (Witte, [0095]).
Additional References
The prior art made of record and not relied upon is considered pertinent to applicants disclosure.
Naanaa et al., US 12,238,213 B1: Methods and Systems for Verifying a Worker Agent.
Singh et al., US 2025/0193244 A1: Intent-Based Policy Configuration Using Natural Language.
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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/J.C.T/Examiner, Art Unit 2446
/BRIAN J. GILLIS/Supervisory Patent Examiner, Art Unit 2446