DETAILED ACTION
Claims 1-20 are pending in this action.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 9/9/2025, 12/4/2025 and 3/10/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
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 nonobviousness.
Claims 1, 2, 5, 6, 8, 14, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Keenan et al. (US PGPUB No. 2025/0005046) [hereinafter “Keenan”] in view of LaRhette et al. (US PGPUB No. 2024/0281472) [hereinafter “LaRhette”].
As per claim 1, Keenan teaches a system, comprising: a plurality of computing devices, respective comprising at least one processor and a memory, configured to implement a natural language generative application service, wherein the natural language generative application service is configured to: receive, via an interface of the natural language generative application service, a natural language request to perform a natural language task for a generative natural language application using one or more data repositories associated with the generative natural language application ([0006], receive a natural language request from the user including a textual question); cause processing of the natural language request for a single iteration through the retrieval pipeline, wherein the retrieval pipeline comprises: rewriting the natural language request to perform the natural language task (Abstract and [0042], taking natural language request from the user and translating to an appropriate prompt to submit to the AI model); retrieving at least some of the data to perform the natural language task from the one or more data repositories ([0040], retrieving data from bases and primitive databases to assist in generating prompts to submit to LLM to perform task); generate a prompt for a generative machine learning model based, at least in part, on the retrieved data, to perform the natural language task ([0042], generating prompt to submit to LLM to perform task); instruct the generative machine learning model according to the prompt to generate a result ([0050], submitting prompt to a selected Gen-AI model); and return, via the interface of the natural language generative application service, a response to the natural language request based, at least in part, on a result received from the generative machine learning model ([0057]-[0058], taking response from Gen-AI model a displaying at client device).
Keenan does not explicitly teach a classification machine learning model, trained to determine intents of natural language requests, to determine an intent for the natural language request; and determine a number of iterations to perform a retrieval pipeline to perform the natural language task of the natural language request, wherein the number of iterations is determined based, at least in part, on the intent for the natural language request. LaRhette teaches a classification machine learning model, trained to determine intents of natural language requests, to determine an intent for the natural language request ([0032], query processor analyzes and interprets user’s search query intent – query is analyzed for search intent and classification see [0048]); and determine a number of iterations to perform a retrieval pipeline to perform the natural language task of the natural language request, wherein the number of iterations is determined based, at least in part, on the intent for the natural language request ([0046], using interpretation of user’s query, determining number of search results to retrieve and post to user).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan with the teachings of LaRhette, a classification machine learning model, trained to determine intents of natural language requests, to determine an intent for the natural language request; and determine a number of iterations of a retrieval pipeline to perform the natural language task of the natural language request based, at least in part, on the intent for the natural language request, to allow the user or an admin to tailor the depth of the intent discovery which can balance concerns of accuracy and efficiency.
As per claim 5, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale.
As per claim 8, the combination of Keenan, LaRhette and Billsus teaches the method of claim 5, further comprising including one or more source attributions based on the one or more data repositories in the result (Keenan; [0093], users have access to identities of primary and secondary sources of the datastores).
As per claim 14, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale.
As per claim 17, the substance of the claimed invention is identical or substantially similar to that of claim 8. Accordingly, this claim is rejected under the same rationale.
Claims 2, 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Keenan and LaRhette in further view of Billsus et al. (US PGPUB No. 2011/0218883) [hereinafter “Billsus”].
As per claim 2, the combination of Lewis and LaRhette teaches the system of claim 1.
The combination of Lewis and LaRhette does not explicitly teach wherein the intent labels the natural language request as a non-retrieval instruction, wherein the determined number of iterations is zero, and wherein the natural language request is provided to the generative machine learning model to obtain the result. Billsus wherein the intent labels the natural language request as a non-retrieval instruction, wherein the determined number of iterations is zero, and wherein the natural language request is provided to the generative machine learning model to obtain the result.
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan and LaRhette with the teachings of Billsus, wherein the intent labels the natural language request as a non-retrieval instruction, wherein the determined number of iterations is zero, and wherein the natural language request is provided to the generative machine learning model to obtain the result, to evaluate user intent for a query in a multiple of perspectives.
As per claim 6, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale.
As per claim 15, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale.
Claims 3, 7, 10-12, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Keenan and LaRhette in further view of Mishchenko et al. (US PGPUB No. Patent No. 11,922,144).
As per claim 3, the combination of Keenan and LaRhette teaches the system of claim 1.
The combination of Keenan and LaRhette does not explicitly teach obtain local user information and local group information for data sources with the natural language generative application; create an application principal store that maps one or more local users found in the local user information to a service user; and provide the application principal store for enforcing access control at the one or more data access repositories associated with the natural language generative application. Mishchenko teaches obtain local user information and local group information for data sources with the natural language generative application (Col. 9, lines 54-67, authentication information for users whom are grouped into various groups like administrators, developers, end-users, etc. see Col. 14, lines 28-31); create an application principal store that maps one or more local users found in the local user information to a service user (Col. 2, lines 15-17, there are different users mapped to different types of API’s see Col. 9, lines 13-20); and provide the application principal store for enforcing access control at the one or more data access repositories associated with the natural language generative application (Col. 9, lines 54-67, authentication linked to particular API’s and/or users).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan and LaRhette with the teachings of Mishchenko, obtain local user information and local group information for data sources with the natural language generative application; create an application principal store that maps one or more local users found in the local user information to a service user; and provide the application principal store for enforcing access control at the one or more data access repositories associated with the natural language generative application, to provide protection against unauthorized access to potentially sensitive and valuable information.
As per claim 7, the combination of Keenan and LaRhette teaches the method of claim 5.
The combination of Keenan and LaRhette obtaining conversation history for the generative natural language application, wherein the natural language request through the retrieval pipeline is processed based, at least in part, on the conversation history. Mishchenko teaches obtaining conversation history for the generative natural language application (Col. 6, lines 1-5, obtaining conversation with user), wherein the natural language request through the retrieval pipeline is processed based, at least in part, on the conversation history (Col. 6, lines 2-7, using conversation history with user to fulfill user intent and/or request).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan and LaRhette with the teachings of Mishchenko, obtaining conversation history for the generative natural language application, wherein the natural language request through the retrieval pipeline is processed based, at least in part, on the conversation history, to train and model API functionality based on behavior and expression of the user giving the request.
As per claim 10, the combination of Keenan and LaRhette teaches the method of claim 5.
The combination of Keenan and LaRhette does not explicitly teach validating, by the generative machine learning service, the result of the generative machine learning model before providing as the result. Mishchenko teaches validating, by the generative machine learning service, the result of the generative machine learning model before providing as the result (Col. 4, lines 57-65, using validation data to modify and improve output response to user requests).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan and LaRhette with the teachings of Mishchenko, validating, by the generative machine learning service, the result of the generative machine learning model before providing as the result, to train and improve model output based on behavior and expression of the user giving the request.
As per claim 11, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale.
As per claim 12, the combination of Keenan and LaRhette teaches the system of claim 1.
The combination of Keenan and LaRhette does not explicitly teach retrieving the data to perform the natural language task from the one or more data repositories accesses the service user to obtain a local user to access at least one of the one or more data repositories. Mishchenko teaches retrieving the data to perform the natural language task from the one or more data repositories accesses the service user to obtain a local user to access at least one of the one or more data repositories (Col. 2, lines 11-20, obtaining the description of an API which includes information about different users to determine which functionality is used and how that functionality is used).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan and LaRhette with the teachings of Mishchenko, retrieving the data to perform the natural language task from the one or more data repositories accesses the service user to obtain a local user to access at least one of the one or more data repositories, to provide protection against unauthorized access to potentially sensitive and valuable information.
As per claim 16, the substance of the claimed invention is identical or substantially similar to that of claim 7. Accordingly, this claim is rejected under the same rationale.
As per claim 19, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Keenan and LaRhette in further view of Quader et al. (US PGPUB No. PGPUB No. 2021/0357776) [hereinafter “Quader”].
As per claim 4, the combination of Keenan and LaRhette teaches the system of claim 1 as well as a generative natural language application (Keenan; [0040], user interface and generative module) and natural language generative application service (Keenan; [0040], server provides entire service).
The combination of Keenan and LaRhette does not explicitly teach receive a request to create an application to be hosted by an application service; provision one or more computing resources to host the application; and provide a network endpoint for accessing the application at the one or more computing resources, wherein a request is submitted via an application interface of the application. Quader teaches receive a request to create an application to be hosted by an application service ([0094], cloud computing as a model of service creating a shared pool of resources including applications to provide the service); provision one or more computing resources to host the application ([0098], assigning physical and virtual resources to provide application services to consumers); and provide a network endpoint for accessing the application at the one or more computing resources, wherein a request is submitted via an application interface of the application ([0102], providing various client devices through thin client interfaces, i.e. API’s, which can make application and configuration requests).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan and LaRhette with the teachings of Quader, receive a request to create an application to be hosted by an application service; provision one or more computing resources to host the application; and provide a network endpoint for accessing the application at the one or more computing resources, wherein a request is submitted via an application interface of the application, to efficiently allocate resources when hosting an AI interface.
As per claim 13, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Keenan and LaRhette in further view of Gardner et al. (US Patent No. 12,008,332) [hereinafter “Garnder”].
As per claim 9, the combination of Keenan and LaRhette teaches the method of claim 5, wherein the intent labels a natural language task (LaRhette; [0014], intent labels a natural language task like booking a flight).
The combination of Keenan and LaRhette does not explicitly teach the natural language task as including a plurality of subtasks and wherein the determined number of iterations corresponds to two or more of the plurality of sub-tasks. Gardner teaches the natural language task as including a plurality of subtasks and wherein the determined number of iterations corresponds to two or more of the plurality of sub-tasks (Col. 21, lines 15-25, iteratively querying different models using sub-tasks to accomplish the original task).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan and LaRhette with the teachings of Gardner, the natural language task as including a plurality of subtasks and wherein the determined number of iterations corresponds to two or more of the plurality of sub-tasks, to provide a modular approach allowing for better control and assignment of resources to accomplishing a task.
As per claim 18, the substance of the claimed invention is identical or substantially similar to that of claim 9. Accordingly, this claim is rejected under the same rationale.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Keenan and LaRhette in further view of Jain et al. (US PGPUB No. 2010/0262632) [hereinafter “Jain”] in further view of Mishchenko.
As per claim 20, the combination of Keenan and LaRhette teaches the one or more non-transitory, computer-readable storage media of claim 14 as well as a generative natural language application (Keenan; [0040], user interface and generative module) and natural language generative application service (Keenan; [0040], server provides entire service).
The combination of Keenan and LaRhette does not explicitly teach receiving by a service, a request to create an application, wherein the application is specified by the request not to be hosted by the natural language generative application service. Jain teaches receiving by a service, a request to create an application, wherein the application is specified by the request not to be hosted by the natural language generative application service (Claim 1, request to create a non-hosted version of a business application on a second deployment).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan and LaRhette with the teachings of Jain, receiving by a service, a request to create an application, wherein the application is specified by the request not to be hosted by the natural language generative application service, to provide a modular approach allowing for better control and assignment of resources to accomplishing a task.
The combination of Keenan, LaRhette and Jain does not explicitly teach providing, by the generative machine learning service, an identifier for associating requests with the generative natural language application. Mishchenko teaches providing, by the generative machine learning service, an identifier for associating requests with the generative natural language application (Col. 1, lines 45-55, request made to system to load a particular API an identifying the particular API using the request).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Keenan, LaRhette and Jain with the teachings of Mishchenko, providing, by the generative machine learning service, an identifier for associating requests with the generative natural language application, to allow the system to load and provide the user the ability to access the desired functionality and service.
Response to Arguments
Applicant’s arguments with respect to the objections of claims 4 and 13 have been fully considered and are persuasive. The objections have been withdrawn.
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C. 112 have been fully considered and are persuasive. The rejections have been withdrawn.
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C. 103 have been fully considered. In light of the new amendments, new prior art references, LaRhette and Jain, have been introduced and cited to.
To expedite prosecution, Examiner is open to conducting an after-final interview to discuss claim amendments to overcome the current rejection and/or place the application in condition for allowance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wang et al. (US PGPUB No. 2025/0225165), Sharma et al. (US PGPUB No. 2025/0111164), Vishnoi et al. (US PGPUB No. 2025/0094725), Papenmeier et al. ("'A Modern Up-To-Date Laptop' -- Vagueness in Natural Language Queries for Product Search," arXiv:2008.02114, August 5, 2020), Wu et al. ("Identification of Web Query Intent Based on Query Text and Web Knowledge," 2010 First International Conference on Pervasive Computing, Signal Processing and Applications, Harbin, China, 2010, pp. 128-131, doi: 10.1109/PCSPA.2010.40) and Jensen ("Querying the Web with Local Intent," 2013 IEEE 14th International Conference on Mobile Data Management, Milan, Italy, 2013, pp. 1-1, doi: 10.1109/MDM.2013.101) all disclose various aspects of the claimed invention including determining intent iteratively and a prompt to accomplish a requested natural language task.
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 PETER C SHAW whose telephone number is (571)270-7179. The examiner can normally be reached Max Flex.
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/PETER C SHAW/Primary Examiner, Art Unit 2493 March 24, 2026