DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
3. Claims 1-20, filed on 8/8/2025, are pending in this office action.
Priority
4. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 103
5. 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.
6. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramanan et al. (US Publication 2024/0296231 A1) in view of Cegielski-Johnson et al. (US Publication 2025/0063101 A1)
As per claim 1, Ramanan teaches A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising: (see Abstract)
converting, into embeddings, static information comprising tenancy definitions from custom resource definitions, system documentation, and operational workflows associated with a plurality of services in a containerized Software-as-a-Service (SaaS) application running on a container orchestration platform, wherein the SaaS application supports multi-tenancy in a single instance; (paragraph 0060, 0073, content are converted into vectors to be used by an SaaS application to implement security features, paragraph 0018, 0050, 0052, the security features of an SaaS application being part of an embedding layer)
storing the embeddings in a vector database; (paragraphs 0015, 0034, 0048, vectors associated with a machine learning model are stored in database)
receiving, via a natural language interface, a query related to tenancy management or resource usage of the SaaS application; (paragraph 0018, 0032, a search engine query is received and processed using SaaS applications and security features, paragraph 0037, 0048, a natural language processor can receive input including the query to be processed)
retrieving, in response to the query, relevant static information from the vector database using a similarity search based on the embeddings; (paragraph 0034, 0037, 0046, 0051, matches are identified and utilized for ranking based on vector information and confidence values, paragraph 0028, 0034, 0037, security features of documents collected, interpreted as static information, paragraph 0060, 0073, the semantic similarity being preserved)
obtaining live data from the container orchestration platform by generating and executing one or more application programming interface (API) calls based on the query and the relevant static information; (paragraph 0045, 0070, 0074, the API of a search engine is utilized by a generator to rank data as data is received, paragraph 0074, 0076, 0077, the security features utilized to process a query)
processing the query, the relevant static information, and the live data to generate a contextually relevant response in natural language; (paragraph 0015, 0028, 0032, 0037, responses from each query is received based on document context)
and providing the contextually relevant response to the natural language interface. (paragraph 0037, 0047, 0070, responses are retrieved in response to the query processed by a natural language processor)
Ramanan does not explicitly indicate processing the query, the relevant static information, and the live data using a large language model to generate a contextually relevant response in natural language.
Cegielski-Johnson teaches processing the query, the relevant static information, and the live data using a large language model to generate a contextually relevant response in natural language. (paragraph 0024, 0025, 0032, 0033, 0047, A large language model is utilized to generate insights and recommendations as natural language output regarding an SaaS product)
It would have been obvious for one of ordinary skill in the art at the time the invention was made to combine Ramanan’s method of implementing security features associated with SaaS applications when processing natural language queries with Cegielski-Johnson’s ability to utilize a large language model to generate a natural language output associated with an SaaS product. This gives the user the ability to generate relevant, natural language output to natural language queries using a large language model. The motivation for doing so would be to better understand user interactions with software products (paragraph 0003).
As per claim 2, Ramanan teaches the natural language interface comprises a chatbot interface configured to provide responses in layman's terms for non-technical users. (paragraph 0048, natural language processor)
As per claim 3, Ramanan teaches the natural language interface comprises a command line interface configured to provide technical responses for advanced users. (paragraph 0048, natural language processing techniques)
As per claim 4, Ramanan teaches the vector database utilizes cosine similarity to match the query with relevant content. (paragraph 0060, 0073, semantic similarity)
As per claim 5, Ramanan teaches the operations further comprise dynamically ingesting updated tenancy definitions or documentation into the vector database at runtime in response to changes in service tenancy requirements. (paragraph 0050, multiple confidence values)
As per claim 6, Ramanan teaches the obtaining the live data comprises generating Kubernetes commands to obtain live resource availability data from the container orchestration platform. (paragraph 0033, 0071, container)
As per claim 7, Ramanan teaches the operations further comprise updating the vector database with new or modified static information in response to changes in service tenancy definitions or operational workflows at runtime. (paragraph 0033, 0049, corresponding security features)
As per claim 8, Ramanan teaches the large language model is configured to generate Kubernetes commands based on the query and the relevant static information to obtain the live data from the container orchestration platform. (paragraph 0033, 0071, container)
As per claim 9, Ramanan and Cegielski-Johnson are taught as per claim 1 above. Cegielski-Johnson additionally teaches the contextually relevant response generated by the large language model includes actionable recommendations for resource scaling or tenant redistribution based on the relevant static information and the live data. (paragraph 0018, 0033, generate recommendations)
As per claim 10, Ramanan teaches the operations further comprise updating the vector database with new or modified onboarding requirements in response to changes in service tenancy definitions. (paragraph 0029, security policy)
As per claim 11, Ramanan teaches A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising: (*see Abstract)
receiving, via a natural language interface, a query related to tenancy management of a containerized Software-as-a-Service (SaaS) application running on a container orchestration platform, wherein the containerized SaaS application supports multi- tenancy in a single instance and comprises a plurality of services, each service providing tenancy definitions via custom resource definitions; (paragraph 0018, 0032, a search engine query is received and processed using SaaS applications and security features, paragraph 0037, 0048, a natural language processor can receive input including the query to be processed, paragraph 0060, 0073, content are converted into vectors to be used by an SaaS application to implement security features)
retrieving, in response to the query, static tenancy information from a vector database, the vector database comprising embeddings of tenancy definitions, documentation, and operational workflows associated with the plurality of services; (paragraph 0034, 0037, 0046, 0051, matches are identified and utilized for ranking based on vector information and confidence values, paragraph 0028, 0034, 0037, security features of documents collected, interpreted as static information, paragraph 0060, 0073, the semantic similarity being preserved, paragraphs 0015, 0034, 0048, vectors associated with a machine learning model are stored in database)
processing the query and the static tenancy information to generate a contextually relevant response in natural language; (paragraph 0015, 0028, 0032, 0037, responses from each query is received based on document context)
and providing the contextually relevant response to a user via the natural language interface. (paragraph 0037, 0047, 0070, responses are retrieved in response to the query processed by a natural language processor)
Ramanan does not explicitly indicate processing the query and the static tenancy information using a large language model to generate a contextually relevant response in natural language.
Cegielski-Johnson teaches processing the query and the static tenancy information using a large language model to generate a contextually relevant response in natural language (paragraph 0024, 0025, 0032, 0033, 0047, A large language model is utilized to generate insights and recommendations as natural language output regarding an SaaS product).
It would have been obvious for one of ordinary skill in the art at the time the invention was made to combine Ramanan’s method of implementing security features associated with SaaS applications when processing natural language queries with Cegielski-Johnson’s ability to utilize a large language model to generate a natural language output associated with an SaaS product. This gives the user the ability to generate relevant, natural language output to natural language queries using a large language model. The motivation for doing so would be to better understand user interactions with software products (paragraph 0003).
As per claim 12, Ramanan teaches the operations further comprise obtaining live data from the container orchestration platform by generating and executing one or more application programming interface (API) calls based on the query and the static tenancy information. (paragraph 0045, 0070, 0074, the API of a search engine is utilized by a generator to rank data as data is received, paragraph 0074, 0076, 0077, the security features utilized to process a query)
As per claim 13, Ramanan teaches the containerized SaaS application is deployed in a common Kubernetes namespace. (paragraph 0033, 0071, container)
As per claim 14, Ramanan teaches the natural language interface comprises a chatbot interface configured to provide responses in layman's terms for non-technical users. (paragraph 0048, natural language processor)
As per claim 15, Ramanan teaches the natural language interface comprises a command line interface configured to provide technical responses for advanced users. (paragraph 0048, natural language processing techniques)
As per claim 16, Ramanan teaches the vector database utilizes cosine similarity to match the query with relevant content. (paragraph 0060, 0073, semantic similarity)
As per claim 17, Ramanan teaches the operations further comprise dynamically ingesting updated tenancy definitions or documentation into the vector database at runtime in response to changes in service tenancy requirements. (paragraph 0050, multiple confidence values)
As per claim 18, Ramanan teaches the contextually relevant response includes an assessment of a feasibility of onboarding a new tenant with a specified profile based on current resource availability and predefined tenancy criteria. (paragraph 0033, 0049, corresponding security features)
As per claim 19, Ramanan teaches A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising: (see Abstract)
receiving, via a natural language interface, a request for resource usage information related to a containerized Software-as-a-Service (SaaS) application running on a container orchestration platform, wherein the containerized SaaS application supports multi-tenancy in a single instance; (paragraph 0018, 0032, a search engine query is received and processed using SaaS applications and security features, paragraph 0037, 0048, a natural language processor can receive input including the query to be processed, paragraph 0060, 0073, content are converted into vectors to be used by an SaaS application to implement security features)
obtaining live resource usage data from the container orchestration platform by generating and executing one or more application programming interface (API) calls based on the request; (paragraph 0045, 0070, 0074, the API of a search engine is utilized by a generator to rank data as data is received, paragraph 0074, 0076, 0077, the security features utilized to process a query)
processing the request and the live resource usage data using a large language model to generate a contextually relevant response in natural language; (paragraph 0015, 0028, 0032, 0037, responses from each query is received based on document context)
and providing the contextually relevant response to a user via the natural language interface. (paragraph 0037, 0047, 0070, responses are retrieved in response to the query processed by a natural language processor)
Ramanan does not explicitly indicate processing the request and the live resource usage data using a large language model to generate a contextually relevant response in natural language.
Cegielski-Johnson teaches processing the request and the live resource usage data using a large language model to generate a contextually relevant response in natural language (paragraph 0024, 0025, 0032, 0033, 0047, A large language model is utilized to generate insights and recommendations as natural language output regarding an SaaS product).
It would have been obvious for one of ordinary skill in the art at the time the invention was made to combine Ramanan’s method of implementing security features associated with SaaS applications when processing natural language queries with Cegielski-Johnson’s ability to utilize a large language model to generate a natural language output associated with an SaaS product. This gives the user the ability to generate relevant, natural language output to natural language queries using a large language model. The motivation for doing so would be to better understand user interactions with software products (paragraph 0003).
As per claim 20, Ramanan teaches the containerized SaaS application is deployed in a common Kubernetes namespace. (paragraph 0033, 0071, container)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Barkan (US Publication 2025/0156413 A1)
Almeida (US Patent 12,586,112 B2)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANGELINO N GORTAYO whose telephone number is (571)272-7204. The examiner can normally be reached Monday-Friday 7:00am - 3:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANGELINO N GORTAYO/Primary Examiner, Art Unit 2168
Prosecution Insights