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 .
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8-11, and 14-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Reddy et al (Pub. No.: US 20250086394 A1), hereafter Reddy.
Regarding claims 1, 11, and 16, Reddy teaches a system and method comprising: at least one memory storing instructions; at least one processor configured to execute the instructions to perform operations comprising (“Any of the systems herein, including the system 100, can comprise at least one hardware processor and at least one memory coupled to the at least one hardware processor.”, P0034): receiving a query to create an AI agent, the query comprising agent features (large language model may receive documents describing functionality of a digital assistant to be generated, P0020, P0025); configuring the AI agent based on the agent features, the configuring of the AI agent comprising establishing connectivity with at least one API or plugin, the at least one API or plugin associating the AI agent with a large language model (large language model may receive the documents via API calls for generating a digital assistant, P0020, P0029, P0136); generating an interface for interacting with the AI agent (“The SAP Conversational Al service (formerly known as Recast.Al) is an Al-powered chatbot platform that allows businesses to easily build and deploy conversational interfaces for various channels such as messaging apps, websites, and voice assistants.”, P0148); and deploying the AI agent (conversational interface may be deployed, including deploying skills to be activated by a generated digital assistant, P0148, P0143, P0171).
Regarding claim 2, Reddy teaches the limitations of claim 1 as outlined above. Reddy further teaches the features comprise model-specific instructions and configuring the AI agent comprises configuring the AI agent based on the model-specific instructions (large language models may be used to generate digital assistant design-time artifact configuration components based on documents input by the user, P0100-P0102, figure 3); configuring the AI agent based on the agent features comprises connecting the AI agent with the large language model through the at least one API or plugin (“At 940, the large language model is prompted to provide a list of possible intents that can be performed by users using the APIs.”, P0137. “The large language model can be used to understand the API documentation (e.g., the Swagger specification, API descriptions, developer guide, or the like). Then, the APIs can be correlated with target business process intents. Then, a large language model such as Codex can be used to generate code (e.g., Python code) to invoke the APIs.”, P0097); and the operations further comprise resolving user prompts sent through the AI agent using the large language model (digital assistant configured by large language model receives prompts by users and outputs answers to the users’ prompts, P0211-P0213, P0255-P0258).
Regarding claim 3, Reddy teaches the limitations of claim 1 as outlined above. Reddy further teaches wherein the AI agent is deployed via at least one of a website, a mobile application, or an application programming interface (bot may be deployed in the form of a website or onto a particular platform, P0147, P0189, P0171).
Regarding claim 4, Reddy teaches the limitations of claim 1 as outlined above. Reddy further teaches wherein the agent features comprise an agent default personality and a training library (API calls may be used to input training data and manage intents of a bot including expressions, entities, and actions, P0181, P0183).
Regarding claim 5, Reddy teaches the limitations of claim 1 as outlined above. Reddy further teaches generating a dashboard, the dashboard comprising metrics associated with the AI agent (“’Analytics’ provides information on how to track and analyze the performance of your chatbot using the SAP Conversational Al analytics dashboard.”, P0156).
Regarding claim 6, Reddy teaches the limitations of claim 1 as outlined above. Reddy further teaches receiving finetuning instructions (API may be used to manage updates made to bots, P0181); and updating the AI agent according to the instructions (API may be used to train bots and update entities of bots, P0182-P0183).
Regarding claim 8, Reddy teaches the limitations of claim 1 as outlined above. Reddy further teaches wherein: the feature comprising a knowledge base and a capability (Large language models may also be used to load documents online and provide clarification or interpretation of contents from the documents, P0174-P0177); configuring the AI agent based on the agent features comprises finetuning a base model with the knowledge base and by generating a set of instructions to configure the capability (intents API allows for updating intents of bots based on user inputs, P0181. User input may include documents 120, P0025-P0026, figure 1); and the at least one API or plugin establishes communication between the AI agent and a networked service (API may be used to manage updates made to bots, P0181).
Regarding claim 9, Reddy teaches the limitations of claim 1 as outlined above. Reddy further teaches wherein the operations further comprise: receiving finetuning instructions (API may be used to manage updates made to bots, P0181); and updating the AI agent according to the finetuning instructions by generating a finetuning dataset that incorporates the finetuning instructions (intents API allows for updating intents of bots based on user inputs, P0181. User input may include documents 120, P0025-P0026, figure 1).
Regarding claim 10, Reddy teaches the limitations of claim 1 as outlined above. Reddy further teaches wherein the AI agent has an assigned memory space in the system (“The memory 1020, 1025 stores software 1080 implementing one or more innovations described herein”, P0287).
Regarding claim 14, Reddy teaches the limitations of claim 11 as outlined above. Reddy further teaches wherein the configuring of the AI agent comprises including a callback function (API calls may be used to retrieve information about bots, P0180-P0182, P0092), an upload/download function (users may upload data using an API, P0183), a security function (“there can be additional models 130, 140, different configuration components, and the like. Additional components can be included to implement security”, P0037), and a model connector function (system 100 contains multiple large language models that may be connected through wireless network connections, including an internet or intranet connection, P0038-P0039).
Regarding claim 15, Reddy teaches the limitations of claim 11 as outlined above. Reddy further teaches wherein: the instructions to initialize the AI agent are received via an interface and the instructions comprise a knowledge base (Large language models may also be used to load documents online and provide clarification or interpretation of contents from the documents, P0174-P0177. API may be used to manage updates made to bots, P0181); and configuring the AI agent based on the instructions comprises finetuning the AI agent using the knowledge base (intents API allows for updating intents of bots based on user inputs, P0181. User input may include documents 120, P0025-P0026, figure 1).
Regarding claim 17, Reddy teaches the limitations of claim 16 as outlined above. Reddy further teaches wherein the interface comprises a chat tab (“A simple call to an API can be supported. The response can be parsed and given in the conversation user interface of the digital assistant”, P0092) and a configuration tab (large language models may be used to generate digital assistant design-time artifact configuration components based on user inputs, P0100-P0102, figure 3).
Regarding claim 18, Reddy teaches the limitations of claim 17 as outlined above. Reddy further teaches wherein the configuration tab comprises an instruction section, an action section, and a knowledge section (large language model may be instructed to perform tasks such as organize data, perform actions such as check whether data has been generated, or P0103. Large language models may also be used to load documents online and provide clarification or interpretation of contents from the documents, P0174-P0177).
Regarding claim 19, Reddy teaches the limitations of claim 17 as outlined above. Reddy further teaches wherein: the interface comprises an application programming interface (API) (intents API allows for updating intents of bots based on user inputs, P0181); and the operations further comprise receiving finetuning instructions via the chat tab and updating the AI agent according to the finetuning instructions (intents API allows for updating intents of bots based on user inputs, P0181. User input may include documents 120, P0025-P0026, figure 1).
Regarding claim 20, Reddy teaches the limitations of claim 19 as outlined above. Reddy further teaches wherein the operations further comprise communicating with the API via an action section (large language model may be instructed to perform tasks such as organize data, perform actions such as check whether data has been generated, or P0103. Instructions for actions such as drilldown, delete, edit, or the like may be provided with API documents, P0092. API documentation may be used to create bots, P0084).
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 7, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Reddy in view of Talavera (Pub. No.: US 20240256345 A1).
Regarding claim 7, Reddy teaches the limitations of claim 1 as outlined above. Reddy does not appear to explicitly teach “wherein: the interface for interacting with the AI agent is a custom interface; and the operations further comprise: receiving a user query or prompt via the custom interface; and resolving the query or prompt using the AI agent and generating a response via the custom interface”.
Talavera teaches wherein: the interface for interacting with the AI agent is a custom interface (“Artificial intelligence, machine learning, virtual assistant, machine assistant may be able to automatically, manually or learn to perform automatically or by command any task or item listed here, combining any ‘live/content user interfaces' with ‘pre-made content or other content user interfaces' to create a custom user interface, combining multiple pages or user interfaces to create a custom user interface”, P0977); and the operations further comprise: receiving a user query or prompt via the custom interface (human user may request content to be made by AI system, P0633); and resolving the query or prompt using the AI agent and generating a response via the custom interface (AI system may produce content in response to the user request, P0633).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Reddy and Talavera before them, to include Talavera’s specific teaching of creating a custom user interface in Reddy’s system of Digital Assistant Generation Via Large Language Models. One would have been motivated to make such a combination of creating a custom user interface (see Talavera P0977) and building and deploying conversational interfaces for various channels such as messaging apps, websites, and voice assistants (see Reddy P0148) for improved artificial intelligence and machine learning operations based on learning the user's preferences as the user may use the interface (see Talavera P0165).
Regarding claim 12, Reddy teaches the limitations of claim 11 as outlined above. Reddy further teaches wherein configuring the AI agent comprises finetuning a base model according to the instructions (intents API allows for updating intents of bots based on user inputs, P0181. User input may include documents 120, P0025-P0026, figure 1); connecting the AI agent with the LM comprises configuring the AI agent to communicate with the LM via an API (“At 940, the large language model is prompted to provide a list of possible intents that can be performed by users using the APIs.”, P0137).
Reddy does not appear to explicitly teach “generating a custom interface for interacting with the AI agent; and the query is received through the custom interface”.
Talavera teaches generating a custom interface for interacting with the AI agent (“Artificial intelligence, machine learning, virtual assistant, machine assistant may be able to automatically, manually or learn to perform automatically or by command any task or item listed here, combining any ‘live/content user interfaces' with ‘pre-made content or other content user interfaces' to create a custom user interface, combining multiple pages or user interfaces to create a custom user interface”, P0977); and the query is received through the custom interface (human user may request content to be made by AI system, P0633).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Reddy and Talavera before them, to include Talavera’s specific teaching of creating a custom user interface in Reddy’s system of Digital Assistant Generation Via Large Language Models. One would have been motivated to make such a combination of creating a custom user interface (see Talavera P0977) and building and deploying conversational interfaces for various channels such as messaging apps, websites, and voice assistants (see Reddy P0148) for improved artificial intelligence and machine learning operations based on learning the user's preferences as the user may use the interface (see Talavera P0165).
Regarding claim 13, Reddy in view of Talavera teaches the limitations of claim 12 as outlined above. Talavera further teaches wherein resolving the queries comprises using the AI agent and communicating a response via the custom interface (human user may make requests to the AI system using the user interface, P0633. User interface may be a custom user interface, P0977). Reddy further teaches configuring the AI agent comprises performing at least one of: a consumer creator flow; an enterprise creator flow; or an API agent flow (“Enterprise software applications may support a variety of enterprise workflows such as billing, invoicing, procurement, payroll, time and attendance management, recruiting and onboarding, learning and development, performance and compensation, workforce planning, and the like. In some cases, user interactions with an enterprise software application can be conducted via a conversation simulation application (e.g., a chatbot or the like)”, P0015).
Conclusion
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/I.M./ Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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