# US 2025/0094733 A1 · Digital assistant using generative artificial intelligence > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0094733 A1 - Document ID: US20250094733A1 - Status: Published application - Kind: A1 - Application number: 18/798,049 - Filed: 2024-08-08 - Published: 2025-03-20 - Earliest claimed: 2023-09-15 - Assignees: Oracle International Corporation - Applicants: Oracle International Corporation - Inventors: Xin Xu; Vishal Vishnoi; Srinivasa Phani Kumar Gadde; Ying Xu; Diego Andres Cornejo Barra; Raman Grover; Stephen Andrew McRitchie - Web page: https://patentgrep.com/patent/US20250094733A1 ## CPC - G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Techniques are disclosed herein for configuring agents for use by digital assistants that use generative artificial intelligence. An agent may be in the form of a container that is configured to have one or more actions that can be executed by a digital assistant. The agent may be configured by initially defining specification parameters for the agent based on natural language input from a user. Configuration information for the one or more assets can be imported into the agent. One or more actions may then be defined for the agent based on importing of the configuration information, the natural language input from the user, or both. A specification document can be generated for the agent and can comprise various description metadata, such as agent, asset, or action metadata, or combinations thereof. The specification document may be stored in a data store that is communicatively coupled to the digital assistant. ## Claims ### Claim 1 A computer-implemented method comprising: accessing a container defining an agent configurable to have one or more actions; configuring the agent for use by a digital assistant, wherein the configuring comprises: defining, based on natural language input from a user, specification parameters including an identification of the agent, a purpose of the agent, and identification of one or more assets for implementing the purpose, defining configuration information for the one or more assets, and defining the one or more actions based on the configuration information, the natural language input from the user, or both; generating a specification document that characterizes the agent, wherein generating the specification document comprises: acquiring, from the agent, metadata associated with: the specification parameters, the one or more assets, the one or more actions, or any combination thereof, and writing the specification document to include the metadata and the identification of the agent; and storing the specification document in a data store that is communicatively coupled to the digital assistant. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising: receiving, by a generative artificial intelligence model, a natural language utterance from the user; and testing, by the generative artificial intelligence model using the natural language utterance, functionality of the agent to implement the purpose of the agent, wherein configuring the agent is performed by the generative artificial intelligence model. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the testing is an interactive process between the user and the generative artificial intelligence model, the interactive process comprising: (i) evaluating the natural language utterance based on the specification document; (ii) selecting the agent for use in responding to the natural language utterance based on evaluating the natural language utterance; (iii) generating an execution plan comprising the one or more actions defined for the agent; (iv) executing, based on the execution plan and the configuration information for the one or more assets, the one or more actions to obtain output data; (v) generating a response to the natural language utterance based on the output data; and (vi) evaluating, based on the response, the functionality of the agent to implement the purpose of the agent. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the interactive process further comprises: (vii) determining, based on evaluating the purpose of the agent, the functionality of the agent to implement the purpose of the agent is unacceptable; (viii) in response to determining the functionality is unacceptable, receiving additional natural language input from the user concerning a change to the specification parameters; (ix) reconfiguring the agent based on the change to the specification parameters; and (x) reperforming steps (i)-(vi) of the interactive process. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein defining the configuration information for the one or more assets comprises: accessing a specification for each of the one or more assets; extracting the configuration information from the specification for each of the one or more assets; and writing the configuration information into the container. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, further comprising: receiving a natural language utterance as a user input to the digital assistant; and generating a response to the natural language utterance based on the specification document and the agent. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, wherein generating the response comprises: executing, using the natural language utterance, a search on specification documents in the data store, wherein the specification documents include the specification document for the agent; identifying, based on the search, one or more candidate agents and associated actions for generating the response, wherein the one or more candidate agents include the agent; generating an input prompt by combining metadata obtained from specification documents for the one or more candidate agents with the natural language utterance, wherein the metadata comprises the metadata written in the specification document for the agent; generating, by a first generative artificial intelligence model and based on the input prompt, an execution plan for responding to the natural language utterance, wherein the execution plan includes an order list of actions comprising the one or more actions; executing, based on the execution plan and the configuration information for the one or more assets, the one or more actions to obtain output data; generating, using the output data, an output prompt for a second generative artificial intelligence model of the digital assistant, the second generative artificial intelligence model being the same or different from the first generative artificial intelligence model; and generating, by the second generative artificial intelligence model and based on the output prompt, the response to the natural language utterance. ### Claim 8 A system comprising: one or more processors; and one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the system to perform operations comprising: accessing a container defining an agent configurable to have one or more actions; configuring the agent for use by a digital assistant, wherein the configuring comprises: defining, based on natural language input from a user, specification parameters including an identification of the agent, a purpose of the agent, and identification of one or more assets for implementing the purpose, defining configuration information for the one or more assets, and defining the one or more actions based on the configuration information, the natural language input from the user, or both; generating a specification document that characterizes the agent, wherein generating the specification document comprises: acquiring, from the agent, metadata associated with: the specification parameters, the one or more assets, the one or more actions, or any combination thereof, and writing the specification document to include the metadata and the identification of the agent; and storing the specification document in a data store that is communicatively coupled to the digital assistant. ### Claim 9 (depends on 8) The system of claim 8, wherein the operations further comprise: receiving, by a generative artificial intelligence model, a natural language utterance from the user; and testing, by the generative artificial intelligence model using the natural language utterance, functionality of the agent to implement the purpose of the agent, wherein configuring the agent is performed by the generative artificial intelligence model. ### Claim 10 (depends on 9) The system of claim 9, wherein the testing is an interactive process between the user and the generative artificial intelligence model, the interactive process comprising: (i) evaluating the natural language utterance based on the specification document; (ii) selecting the agent for use in responding to the natural language utterance based on evaluating the natural language utterance; (iii) generating an execution plan comprising the one or more actions defined for the agent; (iv) executing, based on the execution plan and the configuration information for the one or more assets, the one or more actions to obtain output data; (v) generating a response to the natural language utterance based on the output data; and (vi) evaluating, based on the response, the functionality of the agent to implement the purpose of the agent. ### Claim 11 (depends on 10) The system of claim 10, wherein the interactive process further comprises: (vii) determining, based on evaluating the purpose of the agent, the functionality of the agent to implement the purpose of the agent is unacceptable; (viii) in response to determining the functionality is unacceptable, receiving additional natural language input from the user concerning a change to the specification parameters; (ix) reconfiguring the agent based on the change to the specification parameters; and (x) reperforming steps (i)-(vi) of the interactive process. ### Claim 12 (depends on 8) The system of claim 8, wherein the defining the configuration information for the one or more assets comprises: accessing a specification for each of the one or more assets; extracting the configuration information from the specification for each of the one or more assets; and writing the configuration information into the container. ### Claim 13 (depends on 8) The system of claim 8, wherein the operations further comprise: receiving a natural language utterance as a user input to the digital assistant; and generating a response to the natural language utterance based on the specification document and the agent. ### Claim 14 (depends on 13) The system of claim 13, wherein the generating the response comprises: executing, using the natural language utterance, a search on specification documents in the data store, wherein the specification documents include the specification document for the agent; identifying, based on the search, one or more candidate agents and associated actions for generating the response, wherein the one or more candidate agents include the agent; generating an input prompt by combining metadata obtained from specification documents for the one or more candidate agents with the natural language utterance, wherein the metadata comprises the metadata written in the specification document for the agent; generating, by a first generative artificial intelligence model and based on the input prompt, an execution plan for responding to the natural language utterance, wherein the execution plan includes an order list of actions comprising the one or more actions; executing, based on the execution plan and the configuration information for the one or more assets, the one or more actions to obtain output data; generating, using the output data, an output prompt for a second generative artificial intelligence model of the digital assistant, the second generative artificial intelligence model being the same or different from the first generative artificial intelligence model; and generating, by the second generative artificial intelligence model and based on the output prompt, the response to the natural language utterance. ### Claim 15 One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations comprising: accessing a container defining an agent configurable to have one or more actions; configuring the agent for use by a digital assistant, wherein the configuring comprises: defining, based on natural language input from a user, specification parameters including an identification of the agent, a purpose of the agent, and identification of one or more assets for implementing the purpose, defining configuration information for the one or more assets, and defining the one or more actions based on the configuration information, the natural language input from the user, or both; generating a specification document that characterizes the agent, wherein generating the specification document comprises: acquiring, from the agent, metadata associated with: the specification parameters, the one or more assets, the one or more actions, or any combination thereof, and writing the specification document to include the metadata and the identification of the agent; and storing the specification document in a data store that is communicatively coupled to the digital assistant. ### Claim 16 (depends on 15) The one or more non-transitory computer-readable media of claim 15, wherein the operations further comprise: receiving, by a generative artificial intelligence model, a natural language utterance from the user; and testing, by the generative artificial intelligence model using the natural language utterance, functionality of the agent to implement the purpose of the agent, wherein configuring the agent is performed by the generative artificial intelligence model. ### Claim 17 (depends on 16) The one or more non-transitory computer-readable media of claim 16, wherein the testing is an interactive process between the user and the generative artificial intelligence model, the interactive process comprising: (i) evaluating the natural language utterance based on the specification document; (ii) selecting the agent for use in responding to the natural language utterance based on evaluating the natural language utterance; (iii) generating an execution plan comprising the one or more actions defined for the agent; (iv) executing, based on the execution plan and the configuration information for the one or more assets, the one or more actions to obtain output data; (v) generating a response to the natural language utterance based on the output data; (vi) evaluating, based on the response, the functionality of the agent to implement the purpose of the agent; (vii) determining, based on evaluating the purpose of the agent, the functionality of the agent to implement the purpose of the agent is unacceptable; (viii) in response to determining the functionality is unacceptable, receiving additional natural language input from the user concerning a change to the specification parameters; (ix) reconfiguring the agent based on the change to the specification parameters; and (x) reperforming steps (i)-(vi) of the interactive process. ### Claim 18 (depends on 16) The one or more non-transitory computer-readable media of claim 16, wherein the defining the configuration information for the one or more assets comprises: accessing a specification for each of the one or more assets; extracting the configuration information from the specification for each of the one or more assets; and writing the configuration information into the container. ### Claim 19 (depends on 16) The one or more non-transitory computer-readable media of claim 16, wherein the operations further comprise: receiving a natural language utterance as a user input to the digital assistant; and generating a response to the natural language utterance based on the specification document and the agent. ### Claim 20 (depends on 19) The one or more non-transitory computer-readable media of claim 19, wherein the generating the response comprises: executing, using the natural language utterance, a search on specification documents in the data store, wherein the specification documents include the specification document for the agent; identifying, based on the search, one or more candidate agents and associated actions for generating the response, wherein the one or more candidate agents include the agent; generating an input prompt by combining metadata obtained from specification documents for the one or more candidate agents with the natural language utterance, wherein the metadata comprises the metadata written in the specification document for the agent; generating, by a first generative artificial intelligence model and based on the input prompt, an execution plan for responding to the natural language utterance, wherein the execution plan includes an order list of actions comprising the one or more actions; executing, based on the execution plan and the configuration information for the one or more assets, the one or more actions to obtain output data; generating, using the output data, an output prompt for a second generative artificial intelligence model of the digital assistant, the second generative artificial intelligence model being the same or different from the first generative artificial intelligence model; and generating, by the second generative artificial intelligence model and based on the output prompt, the response to the natural language utterance.