公开(公告)号：US20250094733A1

公开(公告)日：2025-03-20

申请号：US18798049

申请日：2024-08-08

Applicant: Oracle International Corporation

Inventor： Xin Xu ,  Vishal Vishnoi ,  Srinivasa Phani Kumar Gadde ,  Ying Xu ,  Diego Andres Cornejo Barra ,  Raman Grover ,  Stephen Andrew McRitchie

IPC: G06F40/40

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.

公开(公告)号：US20250094466A1

公开(公告)日：2025-03-20

申请号：US18830344

申请日：2024-09-10

Applicant: Oracle International Corporation

Inventor： Raman Grover ,  Amitabh Saikia

IPC: G06F16/33 ,  G06F16/332 ,  G06F16/383

Abstract: Techniques are disclosed for storage and retrieval mechanisms for knowledge artifacts acquired and applicable across conversations to enrich user interactions with a digital assistant. In one aspect, a method includes receiving a natural language utterance form a user during a session between the user and the digital assistant and obtaining a topic context instance for the utterance. The obtaining includes executing a search, determining whether the utterance satisfies a threshold of similarity with one or more topics, identifying the topic context instance associated with the topics, and associating the utterance with the topic context instance. A first generative artificial intelligence model can then be used to generate a list of executable actions. An execution plan is then created, and the topic context instances is updated with the execution plan. The execution plan is then executed, and an output or communication derived from the output is sent to the user.