DIGITAL ASSISTANT USING GENERATIVE ARTIFICIAL INTELLIGENCE

Smart overview of the Invention

Techniques are introduced for configuring agents within digital assistants using generative artificial intelligence. These agents, structured as containers, are equipped to execute specific actions based on user input. The configuration process involves defining specification parameters from natural language input, importing configuration information for assets, and setting up actions based on this data. A specification document is created, detailing metadata about the agent and its actions, which is then stored in a data store linked to the digital assistant.

Background

Digital assistants have evolved from simple chatbots relying on predefined keywords to sophisticated systems using Large Language Models (LLMs). Initially, chatbots required users to communicate through fixed commands, limiting their conversational flexibility. The integration of LLMs has transformed these systems, allowing for more natural and context-aware interactions. LLMs use deep learning algorithms to understand language patterns, enabling digital assistants to engage more intelligently and fluidly with users.

Methodology

The proposed method involves accessing a container that defines an agent with configurable actions. The agent is configured using user-provided natural language input to establish specification parameters, identify assets, and define actions. A specification document is generated from metadata related to these parameters and stored in a data store. This document is used by the digital assistant to respond effectively to user queries through an interactive process involving evaluation and execution of actions.

Interactive Testing

The system supports interactive testing of the agent's functionality using natural language utterances. This process includes evaluating the utterance against the specification document, selecting appropriate agents, generating execution plans, and executing actions to produce output data. If the agent's functionality is deemed inadequate, users can provide additional input to refine specification parameters and improve performance through iterative testing.

Implementation

The described techniques can be implemented via a system comprising processors and computer-readable media storing executable instructions. These instructions enable the system to perform the outlined operations and methods. The approach allows for flexible integration into various contexts and applications, enhancing the capability of digital assistants to understand and respond accurately to diverse user inputs.