System-Level LLM Agent Services for In-App Function Access
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Solution Overview
Problem
Digital assistants struggle to intelligently deploy application functions across various settings, forcing users to leave applications and limiting developer incentives due to cumbersome implementation and sandboxing restrictions.
Innovation Solution
A system-level large language model (LLM) agent service that allows users to interact with applications through natural language inputs, instantiating agents within the application context to access functionality, permissions, and user data without exiting, enabling seamless integration and collaboration between applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digital assistant functionality is implemented through traditional methods, then the assistant can access application functions, but users are forced to leave the application interface and implementation becomes cumbersome due to sandboxing restrictions
Solution Approach 1:
The patent introduces an intermediary component (digital assistant service with LLM) that mediates between the user and application functions. This intermediary can be instantiated within the application context, allowing users to interact with the assistant without leaving the application interface, while the assistant maintains access to necessary application functions through controlled interfaces
Solution Approach 2:
The digital assistant is designed as a universal service that can be instantiated across multiple applications and contexts. The LLM-based assistant can adapt to different application types and user needs, providing consistent functionality while respecting application-specific constraints and sandboxing rules
2Reliability
If digital assistant functionality is integrated into applications, then user experience improves, but application developers face cumbersome implementation processes and sandboxing limitations
Solution Approach 1:
The system provides an intermediary layer that simplifies developer integration. Instead of requiring developers to directly implement complex LLM functionality, the patent offers a standardized digital assistant service that can be instantiated with minimal configuration, handling the complexity of LLM integration, prompt management, and response processing
Solution Approach 2:
The digital assistant service performs preliminary actions by pre-configuring LLM instances, establishing communication channels, and setting up interaction frameworks before the application needs to use them. This allows developers to leverage pre-established infrastructure rather than building from scratch
3Adaptability or versatility
If users access digital assistant functionality, then they gain enhanced capabilities, but they must exit the application interface they were using
Solution Approach 1:
The patent implements a nested architecture where the digital assistant is instantiated within the application context. The assistant interface is embedded inside the application UI, allowing users to access assistant functionality without exiting the application. The assistant can be thought of as a nested component that operates within the parent application's context while maintaining its own functional capabilities
Data Source
AI summary
This document describes systems and techniques directed at exposing application functionality using system-level large language model (LLM) agent services. An electronic device accesses one or more LLMs. An input prompt is received, the input prompt including a plurality of words in a natural-language format. The input prompt is used as an input for the one or more LLMs, which generates an inference output indicative of an intent of the input prompt. An action output is performed based on the intent of the input prompt. An application agent instantiated within an application interface generates the input prompt, parses the input prompt, receives the input prompt, or limits the action output.