LLM Agent UI Automation With Caching for Precise Action Execution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently automating user interface (UI) operations using large language models (LLMs) due to the complexity of translating natural language instructions into precise UI actions, particularly in software applications, leading to inefficiencies and resource wastage.
Innovation Solution
A system utilizing an LLM-based UI assistant that includes an autonomous agent leveraging application and UI platform documentation, parameter and prompt caching, and a feedback mechanism to generate, execute, and optimize UI actions, ensuring efficient translation and execution of natural language instructions into software application tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If LLMs are used to translate natural language instructions into UI actions, then the automation capability is improved, but the computational resources and time consumption increase
Solution Approach 1:
The system performs preliminary actions by caching parameters and prompts before they are needed for UI automation. Parameters such as element identifiers, component hierarchies, and action templates are pre-computed and stored, so when a natural language instruction arrives, the system can retrieve cached results instead of performing full LLM translations from scratch, significantly reducing computational resource consumption while maintaining automation capability.
Solution Approach 2:
The system creates copies of successful UI action sequences and stores them in a database for future reference. When similar tasks arise, instead of generating new actions through LLM inference, the system retrieves and reuses cached action sequences, reducing the computational burden on LLMs while maintaining high automation effectiveness.
2Extent of automation
If LLMs are used to translate natural language instructions into UI actions, then the automation capability is improved, but the translation speed and precision decrease
Solution Approach 1:
The system segments the translation process into multiple stages: first parsing the natural language instruction to extract key parameters, then retrieving cached parameter templates, and finally generating UI actions using a parameterized prompt. This segmentation allows each stage to be optimized independently, improving overall translation precision while reducing the computational load on the LLM.
Solution Approach 2:
The system introduces an intermediary layer of parameterized prompts that bridges the natural language instruction and the final UI actions. Instead of directly translating natural language to UI actions, the system uses parameterized prompts as intermediaries that incorporate cached parameter information, ensuring more precise and accurate action generation while reducing direct LLM computational requirements.
3Use of energy by moving object
If parameter and prompt caching is implemented, then the computational resources are reduced, but the system complexity increases
Solution Approach 1:
The caching system is designed to serve multiple functions: it caches parameters for different UI elements, stores prompt templates for various action types, and maintains action sequence histories. This multi-functional caching approach consolidates what would otherwise be multiple separate systems into a single unified mechanism, reducing overall system complexity while achieving significant computational resource savings.
Data Source
AI summary
In some implementations, the techniques described herein relate to a method including: receiving, by a processor, a natural language instruction from a client device, the natural language instruction describing a task utilizing a software application; generating, by the processor, a user interface action representing the natural language instruction, the user interface action generated by a large language model responsive to an input prompt; executing, by the processor, the user interface action within the software application; and transmitting, by the processor, a result of executing the user interface action to the client device.