Hierarchical UI Agents With Context Filtering for Real-Time Interaction
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Solution Overview
Problem
Conventional machine learning models for UI-based data integration are prone to breaking with minor UI updates and are computationally expensive, making them impractical for real-time applications, and unrestricted data availability can lead to inaccurate results.
Innovation Solution
A hierarchical data integration system with context-managed hierarchical agents operates in a web application's runtime environment, decomposing UI elements, categorizing input types, and restricting data access to child agents, using a propagated subset to generate input data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional machine learning models are used for UI-based data integration, then data integration operations can be performed, but the models are computationally expensive and impractical for real-time applications
Solution Approach 1:
The patent segments the monolithic machine learning model into a hierarchical agent system with multiple specialized agents (root agent, domain agents, task agents). Each agent handles specific aspects of UI interaction, dividing the computationally expensive operations into smaller, more efficient tasks that can be executed in real-time.
Solution Approach 2:
The patent introduces context management as an intermediary layer between the agent system and the UI elements. This context manager filters and manages the state space, preventing agents from processing unnecessary data and reducing computational overhead while maintaining real-time responsiveness.
2Measurement precision
If unrestricted data is made available to agents, then comprehensive analysis can be performed, but the volume of data makes deep learning operations computationally expensive and increases likelihood of inaccurate results
Solution Approach 1:
The patent extracts only the necessary subset of UI state and context information that each agent needs to perform its specific task. Instead of providing agents with unrestricted access to all UI data, the context manager filters and provides only relevant information, reducing computational cost while maintaining accuracy.
Solution Approach 2:
Different agents are provided with different levels and types of context information based on their specific needs. The root agent receives comprehensive UI state, while domain agents and task agents receive filtered, task-specific subsets. This local quality approach ensures each agent has precisely the information it needs without unnecessary computational overhead.
3Reliability
If machine learning models are designed for specific interface types, then accurate results can be obtained, but minor updates to UI structure break the models
Solution Approach 1:
The patent implements a dynamic context management system that adapts to UI changes in real-time. Instead of static models tied to specific UI structures, the context manager dynamically tracks and manages UI state, allowing the agent system to adapt to structural changes while maintaining reliable interactions. The context is updated as the UI evolves, ensuring models remain valid without requiring redesign.
Data Source
AI summary
In some embodiments, a method may include determining hierarchical agents including a first agent and a second agent based on a document defining a UI, the hierarchical agents having access to initial context data. The method may include delegating a task related to a UI element to the first agent based on information associated with the UI element and restricting portions of the initial context data available to the first agent to a propagated subset based on input data types mapped to the first agent and portions of the initial context data available to the second agent. The method may include generating interaction data by providing a machine learning model with the propagated subset and updating the document (e.g., by populating or interacting with the UI element based on the interaction data).