Dynamic Runtime UI Hierarchy for Adaptive Data Integration
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Solution Overview
Problem
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 that decompose UI elements in a runtime environment, restrict data access to relevant subsets, and use machine learning models efficiently to populate UI elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used for UI-based data integration, then data integration operations can be automated, 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, UI agents). Each agent handles specific aspects of data integration, reducing the computational burden on any single model while maintaining automation capabilities.
Solution Approach 2:
The patent introduces context variables and intermediate processing layers between the machine learning models and the UI elements. These intermediaries pre-process and filter information before it reaches the models, reducing the complexity of computations required for real-time decisions.
2Extent of automation
If machine learning models are used for UI-based data integration, then data integration operations can be automated, but minor UI updates can break the models
Solution Approach 1:
The hierarchical agent architecture divides the system into modular components where UI agents specifically handle UI element identification and mapping. This segmentation isolates the impact of UI changes to specific agents rather than breaking the entire model, improving reliability during UI updates.
Solution Approach 2:
The system dynamically adapts to UI changes by using context variables that can be updated without retraining the entire model. The hierarchical agents can adjust their behavior based on current UI state, making the system more resilient to structural changes in the interface.
3Adaptability or versatility
If unrestricted data is made available to agents, then more comprehensive analysis can be performed, but computational cost increases and accuracy decreases
Solution Approach 1:
The patent applies local quality by providing different levels of data access to different agents based on their specific needs. Context variables are selectively propagated down the hierarchy, with each agent receiving only the data relevant to its function. This improves accuracy by reducing noise while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The hierarchical structure segments the data flow into domain-specific streams. Domain agents receive filtered data relevant to their expertise areas, and UI agents receive only the specific context variables needed for UI element population. This segmentation maintains comprehensiveness while improving precision through targeted data delivery.
Data Source
AI summary
In some embodiments, a method and related system for dynamically responding to evolving UIs includes using hierarchical agents in a same runtime as the UI. In some embodiments, the method includes determining hierarchical agents within a runtime environment based on UI elements of a document in the runtime environment, and delegating a task related to a target UI element to an agent based on UI element information in the first runtime environment. The method may include generating, via the agent, interaction data for the target UI element based on the information related to the target UI element and user information and may further include updating the document based on the interaction data.