Generative UI Generation via LLM Query Decomposition
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Solution Overview
Problem
Existing user interfaces are static and preprogrammed, making it difficult and costly to develop interfaces that can serve diverse user needs while providing a user experience that aligns with the data or information type and the functionalities offered.
Innovation Solution
The method generates dynamic user interfaces by mapping user queries to relevant variables using a large language model, decomposing queries into observed and hidden variables, and constructing query-specific UI code to infer response variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static and preprogrammed UIs are used, then development cost and complexity are reduced, but adaptability to diverse user needs deteriorates
Solution Approach 1:
The patent implements dynamic UI generation by using large language models to create customized interfaces in real-time based on user queries and context. Instead of static preprogrammed interfaces, the system dynamically generates UI code that adapts to each user's specific needs, resolving the contradiction between development simplicity and adaptability.
Solution Approach 2:
The system changes UI parameters dynamically by using LLMs to generate different interface configurations based on user input. The UI structure, elements, and behavior are parameterized through natural language processing, allowing the same base system to produce infinitely varied customized interfaces without requiring separate development for each scenario.
2Adaptability or versatility
If rule-based systems with large sets of predefined responses are used, then ability to match diverse queries is improved, but complexity and maintenance burden increase
Solution Approach 1:
The patent replaces mechanical rule-based systems with an intelligent LLM-based system. Instead of maintaining complex sets of predefined rules and responses, the system uses natural language processing capabilities of LLMs to understand and respond to diverse queries dynamically, significantly reducing system complexity while maintaining or improving adaptability.
3Adaptability or versatility
If machine learning models are used to learn probable responses, then ability to handle wider range of queries is improved, but data requirements and training complexity increase
Solution Approach 1:
The patent leverages the universal capabilities of pre-trained large language models that have already learned from vast amounts of data. Instead of training specialized models for each UI task, the system uses the LLM's general language understanding and code generation capabilities across multiple functions, reducing the need for task-specific data collection and training while maintaining high adaptability.
4Productivity
If static UIs are used, then development time is reduced, but user experience customization deteriorates
Solution Approach 1:
The system enables self-service UI generation where the LLM automatically creates customized interfaces based on user queries without requiring manual design intervention. This maintains fast development throughput while delivering highly customized user experiences that align with specific user needs and contexts.
Data Source
AI summary
In a method of generating a user interface, a user query is submitted to an encoder LLM which provides variable elements used to infer novel variable elements using a factor graph document database, which are in turn passed to a decoder LLM to generate user interface elements.