View Coordinator for LLM Response Visualization and Context Tracking
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Solution Overview
Problem
Conversational user interfaces powered by large language models (LLMs) often fail to clearly present responses, as text or image formats may not accurately convey information.
Innovation Solution
A view coordinator that receives responses from an LLM-powered search engine, including a natural language summary, data payload, and action recommendation, dynamically renders domain-specific visualizations in a user interface, orchestrates viewing tools, and maintains conversational and viewer context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If text or image formatted responses are used to convey LLM output, then the interface is simple to implement, but the information presentation is unclear and not concise
Solution Approach 1:
The patent introduces a view coordinator as an intermediary component that receives LLM responses and dynamically selects appropriate viewing tools to render visualizations. This mediator translates text/image responses into domain-specific visual representations, improving information clarity while maintaining implementation simplicity through modular architecture
Solution Approach 2:
The system changes the presentation parameter from text/image format to visual representation format based on the data type and domain. The view coordinator transforms the response format dynamically, converting tabular data into charts, documents into visual layouts, and code into syntax-highlighted displays, thereby improving information conciseness and clarity
2Adaptability or versatility
If multiple viewing tools are integrated to handle different data types, then the system becomes versatile, but the device complexity increases
Solution Approach 1:
The view coordinator serves as a universal controller that manages multiple viewing tools through a single interface. Instead of having separate controllers for each data type, the view coordinator dynamically routes to the appropriate viewing tool based on the data type, achieving multi-functionality while reducing overall system complexity
Solution Approach 2:
The patent segments the visualization functionality into independent viewing tools, each specialized for specific data types (tables, documents, code, charts). The view coordinator orchestrates these segmented functions, allowing the system to handle diverse data types while maintaining modular architecture that reduces complexity through separation of concerns
3Loss of information
If dynamic visualization rendering is implemented, then information presentation becomes clear and concise, but the processing time increases
Solution Approach 1:
The system performs preliminary classification of the LLM response type before rendering. The view coordinator analyzes the response structure in advance to determine the appropriate viewing tool, avoiding unnecessary rendering operations for text-based responses and only initiating visualization rendering when visual formats are needed, thereby reducing processing time while maintaining clarity
Data Source
AI summary
In general, a view coordinator receives a response to a user query from an LLM-powered search engine, including a natural language summary and a data payload. The natural language summary includes a reference to a data element of the data payload. The view coordinator displays the natural language summary in a first section of a user interface of a user application. The view coordinator further detects a selection of the reference. Upon selection of the reference, the view coordinator further identifies a viewer type corresponding to the data element, and invokes a corresponding viewing tool to generate a visualization of the data element. The viewing tool renders the visualization in a first viewer section of the user interface. The view coordinator further monitors the first viewer section for user interactions to detect selection of a second reference.