Conversational Data Querying With AI-Generated Structural Visualizations
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Solution Overview
Problem
Current data analysis tools lack flexibility and fail to facilitate understanding of data relevance, often requiring rigid syntax and lacking mechanisms for revising inaccurate data results or providing meaningful visualizations.
Innovation Solution
A system and method for natural language query processing and visualization that allows users to input conversational queries, receive responsive data, and generate customizable graphical visualizations based on structural characteristics, iteratively refining queries for enhanced understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current search mechanisms employ syntax requirements, then data retrieval capability is improved, but ease of operation deteriorates due to rigid and difficult-to-use syntax
Solution Approach 1:
The patent replaces the mechanical syntax-based query system with an AI model that processes natural language questions. The AI model translates conversational queries into executable database queries, eliminating the need for users to learn rigid syntax while maintaining precise data retrieval capabilities through intelligent question interpretation and transformation.
2Loss of information
If current systems provide data results, then information availability is improved, but understanding of data relevance deteriorates due to lack of visualization mechanisms
Solution Approach 1:
The patent adds a visualization dimension to data presentation by generating graphical representations alongside tabular results. The system creates charts, graphs, and visual summaries that transform raw data into intuitive visual formats, enabling users to quickly grasp data patterns, relationships, and relevance without merely scanning text-based results.
3Productivity
If current systems execute queries successfully, then productivity is improved, but adaptability deteriorates due to lack of mechanisms for revising inaccurate results
Solution Approach 1:
The patent implements a feedback loop where the AI model continuously interacts with users to refine query results. When results are inaccurate or incomplete, users can provide feedback through follow-up questions or corrections, and the AI model adjusts subsequent queries accordingly. This enables iterative refinement of results while maintaining efficient query execution through learned optimizations.
Data Source
AI summary
Systems and methods for natural language query processing and visualization. In embodiments, a structure associated with a dataset and a natural language question are obtained and provided to an AI model to request, from the AI model, a query that may be used for retrieving data from the dataset responsive to the natural language question. The query is received form the AI model and executed against the data in the dataset to retrieve data responsive to the natural language question. In embodiments, the data responsive to the natural language question is analyzed to determine one or more structural characteristics of the data responsive to the natural language question, and a graphical visualization of the data responsive to the natural language question is generated based on the one or more structural characteristics of the data responsive to the natural language question.