Natural Language Data Integration for Multidimensional Visualization
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Solution Overview
Problem
Existing data visualization tools lack the ability to efficiently integrate and manipulate multidimensional data in response to natural language requests, limiting user interaction and functionality.
Innovation Solution
A computer-implemented method that utilizes a large language model (LLM) to generate structured objects based on natural language requests, allowing for the intelligent triggering of user interface functionality and data manipulation, including generation of textual transformations and visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data visualization tools are used, then data can be displayed and manipulated through graphical interfaces, but user interaction is limited and requires complex operations
Solution Approach 1:
The patent replaces the mechanical graphical interface system with a natural language processing system. Instead of requiring users to manually navigate complex graphical interfaces, the system uses LLMs to interpret natural language queries and automatically generate the necessary data manipulation and visualization operations, thereby simplifying user interaction while maintaining powerful data analysis capabilities
Solution Approach 2:
The patent introduces a large language model as an intermediary between the user and the data visualization system. This intermediary translates natural language requests into executable data operations, acting as a mediator that bridges the gap between simple user input and complex data processing requirements, thus reducing the perceived complexity of the system
2Adaptability or versatility
If multidimensional data is integrated, then data functionality is enhanced, but data integration complexity increases
Solution Approach 1:
The patent implements a universal data integration layer that can handle multiple data sources and dimensions through a single standardized interface. The system uses LLMs to understand and navigate complex data structures, providing multi-functional capabilities for data retrieval, transformation, and visualization without requiring users to manage the underlying integration complexity
Solution Approach 2:
The patent employs a data abstraction layer as an intermediary that consolidates multiple data sources and dimensions into a unified view. This layer hides the complexity of data integration from the user while providing simplified access through natural language queries, enabling versatile data functionality without exposing the user to integration complexities
3Ease of operation
If natural language processing is used, then user interaction is simplified, but processing time increases
Solution Approach 1:
The patent applies partial action by having the LLM generate only the necessary data operations required to answer the user's query, rather than processing all available data. The system can selectively retrieve and transform only the relevant portions of multidimensional data, reducing processing time while maintaining the simplicity of natural language interaction
Solution Approach 2:
The patent implements preliminary action by pre-processing and indexing data structures to enable faster query response. The system prepares data in advance and organizes it in ways that facilitate rapid retrieval and transformation when natural language queries are received, thereby reducing the processing time penalty associated with LLM-based interaction
Data Source
AI summary
Systems, methods, and computer-readable media are provided for triggering functionality on data to be generated in a user interface and/or data shown or visualized in a user interface based on a natural language request that references actions to be performed and data items to use in performing the actions. The user interface actions are triggered based on a structured object generated by a large language model (LLM), which may then be processed, validated, and used to carry out the actions. The LLM may be further instructed based on available interface functionality control(s) and which content has been selected on the user interface. The structured object may be used to generate output content that is based on the selected content, such as a summary or other text transformation, a targeted visualization, output document for consumption by another application, or other consumable content. The output content may be stored in association with a content consumer for display in a user interface.