Natural-Language Data Filtering With LLM Database Command Mapping
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Solution Overview
Problem
Database tables with thousands of records and columns require complex manual configuration of filters, necessitating technical knowledge and skills to access relevant data effectively.
Innovation Solution
A system utilizing large language models (LLMs) to convert natural-language data requests into database commands, allowing users to filter data without specific knowledge of the database structure, with attention data highlighting understood text and manual filter options for modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual filter configuration is used to access data in database tables, then data filtering precision is improved, but ease of operation deteriorates due to requiring technical knowledge and skills
Solution Approach 1:
The patent introduces an LLM-based intermediary service that translates natural language queries into database filter commands. This mediator layer eliminates the need for users to directly configure complex filters while maintaining precise data retrieval, resolving the contradiction between filtering precision and operational ease.
Solution Approach 2:
The patent replaces the mechanical manual filter configuration process with an automated LLM-based system. Instead of requiring users to manually set filter parameters, the system uses AI to automatically generate and apply filters based on natural language descriptions, thereby improving ease of operation while preserving filtering accuracy.
2Adaptability or versatility
If manual filter configuration is used to access data, then control over data selection is improved, but device complexity increases due to requiring knowledge of database structure
Solution Approach 1:
The LLM service acts as an intermediary that abstracts the complex database structure from the user interface. Users interact with simplified natural language commands while the system handles the complexity of mapping these to appropriate database filters, maintaining control versatility without increasing user-facing complexity.
Solution Approach 2:
The patent creates a universal interface where users can request data using natural language regardless of the underlying database structure. The LLM adapts to different database schemas and generates appropriate filters automatically, providing consistent ease of use across various data sources without requiring users to learn database-specific complexities.
3Ease of operation
If LLM is used to generate database commands from natural language, then ease of operation is improved, but reliability may deteriorate due to potential errors in command generation
Solution Approach 1:
The patent implements feedback mechanisms where the system provides explanations for generated filters and allows users to review and modify results before execution. This feedback loop enables users to correct potential LLM errors while maintaining the ease of natural language interaction, thereby preserving both operational simplicity and result reliability.
Solution Approach 2:
The system performs preliminary validation of generated database commands against the actual database schema before execution. This preliminary check ensures that generated filters are syntactically correct and semantically meaningful, preventing obvious errors while maintaining the automated ease of operation provided by LLM generation.
Data Source
AI summary
A user provides natural-language filtering instructions to an application server. The natural-language filtering instructions are provided to a large language model (LLM) and the LLM generates filtering commands. The filtering commands may be in a format expected by a database or in a format suitable for post-processing to generate database commands. Manual filter options may also be received from the user and used to generate additional filtering commands for the database. Responsive data is provided by a user interface. The LLM may be configured for the database or database tables being filtered. For example, metadata for the database or database tables may be used to programmatically generate a data format to be used to provide filtering commands. The LLM is instructed to generate output using the data format.