Natural Language Query Generation System
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Solution Overview
Problem
Existing database query systems require specialized knowledge and fail to account for user preferences, leading to inefficiencies and wasted resources due to the need for formal query languages inaccessible to general users.
Innovation Solution
A query generation system that converts natural language inputs into user-specific database queries compatible with database management systems, using a cleaning layer and data quality layers to preprocess and generate Structured Query Language (SQL) queries tailored to individual user needs and project-specific requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If formal query languages are used to access database information, then query precision and reliability are improved, but ease of operation deteriorates because specialized knowledge and training are required
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into formal database query languages. This mediator handles the conversion between informal user input and structured query syntax, allowing users to interact with databases using natural language while maintaining the precision of formal query processing.
Solution Approach 2:
The system segments the query processing into distinct layers: a user interface layer that accepts natural language input, a translation layer that converts natural language to formal query syntax, and a database execution layer that processes the formal queries. This segmentation allows each layer to specialize in its function, improving both accessibility and query reliability.
2Reliability
If iterative approaches are used by trained individuals to create queries meeting user requirements, then query reliability is improved, but productivity deteriorates due to inefficiencies and wasted system resources
Solution Approach 1:
The system enables self-service query generation by automatically translating natural language input into executable database queries without requiring trained individuals to manually craft queries. The automated translation process eliminates iterative manual adjustments while maintaining query correctness through sophisticated natural language processing and context understanding.
Solution Approach 2:
The system performs preliminary analysis and translation of natural language queries into formal query syntax before database execution. This preliminary action includes understanding user intent, identifying required data fields, and constructing appropriate query structures in advance, eliminating the need for iterative refinement during the querying process.
3Device complexity
If conventional query tools are used that fail to account for user context and preferences, then device complexity is reduced, but loss of information deteriorates because user-specific requirements are not met
Solution Approach 1:
The patent adds a new dimension to query processing by incorporating user context and preferences as additional parameters. The system analyzes user profiles, project associations, and historical query patterns to enrich basic queries with user-specific requirements, transforming simple query processing into a multi-dimensional process that considers both data requirements and user characteristics.
Solution Approach 2:
The system performs preliminary analysis of user context, preferences, and project associations before generating queries. This preliminary action includes retrieving user profiles, understanding project requirements, and anticipating user needs based on historical behavior, ensuring that generated queries automatically align with user-specific requirements without adding complex post-processing steps.
Data Source
AI summary
A query generation system receives, from a first device, a first input and a first project identifier and receives, from a second device, a second input and a second project identifier. The first and second inputs are the same and are in a natural language format that is not compatible with a downstream database management system. The system generates, based on the first input, a first database query. The system generates, based on the second input, a second database query. The first and second database queries are compatible with the downstream database management system. The system receives a first response to the first database query and a second response to the second database query from the downstream database management system. The system transmits the first response to the first device and the second response to the second device.