Natural Language Query Translation System for Database Access
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Solution Overview
Problem
Conventional database query systems are inflexible and inaccessible to non-technical users, as they require knowledge of query languages like SQL, making it difficult for users to retrieve specific data from databases using natural language queries.
Innovation Solution
A system and method that translates natural language questions into database queries using a supervised template-driven process, involving heuristic search and machine learning models to identify query parameters and simplify queries, allowing users to input queries in plain language and generate accurate database queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional query languages (SQL) are used for data retrieval, then data access precision is improved, but ease of operation deteriorates due to requiring technical knowledge
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates between user-friendly natural language queries and formal SQL queries. This mediator system includes language models trained on query-data pairs, which automatically convert casual user questions into precise database queries without requiring users to learn SQL syntax, thus resolving the contradiction between precision and ease of operation
Solution Approach 2:
The patent replaces the mechanical system of manual SQL query construction with an automated language model-based translation system. Instead of requiring users to manually craft structured queries, the system uses trained language models to automatically generate accurate SQL queries from natural language input, substituting the complex mechanical process of query construction with an intelligent automated system
2Productivity
If sophisticated query processing systems are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the query processing system into distinct functional modules: a language model component for natural language understanding, a query generation component for SQL construction, and a query execution component for data retrieval. This segmentation allows each module to be optimized independently while working together to improve overall productivity without proportionally increasing system complexity
Solution Approach 2:
The patent implements a universal language model that can handle multiple types of queries and natural language variations through a single trained system. Rather than requiring separate processing systems for different query types, the universal model learns to generate appropriate SQL queries for various natural language inputs, improving productivity while avoiding the complexity of multiple specialized systems
Data Source
AI summary
Systems and methods for determining a query for a data store are described. A natural language text may be analyzed using heuristic processing and one or more machine learning models. Query parameters may be determined from the heuristic processing and machine learning and combined to form a query in a query language. In the heuristic processing, parsing rules may be used to remove conditional terms to generate a base question. The base question may be input to the one or more machine learning model to generate a base query which may be combined with query parameters related to the conditional terms.