Natural Language SQL Generation With AI Chatbot Mediation
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Solution Overview
Problem
Existing SQL generation systems are cumbersome for users due to their reliance on hard-coded rules and complex syntax, making it difficult for non-technical users to effectively interact with databases and generate structured query language statements.
Innovation Solution
A computer system utilizing a deep learning model, such as a large language model, to assist users in generating SQL statements through a chatbot-like interface, enabling natural language input and output, and providing interactive features to enhance database interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If hard-coded rules and complex syntax are used for SQL generation, then SQL statements can be generated with precise syntax, but the system becomes cumbersome and difficult for non-technical users to operate
Solution Approach 1:
The patent replaces the mechanical system of hard-coded SQL generation rules with an AI/ML-based natural language processing system. Users can now input natural language queries instead of needing to learn complex SQL syntax, while the AI model translates these into precise SQL statements, maintaining syntax accuracy while dramatically improving ease of operation.
Solution Approach 2:
The patent introduces an AI/ML model as an intermediary between the user's natural language input and the database system. This intermediary automatically translates casual language into precise SQL queries, eliminating the need for users to directly manipulate complex syntax while ensuring accurate database interactions.
2Ease of operation
If AI/ML models are used to generate SQL statements from natural language, then ease of operation improves for non-technical users, but the system complexity increases
Solution Approach 1:
The patent implements a self-service system where the AI/ML model automatically handles the complex task of translating natural language into SQL. The system serves itself by learning from training data and continuously improving its translation capabilities without requiring manual configuration of SQL generation rules, thereby managing complexity internally while maintaining simplicity for users.
Solution Approach 2:
The patent changes the fundamental parameter of query input from structured SQL syntax to unstructured natural language. This parameter change enables the system to accept diverse, user-friendly inputs while the AI model internally manages the complexity of converting these varied inputs into precise database queries, effectively hiding system complexity from users.
3Device complexity
If existing rules-based SQL generation systems are used, then system complexity is kept low with simple rules, but productivity decreases due to cumbersome operation
Solution Approach 1:
The patent applies preliminary action by pre-training the AI/ML model on extensive SQL and natural language data before deployment. This preliminary training equips the model with the knowledge to efficiently translate various natural language queries into accurate SQL statements, significantly improving query generation productivity while the model handles the complexity internally rather than requiring complex rule-based systems.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for generating structured query language using machine learning. In some implementations, a system provides an interface configured to receive input comprising data processing criteria expressed in natural language text. The system receives natural language input through the interface, where the natural language input specifies one or more criteria for retrieving or generating data from a data set. The system obtains a structured query language (SQL) statement generated by an artificial intelligence or machine learning (AI/ML) model based on the natural language input, and the system provides the SQL statement through the interface.