Text-to-SQL AI Chat With Personalized Database Responses
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Solution Overview
Problem
Interacting with SQL databases can be challenging due to complex query requirements and the need for specialized knowledge, making it difficult for users to effectively access and modify data without writing SQL code.
Innovation Solution
A computing system utilizing generative artificial intelligence to process natural language questions, generating SQL queries and personalized responses, allowing users to interact with databases through plain language queries and receiving personalized results, including graphics and audio outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users interact with SQL databases using traditional methods, then data access and modification can be achieved, but users require specialized SQL knowledge and complex query skills
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly text queries into SQL code. This mediator handles the complexity of SQL syntax and database operations, allowing users to interact with databases using simple natural language without needing to learn SQL, thus resolving the contradiction between ease of operation and query complexity
Solution Approach 2:
The patent replaces the mechanical requirement of writing and executing SQL code with an automated natural language processing system. The system automatically generates, executes, and interprets SQL queries based on user input, substituting the manual mechanical process of SQL programming with an intelligent automated system, thereby improving ease of operation without requiring users to master SQL complexity
2Adaptability or versatility
If standard SQL query results are returned, then data can be retrieved, but responses lack personalization and user preference adaptation
Solution Approach 1:
The patent implements preliminary action by pre-processing user input to extract personalization requirements and preferences before executing the SQL query. The system analyzes user profiles, historical interactions, and contextual information in advance, then uses this pre-processed information to personalize the query results and presentation, enabling adaptability without requiring complex real-time processing during query execution
Solution Approach 2:
The patent segments the system into distinct functional modules: a natural language processing component, a SQL generation component, a query execution component, and a result personalization component. This segmentation allows each module to handle specific tasks independently, managing system complexity through modular architecture while enabling comprehensive personalization capabilities across the entire system
3Adaptability or versatility
If multiple response formats (graphics, audio, text) are provided, then user experience is enhanced, but processing complexity increases
Solution Approach 1:
The patent implements dynamics by making the response format flexible and adaptive based on user preferences, device capabilities, and query context. The system dynamically selects and generates appropriate response formats (text, graphics, audio) rather than providing all formats statically, allowing versatility in response delivery while managing processing complexity through context-aware format selection and generation
Data Source
AI summary
Systems and methods are provided for processing a natural language question using structured query language (SQL). A computing system includes a personalization large language model (LLM), a retrieval system, and a structured query language (SQL) LLM. The processor receives a natural language question, obtains user profile data, and generates a prompt with the retrieval system. The prompt identifies relevant tables in the database and generates an augmented prompt. The augmented prompt is used to generate a set of SQL code. The set of SQL code is executed on the database, and the result is inputted into the personalization LLM to generate a personalized result message that is outputted in response to the natural language question.