Intent-Based Database Query Composition for Non-Technical Users
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Solution Overview
Problem
Non-technical users face challenges in retrieving relevant data from databases due to the complexity and technical nature of database-specific queries, necessitating a solution that allows for natural language queries without requiring technical expertise.
Innovation Solution
A method and system utilizing an AI and ML-based NLP model to receive, analyze, and execute natural language queries, identify intent and entities, compose database-specific queries, and display responses visually, with features like auto-complete and query recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If database-specific queries are used to retrieve data, then data retrieval accuracy is improved, but user accessibility deteriorates due to technical complexity
Solution Approach 1:
The patent introduces an intermediary system consisting of an NLP model and query translator that mediates between the user's natural language queries and the database's structured query requirements. The intermediary translates user-friendly natural language into precise database-specific queries, thereby maintaining data retrieval accuracy while improving user accessibility.
Solution Approach 2:
The patent replaces the mechanical system of manual query composition with an automated NLP-based translation system. Instead of requiring users to mechanically construct complex database queries, the system uses natural language processing to automatically generate appropriate queries, thus maintaining precision while reducing operational complexity.
2Ease of operation
If natural language queries are used, then ease of operation is improved, but query precision deteriorates due to lack of technical knowledge requirements
Solution Approach 1:
The patent replaces manual query construction with automated NLP translation, allowing users to express queries in natural language while the system ensures precision through intelligent translation into accurate database queries.
Solution Approach 2:
The NLP model acts as an intermediary that bridges the gap between imprecise natural language and precise database queries, translating user intentions accurately while maintaining ease of operation.
3Ease of operation
If trained NLP models are deployed, then ease of operation is improved for non-technical users, but device complexity increases
Solution Approach 1:
The patent encapsulates system complexity within an intermediary NLP translation layer, isolating the complexity from the user interface. This allows non-technical users to interact simply while the underlying system handles complexity through automated translation.
Solution Approach 2:
The system performs self-service translation of queries automatically, eliminating the need for users to manually handle complex query construction. The NLP model autonomously translates natural language into database queries, improving ease of operation while managing complexity internally.
4Productivity
If automated query translation is implemented, then productivity is improved by reducing expert dependency, but device complexity increases due to NLP model requirements
Solution Approach 1:
The system enables self-service query translation where the NLP model automatically converts natural language queries into database queries without requiring technical expertise. This improves productivity by eliminating expert dependency while the system manages NLP processing complexity internally.
Solution Approach 2:
The NLP translation layer serves as an intermediary that handles complexity internally while providing simple interfaces to users, thereby improving productivity without exposing users to underlying system complexity.
Data Source
AI summary
A method and a system for displaying a response to at least one natural language query are disclosed. The method includes receiving the at least one natural language query. The method further includes analyzing, using a trained model, the at least one natural language query to identify an intent and entities associated with the at least one natural language query. The method further includes composing a database-specific query using the identified intent and entities associated with the at least one natural language query. The method includes executing the database-specific query to retrieve the response to the at least one natural language query from at least one database. The method further includes displaying, via a display, the response that is retrieved from the at least one database.