Context-Aware Natural Language Data Query System
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Solution Overview
Problem
Current data analytics approaches are challenging for users, particularly non-technical individuals, as they require specialized technical expertise and are limited by high-level, inflexible tools that struggle to handle complex data queries and custom analyses.
Innovation Solution
A computer-implemented method and system that enables users to query, explore, and analyze datasets through natural language (and/or structured query language) data conversations, utilizing context-based natural language processing (NLP) techniques and artificial intelligence to generate executable SQL statements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional data analytics tools are used, then data analysis capability is provided, but the ease of operation deteriorates for non-technical users
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into SQL statements. This mediator handles the complexity of database querying, allowing non-technical users to interact with data using everyday language while the system automatically generates and executes the appropriate SQL code, thus improving ease of operation without sacrificing adaptability.
Solution Approach 2:
The patent replaces the mechanical requirement for users to manually write and understand SQL code with an automated natural language processing system. Instead of requiring users to learn complex query syntax and database schemas, the system uses AI to interpret natural language intent and generate executable SQL statements, significantly lowering the barrier to entry for non-technical users.
2Ease of operation
If high-level analytics tools are used, then ease of operation is improved, but the ability to perform custom data analyses deteriorates
Solution Approach 1:
The patent implements a dynamic system that adapts to varying user needs. The natural language processing engine can handle both simple exploratory queries and complex analytical requests, adjusting its SQL generation capabilities based on the sophistication of the user's natural language input. This allows the system to maintain ease of operation while providing full flexibility for custom data analyses.
Solution Approach 2:
The patent creates a universal interface that handles multiple types of data analysis tasks through a single natural language processing system. Whether users need simple data retrieval, complex aggregations, or custom analytical queries, the same natural language interface accommodates all these functions, eliminating the need for separate tools for different analysis types while maintaining both ease of operation and flexibility.
3Measurement precision
If specialized technical individuals are used for data manipulation, then data analysis precision is improved, but the loss of time increases due to manual processes
Solution Approach 1:
The patent enables self-service data analysis by empowering non-technical users to perform complex data manipulations themselves through natural language queries. The system automatically generates and executes SQL code, performs data transformations, and delivers results without requiring specialized technical personnel. This maintains analysis precision while dramatically reducing time consumption by eliminating manual intervention and expert dependency.
Solution Approach 2:
The patent performs preliminary actions by pre-compiling and optimizing SQL query templates and data transformation logic. When users submit natural language queries, the system leverages pre-prepared code structures and optimization rules to quickly generate efficient SQL statements, reducing the time required for complex data manipulations while maintaining the precision that would otherwise require specialized expertise.
Data Source
AI summary
A method for querying and analyzing datasets via natural language processing (NLP) that can maintain context is disclosed. According to one embodiment, a computer-implemented method includes receiving, by a user interface, at least one of an utterance or a structured query language statement. The method includes identifying zero or more previous data conversation steps indicated by the utterance. The method includes determining, based on the utterance and the zero or more previous data conversation steps, an effective schema targeted by the utterance. The method includes generating, based on the utterance and the effective schema, an intermediate structured query language statement that is representative of the utterance. The method includes generating an executable structured query language statement based on the intermediate structured query language statement. The method includes executing the executable structured query language statement for the data query engine schema. The method includes communicating a result set and metadata.