Natural Language Query Transformation via LLM Validation
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Solution Overview
Problem
Existing database analytic tools are inefficient, costly, and require substantial configuration and training, making it difficult for businesses to effectively access and analyze large volumes of data stored in complex systems.
Innovation Solution
A data access and analysis system that utilizes a large language model to generate artificial data analysis requests by transforming natural language input into structured query language, optimizing data queries and improving accessibility and accuracy through predictive and generative input techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing database analytic tools are used, then data analysis capability is provided, but the tools are inefficient, costly, and require substantial configuration and training
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the database system. This intermediary translates natural language queries into structured query language, eliminating the need for users to directly interact with complex database tools. The intermediary handles the complexity of data analysis internally while presenting a simple interface to users, thereby improving efficiency without requiring extensive configuration or training.
Solution Approach 2:
The patent replaces manual mechanical operations of traditional database tools with automated natural language processing. Instead of requiring users to manually configure and operate complex analytic tools, the system automatically processes natural language input and generates appropriate queries. This substitution eliminates the need for substantial configuration and training while maintaining data analysis capability.
2Quantity of substance
If complex database systems store large volumes of data, then data storage capacity is improved, but access and analysis become difficult
Solution Approach 1:
The natural language processing system serves as an intermediary that simplifies access to large volumes of data stored in complex database systems. Users can query extensive datasets using simple natural language without needing to understand the underlying database structure or complexity. The intermediary handles the complexity of navigating and analyzing large data volumes internally.
Solution Approach 2:
The system creates simplified representations or copies of complex database structures through natural language interfaces. Instead of requiring users to directly interact with complex database schemas, the system translates natural language queries into appropriate structured queries that automatically navigate the complex data structures, making data access easy while maintaining full access to stored volumes.
3Measurement precision
If natural language input is transformed into structured query language, then data query accuracy is improved, but processing complexity increases
Solution Approach 1:
The system performs preliminary processing of natural language input by pre-defining templates and structures for common query types. Before attempting complex transformations, the system prepares standardized query frameworks that can be automatically populated from natural language input. This preliminary action simplifies the transformation process while maintaining query accuracy by leveraging pre-established patterns.
Solution Approach 2:
The transformation process incorporates feedback mechanisms where the system validates natural language input against expected query structures and refines transformations iteratively. If initial transformations do not produce accurate queries, the system uses feedback from validation results to adjust and improve the transformation process, thereby maintaining accuracy while managing complexity through adaptive refinement.
Data Source
AI summary
Obtaining artificial data analysis request data includes obtaining triggering event data including at least one primary data source identifier, obtaining large language model input data for obtaining the artificial data analysis request, obtaining large language model generated data output by a large language model in response to the large language model input data, wherein the large language model generated data includes at least one candidate artificial data analysis request tuple, and validating the large language model generated data, wherein validating the large language model generated data includes validating the at least one candidate artificial data analysis request tuple in accordance with a defined data-analytics grammar implemented by the data access and analysis system.