Natural Language Query Pipeline for Accurate Intent-Based Responses
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Solution Overview
Problem
Traditional query systems struggle to accurately infer user intent from natural language queries, often producing inaccurate or incomplete results due to their reliance on well-structured queries and limited contextual understanding.
Innovation Solution
A query system that utilizes a pre-processor to convert unstructured data into a machine-readable format, leveraging models to determine user intent, and employs a feedback mechanism through a reward model to generate accurate and complete responses using a combination of a function call generator, executor, and natural language generation synthesizer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional query systems rely on well-structured queries, then system simplicity is maintained, but accuracy of query responses deteriorates
Solution Approach 1:
The patent introduces an intermediary natural language processing layer between the user and the database query system. This intermediary layer includes intent detection modules, context understanding components, and query generation algorithms that translate unstructured natural language into structured database queries, thereby improving accuracy without requiring users to write complex queries themselves
Solution Approach 2:
The query system is segmented into multiple specialized modules including natural language processing module, intent detection module, context analysis module, query generation module, and feedback processing module. Each module handles a specific aspect of the query processing pipeline, allowing the system to manage complexity through modular design while improving overall response accuracy
2Ease of operation
If the system processes unstructured natural language data, then ease of operation improves, but reliability of query results worsens
Solution Approach 1:
The patent implements a feedback mechanism where the system analyzes user interactions, query outcomes, and result accuracy to continuously refine its natural language processing and intent detection capabilities. The feedback loop includes performance monitoring, error analysis, and model retraining components that improve reliability over time while maintaining ease of operation
Solution Approach 2:
The system performs preliminary actions by pre-processing natural language input through multiple analysis stages including tokenization, part-of-speech tagging, semantic role labeling, and intent classification before generating queries. This preliminary processing ensures that even unstructured input is systematically analyzed to produce reliable query results
3Measurement precision
If the system uses complex optimization processes, then accuracy improves, but productivity deteriorates
Solution Approach 1:
The patent applies partial action by implementing multi-stage query optimization where only the most critical optimization techniques are applied to each query based on its complexity and type. Simple queries receive minimal processing while complex queries undergo more extensive optimization, balancing accuracy improvement with processing speed maintenance
Data Source
AI summary
A method for query response generation using a query system, the method including receiving a user query from a user, generating an altered query using the user query, wherein the altered query is associated with a user intent of the user, generating a function call using the user intent and the altered query, executing the function call to generate a result, converting the result into a natural language result, making a first determination that the natural language result satisfies a predetermined threshold, and providing, in response to the first determination, the natural language result to the user.