Data-Driven Query Clustering for Trusted Domain Responses
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Solution Overview
Problem
Current knowledge-based search systems lack trustworthiness and reliability, struggle with complex queries, and often require domain expertise, especially for nuanced areas like tax scenarios, and fail to provide timely and accurate responses.
Innovation Solution
A method involving clustering historical queries, generating responses using large language models, receiving feedback, and prioritizing responses based on frequency and user interaction to provide curated, domain-specific natural language responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current knowledge-based search systems provide large sets of search results, then the quantity of information is improved, but the trustworthiness and reliability deteriorate
Solution Approach 1:
The patent extracts and highlights only the most relevant information snippets from search results rather than presenting complete documents. The system identifies and extracts key factual statements, statistics, and data points that directly answer the query, filtering out unnecessary content to improve both reliability and conciseness while maintaining information quantity.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with search results (clicks, selections, corrections) are used to continuously improve the quality and reliability of extracted information. The system learns from user feedback to better identify trustworthy sources and accurate information snippets.
2Measurement precision
If complex research queries are handled by senior personnel with domain knowledge, then the accuracy is improved, but the time required and operational complexity worsen
Solution Approach 1:
The system enables self-service for complex research queries by automatically performing multi-step research tasks, data analysis, and synthesis without requiring senior personnel intervention. The AI system independently handles complex queries by breaking them down into sub-queries, executing them, and synthesizing results, thereby maintaining accuracy while eliminating time loss and operational complexity.
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing domain knowledge bases, pre-computing relationships between entities, and preparing structured data representations in advance. This allows complex queries to be answered quickly by retrieving and combining pre-processed information rather than performing full analysis from scratch.
3Reliability
If domain-specific expertise is required for nuanced areas like tax scenarios, then the reliability is improved, but the ease of operation deteriorates
Solution Approach 1:
The system implements a universal interface that handles multiple domain-specific queries (tax, legal, medical, etc.) through a single unified platform. The same ease-of-use interface adapts to different domains by automatically selecting appropriate domain-specific knowledge bases and analysis methods, making domain expertise accessible without requiring users to learn domain-specific operations.
Solution Approach 2:
The system introduces an intermediary layer between the user and domain-specific knowledge bases. This intermediary translates natural language queries into domain-specific search operations, automatically identifies relevant domain expertise requirements, and presents results in user-friendly formats, thereby maintaining reliability while improving ease of operation.
4Reliability
If multiple sources are cited to support generated responses, then the trustworthiness is improved, but the complexity of the system worsens
Solution Approach 1:
The system segments the complex multi-source verification process into independent modular components: source retrieval module, credibility assessment module, information extraction module, and synthesis module. Each module handles a specific aspect of multi-source verification independently, making the overall system more manageable and maintainable while preserving trustworthiness through comprehensive source validation.
Data Source
AI summary
A system and method are provided for automatic query and response generation. The method may include obtaining a query log of historical queries within a predetermined knowledge domain. The method may also include clustering the query log to identify one or more sets of queries using semantic aggregation. The method may also include generating one or more responses for each of the one or more sets of queries using a large language model. The responses may be within the predetermined knowledge domain. The method may also include receiving feedback data for each of the one or more responses. The feedback data may identify a preferred response. The method may also include, in response to receiving a new query, providing a new response to the new query. The new response may be a preferred response for a set of queries including the new query.