AI Query Response Curation for Trustworthy Complex Search
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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, failing to provide timely and accurate responses, especially for nuanced topics like tax scenarios.
Innovation Solution
Implementing AI-based query and response generation systems that utilize generative AI models to generate and curate queries and responses, integrating feedback mechanisms, and employing unsupervised learning to improve query clustering and response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current knowledge-based search systems are used to provide search results, then large sets of responsive documents can be provided, but the systems lack trustworthiness and reliability
Solution Approach 1:
The system implements feedback loops where user interactions with search results (selections, views, engagements) are continuously collected and used to refine and re-rank results. This feedback mechanism improves reliability by learning from actual user behavior patterns and adjusting the trustworthiness weighting of different sources over time.
Solution Approach 2:
The patent replaces traditional mechanical search ranking systems with AI-based generative models that can evaluate document credibility, synthesize information from multiple sources, and generate ranked results based on learned patterns of trustworthiness rather than simple keyword matching algorithms.
2Speed
If simple research queries are handled by search engines, then quick fact look-up can be achieved, but complex research queries requiring expert domain knowledge cannot be properly addressed
Solution Approach 1:
The system dynamically adapts its processing approach based on query complexity detection. Simple queries receive fast, direct search engine responses, while complex queries are automatically routed to AI-generated synthesis processes that aggregate information from multiple documents, providing appropriate response depth and speed for each query type.
Solution Approach 2:
The search system is designed to handle multiple types of queries through a unified platform that can perform both simple keyword search and complex AI-generated synthesis, eliminating the need for separate systems for different query complexities and enabling a single interface to serve diverse research needs.
3Reliability
If organizational experts are used to respond to complex queries, then domain knowledge can be provided, but the organization may not have the right experts available for specific queries
Solution Approach 1:
The system creates synthetic expert responses by generating AI-synthesized answers based on aggregated information from multiple organizational documents and data sources. These generated responses replicate the knowledge that would come from human experts without requiring actual expert availability, effectively copying expert-level information synthesis at scale.
Solution Approach 2:
The search system automatically synthesizes complex responses using AI models that aggregate and analyze organizational documents without requiring human expert intervention. The system serves itself by generating knowledgeable responses from its own access to organizational data, eliminating dependency on expert availability while maintaining knowledge quality.
4Productivity
If generative AI is used to generate responses in real time, then response capability is improved, but trust and transparency foundations are compromised
Solution Approach 1:
The system introduces an intermediary layer between the AI generation process and the user that provides transparency mechanisms. This includes showing source document references, allowing users to trace generated claims back to original sources, and providing visibility into the synthesis process, thereby maintaining trust while enabling real-time generation.
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing organizational documents into structured formats before AI generation occurs. This includes pre-tagging, indexing, and validating source materials, which enables faster real-time generation while maintaining transparency because the preparation work is already done and verifiable before the actual response generation.
Data Source
AI summary
A system and method are provided for automatic query and response generation. A first query is obtained, a plurality documents that are relevant to the first query is identified, and the plurality of documents is presented via an interface. In response to receiving a selection of one or more documents from the plurality of documents, and for each document of the one or more documents, a set of textual content elements is generated and feedback data is received indicating a selection of one or more of the set of textual content elements. The one or more of the set of textual content elements is associated with a set of queries including the first query. In response to receiving a second query in the set of queries, a preferred response associated with the set of queries is selected and displayed.