Generative Search Result Documents With LGM Curation
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Solution Overview
Problem
Current search result systems require significant user effort to find relevant answers, as they often present lists of links and resources without providing direct and comprehensible answers, leading to inefficient information retrieval.
Innovation Solution
A generative document system utilizing large generative models (LGMs) to create interactive and dynamic search result documents by generating text narrative responses, matching them with answer cards, and formatting the layout to provide streamlined and understandable answers alongside search result links.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current search result systems present lists of links and resources, then comprehensive information coverage is achieved, but user effort and time required to find relevant answers increases significantly
Solution Approach 1:
The patent introduces an intermediary system (the generative document system with LGM) that mediates between the search query and the search result links. This intermediary automatically generates curated documents that synthesize information from multiple links, eliminating the need for users to manually examine each link while maintaining comprehensive information coverage.
Solution Approach 2:
The system performs self-service by automatically generating structured documents from search result links without requiring user intervention. The LGM autonomously processes the links, extracts relevant information, and presents synthesized answers, allowing the system to serve itself rather than relying on user effort to navigate and synthesize information.
2Ease of operation
If current search result systems provide direct answers for frequently searched topics, then ease of operation improves, but flexibility and adaptability to diverse search queries decreases
Solution Approach 1:
The patent implements a universal system that handles both frequently searched topics and diverse, unique queries through the same LGM-based architecture. The system adapts its behavior based on the query type, providing direct answers for common topics and synthesized documents for complex queries, thereby achieving multi-functionality that maintains ease of operation across different query types.
Solution Approach 2:
The system dynamically adjusts its response format and processing approach based on the characteristics of each search query. For frequently searched topics, it provides quick direct answers; for diverse and complex queries, it generates comprehensive curated documents. This dynamic adaptation maintains ease of operation while preserving flexibility across different query types.
3Measurement precision
If users manually examine lists of results and review multiple entries, then measurement precision of information retrieval improves, but productivity and efficiency decreases
Solution Approach 1:
The system performs preliminary action by automatically synthesizing and curating information from multiple search result links before presenting it to the user. The LGM pre-processes the information, extracts key points, and organizes them into a coherent document, eliminating the need for users to manually review multiple entries while maintaining retrieval accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual information examination with an automated computational system. The LGM performs the cognitive tasks of information synthesis, evaluation, and presentation automatically, substituting the mechanical user action of reading and analyzing multiple links with an automated intelligent system that achieves the same precision more efficiently.
Data Source
AI summary
This disclosure describes utilizing a generative document system to dynamically build and provide generative search result documents. The generative document system utilizes an aggregated framework that leverages one or more large generative models (LGMs). For example, the aggregated framework includes three stages where local processes are applied to generative outputs from LGMs, with each stage building upon the generative inputs from previous stages. The generative document system uses the aggregated framework to create generative search result documents based on search queries and their corresponding search result links. These generative search result documents provide interactive, intuitive, comprehensive, and flexible curation of answers that address the respective search queries.