Generative Search Documents With Curated Answer Cards
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current search result systems require significant user effort to find relevant answers, as they often present lists of links and resources that need manual examination, and direct answers are not always helpful.
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 them into logical layouts, leveraging multiple LGM prompts to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
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 search system) that mediates between the comprehensive search results and the user. This intermediary automatically synthesizes, curates, and presents relevant information in a structured format, eliminating the need for users to manually examine numerous links while preserving complete information coverage.
Solution Approach 2:
The system enables self-service by automatically processing search results without requiring user intervention. The generative model autonomously analyzes search results, identifies relevant information, and presents synthesized answers, allowing the system to serve itself in reducing user effort while maintaining comprehensive information coverage.
2Ease of operation
If direct answers are provided for frequently searched topics, then user effort is reduced, but helpfulness and relevance of answers deteriorates when answers are not always helpful
Solution Approach 1:
The patent implements a dynamic answer generation system that adapts to each specific search query and context. Rather than providing static direct answers, the system dynamically generates customized responses based on real-time analysis of search results and user intent, ensuring both ease of operation and reliability of answer helpfulness.
Solution Approach 2:
The system applies local quality by tailoring the level and type of detail in answers to the specific needs of each query. Some queries receive concise direct answers while others receive more detailed synthesized responses, optimizing both user effort and answer helpfulness for each local context rather than applying a uniform approach.
3Measurement precision
If users manually examine lists of results and review multiple entries, then comprehensive review is achieved, but productivity and efficiency of finding satisfactory answers decreases
Solution Approach 1:
The system performs preliminary action by pre-analyzing and synthesizing search results before presenting them to users. The generative model proactively processes comprehensive search results, identifies key information, and prepares synthesized answers in advance, maintaining review completeness while dramatically improving answer-finding efficiency.
Solution Approach 2:
The system extracts essential information from comprehensive search results and presents only the most relevant synthesized content. By taking out and highlighting key findings while maintaining comprehensive review through automated analysis, the system achieves both review completeness and high productivity in answer discovery.
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.