Semantic Search Engine Summarization
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Solution Overview
Problem
Classical search engines only retrieve and rank relevant content based on user queries without providing additional information or analysis, requiring users to navigate multiple results to find relevant information.
Innovation Solution
A semantic search engine system that organizes and summarizes information from retrieval-based search engines into a semantically meaningful format, using large language models to generate answers, provide overviews, and disambiguate queries by gathering information from various data sources, including local document stores and third-party platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a classical search engine retrieves and ranks relevant content based on user queries, then the search results are provided, but users are required to navigate multiple results to determine information relevant to their query
Solution Approach 1:
The patent introduces an intermediary system that sits between the classical search engine and the user. This intermediary automatically analyzes search results, extracts key information, and presents it in a synthesized format. The intermediary performs functions such as summarizing content, identifying relevant facts, and organizing information hierarchically, thereby mediating the gap between raw search results and user comprehension needs.
Solution Approach 2:
The system performs preliminary analysis and synthesis of search results before presenting them to the user. Instead of requiring users to manually examine multiple results, the system pre-processes the information by extracting essential content, generating summaries, and organizing key points in advance. This preliminary action saves user time and effort by preparing the information in a consumable format before the user even views it.
2Loss of information
If a classical search engine only retrieves and ranks content, then the system remains simple, but it cannot provide additional information or analysis
Solution Approach 1:
The patent implements a multi-functional system that performs multiple tasks within a unified architecture. The same system components that retrieve and rank search results also analyze, summarize, and synthesize the content. Rather than adding separate independent systems, the solution integrates multiple functions (retrieval, ranking, analysis, summarization) into a cohesive platform, where shared resources and algorithms serve multiple purposes.
Solution Approach 2:
The system performs self-service by automatically analyzing and synthesizing search results without requiring external intervention or complex manual processing. The algorithms autonomously extract key information, generate summaries, and organize content based on the retrieved results. This self-service capability allows the system to provide enhanced information and analysis while maintaining operational simplicity.
3Productivity
If users navigate multiple search results to find relevant information, then comprehensive coverage is achieved, but user productivity decreases
Solution Approach 1:
The patent applies the extraction principle by selectively removing and isolating the most relevant information from multiple search results. Instead of presenting users with complete sets of search results requiring manual navigation, the system extracts key facts, essential content, and critical information points from each result and presents only these extracted elements in a synthesized format. This extraction approach maintains comprehensive coverage of relevant information while dramatically reducing the time users spend navigating through full results.
Data Source
AI summary
The present disclosure relates to generating semantic search engine results. Aspects of the present disclosure retrieve relevant information from a search engine based on user's search query. The query can be a classic search query (keyword or short phrase) or a conversational query (e.g., a chat messages between users and/or chatbots), a query based upon an email or other type of message, or a query generate based upon a content item (e.g., a webpage, image, video, document, etc.). Aspects of the disclosure leverage a large language model (LLM), such as, for example, a generative model, to summarizes the content according to the intent detected from the query. In some cases, aspects of the present disclosure may generate a direct answer to the query and provide relevant references to support the information.