Generative Search Result Categorization to Reduce Redundant Retrieval
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Solution Overview
Problem
Traditional search engines provide redundant information in response to general queries, leading to unnecessary resource consumption due to redundant content retrieval and processing.
Innovation Solution
A search system utilizing generative models to generate topic-organized search results by processing user queries with context data, generating topic tuples, and retrieving results from specific backend indices, then integrating them into a structured search results page with model-generated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional search engines provide comprehensive search results for general queries, then users receive more information options, but redundant information increases leading to resource consumption
Solution Approach 1:
The patent segments search results into distinct topic categories using generative models. Instead of presenting a flat list of results, the system clusters results by topic and presents them in an organized hierarchy, reducing redundancy while maintaining comprehensive coverage. This segmentation allows users to efficiently navigate relevant topics without processing duplicate information across multiple results.
Solution Approach 2:
The patent extracts and removes redundant information from search results through generative model processing. The system identifies overlapping content across multiple results and consolidates them, keeping only essential unique information. This extraction process reduces the quantity of redundant data presented to users while preserving the core informational value.
2Loss of information
If users explore multiple search results to find relevant information, then they may discover comprehensive answers, but significant computational expenditure occurs on user devices and network components
Solution Approach 1:
The patent performs preliminary organization and filtering of search results on the server side before presenting them to users. The generative model pre-processes results to identify and highlight the most relevant topics and information, so users receive a curated set of results that are more likely to contain comprehensive answers. This preliminary action reduces the need for users to extensively process multiple results on their own devices.
Solution Approach 2:
The patent introduces an intermediary processing layer (the generative model) between the search index and the user interface. This intermediary consolidates and organizes information from multiple sources into a coherent structure, reducing the computational burden on user devices. Users interact with the organized output rather than processing raw, unstructured results themselves.
3Device complexity
If traditional search engines organize results by basic relevance, then implementation is simple, but redundant information across multiple results increases
Solution Approach 1:
The patent applies segmentation by organizing search results into distinct topic categories rather than a single relevance-based list. The generative model identifies thematic groupings and structures results hierarchically, which reduces redundancy by presenting similar information under unified topic headings rather than as separate overlapping results.
Data Source
AI summary
Provided are systems and methods that use generative models such as large language models (LLMs) to assist in generating topic-organized search result pages. In particular, an example system can use a generative model to generate a number of different topics in response to a user query. A number of search results can be retrieved for each different topic. The search system can integrate all of the search results into a coherent search results page that is structured by topic. The resulting generating topic-organized search result page can provide a more intuitive and easily-navigable search experience.