Client-Side Search Filtration Layer for Relevance Filtering
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Solution Overview
Problem
Users face the challenge of receiving irrelevant and outdated search engine results, requiring multiple searches to find accurate information, due to the lack of context-based filtering in existing search engines.
Innovation Solution
A computer-implemented method and system that builds a user search interaction model based on a user's profile and historic search results, using topic analysis to filter search results on a client device, thereby selecting relevant topics and improving the relevance of search engine outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search engines return all search results without filtering, then the quantity of search results is maximized, but the relevance and accuracy of information decreases
Solution Approach 1:
The patent segments search results into different categories or tiers based on relevance metrics, user profiles, and interaction history. The filtration layer divides the result set into high-relevance, medium-relevance, and low-relevance segments, presenting only the most relevant results to the user while maintaining the option to access additional results if needed.
Solution Approach 2:
The patent introduces a filtration layer as an intermediary component between the search engine and the user interface. This intermediary layer processes search results through multiple filtering stages, applying relevance algorithms and user-specific criteria to select and rank results before presentation, thereby improving reliability without completely eliminating result quantity.
2Measurement precision
If users perform multiple searches to find accurate information, then the accuracy of information is improved, but the time spent searching increases
Solution Approach 1:
The patent performs preliminary filtering and ranking of search results before they are presented to the user. By pre-processing results through relevance algorithms, user profile matching, and interaction history analysis, the system prepares optimized result sets in advance, ensuring accurate information is presented first without requiring users to perform multiple iterative searches.
Solution Approach 2:
The patent implements feedback mechanisms that learn from user interactions with search results. User behavior data such as click-through rates, time spent on results, and refinement queries are fed back into the system to continuously improve the accuracy of future search result rankings, reducing the time users need to spend searching over time.
3Reliability
If a filtration layer is implemented to improve search result relevance, then the quality of search results is improved, but the system complexity increases
Solution Approach 1:
The patent designs the filtration layer to perform multiple functions simultaneously: filtering results by relevance, ranking results according to user profiles, analyzing interaction patterns, and adapting to user preferences. By consolidating these functions into a single multi-functional component rather than separate systems, the patent improves result quality while managing system complexity.
Data Source
AI summary
A computer-implemented method for filtering search engine results for a user is provided. The method includes maintaining a filtration layer that is opted into by a search engine and a client device. The method further includes building a user search interaction model, operatively coupled to the filtration layer, based on a user's profile and historic search results by performing a topic analysis on a user's interactions with the historic search results and selecting a subset of relevant topics based on respective amounts of user interaction. The user interaction includes interactions on a plurality of different devices. The method also includes filtering search results produced for a particular user search query on the client device using the user search interaction model and the filtration layer.