Dynamic Search Result Filtering for Changing User Intent
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Solution Overview
Problem
Existing search engines struggle with providing effective and responsive search filters that adapt to user interactions, often resulting in irrelevant search results due to static and non-responsive filtering mechanisms.
Innovation Solution
A system that dynamically filters search results based on user interaction data, analyzing scrolling behavior and other interactions to generate and apply result filter rules, enhancing relevance and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static search filters are used, then device complexity is reduced, but search result relevance deteriorates
Solution Approach 1:
The patent applies dynamics by transforming static search filters into dynamic filters that automatically adapt to user interactions. The system monitors user behavior (scrolling, clicking, time spent) and dynamically adjusts filter criteria in real-time, allowing the filtering mechanism to evolve from fixed to adaptive without increasing perceived user effort or system complexity.
Solution Approach 2:
The patent implements feedback loops where user interaction data is continuously collected, analyzed, and fed back into the filtering system. This feedback mechanism enables the system to learn from user behavior patterns and automatically refine search results, improving relevance while maintaining operational simplicity through automated decision-making algorithms.
2Reliability
If dynamic filtering based on user interaction data is implemented, then search result relevance is improved, but device complexity increases
Solution Approach 1:
The patent applies self-service by enabling the filtering system to automatically manage its own complexity. The system autonomously collects interaction data, generates filter rules, and applies filtering without requiring manual configuration or complex user input. This self-managing approach handles the complexity internally while presenting a simple interface to users.
Solution Approach 2:
The patent implements preliminary action by pre-establishing the framework for dynamic filtering during system initialization. Filter templates, data collection mechanisms, and analysis algorithms are prepared in advance, allowing the system to quickly adapt to user interactions without requiring complex real-time decision-making infrastructure.
3Measurement precision
If comprehensive user interaction data is collected and analyzed, then filter rule accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by selectively analyzing only the most relevant user interaction data points rather than processing all possible data. The system identifies key behavioral indicators (such as scroll depth, click patterns, and time-on-page) and focuses computational resources on these critical metrics, achieving accurate filter rules with reduced processing overhead.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the granularity and depth of data analysis based on search context and user behavior stages. The system varies computational intensity, data collection scope, and analysis depth according to situational needs, optimizing the balance between filter accuracy and resource consumption through adaptive parameter tuning.
Data Source
AI summary
A computerized method filters search result content using user interaction data. Search result content, including search result entries, is presented. User interaction data is received that is indicative of a user's interactions with the presented search result content and a result filter rule is determined using the user interaction data. Filtered search result content is generated using the determined result filter rule and the generated filtered search result content, including a portion of the search result entries, is presented. In some examples, the user is prompted to accept the filtering of the search result content prior to the generation of the filtered search result content. Thus, the search result content is dynamically filtered based on the user's interactions with that content while the user reviews the content.