Customized Search Platform With User-Controlled Data Source Ranking
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Solution Overview
Problem
Existing search engines provide limited user control and transparency over how and where searches are conducted, often resulting in inefficient and unsatisfactory search experiences, particularly when users need access to specific and dedicated databases.
Innovation Solution
A customized search platform that allows users to prioritize and control data sources based on search query characteristics and personal preferences, using neural networks to intelligently recommend and rank relevant data sources, and enable users to select or deselect data sources for their searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing search engines crawl web data to collect search results from all data sources, then the quantity and variety of search results is improved, but user control and transparency over search processes deteriorates
Solution Approach 1:
The patent segments the search process into distinct components: (1) user-controlled selection of data sources, (2) neural network-based relevance determination, and (3) result generation. This segmentation allows users to control which data sources are searched while the system independently handles result generation, resolving the contradiction between comprehensive results and user control.
Solution Approach 2:
The patent implements preliminary action by requiring users to pre-select their preferred data sources before conducting searches. The system stores these selections and automatically applies them when processing search queries, giving users advance control over the search scope and improving both transparency and ease of operation.
2Productivity
If a customized search platform filters results based on user preferences and neural network models, then user control and search efficiency are improved, but computational complexity increases
Solution Approach 1:
The patent extracts the computationally intensive task of determining data source relevance from the main search execution flow. By using pre-trained neural network models that have already learned relevance patterns, the system performs quick inference during actual searches rather than complex analysis, thereby improving search efficiency while managing computational complexity through model optimization.
3Measurement precision
If neural network models are used to determine relevant data sources, then measurement precision of relevance assessment is improved, but device complexity increases
Solution Approach 1:
The patent uses copying by deploying pre-trained neural network models that have been trained on extensive data to perform relevance assessment. Instead of implementing complex algorithms from scratch, the system copies proven model architectures and training approaches, achieving high measurement precision while managing complexity through the use of established, optimized model implementations.
Data Source
AI summary
Embodiments described herein provide systems and methods for a customized search platform that provides users control and transparency in their searches. The system may use a ranker and parser to utilize input data and contextual information to identify search applications, sort the search applications, and present search results via user-engageable elements. The system may also use input from a user to personalize and update search results based on a user's interaction with user-engageable elements.