AI Compatibility Search Ranking Beyond Keyword Matching
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Solution Overview
Problem
Current online search engines rely on keyword-based searching, which leads to over-inclusive and under-inclusive search results, distorted rankings, and inefficiencies in finding relevant employment opportunities or social engagements.
Innovation Solution
The implementation of an artificial intelligence-driven system that automates Internet-based searching and selection processes using user-determined characteristics and customizable parameters, providing personalized search results and rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If keyword-based searching is used, then search coverage is broad, but search result quality and relevance deteriorate
Solution Approach 1:
The patent segments the search process into multiple stages: initial keyword-based broad search followed by AI-driven refinement. The search results are segmented and re-ranked based on multiple criteria including user profile matching, content quality signals, and relevance algorithms, thereby maintaining broad coverage while improving result quality through systematic division of the search workflow.
Solution Approach 2:
The patent introduces an AI-based re-ranking system as an intermediary between the keyword search engine and the user. This intermediary layer processes the initial search results, applies quality filters, and re-ranks results based on relevance algorithms that consider user profiles and content quality signals, thus bridging the gap between broad keyword matching and precise result delivery.
2Measurement precision
If AI-based personalization is implemented, then search result relevance improves, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional AI system that simultaneously performs user profile matching, content quality assessment, result re-ranking, and bias mitigation. By consolidating these multiple functions into a single integrated AI processing layer, the system achieves high result relevance without proportionally increasing complexity, as the AI model handles diverse tasks through unified algorithms.
Solution Approach 2:
The patent utilizes parameter changes in the AI model to adjust search behavior dynamically. By modifying weights and parameters in the relevance algorithm based on user feedback and performance metrics, the system adapts to improve relevance without requiring fundamental architectural changes, thereby managing complexity through parameter optimization rather than structural complexity.
3Loss of energy
If comprehensive filtering is applied, then data transmission volume decreases, but information completeness may be compromised
Solution Approach 1:
The patent applies preliminary filtering and re-ranking of search results before transmission to the user. By pre-processing results to remove low-quality matches and organize relevant content, the system reduces the volume of data that needs to be transmitted while ensuring that the most relevant information is preserved and prioritized in the transmitted results.
Solution Approach 2:
The patent extracts and removes irrelevant, low-quality, or duplicate results from the search output before transmission. By taking out only the essential high-value information and filtering out noise, the system decreases data transmission volume while maintaining information completeness for the most relevant results.
Data Source
AI summary
A computer server system and method are disclosed for personalization and customizable filtering of network search results and search result rankings, such as for Internet searching. A representative server system comprises: a network interface to receive a query from a respondent or co-respondent; at least one data storage device storing a plurality of return queries; and one or more processors adapted to access the data storage device and using the query, to select the return queries for transmission; to search the data storage device for corresponding pluralities of responses to the return queries from other co-respondents or respondents; to pair-wise score the responses and generate pair-wise alignment scores for respondent and co-respondent combinations; to sort and rank the combinations according to the alignment scores; and to output a listing of the sorted and ranked respondents or co-respondents to form the personalized network search results and search result rankings.