Intelligent Query System Using PCF-IPCDF Scoring for Contextual Search
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Solution Overview
Problem
Existing search and retrieval systems fail to efficiently distinguish between different contexts of phrases, leading to irrelevant search results when terms like 'strike outs' or 'home run' are used, as they can relate to both baseball and financial contexts, causing user frustration.
Innovation Solution
An intelligent query system that categorizes documents using a taxonomy engine, filtering and correlating terms with taxonomy elements, employing a PCF-IPCDF scoring system to generate an IQ map that recommends the strongest correlation, such as marking baseball documents with 'BASE' and financial documents with 'EQUITIES', thereby providing relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing search systems retrieve all documents containing a search term, then comprehensive search coverage is achieved, but search result relevance deteriorates due to contextual ambiguity
Solution Approach 1:
The patent applies local quality by associating different contextual meanings with the same search term based on the document's category. For example, the term 'strike outs' is interpreted differently in baseball documents versus financial documents. The system enhances the local contextual understanding of each term within its specific document category, allowing the same term to have different relevant meanings depending on where it appears.
Solution Approach 2:
The patent segments the search process into two distinct phases: first retrieving all documents containing the search term (comprehensive coverage), then filtering and re-ranking results based on contextual relevance scores calculated from category-specific term frequencies. This segmentation allows the system to maintain both broad search coverage and high result relevance by applying different operations at different stages.
2Speed
If search systems use simple term matching, then search speed is improved, but search accuracy deteriorates due to inability to distinguish contextual meanings
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing term frequency statistics for each category before the actual search occurs. The system builds category-specific term frequency tables in advance, so that during search operations, it can quickly retrieve and apply these pre-computed contextual weights without performing complex calculations in real-time, thus maintaining high search speed while improving accuracy.
3Measurement precision
If the system analyzes contextual correlations for all terms, then search relevance is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by using a single, unified statistical approach (term frequency analysis) that serves multiple purposes: it identifies relevant terms, determines contextual meanings, and ranks search results. The same category-specific term frequency tables are used across different search queries and document types, creating a multi-functional system that handles diverse search scenarios without requiring separate complex mechanisms for each case.
Data Source
AI summary
An intelligent query system and method used in a search and retrieval system provides an end-user the most relevant, meaningful, up-to-date, and precise search results. The system and method allows an end-user to benefit from an experienced recommendation that is tailored to a specific industry. The system and method recognizes that the phrases “strike outs” and “home run” are much more strongly correlated with “BASE” as opposed to “EQUITIES.” When a search is conducted or a lookup is done in a map, the system and method recommends the strongest correlation as “BASE.”