Document Segment Indexing via Machine Learning Relevance Ranking
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Solution Overview
Problem
Current information retrieval systems are inefficient in identifying and ranking relevant segments within documents, as they rely on traditional keyword searching and fail to distinguish between segments with peripheral and primary relevance to search queries, and do not effectively handle temporal relevance.
Innovation Solution
A computing system that analyzes documents to identify segments based on features like casing, spacing, and keyword density, and uses machine learning to determine statistical likelihoods of segment relevance, incorporating recency scores and concept hierarchies to rank segments and index concepts, allowing for precise querying and retrieval of relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword searching is used, then the system is simple to implement, but it fails to distinguish between segments with peripheral and primary relevance to search queries
Solution Approach 1:
The patent divides documents into segments and creates separate index entries for each segment rather than indexing entire documents. This segmentation allows the system to evaluate and rank individual segments based on their relevance to search queries, enabling distinction between primary and peripheral relevance while maintaining manageable complexity through localized analysis.
Solution Approach 2:
The system introduces multiple parameters for segment evaluation including keyword density, concept hierarchy matching scores, and recency weights. By changing from simple keyword presence to multi-parameter scoring, the system achieves precise relevance distinction through statistical likelihood calculations that combine multiple factors.
2Measurement precision
If comprehensive document analysis is performed to improve retrieval accuracy, then search accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary analysis by pre-processing documents into segments, calculating keyword densities, and building concept hierarchy indexes before actual search queries. This preliminary action stores processed data in index structures that enable rapid retrieval during search operations, reducing processing time while maintaining comprehensive analysis benefits.
Solution Approach 2:
The patent extracts key features from documents including keyword frequencies, concept matches, and segment metadata, separating these from full document text. By extracting only essential information for indexing and storing it in optimized data structures, the system achieves high search accuracy without the computational overhead of analyzing complete documents during query processing.
3Measurement precision
If the system indexes all segments equally, then completeness is maintained, but temporal relevance cannot be distinguished
Solution Approach 1:
The system applies different quality attributes to different segments based on their characteristics. Recency weights are applied locally to segments based on their creation or modification timestamps, allowing temporal relevance to be distinguished without re-indexing entire documents. Each segment receives appropriate weighting based on its specific temporal properties.
Data Source
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AI summary
Systems and techniques for indexing and/or querying a database are described herein. Discrete sections and/or segments from documents may be determined by a concept indexing system. The segments may be indexed by concept and/or higher-level category of interest to a user. A user may query the segments by one or more concepts. The segments may be analyzed to rank the segments by statistical accuracy and/or relatedness to one or more particular concepts. The rankings may be used for presentation of search results in a user interface. Furthermore, segments and/or documents may be ranked based on recency decay functions that distinguish between segments that maintain their relevance over time in contrast with temporal segments whose relevance decays quicker over time, for example.