Document Fusion Score via Hierarchical Structure Comparison
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Solution Overview
Problem
The challenge of efficiently retrieving relevant information from voluminous electronic documents is exacerbated by the difficulty in comparing and matching the structural and semantic features across documents, leading to ineffective search results.
Innovation Solution
A document management system generates a fusion score by extracting and comparing sets of features, including hierarchical structures and semantic data, using machine learning models to compute similarity scores and weighted features, thereby determining the relevance between electronic documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple feature types including hierarchical structure are extracted and compared, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments document features into distinct types including hierarchical structure, semantic content, and metadata. Each feature type is extracted and processed separately through dedicated processing steps, allowing the system to handle complex multi-dimensional document comparison by breaking it down into manageable feature segments that can be independently analyzed and then integrated.
2Manufacturing precision
If machine learning models are used to generate weighted features, then manufacturing precision is improved, but ease of manufacture deteriorates
Solution Approach 1:
The patent employs machine learning models to pre-compute optimal weights for different feature types during a training phase. This preliminary action allows the system to learn the relative importance of various document features beforehand, so that during actual document comparison operations, the pre-determined weights can be directly applied without requiring complex real-time calculations, thus improving precision while reducing operational complexity.
3Reliability
If fusion scores are generated by comparing multiple feature sets, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent implements a tiered fusion score computation approach where not all feature types are compared with equal depth for every document pair. The system computes fusion scores using a combination of feature types, applying more rigorous comparison to critical features while using simpler comparison methods for less important features. This partial action approach maintains reliable retrieval accuracy by focusing computational effort on the most discriminative features rather than exhaustively processing all features at maximum detail.
Data Source
AI summary
Systems and methods for generating a fusion score between electronic documents. The method includes receiving a first electronic document by a document management system. The method further includes extracting a first set of features from the first electronic document including at least one feature type indicating the hierarchical structure of the first electronic document. The method also includes receiving a second electronic document by the document management server. The method further includes extracting a second set of features from the second electronic document including at least one feature type indicating the hierarchical structure of the second electronic document. The method further includes generating a fusion score based on a comparison of the first set of features and the second set of features.