Document Classification Using Multi-Dimensional Feature Vectors
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Solution Overview
Problem
Existing document classification methods are inefficient in accurately identifying desired documents within large sets, often requiring manual review to filter out unnecessary documents, and struggle to provide a general overview of document sets, leading to increased time and effort in searching and analysis.
Innovation Solution
A document classifying device that generates multi-dimensional feature vectors using classification codes and employs cluster analysis or latent topic analysis to classify documents, allowing for efficient identification of relevant documents and capturing the general picture of a document set by grouping similar content together.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing document classification methods are used, then documents can be classified using basic classification codes, but the accuracy of identifying desired documents is insufficient and manual review is required
Solution Approach 1:
The patent transforms classification codes into multi-dimensional feature vectors, adding dimensional depth to the classification representation. This allows documents to be classified not just by single codes but by complex patterns across multiple dimensions, significantly improving classification accuracy and enabling automated identification of desired documents without manual review
Solution Approach 2:
The patent combines multiple classification codes into composite feature vectors that capture nuanced document characteristics. By integrating information from multiple classification dimensions into a unified vector representation, the system achieves higher classification precision and can automatically distinguish desired documents from irrelevant ones
2Loss of information
If existing document classification methods are used, then basic classification can be performed, but the ability to provide a general overview of document sets is limited
Solution Approach 1:
The patent extracts essential classification information from multiple codes and consolidates it into representative feature vectors. This extraction process captures the general overview of document sets by identifying dominant classification patterns and characteristics, providing comprehensive insights without requiring complex manual analysis
Solution Approach 2:
The patent creates a multi-functional classification system where the same feature vector representation serves multiple purposes: detailed document classification, general overview generation, and pattern recognition across document sets. This universal approach enables both specific and general analysis functions within a unified framework
3Reliability
If manual review is used to filter documents, then accurate identification is possible, but the process requires increased time and effort
Solution Approach 1:
The patent enables the classification system to automatically perform the filtering and identification functions that previously required manual review. By using multi-dimensional feature vectors and advanced classification algorithms, the system achieves reliable automated document identification, maintaining high accuracy while dramatically improving searching efficiency and reducing human effort
Data Source
AI summary
A document classifying device (10) includes: a unit (22) configured to acquire information regarding a to-be-classified document set in which classification codes based on multi-viewpoint classification are assigned to each document in advance; a unit (23) configured to generate a multi-dimensional feature vector for each document in the to-be-classified document set, the multi-dimensional feature vector having, as elements, all or part of the classification codes assigned to the to-be-classified document set; classifying unit (24) configured to classify the to-be-classified document set using the feature vector of each document; and a generating unit (25) that generates document classification information indicating a result of the classification.