Document Classification via Dynamic Structural Thresholds
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Solution Overview
Problem
Current document classification methods are inefficient in managing and protecting large numbers of documents across different tiers, as they rely on textual rules engines and manual categorization, which can be inconsistent and lack a multifaceted approach to cover various dimensions of document analysis.
Innovation Solution
A method and system for generating a document mapping classifying dataset that uses document feature datasets to categorize documents based on similarity, adjusting a dynamic similarity threshold according to structurality levels, allowing for accurate automated classification and consistent policy enforcement across different categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content based classification is used, then documents can be classified based on their content, but the classification becomes inconsistent and lacks multifaceted approach
Solution Approach 1:
The patent segments document classification into multiple dimensions by extracting different types of features (textual, structural, visual, metadata) and analyzing them separately through specialized modules. Each feature type is processed independently and then combined, allowing consistent application of multiple classification criteria without the inconsistencies of single-dimension approaches.
Solution Approach 2:
The patent transitions from traditional one-dimensional content-based classification to multi-dimensional classification by adding structural, visual, and metadata dimensions. This dimensional expansion enables the system to capture documents from multiple perspectives simultaneously, improving both accuracy and consistency through comprehensive feature analysis.
2Measurement precision
If manual categorization is used, then documents can be reviewed for accuracy, but the process is time-consuming and cannot scale
Solution Approach 1:
The system performs self-service classification by automatically extracting features, determining document categories, and assigning metadata without requiring manual intervention. The automated pipeline processes documents through multiple analysis modules and generates classifications independently, eliminating time-consuming manual review while maintaining high accuracy through comprehensive feature extraction.
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated computational systems that use algorithms to extract and analyze document features. This substitution of human-based mechanical review with automated digital processing enables rapid scaling while maintaining precision through sophisticated feature extraction and matching algorithms.
3Ease of manufacture
If textual rules engines are used, then classification can be implemented, but the system lacks multifaceted approach to cover various dimensions
Solution Approach 1:
The patent implements a universal classification system that handles multiple document types and dimensions through a single integrated platform. The system can process textual, structural, visual, and metadata features simultaneously, providing multifaceted analysis capabilities that adapt to various document categories and organizational requirements without requiring separate specialized systems.
Solution Approach 2:
The system achieves adaptability by dynamically adjusting feature extraction parameters and classification thresholds based on document type and organizational requirements. The configurable nature of the feature extraction modules allows the same system to adapt to different classification needs by modifying parameters rather than requiring system redesign, enabling versatile multi-dimensional coverage.
Data Source
AI summary
A method of classifying documents. The method comprises providing a document mapping classifying dataset comprising document feature datasets, each one of the document feature datasets documenting document features of one of a plurality of documents, each one of the documents is associated with a structurality level and classified as related to one of a plurality of database specific categories, extracting a current document feature dataset from a document, performing an analysis of each of at least some of the document feature datasets to identify a similarity to the current document feature dataset while adjusting a dynamic similarity threshold according to a respective the structurality level of an associated document from the documents, selecting one of the documents according to the similarity, and classifying the current document as a member of a respective the database specific category of the selected document.