Document Classification via Pattern and Anti-Pattern Engines
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Solution Overview
Problem
Classifying documents into topical and non-topical categories within a document repository is a tedious task, as existing methods lack efficient and accurate techniques for identifying relevant patterns and anti-patterns across diverse resources.
Innovation Solution
A method and system utilizing hand-crafted topical text patterns and anti-patterns, combined with frequent pattern growth procedures, to classify documents through a patterns engine, anti-patterns engine, topical probability engine, and non-topical probability engine, determining confidence scores for ranking documents based on their relevance to a specific topic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of documents is performed, then classification accuracy can be maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system enables automatic self-classification of documents by extracting features and matching them against predefined classification criteria. The document classification system processes documents autonomously without requiring manual intervention, thereby maintaining accuracy while eliminating time-consuming manual sorting operations.
Solution Approach 2:
The patent replaces manual mechanical classification processes with an automated computational system that uses feature extraction algorithms, pattern recognition, and decision trees to classify documents. This substitution eliminates human labor while maintaining or improving classification precision through computational methods.
2Ease of manufacture
If existing classification methods are used, then implementation is simple, but classification accuracy and reliability are insufficient
Solution Approach 1:
The classification system is divided into distinct modular components: feature extraction module, feature selection module, classification model module, and evaluation module. This segmentation allows each component to be independently optimized and maintained, ensuring both ease of implementation and high reliability through specialized processing at each stage.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results are evaluated against ground truth or user feedback, and the model is iteratively refined. This feedback loop ensures continuous improvement of classification reliability while maintaining simple implementation through automated model retraining and adjustment processes.
3Measurement precision
If manual pattern identification is performed, then pattern accuracy can be maintained, but the process becomes tedious and inefficient
Solution Approach 1:
The patent replaces manual pattern identification with automated computational algorithms that extract linguistic patterns, semantic structures, and contextual relationships from documents. This substitution maintains high pattern identification accuracy through sophisticated NLP techniques while dramatically improving processing efficiency by handling large volumes of text automatically.
Solution Approach 2:
The system performs continuous pattern extraction and classification operations without interruption, processing documents in a streamlined sequential manner. The automated pipeline maintains constant operation from feature extraction through classification decision-making, eliminating the intermittent and tedious nature of manual pattern identification while sustaining high accuracy levels.
Data Source
AI summary
A method for classifying a document using identified patterns includes determining frequent patterns based on a group of resources, where the frequent patterns include sets of words associated with resources that are related to a particular topic; determining frequent anti-patterns based on another group of resources, where the frequent anti-patterns include sets of words associated with resources that are not related to the particular topic, where the second group of resources is different from the first group of resources; determining a probability that the document is related to the particular topic based on the frequent patterns and the frequent anti-patterns; and determining a topic classification of the document based on the determined probability.