Hybrid Document Classification Trees for Unstructured Content
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Solution Overview
Problem
Traditional rule-based document classification systems struggle with adaptability, scalability, and accuracy when handling unstructured or poorly structured documents, particularly those containing complex or ambiguous content, leading to inefficiencies and increased manual intervention.
Innovation Solution
A hybrid document classification system that integrates both rule-based nodes and machine learning (ML) model-based nodes, utilizing decision trees to dynamically adapt to document content, evaluate confidence levels, and iteratively refine classifications, allowing for nuanced pattern recognition and reduced manual maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based classification is used, then the system is simple to implement, but it lacks adaptability to evolving document types
Solution Approach 1:
The patent combines rule-based classification nodes with machine learning model-based nodes into a unified decision tree structure. This merging allows the system to leverage both the simplicity and interpretability of rule-based approaches and the adaptability and pattern recognition capabilities of machine learning, resolving the contradiction between ease of implementation and adaptability to evolving document types.
2Ease of repair
If rule-based nodes are used, then the system is easy to maintain, but it struggles with unstructured or poorly structured documents
Solution Approach 1:
The patent introduces machine learning model-based nodes as intermediaries between the input documents and the final classification output. These ML nodes specifically handle unstructured or poorly structured documents by learning patterns from training data, while rule-based nodes continue to maintain simple, interpretable classification logic for structured documents. This intermediary approach resolves the contradiction by assigning different document types to the most appropriate processing mechanism.
3Measurement precision
If extensive training models are used, then classification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the classification system into multiple decision nodes within a decision tree, where each node can be either rule-based or machine learning model-based. This segmentation allows the system to achieve high classification accuracy through specialized nodes while maintaining overall manageability by organizing complexity in a hierarchical, modular structure. Not all nodes require extensive training models - only those where ML provides added value.
4Adaptability or versatility
If machine learning models are integrated, then adaptability improves, but manual intervention increases
Solution Approach 1:
The patent implements a decision tree structure where machine learning model-based nodes provide confidence levels for their classifications. When confidence is high, automated classification occurs without manual intervention. When confidence is low or documents are ambiguous, the system can route to human reviewers for manual classification. This feedback mechanism allows the system to achieve high adaptability while minimizing manual intervention by automatically handling only the cases where ML models are sufficiently confident.
Data Source
AI summary
The technology automatically classifies documents using a decision tree integrating both rule-based nodes and machine learning (ML) model-based nodes. Rule-based nodes evaluate document information against predefined rules to generate classifications, while ML model-based nodes provide classifications along with the corresponding confidence level probabilities. Upon receiving an unclassified set of documents, the technology classifies each document by traversing the decision tree. At rule-based nodes, document evaluation entails comparing outcomes of logical conditions within the node. At ML model-based nodes, the evaluation depends on confidence level probabilities meeting predefined thresholds for each node. Using the evaluations, the technology assigns a proposed classification to each document. Once all documents have been classified, the technology generates a set of classified documents.