Dynamic Document Classification with Confidence Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Static labeling schemes fail to adapt to changing topics and term meanings over time, leading to inadequate classification of documents in dynamic environments such as customer support centers.
Innovation Solution
A computer-based method and system that generates document classifiers using a classification algorithm on a trusted corpus, associates classification confidence levels with classified documents, and prompts users to manually classify low-confidence documents, allowing for continuous refinement of the classification system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a static labeling scheme is used to classify documents, then the classification process is simple and fast, but the classification accuracy deteriorates over time as topics and term meanings change
Solution Approach 1:
The patent implements a dynamic classification system where the taxonomy structure and document labels are continuously updated based on incoming documents and user feedback. The system transitions from static pre-defined labels to dynamic labels that evolve with changing topics and term meanings, resolving the contradiction between initial simplicity and long-term accuracy.
Solution Approach 2:
The system incorporates feedback loops where user corrections and new document classifications are fed back into the taxonomy structure. This allows the classification system to learn from errors and improvements, continuously refining its accuracy while adapting to changing topics without requiring complete system redesign.
2Measurement precision
If manual review is used to label all documents, then classification accuracy is high, but the processing time and cost increase significantly
Solution Approach 1:
The system applies partial manual review by using automated classification for the majority of documents and reserving manual review only for low-confidence cases. This selective approach maintains high label quality for critical documents while preserving processing throughput by automating routine classifications.
Solution Approach 2:
The system enables self-service classification where the automated classifier handles most documents independently, and only uncertain cases require human intervention. This reduces the burden on manual reviewers and increases overall processing capacity while maintaining quality standards.
3Productivity
If automated classification is applied to all documents, then processing speed increases, but classification reliability decreases for ambiguous or low-confidence cases
Solution Approach 1:
The system applies different classification strategies to different documents based on their characteristics. High-confidence documents are classified automatically with high speed, while low-confidence or ambiguous documents are flagged for manual review, ensuring local optimization of both speed and reliability for each document type.
Data Source
AI summary
A method and system for classifying documents is provided. A set of document classifiers is generated by applying a classification algorithm to a trusted corpus that includes a set of training documents representing a taxonomy. One or more of the generated document classifiers are executed against a plurality of input documents to create a plurality of classified documents. Each classified document is associated with a classification within the taxonomy and a classification confidence level. One or more classified documents that are associated with a classification confidence level below a predetermined threshold value are selected to create a set of low-confidence documents. The low-confidence documents are disassociated from each of the associated classifications. A user is prompted to enter a classification within the taxonomy for at least one low-confidence document. The low-confidence document is associated with the entered classification and with a predetermined confidence level to create a newly classified document.