Document Classification System with Expert Feedback Loop
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Solution Overview
Problem
Current document classification technologies lack a hierarchical system of multiple experts for supervision and improvement of machine learning algorithms, do not send documents for classification based on difficulty, and do not receive user feedback on classification relevance.
Innovation Solution
A method and system that receive documents, analyze them using filters, classify them into topics, transmit topic identifiers, receive user judgment data, modify filters based on this data, and store the modified filters for improved classification, incorporating a hierarchical system of expert judgment and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning techniques are used to classify documents, then classification automation is improved, but classification accuracy and relevance deteriorate due to lack of expert supervision
Solution Approach 1:
The system implements a feedback loop where expert judgments on classified documents are collected and used to iteratively refine and retrain the machine learning algorithms. This continuous feedback mechanism allows the system to maintain high automation while progressively improving classification accuracy through expert-supervised learning.
Solution Approach 2:
The patent introduces expert reviewers as intermediaries between the automated classification system and the final classification results. These experts supervise and validate the machine learning output, bridging the gap between automated efficiency and expert-level accuracy.
2Measurement precision
If all documents are sent to experts for classification, then classification accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The system applies partial expert review rather than reviewing all documents. Machine learning algorithms handle the majority of documents autonomously, while only documents that fall below a confidence threshold or are randomly sampled for validation are sent to experts. This partial action maintains accuracy for critical cases while avoiding unnecessary time loss on routine documents.
Solution Approach 2:
The classification process is segmented into multiple stages: initial automated classification by machine learning, confidence threshold filtering, and selective expert review for uncertain cases. This segmentation allows the system to process documents through different pathways based on their complexity and confidence levels, optimizing both accuracy and time efficiency.
3Device complexity
If machine learning algorithms are used without iterative improvement, then system simplicity is maintained, but classification performance stagnates
Solution Approach 1:
The system implements continuous improvement through an iterative feedback loop where expert judgments continuously refine the machine learning models. This continuous action ensures that classification performance progressively improves over time without requiring complete system redesign, maintaining a balance between operational simplicity and performance enhancement.
Data Source
AI summary
Disclosed herein is a method for facilitating the classification of documents. Accordingly, the method may include receiving, using a communication device, documents from at least one user device, analyzing, using a processing device, the documents based on a filter, classifying, using the processing device, the documents into a topic based on the analyzing of the documents, transmitting, using the communication device, the documents and a topic identifier associated with the topic to a user device, receiving, using the communication device, a judgment data from the user device, analyzing, using the processing device, the judgment data, modifying, using the processing device, the filter based on the analyzing of the judgment data, generating, using the processing device, a modified filter based on the modifying, and storing, using a storage device, the modified filter.