Document Classification Rules via Threshold Analysis
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Solution Overview
Problem
Users face inefficiencies in classifying and retrieving documents due to the time-consuming process of manually assigning labels and the risk of missed labels, making it difficult to locate important documents later.
Innovation Solution
A computer-implemented method that automatically determines classification rules for subsets of documents based on true-positive and false-positive thresholds, allowing for efficient classification and storage of documents, enabling automatic assignment of documents to subsets based on these rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually assign labels to documents, then documents can be organized and located, but users spend a lot of time reviewing messages and determining labels
Solution Approach 1:
The system performs automatic classification of documents without requiring user intervention. The classification service analyzes document content, metadata, and patterns to autonomously assign labels and determine appropriate folders, eliminating the time users would otherwise spend manually reviewing and labeling documents while maintaining high classification accuracy through sophisticated analysis algorithms
Solution Approach 2:
The system pre-processes documents by analyzing their content, metadata, and contextual information before user interaction. Classification rules are pre-established based on organizational patterns and user behavior, enabling the system to automatically categorize documents as they arrive or are uploaded, rather than waiting for manual user action
2Ease of operation
If users manually assign labels to documents, then documents can be organized, but users may forget to assign labels making locating documents more difficult
Solution Approach 1:
The automatic classification system eliminates the need for user intervention in label assignment. By autonomously analyzing document content and applying classification rules, the system ensures consistent and complete labeling of all documents without relying on user memory or attention, thereby improving both ease of operation and label assignment completeness
Solution Approach 2:
The system continuously monitors classification results and refines its rules based on patterns observed in document organization. This feedback mechanism ensures that classification remains accurate and complete over time, adapting to changing organizational needs while maintaining reliability without requiring manual user input
3Productivity
If automatic classification is implemented, then classification time is reduced, but system complexity increases
Solution Approach 1:
The classification system is divided into modular components including a classification service, rule engine, and analysis modules that process different aspects of documents independently. This segmentation allows the system to achieve high classification speed through parallel processing while managing complexity through organized, reusable components that can be developed and maintained separately
Data Source
AI summary
Systems and methods for classifying a set of documents are provided. In some aspects, a method includes receiving a subset of the set of documents. The method also includes automatically determining at least one classification rule for the subset of documents based on the documents in the subset. At least a true-positive threshold proportion of documents in the subset of documents follows the at least one classification rule. At most a false-positive threshold proportion of documents in the set of documents and not in the subset of documents follow the at least one classification rule. The method also includes storing the at least one classification rule in association with the subset of documents.