Document Classification Using Topic Clustering and Access Control
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Solution Overview
Problem
Current information handling systems lack efficient methods for automatic document classification and grouping based on document topic, leading to potential misclassification and misgrouping of sensitive information.
Innovation Solution
A document handling system that employs a hierarchical classification system and grouping system, utilizing a document handler with a topic cluster miner, document correlator, and class/group database to evaluate and correct document classifications and groupings using TF-IDF, LDA, and LSI analyses, ensuring appropriate access control and security levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual document classification and grouping methods are used, then flexibility and adaptability are maintained, but efficiency and productivity are reduced due to time-consuming processes
Solution Approach 1:
The system enables documents to automatically classify and group themselves based on extracted topic clusters without requiring manual intervention. The document handler autonomously analyzes document content, identifies topic clusters using TF-IDF, LDA, and LSI, and assigns appropriate classifications and groupings, making the system self-sufficient and highly productive.
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated computational methods. Instead of human reviewers manually categorizing documents, the system uses topic cluster mining algorithms (TF-IDF, LDA, LSI) to automatically extract topics and assign classifications, significantly improving efficiency while managing complexity through algorithmic automation.
2Measurement precision
If simple classification methods are used, then ease of operation is maintained, but measurement precision and classification accuracy deteriorate
Solution Approach 1:
The system segments the document analysis process into distinct analytical components: TF-IDF for term importance, LDA for topic distribution, and LSI for semantic relationships. Each method addresses a specific aspect of topic identification, and their combined results provide high-precision classification while managing complexity through modular processing stages.
Solution Approach 2:
The patent combines multiple analytical methods (TF-IDF, LDA, LSI) into a composite classification system. Just as composite materials combine different substances to achieve superior properties, this system combines multiple topic modeling techniques to achieve higher classification accuracy than any single method could provide alone, while the integrated approach manages the inherent complexity of each individual method.
3Productivity
If automated classification systems are implemented, then productivity is improved, but reliability may worsen due to potential misclassification and misgrouping of sensitive information
Solution Approach 1:
The system incorporates feedback mechanisms where classification results are continuously evaluated and refined. The document handler analyzes classification outcomes and adjusts topic cluster extraction parameters based on identified misclassifications, creating a self-correcting system that maintains high productivity while improving reliability through iterative optimization of the classification process.
Data Source
AI summary
A document handling system includes a memory and a processor, in communication with the memory, to receive first information from a first document, determine that the first document includes a first topic based on the first information, determine a first classification level of the first document, determine a first grouping of the first document, associate the first classification level and the first grouping with the first topic, receive second information from a second document, determine that the second document includes the first topic based on the second information, and modify the second document to ascribe the first classification level and the first grouping to the second document.