Metadata-Based Document Reorganization With Duplicate Removal
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Solution Overview
Problem
Current document management systems require manual organization of bulk documents after upload, which is time-consuming and inefficient.
Innovation Solution
A computer-implemented method for automatically categorizing and organizing bulk documents using metadata, generating new folders if necessary, and detecting and deleting duplicates, with the ability to revise categorization maps and utilize a configurable look-up file for uncategorizable documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual organization of bulk documents is performed after upload, then document organization can be done with existing folder structures, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by automatically categorizing documents during the upload process itself, rather than requiring manual organization after upload. The categorization is performed in advance using metadata extraction and automatic folder assignment, eliminating the need for subsequent manual sorting operations.
Solution Approach 2:
The document management system performs self-service by automatically organizing uploaded documents without requiring manual intervention. The system extracts metadata, determines appropriate categories, and assigns documents to folders autonomously, replacing the manual organization process that would otherwise be required.
2Productivity
If automatic categorization is implemented, then document organization efficiency is improved, but system complexity increases due to categorization maps and metadata processing
Solution Approach 1:
The categorization system is segmented into manageable components: metadata extraction modules, categorization map definitions, and folder assignment processes. Each component handles a specific aspect of categorization independently, making the overall complex system more manageable and easier to implement.
Solution Approach 2:
The system uses parameter changes by extracting and transforming metadata from various document formats into standardized categorization fields. By converting different document attributes into consistent parameters that map to folder structures, the system handles complexity through standardized data transformation rather than complex decision logic.
3Loss of substance
If duplicate detection is performed, then storage costs are reduced by removing duplicates, but processing time increases due to content comparison
Solution Approach 1:
Duplicate detection is performed as a preliminary action during the upload process, before documents are fully processed and stored. By identifying and marking duplicates early in the workflow, the system prevents unnecessary storage of duplicate documents while minimizing the impact on overall processing time.
Solution Approach 2:
The system extracts and compares only the essential metadata and content identifiers to detect duplicates, rather than processing entire document contents. By taking out only the necessary comparison elements, the system reduces processing time while maintaining effective duplicate detection capability.
Data Source
AI summary
A document management method for categorizing uploaded documents from a legacy system and re-organizing existing documents saved in a documents management system is disclosed. The method categorizes the uploaded documents and existing documents based on metadata embedded therein and saves the uploaded documents and existing documents in a plurality of category folders. The method further generates new category folders and detects and deletes duplicated documents in the uploaded documents and existing documents. A document management system for categorizing uploaded documents from a legacy system and re-organizing existing documents saved in a documents management system is also disclosed.