Self-organizing document vault for user-centric clustering
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Solution Overview
Problem
Existing computerized document storage and organization systems are enterprise-centric, making them difficult for individuals to use as they require manual configuration and classification, which is tedious and not intuitive for personal document management.
Innovation Solution
Documents are automatically clustered around entities associated with individual users by identifying textual and graphical elements, performing optical character recognition, and classifying documents based on type and layout, with cluster keys being determined to organize documents intuitively around user-specific entities, allowing for user-driven cluster management and access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If enterprise-centric document storage systems are used, then document organization structure is established, but ease of operation deteriorates for individual users requiring manual configuration
Solution Approach 1:
The system performs self-organization by automatically clustering documents around entities associated with individual users. The system identifies textual and graphical elements, performs optical character recognition on image-based documents, classifies documents by type and layout, and determines cluster keys without requiring manual user configuration. This self-service approach eliminates the need for users to manually configure folder structures while maintaining effective document organization.
Solution Approach 2:
The system changes the organizational parameters from enterprise-centric hierarchical folders to user-centric entity-based clusters. Documents are organized around entities associated with individual users rather than predetermined business categories. The system dynamically determines cluster keys based on document content, classification, and user associations, allowing flexible reorganization without manual intervention.
2Productivity
If manual configuration and document classification are required, then system flexibility is maintained, but productivity deteriorates due to tedious manual effort
Solution Approach 1:
The system performs preliminary processing of documents by automatically identifying textual and graphical elements, classifying documents by type and layout, and determining cluster keys before user access is needed. This preliminary action eliminates the need for users to perform manual classification tasks, significantly improving productivity while maintaining high automation in the document organization process.
3Adaptability or versatility
If default enterprise folders are used, then system simplicity is maintained, but adaptability deteriorates for individual user needs
Solution Approach 1:
The system dynamically adapts to individual user needs by automatically determining entity associations and cluster keys based on the specific documents and users involved. Rather than using static default folders, the system creates flexible, user-specific organizational structures that can adapt to different user requirements. The dynamic nature of entity-based clustering allows the system to adapt to various document types and user preferences without requiring complex manual configuration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables individual users to intuitively identify and access documents organized around personal entities, improving usability and reducing manual effort in document management by automatically clustering and managing documents based on user-specific associations.
Implementation Method 1
Where a retrieved document is in an image based format, optical character recognition can be performed to allow the identification of textual elements
Data Source
AI summary
Multiple documents associated with a user are retrieved from one or more sources. Textual elements in the documents are identified, and the documents are classified according to document type. Cluster keys are identified in the documents, based on document content and document classification. A cluster key comprises an association between a document and a specific entity associated with the individual user, around which to cluster associated documents. Identifying cluster keys for a document can take the form of performing feature reduction, and identifying any features remaining thereafter as cluster keys. Names and addresses other than those of the document recipient can be identified as cluster keys. Retrieved documents, identified cluster keys and associations between them are stored, thereby organizing documents into clusters based on entities associated with the individual user. The user is provided with access to the documents according to the clusters into which they are organized.