Universal Document Redaction via Classification and Archiving
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Solution Overview
Problem
Current systems lack an effective means to automate the process of removing sensitive information from documents across different types, making it labor-intensive for businesses and government agencies to share information while maintaining confidentiality.
Innovation Solution
A method involving a 'Universal Viewer' and a redaction engine that creates a universal view of documents, applies classifications, and uses rule sets to automatically identify and exclude sensitive information, allowing for the creation of redacted documents in various formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual redaction methods are used to remove sensitive information from documents, then confidentiality can be maintained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical redaction processes with an automated electronic system that uses optical character recognition (OCR), pattern recognition algorithms, and machine learning to automatically identify and redact sensitive information. The system substitutes human operators with computational mechanisms that can process documents at much higher speeds while maintaining consistent application of redaction rules.
Solution Approach 2:
The system enables documents to redact themselves automatically through self-learning algorithms. The redaction engine analyzes document content, identifies sensitive information patterns, and applies appropriate redaction measures without requiring manual review for each document. The system continuously improves its redaction accuracy through feedback from previous operations.
2Productivity
If automated rediction systems are implemented to increase productivity, then redaction efficiency improves, but the system becomes complex and difficult to implement
Solution Approach 1:
The patent creates a universal redaction platform that handles multiple document types (PDF, Word, Excel, images), various sensitive information categories (SSN, addresses, names, financial data), and different redaction methods (blurring, blacking out, masking) through a single integrated system. This multi-functional approach consolidates what would otherwise require multiple separate tools into one cohesive solution.
Solution Approach 2:
The system introduces an intelligent intermediary layer between document input and redaction output. This intermediary uses OCR technology to convert various document formats into a standardized processing format, applies pattern recognition as a mediating step to identify sensitive information, and then generates the final redacted output. This intermediary architecture simplifies the overall process by creating clear separation between input handling, analysis, and output generation.
3Adaptability or versatility
If a universal redaction system is designed to work with different document types, then adaptability improves, but the device complexity increases
Solution Approach 1:
The patent segments the redaction system into distinct modular components: an OCR module for text extraction, a pattern recognition module for sensitive information identification, a classification module for categorizing different types of sensitive data, and a redaction application module for executing the actual redaction. Each module handles specific tasks independently, allowing the system to process different document types without requiring complete system redesign.
Solution Approach 2:
The system dynamically adjusts processing parameters based on document type and content characteristics. It changes OCR recognition parameters for different file formats, modifies pattern recognition sensitivity thresholds based on document context, and adapts redaction methods according to the identified sensitive information type. This parameter adaptability allows a single system to effectively handle diverse document types without increasing structural complexity.
Data Source
AI summary
A method including creating a universal view of a document in an archive, where the universal view comprises individual portions of information from the document as individual elements of the universal view; applying classifications to at least some of the individual elements; and supplying the individual elements with their respectively applied classifications from the archive.