Document Clearance System Using Blockchain and NLP
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Solution Overview
Problem
Current document clearance systems rely on manual processes that are time-consuming and resource-intensive, lacking efficiency and security, especially when handling documents with varying levels of sensitivity, which can lead to the risk of publishing confidential content.
Innovation Solution
The implementation of a document clearance system that utilizes natural language processing (NLP) and machine learning (ML) to analyze document content, determine sensitivity levels, and route documents to appropriate approval committees, combined with blockchain technology for transparent and secure approval processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual document clearance processes are used, then human review can identify sensitive content, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary automated analysis of document content using NLP and ML algorithms before human review. This pre-screening extracts features, determines sensitivity levels, and routes documents to appropriate approvers, reducing the time burden on manual reviewers while maintaining accurate identification of sensitive content through automated content analysis.
2Adaptability or versatility
If manual document clearance processes are used, then flexible human judgment can be applied, but the process lacks security and transparency
Solution Approach 1:
The system introduces an intermediary automated analysis layer between document submission and human approval. This intermediary uses NLP and ML to objectively assess content sensitivity, determine appropriate approval levels, and route documents to designated approvers. This intermediary maintains security and transparency through consistent, rule-based routing while preserving human flexibility in making final approval decisions.
Solution Approach 2:
The system implements feedback loops where approval decisions and outcomes are tracked and used to refine the automated analysis models. This feedback mechanism enhances security and transparency by continuously improving the accuracy of sensitivity detection and routing decisions, while maintaining adaptability through iterative model training on real-world approval patterns.
3Productivity
If automated content analysis is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The system segments the document clearance process into distinct modular components: content extraction module, feature analysis module, sensitivity determination module, routing module, and approval management module. Each module performs a specific function and can be independently optimized or maintained. This segmentation increases processing speed through specialized automated analysis while managing complexity by organizing functions into separate, manageable components.
Data Source
AI summary
A method, computer system, and a computer program product for document clearance is provided. The present invention may include receiving content. The present invention may also include extracting the received content features. The present invention may then include determining a level of sensitivity based on the extracted content features. The present invention may further include identifying an approver based on the determined level of sensitivity. The present invention may also include transmitting the content to the identified approver.