AI Document Redaction System for Privacy and Grammatical Integrity
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Solution Overview
Problem
Existing document redaction systems are inefficient in processing large volumes of documents, as they fail to infer patterns, perform grammatical analysis, and introduce noise in OCR conversion, making it difficult to analyze and improve document data collections while maintaining privacy and grammatical integrity.
Innovation Solution
A system comprising a parser that identifies structured, semi-structured, and unstructured data, a candidates generator that lists words for redaction, and a replacement engine that replaces confidential information with random characters or numbers, maintaining the original document's look and feel while ensuring information extraction systems function seamlessly with redacted documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing document redaction systems are used to process large volumes of documents, then document processing can be automated, but the systems fail to infer patterns and perform grammatical analysis, introducing noise in OCR conversion and reducing processing quality
Solution Approach 1:
The patent replaces traditional mechanical redaction methods (simple blacklisting or pattern matching) with an AI-based natural language processing system that performs grammatical analysis and semantic understanding. This substitution enables the system to distinguish between confidential information and other text elements, accurately identifying what should be redacted while preserving the rest of the document's integrity, thereby resolving the contradiction between automation efficiency and redaction accuracy
Solution Approach 2:
The patent introduces an intermediary AI processing layer between the original document and the final redacted output. This intermediary system performs pattern inference, grammatical analysis, and semantic understanding to generate accurate redaction masks. The intermediary also handles OCR conversion with noise reduction, serving as a bridge that maintains both processing efficiency and redaction precision
2Reliability
If private data is removed and human reviewer access is prohibited to maintain privacy, then user privacy is protected, but analyzing and improving the quality of the document data collection becomes very difficult
Solution Approach 1:
The patent creates a copy of the document data collection that is processed through the AI redaction system. The system generates redacted versions of documents while maintaining the ability to analyze the original data patterns. This copying approach allows quality analysis and improvement without exposing private information, as the AI system works with the copied data structure and patterns rather than the actual private content
Solution Approach 2:
The patent introduces an intermediary AI analysis system that operates on redacted or anonymized data copies. This intermediary enables quality analysis, pattern recognition, and service improvement while maintaining privacy safeguards. The system can identify data collection quality issues and suggest improvements without requiring human reviewers to access private information
3Speed
If simple redaction methods are used to remove confidential information, then processing speed is maintained, but the systems introduce noise in OCR conversion and fail to maintain grammatical integrity
Solution Approach 1:
The patent replaces simple mechanical rediction methods with an AI-based natural language processing system that performs grammatical analysis. This system understands sentence structure, part of speech, and semantic relationships, allowing it to redact confidential information while maintaining grammatical integrity. The AI system processes documents at high speed while preserving the grammatical correctness of the remaining text
Solution Approach 2:
The patent performs preliminary grammatical analysis and pattern recognition before executing redaction. The AI system pre-identifies confidential information patterns, understands the grammatical context, and plans the redaction strategy in advance. This preliminary action ensures that when rediction occurs, it maintains grammatical integrity without requiring slow, iterative processing
Data Source
AI summary
A system and method for advanced document redaction are disclosed. According to one embodiment, a system comprises a parser that analyzes documents to identify structured, semi-structured, and unstructured data from a document. A candidates generator generates a list of words for redaction from the structured, semi-structured, and unstructured data. A replacement engine replaces one or more words from the list of words with one or more of a replacement word, random characters, and random numbers.