Cross-Redaction Alignment Across Multi-Device Incident Recordings
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Solution Overview
Problem
Existing systems face inefficiencies in redacting data from multiple recording devices at an incident, as each device captures the event from a different perspective, requiring extensive processing to identify and apply redactions across all devices.
Innovation Solution
A method of cross-redaction is employed, where data from a subset of recording devices is analyzed to determine redactions, which are then applied to all devices, leveraging alignment and redaction criteria to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate redaction systems are used for different document types, then each system can be optimized for its specific document type, but the overall system complexity increases and consistency across different document types deteriorates
Solution Approach 1:
The patent implements a universal redaction system that handles multiple document types (medical records, legal documents, financial statements) through a single platform. The system uses configurable redaction patterns and templates that can be adapted to different document types without requiring separate specialized systems, thereby reducing overall system complexity while maintaining redaction accuracy through type-specific optimization rules.
Solution Approach 2:
The patent segments the redaction process into distinct modular components: document type identification, pattern matching modules, redaction template selection, and execution engine. Each module can be independently configured and optimized for specific document types, allowing the system to maintain high reliability for each document type while managing overall complexity through modular architecture.
2Reliability
If manual review processes are implemented to ensure redaction accuracy, then redaction reliability improves, but processing time and operational complexity increase
Solution Approach 1:
The patent implements automated feedback mechanisms where the system analyzes redaction results and compares them against predefined accuracy criteria. The feedback loop automatically adjusts redaction parameters and patterns based on detected issues, reducing the need for manual review while maintaining high accuracy. Confidence scores are generated for each redaction, allowing prioritized manual review only when needed.
Solution Approach 2:
The system performs self-validation through automated confidence scoring and quality checks. The redaction engine automatically evaluates its own output, identifies potential errors, and corrects them without human intervention. This self-service capability maintains high redaction reliability while minimizing the time loss associated with manual review processes.
3Reliability
If comprehensive redaction patterns are applied to all documents, then redaction completeness improves, but false redactions of sensitive information increase
Solution Approach 1:
The patent applies different redaction patterns and sensitivity levels to different sections and contexts within documents. Instead of uniform redaction, the system analyzes local document characteristics (document type, section headers, context surrounding potential PII) to determine appropriate redaction intensity. This localized approach ensures comprehensive redaction where needed while preserving non-sensitive information that might be incorrectly flagged by blanket redaction patterns.
Solution Approach 2:
The redaction system dynamically adjusts its patterns and sensitivity based on document type, context, and detected information categories. The engine learns from document structure and adapts redaction behavior in real-time, increasing completeness for high-risk sections while reducing false redactions in low-risk areas. This dynamic adjustment resolves the contradiction between comprehensive redaction and avoiding false positives.
4Productivity
If automated redaction systems are used to reduce manual effort, then productivity improves, but the ability to handle complex document variations deteriorates
Solution Approach 1:
The patent implements preliminary configuration of redaction templates, patterns, and rules for different document types before actual redaction processing. Document type classification and template selection are performed automatically in advance, allowing the automated engine to apply pre-optimized redaction strategies. This preliminary action enables high productivity through automation while maintaining adaptability to complex document variations through pre-configured flexibility.
Solution Approach 2:
The system dynamically changes redaction parameters (sensitivity thresholds, pattern matching strictness, redaction methods) based on detected document type and characteristics. The automated engine adjusts these parameters in real-time to handle different document variations, maintaining both high productivity through automation and versatility in handling complex document types without requiring manual reconfiguration.
Data Source
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AI summary
Systems and methods for analyzing recorded data from one recording device, or a subset of recording devices to identify redactions that should be made to the recorded data. The identified redactions may be in accordance with a redaction policy. The identified redactions may be applied to recorded data recorded by other devices that recorded data the same incident. The redactions may be made to recorded data that was not analyzed prior to performing the redactions, so the redactions identified in one recorded data are performed in other recorded data that was not used to identify the types of redactions that should be made. Applying redactions to data that was not analyze to determine what types of redactions should be made reduces the amount of time required to analyze recorded data for redaction.