Document Image Sensitive Pattern Recognition
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Solution Overview
Problem
Identifying sensitive information in scanned document images is challenging due to varied orientations, handwritten or printed content, and mixed lengths of sensitive data elements.
Innovation Solution
Utilizing multiple trained models to independently predict sensitive data elements in document image files and combine their outputs to efficiently identify sensitive data patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-model approaches are used to identify sensitive information, then the system complexity is low, but the detection precision and coverage are insufficient
Solution Approach 1:
The patent divides the sensitive information detection task into multiple specialized models, each trained to detect specific types of sensitive data (e.g., PII, financial information, medical records). This segmentation allows each model to focus on particular patterns, improving detection precision while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent creates a multi-functional detection system where multiple trained models work together to identify various types of sensitive information across different document formats and orientations. Each model contributes specific detection capabilities, and their combined outputs provide comprehensive sensitive information identification that exceeds what a single model could achieve.
2Reliability
If multiple trained models are used to detect sensitive information, then the sensitive element coverage improves, but the computational cost increases
Solution Approach 1:
The patent performs preliminary filtering and preprocessing of document images before applying multiple trained models. This includes initial scanning, orientation detection, and region of interest identification, which reduces the computational burden on subsequent models by focusing processing only on relevant areas, thereby lowering overall computational cost while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces an intermediary coordination layer that manages the outputs of multiple trained models. This intermediary synthesizes detection results, resolves conflicts between models, and optimizes resource allocation, allowing the system to achieve high sensitive element coverage without linearly increasing computational cost as more models are added.
3Reliability
If comprehensive sensitive information detection is performed on all document types, then the detection coverage is maximized, but the processing time increases
Solution Approach 1:
The patent applies different detection strategies and model combinations based on local document characteristics. Instead of uniformly applying all models to every document, the system adapts its approach according to document type, orientation, and content, processing high-risk areas with multiple models while using faster methods for lower-risk regions, thereby maintaining maximum detection coverage with reduced overall processing time.
Data Source
AI summary
Some implementations of the disclosure describe a method, comprising: obtaining, at a computing device, a document image file; generating, at the computing device, using a first trained model, a first prediction including a first location of a sensitive information pattern within the document image file that contains a sensitive element and an identification of the sensitive element; generating, at the computing device, using a second trained model, a second prediction including a second location of a first sensitive sequence of characters within the document image file; determining, at the computing device, that the second location is within the first location; and after determining that the second location is within the first location, generating, at the computing device, an output including the second location or a redaction of the first sensitive sequence of characters within the document image file.