Machine Learning PII Redaction for Automated Document Processing
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Solution Overview
Problem
Existing techniques for protecting personal information in software applications require extensive manual processing, which is time-consuming, resource-intensive, and prone to errors, and are limited in application, leading to increased costs and decreased downstream efficiency.
Innovation Solution
Utilizing a machine learning model to automatically identify, classify, and replace protected entities with corresponding unprotected entities in documents, guided by a prompt, to enhance data privacy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing techniques are used to protect personal information, then data privacy protection can be achieved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical processing with an automated machine learning system that uses natural language processing to identify and redact PII. The system substitutes human operators with computational algorithms that can process documents automatically, eliminating the time-consuming manual review process while maintaining privacy protection standards.
Solution Approach 2:
The machine learning model performs self-service by automatically identifying protected entities, classifying them, and generating redacted versions without human intervention. The system serves itself through automated prompt generation, model inference, and output production, eliminating the need for continuous manual processing while maintaining high accuracy in PII protection.
2Reliability
If manual processing techniques are used to protect personal information, then data privacy protection can be achieved, but resource consumption increases
Solution Approach 1:
The patent replaces resource-intensive manual processing with a computational machine learning system that consumes fewer human resources. While the system uses computational energy, it eliminates the need for human time and effort, overall reducing the type of resource consumption that limits scalability and increases operational costs in manual processing environments.
Solution Approach 2:
The system changes the operational parameters from manual human processing to automated computational processing. This parameter change transforms the resource consumption profile from human labor (time, cognitive effort) to computational resources (processing power, memory), which can be scaled more efficiently and cost-effectively for large volumes of documents.
3Adaptability or versatility
If manual processing techniques are used to protect personal information, then specific application requirements can be met, but adaptability to different applications is limited
Solution Approach 1:
The machine learning system is designed with universal applicability across multiple document types and industries through configurable prompts and flexible entity recognition capabilities. The same core system can adapt to different applications (medical, legal, financial) by adjusting the prompt instructions and entity classification criteria, eliminating the need for separate manual processing procedures for each application.
Solution Approach 2:
The system employs dynamic adaptability where the machine learning model can adjust its behavior based on the specific application context provided in the prompt. The entity recognition and redaction strategies dynamically change according to the document type, industry requirements, and sensitivity levels, allowing a single system to serve multiple applications without requiring complex specialized configurations for each.
4Measurement precision
If manual processing techniques are used to protect personal information, then accuracy can be maintained, but error rates increase due to human factors
Solution Approach 1:
The patent replaces human processing with machine learning-based automated processing to eliminate human errors such as fatigue, distraction, and inconsistency. The machine learning model provides consistent, repeatable identification and redaction of PII entities across all documents, improving both measurement precision in entity detection and overall processing reliability by removing human variability from the equation.
Data Source
AI summary
Aspects of the present disclosure relate to automated data extraction and prediction using machine learning models. Embodiments include extracting, by a text extraction engine, a set of data from each document of one or more documents provided to the text extraction engine. Embodiments include instructing a machine learning model, via a prompt, to identify and classify one or more protected entities contained in the set of data from each document by parsing the set of data from each document. Embodiments include instructing the machine learning model, via the prompt, to generate, for each protected entity of the one or more protected entities identified in the set of data from each document, a corresponding unprotected entity and to replace each protected entity with the corresponding unprotected entity. Embodiments include receiving an output from the machine learning model in response to the prompt and performing an action based on the output.