AI Data Redaction With Text Mapping for Document Integrity

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Solution Overview

Problem

Conventional data redaction techniques struggle with inconsistent and resource-intensive removal of sensitive information from structured and unstructured data sets, often compromising document integrity due to varying data conversions.

Innovation Solution

An automated data redaction system using artificial intelligence to identify, map, and redact sensitive information in real-time from structured and unstructured data sets, maintaining document integrity through machine learning models and image processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional data redaction techniques are used to handle large quantities of structured and unstructured data, then data redaction can be performed, but the process becomes resource-intensive and time-consuming

Engineering Contradiction:

Improvedata redaction speedVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent replaces conventional mechanical data processing systems with an AI-based system that uses machine learning models to automatically detect, identify, and redact sensitive information. The system employs natural language processing and computer vision algorithms to handle both structured and unstructured data, eliminating the need for manual review and significantly reducing computational resource consumption while maintaining high accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service by automatically learning from training data to identify patterns of sensitive information without requiring human intervention. The machine learning models continuously improve their accuracy through feedback mechanisms, enabling the system to autonomously handle diverse data types and adapt to new redaction requirements without external assistance.

Inventive Principle:

Principle #25Self-service

2Reliability

If conventional data redaction techniques are applied to ensure consistency across different data sets, then redaction can be standardized, but document integrity is compromised due to repeated data transformations

Engineering Contradiction:

Improveredaction consistencyVSAvoiddocument integrity

Core Design Contradiction:

ReliabilityVSStability of the object's composition

Solution Approach 1:

The patent segments the redaction process into distinct stages: data ingestion, AI-based analysis, sensitivity detection, and selective redaction. This segmentation allows each component to specialize in specific tasks, maintaining document integrity while ensuring consistent redaction application. The system processes different data types (structured and unstructured) through separate but coordinated pathways, preventing the need for repeated transformations.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes the approach by using AI models that directly analyze data in its original format rather than requiring standardization to predetermined schemas. The machine learning models adapt to various data structures and formats, maintaining document integrity while achieving consistent redaction results across different data types without repeated transformations.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If manual data redaction is performed to ensure accuracy, then sensitive information can be identified, but the process becomes time-consuming and cannot keep pace with large data volumes

Engineering Contradiction:

Improvesensitive information detection accuracyVSAvoidredaction processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual redaction processes with AI-based systems that use machine learning models trained to accurately identify sensitive information patterns. The system employs natural language processing for text data and computer vision for image data, achieving human-level or superior accuracy while processing millions of records simultaneously, thereby eliminating the time consumption associated with manual review.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system maintains continuous operation by processing data streams in real-time without interruption. The machine learning models continuously analyze incoming data, making immediate redaction decisions, which ensures both high accuracy and rapid processing speed. The system can operate 24/7 without human fatigue, maintaining consistent performance across large data volumes.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS12613995B2Method and system for providing automated data redaction

Publication Date: 2026.04.28 JPMORGAN CHASE BANK NA

AI summary

A method for providing automated data redaction is disclosed. The method includes receiving a data set from a source, the data set including an unstructured electronic document and a structured electronic document; extracting, by using a model, texts from the data set; mapping each of the texts to a location in the data set, the location relating to a position of each of the texts on a page; determining, by using the model, whether the texts include restricted information; redacting the texts from the data set based on the mapping when the texts are determined to include restricted information; and generating a redacted data set based on a result of the redacting.