Patient Identifier Anonymization Using Contextual False-Positive Filtering

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Solution Overview

Problem

Managing sensitive patient data in an interconnected world poses significant challenges due to increased data security threats, requiring labor-intensive processes for anonymization.

Innovation Solution

An apparatus and method for anonymizing user data using a processor and memory to identify patient identifiers, generate contextual data, and recognize false positives, employing machine learning and natural language processing techniques to enhance data security.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional manual anonymization processes are used, then data security can be maintained through careful review, but the process becomes labor-intensive and time-consuming

Engineering Contradiction:

Improvedata securityVSAvoidanonymization efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical review processes with automated machine learning models and natural language processing systems that can identify and contextualize patient identifiers at scale, maintaining security through algorithmic consistency while dramatically improving processing throughput

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces contextual data as an intermediary layer between raw patient data and anonymization decisions, allowing the system to understand the meaning and significance of identified identifiers before determining whether and how to anonymize them

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If automated identifier detection algorithms are used, then processing speed increases, but false positive identification rates increase

Engineering Contradiction:

Improveprocessing speedVSAvoididentifier detection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements feedback loops where contextual analysis results feed back into the identifier detection process, allowing the system to refine its identification accuracy by considering the semantic meaning and surrounding context of potential identifiers rather than relying solely on pattern matching

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary contextual data generation and analysis before final identifier classification, preparing classification labels and semantic understanding in advance to enable more accurate differentiation between true identifiers and false positives

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12530498B2Apparatus and a method for the anonymization of user data

Publication Date: 2026.01.20 NFERENCE INC

AI summary

An apparatus for the anonymization of user data is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive a plurality of user data. The memory contains instructions configuring the processor to identify a plurality of patient identifiers within the plurality of user data. The memory contains instructions configuring the processor to generate contextual data associated with each patient identifier of the plurality of patient identifier. The memory contains instructions configuring the processor to identify one or more false positive patient identifiers within the plurality of patient identifiers as a function of the contextual data.