Universal Reference Token Repository for Secure Record Matching
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Solution Overview
Problem
Entities face inefficiencies and risks in reconciling incomplete and inconsistent data records, leading to potential exposure of personally identifiable information and decreased data integrity, due to challenges in data collection, reconciliation, and multi-channel data management.
Innovation Solution
A system utilizing a universal reference token repository and intelligent source creation for secure data matching, involving tokenization and encryption of client data, comparison with a reference token repository, and transitive matching to create composite records, ensuring efficient and secure data consolidation and enrichment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to combine incomplete data records, then data reconciliation can be performed, but the process becomes inefficient and time-consuming
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between disparate data records. This system uses probabilistic matching algorithms and transitive closure properties to automatically link records across multiple channels without manual intervention, thereby resolving the contradiction between reconciliation capability and processing efficiency
Solution Approach 2:
The patent replaces the mechanical manual process of record combination with an automated computational system. The system uses algorithms including entity resolution, probabilistic matching, and graph-based transitive closure to automatically reconcile records, eliminating the need for manual data processing while maintaining accuracy
2Reliability
If manual methods are used to reconcile data records, then data matching can be achieved, but exposure to personally identifiable information increases
Solution Approach 1:
The patent extracts and separates personally identifiable information (PII) from the data matching process. By using PII-agnostic features such as transitive closure properties, probabilistic matching scores, and entity relationship graphs, the system achieves accurate record linkage without exposing sensitive personal data, thus resolving the contradiction between matching accuracy and PII exposure
Solution Approach 2:
The system introduces an intermediary processing layer that handles data matching using anonymized features and transitive properties. This intermediary mechanism enables accurate record reconciliation while preventing direct exposure to PII, as the matching is performed on transformed, non-sensitive representations of the data
3Adaptability or versatility
If multiple incomplete records are maintained for the same subject, then data collection flexibility is improved, but data integrity decreases due to inconsistencies
Solution Approach 1:
The patent segments the data integrity problem into manageable components by treating each record as a separate entity with its own quality score and confidence level. The system uses entity resolution to identify relationships between segmented records and applies transitive closure to propagate integrity information across the network of records, maintaining both collection flexibility and overall data integrity
Solution Approach 2:
The system creates a universal framework that handles multiple incomplete records through a unified approach. By implementing a multi-functional entity resolution system that combines probabilistic matching, transitive closure, and quality scoring, the system can process diverse record types and sources while maintaining consistent integrity standards across all data
4Reliability
If data records are frequently updated to reflect changes over time, then data currency is improved, but reconciliation difficulty increases
Solution Approach 1:
The patent implements a dynamic entity resolution system that adapts to changing data conditions. The system continuously updates entity relationships and transitive closure properties as new records arrive or existing records change, using probabilistic matching that can accommodate temporal variations. This dynamic approach maintains data currency while managing reconciliation complexity through adaptive algorithms
Data Source
AI summary
The present disclosure is directed to systems and methods for reference source matching. Specifically, the systems and methods disclosed enable matching among tokens using a reference source. In one example, a Consolidation Platform may receive tokens from a customer environment and tokens from a reference source environment. The customer tokens may be compared to each other using AB matching. If a match does not occur, the customer tokens may further be compared to the reference source tokens via transitive matching. If a match does occur, then the customer tokens may be denoted as a match. In further example aspects, the reference source may be a universal reference token repository that comprises unique tokens. If, after a match is indicated, the matched token(s) may be compared to the universal reference token repository. If the matched token(s) does not exist, it may be added to the repository for future use.