Unique Person Identification via Multi-Source Data Reconciliation
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Solution Overview
Problem
Current methods for uniquely identifying individuals within a population face challenges due to common name pairings, shared birth dates, and incomplete or inaccurate digital records, particularly when a unique identifier like a Social Security Number is not available.
Innovation Solution
The method involves using at least two unique data sources to compare and validate individual data, searching for potential matches, and reconciling these matches to identify a unique identifier that distinguishes the individual from others within the population.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If common identification methods (name, birth date, address) are used, then ease of operation is improved, but measurement precision deteriorates due to common name pairings and shared information
Solution Approach 1:
The identification process is divided into multiple stages: initial screening using common identifiers (name, birth date, address), followed by sequential verification through additional data points from multiple sources. This segmentation allows the system to start with easy-to-obtain information while progressively adding layers of verification to achieve unique identification.
Solution Approach 2:
The system introduces an intermediary verification process that bridges the gap between common identifiers and unique identification. By using multiple data sources and cross-referencing information, the system creates an intermediate layer of validation that confirms whether common identifiers uniquely identify an individual or require further verification.
2Measurement precision
If Social Security Number is used as identifier, then measurement precision is improved for unique identification, but ease of operation deteriorates due to missing or inaccurate data in digital records
Solution Approach 1:
The system performs preliminary identification attempts using available common identifiers before resorting to Social Security Number verification. By pre-screening with name, birth date, and address from multiple sources, the system can often identify individuals without needing SSN, and only requests SSN verification when absolutely necessary for disambiguation.
Solution Approach 2:
The system dynamically adjusts the identification parameters based on data availability and quality. When SSN is missing or inaccurate in digital records, the system compensates by increasing the weight and verification of alternative identifiers from multiple sources, effectively changing the identification parameters to match the available data quality.
3Measurement precision
If multiple data sources are compared and validated, then measurement precision is improved for singular identification, but device complexity increases due to multiple sources and verification steps
Solution Approach 1:
The complex task of unique identification is segmented into manageable modules: data collection from multiple sources, initial matching algorithms, verification processes, and final confirmation. Each module handles a specific aspect of the identification process, making the overall complex system manageable and maintainable while achieving high precision through the coordinated operation of these segmented components.
Data Source
AI summary
Methods, program products, computer program products and systems for uniquely identifying an individual within a population to the exclusion of all others within the population by comparing data from unique data sources based on the name of the individual for providing a collection of preliminary suspects. An individual search service provider is then searched for additional data relating to the collection of preliminary suspects to locate any potential matches, which are reconciled with data from the unique data sources to locate at least a portion of a unique identifier that may be associated with the individual. The potential matches and portion of the unique identifier are compared with other data records within the individual search data source for locating a complete unique identifier that may be associated with the individual, followed by determining whether or not this complete unique identifier uniquely identifies the individual.