Personal Identity Data Matching with Predictive Attributes
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Solution Overview
Problem
Existing database searching technologies are inefficient in matching personal identity inquiries that include both expected and unexpected data attributes, as they rely on character-for-character comparisons and assume a specific structure, failing to utilize high-value predictive data elements and providing limited actionable feedback.
Innovation Solution
A method that receives an inquiry, determines a search strategy, searches a reference database, and outputs matches with feedback on the match quality, utilizing processing rules, attribute tables, and frequency tables to handle both expected and unexpected data components, and provide confidence indicators and actionable feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If character-for-character comparison methods are used for matching personal identity data, then the matching process is simple to implement, but the system cannot effectively handle unexpected data attributes and provides limited actionable feedback
Solution Approach 1:
The matching system is segmented into distinct functional modules: data normalization module, predictive attribute generation module, similarity calculation module, and feedback generation module. Each module handles specific aspects of the matching process, allowing the system to handle unexpected attributes without requiring complete redesign of the entire system.
Solution Approach 2:
The system employs a universal matching framework that can process both expected and unexpected data attributes through the same pipeline. The predictive attribute generation module creates standardized representations of any input attribute, enabling the system to handle diverse data types uniformly without requiring attribute-specific processing logic.
2Measurement precision
If traditional matching algorithms are used that rely on predefined data structures, then the system is easier to operate, but it cannot utilize high-value predictive data elements and synthesized indicia
Solution Approach 1:
The system performs preliminary actions by pre-generating predictive attributes and synthesized indicia from the input data before the actual matching process. This includes normalizing data formats, generating predictive attributes based on data relationships, and creating confidence scores in advance, which improves matching accuracy without requiring complex real-time processing during operation.
Solution Approach 2:
The system introduces intermediary components including predictive attribute tables, frequency tables, and confidence indicator mechanisms that mediate between raw input data and final match results. These intermediaries transform unstructured or semi-structured data into standardized, comparable formats while preserving high-value information, thereby improving accuracy without complicating the user interface.
3Ease of operation
If the system provides detailed actionable feedback for each match, then the usability for business decisions is improved, but the amount of data processing and feedback generation increases
Solution Approach 1:
The system implements partial feedback generation by providing detailed actionable feedback only for the most relevant match attributes and confidence indicators, rather than analyzing every possible data element. This selective approach delivers sufficient information for business decisions while avoiding the processing overhead of generating exhaustive feedback on all attributes, thus maintaining productivity while improving usability.
Data Source
AI summary
There is provided a method that includes (a) receiving an inquiry to initiate a search for data for a specific individual, (b) determining, based on the inquiry, a strategy and flexible predictiveness equations to search a reference database, (c) searching the reference database, in accordance with the strategy, for a match to the inquiry; and (d) outputting the match. The method may also output flexible feedback related to the match that reflects inferred quality of the match experience which can be used by an end-user to determine the degree to which the matched entity meets that end-user's quality-based criteria. There is also provided a system that performs the method, and a storage medium that contains instructions that control a processor to perform the method.