Match Score Modeling for Unstructured Profile Data Filtering
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Solution Overview
Problem
Existing data matching systems face challenges in accurately identifying matches (false positives) or missing matches (false negatives), particularly when dealing with stringent requirements for unstructured data.
Innovation Solution
A machine learning model is trained using unstructured profile and reference data to generate a match score by calculating the degree of similarity between metrics and criteria, employing filtering and matching algorithms, and displaying outputs on a client device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional matching algorithms are used to match unstructured data, then the system can process data quickly, but the accuracy of matching deteriorates with false positives and false negatives
Solution Approach 1:
The patent segments the matching process into multiple independent components: profile data extraction, reference data extraction, feature vector generation, and similarity calculation. Each component processes specific aspects of the data separately, allowing for more precise control over the matching accuracy and reducing errors that would occur in a monolithic algorithm.
Solution Approach 2:
The patent transforms unstructured data into structured feature vectors with specific parameters and dimensions. By changing the representation parameters of the data (from unstructured text to structured numerical vectors), the system achieves more accurate and reliable matching while maintaining processing efficiency.
2Measurement precision
If filtering is applied to unstructured profile data for each reference datum, then the quality of matches improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary extraction of relevant features from profile data and reference data before the actual matching process. By pre-processing and organizing the data into structured feature vectors in advance, the filtering and matching operations become more efficient, reducing the time penalty associated with quality filtering.
Solution Approach 2:
The patent replaces traditional mechanical filtering methods with machine learning-based feature vector comparison. This substitution allows for more intelligent and efficient filtering that maintains high match quality while reducing processing time through optimized similarity calculations.
Data Source
AI summary
An apparatus and method for training a machine learning model to generate a match score using unstructured profile data and unstructured reference data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of unstructured profile data, receive a plurality of unstructured reference data, filter, for each unstructured reference datum of the plurality of unstructured reference data, the plurality of unstructured profile data, generate, for each unstructured profile datum of the filtered plurality of unstructured profile data, a match score by calculating, using a matching algorithm, a degree of similarity between the plurality of metrics and the plurality of criteria, and display, an output based on the generated match score.