ML Match Classifier for Unstructured Descriptor Validation
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Solution Overview
Problem
Existing systems face challenges in identifying and routing alert messages in computer networks due to unstructured descriptors, which can lead to mismatch errors and difficulties in decoding data elements, resulting in alert messages not being forwarded to the correct recipient entities.
Innovation Solution
A computer system is implemented with a matching subsystem and a machine learning-based match classifier (ML match classifier) to extract and match data elements from unmatched descriptors, validate matches, and route alert messages to the correct entities by training on similarity metrics and generating feature vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional decoding methods are used to match descriptors to known entities, then the system can attempt to identify recipient entities, but the process is computationally difficult and prone to mismatch errors due to unstructured descriptor variability
Solution Approach 1:
The patent replaces traditional mechanical decoding methods with a machine learning-based match classifier that uses neural networks to process unstructured descriptors. The system extracts features from descriptors and compares them against known entities using ML algorithms, which automatically handles variability and structural complexity without requiring manual decoding rules.
Solution Approach 2:
The system transforms unstructured descriptor data into structured feature vectors through parameter extraction and transformation. By converting variable-length, unstructured descriptors into standardized feature representations, the system enables reliable comparison and matching while reducing computational complexity of the matching process.
2Adaptability or versatility
If the system attempts to decode unstructured descriptors to identify entities, then more entities can be potentially identified, but false positive matches increase due to descriptor variability and errors
Solution Approach 1:
The system incorporates feedback mechanisms where the match classifier continuously learns from labeled data and feedback loops. The classifier receives feedback about correct and incorrect matches, allowing it to refine its decision boundaries and improve reliability. This feedback-driven optimization enables the system to handle descriptor variability while reducing false positives.
Solution Approach 2:
The patent introduces an intermediary match classifier layer between the descriptor extraction and entity identification processes. This intermediary component acts as a mediator that evaluates the relationship between extracted features and known entities, filtering out false positives while maintaining the ability to identify diverse entity types through the learned representations.
3Productivity
If traditional matching algorithms are used, then the system can process descriptors, but it cannot effectively handle variability in data element content, order, and structure
Solution Approach 1:
The system replaces traditional rule-based matching algorithms with a machine learning model that processes unstructured data through neural networks. The ML classifier automatically adapts to variations in data element content, order, and structure by learning patterns from training data, enabling both high processing speed and effective handling of descriptor variability simultaneously.
Solution Approach 2:
The match classifier is designed as a universal system that can handle multiple types of unstructured descriptors and entity representations through a single unified model. This multi-functional approach allows the system to process various descriptor formats and entity types without requiring separate specialized algorithms for each case, improving both productivity and adaptability.
Data Source
AI summary
The disclosure relates to methods and systems of generating matches between unmatched descriptors and known entities and training and using a machine learning-based match classifier (ML match classifier) to generate match classifications. For example, a system may access an unmatched descriptor having unstructured content and extract one or more data elements from the unmatched descriptor. The system may compare each of the extracted data elements with data records of known entities to identify candidate matches. The system may train and use the ML match classifier to validate the candidate matches. The ML match classifier may be trained based on labeled features derived from similarity metrics between two strings such as a name associated with the unmatched descriptor and a name of a candidate entity.