Device Fingerprint Chain Analysis for Fraud Detection

The patent divides the large fingerprint database into multiple clusters or groups based on similarity metrics. Instead of comparing a query fingerprint against all fingerprints in the database, the system first identifies relevant clusters and performs detailed comparison only within those segments. This segmentation reduces the search space from millions of fingerprints to a manageable subset, maintaining identification accuracy while dramatically reducing processing time.

The patent performs preliminary actions by pre-processing fingerprints during enrollment to extract key features and organize them into structured representations. Fingerprints are pre-clustered and indexed based on their feature vectors, creating a prepared data structure that enables efficient querying. This preliminary organization reduces the complexity of real-time fraud detection, as the system only needs to perform targeted comparisons rather than brute-force searches through all fingerprints.

The patent incorporates feedback mechanisms where the results of fingerprint comparisons are used to refine and adjust the clustering parameters. When false positives are detected, the system learns from these errors by adjusting similarity thresholds and re-evaluating cluster boundaries. This feedback loop continuously optimizes the balance between processing efficiency and accuracy, reducing false positives while maintaining the benefits of grouped processing.