Adaptive Record Linking via Dynamic Matching Rules

The patent implements dynamic matching rules that adapt based on data quality and context. The system adjusts matching thresholds and criteria dynamically rather than using fixed rigid rules, allowing it to handle incomplete, erroneous, or non-standard data while maintaining precision through context-aware adjustments.

The system changes matching parameters adaptively based on the characteristics of the data being processed. When encountering incomplete or erroneous records, the system modifies matching thresholds, weightings, and criteria to accommodate varying data qualities while still achieving accurate matches.

The patent segments the matching process into multiple stages: initial filtering using quick criteria to eliminate obviously mismatched records, followed by more comprehensive evaluation only for promising candidates. This segmentation maintains high recall while improving processing speed by avoiding exhaustive evaluation of all records.

The system applies partial matching criteria initially to quickly identify candidate records, then applies full comprehensive criteria only to those candidates. This partial action approach achieves high recall for the final result set while maintaining processing speed by limiting exhaustive evaluation to a smaller subset.

The patent implements self-service matching where the system automatically learns and adjusts matching rules based on observed data patterns and user feedback, eliminating the need for manual rule creation and maintenance. The system serves itself by continuously improving its matching algorithms through adaptive learning while maintaining precision.

The system incorporates feedback mechanisms where matching results and user corrections are fed back into the rule generation process. This automatic feedback loop allows the system to refine its matching rules continuously without manual intervention, reducing complexity while maintaining or improving precision over time.