Patent ID: 11914621
Assignee: KOMODO HEALTH
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A method performed by at least one processor, the method comprising:
obtaining pairs of training records for training an entity resolution model, wherein the pairs of training records comprise a first pair of training records, wherein the first pair of training records comprises a first record that indicates a first set of values, including a first value, for a first attribute and a second record that indicates a second set of values, including a second value, for the first attribute;
extracting, from the first set of values, a third set of values for a first portion of the first attribute and a fourth set of values for a second portion of the first attribute, wherein a third value, of the third set of values, and a fourth value, of the fourth set of values, is extracted from the first set of values for the first attribute;
extracting, from the second set of values, a fifth set of values for the first portion of the first attribute and a sixth set of values for the second portion of the first attribute;
determining a set of association metrics corresponding to the pairs of training records at least by:
determining a first set of preliminary association metrics corresponding respectively to comparisons between the third value, of the third set of values, and each value of the fifth set of values;
determining a second set of preliminary association metrics corresponding respectively to comparisons between the fourth value, of the fourth set of values, and each value of the sixth set of values;
based on at least the first set of preliminary association metrics and the second set of preliminary association metrics, determining a first set of individual association metrics corresponding respectively to comparisons between the first value and each value of the second set of values;
executing a first-level reduction operation for the first set of individual association metrics, across the second set of values, to generate a first reduced association metric;
storing the first reduced association metric in a set of association metrics;
executing the first-level reduction operation for a second set of individual association metrics, across the second set of values, to generate a second reduced association metric;
storing the second reduced association metric in the set of association metrics;
determining a presence of any more values in the first set of values;
based on a determination that no more values are present in the first set of values, executing a second-level reduction operation for the set of reduced association metrics to generate a set of association metrics corresponding to the first pair of training records;

applying a machine learning algorithm to the set of association metrics corresponding to the pairs of training records to train the entity resolution model; and
applying the entity resolution model to target association metrics for a pair of target records to determine a classification of the pair of target records as being associated with a same entity or being associated with different entities.