# US 12,602,359 B2 · Systems and methods for data entity matching between disparate datasets > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,359 B2 - Document ID: US12602359B2 - Status: Granted - Kind: B2 - Application number: 18/402,232 - Filed: 2024-01-02 - Published: 2025-05-01 - Granted: 2026-04-14 - Earliest claimed: 2023-10-30 - Assignees: Optum Services (Ireland) Limited - Applicants: Optum Services (Ireland) Limited - Inventors: Conor Brian Breen; Conor Power; Fadong Yan; Kashyap Krishnamurthy - Web page: https://patentgrep.com/patent/US12602359B2 ## CPC - G06F 16/215: Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors - G06F 16/2365: {Ensuring data consistency and integrity} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Embodiments of the present disclosure provide systems and methods for matching data entities across disparate datasets. One method may include generating a reference numeric vector for a reference data object and generating a plurality of match candidate numeric vectors for a plurality of match candidate data objects. The method may also include identifying a subset of match candidate data objects from the plurality of match candidate data objects and generating a plurality of data features based on a comparison between one or more reference object attributes corresponding to the reference data object and one or more matching object attributes corresponding to the subset of match candidate data objects. The method may also include identifying a match between the reference data object and a match candidate data object of the subset of match candidate data objects based on the plurality of data features. ## Claims ### Claim 1 A computer-implemented method comprising: receiving, by one or more processors, from a first data source, a reference data object comprising a plurality of data elements; generating, by the one or more processors, a text representation for the reference data object according to a predefined sequence, wherein the text representation comprises a string value comprising at least two data elements, of the plurality of data elements, positioned in accordance with the predefined sequence; generating, by the one or more processors, a reference numeric vector for the reference data object based on the text representation; generating, by the one or more processors, a plurality of match candidate numeric vectors for a plurality of match candidate data objects from a second data source based on a plurality of text representations associated with the plurality of match candidate data objects; identifying, by the one or more processors, a subset of match candidate data objects from the plurality of match candidate data objects based on a comparison between the reference numeric vector and the plurality of match candidate numeric vectors; generating, by the one or more processors, a plurality of data features based on a comparison between (a) a plurality of reference object attributes corresponding to the reference data object and (b) a plurality of matching object attributes corresponding to the subset of match candidate data objects; providing, by the one or more processors, the plurality of data features to an entity matching machine learning model to generate a plurality of match scores, wherein a match score of the plurality of match scores comprises a probabilistic value of a match likelihood between one of the subset of match candidate data objects and the reference data object; and identifying, by the one or more processors, a match between the reference data object and a match candidate data object of the subset of match candidate data objects based on the plurality of match scores and a threshold. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of data features comprises a string matching feature corresponding to a text attribute pair, wherein the text attribute pair comprises: (i) a reference object attribute of the plurality of reference object attributes that comprises a first text string corresponding to the reference data object, and (ii) a matching object attribute of the plurality of matching object attributes that comprises a second text string corresponding to the match candidate data object of the subset of match candidate data objects. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the string matching feature comprises a Levenshtein ratio between the first text string and the second text string. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of data features comprises a geographic distance feature corresponding to a region attribute pair, wherein the region attribute pair comprises: (i) a reference object attribute of the plurality of reference object attributes that comprises a first region identifier corresponding to the reference data object, and (ii) a matching object attribute of the plurality of matching object attributes that comprises a second region identifier corresponding to the match candidate data object of the subset of match candidate data objects. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein the geographic distance feature identifies a geographic distance between the reference data object and the match candidate data object. ### Claim 6 (depends on 4) The computer-implemented method of claim 4, wherein the geographic distance feature comprises a Haversine distance between the first region identifier and the second region identifier. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of data features comprises a vector similarity score corresponding to a vector attribute pair, wherein the vector attribute pair comprises: (i) the reference numeric vector corresponding to the reference data object, and (ii) a respective candidate numeric vector of the plurality of match candidate numeric vectors that corresponds to the match candidate data object of the subset of match candidate data objects. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the reference data object is associated with a ground truth data object and the computer-implemented method further comprises: modifying a parameter of the entity matching machine learning model based on a comparison between the ground truth data object and the match candidate data object. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein identifying the subset of match candidate data objects from the plurality of match candidate data objects comprises: generating a vector similarity score between the reference numeric vector and a respective match candidate numeric vector of the plurality of match candidate numeric vectors; and identifying the subset of match candidate data objects from the plurality of match candidate data objects based on the vector similarity score. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein identifying the subset of match candidate data objects from the plurality of match candidate data objects comprises: selecting the subset of match candidate data objects from the plurality of match candidate data objects based on a comparison between the vector similarity score and a threshold similarity score. ### Claim 11 (depends on 9) The computer-implemented method of claim 9, wherein identifying the subset of match candidate data objects from the plurality of match candidate data objects comprises: selecting the subset of match candidate data objects from the plurality of match candidate data objects based on the vector similarity score, a plurality of vector similarity scores corresponding to the plurality of match candidate data objects, and a threshold number of candidate data objects. ### Claim 12 A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to: receive from a first data source, a reference data object comprising a plurality of data elements; generate a text representation for the reference data object according to a predefined sequence, wherein the text representation comprises a string value comprising at least two data elements, of the plurality of data elements, positioned in accordance with the predefined sequence; generate a reference numeric vector for a reference data object from a first data source based on a text representation associated with the reference data object; generate a plurality of match candidate numeric vectors for a plurality of match candidate data objects from a second data source based on a plurality of text representations associated with the plurality of match candidate data objects; identify a subset of match candidate data objects from the plurality of match candidate data objects based on a comparison between the reference numeric vector and the plurality of match candidate numeric vectors; generate a plurality of data features based on a comparison between (a) a plurality of reference object attributes corresponding to the reference data object and (b) a plurality of matching object attributes corresponding to the subset of match candidate data objects; provide the plurality of data features to an entity matching machine learning model to generate a plurality of match scores, wherein a match score of the plurality of match scores comprises a probabilistic value of a match likelihood between one of the subset of match candidate data objects and the reference data object; and identify a match between the reference data object and a match candidate data object of the subset of match candidate data objects based on the plurality of match scores and a threshold. ### Claim 13 (depends on 12) The computing system of claim 12, wherein the plurality of data features comprises a string matching feature corresponding to a text attribute pair, wherein the text attribute pair comprises: (i) a reference object attribute of the plurality of reference object attributes that comprises a first text string corresponding to the reference data object, and (ii) a matching object attribute of the plurality of matching object attributes that comprises a second text string corresponding to the match candidate data object of the subset of match candidate data objects. ### Claim 14 (depends on 13) The computing system of claim 13, wherein the string matching feature comprises a Levenshtein ratio between the first text string and the second text string. ### Claim 15 (depends on 12) The computing system of claim 12, wherein the plurality of data features comprises a geographic distance feature corresponding to a region attribute pair, wherein the region attribute pair comprises: (i) a reference object attribute of the plurality of reference object attributes that comprises a first region identifier corresponding to the reference data object, and (ii) a matching object attribute of the plurality of matching object attributes that comprises a second region identifier corresponding to the match candidate data object of the subset of match candidate data objects. ### Claim 16 (depends on 15) The computing system of claim 15, wherein the geographic distance feature identifies a geographic distance between the reference data object and the match candidate data object. ### Claim 17 (depends on 15) The computing system of claim 15, wherein the geographic distance feature comprises a Haversine distance between the first region identifier and the second region identifier. ### Claim 18 (depends on 12) The computing system of claim 12, wherein the plurality of data features comprises a vector similarity score corresponding to a vector attribute pair, wherein the vector attribute pair comprises: (i) the reference numeric vector corresponding to the reference data object, and (ii) a respective candidate numeric vector of the plurality of match candidate numeric vectors that corresponds to the match candidate data object of the subset of match candidate data objects. ### Claim 19 (depends on 12) The computing system of claim 12, wherein the reference data object is associated with a ground truth data object and the one or more processors are further configured to: modify a parameter of the entity matching machine learning model based on a comparison between the ground truth data object and the match candidate data object. ### Claim 20 One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: receive from a first data source, a reference data object comprising a plurality of data elements; generate a text representation for the reference data object according to a predefined sequence, wherein the text representation comprises a string value comprising at least two data elements, of the plurality of data elements, positioned in accordance with the predefined sequence; generate a reference numeric vector for a reference data object from a first data source based on a text representation associated with the reference data object; generate a plurality of match candidate numeric vectors for a plurality of match candidate data objects from a second data source based on a plurality of text representations associated with the plurality of match candidate data objects; identify a subset of match candidate data objects from the plurality of match candidate data objects based on a comparison between the reference numeric vector and the plurality of match candidate numeric vectors; generate a plurality of data features based on a comparison between (a) a plurality of reference object attributes corresponding to the reference data object and (b) a plurality of matching object attributes corresponding to the subset of match candidate data objects; provide the plurality of data features to an entity matching machine learning model to generate a plurality of match scores, wherein a match score of the plurality of match scores comprises a probabilistic value of a match likelihood between one of the subset of match candidate data objects and the reference data object; and identify a match between the reference data object and a match candidate data object of the subset of match candidate data objects based on the plurality of data features.