IP Library › Granted Patent US 12,298,969
Granted Patent B2
US 12,298,969 · App. 17/093,151 · Granted May 13, 2025

Apparatus, systems, and methods for grouping data records

Inventors: Boris Shimanovsky (Los Angeles, CA); Manuel Lagang (Pasadena, CA); Leonid Polovets (Menlo Park, CA)
Assignee: FOURSQUARE LABS, INC.
G06F16/21G05B13/0265G06F16/23G06F16/235G06F16/2379G06F16/2386G06F16/24564G06F16/2477G06F16/282G06F16/285G06F16/29G06F16/313G06F16/35G06F16/951G06N5/022G06N20/00G06Q10/101G06Q30/0261G06Q30/0282G06Q50/01H04L41/14H04W4/02H04W4/021H04W4/025H04W4/029H04W4/50H04W8/08H04W8/16H04W8/18H04W16/24H04W64/00H04W64/003H04W76/38H04W88/02G06F16/337H04W16/00H04W16/30H04W16/32H04W88/00
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Quick Facts
Patent No.
US 12,298,969
App. No.
17/093,151
Granted
May 13, 2025
Kind
B2
Abstract

The present application relates to apparatus, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining a pair-wise similarity between a large number of data records, and clustering a subset of the data records based on their pair-wise similarity.

Claims (56)

1. An apparatus comprising:

a processor configured to run one or more modules stored in memory, wherein the one or more modules are configured to:

receive a plurality of pairs of data records;

determine whether one or more pairs of data records are eligible to be clustered, the determination is based upon an analysis of a set of attributes included in the one or more pairs, wherein the analysis is performed by:

converting a first data record in the pair of data records into a first hash;

converting a second data record in the pair of data records into a second hash; and

comparing bits of the first hash and second hash; and

when it is determined that at least one pair of data records can be clustered:

determine a similarity value for the at least one pair of data records based, at least in part, on a plurality of attributes associated with the at least one pair of data records, wherein the similarity value is determined using a machine learning model trained using a supervised learning function operating on ground-truth clusters of data records; and

associate the at least one pair of data records with one or more clusters, each associated with a unique entity, based on the similarity value for the at least one pair of data records.

2. The apparatus of claim 1 , wherein the one or more modules are further configured to identify one or more pairs of data records for which a similarity value need not be determined based on a predetermined set of attributes.

3. The apparatus of claim 2 , wherein the one or more modules are configured to adjust the predetermined set of attributes based on association of data records to clusters from a previous iteration.

4. The apparatus of claim 1 , wherein the similarity model is designed to infer an importance of a particular component associated with a particular attribute of a data record, wherein the similarity model is learned by:

determining differences between components associated with the particular attribute of the training data records, wherein the training data records are known belong to the same cluster; and

determining the importance of the particular component based on a number of times the particular component appears in the differences.

5. The apparatus of claim 1 , wherein the similarity model is designed to infer a likelihood of interchanging a first component in a particular attribute of a data record with a second component, wherein the similarity model is learned by:

determining differences between components associated with the particular attribute of the training data records, wherein the training data records are known belong to the same cluster; and

determining the likelihood of interchanging the first component with the second component based on a number of times the first component and the second component appears in the differences at the same time.

6. The apparatus of claim 1 , wherein the similarity model is designed to determine a conditional likelihood that a missing attribute of a data record has a particular component, wherein the conditional likelihood is determined by:

determining a combination of known attributes corresponding to a particular entity;

determining all variations of a missing attribute amongst data records of the particular entity having the combination of known attributes; and

determining a conditional probability, based on the variations of the missing attribute, that the missing attribute has a particular component given that the data record has the particular combination of known attributes.

7. The apparatus of claim 1 , wherein the one or more modules are configured to:

represent the plurality of data records as a plurality of nodes in a graph;

represent the similarity value for the at least one pair of data records as at least one edge between nodes, in the graph, corresponding to the at least one pair of data records; and

determine the one or more clusters from based on the graph.

8. The apparatus of claim 7 , wherein the one or more modules are configured to determine the one or more clusters based on the graph using a graph clustering technique.

9. The apparatus of claim 1 , wherein the one or more modules are configured to receive a clustering directive requiring the one or more modules to associate two data records with the same cluster.

10. The apparatus of claim 1 , wherein the one or more modules are configured to associate at least one of the plurality of data records to one or more clusters using a clustering technique; and

adjust a parameter for the clustering technique for each of the one or more clusters independently, based on data records in the one or more clusters.

11. The apparatus of claim 1 , wherein the one or more modules are configured to determine the similarity value for the at least one pair of data records by receiving the similarity value for the at least one pair of data records from another computing device.

12. The apparatus of claim 1 , wherein the one or more modules are configured to:

receive, from a plurality of computing devices, a plurality of sub-clusters independently identified at the plurality of computing devices; and

perform a union-find operation on the plurality of sub-clusters to identify the one or more clusters.

13. A method for clustering a plurality of data records into at least one cluster, the method comprising:

receiving a plurality of pairs of data records;

determining whether one or more pairs of data records are eligible to be clustered, the determination is based upon an analysis of a set of attributes included in the one or more pairs, wherein the analysis is performed by:

converting a first data record in the pair of data records into a first hash;

converting a second data record in the pair of data records into a second hash; and

comparing bits of the first hash and second hash; and

when it is determined that at least one pair of data records can be clustered:

determining in communication with the candidate reduction module, a similarity value for the at least one pair based, at least in part, on a plurality of attributes associated with the at least one pair of data records, wherein the similarity value is determined using a machine learning model trained using a supervised learning function operating on ground-truth clusters of data records; and

associating, the at least one pair of data records with one or more clusters, each associated with a unique entity, based on the similarity value for the at least one pair of data records.

14. The method of claim 13 , further comprising identifying one or more pairs of the plurality of data records for which a similarity value need not be determined based on a predetermined set of attributes.

15. The method of claim 14 , further comprising adjusting the predetermined set of attributes based on association of data records to clusters from a previous iteration.

16. The method of claim 13 , wherein the similarity model is designed to infer an importance of a particular component associated with a particular attribute of a data record.

17. The method of claim 13 , wherein the similarity model is designed to infer a likelihood of interchanging a first component in a particular attribute of a data record with a second component.

18. A computer program product, tangibly embodied in a non-transitory computer-readable storage medium, the computer program product including instructions operable to cause a data processing system to:

receive a plurality of pairs of data records;

determine whether one or more pairs of data records are eligible to be clustered, the determination is based upon an analysis of a set of attributes included in the one or more pairs, wherein the analysis is performed by:

converting a first data record in the pair of data records into a first hash;

converting a second data record in the pair of data records into a second hash; and

comparing bits of the first hash and second hash; and

when it is determined that at least one pair of data records can be clustered:

determine a similarity value for the at least one pair of data records based, at least in part, on a plurality of attributes associated with the at least one pair of data records, wherein the similarity value is determined using a machine learning model trained using a supervised learning function operating on ground-truth clusters of data records; and

associate the at least one pair of data records with one or more clusters, each associated with a unique entity, based on the similarity value for the at least one pair of data records.

Assignments (3)
SECURITY INTEREST Recorded Jul 13, 2022
From: FOURSQUARE LABS, INC.
To: WELLS FARGO BANK, NATIONAL ASSOCIATION, AS AGENT
Reel/Frame 060649/0366 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 22, 2022
From: FACTUAL, INC.
To: FOURSQUARE LABS, INC.
Reel/Frame 059977/0688 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 12, 2022
From: SHIMANOVSKY, BORIS; LAGANG, MANUEL; POLOVETS, LEONID
To: FACTUAL, INC.
Reel/Frame 059573/0780 →
Continuity (7)
Continuation 14214231 · Mar 14, 2014
Provisional Application 61800036 · Mar 15, 2013
Provisional Application 61799846 · Mar 15, 2013
Provisional Application 61799131 · Mar 15, 2013
Provisional Application 61799986 · Mar 15, 2013
Provisional Application 61799817 · Mar 15, 2013
Related Publication 20210303531A1 · Sep 30, 2021
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