Determining a likelihood that two entities are the same

• US 9,830,325 B1
• Filed: 09/11/2013
• Issued: 11/28/2017
• Est. Priority Date: 09/11/2013
• Status: Active Grant

• Current Assignee: Intuit, Inc.
• Original Assignee: Intuit, Inc.
• Inventors: Joseph, Sony, Tripathy, Sunil K., Izrailevsky, Ilya A.
• Primary Examiner(s): Coby, Frantz
• Application Number:
  US14/024,351
• Time in Patent Office: 1,539 Days
• Field of Search: 707723, 707727, 707767, 707738, 707758, 707740, 707770, 707609, 705 30, 705 35, 705 38, 705 39, 705 44
• CPC Class Codes: G06F 16/14 Details of searching files based on file metadata
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/24 Querying
• CPC Class Codes: G06F 16/2477 Temporal data queries
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 50/00 Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism healthcare informatics G16H

Abstract

The disclosed embodiments provide a system that determines a likelihood that a first entity and a second entity are the same entity. During operation, the system obtains financial data related to the first entity and obtains financial data related to the second entity. Next, the system determines the likelihood that the first entity and the second entity are the same entity based on the relationship between the financial data for the first entity and the financial data for the second entity. Then, the system alters a record related to the first entity or the second entity based on the likelihood.

Claims

1. A computer-implemented method executed by processor circuitry for determining a likelihood that a first entity and a second entity are a same entity, the method comprising:

retrieving, from a first database, a first data related to the first entity and retrieving, from a second database, a second data related to the second entity;

segmenting the retrieved first data into a first set of individual components;

segmenting the retrieved second data into a second set of individual components, wherein each component in the first set of individual components corresponds to a component in the second set of individual components;

determining a characteristic value for each component in the first and second sets of individual components;

determining, for each component in the first set of individual components, a distance value based on a distance of the determined characteristic value of the component from the determined characteristic value of the corresponding component in the second set of individual components;

determining the likelihood that the first entity and the second entity are the same entity based on the determined distance values; and

altering a record related to the first entity or the second entity upon determining the likelihood is greater than or equal to a first likelihood; and

adding a second record from the first data related to the first entity to the second data related to the second entity if the likelihood is greater than or equal to a second likelihood.

2. The computer-implemented method of claim 1, wherein the first data and second data include financial transactional data.

3. The computer-implemented method of claim 2, wherein the financial transactional data includes at least one of:

an industry category;

a radius of activity;

a vendor industry category;

a set of vendor names;

a set of customer names;

an average transaction size;

an average vendor size;

an average customer size;

an inventory;

a debit;

a credit;

an account payable; and

an account receivable.

4. The computer-implemented method of claim 1, wherein the determined distance values are based on distance metrics.

5. The computer-implemented method of claim 1, wherein the likelihood is further determined based on a weight for each distance.

6. The computer-implemented method of claim 1, wherein retrieving the first data includes retrieving financial data related to the first entity from financial data associated with a first online application, and retrieving the second data includes retrieving financial data related to the second entity from financial data associated with a second online application.

7. The computer-implemented method of claim 1 wherein retrieving the first data includes retrieving non-financial data related to the first entity, and retrieving the second data includes retrieving non-financial data related to the second entity.

8. A system for determining a likelihood that a first entity and a second entity are a same entity, comprising:

processing circuitry configured to;

retrieve, from a first database, a first data related to the first entity and retrieve, from a second database, a second data related to the second entity;

segment the retrieved first data into a first set of individual components;

segment the retrieved second data into a second set of individual components, wherein each component in the first set of individual components corresponds to a component in the second set of individual components;

determine a characteristic value for each component in the first and second sets of individual components;

determine, for each component in the first set of individual components, a distance value based on a distance of the determined characteristic value of the component from the determined characteristic value of the corresponding component in the second set of individual components;

determine the likelihood that the first entity and the second entity are the same entity based on the determined distance values; and

alter a record related to the first entity or the second entity upon determining the likelihood is greater than or equal to a first likelihood; and

add a second record from the first data related to the first entity to the second data related to the second entity if the likelihood is greater than or equal to a second likelihood.

9. The system of claim 8, wherein the first data and second data include financial transactional data.

10. The system of claim 9, wherein the financial transactional data includes at least one of:

an industry category;

a radius of activity;

a vendor industry category;

a set of vendor names;

a set of customer names;

an average transaction size;

an inventory;

an average vendor size;

an average customer size;

a debit;

a credit;

an account payable; and

an account receivable.

11. The system of claim 8, wherein the processing apparatus is configured to determine the distance value based on a distance metric.

12. The system of claim 8, wherein the processing apparatus is configured to determine the likelihood based on a weight for each distance.

13. The system of claim 8, wherein the processing apparatus is configured to retrieve the first data by retrieving financial data related to the first entity from a first online application, and retrieving financial data related to the second entity by retrieving the financial data related to the second entity from a second online application.

14. The system of claim 8, wherein the processing apparatus is further configured to:

retrieve the first data by retrieving non-financial data related to the first entity and retrieve the second data by retrieving non-financial data related to the second entity.

15. A non transitory computer-readable storage medium storing instructions that when executed by a computer cause processing circuitry to perform a method for determining a likelihood that a first entity and a second entity are a same entity, the method comprising:

retrieving, from a first database, a first data related to the first entity and retrieving, from a second database, a second data related to the second entity;

segmenting the retrieved first data into a first set of individual components;

segmenting the retrieved second data into a second set of individual components, wherein each component in the first set of individual components corresponds to a component in the second set of individual components;

determining a characteristic value for each component in the first and second sets of individual components;

determining, for each component in the first set of individual components, a distance value based on a distance of the determined characteristic value of the component from the determined characteristic value of the corresponding component in the second set of individual components;

determining the likelihood that the first entity and the second entity are the same entity based on the determined distance values; and

altering a record related to the first entity or the second entity upon determining the likelihood is greater than or equal to a first likelihood; and

adding a second record from the first data related to the first entity to the second data related to the second entity if the likelihood is greater than or equal to a second likelihood.

16. The non transitory computer-readable storage medium of claim 15, wherein the first and second data includes financial transactional data, wherein the financial transactional data includes at least one of:

an industry category;

a radius of activity;

a vendor industry category;

a set of vendor names;

a set of customer names;

an average transaction size;

an inventory;

an average vendor size;

an average customer size;

a debit;

a credit;

an account payable; and

an account receivable.

17. The non transitory computer-readable storage medium of claim 15, wherein the determined distance values are based on distance metrics.

18. The non transitory computer-readable storage medium of claim 15, wherein retrieving the first data includes retrieving financial data related to the first entity from a first online application, and retrieving the second data includes retrieving financial data related to the second entity from a second online application.

19. The non transitory computer-readable storage medium of claim 15 wherein retrieving the first data includes retrieving non-financial data related to the first entity, and retrieving the second data includes retrieving non-financial data related to the second entity.