SYSTEM AND METHOD FOR DETERMINING A PROPENSITY OF ENTITY TO TAKE A SPECIFIED ACTION

• US 20160026923A1
• Filed: 12/05/2014
• Published: 01/28/2016
• Est. Priority Date: 07/22/2014
• Status: Abandoned Application

• Current Assignee: Palantir Technologies Incorporated
• Original Assignee: Palantir Technologies Incorporated
• Inventors: ERENRICH, Daniel, MUKHERJEE, Anirvan
• Application Number:
  US14/562,524
• Publication Number:
  US 20160026923A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 30/01 Customer relationship services
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Systems and methods are disclosed for determining a propensity of an entity to take a specified action. In accordance with one implementation, a method is provided for determining the propensity. The method includes, for example, accessing one or more data sources, the one or more data sources including information associated with the entity, forming a record associated with the entity by integrating the information from the one or more data sources, generating, based on the record, one or more features associated with the entity, processing the one or more features to determine the propensity of the entity to take the specified action, and outputting the propensity.

Claims

1. A system for determining a propensity of an entity to take a specified action, the system comprising:

one or more computer-readable storage media configured to store instructions; and

one or more processors configured to execute the instructions to;

acquire information associated with the entity from one or more data sources;

form a record associated with the entity by integrating the information from the one or more data sources;

generate, based on the record, one or more features associated with the entity;

process the one or more features to determine the propensity of the entity to take the specified action; and

output the propensity.

2. The system of claim 1, wherein the one or more processors are further configured to filter the record for information associated with the specified action.

3. The system of claim 1, wherein the one or more processors are further configured to train a model to predict the propensity of the entity to take the specified action.

4. The system of claim 3, wherein the one are more processors are further configured to determine, based on the trained model and the record, the relative importance of the one or more features.

5. The system of claim 1, wherein the one or more processors are further configured to:

acquire a temporal period; and

determine the propensity of the entity to take the specified action within the temporal period.

6. The system of claim 1, wherein the one or more processors are further configured to generate a user interface to display the propensity of the entity to take the specified action.

7. The system of claim 1, wherein the entity is a household and the specified action is churn.

8. A method for determining a propensity of an entity to take a specified action, the method being performed by one or more processors and comprising:

acquiring information associated with the entity from one or more data sources;

forming a record associated with the entity by integrating the information from the one or more data sources;

generating, based on the record, one or more features associated with the entity;

processing the one or more features to determine the propensity of the entity to take the specified action; and

outputting the propensity.

9. The method of claim 8, further comprising filtering the record for information associated with the specified action.

10. The method of claim 8, further comprising training a model to predict the propensity of the entity to take the specified action.

11. The method of claim 10, further comprising determining, based on the trained model and the record, the relative importance of the one or more features.

12. The method of claim 8, further comprising:

acquiring a temporal period; and

determining the propensity of the entity to take the specified action within the temporal period.

13. The method of claim 8, further comprising generating a user interface to display the propensity of the entity to take the specified action.

14. The method of claim 8, wherein the entity is a household and the specified action is churn.

15. A non-transitory computer-readable medium storing a set of instructions that are executable by one or more processors to cause the one or more processors to perform a method for determining a propensity of an entity to take a specified action, the method comprising:

acquiring information associated with the entity one or more data sources;

forming a record associated with the entity by integrating the information from the one or more data sources;

generating, based on the record, one or more features associated with the entity;

processing the one or more features to determine the propensity of the entity to take the specified action; and

outputting the propensity.

16. The non-transitory computer-readable medium of claim 15, further comprising instructions executable by the one or more processors to cause the one or more processors to perform:

training a model to predict the propensity of the entity to take the specified action.

17. The non-transitory computer-readable medium of claim 16, further comprising instructions executable by the one or more processors to cause the one or more processors to perform:

determining, based on the trained model and the record, the relative importance of the one or more features.

18. The non-transitory computer-readable medium of claim 15, further comprising instructions executable by the one or more processors to cause the one or more processors to perform:

acquiring a temporal period; and

determining the propensity of the entity to take the specified action within the temporal period.

19. The non-transitory computer-readable medium of claim 15, further comprising instructions executable by the one or more processors to cause the one or more processors to perform:

generating a user interface to display the propensity of the entity to take the specified action.

20. The non-transitory computer-readable medium of claim 15, wherein the entity is a household and the specified action is churn.