Granted Patent B1

US 12,437,111 · App. 18/504,466 · Granted Oct 7, 2025

Inventors: Fredrick William Rodgers (Northlake, TX); Michael P. Gout (San Antonio, TX)

Assignee: United Services Automobile Association (USAA)

G06F21/6254G06F18/214G06N5/04G06N20/00H04L9/3213

Abstract

Claims (68)

1. A computer-implemented method performed by at least one processor, the method comprising:

generating, by at least one processor, for each of a plurality of events, a respective token that represents the event, wherein the respective token representing the event is encrypted using a private key associated with an organization and allows the organization to identify the event but does not allow a third party to identify the event;

for each event of the plurality of events:

identifying, by at least one processor, at least one entity associated with the event;

associating the respective token encrypted by the private key of the organization and representing the event with the at least one entity;

communicating, by at least one processor, the respective token and the at least one entity as label data for training of a machine learning system; and

associating the respective token encrypted by the private key and representing the event and the at least one entity with environmental data of the at least one entity to generate feature data for training of the machine learning system, wherein the environmental data indicates online activities taken by the at least one entity for a predetermined period of time before a date of the event;

combining the label data and the feature data to generate training data for training the machine learning system;

training the machine learning system on the training data comprising both (i) the label data and (ii) the feature data generated from the environmental data indicating online activities taken by the at least one entity; and

providing other environmental data indicating one or more other recent online activities of another entity as input to the trained machine learning system;

processing, using the trained machine learning system, the other environmental data indicating the one or more other recent online activities of another entity to generate an output that indicates that a particular event will likely occur within a predetermined time frame;

determining whether the particular event occurred based on subsequent observation data associated with the other entity;

providing feedback data to the machine learning system based on the determination, the feedback data comprising a positive example when the particular event occurred and a negative example when the particular event did not occur; and

retraining the machine learning system on the training data and the feedback data.

2. The computer-implemented method of claim 1 , wherein the at least one entity has an identifier shared with the machine learning system.

3. The computer-implemented method of claim 1 , wherein using the trained machine learning system to make one or more inferences on entities for the respective tokens comprises:

for each of one or more events in the plurality of events,

identifying, using the trained machine learning system, a set of new entities that each having a probability of experiencing the event within a predetermined time frame, wherein each probability is greater than zero, and

filtering, by at least one processor, from the set of new entities, entities for which the probability of experiencing the event is below a threshold to obtain an updated set of new entities.

4. The computer-implemented method of claim 3 , further comprising: providing, over a computer network, information on the updated set of new entities to the organization.

5. The computer-implemented method of claim 1 , wherein the plurality of events comprises one or more life events comprising at least one of a marriage, a divorce, a childbirth, or a job change.

6. The computer-implemented method of claim 1 , wherein the plurality of events comprises one or more major purchases comprising at least one of a purchase of a house, a purchase of a car, or a purchase of a vacation package.

7. One or more non-transitory computer storage media storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

generating, for each of a plurality of events, a respective token that represents the event, wherein the respective token representing the event is encrypted using a private key associated with an organization and allows the organization to identify the event but does not allow a third party to identify the event;

for each event of the plurality of events:

identifying at least one entity associated with the event;

associating the respective token encrypted by the private key of the organization and representing the event with the at least one entity;

communicating the respective token and associated entity as label data for training of a machine learning system; and

associating the respective token encrypted by the private key and representing the event and the at least one entity with environmental data of the at least one entity to generate feature data for training of the machine learning system, wherein the environmental data indicates online activities taken by the at least one entity for a predetermined period of time before a date of the event;

combining the label data and the feature data to generate training data for training the machine learning system;

training the machine learning system on the training data comprising both (i) the label data and ii the feature data generated from the environmental data indicating online activities taken by the at least one entity; and

providing other environmental data indicating one or more other recent online activities of another entity as input to the trained machine learning system;

processing, using the trained machine learning system, the other environmental data indicating the one or more other recent online activities of another entity to generate an output that indicates that a particular event will likely occur within a predetermined time frame;

determining whether the particular event occurred based on subsequent observation data associated with the other entity;

providing feedback data to the machine learning system based on the determination, the feedback data comprising a positive example when the particular event occurred and a negative example when the particular event did not occur; and

retraining the machine learning system on the training data and the feedback data.

8. The one or more non-transitory computer storage media of claim 7 , wherein the at least one entity has an identifier shared with the machine learning system.

9. The one or more non-transitory computer storage media of claim 7 , wherein the operations for using the trained machine learning system to make one or more inferences on entities for the respective tokens comprise:

for each of one or more events in the plurality of events,

identifying, using the trained machine learning system, a set of new entities that each having a probability of experiencing the event within a predetermined time frame, wherein each probability is greater than zero, and

filtering, by at least one processor, from the set of new entities, entities for which the probability of experiencing the event is below a threshold to obtain an updated set of new entities.

10. The one or more non-transitory computer storage media of claim 9 , wherein the operations further comprise: providing, over a computer network, information on the updated set of new entities to the organization.

11. The one or more non-transitory computer storage media of claim 7 , wherein the plurality of events comprises one or more life events comprising at least one of a marriage, a divorce, a childbirth, or a job change.

12. The one or more non-transitory computer storage media of claim 7 , wherein the plurality of events comprises one or more major purchases comprising at least one of a purchase of a house, a purchase of a car, or a purchase of a vacation package.

13. A system, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

generating, by at least one processor, for each of a plurality of events, a respective token that represents the event, wherein the respective token representing the event is encrypted using a private key associated with an organization and allows the organization to identify the event but does not allow a third party to identify the event;

for each event of the plurality of events:

identifying, by at least one processor, at least one entity associated with the event;

associating the respective token encrypted by the private key of the organization and representing the event with the at least one entity;

communicating, by at least one processor, the respective token and the at least one entity as label data for training of a machine learning system; and

associating the respective token encrypted by the private key and representing the event and the at least one entity with environmental data of the at least one entity to generate feature data for training of the machine learning system, wherein the environmental data indicates online activities taken by the at least one entity for a predetermined period of time before a date of the event;

combining the label data and the feature data to generate training data for training the machine learning system;

training the machine learning system on the training data comprising both (i) the label data and ii the feature data generated from the environmental data indicating online activities taken by the at least one entity; and

providing other environmental data indicating one or more other recent online activities of another entity as input to the trained machine learning system;

processing, using the trained machine learning system, the other environmental data indicating the one or more other recent online activities of another entity to generate an output that indicates that a particular event will likely occur within a predetermined time frame;

determining whether the particular event occurred based on subsequent observation data associated with the other entity;

providing feedback data to the machine learning system based on the determination, the feedback data comprising a positive example when the particular event occurred and a negative example when the particular event did not occur; and

retraining the machine learning system on the training data and the feedback data.

14. The system of claim 13 , wherein the at least one entity has an identifier shared with the machine learning system.

15. The system of claim 13 , wherein the operations for using the trained machine learning system to make one or more inferences on entities for the respective tokens comprise:

for each of one or more events in the plurality of events,

identifying, using the trained machine learning system, a set of new entities that each having a probability of experiencing the event within a predetermined time frame, wherein each probability is greater than zero, and

filtering, by at least one processor, from the set of new entities, entities for which the probability of experiencing the event is below a threshold to obtain an updated set of new entities.

16. The system of claim 15 , wherein the operations further comprise: providing, over a computer network, information on the updated set of new entities to the organization.

17. The system of claim 13 , wherein the plurality of events comprises one or more life events comprising at least one of a marriage, a divorce, a childbirth, or a job change.

18. The system of claim 13 , wherein the plurality of events comprises one or more major purchases comprising at least one of a purchase of a house, a purchase of a car, or a purchase of a vacation package.

Assignments (2)

Continuity (2)

References Cited (78)