Systems and methods for updating creatives generation models

Patent metadata

Field	Value
Publication number	US-11451495-B2
Application number	US-202117200572-A
Country	US
Kind code	B2
Filing date	Mar 12, 2021
Priority date	Mar 4, 2020
Publication date	Sep 20, 2022
Grant date	Sep 20, 2022

Abstract

Disclosed embodiments provide systems and methods related to updating creatives generation models. The system may include at least one memory unit for storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving a feedback indication concerning an email message sent to a target, the email message constructed using a first template and associated with a first context, the feedback indication including a recommendation identifier; updating, in response to receiving the feedback indication, a feedback value for the email message stored in a delay buffer; obtaining the updated feedback value upon satisfaction of a time delay condition; updating, using the updated feedback data and the recommendation identifier, a machine learning model configured to recommend templates based on contexts; and constructing and providing a second email message using a second template recommended by the updated machine learning model for a second context.

First claim

What is claimed is:

1. A system comprising:
at least one memory containing instructions; and
at least one processor configured to execute the instructions to perform operations comprising:
receiving a request to construct a first email message, the request including an identifier of a first target;
receiving a feedback indication concerning the first email message sent to the first target, the first email message constructed using a first template and associated with a first context;
updating, in response to receiving the feedback indication, a feedback value for the first email message;
updating, using the updated feedback value and the identifier, a machine learning model configured to recommend templates based on contexts; and
constructing and providing a second email message using a second template recommended by the updated machine learning model for a second context.

2. The system of claim 1 , wherein the feedback indication indicates selection of a link in the first email message.

3. The system of claim 1 , wherein the first context includes descriptive data for a first target and interaction data for the first target.

4. The system of claim 1 , wherein updating a feedback value further comprises changing a negative feedback value into a positive feedback value.

5. The system of claim 1 , wherein the operations further comprise obtaining the updated feedback value upon satisfaction of time delay.

6. The system of claim 1 , wherein the time delay condition is satisfied when a time between 8 and 30 hours has elapsed since mailing of the first email message.

7. The system of claim 1 , wherein the machine learning model is updated to increase a probability of recommending the first template based on the first context.

8. The system of claim 1 , wherein updating a feedback value further comprises storing the first email message in a delay buffer.

9. The system of claim 1 , wherein constructing and providing the second email message further comprises:
receiving a request to generate the second email message, the request including an identifier of the machine learning model and an identifier of a second target;
retrieving, from at least one database, information for the second target, using the identifier of the second target;
applying at least a portion of the information to the machine learning model to generate a template recommendation;
constructing the second email message using the template recommendation; and
providing the constructed second email message.

10. The system of claim 9 , wherein constructing the second email message using the template recommendation further comprises retrieving, from a database storing a set of templates, the second template using the template recommendation.

11. The system of claim 9 , wherein the operations further comprise retrieving a campaign definition that specifies the at least a portion of the information applied to the machine learning model.

12. The system of claim 9 , wherein the operations further comprise storing a negative feedback value for the second email message in a delay buffer with a delay time.

13. The system of claim 12 , wherein the negative feedback value is stored in the delay buffer in response to receiving a delivery indication for the first email.

14. A system comprising:
at least one memory containing instructions; and
at least one processor configured to execute the instructions to perform operations comprising:
receiving a request to generate a first email message, the request including an identifier of a first target;
retrieving, from at least one database, information for the first target, using the identifier of the first target;
applying at least a portion of the information to a machine learning model to generate a template recommendation;
constructing the first email message using the template recommendation; and
providing the constructed first email message.

15. The system of claim 14 , wherein the operations further comprise storing a negative feedback value for the first email message in a delay buffer with a delay time.

16. The system of claim 14 , wherein the operations further comprise retrieving a campaign definition that specifies the at least a portion of the information applied to the machine learning model.

17. The system of claim 14 , wherein the delay time is between 8 and 30 hours.

18. The system of claim 14 , wherein the operations further comprise:
receiving a feedback indication concerning the first email message; and
updating, in response to receiving the feedback indication, the negative feedback value for the first email message in the delay buffer to a positive feedback value.

19. The system of claim 18 , where in the operations further comprise:
updating the machine learning model using the positive feedback value; and
constructing and providing a second email message using the updated machine learning model.

20. A non-transitory computer readable medium containing instructions that, when executed by at least one processor of a system, cause the system to perform operations comprising:
receiving a feedback indication concerning a first email message sent to a first target, the first email message constructed using a first template and associated with a first context, the feedback indication including an identifier of the first target;
updating, in response to receiving the feedback indication, a feedback value for the first email message;
updating, using the updated feedback data and the identifier, a machine learning model configured to recommend templates based on contexts; and
constructing and providing a second email message using a second template recommended by the updated machine learning model for a second context.

Assignees

• Capital One Services Llc

Inventors

• Kulkarni Kirankumar
• Darivemula Savio Joseph
• Konduru Anil
• Patel Gunjan
• Shen Jie
• Birch Kelly L
• Manion Patrick James

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06F16/9535

  Search customisation based on user profiles and personalisation · CPC title

• G06N20/00

  Machine learning · CPC title

• H04L51/02 Primary

  using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages · CPC title