US 11,936,606 B2
Methods and systems for using machine learning to determine times to send message notifications
Austin Walters, Savoy, IL (US); Jeremy Goodsitt, Champaign, IL (US); and Galen Rafferty, Washington, DC (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Jan. 9, 2023, as Appl. No. 18/151,516.
Application 18/151,516 is a continuation of application No. 17/319,796, filed on May 13, 2021, granted, now 11,588,775.
Prior Publication US 2023/0164104 A1, May 25, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 15/16 (2006.01); G06F 40/30 (2020.01); H04L 51/02 (2022.01); H04L 51/224 (2022.01)
CPC H04L 51/224 (2022.05) [G06F 40/30 (2020.01); H04L 51/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for using machine learning to determine a time to send a message notification to a user device, the system comprising:
one or more processors configured to execute computer program instructions that, when executed, cause the one or more processors to perform operations comprising:
receiving a message comprising text and metadata indicating a sender of the message, the user device intended to receive the message, and a timestamp;
generating, via a message embedding model, a vector representation of the message, wherein the vector representation is indicative of the text and the metadata of the message;
inputting the vector representation into a sentiment detection model to obtain a sentiment identifier associated with the message;
inputting the vector representation into an urgency detection model to obtain an urgency level associated with the message;
inputting an indication of the sentiment identifier, the urgency level, the timestamp, and user device information into a response prediction model to obtain a predicted response time for the message indicative of a quantity of time predicted to transpire between a first time at which a notification is received at the user device and a second time at which a response to the message is predicted to be sent;
determining a time to send the message notification to the user device;
sending the notification to the user device;
receiving, from the user device, feedback information indicating a preferred time for receiving the message notification and a second sentiment identifier of the message indicating an interpretation of the message by a user; and
training, based on the feedback information, the sentiment detection model, the urgency detection model, and the response prediction model.