US 12,136,104 B2
Automatic personalized email triggers
Kannan Achan, Saratoga, CA (US); Sushant Kumar, Sunnyvale, CA (US); Kaushiki Nag, Santa Clara, CA (US); and Venkata Syam Prakash Rapaka, Cupertino, CA (US)
Assigned to WALMART APOLLO, LLC, Bentonville, AR (US)
Filed by Walmart Apollo, LLC, Bentonville, AR (US)
Filed on Mar. 28, 2022, as Appl. No. 17/706,300.
Application 17/706,300 is a continuation of application No. 16/259,626, filed on Jan. 28, 2019, granted, now 11,288,700.
Claims priority of provisional application 62/622,511, filed on Jan. 26, 2018.
Prior Publication US 2022/0215428 A1, Jul. 7, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/00 (2023.01); G06Q 30/0242 (2023.01); G06Q 30/0251 (2023.01); G06N 7/01 (2023.01)
CPC G06Q 30/0246 (2013.01) [G06Q 30/0255 (2013.01); G06Q 30/0271 (2013.01); G06N 7/01 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:
training a logistic regression machine-learning model to create a trained model to provide respective probabilities of users clicking on emails of one or more email campaigns within each of multiple different time periods, wherein input predictor variables of the logistic regression machine-learning model comprise (i) user feature data comprising personal user features and online activity history for users in the multiple different time periods and (ii) email feature data comprising sent times and item category data for multiple different emails in the one or more email campaigns, wherein the personal user features comprise, for each of the multiple different time periods, a location of a user of the users, a brand affinity of the user, and a price affinity of the user, wherein the email feature data further comprise, for each email of the multiple different emails in the one or more email campaigns: a category of one or more items featured in the email, one or more brands of the one of more items featured in the email, and at least one of one or more discount values of the one or more items featured in the email or one or more special promotional values of the one or more items featured in the email, and wherein output dependent variables of the logistic regression machine-learning model comprise responses by the users to the one or more email campaigns; and
triggering sending a first email of the one or more email campaigns to a first user of the users at a selected time period based at least in part on the trained model.