US 11,741,524 B2
Systems and methods for generating basket and item quantity predictions using machine learning architectures
Sonal Bathe, Sunnyvale, CA (US); Aleksandra Cerekovic, Sunnyvale, CA (US); Rahul Sridhar, Santa Clara, CA (US); Sinduja Subramaniam, San Jose, CA (US); Evren Korpeoglu, San Jose, CA (US); and Kannan Achan, Saratoga, CA (US)
Assigned to WALMART APOLLO, LLC, Bentonville, AR (US)
Filed by Walmart Apollo, LLC, Bentonville, AR (US)
Filed on Jan. 30, 2021, as Appl. No. 17/163,402.
Prior Publication US 2022/0245707 A1, Aug. 4, 2022
Int. Cl. G06Q 30/00 (2023.01); G06Q 30/0601 (2023.01); G06N 20/20 (2019.01); G06N 5/04 (2023.01); G06N 5/01 (2023.01)
CPC G06Q 30/0631 (2013.01) [G06N 5/01 (2023.01); G06N 5/04 (2013.01); G06N 20/20 (2019.01); G06Q 30/0633 (2013.01); G06Q 30/0641 (2013.01)] 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 functions comprising:
generating a feature vector for a user based, at least in part, on historical data pertaining to previous transactions of the user, wherein the feature vector comprises basket features;
generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user by:
creating a probability distribution of previous item-specific purchase quantities of the user; and
utilizing previous quantity data as a prior probability to predict a purchase quantity for each of the one or more items for a future transaction of the user; and
populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items.