US 12,217,296 B2
Systems and methods using deep joint variational autoencoders
Venugopal Mani, Sunnyvale, CA (US); Jianpeng Xu, San Jose, CA (US); Hyun Duk Cho, San Francisco, CA (US); Sushant Kumar, San Jose, CA (US); Kannan Achan, Saratoga, CA (US); and Aysenur Inan, Mountain View, CA (US)
Assigned to Walmart Apollo, LLC, Bentonville, AR (US)
Filed by Walmart Apollo, LLC, Bentonville, AR (US)
Filed on Jan. 31, 2022, as Appl. No. 17/589,229.
Prior Publication US 2023/0245204 A1, Aug. 3, 2023
Int. Cl. G06Q 30/00 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06Q 30/0601 (2023.01)
CPC G06Q 30/0631 (2013.01) [G06N 3/045 (2023.01); G06N 3/047 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a processor; and
a non-transitory memory storing instructions, that when executed, cause the processor to:
receive a user identifier corresponding to a set of prior interactions;
generate a set of candidate items;
generate a set of latent space representations of the set of prior interactions and the candidate items using a trained inference model, wherein the trained inference model generates distribution likelihoods for hidden variables and includes a joint variational autoencoder model;
generate a set of k-recommended items based on a comparison of the set of latent space representations of the set of prior interactions and the set of latent space representations of the candidate items;
rank the set of k-recommended items based on the candidate items and a set of all available items; and
generate a user interface including the ranked set of k-recommended items.