US 12,067,469 B2
System and method for machine learning-based delivery tagging
Omker Mahalanobish, Kolkata (IN); Rahul Agarwal, London (GB); Nicholas William Sinai, New York, NY (US); and Girish Thiruvenkadam, Bangalore (IN)
Assigned to WALMART APOLLO, LLC, Bentonville, AR (US)
Filed by Walmart Apollo, LLC, Bentonville, AR (US)
Filed on Mar. 15, 2021, as Appl. No. 17/201,277.
Prior Publication US 2022/0292407 A1, Sep. 15, 2022
Int. Cl. G06Q 30/02 (2023.01); G06N 20/20 (2019.01); G06Q 10/083 (2023.01)
CPC G06N 20/20 (2019.01) [G06Q 10/0838 (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 configured to run on the one or more processors and perform:
training a first submodel of a machine learning model by at least:
creating a cumulative addition of light gradient boosting models using features;
training each of the light gradient boosting models to reduce binary log loss, increase precision scores, and increase recall scores; and
determining, using a Bayesian Model Combination, (i) weights for aggregation with probabilities from the light gradient boosting models and (ii) a tuned threshold;
receiving historical delivery records over a predetermined time period from partners associated with items offered to subregions through an online platform;
generating nodes for combinations each comprising a respective one of the partners, a respective one of the items offered by the partners, and a respective one of the subregions;
generating, using the machine learning model, as trained, a respective classification for each respective node on whether to tag each respective node as deliverable in a predetermined time window, wherein the respective classification comprises a union of a respective output of the first submodel of the machine learning model and a respective output of a second submodel of the machine learning model; and
based on the respective classification for each respective node, automatically tagging a portion of the nodes as deliverable in the predetermined time window in the online platform.