Patent ID: 11966434
Assignee: WALMART APOLLO, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:
training, based at least in part on sample items in a sample database, a machine learning model to determine a respective embedding vector for a respective image for each of the sample items;
retrieving, from the sample database, the sample items stored and indexed in the sample database based on the respective embedding vector for the respective image for each of the sample items;
determining automatically a query embedding vector for a query image of a query item by the machine learning model, as trained;
determining neighboring items from among the sample items based on a respective embedding distance between the query image of the query item and a respective image of each of the neighboring items, calculated based on the query embedding vector for the query image, as determined, and the respective embedding vector for the respective image of each of the neighboring items, wherein:
the each of the sample items comprises the respective image and at least one respective item label;

determining a respective normalized weight for each of the neighboring items based on the respective embedding distance between the query image and the respective image of the each of the neighboring items;
determining a query item label of the query item based on a weighted majority vote by the neighboring items via the respective normalized weight for the each of the neighboring items;
determining that a designation error exists when the query item label of the query item is different from an assigned item label of the query item;
after determining that the designation error exists when the query item label of the query item is different from the assigned item label of the query item, storing the query item with the query item label, as determined, in a product database;
selectively updating the sample items stored in the sample database from the product database; and
re-training the machine learning model based at least in part on the sample items in the sample database, as updated.