US 12,406,295 B2
Providing information for locating an item within a warehouse from a shopper to other shoppers retrieving the item from the warehouse
Mingzhe Zhuang, Los Altos Hills, CA (US); Camille van Horne, San Francisco, CA (US); Christopher Rudnick, Pacifica, CA (US); Benjamin Knight, Oakland, CA (US); Chris Jenkins, Thornton, CO (US); Viktoriya Andonova, San Francisco, CA (US); Djordje Gluhovic, Oakland, CA (US); Riddhima Sejpal, Dublin, CA (US); Maksim Golivkin, Oakland, CA (US); and Sharath Rao Karikurve, Berkeley, CA (US)
Assigned to Maplebear Inc., San Francisco, CA (US)
Filed by Maplebear Inc., San Francisco, CA (US)
Filed on Nov. 14, 2023, as Appl. No. 18/509,157.
Application 18/509,157 is a continuation of application No. 16/734,273, filed on Jan. 3, 2020, granted, now 11,854,063.
Claims priority of provisional application 62/788,726, filed on Jan. 4, 2019.
Prior Publication US 2024/0078591 A1, Mar. 7, 2024
Int. Cl. G06Q 30/00 (2023.01); G06Q 30/0601 (2023.01)
CPC G06Q 30/0635 (2013.01) [G06Q 30/0639 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method performed by one or more processors of an online system, the computer-implemented method comprising:
training a machine-learned availability model for determining availabilities of items in warehouses, wherein training of the machine-learned availability model comprises:
applying a plurality of training samples to train the machine-learned availability model, the plurality of training samples comprising previous delivery orders, wherein at least one training sample comprises a training label indicating whether an item was found at a warehouse and a plurality of characteristics associated with the item;
receiving a first set of notifications regarding availabilities of a plurality of items, wherein a notification includes whether one or more items were found at the warehouse;
determining, based on the first set of notifications, that a set of items were reported to be not found, the set of items being reported to be not found for at least a threshold number of times;
retraining, based on the set of items, the machine-learned availability model to improve accuracy of predictions of the machine-learned availability model, wherein retraining the machine-learned availability model comprises:
generating an additional set of training samples using the set of items that were not found for at least the threshold number of times,
inputting the additional training samples to the machine-learned availability model for the machine-learned availability model to generate a confidence score indicative of an accuracy of a prediction of availability of one or more items in the set,
responsive to the confidence score indicative of the accuracy of the prediction of availability of the specific item being below a threshold, sending instructions to indicate the availability of the one or more items need to be verified,
receiving a second set of notifications indicating one or more items in the set of items were found in the warehouse,
updating the additional set of training samples based on the second set of notifications,
applying the additional set of training samples to the machine-learned availability model, and
adjusting weights of the machine-learned availability model based on the applying of the additional set of training samples;
receiving, from a user device, a selection of a particular item;
applying the retrained machine-learned availability model to generate a prediction of whether the particular item is available at the warehouse; and
causing to display a result based on the prediction generated by the machine-learned availability model regarding the particular item.