Patent ID: 11875394
Assignee: MAPLEBEAR INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A method comprising:
receiving an order at an online concierge system, the order identifying a time for fulfillment;
determining, by one or more processors of the online concierge system, a predicted amount of time to fulfill the order based on characteristics of the order;
determining, by one or more processors of the online concierge system, a predicted benefit to the online concierge system for delaying display of the order to one or more shoppers for each of a plurality of candidate time intervals, wherein determining the predicted benefit for a candidate time interval comprises:
determining, by one or more processors of the online concierge system, an overall fulfillment time for the order as a combination of the predicted amount of time to fulfill the order and the candidate time interval;
determining, by one or more processors of the online concierge system, a probability of the order being fulfilled later than the time identified by the order when the order is delayed from display to shoppers by the candidate time interval time by applying a late fulfillment machine learning model to determine the probability of the order being fulfilled later than the time identified by the order, wherein training of the late fulfillment machine learning model comprises:
obtaining, by one or more processors of the online concierge system, training data including a plurality of examples, each example including an example late fulfillment order, a fulfillment time for the example late fulfillment order, and a label applied to each example indicating whether the example late fulfillment order was fulfilled later than a time identified in the example late fulfillment order;
backpropagating, by one or more processors of the online concierge system, one or more error terms obtained from one or more loss functions associated with the late fulfillment machine learning model to update a set of parameters of the late fulfillment machine learning model, the backpropagating comprising updating, by the one or more processors, one or more of the error terms based on a difference between the label applied to an example of the training data and a predicted probability of the example late fulfillment order being fulfilled later than the time identified by the example late fulfillment order; and

stopping the backpropagation after the one or more loss functions satisfy one or more criteria;

selecting a time interval from the plurality of candidate time intervals for delaying display of the order to the one or more shoppers based on the determined probabilities;
evaluating one or more additional orders for inclusion of the one or more additional orders in a batch of orders that includes the order; and
displaying the batch of orders to the one or more shoppers after the selected time interval lapses.