公开(公告)号：US20240428320A1

公开(公告)日：2024-12-26

申请号：US18213756

申请日：2023-06-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Rebecca Riso ,  Bo Xu ,  Kenneth Jason Sanchez ,  Ashish Sinha ,  Chencheng Wu

IPC: G06Q30/0601 ,  G06Q20/40

Abstract: An online system receives a request to confirm a transaction that is associated with an order. The system accepts or declines the transaction based on whether an amount associated with the pending transaction is likely to exceed an expected amount of the order by more than a threshold value. To determine the threshold, the system trains a first model to predict an overspend for an order and then trains a second model to predict an amount of error associated with the predictions from the first model. The outputs of the first model and the second model provide a mean and a variance for an expected distribution of the overspend. If the actual overspend amount for the transaction exists in too high of a percentile of the distribution, the transaction may be flagged for review or declined.

公开(公告)号：US20240362582A1

公开(公告)日：2024-10-31

申请号：US18141398

申请日：2023-04-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Haochen Luo ,  Kenneth Jason Sanchez ,  Eric Hermann

IPC: G06Q10/087

CPC classification number: G06Q10/087

Abstract: An inventory interaction model predicts user interactions with items to be included in an item assortment in a warehouse. The item is described with features that include the co-located items and the respective user interactions, so that the item interactions for the evaluated item incorporate item-item effects in its predictions. To train the model effectively in the absence of prior interaction data for an item, training examples are generated from existing item and user interaction data of co-located items by selecting a portion of the items for the examples and including co-located item data, labeling the training example output with item interactions for the item. The trained model is then applied for an item assortment by describing co-located item features of the item assortment in evaluating candidate items.

公开(公告)号：US20240289739A1

公开(公告)日：2024-08-29

申请号：US18113868

申请日：2023-02-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Kenneth Jason Sanchez ,  Haochen Luo ,  Eric Hermann

IPC: G06Q10/087 ,  G06Q10/0833

CPC classification number: G06Q10/087

Abstract: An online concierge system facilitates ordering of items by customers, procurement of the items from physical retailers by pickers assigned to the orders, and delivery of the orders to customers. To enable efficient procurement, the online concierge system may facilitate preemptive picking of items for staging at a rapid fulfillment area of the physical retailer, and pickers may selectively pick items from the rapid fulfillment area instead of their standard storage locations. Decisions on which items to preemptively pick may be based on a predictive optimization model that scores and ranks items for predictive picking in accordance with various optimization criteria. In the course of fulfilling orders, pickers may furthermore be assigned to replenish items from the standard storage locations to the rapid fulfillment area to satisfy future predicted or actual orders in a manner that optimizes a cost metric.

公开(公告)号：US20240289738A1

公开(公告)日：2024-08-29

申请号：US18113866

申请日：2023-02-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Kenneth Jason Sanchez ,  Haochen Luo ,  Eric Hermann

IPC: G06Q10/087 ,  G06Q10/04 ,  G06Q10/083

CPC classification number: G06Q10/087 ,  G06Q10/04 ,  G06Q10/083

Abstract: An online concierge system facilitates ordering of items by customers, procurement of the items from physical retailers by pickers assigned to the orders, and delivery of the orders to customers. To enable efficient procurement, the online concierge system may facilitate preemptive picking of items for staging at a rapid fulfillment area of the physical retailer, and pickers may selectively pick items from the rapid fulfillment area instead of their standard storage locations. Decisions on which items to preemptively pick may be based on a predictive optimization model that scores and ranks items for predictive picking in accordance with various optimization criteria. In the course of fulfilling orders, pickers may furthermore be assigned to replenish items from the standard storage locations to the rapid fulfillment area to satisfy future predicted or actual orders in a manner that optimizes a cost metric.