公开(公告)号：US20240362580A1

公开(公告)日：2024-10-31

申请号：US18141394

申请日：2023-04-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Kenneth Jason Sanchez ,  Haochen Luo ,  Rishab Saraf ,  Eric Hermann ,  Dario Fidanza

IPC: G06Q10/087 ,  G06Q30/0202

CPC classification number: G06Q10/087 ,  G06Q30/0202

Abstract: An online system evaluates different item assortments for a physical warehouse having limited capacity to stock items. Each item assortment is stocked at the physical warehouse in proportion to an assortment split weight. The items at the warehouse are available for users to order, for example to be gathered by a picker and physically delivered to users near the warehouse. Rather than display all items actually stocked at the physical warehouse to all users, the different item assortments are displayed to different users. Users may order items from the assigned item assortment and, because both item assortments are actually stocked at the physical warehouse, orders from either item assortment may be successfully fulfilled for delivery. The different user interfaces thus permit evaluation of the preferred item assortment by users while maintaining expected delivery capability and while using the same storage capacity of the physical warehouse.

公开(公告)号：US20240362579A1

公开(公告)日：2024-10-31

申请号：US18141393

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 of a location for a physical warehouse included with other warehouses in a region. The location is described with features that include the nearby locations and the respective user interactions with the respective item assortments, so that the item interactions for the evaluated location incorporate location-location effects in model predictions. To effectively train the model in the absence of prior interaction data for a location, training examples are generated from existing locations and user interaction data of item assortments by selecting a portion of the locations for the training examples and including nearby location interaction data, labeling the training example output with item interactions for the location. The trained model is then applied for an item assortment at a location by describing nearby locations in evaluating candidate locations and item assortments.

公开(公告)号：US20240331015A1

公开(公告)日：2024-10-03

申请号：US18129464

申请日：2023-03-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Kenneth Jason Sanchez ,  Eric Hermann

IPC: G06Q30/0601 ,  G06Q30/02

CPC classification number: G06Q30/0635 ,  G06Q30/0281 ,  G06Q30/0639

Abstract: An online concierge system facilitates creation of shopping lists of items for ordering from a physical retail store and at least partial self-service fulfillment of orders by the customer. To support fulfillment by the customer, the online concierge system may intelligently select one or more items of the order to be picked by a third-party picker and prepopulated to a shopping cart reserved for the customer in advance of the customer arriving at the retail location. The items for prepopulating may be selected based on various factors that optimize prepopulation decisions on an item-by-item basis in accordance with various machine learning models. The online concierge system may furthermore facilitate procurement of the remaining items by the customer through a customer client device that may track item procurement and/or provide guidance for locating items.

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.