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：US20240242145A1

公开(公告)日：2024-07-18

申请号：US18156347

申请日：2023-01-18

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Haochen Luo ,  Eric Hermann ,  Rishab Saraf ,  Abhinav Darbari ,  Teodor Lefter ,  Jason Sanchez ,  Jagannath Putrevu

IPC: G06Q10/0631 ,  G06Q10/0639 ,  G06Q10/0835 ,  G06Q10/087 ,  G06Q30/0202 ,  G06Q30/0601

CPC classification number: G06Q10/063118 ,  G06Q10/06398 ,  G06Q10/08355 ,  G06Q10/087 ,  G06Q30/0202 ,  G06Q30/0635

Abstract: An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.

公开(公告)号：US20240193627A1

公开(公告)日：2024-06-13

申请号：US18079836

申请日：2022-12-12

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Jason Sanchez ,  Eric Hermann ,  Abhinav Darbari ,  Haochen Luo ,  Maksym Brodin ,  Sam Crocker

IPC: G06Q30/0202 ,  G06Q10/083

CPC classification number: G06Q30/0202 ,  G06Q10/083

Abstract: An online concierge system applies a predictive model to predict demand of items, and facilitates preemptive picking of items in advance of receiving orders to enable efficient procurement and delivery. The online concierge system may apply a time-series model and/or machine learning model that predicts demand based on historical data. Depending on the predicted demand, items may be preemptively moved from a storage location to a staging area that enables the items to be more rapidly processed and delivered to customers when orders come in.

公开(公告)号：US20240070603A1

公开(公告)日：2024-02-29

申请号：US17899977

申请日：2022-08-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Jagannath Putrevu ,  Haochen Luo ,  Xiangpeng Li ,  Rishab Saraf

IPC: G06Q10/08 ,  G06F16/29 ,  G06Q30/02

CPC classification number: G06Q10/08355 ,  G06F16/29 ,  G06Q30/0205

Abstract: A grid is created for a map of a geographic region based on a location planning request received from a user device. A plurality of candidate cells are identified from among a plurality of cells of the grid. Each of the candidate cells including a candidate location for a warehouse. Respective isochrones are generated relative to the candidate locations of the plurality of candidate cells based on a delivery time threshold indicated in the location planning request. Respective isochrone scores are determined for the generated isochrones based at least on data indicating a past volume of sales in the isochrone. Based on the respective isochrone scores of the candidate locations, a subset of the candidate locations is selected as a recommended set of locations for warehouses to cover the geographic region. A notification indicating the recommended set of locations is transmitted to the user device.

公开(公告)号：US12288172B2

公开(公告)日：2025-04-29

申请号：US18156347

申请日：2023-01-18

Applicant: Maplebear Inc.

Inventor： Haochen Luo ,  Eric Hermann ,  Rishab Saraf ,  Abhinav Darbari ,  Teodor Lefter ,  Kenneth Jason Sanchez ,  Jagannath Putrevu

IPC: G06Q10/0631 ,  G06Q10/0639 ,  G06Q10/0835 ,  G06Q10/087 ,  G06Q30/0202 ,  G06Q30/0601

Abstract: An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.

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.