公开(公告)号：EP4062321A1

公开(公告)日：2022-09-28

申请号：EP21703127.7

申请日：2021-01-11

Applicant: Maplebear, Inc. (DBA Instacart)

Inventor： GAO, Lin ,  HUANG, Yilin ,  YANG, Shiyuan ,  BESHRY, Ahmed ,  SANZARI, Michael ,  WOO, Jungsoo ,  ZAMBARE, Sarang ,  KELLY, Griffin

IPC: G06K9/62 ,  G06K9/46

公开(公告)号：US20250156930A1

公开(公告)日：2025-05-15

申请号：US18510554

申请日：2023-11-15

Applicant: Maplebear Inc.

Inventor： Brent Scheibelhut ,  Shaun Navin Maharaj ,  Madeline Mesard

IPC: G06Q30/0601

Abstract: An online concierge system identifies churn of a customer, which occurs when the customer does not perform a specific action within a threshold time period. The online concierge system determines an event causing churn of the customer based on characteristics of the customer and attributes describing prior fulfillment of an order for the customer. To mitigate different events causing churn, the online concierge system maps areas of expertise of pickers for different aspects of order fulfillment to corresponding events. Through a trained picker scoring model, the online concierge system determines picker scores for different pickers fulfilling an order for a customer using characteristics of pickers, including an expertise, characteristics of the customer, and an event causing churn of the customer. Based on the picker scores, the online concierge system selects a specific picker for fulfilling a subsequent order from the customer.

公开(公告)号：US20250148418A1

公开(公告)日：2025-05-08

申请号：US19013722

申请日：2025-01-08

Applicant: Maplebear Inc.

Inventor： Joey Loi ,  Viswa Mani Kiran Peddinti

IPC: G06Q10/087

Abstract: A warehouse from which shoppers fulfill orders for an online concierge system maintains an online concierge system-specific portion for which the online concierge system specifies placement of items in regions. To place items in the online concierge system-specific portion, the online concierge system accounts for co-occurrences of different items in orders and measures of similarity between different items. From the co-occurrences of items, the online concierge system generates an affinity graph. The online concierge system also generates a colocation graph based on distances between different regions in the online concierge system-specific portion. Using an optimization function with the affinity graph and the colocation graph, the online concierge system selects regions within the online concierge system-specific portion for different items to minimize an amount of time for shoppers to obtain items in the online concierge-system specific portion.

公开(公告)号：US20250148412A1

公开(公告)日：2025-05-08

申请号：US18500930

申请日：2023-11-02

Applicant: Maplebear Inc.

Inventor： Charles Wesley ,  Brent Scheibelhut ,  Hua Xiao

IPC: G06Q10/083 ,  G08B21/18

Abstract: A trained computer model for automatic identification of a wrong delivery location for an order placed at an online system. The online system receives, via a user interface, a user input that includes a delivery location for the order. The online system compares the received delivery location with a stored delivery location for the user. Responsive to identifying that the received and stored delivery locations are different, the online system accesses and applies a computer model to predict, based on features of the order, a likelihood of the received delivery location being correct. The online system generates, based on the predicted likelihood, a confidence score of the received delivery location being correct. Responsive to the confidence score being below a threshold score, the online system causes a device of the user to display a user interface with a message prompting the user to verify accuracy of the received delivery location.

公开(公告)号：US12283165B2

公开(公告)日：2025-04-22

申请号：US18408893

申请日：2024-01-10

Applicant: Maplebear Inc.

Inventor： Lin Gao ,  Shiyuan Yang

IPC: G01G19/415 ,  A47F9/04 ,  G01G21/22 ,  G06Q20/20 ,  G06V20/20 ,  G06V20/40 ,  G07G1/00 ,  G07G1/14 ,  G06V20/52 ,  G06V20/68

Abstract: Disclosed are visual recognition and sensor fusion weight detection system and method. An example method includes: tracking, by a sensor system, objects and motions within a selected area of a store; activating, by the sensor system, a first computing device positioned in the selected area in response to detecting a presence of a customer within the selected area: identifying, by the sensor system, the customer and at least one item carried by the customer; transmitting, by the sensor system, identifying information of the customer and the at least one item to a computing server system via a communication network; measuring, by the first computing device, a weight of the at least one item; transmitting, by the first computing device, the weight to the computing server system via the communication network; and generating, by the computing server system, via the communication network, transaction information of the at least one item.

公开(公告)号：US20250124484A1

公开(公告)日：2025-04-17

申请号：US18485581

申请日：2023-10-12

Applicant: Maplebear Inc.

Inventor： Madeline Mesard ,  Brent Scheibelhut ,  Shaun Navin Maharaj

IPC: G06Q30/0601 ,  G06F40/40 ,  G06Q30/0204

Abstract: An online system automatically generates a personalized collection of items around a theme. The online system generates a prompt for input into a language model, the prompt including information about a plurality of items and a text describing the theme around which the collection of items will be built. The online system requests the language model to generate, based on the prompt, a list of products eligible for building the collection of items. The online system accesses a computer model trained to identify a set of items personalized for a user of the online system. The computer model identifies, based on the list of eligible products and information about the user, the set of items for populating the collection of items. The online system causes a device of the user to display a user interface with the collection of items for inclusion into a cart of the user.

公开(公告)号：US12277584B2

公开(公告)日：2025-04-15

申请号：US17493780

申请日：2021-10-04

Applicant: Maplebear Inc.

Inventor： Greg Reda ,  Jagannath Putrevu ,  Kevin Charles Ryan

IPC: G06Q30/0601 ,  G06F18/214 ,  G06N3/04

Abstract: An online concierge system receives orders from users identifying items and a warehouses from which the items are obtained. The online concierge system displays groups of one or more orders to shoppers, allowing a shopper to select a group of orders for fulfillment. When selecting groups of orders to display to shoppers, the online concierge system accounts for costs for fulfilling different groups and displays groups having costs satisfying one or more criteria, while maintaining one or more restrictions on times to fulfill orders. The online concierge system trains a selection prediction model to predict an amount of time for a shopper to select a group of orders and determines an estimated fulfillment time for the group from the predicted amount of time. Accounting for the predicted selection time allows the online concierge system to identify a larger number of groups for which costs of fulfillment are determined.

公开(公告)号：US12271939B2

公开(公告)日：2025-04-08

申请号：US17855788

申请日：2022-06-30

Applicant: Maplebear Inc.

Inventor： Sonali Deepak Chhabria ,  Xiangyu Wang ,  Aman Jain ,  Ganesh Krishnan ,  Trace Levinson ,  Jian Wang

IPC: G06Q30/0601 ,  G01C21/34 ,  G06Q10/0631 ,  G06Q10/0637 ,  G06Q10/087

Abstract: An online concierge system includes a marketplace automation engine for setting various control parameters affecting marketplace operation. The marketplace automation engine applies a hyperparameter learning model to the marketplace state data to predict a set of hyperparameters affecting a set of respective parameterized control decision models. The hyperparameter learning model is trained on historical marketplace state data and a configured outcome objective for the online concierge system. The marketplace automation engine independently applies the set of parameterized control decision models to the marketplace state data using the hyperparameters to generate a respective set of control parameters affecting marketplace operation of the online concierge system. The marketplace automation engine applies the respective set of control parameters to operation of the online concierge system.

公开(公告)号：US20250111782A1

公开(公告)日：2025-04-03

申请号：US18916461

申请日：2024-10-15

Applicant: Maplebear Inc.

Inventor： Mathieu Ripert ,  Jagannath Putrevu ,  Deepak Tirumalasetty ,  Bala Subramanian ,  Andrew Kane

IPC: G08G1/00 ,  B65G1/04 ,  B65G1/137 ,  G01C21/34 ,  G05D1/644 ,  G05D1/69 ,  G06Q10/0631 ,  G06Q10/0833 ,  G06Q10/087 ,  G06Q20/32 ,  G06Q30/0601

Abstract: A method for optimizing delivery assignments in an online system. The system processes delivery orders from user devices, associates the orders with available delivery agents, and allocates them based on real-time data such as inventory availability at different warehouses, delivery agent locations, and order preparation progress. The system dynamically updates order allocations by periodically reallocating orders to different delivery agents based on travel progress, order preparation progress, warehouse proximity, and inventory availability at various warehouses.

公开(公告)号：US12265980B2

公开(公告)日：2025-04-01

申请号：US18240798

申请日：2023-08-31

Applicant: Maplebear Inc.

Inventor： Shuo Feng ,  Chia-Eng Chang ,  Aoshi Li ,  Pak Hong Wong ,  Leo Kwan ,  Mengyu Zhang ,  Van Nguyen ,  Aman Jain ,  Ziwei Shi ,  Ajay Pankaj Sampat ,  Rucheng Xiao

IPC: G06Q30/0201

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.