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.

公开(公告)号：US20240403826A1

公开(公告)日：2024-12-05

申请号：US18204200

申请日：2023-05-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Youdan Xu ,  Aoshi Li ,  Jaclyn Tandler ,  Roman Hayran ,  Brendan Evans Ashby ,  Emily Silberstein ,  Ajay Pankaj Sampat

IPC: G06Q10/0875 ,  G06N3/084 ,  G06Q20/12

Abstract: An online concierge system allows customers to place orders to be fulfilled by pickers. An order includes an amount of compensation a customer provides to a picker when the order is fulfilled. A customer may modify the amount of compensation provided to a picker, so some customers may initially specify a large amount of compensation to entice a picker to fulfill an order and then reduce the amount of compensation when the order is fulfilled. To prevent penalizing pickers who fulfilled an order without a problem, the online concierge system trains a model to determine a probability that a reduction in compensation to a picker was unrelated to a problem with order fulfillment. The online concierge system may perform one or more remedial actions for a picker based on the probability determined by the model.

公开(公告)号：US20250111303A1

公开(公告)日：2025-04-03

申请号：US18374457

申请日：2023-09-28

Applicant: Maplebear Inc.

Inventor： Rucheng Xiao ,  Aoshi Li ,  Youdan Xu ,  Mengyu Zhang ,  Chen Zhang ,  Ziwei Shi ,  Matthew Donghyun Kim

IPC: G06Q10/0631

Abstract: An online concierge system identifies a set of attributes of one or more future time periods and accesses a machine learning model trained to predict a set of working hours for a picker during a future time period, in which the set of working hours describes an availability of the picker to service orders placed with the online concierge system. The online concierge system then applies the machine learning model to the set of attributes to predict the set of working hours for the picker during the future time periods and stores the predicted set of working hours for the picker during the future time periods.

公开(公告)号：US20240427559A1

公开(公告)日：2024-12-26

申请号：US18213773

申请日：2023-06-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Aoshi Li ,  Kevin Green ,  Zhongqiang Liang ,  Francois Campbell ,  Mengyu Zhang

IPC: G06F8/20 ,  G06F8/70

Abstract: A system validates code ownership of software components identified in a build process. The system receives a pull request identifying a set of software components. The system analyzes code ownership of each software component using machine learning. The system provides features describing the software components as input to a machine learning model. The system determines based on the output of the machine learning model, whether the code ownership of the software component can be determined accurately. If the system determines that a software component identified by the pull request cannot be determined with high accuracy, the system may block the pull request or send a message indicating that the code ownership of a software component cannot be determined accurately.

公开(公告)号：US20250078105A1

公开(公告)日：2025-03-06

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.

公开(公告)号：US20250078056A1

公开(公告)日：2025-03-06

申请号：US18240719

申请日：2023-08-31

Applicant: Maplebear Inc.

Inventor： Aoshi Li ,  Prithvishankar Srinivasan ,  Shang Li ,  Mengyu Zhang ,  Daniel Haugh ,  Cheryl D’Souza ,  Syed Wasi Hasan Rizvi ,  William Halbach ,  Ziwei Shi ,  Annie Zhang ,  Giovanny Castro ,  Sonali Parthasarathy ,  Shishir Kumar Prasad

IPC: G06Q20/14 ,  G06Q10/087 ,  G06Q30/0601

Abstract: An online concierge system compensates pickers who fulfill orders including one or more items based in part on weights of the items included in an order. Because the online concierge system does not physically possess the items that are obtained, the online concierge system cannot directly weigh the items and weights specified for items in a catalog from a retailer may be inaccurate. To more accurately determine weights of items, the online concierge system trains a weight prediction model to estimate an item's weight from attributes of the item and uses the output of the weight prediction model to determine compensation to a picker. The weight prediction model may output a predicted weight of an item or a classification of the item as heavy or light. Where discrepancies are found between a predicted weight and the catalog weight of an item, additional information about the item is obtained.