公开(公告)号：US20250029053A1

公开(公告)日：2025-01-23

申请号：US18224795

申请日：2023-07-21

Applicant: Maplebear Inc.

Inventor： Kevin Charles Ryan ,  Krishna Kumar Selvam ,  Tahmid Shahriar ,  Ajay Pankaj Sampat ,  Shouvik Dutta ,  Sawyer Bowman ,  Nicholas Rose ,  Ziwei Shi

IPC: G06Q10/0834 ,  G06Q10/083

Abstract: An online concierge system receives information describing the progress of a picker servicing a batch of existing orders and a service request for an order. The system identifies picker attributes of the picker and order attributes of the order and each existing order of the set and accesses a machine learning model trained to predict a likelihood the picker will accept an add-on request to add the order to the batch of existing orders. To predict the likelihood, the system applies the model to the picker attributes, the progress of the picker, and the order attributes. The system determines a cost associated with sending the add-on request to the picker based on the likelihood and assigns the order to a set of orders based on the cost. The system sends the add-on request to the picker responsive to determining the order is assigned to the batch of existing orders.

公开(公告)号：US20250053898A1

公开(公告)日：2025-02-13

申请号：US18233252

申请日：2023-08-11

Applicant: Maplebear Inc.

Inventor： Kevin Charles Ryan ,  Krishna Kumar Selvam ,  Tahmid Shahriar ,  Sawyer Bowman ,  Nicholas Rose ,  Ajay Pankaj Sampat ,  Ziwei Shi

IPC: G06Q10/0631 ,  G06Q10/0833

Abstract: An online concierge system receives information describing the progress of a picker servicing a batch of existing orders and predicts a first likelihood the picker will finish servicing the batch within a threshold amount of time based on the picker's progress and information describing the batch. If the first likelihood exceeds a threshold likelihood, the system accesses a machine learning model trained to predict a second likelihood the picker will accept a batch of new orders for servicing while servicing the batch of existing orders. The system applies the model to inputs including a set of attributes of the picker and the picker's progress to predict the second likelihood. The system matches batches of new orders with pickers based on the second likelihood and sends one or more requests to service one or more batches matched with the picker to a client device associated with the picker.

公开(公告)号：US20250021772A1

公开(公告)日：2025-01-16

申请号：US18769970

申请日：2024-07-11

Applicant: Maplebear Inc.

Inventor： Pak Hong Wong ,  Shengwen Fang ,  Tahmid Shahriar ,  Zyshia Williams

IPC: G06F40/58 ,  G06F40/106 ,  G06F40/166 ,  G06F40/232

Abstract: An online system performs a message transformation task in conjunction with the model serving system or the interface system to transform a message input to a chat message. The online system receives the message input in a conversation between a picker and a customer. The online system may transform the message input to a text string that is properly formed and contextually appropriate, format the text string into a chat message, and send the chat message to a receiving party on behalf of the sending party.

公开(公告)号：US20240104449A1

公开(公告)日：2024-03-28

申请号：US17955395

申请日：2022-09-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Krishna Kumar Selvam ,  Mouna Cheikhna ,  Michael Chen ,  Dylan Wang ,  Joseph Cohen ,  Tahmid Shahriar ,  Graham Adeson ,  Ajay Pankaj Sampat

IPC: G06Q10/06 ,  G06Q30/06

CPC classification number: G06Q10/06311 ,  G06Q10/06398 ,  G06Q30/0635

Abstract: An online concierge system iteratively makes a batch of one or more orders available to an increasing number of shoppers to choose to fulfill. Each shopper may choose to accept or reject a batch for fulfillment. To improve batch acceptance and matching between batches and shoppers, the batches are scored with respect to expected resource costs, likelihood of acceptance by the shopper, and/or other quality metrics to iteratively offer the batch to an increasing number of shoppers (prioritizing the scoring factors) until a shopper accepts. The number of shoppers notified of the batch and the frequency that additional shoppers are selected may vary based on characteristics of the batch and likelihood the batch will be accepted by a shopper.