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：US20240386471A1

公开(公告)日：2024-11-21

申请号：US18199938

申请日：2023-05-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Peter Vu ,  Ziwei Shi ,  Joseph Cohen ,  Emily Silberstein ,  Krishna Kumar Selvam ,  Jaclyn Tandler ,  Adrian McLean ,  Nicholas Rose

IPC: G06Q30/0601

Abstract: A concierge system sends batches of orders to pickers that they can review and accept in a batch list on a client device. Each batch in the batch list is presented with a hide option that enables the picker to hide a batch that they do not intend to accept. In response to receiving a hide signal, the system extracts features associated with the batch and stores those features with a negative indication of the picker towards the batch. The hide signal provides the system with a higher quality signal indicating the picker's negative intent regarding an order, as compared to simply ignoring the order in favor of fulfilling another order. This higher quality signal is then used to train models to better predict events related to the pickers' acceptance of orders, such as for ranking orders for pickers or for predicting fulfillment times.

公开(公告)号：US20240070577A1

公开(公告)日：2024-02-29

申请号：US17823850

申请日：2022-08-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Krishna Kumar Selvam ,  Joseph Cohen ,  Tahmid Sharjar ,  Neel Sarwal ,  Darren Johnson ,  Nicholas Rose ,  Ajay Pankaj Sampat ,  Joey Dong

IPC: G06Q10/06

CPC classification number: G06Q10/063114 ,  G06Q10/06316

Abstract: The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.