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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.
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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.
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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.
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