公开(公告)号：US20250078025A1

公开(公告)日：2025-03-06

申请号：US18952836

申请日：2024-11-19

Applicant: Maplebear Inc.

Inventor： Jeremy Stanley ,  Montana Low ,  Nima Zahedi

IPC: G06Q10/087 ,  G06N3/04 ,  G06N3/08 ,  G06N5/01 ,  G06N20/20

Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

公开(公告)号：US20230113122A1

公开(公告)日：2023-04-13

申请号：US18080118

申请日：2022-12-13

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Sharath Rao ,  Shishir Prasad ,  Jeremy Stanley

IPC: G06Q10/087 ,  G06Q10/0631 ,  G06Q10/067

Abstract: A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.

公开(公告)号：US10810543B2

公开(公告)日：2020-10-20

申请号：US16048800

申请日：2018-07-30

Applicant: Maplebear, Inc.

Inventor： Jonathan Hsieh ,  Oliver Gothe ,  Jeremy Stanley

IPC: G06Q10/08 ,  G06N3/08

Abstract: A method for populating an inventory catalog includes receiving an image showing an item in the inventory catalog and comprising a plurality of pixels. A machine learned segmentation neural network is retrieved to determine location of pixels in an image that are associated with an image label and the property. The method determines a subset of pixels associated with the item label in the received image and identifies locations of the subset of pixels of the received image, and extracts the subset of pixels from the received image. The method retrieves a machine learned classifier to determine whether an image shows the item label. The method determines, using the machine learned classifier, that the extracted subset of pixels shows the item label. The method updates the inventory catalog for the item to indicate that the item has the property associated with the item label.

公开(公告)号：US12182760B2

公开(公告)日：2024-12-31

申请号：US18236575

申请日：2023-08-22

Applicant: Maplebear Inc.

Inventor： Jeremy Stanley ,  Montana Low ,  Nima Zahedi

IPC: G06Q10/087 ,  G06N3/04 ,  G06N3/08 ,  G06N5/01 ,  G06N20/20

Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

公开(公告)号：US20230394432A1

公开(公告)日：2023-12-07

申请号：US18236575

申请日：2023-08-22

Applicant: Maplebear Inc.

Inventor： Jeremy Stanley ,  Montana Low ,  Nima Zahedi

IPC: G06Q10/087 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06N5/01

CPC classification number: G06Q10/087 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06N5/01

Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

公开(公告)号：US11775926B2

公开(公告)日：2023-10-03

申请号：US15882934

申请日：2018-01-29

Applicant: Maplebear, Inc.

Inventor： Jeremy Stanley ,  Montana Low ,  Nima Zahedi

IPC: G06Q10/087 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06N5/01

CPC classification number: G06Q10/087 ,  G06N3/04 ,  G06N3/08 ,  G06N5/01 ,  G06N20/20

Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

公开(公告)号：US11544810B2

公开(公告)日：2023-01-03

申请号：US15885492

申请日：2018-01-31

Applicant: Maplebear, Inc.

Inventor： Sharath Rao ,  Shishir Prasad ,  Jeremy Stanley

IPC: G06Q50/28 ,  G06Q10/06 ,  G06Q10/08

Abstract: A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.

公开(公告)号：US20200034782A1

公开(公告)日：2020-01-30

申请号：US16048800

申请日：2018-07-30

Applicant: Maplebear, Inc. (dba Instacart)

Inventor： Jonathan Hsieh ,  Oliver Gothe ,  Jeremy Stanley

IPC: G06Q10/08 ,  G06N3/08

Abstract: A method for populating an inventory catalog includes receiving an image showing an item in the inventory catalog and comprising a plurality of pixels. A machine learned segmentation neural network is retrieved to determine location of pixels in an image that are associated with an image label and the property. The method determines a subset of pixels associated with the item label in the received image and identifies locations of the subset of pixels of the received image, and extracts the subset of pixels from the received image. The method retrieves a machine learned classifier to determine whether an image shows the item label. The method determines, using the machine learned classifier, that the extracted subset of pixels shows the item label. The method updates the inventory catalog for the item to indicate that the item has the property associated with the item label.

公开(公告)号：US20190236740A1

公开(公告)日：2019-08-01

申请号：US15885492

申请日：2018-01-31

Applicant: Maplebear, Inc. (dba Instacart)

Inventor： Sharath Rao ,  Shishir Prasad ,  Jeremy Stanley

IPC: G06Q50/28 ,  G06N99/00 ,  G06N7/00 ,  G06Q10/06

CPC classification number: G06Q50/28 ,  G06N7/005 ,  G06N20/00 ,  G06Q10/06315 ,  G06Q10/067

Abstract: A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.

公开(公告)号：US20190236525A1

公开(公告)日：2019-08-01

申请号：US15882934

申请日：2018-01-29

Applicant: Maplebear, Inc. (dba Instacart)

Inventor： Jeremy Stanley ,  Montana Low ,  Nima Zahedi

IPC: G06Q10/08 ,  G06N3/08

CPC classification number: G06Q10/087 ,  G06N3/08

Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.