公开(公告)号：US20230132730A1

公开(公告)日：2023-05-04

申请号：US17515399

申请日：2021-10-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shishir Kumar Prasad ,  Natalia Botía ,  Diego Goyret ,  Allan Stewart ,  Douglas Mill ,  Andrew Wong ,  Yao Zhou

IPC: G06Q30/06 ,  G06F16/2457

Abstract: An online concierge system maintains a taxonomy associating one or more specific items offered by a warehouse with a category. When the online concierge system receives a selection of an item from a user for inclusion in an order, the online concierge system determines a category including the selected item. From prior received orders, the online concierge system 102 identifies additional categories including one or more items included in various prior received orders. Based on cooccurrences of the category and the additional categories, the online concierge system generates scores for the additional categories. An additional category is selected based on the scores and specific items from the selected additional category are displayed via an interface for selection by the user.

公开(公告)号：US20230056148A1

公开(公告)日：2023-02-23

申请号：US17406027

申请日：2021-08-18

Applicant: Maplebear Inc.(dba Instacart)

Inventor： Negin Entezari ,  Sharath Rao Karikurve ,  Shishir Kumar Prasad ,  Haixun Wang

IPC: G06Q30/06 ,  G06Q10/08

Abstract: An online concierge shopping system identifies candidate items to a user for inclusion in an order based on prior user inclusion of items in orders and items currently included in the order. From a multi-dimensional tensor generated from cooccurrences of items in orders from various users, the online concierge system generates item embeddings and user embeddings in a common latent space by decomposing the multi-dimensional tensor. From items included in an order, the online concierge system generates an order embedding from item embeddings of the items included in the order. Scores for candidate items are determined based on similarity of item embeddings for the candidate items to the order embedding. Candidate items are selected based on their scores, with the selected candidate items identified to the user.

公开(公告)号：US20230035687A1

公开(公告)日：2023-02-02

申请号：US17938405

申请日：2022-10-06

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Supriyo Chakraborty ,  Keith Grueneberg ,  Bongjun Ko ,  Christian Makaya ,  Jorge J. Ortiz ,  Swati Rallapalli ,  Theodoros Salonidis ,  Rahul Urgaonkar ,  Dinesh Verma ,  Xiping Wang

IPC: G06Q30/02

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

公开(公告)号：US20220358560A1

公开(公告)日：2022-11-10

申请号：US17308993

申请日：2021-05-05

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Weian Sheng ,  Peng Qi ,  Changyao Chen

IPC: G06Q30/06 ,  G06N20/00 ,  G06N5/04

Abstract: An online concierge system maintains a taxonomy associating one or more specific items offered by a warehouse with a generic item description. When the online concierge system receives a generic item description from a user for inclusion in an order, the online concierge system uses the taxonomy to select a set of items associated with the generic item description. Based on probabilities of the user purchasing various items of the set, the online concierge system selects an item of the set for inclusion in the order For example, the online concierge system selects an item of the set for which the user has a maximum probability of being purchased. Subsequently, the online concierge system displays an interface for the user that is prepopulated with information identifying the selected item of the set.

公开(公告)号：US20220335493A1

公开(公告)日：2022-10-20

申请号：US17232651

申请日：2021-04-16

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Weian Sheng ,  Peng Qi ,  Changyao Chen

IPC: G06Q30/06 ,  G06Q30/02 ,  G06Q10/08 ,  G06N7/00

Abstract: An online concierge system maintains historical orders received from a user that include one or more items. For items included in one more historical orders, the online concierge system determines an interval between orders including an item, providing an indication of a frequency with which the user orders the item. When the online concierge system receives a request to create an order from the user, in response to an amount of time between a most recently received order including the item and a time when the request was received is within a threshold duration of the interval between orders including the item, the online concierge system selects an item from a category including the item. The selected item may be the item or an alternative item in the category. Subsequently, the online concierge system displays an interface for the user that is prepopulated with information identifying the selected item.

公开(公告)号：US20220269748A1

公开(公告)日：2022-08-25

申请号：US17704101

申请日：2022-03-25

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ogden Kent ,  Benjamin David Bader ,  Jeffrey Bernard Arnold

IPC: G06F16/957 ,  G06F16/958 ,  G06F11/34 ,  G06F40/143

Abstract: A variation testing system environment for performing variation testing of web pages and applications is disclosed. The variation testing system applies a weighted consistent hash function to user attributes of users to assign the users to a variant of a web page that is undergoing experimentation. The usage of the weighted consistent hash function allows for a stable experimental population.

公开(公告)号：US20210287271A1

公开(公告)日：2021-09-16

申请号：US16815846

申请日：2020-03-11

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Shishir Kumar Prasad ,  Sharath Rao

IPC: G06Q30/06 ,  G06F16/953

Abstract: An online concierge system may determine recommended search terms for a user. The online concierge system may receive a request from a user to view a user interface configured to receive a search query. The online concierge system retrieves long-term activity data including previous search terms entered by the user while searching for items to add to an online shopping cart. For each previous search term, the online concierge system retrieves categorical search terms corresponding to one or more categories to which the previous search term was mapped. The online concierge system determines a set of nearby categorical search terms and sends, for display via a client device, the set of nearby categorical search terms as recommended search terms.

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.