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.

公开(公告)号：US20180068374A1

公开(公告)日：2018-03-08

申请号：US15258942

申请日：2016-09-07

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Emmanuel Jean Yves Turlay ,  Elizabeth Ruth Barnum ,  Ashu Khaitan ,  Moses Yung Kyu Lee ,  Denise Hoi Shuen Leung ,  Arnaud Nicolas Ferreri

IPC: G06Q30/06 ,  G06Q20/18

CPC classification number: G06Q30/0633 ,  G06Q20/18 ,  G06Q20/20 ,  G06Q20/3276 ,  G07G1/0045 ,  G07G1/0081 ,  G07G1/009

Abstract: An online shopping concierge service allows shoppers to purchase items on behalf of customers and checkout using a mobile application, circumventing traditional point-of-sale check-out systems. A customer places an order using a mobile application or website associated with the online shopping concierge service. The online shopping concierge service charges a payment instrument of the customer in the value of the selected items. The system transmits the order to a shopper, who receives an order for fulfillment on a mobile device. The shopper collects and scans items using a mobile application. The mobile application transmits an identification of the items for purchase and their total cost to the online shopping concierge service, which transmits payment to the retailer. Alternatively, the mobile application encodes an identification of the items for purchase into an encoded image, which is scanned by a cashier, allowing the shopper to complete an accelerated check-out.

公开(公告)号：US20240428324A1

公开(公告)日：2024-12-26

申请号：US18213761

申请日：2023-06-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Tianshu Ren

IPC: G06Q30/08 ,  G06Q30/0601

Abstract: An online concierge system includes a content selection simulation module that performs offline simulations of a content selection process to enable rapid testing of various content selection parameters. The content selection simulation module obtains historical content selection data including content delivery opportunities and candidate content items associated with those content delivery opportunities. The content selection simulation module simulates the filtering, ranking, and auction stages of a content selection process using a set of configurable content selection parameters that affects selection of a winning content item and price. The winning content items from the simulation may be used to compute performance metrics associated with the configured content selection parameters. Different content selection parameters may be compared to determine an effect of changes to the parameters.

公开(公告)号：US20240428314A1

公开(公告)日：2024-12-26

申请号：US18212122

申请日：2023-06-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ryan McColeman ,  Brent Scheibelhut ,  Mark Oberemk ,  Shaun Navin Maharaj

IPC: G06Q30/0601 ,  G06Q30/0201

Abstract: The present disclosure is directed to determining purchase suggestions for an online shopping concierge platform. In particular, the methods and systems of the present disclosure may receive, from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform; determine, based at least in part on one or more machine learning (ML) models and the data indicating the interaction(s), a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item; and generate and communicate data describing a graphical user interface (GUI) comprising at least a portion of a listing of one or more purchase suggestions including the suggestion to purchase the particular item.