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.

公开(公告)号：US20240177219A1

公开(公告)日：2024-05-30

申请号：US18070382

申请日：2022-11-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Bradley Colthurst ,  Ryan McColeman

IPC: G06Q30/0601 ,  G06Q10/087

CPC classification number: G06Q30/0633 ,  G06Q10/087 ,  G06Q30/0631

Abstract: An online concierge system facilitates ordering, procurement, and delivery of items to a customer from physical retailers based on shared cart recommendations. Based on customer identifying information and other data sources, the online concierge system may recommend prepopulated shared carts that may be of interest to a customer. The prepopulated carts may be associated with other users of the online concierge system or may be associated with specific events, locations, or other metadata. Prepopulated carts may be created by other users that select to share their carts. Additionally, prepopulated carts may be created and shared by retailers, manufacturers, wholesalers, or other stakeholders in the selling of items through the online concierge system. Furthermore, recommended carts may be automatically generated based on machine learning techniques.

公开(公告)号：US20240428125A1

公开(公告)日：2024-12-26

申请号：US18339203

申请日：2023-06-21

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Amalia Rothschild-Keita ,  Brent Scheibelhut ,  Mark Oberemk ,  Hua Xiao ,  Shaun Navin Maharaj ,  Taha Amjad

IPC: G06N20/00

Abstract: An online concierge system uses a findability machine-learning model to predict the findability of items within a physical area. The findability model is a machine-learning model that is trained to compute findability scores, which are scores that represent the ease or difficulty of finding items within a physical area. The findability model computes findability scores for items based on an item map describing the locations of items within a physical area. The findability model is trained based on data describing pickers that collect items to service orders for the online concierge system. The online concierge system aggregates this information across a set of pickers to generate training examples to train the findability model. These training examples include item data for an item, an item map data describing an item map for the physical area, and a label that indicates a findability score for that item/item map pair.

公开(公告)号：US20240193663A1

公开(公告)日：2024-06-13

申请号：US18064129

申请日：2022-12-09

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Mark Oberemk

IPC: G06Q30/0601

CPC classification number: G06Q30/0631 ,  G06F40/279

Abstract: A system or a method for using machine learning to automatically route user inquiries to a retailer are presented. The system receives an inquiry from a client device associated with a user. The inquiry includes text content and an image. The system uses a natural language model to analyze the received text to identify a first category of items. The system applies the received image to an image recognition model to identify a second category of items contained in the received image. The system then identifies a retailer that carries items in at least one of the first or second category of items, and suggests the retailer to the user via the client device associated with the user. A retail associate at the retailer can then respond to the inquiry via a client device associated with the retailer.

公开(公告)号：US20240104494A1

公开(公告)日：2024-03-28

申请号：US17955415

申请日：2022-09-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Brent Scheibelhut ,  Shaun Maharaj

IPC: G06Q10/08 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V20/68

CPC classification number: G06Q10/087 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V20/68

Abstract: An online concierge system may receive multi-angle images of a plurality of instances of a grocery item carried at a physical store. Each instance of the grocery item is associated with one or more multi-angle images that are captured through a checkout process of the instance of the grocery item. The online concierge system may apply a machine learning model to the multi-angle images to identify expiration information of the plurality of instances of the grocery item. The online concierge system may use the identified expiration information to predict that a batch of the grocery item remaining in inventory of the physical store is close to expiration. The online concierge system may generate one or more item-specific suggestions associated with the expiration information with respect to the grocery item offered in the physical store.

公开(公告)号：US20240289857A1

公开(公告)日：2024-08-29

申请号：US18113874

申请日：2023-02-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Mark Oberemk

IPC: G06Q30/0601

CPC classification number: G06Q30/0623 ,  G06Q30/0603

Abstract: An online concierge system delivers items from multiple retailers to customers. To avoid delivery of expired or near-expired items, the online concierge system obtains attributes of items offered by a retailer, such as from images of items at the retailer from client devices and uses a trained desirability model to predict a desirability score of an item based on the item's attributes. The desirability model is trained using training examples with labels indicating whether an item was suitable for inclusion in an order. The desirability model may be used to determine if an item is suitable for inclusion in an order, to provide suggestions for a retailer for using the item, or to select a retailer for fulfilling an order.

公开(公告)号：US20230419381A1

公开(公告)日：2023-12-28

申请号：US17846887

申请日：2022-06-22

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Leho Nigul ,  Shaun Navin Maharaj ,  Brent Scheibelhut

IPC: G06Q30/06

CPC classification number: G06Q30/0613

Abstract: An online concierge system receives, from a client device comprising a customer mobile application, an order comprising a list of one or more items for delivery to a destination location from a warehouse. The customer mobile application comprises a user interface. The online concierge system identifies a set of item groupings from a database that match the list of one or more items. The online concierge system applies the order and the set of item groupings to a machine learning model to produce a set of foundational items. The online concierge system sends for display, to the client device, an updated user interface comprising a foundational items graphical element that visually distinguishes the set of foundational items from other items in the list of one or more items.