公开(公告)号：US20220335505A1

公开(公告)日：2022-10-20

申请号：US17230816

申请日：2021-04-14

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Shishir Kumar Prasad ,  Sharath Rao Karikurve ,  Diego Goyret

IPC: G06Q30/06 ,  G06Q10/08 ,  G06Q30/02 ,  G06N20/00 ,  G06N5/04

Abstract: An online concierge system allows users to order items from a warehouse having multiple physical locations, allowing a user to order items at any given warehouse location. To select a warehouse location for a warehouse selected by a user, the online concierge system identifies a set of items that the user has a threshold likelihood of purchasing from prior orders by the user. For each of a set of warehouse locations, the online concierge system applies a machine-learned item availability model to each item of the identified set. From the availabilities of items of the set at each warehouse location of the set, the online concierge system selects a warehouse location. The online concierge system identifies an inventory of items from the selected warehouse location to the user for inclusion in an order.

公开(公告)号：US20230078450A1

公开(公告)日：2023-03-16

申请号：US17474408

申请日：2021-09-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chuanwei Ruan ,  Diego Goyret ,  Tilman Drerup ,  Rob Donnelly

IPC: G06Q30/06 ,  G06F3/0482 ,  G06Q30/02 ,  G06F16/9535 ,  G06F16/2457

Abstract: An online concierge system allows users to purchase items from warehouses and delivers the purchased items to the users. When displaying items offered by a warehouse, the online concierge system predicts an availability of the items at the warehouse using a trained model. When displaying items offered by the warehouse to a user, the online concierge system accounts for the predicted availabilities of different items. For example, the online concierge system determines scores for different items at the warehouse based on relevance to the user and adjusts a score for an item by its predicted availability. The online concierge system uses the adjusted scores for items when displaying items, demoting positions in an interface in which items with lower predicted availabilities are displayed. Additionally, the online concierge system may display a visual indication of a predicted availability of certain items, such as items with less than a threshold predicted availability.

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：US20240354828A1

公开(公告)日：2024-10-24

申请号：US18137404

申请日：2023-04-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Luis Manrique ,  Sanchit Gupta ,  Aref Kashani Nejad ,  Diego Goyret ,  Kurtis Mirick ,  Joshua Roberts

IPC: G06Q30/0601

CPC classification number: G06Q30/0631 ,  G06Q30/0641

Abstract: An online system receives a request from a user to access an ordering interface for a retailer and identifies a retailer location based on the user's location. The system uses a machine learning model to predict availabilities of items at the retailer location and identifies anchor items the user previously ordered from the retailer that are likely available. The system computes a first score for each anchor item based on an expected value associated with it and/or a likelihood the user will re-order it, determines categories associated with the anchor items, and ranks the categories based on the first score. For each category, the system identifies associated candidate items likely to be available and ranks them based on a second score for each candidate item computed based on a probability of user satisfaction with it as an anchor item replacement. The ordering interface is then generated based on the rankings.