公开(公告)号：USD976270S1

公开(公告)日：2023-01-24

申请号：US29773062

申请日：2021-03-05

Applicant: Maplebear, Inc.

Designer： Christopher Hans Nietes Rudnick ,  Min Ho Kim ,  Matthew Ryan Marcuccio ,  Aref Kashani Nejad ,  John Alexander Wilde ,  Laimonas Turauskas ,  Brian Patrick Mahlstedt ,  Imaan Munir

公开(公告)号：US20230113386A1

公开(公告)日：2023-04-13

申请号：US17496829

申请日：2021-10-08

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Aref Kashani Nejad ,  Skyler Moses ,  Logan Murdock ,  Evan Smith ,  Bonnie Hoang ,  Nikita Pahadia ,  Rezwan Islam

IPC: G06Q30/06 ,  G06Q10/08

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 request to create an order from a user, the online concierge system selects a set of generic item descriptions from previously received orders and displays depictions of each generic item of the set to the user via an interface. In response to the user selecting a generic item description, the online concierge system identifies specific items associated with the selected generic item description from the taxonomy. Different identified specific items are displayed via the interface, for example as a scrollable list, allowing the user to select specific items for an order via the interface after selecting one or more generic item descriptions via the interface.

公开(公告)号：US20250005644A1

公开(公告)日：2025-01-02

申请号：US18217324

申请日：2023-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chuanwei Ruan ,  Yunzhi Ye ,  Han Li ,  David Vengerov ,  Allan Stewart ,  Aref Kashani Nejad

IPC: G06Q30/0601

Abstract: An online system accesses a two-tower model trained to identify candidate items for presentation to users, in which the model includes an item tower trained to compute item embeddings and a user tower trained to compute user embeddings. The user tower includes a long-term sub-tower trained to compute long-term embeddings for users and a short-term sub-tower trained to compute short-term embeddings for users. The model is trained based on item data associated with items, user data associated with users, and session data associated with user sessions. The system uses the item tower to compute an item embedding for each of multiple candidate items. The system also uses the long-term sub-tower to compute a long-term embedding for a user. The system then receives session data associated with a current session of the user and uses the short-term sub-tower to compute a short-term embedding for the user based on this session data.

公开(公告)号：US20250022036A1

公开(公告)日：2025-01-16

申请号：US18772774

申请日：2024-07-15

Applicant: Maplebear Inc.

Inventor： Chuanwei Ruan ,  Allan Stewart ,  Li Tan ,  Yunzhi Ye ,  Aref Kashani Nejad

IPC: G06Q30/0601 ,  G06N3/0475 ,  G06N3/09

Abstract: An online system selects an item to present to a user of the online system. The online system accesses user interaction data for the user. The online system transmits the user interaction data to a model serving system and receives, from the model serving system, item embeddings for the items with which the user interacted. The model serving system may use an LLM to generate the item embeddings based on the user interaction data. The online system generates a user embedding array based on the item embeddings. The online system applies a transformer network to the user embedding array to generate a user embedding describing the user. To select an item to present to the user, the online system compares the generated user embedding to item embeddings for a set of candidate items. The online system selects a candidate item based on the interaction scores.

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.