公开(公告)号：US20240303711A1

公开(公告)日：2024-09-12

申请号：US18596592

申请日：2024-03-05

Applicant: Maplebear Inc.

Inventor： Li Tan ,  Tejaswi Tenneti ,  Shishir Kumar Prasad ,  Huapu Pan ,  Allan Stewart ,  Taesik Na ,  Tyler Russell Tate ,  Joshua Roberts ,  Haixun Wang

IPC: G06Q30/0601 ,  G06F16/9532

CPC classification number: G06Q30/0627 ,  G06F16/9532 ,  G06Q30/0635

Abstract: A system, for example, an online system uses a machine learning based language model, for example, a large language model (LLM) to process high-level natural language queries received from users. The system receives a natural language query from a user of a client device. The system determines contextual information associated with the query. Based on this information, the system generates a prompt for the machine learning based language model. The system receives a response from the machine learning based language model. The system uses the response to generate a search query for a database. The system obtains results returned by the database in response to the search query and provides them to the user. The system allows users to specify high level natural language queries to obtain relevant search results, thereby improving the overall user experience.

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：US12033172B2

公开(公告)日：2024-07-09

申请号：US17572450

申请日：2022-01-10

Applicant: Maplebear Inc.

Inventor： Ze He ,  Asif Haque ,  Allan Stewart ,  Haixun Wang ,  Xinyu Li

IPC: G06Q30/0204 ,  G06N3/049 ,  G06N3/084 ,  G06Q30/0282 ,  G06Q30/0601

CPC classification number: G06Q30/0205 ,  G06N3/049 ,  G06N3/084 ,  G06Q30/0282 ,  G06Q30/0635 ,  G06Q30/0639 ,  G06Q30/0641

Abstract: An online concierge system allows users to order items from a warehouse, which may have multiple warehouse locations. The online concierge system provides a user interface to users for ordering the items, with the user interface providing an indication of whether an item is predicted to be available at the warehouse at different times. To predict availability of an item model at different times, the online concierge system selects data from historical information about availability of items at one or more warehouses based on temporal, geospatial, and socioeconomic information about observations of historical availability of items at warehouses. The online concierge system accounts for distances between observations and a time and geographic location in a feature space to select observations for predicting item availability at the time and the geographic location.

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：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：US20240354793A1

公开(公告)日：2024-10-24

申请号：US18761756

申请日：2024-07-02

Applicant: Maplebear Inc.

Inventor： Ze He ,  Asif Haque ,  Allan Stewart ,  Haixun Wang ,  Xinyu Li

IPC: G06Q30/0204 ,  G06N3/049 ,  G06N3/084 ,  G06Q30/0282 ,  G06Q30/0601

CPC classification number: G06Q30/0205 ,  G06N3/049 ,  G06N3/084 ,  G06Q30/0282 ,  G06Q30/0635 ,  G06Q30/0639 ,  G06Q30/0641

Abstract: An online concierge system allows users to order items from a warehouse, which may have multiple warehouse locations. The online concierge system provides a user interface to users for ordering the items, with the user interface providing an indication of whether an item is predicted to be available at the warehouse at different times. To predict availability of an item model at different times, the online concierge system selects data from historical information about availability of items at one or more warehouses based on temporal, geospatial, and socioeconomic information about observations of historical availability of items at warehouses. The online concierge system accounts for distances between observations and a time and geographic location in a feature space to select observations for predicting item availability at the time and the geographic location.

公开(公告)号：US20230222529A1

公开(公告)日：2023-07-13

申请号：US17572450

申请日：2022-01-10

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ze He ,  Asif Haque ,  Allan Stewart ,  Haixun Wang ,  Xinyu Li

IPC: G06Q30/02 ,  G06Q30/06 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06Q30/0205 ,  G06Q30/0282 ,  G06Q30/0639 ,  G06Q30/0635 ,  G06Q30/0641 ,  G06N3/049 ,  G06N3/084

Abstract: An online concierge system allows users to order items from a warehouse, which may have multiple warehouse locations. The online concierge system provides a user interface to users for ordering the items, with the user interface providing an indication of whether an item is predicted to be available at the warehouse at different times. To predict availability of an item model at different times, the online concierge system selects data from historical information about availability of items at one or more warehouses based on temporal, geospatial, and socioeconomic information about observations of historical availability of items at warehouses. The online concierge system accounts for distances between observations and a time and geographic location in a feature space to select observations for predicting item availability at the time and the geographic location.