公开(公告)号：US20250131482A1

公开(公告)日：2025-04-24

申请号：US18490683

申请日：2023-10-19

Applicant: Maplebear Inc.

Inventor： Chakshu Ahuja ,  Karuna Ahuja ,  Radhika Goel

IPC: G06Q30/0601 ,  G06F16/2457

Abstract: An online concierge system receives a free-text query describing items and constraints from a client device associated with a user. The system generates a prompt including the query and a request to identify the items and constraints. The system provides the prompt to a large language model, extracts, from an output of the model, the constraints and one or more categories associated with the items, and identifies retailers based on user data associated with the user. For each retailer, the system identifies a set of items associated with each category, determines, based on the constraints, a combination of a subset of items associated with each category, and computes a score for the combination based on the user data and item data associated with items in the combination. The system ranks the combinations based on the scores and sends information describing a ranked set of the combinations to the client device.

公开(公告)号：US20250069723A1

公开(公告)日：2025-02-27

申请号：US18455498

申请日：2023-08-24

Applicant: Maplebear Inc.

Inventor： Bhavya Gulati ,  Chakshu Ahuja ,  Girija Narlikar ,  Karuna Ahuja ,  Radhika Goel

IPC: G16H20/60 ,  G16H50/20

Abstract: The online concierge system accesses item data for a target item and item data for a candidate item. The online concierge system generates a replacement score based on the accessed item data and generates a nutrition score based on the item data for the candidate item. The online concierge system generates a nutrition replacement score based on the replacement score and the nutrition score and stores a training example based on the item data and the nutrition replacement score. The training example may include the item data for the target item and the candidate item and a label based on the nutrition replacement score.

公开(公告)号：US20240144172A1

公开(公告)日：2024-05-02

申请号：US17977724

申请日：2022-10-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Apurvaa Subramanian ,  Girija Narlikar ,  Chakshu Ahuja ,  Karuna Ahuja ,  Radhika Goel ,  Sneha Chandrababu

IPC: G06Q10/08 ,  G06Q30/02 ,  G06Q30/06

CPC classification number: G06Q10/087 ,  G06Q10/083 ,  G06Q30/0206 ,  G06Q30/0635

Abstract: An online concierge system facilitates a concierge service for ordering, procurement, and delivery of food items from physical retailers. The order fulfillment is based in part on automatically inferring one or more quality metrics, such as remaining shelf-life, associated with perishable food items. A picker shopping on behalf of a customer may capture images of available food items for the order using a picker client device. The images are processed through a machine learning model to infer the one or more quality metrics, and a price is then determined based in part on a dynamic pricing model. The online concierge system communicates with a customer client device to meet quality characteristics and pricing preferences set by the customer. The online concierge system may further facilitate a checkout process for the items obtained by the picker and may facilitate delivery of the items by the picker to the customer.

公开(公告)号：US20240112238A1

公开(公告)日：2024-04-04

申请号：US17956217

申请日：2022-09-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Girija Narlikar ,  Karuna Ahuja ,  Radhika Goel ,  Chakshu Ahuja ,  Xiaoming Zhang ,  Devlina Das

IPC: G06Q30/06 ,  G06Q30/02 ,  G06Q30/08 ,  G06Q50/00

CPC classification number: G06Q30/0631 ,  G06Q30/0203 ,  G06Q30/0603 ,  G06Q30/08 ,  G06Q50/01

Abstract: An online concierge system receives a request to purchase a gift for a user of the system and retrieves a profile associated with the user. Based on the profile and attributes of items included among inventories of one or more retailer locations, the system identifies a set of candidate items for which the user is likely to have an affinity. The system accesses a machine learning model trained to predict a giftability score for an item and applies the model to attributes of each candidate item to predict its giftability score. Based on its giftability score and the profile, the system computes a composite score for each candidate item indicating an appropriateness of gifting the candidate item to the user. The system ranks the set of candidate items based on the composite scores and selects one or more suggested items for gifting to the user based on the ranking.