公开(公告)号：US20240070746A1

公开(公告)日：2024-02-29

申请号：US17899483

申请日：2022-08-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Girija Narlikar ,  Sharath Rao Karikurve

IPC: G06Q30/06 ,  G06N20/00 ,  G06Q30/02

CPC classification number: G06Q30/0631 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q30/0633

Abstract: A method implemented at a computer system includes, responsive to identifying an opportunity to present content to a target user, accessing a machine learning model trained on a dataset containing input features of a plurality of users and labels indicating openness metrics of the respective plurality of users. The machine learning model is then applied to a set of features of the target user to output an openness metric that predicts a loss in the target user's response rate when contextual relevance is not considered in selection of recommendation for the target user. A recommendation is then selected from a plurality of candidate recommendations based on the openness metric and sent for display to the target user.

公开(公告)号：US20240185324A1

公开(公告)日：2024-06-06

申请号：US18442466

申请日：2024-02-15

Applicant: Maplebear Inc.

Inventor： Ramasubramanian Balasubramanian ,  Girija Narlikar ,  Omar Alonso

IPC: G06Q30/0601 ,  G06N3/08

CPC classification number: G06Q30/0631 ,  G06N3/08

Abstract: An online concierge system generates recipe embeddings for recipes including multiple items and user embeddings for users, with the recipe embeddings and user embeddings in a common latent space. To generate the user embeddings and the recipe embeddings, a model includes separate layers for a user model outputting user embeddings and for a recipe model outputting recipe embeddings. When training the model, a weight matrix generates a predicted dietary preference type for a user embedding and for a recipe embedding and adjusts the user model or the recipe model based on differences between the predicted dietary preference type and a dietary preference type applied to the user embedding and to the recipe embedding. Additionally cross-modal layers generate a predicted user embedding from a recipe embedding and generate a predicted recipe embedding from a user embedding that are used to further refine the user model and the recipe model.

公开(公告)号：US20230147670A1

公开(公告)日：2023-05-11

申请号：US17524469

申请日：2021-11-11

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Girija Narlikar

IPC: G06Q30/06

CPC classification number: G06Q30/0631 ,  G06Q30/0641

Abstract: An online concierge system modifies generic item descriptions included in a recipe displayed to a user based on the user's preferences. The online concierge system generates a replacement graph identifying a replacement generic item description for a generic item description, one or more preferences causing replacement of the generic item description with the replacement generic item description, and a replacement quantity of the replacement generic item description. To customize a recipe for the user, the online concierge system selects replacement generic item descriptions for one or more generic item descriptions in the recipe satisfying one or more stored preferences for the user based on the replacement graph.

公开(公告)号：US20240362678A1

公开(公告)日：2024-10-31

申请号：US18141396

申请日：2023-04-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chakshu Ahuja ,  Girija Narlikar ,  Karuna Ahuja

IPC: G06Q30/0251 ,  G06N20/00

CPC classification number: G06Q30/0261 ,  G06N20/00

Abstract: For each retailer location associated with multiple retailers, an online system associated with the retailers receives video data captured within the retailer location by a camera of a client device associated with an online system user. The online system detects, based at least in part on the video data, a location associated with the user within the retailer location and/or an interaction by the user with an item included among an inventory of the retailer location. The online system generates a set of signals associated with the user based at least in part on the detection of the location and/or the interaction. Based at least in part on the set of signals, the online system determines a set of preferences associated with the user, trains a machine learning model to predict a metric associated with the user, and/or sends content for display to a client device associated with the user.

公开(公告)号：US20240420210A1

公开(公告)日：2024-12-19

申请号：US18211107

申请日：2023-06-16

Applicant: Maplebear Inc. (dba Instacart)

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

IPC: G06Q30/0601

Abstract: An online concierge system receives information describing items in orders placed by a customer and a sequence of events associated with each order and identifies an impulse item included in the orders based on a set of rules, attributes of each item, and/or the sequence of events. The system applies a model to predict a measure of similarity between the impulse item and each of multiple candidate items and identifies larger-size variants of the impulse item based on this prediction and attributes of the impulse item and each candidate item. The system applies another model to predict a likelihood the customer will order each variant, computes a recommendation score for each variant based on this prediction, and determines whether to recommend each variant based on the score. Based on the determination, the system generates and sends a recommendation for a variant to a client device associated with the customer.

公开(公告)号：US20240338746A1

公开(公告)日：2024-10-10

申请号：US18627280

申请日：2024-04-04

Applicant: Maplebear Inc.

Inventor： Karuna Ahuja ,  Girija Narlikar ,  Chakshu Ahuja ,  Apurvaa Subramaniam

IPC: G06Q30/0601

CPC classification number: G06Q30/0631

Abstract: An online system employs real-time and pre-generated images for recommendation. The system leverages generative machine-learning models, such as diffusion models, to generate images dynamically. The selection and creation of these images rely upon user data and session data, which are collected during a user's application session. These data are employed to generate a text prompt string, which directs the image generation process. For instances where real-time computation may be a resource constraint, the system utilizes pre-generated images linked to user-context clusters—data set groupings related to user characteristics and session context. This method enables the system to present tailored recommendations to the user, making use of both dynamic generation and pre-existing image resources, thereby optimizing the balance between customization, computational resources, and latency.

公开(公告)号：US20240144173A1

公开(公告)日：2024-05-02

申请号：US17977734

申请日：2022-10-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Karuna Ahuja ,  Girija Narlikar ,  Sneha Chandrababu ,  Gowri Rajeev ,  Lan Wang ,  Chakshu Ahuja ,  Sonal Jain

IPC: G06Q10/08 ,  G06K7/10 ,  G06K7/14 ,  G06Q30/02 ,  G06Q30/06

CPC classification number: G06Q10/087 ,  G06K7/10366 ,  G06K7/1417 ,  G06Q30/0202 ,  G06Q30/0623

Abstract: An online concierge system detects acquired items included among an inventory of a customer and identifies one or more candidate available items from the acquired items based on a predicted perishability of each item and a predicted amount of each item that was used. The system retrieves recipes, matches the item(s) likely to be available to a set of recipes based on their ingredients, and identifies any remaining items for each matched recipe not likely to be available. The system retrieves a set of attributes associated with the customer and the set of recipes and computes a suggestion score for each recipe based on the attributes. The system ranks the recipes based on their scores, identifies one or more recipes for suggesting to the customer based on the ranking, and sends the recipe(s) and any remaining items for each recipe to a client device associated with the customer.

公开(公告)号：US12243008B2

公开(公告)日：2025-03-04

申请号：US17977734

申请日：2022-10-31

Applicant: Maplebear Inc.

Inventor： Karuna Ahuja ,  Girija Narlikar ,  Sneha Chandrababu ,  Gowri Rajeev ,  Lan Wang ,  Chakshu Ahuja ,  Sonal Jain

IPC: G06Q10/00 ,  G06K7/10 ,  G06K7/14 ,  G06Q10/087 ,  G06Q30/0202 ,  G06Q30/0601

Abstract: An online concierge system detects acquired items included among an inventory of a customer and identifies one or more candidate available items from the acquired items based on a predicted perishability of each item and a predicted amount of each item that was used. The system retrieves recipes, matches the item(s) likely to be available to a set of recipes based on their ingredients, and identifies any remaining items for each matched recipe not likely to be available. The system retrieves a set of attributes associated with the customer and the set of recipes and computes a suggestion score for each recipe based on the attributes. The system ranks the recipes based on their scores, identifies one or more recipes for suggesting to the customer based on the ranking, and sends the recipe(s) and any remaining items for each recipe to a client device associated with the customer.

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：US12033205B2

公开(公告)日：2024-07-09

申请号：US17524469

申请日：2021-11-11

Applicant: Maplebear Inc.

Inventor： Girija Narlikar

IPC: G06Q30/0601

CPC classification number: G06Q30/0631 ,  G06Q30/0641

Abstract: An online concierge system modifies generic item descriptions included in a recipe displayed to a user based on the user's preferences. The online concierge system generates a replacement graph identifying a replacement generic item description for a generic item description, one or more preferences causing replacement of the generic item description with the replacement generic item description, and a replacement quantity of the replacement generic item description. To customize a recipe for the user, the online concierge system selects replacement generic item descriptions for one or more generic item descriptions in the recipe satisfying one or more stored preferences for the user based on the replacement graph.