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：US20240289873A1

公开(公告)日：2024-08-29

申请号：US18113562

申请日：2023-02-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chakshu Ahuja ,  Ramasubramanian Balasubramanian ,  Karuna Ahuja

IPC: G06Q30/08 ,  G06N20/00 ,  G06Q30/0601

CPC classification number: G06Q30/08 ,  G06N20/00 ,  G06Q30/0613

Abstract: An online system manages campaign participation by a plurality of sub-campaigns with a reinforcement learning model. The reinforcement learning model determines a current context and determines an action that affects the participation of the individual sub-campaigns. The reinforcement learning model may thus dynamically control the participation over time as different objectives are achieved by the sub-campaigns and may account for the different contexts that change over time.

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：US20240070210A1

公开(公告)日：2024-02-29

申请号：US17899441

申请日：2022-08-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ramasubramanian Balasubramanian ,  Taesik Na ,  Karuna Ahuja

IPC: G06F16/9532 ,  G06Q30/06

CPC classification number: G06F16/9532 ,  G06Q30/0631

Abstract: A computer-implemented method for suggesting keywords as a search term of a content item includes receiving, from a content provider, information about the content item in a database of content items. The method further includes generating a set of seed keywords related to the content item, and expanding the set of seed keywords to a plurality of candidate keywords. The plurality of candidate keywords are then scored based, at least in part, on an engagement metric measuring a user engagement with the content item in response to being presented with results from a search query comprising the candidate keyword. A candidate keyword is then selected from the plurality of candidate keywords based on the scoring, and stored relationally to the content item to define an audience for a recommendation about the content item, providing a suggestion to the content provider.