公开(公告)号：US20250157089A1

公开(公告)日：2025-05-15

申请号：US18510560

申请日：2023-11-15

Applicant: Maplebear Inc.

Inventor： Levi Boxell ,  Tilman Drerup

IPC: G06T11/00 ,  G06F16/56 ,  G06F16/953

Abstract: A system generates item images using an item image generation model. The system receives a prompt for the model. The prompt is configured to request the model generate item images for an item. The system executes the model using the prompt to generate a set of item images. The system evaluates each of the set of item images to determine performance data of each of the set of item images. The system iteratively improves the set of item images by performing the following steps. The system updates the prompt based on the performance data of each of the set of item images to obtain a new prompt. The system executes, using the new prompt, the model to generate a new set of item images, and the system evaluates the new set of item images to determine performance data of each of the new set of item images.

公开(公告)号：US20250045673A1

公开(公告)日：2025-02-06

申请号：US18228669

申请日：2023-07-31

Applicant: Maplebear Inc.

Inventor： Levi Boxell ,  Rustin Partow ,  Tilman Drerup

IPC: G06Q10/0637 ,  G06Q10/0639

Abstract: An embedding model is trained to learn latent representations of users describing information related to conditional treatment effect for users relative to different potential treatments. The user embeddings may be used to determine the types of situations in which a user responds differently to different conditions or situations. To train this model, a plurality of experiments with users may be performed to determine user responses to different treatment conditions in the experiments. The conditional treatment effect for users in the experiments may be determined, e.g., with counterfactual predictions of a treatment not experienced by a user in the experiment. The embedding model may be trained with decoders that each predict the conditional treatment effect with respect to one of the experiments, enabling a loss for each experiment with respect to the conditional treatment effect to jointly train the embedding model.

公开(公告)号：US20240220805A1

公开(公告)日：2024-07-04

申请号：US18393338

申请日：2023-12-21

Applicant: Maplebear Inc.

Inventor： Jonathan Gu ,  Bo Xiao ,  Yixi Ouyang ,  Jennifer Wiersema ,  Sophia Li ,  Matias Cersosimo ,  Rustin Partow ,  Levi Boxell ,  Tilman Drerup ,  Oleksii Stepanian

IPC: G06N3/084

CPC classification number: G06N3/084

Abstract: A system accesses user data describing characteristics of a user and generates a content item score for each content item of a plurality of content items. The system generates the content item score by applying a machine-learning model to the user data, and then generates a plurality of content bundles. The system also generates a bundle score for each content bundle based on corresponding content item scores for the content item associated with each content bundle, randomly selects a bundle of the plurality of content bundles based on the generated bundle scores, and transmits the randomly selected bundle to a client device associated with the user for display to the user. Finally, the system applies the model to each of the generated training examples and updates the parameters of the model based on the model output.

公开(公告)号：US20230298080A1

公开(公告)日：2023-09-21

申请号：US18108916

申请日：2023-02-13

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Tilman Drerup ,  Nour Alkhatib ,  Jonathan Gu ,  Amin Akbari ,  Changyao Chen

IPC: G06Q30/0601 ,  G06N3/092

CPC classification number: G06Q30/0617 ,  G06N3/092

Abstract: An online system may receive, from a content provider, a content presentation campaign that includes one or more objectives. The online system may define a set of one or more policy functions that automatically controls the content presentation campaign. A policy function may control one or more criteria in bidding content slots. The online system may monitor a realized outcome of the content presentation campaign. The online system may apply a reinforcement learning algorithm in adjusting the set of policy functions. The reinforcement learning algorithm adjusts one or more parameters in the set of policy functions to reduce a difference between the realized outcome and the desired outcome set by the content provider. The online system generates an adjusted set of policy functions and uses the adjusted set of policy functions in bidding content slots to present one or more content items provided by the content provider.

公开(公告)号：US20230078450A1

公开(公告)日：2023-03-16

申请号：US17474408

申请日：2021-09-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chuanwei Ruan ,  Diego Goyret ,  Tilman Drerup ,  Rob Donnelly

IPC: G06Q30/06 ,  G06F3/0482 ,  G06Q30/02 ,  G06F16/9535 ,  G06F16/2457

Abstract: An online concierge system allows users to purchase items from warehouses and delivers the purchased items to the users. When displaying items offered by a warehouse, the online concierge system predicts an availability of the items at the warehouse using a trained model. When displaying items offered by the warehouse to a user, the online concierge system accounts for the predicted availabilities of different items. For example, the online concierge system determines scores for different items at the warehouse based on relevance to the user and adjusts a score for an item by its predicted availability. The online concierge system uses the adjusted scores for items when displaying items, demoting positions in an interface in which items with lower predicted availabilities are displayed. Additionally, the online concierge system may display a visual indication of a predicted availability of certain items, such as items with less than a threshold predicted availability.