公开(公告)号：US20250086189A1

公开(公告)日：2025-03-13

申请号：US18367185

申请日：2023-09-12

Applicant: Maplebear Inc.

Inventor： Levi Boxell ,  Esther Vasiete Allas ,  Tejaswi Tenneti ,  Tilman Drerup ,  Yueyang Rao

IPC: G06F16/2457 ,  G06F16/248

Abstract: A computer system allowing users to search for items of interest provides a search query interface. The system receives characters of a search query in the search interface as the user enters the characters and interactively calculates, ranks, and displays a set of possible search query options from which the user can select. To rank the set of possible search query options, the system modifies rankings of candidate search queries based on factors associated with third parties. More specifically, contextual relevance scores are computed for the candidate search queries based on the context, such as a user to whom the search results are provided. These contextual relevance scores are in turn adjusted using factors associated with third parties, such as values calculated based on consideration offered by third parties. Users are shown the search query options, ranked in order of the adjusted relevance scores, as possible query selections.

公开(公告)号：US20240220859A1

公开(公告)日：2024-07-04

申请号：US18393349

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: G06N20/00 ,  G06Q30/01

CPC classification number: G06N20/00 ,  G06Q30/01

Abstract: An online system uses an offline iterative clustering process to evaluate the performance of a set of content selection frameworks. To perform an iteration of the iterative clustering process, an online system clusters the testing example data into a set of clusters. An online system computes a set of framework scores for each of the generated clusters. An online system computes an improvement score for each cluster based on the performance scores of the clusters. To determine whether to perform another iteration, an online system computes an aggregated improvement score based on the improvement scores of the clusters. If an online system determines that the aggregated improvement score does not meet the threshold, an online system performs another iteration of the process above. When an online system finishes the iterative process, an online system outputs the improvement scores of the most-recent iteration.

公开(公告)号：US20250077529A1

公开(公告)日：2025-03-06

申请号：US18241093

申请日：2023-08-31

Applicant: Maplebear Inc.

Inventor： Levi Boxell ,  Vinesh Reddy Gudla ,  Michael Kurish ,  Raochuan Fan ,  Tilman Drerup ,  Tejaswi Tenneti

IPC: G06F16/2457 ,  G06F16/248 ,  G06N20/00

Abstract: An online system displays items to a user in search results based on appeasement scores for the items, adjusted according to how specific the search query is. The online system receives a search query from a user of an online system. The online system computes a query specificity score, a measure of the specificity of the search query. The online system accesses candidate items from a database that potentially match the search query. For each candidate item, the online system may compute or predict an appeasement score. The online system adjusts the appeasement score based on the query specificity score such that a more specific query weights the appeasement score lower than a less specific query. The online system may then compute a ranking score based on the adjusted appeasement score and display the candidate items to the user based on their ranking scores.

公开(公告)号：US20250068988A1

公开(公告)日：2025-02-27

申请号：US18238330

申请日：2023-08-25

Applicant: Maplebear Inc.

Inventor： Levi Boxell ,  Rustin Partow

IPC: G06Q10/04

Abstract: A computing system automatically selects treatments for users by generating a propensity vector for a set of treatments and selecting a treatment based on the propensity vector. The propensity vector is determined based on one or more computer models that predict user actions responsive to the treatments and the propensity vector is determined based on the value of a treatment parameter. The treatment parameter is perturbed to determine an adjusted propensity vector. Treatments are applied and outcomes determined with the propensities determined by the current value of the treatment parameter, and counterfactuals for the adjusted treatment vector are determined to evaluate the effect of modifying the treatment parameter. When the perturbed treatment parameter value yields improved results in the counterfactual, the current value is modified to improve performance of the model as a whole without requiring retraining of underlying predictive models.

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.