公开(公告)号：US12175525B2

公开(公告)日：2024-12-24

申请号：US17493150

申请日：2021-10-04

Applicant: Maplebear Inc.

Inventor： Jeffrey Bernard Arnold ,  Rob Donnelly ,  Sumit Garg ,  Jonathan Gu ,  Bill Lundberg ,  David Pal ,  Sharath Rao Karikurve ,  Peng Qi

IPC: G06Q30/0601 ,  G06F9/451 ,  G06Q30/02

Abstract: An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

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：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：US20250095055A1

公开(公告)日：2025-03-20

申请号：US18965960

申请日：2024-12-02

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Jeffrey Bernard Arnold ,  Rob Donnelly ,  Sumit Garg ,  Jonathan Gu ,  Bill Lundberg ,  David Pal ,  Sharath Rao Karikurve ,  Peng Qi

IPC: G06Q30/0601 ,  G06F9/451 ,  G06Q30/02

Abstract: An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

公开(公告)号：US20230368236A1

公开(公告)日：2023-11-16

申请号：US17744526

申请日：2022-05-13

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Tilman Drerup ,  Anne Moxie ,  Sophia Li ,  Vibin Kundukulam ,  Jonathan Gu ,  Ashley Denney

IPC: G06Q30/02 ,  G06Q30/06

CPC classification number: G06Q30/0211 ,  G06Q30/0239 ,  G06Q30/0617

Abstract: An online concierge system uses a new treatment engine to score users for applying treatments of a new treatment type. The new treatment engine uses treatment models to generate treatment lift scores for the user. The new treatment engine applies an aggregation function model to the treatment lift scores to generate an aggregated lift score for the user. If the aggregated lift score exceeds a threshold, the new treatment engine applies a treatment of the new treatment type to the user. The new treatment engine trains the aggregation function model based on training examples used to train the treatment models. For a training example associated with a particular treatment type, the new treatment engine uses a target lift score generated by the treatment model for the treatment type to evaluate the performance of the aggregation function model, and to update the aggregation function model accordingly.