公开(公告)号：US12266005B2

公开(公告)日：2025-04-01

申请号：US18072316

申请日：2022-11-30

Applicant: Maplebear Inc.

Inventor： Ramasubramanian Balasubramanian ,  Lynn Fink ,  Alexandra Hart ,  Sanam Alavizadeh ,  Lauren Scully ,  Samuel Lederer ,  Anna Vitti ,  Lukasz Czekaj ,  Joseph Olivier ,  Michael Prescott ,  Jeong Eun Woo ,  Nicole Yin Chuen Lee Altman

IPC: G06Q30/06 ,  G06Q30/0601 ,  G06Q30/08

Abstract: An online concierge system suggests replacement items when an ordered item may be unavailable. To promote similarity of sources between the replacement item with the ordered item, candidate replacement items are scored, in part, based on a source similarity score based on a source of the candidate replacement item and a source of the ordered item. The source similarity score may be determined by a computer model based on user interactions with item sources. The similarity score may be based on source embeddings that may be determined based on respective item embeddings or may be determined by training source embeddings directly from user-source interactions. The similarity score for a candidate replacement item may be combined with a replacement score indicating the user's likelihood of selecting the candidate replacement item as a replacement to yield a total score for selection as suggestion as a replacement for the ordered item.

公开(公告)号：US20250156925A1

公开(公告)日：2025-05-15

申请号：US18509882

申请日：2023-11-15

Applicant: Maplebear Inc.

Inventor： Brent Luna ,  Mridul Singhai ,  Nathan Marks ,  Lukasz Czekaj ,  Joseph Olivier

IPC: G06Q30/0601

Abstract: A trained computer model to identify a list of representative previously purchased items for recommendation to a user of an online system. The online system clusters, based on a similarity score for each pair of items, a set of previously purchased items into multiple clusters. The online system accesses a computer model trained to predict a likelihood of engagement by the user for each item in each cluster, and applies the computer model to predict, based on one or more features of each item, the likelihood of engagement for each item in each cluster. The online system generates, based on the likelihood of engagement, a score for each item in each cluster. The online system selects, based on the score for each item, a representative item from each cluster. The online system causes a device associated with the user to display the representative item from each cluster.

公开(公告)号：US20240378654A1

公开(公告)日：2024-11-14

申请号：US18660901

申请日：2024-05-10

Applicant: Maplebear Inc.

Inventor： Joseph Olivier ,  Lukasz Czekaj ,  Domenico Matteo ,  Brent Luna ,  Mark Ding ,  Mathieu Hartvick

IPC: G06Q30/0601 ,  G06Q30/0282

Abstract: An online system determines whether to recommend a replacement item to a user based on a predicted sentiment score. The online system receives one or more comments from user feedback on the replacement items. The online system generates a prompt for each user comment for input to a machine-learned model. The online system generates a sentiment score for the ordered item and a replacement item based on the inferred sentiments by the model serving system. Using the sentiment score, the online system determines whether to recommend the replacement item.

公开(公告)号：US20250086685A1

公开(公告)日：2025-03-13

申请号：US18243600

申请日：2023-09-07

Applicant: Maplebear Inc.

Inventor： Shubhanshu Mishra ,  Gia Young ,  Jennie Morgan Burger ,  Joseph Olivier ,  Brent Luna ,  Yuanzheng Zhu ,  Mackenzie Cala ,  Armand Raquel-Santos ,  David Zandman ,  Mia Martinez Barnett ,  Callie Bleckner ,  Mohammad Abdul-Rahim

IPC: G06Q30/0601 ,  G06V20/50

Abstract: An online concierge system assists users in identifying additional information about items in an image. Image regions are identified in the image that may correspond to unknown items and an item search space is determined for detecting items in the image regions based on a context of the image, such as items in a warehouse or a list of items delivered to a customer. The identified items are used to retrieve relevant item information that is included in a prompt for a language model to extract relevant information for the item. As such, the process may automatically process the image into relevant textual information about the pictured items. Applications may be used to assist vision-impaired users in distinguishing delivered items or quickly identifying and evaluating relevant information about items.

公开(公告)号：US20240403907A1

公开(公告)日：2024-12-05

申请号：US18204074

申请日：2023-05-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Lukasz Czekaj ,  Joseph Olivier ,  Mark Ding ,  Mathieu Hartvick ,  Kenny Hwu ,  Yiheng Wang ,  Domenico Matteo

IPC: G06Q30/0207 ,  G06Q30/0601

Abstract: Embodiments relate to automatic determination of a directed spend program eligibility for items offered by retailers associated with an online system. The online system provides inputs into a computer model, where the inputs include information about at least one property for each candidate item in a set of candidate items and at least one requirement for a directed spend program. The online system applies the computer model to generate, based on the inputs, an output that comprises an indication of an eligibility for each candidate item in the set for the at least one directed spend program. The online system sends a message causing a device of a user of the online system to display a user interface including an option for the user to add into a cart at least one of the candidate items determined to be eligible for the directed spend program.

公开(公告)号：US20240177211A1

公开(公告)日：2024-05-30

申请号：US18072316

申请日：2022-11-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ramasubramanian Balasubramanian ,  Lynn Fink ,  Alexandra Hart ,  Sanam Alavizadeh ,  Lauren Scully ,  Samuel Lederer ,  Anna Vitti ,  Lukasz Czekaj ,  Joseph Olivier ,  Michael Prescott ,  Jeong Eun Woo ,  Nicole Yin Chuen Lee Altman

IPC: G06Q30/0601 ,  G06Q30/08

CPC classification number: G06Q30/0631 ,  G06Q30/0629 ,  G06Q30/08

Abstract: An online concierge system suggests replacement items when an ordered item may be unavailable. To promote similarity of sources between the replacement item with the ordered item, candidate replacement items are scored, in part, based on a source similarity score based on a source of the candidate replacement item and a source of the ordered item. The source similarity score may be determined by a computer model based on user interactions with item sources. The similarity score may be based on source embeddings that may be determined based on respective item embeddings or may be determined by training source embeddings directly from user-source interactions. The similarity score for a candidate replacement item may be combined with a replacement score indicating the user's likelihood of selecting the candidate replacement item as a replacement to yield a total score for selection as suggestion as a replacement for the ordered item.