公开(公告)号：US20240320523A1

公开(公告)日：2024-09-26

申请号：US18605580

申请日：2024-03-14

Applicant: Maplebear Inc.

Inventor： Ryan McColeman ,  Ryan Martin ,  Brent Scheibelhut ,  Shaun Navin Maharaj ,  Mark Oberemk

IPC: G06N5/022 ,  G06N5/04

CPC classification number: G06N5/022 ,  G06N5/04

Abstract: An online system performs an inference task in conjunction with the model serving system or the interface system to continuously monitor conversations between requesting users and fulfillment users to determine whether the online system can intervene to automatically respond to a message sent by a sending party, rather than prompting the receiving party for a manual reply. Upon inferring that a message can be automatically responded to, the online system automatically provides a response to the message without the receiving party's manual involvement. The online system can further be augmented to classify and reroute certain requesting user or fulfillment user queries that impact an order's end state by intercepting the conversation on behalf of either party and performing one or more automated actions. If the message is action-oriented, the online system may perform one or more automated actions in response to the message.

公开(公告)号：US20250140403A1

公开(公告)日：2025-05-01

申请号：US18495651

申请日：2023-10-26

Applicant: Maplebear Inc.

Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Mark Oberemk ,  Ryan McColeman ,  Ryan Martin

IPC: G16H50/20 ,  G06Q30/0601 ,  G06Q40/08 ,  G16H10/20

Abstract: A trained computer model for generating an aggregated health score for a business user of an online system. The online system obtains a set of health scores for a set of individual employees of a business user of an online system. The online system accesses a computer model of the online system trained to determine an aggregated health score for the business user. The online system applies the computer model to generate, based at least in part on the set of health scores and content of a set of orders placed by the business user, the aggregated health score for the business user. The online system causes a device of the business user to display a user interface with the aggregated health score.

公开(公告)号：US20240428314A1

公开(公告)日：2024-12-26

申请号：US18212122

申请日：2023-06-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ryan McColeman ,  Brent Scheibelhut ,  Mark Oberemk ,  Shaun Navin Maharaj

IPC: G06Q30/0601 ,  G06Q30/0201

Abstract: The present disclosure is directed to determining purchase suggestions for an online shopping concierge platform. In particular, the methods and systems of the present disclosure may receive, from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform; determine, based at least in part on one or more machine learning (ML) models and the data indicating the interaction(s), a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item; and generate and communicate data describing a graphical user interface (GUI) comprising at least a portion of a listing of one or more purchase suggestions including the suggestion to purchase the particular item.

公开(公告)号：US20240403923A1

公开(公告)日：2024-12-05

申请号：US18677640

申请日：2024-05-29

Applicant: Maplebear Inc.

Inventor： Bryan Pham ,  Shaun Navin Maharaj ,  Brent Scheibelhut ,  Mark Oberemk ,  Fabien Mouvet

IPC: G06Q30/0241

Abstract: An online system generates digital flyers using a generative model. The online system receives, from a client device, a request to generate a digital flyer. The request includes one or more design conditions for the digital flyer. For example, the design conditions may specify one or more cornerstone items, a theme, a template flyer, other target characteristics, etc. The online system further accesses an item catalog storing item data. The online system generates a query for a generative model including a prompt to generate the digital flyer, the one or more design conditions, and item data accessed from the item catalog. The online system provides the query to a model serving system, which executes the generative model with the query to return a batch of one or more digital flyers. The online system provides a first digital flyer in the batch of one or more digital flyers for presentation.

公开(公告)号：US20240346441A1

公开(公告)日：2024-10-17

申请号：US18632540

申请日：2024-04-11

Applicant: Maplebear Inc.

Inventor： Ryan McColeman ,  Ryan Martin ,  Brent Scheibelhut ,  Shaun Navin Maharaj ,  Mark Oberemk

IPC: G06Q10/087 ,  G06N5/04

CPC classification number: G06Q10/087 ,  G06N5/04

Abstract: An online system performs an inference task in conjunction with the model serving system or the interface system to continuously monitor conversations between users and shoppers to determine whether a message sent by a sending party can be automatically responded to rather than prompting the receiving party for a manual response. The online system automatically provides a response to the message without the receiving party's manual involvement. In one or more embodiments, the online system can infer whether a question can be intercepted and/or suggests one or more available answers the sender can consider as feedback without a manual response from the receiver.

公开(公告)号：US20240289857A1

公开(公告)日：2024-08-29

申请号：US18113874

申请日：2023-02-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Mark Oberemk

IPC: G06Q30/0601

CPC classification number: G06Q30/0623 ,  G06Q30/0603

Abstract: An online concierge system delivers items from multiple retailers to customers. To avoid delivery of expired or near-expired items, the online concierge system obtains attributes of items offered by a retailer, such as from images of items at the retailer from client devices and uses a trained desirability model to predict a desirability score of an item based on the item's attributes. The desirability model is trained using training examples with labels indicating whether an item was suitable for inclusion in an order. The desirability model may be used to determine if an item is suitable for inclusion in an order, to provide suggestions for a retailer for using the item, or to select a retailer for fulfilling an order.

公开(公告)号：US20250139686A1

公开(公告)日：2025-05-01

申请号：US18496679

申请日：2023-10-27

Applicant: Maplebear Inc.

Inventor： Brent Scheibelhut ,  Shaun Navin Maharaj ,  Mark Oberemk ,  Ryan Martin ,  Ryan McColeman

IPC: G06Q30/0601

Abstract: Different possible candidate routes for efficiently obtaining a set of items at given retailer premises are generated and simulated to estimate degrees of difficulty of the various routes, such as how long they are expected to take. The current conditions can be inferred based on analysis of environment data received from a plurality of devices associated with users shopping for items on the retailer premises, such as location data, camera data, or comments related to the retailer premises. The simulation takes into account current or expected conditions in the environment of the retailer premises, such as obstructions, alternative placements of items, etc. Routes with least degrees of difficulty may be presented to the users shopping for the items so that the users can use the most efficient routes when obtaining the items.

公开(公告)号：US20250111329A1

公开(公告)日：2025-04-03

申请号：US18375109

申请日：2023-09-29

Applicant: Maplebear Inc.

Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Mark Oberemk ,  Ryan McColeman ,  Ryan Martin

IPC: G06Q10/087 ,  G06Q10/0631 ,  G06Q30/0601

Abstract: An online concierge system uses images captured for fulfillment of a first order to affect item information of a second order. When a picker fulfills the first order in a physical warehouse, the picker captures an image of the physical warehouse, for example to capture an image of potential replacement items. The online concierge system detects items in the image along with a location of the item in the physical warehouse based on the image. The detected items and respective locations may then be used to modify a second order, for example to route a picker for the second order to updated or alternate locations of the detected items.

公开(公告)号：US20250078980A1

公开(公告)日：2025-03-06

申请号：US18457298

申请日：2023-08-28

Applicant: Maplebear Inc.

Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Mark Oberemk

IPC: G16H20/60 ,  G16H50/20

Abstract: An online system uses a meal plan scoring model to generate candidate replacement meal plans for a user in response to a triggering event. In response to identifying a triggering event, the online system generates a set of candidate meal plans. Each of the candidate replacement meal plans comply with nutritional constraints established by the user. The online system scores each of the candidate meal plans using a meal plan scoring model. A meal plan scoring model is a machine-learning model that is trained to predict a likelihood that a user will select a candidate replacement meal plan. The online system selects a subset of the candidate replacement meal plans and transmits the selected candidate replacement meal plans to a client device associated with the user. The user can select one of the candidate replacement meal plans to replace their initial meal plan with their selected meal plan.

公开(公告)号：US20240428125A1

公开(公告)日：2024-12-26

申请号：US18339203

申请日：2023-06-21

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Amalia Rothschild-Keita ,  Brent Scheibelhut ,  Mark Oberemk ,  Hua Xiao ,  Shaun Navin Maharaj ,  Taha Amjad

IPC: G06N20/00

Abstract: An online concierge system uses a findability machine-learning model to predict the findability of items within a physical area. The findability model is a machine-learning model that is trained to compute findability scores, which are scores that represent the ease or difficulty of finding items within a physical area. The findability model computes findability scores for items based on an item map describing the locations of items within a physical area. The findability model is trained based on data describing pickers that collect items to service orders for the online concierge system. The online concierge system aggregates this information across a set of pickers to generate training examples to train the findability model. These training examples include item data for an item, an item map data describing an item map for the physical area, and a label that indicates a findability score for that item/item map pair.