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.

公开(公告)号：US20240193663A1

公开(公告)日：2024-06-13

申请号：US18064129

申请日：2022-12-09

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Mark Oberemk

IPC: G06Q30/0601

CPC classification number: G06Q30/0631 ,  G06F40/279

Abstract: A system or a method for using machine learning to automatically route user inquiries to a retailer are presented. The system receives an inquiry from a client device associated with a user. The inquiry includes text content and an image. The system uses a natural language model to analyze the received text to identify a first category of items. The system applies the received image to an image recognition model to identify a second category of items contained in the received image. The system then identifies a retailer that carries items in at least one of the first or second category of items, and suggests the retailer to the user via the client device associated with the user. A retail associate at the retailer can then respond to the inquiry via a client device associated with the retailer.