公开(公告)号：US20220358562A1

公开(公告)日：2022-11-10

申请号：US17682444

申请日：2022-02-28

Applicant: Maplebear Inc.(dba Instacart)

Inventor： Manmeet Singh ,  Tyler Russell Tate ,  Tejaswi Tenneti ,  Sharath Rao Karikurve

IPC: G06Q30/06

Abstract: An online concierge shopping system identifies recipes to users to encourage them to include items from the recipes in orders. The online concierge system maintains user embeddings for users and recipe embeddings for recipes. For users who have not placed orders, recipes are recommended based on global user interactions with recipes. Users who have previously ordered items from recipes are suggested recipes selected based on a similarity of their user embedding to recipe embeddings. Users who have purchased items but not from recipes are compared to a set of similar users based on the user embeddings, and recipes with which users of the set of similar users interacted are used for identifying recipes to the users. A recipe graph may be maintained by the online concierge system to identify similarities between recipes for expanding candidate recipes to suggest to users.

公开(公告)号：US20220343272A1

公开(公告)日：2022-10-27

申请号：US17240977

申请日：2021-04-26

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Dylan Wang

IPC: G06Q10/08 ,  G06F21/32 ,  G06Q50/30

Abstract: When an online system receives an order from a customer, the online system can fulfill the order (for example, using a picker to acquire and deliver the ordered items) and use audio verification to verify delivery. When using audio verification, a customer or picker's mobile device plays verification audio including a verification code specific to the order. If the mobile devices (and, by proxy, the customer and picker) are nearby (for example, within 10 feet), the other party's mobile device can detect the verification audio through a microphone and decode the verification code from the captured audio. In some implementations, audio verification can also be performed using a smart doorbell or smart home without the presence of the customer.

公开(公告)号：US20220335505A1

公开(公告)日：2022-10-20

申请号：US17230816

申请日：2021-04-14

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Shishir Kumar Prasad ,  Sharath Rao Karikurve ,  Diego Goyret

IPC: G06Q30/06 ,  G06Q10/08 ,  G06Q30/02 ,  G06N20/00 ,  G06N5/04

Abstract: An online concierge system allows users to order items from a warehouse having multiple physical locations, allowing a user to order items at any given warehouse location. To select a warehouse location for a warehouse selected by a user, the online concierge system identifies a set of items that the user has a threshold likelihood of purchasing from prior orders by the user. For each of a set of warehouse locations, the online concierge system applies a machine-learned item availability model to each item of the identified set. From the availabilities of items of the set at each warehouse location of the set, the online concierge system selects a warehouse location. The online concierge system identifies an inventory of items from the selected warehouse location to the user for inclusion in an order.

公开(公告)号：US20220327603A1

公开(公告)日：2022-10-13

申请号：US17851037

申请日：2022-06-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Camille Van Horne ,  Phillip Goolkasian

IPC: G06Q30/06 ,  H04L51/046 ,  G06F3/0482

Abstract: In a delivery service, a picker retrieves items specified in an order by a customer. If a picker encounters an issue with an item in the order, the picker may select, via a user interface, the item and an associated template message, which requests input from the customer regarding a course of action for the item, to send to the customer. The customer may select, via another user interface, a template message describing a course of action for the item. In response to receiving one of a subset of template messages, the online concierge system displays via the user interface, a set of replacement options to the customer, who may select one of the replacement options to be sent to the picker with the template message.

公开(公告)号：US20220284379A1

公开(公告)日：2022-09-08

申请号：US17191601

申请日：2021-03-03

Applicant: Maplebear, Inc. (dba Instacart)

Inventor： Robert Russel Adams

IPC: G06Q10/08 ,  G06Q20/20 ,  G06Q20/04 ,  G06Q40/00 ,  G06F16/25

Abstract: A receipt capture device can collect transaction information from transactions conducted at a point of sale system by capturing receipt data transmitted from the point of sale system for the purpose of printing receipts at an external receipt printer. The receipt capture device can then send the collected receipt data to an online system for analysis. At the online system, received receipt data can be decoded from the printer-readable format it is transmitted in and used to enhance the online system's understanding of transactions occurring at a retailer associated with the point of sale system. For example, the online system can determine an approximate inventory of items available at purchase at the retailer by aggregating items recently purchased in transactions at the point of sale system.

公开(公告)号：US20220114648A1

公开(公告)日：2022-04-14

申请号：US17068594

申请日：2020-10-12

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Camille van Horne ,  Sean Cashin

IPC: G06Q30/06 ,  G06Q30/02 ,  G06Q40/00 ,  G06Q20/08 ,  G06Q10/08 ,  G06Q20/04 ,  G06K9/00 ,  G06K9/03 ,  G06K9/62

Abstract: A customer places an order of items to be purchased with an online concierge system. The online concierge system provides the order to a picker who shops for the items at a retailer and delivers them. The online concierge system requests an image of a receipt of the order from the picker. The online concierge system performs image processing on the image of the receipt and uses machine learning and optical character recognition to determine the actual amounts purchased of items. The online concierge system charges the customer based on the actual amounts purchased of each item.

公开(公告)号：US20190114583A1

公开(公告)日：2019-04-18

申请号：US15787286

申请日：2017-10-18

Applicant: Maplebear, Inc. (dba Instacart)

Inventor： Mathieu Ripert ,  Jagannath Putrevu ,  Deepak Tirumalasetty ,  Bala Subramanian ,  Andrew Kane

IPC: G06Q10/08 ,  G06Q10/06 ,  G06Q30/06

CPC classification number: G06Q10/0833 ,  G06Q10/063116 ,  G06Q30/0635

Abstract: An online shopping concierge system identifies a set of delivery orders and a set of delivery agents associated with a location. The system allocates the orders among the agents, each agent being allocated at least one order. The system obtains agent progress data describing travel progress of the agents to the location, and order preparation progress data describing progress of preparing the orders for delivery. The system periodically updates the allocation of the orders among the agents based on the agent progress data and the order preparation progress data. This involves re-allocating at least one order to a different delivery agent. When a first agent arrives at the location, the system assigns to the first agent the orders allocated to the first agent. The system then removes the first agent from the set of available delivery agents, and removes the assigned delivery orders from the set of delivery orders.

公开(公告)号：US20250005644A1

公开(公告)日：2025-01-02

申请号：US18217324

申请日：2023-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chuanwei Ruan ,  Yunzhi Ye ,  Han Li ,  David Vengerov ,  Allan Stewart ,  Aref Kashani Nejad

IPC: G06Q30/0601

Abstract: An online system accesses a two-tower model trained to identify candidate items for presentation to users, in which the model includes an item tower trained to compute item embeddings and a user tower trained to compute user embeddings. The user tower includes a long-term sub-tower trained to compute long-term embeddings for users and a short-term sub-tower trained to compute short-term embeddings for users. The model is trained based on item data associated with items, user data associated with users, and session data associated with user sessions. The system uses the item tower to compute an item embedding for each of multiple candidate items. The system also uses the long-term sub-tower to compute a long-term embedding for a user. The system then receives session data associated with a current session of the user and uses the short-term sub-tower to compute a short-term embedding for the user based on this session data.

公开(公告)号：US20240428309A1

公开(公告)日：2024-12-26

申请号：US18214150

申请日：2023-06-26

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Robert Fletcher ,  Ramasubramanian Balasubramanian ,  Tilman Drerup ,  Sharath Rao Karikurve

IPC: G06Q30/0601

Abstract: Based on logged information about prior events, an online concierge system generates a set of location metrics that quantify properties of locations such as retailers at which items may be acquired, and residences to which the items are brought. The location metrics can be used for a variety of purposes to aid customers or other users of the online concierge system, such as providing the users with more information (e.g., likely delivery delays) or alternative options (e.g., pricing options), or emphasizing options that the location metrics indicate would be of particular value to the user. To determine whether to emphasize a particular option, the online concierge system applies a machine-learned model that predicts whether emphasizing that option would effect a positive change in user behavior, relative to not emphasizing it.

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.