公开(公告)号：US20240289739A1

公开(公告)日：2024-08-29

申请号：US18113868

申请日：2023-02-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Kenneth Jason Sanchez ,  Haochen Luo ,  Eric Hermann

IPC: G06Q10/087 ,  G06Q10/0833

CPC classification number: G06Q10/087

Abstract: An online concierge system facilitates ordering of items by customers, procurement of the items from physical retailers by pickers assigned to the orders, and delivery of the orders to customers. To enable efficient procurement, the online concierge system may facilitate preemptive picking of items for staging at a rapid fulfillment area of the physical retailer, and pickers may selectively pick items from the rapid fulfillment area instead of their standard storage locations. Decisions on which items to preemptively pick may be based on a predictive optimization model that scores and ranks items for predictive picking in accordance with various optimization criteria. In the course of fulfilling orders, pickers may furthermore be assigned to replenish items from the standard storage locations to the rapid fulfillment area to satisfy future predicted or actual orders in a manner that optimizes a cost metric.

公开(公告)号：US20240289738A1

公开(公告)日：2024-08-29

申请号：US18113866

申请日：2023-02-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Kenneth Jason Sanchez ,  Haochen Luo ,  Eric Hermann

IPC: G06Q10/087 ,  G06Q10/04 ,  G06Q10/083

CPC classification number: G06Q10/087 ,  G06Q10/04 ,  G06Q10/083

Abstract: An online concierge system facilitates ordering of items by customers, procurement of the items from physical retailers by pickers assigned to the orders, and delivery of the orders to customers. To enable efficient procurement, the online concierge system may facilitate preemptive picking of items for staging at a rapid fulfillment area of the physical retailer, and pickers may selectively pick items from the rapid fulfillment area instead of their standard storage locations. Decisions on which items to preemptively pick may be based on a predictive optimization model that scores and ranks items for predictive picking in accordance with various optimization criteria. In the course of fulfilling orders, pickers may furthermore be assigned to replenish items from the standard storage locations to the rapid fulfillment area to satisfy future predicted or actual orders in a manner that optimizes a cost metric.

公开(公告)号：US20240202748A1

公开(公告)日：2024-06-20

申请号：US18066257

申请日：2022-12-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Radhika Anand ,  Ajay Pankaj Sampat ,  Caleb Grisell ,  Youdan Xu ,  Krishna Kumar Selvam ,  Bita Tadayon

IPC: G06Q30/0202

CPC classification number: G06Q30/0202

Abstract: Techniques for predicting a wait time for a shopper based on a location the shopper's client device are presented. A system identifies a shopper's current location and uses a machine learning model to predict a wait time until the shopper will receive one or more orders. The machine learning model is trained to use input features including a number of orders received during a current time period for fulfillment near the current location, a number of other shoppers available for fulfilling orders during the current time period near the current location, historical information about a presentation of a plurality of orders to a plurality of shoppers near the current location, and historical information about the shopper and the other nearby available shoppers. The system then sends the predicted wait time to the client device for presentation to the shopper.

公开(公告)号：US20240202661A1

公开(公告)日：2024-06-20

申请号：US18085396

申请日：2022-12-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ashish Sinha ,  Collin Yen

IPC: G06Q10/0875 ,  G01C21/34 ,  G06F18/23

CPC classification number: G06Q10/0875 ,  G01C21/3476 ,  G06F18/23

Abstract: An online concierge system receives data points associated with picking up and delivering orders and arriving at retailer/delivery locations from picker client devices and executes a clustering process on one or more sets of the data points. The system determines a geolocation associated with each location based on the clustering process and identifies one or more points of interest associated with each location based on rules applied to the data points. The system receives information describing an order, identifies pairs of data points associated with a location associated with the order, and determines a navigation path including a sequence of points of interest for servicing the order based on points of interest associated with the location and a difference between times associated with each pair of data points. The system sends the geolocation and navigation path to a picker client device associated with a picker servicing the order.

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.

公开(公告)号：US20240144688A1

公开(公告)日：2024-05-02

申请号：US18060473

申请日：2022-11-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Lin Gao ,  Yilin Huang ,  Shiyuan Yang ,  Xiaofei Zhou ,  Xiao Zhou ,  Qunwei Liu

IPC: G06V20/52 ,  G06Q30/0601 ,  G06V10/74

CPC classification number: G06V20/52 ,  G06Q30/0633 ,  G06V10/761

Abstract: An automated checkout system accesses an image of an item inside a shopping cart and a location of the shopping cart within a store. The automated checkout system identifies a set of candidate items located within a threshold distance of the location of the shopping cart based on an item map. The item map describes a location of each item within the store and the location of each candidate item corresponds to a location of the candidate item on the item map. The automated checkout system inputs visual features of the item extracted from the image to a machine-learning model to identify the item by determining a similarity score between the item and each candidate item of the set of candidate items. After identifying the item, the automated checkout system displays a list comprising the item and additional items within the shopping cart to a user.

公开(公告)号：US20240144355A1

公开(公告)日：2024-05-02

申请号：US17977712

申请日：2022-10-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Liang Chen ,  Aman Jain ,  Xiangyu Wang ,  Houtao Deng ,  Jae Cho

IPC: G06Q30/06 ,  G06Q30/02

CPC classification number: G06Q30/0641 ,  G06Q30/0201 ,  G06Q30/0607

Abstract: The present disclosure is directed to selecting order checkout options. In particular, the methods and systems of the present disclosure may, responsive to receiving data describing a potential order for an online shopping concierge platform: generate, based at least in part on the data describing the potential order, a plurality of different and distinct checkout options for the potential order; determine, for each checkout option of the plurality of different and distinct checkout options and based at least in part on one or more machine learning (ML) models, a probability that a customer associated with the potential order will proceed with the potential order if presented with the checkout option; and select a subset of checkout options for presentation to the customer based on their respective determined probabilities that the customer will proceed with the potential order if presented with the subset of checkout options.

公开(公告)号：US20240104632A1

公开(公告)日：2024-03-28

申请号：US17935916

申请日：2022-09-27

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Creagh Briercliffe ,  Chuan Lei ,  Saurav Manchanda ,  Min Xie

IPC: G06Q30/06

CPC classification number: G06Q30/0635 ,  G06Q30/0613 ,  G06Q30/0627 ,  G06Q30/0639

Abstract: An online concierge system uses a co-engagement graph to assign attribute values to items for which those attribute values are uncertain. A co-engagement graph is a graph with nodes that represent items and edges that represent co-engagement between items. The online concierge system generates a co-engagement graph for a set of items based on item engagement data and item data for the items. The set of items includes items for which the online concierge system has an attribute value for a target attribute and items for which the online concierge system does not have an attribute value for the target attribute. The online concierge system identifies a node that corresponds to an unknown item and identifies a node connected to that first node that corresponds to a known item. The online concierge system assigns the attribute value for the known item to the unknown item.

公开(公告)号：US20240005377A1

公开(公告)日：2024-01-04

申请号：US17855377

申请日：2022-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Zhenbang Chen ,  Jingying Zhou ,  Peng Qi

IPC: G06Q30/06 ,  G06Q30/02

CPC classification number: G06Q30/0631 ,  G06Q30/0222 ,  G06Q30/0205

Abstract: An online system trains a machine-learned lift prediction model configured as a neural network. The machine-learned lift prediction model can be used during the inference process to determine lift predictions for users and items associated with the online system. By configuring the lift prediction model as a neural network, the lift prediction model can capture and process information from users and items in various formats and more flexibly model users and items compared to existing methods. Moreover, the lift prediction model includes at least a first portion for generating control predictions and a second portion for generating treatment predictions, where the first portion and the second portion share a subset of parameters. The shared subset of parameters can capture information important for generating both control and treatment predictions even when the training data for a control group of users might be significantly smaller than that of the treatment group.

公开(公告)号：US20240005132A1

公开(公告)日：2024-01-04

申请号：US17855366

申请日：2022-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Zhenbang Chen ,  Jingying Zhou ,  Peng Qi

IPC: G06N3/04 ,  G06N3/08 ,  G06Q30/06

CPC classification number: G06N3/0481 ,  G06N3/08 ,  G06Q30/0631

Abstract: An online system trains a machine-learned lift prediction model configured as a neural network. The machine-learned lift prediction model can be used during the inference process to determine lift predictions for users and items associated with the online system. By configuring the lift prediction model as a neural network, the lift prediction model can capture and process information from users and items in various formats and more flexibly model users and items compared to existing methods. Moreover, the lift prediction model includes at least a first portion for generating control predictions and a second portion for generating treatment predictions, where the first portion and the second portion share a subset of parameters. The shared subset of parameters can capture information important for generating both control and treatment predictions even when the training data for a control group of users might be significantly smaller than that of the treatment group.