公开(公告)号：US20250139574A1

公开(公告)日：2025-05-01

申请号：US18498016

申请日：2023-10-30

Applicant: Maplebear Inc.

Inventor： Shang Li ,  Ashish Sinha ,  Krishna Kumar Selvam ,  Qi Xi ,  Amirali Darvishzadeh ,  David Zandman ,  Christopher Billman

IPC: G06Q10/087 ,  G06N5/022

Abstract: A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

公开(公告)号：US20240428320A1

公开(公告)日：2024-12-26

申请号：US18213756

申请日：2023-06-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Rebecca Riso ,  Bo Xu ,  Kenneth Jason Sanchez ,  Ashish Sinha ,  Chencheng Wu

IPC: G06Q30/0601 ,  G06Q20/40

Abstract: An online system receives a request to confirm a transaction that is associated with an order. The system accepts or declines the transaction based on whether an amount associated with the pending transaction is likely to exceed an expected amount of the order by more than a threshold value. To determine the threshold, the system trains a first model to predict an overspend for an order and then trains a second model to predict an amount of error associated with the predictions from the first model. The outputs of the first model and the second model provide a mean and a variance for an expected distribution of the overspend. If the actual overspend amount for the transaction exists in too high of a percentile of the distribution, the transaction may be flagged for review or declined.

公开(公告)号：US20240330846A1

公开(公告)日：2024-10-03

申请号：US18129021

申请日：2023-03-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Sharath Rao Karikurve ,  Ramasubramanian Balasubramanian ,  Ashish Sinha

IPC: G06Q10/0835 ,  G06Q10/087 ,  G06Q30/0203

CPC classification number: G06Q10/08355 ,  G06Q10/087 ,  G06Q30/0203 ,  G06N20/00

Abstract: An online concierge system receives, from a client device associated with a user of the online concierge system, order data associated with an order placed with the online concierge system, in which the order data describes a delivery location for the order. The online concierge system receives information describing a set of attributes associated with the delivery location and accesses a machine learning model trained to predict a difference between an arrival time and a delivery time for the delivery location. The online concierge system applies the model to the set of attributes associated with the delivery location to predict the difference between the arrival time and the delivery time for the delivery location and determines an estimated delivery time for the order based at least in part on the predicted difference. The online concierge system sends the estimated delivery time for the order for display to the client device.

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.