公开(公告)号：US20240403826A1

公开(公告)日：2024-12-05

申请号：US18204200

申请日：2023-05-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Youdan Xu ,  Aoshi Li ,  Jaclyn Tandler ,  Roman Hayran ,  Brendan Evans Ashby ,  Emily Silberstein ,  Ajay Pankaj Sampat

IPC: G06Q10/0875 ,  G06N3/084 ,  G06Q20/12

Abstract: An online concierge system allows customers to place orders to be fulfilled by pickers. An order includes an amount of compensation a customer provides to a picker when the order is fulfilled. A customer may modify the amount of compensation provided to a picker, so some customers may initially specify a large amount of compensation to entice a picker to fulfill an order and then reduce the amount of compensation when the order is fulfilled. To prevent penalizing pickers who fulfilled an order without a problem, the online concierge system trains a model to determine a probability that a reduction in compensation to a picker was unrelated to a problem with order fulfillment. The online concierge system may perform one or more remedial actions for a picker based on the probability determined by the model.

公开(公告)号：US20240394720A1

公开(公告)日：2024-11-28

申请号：US18202876

申请日：2023-05-26

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Youdan Xu ,  Michael Chen ,  Marina Tanasyuk ,  Matthew Donghyun Kim ,  Ajay Pankaj Sampat ,  Caleb Grisell ,  Yuan Gao

IPC: G06Q10/08 ,  G08G1/14 ,  H04W4/02

Abstract: An online concierge system uses a machine-learned parking quality model to quantify the suitability of a particular parking location (e.g., a parking lot, or a street) for use when performing purchases at a retail location on behalf of customers. The parking quality model's output is determined according to input features related to parking at a candidate parking location, such as a current time, a current degree of demand for shoppers at the retail location, or a current average shopper wait time at the retail location before receiving an order. The online concierge system provides suggested alternate parking locations to a client device of the shopper, where they may be displayed, e.g., as part of an electronic map. Use of the suggested alternate parking locations helps to preserve parking availability in restricted areas such as retailer parking lots and to reduce traffic congestion in the area of the retailer.

公开(公告)号：US12265980B2

公开(公告)日：2025-04-01

申请号：US18240798

申请日：2023-08-31

Applicant: Maplebear Inc.

Inventor： Shuo Feng ,  Chia-Eng Chang ,  Aoshi Li ,  Pak Hong Wong ,  Leo Kwan ,  Mengyu Zhang ,  Van Nguyen ,  Aman Jain ,  Ziwei Shi ,  Ajay Pankaj Sampat ,  Rucheng Xiao

IPC: G06Q30/0201

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

公开(公告)号：US20250029053A1

公开(公告)日：2025-01-23

申请号：US18224795

申请日：2023-07-21

Applicant: Maplebear Inc.

Inventor： Kevin Charles Ryan ,  Krishna Kumar Selvam ,  Tahmid Shahriar ,  Ajay Pankaj Sampat ,  Shouvik Dutta ,  Sawyer Bowman ,  Nicholas Rose ,  Ziwei Shi

IPC: G06Q10/0834 ,  G06Q10/083

Abstract: An online concierge system receives information describing the progress of a picker servicing a batch of existing orders and a service request for an order. The system identifies picker attributes of the picker and order attributes of the order and each existing order of the set and accesses a machine learning model trained to predict a likelihood the picker will accept an add-on request to add the order to the batch of existing orders. To predict the likelihood, the system applies the model to the picker attributes, the progress of the picker, and the order attributes. The system determines a cost associated with sending the add-on request to the picker based on the likelihood and assigns the order to a set of orders based on the cost. The system sends the add-on request to the picker responsive to determining the order is assigned to the batch of existing orders.

公开(公告)号：US20240362581A1

公开(公告)日：2024-10-31

申请号：US18141397

申请日：2023-04-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Vladimir Katz ,  Ajay Pankaj Sampat ,  Fangzhou Wang ,  Wenqi Ge ,  Charles Durham ,  Kevin Shepherd

IPC: G06Q10/087 ,  G06N7/01 ,  G06N20/00

CPC classification number: G06Q10/087 ,  G06N7/01 ,  G06N20/00

Abstract: An online concierge system allows users to place orders for fulfillment by pickers. Orders have various attributes (e.g., dimensions, weight, contents, etc.), and the pickers may have corresponding characteristics affecting capability of fulfilling orders. To optimize allocation of orders to pickers for fulfillment, the online concierge system trains an order validation model that predicts a probability of a picker encountering a problem fulfilling an order based on characteristics of the picker and attributes of the order. The order validation model is trained from training examples based on previous orders and labels indicating whether a picker encountered a problem with fulfilling the order. The order validation model can then be used to predict deliverability of future orders or to specify limits on one or more attributes of orders for fulfillment.

公开(公告)号：US20250111410A1

公开(公告)日：2025-04-03

申请号：US18374462

申请日：2023-09-28

Applicant: Maplebear Inc.

Inventor： Van Nguyen ,  Fangzhou Wang ,  Ajay Pankaj Sampat ,  Ann Barzman ,  Yuan Gao ,  Amsal Lakhani

IPC: G06Q30/0241 ,  G06F40/197 ,  G06Q30/0204 ,  G06Q30/0242 ,  G06Q30/0251

Abstract: An online system receives user data for users of the online system and assigns the users to one or more user cohorts based on the user data. The online system generates a prompt for content to be included in a landing page presented to each user cohort, in which the prompt includes a template for the landing page and information describing the user cohorts. The online system then provides the prompt to a generative artificial intelligence model to obtain an output and extracts, from the output, a set of content to be included in the landing page for each user cohort. The online system generates variants of the landing page for each user cohort based on the extracted set of content.

公开(公告)号：US20250078105A1

公开(公告)日：2025-03-06

申请号：US18240798

申请日：2023-08-31

Applicant: Maplebear Inc.

Inventor： Shuo Feng ,  Chia-Eng Chang ,  Aoshi Li ,  Pak Hong Wong ,  Leo Kwan ,  Mengyu Zhang ,  Van Nguyen ,  Aman Jain ,  Ziwei Shi ,  Ajay Pankaj Sampat ,  Rucheng Xiao

IPC: G06Q30/0201

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

公开(公告)号：USD1050175S1

公开(公告)日：2024-11-05

申请号：US29851808

申请日：2022-08-31

Applicant: Maplebear Inc.

Designer： Adrian Mclean ,  Joseph Cohen ,  Jaclyn Tandler ,  Sawyer Bowman ,  Rafael Moreno Cesar ,  Ajay Pankaj Sampat

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：US20250053898A1

公开(公告)日：2025-02-13

申请号：US18233252

申请日：2023-08-11

Applicant: Maplebear Inc.

Inventor： Kevin Charles Ryan ,  Krishna Kumar Selvam ,  Tahmid Shahriar ,  Sawyer Bowman ,  Nicholas Rose ,  Ajay Pankaj Sampat ,  Ziwei Shi

IPC: G06Q10/0631 ,  G06Q10/0833

Abstract: An online concierge system receives information describing the progress of a picker servicing a batch of existing orders and predicts a first likelihood the picker will finish servicing the batch within a threshold amount of time based on the picker's progress and information describing the batch. If the first likelihood exceeds a threshold likelihood, the system accesses a machine learning model trained to predict a second likelihood the picker will accept a batch of new orders for servicing while servicing the batch of existing orders. The system applies the model to inputs including a set of attributes of the picker and the picker's progress to predict the second likelihood. The system matches batches of new orders with pickers based on the second likelihood and sends one or more requests to service one or more batches matched with the picker to a client device associated with the picker.