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11.发明授权公开(公告)号：US12033109B1
公开(公告)日：2024-07-09
申请号：US18114858
申请日：2023-02-27
Applicant: Maplebear Inc.
Inventor： Shuai Wang , Zi Wang , Liang Chen , Houtao Deng , Xiangyu Wang , Aman Jain , Jian Wang
IPC: G06Q10/0835 , G06Q10/0833
CPC classification number: G06Q10/0835 , G06Q10/0833
Abstract: An online concierge system delivers items from retailers to customers. The online concierge predicts a range of times during which an order may be fulfilled for presentation to a user. The online concierge system uses a trained maximum time prediction model to determine a maximum time for order fulfillment based on an order. A trained minimum time prediction model determines a minimum time for order fulfillment from the order and the maximum time. The minimum time may account for one or more rules (e.g., a percentage of orders fulfilled before the minimum time, a desired rate of selection of a range including the minimum time). A range bounded by the maximum time and the minimum time is transmitted to a customer to enable the customer to select a time interval for order fulfillment.
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公开(公告)号：US12008590B2
公开(公告)日：2024-06-11
申请号：US18149646
申请日：2023-01-03
Applicant: Maplebear Inc.
Inventor： Wa Yuan , Ganesh Krishnan , Qianyi Hu , Aishwarya Balachander , George Ruan , Soren Zeliger , Mike Freimer , Aman Jain
IPC: G06Q30/02 , G06N3/084 , G06Q10/0631 , G06Q10/087 , G06Q30/0201 , G06Q30/0202 , G06Q30/0601
CPC classification number: G06Q30/0202 , G06N3/084 , G06Q10/06312 , G06Q10/087 , G06Q30/0201 , G06Q30/0607 , G06Q30/0633
Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.
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公开(公告)号：US20230351279A1
公开(公告)日：2023-11-02
申请号：US17731810
申请日：2022-04-28
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Soren Zeliger , Aman Jain , Zhaoyu Kou , Ji Chen , Trace Levinson , Ganesh Krishnan
IPC: G06Q10/06
CPC classification number: G06Q10/063116 , G06Q10/04
Abstract: An online concierge system assigns shoppers to fulfill orders from users. To allocate shoppers, the online concierge system predicts future supply and demand for the shoppers' services for different time windows. To forecast a supply of shoppers, the online concierge system trains a machine learning model that estimates future supply based on access to a shopper mobile application through which the shoppers obtain new assignments by shoppers. The online concierge system also forecasts future orders. The online concierge system estimates a supply gap in a future time period by selecting a target time to accept for shoppers to accept orders and determining a corresponding ratio of number of shoppers and number of orders. The online concierge system may adjust a number of shoppers allocated to the future time period to achieve the determined ratio number of shoppers and number of orders.
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公开(公告)号：US20230153847A1
公开(公告)日：2023-05-18
申请号：US18149646
申请日：2023-01-03
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Wa Yuan , Ganesh Krishnan , Qianyi Hu , Aishwarya Balachander , George Ruan , Soren Zeliger , Mike Freimer , Aman Jain
IPC: G06Q30/0202 , G06N3/084 , G06Q30/0201 , G06Q30/0601 , G06Q10/087 , G06Q10/0631
CPC classification number: G06Q30/0202 , G06N3/084 , G06Q30/0201 , G06Q30/0633 , G06Q10/087 , G06Q30/0607 , G06Q10/06312
Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.
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15.发明授权公开(公告)号：US12271939B2
公开(公告)日：2025-04-08
申请号：US17855788
申请日：2022-06-30
Applicant: Maplebear Inc.
Inventor： Sonali Deepak Chhabria , Xiangyu Wang , Aman Jain , Ganesh Krishnan , Trace Levinson , Jian Wang
IPC: G06Q30/0601 , G01C21/34 , G06Q10/0631 , G06Q10/0637 , G06Q10/087
Abstract: An online concierge system includes a marketplace automation engine for setting various control parameters affecting marketplace operation. The marketplace automation engine applies a hyperparameter learning model to the marketplace state data to predict a set of hyperparameters affecting a set of respective parameterized control decision models. The hyperparameter learning model is trained on historical marketplace state data and a configured outcome objective for the online concierge system. The marketplace automation engine independently applies the set of parameterized control decision models to the marketplace state data using the hyperparameters to generate a respective set of control parameters affecting marketplace operation of the online concierge system. The marketplace automation engine applies the respective set of control parameters to operation of the online concierge system.
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Patent Agency Ranking
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.
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17.发明公开公开(公告)号：US20240005381A1
公开(公告)日：2024-01-04
申请号：US17855788
申请日：2022-06-30
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Sonali Deepak Chhabria , Xiangyu Wang , Aman Jain , Ganesh Krishnan , Trace Levinson , Jian Wang
CPC classification number: G06Q30/0635 , G06Q10/06311 , G06Q10/087 , G01C21/3407
Abstract: An online concierge system includes a marketplace automation engine for setting various control parameters affecting marketplace operation. The marketplace automation engine applies a hyperparameter learning model to the marketplace state data to predict a set of hyperparameters affecting a set of respective parameterized control decision models. The hyperparameter learning model is trained on historical marketplace state data and a configured outcome objective for the online concierge system. The marketplace automation engine independently applies the set of parameterized control decision models to the marketplace state data using the hyperparameters to generate a respective set of control parameters affecting marketplace operation of the online concierge system. The marketplace automation engine applies the respective set of control parameters to operation of the online concierge system.
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公开(公告)号：US20230049669A1
公开(公告)日：2023-02-16
申请号：US17403400
申请日：2021-08-16
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Wa Yuan , Ganesh Krishnan , Qianyi Hu , Aishwarya Balachander , George Ruan , Soren Zeliger , Mike Freimer , Aman Jain
Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.
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公开(公告)号：US11574325B1
公开(公告)日：2023-02-07
申请号：US17403400
申请日：2021-08-16
Applicant: Maplebear Inc.
Inventor： Wa Yuan , Ganesh Krishnan , Qianyi Hu , Aishwarya Balachander , George Ruan , Soren Zeliger , Mike Freimer , Aman Jain
Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.
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20.发明申请公开(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.
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