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公开(公告)号：US11830018B2
公开(公告)日：2023-11-28
申请号：US17389281
申请日：2021-07-29
Applicant: Maplebear Inc.
Inventor： Houtao Deng , Ji Chen , Zi Wang , Soren Zeliger , Ganesh Krishnan , Wa Yuan , Michael Scheibe
IPC: G06Q10/00 , G06Q30/0201 , G06Q30/0601 , G06Q10/0631 , G06N5/04 , G06Q30/0204 , G06N20/00
CPC classification number: G06Q30/0206 , G06N5/04 , G06N20/00 , G06Q10/06312 , G06Q10/06313 , G06Q10/06315 , G06Q30/0205 , G06Q30/0635 , G06Q30/0641
Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received or for short-term fulfillment when the order was received. To account for a number of shoppers available to fulfill orders during different discrete time intervals and numbers of orders for fulfillment during different discrete time intervals, the online concierge system specifies a target rate for orders fulfilled later than a specified discrete time interval and a threshold from the target rate. A trained machine learning model periodically predicts a percentage of orders being fulfilled late, with an order associated with a predicted percentage when the order was received. The online concierge system increases a price of orders associated with predicted percentages greater than the threshold from the target rate. The increased price of an order is determined from a price elasticity curve and the predicted percentage.
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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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公开(公告)号：US12175487B2
公开(公告)日：2024-12-24
申请号：US18503078
申请日：2023-11-06
Applicant: Maplebear Inc.
Inventor： Houtao Deng , Ji Chen , Zi Wang , Soren Zeliger , Ganesh Krishnan , Wa Yuan , Michael Scheibe
IPC: G06Q10/00 , G06N5/04 , G06N20/00 , G06Q10/0631 , G06Q30/0201 , G06Q30/0204 , G06Q30/0601
Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received or for short-term fulfillment when the order was received. To account for a number of shoppers available to fulfill orders during different discrete time intervals and numbers of orders for fulfillment during different discrete time intervals, the online concierge system specifies a target rate for orders fulfilled later than a specified discrete time interval and a threshold from the target rate. A trained machine learning model periodically predicts a percentage of orders being fulfilled late, with an order associated with a predicted percentage when the order was received. The online concierge system increases a price of orders associated with predicted percentages greater than the threshold from the target rate. The increased price of an order is determined from a price elasticity curve and the predicted percentage.
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Patent Agency Ranking
公开(公告)号：US20240104458A1
公开(公告)日：2024-03-28
申请号：US17955407
申请日：2022-09-28
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Wa Yuan , Jae Cho , Yijia Chen , Houtao Deng , Soren Zeliger , Aman Jain , Jian Wang , Ji Chen
CPC classification number: G06Q10/063116 , G06N5/022 , G06Q10/06393 , G06Q30/0637
Abstract: An online concierge system determines a quantity of a resource available in a timeslot to fulfill orders during the timeslot. The orders include immediate orders placed during the timeslot and scheduled orders that are scheduled for fulfillment during the timeslot. The online concierge system applies the quantity of the resource to a machine learning model to produce a predicted relationship between a value of a fulfillment metric and an allocation of the quantity of the resource reserved for immediate orders. The online concierge system determines, based on the predicted relationship, an expected optimal allocation of the quantity of the resource that maximizes the fulfillment metric. The online concierge system reserves the expected optimal allocation of the quantity of the resource for immediate orders.
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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：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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8.发明公开公开(公告)号：US20240070697A1
公开(公告)日：2024-02-29
申请号：US18503078
申请日：2023-11-06
Applicant: Maplebear Inc.
Inventor： Houtao Deng , Ji Chen , Zi Wang , Soren Zeliger , Ganesh Krishnan , Wa Yuan , Michael Scheibe
IPC: G06Q30/0201 , G06N5/04 , G06N20/00 , G06Q10/0631 , G06Q30/0204 , G06Q30/0601
CPC classification number: G06Q30/0206 , G06N5/04 , G06N20/00 , G06Q10/06312 , G06Q10/06313 , G06Q10/06315 , G06Q30/0205 , G06Q30/0635 , G06Q30/0641
Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received or for short-term fulfillment when the order was received. To account for a number of shoppers available to fulfill orders during different discrete time intervals and numbers of orders for fulfillment during different discrete time intervals, the online concierge system specifies a target rate for orders fulfilled later than a specified discrete time interval and a threshold from the target rate. A trained machine learning model periodically predicts a percentage of orders being fulfilled late, with an order associated with a predicted percentage when the order was received. The online concierge system increases a price of orders associated with predicted percentages greater than the threshold from the target rate. The increased price of an order is determined from a price elasticity curve and the predicted percentage.
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公开(公告)号：US20230034221A1
公开(公告)日：2023-02-02
申请号：US17389281
申请日：2021-07-29
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Houtao Deng , Ji Chen , Zi Wang , Soren Zeliger , Ganesh Krishnan , Wa Yuan , Michael Scheibe
Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received or for short-term fulfillment when the order was received. To account for a number of shoppers available to fulfill orders during different discrete time intervals and numbers of orders for fulfillment during different discrete time intervals, the online concierge system specifies a target rate for orders fulfilled later than a specified discrete time interval and a threshold from the target rate. A trained machine learning model periodically predicts a percentage of orders being fulfilled late, with an order associated with a predicted percentage when the order was received. The online concierge system increases a price of orders associated with predicted percentages greater than the threshold from the target rate. The increased price of an order is determined from a price elasticity curve and the predicted percentage.
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