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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：US12223538B2
公开(公告)日：2025-02-11
申请号：US18503055
申请日：2023-11-06
Applicant: Maplebear Inc.
Inventor： Amy Luong , Michael Righi , Graham Adeson , Ross Stuart Williams , Aman Jain , Radhika Anand , Ganesh Krishnan
IPC: G06Q30/00 , G06Q30/0601
Abstract: For each retailer in the geographic region, an online system predicts a number of orders placed at the retailer and a capacity to fulfill orders during a forecast time period. The capacity of the retailer is predicted based on a number of pickers expected to be available to the retailer during the forecast time period. The online system determines demand for the services of a picker at the retailer based on a comparison of the predicted number of orders and the predicted capacity to fulfill those orders. The online system displays a user interactive map of the geographic region to the picker. The map displays a pin at the location of each retailer in the geographic region, which describes the categorization determined for the retailer. The picker selects a pin, which causes the user interactive map to display a notification characterizing the demand for services at the retailer.
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公开(公告)号：US20240403812A1
公开(公告)日：2024-12-05
申请号：US18203578
申请日：2023-05-30
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Liang Chen , Xiangyu Wang , Houtao Deng , Ganesh Krishnan , Kevin Charles Ryan , Aman Jain , Jian Wang
IPC: G06Q10/0834 , G06Q10/083 , G06Q10/0833 , G06Q30/0601
Abstract: An online concierge system generates a set of candidate estimated times of arrival (ETAs) for delivery of a set of items being purchased by a user. Each candidate ETA is scored by using a machine-learned model to estimate values for different criteria of interest, such as likelihood of acceptance of the ETA, cost of delivery of the items to the user, and the like. The values for the different criteria may be combined to generate the overall score for a candidate ETA. One or more of the highest-scoring ETAs are selected and provided to the user, who may then approve one of the ETAs for use with delivery of the user's set of items.
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15.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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Patent Agency Ranking
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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公开(公告)号：US12265933B2
公开(公告)日：2025-04-01
申请号：US17731810
申请日：2022-04-28
Applicant: Maplebear Inc.
Inventor： Soren Zeliger , Aman Jain , Zhaoyu Kou , Ji Chen , Trace Levinson , Ganesh Krishnan
IPC: G06Q10/0631 , 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：US20250061350A1
公开(公告)日：2025-02-20
申请号：US18233828
申请日：2023-08-14
Applicant: Maplebear Inc.
Inventor： Ganesh Krishnan , Sharath Rao Karikurve , Angadh Singh , Changyao Chen , Tilman Drerup
IPC: G06N5/022 , G06Q10/087
Abstract: An online system trains a churn prediction model to attribute a churn event to one or more causal events. The churn prediction model receives customer features and online system features as inputs. Various causal events that occur affect one or more online system features. To avoid biasing the churn prediction model using input features that are related to possible causal events, the online system determines customer features and online system features based on customer interactions occurring in different time intervals. The customer features are determined from interactions in a time interval that is earlier than a time interval from which interactions are used to determine online system features. Such time segmenting decorrelates the features input to the model from the events, reducing potential bias from the causal events on the churn prediction model.
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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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20.发明公开公开(公告)号：US20240070491A1
公开(公告)日：2024-02-29
申请号：US17900533
申请日：2022-08-31
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Lanchao Liu , George Ruan , Zhiqiang Wang , Xiangdong Liang , Jagannath Putrevu , Ganesh Krishnan , Ryan Dick
Abstract: An online system accesses a machine learning model trained to predict behaviors of users of the online system, in which the model is trained based on historical data received by the online system that is associated with the users and demand and supply sides associated with the online system. The online system identifies a treatment for achieving a goal of the online system and simulates application of the treatment on the demand and supply sides based on the historical data and a set of behaviors predicted for the users. Application of the treatment is simulated by replaying the historical data in association with application of the treatment and applying the model to predict the set of behaviors while replaying the data. The online system measures an effect of application of the treatment on the demand and supply sides based on the simulation, in which the effect is associated with the goal.
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