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公开(公告)号：US20250111782A1
公开(公告)日：2025-04-03
申请号：US18916461
申请日：2024-10-15
Applicant: Maplebear Inc.
Inventor： Mathieu Ripert , Jagannath Putrevu , Deepak Tirumalasetty , Bala Subramanian , Andrew Kane
IPC: G08G1/00 , B65G1/04 , B65G1/137 , G01C21/34 , G05D1/644 , G05D1/69 , G06Q10/0631 , G06Q10/0833 , G06Q10/087 , G06Q20/32 , G06Q30/0601
Abstract: A method for optimizing delivery assignments in an online system. The system processes delivery orders from user devices, associates the orders with available delivery agents, and allocates them based on real-time data such as inventory availability at different warehouses, delivery agent locations, and order preparation progress. The system dynamically updates order allocations by periodically reallocating orders to different delivery agents based on travel progress, order preparation progress, warehouse proximity, and inventory availability at various warehouses.
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公开(公告)号：US20240242145A1
公开(公告)日：2024-07-18
申请号：US18156347
申请日：2023-01-18
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Haochen Luo , Eric Hermann , Rishab Saraf , Abhinav Darbari , Teodor Lefter , Jason Sanchez , Jagannath Putrevu
IPC: G06Q10/0631 , G06Q10/0639 , G06Q10/0835 , G06Q10/087 , G06Q30/0202 , G06Q30/0601
CPC classification number: G06Q10/063118 , G06Q10/06398 , G06Q10/08355 , G06Q10/087 , G06Q30/0202 , G06Q30/0635
Abstract: An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.
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公开(公告)号：US20240112247A1
公开(公告)日：2024-04-04
申请号：US18528738
申请日：2023-12-04
Applicant: Maplebear Inc.
Inventor： Reza Faturechi , Site Wang , Jagannath Putrevu
IPC: G06Q30/0601 , G06N3/084 , G06Q10/0633
CPC classification number: G06Q30/0635 , G06N3/084 , G06Q10/0633
Abstract: An online concierge identifies orders to shoppers, allowing shoppers to select orders for fulfillment. The online concierge system may generate batches that include multiple orders, allowing a shopper to select a batch to fulfill multiple orders. As orders are continuously being received, delaying identification of orders to shoppers may allow greater batching of orders. To allow greater opportunities for batching, the online concierge system estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fulfill the order. The online concierge system then delays assigning orders for which there is a threshold benefit for delaying and selects a time interval for delaying identification of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.
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公开(公告)号：US20220391965A1
公开(公告)日：2022-12-08
申请号：US17338421
申请日：2021-06-03
Applicant: Maplebear, Inc.(dba Instacart)
Inventor： Jagannath Putrevu , Reza Faturechi
Abstract: An online concierge system receives orders from users and assigns orders to shoppers for fulfillment. Each order specifies a destination location and a warehouse from which items in the order are obtained. When assigning orders to shoppers, the online concierge system seeks to minimize distances traveled by shoppers fulfilling orders. To more efficiently assign orders to shoppers, the online concierge system trains a distance prediction model to predict a distance traveled between a starting location and a destination location from the starting location, the destination location, and a Haversine distance between the destination location and the starting location. Information identifying distances traveled by shoppers when fulfilling previous orders or information about distances between locations from a third party system may be used to train the distance prediction model.
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公开(公告)号：US10818186B2
公开(公告)日：2020-10-27
申请号：US15787286
申请日：2017-10-18
Applicant: Maplebear, Inc.
Inventor： Mathieu Ripert , Jagannath Putrevu , Deepak Tirumalasetty , Bala Subramanian , Andrew Kane
IPC: G08G1/00 , G06Q10/08 , G06Q30/06 , G06Q10/06 , G05D1/02 , G06Q20/32 , G01C21/34 , B65G1/137 , B65G1/04
Abstract: An online shopping concierge system identifies a set of delivery orders and a set of delivery agents associated with a location. The system allocates the orders among the agents, each agent being allocated at least one order. The system obtains agent progress data describing travel progress of the agents to the location, and order preparation progress data describing progress of preparing the orders for delivery. The system periodically updates the allocation of the orders among the agents based on the agent progress data and the order preparation progress data. This involves re-allocating at least one order to a different delivery agent. When a first agent arrives at the location, the system assigns to the first agent the orders allocated to the first agent. The system then removes the first agent from the set of available delivery agents, and removes the assigned delivery orders from the set of delivery orders.
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Patent Agency Ranking
公开(公告)号：US20240303710A1
公开(公告)日：2024-09-12
申请号：US18596590
申请日：2024-03-05
Applicant: Maplebear Inc.
Inventor： Li Tan , Haixun Wang , Shishir Kumar Prasad , Tejaswi Tenneti , Aomin Wu , Jagannath Putrevu
IPC: G06Q30/0601 , G06Q30/0201 , G06Q30/0282
CPC classification number: G06Q30/0627 , G06Q30/0201 , G06Q30/0282
Abstract: A system, for example, an online system uses a machine learning based language model, for example, a large language model (LLM) to process crowd-sourced information provided by users. The crowd-sourced information may include comments from users represented as unstructured text. The system further receives queries from users and answers the queries based on the crowd-sourced information collected by the system. The system generates a prompt for input to a machine-learned language model based on the query. The system provides the prompt to the machine-learned language model for execution and receives a response from the machine-learned language model. The response comprises the insight on the topic and evidence for the insight. The evidence identifies one or more comments used to obtain the insight.
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公开(公告)号：US20240070583A1
公开(公告)日：2024-02-29
申请号：US17823838
申请日：2022-08-31
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Amod Mital , Sherin Kurian , Kevin Ryan , Shouvik Dutta , Jason He , Aneesh Mannava , Ralph Samuel , Jagannath Putrevu , Deepak Tirumalasetty , Krishna Kumar Selvam , Wei Gao , Xiangpeng Li
CPC classification number: G06Q10/06316 , G06Q10/087 , G06Q10/06311 , G06Q10/08355
Abstract: The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.
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18.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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公开(公告)号：US11875394B2
公开(公告)日：2024-01-16
申请号：US17591584
申请日：2022-02-02
Applicant: Maplebear Inc.
Inventor： Reza Faturechi , Site Wang , Jagannath Putrevu
IPC: G06Q10/0633 , G06Q30/0601 , G06N3/084
CPC classification number: G06Q30/0635 , G06N3/084 , G06Q10/0633
Abstract: An online concierge identifies orders to shoppers, allowing shoppers to select orders for fulfillment. The online concierge system may generate batches that include multiple orders, allowing a shopper to select a batch to fulfill multiple orders. As orders are continuously being received, delaying identification of orders to shoppers may allow greater batching of orders. To allow greater opportunities for batching, the online concierge system estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fulfill the order. The online concierge system then delays assigning orders for which there is a threshold benefit for delaying and selects a time interval for delaying identification of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.
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公开(公告)号：US20230146832A1
公开(公告)日：2023-05-11
申请号：US18149652
申请日：2023-01-03
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Mathieu Ripert , Jagannath Putrevu , Deepak Tirumalasetty , Bala Subramanian , Andrew Kane
IPC: G08G1/00 , G06Q10/0833 , G06Q30/0601 , G06Q10/0631 , G05D1/02 , G06Q20/32 , G01C21/34 , G06Q10/087 , B65G1/137 , B65G1/04
CPC classification number: G08G1/20 , G06Q10/0833 , G06Q30/0635 , G06Q10/063116 , G05D1/0217 , G06Q20/322 , G01C21/34 , G06Q10/087 , B65G1/1373 , B65G1/0492 , G05D1/0291
Abstract: An online shopping concierge system identifies a set of delivery orders and a set of delivery agents associated with a location. The system allocates the orders among the agents, each agent being allocated at least one order. The system obtains agent progress data describing travel progress of the agents to the location, and order preparation progress data describing progress of preparing the orders for delivery. The system periodically updates the allocation of the orders among the agents based on the agent progress data and the order preparation progress data. This involves re-allocating at least one order to a different delivery agent. When a first agent arrives at the location, the system assigns to the first agent the orders allocated to the first agent. The system then removes the first agent from the set of available delivery agents, and removes the assigned delivery orders from the set of delivery orders.
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