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：US20250139574A1

公开(公告)日：2025-05-01

申请号：US18498016

申请日：2023-10-30

Applicant: Maplebear Inc.

Inventor： Shang Li ,  Ashish Sinha ,  Krishna Kumar Selvam ,  Qi Xi ,  Amirali Darvishzadeh ,  David Zandman ,  Christopher Billman

IPC: G06Q10/087 ,  G06N5/022

Abstract: A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

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.

公开(公告)号：US20240386471A1

公开(公告)日：2024-11-21

申请号：US18199938

申请日：2023-05-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Peter Vu ,  Ziwei Shi ,  Joseph Cohen ,  Emily Silberstein ,  Krishna Kumar Selvam ,  Jaclyn Tandler ,  Adrian McLean ,  Nicholas Rose

IPC: G06Q30/0601

Abstract: A concierge system sends batches of orders to pickers that they can review and accept in a batch list on a client device. Each batch in the batch list is presented with a hide option that enables the picker to hide a batch that they do not intend to accept. In response to receiving a hide signal, the system extracts features associated with the batch and stores those features with a negative indication of the picker towards the batch. The hide signal provides the system with a higher quality signal indicating the picker's negative intent regarding an order, as compared to simply ignoring the order in favor of fulfilling another order. This higher quality signal is then used to train models to better predict events related to the pickers' acceptance of orders, such as for ranking orders for pickers or for predicting fulfillment times.

公开(公告)号：US20240193540A1

公开(公告)日：2024-06-13

申请号：US18079317

申请日：2022-12-12

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Krishna Kumar Selvam ,  Ali Soltani Sobh ,  Kevin Charles Ryan ,  Bing Hong Leonard How ,  Rahul Makhijani ,  Bita Tadayon

IPC: G06Q10/087 ,  G06N20/00

CPC classification number: G06Q10/087 ,  G06N20/00

Abstract: An online concierge system accesses and applies a model to predict likelihoods of acceptance of a service request for an order by pickers. The system accesses timespan distributions for accepted service requests and identifies sets of pickers based on the order. Based on the likelihoods and distributions, the system generates simulated responses of the sets of pickers to the service request and trains an additional model based on attributes of the order, the simulated responses, and information associated with corresponding sets of pickers. The system receives a new order, identifies additional sets of pickers based on the new order, and applies the additional model to predict responses of the additional sets of pickers to an additional service request for the new order. Based on the predicted responses and a delivery time associated with the new order, a minimum number of pickers to send the additional service request is determined.

公开(公告)号：US20240177108A1

公开(公告)日：2024-05-30

申请号：US18072311

申请日：2022-11-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Youdan Xu ,  Krishna Kumar Selvam ,  Michael Chen ,  Radhika Anand ,  Rebecca Riso ,  Ajay Sampat

IPC: G06Q10/087 ,  G06Q30/0202

CPC classification number: G06Q10/087 ,  G06Q30/0202

Abstract: An online concierge system receives location information associated with pickers and actual orders associated with a geographical zone. A model trained to predict a likelihood an actual order associated with the zone will be available for servicing within a timeframe is accessed and applied to forecasted orders. Each picker is matched to an order for servicing by minimizing a value of a function that is based on a difference between a location associated with each picker matched to an actual order and an associated retailer location, a difference between the location associated with each picker matched to a forecasted order and an associated retailer location, and the predicted likelihood. Recommendations for accepting an actual order, moving to a retailer location associated with a forecasted order, or checking back later with the system are generated based on the matches and sent for display to a client device associated with each picker.

公开(公告)号：US20240104449A1

公开(公告)日：2024-03-28

申请号：US17955395

申请日：2022-09-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Krishna Kumar Selvam ,  Mouna Cheikhna ,  Michael Chen ,  Dylan Wang ,  Joseph Cohen ,  Tahmid Shahriar ,  Graham Adeson ,  Ajay Pankaj Sampat

IPC: G06Q10/06 ,  G06Q30/06

CPC classification number: G06Q10/06311 ,  G06Q10/06398 ,  G06Q30/0635

Abstract: An online concierge system iteratively makes a batch of one or more orders available to an increasing number of shoppers to choose to fulfill. Each shopper may choose to accept or reject a batch for fulfillment. To improve batch acceptance and matching between batches and shoppers, the batches are scored with respect to expected resource costs, likelihood of acceptance by the shopper, and/or other quality metrics to iteratively offer the batch to an increasing number of shoppers (prioritizing the scoring factors) until a shopper accepts. The number of shoppers notified of the batch and the frequency that additional shoppers are selected may vary based on characteristics of the batch and likelihood the batch will be accepted by a shopper.

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

IPC: G06Q10/06 ,  G06Q10/08

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.

公开(公告)号：US20240070577A1

公开(公告)日：2024-02-29

申请号：US17823850

申请日：2022-08-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Krishna Kumar Selvam ,  Joseph Cohen ,  Tahmid Sharjar ,  Neel Sarwal ,  Darren Johnson ,  Nicholas Rose ,  Ajay Pankaj Sampat ,  Joey Dong

IPC: G06Q10/06

CPC classification number: G06Q10/063114 ,  G06Q10/06316

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.