公开(公告)号：US20240249333A1

公开(公告)日：2024-07-25

申请号：US18100739

申请日：2023-01-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Sharath Rao Karikurve ,  Ramasubramanian Balasubramanian ,  Shrikar Archak

IPC: G06Q30/0601 ,  G06N20/00

CPC classification number: G06Q30/0631 ,  G06N20/00

Abstract: An online concierge system may receive, from a customer, a selection of an item that is associated with a first brand. The online concierge system may extract features associated with the customer and features associated with the item. The online concierge system may input the extracted features to a machine learning model that is trained to predict a degree of association between the customer and the first brand associated with the item. The online concierge system may identify candidate alternatives for replacing the item. The candidate alternatives may include a first alternative that is associated with the first brand and a second alternative that is associated with a second brand different from the first brand. The online concierge system may select, based on the degree of association between the customer and the first brand, one or more candidate alternatives to be presented to the customer to replace the item.

公开(公告)号：US20240211842A1

公开(公告)日：2024-06-27

申请号：US18087547

申请日：2022-12-22

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Cameron Nicholas Taylor ,  Robert Fletcher ,  Pedro Tanure Veloso ,  Tilman Drerup ,  Rob Donnelly ,  Ben Lowenstein ,  Matthew Wean

IPC: G06Q10/0637 ,  G06Q10/087

CPC classification number: G06Q10/06375 ,  G06Q10/087

Abstract: An online concierge system fulfills orders for items offered by retailers and may increase the price of an item offered by a retailer in some instances. The online concierge system applies a markup to an item by applying a pricing policy to a category including the item. To optimize application of pricing policies to categories, the online concierge system categorizes items offered by the retailer and applies an outcome model to combinations of categories and pricing policies. From the output of the outcome model, the online concierge system selects a set of categories and corresponding pricing policies. Using a price adjustment model, the online concierge system determines modifications to one or more of the pricing policies of the set to enforce one or more constraints across multiple pricing policies.

公开(公告)号：US20240202784A1

公开(公告)日：2024-06-20

申请号：US18085458

申请日：2022-12-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Calder Hayes ,  Aakash Singh ,  Jaclyn Tandler ,  Alicia Wei ,  Gareth Pennington ,  Shengwen Fang

IPC: G06Q30/0282 ,  G06Q10/087

CPC classification number: G06Q30/0282 ,  G06Q10/087

Abstract: An online concierge system provides orders to a picker who fulfills the order by delivering items from the order to a customer. Customers provide feedback to the online concierge system about pickers, which is used when the online concierge system allocates orders to pickers. In some cases, negative feedback may be caused by conditions external to a picker, such as weather conditions or an inability to access the online concierge system. To avoid penalizing pickers because of external conditions, the online concierge system identifies forgiveness events when external conditions affect order fulfillment. Feedback affected by a forgiveness event is identified by the online concierge system when evaluating pickers. To facilitate matching of feedback to forgiveness events, a forgiveness event table indexes identified forgiveness events to reduce computing complexity and resources, while simplifying addition of new forgiveness events.

公开(公告)号：US20240193657A1

公开(公告)日：2024-06-13

申请号：US18079544

申请日：2022-12-12

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Sneha Chandrababu ,  Karuna Ahuja

IPC: G06Q30/0601

CPC classification number: G06Q30/0617 ,  G06Q30/0633

Abstract: An online concierge system generates an order including multiple items based on unstructured data received from a user through a chat interface instead of manually adding items to the order. The user provides unstructured data to the online concierge system through the chat interface, and the online concierge system extracts an intent from the unstructured data using a natural language process. Based on the intent, the online concierge system identifies a group of items associated with the intent and selects a group of items. The online concierge system generates an order for the user that includes the items comprising the selected group of items.

公开(公告)号：US20240177219A1

公开(公告)日：2024-05-30

申请号：US18070382

申请日：2022-11-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Bradley Colthurst ,  Ryan McColeman

IPC: G06Q30/0601 ,  G06Q10/087

CPC classification number: G06Q30/0633 ,  G06Q10/087 ,  G06Q30/0631

Abstract: An online concierge system facilitates ordering, procurement, and delivery of items to a customer from physical retailers based on shared cart recommendations. Based on customer identifying information and other data sources, the online concierge system may recommend prepopulated shared carts that may be of interest to a customer. The prepopulated carts may be associated with other users of the online concierge system or may be associated with specific events, locations, or other metadata. Prepopulated carts may be created by other users that select to share their carts. Additionally, prepopulated carts may be created and shared by retailers, manufacturers, wholesalers, or other stakeholders in the selling of items through the online concierge system. Furthermore, recommended carts may be automatically generated based on machine learning techniques.

公开(公告)号：US20240176674A1

公开(公告)日：2024-05-30

申请号：US18072700

申请日：2022-11-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Sahil Khanna ,  Reza Sadri ,  Jacob Jensen

IPC: G06F9/50 ,  G06F11/34

CPC classification number: G06F9/5077 ,  G06F11/3442

Abstract: An online system facilitates various functions using machine learning model microservices. A tuning mechanism tunes various configuration parameters for each microservice that control allocation of computing resources and other configurations of physical and/or virtual machines that implement the microservices. Tuning may be performed in part by executing tests under various configurations and evaluating an objective function associated with the different configurations. Furthermore, parameters of the objective function may be set based on a trained learning model that learns baseline parameters and weights of the objective function based on historical data.

公开(公告)号：US20240144191A1

公开(公告)日：2024-05-02

申请号：US17977759

申请日：2022-10-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ganesh Krishnan ,  Xiaofan Xu ,  Kevin Ryan

IPC: G06Q10/10

CPC classification number: G06Q10/1093

Abstract: An online concierge system receives a goal and availability information for a picker, in which the availability information describes time slot-location pairs for which the picker is available. The system accesses and applies a first and a second machine learning model to predict a likelihood that an order will be available for service and an amount of earnings for servicing the order, respectively, for each time slot-location pair. The system computes an estimated amount of earnings for each time slot-location pair based on the predictions and generates suggested schedules that each includes one or more time slot-location pairs. For each suggested schedule, the system computes a total estimated amount of earnings based on the estimated amount of earnings and one or more costs. The system identifies a suggested schedule for achieving the goal based on the total estimated amount of earnings or an estimated amount of time included in the suggested schedule.

公开(公告)号：US20240144172A1

公开(公告)日：2024-05-02

申请号：US17977724

申请日：2022-10-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Apurvaa Subramanian ,  Girija Narlikar ,  Chakshu Ahuja ,  Karuna Ahuja ,  Radhika Goel ,  Sneha Chandrababu

IPC: G06Q10/08 ,  G06Q30/02 ,  G06Q30/06

CPC classification number: G06Q10/087 ,  G06Q10/083 ,  G06Q30/0206 ,  G06Q30/0635

Abstract: An online concierge system facilitates a concierge service for ordering, procurement, and delivery of food items from physical retailers. The order fulfillment is based in part on automatically inferring one or more quality metrics, such as remaining shelf-life, associated with perishable food items. A picker shopping on behalf of a customer may capture images of available food items for the order using a picker client device. The images are processed through a machine learning model to infer the one or more quality metrics, and a price is then determined based in part on a dynamic pricing model. The online concierge system communicates with a customer client device to meet quality characteristics and pricing preferences set by the customer. The online concierge system may further facilitate a checkout process for the items obtained by the picker and may facilitate delivery of the items by the picker to the customer.

公开(公告)号：US20240135423A1

公开(公告)日：2024-04-25

申请号：US18047990

申请日：2022-10-18

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shitao Wang ,  Apurvaa Subramaniam ,  Nicholas George Gordenier

IPC: G06Q30/06

CPC classification number: G06Q30/0631

Abstract: An online concierge system generates an aggregated lift score for a test feature for the online concierge system. The online concierge presents prioritized items from a set of item groups to two sets of users: a test set and a control set. The online concierge system uses the test feature to present prioritized items to users in the test set, and the online concierge system uses existing functionality to present prioritized items to users in the control set. For each test group, the online concierge system creates holdout subsets out of the test set and the control set. The online concierge system tracks user interactions with items in an item group and computes a group lift score for the item group. The online concierge system generates an aggregated lift score for the test feature based on the group lift scores and presents items to users based on the aggregated lift score.

公开(公告)号：US20240112238A1

公开(公告)日：2024-04-04

申请号：US17956217

申请日：2022-09-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Girija Narlikar ,  Karuna Ahuja ,  Radhika Goel ,  Chakshu Ahuja ,  Xiaoming Zhang ,  Devlina Das

IPC: G06Q30/06 ,  G06Q30/02 ,  G06Q30/08 ,  G06Q50/00

CPC classification number: G06Q30/0631 ,  G06Q30/0203 ,  G06Q30/0603 ,  G06Q30/08 ,  G06Q50/01

Abstract: An online concierge system receives a request to purchase a gift for a user of the system and retrieves a profile associated with the user. Based on the profile and attributes of items included among inventories of one or more retailer locations, the system identifies a set of candidate items for which the user is likely to have an affinity. The system accesses a machine learning model trained to predict a giftability score for an item and applies the model to attributes of each candidate item to predict its giftability score. Based on its giftability score and the profile, the system computes a composite score for each candidate item indicating an appropriateness of gifting the candidate item to the user. The system ranks the set of candidate items based on the composite scores and selects one or more suggested items for gifting to the user based on the ranking.