DETERMINING PURCHASE SUGGESTIONS FOR AN ONLINE SHOPPING CONCIERGE PLATFORM

    公开(公告)号:US20240428314A1

    公开(公告)日:2024-12-26

    申请号:US18212122

    申请日:2023-06-20

    Abstract: The present disclosure is directed to determining purchase suggestions for an online shopping concierge platform. In particular, the methods and systems of the present disclosure may receive, from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform; determine, based at least in part on one or more machine learning (ML) models and the data indicating the interaction(s), a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item; and generate and communicate data describing a graphical user interface (GUI) comprising at least a portion of a listing of one or more purchase suggestions including the suggestion to purchase the particular item.

    PREDICTING CAUSES OF CHURN AND ALLOCATING FULFILLMENT RESOURCES BASED ON PREDICTION

    公开(公告)号:US20250156930A1

    公开(公告)日:2025-05-15

    申请号:US18510554

    申请日:2023-11-15

    Applicant: Maplebear Inc.

    Abstract: An online concierge system identifies churn of a customer, which occurs when the customer does not perform a specific action within a threshold time period. The online concierge system determines an event causing churn of the customer based on characteristics of the customer and attributes describing prior fulfillment of an order for the customer. To mitigate different events causing churn, the online concierge system maps areas of expertise of pickers for different aspects of order fulfillment to corresponding events. Through a trained picker scoring model, the online concierge system determines picker scores for different pickers fulfilling an order for a customer using characteristics of pickers, including an expertise, characteristics of the customer, and an event causing churn of the customer. Based on the picker scores, the online concierge system selects a specific picker for fulfilling a subsequent order from the customer.

    TRAINED COMPUTER MODEL FOR IDENTIFICATION OF WRONG DELIVERY LOCATION FOR AN ORDER PLACED AT AN ONLINE SYSTEM

    公开(公告)号:US20250148412A1

    公开(公告)日:2025-05-08

    申请号:US18500930

    申请日:2023-11-02

    Applicant: Maplebear Inc.

    Abstract: A trained computer model for automatic identification of a wrong delivery location for an order placed at an online system. The online system receives, via a user interface, a user input that includes a delivery location for the order. The online system compares the received delivery location with a stored delivery location for the user. Responsive to identifying that the received and stored delivery locations are different, the online system accesses and applies a computer model to predict, based on features of the order, a likelihood of the received delivery location being correct. The online system generates, based on the predicted likelihood, a confidence score of the received delivery location being correct. Responsive to the confidence score being below a threshold score, the online system causes a device of the user to display a user interface with a message prompting the user to verify accuracy of the received delivery location.

    AUTOMATIC GENERATION OF PERSONALIZED COLLECTION OF ITEMS AROUND A THEME AT AN ONLINE SYSTEM

    公开(公告)号:US20250124484A1

    公开(公告)日:2025-04-17

    申请号:US18485581

    申请日:2023-10-12

    Applicant: Maplebear Inc.

    Abstract: An online system automatically generates a personalized collection of items around a theme. The online system generates a prompt for input into a language model, the prompt including information about a plurality of items and a text describing the theme around which the collection of items will be built. The online system requests the language model to generate, based on the prompt, a list of products eligible for building the collection of items. The online system accesses a computer model trained to identify a set of items personalized for a user of the online system. The computer model identifies, based on the list of eligible products and information about the user, the set of items for populating the collection of items. The online system causes a device of the user to display a user interface with the collection of items for inclusion into a cart of the user.

    AUTOMATIC QUALITY ASSESSMENT OF AN ITEM DURING ORDER FULFILLMENT

    公开(公告)号:US20250139137A1

    公开(公告)日:2025-05-01

    申请号:US18495581

    申请日:2023-10-26

    Applicant: Maplebear Inc.

    Abstract: Use of a language model to automatically perform visual assessment of quality of an item being fulfilled by a picker. The online system receives an image of the item and identifies a set of potential problems associated with the item. The online system generates a plurality of prompts for input into the language model including the image and one or more questions each corresponding to a respective potential problem of the set potential problems. The online system requests the language model to generate, based on the plurality of prompts, a feedback response for each potential problem. The online system generates an aggregated output by aggregating the feedback response for each potential problem, and based on the aggregated output, a second message that identifies one or more relevant problems associated with the item. The online system causes a device of the picker to display the second message.

    Replacing Online Conversations Using Large Language Machine-Learned Models

    公开(公告)号:US20240320523A1

    公开(公告)日:2024-09-26

    申请号:US18605580

    申请日:2024-03-14

    Applicant: Maplebear Inc.

    CPC classification number: G06N5/022 G06N5/04

    Abstract: An online system performs an inference task in conjunction with the model serving system or the interface system to continuously monitor conversations between requesting users and fulfillment users to determine whether the online system can intervene to automatically respond to a message sent by a sending party, rather than prompting the receiving party for a manual reply. Upon inferring that a message can be automatically responded to, the online system automatically provides a response to the message without the receiving party's manual involvement. The online system can further be augmented to classify and reroute certain requesting user or fulfillment user queries that impact an order's end state by intercepting the conversation on behalf of either party and performing one or more automated actions. If the message is action-oriented, the online system may perform one or more automated actions in response to the message.

    SHARING AND GENERATING PREPOPULATED CARTS BY AN ONLINE CONCIERGE SYSTEM

    公开(公告)号:US20240177219A1

    公开(公告)日:2024-05-30

    申请号:US18070382

    申请日:2022-11-28

    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.

    USING GENERATIVE ARTIFICIAL INTELLIGENCE (AI) FOR AUTOMATED DIGITAL FLYER CONTENT GENERATION

    公开(公告)号:US20240403923A1

    公开(公告)日:2024-12-05

    申请号:US18677640

    申请日:2024-05-29

    Applicant: Maplebear Inc.

    Abstract: An online system generates digital flyers using a generative model. The online system receives, from a client device, a request to generate a digital flyer. The request includes one or more design conditions for the digital flyer. For example, the design conditions may specify one or more cornerstone items, a theme, a template flyer, other target characteristics, etc. The online system further accesses an item catalog storing item data. The online system generates a query for a generative model including a prompt to generate the digital flyer, the one or more design conditions, and item data accessed from the item catalog. The online system provides the query to a model serving system, which executes the generative model with the query to return a batch of one or more digital flyers. The online system provides a first digital flyer in the batch of one or more digital flyers for presentation.

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