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.

    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.

    Route Selection for Obtaining Items in a Warehouse

    公开(公告)号:US20250139686A1

    公开(公告)日:2025-05-01

    申请号:US18496679

    申请日:2023-10-27

    Applicant: Maplebear Inc.

    Abstract: Different possible candidate routes for efficiently obtaining a set of items at given retailer premises are generated and simulated to estimate degrees of difficulty of the various routes, such as how long they are expected to take. The current conditions can be inferred based on analysis of environment data received from a plurality of devices associated with users shopping for items on the retailer premises, such as location data, camera data, or comments related to the retailer premises. The simulation takes into account current or expected conditions in the environment of the retailer premises, such as obstructions, alternative placements of items, etc. Routes with least degrees of difficulty may be presented to the users shopping for the items so that the users can use the most efficient routes when obtaining the items.

    Routing Based on Cross-Order Image Recognition

    公开(公告)号:US20250111329A1

    公开(公告)日:2025-04-03

    申请号:US18375109

    申请日:2023-09-29

    Applicant: Maplebear Inc.

    Abstract: An online concierge system uses images captured for fulfillment of a first order to affect item information of a second order. When a picker fulfills the first order in a physical warehouse, the picker captures an image of the physical warehouse, for example to capture an image of potential replacement items. The online concierge system detects items in the image along with a location of the item in the physical warehouse based on the image. The detected items and respective locations may then be used to modify a second order, for example to route a picker for the second order to updated or alternate locations of the detected items.

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