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.

    RECOMMENDATION SYSTEM USING A RECIPE DATABASE AND CO-OCCURRENCES OF HISTORICAL ITEM SELECTIONS

    公开(公告)号:US20250069126A1

    公开(公告)日:2025-02-27

    申请号:US18236342

    申请日:2023-08-21

    Applicant: Maplebear Inc.

    Abstract: An online system identifies recipes that are most likely to be pertinent to particular users of the system. To do so, the online system uses an association table containing degrees of association between pairs of possible ingredients, identifying degrees of association between the constituent ingredients of various possible recipes and between ingredients from known user personalization data about the user to whom recipes are being recommended. These degrees of association are used to compute a score for each recipe as a whole, with the highest scores indicating the most pertinent recipes for the user in question. The most pertinent recipes, and/or the constituent ingredients of those recipes, are recommended to the user, and the system may additionally aid the user in obtaining the full complement of ingredients for a recommended recipe. The system may also build the association table as a co-occurrence graph of pairs of items that were previously purchased together by users of the system.

    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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