Forward contracts in e-commerce
    11.
    发明授权

    公开(公告)号:US11854052B2

    公开(公告)日:2023-12-26

    申请号:US17396900

    申请日:2021-08-09

    Applicant: eBay Inc.

    CPC classification number: G06Q30/0283 G06N20/00 G06Q30/0206 G06Q30/0619

    Abstract: A method of training a machine learning model to determine an item margin is provided. The method includes monitoring a first value for a first item having attributes and monitoring a first value for a second type of item having attributes where an attribute of the first attributes is the same as an attribute of the second attributes. The method also includes determining a first margin based on the first values. The first attributes, the second attributes, and the first margin are input as training data for the machine learning model where the machine learning model is trained with the training data. The monitoring operations for the first item and the second item are repeated to obtain a second value for the first and second items. Furthermore, the trained machine learning model is applied to the second values to determine a second margin.

    Adaptive Timing Prediction For Updating Information

    公开(公告)号:US20230410189A1

    公开(公告)日:2023-12-21

    申请号:US18239253

    申请日:2023-08-29

    Applicant: eBay Inc.

    CPC classification number: G06Q30/08 G06N5/02

    Abstract: Technologies are disclosed herein for distributing information. The disclosed technologies determine an application element configured to receive information that is updated at a variable rate, the information pertaining to an object. Feature data is received that is associated with the object and data associated with use of the application element. The feature data includes a time horizon for the object and supplemental information associated with the object. Based on the feature data and the data associated with use of the application element, a first rate is predicted for sending the information about the object to the application element. The information is sent to the application element at the first rate.

    PROMOTED OFFERS
    13.
    发明申请

    公开(公告)号:US20230065751A1

    公开(公告)日:2023-03-02

    申请号:US17409933

    申请日:2021-08-24

    Applicant: eBay Inc.

    Abstract: A method of training a machine learning model to promote an item is provided. Acceptance activity of a first entity is monitored and a determination of a first likelihood that the first entity accepts a future offer is made. The acceptance activity and the first likelihood are input as training data for the machine learning model for training purposes. An offer having an acceptance window and a characteristic is received from a second entity for a first item associated with the first entity. A second likelihood that the first entity will accept the offer is determined and an acceptance window is adjusted based on the determination. When the acceptance window closes, the method searches for a second item associated with a third entity based having the characteristic where an offer for the second item is sent to the second entity on behalf of the third entity.

    Techniques of prefetching operation cost based digital content and digital content with emphasis

    公开(公告)号:US11321737B2

    公开(公告)日:2022-05-03

    申请号:US16713893

    申请日:2019-12-13

    Applicant: eBay Inc.

    Abstract: Techniques for prefetching operation cost based digital content and digital content with emphasis that overcome the challenges of conventional systems are described. In one example, a computing device may receive digital content representations of digital content from a service provider system, which are displayed on a user interface of the computing device. Thereafter, the computing device may also receive digital content as prefetches having a changed display characteristic as emphasizing a portion of the digital content based on a model trained using machine learning. Alternatively, the computing device may receive digital content as a prefetch based on a model trained using machine learning in which the model addresses a likelihood of conversion of a good or service and an operation cost of providing the digital content. Upon receiving a user input selecting one of the digital content representations, digital content is rendered in the user interface of the computing device.

    Forward contracts in e-commerce
    17.
    发明授权

    公开(公告)号:US12198169B2

    公开(公告)日:2025-01-14

    申请号:US18388013

    申请日:2023-11-08

    Applicant: eBay Inc.

    Abstract: A method of training a machine learning model to determine an item margin is provided. The method includes monitoring a first value for a first item having attributes and monitoring a first value for a second type of item having attributes where an attribute of the first attributes is the same as an attribute of the second attributes. The method also includes determining a first margin based on the first values. The first attributes, the second attributes, and the first margin are input as training data for the machine learning model where the machine learning model is trained with the training data. The monitoring operations for the first item and the second item are repeated to obtain a second value for the first and second items. Furthermore, the trained machine learning model is applied to the second values to determine a second margin.

    Different Action User-Interface Components In A Comparison View

    公开(公告)号:US20240412270A1

    公开(公告)日:2024-12-12

    申请号:US18811577

    申请日:2024-08-21

    Applicant: eBay Inc.

    Abstract: Different action user-interface components in a comparison view are described. Initially, a selection is received to display a comparison view via a user interface of a listing platform. Multiple listings of the listing platform are selected for inclusion in the comparison view. A comparison view system determines which action of a plurality of actions, used by the listing platform, to associate with each of the listings. A display device displays the multiple listings concurrently in a comparison view via a user interface of the listing platform and also displays an action user-interface component (e.g., a button) in each of the plurality of listings. The action user-interface component is selectable to initiate the action associated with the respective listing. In accordance with the described techniques, the action user-interface component displayed in at least two of the multiple listings is selectable to initiate different actions in relation to the respective listing.

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