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    Enhancing a digital image
    23.
    发明授权
    Enhancing a digital image 有权

    公开(公告)号：US10290086B2

    公开(公告)日：2019-05-14

    申请号：US15722806

    申请日：2017-10-02

    Applicant: DROPBOX, INC.

    Inventor： Jongmin Baek

    IPC: G06T1/00 ,  G06T5/00 ,  G06T5/20 ,  G06T7/11 ,  G06T7/12 ,  G06T7/136 ,  G06T7/168

    Abstract: One or more embodiments of an image enhancement system enable a computing device to generate an enhanced digital image. In particular, a computing device can enhance a digital image including, for example, a photograph of a whiteboard, document, chalkboard, or other object having a uniform background. The computing device can determine modifications to apply to the digital image by minimizing an energy heuristic that both causes pixels of the digital image to change to a uniform color (e.g., white) and preserves gradients from the digital image. The computing device can further generate an enhanced digital image by applying the determined modifications to the digital image.

    CROSS-MODEL SCORE NORMALIZATION
    26.
    发明公开
    CROSS-MODEL SCORE NORMALIZATION 审中-公开

    公开(公告)号：US20240296388A1

    公开(公告)日：2024-09-05

    申请号：US18659482

    申请日：2024-05-09

    Applicant: Dropbox, Inc.

    Inventor： Jongmin Baek

    IPC: G06N20/00 ,  G06F16/16 ,  G06F16/93 ,  G06N5/04 ,  H04L67/10

    CPC classification number: G06N20/00 ,  G06F16/16 ,  G06F16/93 ,  G06N5/04 ,  H04L67/10

    Abstract: Computer-implemented techniques encompass using distinct machine learning sub-models to score respective types of candidate content for the purpose of providing personalized content suggestions to end-users of a content management system. The relevancy scores generated by the distinct sub-models are mapped to expected end-user interaction scores of the candidate content scored. Content suggestions are provided at end-users' computing devices where the suggested content is selected from the candidate content based on the expected end-user interaction scores of the candidate content. For each distinct sub-model, a normalizing mapping function is solved using an optimizer that maps the relevancy scores generated by the sub-model for the candidate content to expected end-user interaction scores for the candidate content. The expected end-user interaction scores are comparable across the distinct sub-models and can be used to rank content suggestions across the distinct sub-models.

    CROSS-MODEL SCORE NORMALIZATION
    29.
    发明申请
    CROSS-MODEL SCORE NORMALIZATION 有权

    公开(公告)号：US20210089963A1

    公开(公告)日：2021-03-25

    申请号：US16681599

    申请日：2019-11-12

    Applicant: Dropbox, Inc.

    Inventor： Jongmin Baek

    IPC: G06N20/00 ,  G06N5/04 ,  G06F16/93 ,  G06F16/16

    Abstract: Computer-implemented techniques encompass using distinct machine learning sub-models to score respective types of candidate content for the purpose of providing personalized content suggestions to end-users of a content management system. The relevancy scores generated by the distinct sub-models are mapped to expected end-user interaction scores of the candidate content scored. Content suggestions are provided at end-users' computing devices where the suggested content is selected from the candidate content based on the expected end-user interaction scores of the candidate content. For each distinct sub-model, a normalizing mapping function is solved using an optimizer that maps the relevancy scores generated by the sub-model for the candidate content to expected end-user interaction scores for the candidate content. The expected end-user interaction scores are comparable across the distinct sub-models and can be used to rank content suggestions across the distinct sub-models.

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