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    UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS
    41.
    发明申请
    UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS 有权

    公开(公告)号：US20220197961A1

    公开(公告)日：2022-06-23

    申请号：US17131488

    申请日：2020-12-22

    Applicant: Dropbox, Inc.

    Inventor： Jongmin Baek ,  Jiarui Ding ,  Ermo Wei ,  Scott McCrae

    IPC: G06F16/958 ,  G06F40/284 ,  G06N3/04 ,  G06N20/00

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model. Based on the digital connections, the disclosed systems can surface one or more digital content suggestions to a user interface of a client device.

    CONTENT TYPE EMBEDDINGS
    42.
    发明申请
    CONTENT TYPE EMBEDDINGS 有权

    公开(公告)号：US20210089822A1

    公开(公告)日：2021-03-25

    申请号：US16675671

    申请日：2019-11-06

    Applicant: Dropbox, Inc.

    Inventor： Jongmin Baek ,  Jiarui Ding ,  Neeraj Kumar

    IPC: G06K9/62 ,  G06F16/2457 ,  G06F16/178 ,  G06N20/00

    Abstract: Techniques for learning and using content type embeddings. The content type embeddings have the useful property that a distance in an embedding space between two content type embeddings corresponds to a semantic similarity between the two content types represented by the two content type embeddings. The closer the distance in the space, the more the two content types are semantically similar. The farther the distance in the space, the less the two content types are semantically similar. The learned content type embeddings can be used in a content suggestion system as machine learning features to improve content suggestions to end-users.

    CROSS-MODEL SCORE NORMALIZATION
    44.
    发明申请
    CROSS-MODEL SCORE NORMALIZATION 有权

    公开(公告)号：US20250131337A1

    公开(公告)日：2025-04-24

    申请号：US19005559

    申请日：2024-12-30

    Applicant: Dropbox, Inc.

    Inventor： Jongmin Baek

    IPC: 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
    45.
    发明授权
    Cross-model score normalization 有权

    公开(公告)号：US12210948B2

    公开(公告)日：2025-01-28

    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

    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.

    UTILIZING A NATURAL LANGUAGE MODEL TO DETERMINE A PREDICTED ACTIVITY EVENT BASED ON A SERIES OF SEQUENTIAL TOKENS
    48.
    发明申请
    UTILIZING A NATURAL LANGUAGE MODEL TO DETERMINE A PREDICTED ACTIVITY EVENT BASED ON A SERIES OF SEQUENTIAL TOKENS 有权

    公开(公告)号：US20220107852A1

    公开(公告)日：2022-04-07

    申请号：US17065266

    申请日：2020-10-07

    Applicant: Dropbox, Inc.

    Inventor： Ranjitha Gurunath Kulkarni ,  Xingyu Xiang ,  Jongmin Baek ,  Ermo Wei

    IPC: G06F9/54 ,  G06N5/02 ,  G06N3/08 ,  G06F40/284

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that can leverage a natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity. In particular, the disclosed systems can tokenize activity event vectors to generate a series of sequential tokens that correspond to recent user activity of one or more user accounts. In addition, the disclosed systems can, for each candidate (e.g., hypothetical) user activity, augment the series of sequential tokens to include a corresponding token. Based on respective probability scores for each of the augmented series of sequential tokens, the disclosed systems can identify as the predicted user activity, a candidate user activity corresponding to one of the augmented series of sequential tokens associated with a highest probability score. Based on the predicted user activity, the disclosed systems can surface one or more suggestions to a client device.

    DATA STORAGE SCHEME SWITCHING IN A DISTRIBUTED DATA STORAGE SYSTEM
    49.
    发明申请
    DATA STORAGE SCHEME SWITCHING IN A DISTRIBUTED DATA STORAGE SYSTEM 有权

    公开(公告)号：US20210240372A1

    公开(公告)日：2021-08-05

    申请号：US16839605

    申请日：2020-04-03

    Applicant: Dropbox, Inc.

    Inventor： Michael Loh ,  Daniel R. Horn ,  Andraz Kavalar ,  David Lichtenberg ,  Austin Sung ,  Shi Feng ,  Jongmin Baek

    IPC: G06F3/06 ,  H03M13/15 ,  G06N20/00 ,  G06N7/00

    Abstract: Systems and methods for dynamic and automatic data storage scheme switching in a distributed data storage system. A machine learning-based policy for computing probable future content item access patterns based on historical content item access patterns is employed to dynamically and automatically switch the storage of content items (e.g., files, digital data, photos, text, audio, video, streaming content, cloud documents, etc.) between different data storage schemes. The different data storage schemes may have different data storage cost and different data access cost characteristics. For example, the different data storage schemes may encompass different types of data storage devices, different data compression schemes, and/or different data redundancy schemes.

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