公开(公告)号：US11947601B2

公开(公告)日：2024-04-02

申请号：US17815478

申请日：2022-07-27

Applicant: Dropbox, Inc.

Inventor： Jongmin Baek ,  Jiarui Ding

IPC: G06F16/906 ,  G06F3/0482 ,  G06F16/9035

CPC classification number: G06F16/906 ,  G06F3/0482 ,  G06F16/9035

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and suggesting content collections for user accounts of a content management system using combinations of content-based features such as textual signals and visual signals. In some embodiments, the disclosed systems select a seed content item from among a plurality of content items associated with a user account within a content management system. From the seed content item, the disclosed systems can determine one or more germane topics and can cluster additional content items in relation to the germane topic(s). In addition, the disclosed systems can select one or more content items from a content cluster to provide as a suggested content collection.

公开(公告)号：US11568018B2

公开(公告)日：2023-01-31

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 ,  G06N20/00 ,  G06N3/04 ,  G06F40/284

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.

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.

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.