公开(公告)号：US20220277020A1

公开(公告)日：2022-09-01

申请号：US17663628

申请日：2022-05-16

Applicant: Dropbox, Inc.

Inventor： Ermo Wei ,  Jialiang Li ,  Kaiyue Sun ,  Li Chen Koh ,  Mingye Xia ,  Yu Zhang ,  Yuyang Guo

IPC: G06F16/27

Abstract: One or more embodiments of a synchronization system facilitate selectivity synchronizing digital content items from a collection of digital content items to a local storage of a client device. In particular, one or more embodiments described herein collect and analyze recall data for users of a digital content management system with respect to digital content items to determine synchronization scores for the digital content items. One or more embodiments described herein further include selectively identifying a subset of the digital content items based on the synchronization scores to recommend for synchronization to a local storage of a client device.

公开(公告)号：US11853817B2

公开(公告)日：2023-12-26

申请号：US18156275

申请日：2023-01-18

Applicant: Dropbox, Inc.

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

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

CPC classification number: G06F9/542 ,  G06F40/284 ,  G06N3/08 ,  G06N5/02

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.

公开(公告)号：US20230186071A1

公开(公告)日：2023-06-15

申请号：US17548519

申请日：2021-12-11

Applicant: Dropbox, Inc.

Inventor： Tristan Frederick Rice ,  Jongmin Baek ,  Ermo Wei ,  Morgan Zerby ,  Win Suen ,  David Lichtenberg ,  Thomas Berg ,  Christopher Lesniewski-Laas ,  Brandon Obas ,  Mingming Liu ,  Zachary Smetana ,  Bryan Guillemette ,  Panashe Machinda Fundira ,  Kevin Li ,  Vidit Bhargava

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine-learning models to classify content items and automatically organize the content items within a file structure according to their content item classifications. For instance, a content item classification system generates one or more content item classification models to determine classifications for content items and/or folders. In some instances, the classification system detects when new content items are added to a smart folder, determines destination folders to which the content items belong based on classifying the content items, and automatically moves the content items accordingly. In various instances, the classification system generates and utilizes a classification model to organize content items into dynamically-generated folders. In example implementations, the classification system generates and utilizes a classification model to automatically organize existing content items into existing folders.

公开(公告)号：US20230185768A1

公开(公告)日：2023-06-15

申请号：US17548516

申请日：2021-12-11

Applicant: Dropbox, Inc.

Inventor： Tristan Inghelbrecht ,  Jongmin Baek ,  Ermo Wei ,  Morgan Zerby ,  Win Suen ,  Shubham Goel

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine-learning models to classify content items and automatically organize the content items within a file structure according to their content item classifications. For instance, a content item classification system generates one or more content item classification models to determine classifications for content items and/or folders. In some instances, the classification system detects when new content items are added to a smart folder, determines destination folders to which the content items belong based on classifying the content items, and automatically moves the content items accordingly. In various instances, the classification system generates and utilizes a classification model to organize content items into dynamically-generated folders. In example implementations, the classification system generates and utilizes a classification model to automatically organize existing content items into existing folders.

公开(公告)号：US20240320288A1

公开(公告)日：2024-09-26

申请号：US18680956

申请日：2024-05-31

Applicant: Dropbox, Inc.

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

IPC: G06F16/958 ,  G06F16/14 ,  G06F40/284 ,  G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N5/02 ,  G06N20/00

CPC classification number: G06F16/958 ,  G06F16/14 ,  G06F40/284 ,  G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N20/00 ,  G06N5/02

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：US12008065B2

公开(公告)日：2024-06-11

申请号：US18153960

申请日：2023-01-12

Applicant: Dropbox, Inc.

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

IPC: G06F16/958 ,  G06F16/14 ,  G06F40/284 ,  G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N20/00 ,  G06N5/02

CPC classification number: G06F16/958 ,  G06F16/14 ,  G06F40/284 ,  G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N20/00 ,  G06N5/02

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：US11334596B2

公开(公告)日：2022-05-17

申请号：US15964267

申请日：2018-04-27

Applicant: Dropbox, Inc.

Inventor： Ermo Wei ,  Jialiang Li ,  Kaiyue Sun ,  Li Chen Koh ,  Mingye Xia ,  Yu Zhang ,  Yuyang Guo

IPC: G06F16/27 ,  G06F3/0482 ,  H04L67/306

Abstract: One or more embodiments of a synchronization system facilitate selectivity synchronizing digital content items from a collection of digital content items to a local storage of a client device. In particular, one or more embodiments described herein collect and analyze recall data for users of a digital content management system with respect to digital content items to determine synchronization scores for the digital content items. One or more embodiments described herein further include selectively identifying a subset of the digital content items based on the synchronization scores to recommend for synchronization to a local storage of a client device.

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.

公开(公告)号：US20230161648A1

公开(公告)日：2023-05-25

申请号：US18156275

申请日：2023-01-18

Applicant: Dropbox, Inc.

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

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

CPC classification number: G06F9/542 ,  G06F40/284 ,  G06N3/08 ,  G06N5/02

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.

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.