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：US11567812B2

公开(公告)日：2023-01-31

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 ,  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：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：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.