公开(公告)号：US20230169139A1

公开(公告)日：2023-06-01

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 ,  G06N20/00 ,  G06F40/284 ,  G06N3/045

CPC classification number: G06F16/958 ,  G06F40/284 ,  G06N3/045 ,  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：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：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.