Dropbox, Inc.

Bryan Guillemette

Nipunn Koorapati

Royce Ausburn

Pranav Vishnu Ramabhadran

Yuyang Guo

Gautam Gupta

Parker Timmerman

Adam Arbree

Sourabh Yerfule

Jonathan Chien

Rishabh Jain

Zihao Yang

David Aeschlimann

David Lichtenberg

Panashe Machinda Fundira

Brandon Obas

Mingming Liu

Zachary Smetana

Vidit Bhargava

Michael Nilsson

Stanimir Pehlivanov

Kevin Li

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.

Classifying and organizing digital content items automatically utilizing content item classification models

Technologies for implementing customized behaviors for content items are provided. An example method can include receiving, from a user account registered with a content management system, a request to access a content item managed by the content management system for the user account, the content item having one or more behaviors configured for an attribute associated with the content item and/or the content item associated with the attribute; obtaining, from a representation of a remote state of content items associated with the user account, metadata defining the attribute associated with the content item; based on the metadata, determining the one or more behaviors configured for the attribute and/or the content item associated with the attribute; and applying the one or more behaviors to the content item.

Per-node metadata for custom node behaviors across platforms

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Palo Alto, CA, United States of America

Bryan Guillemette