Dropbox, Inc.

Neeraj Kumar

Jongmin Baek

Jiarui Ding

Sigalit Sadeh

Vasanth Krishna Namasivayam

Christopher T Lesniewski-Laas

Tal Tendler

Sohrab Seyed Fathi

Renana Albeg

Orly Tishbi

Nadav Goldin

This disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that can detect scene types across various portions of media content and display collections that organize segments (or portions) of media content (e.g., videos or images) according to the detected scene types for the media content files. For example, the disclosed systems can automatically identify content segments of media content that belong to one or more identified scene types and display the content segments organized by the different scene types. In order to determine the scene types for the content segments of the media content files, the disclosed systems can utilize machine learning that determines relevancies between data of the media content files and the scene types. Furthermore, the disclosed systems can display, within a GUI, the groupings of media content segments organized by the different scene types.

Organizing media content items utilizing detected scene types

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.

Content type embeddings

One or more embodiments of a document organization system quickly and conveniently provide digital documents to a user on a client device based on a physical object. In particular, the document organization system can receive an image of a physical document and an identifier from a first client device, identify digital documents that match the physical document, and provide the matching digital documents to a second client device, which displays the identifier. In another embodiment, the document organization system allows a user to bind digital documents to a physical object and later recall the digital documents using the physical object. In addition, the document organization system can store and recall the layout arrangement of digital documents on a client device when binding and recalling the digital documents to the physical object.

Intelligently identifying and presenting digital documents

One or more embodiments of a content naming system provide machine-learned name suggestions to a user for naming content items. Specifically, an online content management system can train a machine-learning model to identify a naming pattern from previously stored content items corresponding to a user account of the user. The online content management system uses the machine-learning model to determine a plurality of name suggestions for naming a content item associated with the user account. One or more embodiments provide graphical elements corresponding to the name suggestions within a graphical user interface. The user can select one or more graphical elements to add the corresponding name suggestion(s) to the name of the content item.

Providing intelligent file name suggestions

One or more embodiments of a content system provide machine-learned storage location recommendations for storing content items. Specifically, an online content management system can train a machine-learning model to identify a storage pattern from previously stored content items in a plurality of storage locations corresponding to a user account of a user. Training the machine-learning model includes training a plurality of classifiers for the plurality of storage locations. The online content management system uses the classifiers to determine whether a content item is similar to the content items in any of the storage locations, and based on the output of the classifiers, provides graphical elements indicating recommended storage locations within a graphical user interface. The user can select a graphical element to move the content item to the corresponding storage location.

Growing community of inventors

San Francisco, CA, United States of America

Neeraj Kumar