Dropbox, Inc.

Morgan Zerby

Shubham Goel

Sigalit Sadeh

Jiarui Ding

Ermo Wei

Maor Bar Asher

Christopher T Lesniewski-Laas

Vasanth Krishna Namasivayam

Tal Tendler

Rottem Shaul Shiloni

Win Suen

Tristan Inghelbrecht

Sohrab Seyed Fathi

Prasang Upadhyaya

Aleksander Dash

Simon Shubbar

Wesley Liao

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

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating a dynamic facet by using a large language model. For example, the disclosed systems extract raw facet data from a plurality of content items stored in a content management system. In addition, the disclosed systems determine one or more facet content groups by grouping the plurality of content items according to the raw facet data. Further, the disclosed systems generate a facet prompt from the one or more facet content groups. Moreover, the disclosed systems generate a dynamic facet by providing the facet prompt to a large language model.

Dynamic facet generation using large language models

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing synthetic visualizations representative of content collections within a content management system. In some cases, the disclosed systems generate a synthetic visualization based on content features that indicate relevance of content items with respect to a user account to emphasize more relevant content items within the synthetic visualization and/or to represent descriptive content attributes of the content items. For example, the disclosed systems can generate a synthetic phrase that represents a content collection and can further generate a synthetic visualization from the synthetic phrase utilizing a synthetic visualization machine learning model.

Synthesizing visualizations for content collections

The present technology provides an improved search interface that provides suggested search terms in a variety of categories. The suggested search terms are first presented in an 'empty search state' i.e., before the user has entered any search terms. And the suggested search terms are repeatedly refined as the user provides inputs into the search interface until a number of search results are few enough that the interface provides search results. The present technology also provides improved search suggestions. In particular, the present technology utilizes predictive algorithms including machine learning algorithms to intelligently provide suggested search terms in the variety of categories.

Empty search state

Growing community of inventors

Kirkland, WA, United States of America

Morgan Zerby