Dropbox, Inc.

Devin Mancuso

Bonita Leung

Benjamin Joseph Calabrese

Jason Stakelon

Austin Sung

Arunsunai Anbukarasi Anbalagapandian

Viksit Gaur

Rituparna Vincent

Maor Bar Asher

Ryan YeePin Yheng

Brett Bergeron

Shweta Kode

Alan C Chu

Richard Chan

Walter Somerville

Udara Jayawardena

Christopher Meeks

Theo Richardson

Alan Chu

Fiona Rolander

Willa Peng

Austin Adams

Kris Puckett

Eric Alves

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating content clusters from topic data and focus data, generating content collections from content clusters, storing and restoring desktop scene layouts, and storing and arranging video call scenes. In some embodiments, the disclosed systems generate content clusters based on topic data and focus data associated with content items within a content management system and/or accessed via the internet. The disclosed systems can also generate content collections for a user account of the content management system from the content clusters. In some embodiments, the content scene system can further store and restore desktop scene layouts for arranging application windows presenting content items. Further, the disclosed systems can store and arrange particular desktop scene layouts for video call scenes.

Generating content collections from digital content clusters

The present disclosure relates to systems, methods, and non-transitory computer-readable media that dynamically capture, organize, and utilize digital media clips. For example, in one or more implementations, the disclosed systems can capture and generate digital media clips of content items that include both content metadata of the content items as well as contextual metadata of contextual signals surrounding the content item. Additionally, in some implementations, the disclosed systems analyze contextual metadata to search, retrieve, discover, and organize new and existing digital media clips. Further, in various implementations, the disclosed systems facilitate generating digital media clip libraries as well as the creation of digital media collections, where different types of digital media clips can be combined in a cohesive interactive graphical user interface.

Generating and utilizing digital media clips based on contextual metadata from digital environments

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and maintaining tokenized assets associated with a user account of a content management system. In particular, the disclosed systems can facilitate generating tokenized assets from content items within a content management system. In addition, the disclosed systems can identify tokenized assets associated with a user account within a content management system for facilitating access to gated content. For example, the disclosed systems can generate and provide a digital passport that serves as a centralized location for accessing and managing tokenized assets associated with a user account.

Generating and managing tokenized assets utilizing blockchain minting and a digital passport

Generating digital content clusters based on web activity

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a content stack utilizing one or more machine-learning models. In some implementations, the disclosed systems generate and provide, to a user account, a content stack that includes content items corresponding to a topic prompt for the user account. For instance, in some implementations, the disclosed systems utilize content-based signals and account-based signals to generate an account-specific stack formulation graph that represents a plurality of content items and relationships of the content items with each other and with the user account. Additionally, in some implementations, the disclosed systems analyze the account-specific stack formulation graph to generate a content stack from the plurality of content items, the content stack comprising a set of content items corresponding to the topic prompt.

Generating and providing content stacks utilizing machine-learning models

Storing and restoring desktop application scenes

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and searching a hybrid search index. In some embodiments, the disclosed systems generate a hybrid search index that comprises one or more content items stored at a content management system or at external network locations linked to the content management system via software connectors along with world state data associated with the one or more content items. The disclosed systems can generate a search result from the hybrid search index in response to receiving a search query of the hybrid search index. In some cases, the disclosed systems can rank one or more content items included in the search result based on observation layer data of the one or more content items.

Generating a hybrid search index for unified search

Performing unified search using a hybrid search index

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing coaching insights using a large language model to process coaching prompts. In some embodiments, the disclosed systems generate a coaching prompt from a knowledge graph encoding data from data sources, such as an observation layer and a world state. The disclosed systems also determine a pulse status of a user account to inform a coaching prompt. Additionally, the disclosed systems provide the coaching prompt to a large language model for generating a coaching insight to improve the pulse status.

Generating coaching insights using large language models to improve pulse status

This disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that can utilize language neural networks to automatically generate draft electronic communications for a user account. For example, the disclosed systems leverage composition parameters of a user account (determined from historical electronic communications of the user account, digital content items corresponding to the user account, and/or other application data) with a neural network to automatically generate draft electronic communications that reflect a composition style of a user account and accurately addresses a context of a communication thread. In addition, the disclosed systems can generate electronic communications using the communication generation neural network and save the electronic communication as a draft (e.g., for review by a user of the user account) and/or automatically transmit the electronic message to a recipient user account.

Generating context specific electronic communications utilizing a neural network

Storing and arranging application windows for video call scenes

Growing community of inventors

Alameda, CA, United States of America

Devin Mancuso