Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: This document describes techniques and devices for a refined search with machine learning. These techniques improve computer-aided searches through enabling selection of search criteria used in a prior search and providing a refined search result based on that selection. Furthermore, a machine-learning component of a search engine can be altered to improve future search results based on the selection and an indication of the desirability of the refined search result.

Abstract: This disclosure describes systems that aggregate activity data items for one or more data sources for a user account. The disclosed systems can utilize a large language model to transform the activity data items to synthesized activity items. For example, the disclosed systems can utilize the large language model to transform the activity data items to the synthesized activity items by extracting the contextual content from the activity data items. The disclosed systems can utilize the large language model to process the synthesized activity items to generate an activity meta summary for a specified timeframe. Based on the activity meta summary, the disclosed systems can further identify a subset of synthesized activity items for the specified timeframe. Thereafter, the disclosed systems can provide the subset of synthesized activity items for the specified timeframe for display on a client device associated with the user account.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a large language model to generate computer code executable to generate a series of calendar events for completing a target objective. Furthermore, the disclosed systems provide a catalyst calendar interface which includes a chat window and an integrated calendar window to interface to display the series of calendar events. For instance, the disclosed systems utilize the large language model to generate a task curriculum which includes a set of executable tasks whose completion accomplishes the target objective. Furthermore, the disclosed systems can generate computer code executable by a calendar application to generate the series of calendar events corresponding to the set of executable tasks.

Abstract: 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.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a large language model to generate computer code executable to generate a series of calendar events for completing a target objective. Furthermore, the disclosed systems provide a catalyst calendar interface which includes a chat window and an integrated calendar window to interface to display the series of calendar events. For instance, the disclosed systems utilize the large language model to generate a task curriculum which includes a set of executable tasks whose completion accomplishes the target objective. Furthermore, the disclosed systems can generate computer code executable by a calendar application to generate the series of calendar events corresponding to the set of executable tasks.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating, arranging, and providing subgroupings of content items according to content attributes and dynamic facets. For example, the disclosed systems generate dynamic facets reflecting content attributes, where the dynamic facets are selectable interface elements for arranging content items into subgroups. In certain cases, as part of generating a subgrouping of content items, the disclosed systems can identify portions of content items (e.g., segments of a digital video or sections of a digital document) that correspond to a dynamic facet. The disclosed systems can also provide filtering options for creating or refining subgroupings from a content collection by, for example, filtering according to recency criteria such as file type, recency of interaction, collaborative user accounts, or others.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for utilizing a catalyst application to analyze digital content of another computer application to generate or extract tasks. The catalyst application can accompany and operate in conjunction with another computer application to extract data from the application and generate tasks. For example, the disclosed systems can scan displayed (or otherwise presented) digital content in an application and can generate a prompt for causing a large language model to identify or extract tasks from the digital content. Through the catalyst application, the disclosed systems can thus implement a large language model to generate a task list from the digital content of another computer application. The disclosed systems can further utilize the catalyst application to automatically execute or perform extracted tasks.