Abstract: A method of notifying a document to a user of a cloud-based content management platform including identifying a first set of documents, wherein the first set of documents is hosted by the cloud-based content management platform and does not include one or more documents recently opened by the user, identifying one or more target documents from the first set of documents for the user based on an amount of overlap in topicality between a respective document and a users current working set of documents, a number of view events of the respective document, and a number of collaborative events associated with the respective document, wherein the users current working set of documents comprises documents the user has accessed within a last predetermined time period via the cloud-based content management platform, providing a graphical user interface (GUI) of a cloud storage of the user hosted by the cloud-based content management platform for presentation to the user, the GUI identifying the one or more target docum

Abstract: Implementations are provided for organizing and/or managing files and/or folders within filesystem hierarchies. In various implementations, an input prompt for a generative model may be assembled to include data indicative of: one or more files to be filed within a given filesystem hierarchy, and one or more filesystem hierarchies. The input prompt may be processed using the generative model to generate one or more recommendations of one or more folders within the given filesystem hierarchy for storing one or more of the files. Output may be rendered to convey one or more of the recommendations, which may be accepted to automatically implement the recommendations in some cases.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Implementations are provided for organizing and/or managing files and/or folders within filesystem hierarchies. In various implementations, an input prompt for a generative model may be assembled to include data indicative of: one or more files to be filed within a given filesystem hierarchy, and one or more filesystem hierarchies. The input prompt may be processed using the generative model to generate one or more recommendations of one or more folders within the given filesystem hierarchy for storing one or more of the files. Output may be rendered to convey one or more of the recommendations, which may be accepted to automatically implement the recommendations in some cases.

Abstract: Implementations relate to leveraging a generative model in summarizing a folder having different files and/or sub-folder(s). Various types of information associated with the folder, one or more files within the folder, user metadata associated with a user that requests summarization of the folder, and/or other types of information can be utilized to generate a folder summary request. The folder summary request can be processed using the generative model, to generate a model output reflecting a folder summary. The folder summary request can include one or more instructions that prompt the generative model, so that the folder summary generated for the folder using the generative model can include, for instance, an overview of the folder, key topics of the folder, and/or key files of the folder. The folder summary can also vary in dependence on the user (e.g., a first-time user vs. a frequent user frequently visits the folder, etc.).

Abstract: A method includes predicting one or more collaborators for a first user among other users that are associated with electronic documents hosted by the cloud-based content management platform. A pending action corresponding to an electronic document and directed to the first user by a second user of the one or more predicted collaborators is identified. A response of the first user to the pending action is predicted by identifying one or more action attributes of the pending action, and generating, based on the one or more action attributes, information identifying i) a predicted response by the first user to the pending action, and 2) a likelihood the first user will respond to the pending action using the predicted response. Upon generating the information, a user interface (UI) identifying the predicted response to the pending action is provided for presentation at a client device of the first user.

Abstract: A method is disclosed that includes obtaining a generative machine learning model (MLM) prompt that prompt includes an indication of a user request to generate content based on one or more of a plurality of documents stored in a cloud-based content management platform, selecting a subset of the plurality of documents based on the generative MLM prompt and a first query embedding corresponding to the generative MLM prompt, inputting the generative MLM prompt and the subset of the plurality of documents into a first generative MLM, and generating, using the first generative MLM, a response, wherein the response comprises content generated by the first generative MLM, and one or more citations to one or more documents of the subset.

Abstract: A method for predicting one or more collaborators provided by a cloud-based content management platform includes identifying, for a user of a cloud-based content management platform, a plurality of other users of the cloud-based content management platform that have a relationship with the user and are associated with a plurality of documents hosted by the cloud-based content management platform, predicting one or more collaborators for the user based on collaboration attributes of the plurality of other users, and providing for presentation to the user, information identifying the one or more collaborators to direct the user to a subset of documents from the plurality of documents hosted by the cloud-based content management platform, the subset of documents each being associated with one of the predicted one or more collaborators.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: A method of notifying a user of a cloud-based content management platform of a comment made in a file associated with a user account of the user includes identifying a subset of files with comments to be of interest to a user of cloud-based content management platform, and providing a graphical user interface (GUI) of the cloud-based content management platform for presentation to the user, the GUI identifying the subset of files and, for each identified file, a respective selected comment included in the identified file, and a date of the respective comment.

Abstract: A method of providing a workspace graphical user interface (GUI) for a user of a cloud-based content management platform includes providing the workspace GUI for the user via the cloud-based content management platform. The workspace GUI presents visual representations of documents stored on the user's cloud storage of the cloud-based content management platform and visual representations of workspaces created by the user. Each workspace includes a set of documents previously added to a respective workspace by the user and stored on the user's cloud storage. The method further includes receiving, via the workspace GUI, a user input with respect to a corresponding document stored on the user's cloud storage. The user input indicates a request to add the corresponding document to a particular workspace.

Abstract: A method of notifying a user of a cloud-based content management platform of a comment made in a file associated with a user account of the user includes identifying a subset of files with comments to be of interest to a user of cloud-based content management platform, and providing a graphical user interface (GUI) of the cloud-based content management platform for presentation to the user, the GUI identifying the subset of files and, for each identified file, a respective selected comment included in the identified file, and a GUI element allowing the user to request that the identified file be opened for editing.

Abstract: A method of notifying a user of a cloud-based content management platform of a comment made in a file associated with a user account of the user includes identifying a subset of files with comments to be of interest to a user of cloud-based content management platform, and providing a graphical user interface (GUI) of the cloud-based content management platform for presentation to the user, the GUI identifying the subset of files and, for each identified file, a respective selected comment included in the identified file, and a GUI element allowing the user to request that the identified file be opened for editing.

Abstract: A method of notifying a user of a cloud-based content management platform of a comment made in a document associated with the user includes determining a set of comments associated with documents to which the user has access via the cloud-based content management platform, the set of comments including one or more comments added by other users to each document within a last predetermined time period, selecting one or more comments from the set of comments for notification to the user, the selecting being based at least on interactions of the user with a comment thread associated with each comment from the set of comments and characteristics of the comment thread, wherein a comment thread is a group of a plurality of comments comprising a first comment and a second comment added in reply to the first comment, determining one or more documents associated with the one or more selected comments, and providing a graphical user interface (GUI) of a cloud storage of the user hosted by the cloud-based content managem

Abstract: A method for predicting one or more collaborators provided by a cloud-based content management platform includes identifying, for a user of a cloud-based content management platform, a plurality of other users of the cloud-based content management platform that have a relationship with the user and are associated with a plurality of documents hosted by the cloud-based content management platform, predicting one or more collaborators for the user based on collaboration attributes of the plurality of other users, and providing for presentation to the user, information identifying the one or more collaborators to direct the user to a subset of documents from the plurality of documents hosted by the cloud-based content management platform, the subset of documents each being associated with one of the predicted one or more collaborators.

Abstract: A method of providing a workspace graphical user interface (GUI) for a user of a cloud-based content management platform includes providing the workspace GUI for the user via the cloud-based content management platform. The workspace GUI presents visual representations of documents stored on the user's cloud storage of the cloud-based content management platform and visual representations of workspaces created by the user. Each workspace includes a set of documents previously added to a respective workspace by the user and stored on the user's cloud storage. The method further includes receiving, via the workspace GUI, a user input with respect to a corresponding document stored on the user's cloud storage. The user input indicates a request to add the corresponding document to a particular workspace.