Abstract: An artificial intelligence (“AI”) based system is disclosed for associating low-level user content, such as documents, email messages, and calendar invites, with high-level user activities using topics as an abstraction. The associations can enable a computing system to provide, among other things, activity-specific views that present a specific selection of low-level user content that is most relevant to a user at a particular point in time. The activity-specific views present the right information to users at the right time based on a context of a user and a user's past activities.

Abstract: An artificial intelligence (“AI”)-based system is disclosed for utilizing a schema to auto-generate an application for a specific context. An AI engine selects an activity schema associated with an activity. The schema identifies data sources for obtaining activity-specific content for the activity and can be selected based upon topics associated with the activity. The AI engine also selects a view definition that defines an arrangement of an activity-specific user interface (“UI”) for presenting relevant activity-specific content obtained from the data sources identified by the schema. An application is then generated using the schema and the view definition. The application can generate the activity-specific UI for presenting the relevant activity-specific content.

Abstract: An artificial intelligence (“AI”)-based system is disclosed for presenting activity-specific views of relevant activity-specific content. An AI engine selects activity-specific content relating to a multitude of activities. The selected activities can have associated relevance scores exceeding a predefined threshold value. The selected activity-specific content can be used to render user interface (“UI”) elements in a UI for the activities. The UI elements present an activity-specific view of the activity-specific content relating to each activity.

Abstract: An artificial intelligence (“AI”) based system is disclosed for associating low-level user content, such as documents, email messages, and calendar invites, with high-level user activities using topics as an abstraction. The associations can enable a computing system to provide, among other things, activity-specific views that present a specific selection of low-level user content that is most relevant to a user at a particular point in time. The activity-specific views present the right information to users at the right time based on a context of a user and a user's past activities.

Abstract: An artificial intelligence (“AI”) based system is disclosed for associating low-level user content, such as documents, email messages, and calendar invites, with high-level user activities using topics as an abstraction. The associations can enable a computing system to provide, among other things, activity-specific views that present a specific selection of low-level user content that is most relevant to a user at a particular point in time. The activity-specific views present the right information to users at the right time based on a context of a user and a user's past activities.

Abstract: An artificial intelligence (“AI”) based system is disclosed for associating low-level user content, such as documents, email messages, and calendar invites, with high-level user activities using topics as an abstraction. The associations can enable a computing system to provide, among other things, activity-specific views that present a specific selection of low-level user content that is most relevant to a user at a particular point in time. The activity-specific views present the right information to users at the right time based on a context of a user and a user's past activities.

Abstract: An artificial intelligence (“AI”)-based system is disclosed for presenting activity-specific views of relevant activity-specific content. An AI engine selects activity-specific content relating to a multitude of activities. The selected activities can have associated relevance scores exceeding a predefined threshold value. The selected activity-specific content can be used to render user interface (“UI”) elements in a UI for the activities. The UI elements present an activity-specific view of the activity-specific content relating to each activity.

Abstract: An artificial intelligence (“AI”) engine generates an activity graph that includes nodes corresponding to activities and that defines clusters of content associated with the activities. A natural language (“NL”) search engine can receive a NL query and parse the NL query to identify entities and intents specified by the NL query. Clusters of content defined by the activity graph can be identified based upon the identified entities and intents. A search can then be made of the identified clusters of content using the entities and intents. Search results identifying the content located by the search can then be returned in response to the NL query.

Abstract: An artificial intelligence (“AI”) engine selects a schema that defines an activity-specific user interface (“UI”) for presenting activity-specific content based upon one or more topics associated with an activity. A UI can then be presented for receiving edits to the selected schema and the edits can be published for utilization by other users. Data identifying the edits, selection of a different schema for the activity, modification of properties associated with the selected schema, and data describing usage of the schema can be provided to the AI engine for using in improving an AI model utilized to select the schema.

Abstract: An artificial intelligence (“AI”)-based system is disclosed for utilizing a schema to auto-generate an application for a specific context. An AI engine selects an activity schema associated with an activity. The schema identifies data sources for obtaining activity-specific content for the activity and can be selected based upon topics associated with the activity. The AI engine also selects a view definition that defines an arrangement of an activity-specific user interface (“UT”) for presenting relevant activity-specific content obtained from the data sources identified by the schema. An application is then generated using the schema and the view definition. The application can generate the activity-specific UT for presenting the relevant activity-specific content.

Abstract: Architecture that synchronizes a job to shared notebook eliminating the need for user intervention and guaranteeing that only one instance of the notebook client performs the task. A job tracking component creates and maintains tracking information of jobs processed against shared notebook information. A scheduling component synchronizes a new job against the shared notebook information based on the tracking information. The tracking information can be a file or cells stored at a root level of a hierarchical data collection that represents the electronic notebook. The file includes properties related to a job that has been processed. The properties are updated as new jobs are processed. Job scheduling includes whole file updates and/or incremental updates to the shared notebook information.

Abstract: Embodiments provide notes application features including notes page sharing and notification features, but the embodiments are not so limited. In an embodiment, a computer-based method can be configured to provide a communication to one or more designated recipients, wherein the communication includes one of a view link to a shared page or an editable shared notes page or link thereto. A system of an embodiment includes a server computer configured in part to process notes page sharing requests as part of creating view links to shared notes pages and/or providing users with a means to collaboratively edit a shared notes page. A user interface of an embodiment is configured to share notes pages including displaying notification information regarding other user shared notes page views and/or edits. Other embodiments are included.

Abstract: Architecture that synchronizes a job to shared notebook eliminating the need for user intervention and guaranteeing that only one instance of the notebook client performs the task. A job tracking component creates and maintains tracking information of jobs processed against shared notebook information. A scheduling component synchronizes a new job against the shared notebook information based on the tracking information. The tracking information can be a file or cells stored at a root level of a hierarchical data collection that represents the electronic notebook. The file includes properties related to a job that has been processed. The properties are updated as new jobs are processed. Job scheduling includes whole file updates and/or incremental updates to the shared notebook information.