Abstract: The present disclosure relates to processing operations configured to tailor notifications of productivity feature suggestions based on predictive relevance to a context associate with user access to an electronic document. Machine learning modeling executes a contextual evaluation of user access to predictively determine relevance of a suggestion that relates to: 1) a confidence in the quality of the suggestion; and 2) a timing prediction as to the urgency for surfacing the suggestion to the user so that the suggestion is most applicable. Example notifications are proactive interruptions that aim to aid processing efficiency in task execution as well as an improve user interface experience when users work with an application/service and/or an application platform that comprises a suite of applications/services. A manner in which the notification is presented may vary based on the confidence in the relevance of the suggestion and timing relevance for interrupting a user's workflow.

Abstract: The present disclosure relates to processing operations configured to identify and present productivity features that are contextually relevant for user access to an electronic document. In doing so, signal data is evaluated to determine a context associated with user access to an electronic document and insights, from the determined context, are utilized to rank productivity features for relevance to a user workflow. As an example, an intelligent learning model is trained and implemented to identify what productivity features are most relevant to a current task of a user. Productivity features are identified and ranked for contextual relevance. A notification comprising one or more ranked productivity features is presented to a user. In one example, the notification is presented through a user interface of an application/service. For instance, a user interface pane is surfaced to present suggestions.

Abstract: The present disclosure relates to processing operations configured to identify and present productivity features that are contextually relevant for user access to an electronic document. In doing so, signal data is evaluated to determine a context associated with user access to an electronic document and insights, from the determined context, are utilized to rank productivity features for relevance to a user workflow. As an example, an intelligent learning model is trained and implemented to identify what productivity features are most relevant to a current task of a user. Productivity features are identified and ranked for contextual relevance. A notification comprising one or more ranked productivity features is presented to a user. In one example, the notification is presented through a user interface of an application/service. For instance, a user interface pane is surfaced to present suggestions.

Abstract: The present disclosure relates to processing operations configured to tailor notifications of productivity feature suggestions based on predictive relevance to a context associate with user access to an electronic document. Machine learning modeling executes a contextual evaluation of user access to predictively determine relevance of a suggestion that relates to: 1) a confidence in the quality of the suggestion; and 2) a timing prediction as to the urgency for surfacing the suggestion to the user so that the suggestion is most applicable. Example notifications are proactive interruptions that aim to aid processing efficiency in task execution as well as an improve user interface experience when users work with an application/service and/or an application platform that comprises a suite of applications/services. A manner in which the notification is presented may vary based on the confidence in the relevance of the suggestion and timing relevance for interrupting a user's workflow.