Proactive notification of relevant feature suggestions based on contextual analysis
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.
Microsoft Technology Licensing, LLC
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Alyssa Rachel Gwenyth Alanna Vabalis Yu Been Penelope Ann Patricia Hendricks Anav Shikha Devesh Susan Michele
Mayo Hardiman Lee Collisson Balik Silverman Desai Hendrich