Microsoft Technology Licensing, LLC

Yair Zohav Zagdanski

Daniel Sitton

Yossef Hai Ben David

Shay Ben-elazar

Amnon Catav

Non-limiting examples of the present disclosure relate to application of artificial intelligence (AI) processing to generate classifications of user activity for a group of users. For example, a classification prediction is generated indicating whether students in an educational class are predicted, over a predetermined time period, to have a high or low activity level based on contextual analysis of multiple types of user-driven events. As user activity data is typically quite robust, the present disclosure applies dimensionality reduction processing to efficiently manage user activity data and further improve accuracy in generating downstream binary classifications. A dimensionality reduction transformation of user activity data results in a low-dimensional representation of input feature data that is contextually relevant for generating a binary classification. Derived classifications are then utilized to generate data insights pertaining to user activity levels of one or more users. Data insights are can then be rendered for presentation.

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Tel Aviv, Israel

Yair Zohav Zagdanski