Microsoft Technology Licensing, LLC

Yossef Hai Ben David

Daniel Sitton

Shay Ben-Elazar

Ori Bar-Ilan

Amnon Catav

Meitar Ronen

Shay Ben-elazar

Yair Zohav Zagdanski

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.

Automated generation of predictive insights classifying user activity

The present disclosure describes automated generation of early warning predictive insights derived from contextual analysis of user activity data of a distributed software platform. Predictive insights are automatically generated from analysis of user activity through implementation of trained artificial intelligence (AI) modeling. User activity data is accessed pertaining to user interactions by a plurality of users a software data platform. The trained AI modeling generates a plurality of mobility determinations that identify changes in patterns of user behavior over a current temporal filter associated with the user activity data. The plurality of mobility determinations is curated using business logic rules that evaluate a relevance of the mobility determinations. One or more predictive insights may be generated and presented via a graphical user interface notification. Exemplary notifications help provides insights into how user behavior has changed and why that is, thereby fostering understanding of predictions that can lead to actionable results.

Automated generation of early warning predictive insights about users

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Tel Aviv, Israel

Yossef Hai Ben David