Microsoft Technology Licensing, LLC

Mark James Encarnación

Michael Gamon

Victor Poznanski

Chenguang Yang

Nalin Singal

Shawon Sarkar

Nouran Soliman

A computing system for identifying tasks at risk in a collaborative project includes one or more processors configured to execute, during an inference-time phase, a collaborative project management program and a machine learning model. The collaborative project management program is configured to receive telemetry data associated with a task, process the telemetry data based at least in part on one or more task attributes, and output at least one feature associated with the task. The machine learning model is configured to receive, as inference-time input, the at least one feature associated with the task, and, responsive to receiving the at least one feature, output a risk prediction for the task. The system is configured to output an alert when the task is predicted to be at risk of not being completed by a predetermined due date.

Identification of tasks at risk in a collaborative project

In non-limiting examples of the present disclosure, systems, methods and devices for exposing computing service skills are presented. A task intent associated with a natural language input may be identified. A plurality of add-in matching language models may be applied to the natural language input, wherein each add-in matching language model corresponds to a computing service skill. A score for each of the computing service skills may be generated. Each of the computing service skills may be ranked. A selectable option to execute an add-in associated with a top ranked computing service skill may be caused to be displayed. An indication that a selection of the option has been made may be received. The add-in associated with the top ranked computing service skill may be executed.

Contextual extensible skills framework across surfaces

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Bellevue, WA, United States of America

Mark James Encarnación