Microsoft Technology Licensing, LLC

Sebastien Nathan R Levy

Emily Nicole Wilson

Brett Kenneth Dodds

Daniel Sebastian Berger

John A Strange

Ramakoti R Bhimanadhuni

Peter Hanpeng Jiang

Pallavi Baral

Bhuvan Malladihalli Shashidhara

Charlotte Gauchet

Michael Wayne Stephenson

Farah Farzana

Josh Charles Moore

Jasmine Grace Schlichting

Ahu Oral

Arhatha Bramhanand

Juan Arturo Herrera Ortiz

Christina Margaret Tobias

The disclosure herein describes training and using an uncorrectable error (UE) state prediction model based on telemetry error data. Sets of UE state labels and non-UE state labels are generated from a first set of collected telemetry data, wherein the UE state labels each reference a UE and telemetry data of an interval prior to the referenced UE. Statistical features are extracted from telemetry data of the sets of UE state labels and non-UE state labels, and the extracted statistical features are used to train a UE state prediction model. A second set of collected telemetry data is obtained, and a UE event is predicted based on the second set of collected telemetry data using the trained UE state prediction model. A preventative operation is performed on a memory page of the system based on the predicted UE event, whereby the predicted UE event is prevented from occurring.

Training and using a memory failure prediction model

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Seattle, WA, United States of America

Sebastien Nathan R Levy