View source for 17531158. TRAINING AND USING A MEMORY FAILURE PREDICTION MODEL simplified abstract (Microsoft Technology Licensing, LLC)

=TRAINING AND USING A MEMORY FAILURE PREDICTION MODEL=


==Organization Name==

[[:Category:Microsoft Technology Licensing, LLC|Microsoft Technology Licensing, LLC]]


[[Category:Microsoft Technology Licensing, LLC]]

==Inventor(s)==

[[:Category:Jasmine Grace Schlichting of Seattle WA (US)|Jasmine Grace Schlichting of Seattle WA (US)]][[Category:Jasmine Grace Schlichting of Seattle WA (US)]]

[[:Category:Bhuvan Malladihalli Shashidhara of Bellevue WA (US)|Bhuvan Malladihalli Shashidhara of Bellevue WA (US)]][[Category:Bhuvan Malladihalli Shashidhara of Bellevue WA (US)]]

[[:Category:Ramakoti R. Bhimanadhuni of Bothell WA (US)|Ramakoti R. Bhimanadhuni of Bothell WA (US)]][[Category:Ramakoti R. Bhimanadhuni of Bothell WA (US)]]

[[:Category:Emily Nicole Wilson of Seattle WA (US)|Emily Nicole Wilson of Seattle WA (US)]][[Category:Emily Nicole Wilson of Seattle WA (US)]]

[[:Category:Farah Farzana of Redmond WA (US)|Farah Farzana of Redmond WA (US)]][[Category:Farah Farzana of Redmond WA (US)]]

[[:Category:Michael Wayne Stephenson of Woodinville WA (US)|Michael Wayne Stephenson of Woodinville WA (US)]][[Category:Michael Wayne Stephenson of Woodinville WA (US)]]

[[:Category:Pallavi Baral of Redmond WA (US)|Pallavi Baral of Redmond WA (US)]][[Category:Pallavi Baral of Redmond WA (US)]]

[[:Category:Josh Charles Moore of Lynnwood WA (US)|Josh Charles Moore of Lynnwood WA (US)]][[Category:Josh Charles Moore of Lynnwood WA (US)]]

[[:Category:Christina Margaret Tobias of Seattle WA (US)|Christina Margaret Tobias of Seattle WA (US)]][[Category:Christina Margaret Tobias of Seattle WA (US)]]

[[:Category:John A. Strange of Everett WA (US)|John A. Strange of Everett WA (US)]][[Category:John A. Strange of Everett WA (US)]]

[[:Category:Peter Hanpeng Jiang of Kirkland WA (US)|Peter Hanpeng Jiang of Kirkland WA (US)]][[Category:Peter Hanpeng Jiang of Kirkland WA (US)]]

[[:Category:Sebastien Nathan R Levy of Seattle WA (US)|Sebastien Nathan R Levy of Seattle WA (US)]][[Category:Sebastien Nathan R Levy of Seattle WA (US)]]

[[:Category:Brett Kenneth Dodds of Boise ID (US)|Brett Kenneth Dodds of Boise ID (US)]][[Category:Brett Kenneth Dodds of Boise ID (US)]]

[[:Category:Arhatha Bramhanand of Redmond WA (US)|Arhatha Bramhanand of Redmond WA (US)]][[Category:Arhatha Bramhanand of Redmond WA (US)]]

[[:Category:Juan Arturo Herrera Ortiz of Seattle WA (US)|Juan Arturo Herrera Ortiz of Seattle WA (US)]][[Category:Juan Arturo Herrera Ortiz of Seattle WA (US)]]

[[:Category:Ahu Oral of Seattle WA (US)|Ahu Oral of Seattle WA (US)]][[Category:Ahu Oral of Seattle WA (US)]]

[[:Category:Charlotte Gauchet of Redmond WA (US)|Charlotte Gauchet of Redmond WA (US)]][[Category:Charlotte Gauchet of Redmond WA (US)]]

[[:Category:Daniel Sebastian Berger of Seattle WA (US)|Daniel Sebastian Berger of Seattle WA (US)]][[Category:Daniel Sebastian Berger of Seattle WA (US)]]

==TRAINING AND USING A MEMORY FAILURE PREDICTION MODEL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17531158 titled 'TRAINING AND USING A MEMORY FAILURE PREDICTION MODEL

==Simplified Explanation==

The patent application describes a method for training and using a prediction model to identify uncorrectable errors (UE) in telemetry data. 

* Sets of UE state labels and non-UE state labels are generated from collected telemetry data.
* Statistical features are extracted from the telemetry data of the sets of UE and non-UE state labels.
* The extracted statistical features are used to train a UE state prediction model.
* A second set of telemetry data is obtained and used to predict a UE event using the trained prediction model.
* A preventative operation is performed on a memory page based on the predicted UE event to prevent it from occurring.

== Potential Applications ==
This technology can be applied in various industries and systems where telemetry data is collected and analyzed. Some potential applications include:

* Computer systems: Predicting and preventing uncorrectable errors in computer memory to improve system reliability.
* Manufacturing: Identifying potential faults in production equipment based on telemetry data to prevent breakdowns and optimize maintenance schedules.
* Energy systems: Predicting and preventing failures in power generation equipment based on telemetry data to minimize downtime and improve efficiency.

== Problems Solved ==
This technology addresses the following problems:

* Uncorrectable errors: Identifying and predicting uncorrectable errors in telemetry data helps prevent system failures and improve overall reliability.
* Reactive maintenance: By predicting potential errors, preventative operations can be performed to avoid costly breakdowns and reduce the need for reactive maintenance.
* Data analysis efficiency: The use of statistical features and prediction models streamlines the analysis process, allowing for quicker identification and prevention of errors.

== Benefits ==
The use of this technology offers several benefits:

* Improved system reliability: By predicting and preventing uncorrectable errors, system downtime and failures can be minimized, leading to increased reliability.
* Cost savings: Preventative operations based on predicted errors help avoid costly breakdowns and reduce maintenance expenses.
* Efficient data analysis: The use of statistical features and prediction models allows for faster and more accurate analysis of telemetry data, saving time and resources.


==Original Abstract Submitted==

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.



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