The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 13, 2020

Filed:

Dec. 28, 2018
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Mats Agerstam, Portland, OR (US);

Bahareh Sadeghi, Portland, OR (US);

Jason Martin, Beaverton, OR (US);

Jeffrey Ota, Morgan Hill, CA (US);

Justin Gottschlich, Santa Clara, CA (US);

Marcos Carranza, Portland, OR (US);

Maria Ramirez Loaiza, Beaverton, OR (US);

Alexander Heinecke, San Jose, CA (US);

Mohammad Mejbah Ul Alam, Milpitas, CA (US);

Robert Colby, Granite Bay, CA (US);

Sara Baghsorkhi, Los Gatos, CA (US);

Shengtian Zhou, Palo Alto, CA (US);

Assignee:

Intel Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 11/34 (2006.01); H04L 12/24 (2006.01); G06F 11/00 (2006.01); H04L 29/06 (2006.01); H04L 29/08 (2006.01); G06K 9/62 (2006.01); H04L 12/66 (2006.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 11/3447 (2013.01); G06F 11/008 (2013.01); G06F 11/3452 (2013.01); G06F 11/3466 (2013.01); G06K 9/6217 (2013.01); G06N 20/00 (2019.01); H04L 12/66 (2013.01); H04L 41/142 (2013.01); H04L 41/145 (2013.01); H04L 41/147 (2013.01); H04L 63/1425 (2013.01); H04L 67/12 (2013.01); G06F 2201/875 (2013.01);
Abstract

An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.


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