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:
Jul. 09, 2024

Filed:

Dec. 17, 2019
Applicant:

Tignis, Inc., Seattle, WA (US);

Inventors:

Jonathan L. Herlocker, Seattle, WA (US);

Matt McLaughlin, Seattle, WA (US);

Alexander Fry, Seattle, WA (US);

Assignee:

Tignis, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/088 (2023.01); F24F 11/38 (2018.01); G06F 17/18 (2006.01); G06N 3/08 (2023.01); G16Y 40/10 (2020.01); G16Y 40/20 (2020.01);
U.S. Cl.
CPC ...
F24F 11/38 (2018.01); G06F 17/18 (2013.01); G06N 3/08 (2013.01); G06N 3/088 (2013.01); G16Y 40/10 (2020.01); G16Y 40/20 (2020.01);
Abstract

A method for detecting anomalies in a physical system generates from a set of physics rules and a process graph representing the system a set of candidate physics models that assign physics rules to portions of the process graph representing sensors. Candidate physics models are rejected if an error between the models and sensor data exceed a predetermined error tolerance. Supervised learning is used to train a machine learning model to predict an error between the physics models and the sensor data. The predicted error and predicted sensor measurements from the physics models are then used to detect anomalies using unsupervised learning on a distribution of error between the predicted sensor measurements and the sensor data.


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