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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.

Patent No.:

US 11662698 B1

PDFFull Text
Date of Patent:
May. 30, 2023

Filed:

Jul. 16, 2019

Methods and apparatuses for detecting tamper using machine learning models

Applicant:

Caci, Inc.—federal, Arlington State/Province, VA (US);

Inventors:

James Andrew Cook, Lompoc, CA (US);

Brian Andrew Rowe, Reston, VA (US);

Eric David Nystrom, Carpinteria, CA (US);

Assignee:

CACI, Inc.—Federal, Reston, VA (US);

Attorneys:

BakerHostetler

Tayan B. Patel

Primary Examiner:

Shane D Woolwine

Int. Cl.
CPC ...
G05B 13/04 (2006.01); G01P 13/00 (2006.01); G05B 19/042 (2006.01); G06N 20/10 (2019.01); G06N 3/04 (2023.01); G05B 23/02 (2006.01); G01C 21/16 (2006.01); G06F 18/24 (2023.01); H04W 4/38 (2018.01); H04W 4/70 (2018.01); G06N 3/084 (2023.01); H04W 4/02 (2018.01); H04W 4/029 (2018.01); H04W 4/80 (2018.01); G06N 3/045 (2023.01); G06N 3/048 (2023.01); G06N 7/01 (2023.01);
U.S. Cl.
CPC ...
G05B 13/048 (2013.01); G01C 21/1654 (2020.08); G01P 13/00 (2013.01); G05B 19/0428 (2013.01); G05B 23/024 (2013.01); G06F 18/24 (2023.01); G06N 3/04 (2013.01); G06N 20/10 (2019.01); G05B 2219/25257 (2013.01); G06N 3/045 (2023.01); G06N 3/048 (2023.01); G06N 3/084 (2013.01); G06N 7/01 (2023.01); H04W 4/02 (2013.01); H04W 4/027 (2013.01); H04W 4/029 (2018.02); H04W 4/38 (2018.02); H04W 4/70 (2018.02); H04W 4/80 (2018.02); Y02D 30/70 (2020.08);
Abstract

The present application describes a machine learning method for detecting tamper. The method includes a step of training a model using one or more values obtained from one or more different sensors on an integrated module. The one or more values act as training data with respect to one or more of light, acceleration, magnetic field, rotation, temperature, pressure, humidity, and audio. The method also includes a step of predicting, via the trained model, tampering of the of the integrated module. The present application also describes a system for detecting tamper.

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