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. 21, 2025

Filed:

Feb. 13, 2024
Applicant:

Lyten, Inc., San Jose, CA (US);

Inventors:

Daniel Cook, Woodside, CA (US);

Michael Stowell, Sunnyvale, CA (US);

Karel Vanheusden, Woodside, CA (US);

George Clayton Gibbs, Santa Clara, CA (US);

Jacques Nicole, Palo Alto, CA (US);

Carlos Montalvo, Cambria, CA (US);

Kyle Matthys, Los Altos Hills, CA (US);

Bruce Lanning, Littleton, CO (US);

Sung Lim, Mountain View, CA (US);

John Chmiola, San Francisco, CA (US);

Assignee:

LYTEN, INC., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01N 27/02 (2006.01); G01N 27/22 (2006.01); G01N 27/414 (2006.01); G01N 27/447 (2006.01); G01N 27/72 (2006.01); G02F 1/167 (2019.01); G06Q 30/018 (2023.01); G06Q 50/06 (2012.01); H04L 9/32 (2006.01); H04L 9/40 (2022.01);
U.S. Cl.
CPC ...
G01N 27/02 (2013.01); G01N 27/221 (2013.01); G01N 27/4145 (2013.01); G01N 27/447 (2013.01); G01N 27/72 (2013.01); G02F 1/167 (2013.01); G06Q 30/018 (2013.01); G06Q 50/06 (2013.01); H04L 9/3247 (2013.01); H04L 63/1416 (2013.01); G01N 2027/222 (2013.01);
Abstract

Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.


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