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:
Dec. 20, 2022

Filed:

Dec. 06, 2019
Applicant:

X Development Llc, Mountain View, CA (US);

Inventors:

Anupama Thubagere Jagadeesh, San Jose, CA (US);

Brian Lance Hie, San Diego, CA (US);

Assignee:

X Development LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G16H 50/00 (2018.01); G16H 50/30 (2018.01); G06Q 30/06 (2012.01); G16H 10/60 (2018.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06T 7/44 (2017.01); G06T 5/40 (2006.01); G06T 7/00 (2017.01); G06T 5/50 (2006.01); G06T 7/11 (2017.01); G06T 7/90 (2017.01); A61B 5/00 (2006.01); G16H 20/00 (2018.01);
U.S. Cl.
CPC ...
G16H 50/30 (2018.01); A61B 5/0071 (2013.01); A61B 5/442 (2013.01); A61B 5/443 (2013.01); A61B 5/445 (2013.01); A61B 5/486 (2013.01); A61B 5/7267 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06Q 30/0631 (2013.01); G06T 5/40 (2013.01); G06T 5/50 (2013.01); G06T 7/0016 (2013.01); G06T 7/11 (2017.01); G06T 7/44 (2017.01); G06T 7/90 (2017.01); G16H 10/60 (2018.01); G16H 20/00 (2018.01); A61B 2576/02 (2013.01); G06N 3/0445 (2013.01); G06T 2207/10064 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30088 (2013.01);
Abstract

A system, method, and computer readable media are provided for obtaining a first set of skin data from an image capture system including at least one ultraviolet (UV) image of a user's skin. Performing a correction on the skin data using a second set of skin data associated with the user. Quantifying a plurality of skin parameters of the user's skin based on the first skin data, including quantifying a bacterial load. Quantifying the bacterial load by applying a brightness filter to isolate portions of the at least one UV image containing fluorescence, applying a dust filter, identifying portions of the at least one UV image that contain fluorescence due to bacteria, and determining a quantity of bacterial load in the users skin. Determining, using a machine learning model, an output associated with a normal skin state of the user and a current skin state of the user.


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