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. 16, 2024

Filed:

Sep. 11, 2020
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Robert Carter Dunn, San Carlos, CA (US);

Ayush Jain, Los Altos, CA (US);

Peggy Yen Phuong Bui, San Francisco, CA (US);

Clara Eng, San Carlos, CA (US);

David Henry Way, Redwood City, CA (US);

Kang Li, Sunnyvale, CA (US);

Vishakha Gupta, Sunnyvale, CA (US);

Jessica Gallegos, San Francisco, CA (US);

Dennis Ai, Redwood City, CA (US);

Yun Liu, Mountain View, CA (US);

David Coz, Mountain View, CA (US);

Yuan Liu, Santa Clara, CA (US);

Assignee:

GOOGLE LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 30/40 (2018.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01);
Abstract

The present disclosure is directed to a deep learning system for differential diagnoses of skin diseases. In particular, the system performs a method that can include obtaining a plurality of images that respectively depict a portion of a patient's skin. The method can include determining, using a machine-learned skin condition classification model, a plurality of embeddings respectively for the plurality of images. The method can include combining the plurality of embeddings to obtain a unified representation associated with the portion of the patient's skin. The method can include determining, using the machine-learned skin condition classification model, a skin condition classification for the portion of the patients skin, the skin condition classification produced by the machine-learned skin condition classification model by processing the unified representation, wherein the skin condition classification identifies one or more skin conditions selected from a plurality of potential skin conditions.


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