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:
Sep. 17, 2024

Filed:

Oct. 23, 2022
Applicant:

Blink Technologies Inc., Palo Alto, CA (US);

Inventors:

Oren Haimovitch-Yogev, Los Altos, CA (US);

Tsahi Mizrahi, Yoqneam Ilit, IL;

Andrey Zhitnikov, Haifa, IL;

Almog David, Kiryat Motzkin, IL;

Artyom Borzin, Haifa, IL;

Gilad Drozdov, Haifa, IL;

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/088 (2023.01); G06F 18/214 (2023.01); G06N 3/04 (2023.01); G06N 3/0455 (2023.01); G06N 3/0464 (2023.01); G06N 3/08 (2023.01); G06N 20/10 (2019.01); G06T 7/11 (2017.01); G06V 10/75 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 40/16 (2022.01); G06V 40/18 (2022.01); G06V 40/19 (2022.01);
U.S. Cl.
CPC ...
G06N 3/088 (2013.01); G06F 18/2155 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G06V 10/755 (2022.01); G06V 10/82 (2022.01); G06V 40/165 (2022.01); G06V 40/171 (2022.01); G06V 40/19 (2022.01); G06V 40/193 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01);
Abstract

Unsupervised, deep learning of eye-landmarks in a user-specific eyes' image data by capturing an unlabeled image comprising an eye region of a user, using an initial geometrically regularized loss function, training a plurality of convolutional autoencoders on the unlabeled image comprising the eye region of the user to recover a plurality of user-specific eye landmarks, training a convolutional neural network for autoencoded landmarks-based recovery from the unlabeled image, and where the initial geometrically regularized loss function is represented by the formula L=λL+λL+λL+λLwhere Lis total AutoEncoder Loss, λLis λ-weighted reconstruction loss, λLis λ-weighted concentration loss, λLis λ-weighted separation loss, and λLis λ-weighted equivalence loss.


Find Patent Forward Citations

Loading…