Patents by Inventor Artyom BORZIN

Artyom BORZIN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Geometrically constrained, unsupervised training of convolutional autoencoders for extraction of eye landmarks
    Patent number: 12093835
    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 LAE=?reconLrecon+?concLconc+?sepLsep+?eqvLeqv where LAE is total AutoEncoder Loss, ?reconLrecon is ?-weighted reconstruction loss, ?concLconce is ?-weighted concentration loss, ?sepLsep is ?-weighted separation loss, and ?eqvLeqv is ?-weighted equivalence loss.
    Type: Grant
    Filed: October 23, 2022
    Date of Patent: September 17, 2024
    Inventors: Oren Haimovitch-Yogev, Tsahi Mizrahi, Andrey Zhitnikov, Almog David, Artyom Borzin, Gilad Drozdov
  • Pupil ellipse-based, real-time iris localization
    Patent number: 12026980
    Abstract: The disclosure relates to systems, methods and programs for developing real-time user-specific eye model based on iris localization using solely pupil-ellipse analysis.
    Type: Grant
    Filed: July 15, 2021
    Date of Patent: July 2, 2024
    Assignee: BLINK TECHNOLOGIES INC.
    Inventors: Gilad Drozdov, Nadav Arbel, Tsahi Mizrahi, Soliman Nasser, Artyom Borzin, Uri Wollner, Oren Haimovitch-Yogev
  • GEOMETRICALLY CONSTRAINED, UNSUPERVISED TRAINING OF CONVOLUTIONAL AUTOENCODERS FOR EXTRACTION OF EYE LANDMARKS
    Publication number: 20230334326
    Abstract: Geometrically constrained unsupervised training of convolutional autoencoders on unlabeled images for extracting eye landmarks by capturing an unlabeled image including the eye region of a user and training multiple convolutional autoencoders on the unlabeled image using an initial geometrically regularized loss function to determine multiple eye landmarks.
    Type: Application
    Filed: October 23, 2022
    Publication date: October 19, 2023
    Inventors: OREN HAIMOVITCH-YOGEV, TSAHI MIZRAHI, ANDREY ZHITNIKOV, ALMOG DAVID, ARTYOM BORZIN, GILAD DROZDOV
  • Geometrically constrained, unsupervised training of convolutional autoencoders for extraction of eye landmarks
    Patent number: 11514720
    Abstract: The disclosure relates to systems, methods and programs for geometrically constrained, unsupervised training of convolutional autoencoders on unlabeled images for extracting eye landmarks. Disclosed systems for unsupervised deep learning of gaze estimation in eyes' image data are implementable in a computerized system. Disclosed methods include capturing an unlabeled image comprising the eye region of a user; and training a plurality of convolutional autoencoders on the unlabeled image comprising the eye region of a user using an initial geometrically regularized loss function to determine a plurality of eye landmarks.
    Type: Grant
    Filed: January 2, 2020
    Date of Patent: November 29, 2022
    Assignee: BLINK TECHNOLOGIES INC.
    Inventors: Oren Haimovitch-Yogev, Tsahi Mizrahi, Andrey Zhitnikov, Almog David, Artyom Borzin, Gilad Drozdov
  • PUPIL ELLIPSE-BASED, REAL-TIME IRIS LOCALIZATION
    Publication number: 20220019791
    Abstract: The disclosure relates to systems, methods and programs for developing real-time user-specific eye model based on iris localization using solely pupil-ellipse analysis.
    Type: Application
    Filed: July 15, 2021
    Publication date: January 20, 2022
    Applicant: Blink.O.G.Ltd
    Inventors: Gilad DROZDOV, Nadav ARBEL, Tsahi MIZRAHI, Soliman NASSER, Artyom BORZIN, URI WOLLNER, Oren HAIMOVITCH-YOGEV
  • GEOMETRICALLY CONSTRAINED, UNSUPERVISED TRAINING OF CONVOLUTIONAL AUTOENCODERS FOR EXTRACTION OF EYE LANDMARKS
    Publication number: 20210034836
    Abstract: The disclosure relates to systems, methods and programs for geometrically constrained, unsupervised training of convolutional autoencoders on unlabeled images for extracting eye landmarks.
    Type: Application
    Filed: January 2, 2020
    Publication date: February 4, 2021
    Applicant: Blink O.G. Ltd.
    Inventors: Oren HAIMOVITCH-YOGEV, Tsahi MIZRAHI, Andrey ZHITNIKOV, Almog DAVID, Artyom BORZIN, Gilad DROZDOV