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. 16, 2025

Filed:

Jan. 21, 2021
Applicant:

Carl Zeiss Meditec Ag, Jena, DE;

Inventors:

Hendrik Burwinkel, Munich, DE;

Holger Matz, Unterschneidheim, DE;

Stefan Saur, Aalen, DE;

Christoph Hauger, Aalen, DE;

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 3/10 (2006.01); A61B 3/12 (2006.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 3/102 (2013.01); A61B 3/1225 (2013.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/10101 (2013.01); G06T 2207/20072 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01);
Abstract

The invention relates to a computer-implemented method and a corresponding system for a machine-learning-supported determining of refractive power for a measure for correcting the eyesight of a patient. The method involves providing a scan result of an eye, wherein the scan result represents an image of an anatomical structure of the eye. The method also involves supplying the scan result as input data to a first machine-learning system in the form of a convolutional neural network, and using output values of the first machine-learning system as input data for a second machine-learning system in the form of a multi-layer perceptron, and a target refraction value for the second machine-learning system is used as an additional input value for the second machine-learning system. Finally, the method involves determining parameters for the measure for correcting the eyesight of a patient via an immediate and direct cooperation of the first machine-learning system and the second machine-learning system.


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