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. 01, 2025

Filed:

Aug. 17, 2022
Applicant:

Carl Zeiss Microscopy Gmbh, Jena, DE;

Inventors:

Manuel Amthor, Jena, DE;

Daniel Haase, Zoellnitz, DE;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/60 (2017.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/94 (2022.01); G06V 20/69 (2022.01);
U.S. Cl.
CPC ...
G06V 20/698 (2022.01); G06T 7/60 (2013.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/95 (2022.01); G06T 2207/10056 (2013.01);
Abstract

A computer-implemented method comprises the following steps. In one step an image is acquired which is captured in the context of a microscopy measurement and images a sample to be examined. In one step the microscopy measurement is monitored in an automated manner. On the basis of the automated monitoring of the microscopy measurement, one or more labels are created, wherein said one or more labels comprise semantic context information of the microscopy measurement. On the basis of the image as input and said one or more labels as ground truth, a machine-learned algorithm is trained which provides semantic context information on the basis of images captured in the context of microscopy measurements. In a further step a further image is acquired, which is captured in the context of the microscopy measurement or a further microscopy measurement by the microscope and images the sample or a further sample. In a further step the trained machine-learned algorithm is applied to the further image in order to predict further semantic context information for the further image.


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