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. 08, 2026

Filed:

Jun. 07, 2019
Applicant:

Leica Microsystems Cms Gmbh, Wetzlar, DE;

Inventor:

Constantin Kappel, Schriesheim, DE;

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G16B 40/20 (2019.01); G06F 18/22 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/048 (2023.01); G06N 3/063 (2023.01); G06N 3/08 (2023.01); G16B 30/20 (2019.01); G16B 40/30 (2019.01); G06N 5/01 (2023.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01);
U.S. Cl.
CPC ...
G16B 40/20 (2019.02); G06F 18/22 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/048 (2023.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G16B 30/20 (2019.02); G16B 40/30 (2019.02); G06N 5/01 (2023.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01);
Abstract

A system () comprises one or more processors () and one or more storage devices (), wherein the system () is configured to generate a first high-dimensional representation of the biology-related language-based input training data () by a language recognition machine-learning algorithm executed by the one or more processors (). Further, the system () is configured to generate biology-related language-based output training data based on the first high-dimensional representation by the language recognition machine-learning algorithm and adjust the language recognition machine-learning algorithm based on a comparison of the biology-related language-based input training data () and the biolo-gy-related language-based output training data. Additionally, the system () is configured to generate a second high-dimensional representation of the biology-related image-based input training data () by a visual recognition machine-learning algorithm executed by the one or more processors () and adjust the visual recognition machine-learning algorithm based on a comparison of the first high-dimensional representation and the second high-dimensional representation.


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