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:
Mar. 31, 2026

Filed:

Nov. 09, 2022
Applicant:

E.g.o. Elektro-geraetebau Gmbh, Oberderdingen, DE;

Inventors:

Christoph Milz, Fairfield, CA (US);

Uwe Schaumann, Oberderdingen, DE;

Antonio Di Maggio, Schwaigern, DE;

Juergen Eiselt, Illertissen, DE;

Assignee:

E.G.O. Elektro-Geraetebau GmbH, Oberderdingen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
D06F 39/08 (2006.01); D06F 39/02 (2006.01); D06F 39/10 (2006.01); A47L 15/00 (2006.01); A47L 15/42 (2006.01); D06F 25/00 (2006.01); D06F 33/43 (2020.01); D06F 34/14 (2020.01); D06F 103/00 (2020.01); D06F 103/16 (2020.01); D06F 103/20 (2020.01); D06F 103/70 (2020.01); D06F 105/58 (2020.01);
U.S. Cl.
CPC ...
D06F 39/088 (2013.01); D06F 39/02 (2013.01); D06F 39/10 (2013.01); A47L 15/0049 (2013.01); A47L 15/4297 (2013.01); A47L 2401/11 (2013.01); A47L 2401/12 (2013.01); A47L 2401/18 (2013.01); A47L 2401/22 (2013.01); A47L 2401/34 (2013.01); A47L 2501/26 (2013.01); D06F 25/00 (2013.01); D06F 33/43 (2020.02); D06F 34/14 (2020.02); D06F 2103/00 (2020.02); D06F 2103/16 (2020.02); D06F 2103/20 (2020.02); D06F 2103/70 (2020.02); D06F 2105/58 (2020.02);
Abstract

A water bearing appliance, control method, and system are provided. Operating data from appliances are transmitted to a receiver over a network such as the Internet. Maintenance, cleaning, and repairs are initiated based on the operating data, and can be triggered remotely and automatically. A machine learning model(s) is trained according to operating conditions of various appliances and respective outcomes, such as maintenance, repairs, damages, life expectancy, and/or the like, to learn correlations therebetween. A digital twin appliance data object is generated for an appliance and applied to the machine learning model(s) to provide a predictive maintenance data object. Cleaning regimens, maintenance, repairs, and/or the like are configured to particular operating circumstances of an appliance.


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