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. 02, 2025

Filed:

Dec. 17, 2021
Applicant:

Tetra Laval Holdings & Finance S.a., Pully, CH;

Inventors:

Gabriele Borelli, Spilamberto, IT;

Stefano Fornasari, Modena, IT;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B65B 9/213 (2012.01); B65B 3/00 (2006.01); B65B 3/26 (2006.01); B65B 9/20 (2012.01); B65B 37/00 (2006.01); B65B 41/16 (2006.01); B65B 43/08 (2006.01); B65B 43/42 (2006.01); B65B 51/14 (2006.01); B65B 57/00 (2006.01); B65B 57/02 (2006.01); B65B 57/04 (2006.01); B65B 57/14 (2006.01); B65B 59/00 (2006.01); B65H 23/04 (2006.01); B65H 23/188 (2006.01); G05B 13/02 (2006.01);
U.S. Cl.
CPC ...
B65B 41/16 (2013.01); B65B 3/00 (2013.01); B65B 3/26 (2013.01); B65B 9/20 (2013.01); B65B 9/213 (2013.01); B65B 37/00 (2013.01); B65B 43/08 (2013.01); B65B 43/42 (2013.01); B65B 51/146 (2013.01); B65B 57/00 (2013.01); B65B 57/02 (2013.01); B65B 57/04 (2013.01); B65B 57/145 (2013.01); B65B 59/003 (2019.05); B65H 23/048 (2013.01); B65H 23/1888 (2013.01); G05B 13/024 (2013.01); G05B 13/027 (2013.01); B65B 2210/04 (2013.01); B65H 2511/112 (2013.01); B65H 2557/24 (2013.01); B65H 2557/264 (2013.01); B65H 2801/69 (2013.01);
Abstract

Methods and apparatus described for forming individual packages in a food packaging machine comprising a plurality of sub-systems. One or more local variable values are received, which indicate measurements by the food packaging machine of one or more physical parameters for a local sub-system. One or more remote variable values are received, which indicate measurements by the food packaging machine of one or more physical parameters for one or remote sub-systems. One or more control parameter values are determined for the local sub-system of the food packaging machine, by processing the remote variable values and the local variable values using a reinforcement learning model and a local control model. One or more control parameters of the local sub-system are adjusted in accordance with the determined control parameter values. The formation of individual packages by the food packaging machine is controlled in accordance with the adjusted one or more control parameters.


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