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. 12, 2023

Filed:

Sep. 17, 2020
Applicant:

Microtec S.r.l., Bressanone, IT;

Inventors:

Enrico Ursella, Mestre, IT;

Davide Boschetto, Vigonza, IT;

Federico Giudiceandrea, Bressanone, IT;

Assignee:

MICROTEC S.R.L., Bressanone, IT;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06T 7/73 (2017.01); B27B 1/00 (2006.01); G06T 7/00 (2017.01); G06V 10/44 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); B27B 1/007 (2013.01); G06T 7/0004 (2013.01); G06T 7/73 (2017.01); G06V 10/454 (2022.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30161 (2013.01);
Abstract

A computer-implemented method for training a software infrastructure based on machine-learning techniques to analyse data obtained from a instrumental examination of objects of a predetermined type, where each of the objects has been obtained by splitting a product into smaller pieces, wherein the software infrastructure receives, for each object in a training set, training input data comprising the data obtained from the instrumental examination and training output data comprising information on the characteristics of interest of the training object, wherein the information on the characteristics of interest is, at least in part, information that has been obtained from the results of a tomographic examination of the product from which the training object was obtained, and wherein the software infrastructure processes, through its own training unit, the training input data and the training output data for each training object in order to set internal processing parameters for the software infrastructure which correlate the training input data to the training output data.


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