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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.

Patent No.:

US 12159386 B1

PDFFull Text
Date of Patent:
Dec. 03, 2024

Filed:

Mar. 13, 2019

Inspection system, discrimination system, and learning data generation device

Applicant:

Omron Corporation, Kyoto, JP;

Inventor:

Yoshihisa Ijiri, Tokyo, JP;

Assignee:

OMRON Corporation, Kyoto, JP;

Attorney:

JCIPRNET

Primary Examiner:

Emily C Terrell

Assistant Examiner:

Nathan J Bloom

Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06F 18/2115 (2023.01); G06F 18/2132 (2023.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06N 3/045 (2023.01); G06N 3/0464 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/771 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0004 (2013.01); G06F 18/2115 (2023.01); G06F 18/2132 (2023.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06N 3/045 (2023.01); G06N 3/0464 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/771 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A characteristic included in data is determined with relatively high precision. An inspection system according to one aspect of the present invention acquires a plurality of learning data sets respectively including a combination of image data and correct answer data, and sets a difficulty level of determination for each of the learning data sets in accordance with a degree to which a result which is obtained by determining the acceptability of a product in the image data of each of the learning data sets by a first discriminator conforms to a correct answer indicated by the correct answer data. Besides, the inspection system constructs a second discriminator that determines the acceptability of the product by executing stepwise machine learning in which the learning data sets are utilized in ascending order of the set difficulty level.

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