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:
Jun. 18, 2024

Filed:

Dec. 21, 2018
Applicant:

Hitachi High-tech Corporation, Tokyo, JP;

Inventors:

Ryou Yumiba, Tokyo, JP;

Yasutaka Toyoda, Tokyo, JP;

Hiroyuki Shindo, Tokyo, JP;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/44 (2022.01); G06F 18/20 (2023.01); G06F 18/2113 (2023.01); G06F 18/213 (2023.01); G06N 20/00 (2019.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01);
U.S. Cl.
CPC ...
G06V 10/44 (2022.01); G06F 18/2113 (2023.01); G06F 18/213 (2023.01); G06F 18/285 (2023.01); G06N 20/00 (2019.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01);
Abstract

In order to select an optimal learning model for an image when inference is carried out in the extraction of a profile line using machine learning, without requiring a correct value or degree of certainty, a feature extraction learning model group containing a plurality of learning models is used for feature extraction. A recall learning model group containing recall learning models is paired with the feature extraction learning models. A feature amount extraction unit for referencing a feature extraction learning model and extracting a feature amount from input data; a data-to-data recall unit for referencing a recall learning model and outputting a recall result with the feature amount subjected to dimensional compression; and a learning model selection unit for selecting a feature extraction learning model from the feature extraction learning model group under the condition that the difference between the feature amount and the recall result is minimized are provided.


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