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. 19, 2024

Filed:

Aug. 10, 2021
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Hiroki Nakano, Otsu, JP;

Masaharu Sakamoto, Yokohama, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01N 33/48 (2006.01); G01N 33/50 (2006.01); G06F 18/21 (2023.01); G06F 18/2413 (2023.01); G06F 18/243 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06T 1/20 (2006.01); G06T 7/00 (2017.01); G06V 10/764 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06F 18/217 (2023.01); G06F 18/2185 (2023.01); G06F 18/24143 (2023.01); G06F 18/24317 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 1/20 (2013.01); G06V 10/764 (2022.01); G06V 10/7788 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06F 2218/12 (2023.01); G06T 2207/30061 (2013.01);
Abstract

Neural network classification may be performed by inputting a training data set into each of a plurality of first neural networks, the training data set including a plurality of samples, obtaining a plurality of output value sets from the plurality of first neural networks, each output value set including a plurality of output values corresponding to one of the plurality of samples, each output value being output from a corresponding first neural network in response to the inputting of one of the samples of the training data set, inputting the plurality of output value sets into a second neural network, and training the second neural network to output an expected result corresponding to each sample in response to the inputting of a corresponding output value set.


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