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:
Apr. 08, 2025

Filed:

Feb. 21, 2019
Applicant:

Nippon Telegraph and Telephone Corporation, Tokyo, JP;

Inventors:

Makoto Morishita, Tokyo, JP;

Jun Suzuki, Tokyo, JP;

Masaaki Nagata, Tokyo, JP;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 16/33 (2019.01); G06F 16/3332 (2025.01); G06F 40/20 (2020.01); G06F 40/30 (2020.01); G06F 40/40 (2020.01); G06N 3/0499 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06F 40/45 (2020.01); G06N 3/04 (2023.01); G06N 3/0455 (2023.01);
U.S. Cl.
CPC ...
G06F 16/3337 (2019.01); G06F 40/20 (2020.01); G06F 40/30 (2020.01); G06F 40/40 (2020.01); G06N 3/0499 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G05B 2219/32018 (2013.01); G05B 2219/32188 (2013.01); G05B 2219/32193 (2013.01); G05B 2219/32194 (2013.01); G05B 2219/32195 (2013.01); G06F 40/45 (2020.01); G06N 3/04 (2013.01); G06N 3/0455 (2023.01);
Abstract

This disclosure relates to a device, a method, and a program capable of removing erroneous data from learning data used for machine learning used in natural language processing, for example. The method includes storing a forward direction learned model of a discrete series converter. The model is trained based on a plurality of pairs of discrete series of texts. Each pair comprises a first discrete series indicates an input of discrete series. A second discrete series indicates an output of discrete series. The first discrete series and the second discrete series are correctly associated. The method further includes converting the first discrete series to the second discrete series, and generating a quality score using the forward direction learned model, using a second learning pair of discrete series texts including an error in relationship.


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