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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 12518519 B1

PDFFull Text
Date of Patent:
Jan. 06, 2026

Filed:

Nov. 22, 2021

Predictor creation device and predictor creation method

Applicant:

Hitachi, Ltd., Tokyo, JP;

Inventors:

Masaki Hamamoto, Tokyo, JP;

Masashi Egi, Tokyo, JP;

Masakazu Takahashi, Tokyo, JP;

Hiroyuki Namba, Tokyo, JP;

Assignee:

HITACHI, LTD., Tokyo, JP;

Attorney:

MATTINGLY & MALUR, PC

Primary Examiner:

Henok Shiferaw

Assistant Examiner:

Dennis undefined Rosario

Int. Cl.
CPC ...
G06V 10/80 (2022.01); G06F 18/214 (2023.01); G06N 3/02 (2006.01); G06N 3/08 (2023.01); G06N 3/0985 (2023.01); G06N 5/04 (2023.01); G06N 5/045 (2023.01); G06N 7/02 (2006.01); G06N 20/00 (2019.01); G06V 10/40 (2022.01); G06V 10/70 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 10/80 (2022.01); G06F 18/214 (2023.01); G06N 3/02 (2013.01); G06N 3/08 (2013.01); G06N 3/0985 (2023.01); G06N 5/04 (2013.01); G06N 5/045 (2013.01); G06N 7/023 (2013.01); G06N 20/00 (2019.01); G06V 10/40 (2022.01); G06V 10/70 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/7788 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A predictor creation device including a processor configured to execute a program and a storage device that stores the program acquires a calibration target ensemble predictor obtained by combining a plurality of predictors based on a training data set which is a combination of training data and ground truth data, calculates a prediction basis characteristic related to a feature of the training data for each of the plurality of predictors, acquires an expected prediction basis characteristic related to the feature based on the prediction basis characteristic related to the feature as a result of outputting the prediction basis characteristic related to the calculated feature, determines a combination coefficient of each of the plurality of predictors based on the prediction basis characteristic related to the feature and the expected prediction basis characteristic related to the feature, and calibrates the calibration target ensemble predictor based on the combination coefficient.

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