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. 30, 2026

Filed:

Feb. 22, 2023
Applicant:

Fujitsu Limited, Kawasaki, JP;

Inventors:

Masaru Todoriki, Kita, JP;

Masafumi Shingu, Mitaka, JP;

Koji Maruhashi, Hachioji, JP;

Assignee:

Fujitsu Limited, Kawasaki, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/00 (2023.01); G06F 40/56 (2020.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06N 3/082 (2023.01); G06N 5/01 (2023.01); G06N 5/045 (2023.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 40/56 (2020.01); G06N 3/04 (2013.01); G06N 3/082 (2013.01); G06N 5/01 (2023.01); G06N 5/045 (2013.01); G06N 20/00 (2019.01);
Abstract

A non-transitory computer-readable recording medium storing a model generation program for causing a computer to perform processing including: changing first data and generating a plurality of pieces of data; calculating a plurality of values indicating a distance between the first data and each of the plurality of pieces of data; determining whether or not a value indicating uniformity of distribution of the distance between the first data and each of the plurality of pieces of data is equal to or greater than a threshold based on the plurality of values; and in a case where the value indicating the uniformity is determined to be equal to or greater than the threshold, generating a linear regression model using a result obtained by inputting the plurality of pieces of data into a machine learning model as an objective variable and using the plurality of pieces of data as explanatory variables.


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