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:
Aug. 20, 2024

Filed:

Jan. 26, 2022
Applicants:

Fanuc Corporation, Yamanashi, JP;

Preferred Networks, Inc., Tokyo, JP;

Inventors:

Shougo Inagaki, Yamanashi, JP;

Hiroshi Nakagawa, Yamanashi, JP;

Daisuke Okanohara, Tokyo, JP;

Ryosuke Okuta, Tokyo, JP;

Eiichi Matsumoto, Tokyo, JP;

Keigo Kawaai, Tokyo, JP;

Assignees:

FANUC CORPORATION, Yamanashi, JP;

PREFERRED NETWORKS, INC., Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 13/02 (2006.01); B25J 9/16 (2006.01); G05B 15/02 (2006.01); G05B 19/4063 (2006.01); G05B 23/02 (2006.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06N 5/048 (2023.01); G06N 20/00 (2019.01); G06N 3/044 (2023.01); G06N 3/084 (2023.01);
U.S. Cl.
CPC ...
G05B 13/0265 (2013.01); B25J 9/163 (2013.01); G05B 15/02 (2013.01); G05B 19/4063 (2013.01); G05B 23/024 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 5/048 (2013.01); G06N 20/00 (2019.01); G05B 2219/31359 (2013.01); G05B 2219/33321 (2013.01); G06N 3/044 (2023.01); G06N 3/084 (2013.01); Y02P 90/02 (2015.11); Y10S 901/47 (2013.01);
Abstract

An anomality prediction system, which predicts an anomality of a machine, includes: one or more memories; and one or more processors configured to: obtain a state variable including at least one of output data from at least one sensor that detects a state of at least one of the machine or a surrounding environment, internal data of control software controlling the machine, or computational data obtained based on at least one of the output data or the internal data; generate, by inputting the obtained state variable into a machine learning model, a degree of anomality of the machine based on output from the machine learning model; and notify information based on the generated degree of anomality, wherein the notified information includes at least one of the generated degree of anomality at one or more time points, or one or more levels of anomality based on the generated degree of anomality.


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