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. 30, 2024

Filed:

Jul. 23, 2021
Applicants:

Mohammad Esmalifalak, Toronto, CA;

Akshay Iyengar, Toronto, CA;

Seyedmorteza Mirhoseininejad, Toronto, CA;

Peter Doulas, Toronto, CA;

Francis Emery, Toronto, CA;

Taylor Mathewson, Toronto, CA;

William Hogan, Toronto, CA;

Min Hua Yu, Toronto, CA;

Inventors:

Mohammad Esmalifalak, Toronto, CA;

Akshay Iyengar, Toronto, CA;

Seyedmorteza Mirhoseininejad, Toronto, CA;

Peter Doulas, Toronto, CA;

Francis Emery, Toronto, CA;

Taylor Mathewson, Toronto, CA;

William Hogan, Toronto, CA;

Min Hua Yu, Toronto, CA;

Assignee:

FIIX INC., Toronto, CA;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/20 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/0635 (2023.01); G06Q 10/0875 (2023.01);
U.S. Cl.
CPC ...
G06Q 10/20 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/0635 (2013.01); G06Q 10/0875 (2013.01);
Abstract

An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.


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