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:
Nov. 22, 2022

Filed:

May. 01, 2020
Applicants:

Chevron U.s.a. Inc., San Ramon, CA (US);

University of Southern California, Los Angeles, CA (US);

Inventors:

Yining Dong, San Jose, CA (US);

Alisha Deshpande, Houston, TX (US);

Yingying Zheng, Thousand Oaks, CA (US);

Lisa Ann Brenskelle, Houston, TX (US);

Si-Zhao Qin, Los Angeles, CA (US);

Assignees:

Chevron U.S.A. Inc., San Ramon, CA (US);

University of Southern California, Los Angeles, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/44 (2018.01); G06F 9/455 (2018.01); G06F 9/445 (2018.01); G06F 11/00 (2006.01); G05B 19/418 (2006.01); G05B 19/408 (2006.01); G05B 19/406 (2006.01); G06F 9/54 (2006.01); G06K 9/62 (2022.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G05B 19/41885 (2013.01); G05B 19/406 (2013.01); G05B 19/408 (2013.01); G06F 9/542 (2013.01); G06K 9/6218 (2013.01); G06K 9/6247 (2013.01); G06N 20/00 (2019.01);
Abstract

Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.


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