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:
Jan. 31, 2023

Filed:

Feb. 25, 2019
Applicant:

Sap SE, Walldorf, DE;

Inventors:

Lukas Carullo, Menlo Park, CA (US);

Patrick Brose, San Francisco, CA (US);

Kun Bao, Sunnyvale, CA (US);

Anubhav Bhatia, Sunnyvale, CA (US);

Rashmi Shetty B, San Ramon, CA (US);

Leonard Brzezinski, San Jose, CA (US);

Lauren McMullen, El Dorado Hills, CA (US);

Harpreet Singh, Fremont, CA (US);

Karthik Mohan Mokashi, San Ramon, CA (US);

Simon Lee, San Ramon, CA (US);

Assignee:

SAP SE, Walldorf, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 13/02 (2006.01); G06F 9/54 (2006.01); G06N 20/20 (2019.01); G06N 7/00 (2006.01);
U.S. Cl.
CPC ...
G05B 13/0265 (2013.01); G06F 9/542 (2013.01); G06N 7/005 (2013.01); G06N 20/20 (2019.01);
Abstract

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.


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