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:
Dec. 26, 2023

Filed:

Jun. 26, 2018
Applicant:

Siemens Aktiengesellschaft, Munich, DE;

Inventors:

Arquimedes Martinez Canedo, Plainsboro, NJ (US);

Jiang Wan, Irvine, CA (US);

Blake Pollard, Jupiter, FL (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 5/02 (2023.01); G06N 5/04 (2023.01); G06F 17/15 (2006.01); G06N 20/00 (2019.01); G06F 16/90 (2019.01); G06Q 10/04 (2023.01); G06N 3/04 (2023.01); G06N 5/022 (2023.01); G06N 5/046 (2023.01); G06F 16/901 (2019.01); G06F 18/21 (2023.01); G06N 3/045 (2023.01); G06Q 50/00 (2012.01);
U.S. Cl.
CPC ...
G06N 5/022 (2013.01); G06F 16/9024 (2019.01); G06F 17/15 (2013.01); G06F 18/21 (2023.01); G06N 3/045 (2023.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06Q 10/04 (2013.01); G06Q 50/01 (2013.01);
Abstract

A computer-implemented method for learning structural relationships between nodes of a graph includes generating a knowledge graph comprising nodes representing a system and applying a graph-based convolutional neural network (GCNN) to the knowledge graph to generate feature vectors describing structural relationships between the nodes. The GCNN comprises: (i) a graph feature compression layer configured to learn subgraphs representing embeddings of the nodes of the knowledge graph into a vector space, (ii) a neighbor nodes aggregation layer configured to derive neighbor node feature vectors for each subgraph and aggregate the neighbor node feature vectors with their corresponding subgraphs to yield aggregated subgraphs, and (iii) a subgraph convolution layer configured to generate the feature vectors based on the aggregated subgraphs. Functional groups of components included in the system may then be identified based on the plurality of feature vectors.


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