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. 20, 2026

Filed:

Feb. 25, 2022
Applicant:

Accenture Global Solutions Limited, Dublin, IE;

Inventors:

Zaid Tashman, San Francisco, CA (US);

Matthew Kujawinski, San Jose, CA (US);

Neda Abolhassani, San Mateo, CA (US);

Sanjoy Paul, Sugar Land, TX (US);

Thien Quang Nguyen, San Jose, CA (US);

Eric Annong Tang, Fremont, CA (US);

Jessica Huey-Jen Yeh, Sunnyvale, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 7/01 (2023.01); G06N 3/008 (2023.01); G06N 5/02 (2023.01); G06N 5/022 (2023.01); G06N 7/06 (2006.01);
U.S. Cl.
CPC ...
G06N 7/01 (2023.01); G06N 3/008 (2013.01); G06N 5/02 (2013.01); G06N 5/022 (2013.01); G06N 7/06 (2013.01);
Abstract

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support ontology driven processes to create digital twins that extend the capabilities of knowledge graphs. A dataset including an ontology and domain data corresponding to a domain associated with the ontology is obtained. A knowledge graph is constructed based on the ontology and the domain data is incorporated into the knowledge graph. The knowledge graph is exploited to derive random variables of a probabilistic graph model. The random variables may be associated with probability distributions, which may include unknown parameters. A learning process is executed to learn the unknown parameters and obtain a joint distribution of the probabilistic graph model, which may enable querying of the probabilistic graph model in a probabilistic and deterministic manner.


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