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. 02, 2025

Filed:

Aug. 21, 2020
Applicant:

Nxgen Partners Ip, Llc, Dallas, TX (US);

Inventor:

Solyman Ashrafi, Plano, TX (US);

Assignee:

NxGen Partners IP, LLC, Dallas, TX (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 7/08 (2006.01); G06F 16/901 (2019.01); G06N 10/60 (2022.01); G06N 20/00 (2019.01); G06T 1/20 (2006.01);
U.S. Cl.
CPC ...
G06N 7/08 (2013.01); G06F 16/9024 (2019.01); G06N 10/60 (2022.01); G06N 20/00 (2019.01); G06T 1/20 (2013.01);
Abstract

A method for predicting future behavior for a dynamic system using an artificial intelligence system implemented within a computer hardware system. A predetermined amount of a time series group of data from the dynamic system defining previous behavior of the dynamic system are received at the artificial intelligence system. An attractor is constructed from the time series group of data that defines the previous behavior of the dynamic system using the artificial intelligence system. The attractor models the previous behavior of the dynamic system based on the predetermined amount of the time series group of data of the dynamic system. A prediction horizon for the predetermined amount of the time series group of data is determined with the artificial intelligence system using an attractor dimension of the constructed attractor and a Lyapunov exponent of the constructed attractor. The prediction horizon increases logarithmically as a length of the predetermined amount of the time series group of data from the dynamic system increases linearly. Prediction values of future behavior of the dynamic system are generated with the artificial intelligence system using the constructed attractor and the determined prediction horizon.


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