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.
Patent No.:
Date of Patent:
Mar. 19, 2024
Filed:
Mar. 04, 2021
Applicant:
The Bank of New York Mellon, New York, NY (US);
Inventor:
Ibrahim Ghalyan, New York, NY (US);
Assignee:
THE BANK OF NEW YORK MELLON, New York, NY (US);
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 40/30 (2022.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/23213 (2023.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01); G06V 10/40 (2022.01); G06V 10/44 (2022.01); G06V 10/74 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 40/33 (2022.01); G06F 18/214 (2023.01); G06F 18/2163 (2023.01); G06F 18/22 (2023.01); G06F 18/23213 (2023.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01); G06V 10/40 (2022.01); G06V 10/44 (2022.01); G06V 10/74 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
Abstract
The systems and methods provide a machine learning model that can exploit long time dependency for time-series sequences, perform end-to-end learning of dimension reduction and clustering, or train on long time-series sequences with low computation complexity. For example, the methods and systems use a novel, unsupervised temporal representation learning model. The model may generate cluster-specific temporal representations for long-history time series sequences and may integrate temporal reconstruction and a clustering objective into a joint end-to-end model.