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:
Feb. 25, 2025

Filed:

Jan. 18, 2020
Applicant:

Royal Bank of Canada, Toronto, CA;

Inventors:

Janahan Mathuran Ramanan, Toronto, CA;

Jaspreet Sahota, Toronto, CA;

Rishab Goel, Toronto, CA;

Sepehr Eghbali, Toronto, CA;

Seyed Mehran Kazemi, Toronto, CA;

Assignee:

ROYAL BANK OF CANADA, Toronto, CA;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/084 (2023.01); G06F 17/10 (2006.01); G06F 17/14 (2006.01); G06F 17/15 (2006.01); G06F 17/18 (2006.01); G06N 3/044 (2023.01); G06N 3/049 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01);
U.S. Cl.
CPC ...
G06N 3/049 (2013.01); G06F 17/10 (2013.01); G06F 17/14 (2013.01); G06F 17/142 (2013.01); G06F 17/156 (2013.01); G06F 17/18 (2013.01); G06N 3/044 (2023.01); G06N 3/084 (2013.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01);
Abstract

Described in various embodiments herein is a technical solution directed to decomposition of time as an input for machine learning, and various related mechanisms and data structures. In particular, specific machines, computer-readable media, computer processes, and methods are described that are utilized to improve machine learning outcomes, including, improving accuracy, convergence speed (e.g., reduced epochs for training), and reduced overall computational resource requirements. A vector representation of continuous time containing a periodic function with frequency and phase-shift learnable parameters is used to decompose time into output dimensions for improved tracking of periodic behavior of a feature. The vector representation is used to modify time inputs in machine learning architectures.


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