US 12,007,759 B2
Geometric aging data reduction for machine learning applications
Dieter Gawlick, Palo Alto, CA (US); Matthew Torin Gerdes, Oakland, CA (US); Kirk Bradley, San Francisco, CA (US); Anna Chystiakova, Sunnyvale, CA (US); Zhen Hua Liu, San Mateo, CA (US); Guang Chao Wang, San Diego, CA (US); and Kenny C. Gross, Escondido, CA (US)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Jun. 28, 2021, as Appl. No. 17/361,189.
Prior Publication US 2022/0413481 A1, Dec. 29, 2022
Int. Cl. G05B 23/02 (2006.01); G06F 11/34 (2006.01); G06F 16/215 (2019.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01)
CPC G05B 23/0283 (2013.01) [G06F 11/3409 (2013.01); G06F 16/215 (2019.01); G06F 18/2148 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a first time-series dataset that tracks at least one metric value over time;
generating a second time-series dataset that includes a reduced version of a first portion of the first time-series dataset and a non-reduced version of a second portion of the first time-series dataset, wherein the second portion of the first time-series dataset includes metric values that are more recent than the first portion of the first time-series dataset;
determining one or more virtual metrics for the first portion of the first time-series dataset based on a non-reduced version of the first portion of the first time-series dataset, wherein the one or more virtual metrics are not discernable from the reduced version of the first portion of the first time-series dataset; and
training at least one machine learning model using the second time-series dataset that includes the reduced version of the first portion of the first time-series dataset and the non-reduced version of the second portion of the first time-series dataset.