Capacity planning using machine learning
US12393855B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 22, 2021 |
| Grant date | Aug 19, 2025 |
| Priority date | — |
| Expiry date | Mar 28, 2044 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L41/5009
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
Systems, devices, and methods are provided for training and/or inferencing capacity planning using a machine learning model. A first time series may be provided as an input to a machine learning model, which may be an Auto Regressive Integrated Moving Average (ARIMA)-based forecasting model. The machine learning model may be trained solve a conditional maximum likelihood problem by performing quantile regression. The machine learning model may forecast one or more innovations using Monte-Carlo simulations. The machine learning model may generate, as an output, a value that corresponds to an amount of computing resources that is predicted, over a second time series, to be sufficient to satisfy a threshold level of availability or quality.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.