Machine learning with partial inversion
US10339441B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 21, 2017 |
| Grant date | Jul 2, 2019 |
| Priority date | — |
| Expiry date | Jan 11, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F11/3684
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n−1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n−1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.