Perform preemptive identification and reduction of risk of failure in computational systems by training a machine learning module
US11200142B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 26, 2018 |
| Grant date | Dec 14, 2021 |
| Priority date | — |
| Expiry date | Feb 3, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A machine learning module is trained by receiving inputs comprising attributes of a computing environment, where the attributes affect a likelihood of failure in the computing environment. In response to an event occurring in the computing environment, a risk score that indicates a predicted likelihood of failure in the computing environment is generated via forward propagation through a plurality of layers of the machine learning module. A margin of error is calculated based on comparing the generated risk score to an expected risk score, where the expected risk score indicates an expected likelihood of failure in the computing environment corresponding to the event. An adjustment is made of weights of links that interconnect nodes of the plurality of layers via back propagation to reduce the margin of error, to improve the predicted likelihood of failure in the computing environment.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.