Deep causal learning for continuous testing, diagnosis, and optimization
US12282303B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 11, 2019 |
| Grant date | Apr 22, 2025 |
| Priority date | — |
| Expiry date | Dec 28, 2040 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02P90/82
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.