Deep machine learning to predict and prevent adverse conditions at structural assets
US10664750B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Aug 10, 2016 |
| Grant date | May 26, 2020 |
| Priority date | — |
| Expiry date | Dec 1, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/56
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure provides systems and methods that use machine-learned models, such as deep neural networks, to predict and prevent adverse conditions at structural assets. One example method includes obtaining data descriptive of a plurality of images that depict at least a portion of a geographic area that contains a first structural asset. The plurality of images include at least a first image captured at a first time and a second image captured at a second time that is different than the first time. The method includes inputting data descriptive of at least the first image, the first time, the second image, and the second time into a condition prediction model. The method includes receiving, as an output of the condition prediction model, at least one prediction regarding the occurrence of an adverse condition at the first structural asset during one or more future time periods.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.