Explainable machine learning based on heterogeneous data
US11604994B2 · kind B2 · utility
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20Claims
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Key dates
| Filing date | Jul 26, 2019 |
| Grant date | Mar 14, 2023 |
| Priority date | — |
| Expiry date | Jan 3, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2218/12
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for explainable machine learning are described. In an example, a processor can receive a data set from a plurality of data sources corresponding to a plurality of domains. The processor can train a machine learning model to learn a plurality of vectors that indicate impact of the plurality of domains on a plurality of assets. The machine learning model can be operable to generate forecasts relating to performance metrics of the plurality of assets based on the plurality of vectors. In some examples, the machine learning model can be a neural attention network with shared hidden layers.
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