Method to explain factors influencing AI predictions with deep neural networks
US11501161B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 4, 2019 |
| Grant date | Nov 15, 2022 |
| Priority date | — |
| Expiry date | Sep 15, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q10/067
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present invention provide systems, methods, and computer storage media for providing factors that explain the generated results of a deep neural network (DNN). In embodiments, multiple machine learning models and a DNN are trained on a training dataset. A preliminary set of trained machine learning models with similar results to the trained DNN are selected for further evaluation. The preliminary set of machine learning models may be evaluated using a distribution analysis to select a reduced set of machine learning models. Results produced by the reduced set of machine learning models are compared, point-by-point, to the results produced by the DNN. The best performing machine learning model with generated results that performs closest to the DNN generated results may be selected. One or more factors used by the selected machine learning model are determined. Those one or more factors from the selected best performing machine learning model may be provided to explain the results of the DNN and increase confidence in the understanding and accuracy of the results generated by the DNN.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.