Neural network categorization accuracy with categorical graph neural networks
US11551039B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 28, 2020 |
| Grant date | Jan 10, 2023 |
| Priority date | — |
| Expiry date | Mar 19, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F40/289
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Neural network-based categorization can be improved by incorporating graph neural networks that operate on a graph representing the taxonomy of the categories into which a given input is to be categorized by the neural network based-categorization. The output of a graph neural network, operating on a graph representing the taxonomy of categories, can be combined with the output of a neural network operating upon the input to be categorized, such as through an interaction of multidimensional output data, such as a dot product of output vectors. In such a manner, information conveying the explicit relationships between categories, as defined by the taxonomy, can be incorporated into the categorization. To recapture information, incorporate new information, or reemphasize information a second neural network can also operate upon the input to be categorized, with the output of such a second neural network being merged with the output of the interaction.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.