Method and system for predicting outcomes based on spatio/spectro-temporal data
US10579925B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 26, 2014 |
| Grant date | Mar 3, 2020 |
| Priority date | — |
| Expiry date | Apr 19, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This invention involves use of temporal or spatio/spector-temporal data (SSTD) for early classification of outputs that are results of spatio-temporal patterns of data. Classification models are based on spiking neural networks (SNN) suitable to learn and classify SSTD. The invention may predict early events in many applications, i.e. engineering, bioinformatics, neuroinformatics, predicting response to treatment of neurological and brain disease, ecology, environment, medicine, and economics, among others. The invention involves a method and system for personalized modelling of SSTD and early prediction of events based on evolving spiking neural network reservoir architecture (eSNNr). The system includes a spike-time encoding module to encode continuous value input information into spike trains, a recurrent 3D SNNr and an eSSN as an output classification module.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.