Patents by Inventor Valery Feigin

Valery Feigin has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Method and system for predicting outcomes based on spatio/spectro-temporal data
    Patent number: 10579925
    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.
    Type: Grant
    Filed: August 26, 2014
    Date of Patent: March 3, 2020
    Assignee: AUT Ventures Limited
    Inventors: Nikola Kirilov Kasabov, Zeng-Guang Hou, Valery Feigin, Yixiong Chen
  • Improved Method And System For Predicting Outcomes Based On Spatio/Spectro-Temporal Data
    Publication number: 20160210552
    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.
    Type: Application
    Filed: August 26, 2014
    Publication date: July 21, 2016
    Inventors: Nikola Kirilov Kasabov, Zeng-Guang Hou, Valery Feigin, Yixiong Chen