Patents by Inventor Seyed Koosha Sadeghi Oskooyee

Seyed Koosha Sadeghi Oskooyee 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).

  • Systems and methods for minimally intrusive displays with heart rate monitoring and workouts
    Patent number: 11766185
    Abstract: A system includes a minimally intrusive display system (MIDS) configured to be disposed on an eyewear. The MIDS includes a display system and a sensor system configured to provide for a sensor data. The MIDS further includes a processor configured to download a workout and to process the sensor data to monitor a user wearing the MIDS during the workout. The processor is further configured to display, via the display system, a workout progress based on the monitoring.
    Type: Grant
    Filed: August 18, 2021
    Date of Patent: September 26, 2023
    Assignee: Flipper Inc.
    Inventors: Yuri Zhovnirovsky, Cory Borovicka, Paul M. Hanson, Anthony A. Ambuehl, Seyed Koosha Sadeghi Oskooyee
  • SYSTEMS AND METHODS FOR MINIMALLY INTRUSIVE DISPLAYS WITH HEART RATE MONITORING AND WORKOUTS
    Publication number: 20210369127
    Abstract: A system includes a minimally intrusive display system (MIDS) configured to be disposed on an eyewear. The MIDS includes a display system and a sensor system configured to provide for a sensor data. The MIDS further includes a processor configured to download a workout and to process the sensor data to monitor a user wearing the MIDS during the workout. The processor is further configured to display, via the display system, a workout progress based on the monitoring.
    Type: Application
    Filed: August 18, 2021
    Publication date: December 2, 2021
    Inventors: Yuri Zhovnirovsky, Cory Borovicka, Paul M. Hanson, Anthony M. Ambuehl, Seyed Koosha Sadeghi Oskooyee
  • Brain-mobile interface optimization using internet-of-things
    Patent number: 10796246
    Abstract: A Brain-Mobile Interface (BMoI) system is provided. A control circuit is configured to execute a predictive model to generate a defined number of predicted signal features in future time based on a number of signal features extracted from a first type sensory data (e.g., electroencephalogram (EEG) data). A predicted future mental state(s) can thus be generated based on the number of predicted signal features and used to trigger a corresponding action(s) in a BMoI application(s). In a non-limiting example, a second type sensory data (e.g., electrocardiogram (ECG) data) can be used to improve accuracy of the predictive model. By using the predicted signal features to generate the predicted future mental state(s) to control the BMoI application(s), it is possible to duty-cycle the BMoI system to help reduce power consumption and processing latency, thus allowing the BMoI application(s) to operate in real-time with improved accuracy and power consumption.
    Type: Grant
    Filed: December 29, 2017
    Date of Patent: October 6, 2020
    Assignee: Arizona Board of Regents on Behalf of Arizona State University
    Inventors: Sandeep Gupta, Ayan Banerjee, Mohammad Javad Sohankar Esfahani, Seyed Koosha Sadeghi Oskooyee
  • Framework for security strength and performance analysis of machine learning based biometric systems
    Patent number: 10671735
    Abstract: A framework for measuring the security strength of bio-metric security systems against spoofing attacks considering the adversary's knowledge about the system is disclosed.
    Type: Grant
    Filed: April 10, 2018
    Date of Patent: June 2, 2020
    Assignee: Arizona Board of Regents on Behalf of Arizona State University
    Inventors: Sandeep K. S. Gupta, Ayan Banerjee, Seyed Koosha Sadeghi Oskooyee, Mohammad Javad Sohankar Esfahani
  • FRAMEWORK FOR SECURITY STRENGTH AND PERFORMANCE ANALYSIS OF MACHINE LEARNING BASED BIOMETRIC SYSTEMS
    Publication number: 20180300487
    Abstract: A framework for measuring the security strength of bio-metric security systems against spoofing attacks considering the adversary's knowledge about the system is disclosed.
    Type: Application
    Filed: April 10, 2018
    Publication date: October 18, 2018
    Applicant: Arizona Board of Regents on Behalf of Arizona State University
    Inventors: Sandeep K.S. Gupta, Ayan Banerjee, Seyed Koosha Sadeghi Oskooyee, Mohammad Javad Sohankar Esfahani
  • BRAIN-MOBILE INTERFACE OPTIMIZATION USING INTERNET-OF-THINGS
    Publication number: 20180189678
    Abstract: A Brain-Mobile Interface (BMoI) system is provided. A control circuit is configured to execute a predictive model to generate a defined number of predicted signal features in future time based on a number of signal features extracted from a first type sensory data (e.g., electroencephalogram (EEG) data). A predicted future mental state(s) can thus be generated based on the number of predicted signal features and used to trigger a corresponding action(s) in a BMoI application(s). In a non-limiting example, a second type sensory data (e.g., electrocardiogram (ECG) data) can be used to improve accuracy of the predictive model. By using the predicted signal features to generate the predicted future mental state(s) to control the BMoI application(s), it is possible to duty-cycle the BMoI system to help reduce power consumption and processing latency, thus allowing the BMoI application(s) to operate in real-time with improved accuracy and power consumption.
    Type: Application
    Filed: December 29, 2017
    Publication date: July 5, 2018
    Applicant: Arizona Board of Regents on behalf of Arizona Stat e University
    Inventors: Sandeep Gupta, Ayan Banerjee, Mohammad Javad Sohankar Esfahani, Seyed Koosha Sadeghi Oskooyee