Patents by Inventor Jonathan C. Kao

Jonathan C. Kao 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).

  • Patent number: 10779746
    Abstract: A brain machine interface (BMI) for improving a performance of a subject is provided. The BMI has two decoders that act in real-time and in parallel to each other. The first decoder is for intention execution of a subject's intention. The second decoder is for error detection in a closed-loop error fashion with the first detector and to improve the performance of the first detector. Embodiments of this invention may enable an entirely new way to substantially increase the performance and robustness, user experience, and ultimately the clinical viability of BMI systems.
    Type: Grant
    Filed: August 11, 2016
    Date of Patent: September 22, 2020
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Nir Even-Chen, Krishna V. Shenoy, Jonathan C. Kao, Sergey Stavisky
  • Publication number: 20190205731
    Abstract: A brain machine interface (BMI) to control a device is provided. The BMI has a neural decoder, which is a neural to kinematic mapping function with neural signals as input to the neural decoder and kinematics to control the device as output of the neural decoder. The neural decoder is based on a continuous-time multiplicative recurrent neural network, which has been trained as a neural to kinematic mapping function. An advantage of the invention is the robustness of the decoder to perturbations in the neural data; its performance degrades less—or not at all in some circumstances—in comparison to the current state decoders. These perturbations make the current use of BMI in a clinical setting extremely challenging. This invention helps to ameliorate this problem. The robustness of the neural decoder does not come at the cost of some performance, in fact an improvement in performance is observed.
    Type: Application
    Filed: March 4, 2019
    Publication date: July 4, 2019
    Inventors: David Sussillo, Jonathan C. Kao, Sergey Stavisky, Krishna V. Shenoy
  • Patent number: 10223634
    Abstract: A brain machine interface (BMI) to control a device is provided. The BMI has a neural decoder, which is a neural to kinematic mapping function with neural signals as input to the neural decoder and kinematics to control the device as output of the neural decoder. The neural decoder is based on a continuous-time multiplicative recurrent neural network, which has been trained as a neural to kinematic mapping function. An advantage of the invention is the robustness of the decoder to perturbations in the neural data; its performance degrades less—or not at all in some circumstances—in comparison to the current state decoders. These perturbations make the current use of BMI in a clinical setting extremely challenging. This invention helps to ameliorate this problem. The robustness of the neural decoder does not come at the cost of some performance, in fact an improvement in performance is observed.
    Type: Grant
    Filed: August 14, 2015
    Date of Patent: March 5, 2019
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: David Sussillo, Jonathan C. Kao, Sergey Stavisky, Krishna V. Shenoy
  • Publication number: 20170042440
    Abstract: A brain machine interface (BMI) for improving a performance of a subject is provided. The BMI has two decoders that act in real-time and in parallel to each other. The first decoder is for intention execution of a subject's intention. The second decoder is for error detection in a closed-loop error fashion with the first detector and to improve the performance of the first detector. Embodiments of this invention may enable an entirely new way to substantially increase the performance and robustness, user experience, and ultimately the clinical viability of BMI systems.
    Type: Application
    Filed: August 11, 2016
    Publication date: February 16, 2017
    Inventors: Nir Even-Chen, Krishna V. Shenoy, Jonathan C. Kao, Sergey Stavisky
  • Patent number: 9471870
    Abstract: A brain machine interface (BMI) for restoring performance of poorly performing decoders is provided. The BMI has a decoder for decoding neural signals for controlling the brain machine interface. The decoder separates in part neural signals associated with a direction of movement and neural signals associated with a speed of movement of the brain machine interface. The decoder assigns relatively greater weight to the neural signals associated with a direction of movement.
    Type: Grant
    Filed: February 3, 2016
    Date of Patent: October 18, 2016
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Jonathan C. Kao, Chethan Pandarinath, Paul Nuyujukian, Krishna V. Shenoy
  • Publication number: 20160224891
    Abstract: A brain machine interface (BMI) for restoring performance of poorly performing decoders is provided. The BMI has a decoder for decoding neural signals for controlling the brain machine interface. The decoder separates in part neural signals associated with a direction of movement and neural signals associated with a speed of movement of the brain machine interface. The decoder assigns relatively greater weight to the neural signals associated with a direction of movement.
    Type: Application
    Filed: February 3, 2016
    Publication date: August 4, 2016
    Inventors: Jonathan C. Kao, Chethan Pandarinath, Paul Nuyujukian, Krishna V. Shenoy
  • Patent number: 9373088
    Abstract: A brain machine interface for control of prosthetic devices is provided. In its control, the interface utilizes parallel control of a continuous decoder and a discrete action state decoder. In the discrete decoding, we not only learn states affiliated with the task, but also states related to the velocity of the prosthetic device and the engagement of the user. Moreover, we not only learn the distributions of the neural signals in these states, but we also learn the interactions/transitions between the states, which is crucial to enabling a relatively higher level of performance of the prosthetic device. Embodiments according to this parallel control system enable us to reliably decode not just task-related states, but any “discrete action state,” in parallel with a neural prosthetic “continuous decoder,” to achieve new state-of-the-art levels of performance in brain-machine interfaces.
    Type: Grant
    Filed: September 12, 2013
    Date of Patent: June 21, 2016
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Paul Nuyujukian, Jonathan C. Kao, Krishna V Shenoy
  • Publication number: 20160048753
    Abstract: A brain machine interface (BMI) to control a device is provided. The BMI has a neural decoder, which is a neural to kinematic mapping function with neural signals as input to the neural decoder and kinematics to control the device as output of the neural decoder. The neural decoder is based on a continuous-time multiplicative recurrent neural network, which has been trained as a neural to kinematic mapping function. An advantage of the invention is the robustness of the decoder to perturbations in the neural data; its performance degrades less—or not at all in some circumstances—in comparison to the current state decoders. These perturbations make the current use of BMI in a clinical setting extremely challenging. This invention helps to ameliorate this problem. The robustness of the neural decoder does not come at the cost of some performance, in fact an improvement in performance is observed.
    Type: Application
    Filed: August 14, 2015
    Publication date: February 18, 2016
    Inventors: David Sussillo, Jonathan C. Kao, Sergey Stavisky, Krishna V. Shenoy
  • Publication number: 20150245928
    Abstract: A brain machine interface (BMI) for restoring performance of poorly performing decoders is provided. The BMI has a decoder for decoding neural signals for controlling the brain machine interface. The decoder separates in part neural signals associated with a direction of movement and neural signals associated with a speed of movement of the brain machine interface. The decoder assigns relatively greater weight to the neural signals associated with a direction of movement.
    Type: Application
    Filed: June 19, 2014
    Publication date: September 3, 2015
    Inventors: Jonathan C. Kao, Chethan Pandarinath, Paul Nuyujukian, Krishna V. Shenoy
  • Patent number: 9095455
    Abstract: A brain-machine interface is provided that incorporates a neural dynamical structure in the control of a prosthetic device to restore motor function and is able to significantly enhance the control performance compared to existing technologies. In one example, a neural dynamical state is inferred from neural observations, which are obtained from a neural implant. In another example, the neural dynamical state can be inferred from both the obtained neural observations and from the kinematics. A controller interfaced with the prosthetic device uses the inferred neural dynamical state as input to the controller to control kinematic variables of the prosthetic device.
    Type: Grant
    Filed: February 24, 2014
    Date of Patent: August 4, 2015
    Assignees: The Board of Trustees of the Leland Stanford Junior University, Cambridge Enterprise Limited
    Inventors: Jonathan C. Kao, Paul Nuyujukian, Mark M. Churchland, John P. Cunningham, Krishna V. Shenoy
  • Publication number: 20140257520
    Abstract: A brain-machine interface is provided that incorporates a neural dynamical structure in the control of a prosthetic device to restore motor function and is able to significantly enhance the control performance compared to existing technologies. In one example, a neural dynamical state is inferred from neural observations, which are obtained from a neural implant. In another example, the neural dynamical state can be inferred from both the obtained neural observations and from the kinematics. A controller interfaced with the prosthetic device uses the inferred neural dynamical state as input to the controller to control kinematic variables of the prosthetic device.
    Type: Application
    Filed: February 24, 2014
    Publication date: September 11, 2014
    Inventors: Jonathan C. Kao, Paul Nuyujukian, Mark M. Churchland, John P. Cunningham, Krishna V. Shenoy
  • Publication number: 20140081454
    Abstract: A brain machine interface for control of prosthetic devices is provided. In its control, the interface utilizes parallel control of a continuous decoder and a discrete action state decoder. In the discrete decoding, we not only learn states affiliated with the task, but also states related to the velocity of the prosthetic device and the engagement of the user. Moreover, we not only learn the distributions of the neural signals in these states, but we also learn the interactions/transitions between the states, which is crucial to enabling a relatively higher level of performance of the prosthetic device. Embodiments according to this parallel control system enable us to reliably decode not just task-related states, but any “discrete action state,” in parallel with a neural prosthetic “continuous decoder,” to achieve new state-of-the-art levels of performance in brain-machine interfaces.
    Type: Application
    Filed: September 12, 2013
    Publication date: March 20, 2014
    Inventors: Paul Nuyujukian, Jonathan C. Kao, Krishna V. Shenoy