Patents by Inventor Jonathan Forsberg

Jonathan Forsberg 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).

  • Publication number: 20200335179
    Abstract: An embodiment of the invention provides a method for determining a patient-specific probability of disease. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of the healing rate of an acute traumatic wound is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of the healing rate of an acute traumatic wound.
    Type: Application
    Filed: July 7, 2020
    Publication date: October 22, 2020
    Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth
  • Patent number: 10726943
    Abstract: An embodiment of the invention provides a method for determining a patient-specific probability of disease. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of the healing rate of an acute traumatic wound is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of the healing rate of an acute traumatic wound.
    Type: Grant
    Filed: April 8, 2011
    Date of Patent: July 28, 2020
    Assignees: THE UNITED STATES OF AMERICA AS REPRESENTED BY THE SECRETARY OF THE NAVY, THE GOVERNMENT OF THE UNITED STATES, AS REPRESENTED BY THE SECRETARY OF THE ARMY, DECISIONQ CORPORATION
    Inventors: Alexander Stojadinovic, Eric Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth
  • Publication number: 20190038317
    Abstract: An orthopaedic fixation assembly for prosthetic biologic attachment. The orthopaedic fixation assembly may include a main body with a longitudinally-extending stem having a proximal end, a distal end, and a cavity body. An anchor plug may be configured to be received within the stem cavity, and securable thereto via complementary mating surfaces. A spindle structure may be fixedly attached to the proximal end of the longitudinally-extending stem and protrude outwardly therefrom such that a portion of the structure extends externally beyond the resected cavity of the bone that may prevent rotational motion of the spindle. The spindle structure may have at least one compliant biasing member configured to apply a compressive force to the surrounding bone. A porous coating may be at the juncture between stem and spindle structure, on the spindle, and the splines and anti-rotation chocks, improving the initial stability of the implant and facilitating long-term bone ingrowth.
    Type: Application
    Filed: August 1, 2018
    Publication date: February 7, 2019
    Applicant: The Solsidan Group, LLC
    Inventor: Jonathan A. FORSBERG
  • Publication number: 20160353994
    Abstract: A method for detecting and monitoring the progression of heterotopic ossification by Raman spectral analysis. Analysis of heterotopic ossification progress can be conducted using invasive or invasive means using specific Raman spectroscopy. Analysis is by determination of a number of Raman spectral parameters including the area under one or more of the vibrational bands, band area ratios, band height, ratios of band heights and shift in band center.
    Type: Application
    Filed: January 2, 2014
    Publication date: December 8, 2016
    Inventors: Nicole Crane, Eric A. Elster, Jonathan Forsberg, Douglas Tadaki
  • Publication number: 20150182119
    Abstract: A method for detecting and monitoring the progression of heterotopic ossification by Raman spectral analysis. Analysis of heterotopic ossification progress can be conducted using invasive or invasive means using specific Raman spectroscopy. Analysis is by determination of a number of Raman spectral parameters including the area under one or more of the vibrational bands, band area ratios, band height, ratios of band heights and shift in band center.
    Type: Application
    Filed: January 2, 2014
    Publication date: July 2, 2015
    Inventors: Nicole Crane, Eric A. Elster, Jonathan Forsberg, Douglas Tadaki
  • Patent number: 8510245
    Abstract: An embodiment of the invention provides a method for determining a patient-specific probability of transplant glomerulopathy. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of transplant glomerulopathy is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant glomerulopathy.
    Type: Grant
    Filed: April 8, 2011
    Date of Patent: August 13, 2013
    Assignee: The United States of America as Represented by the Secretary of the Army
    Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth, Roslyn Mannon
  • Publication number: 20110295782
    Abstract: An embodiment of the invention provides a method for determining a patient-specific probability of disease. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of disease.
    Type: Application
    Filed: October 15, 2009
    Publication date: December 1, 2011
    Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor S. Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth, Roslyn Mannon, Aviram Nissan
  • Publication number: 20110289035
    Abstract: An embodiment of the invention provides a method for determining a patient-specific probability of disease. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of the healing rate of an acute traumatic wound is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of the healing rate of an acute traumatic wound.
    Type: Application
    Filed: April 8, 2011
    Publication date: November 24, 2011
    Inventors: Alexander Stojadinovic, Eric Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth
  • Publication number: 20110289036
    Abstract: An embodiment of the invention provides a method for determining a patient-specific probability of transplant glomerulopathy. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of transplant glomerulopathy is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant glomerulopathy.
    Type: Application
    Filed: April 8, 2011
    Publication date: November 24, 2011
    Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth, Roslyn Mannon