Patents by Inventor John J. Feeney

John J. Feeney 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: 20220386967
    Abstract: Systems and methods for supporting medical therapy decisions are disclosed that utilize predictive models and electronic medical records (EMR) data to provide predictions of health conditions over varying time horizons. Embodiments also determine a 0-100 health risk index value that represents the “risk” for a patient to acquire a health condition based on a combination of real-time and predicted EMR data. The systems and methods receive EMR data and use the predictive models to predict one or more data values from the EMR data as diagnostic criteria. In some embodiments, the health condition trying to be avoided is Sepsis and the health risk index is a Sepsis Risk Index (SRI). In some embodiments, the predictive models are neural network models such as time delay neural networks.
    Type: Application
    Filed: August 15, 2022
    Publication date: December 8, 2022
    Applicant: Aptima, Inc.
    Inventors: Scott M. Pappada, John J. Feeney, William N. DePriest
  • Patent number: 11464456
    Abstract: Systems and methods for supporting medical therapy decisions are disclosed that utilize predictive models and electronic medical records (EMR) data to provide predictions of health conditions over varying time horizons. Embodiments also determine a 0-100 health risk index value that represents the “risk” for a patient to acquire a health condition based on a combination of real-time and predicted EMR data. The systems and methods receive EMR data and use the predictive models to predict one or more data values from the EMR data as diagnostic criteria. In some embodiments, the health condition trying to be avoided is Sepsis and the health risk index is a Sepsis Risk Index (SRI). In some embodiments, the predictive models are neural network models such as time delay neural networks.
    Type: Grant
    Filed: August 7, 2016
    Date of Patent: October 11, 2022
    Assignee: Aptima, Inc.
    Inventors: Scott M. Pappada, John J. Feeney, William N. DePriest
  • Patent number: 10783801
    Abstract: In one example embodiment of the invention, a simulation based training system is provided having a sensor that unobtrusively collects objective data for individuals and teams experiencing training content to determine the cognitive states of individuals and teams; time-synchronizes the various data streams; automatically determines granular and objective measures for individual cognitive load (CL) of individuals and teams; and automatically determines a cognitive load balance (CLB) and a relative cognitive load (RCL) measure in real or near-real time. Data is unobtrusively gathered through physiological or other activity sensors such as electroencephalogram (EEG) and electrocardiogram (ECG) sensors. Some embodiments are further configured to also include sociometric data in the determining cognitive load. Sociometric data may be obtained through the use of sociometric badges.
    Type: Grant
    Filed: December 21, 2017
    Date of Patent: September 22, 2020
    Assignee: Aptima, Inc.
    Inventors: Jeffrey Beaubien, John J. Feeney, William N. DePriest, Scott M. Pappada
  • Publication number: 20180168516
    Abstract: Systems and methods for supporting medical therapy decisions are disclosed that utilize predictive models and electronic medical records (EMR) data to provide predictions of health conditions over varying time horizons. Embodiments also determine a 0-100 health risk index value that represents the “risk” for a patient to acquire a health condition based on a combination of real-time and predicted EMR data. The systems and methods receive EMR data and use the predictive models to predict one or more data values from the EMR data as diagnostic criteria. In some embodiments, the health condition trying to be avoided is Sepsis and the health risk index is a Sepsis Risk Index (SRI). In some embodiments, the predictive models are neural network models such as time delay neural networks.
    Type: Application
    Filed: August 7, 2016
    Publication date: June 21, 2018
    Applicant: Aptima, Inc.
    Inventors: Scott M. Pappada, John J. Feeney, William N. DePriest
  • Patent number: 4116432
    Abstract: A stablized gasoline powered model airplane and pylon assembly has a thin vertically extending pylon of substantially uniform cross-section, a base for supporting the pylon vertically without circumferential lateral interference, a gasoline powered model airplane mounted for powered flight around the pylon by a small ring freely mounted on the pylon and a restraint line extending between the model airplane and the ring. A circular element of resilient plastic is secured near the upper end of the pylon for engaging the ring on the restraining wire to limit vertical travel, and oscillation of the model during flight. The model airplane is constrained to take-off, circle about the pylon, climb to the height of the pylon, and, following engagement with the flexible damping and stop element, to continue circular flight about the upper end of the pylon until fuel is exhausted and then to glide to a landing.
    Type: Grant
    Filed: March 28, 1977
    Date of Patent: September 26, 1978
    Inventor: John J. Feeney