Jeffrey C Humpherys

US Patent:

20110228971, Sep 22, 2011

Filed:

Mar 16, 2011

Appl. No.:

13/049543

Inventors:

Jared Webb - Brigham City UT, US
Rodney Forcade - Orem UT, US
Christopher Guzman - Middletown NY, US
Jeffrey Humpherys - Provo UT, US
C. Shane Reese - Mapleton UT, US

Assignee:

BRIGHAM YOUNG UNIVERSITY - Provo UT

International Classification:

G06K 9/00

US Classification:

382100

Abstract:

Embedding a watermark includes organizing variation locations in a data stream, partitioning the data stream into small blocks, determining the variation of small blocks based on the variation, categorizing small blocks into big blocks, identifying those big blocks that have a threshold level of variation, and embedding into those identified big blocks a watermark value.

US Patent:

20220269763, Aug 25, 2022

Filed:

Feb 22, 2022

Appl. No.:

17/677079

Inventors:

- Ira MI, US
Jeffrey Humpherys - Lehi UT, US
Ruth Fisher - Mountain View CA, US
Chosen Lee - Fort Washington PA, US
Caleb McClain - Ira MI, US

International Classification:

G06F 21/32
G06F 16/28
A24F 40/53
A24F 40/42
A24F 40/57
A24F 40/65
A61M 11/04

Abstract:

The present invention extends to methods, systems, and computer program products for tailoring administration of aerosolized bioactive material. Aspects include communicating biological and subjective information to support formulation of personalized bioactive material compositions. Aerosolized bioactive materials can be administered to humans, via inhalation, using a vaporizer device which is engineered for real time data capture, and communication, with a software application and backend computational system. Smart vaporizer cartridges can be identified and their contents recalled. Based at least on contents, appropriate (e.g., precision) doses of bioactive material can be aerosolized and administered. Users have the ability to electronically exchange and communicate in real time with the provisioning organization and medical practitioners via telemedicine and can directly participate in clinical trials pertaining to the bioactive material(s) in formulations within the smart vaporizer cartridge and elsewhere.

US Patent:

20210106240, Apr 15, 2021

Filed:

Oct 7, 2020

Appl. No.:

17/065454

Inventors:

- Lehi UT, US
Elliot Brown - Provo UT, US
Tanner Christensen - Provo UT, US
Chris Hettinger - Worcester MA, US
Jeffrey Humpherys - Lehi UT, US

International Classification:

A61B 5/024
A61B 5/308
A61B 5/00
A61B 5/366

Abstract:

A technology for obtaining a fetal heart rate from an electrocardiogram (ECG) signal. In one example, an artificial neural network model can be trained to predict a fetal heart rate using a training dataset containing ECG data. The artificial neural network model can include a first series of convolutional layers to separate a fetal ECG signal from a maternal ECG signal, a fast Fourier transform (FFT) layer to convert the fetal ECG signal to ECG frequency representations, and a dense layer to decode the ECG frequency representations to fetal heart rate predictions. After training the artificial neural network model, ECG data generated by an ECG monitor can be obtained, and the ECG data can be input to the artificial neural network model. The artificial neural network model outputs a fetal heart rate prediction, wherein the fetal heart rate prediction represents the fetal heart rate obtained from the ECG signal.

US Patent:

20210106241, Apr 15, 2021

Filed:

Oct 7, 2020

Appl. No.:

17/065460

Inventors:

- Lehi UT, US
Tanner Christensen - Provo UT, US
Chris Hettinger - Worcester MA, US
Jeffrey Humpherys - Lehi UT, US

International Classification:

A61B 5/024
A61B 5/00

Abstract:

A technology for obtaining a heart rate from a photoplethysmogram (PPG) signal. In one example, an artificial neural network model can be trained to predict a heart rate using a training dataset containing PPG data. The artificial neural network model can include a series of convolutional layers to remove artifacts from a PPG signal, a fast Fourier transform (FFT) layer to convert the PPG signal to PPG frequency representations, and a dense layer to decode the PPG frequency representations to heart rate predictions. After training the artificial neural network model, PPG data generated by a pulse oximeter monitor can be obtained, and the PPG data can be input to the artificial neural network model. The artificial neural network model outputs a heart rate prediction, wherein the heart rate prediction represents the heart rate obtained from the PPG signal.

US Patent:

20210110927, Apr 15, 2021

Filed:

Oct 7, 2020

Appl. No.:

17/065453

Inventors:

- Lehi UT, US
Tanner Christensen - Provo UT, US
Chris Hettinger - Worcester MA, US
Jeffrey Humpherys - Lehi UT, US

International Classification:

G16H 50/20
A61B 5/08
A61B 5/00
G06N 3/04
G06N 3/08
G16H 50/30

Abstract:

A technology for obtaining a respiratory rate from a photoplethysmogram (PPG) signal. In one example, an artificial neural network model can be trained to predict a respiratory rate using a training dataset containing PPG data. The artificial neural network model can include a first series of convolutional layers to remove artifacts from a PPG signal, a fast Fourier transform (FFT) layer to convert the PPG signal to PPG frequency representations, and a dense layer to decode the PPG frequency representations to respiratory rate predictions. After training the artificial neural network model, PPG data generated by a pulse oximeter monitor can be obtained, and the PPG data can be input to the artificial neural network model. The artificial neural network model outputs a respiratory rate prediction, wherein the respiratory rate prediction represents the respiratory rate obtained from the PPG signal.