David Hunter

US Patent:

7625350, Dec 1, 2009

Filed:

Jun 3, 2006

Appl. No.:

11/446331

Inventors:

David Hunter - Chestnut Hill MA, US
Glenn Colaco - Caringbah, NSW, AU

International Classification:

A61F 5/00
A61F 13/00
A41D 13/08
A41D 13/00
A61F 13/06

US Classification:

602 62, 602 61, 602 60, 602 41, 602 26, 602 23, 602 5, 602 1, 2 16, 2 17, 2 18, 2 19, 2 20, 2 21, 2 22, 2 23, 2 24, 128892, 128889, 128846

Abstract:

A patellofemoral brace consisting of a sleeve adapted to be worn by a user in the knee area and having a patella interior opening is provided with a glide which is adjustably carried by the sleeve and which includes a patella engaging brace pad which may be silicon filled and which is generally U-shaped having one longer leg and one shorter leg. The brace is used to influence proper positioning of the patella by the generation of a force applied to the superolateral aspect of the patella to correct misalignment of the patella.

US Patent:

7959292, Jun 14, 2011

Filed:

Mar 27, 2007

Appl. No.:

12/294120

Inventors:

David G. Hunter - Wayland MA, US
Nadezhda V. Piskun - Sudbury MA, US

Assignee:

Children's Medical Center Corporation - Boston MA

International Classification:

A61B 3/00

US Classification:

351246, 351201, 351202, 351209

Abstract:

Generally, the present invention relates to medical devices and a method of vision screening, and more particularly to a pediatric vision screening system and method thereof that identifies a risk factor for amblyopia or diagnoses amblyopia by measurement of microstrabismus. An embodiment of the invention is directed to a method of patient screening for risk factors for amblyopia which includes the steps of illuminating the eye with polarized light, scanning the polarized light about the eye, capturing the retro-reflected light emanating back from the eye, analyzing the retro-reflected light to determine ocular misalignment; and calculating a metric to determine if the patient passes or fails the screening test thereby providing an indication that the patient may have a risk of amblyopia based on either strabismus or anisometropia. The method is effective at detecting amblyopia related to focusing problems without the measuring the focus of the eye directly.

US Patent:

20100241041, Sep 23, 2010

Filed:

Jul 18, 2008

Appl. No.:

12/668673

Inventors:

David J. Hunter - Chestnut Hill MA, US
Glenn Colaco - Caringbah, AU
Dimitrije Stamenovic - Brookline MA, US

Assignee:

TRUSTEES OF BOSTON UNIVERSITY - Boston MA

International Classification:

A61F 5/00

US Classification:

602 13, 602 26

Abstract:

A knee brace () has a body () supporting first, second, and third expandable members (). When expanded, the first, second, and third expandable members are positioned on the brace body to provide a counteracting force to the adduction moment of the knee.

US Patent:

20220314010, Oct 6, 2022

Filed:

Nov 15, 2021

Appl. No.:

17/526254

Inventors:

- Baltimore MD, US
Ehud J. Schmidt - Boston MA, US
Ronald D. Watkins - Stanford CA, US
Harikrishna Tandri - Ellicott City MD, US
David Hunter - Baltimore MD, US
Leslie Tung - Columbia MD, US
Ronald D. Berger - Baltimore MD, US

Assignee:

The Johns Hopkins University - Baltimore MD

International Classification:

A61N 1/39
A61B 5/055

Abstract:

A magnetic-resonance-imaging-compatible (MRI-compatible) cardiac defibrillator includes: a defibrillator generator; first and second electric wires, each being electrically connected to said defibrillator generator; first and second defibrillation pads, each being electrically connected to a respective one of said first and second electric wires; and a low pass filter electrically connected between said defibrillator generator and said first and second electric wires to prevent a noise in an MRI image caused by a radiofrequency interference from the defibrillator as well as protect a patient and the defibrillator from MRI radiofrequency imaging signals, wherein said low pass filter has a cutoff frequency set such that differential mode noise at an MRI Larmor frequency is in an attenuated band while a system-test signal by said defibrillator generator is in a pass band of said low pass filter.

US Patent:

20200086130, Mar 19, 2020

Filed:

Dec 8, 2017

Appl. No.:

16/467373

Inventors:

- Baltimore MD, US
Ehud J. Schmidt - Boston MA, US
Ronald D. Watkins - Stanford CA, US
Harikrishna Tandri - Ellicott City MD, US
David Hunter - Baltimore MD, US
Leslie Tung - Columbia MD, US
Ronald D. Berger - Baltimore MD, US

Assignee:

The Johns Hopkins University - Baltimore MD

International Classification:

A61N 1/39
A61B 5/055

Abstract:

A magnetic-resonance-imaging-compatible (MRI-compatible) cardiac defibrillator includes: a defibrillator generator; first and second electric wires, each being electrically connected to said defibrillator generator; first and second defibrillation pads, each being electrically connected to a respective one of said first and second electric wires; and a low pass filter electrically connected between said defibrillator generator and said first and second electric wires to prevent a noise in an MRI image caused by a radiofrequency interference from the defibrillator as well as protect a patient and the defibrillator from MRI radiofrequency imaging signals, wherein said low pass filter has a cutoff frequency set such that differential mode noise at an MRI Larmor frequency is in an attenuated band while a system-test signal by said defibrillator generator is in a pass band of said low pass filter.

US Patent:

20190385100, Dec 19, 2019

Filed:

Jun 11, 2019

Appl. No.:

16/437082

Inventors:

- Cambridge MA, US
David Hunter - Cambridge MA, US
Ajay Saini - Shrewsbury MA, US

International Classification:

G06Q 10/06
G06F 17/16
G06N 7/00

Abstract:

A system for modeling an entity includes a prediction module configured to identify one of a plurality of entities using a set of criteria filters to construct a model for the entity with model features built from data about the entity. The prediction module computes a diffusion model coefficient σ based on a diffusion parameter vector γ, and a drift model coefficient μ based on a drift parameter vector β. The module computes a predicted entity success probability and a success interval confidence interval. A portfolio selection module is configured to receive a score measuring success for the plurality of entities based on the model, order the scores in a rank order, and form a portfolio from top n scoring entities. A prediction interpretation module receives parameter vectors β and γ and entity model coefficients μ and σ and uses distributions of β and γ to correlate entity features with success prediction.