Jason Brian Jenkins

Address:

10108 Whisper Pointe Dr, Tampa, FL 33647

Phone:

305 393-3769

VIN:

5FNYF3H52CB036720

Make:

HONDA

Model:

PILOT

Year:

2012

US Patent:

62557998, Jul 3, 2001

Filed:

Dec 29, 1999

Appl. No.:

9/474515

Inventors:

Binh Q. Le - Vienna VA
Ark L. Lew - Ellicott City MD
Paul D. Schwartz - Arnold MD
Albert C. Sadilek - Elkridge MD
Joseph J. Suter - Ellicott City MD
Jason E. Jenkins - Columbia MD
Sharon X. Ling - Clarksville MD

Assignee:

The Johns Hopkins University - Baltimore MD

International Classification:

H02J 700
H02K 500
A43B 702

US Classification:

320107

Abstract:

The invention comprises a means for generating energy while walking or running for storage in a rechargeable battery. One embodiment uses lever arm movement in the heel of a shoe resulting from normal walking or running to generate energy from a built-in generator. The linear or rotational motion of the lever arm engages the circular gear assembly and turns the generator/motor/turbine, thus generating power. The second embodiment uses fluid reservoirs embedded in the shoes. Pressure changes resulting from normal walking or running moves the fluid through a narrow channel connecting two reservoirs, thus generating power by rotating a flywheel and an attached motor/generator/turbine in the middle of the channel. Secondary (rechargeable) batteries are incorporated into the invention either in an integrated form or as an add-on design. Additional features include a digital diagnostic data output, which would serves as a "fuel gauge" for the secondary batteries, and a smart charging circuit that efficiently controls battery charging from a generator output that varies with step rate and force.

US Patent:

20200259999, Aug 13, 2020

Filed:

Jan 15, 2020

Appl. No.:

16/743855

Inventors:

- Burbank CA, US
Jere M. KIRKLEY - Winter Garden FL, US
Jason B. JENKINS - Tampa FL, US

International Classification:

H04N 5/232
G06N 3/08

Abstract:

Embodiments of the present disclosure relate to intelligent photography with machine learning. Embodiments include receiving a video stream from a control camera. Embodiments include providing inputs to a trained machine learning model based on the video stream. Embodiments include determining, based on data output by the trained machine learning model in response to the inputs, at least a first time for capturing a first picture during a session. Embodiments include programmatically instructing a first camera to capture the first picture at the first time during the session.