IP Library Granted Patent US 11,333,514
Granted Patent B2
US 11,333,514 · App. 16/694,733 · Granted May 17, 2022

Intelligent transportation systems

Inventor: Charles Howard Cella (Pembroke, MA)
Assignee: Strong Force Intellectual Capital, LLC
G01C21/3469B60W40/08G01C21/3438G05B13/027G05D1/0088G05D1/0212G05D1/0287G06F40/40G06N3/0418G06N3/0454G06N3/08G06N20/00G06Q50/188G06Q50/30G06V20/64G07C5/006G07C5/008G07C5/02G07C5/08G07C5/0816B60W2040/0881G05D2201/0213G06N3/02G06Q30/0281G06Q50/01
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 11,333,514
App. No.
16/694,733
Granted
May 17, 2022
Kind
B2
Abstract

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

Claims (16)

1. A system for transportation to operate a vehicle having a continuously variable powertrain, comprising:

a hybrid neural network for optimizing an operating state of the continuously variable powertrain of the vehicle wherein

a portion of the hybrid neural network is to operate to classify a state of the vehicle based on output from a set of sensors thereby generating a classified state of the vehicle, wherein the set of sensors includes a physiological monitor within the vehicle to monitor a rider, wherein the classified state of the vehicle is a vehicle rider state and wherein the vehicle rider state is indicative of an emotional state of the rider, and

an other portion of the hybrid neural network is to operate to optimize at least one operating parameter of a continuously variable transmission portion of the continuously variable powertrain wherein the at least one operating parameter of the continuously variable transmission portion includes a transmission ratio and the other portion of the hybrid neural network is to operate to optimize the transmission ratio to achieve an optimized combination of vehicle speed, steering, braking, and acceleration for achieving a favorable emotional state of the rider; and

an artificial intelligence system in communication with the set of sensors, the artificial intelligence system operative on at least one processor having access to a non-transitory storage medium that stores computer executable instructions to be executed by the at least one processor, the artificial intelligence system to operate the portion of the hybrid neural network to operate to classify the state of the vehicle and the artificial intelligence system to operate the other portion of the hybrid neural network to optimize the at least one operating parameter of the transmission portion of the continuously variable powertrain based on the classified state of the vehicle.

2. The system for transportation of claim 1 wherein the vehicle comprises a system for automating at least one control parameter of the vehicle.

3. The system for transportation of claim 2 wherein the vehicle is at least a semi-autonomous vehicle.

4. The system for transportation of claim 3 wherein the vehicle is to be automatically routed.

5. The system for transportation of claim 4 wherein the vehicle is a self-driving vehicle.

6. The system for transportation of claim 1 wherein the physiological monitor includes a vision-based sensor that observes and captures images of the rider.

7. The system for transportation of claim 6 wherein captured images of the rider include a face of the rider, and wherein the hybrid neural network is to process feature vectors of a face of the rider in the captured images to determine the emotional state of the rider.

8. The system for transportation of claim 6 wherein the other portion of the hybrid neural network includes a radial basis function neural network to optimize the transmission ratio in response to the indication of change in emotional state of the rider.

9. The system for transportation of claim 1 wherein the portion of the hybrid neural network includes a recurrent neural network to indicate a change in the emotional state of the rider through recognition of patterns of physiological data of the rider captured by the physiological monitor.

10. The system for transportation of claim 1 wherein the physiological monitor includes a galvanic skin response sensor to detect galvanic skin response of the rider wherein the galavanic skin response of the rider is indicative of the emotional state of the rider.

11. The system for transportation of claim 1 wherein the physiological monitor includes electrodes for detecting electrical activity of the brain and recording an electroencephalogram (EEG) of the rider wherein the EEG is indicative of the emotional state of the rider.

12. The system for transportation of claim 1 wherein the favorable emotional state of the driver is associated with lower cortisol levels and wherein the transmission ratio is optimized to achieve a gentle driving motion in the optimized combination of vehicle speed, steering, braking and acceleration.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 2, 2022
From: STRONG FORCE INTELLECTUAL CAPITAL, LLC
To: STRONG FORCE TP PORTFOLIO 2022, LLC
Reel/Frame 061960/0235 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 26, 2020
From: CELLA, CHARLES HOWARD
To: STRONG FORCE INTELLECTUAL CAPITAL, LLC
Reel/Frame 052753/0215 →
Continuity (3)
Continuation PCTUS2019053857 · Sep 30, 2019
Provisional Application 62739335 · Sep 30, 2018
Related Publication 20200103244A1 · Apr 2, 2020
Cited By (1)
US 12,415,539