Smart device

• US 10,034,066 B2
• Filed: 06/15/2017
• Issued: 07/24/2018
• Est. Priority Date: 05/02/2016
• Status: Active Grant

• Current Assignee: Bao Tran
• Original Assignee: Bao Tran
• Inventors: Tran, Bao, Tran, Ha
• Primary Examiner(s): Ahmed, Masud
• Application Number:
  US15/624,014
• Publication Number:
  US 20170318360A1
• Time in Patent Office: 404 Days
• Field of Search: 463 16- 42
• CPC Class Codes: A42B 3/0433 Detecting, signalling or lighting devices
• CPC Class Codes: A42B 3/046 Means for detecting hazards or accidents
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/224 Measuring muscular strength
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/332 Portable devices specially adapted therefor
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/6804 Garments; Clothes
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A63F 13/211 using inertial sensors, e.g. accelerometers or gyroscopes
• CPC Class Codes: A63F 13/212 using sensors worn by the player, e.g. for measuring heart beat or leg activity
• CPC Class Codes: A63F 13/245 specially adapted to a particular type of game, e.g. steering wheels
• CPC Class Codes: A63F 13/25 Output arrangements for video game devices
• CPC Class Codes: A63F 13/5255 according to dedicated instructions from a player, e.g. using a secondary joystick to rotate the camera around a player's character
• CPC Class Codes: A63F 2300/8082 : Virtual reality
• CPC Class Codes: B33Y 10/00 : Processes of additive manufacturing
• CPC Class Codes: B33Y 80/00 : Products made by additive manufacturing
• CPC Class Codes: G01L 5/0052 : measuring forces due to impact G01L5/0061, G01L5/14 take precedence; impact testing of structures G01M7/08; impact testing of material G01N3/00
• CPC Class Codes: G01L 5/10 : using electrical means
• CPC Class Codes: G01L 5/226 : to manipulators, e.g. the force due to gripping
• CPC Class Codes: G01M 17/02 : Tyres
• CPC Class Codes: G01M 17/06 : Steering behaviour; Rolling behaviour
• CPC Class Codes: G01N 2291/0258 : Structural degradation, e.g. fatigue of composites, ageing of oils
• CPC Class Codes: G01N 29/00 : Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object G01N3/00, G01N5/00, G01N7/00, G01N9/00, G01N11/00, G01N13/00, G01N15/00, G01N17/00, G01N19/00, G01N21/00, G01N22/00, G01N23/00, G01N24/00, G01N25/00, G01N27/00 take precedence
• CPC Class Codes: G01N 29/14 : using acoustic emission techniques echo of particles G01N29/046; measuring mechanical vibrations or acoustic waves in solids in general G01H1/00
• CPC Class Codes: G01N 29/4436 : with a reference signal amplitude comparison G01N29/48
• CPC Class Codes: G01P 15/0802 : Details
• CPC Class Codes: G01P 15/18 : in two or more dimensions
• CPC Class Codes: G06F 1/163 : Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 3/017 : Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 40/113 : Recognition of static hand signs
• CPC Class Codes: G06V 40/174 : Facial expression recognition
• CPC Class Codes: G06V 40/28 : Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113
• CPC Class Codes: G09B 19/0038 : Sports
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: H04B 1/38 : Transceivers, i.e. devices in which transmitter and receiver form a structural unit and in which at least one part is used for functions of transmitting and receiving
• CPC Class Codes: H04B 1/385 : Transceivers carried on the body, e.g. in helmets
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/52 : specially adapted for the location of the user terminal
• CPC Class Codes: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: H04Q 2209/40 : using a wireless architecture
• CPC Class Codes: H04Q 9/00 : Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom

Abstract

An Internet of Thing (IoT) device includes a body with a processor, a camera and a wireless transceiver coupled to the processor.

Claims

1. A device, comprising:

a processor coupled to a wireless transceiver;

a camera to capture device environment and provide 3D models for neighboring objects, wherein the camera determines distances of the neighboring objects from the camera;

an accelerometer to detect acceleration of the device;

a first module to navigate the device from a start to a destination; and

a second module to analyze a neighboring object motion based on camera and accelerometer outputs and to apply a control motion of a professional to improve navigation of the device;

wherein device further comprising a code to collect travel data

from neighboring vehicles and coordinate travel path for a plurality of vehicles to decrease travel cost, travel time, energy cost, air pollution.

2. The device of claim 1, wherein the neighboring object comprises one of:

a car, a truck, a ship, a boat, a plane, a helicopter, a vertical take off and landing (VTOL) vehicle, a drone, a flying vehicle, a bicycle, a motorcycle, a movable unit with at least one wheel, a moveable unit with at least one propeller.

3. The device of claim 1, wherein the neighboring object comprises a road contacting surface, air contacting surface or water contacting surface.

4. The device of claim 1, comprising a gesture identifying component configured to identify at least one posture or one gesture including hand, eye, mouth, body or finger gesture.

5. The device of claim 1, comprising code to match people or package needing transportation including one of:

ride-sharing code, car-pooling code, van-pooling code.

6. The device of claim 1, comprising a radar, a laser positioning system or an optical positioning system.

7. The device of claim 1, comprising a sensor to detect an imminent impact and activate crash protection for people about to contact the device.

8. The device of claim 1, comprising a hidden markov model (HMM) machine, graphical processing units (GPUs), or a deep learning machine to perform image recognition or deep learning to detect patterns from the camera and the accelerometer.

9. The device of claim 1, comprising a module to learn driver behavior and adjust driving based on environment and predicted driver behavior.

10. The device of claim 1, comprising a vehicle-to-vehicle wireless module, a vehicle-to-infrastructure wireless module, or a vehicle-to-building wireless module, wherein the wireless module communicates over optical path or radio path.

11. The device of claim 1, comprising an automatic parking module with a plurality of sensors.

12. The device of claim 1, comprising code to learn driver behavior and adjust driving based on environment, and driving behavior from a group of neighboring drivers.

13. The device of claim 1, comprising code to organize with an object having a common travel segment to form a traveling flock to optimize traffic flow or energy efficiency.

14. The device of claim 1, comprising code for lane keeping assist, adaptive cruise control, and controlled brake to reduce a collision risk.

15. The device of claim 1, comprising code to adjust for weather condition, code to interpret traffic signal, code to handle people in proximity of the device, code to handle a cyclist in proximity of the device, code to handle railroad crossing, code to avoid animal or obstacle, or code to handle instruction from an officer.

16. The device of claim 1, comprising code to process a street warning including one of:

red light warning, reduce speed warning, work zone warning, pedestrian warning, curve speed warning, emergency vehicle preemption warning, transit signal priority warning.

17. The device of claim 1, comprising code to operate the device at a predetermined fixed or variable speed to smooth traffic flow or to minimize environmental emission.

18. The device of claim 1, comprising code to determine a common travel path for riders and packages to deliver the riders and packages while minimizing delivery costs for riders and packages.