SMART DEVICE

• US 20170318360A1
• Filed: 06/15/2017
• Published: 11/02/2017
• Est. Priority Date: 05/02/2016
• Status: Active Grant

• Current Assignee: Bao Tran
• Original Assignee: Bao Tran
• Inventors: Tran, Bao, Tran, Ha
• Granted Patent: US 10,034,066 B2
• Time in Patent Office: Days
• 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;

an accelerometer to detect acceleration of the device; and

a module to follow a third-party motion or another device motion based on camera and accelerometer outputs.

2. The device of claim 1, comprising at least one sensor selected from a sensor set comprising:

a pressure sensor, a motion sensor, a finger sensor, a digit motion sensor, an EKG sensor, a bio sensor, a medical sensor, a temperature sensor.

3. The device of claim 1, comprising a module to follow other third parties or other devices in a flock.

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

5. The device of claim 1, comprising a gesture identifying component configured to identify at least one hand or finger gesture detected by at least one sensor;

the memory configured to, upon receiving an indication of a hand or finger gesture identified by the gesture identifying component, store data corresponding to the hand or finger gesture in the memory; and

the device interface configured to, upon connecting to the device, provide at least some of the stored data corresponding to the hand or finger gesture to the device.

6. The device of claim 1, comprising an optical positioning system.

7. The device of claim 6, wherein the positioning system comprises a laser positioning system.

8. The device of claim 1, comprising a sensor to detect an imminent impact and activate crash protection for a user.

9. The device of claim 1, comprising a hidden markov model (HMM) or a deep learning machine to detect patterns from the camera and the accelerometer.

10. The device of claim 1, comprising an emotion detector to detect a user condition.

11. A system, comprising:

an accelerometer and a camera to detect a potential impact;

a radio frequency transmitter coupled to the accelerometer for transmitting impact measurements;

a radio frequency receiver for receiving the impact measurements; and

a cloud computer coupled to the receiver to predict the impact and to protect a user for the potential impact.

12. The system of claim 11, comprising at least one sensor selected from a sensor set comprising:

a pressure sensor, a motion sensor, a finger sensor, a digit motion sensor, an EKG sensor, a bio sensor, a medical sensor, a temperature sensor.

13. The system of claim 11, comprising a module to follow other third parties or other devices in a flock.

14. The system of claim 11, wherein the device comprises a road contacting surface, further comprising a positioning system.

15. The system of claim 11, comprising a gesture identifying component configured to identify at least one hand or finger gesture detected by at least one sensor;

the memory configured to, upon receiving an indication of a hand or finger gesture identified by the gesture identifying component, store data corresponding to the hand or finger gesture in the memory; and

the device interface configured to, upon connecting to the device, provide at least some of the stored data corresponding to the hand or finger gesture to the device.