Portable Multi-Modal Emergency Situation Anomaly Detection and Response System

• US 20100305806A1
• Filed: 06/02/2009
• Published: 12/02/2010
• Est. Priority Date: 06/02/2009
• Status: Abandoned Application

• Current Assignee: Chadwick Todd Hawley
• Original Assignee: Chadwick Todd Hawley
• Inventors: Hawley, Chadwick Todd
• Application Number:
  US12/477,079
• Publication Number:
  US 20100305806A1
• Time in Patent Office: Days
• US Class Current: 701/33
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G08B 29/188 Data fusion; cooperative systems, e.g. voting among different detectors
• 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: H04L 67/55 Push-based network services
• CPC Class Codes: H04M 1/72421 with automatic activation of emergency service functions, e.g. upon sensing an alarm
• CPC Class Codes: H04M 2250/12 including a sensor for measuring a physical value, e.g. temperature or motion telephones including GPS signal receivers H04M2250/10
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/90 Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
• CPC Class Codes: H04W 76/50 for emergency connections

Abstract

A portable multi-modal physics and environmental based signature information collection, analysis, and alerting apparatus, device, and method capable of operating independently of vehicle diagnostic or alerting systems. Device uses algorithms and models to calculate, determine, and detect signature anomalies from accelerometer and other imbedded sensors for anomaly response to individual situations within the Automotive Global Information Grid. Wireless infrastructure provisioning of uniquely identified publisher and subscriber Automotive Network Centric Enterprise Services host subscriber and device holder as a persistent publisher presenting signature information on operational and environmental status and conditions. Onboard signature collection and anomaly detection devices support ISO MME, manufacture defined data formats, standard crash analysis algorithms based on ISO, SAE, FMVSS, CMVSS, EuoNCAP, and others. Communications infrastructure is multi-modal providing auto alert capabilities. Machine to machine interface employs W3C standard telematics, and event mark-up language, and the wireless communications utilizing cell phone, satellite, and other communications platforms.

Claims

1. A highly portable device to detect multiple physics and environmental based signatures associated with emergency or non emergency situations which includes multiple sensors to automatically detect anomalies and emergency situations;

and to automatically communicate the location of the subscriber to the call center; and

to self activate a communication system to allow the call center to communicate with the user, assess the situation with connectivity between the device, the call center, and the appropriate emergency response system or service to an individual or set of individuals comprising;

a) a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities onboard the vehicle,

b) a portable multi-mode signature sensor and processing suite device capable of publishing sensor information onboard the vehicle to a global information grid,

c) a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities external to the vehicle,

d) a portable multi-mode signature sensor and processing suite device capable of publishing sensor information and external environmental information to a global information grid,

e) a global positioning system capable of identification of position and tracking of position,

f) a two way communication system to a Network Centric Enterprise Service Center

g) an emergency alert two way communications system

2. A Portable Multi-Modal Emergency Situation Anomaly Detection and Response System according to claim 1, comprising;

a) methodology and procedures to support a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities onboard the vehicle,

b) methodology and procedures to support a portable multi-mode signature sensor and processing suite device capable of publishing sensor information onboard the vehicle to a global information grid,

c) methodology and procedures to support a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities external to the vehicle,

d) methodology and procedures to support a portable multi-mode signature sensor and processing suite device capable of publishing sensor information and external environmental information to a global information grid,

e) methodology and procedures to support a global positioning system capable of identification of position and tracking of position,

f) methodology and procedures to support a two way communication system to a Network Centric Enterprise Service Center

g) methodology and procedures to support an emergency alert two way communications system

9. An anomaly detection and vehicle alerting system which is comprised of:

a) the sensors, devices, analytic and anomaly detection models of claims 1-8 which alerts into the Automotive Global Information Grid Network Centric Enterprise Service Center of automated and human agents,

b) a geospatial locator that automatically reports the vehicle location and route,

c) a communication devise which is capable of automatically reporting and publishing alerts into the Global Information Grid Network Centric Enterprise Service center of automated and human agents and subscribing down information from the Network Centric Enterprise Service Center.

d) a system which detects, reports, and analyzes environmental conditions.

3. A highly portable set of algorithms to detect and analyze multiple signatures associated with emergency or non emergency situations which includes multiple sensors to automatically detect anomalies and emergency situations and automatically communicate the location of the subscriber to the call center and to activate a communication system to allow the call center to communicate with the user and assess the situation with connectivity between the device, the call center and the appropriate response system or emergency service to an individual or set of individuals comprising:

a) algorithms and models to support a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities onboard the vehicle,

b) algorithms and models a portable multi-mode signature sensor and processing suite device capable of publishing sensor information onboard the vehicle to a global information grid,

c) algorithms and models a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities external to the vehicle,

d) algorithms and models a portable multi-mode signature sensor and processing suite device capable of publishing sensor information and external environmental information to a global information grid,

e) algorithms and models a global positioning system capable of identification of position and tracking of position,

f) algorithms and models a two way communication system to a Network Centric Enterprise Service Center

g) algorithms and models an emergency alert two way communications system

4. A highly portable set of event models to detect multiple signatures associated with emergency or non emergency situations which includes multiple sensors to automatically detect anomalies and emergency situations and automatically communicate the location of the subscriber to the call center and to activate a communication system to allow the call center to communicate with the user and assess the situation with connectivity between the device, the call center and the appropriate response system or emergency service to an individual or set of individuals comprising:

a) anomaly and event models to support a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities onboard the vehicle,

b) anomaly and event models to support a portable multi-mode signature sensor and processing suite device capable of publishing sensor information onboard the vehicle to a global information grid,

c) anomaly and event models to support a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities external to the vehicle,

d) anomaly and event models to support a portable multi-mode signature sensor and processing suite device capable of publishing sensor information and external environmental information to a global information grid,

e) anomaly and event models to support a global positioning system capable of identification of position and tracking of position,

f) anomaly and event models to support a two way communication system to a Network Centric Enterprise Service Center

g) anomaly and event models to support an emergency alert two way communications system

5. The device which utilizes sophisticated composite signatures to reduce false positive alerts and alarms. These composite signatures comprise system learning of physics, environmental and behavioral based signatures through a sequence of feed back and feed forward analysis based on rules based templates comprising of operational steps which:

a) receive and analyze temporal parameters recorded by the onboard system

b) receive and analyze intensity parameters recorded by the onboard system

c) receive and analyze frequency parameters recorded by the onboard system

d) receive and analyze feature, temperature, and pressure parameters recorded by the onboard system and compared to known and trusted data source information

e) receive and analyze geospatial parameters recorded by the onboard system and compared to known and trusted data source information

f) combined temporal, intensity, frequency, direction, feature, temperature, pressure, geospatial, space and other sensor data into analytic models to indicate that a personal emergency situation is occurring as apposed to a false positive.

6. The methodology which utilizes sophisticated composite signatures to reduce false positive alerts and alarms. These composite signatures comprise system learning of physics, environmental and behavioral based signatures through a sequence of feed back and feed forward analysis based on rules based templates comprising of operational steps and personnel which:

a) receive and analyze temporal parameters recorded by the onboard system

b) receive and analyze intensity parameters recorded by the onboard system

c) receive and analyze frequency parameters recorded by the onboard system

d) receive and analyze feature, temperature, and pressure parameters recorded by the onboard system and compared to known and trusted data source information

e) receive and analyze geospatial parameters recorded by the onboard system and compared to known and trusted data source information

f) combined temporal, intensity, frequency, direction, feature, temperature, pressure, geospatial, space and other sensor data into analytic models to indicate that a personal emergency situation is occurring as apposed to a false positive.

7. The algorithms which utilizes sophisticated composite signatures to reduce false positive alerts and alarms. These composite signatures comprise system learning of physics, environmental and behavioral based signatures through a sequence of feed back and feed forward analysis based on rules based templates comprising of operational steps which:

a) receive and analyze temporal parameters recorded by the onboard system

b) receive and analyze intensity parameters recorded by the onboard system

c) receive and analyze frequency parameters recorded by the onboard system

d) receive and analyze feature, temperature, and pressure parameters recorded by the onboard system and compared to known and trusted data source information

e) receive and analyze geospatial parameters recorded by the onboard system and compared to known and trusted data source information

f) combined temporal, intensity, frequency, direction, feature, temperature, pressure, geospatial, space and other sensor data into analytic models to indicate that a personal emergency situation is occurring as apposed to a false positive.

8. The processing models which utilizes sophisticated composite signatures to reduce false positive alerts and alarms. These composite signatures comprise system learning of physics, environmental and behavioral based signatures through a sequence of feed back and feed forward analysis based on rules based templates comprising of operational steps which:

a) receive and analyze temporal parameters recorded by the onboard system

b) receive and analyze intensity parameters recorded by the onboard system

c) receive and analyze frequency parameters recorded by the onboard system

d) receive and analyze feature, temperature, and pressure parameters recorded by the onboard system and compared to known and trusted data source information

e) receive and analyze geospatial parameters recorded by the onboard system and compared to known and trusted data source information

f) combined temporal, intensity, frequency, direction, feature, temperature, pressure, geospatial, space and other sensor data into analytic models to indicate that a personal emergency situation is occurring as apposed to a false positive.