SYSTEMS AND METHODS FOR TELEMATICS MONTORING AND COMMUNICATIONS

• US 20150379789A1
• Filed: 08/28/2015
• Published: 12/31/2015
• Est. Priority Date: 01/24/2011
• Status: Active Grant

• Current Assignee: LexisNexis Risk Solutions, Inc. (RELX PLC)
• Original Assignee: LexisNexis Risk Solutions, Inc. (RELX PLC)
• Inventors: Hassib, Ash, Kaminski, Charles, Pierce, Aaron G.
• Granted Patent: US 10,438,424 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0816 Indicating performance data, e.g. occurrence of a malfunction
• CPC Class Codes: G07C 5/085 using electronic data carriers
• CPC Class Codes: G07C 5/0858 wherein the data carrier is removable

Abstract

Certain example embodiments of the disclosed technology may include systems and methods for telematics monitoring. An example method is provided that includes receiving, at a mobile computing device, and from a Vehicle Identification Unit (VIU), identification (ID) data representing a first vehicle. The method further includes receiving, by the mobile computing device, sensor data from one or more sensors associated with the mobile computing device. Certain embodiments may further include receiving, at an Operational Measurement Unit (OMU), an operation indication associated with the first vehicle. The OMU may include an operational measurement component configured to advance an operational count in response to receiving the operation indication. Certain example embodiments may include transmitting telematics data by the mobile computing device. In certain embodiments, the telematics data may include least a portion of one or more of the ID data, the sensor data, and/or the operational count data.

Claims

1. A computer-implemented method comprising:

receiving, at a processor, an operation indication associated with starting an operation of a vehicle;

responsive to receiving the operation indication, advancing an operational count of a counter;

storing the operational count;

comparing-the stored operational count with a previously stored count, wherein the previously stored count corresponds to a previously stored operational count, and wherein the stored operational count corresponds to a start-of-vehicle-operation operational count; and

generating, responsive to the comparing, a monitor indication representing whether the vehicle has been monitored during a previous operation.

2. The method of claim 1, wherein the operation indication comprises electrical power being supplied to at least one electronic component associated with the vehicle responsive to starting the vehicle.

3. The method of claim 1, wherein the processor is configured to provide an alert in response to the stored operational count and the previously stored count differing by greater than a predefined threshold.

4. The method of claim 1, further comprising receiving vehicular identification data related to the vehicle, wherein the vehicular identification data comprises identification (ID) data representing the vehicle.

5. The method of claim 1, further comprising:

receiving and storing, at a server, telematics data associated with the vehicle;

comparing at least a portion of the received telematics data with telematics data associated with a plurality of other vehicles to determine one or more risk factors associated with operating the vehicle.

6. The method of claim 1, further comprising:

determining one or more entity scenarios comprising one or more assertions;

comparing one or more attributes of telematics data with information from one or more data sources to determine a plurality of correlations;

calculating a confidence level associated with the one or more assertions based at least in part on the determined plurality of correlations; and

outputting a score representative of the confidence level that the telematics data comprises one or more attributes that correspond to the one or more assertions.

7. The method of claim 6, wherein the information from one or more data sources comprises previously stored information having a class type corresponding to the telematics data, wherein the class type comprises one or more of accelerometer data, magnetometer data, gyroscope data, clock data, global positioning system (GPS) data, Bluetooth system data comprising one or more of paring or disconnects, microphone signals, or Wi-Fi system data.

8. The method of claim 6, wherein determining the one or more entity scenarios comprising one or more assertions comprises identifying one or more of an individual in possession of or associated with the mobile computing device, characteristics of an individual in possession of or associated with the mobile computing device, and a vehicle in proximity to the mobile device and associated with identification (ID) data representing the vehicle.

9. The method of claim 6, wherein the comparing one or more attributes of the telematics data with information from one or more data sources comprises comparing one or more of repeat location visits, repeatable events, velocity, acceleration, location at a specific operational, body patterns, habits, data attributes indicative of education level, data attributes indicative of age, data attributes indicative of gender, data attributes indicative of behavior risks, or groups of events.

10. The method of claim 6, further comprising transforming the telematics data, wherein the transforming comprises one or more of formatting, filtering, compressing, or validating the received telematics data.

11. A system comprising:

at least one memory for storing data and computer-executable instructions; and

at least one processor configured to access the at least one memory and further configured to execute the computer-executable instructions to;

receive an operation indication associated with starting an operation of a vehicle;

advance an operational count of a counter responsive to the operation indication;

store, in the memory, the operational count;

compare the stored operational count with a previously stored count, wherein the previously stored count corresponds to a previously stored operational count, and wherein the stored operational count corresponds to a start-of-vehicle-operation operational count;

generate, responsive to the comparing, a monitor indication representing whether the vehicle has been monitored during a previous operation; and

transmit telematics data, wherein the telematics data comprises at least a portion of one or more of the operational count and the monitor indication.

12. The system of claim 11, wherein the operation indication comprises electrical power being supplied to at least one electronic component associated with the vehicle responsive to starting the vehicle.

13. The system of claim 11, wherein the at least one processor is further configured to provide an alert in response to the stored operational count and the previously stored count differing by greater than a predefined threshold

14. The system of claim 11, further comprising one or more sensors in communication with the at least one processor, and wherein the at least one processor is further configured to receive sensor date from the one or more sensors;

and wherein the telematics data further comprises at least a portion of the sensor data.

15. The system of claim 14, wherein the sensor data comprises accelerometer data, magnetometer data, gyroscope data, clock data, global positioning system (GPS) data, Bluetooth system data comprising one or more of paring or disconnects, microphone signals, and Wi-Fi system data.

16. The system of claim 11, wherein the at least one processor is further configured to receive vehicle identification (ID) data, and wherein the telematics data further comprises at least a portion of the ID data.

17. The system of claim 11, wherein the telematics data further comprises one or more of repeat location visits, repeatable events, velocity, acceleration, location at a specific operational, body patterns, habits, data attributes indicative of education level, data attributes indicative of age, data attributes indicative of gender, data attributes indicative of behavior risks, or groups of events.

18. The system of claim 11, wherein the at least one processor is further configured to execute the computer-executable instructions to:

determine one or more entity scenarios comprising one or more assertions;

compare one or more attributes of telematics data with information from one or more data sources to determine a plurality of correlations;

calculate a confidence level associated with the one or more assertions based at least in part on the determined plurality of correlations; and

output a score representative of the confidence level that the telematics data comprises one or more attributes that correspond to the one or more assertions.

19. The system of claim 18, wherein the one or more entity scenarios comprise one or more assertions including identifying one or more of an individual in possession of or associated with the mobile computing device, characteristics of an individual in possession of or associated with the mobile computing device, and a vehicle in proximity to the mobile device and associated with identification (ID) data representing the vehicle.

20. The system of claim 18, wherein the one or more attributes of the telematics data comprise one or more of repeat location visits, repeatable events, velocity, acceleration, location at a specific operational, body patterns, habits, data attributes indicative of education level, data attributes indicative of age, data attributes indicative of gender, data attributes indicative of behavior risks, or groups of events.