ANOMALY DETECTION SYSTEMS AND METHODS

• US 20170352245A1
• Filed: 06/06/2017
• Published: 12/07/2017
• Est. Priority Date: 06/06/2016
• Status: Active Grant

• Current Assignee: Intertrust Technologies Corporation (Fidelio Acquisition Co LLC)
• Original Assignee: Intertrust Technologies Corporation (Fidelio Acquisition Co LLC)
• Inventors: Maher, David P., Tamir, Michael
• Granted Patent: US 10,360,783 B2
• Time in Patent Office: Days
• CPC Class Codes: G07C 3/14 Quality control systems
• CPC Class Codes: G08B 21/18 Status alarms G08B21/02 takes precedence
• CPC Class Codes: G08B 21/182 Level alarms, e.g. alarms responsive to variables exceeding a threshold
• CPC Class Codes: G08B 21/185 Electrical failure alarms
• CPC Class Codes: G08B 21/187 Machine fault alarms
• CPC Class Codes: H04L 12/28 characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks] wireless communication networks H04W ; arrangements for dividing the transmission path H04W40/00
• CPC Class Codes: H04L 12/2803 Home automation networks
• CPC Class Codes: H04L 12/2807 Exchanging configuration information on appliance services in a home automation network arrangements for maintenance or administration involving network analysis for automatically determining the actual topology of a network H04L41/12; hardware or software tools for network management using graphical user interfaces H04L41/22; address allocation H04L61/50
• CPC Class Codes: H04L 63/0428 wherein the data content is protected, e.g. by encrypting or encapsulating the payload
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: Y02B 10/30 Wind power
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

This disclosure relates to the analysis of data generated by one or more connected systems and devices. Operational data obtained by one or more connected devices and/or systems, such as a connected thermostat and/or wind turbine system, may be used to detect and/or predict impending failures and/or suboptimal performance. By detecting and/or predicting anomalous system and device performance, various actions may be taken to improve system and device performance and mitigate failure conditions.

Claims

1. A method for managing operational data associated with one or more connected devices performed by a trusted service comprising a processor and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the trusted service to perform the method, the method comprising:

receiving first operational data from a plurality of first connected devices;

receiving second operational data from a second connected device;

identifying, based on an analysis of the first operational data and the second operational data, at least one anomalous condition associated with the second connected device; and

generating at least one response based on identifying the at least one anomalous condition associated with the second connected device.

2. The method of claim 1, where the first operational data comprises at least one of device sensor data, device command information, duty cycle information, and device set point information.

3. The method of claim 2, wherein the device sensor data comprises at least one of data provided by an internal temperature sensor, an external temperature sensor, a humidity sensor, a current sensor, a voltage sensor, a fluid level sensor, an atmospheric sensor, and an environmental sensor.

4. The method of claim 1, wherein the plurality of first connected devices comprise at least one of a security system, a vehicle infotainment system, a streaming media device, a gaming device, an entertainment system, a networked lock, a connected thermostat, a connected furnace, a connected air conditioning system, an irrigation system, a water control system, a pump system, a utility meter, a network gateway, an activity sensor, a home alarm, security systems, vehicle infotainment systems, streaming media devices, gaming devices, a connected appliance, a connected vehicle, a mobile communication device, a wind turbine system, a solar panel system, and an industrial manufacturing system.

5. The method of claim 1 wherein the plurality of second connected device comprises at least one of a security system, a vehicle infotainment system, a streaming media device, a gaming device, an entertainment system, a networked lock, a connected thermostat, a connected furnace, a connected air conditioning system, an irrigation system, a water control system, a pump system, a utility meter, a network gateway, an activity sensor, a home alarm, security systems, vehicle infotainment systems, streaming media devices, gaming devices, a connected appliance, a connected vehicle, a mobile communication device, a wind turbine system, a solar panel system, and an industrial manufacturing system.

6. The method of claim 1, wherein the first operational data comprises an indication of at least one known anomalous condition associated with at least one connected device of the plurality of first connected devices.

7. The method of claim 6, wherein identifying the anomalous condition comprises:

comparing the first operational data and the second operational data,

determining, based on the comparison, that operational data associated with the at least one connected device of the plurality of first connected devices is within a threshold difference of the second operational data; and

identifying the at least one anomalous condition associated with the second connected device as the at least one known anomalous condition specified in the first operational data.

8. The method of claim 1, wherein identifying the at least one anomalous condition comprises generating at least one anomalous condition decision function based on the first operational data.

9. The method of claim 8, wherein identifying the at least one anomalous condition further comprises comparing the second operational data with the at least one anomalous condition decision function.

10. The method of claim 1, wherein the method further comprises:

receiving data from a data service provider; and

wherein identifying the at least one anomalous condition associated with the second connected device is further based on the data received from the data service provider.

11. The method of claim 10, wherein the data service provider comprises a weather data search.

12. The method of claim 1, wherein the first operational data comprises an indication of a maintenance status code associated with at least one connected device of the plurality of first connected devices and wherein identifying the at least one anomalous condition associated with the second connected device comprises identifying the maintenance status code.