SMART-HOME SYSTEM FACILITATING INSIGHT INTO DETECTED CARBON MONOXIDE LEVELS

• US 20150096352A1
• Filed: 10/07/2014
• Published: 04/09/2015
• Est. Priority Date: 10/07/2013
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Sloo, David, Matsuoka, Yoky, Webb, Nicholas Unger, PETERSON, KEVIN
• Granted Patent: US 10,546,469 B2
• Time in Patent Office: Days
• US Class Current: 73/31.02
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/33 to fire, excessive heat or smoke
• CPC Class Codes: F24F 11/34 by opening air passages
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/58 using Internet communication
• CPC Class Codes: F24F 11/70 Control systems characterised by their outputs; Constructional details thereof
• CPC Class Codes: F24F 11/75 for maintaining constant air flow rate or air velocity
• CPC Class Codes: F24F 11/89 Arrangement or mounting of control or safety devices
• CPC Class Codes: F24F 2120/10 Occupancy
• CPC Class Codes: G01J 1/4204 with determination of ambient light solar light G01J2001/4266
• CPC Class Codes: G01N 27/02 by investigating impedance
• CPC Class Codes: G01N 27/121 for determining moisture content, e.g. humidity, of the fluid moisture content of the tested material G01N27/048
• CPC Class Codes: G01N 33/0031 comprising two or more sensors, e.g. a sensor array
• CPC Class Codes: G01N 33/004 CO or CO2
• CPC Class Codes: G01V 8/10 Detecting, e.g. by using light barriers by reflection from the object G01S17/00
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06V 20/46 Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
• CPC Class Codes: G08B 17/10 Actuation by presence of smoke or gases , e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
• CPC Class Codes: G08B 17/117 by using a detection device for specific gases, e.g. combustion products, produced by the fire G08B17/103, G08B17/11 take precedence
• CPC Class Codes: G08B 19/005 combined burglary and fire alarm systems
• CPC Class Codes: G08B 21/12 : responsive to undesired emission of substances, e.g. pollution alarms
• CPC Class Codes: G08B 21/14 : Toxic gas alarms G08B21/16 takes precedence
• 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 25/002 : Generating a prealarm to the central station
• CPC Class Codes: G08B 25/008 : Alarm setting and unsetting, i.e. arming or disarming of the security system
• CPC Class Codes: G08B 25/012 : using recorded signals, e.g. speech G08B25/08 takes precedence
• CPC Class Codes: G08B 25/08 : using communication transmission lines G08B13/19658, G08B21/0286, G08B25/016 take precedence
• CPC Class Codes: G08B 29/02 : Monitoring continuously signalling or alarm systems
• CPC Class Codes: G08B 29/04 : Monitoring of the detection circuits
• CPC Class Codes: G08B 29/145 : of fire detection circuits
• CPC Class Codes: G08B 29/185 : Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
• CPC Class Codes: G08B 29/22 : Provisions facilitating manual calibration, e.g. input or output provisions for testing; Holding of intermittent values to permit measurement
• CPC Class Codes: G08B 29/26 : by updating and storing reference thresholds
• CPC Class Codes: G08B 3/10 : using electric transmission; using electromagnetic transmission
• CPC Class Codes: G08B 5/22 : using electric transmission; using electromagnetic transmission
• CPC Class Codes: G08B 5/36 : using visible light sources
• CPC Class Codes: H04L 12/2803 : Home automation networks
• CPC Class Codes: H04L 12/2809 : indicating that an appliance service is present in a home automation network monitoring functionality H04L43/0817; discovery or management thereof, e.g. service location protocol [SLP] or web services, H04L67/51
• CPC Class Codes: H04L 12/2818 : from a device located outside both the home and the home network access arrangements H04L12/2856; for remote control or remote monitoring of applications H04L67/025; arrangements for transmitting signals characterised by the use of a wireless eletrical link G08C17/00; telephonic communication systems adapted for combination with remote control systems H04M11/007
• CPC Class Codes: H04L 12/282 : based on user interaction within the home receiver circuitry for displaying additional information being controlled by a remote control apparatus H04N21/42204
• CPC Class Codes: H04L 67/025 : for remote control or remote monitoring of applications
• 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 67/54 : Presence management, e.g. monitoring or registration for receipt of user log-on information, or the connection status of the users
• CPC Class Codes: H04M 1/72445 : for supporting Internet browser applications
• CPC Class Codes: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: H05B 45/10 : Controlling the intensity of the light
• CPC Class Codes: H05B 45/20 : Controlling the colour of the light
• CPC Class Codes: H05B 47/11 : by determining the brightness or colour temperature of ambient light
• CPC Class Codes: H05B 47/19 : via wireless transmission
• CPC Class Codes: H05B 47/196 : characterised by user interface arrangements
• CPC Class Codes: H05B 47/1965 : using handheld communication devices
• CPC Class Codes: H05B 47/197 : Sound control or voice control
• CPC Class Codes: H05B 47/1975 : Gesture control
• CPC Class Codes: Y02A 50/20 : Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters

Abstract

In an embodiment, a method determines one or more sources of carbon monoxide (CO) in a smart-home environment that includes a plurality of smart devices that have at least measurement and communication capabilities. The method includes measuring a level of CO in the smart-home environment to generate a CO measurement, and providing the CO measurement and one or more current characteristics of the smart-home environment, from one or more of the smart devices to an analyzing device. The method further includes evaluating, by the analyzing device and with the CO measurement and the current characteristics of the smart-home environment, a set of CO correlation scenarios that attribute generation of CO to a corresponding one of a set of specific sources, and selecting one or more of the specific sources as the most likely source of the CO, by aggregating results of the correlation scenarios.

Claims

1. A method for determining one or more sources of carbon monoxide (CO) in a smart-home environment, the smart-home environment including a plurality of smart devices that have at least measurement and communication capabilities, the method comprising:

measuring a level of CO in the smart-home environment, by one of the smart devices, to generate a CO measurement;

providing the CO measurement and one or more current characteristics of the smart-home environment, from one or more of the smart devices to an analyzing device;

evaluating, by the analyzing device and with the CO measurement and the current characteristics of the smart-home environment, a set of CO correlation scenarios that attribute generation of CO to a corresponding one of a set of specific sources; and

selecting one or more of the specific sources as the most likely source of the CO, by aggregating results of the correlation scenarios.

2. The method of claim 1, wherein

the result of evaluating each correlation scenario is generation of a confidence metric that the CO measurement is due to a corresponding one of the specific sources, and

aggregating the results comprises generating a sum of the confidence metrics for each of the specific sources.

3. The method of claim 2, wherein evaluating one or more of the correlation scenarios comprises:

generating a positive or negative result state for each of the one or more of the correlation scenarios, and

assigning a fixed value as the confidence metric responsive to generating the positive value.

4. The method of claim 2, wherein evaluating one or more of the correlation scenarios comprises generating a variable result for each of the one or more of the correlation scenarios, and assigning a variable value that is responsive to a degree of fit between the CO measurement and the current characteristics, as the confidence metric.

5. The method of claim 2, wherein selecting one or more of the specific sources as the most likely source of the CO comprises comparing the sums of the confidence metrics for the corresponding sources and determining that one of the corresponding sources is more likely than all of the other sources.

6. The method of claim 2, wherein selecting one or more of the specific sources as the most likely source of the CO comprises determining that two or more of the corresponding sources are possible sources, responsive to the sums of the confidence metrics for each of the two or more corresponding sources being within a confidence window of one another.

7. The method of claim 1, wherein the analyzing device is the device that measures the CO, and others of the smart-home devices provide at least some of the current characteristics to the device that measures the CO.

8. The method of claim 1, wherein the analyzing device is a computer that receives the CO measurement and the current characteristics.

9. The method of claim 1, wherein:

one of the correlation scenarios includes CO measurements taken by a hazard detector in or adjacent to a garage;

the home characteristics include a time of day, and

the specific source is automobile exhaust in the garage.

10. The method of claim 9, wherein the home characteristics further include a day of the week.

11. The method of claim 9, wherein the home characteristics further include an indication that a door associated with the garage has opened or closed.

12. The method of claim 1, wherein:

one of the correlation scenarios includes CO measurements taken by a hazard detector in or adjacent to a kitchen;

the home characteristics include detection of a temperature increase in the kitchen, and

the specific source is operation of a gas stove in the kitchen.

13. The method of claim 12, wherein the home characteristics further include detection of smoke in the kitchen.

14. The method of claim 12, wherein the CO measurements taken by a hazard detector in or adjacent to a kitchen rise rapidly, while CO measurements taken by other hazard detectors do not change significantly.

15. The method of claim 1, wherein:

one of the correlation scenarios includes CO measurements taken by a hazard detector in or adjacent to a location of a wood burning appliance;

the home characteristics include;

detection of a temperature increase in the location of the hazard detector, and

information that an HVAC system is not operating as a heat source; and

the specific source is wood burning in the wood burning appliance.

16. The method of claim 1, wherein:

one of the correlation scenarios includes CO measurements taken by a hazard detector in or adjacent to a location of a water heater;

the home characteristics include detection of a humidity increase in or adjacent to a bathroom, and

the specific source is a CO leak from the water heater.

17. The method of claim 1, further comprising generating one of the correlation scenarios by:

periodically generating CO measurements by a hazard detector of the smart devices;

storing the CO measurements;

periodically generating home characteristics of the smart-home environment that are not the CO measurements;

storing the home characteristics; and

generating the one of the correlation scenarios by evaluating a correlation possibility between the CO measurements and one or more of the home characteristics, by;

determining correlation of the CO measurements with the one or more of the home characteristics; and

discarding the correlation possibility responsive to no correlation being present between the CO measurements and the one or more of the home characteristics included in the correlation possibility;

or

retaining the correlation possibility as the correlation scenario responsive to a correlation being present between the CO measurements and the one or more of the home characteristics included in the correlation possibility.

18. The method of claim 17, wherein storing the CO measurements comprises associating each of the CO measurements with a corresponding time identifier and location of the hazard detector, and storing the home characteristics comprises associating each of the home characteristics with a corresponding time identifier and a location of one of the smart-home devices that generated the home characteristic.

19. The method of claim 17, wherein:

one of the correlation possibilities is generated by randomly correlating the CO measurements with ones of the home characteristics.