Detecting location within a network

• US 10,064,014 B2
• Filed: 09/22/2017
• Issued: 08/28/2018
• Est. Priority Date: 09/16/2015
• Status: Active Grant

• Current Assignee: Ivani, LLC
• Original Assignee: Ivani, LLC
• Inventors: Wootton, John, Wootton, Matthew, Nissman, Chris, Preston, Victoria, Clark, Jonathan, McKinney, Justin, Barnes, Claire, Wang, Zhecan, Xiao, Xinyu
• Primary Examiner(s): HEIBER, SHANTELL LAKETA
• Application Number:
  US15/713,309
• Publication Number:
  US 20180012641A1
• Time in Patent Office: 340 Days
• Field of Search: None
• CPC Class Codes: G01V 1/001 Acoustic presence detection
• CPC Class Codes: G01V 3/12 operating with electromagnetic waves operating with millimetre waves G01V8/005
• CPC Class Codes: G11C 11/221 using ferroelectric capacitors
• CPC Class Codes: G11C 11/2257 Word-line or row circuits
• CPC Class Codes: G11C 11/2259 Cell access
• CPC Class Codes: G11C 11/2273 Reading or sensing circuits or methods
• CPC Class Codes: G11C 11/2293 Timing circuits or methods
• CPC Class Codes: G11C 11/2297 Power supply circuits
• CPC Class Codes: H04B 17/27 for locating or positioning the transmitter
• CPC Class Codes: H04B 17/318 Received signal strength
• CPC Class Codes: H04L 1/0018 based on latency requirement
• CPC Class Codes: H04L 5/006 Quality of the received signal, e.g. BER, SNR, water filling
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/30 Services specially adapted for particular environments, situations or purposes
• CPC Class Codes: H04W 4/33 for indoor environments, e.g. buildings
• CPC Class Codes: H04W 4/80 Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

Systems and methods for detecting the presence of a body in a network without fiducial elements, using signal absorption, and signal forward and reflected backscatter of radio frequency (RF) waves caused by the presence of a biological mass in a communications network.

Claims

1. A method for detecting the presence of a human comprising:

providing a first transceiver disposed at a first location within a detection area;

providing a second transceiver disposed at a second location within said detection area;

a computer server communicably coupled to said first transceiver;

said first transceiver receiving a first set of wireless signals from said second transceiver;

said computer server;

receiving a first set of signal data from said first transceiver, said first set of signal data comprising data about properties of said first set of wireless signals;

inferring that said first set of signal data is indicative of the presence of a human in said detection area;

creating a detection signal profile for wireless communications from said second transceiver to said first transceiver based at least in part on said properties of said first set of wireless signals in said first set of signal data when a human is inferred present in said detection area;

said first transceiver receiving a second set of wireless signals from said second transceiver;

said computer server;

receiving a second set of signal data from said first transceiver, said second set of signal data comprising data about properties of said second set of wireless signals;

inferring that said second set of signal data is indicative of the absence of any humans in said detection area;

creating a baseline signal profile for wireless communications from said second transceiver to said first transceiver based at least in part on said properties of said second set of wireless signals in said second set of signal data when the absence of any humans in in said detection area is inferred;

said first transceiver receiving a third set of wireless signals from said second transceiver;

said computer server receiving a third set of signal data from said first transceiver, said third set of signal data comprising data about properties of said third set of wireless signals;

determining whether said third set of signal data is indicative of the presence of a human, or absence of any humans, in said detection area, said determining based at least in part a comparison of said third set of signal data to said detection signal profile and said baseline signal profile.

2. The method of claim 1, wherein:

in said inferring that said first set of signal data is indicative of the presence of a human in said detection area, said inferring is based at least in part on additional signal data sets for signals received by said first transceiver from other transceivers in the detection area; and

in said inferring that said second set of signal data is indicative of the absence of any humans in said detection area, said inferring is based at least in part on additional signal data sets for signals received by said first transceiver from other transceivers in the detection area.

3. The method of claim 1, further comprising:

said computer server storing a plurality of historical data records indicative of whether a human was determined to be present in said detection area over a period of time, each of said historical data records comprising an indication of a number of humans determined to be present in said detection area and a date and time when each of said number of humans was determined to be present in said detection area; and

said computer server making at least some of said plurality of historical data records available to one or more external computer systems via an interface.

4. The method of claim 1, wherein:

said computer server is operatively coupled to a second system; and

only after said computer server determines a human is present in said detection area, said computer server operates said second system.

5. The method of claim 4, wherein said first transceiver and said second system are configured to communicate using an identical communication protocol.

6. The method of claim 4, wherein said second system is selected from the group consisting of:

an electrical system;

a lighting system;

a heating, venting, and cooling (HVAC) system;

a security system; and

, an industrial automation system, an electrical system.

7. The method of claim 1, wherein said wireless communication utilizes a protocol selected from the group consisting of:

Bluetooth™

, Bluetooth™

Low Energy, ANT, ANT+, WiFi, Zigbee, Thread, and Z-Wave.

8. The method of claim 1, wherein said wireless communications from said second transceiver to said first transceiver have a carrier frequency in the range of 850 MHz and 17.5 GHz inclusive.

9. The method of claim 1 wherein said computer server determining whether said third set of signal data is indicative of the presence of a human includes a confidence metric.

10. The method of claim 1 wherein said first transceiver and said second transceiver are configured to calculate their relative positions within said detection area automatically.

11. A method for estimating the number of humans present in an area comprising:

providing a first transceiver disposed at a first location within a detection area;

providing a second transceiver disposed at a second location within said detection area;

a computer server communicably coupled to said first transceiver;

said first transceiver receiving a first set of wireless signals from said second transceiver when no humans are present within said detection area;

said computer server receiving a first set of signal data from said first transceiver, said first set of signal data comprising data about properties of said first set of wireless signals;

said computer server creating a first baseline signal profile for wireless communications from said second transceiver to said first transceiver, said first baseline signal profile being based at least in part on said properties of said first set of wireless signals in said first set of signal data when no humans are present in said detection area;

said first transceiver receiving a second set of wireless signals from said second transceiver when a first plurality of humans is present in said detection area, said first plurality of humans having a total mass;

said computer server receiving a second set of signal data from said first transceiver, said second set of signal data comprising data about properties of said second set of wireless signals;

said computer server creating a second baseline signal profile for wireless communications from said second transceiver to said first transceiver, said second baseline signal profile being based at least in part on said properties of said second set of wireless signals in said second set of signal data when said first plurality of humans is present in said detection area;

said first transceiver receiving a third set of wireless signals from said second transceiver when a second plurality of humans is present within said detection area;

said computer server receiving a third set of signal data from said first transceiver, said third set of signal data comprising data about properties of said third set of wireless signals;

said computer server estimating the total mass of said second plurality of humans, said estimating based at least in part on a comparison of said properties of said third set of wireless signals in said third set of wireless signal data to said first baseline signal profile and to said second baseline signal profile;

said computer server estimating the total number of humans in said plurality of humans based at least in part on dividing said estimated total mass of said plurality of humans by an average mass per human.

12. The method of claim 11, wherein said computer server estimating the total mass of said second plurality of humans is further based at least in part on a comparison of said properties of said third set of wireless signals in said third set of wireless signal data to said properties of said second set of wireless signals in said second set of wireless signal data.

13. The method of claim 11, wherein said properties of said first set of wireless signals, said second set of wireless signals, and said third set of wireless signals comprise wireless network signal protocol properties determined by said first transceiver.

14. The method of claim 11, wherein each of said wireless network signal protocol properties is selected from the group consisting of:

received signal strength, latency, and bit error rate.

15. The method of claim 11, wherein said computer server estimating comprises interpolating the total mass of humans in said second plurality of humans.

16. The method of claim 15, wherein said interpolating uses an assumed mass of zero for said baseline signal profile and said total mass of humans in said first plurality of humans for said second baseline signal profile.

17. The method of claim 11, wherein said total mass is a discrete user-supplied quantity.

18. The method of claim 11, wherein said average mass per human is a discrete user-supplied quantity.

19. The method of claim 11, further comprising:

said computer server storing a plurality of historical data records indicative of whether a human was present in the detection area over a period of time, each of said historical data records comprising an indication of a number of humans detected in said detection area and a date and time when each of said number of humans was detected in said detection area; and

said computer server making at least some of said plurality of historical data records available to one or more external computer systems via an interface.

20. The method of claim 11, wherein said computer server estimating the total mass of said second plurality of humans is adjusted based on machine learning comprises:

determining a first sample total mass of a plurality of humans having a fiducial element in said detection area, said first sample mass being determined based upon detecting said fiducial element;

determining a second sample total mass of said plurality of humans in said detection area, said second sample mass being determined based at least in part on a comparison of said received second set of signal data to said baseline signal profile;

comparing said first sample mass and said second sample mass; and

adjusting said estimation of said total mass of said second plurality of humans based upon said comparing.

21. The method of claim 11 wherein said computer server estimating the total mass of said second plurality of humans is adjusted based on machine learning comprises:

determining a first sample total mass of a plurality of humans based on inferences in said detection area;

determining a second sample total mass of a plurality of humans in said detection area, said second sample mass being determined based at least in part on a comparison of said received second set of signal data to said baseline signal profile;

comparing said first sample mass and said second sample mass; and

adjusting said determination of said second sample mass based upon said comparing.

22. The method of claim 21, wherein:

said determining a first sample location of a human based on inference in said detection area comprises said computer server inferring from a detected operation of a network element in said detection area that a human is present in said detection area near said network element.

23. The method of claim 22, wherein said network component is a component of an electrical system, a lighting system, a heating, venting, and cooling (HVAC) system, a security system, or an industrial automation system.

24. The method of claim 11 wherein said estimating the total number of humans in said plurality of humans includes a confidence metric.

25. The method of claim 11 wherein said first transceiver and said second transceiver are configured to calculate their relative positions within said detection area automatically.

26. The method of claim 11, wherein said wireless communication utilizes a protocol selected from the group consisting of:

Bluetooth™

, Bluetooth™

Low Energy, ANT, ANT+, WiFi, Zigbee, Thread, and Z-Wave.

27. The method of claim 11, wherein said wireless communications from said second transceiver to said first transceiver have a carrier frequency in the range of 850 MHz and 17.5 GHz inclusive.