Detecting Location within a Network

• US 20170366938A1
• Filed: 08/10/2017
• Published: 12/21/2017
• 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, Xiao, Xinyu, Wang, Zhecan
• Granted Patent: US 10,455,357 B2
• Time in Patent Office: Days
• 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 a 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 when a human is present within said detection area at a first position;

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 baseline signal profile for wireless communications from said second transceiver to said first transceiver, said 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 said human is present in said detection area at said first position;

said human moving from said first position to a second position in said detection area;

said first transceiver receiving a second set of wireless signals from said second transceiver when said human is present at said second position;

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;

and said computer server determining if the position of said human in said detection area has changed, said determination based at least in part on a comparison of said properties of said second set of wireless signals in said second set of wireless signal data to said baseline signal profile.

2. The method of claim 1, wherein said properties of said first set of wireless signals comprise wireless network signal protocol properties determined by said first transceiver.

3. The method of claim 2, wherein said each of said wireless network signal protocol properties is selected from the group consisting of:

received signal strength, latency, and bit error rate.

4. The method of claim 1, further comprising:

said human moving to a new position in said detection area;

said first transceiver receiving a new set of wireless signals from said second transceiver when said human is present in said detection area at said new position;

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

said computer server replacing said baseline signal profile with a new baseline signal for wireless communications from said second transceiver to said first transceiver when said human is at said new position, said second baseline signal profile being created based at least in part on said properties of said new set of wireless signals in said new set of signal data when said human is present in said detection area at said new position.

5. The method of claim 4, further comprising:

before said computer server replacing said baseline signal profile with said new baseline signal, said computer server storing a historical data record of said baseline profile.

6. The method of claim 5, wherein said stored historical data record is indicative of said human being detected in said detection area and comprises a date and time when said humans was detected in said detection area.

7. The method of claim 4, repeating said moving, receiving, and replacing steps one or more times.

8. The method of claim 1, wherein said computer server determining if the position of said human in said detection area has changed is adjusted based on machine learning comprises:

determining a first sample location of a human having a fiducial element in said detection area, said first sample location being determined based upon detecting said fiducial element;

determining a second sample location of said human in said detection area, said second sample location 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 location and said second sample location; and

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

9. The method of claim 1, wherein said computer server determining if the position of said human in said detection area has changed is adjusted based on machine learning comprises:

determining a first sample location of a human based on inferences in said detection area;

determining a second sample location of said human in said detection area, said second sample location 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 location and said second sample location; and

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

10. The method of claim 9, 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.

11. The method of claim 10, 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.

12. The method of claim 1, further comprising:

said computer server storing a plurality of historical data records indicative of whether a human changed locations 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 determined to have changed locations 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.

13. The method of claim 1, further comprising:

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

only after said computer server determines a human has changed locations in said detection area, said computer server operates said second system.

14. The method of claim 3, wherein said first transceiver and said second system are configured to communicate using an identical communication protocol.

15. The method of claim 13, 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.

16. 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.

17. 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.

18. The method of claim 1 wherein said computer server determining if the position of said human in said detection area has changed includes a confidence metric.

19. 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.

20. The method of claim 1 where said first transceiver and said second transceiver are configured to define automatically a detection area including said first transceiver and said second transceiver.