Detecting location within a network

• US 9,693,195 B2
• Filed: 08/03/2016
• Issued: 06/27/2017
• Est. Priority Date: 09/16/2015
• Status: Active Grant

• Current Assignee: Ivani, LLC
• Original Assignee: Ivani, LLC
• Inventors: Nissman, Chris, Preston, Victoria, Clark, Jonathan, McKinney, Justin, Barnes, Claire, Wootton, John, Wootton, Matthew
• Primary Examiner(s): HEIBER, SHANTELL LAKETA
• Application Number:
  US15/227,717
• Publication Number:
  US 20170078845A1
• Time in Patent Office: 328 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: H04B 17/373 Predicting channel quality or other radio frequency [RF] parameters
• 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: 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 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/131 Protocols for games, networked simulations or virtual reality
• 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: H04W 4/02 Services making use of location information
• 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 4/029 Location-based management or tracking services
• 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 4/50 : Service provisioning or reconfiguring
• 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 52/283 : Power depending on the position of the mobile
• 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 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 position 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 via said wireless communications network;

said computer server receiving a first set of signal data from said first transceiver, said first set of signal data comprising data about the properties of said first set of wireless signals, said property data being generated as part of ordinary operation of said first transceiver on said communication network;

said computer server creating a baseline signal profile for communications from said second transceiver to said first transceiver, said baseline signal profile being based at least in part on said wireless signal properties in said received first set of signal data, and representing characteristics of wireless transmissions from said second transceiver to said first transceiver when no human is present in said detection area;

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

said computer server receiving a second set of signal data from said first transceiver, said second set of signal data comprising data about the properties of said second set of wireless signals, said property data being generated as part of ordinary operation of said first transceiver on said communication network; and

said computer server determining whether a human is present within said detection area, said determination based at least in part on a comparison of said wireless signal properties in said received second set of wireless signal data to said baseline signal profile.

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

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

received signal strength, latency, and bit error rate.

4. The method of claim 1, further comprising:

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

said first transceiver receiving a third set of wireless signals from said third transceiver via said wireless communications network;

said computer server receiving a third set of signal data from said first transceiver, said third set of signal data comprising data about the properties of said third set of wireless signals, said property data being generated as part of ordinary operation of said first transceiver on said communication network;

said computer server creating a second baseline signal profile for communications from said third transceiver to said first transceiver, said second baseline signal profile being based at least in part on said wireless signal properties in said received third set of signal data, and representing characteristics of wireless transmissions from said third transceiver to said first transceiver when no human is present in said detection area;

said first transceiver receiving a fourth set of wireless signals from said third transceiver via said wireless communications network;

said computer server receiving a fourth set of signal data from said first transceiver, said fourth set of signal data comprising data about the properties of said fourth set of wireless signals, said property data being generated as part of ordinary operation of said first transceiver on said communication network; and

in said determining step, said computer server determining whether a human is present within said detection area based at least in part on a comparison of said wireless signal properties in said received fourth set of wireless signal data to said second baseline signal profile.

5. The method of claim 1, wherein said determining step applies statistical methods to said second set of wireless signal data to determine the presence of a human.

6. The method of claim 1, further comprising:

said computer server continuously determining the presence or absence of a human within said detection area, said determination based at least in part on a comparison of said baseline signal profile to signal data comprising data about the properties of said first set of wireless signals received continuously at said computer server from said first transceiver; and

said computer continuously updating said baseline signal profile based on said continuously received signal data when said continuously received signal data indicates the absence of a human in said detection area.

7. The method of claim 1, further comprising:

said computer server determining the number of humans is present within said detection area, said determination based at least in part on a comparison of said received second set of signal properties to said baseline signal profile.

8. The method of claim 7, further comprising:

said computer server determining the location of one or more humans within said detection area, said determination based at least in part on a comparison of said received second set of signal properties to said baseline signal profile.

9. The method of claim 1, further comprising:

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

only after said computer server detects the presence of a human in said detection area, said computer operates said second system.

10. The method of claim 9, wherein said detection network and said second system are configured to communicate using the same communication protocol.

11. The method of claim 9, wherein said second system is an electrical system.

12. The method of claim 9, wherein said second system is a lighting system.

13. The method of claim 9, wherein said second system is a heating, venting, and cooling (HVAC) system.

14. The method of claim 9, wherein said second system is a security system.

15. The method of claim 9, wherein said second system is an industrial automation system.

16. The method of claim 1, wherein said wireless communication protocol is selected from the group consisting of:

Bluetooth™

, Bluetooth™

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

17. The method of claim 1, wherein said wireless communication network has a carrier frequency in the range of 850 MHz and 17.5 GHz inclusive.

18. The method of claim 1, wherein said determination whether a human is present within said detection area is adjusted based on machine learning comprising:

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 not utilizing the fiducial element;

comparing said first sample location and said second sample location; and

adjusting said determination step based on non-fiducial element location to improve the location calculating capabilities of the system, said adjusting based upon said comparing step.

19. The method of claim 1, 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 the number of humans detected in the detected area and the date and time of when said number of humans was detected in the detection area; and

said computer server making said historical data records available to one or more external computer systems via an interface.