Detecting location within a network

Patent metadata

Field	Value
Publication number	US-10665284-B2
Application number	US-201916443458-A
Country	US
Kind code	B2
Filing date	Jun 17, 2019
Priority date	Sep 16, 2015
Publication date	May 26, 2020
Grant date	May 26, 2020

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.

First claim

The invention claimed is:

1. A system for estimating the number of humans present in an area comprising:
a first transceiver disposed at a first location within a detection area;
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.

2. The system of claim 1 , 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.

3. The system of claim 1 , 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.

4. The system of claim 1 , wherein each of said wireless network signal protocol properties is selected from the group consisting of: received signal strength, latency, and bit error rate.

5. The system of claim 1 , wherein said computer server estimating comprises interpolating the total mass of humans in said second plurality of humans.

6. The system of claim 5 , 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.

7. The system of claim 1 , wherein said total mass is a discrete user-supplied quantity.

8. The system of claim 1 , wherein said average mass per human is a discrete user-supplied quantity.

9. The system 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 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.

10. The system of claim 1 , 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.

Assignees

• Ivani Llc

Inventors

• Wootton John
• Wootton Matthew
• Nissman Chris
• Preston Victoria
• Clark Jonathan
• Mckinney Justin
• Barnes Claire
• Wang Zhecan
• Xiao Xinyu

Classifications

• G11C11/5657

  using ferroelectric storage elements · CPC title

• G11C11/2273

  Reading or sensing circuits or methods · CPC title

• G11C11/2297

  Power supply circuits · CPC title

• G01V3/12 Primary

  operating with electromagnetic waves {(operating with millimetre waves G01V8/005)} · CPC title

• G11C11/2255

  Bit-line or column circuits · CPC title