Detecting location within a network

• US 10,667,086 B2
• Filed: 06/10/2019
• Issued: 05/26/2020
• 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
• Primary Examiner(s): Heiber, Shantell L
• Application Number:
  US16/435,783
• Publication Number:
  US 20190297459A1
• Time in Patent Office: 351 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: 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 presence detection method comprising:

obtaining, at a machine learning computer server, multiple sets of input training data, each set of input training data based on a statistical analysis of characteristics of wireless signals transmitted through a detection area over a respective time period, each set of the input training data indicating whether a human was detected in the detection area over the respective time period,

wherein the input training data comprises machine learning data, and the statistical analysis includes;

obtaining frequencies and power levels of the wireless signals;

computing statistical parameter values based on the frequencies and power levels of the wireless signals;

populating the statistical parameter values into an initial signal characteristic profile; and

generating the machine learning data based on the initial signal characteristic profile; and

by operation of the machine learning computer server, processing the sets of input training data to parameterize nodes of a machine learning system; and

detecting presence of a human in the detection area, using the machine learning system comprising the parameterized nodes, based on a newly obtained set of input data.

2. The method of claim 1, wherein the statistical parameters comprise a mean and a standard deviation.

3. The method of claim 1, wherein the input training data indicates a category of motion, and parameterizing the nodes configures the machine learning system to detect a category of motion based on the newly obtained set of input data.

4. The method of claim 1, comprising:

obtaining, at the machine learning computer server, additional sets of input training data, each additional set of input training data indicating whether interference was present in the detection area over the respective time period; and

by operation of the machine learning computer server, processing the additional sets of input training data to parameterize nodes of the machine learning system, wherein parameterizing the nodes configures the machine learning system to detect interference based on the newly obtained set of input data.

5. The method of claim 1, wherein the machine learning system comprises a neural network.

6. A machine learning training system comprising:

a data processing apparatus; and

memory comprising instructions that are operable when executed by the data processing apparatus to perform operations comprising;

obtaining multiple sets of input training data, each set of input training data based on a statistical analysis of wireless signals transmitted through a detection area over a respective time period, each set of the input training data indicating whether a human was detected in the detection area over the respective time period;

wherein the input training data comprises machine learning data, and the statistical analysis comprises;

obtaining frequencies and power levels of the wireless signals;

computing statistical parameter values based on the frequencies and power levels of the wireless signals;

populating the statistical parameter values into an initial signal characteristic profile;

generating the machine learning data based on the initial signal characteristic profile;

processing the input training data to parameterize nodes of a machine learning system; and

detecting presence of a human in the detection area, using the machine learning system comprising the parameterized nodes, based on a newly obtained set of input data.

7. The machine learning training system of claim 6, wherein the statistical parameters comprise a mean and a standard deviation.

8. The machine learning training system of claim 6, wherein the input training data indicates a category of motion, and parameterizing the nodes configures the machine learning system to detect a category of motion based on the newly obtained set of input data.

9. The machine learning training system of claim 6, wherein the operations comprise:

obtaining additional sets of input training data, each additional set of input training data indicating whether interference was present in the detection area over the respective time period; and

processing the additional sets of input training data to parameterize nodes of the machine learning system, wherein parameterizing the nodes configures the machine learning system to detect interference based on the newly obtained set of input data.

10. The machine learning training system of claim 6, wherein the machine learning system comprises a neural network.

11. A motion detection method, comprising:

obtaining, at a machine learning system, multiple sets of input training data, each set of input training data based on a statistical analysis of a series of wireless signals transmitted through a detection area over a respective time period,

wherein the input training data comprises machine learning data, and the statistical analysis comprises;

obtaining frequencies and power levels of the wireless signals;

computing statistical parameter values based on the frequencies and power levels of the wireless signals;

populating the statistical parameter values into an initial signal profile; and

generating the machine learning data based on the initial signal profile, and

by operation of the machine learning system, processing the sets of input training data through a plurality of programmed machine learning nodes; and

determining whether motion occurred in the detection area during the respective time period.

12. The method of claim 11, wherein the statistical parameters comprise a mean and a standard deviation.

13. The method of claim 11, wherein determining whether motion occurred in the detection area comprises generating an indication of motion by an object in the detection area, a category of motion that occurred in the detection area, interference present in the detection area, or an absence of motion in the detection area.

14. The method of claim 11, wherein the machine learning system comprises a neural network.

15. A machine learning system comprising:

a data processing apparatus; and

memory comprising instructions that are operable when executed by the data processing apparatus to perform operations comprising;

obtaining multiple sets of input training data, each set of input training data based on a statistical analysis of a series of wireless signals transmitted through a detection area over a respective time period,

wherein the input training data comprises machine learning data, and the statistical analysis comprises;

obtaining frequencies and power levels of the wireless signals;

computing statistical parameter values based on the frequencies and power levels of the wireless signals;

populating the statistical parameter values into an initial signal profile; and

generating the machine learning data based on the initial signal profile; and

processing the sets of input training data through a plurality of programmed machine learning nodes; and

determining whether motion occurred in the detected area during the respective time period.

16. The machine learning system of claim 15, wherein the statistical parameters comprise a mean and a standard deviation.

17. The machine learning system of claim 15, wherein determining whether motion occurred in the detection area comprises generating an indication of motion by an object in the detection area, a category of motion that occurred in the detection area, interference present in the detection area, or an absence of motion in the detection area.

18. The machine learning system of claim 15, wherein the machine learning system comprises a neural network.