Computer-implemented systems utilizing sensor networks for sensing temperature and motion environmental parameters; and methods of use thereof

• US 9,558,447 B2
• Filed: 06/22/2016
• Issued: 01/31/2017
• Est. Priority Date: 03/31/2004
• Status: Expired due to Term

• Current Assignee: Alarm.com Inc. (Alarm.com Holdings, Inc.)
• Original Assignee: TriPlay Incorporated
• Inventors: Jung, Edward K. Y., Tegreene, Clarence T.
• Primary Examiner(s): SYED, FARHAN M
• Application Number:
  US15/189,955
• Publication Number:
  US 20160300136A1
• Time in Patent Office: 223 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G01D 9/005 Solid-state data loggers
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G08B 29/186 Fuzzy logic; neural networks
• CPC Class Codes: G08B 29/188 Data fusion; cooperative systems, e.g. voting among different detectors
• 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: H04Q 2209/20 using a distributed architecture
• CPC Class Codes: H04Q 2209/40 using a wireless architecture
• CPC Class Codes: H04Q 9/00 Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
• CPC Class Codes: H04W 4/38 for collecting sensor information
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

Computer-implemented systems utilizing sensor networks for sensing temperature and motion environmental parameters, and performing at least operations of electronically establishing, based on pattern recognition criteria, correspondence of a plurality of representative features a plurality of characteristics of an occurrence, where a first instance of the occurrence occurred within a first time period of a plurality of time periods; electronically discovering, based on the correspondence, a second instance of the occurrence in an environment during a second time period of the plurality of time periods; and electronically causing, based on the discovery of the second instance of the occurrence, a change in the environment via an electronically-controlled device.

Claims

1. A computer-implemented method comprising:

electronically and automatically receiving, by at least one programmed central processing unit, via at least one communication link, sensed data from at least one sensor network;

wherein the at least one sensor network comprises a plurality of remotely located recording sensor nodes;

wherein the sensed data comprises environmental data for each environment in which each remotely located recording sensor node is located;

wherein the environmental data is being continuously captured, over a plurality of time periods, by the plurality of remotely located recording sensor nodes, based, at least in part, on a plurality of environmental parameters;

wherein the plurality of environmental parameters comprises;

1) at least one temperature environmental parameter, and

2) at least one motion environmental parameter;

wherein each of the plurality of remotely located recording sensor nodes captures the environmental data for at least one respective environmental parameter;

automatically analyzing, by the at least one programmed central processing unit, the sensed data, captured over at least one first time period of the plurality of time periods, to identify a plurality of representative features within the sensed data, wherein each representative feature of the plurality of representative features is represented by particular environmental data of the at least one respective environmental parameter;

electronically storing, in at least one non-transient database, by the at least one programmed central processing unit, data representative of the plurality of representative features related to the at least one first time period of the plurality of time periods;

electronically establishing, based on at least one pattern recognition criterion, by the at least one programmed central processing unit, a first correspondence of at least one first representative feature from the plurality of representative features to at least one first characteristic of at least one first occurrence and a second correspondence of at least one second representative feature from the plurality of representative features to at least one second characteristic of the at least one first occurrence, wherein at least one first instance of the at least one first occurrence has occurred within the at least one first time period of the plurality of time periods;

electronically discovering, based on the first correspondence and the second correspondence, by the at least one programmed central processing unit, at least one second instance of the at least one first occurrence within the sensed data captured over at least one second time period of the plurality of time periods, wherein the at least one first occurrence has taken place in at least one first environment associated with at least one first remotely located recording sensor node; and

electronically causing, based on the discovery of the at least one second instance of the at least one first occurrence in the at least one first environment, by the at least one programmed processing unit, via at least one electronically-controlled device, at least one change in the at least one first environment.

2. The method of claim 1, wherein at least one the remotely located recording sensor node comprises a digital image capture device;

and wherein the environmental data being captured by the at least one the remotely located recording sensor node is related to light images selected from the group consisting of infrared light images, visible light images, ultraviolet light images, and any combination thereof.

3. The method of claim 1, wherein the at least one first occurrence comprises at least one of:

i) a reference,

ii) an incident,

iii) an accident,

iv) an event,

v) a change in data sequence,

vi) a change in time domain, and

vii) any combination thereof.

4. The method of claim 1, wherein each remotely located recording sensor node of the plurality of remotely located recording sensor nodes is associated with at least one location identifier.

5. The method of claim 1, wherein the electronically establishing, based on the at least one pattern recognition criterion, the first correspondence and the second correspondence, further comprising:

analyzing the environmental data by utilizing at least one of the following techniques;

i) a fuzzy logic technique,

ii) an artificial neural network modeling technique,

iii) a genetic algorithm technique,

iv) a rough set technique,

v) a wavelets-based technique, and

vi) any combination thereof.

6. The method of claim 1, wherein the plurality of environmental parameters are related to a plurality of distributed equipment pieces.

7. The method of claim 1, wherein the electronically establishing, based on the at least one pattern recognition criterion, the first correspondence and the second correspondence, further comprising:

comparing a plurality of instances of the sensed data captured from the plurality of the remotely located recording sensor nodes over the at least one first time period of the plurality of time periods.

8. The method of claim 7, wherein the electronically establishing, based on the at least one pattern recognition criterion, the first correspondence and the second correspondence, further comprising:

automatically assigning, by the at least one programmed central processing unit, a weight to each instance of the plurality of instances of the sensed data.

9. The method of claim 8, wherein the electronically establishing, based on the at least one pattern recognition criterion, the first correspondence and the second correspondence, further comprising:

comparing the plurality of instances of the sensed data based on the assigned weights.

10. The method of claim 1, wherein the plurality of environmental parameters further comprises:

3) at least one optical environmental parameter,

4) at least one acoustic environmental parameter,

5) at least one pressure environmental parameter,

6) at least one thermal environmental parameter,

7) at least one acceleration environmental parameter,

8) at least one magnetic environmental parameter,

9) at least biological environmental parameter, and

10) at least chemical environmental parameter.

11. The method of claim 10, wherein the at least one acoustic environmental parameter is a sound frequency.

12. The method of claim 10, wherein the at least one acoustic environmental parameter is a sound pattern.

13. The method of claim 10, wherein the at least one pressure environmental parameter is a vibration frequency.

14. The method of claim 10, wherein the at least one chemical environmental parameter is a presence of airborne carbon particles.

15. The method of claim 1, wherein each time period of the plurality of time periods is determined based on a pre-determined sampling rate.

16. The method of claim 1, wherein the at least one first time period is a day.

17. A computer-implemented system, comprising:

a specially programmed central processing unit, comprising;

a non-transient memory, electronically storing particular computer executable program code; and

at least one computer processor which, when executing the particular computer executable program code, becomes at least one programmed computer processor of the programmed central processing unit that is configured to perform at least the following operations;

electronically and automatically receiving, via at least one communication link, sensed data from at least one sensor network;

wherein the at least one sensor network comprises a plurality of remotely located recording sensor nodes;

wherein the sensed data comprises environmental data for each environment in which each remotely located recording sensor node is located;

wherein the environmental data is being continuously captured, over a plurality of time periods, by the plurality of remotely located recording sensor nodes, based, at least in part, on a plurality of environmental parameters;

wherein the plurality of environmental parameters comprises;

1) at least one temperature environmental parameter, and

2) at least one motion environmental parameter;

wherein each of the plurality of remotely located recording sensor nodes captures the environmental data for at least one respective environmental parameter;

automatically analyzing the sensed data, captured over at least one first time period of the plurality of time periods, to identify a plurality of representative features within the sensed data, wherein each representative feature of the plurality of representative features is represented by particular environmental data of the at least one respective environmental parameter;

electronically storing, in at least one non-transient database, data representative of the plurality of representative features related to the at least one first time period of the plurality of time periods;

electronically establishing, based on at least one pattern recognition criterion, a first correspondence of at least one first representative feature from the plurality of representative features to at least one first characteristic of at least one first occurrence and a second correspondence of at least one second representative feature from the plurality of representative features to at least one second characteristic of the at least one first occurrence, wherein at least one first instance of the at least one first occurrence has occurred within the at least one first time period of the plurality of time periods;

electronically discovering, based on the first correspondence and the second correspondence, at least one second instance of the at least one first occurrence within the sensed data captured over at least one second time period of the plurality of time periods, wherein the at least one first occurrence has taken place in at least one first environment associated with at least one first remotely located recording sensor node; and

electronically causing, based on the discovery of the at least one second instance of the at least one first occurrence in the at least one first environment, at least one change in the at least one first environment via at least one electronically-controlled device.

18. The system of claim 17, wherein at least one the remotely located recording sensor node comprises a digital image capture device;

and wherein the environmental data being captured by the at least one the remotely located recording sensor node is related to light images selected from the group consisting of infrared light images, visible light images, ultraviolet light images, and any combination thereof.

19. The system of claim 17, wherein the at least one first occurrence comprises at least one of:

i) a reference,

ii) an incident,

iii) an accident,

iv) an event,

v) a change in data sequence,

vi) a change in time domain, and

vii) any combination thereof.

20. The system of claim 17, wherein each remotely located recording sensor node of the plurality of remotely located recording sensor nodes is associated with at least one location identifier.

21. The system of claim 17, wherein the electronically establishing, based on the at least one pattern recognition criterion, the first correspondence and the second correspondence, further comprising:

analyzing the environmental data by utilizing at least one of the following techniques;

i) a fuzzy logic technique,

ii) an artificial neural network modeling technique,

iii) a genetic algorithm technique,

iv) a rough set technique,

v) a wavelets-based technique, and

vi) any combination thereof.

22. The system of claim 17, wherein the plurality of environmental parameters are related to a plurality of distributed equipment pieces.

23. The system of claim 17, wherein the electronically establishing, based on the at least one pattern recognition criterion, the first correspondence and the second correspondence, further comprising:

comparing a plurality of instances of the sensed data captured from the plurality of the remotely located recording sensor nodes over the at least one first time period of the plurality of time periods.

24. The system of claim 23, wherein the electronically establishing, based on the at least one pattern recognition criterion, the first correspondence and the second correspondence, further comprising:

automatically assigning, by the at least one programmed central processing unit, a weight to each instance of the plurality of instances of the sensed data.

25. The system of claim 24, wherein the electronically establishing, based on the at least one pattern recognition criterion, the first correspondence and the second correspondence, further comprising:

comparing the plurality of instances of the sensed data based on the assigned weights.

26. The system of claim 17, wherein the plurality of environmental parameters further comprises:

3) at least one optical environmental parameter,

4) at least one acoustic environmental parameter,

5) at least one pressure environmental parameter,

6) at least one thermal environmental parameter,

7) at least one acceleration environmental parameter,

8) at least one magnetic environmental parameter,

9) at least biological environmental parameter, and

10) at least chemical environmental parameter.

27. The system of claim 26, wherein the at least one acoustic environmental parameter is a sound frequency.

28. The system of claim 26, wherein the at least one chemical environmental parameter is a presence of airborne carbon particles.

29. The system of claim 17, wherein each time period of the plurality of time periods is determined based on a pre-determined sampling rate.

30. The system of claim 17, wherein the at least one first time period is a day.