Room labeling drawing interface for activity tracking and detection

1. An intelligent secure networked architecture configured by at least one processor to execute instructions stored in memory, the architecture comprising:

a data retention system and a machine learning system;

a web services layer providing access to the data retention and the machine learning systems; and

an application server layer that:

provides a user-facing application that accesses the data retention and the machine learning systems through the web services layer; and

performs processing based on user interaction with an interactive graphical user interface provided by the user-facing application, the user-facing application configured to execute instructions for a method for room labeling for activity tracking and detection, the method comprising:

making a 2D sketch of a first room on the interactive graphical user interface, comprising:

prompting a user, via guided interaction, to draw the 2D sketch of the first room;

using machine learning to turn the 2D sketch of the first room represented as a matrix of numbers where each number represents a value of an individual pixel into a 3D model in a form of a matrix of numbers representing dimensions of the first room;

correcting an error within the 3D model;

performing the processing transparently to the user without disruption with an exceeded threshold triggering the processing transparently; and

tracking a location and movement of a dementia patient within the 3D model in real-time for an emergency response.

2. The intelligent secure networked architecture of claim 1 , the method further comprising transmitting the 2D sketch of the first room using an internet or cellular network to a series of cloud-based services.

3. The intelligent secure networked architecture of claim 1 , the method further comprising using input data from the 2D sketch of the first room to generate the 3D model of the first room with an estimated dimension.

4. The intelligent secure networked architecture of claim 1 , the method further comprising making a 2D sketch of a second room on the interactive graphical user interface.

5. The intelligent secure networked architecture of claim 4 , the method further comprising using the machine learning to turn the 2D sketch of the second room into a 3D model of the second room.

6. The intelligent secure networked architecture of claim 5 , the method further comprising using the machine learning to combine the 3D model of the first room and the 3D model of the second room.

7. The intelligent secure networked architecture of claim 6 , the method further comprising updating a dimension of the 3D model of the first room and a dimension of the 3D model of the second room.

8. The intelligent secure networked architecture of claim 7 , the method further comprising using the machine learning to create a 3D model of a dwelling.

9. The intelligent secure networked architecture of claim 8 , the method further comprising placing a device having the interactive graphical user interface, an integrated camera and a geolocator in each room of the dwelling.

10. The intelligent secure networked architecture of claim 9 , the method further comprising associating a physical address with the dwelling.

11. The intelligent secure networked architecture of claim 10 , the method further comprising tracking activity in each room of the dwelling.

12. The intelligent secure networked architecture of claim 11 , the method further comprising transmitting the tracking activity in each room of the dwelling using an internet or cellular network to a series of cloud-based services.

13. The intelligent secure networked architecture of claim 1 , wherein the machine learning utilizes a convolutional neural network.

14. The intelligent secure networked architecture of claim 13 , further comprising using backpropagation to train the convolutional neural network.

15. The intelligent secure networked architecture of claim 14 , wherein the 2D sketch of the first room is received by an input layer of the trained convolutional neural network.

16. The intelligent secure networked architecture of claim 15 , further comprising the 2D sketch of the first room being processed through an additional layer of the trained convolutional neural network.

17. The intelligent secure networked architecture of claim 16 , further comprising the 2D sketch of the first room being processed through an output layer of the trained convolutional neural network, resulting in the 3D model of the first room.

18. The intelligent secure networked architecture of claim 1 , further comprising using multiple security tokens.

19. The intelligent secure networked architecture of claim 1 , further comprising using a security token cached on a web browser.

20. The intelligent secure networked architecture of claim 1 , further comprising using a security token between the application server layer and the web services layer.