Quantitative disorder enhanced augmentative/alternative communication device and process

1 . A computer-implemented method of recognizing the content of a human-drawn image, the method comprising:

utilizing a computer to form a modified image from a human-drawn image by replicating a physical image formation process;

utilizing a computer to extract a numerical value corresponding to an order parameter squared (S 2 ) from the modified image, wherein the modified image includes light regions and dark regions, and wherein S 2 comprises a numerical value quantifying a degree of order present in the modified image, and wherein the extracted numerical value comprises a ratio of an area of the light regions to a total area that is equal to the sum of: 1) an area of the light regions, and 2) of an area of the dark regions;

causing an artificial intelligence (AI) program to characterize the human-drawn image utilizing: 1) the human-drawn image, and 2) the numerical value of the order parameter extracted from the modified image formed from the human-drawn image; and:

causing the computer to output at least one of an image and text that identifies the human-drawn image.

2 . The method of claim 1 , further including:

training the AI program by causing the AI program to characterize human-drawn images for a plurality of non-identical human-drawn images using 1) each of the plurality of non-identical human-drawn images, and 2) the numerical value of the order parameter extracted from the modified image formed from each of the plurality of non-identical human-drawn images.

3 . The method of claim 2 , wherein:

the plurality of non-identical human-drawn images are formed by a human utilizing an input device that allows a user to manually form the human-drawn image data.

4 . The method of claim 1 , wherein:

the computer comprises a portable device having a touch screen;

the human-drawn image data includes at least one image drawn by a human using the touch screen.

5 . The method of claim 4 , wherein:

the portable device is selected from the group consisting of smart phones and tablet computers.

6 . The method of claim 1 , wherein:

the human-drawn image comprises a symbolic drawing of an object;

the AI program characterizes the symbolic drawing by outputting a word describing the object.

7 . The method of claim 1 , wherein:

the human-drawn image comprises text;

the AI program characterizes the symbolic drawing by outputting a word describing the text.

8 . The method of claim 1 , wherein:

causing the AI program to characterize the human-drawn image includes

supplying the AI with 1) the human-drawn image, and 2) the numerical value of the order parameter extracted from the modified image formed from the human-drawn image.

9 . The method of claim 1 , wherein:

the computer comprises a draw-to-speech device;

the human-drawn image represents at least one of numbers, letters, words, pictures, or concepts; and including:

causing the draw-to-speech device to generate an audio signal comprising a word corresponding the numbers, letters, words, pictures, or concepts of the human-drawn image.

10 . The method of claim 1 , wherein utilizing a computer to form a modified image from a human-drawn image includes:

utilizing a computer to form a Fourier spectrum by taking a Fourier transform of the human-drawn image that is in the form of digital image data;

utilizing a computer to form an MTF-modified Fourier transform by applying an idealized modulation transfer function (MTF) to the Fourier spectrum, wherein the MTF is constant across all frequencies;

utilizing a computer to form a modified image by taking an inverse Fourier transform of the MTF-modified Fourier transform.

11 . A data processing system for recognizing the content of a human-drawn image comprising means for

forming a modified image from a human-drawn image by replicating a physical image formation process;

extracting a numerical value corresponding to an order parameter squared S 2 from the modified image, wherein the modified image includes light regions and dark regions, and wherein S 2 comprises a numerical value quantifying a degree of order present in the modified image, and wherein the extracted numerical value comprises a ratio of an area of the light regions to a total area that is equal to the sum of: 1) an area of the light regions, and 2) of an area of the dark regions;

using an artificial intelligence (AI) program to characterize the human-drawn image utilizing: 1) the human-drawn image, and 2) the numerical value of the order parameter extracted from the modified image formed from the human-drawn image; and:

outputting at least one of an image and text that identifies the human-drawn image.

12 . The system of claim 11 wherein the system comprises a draw-to-speech device.

13 . The system of claim 11 , wherein said human-drawn image represents at least one of numbers, letters, words, pictures, or concepts; and the draw-to-speech device is capable of generating an audio signal comprising a word corresponding to the numbers, letters, words, pictures, or concepts of the human-drawn image.

14 . The system of claim 11 , wherein the system comprises a portable device having a touch screen; and

the human-drawn image data includes at least one image drawn by a human using the touch screen.

15 . The system of claim 14 , wherein:

the portable device is selected from the group consisting of smart phones and tablet computers.

16 . The system of claim 11 , wherein forming a modified image from a human-drawn image includes:

utilizing a computer to form a Fourier spectrum by taking a Fourier transform of the human-drawn image that is in the form of digital image data;

utilizing a computer to form an MTF-modified Fourier transform by applying an idealized modulation transfer function (MTF) to the Fourier spectrum, wherein the MTF is constant across all frequencies;

utilizing a computer to form a modified image by taking an inverse Fourier transform of the MTF-modified Fourier transform.