Machine-readable label generator
Software tools disclosed herein may allow a user to enter design choices that alter an aesthetic appearance of a machine-readable label such that modules included in the label deviate from a standardized definition for the modules. Such alterations may include changes in size, color and orientation of modules. The alterations may allow a user to create machine-readable labels having unique aesthetic appearances. A software engine may ensure that, despite the aesthetic design choices entered by a user, the generated machine-readable label is reliably scannable.
1. An artificial intelligence (“AI”) method for generating a quick-response (“QR”) code, the method comprising:
receiving an image;
identifying a color in the image;
generating a quick response (“QR”) code based on the color; and
computing how to apply the color to a module of the QR code such that the QR code is associated with a threshold level of error correction,
wherein generating the QR code comprises generating a data zone that:
triggers a target action when scanned; and
is scannable within a target time window.
2. The AI method of claim 1 wherein generating the QR code further comprises computationally altering the color.
3. The AI method of claim 2 wherein computationally altering the color comprises changing hue, tint, shade, tone, saturation, lightness, chromaticity or intensity associated with the color.
4. The AI method of claim 1 further comprising:
applying computational aesthetic techniques to define an aesthetic appearance associated with the image; and
generating the QR code based on the aesthetic appearance.
5. The AI method of claim 4 wherein the aesthetic appearance includes a modification to the color.
6. The AI method of claim 4 wherein generating the QR code based on the aesthetic appearance comprises computing a color for each module in the QR code such that the QR code conforms to the aesthetic appearance.
7. The AI method of claim 1 wherein generating the QR code comprises computing a shape and size of each module within the QR code.
8. An artificial intelligence (“AI”) method for generating a quick-response (“QR”) code, the method comprising:
receiving an image;
identifying a color in the image;
generating a quick response (“QR”) code based on the color,
wherein generating the QR code comprises generating an environmental zone based on the color.
9. An artificial intelligence (“AI”) method for generating a quick-response (“QR”) code, the method comprising:
receiving an image;
identifying a color in the image;
generating a quick response (“QR”) code based on the color, wherein generating the QR code further comprises applying a mask pattern that ensures the QR code does not include a threshold number of adjacent modules having the color.
10. A software platform for generating a quick-response (“QR”) code, the software platform comprising:
a user interface programmed to receive an image; and
an artificial intelligence (“AI”) software engine that generates a machine-readable label based on the image, the machine-readable label comprising a data zone that includes error correction code capable of restoring a threshold percentage of instructions encoded in the data zone,
wherein the AI software engine generates the machine-readable label that is scannable within a threshold time limit and the error correction code is capable of restoring at least 15% of instructions encoded in the data zone.
11. The software platform of claim 10 wherein the AI software engine incorporates a color in the image into the machine-readable label.
12. The software platform of claim 10 wherein the AI software engine generates the machine-readable label by:
applying a machine-generated change to a color included in the image; and
incorporating the machine-generated change into the machine-readable label.
13. The software platform of claim 10 wherein the AI software engine computes a color for each module in the machine-readable label.
14. The software platform of claim 10 wherein the AI software engine:
alters one or more of: hue, tint, shade, tone, saturation, lightness, chromaticity and intensity of a color included in the image; and
includes alterations to the color in the machine-readable label.
15. The software platform of claim 10 wherein the AI software engine computes a color for each module in the machine-readable label by computing how the color impacts human aesthetic judgment.
16. A software platform for generating a quick-response (“QR”) code, the software platform comprising:
a user interface programmed to receive an image; and
an artificial intelligence (“AI”) software engine that generates a machine-readable label based on the image, the machine-readable label comprising a data zone that includes error correction code capable of restoring a threshold percentage of instructions encoded in the data zone, wherein the AI software engine:
generates an environmental zone for the machine-readable label; and
to a human eye, the environmental zone appears to be a contiguous extension of the data zone.
17. A software platform for generating a quick-response (“QR”) code, the software platform comprising:
a user interface programmed to receive an image; and
an artificial intelligence (“AI”) software engine that generates a machine-readable label based on the image, the machine-readable label comprising a data zone that includes error correction code capable of restoring a threshold percentage of instructions encoded in the data zone, wherein:
the data zone comprises a timing pattern and an alignment pattern; and
the instructions encoded in the data zone, when scanned, trigger a target action.
18. An artificial intelligence (“AI”) method for generating a quick-response (“QR”) code, the method comprising:
receiving user-input design choices;
computationally generating an aesthetic appearance that includes a change to at least one of the user-input design choices; and
generating a quick response (“QR”) code based on the aesthetic appearance, wherein the QR code includes a data zone having error correction code that is capable of restoring at least 15% of instructions encoded in the data zone.
19. The AI method of claim 18 wherein computationally generating the aesthetic appearance comprises altering a color included in the user-input design choices.
20. The AI method of claim 18 wherein generating the QR code based on the aesthetic appearance comprises determining a color of each module in the QR code such that the QR code conforms to the aesthetic appearance.
21. The AI method of claim 18 wherein:
computationally generating the aesthetic appearance comprises defining how the user-input design choices impact human aesthetic judgment; and
generating the QR code based on how the user-input design choices impact human aesthetic judgment.
22. The AI method of claim 18 wherein the user-input design choices comprise an image.
23. The AI method of claim 18 wherein computationally generating the aesthetic appearance comprises modifying the user-input design choices such that readability of the QR code by a scanning device is enhanced over applying the user-input design choices without the change included in the aesthetic appearance.
24. The AI method of claim 18 wherein generating the QR code based on the aesthetic appearance comprises generating an environmental zone for the QR code and a data zone of the QR code based on the aesthetic appearance.
25. The AI method of claim 18 wherein generating the aesthetic appearance comprises changing hue, tint, shade, tone, saturation, lightness, chromaticity or intensity associated with a color included in the user-input design choices.
26. The AI method of claim 24 wherein the data zone comprises a timing pattern, an alignment pattern and encoded instructions that, when scanned, trigger a target action.