Machine-Readable Image Encoding Data with Embedded Graphics
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Solution Overview
Problem
Current machine-readable codes, such as QR Codes, lack clear identification of the proprietor, making it difficult to visually distinguish between different proprietors in marketing and advertising applications.
Innovation Solution
A computerized method and system for generating machine-readable images that embed data encoding with graphics, using function patterns, image descriptors, and dot modules positioned relative to the graphic, allowing for the inclusion of error correction and transparency values to create readable images from different viewpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine-readable code is designed to be abstract and compact, then machine readability is improved, but visual identifiability of the proprietor deteriorates
Solution Approach 1:
The code structure is divided into distinct segments: functional patterns (finder patterns, alignment patterns, timing patterns) that ensure machine readability, and a logo mark segment that provides proprietor identification. This segmentation allows each part to fulfill its specific function without interfering with the other.
Solution Approach 2:
Different regions of the image are assigned different qualities: the functional patterns use high-contrast black and white squares for optimal machine detection, while the logo mark region uses the proprietor's branded imagery for visual identification. Each region is optimized for its specific purpose.
2Loss of information
If a logo mark is superimposed on the machine-readable code, then proprietor identification is improved, but code reading accuracy may deteriorate
Solution Approach 1:
The logo mark is extracted and placed in a dedicated region that does not overlap with the functional patterns and data cells. This separation ensures that the logo does not interfere with the machine reading process while still providing proprietor identification.
Solution Approach 2:
Error correction capabilities are built into the code structure beforehand, creating a buffer that can accommodate potential reading errors. This ensures that even if some parts of the code are obscured or damaged, the overall reading accuracy is maintained.
3Difficulty of detecting and measuring
If the code uses high contrast black and white squares, then machine detection is improved, but aesthetic appeal and brand recognition deteriorate
Solution Approach 1:
The functional code elements and the brand logo are merged into a single integrated image. The logo mark is positioned within the overall code structure, creating a unified visual element that serves both machine detection and brand recognition functions simultaneously.
Solution Approach 2:
The final image serves multiple functions: the functional patterns enable machine detection and reading, while the integrated logo mark provides brand recognition and aesthetic appeal. This multi-functionality resolves the contradiction between technical performance and visual appeal.
Data Source
AI summary
There is provided a non-transitory computer readable storage medium tangibly embodying a machine-readable image having data encoded therein and embedded with a graphic, the machine-readable image adapted to be detected by a reader for decoding said encoded data, including: the graphic associated with an image descriptor calculated based on a chosen area of the graphic, the image descriptor being used in a reading process of said machine-readable image; a plurality of function patterns; and a plurality of dot modules having decoded values corresponding to at least said encoded data, the dot modules being positioned in one or more encoding regions of the machine-readable image relative to the function patterns and the chosen area of the graphic.