Pseudorandom Dot Codes for Low-Storage Product Authentication
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Solution Overview
Problem
Existing methods for product authentication are inefficient and costly, particularly for large-scale production, and counterfeit detection systems are vulnerable to sophisticated counterfeiting techniques, often requiring significant storage and computational resources.
Innovation Solution
The use of pseudorandom batch codes printed via two-dimensional (2D) codes using dot codes, generated by a cryptographic algorithm, allows for efficient authentication without the need for extensive storage or database access, leveraging imaging technology to verify product authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing product authentication methods (holographic labels, RFID tags, blockchain tracking) are used, then product authenticity can be verified, but production costs and system complexity increase significantly
Solution Approach 1:
The patent uses disposable, low-cost printed codes (barcodes, QR codes, data matrices) instead of expensive reusable authentication technologies like RFID tags or holographic labels. These codes are printed directly on products using existing printing infrastructure, making authentication reliable while keeping production costs and system complexity low.
Solution Approach 2:
The patent creates digital copies of product information in the form of machine-readable codes that can be scanned and verified. Instead of using physical authentication elements, the system uses copied data representations (codes containing product identifiers) that can be easily reproduced through printing while maintaining authentication reliability.
2Measurement precision
If sophisticated counterfeit detection systems with extensive databases are implemented, then counterfeit detection accuracy improves, but storage and computational resource requirements increase
Solution Approach 1:
The patent extracts only the essential authentication information into compact machine-readable codes printed on products. Instead of requiring extensive databases to store and compare product images or detailed specifications, the system extracts key identifiers into codes that can be quickly scanned and verified with minimal storage requirements while maintaining high counterfeit detection accuracy.
Solution Approach 2:
The patent replaces complex mechanical/image-based verification systems with optical scanning of machine-readable codes. Instead of using imaging analysis systems that require significant computational resources to compare product photographs, the system uses optical scanners to read codes containing condensed product information, dramatically reducing storage and computational requirements while improving detection accuracy.
3Reliability
If manual collection and organization of counterfeit images for machine learning training is performed, then authentication system robustness improves, but time and cost expenditures increase prohibitively
Solution Approach 1:
The patent incorporates authentication information directly into machine-readable codes during the product manufacturing process. This preliminary encoding of verification data eliminates the need for later manual collection, organization, and labeling of counterfeit images for machine learning training. The authentication system achieves robustness through the inherent structure of the codes rather than extensive post-production data gathering.
4Adaptability or versatility
If unique per-item codes are printed and stored in large databases, then product tracking capability improves, but database storage capacity and access requirements increase
Solution Approach 1:
The patent changes the form factor and information density of product codes from traditional barcodes to high-capacity 2D codes (QR codes, data matrices). These codes can encode significantly more information in a smaller space, allowing unique per-item tracking while reducing the storage capacity needed in databases. The increased information density of the codes enables comprehensive product tracking with minimal database requirements.
Data Source
AI summary
Systems and methods are disclosed for authenticating products using two-dimensional (2D) codes and dot codes that involves generating an authenticating sequence of pseudorandom numbers via a cryptographic algorithm. The initial seed value corresponds to a specific product within a batch, with each subsequent number generated directly or indirectly from the previous one. A 2D code printing application generates two dot codes based on different sets of pseudorandom numbers in the sequence and prints them onto corresponding products within the batch. The first dot code is printed onto the first product's 2D code, while the second dot code is printed onto the second product's 2D code, allowing each product to have a unique combination of codes that can be verified to confirm its authenticity. The systems and methods use one or more processors and a printer to execute these steps, allowing for efficient and secure authentication of products within a batch.