Context-Aware Serial Identifier Generation for Counterfeit Resistance
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Solution Overview
Problem
Conventional product serialization methods are static and lack context-based approaches, making them vulnerable to counterfeiting as they do not consider product-specific data, leading to potential human errors in generating strong serial identifiers.
Innovation Solution
A computer-implemented method and system that utilizes a machine learning model to generate context-based product serialization identifiers by accessing and processing product context data, such as general product data, supply chain data, and counterfeit data, to recommend a serial identifier that is optimized for the product's specific context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional static serialization assignment methods are used, then the process is simple and fast, but the serial identifiers are vulnerable to counterfeiting and cracking
Solution Approach 1:
The patent implements a dynamic serial recommendation engine that adapts serialization strategies based on real-time context data analysis. The system dynamically adjusts serial identifier generation parameters by evaluating product attributes, supply chain characteristics, and counterfeit risk factors, transforming the static assignment process into a responsive, context-aware system that enhances counterfeit resistance without requiring overly complex infrastructure
Solution Approach 2:
The system changes multiple serialization parameters simultaneously based on context data, including serial identifier length, character set composition, encoding schemes, and generation algorithms. By dynamically adjusting these parameters according to product risk profiles and supply chain characteristics, the system generates optimized serial identifiers that are significantly more resistant to counterfeiting while maintaining manageable system complexity through automated parameter selection
2Reliability
If context data is collected and processed for each product, then stronger and more resilient serial identifiers can be generated, but the data processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary collection and organization of context data during product onboarding and supply chain operations, storing product attributes, manufacturer information, supply chain node data, and historical counterfeit incident records in structured databases. This preliminary data preparation enables the serial recommendation engine to quickly generate strong serial identifiers without requiring intensive real-time data processing, thus reducing data processing overhead while maintaining identifier strength
Solution Approach 2:
The patent replaces manual or rule-based serial identifier generation with an automated machine learning-based recommendation engine. This substitution eliminates the need for human experts to manually analyze context data and determine serialization parameters, automatically processing large volumes of product and supply chain data to generate optimized serial identifiers, thereby reducing data processing overhead through automation while enhancing identifier strength
3Reliability
If a dynamic serial recommendation engine is implemented, then counterfeit resistance is improved, but the computational resources and processing time are increased
Solution Approach 1:
The system implements a tiered approach where the serial recommendation engine processes only the most critical context data parameters necessary for generating secure serial identifiers, rather than analyzing every available data point. By focusing on key factors such as product value, counterfeit risk indicators, and supply chain vulnerability metrics, the system achieves strong counterfeit resistance while maintaining high generation speed, effectively applying partial action to balance security requirements with productivity constraints
Data Source
AI summary
A computer-implemented method of generating a serial identifier for a product is disclosed. The computer-implemented method includes: detecting, at an application platform associated with a computer server, a request to generate the serial identifier for the product; determining, using a processor associated with the computer server, whether context data is available for the product; accessing, responsive to determining that the context data is available for the product, the context data; providing, subsequent to the accessing, the context data for the product to a serial identifier generation component associated with the computer server; receiving, from the serial identifier generation component, an output comprising a recommended serial identifier; and establishing, subsequent to the receiving, the recommended serial identifier received from the serial identifier generation component as the serial identifier for the product. Other aspects are described and claimed.