Symbolic Mark Decoding with User-Specific Content Personalization
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Solution Overview
Problem
Current systems for decoding information from symbolic marks like QR codes and barcodes provide static data, not tailored to individual users or their contexts, lacking dynamic access to user-specific content.
Innovation Solution
A tool that decodes marks by considering user identity and role, generating personalized content identifiers or URLs based on user authentication, location, and temporal factors, allowing dynamic access to tailored information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static data encoding in symbolic marks is used, then the marking process is simple and fast, but the content cannot be personalized or tailored to specific users
Solution Approach 1:
The system segments the decoding process into multiple components: mark scanning, user authentication verification, role determination, and dynamic content generation. Each component handles a specific aspect of the personalization process, making the overall complex system manageable and modular.
Solution Approach 2:
The system introduces an intermediary processing layer between the static mark and the user. This intermediary component receives the scanned mark data, correlates it with user authentication information, determines user roles, and generates personalized content identifiers, thereby enabling personalization without modifying the original simple mark structure.
2Adaptability or versatility
If user authentication and role determination are added to mark decoding, then personalized content access is enabled, but the decoding process time increases
Solution Approach 1:
User authentication information and role definitions are pre-established and stored in the system before the mark decoding process. When a user scans a mark, the system quickly verifies against pre-configured authentication data and role definitions, rather than performing complex analysis in real-time, thus reducing processing time.
Solution Approach 2:
The system uses pre-defined role templates and content identifier patterns that can be rapidly instantiated. Instead of generating personalized content from scratch, the system copies and adapts pre-established content structures based on the determined user role, significantly accelerating the personalization process.
3Loss of information
If dynamic content identification based on user identity is implemented, then information relevance to users is improved, but the system complexity increases
Solution Approach 1:
The system employs a universal content identifier structure that can serve multiple purposes: it works for both generic and personalized content delivery. The same decoding framework handles both simple mark scanning and complex user-specific content retrieval, reducing the need for separate specialized systems.
Solution Approach 2:
The system changes parameters dynamically during the decoding process - specifically, it modifies the content identifier based on user authentication status and role parameters. The base content identifier remains constant, but user-specific parameters are appended or substituted to create personalized versions, allowing flexible information delivery without restructuring the entire system.
Data Source
AI summary
Apparatuses, systems and methods that operate to customize content or other information indicated by symbolic marks (such as, for example, QR codes and other types of barcodes).