Knowledge Graph Registration for Multiplatform Royalty Collection
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Solution Overview
Problem
Conventional content attribution monitoring systems require manual, specialized knowledge-intensive interactions for content creators to register and manage resource transfers across multiple platforms, leading to inefficiencies, errors, and unmatched royalties.
Innovation Solution
A knowledge graph-based system that generates a content creator profile using generative AI to automate registration with multiple content attribution monitoring systems, utilizing resource transfer templates and machine-learning models to streamline the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual registration processes are used across multiple content attribution monitoring systems, then content creators can register their content, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent combines multiple content attribution monitoring systems into a single unified platform that processes registrations once and distributes information across all connected systems. This eliminates the need for creators to manually register with each system separately, reducing both time consumption and errors while maintaining reliable registration across all platforms.
Solution Approach 2:
The unified registration system performs multiple functions simultaneously: it registers content with the primary system, translates the registration data, and propagates it to multiple other content attribution monitoring systems. This multi-functional approach resolves the contradiction by handling all registration tasks in a single operation rather than requiring separate manual processes for each system.
2Reliability
If specialized knowledge is required for each content attribution monitoring system, then accurate registration can be achieved, but the complexity of the process increases
Solution Approach 1:
The patent introduces an intermediary unified registration system that acts as a mediator between the content creator and multiple content attribution monitoring systems. This intermediary handles the complexity of different system requirements, translation protocols, and registration formats internally, while presenting a simple, standardized interface to creators. This resolves the contradiction by hiding system complexity while maintaining accurate registration across all platforms.
Solution Approach 2:
The system segments the registration process into distinct modular components: data collection, translation/adaptation, and distribution to specific systems. Each component handles specific system requirements independently, allowing the system to manage complexity through modular architecture while maintaining reliable registration across multiple platforms with different specifications.
3Adaptability or versatility
If data is duplicated across multiple content attribution monitoring systems, then comprehensive coverage is achieved, but computational resources are wasted
Solution Approach 1:
The unified system performs preliminary action by registering content once in a central location before distribution to other systems. This pre-registration step creates a master record that can be efficiently replicated and translated, reducing the need for redundant data processing and storage across multiple systems while maintaining comprehensive platform coverage.
Solution Approach 2:
Instead of maintaining fully independent duplicate systems, the patent uses efficient copying mechanisms where a master registration record is replicated and adapted for distribution to other platforms. This copying approach achieves comprehensive coverage across multiple systems while minimizing computational waste by avoiding redundant data entry and processing at each system level.
Data Source
AI summary
Digital service license control techniques are described. In one or more examples, registration signup data is obtained from a plurality of content attribution monitoring systems and a plurality of resource transfer templates are generated for the plurality of content attribution monitoring systems. A content creator profile formed as a knowledge graph is generated as having a plurality of nodes that include the content creator identifier and a composition identifier associated with a composition of content associated with the content creator. A single input is received via a user interface to initiate registration with the plurality of content attribution monitoring systems for resource transfer collection. Resource transfer data is generated for each of the plurality of content attribution monitoring systems, automatically and without user intervention, by mapping the knowledge graph of the content creator profile using respective resource transfer templates of the plurality of resource transfer templates.