Rules Engine for Automated Rights Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for determining reuse rights for content with multiple licenses and subscriptions are time-consuming and expensive, often requiring human intervention to navigate complex and conflicting license agreements, especially for unusual or extensive use requests.
Innovation Solution
A rules engine driven by stored rules automates the determination of whether a request for a right can be granted, calculates the required price, and determines royalty payments, using a standard set of reusable modules that can be linked differently to process various rights, and a graphical user interface to prompt users for necessary information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a rules engine with reusable modules is implemented, then processing efficiency and automation are improved, but system complexity increases
Solution Approach 1:
The rights management system is divided into modular rule components that can be independently configured and executed. Each rule handles a specific aspect of rights determination, allowing the system to process complex license agreements through standardized, reusable modules rather than custom-coded solutions for each scenario.
Solution Approach 2:
The rules engine implements universal processing capabilities that can handle multiple types of license agreements, subscription models, and rights scenarios through a single standardized system. The reusable rule modules can be configured to address diverse rights management needs without requiring system redesign or custom development for each case.
2Measurement precision
If human operators review requests manually, then accuracy in handling complex cases is improved, but processing time and cost increase
Solution Approach 1:
The rules engine performs self-service by automatically evaluating license agreements and determining rights without requiring human operator intervention. The system independently parses license terms, applies relevant rules, and generates determinations for standard cases, freeing human operators to focus only on exceptional or ambiguous scenarios that require expert judgment.
Solution Approach 2:
The system incorporates feedback mechanisms where determination results are continuously evaluated and used to refine rule configurations. This allows the automated system to improve its accuracy over time by learning from edge cases and human operator corrections, creating a self-improving cycle that maintains high precision while preserving automation benefits.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
A rules engine program is driven by one or more stored "rules" to make rights determinations normally made by a human operator; that is whether a request for a right can be granted, what price should be paid by the requestor and how much royalty should be paid to the rightsholder. In order to avoid specific coding for each right, the rules and parameters necessary to make the required determinations are constructed to form a standard set of reusable rule records that can be linked in different ways to process different rights. Additional display rules that are linked to the type of use control a graphic user interface in order to prompt the user for information needed to decide whether the request can be granted and to display to the user a determined price if the request is granted.