DRM System with Temporal Access Restrictions
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Solution Overview
Problem
Current multimedia subscription models force customers into an all-or-nothing subscription structure, where they are provided access to resources they do not consume, due to the binary nature of digital rights management systems, which fail to account for varying customer behaviors and preferences.
Innovation Solution
Implementing a DRM system that uses machine-learning algorithms to intelligently restrict and provide access to multimedia content based on user profiles and preferences, allowing for temporal restrictions and offering options to upgrade subscriptions or pay for one-time access to restricted content, while utilizing efficient data structures to manage different subscription tiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a binary subscription structure is used for multimedia access, then digital rights management is easier to implement, but customers are forced to subscribe to content they do not consume
Solution Approach 1:
The patent segments the binary subscription model into multiple access levels: full subscription, temporal restrictions, and geographic restrictions. This allows the DRM system to maintain ease of implementation while enabling customers to subscribe only to the level of access they need, reducing resource consumption for both providers and consumers.
Solution Approach 2:
The patent introduces dynamic restriction parameters that can be adjusted based on customer behavior and preferences. The DRM system can modify temporal and geographic restrictions in real-time, allowing customers to expand or contract their access rights as needed, rather than being locked into a static binary subscription.
2Adaptability or versatility
If full access to all programming is provided, then customers have access to relevant content, but resources are wasted on non-consumed content
Solution Approach 1:
The patent applies local quality by allowing different access rights for different customers based on their specific needs and behaviors. Instead of providing uniform full access to all customers, the system tailors access rights locally to each customer's consumption patterns, ensuring relevant content is accessible while avoiding waste on non-consumed content.
Solution Approach 2:
The patent changes the parameters of access rights from a binary state (full access or no access) to a multi-dimensional state involving temporal restrictions, geographic restrictions, and content-specific restrictions. This allows the system to optimize resource allocation by adjusting access parameters to match actual customer consumption patterns.
3Productivity
If temporal restrictions are implemented in DRM system, then access is optimized for customer behavior, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-defining temporal and geographic restriction templates that can be applied to customers based on their profiles. Rather than creating complex custom restrictions for each customer, the system uses pre-configured templates that simplify the DRM system complexity while still enabling access optimization based on customer behavior.
Solution Approach 2:
The patent enables self-service by allowing customers to view their restriction parameters and request modifications through automated processes. The DRM system can automatically adjust restrictions based on customer consumption patterns without requiring manual intervention, reducing system complexity while maintaining access optimization.
Data Source
AI summary
The present disclosure is directed to systems and methods for permitting and restricting access to multimedia items based on temporal restrictions associated with certain subscription levels. The subscriptions may be subscriptions to broadcast/satellite television programming and/or Internet-streaming services. The temporal limitations on the subscription may be based on days of the week, time of day, and/or particular types of content that air at certain times. For example, one subscription type may be a weekend-only subscription that permits a user to access certain multimedia items on the weekend but restricts access to those same multimedia items during the week. A machine-learning model that is trained on a user's past viewing history is also disclosed. The system may rely on the machine-learning model to present certain relevant restricted multimedia items to the user that the user can access if the user, e.g., upgrades a subscription and/or remits a one-time fee.