Media Storage Selection via Trick-Play Likelihood Analysis
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Solution Overview
Problem
Modern media delivery systems lack an intelligent method to select the optimal storage location for media assets, often resulting in inefficiencies such as delayed access to trick-play features when data is stored remotely and unnecessary consumption of cloud storage when stored locally.
Innovation Solution
A media guidance application that detects the likelihood of trick-play requests by analyzing user behavior and metadata, storing media assets locally when trick-play is likely and remotely when it is not, using a combination of connection type, scene content analysis, and metadata matching to determine storage location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If media assets are stored in cloud storage, then storage capacity is increased and accessibility from multiple devices is improved, but access speed decreases and latency increases for trick-play functions
Solution Approach 1:
The storage system is segmented into two types: local storage for high-speed access and cloud storage for accessibility. The system determines which storage location to use based on the specific needs of the media asset and user scenario, effectively dividing the storage function into two specialized components.
Solution Approach 2:
The system dynamically selects the storage location based on real-time analysis of media content characteristics and user behavior patterns. The storage decision is not fixed but adapts to the specific media asset and user context, switching between local and cloud storage as needed.
2Speed
If media assets are stored locally, then access speed is improved and latency is reduced, but storage capacity is limited and cloud storage space is wasted
Solution Approach 1:
The system applies different storage strategies to different media assets based on their characteristics. Media with high trick-play likelihood is stored locally with high-speed access, while media with low trick-play likelihood is stored in cloud storage with unlimited capacity, creating a differentiated storage quality system.
Solution Approach 2:
The system changes the storage parameter (local vs. cloud) based on the calculated trick-play likelihood parameter. By analyzing media content and user behavior patterns, the system adjusts the storage location parameter to optimize the balance between access speed and storage capacity utilization.
3Quantity of substance
If media assets are stored in cloud storage, then storage capacity is increased, but unnecessary consumption of cloud storage occurs when media could have been stored locally
Solution Approach 1:
The system uses feedback from analyzing user behavior patterns and media content characteristics to make intelligent storage decisions. By continuously monitoring and learning from user interactions, the system improves its accuracy in predicting trick-play likelihood and optimizing storage location selection.
Solution Approach 2:
The system automatically determines the optimal storage location without requiring manual user input or configuration. The media guidance application autonomously analyzes media assets and user behavior patterns to make storage decisions, reducing the need for user intervention and optimizing storage efficiency automatically.
Data Source
AI summary
Systems and methods are described for selecting a storage location for a media asset. A determination is made as to whether a user device is accessing the media asset outside of the user's home location (e.g., over a wireless connection). When the user device is accessing the media asset outside of the user's home location, the following steps are performed. A determination is made whether the media asset features a set of scenes that increases a likelihood of using trick-play functions. If such scenes are present, the media asset is stored in a local storage of the user device. If such scenes are absent, the media asset is stored in a network storage.