Content Source Ranking for Bandwidth-Aware Playback Quality
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Solution Overview
Problem
Conventional content discovery applications struggle to recommend the highest-quality version of a content item available from multiple OTT providers, as users face uncertainty in selecting among multiple sources due to varying network quality, device capabilities, and service provider experience.
Innovation Solution
A system that determines the quality of content items from multiple sources based on network bandwidth, device resolution, and user experience (QoE) to rank and recommend the highest-quality content item for playback, considering user preferences and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional content discovery applications display all available content items from multiple OTT providers, then users have access to complete information about content availability, but users face uncertainty and difficulty in selecting the highest-quality version
Solution Approach 1:
The patent introduces a recommendation system that acts as an intermediary between multiple OTT providers and the user. This system aggregates content availability information from multiple sources, evaluates quality metrics, and presents a single recommended content item, thereby mediating the complexity of multi-source selection for the user.
Solution Approach 2:
The system automatically performs quality assessment and recommendation generation without requiring user intervention. By implementing self-service mechanisms that autonomously evaluate content quality across multiple OTT providers and select the best option, the system reduces user burden while maintaining information completeness.
2Manufacturing precision
If the system recommends the highest-quality content item, then user satisfaction with content quality improves, but the system complexity increases due to multiple evaluation factors
Solution Approach 1:
The patent segments the content quality assessment into distinct evaluation dimensions including video quality, audio quality, network bandwidth, device capabilities, and user preferences. By dividing the complex assessment task into separable components, the system can process each factor independently and combine results to generate comprehensive recommendations.
Solution Approach 2:
The system dynamically adjusts evaluation parameters based on user device characteristics, network conditions, and personal preferences. By changing parameters such as weightings for different quality factors based on real-time conditions, the system achieves precise content quality assessment without requiring a monolithic complex architecture.
3Measurement precision
If the system considers multiple factors including network bandwidth, device resolution, and user preferences, then recommendation accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing user preference profiles, device capability information, and content metadata before actual recommendation requests. By preparing evaluation data in advance and maintaining updated user profiles, the system reduces real-time processing requirements when generating content recommendations.
Solution Approach 2:
The system incorporates feedback mechanisms that learn from user interactions and preference data to refine recommendations. By using feedback from user selections and explicit preferences to adjust future recommendations, the system improves accuracy over time while reducing the computational burden of evaluating all factors from scratch for each recommendation request.
Data Source
AI summary
Systems and methods are described for identifying a list of sources available for a content item. An input associated with selecting a content item is received at a computing device. The availability of the content item from more than one source is identified. The available bandwidth of the computing device is determined. The respective resolutions of the selected content item from each respective source is determined based on metadata. A recommendation factor for each of the respective resolutions from each of the plurality of source is determined. The recommendation factor is based on whether the respective resolution is supported by the available bandwidth. A list including the sources and a respective resolution of the content item is generated for output. The list is ordered based on the recommendation factor for each respective source. Each available resolution is formatted base on the associated recommendation factor being above or below threshold level.