Dynamic Favorite Channel Sorting via Content Attribute Monitoring
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Solution Overview
Problem
Existing systems for navigating favorite channels are inefficient because they present static lists that do not consider the user's current content interest, leading to wasted time searching for relevant channels.
Innovation Solution
A media guidance application that monitors the content being accessed by the user and sorts favorite content sources based on the current and previously accessed content attributes, ensuring that channels with content of interest are prioritized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a static list of favorite channels is used, then the system structure is simple and easy to implement, but the user has to spend more time searching for relevant channels and the system efficiency decreases
Solution Approach 1:
The patent implements dynamic sorting of favorite channels based on real-time content attributes and user viewing history. The channel list automatically reorders itself according to relevance metrics such as current program content, genre matching, and user preferences, transforming a static structure into an adaptive dynamic system that improves search efficiency without requiring complex manual reconfiguration
Solution Approach 2:
The system performs automatic channel reordering based on content analysis and user behavior patterns without requiring explicit user commands. The media guidance application continuously monitors viewing habits and programmatically adjusts the favorite channels list, allowing the system to serve itself in optimizing the user experience rather than requiring constant user intervention
2Loss of time
If the favorite channels list is reordered dynamically based on content attributes, then the user finds relevant channels faster, but the system becomes more complex and requires more processing
Solution Approach 1:
The system pre-calculates and stores content attributes for multiple channels in advance, including genre classifications, program schedules, and metadata. When a user requests favorite channels, the system quickly retrieves and sorts based on pre-analyzed data rather than performing complex real-time analysis, significantly reducing the time to present relevant channels while managing computational complexity through advance preparation
Solution Approach 2:
The system continuously monitors user channel selection behavior and uses this feedback to refine the sorting algorithm. By tracking which channels users select and spend time watching, the system adjusts the weighting and relevance calculations in the sorting mechanism, making the system progressively more efficient at predicting user preferences and reducing search time while the complexity is amortized over time
Data Source
AI summary
Systems and methods for sorting favorite content sources are provided. A list of favorite content sources arranged in a first order is stored. The favorite content sources in the list are accessed according to the order of the favorite content sources in the list. At a given time, a user request to change a currently accessed content source to one of the favorite content sources in the list is received. In response to receiving the user request, an attribute of content presented on the currently accessed content source at the given time is identified. The favorite content sources in the list are rearranged into a second order based on the identified attribute. The favorite content sources in the list are accessed according to the second order in response to receiving the user request.