Predictive Channel Navigation and Tuner Allocation
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Solution Overview
Problem
The increasing number of available channels and programs in content receivers makes it burdensome for users to navigate and find desired content, discouraging them from exploring other channels due to the cumbersome browsing process.
Innovation Solution
A computer-implemented method for navigating channels in a content receiver that assigns channel numbers and categories, allowing users to select next or previous channels/categories, predicts channel changes based on user behavior, and optimizes tuner allocation for efficient channel switching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional browsing methods are used to navigate through channels, then users can access channel information, but the navigation process becomes burdensome and time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-tuning tuners to predicted channels based on user behavior patterns before the user actually requests them. The predictive tuning mechanism analyzes historical channel selection data and anticipates future channel changes, preparing the receiving apparatus in advance so that when the user requests a channel, it is already available, thereby eliminating browsing delays.
Solution Approach 2:
The system implements self-service by automatically analyzing user behavior patterns and performing predictive tuning without requiring explicit user commands for each channel change. The apparatus monitors channel selection history, identifies patterns, and autonomously prepares tuners for anticipated channel changes, making the navigation process seamless and reducing the perceptible time users spend browsing.
2Speed
If predictive tuning is implemented to speed up channel switching, then channel access becomes faster, but tuner allocation complexity increases
Solution Approach 1:
The system employs feedback mechanisms by continuously monitoring actual user channel selection behavior and using this information to refine predictive tuning algorithms. The apparatus compares predicted channel selections with actual user choices, adjusts behavioral patterns accordingly, and optimizes tuner allocation dynamically. This feedback loop enables the system to improve channel switching speed while managing tuner allocation complexity through data-driven adjustments rather than complex static rules.
3Speed
If multiple tuners are allocated for predictive tuning, then channel access speed improves, but the number of required tuners increases
Solution Approach 1:
The system applies partial action by allocating tuners selectively based on predicted channel change probability rather than maintaining all possible tuners ready at all times. The predictive algorithm identifies high-probability channel transitions and allocates tuners only for those specific predictions, using partial tuner allocation to achieve speed improvements without requiring a full complement of tuners to be actively managed, thus reducing the effective number of tuners needed.
Data Source
AI summary
A computer-implemented method for navigating through channels in a content receiver, wherein the channels have assigned channel numbers and categories, the method comprising: providing a list of categories; providing an ordered list of channels for each category; upon receiving a channel zapping command (SIMILAR+, SIMILAR−), selecting a next channel or a previous channel from the list of channels for the current category; upon receiving a category zapping command (NEXT, PREV), selecting a last watched channel in a next category or a previous category from the list of categories and changing the current category to the next category or the previous category; storing the channel number of the selected channel as the last watched channel on the current channel category; and tuning the content receiver to the selected channel. A suitable predictive channels assignment to respective tuners is also disclosed.