Adaptive On-Screen Guides Using Behavior-Based Channel Sequences
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Solution Overview
Problem
Conventional abbreviated on-screen content guides provide limited navigation and require manual creation and management of favorite channels lists, failing to adapt to user preferences and promote discovery of new channels or programs of interest.
Innovation Solution
Systems and methods that predict channel targets based on past user behavior and viewing activities, generating adaptive channel families using transition matrices and clustering coefficients, and incorporating collaborative filtering to suggest relevant channels and content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a full-screen content guide is used to provide detailed information about channel lineup, then information completeness is improved, but display area occupation increases and viewing experience is interrupted
Solution Approach 1:
The guide information is segmented into multiple pages, with each page displaying a subset of channels (e.g., 5-10 channels) instead of all channels at once. This allows comprehensive guide information to be provided while limiting display area occupation to a manageable portion of the screen at any given time.
Solution Approach 2:
The patent introduces temporal navigation through predicted channel sequences, adding a time-based dimension to channel browsing. Instead of spatial scrolling through all channels, users navigate through time-ordered sequences of predicted channels, reducing the need for extensive spatial display area.
2Area of stationary object
If conventional abbreviated guides are used to minimize display area occupation, then viewing experience is preserved, but navigation capability deteriorates
Solution Approach 1:
The abbreviated guide dynamically adapts its content based on user behavior. The displayed channels are not static but are continuously updated based on predicted channel targets, making the navigation experience more efficient without requiring larger display area.
Solution Approach 2:
The system automatically predicts and presents relevant channel sequences based on user viewing history and behavior patterns, eliminating the need for users to manually scroll through entire channel lineups. The guide serves itself by intelligently selecting what to display.
3Productivity
If manual favorite channels lists are created to improve navigation efficiency, then channel access is sped up, but user intervention and system complexity increase
Solution Approach 1:
The system automatically generates predicted channel sequences based on analysis of user viewing history and behavior patterns, eliminating the need for users to manually create and maintain favorite channels lists. The system serves itself by automatically learning and adapting to user preferences.
Solution Approach 2:
The system continuously learns from user channel selection behavior and adjusts predicted sequences accordingly. This feedback loop enables the system to improve channel access speed over time without requiring manual updates or complex user configuration.
4Device complexity
If conventional abbreviated guides display only current and adjacent channels, then display simplicity is maintained, but channel discovery capability is limited
Solution Approach 1:
The guide content dynamically adapts to user needs by predicting relevant channel sequences. This allows the system to maintain display simplicity while presenting channels that are relevant to user interests, effectively enhancing discovery capability without increasing perceived complexity.
Solution Approach 2:
The system changes the parameter of channel selection from static (fixed adjacent channels) to dynamic (behavior-based predicted channels). This allows the guide to maintain simplicity in presentation while adapting the content to enhance channel discovery based on user behavior patterns.
Data Source
AI summary
Systems and methods for generating a channel sequence for display via an abbreviated on-screen guide are disclosed herein. Channel tuning commands are entered via a user interface of a computing device. Channel tuning data, which describes channel transitions caused by the channel tuning commands, is stored in a buffer. Based on the channel tuning data, a channel family comprising a plurality of channels is generated. A determination is made as to whether a currently tuned channel is included in the channel family. In response to determining that the currently tuned channel is included in the channel family, an on-screen guide, which comprises an abbreviated channel listing of the plurality of channels of the channel family, is generated for display.


