Video Source Scanning Priority Based on Usage Statistics
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Solution Overview
Problem
The increasing number of A/V peripheral devices connected to TVs leads to inefficient manual scanning for video signals, wasting time and causing inconvenience as users must search through each device to find an active video source.
Innovation Solution
A scanning method that groups video sources and prioritizes them based on viewing time or frame number statistics, allowing the display device to automatically determine which source is providing an input signal by scanning the highest-priority groups first.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual scanning of each video source is performed to determine active video signals, then complete detection of all video sources is achieved, but user time is wasted and operation convenience is reduced
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing statistics (viewing time, frame numbers) for each video source group before scanning is needed. When scanning is required, the system uses these pre-computed statistics to determine scanning priority, avoiding the need to manually scan each source sequentially and thus reducing user waiting time while maintaining detection completeness.
Solution Approach 2:
The system changes the parameter of scanning priority by using statistics (viewing time, frame numbers) to dynamically assign different priority levels to different video source groups. This allows the system to intelligently determine which sources to scan first based on historical usage patterns, reducing the time needed to detect active video signals while maintaining reliable detection.
2Reliability
If manual scanning of each video source is performed to determine active video signals, then complete detection of all video sources is achieved, but operation convenience is reduced
Solution Approach 1:
The system performs self-service by automatically scanning video sources according to statistically-determined priorities without requiring user intervention. The system uses pre-calculated statistics to autonomously determine which video source groups to scan first, eliminating the need for users to manually operate the controller to search for video signals, thus improving operation convenience while maintaining detection completeness.
Solution Approach 2:
The system uses feedback from historical usage statistics (viewing time, frame numbers) to dynamically adjust scanning priorities. This feedback mechanism allows the system to learn from past user behavior and automatically optimize the scanning sequence, making the operation more convenient while ensuring reliable detection of active video sources.
3Productivity
If video sources are grouped and scanned according to viewing time statistics priority, then scanning efficiency is improved and user time is saved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing video sources into different groups and assigning scanning priorities to each group based on viewing time statistics. This segmentation allows the system to scan high-priority groups first, improving scanning efficiency. The complexity is managed by organizing sources into structured groups with predefined priority levels.
Solution Approach 2:
The system changes the parameter of scanning priority by using viewing time statistics to dynamically assign different priority levels to video source groups. This parameter change enables intelligent scanning that improves efficiency while the complexity is contained through systematic parameter management rather than complex operational procedures.
4Productivity
If video sources are grouped and scanned according to frame number statistics priority, then scanning efficiency is improved and user time is saved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing video sources into different groups and assigning scanning priorities to each group based on frame number statistics. This segmentation allows the system to scan high-priority groups first, improving scanning efficiency. The complexity is managed by organizing sources into structured groups with predefined priority levels.
Solution Approach 2:
The system changes the parameter of scanning priority by using frame number statistics to dynamically assign different priority levels to video source groups. This parameter change enables intelligent scanning that improves efficiency while the complexity is contained through systematic parameter management rather than complex operational procedures.
Data Source
AI summary
The present invention suggests a simplified and robust scanning method as a scenario for scanning video sources of a display device such as a TV or projector. The inventive method is attained by way of grouping the video sources and prioritizing the scanning priority for each video source group in terms of the viewing time statistic or the frame number statistic of each video source group. Therefore, the display device is capable of scanning each video source group based on the scanning priority to discover if there is a video signal inputted into the video source.