Remote Control Signal Learning for Faster IPTV Service Access
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Solution Overview
Problem
Existing IPTV services lack intuitive and fast access methods due to reliance on complex service middleware, making independent implementation by product manufacturers difficult.
Innovation Solution
A control signal learning-based system that records and analyzes remote control button clicks to predict frequently used services, automating the generation and processing of sequential control signals for streamlined service access using AI-based learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex service middleware is used to provide IPTV services, then service functionality is improved, but service access complexity increases and ease of operation deteriorates
Solution Approach 1:
The system automatically learns and records control signal sequences from user operations without requiring manual configuration. The terminal autonomously builds a database of shortest path control sequences by observing user behavior patterns, enabling self-improving service access that adapts to individual usage habits while maintaining full service functionality
Solution Approach 2:
The system pre-records and stores control signal sequences during initial system operation and continues to accumulate data during normal use. This preliminary action builds a library of optimized control paths in advance, so that when users need to access services, the system can immediately retrieve and execute the pre-learned shortest paths without requiring real-time analysis
2Measurement precision
If manual navigation through complex menus is required, then service access precision is maintained, but access time increases and productivity deteriorates
Solution Approach 1:
The system creates copies of control signal sequences from observed user operations and stores them in a database. Instead of requiring users to manually navigate through menus each time, the system replicates and reuses the learned control signal patterns, maintaining accurate service access while dramatically reducing the time required for repeated operations
Solution Approach 2:
The system continuously monitors and records actual user control operations, using this feedback to refine and update its database of control signal sequences. This feedback mechanism ensures that the system learns the most efficient paths for each user's specific needs, improving both precision and speed over time as usage patterns are better understood
3Productivity
If AI-based learning is implemented to predict service access patterns, then service access efficiency is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex AI-based machine learning algorithms with a simpler pattern recognition approach that analyzes recorded control signal sequences. Instead of using sophisticated neural networks or deep learning models, the system uses rule-based analysis of stored operation data to predict and retrieve optimal control paths, achieving improved efficiency while maintaining manageable system complexity
Solution Approach 2:
The system introduces a control signal sequence database as an intermediary layer between the user interface and the service execution system. This database acts as a buffer that stores pre-analyzed control patterns, allowing the system to quickly retrieve optimized paths without requiring complex real-time decision-making algorithms, thus improving efficiency while keeping the overall architecture relatively simple
Data Source
AI summary
Disclosed is a control signal learning-based shortened service access method and system. A control signal learning-based shortened service access system proposed herein records the control signals generated through RCU (Remote Control Unit) button clicks, and through AI-based learning or configuration of the recorded control signals, can predict frequently used services.