Contextual Smart Switching via Multi-Modal Learning
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Solution Overview
Problem
Existing multimedia processing systems fail to automatically detect and respond to undesired content, such as advertisements or profane language, in video and audio streams, which can distract users and pose safety risks, especially during tasks like driving, as they require manual intervention that can divert attention and lead to accidents.
Innovation Solution
A contextual stream switching system that utilizes multi-modal machine learning to analyze audio and video streams, identify patterns, and predict undesired content, then autonomously switch to alternative streams or adjust volume based on user preferences and historical data, incorporating geolocation sensors to assess contextual risk factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to detect and respond to undesired content, then user control and flexibility are maintained, but user distraction and safety risks increase during critical tasks
Solution Approach 1:
The system performs automatic detection and response to undesired content without requiring user intervention. The machine learning model autonomously identifies advertisements, profane language, and other unwanted content in audio和视频 streams, and automatically switches streams or adjusts volume, allowing the system to serve itself rather than requiring continuous user control
Solution Approach 2:
The patent replaces manual user actions with an automated machine learning-based system. Instead of requiring users to manually detect and respond to undesired content, the system uses computational algorithms to perform detection and response actions, substituting mechanical human intervention with an automated computational system
2Reliability
If automated stream switching is implemented, then user distraction is reduced and safety is improved, but system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it detects various types of undesired content including advertisements, profane language, and other unwanted material across different media streams. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single unified solution, managing complexity through versatility
Solution Approach 2:
The system introduces a machine learning model as an intermediary between the user and the multimedia streams. This intermediary automatically processes and filters content, making decisions about stream switching and volume adjustment, thereby reducing the complexity of direct user-system interaction while maintaining safety and reliability
3Measurement precision
If multi-modal machine learning is used to analyze content, then detection accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system applies multi-modal analysis selectively rather than continuously to all content. By using the machine learning model to analyze only relevant segments of audio和视频 streams where undesired content is likely to occur, the system achieves high detection accuracy while avoiding the excessive computational resource consumption that would result from analyzing every moment of every stream in detail
Data Source
AI summary
The present invention may include a computer receives multimedia data. The computer parses the multimedia data into an audio stream. The computer analyzes the audio stream to identify recognized patterns. The computer calculates a probability of an undesired content based on the recognized patterns and taking an action based on determining the probability is above a threshold.