Media Guidance Application Context-Aware Feature Detection
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Solution Overview
Problem
Viewers of media content often find it inefficient to navigate through large portions of content to find interesting segments, as existing methods rely on guessing whether selected content will be engaging, lacking context and precision.
Innovation Solution
A media guidance application that analyzes frames of media assets to detect specific features, compares them to a database of objects, and performs actions based on context indicators to skip to relevant playback points or transmit data to mobile devices, ensuring users access interesting content efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually navigate through media content to find interesting segments, then they can potentially discover engaging content, but the time required to locate desired segments increases significantly
Solution Approach 1:
The system performs preliminary analysis of media content by detecting features, objects, and context indicators before user viewing. This pre-processing creates a structured representation of the content that enables rapid navigation to interesting segments without requiring users to manually scan through the entire media asset.
Solution Approach 2:
The patent introduces an intermediary system that acts as a bridge between the raw media content and the user. This intermediary detects and analyzes content features, compares them against a database of objects, and generates context indicators that guide users to interesting segments, thereby eliminating the need for direct manual navigation through the content.
2Adaptability or versatility
If users switch between content sources to find entertaining portions, then they may discover interesting content, but the efficiency of content consumption decreases
Solution Approach 1:
The system incorporates feedback mechanisms where context indicators and object detection results are used to guide navigation decisions. The system learns from user interactions and adjusts its feature detection and object comparison processes to better identify content that matches user preferences, thereby improving both adaptability and efficiency simultaneously.
3Measurement precision
If the system analyzes every frame of media assets to detect features, then detection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the media content analysis process into distinct stages: frame-level feature detection, object comparison against a database, and context indicator generation. This segmentation allows the system to process only relevant features from each frame rather than analyzing all pixels, thereby maintaining detection accuracy while reducing computational complexity.
Solution Approach 2:
The system performs partial analysis by focusing on detecting specific features and objects that are most relevant to user interest, rather than comprehensively analyzing all aspects of each frame. This selective approach maintains sufficient detection accuracy for navigation purposes while significantly reducing the overall processing burden.
Data Source
AI summary
Systems and methods are provided herein for performing an action based on a feature in a media asset. In many media assets, specific features appear at portions of a program that users find interesting. These features can be compared with a database of stored objects that commonly appear in media assets to determine the object corresponding to the feature. The context of the object in the media asset then may be determined so that an appropriate action is selected for the system to take.