Mood Vector Generation for Media Library Presentation Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing media library management systems often result in inappropriate themed presentations of media content due to the inability to accurately determine valid moods associated with collections of media content items.
Innovation Solution
A system and method for determining valid moods in a media library by analyzing media content items and metadata, identifying mood sources, and generating mood vectors to identify valid moods that meet specific threshold conditions, thereby affecting the presentation of media content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If media library management systems use traditional content analysis methods, then the system complexity remains low, but the mood determination accuracy is insufficient resulting in inappropriate themed presentations
Solution Approach 1:
The system segments the mood determination process into multiple independent components: identifying mood sources (images, audio, text), extracting features from each source type, and aggregating results. This segmentation allows complex mood analysis to be broken down into manageable stages, improving accuracy while maintaining operational clarity
Solution Approach 2:
The patent introduces mood vectors as intermediary data structures that bridge raw media content and final mood determination. These vectors serve as a standardized intermediate representation that consolidates information from multiple mood sources, enabling accurate mood determination without directly processing all raw data complexity
2Measurement precision
If the system analyzes multiple mood sources (images, audio, text) to improve mood determination accuracy, then the measurement precision improves, but the processing time increases
Solution Approach 1:
The system performs preliminary feature extraction and mood vector generation for individual media items before final mood determination. By pre-processing images, audio, and text into standardized mood vectors, the system reduces the time required for comprehensive mood analysis of entire collections
Solution Approach 2:
The patent applies partial analysis by focusing on the most influential mood sources for each specific collection. Rather than uniformly analyzing all possible media types, the system identifies and prioritizes relevant mood sources based on collection characteristics, reducing unnecessary processing time while maintaining accuracy
3Adaptability or versatility
If the system uses comprehensive mood source types (visual, auditory, textual) to enhance presentation appropriateness, then the adaptability improves, but the device complexity increases
Solution Approach 1:
The patent creates a universal mood vector framework that can accommodate multiple mood source types (visual, auditory, textual) using the same underlying processing architecture. This multi-functional approach allows the system to handle diverse media types through a unified methodology, improving adaptability without proportionally increasing complexity
Solution Approach 2:
The system manages complexity by parameterizing mood source analysis - using different feature extraction parameters for different media types (image processing parameters for visual, audio feature parameters for auditory, text processing parameters for textual) while maintaining a consistent overall framework for mood determination and presentation generation
Data Source
AI summary
Systems, methods, and computer-readable media for determining at least one valid mood for a collection of media content items of a media library are provided.