Expression Recognition Tag for Dynamic Media Metadata
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Solution Overview
Problem
Current web content metadata is limited to static keywords and tags, failing to capture dynamic user emotions expressed during media consumption, which are crucial for understanding user engagement and preferences.
Innovation Solution
An expression recognition system that utilizes SoC components to capture raw expression data, including facial and audible cues, and generates expression tags as metadata, which can be added to media files, allowing for the analysis of user emotions and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static keywords and tags are used for metadata, then the metadata structure is simple and easy to implement, but the ability to capture dynamic user emotions is lost
Solution Approach 1:
The patent segments the metadata structure into multiple layers: traditional static metadata (keywords, tags) and dynamic expression metadata (emotion tags, facial expression data). This segmentation allows the system to incorporate rich emotional information without completely redesigning the existing metadata framework, thus reducing implementation complexity while capturing user emotions.
Solution Approach 2:
The patent creates a universal metadata structure that can accommodate both traditional static information and dynamic expression data. The expression metadata schema is designed to work alongside existing metadata standards, enabling the system to handle diverse data types (images, video, text) and multiple emotion dimensions through a single unified framework.
2Loss of information
If expression recognition technology is integrated into media processing, then user emotion capture is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and pre-tagging media content with expression metadata before actual user consumption. Expression recognition is performed on media files in advance, and the resulting emotion tags are stored as metadata. This allows user emotions to be captured and analyzed without adding significant processing time during actual media playback or interaction.
Solution Approach 2:
The patent employs periodic action by capturing user expressions at strategically selected moments during media consumption rather than continuously. Expression recognition is triggered at key events such as scene transitions, emotional peaks, or at regular intervals, reducing computational overhead while still capturing meaningful emotion data for analysis.
3Measurement precision
If comprehensive expression data is collected during media consumption, then analysis accuracy is improved, but data privacy concerns increase
Solution Approach 1:
The patent extracts and separates personally identifiable information from expression data. The system captures and analyzes facial expressions and emotions, but deliberately removes or anonymizes identifiers that could directly trace back to specific individuals. This allows comprehensive emotion data collection for accurate analysis while mitigating privacy risks by extracting only the necessary emotional information.
Solution Approach 2:
The patent introduces an intermediary layer of anonymization and aggregation between raw expression data and analysis results. Individual expression data is processed through privacy-preserving transformations, and analysis is performed on aggregated data sets rather than individual records. This intermediary processing maintains analysis accuracy through sufficient data volume while protecting user privacy through anonymization.
Data Source
AI summary
An apparatus for tagging content with expression recognition information is disclosed herein. The apparatus can include an input collector to receive raw expression data at a data storage device, the raw expression data to correspond to a media event. The apparatus can include an expression recognition generator to create an expression tag by coding the received raw expression data to follow an expression action coding system. The apparatus can include a content modifier to modify a deliverable content instance for the media event to include the expression tag.