Multimodal Content Tagging via Emotional Profile Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for tagging multimedia content lack the ability to provide granular, contextual, and personalized information, failing to effectively pinpoint specific areas of content relevance, capture true user reactions, and enable scalable and generic tagging and search functionalities.
Innovation Solution
A system and method for granular tagging of multimedia content that captures user-specific data, including emotional profiles and cues, to generate emotional scores and profiles, which are then tagged and stored in a time-granular manner, allowing for personalized and contextual analysis and search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content tagging methods are used, then content classification is achieved, but granular and personalized information is lost
Solution Approach 1:
The patent segments content into discrete time-stamped segments and tags each segment individually with specific user reactions and emotional scores, rather than applying uniform tagging to entire content pieces. This enables granular analysis of user responses to specific content portions while preserving contextual information about when and how users reacted.
Solution Approach 2:
The system applies different tagging qualities to different content segments based on local user reactions. Each segment receives tags reflecting the specific emotional state and reaction type observed at that moment, creating locally optimized tags that capture nuanced user responses rather than applying generic global tags.
2Reliability
If user reactions are captured in real-time, then true user responses are obtained, but system complexity increases
Solution Approach 1:
The patent implements a universal reaction capture system that handles multiple types of user responses (emotional scores, facial expressions, verbal reactions, physiological data) through a single integrated framework. This multi-functional approach captures diverse reaction types using consistent processing methods, reducing overall system complexity despite the variety of data sources.
Solution Approach 2:
The system introduces intermediary processing layers that translate diverse raw user reactions into standardized tag formats. These intermediaries act as mediators between various input sources (cameras, microphones, sensors) and the tagging system, simplifying the integration of multiple data types without requiring complex custom processing for each source.
3Measurement precision
If granular content tagging is implemented, then content relevance is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary segmentation of content into manageable time-stamped segments before the tagging process. This pre-processing step organizes content in advance, allowing the tagging system to work with pre-defined segments rather than processing continuous streams, thereby reducing real-time processing time while maintaining granular tagging capability.
Solution Approach 2:
The system applies tagging at selective intervals rather than continuously processing every moment of content. By tagging key segments and using interpolation or representative tagging for intermediate portions, the system achieves sufficient content relevance without the computational burden of exhaustive frame-by-frame analysis.
Data Source
AI summary
A system and method for media content evaluation based on combining multi-modal inputs from the audiences that may include reactions and emotions that are recorded in real-time on a frame-by-frame basis as the participants are watching the media content is provided. The real time reactions and emotions are recorded in two different campaigns with two different sets of people and which include different participants for each. For the first set of participants facial expression are captured and for the second set of participants reactions are captured. The facial expression analysis and reaction analysis of both set of participants are correlated to identify the segments which are engaging and interesting to all the participants.