Emotion Modeling via Non-Intrusive Video Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for analyzing human emotional reactions to video content face challenges in gathering labeled data due to privacy concerns and scalability issues, particularly with complex emotions, and vary significantly across users.
Innovation Solution
The use of non-intrusive signals such as user feedback like thumbs up/down, emoticons, comments, and playback commands to generate emotional reaction timelines, allowing machine learning systems to model and predict emotional responses, and provide personalized content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video monitoring is used to analyze facial expressions for labeled data, then emotional reaction data can be obtained, but privacy concerns and intrusiveness increase
Solution Approach 1:
The patent introduces an intermediary layer between video monitoring and emotional analysis by using pre-defined emotion detection models that process video data without requiring direct facial expression analysis. This intermediary approach allows emotional reaction data to be obtained while reducing privacy intrusiveness by focusing on broader emotional patterns rather than detailed facial features.
Solution Approach 2:
The patent creates simplified copies or representations of emotional states through multiple pre-defined emotion categories (e.g., happy, sad, angry, surprised) rather than analyzing raw facial expressions directly. This copying approach enables emotional reaction measurement while maintaining participant privacy by working with categorized emotional states instead of detailed biometric data.
2Measurement precision
If video monitoring is used to gather emotional reaction data, then labeled data can be obtained, but scalability is limited
Solution Approach 1:
The patent segments the emotional reaction analysis into multiple independent pre-defined emotion categories that can be processed separately and in parallel. This segmentation allows the system to handle multiple participants and video segments simultaneously, significantly improving scalability while maintaining measurement precision for each emotion type.
Solution Approach 2:
The patent develops a universal emotion detection framework that can process different types of video content and multiple participant reactions using the same pre-defined emotion models. This multi-functional approach enables the system to scale across diverse video datasets and participant groups without requiring separate analysis methodologies for each case.
3Measurement precision
If facial expression analysis is used, then emotional reactions can be identified, but it does not account for individual user variations
Solution Approach 1:
The patent performs preliminary action by collecting and analyzing multiple pre-defined emotion categories for each participant before final emotional reaction identification. This preliminary data collection across different emotion types and participant groups enables the system to establish individual baseline patterns, which are then used to account for user-specific variations in subsequent analysis.
Solution Approach 2:
The patent implements dynamic adaptability by allowing the emotion detection models to adjust and refine their parameters based on individual participant responses across multiple video segments. This dynamic approach enables the system to capture user-specific emotional patterns while maintaining the ability to identify universal emotional reactions, thereby accounting for individual variations in emotional expression.
Data Source
AI summary
A machine learning system builds and uses computer models for identifying or predicting intensity of emotional reactions elicited by a particular video. Such computer models may also determine which particular emotional reaction corresponds to certain times during the video, and whether these reactions are positive or negative for a particular user. The computer models can also predict emotional reactions likely to be elicited by new videos based on learned correlations between video features and elicited emotional reactions.