Emotional Arc Matching for Secondary Content Insertion
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Solution Overview
Problem
Current content-insertion methodologies fail to consider users' emotional states during media consumption, leading to ineffective placement and impact of secondary content, as they do not track emotional responses or adjust content accordingly, resulting in low engagement and high user switching rates.
Innovation Solution
The system generates an average emotional arc based on collective user responses and compares it with real-time emotional arcs to determine the optimal type and timing for inserting secondary content, using biometric data and Russell's circumplex model to align emotional states and enhance engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If supplemental content is inserted based on user search history, then content relevance is improved, but user emotional engagement deteriorates
Solution Approach 1:
The system performs preliminary analysis of user emotional state through biometric data collection and processing before determining supplemental content insertion. Emotional arcs are generated in advance by analyzing biometric responses during primary content consumption, allowing the system to proactively identify optimal insertion points that align with user emotional states rather than reacting after content is consumed.
Solution Approach 2:
The system continuously monitors user biometric data (heart rate, skin conductance, facial expressions) during primary content consumption and uses this real-time feedback to dynamically adjust supplemental content insertion decisions. The emotional arc generated from biometric feedback serves as a continuous signal that guides when and what supplemental content to insert, creating a closed-loop system that adapts to user emotional state.
2Ease of operation
If supplemental content is inserted at random times, then content delivery simplicity is improved, but user attention and impact deteriorate
Solution Approach 1:
The system transitions from static, predetermined content insertion schedules to dynamic, real-time insertion decisions based on user emotional state. The emotional arc continuously evolves as biometric data is collected during primary content consumption, and supplemental content insertion points are dynamically determined to coincide with emotionally significant moments, maximizing user attention and impact.
Solution Approach 2:
The system uses biometric parameters (heart rate, skin conductance, facial muscle activity) to detect changes in user emotional state. By monitoring these physiological parameters in real-time, the system identifies emotionally significant moments in the primary content and uses these parameter changes as triggers for supplemental content insertion, ensuring content is delivered when user attention and emotional engagement are highest.
3Productivity
If emotional tracking is implemented, then user emotional engagement is improved, but system complexity increases
Solution Approach 1:
The system segments the emotional analysis process into distinct functional modules: biometric data collection (sensors capturing physiological signals), emotional arc generation (processing biometric data to create emotional trajectory), and content insertion decision-making (using emotional arc to guide supplemental content placement). This modular segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The emotional arc serves as an intermediary data structure that bridges raw biometric sensor data and content insertion decisions. By converting complex biometric signals into a simplified emotional arc representation that captures the trajectory and intensity of user emotional state, the system creates a manageable intermediate form that can be easily used by the content selection algorithm without requiring direct complex biometric processing at the decision-making stage.
Data Source
AI summary
Systems and methods for determining emotional responses to videos, generating emotional arc based on the responses and reactions, and determining the type and/or point of insertion of secondary content based on the determined emotional responses are disclosed. The methods display primary content to a plurality of users and determine their emotional response as they consume the primary content. An average emotional arc is generated based on the determined emotional response and it is compared to a current emotional arc generated based on emotional response from a current user. When values of the current emotional are within a predefined range of the average emotional arc at a particular play position in the timeline of the primary content, a secondary content that is designated for the value of the current emotional arc is inserted in the primary content at the particular play position.