Emotional Trajectory Matching for Media Stream Delivery
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Solution Overview
Problem
Current media content recommendation systems fail to accurately capture the nuances of user emotional responses to media content, leading to inefficient recommendations that often result in 'filter bubbles' and mismatched content due to their reliance on social graphs, content-based approaches, and collaborative filtering.
Innovation Solution
A method that involves measuring and aggregating emotional trajectories of media consumption using biometric sensors and neural networks to match target emotional states with media streams, enabling personalized recommendations based on continuous emotional state analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If collaborative filtering is used to generate recommendations based on user profiles and clustering, then recommendation efficiency is improved, but recommendation accuracy deteriorates due to filter bubbles and mismatched content
Solution Approach 1:
The patent transforms the recommendation approach by changing from collaborative filtering based on user behavior patterns to affective computing that measures actual emotional responses. This parameter change involves using biometric sensors to detect physiological states and mapping them to emotional trajectories, fundamentally altering how recommendations are generated to improve accuracy while maintaining efficiency
Solution Approach 2:
The patent replaces the mechanical information-processing system of collaborative filtering with an affective computing system that uses biometric sensors and neural networks. This substitution moves from analyzing user interactions to directly measuring emotional states, eliminating filter bubbles while preserving recommendation efficiency through automated emotional trajectory matching
2Device complexity
If content-based recommendation with generic keywords is used, then implementation complexity is reduced, but measurement precision of user preferences deteriorates
Solution Approach 1:
The patent replaces simple keyword-matching content-based recommendation with affective computing that measures actual emotional responses. Instead of analyzing generic content descriptors, the system uses biometric sensors to directly measure user emotional states and maps them to emotional trajectories, achieving high measurement precision without excessive complexity through automated processing
3Quantity of substance
If social graphing is used for recommendations, then data processing requirements are reduced, but recommendation accuracy deteriorates due to assuming similar tastes among connected users
Solution Approach 1:
The patent replaces social graphing's assumption-based recommendation with direct emotional measurement through biometric sensors. Instead of inferring preferences from social connections, the system measures actual emotional responses and creates affective profiles, eliminating the need for complex social network analysis while achieving superior recommendation accuracy through straightforward emotional trajectory matching
Data Source
AI summary
A media stream is delivered to a searcher by identifying a desirable emotional state for the searcher; identifying a current emotional state of the searcher; and estimating a target emotional trajectory that begins with the current emotional state and concludes with the desirable emotional state. Then the target emotional trajectory is matched to an aggregate emotional trajectory of the media stream; the media stream is recommended to the searcher in response to the matching; and a bit stream of the media stream is rendered to the searcher in response to the searcher's acceptance of the recommendation.