Audio Sensor User Reaction Analysis for Personalized Content Delivery
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Solution Overview
Problem
Current technologies lack the ability to effectively analyze and respond to user reactions to media content in real-time, such as emotions and preferences, for personalized content targeting.
Innovation Solution
A system that uses audio/video sensors and speech recognition to detect and analyze user reactions, correlating them with media content, and adjusts content presentation based on user preferences and emotions, employing facial recognition and machine learning for personalized advertising and content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If audio/video sensors and speech recognition are used to detect and analyze user reactions in real-time, then user engagement and advertising relevance are improved, but device complexity and processing requirements increase
Solution Approach 1:
The system segments user reaction analysis into distinct components: audio signal capture, speech recognition processing, emotional state classification, and content correlation. Each component is handled by specialized modules that process specific aspects of user feedback independently, allowing the complex task to be divided into manageable segments that can be processed in parallel.
Solution Approach 2:
The patent introduces intermediate processing layers including audio pre-processing filters, speech-to-text conversion intermediaries, and emotional state classification models that act as mediators between raw sensor data and final content delivery decisions. These intermediaries simplify the overall system architecture by breaking down complex processing chains into standardized intermediate representations.
2Measurement precision
If facial recognition and machine learning are employed for personalized content targeting, then advertising relevance is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing audio signals into standardized features, pre-training machine learning models with extensive user data, and pre-establishing content databases organized by emotional state and preference categories. This preliminary preparation reduces the computational burden during real-time operation, allowing rapid inference without sacrificing accuracy.
Solution Approach 2:
The patent implements partial processing by analyzing only the most salient features of user reactions rather than complete signal processing. For example, it focuses on key acoustic features for emotional detection rather than full spectral analysis, and uses simplified facial expression recognition for immediate response while more detailed analysis occurs asynchronously.
3Adaptability or versatility
If real-time analysis of user emotions and preferences is performed, then personalized content delivery is improved, but system resource consumption increases
Solution Approach 1:
The system implements periodic analysis rather than continuous full-scale processing. It analyzes user reactions at key moments such as scene transitions, ad presentations, or when threshold emotional states are detected, rather than continuously processing all audio and video data at maximum resolution. This periodic approach maintains personalization capability while significantly reducing average energy consumption.
Solution Approach 2:
The patent dynamically changes processing parameters based on context, adjusting analysis depth, sensor sampling rates, and model complexity according to the current viewing situation, user engagement level, and content type. For example, it reduces processing intensity during passive viewing periods and increases it during interactive segments, optimizing the balance between personalization quality and energy consumption.
Data Source
AI summary
Techniques for identifying content displayed by a content presentation system associated with a physical environment, detecting an audible expression by a user located within the physical environment, and storing information associated with the audible expression in relation to the displayed content are disclosed.