Sensor-Based Mood Detection for Content Recommendations
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Solution Overview
Problem
Digital cable service providers fail to account for a subscriber's current mood or demeanor when making content recommendations, relying solely on viewing history and user profiles.
Innovation Solution
A system that utilizes sensor data from biometric and physiological attributes, such as heart rate, skin temperature, and facial expressions, to provide real-time content recommendations by mapping sensor data to mood types and recommending appropriate content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data collection is implemented to detect user mood, then recommendation accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of mood detection into multiple independent sensor components (facial expression sensors, voice analysis sensors, physiological sensors). Each sensor type independently captures a specific aspect of user state, and the recommendation engine integrates these segmented data sources to achieve comprehensive mood detection without requiring a single complex detection system.
Solution Approach 2:
The recommendation engine acts as an intermediary that processes sensor data from multiple sources and translates it into content recommendations. This intermediary layer abstracts the complexity of sensor integration and mood analysis, allowing the system to maintain modularity while achieving accurate real-time recommendations based on integrated sensor inputs.
2Ease of operation
If real-time sensor monitoring is implemented, then user experience is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system implements periodic sensor data collection triggered by specific events such as user interaction with the interface or transitions between content items. This periodic activation reduces energy consumption while maintaining the ability to provide timely recommendations based on user mood changes.
Solution Approach 2:
The system dynamically adjusts sensor monitoring intensity based on contextual factors such as user engagement level, time of day, and current content type. During high-engagement periods or when mood changes are detected, monitoring intensity increases; during low-activity periods, monitoring reduces to conserve energy, optimizing the balance between user experience and power consumption.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, computer readable media, and/or other subject matter that enable use of sensor data to provide content recommendations, but are not so limited. A disclosed system operates to receive attributes of at least one human subject from one or more sensors as part of identifying recommended content items for display. A disclosed method operates to use one or more sensor data mapping parameters as part of identifying recommended content items for display.