Mood Prediction System for Adaptive Device Configuration
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Solution Overview
Problem
Current personalization technologies in electronics lack the ability to automatically adapt and respond to a user's emotional mood, missing opportunities for targeted content delivery, social interaction, and therapeutic interventions based on predicted emotional states.
Innovation Solution
A system that predicts a user's mood by collecting and analyzing data from various sources, including content databases, message servers, social servers, and sensors, and uses this information to deliver targeted advertisements, facilitate mood-based social interactions, and automatically configure device settings, while also providing mood-based notifications and provisioning of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personalization is performed manually by users, then users can customize their preferences, but the system cannot automatically adapt to user's emotional states
Solution Approach 1:
The system automatically monitors user behavior patterns, analyzes emotional states from usage data, and personalizes content delivery without requiring manual user input. The system serves itself by making intelligent adjustments based on observed user states.
Solution Approach 2:
The system continuously collects feedback from user interactions with electronic devices, analyzes this data to determine emotional states, and uses this feedback loop to dynamically adjust personalization settings and content delivery strategies.
2Measurement precision
If the system collects data from multiple sources to predict mood, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system uses a unified data collection framework that handles multiple data sources (usage patterns, sensor data, communication data) through a single analytical engine, reducing complexity by making the system multi-functional rather than requiring separate systems for each data type.
Solution Approach 2:
The patent combines multiple data sources and analysis methods into an integrated mood prediction system, merging disparate data collection and analysis functions into a cohesive system that improves accuracy while managing complexity through integration.
3Productivity
If the system delivers targeted content based on mood, then user engagement improves, but information overload may occur
Solution Approach 1:
The system tailors content delivery to the specific emotional state and contextual needs of the user at each moment, providing different types and amounts of information based on local conditions rather than applying a uniform information delivery strategy.
Solution Approach 2:
The system adjusts the amount of content delivered based on user mood - providing more content when users are receptive and engaged, and reducing content delivery when users show signs of information overload or negative emotional states.
Data Source
AI summary
Methods, systems, and products predict emotional moods. Predicted moods may then be used to configure devices and machinery. A communications device may be configured to a mood of a user. A car may adjust to the mood of an operator. Even assembly lines may be configured, based on the mood of operators. Machinery and equipment may thus adopt performance and safety precautions that account for varying moods.