Mood-Condition Affinity Detection Using Sensor Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies lack effective methods to detect and utilize mood-condition affinities, which are associations between environmental conditions and user moods, limiting personalized interactions and mood improvement strategies.
Innovation Solution
A system and method that utilize sensors on computing devices to collect data, analyze biometric and environmental information, and generate affinity data to determine mood associations, allowing for the projection of moods and provision of insights for improving user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensors and biometric data collection are implemented to detect mood-condition affinities, then personalized mood improvement insights can be provided, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments mood detection into multiple independent sensor modules (biometric sensors, environmental sensors, activity sensors) that collect different types of data separately. This segmentation allows the complex task of mood detection to be divided into manageable components, reducing overall system complexity while maintaining comprehensive monitoring capability.
Solution Approach 2:
The patent introduces an affinity service as an intermediary layer between raw sensor data and mood analysis. This service acts as a mediator that standardizes data collection from various sensors, processes the data through defined affinity relationships, and outputs standardized mood insights. The intermediary layer abstracts the complexity of multi-sensor integration and data processing.
2Measurement precision
If continuous sensor monitoring and biometric data analysis are performed to determine user mood, then mood detection accuracy improves, but energy consumption increases
Solution Approach 1:
The system implements periodic sampling of sensor data rather than continuous monitoring. The affinity service collects biometric and environmental data at defined intervals, analyzes the data to determine mood states, and updates affinity relationships periodically. This periodic action maintains adequate mood detection accuracy while significantly reducing energy consumption compared to continuous monitoring.
Solution Approach 2:
The patent applies different monitoring intensities to different sensors based on their importance for mood detection. Critical biometric sensors (heart rate, skin conductance) are monitored with higher frequency and precision, while less critical environmental sensors are monitored at lower frequencies. This local quality approach optimizes energy usage by allocating processing power selectively to the most important measurement functions.
Data Source
AI summary
Concepts and technologies are disclosed herein for detecting and using mood-condition affinities. A processor that executes an affinity service or affinity application can obtain collected data associated with a user device. The collected data can include sensor readings collected by the user device. The processor can determine a condition at the user device and a mood associated with the condition. The processor can generate an affinity that defines a relationship between the condition and the mood and store the affinity at a data storage device.