Mood Aggregation System Using Segmented Data Collection
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Solution Overview
Problem
Current systems lack the capability to effectively gather, track, and analyze mood data over time, including time of day, day of week, and location, which hinders understanding and reporting of individual moods and their impact on daily activities.
Innovation Solution
A mood aggregation system comprising a server and user computing devices that automatically generate and send mood requests, store and report mood data, including time and location, with features like mood selection, health monitoring, and daily event integration, and includes a reward system for data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a mood aggregation system is implemented to gather and track mood data, then the capability to analyze and report mood trends is improved, but the system complexity and data management requirements increase
Solution Approach 1:
The system segments mood data collection into discrete, standardized mood selections (e.g., excited, good, average, bad, terrible) that users can easily choose. This segmentation simplifies the complex task of mood tracking by breaking it into manageable, pre-defined categories rather than requiring free-form input or continuous monitoring.
Solution Approach 2:
The patent introduces an intermediary processing layer that automatically generates mood requests, collects responses, and aggregates data. This intermediary system handles the complexity of data management, storage, and analysis, shielding users from the underlying system complexity while enabling comprehensive mood tracking capabilities.
2Loss of information
If comprehensive mood data including time, location, and health monitoring is collected, then the analytical value and insights are improved, but the data storage and processing requirements increase
Solution Approach 1:
The system is designed to handle multiple types of data (mood selections, time stamps, location data, health monitoring information) through a universal data collection framework. This multi-functional approach allows the same infrastructure to store and process diverse data types efficiently, reducing overall storage requirements compared to separate specialized systems.
Solution Approach 2:
The system pre-defines mood categories and data collection parameters before actual mood tracking begins. By establishing a standardized framework in advance, the system eliminates the need for complex real-time data processing and classification, thereby reducing storage and processing requirements while maintaining information completeness.
3Ease of operation
If automatic mood request generation and reporting is implemented, then the ease of operation is improved, but the automation extent and system resources increase
Solution Approach 1:
The system implements self-service automation where mood requests are automatically generated and sent to users at appropriate times, and reports are automatically created based on collected data. This self-service approach minimizes manual intervention while maintaining ease of operation, as the system handles routine tasks autonomously without requiring extensive automated infrastructure.
Solution Approach 2:
The system uses periodic action by automatically generating mood requests at scheduled intervals or based on specific triggers (e.g., time of day, location changes). This periodic automation balances system resource usage by activating data collection only when needed, rather than continuous monitoring, thereby maintaining ease of operation without excessive automation overhead.
Data Source
AI summary
Described is a mood aggregation system. The system includes a server having a memory storing user information and a user computing device coupled to the server. The server may be programmed to automatically generate a mood request with response capabilities and send the request to the user computing device for display in response to the user computing device accessing the system. The server may also be programmed to receive and store mood data sent from the user computing device, wherein the mood data comprises a mood selection, a time the mood data was sent and a location of the user computing device when sending the mood data. The user may send a request for a report that is received and stored in the server for a user associated with the user computing device and automatically access the stored mood data and generate a report responsive to the report request.