Personalized Target Value Setting Using User Behavior Statistics
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Solution Overview
Problem
Existing systems fail to set appropriate target values for user behavior change, leading to insufficient motivation or performance due to target values being too high, too low, or not specific enough for individual user needs.
Innovation Solution
A target value setting device that calculates statistical information on user behavior over a period, using a Gaussian mixture model for clustering to determine personalized target values, setting them moderately higher than normal behavior to encourage healthy lifestyle changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a high target value is set for user behavior, then performance improvement is enhanced, but user satisfaction decreases due to excessive difficulty
Solution Approach 1:
The system dynamically adjusts target values by changing parameters based on individual user statistics (average behavior, standard deviation) rather than using fixed high targets. This allows optimization of both performance improvement and user satisfaction by setting scientifically-determined moderate targets.
Solution Approach 2:
The system performs preliminary analysis of user behavior statistics before setting target values. By calculating average behavior and standard deviation in advance, the system pre-determines appropriate target levels that balance challenge and achievability for each user.
2Ease of operation
If a low or non-specific target value is set for user behavior, then user satisfaction is maintained, but performance improvement is reduced
Solution Approach 1:
Instead of using low or generic target values, the system dynamically changes target parameters based on individual user statistics. The target is set at average behavior plus a multiple of standard deviation, ensuring each user receives an optimized target that maximizes performance improvement while remaining achievable.
Solution Approach 2:
The system performs preliminary statistical analysis of user behavior patterns before setting targets. This advance preparation allows the system to determine the optimal challenge level for each user, preventing both under-challenging and over-challenging scenarios.
3Device complexity
If generic target values are used for all users, then system complexity is reduced, but individual appropriateness of targets deteriorates
Solution Approach 1:
The system applies local quality by customizing target values for each individual user based on their specific behavior statistics. Instead of uniform treatment, each user receives a locally-optimized target calculated from their personal average and standard deviation, enhancing individual appropriateness.
Solution Approach 2:
The system enables self-service by automatically calculating and setting individualized target values based on user behavior data. The system serves itself by using statistical methods to determine appropriate targets without requiring manual intervention or complex configuration for each user.
Data Source
AI summary
To provide a target value setting device capable of setting an appropriate target value for each individual.In a target value setting device 100, a target value calculation unit 103 calculates statistical information (for example, variance and an average value) of behavior of one user over a predetermined period of time. The target value calculation unit 103 then calculates a target value for the behavior of the one user on the basis of the statistical information. A result notification unit 104 notifies the one user of the target value. This target value is set on the basis of the statistical information so that a load is higher than normal behavior of one user or healthier than normal.