Machine-Learned Activity Support Through Goal-Linked Indicators
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Solution Overview
Problem
Existing systems fail to provide personalized and effective support for users in achieving their activity goals, as they do not adequately account for individual user characteristics and correlations between activity indicators and goal-related parameters.
Innovation Solution
An activity support system that utilizes machine learning to analyze user data, identify highly correlated indicators with goal-related parameters, and provide targeted advice for improvement based on the practice data of users with similar characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic activity evaluation is provided without considering individual user characteristics, then the system is simpler to operate, but the support becomes less effective and personalized
Solution Approach 1:
The system transitions from generic activity evaluation to personalized support by identifying and addressing specific local weaknesses of each user. It analyzes multiple indicator values to determine which indicators are most relevant to each user's goals and characteristics, then provides tailored advice focused on improving those specific areas rather than offering uniform guidance to all users.
Solution Approach 2:
The system dynamically adjusts its support parameters based on individual user characteristics and goal-related parameters. It selects and emphasizes specific indicator values that are most relevant to each user's situation, changing the evaluation and advice parameters from generic to personalized based on user-specific data patterns and correlations.
2Reliability
If the system analyzes multiple indicator values to identify correlations with goal-related parameters, then the support becomes more personalized and effective, but the system complexity increases
Solution Approach 1:
The system extracts and focuses on the most relevant indicator values from the overall set of measured parameters. Instead of processing all indicators equally, it identifies and isolates those that have strong correlations with goal-related parameters for each user, then provides advice focused only on these extracted key indicators, reducing the complexity of analysis while maintaining effectiveness.
Solution Approach 2:
The system segments the activity evaluation into distinct components based on user characteristics and goals. It divides the multiple indicator values into groups that are most relevant to each user's specific objectives, analyzing and providing advice on each segment separately rather than treating all indicators uniformly, which manages complexity through structured organization.
3Quantity of substance
If the system provides comprehensive advice on all activity indicators, then the information is more complete, but the user cannot focus on the most impactful areas for goal achievement
Solution Approach 1:
The system applies partial action by providing comprehensive advice only on the most relevant indicator values rather than all indicators. It identifies which indicators have the strongest correlations with goal-related parameters for each user and focuses advice on those specific areas, allowing users to concentrate on high-impact improvements rather than being overwhelmed by complete but diluted information across all indicators.
Data Source
AI summary
There is provided an activity support method to be executed by a computer. The method includes: obtaining a first goal of a first user in an activity; obtaining indicator values of the first user, the indicator values indicating an actual result of the activity; identifying a related indicator from the indicator values, the related indicator changing along with a goal-related parameter that corresponds to the first goal; and outputting information for the first user to improve the related indicator in performing the activity.