Adaptive Operation Support via Machine Learning
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Solution Overview
Problem
Existing techniques for supporting user operations in applications, such as PC applications for business use, fail to improve operability as they provide fixed and non-optimal operation support information, which does not adapt to individual user mistakes or proficiency levels.
Innovation Solution
An information processing device and method that utilizes a machine learning model to analyze user operations and dynamically determine and present operation support information, including alerts and guidance, to improve user interaction with applications by identifying abnormal operations and suggesting correct actions based on learned patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed operation support information is presented to users, then the guidance structure is simple and easy to implement, but the operability improvement is limited and cannot adapt to individual users
Solution Approach 1:
The operation support information is made dynamic by using machine learning models that continuously adapt to individual user behavior patterns. The system transitions from static fixed guidance to dynamic personalized guidance that evolves with each user interaction, enabling the support information to automatically adjust to each user's proficiency level and operational patterns.
Solution Approach 2:
The system changes the parameters of operation support information based on user behavior analysis. By monitoring user operations and adjusting support information parameters (such as timing, content, and presentation) according to learned user patterns, the system achieves personalized adaptation without requiring manual configuration for each user.
2Ease of operation
If operation support information is presented to all users, then comprehensive guidance is provided, but unnecessary guidance increases and user experience deteriorates
Solution Approach 1:
The system implements feedback mechanisms where user operations are continuously monitored and analyzed. This feedback loop enables the machine learning model to understand user proficiency levels and adjust guidance presentation accordingly, providing support information only when and where it is most needed based on real-time user behavior analysis.
Solution Approach 2:
Instead of providing comprehensive guidance to all users, the system applies partial action by selectively presenting operation support information only to users who benefit from it. The machine learning model identifies when guidance is necessary versus when users are already proficient, avoiding unnecessary guidance while ensuring helpful support is delivered.
3Ease of operation
If personalized operation support is implemented, then operability is significantly improved, but device complexity increases
Solution Approach 1:
The system achieves self-service personalization by using machine learning models that automatically learn and adapt to individual user patterns without manual intervention. The complexity of personalization is automated through the learning model, which handles user profiling, guidance generation, and presentation timing autonomously based on observed user behavior.
Solution Approach 2:
The patent replaces complex manual personalization mechanisms with machine learning-based automated systems. Instead of requiring manual configuration or complex rule-based personalization logic, the system uses statistical learning models that naturally handle the complexity of adapting to individual users through data-driven patterns.
Data Source
AI summary
Provided is an information processing device including a control unit that performs processing using a learning model trained by machine learning for a user operation in an application and determines operation support information for operation support to be presented to a user according to a result of the processing.