Adaptive Cognitive Training System with Dynamic Difficulty Adjustment
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Solution Overview
Problem
Existing computerized cognitive training systems face challenges in efficiency and efficacy due to limited approaches for personalization and predictive modification of therapeutic interactions.
Innovation Solution
A method and system for configuring user interfaces within computerized cognitive training regimens, utilizing a processor to present adaptive cognitive training tasks, process user input data with a hierarchical statistical model, and predict efficacy to modify task difficulty and presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computerized cognitive training programs are delivered with fixed difficulty levels, then the system is simple to implement, but the efficiency and efficacy of training is limited
Solution Approach 1:
The patent implements dynamic difficulty adjustment where the system automatically modifies task difficulty levels based on real-time user performance data. The difficulty parameter is no longer fixed but dynamically adapts to individual user capabilities, optimizing training efficiency while managing system complexity through algorithmic automation
Solution Approach 2:
The system changes the difficulty parameter dynamically based on user performance metrics. By continuously monitoring and adjusting the difficulty parameter according to user responses, the system optimizes training efficacy without requiring manual intervention, thus improving productivity while controlling complexity through automated parameter modification
2Productivity
If personalized training regimens are implemented, then training efficacy is improved, but the system complexity increases
Solution Approach 1:
The system performs self-service personalization by automatically analyzing user performance data and adjusting training parameters without external intervention. The algorithm independently evaluates user responses and modifies the training regimen accordingly, improving efficacy while minimizing the complexity burden on users and administrators
Solution Approach 2:
The patent implements continuous feedback loops where user performance data is collected, analyzed, and used to adjust training parameters in real-time. This feedback mechanism enables automatic personalization that improves training efficacy while managing system complexity through structured data processing and algorithmic decision-making
3Productivity
If adaptive difficulty adjustment is applied to each task, then training efficiency is optimized, but the computational processing requirements increase
Solution Approach 1:
The system applies adaptive difficulty adjustment selectively rather than uniformly to all tasks. By implementing partial adaptation based on user performance patterns and task relevance, the system optimizes training efficiency for critical learning moments while reducing unnecessary computational processing for less impactful tasks, thus managing energy consumption
Data Source
AI summary
A system, method, and computer platform product for an adaptive cognitive training platform. In accordance with various aspects of the present disclosure, an adaptive cognitive training platform is configured to process user activity data from one or more instances of a computerized cognitive training program to determine a baseline cognitive assessment for one or more cognitive abilities/skills of a user. The cognitive assessment model may be configured to further process the user activity data to predict a relative value or measure of efficacy for one or more computerized stimuli or interactions within the computerized cognitive training program. The adaptive cognitive training platform may be configured to configure, modify and/or present one or more graphical user interface elements for one or more subsequent instances of the computerized cognitive training program according to the predicted value or measure of efficacy of one or more cognitive training tasks.