Cognitive Training Recommendation System Using Dual Evaluation
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Solution Overview
Problem
Existing cognitive training recommendation methods do not accurately account for specific impairments of cognitive functions in users, leading to suboptimal personalized recommendations.
Innovation Solution
A user ability-based personalized cognitive training task recommendation method that combines machine preliminary testing and manual evaluation to create an optimal task list, using cognitive capability models, weight matrices, and dual evaluation scoring to match user capabilities with suitable training tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If disease-based personalized recommendation technology is used to recommend cognitive training tasks, then the recommendation process is simplified, but the accuracy of recommendations deteriorates because specific cognitive function impairments are not considered
Solution Approach 1:
The recommendation system is segmented into multiple independent modules: a machine preliminary test module that performs automated cognitive assessment, a manual evaluation module that incorporates clinical expertise, and a combination sorting module that integrates both evaluations. This segmentation allows each module to specialize in specific functions while maintaining overall system accuracy and simplicity.
Solution Approach 2:
The patent introduces capability weight matrices and task capability weight matrices as intermediary structures that bridge machine evaluation results and manual clinical assessments. These matrices serve as mediators that translate different evaluation formats into a unified recommendation framework, enabling accurate integration of multiple evaluation sources.
2Productivity
If only machine preliminary testing is used for cognitive training recommendation, then the recommendation efficiency is improved, but the reliability deteriorates due to potential decision errors
Solution Approach 1:
The patent merges machine preliminary testing and manual evaluation into a unified recommendation system through combination sorting. The final recommended task training list is generated by combining results from both the machine preliminary test recommended list and the manual evaluation recommended list, thereby leveraging the efficiency of automated testing while incorporating the reliability of human clinical judgment.
Solution Approach 2:
The system implements feedback mechanisms where manual evaluation results are used to verify and adjust machine preliminary test outcomes. The combination sorting module incorporates feedback from both evaluation sources to produce the final recommended list, ensuring that automated efficiency is balanced with clinical reliability.
3Reliability
If only manual evaluation is used for cognitive training recommendation, then the reliability of recommendations is improved, but the time consumption increases
Solution Approach 1:
The patent implements preliminary machine-based cognitive testing before manual evaluation. The machine preliminary test module automatically assesses users' cognitive functions and generates initial recommendations, which then serve as the basis for more focused manual evaluation. This preliminary action reduces the overall time required while maintaining reliability.
Solution Approach 2:
The system performs partial manual evaluation by focusing only on areas where machine preliminary testing results need verification or supplementation. Rather than conducting complete manual evaluations for all users, the system applies manual evaluation selectively to enhance reliability only where necessary, thereby reducing overall time consumption.
4Device complexity
If existing cognitive training recommendation methods are used that do not consider specific cognitive function impairments, then the system complexity is reduced, but the recommendation accuracy deteriorates
Solution Approach 1:
The patent applies local quality by creating specific capability weight matrices for different cognitive domains (e.g., attention, memory, executive function) and task capability weight matrices that match specific impairment types. Rather than using a single generic recommendation algorithm, the system tailors evaluation and recommendation parameters to specific cognitive function impairments, thereby improving accuracy without excessive complexity.
Data Source
AI summary
Disclosed in the present invention are a user ability-based personalized cognitive training task recommendation method and system. The method comprises the following steps: establishing a machine initial test-based recommended training task list; establishing a manual evaluation-based recommended training task list; and merging and sorting to establish an optimal recommended training task list. According to the present invention, personalized cognitive training task recommendation can be performed on the basis of the user's ability, and mutual complementation of treatment schemes is achieved by combining a machine algorithm and manual evaluation, thereby balancing machine and human problems, and reducing decision-making errors.