User Coherency Detection for Adaptive Information Complexity
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Solution Overview
Problem
Computing devices often present information to users without considering individual preferences or coherence levels, resulting in inappropriate information complexity and timing, which can lead to ineffective information delivery.
Innovation Solution
A method and device that determine a user's coherency level based on various factors, such as user input, sensor data, and behavior history, to adjust the complexity and type of information output, ensuring it aligns with the user's predicted ability to comprehend, thereby optimizing information presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the computing device presents the same information to different users without considering individual preferences, then the device complexity is reduced and ease of operation is improved, but the user engagement and comprehension effectiveness deteriorate
Solution Approach 1:
The system performs preliminary detection of user coherency factors (such as cognitive state, context, and preferences) before presenting information. This allows the computing device to pre-adjust information complexity and formatting based on predicted user comprehension ability, resolving the contradiction by preparing personalized content in advance without requiring complex real-time adjustments during interaction
Solution Approach 2:
The system dynamically adjusts information presentation based on detected user coherency levels. Information complexity, detail level, and formatting are made adaptable and variable according to real-time user state detection, allowing the same information to be presented in different forms to different users or to the same user at different times based on their current comprehension ability
2Loss of time
If the computing device presents information without considering user coherency level, then the information delivery process is simplified and time is saved, but the information comprehension effectiveness and user engagement worsen
Solution Approach 1:
The system detects and assesses user coherency factors in advance before information delivery. By predicting user comprehension ability ahead of time, the system prepares appropriately scaled information content without requiring time-consuming adjustments during the actual information delivery moment, thus maintaining efficiency while improving comprehension reliability
Solution Approach 2:
The system uses detected user coherency factors as feedback to continuously optimize information presentation. By monitoring user state and adjusting information complexity based on this feedback loop, the system ensures that information is delivered at an appropriate comprehension level, improving reliability without significantly increasing processing time
3Loss of information
If the computing device presents complex information to users with low coherency level, then the information completeness and detail are improved, but the user comprehension ability is exceeded and engagement deteriorates
Solution Approach 1:
The system applies different levels of information complexity to different users or different portions of information based on detected coherency factors. Instead of presenting uniform complex information to all users, the system tailors the detail level, formatting, and complexity locally to match each user's predicted comprehension ability, ensuring information is neither oversimplified nor overly complex for any given user
4Reliability
If the computing device dynamically adjusts information complexity based on user coherency level, then user engagement and comprehension effectiveness are improved, but the device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary detection and assessment of user coherency factors before information delivery, and uses this pre-assessed data to guide information selection. This approach reduces the need for complex real-time adjustments during information delivery, as the personalization parameters are determined in advance, thereby improving comprehension effectiveness without proportionally increasing system complexity
Solution Approach 2:
The system uses automatically detected user coherency factors (such as contextual data, usage patterns, and device sensors) to self-adjust information presentation without requiring manual user input or complex interaction. This self-service approach allows dynamic personalization while minimizing the complexity of user-system interaction and automated decision-making processes
Data Source
AI summary
A method may include determining, by a computing device and based on at least one user coherency factor, a user coherency level. The coherency level may include a predicted ability of a user to comprehend information. The method may also include determining, by the computing device and based on the user coherency level, information having a complexity that satisfies the predicted ability of the user to comprehend information. The method may further include outputting, by the computing device, at least a portion of the information.