Behavior Training With Segmented Video Self-Modeling and Timely Rewards
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Solution Overview
Problem
Individuals face difficulties in learning new behaviors due to cognitive rigidity, disabilities such as autism or ADHD, or simply due to the complex nature of the behavior, often requiring repeated effort and practice, and existing methods like video self-modeling and generalization can be time-consuming and ineffective for users with attention issues.
Innovation Solution
A behavior training system combining modified video self-modeling, generalization, and applied behavior analysis, using personalized visual representations and rewards to reinforce learning, with AI and machine learning to adapt prompts and rewards based on user interaction and progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional video self-modeling and generalization methods are used to teach new behaviors, then learning completeness is improved, but training time and user engagement are worsened due to the complex and time-consuming nature of the process
Solution Approach 1:
The patent segments the complete behavior sequence into discrete steps, presenting only the critical subset needed for learning. Instead of showing all steps in traditional video self-modeling, the system identifies and presents key steps that capture the essential learning objectives, reducing training time while maintaining learning effectiveness
Solution Approach 2:
The system extracts only the most important steps from the complete behavior sequence, removing redundant or less critical components. This extraction approach allows users to learn the core behavior faster without being overwhelmed by unnecessary details, resolving the contradiction between completeness and time efficiency
2Manufacturing precision
If traditional video self-modeling is used to teach new behaviors, then behavioral accuracy is improved, but user engagement is worsened due to lack of personalization and delayed reinforcement
Solution Approach 1:
The system provides preliminary visual guidance showing the correct behavior before the user attempts it. By presenting the ideal behavior sequence in advance through augmented reality overlays, users can accurately replicate the behavior while remaining engaged through immediate visual feedback and personalization
Solution Approach 2:
The system implements immediate feedback mechanisms through augmented reality visualizations that show users their performance in real-time compared to the target behavior. This continuous feedback loop maintains user engagement while ensuring behavioral accuracy, as users can see their progress and make adjustments instantly
3Manufacturing precision
If complete step sequences are presented for behavior learning, then learning thoroughness is improved, but cognitive load is worsened making it difficult for users with attention issues to maintain focus
Solution Approach 1:
The patent divides the complete behavior sequence into segmented steps, displaying only the critical subset needed for each learning objective. This segmentation reduces cognitive load by presenting information in manageable chunks while maintaining learning thoroughness through progressive disclosure of steps
Solution Approach 2:
The system applies partial action by showing only the necessary portion of the complete behavior sequence rather than all steps. This approach provides sufficient information for effective learning without overwhelming users with excessive details, particularly benefiting those with attention challenges
Data Source
AI summary
The disclosed behavior training system and method uses a combination of modified video self-modeling techniques, generalization and applied behavior analysis techniques to effectively teach new target behaviors. According to an embodiment, a system and/or method includes: receiving a selection of a target behavior; constructing a visual representation of a user environment, sending, to the user electronic computing device, the constructed visual representation of the user environment, generating a behavioral clip related to performing the target behavior, receiving a selection of the stimulus object and in response to receiving the selection of the stimulus object, sending, to the user electronic computing device, the generated behavioral clip and one or more rewards to encourage the user to continue to engage with the behavior training system.