Multi-Sensory Content Generation Using Pattern Completion for User State
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Solution Overview
Problem
Existing training technologies are not scalable, labor-intensive, and resource-intensive, and often fail to adequately engage users, which negatively impacts their ability to achieve therapeutic goals during training sessions.
Innovation Solution
A computer-implemented technique that provides personalized multi-sensory content based on a user's physiological and emotional state, using a pattern completion component to map input information to control instructions for output systems, including audio, visual, odor, and haptic outputs, to deliver guidance aligned with specific therapeutic goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple standalone systems are developed for specific training objectives, then each system can serve its narrow objective effectively, but the overall system complexity increases and scalability decreases
Solution Approach 1:
The patent implements a universal training system that can adapt to multiple training objectives through parameter configuration rather than requiring separate standalone systems. The system uses a common architecture with configurable parameters (training objective, environment settings, sensory modalities) to serve different therapeutic goals such as stress reduction, meditation, sleep induction, and attention enhancement, thereby reducing overall system complexity while maintaining effectiveness for each specific objective
Solution Approach 2:
The system employs dynamic parameter adjustment based on user state feedback. Sensors continuously monitor physiological parameters (heart rate, respiration, brain activity) and the system dynamically modifies training parameters including sensory stimulus intensity, content selection, and delivery timing to optimize effectiveness for each user's current state while maintaining a single unified system
2Reliability
If standalone systems are developed for each training objective, then each system can be optimized for its specific purpose, but development and maintenance become labor-intensive and resource-intensive
Solution Approach 1:
A single unified system platform is developed that can be configured for multiple training objectives through parameter settings rather than requiring separate development projects for each objective. The system includes reusable components such as sensory output modules, user state monitoring, and pattern completion algorithms that can be applied across different training scenarios, significantly reducing development and maintenance time while maintaining objective-specific optimization through configuration
Solution Approach 2:
The system includes pre-configured templates and patterns for common training objectives (stress reduction, meditation, sleep induction, attention enhancement). These pre-established patterns can be quickly adapted to specific needs without requiring extensive new development, allowing the system to maintain optimization for specific objectives while reducing the time and resources needed for implementation and maintenance
3Ease of operation
If traditional training systems are used, then implementation is simpler, but user engagement is insufficient which negatively impacts training success
Solution Approach 1:
The system incorporates dynamic sensory stimulation including audio, visual, and haptic feedback that adapts to user physiological state. This multi-sensory engagement creates heightened user attention and involvement in the training process, improving training success rates while maintaining ease of operation through automated state monitoring and adaptive content delivery that requires minimal user effort to activate
Solution Approach 2:
The system continuously monitors user physiological parameters through sensors and uses this feedback to dynamically adjust training content and delivery. This closed-loop feedback mechanism enhances user engagement by adapting to real-time user state, improving training effectiveness while maintaining operational simplicity through automated adjustment algorithms that eliminate the need for manual intervention
Data Source
AI summary
A technique for providing multi-sensory content receives input information that expresses a physiological state and experienced emotional state of a user. The technique generates prompt information that describes at least an objective of guidance to be delivered and the input information. The technique maps the prompt information to output information using a pattern completion component. The output information contains control instructions for controlling an output system to deliver the guidance via generated content. In some implementations, the pattern completion component is a machine-trained pattern completion model. In some implementations, a reward-driven machine-trained model further processes the input information and/or the output information. The reward-driven machine-trained model is trained by reinforcement learning to promote the objective of the guidance. In other implementations, the reward-driven machine-trained model operates by itself, without the pattern completion component.