Virtual Reality Content Redaction for Personalized Learning
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Solution Overview
Problem
Current computer systems lack an efficient method to dynamically adapt and personalize educational content in virtual reality environments based on learner profiles and physical interactions, leading to suboptimal learning experiences.
Innovation Solution
The system employs an experience creation module that utilizes environment sensor information, instructor input, and learner interactions to generate personalized learning assets and adapt the learning experience in real-time, incorporating physicality assessments and learner profiles to optimize content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If educational content is delivered through recorded lectures with built-in feedback prompts, then the educator can assess student understanding, but the learning experience lacks dynamic adaptation and personalization
Solution Approach 1:
The patent implements dynamic adaptation by continuously adjusting the virtual reality learning environment based on real-time sensor data from wearables and user interactions. The system modifies content difficulty, pacing, and presentation style dynamically rather than using static pre-recorded lectures, resolving the contradiction between adaptability and complexity through algorithmic content adjustment.
Solution Approach 2:
The system incorporates multiple feedback loops including wearable sensor feedback (physiological data), interaction feedback (user responses to prompts), and assessment feedback (comprehension evaluation). This multi-layered feedback mechanism enables the system to adapt content in real-time, achieving dynamic personalization while managing complexity through structured feedback processing.
2Adaptability or versatility
If the system collects environment sensor information, instructor input, and learner interactions to personalize content, then learning experiences become tailored to individual needs, but data processing requirements increase
Solution Approach 1:
The patent extracts and processes only the most relevant data elements from the vast amount of collected information. Rather than analyzing all sensor data equally, the system identifies and processes key indicators such as comprehension level, engagement metrics, and physiological responses, reducing data processing requirements while maintaining personalization effectiveness.
Solution Approach 2:
The system applies different processing intensities to different data sources based on their relevance and reliability. High-priority data (e.g., direct comprehension assessments) receive immediate and detailed processing, while lower-priority data (e.g., ambient environmental sensors) are processed with less computational resources, optimizing the balance between personalization and data processing load.
3Ease of operation
If physical interactions and physicality assessments are incorporated into the learning experience, then learner engagement improves, but the system requires additional sensors and interaction tracking
Solution Approach 1:
The patent implements multi-functional sensors and devices that serve multiple purposes. For example, wearable sensors not only track physiological data for personalization but also detect physical interactions with the environment. This universal approach allows the system to gather engagement data without proportionally increasing hardware complexity.
Solution Approach 2:
The system merges interaction tracking with existing sensor data collection infrastructure. Physical interactions are detected by combining data from wearables, environmental sensors, and interaction prompts rather than requiring separate dedicated tracking systems, reducing overall device complexity while maintaining high learner engagement capabilities.
Data Source
AI summary
A method for execution by a computer generating a virtual reality environment utilizing a group of object representations by identifying an exclusion asset and modifying a set of common illustrative assets to exclude the exclusion asset to produce a redacted set of common illustrative assets. The method further includes rendering a portion of the redacted set of common illustrative asset to produce a redacted set of common illustrative asset video frames and selecting a subset of the redacted set of common illustrative asset video frames to produce a common portion of video frames for the virtual reality environment. The method further includes rendering representations of object representations to produce remaining portions of the video frames for the virtual reality environment. The method further includes linking the common portion and the remaining portions of the video frames to produce the virtual reality environment for interactive consumption.