Adaptive Virtual Training Scenarios via Personality Biometrics

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Solution Overview

Problem

Existing training systems lack personalization and adaptability, leading to linear and repetitive training scenarios that fail to effectively engage users based on their unique personality and behavior.

Innovation Solution

A method utilizing a machine learning agent to generate tailored training scenarios based on a user's personality biometric profile and real-time behavioral data, allowing for dynamic adaptation of the training environment during sessions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If training scenarios are personalized based on user personality and behavior, then training effectiveness and user engagement are improved, but system complexity and computational requirements increase

Engineering Contradiction:

Improvetraining personalizationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by collecting personality biometric data and behavioral patterns before training sessions begin. This pre-processing of user characteristics allows the complex personalization logic to be prepared in advance, reducing real-time computational burden while maintaining high adaptability during actual training delivery.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary layer (the personalization engine that processes personality biometrics and behavioral data) between the user and the training content. This intermediary handles the complex analysis and decision-making, shielding the overall system from direct complexity while enabling sophisticated personalized training scenario generation.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If training scenarios are dynamically adapted in real-time during sessions, then user engagement and learning outcomes are enhanced, but processing power and computational resources are consumed

Engineering Contradiction:

Improvelearning efficiencyVSAvoidcomputational energy

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system merges multiple functions into a unified real-time adaptation process: behavioral data collection, personality assessment, scenario modification, and content delivery are combined into a single integrated workflow. This consolidation reduces redundant processing and optimizes energy utilization while maintaining high learning efficiency through continuous adaptation.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent implements continuous adaptation throughout the training session rather than discrete adjustments. By maintaining a continuous feedback loop that constantly monitors user behavior and adjusts scenarios, the system maximizes learning efficiency without requiring intensive batch processing, thereby optimizing energy consumption during the training delivery phase.

Inventive Principle:

Principle #20Continuity of useful action

3Measurement precision

If personality biometric data is collected and analyzed, then accurate user profiling and personalized training are achieved, but data privacy concerns and security requirements increase

Engineering Contradiction:

Improveuser profiling accuracyVSAvoiddata privacy risks

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system applies local quality by processing and analyzing personality biometric data locally within the training system rather than centralizing all raw data. This distributed approach allows accurate user profiling to be achieved through localized analysis while reducing privacy risks associated with centralized data storage and transmission.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent transforms sensitive personality biometric data into processed personality profiles and behavioral patterns through parameter changes. By converting raw biometric measurements into aggregated personality characteristics and training-relevant metrics, the system maintains measurement precision for personalization while reducing data privacy risks through data transformation and abstraction.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250054406A1User personality traits classification for adaptive virtual environments in non-linear story paths

Publication Date: 2025.02.13 BRITISH TELECOM PLC

AI summary

User personality traits classification for adaptive virtual environments in non-linear story paths A method of providing adaptive training to a user in a training environment is provided. The method comprises generating, by a machine learning agent, a training scenario for a user, wherein the training scenario is generated based on a personality biometric profile and one or more personality characteristics of the user. The user is trained in a training session using the training scenario wherein, during the training session, the machine learning agent modifies the training scenario in real time based on user behavioural data that reflects the user's behaviour in the training session.