Virtual Environment Augmentation via Real-World Non-User Action Detection
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Solution Overview
Problem
Existing virtual environments are often tailored for specific user groups, leading to inefficiencies in computational resource usage and user experience, as they are not intuitive or immersive for all users.
Innovation Solution
A method and system that augment virtual environments by identifying actions of non-users in the real environment and modifying the behavior of characters in the virtual environment based on these actions, allowing for a more personalized and immersive experience without explicit scripting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual environments are tailored for specific user groups, then the environment can be optimized for that group's needs, but computational resource usage increases and other users find the environment less intuitive
Solution Approach 1:
The system automatically adapts the virtual environment by analyzing real-world data about users and non-users, eliminating the need for manual environment creation for each user group. The environment self-adjusts based on captured data, reducing computational resources while maintaining adaptability across diverse user groups
Solution Approach 2:
The system modifies environmental parameters dynamically based on user characteristics extracted from real-world data. By changing parameters such as character behavior, dialogue, and visual elements according to analyzed user patterns, the system achieves adaptability without requiring separate environment creations for each group
2Adaptability or versatility
If multiple separate adaptions are created for different user groups, then each group receives a tailored environment, but computational resource usage increases significantly
Solution Approach 1:
A single virtual environment serves multiple user groups by dynamically adapting to different users based on real-world data analysis. The environment performs multiple functions - it can be customized for different groups without creating separate versions, reducing complexity while maintaining universal applicability
Solution Approach 2:
Instead of creating entirely separate environment copies for each user group, the system uses data from real-world users to generate adapted versions of the same environment. This copying approach maintains the original environment structure while creating variations through data-driven modifications
3Adaptability or versatility
If users manually customise their virtual environment, then the environment can be personalised to their needs, but the customisation process is cumbersome and time consuming
Solution Approach 1:
The system performs automatic personalisation by analyzing real-world data about the user and non-users, eliminating the need for manual customisation. The environment self-adapts to user preferences and contexts, saving time while maintaining personalisation quality
Solution Approach 2:
The system performs customisation actions in advance by pre-analyzing user data and pre-configuring environment parameters before the user interacts with the virtual environment. This preliminary action eliminates the need for time-consuming manual setup while maintaining personalized experience
4Ease of operation
If the degree of user customisation is restricted, then the customisation process remains simple, but users cannot customise the environment to the extent needed for their specific needs
Solution Approach 1:
The system automatically performs extensive customisation based on real-world data analysis, achieving deep personalisation without requiring user effort. The environment self-adjusts to the full extent needed for user needs while maintaining ease of operation through automatic rather than manual control
Data Source
AI summary
There is provided a method for augmenting a virtual environment. The method comprises identifying one or more actions of one or more non-users in a real environment of a user who is interacting with a virtual environment, in dependence on data relating to the non-users captured using one or more sensors, characterising the one or more identified actions of the non-users relative to one or more corresponding reference actions, and modifying an aspect of behaviour of one or more characters in the virtual environment that are not controlled by the user, in dependence on the characterisation of the one or more identified actions of the non-users.