Digital Identity Mediation for Seamless Virtual Environment Transfers
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Solution Overview
Problem
Existing technologies are inefficient and cumbersome when users need to move from one virtual environment to another, requiring users to create new accounts and adjust settings, leading to time consumption and frustration, especially when moving as a group.
Innovation Solution
A computer-implemented method that identifies a cluster of users by learning from their interactions, generates a secure portal, creates a guest profile and avatar in the new environment, and adapts the avatar based on the environment's theme, while prefilling the profile with data from previous environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users create new accounts and adjust settings in each virtual environment, then they can access each environment, but the process becomes time-consuming and cumbersome
Solution Approach 1:
The system performs preliminary actions by pre-generating guest profiles and avatars in target virtual environments before users actually need to access them. Machine learning models analyze user interactions in current environments to predict and prepare digital identities for future environments, eliminating the need for users to manually create accounts when they encounter new virtual spaces.
Solution Approach 2:
The system enables self-service by using machine learning to automatically generate and adapt digital identities without user intervention. The ML models autonomously analyze user behavior patterns, classify avatar characteristics, and create guest profiles in new environments, allowing the system to serve itself in managing cross-environment user identities.
2Ease of operation
If users move as a group between virtual environments, then they can maintain social connections, but the complexity of coordinating multiple users increases
Solution Approach 1:
The system merges multiple user profiles by clustering users based on their interaction patterns and generating coordinated guest profiles for the entire group. The machine learning system identifies user clusters in current environments and creates corresponding guest avatars in target environments, allowing groups to move together seamlessly without individual coordination overhead.
Solution Approach 2:
The system achieves universality by creating a single portal generation mechanism that serves multiple users simultaneously. The portal system is designed to handle individual and group transitions uniformly, generating appropriate guest profiles whether for one user or many, thereby simplifying the coordination complexity for group movements.
3Reliability
If users maintain consistent avatar representation across environments, then their digital identity is preserved, but adapting to different environment themes becomes difficult
Solution Approach 1:
The system implements dynamics by making avatar characteristics adaptable rather than static. Machine learning models classify avatar features and dynamically adjust them based on the theme and requirements of the target virtual environment. This allows digital identities to maintain core consistency while flexibly adapting appearance and properties to suit different environmental contexts.
Solution Approach 2:
The system applies parameter changes by modifying specific avatar attributes (such as appearance, accessories, or behavioral characteristics) based on the target environment's theme. The machine learning system identifies which avatar parameters need adjustment and changes them appropriately, preserving the user's core digital identity while adapting to environmental requirements.
Data Source
AI summary
A computer-implemented method for identifying, by a processor set, a cluster of users by learning from their interactions in a first virtual environment. The processor set may further classify characteristics of an avatar associated with each user of the cluster of users in the first virtual environment and generate a secure portal visible only to users of the cluster of users to teleport to a second virtual environment. The processor set may also generate a guest profile and a guest avatar in the second virtual environment for each user of the cluster of users. The processor may also adapt the guest avatar for each user of the cluster of users based on a theme of the second virtual environment and generate a new profile prefilled with data collected from previous virtual environments that contain a subset of profile data required for the second virtual environment.