Cross-Platform Account Matching for Metaverse Store Normalization
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Solution Overview
Problem
Existing systems face challenges in efficiently utilizing computing, processing, and communication resources in the context of rapidly changing internet content and diverse metaverse environments, particularly in cross-platform data management and user account unification.
Innovation Solution
A matching engine and adaptation engine are introduced to compare metaverse metadata, unify cross-platform accounts, and adapt content and user experiences based on device and platform capabilities, using network interfaces and processors to control physical feedback in metaverse human-machine interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cross-platform metaverse accounts are unified through metadata comparison and matching, then user experience consistency and resource utilization improve, but system complexity and data processing requirements increase
Solution Approach 1:
The matching engine processes metadata by dividing it into discrete comparable elements (user data, connection times, disconnection times, activity durations) and systematically comparing corresponding segments across different metaverse platforms to identify matches, thereby managing complexity through structured segmentation
Solution Approach 2:
The matching engine acts as an intermediary component that receives metadata from multiple metaverse platforms, performs unified comparison and matching operations, and outputs matched account identifiers, thereby simplifying the overall system architecture through a dedicated mediating component
2Measurement precision
If metaverse metadata is collected and processed from multiple platforms, then user identification accuracy improves, but communication and processing resource consumption increase
Solution Approach 1:
The matching engine performs comparisons using selected metadata elements (user data, connection times, disconnection times, activity durations) rather than processing all possible data, achieving sufficient identification accuracy while optimizing resource utilization through partial action
Solution Approach 2:
The system changes the state of metadata from raw unprocessed data to structured comparable parameters (connection times, disconnection times, activity durations), enabling efficient processing and accurate matching while reducing overall resource consumption through parameter transformation
3Ease of operation
If behavior adaptation is implemented across metaverse platforms, then user experience personalization improves, but system control complexity increases
Solution Approach 1:
The adaptation engine implements universal behavior adaptation mechanisms that function across multiple metaverse platforms using the same matched account identifiers, enabling personalized user experiences through a single multi-functional system rather than separate platform-specific solutions
Data Source
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AI summary
The present specification provides a multiplatform virtual retail store engine. The specification can have application to client devices with augmented or virtual reality hardware that interact with different platforms with metaverse capabilities. Rich experiences are provided on client hardware while making efficient use of available processing, memory and communication resources. Embodiments discuss the provision of a single retail store model which is dynamically adapted for generation across the plurality of different platforms according to the different metaverse capabilities. Embodiments also discuss include racking of the same user across different accounts on different metaverse platforms.