Cross-Platform AI Character Data Synchronization via Synchro-Verse Database
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Solution Overview
Problem
Existing artificial intelligence systems for game characters are limited to specific applications and platforms, unable to share data or adapt across different environments, leading to a lack of cross-compatibility and self-evolution.
Innovation Solution
A cross-compatible artificial intelligence character system that allows virtual characters to learn from interactions across multiple applications and platforms, using a 'synchro-verse database' to store and share character data, enabling natural language communication and self-evolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If artificial intelligence systems are implemented for game characters within a single application, then character interaction quality improves, but cross-platform compatibility and data sharing capability deteriorate
Solution Approach 1:
The patent implements a universal character data structure that can be instantiated across multiple applications and platforms. The character system uses a standardized data model with core attributes (personality traits, backstory, skills) that remain consistent across different game environments, while allowing platform-specific extensions. This enables a character created in one application to be seamlessly transferred and recognized in another, resolving the contradiction between maintaining high interaction quality and achieving cross-platform compatibility.
Solution Approach 2:
The patent introduces a character data exchange intermediary layer that sits between the AI system and different application platforms. This intermediary handles data translation, normalization, and synchronization, allowing characters to maintain their identity and learned behaviors across platforms while adapting to platform-specific requirements. The intermediary resolves the contradiction by providing a standardized interface that preserves character integrity during cross-platform transitions.
2Adaptability or versatility
If character data is stored in a centralized database for cross-platform access, then data sharing and character evolution improve, but system complexity and integration requirements increase
Solution Approach 1:
The patent segments the character data storage system into modular components: a core immutable identity layer (stored centrally for consistency), a mutable experience layer (stored locally for quick access), and a synchronization layer (managing data exchange). This segmentation reduces integration complexity by allowing different parts of the system to access appropriate data layers independently, while still achieving comprehensive data sharing across platforms through the standardized synchronization protocol.
Solution Approach 2:
The patent implements parameter-based data access control that dynamically adjusts what character data is accessible based on platform, application, and user permissions. Rather than requiring full centralized access control mechanisms, the system uses simple parameter tags on data elements that automatically govern their accessibility and synchronization behavior, significantly reducing the complexity of implementing cross-platform data sharing while maintaining security and consistency.
3Adaptability or versatility
If characters learn from all interactions across multiple platforms, then character depth and personalization improve, but data management and processing requirements increase
Solution Approach 1:
The patent extracts only the essential learning signals from character interactions—specifically, changes in personality trait values, acquired skills, and significant narrative events—while discarding redundant data such as routine dialogue exchanges and repetitive actions. This extraction approach allows the system to maintain deep, personalized characters based on meaningful experiences across platforms without being overwhelmed by the full volume of interaction data, reducing data management requirements while preserving character depth.
Data Source
AI summary
A system and method where a player's chosen virtual character, either user personalized or pre-made, is able to infinitely learn from all the interactions that it has either with the player, other characters, non-player characters (NPCs), or game match experiences/results, whereby virtual character is then able to communicate with the player in a natural language voice and physical interaction in a separate environment, whereby the personality of the character virtual character is constantly shaped by the backstory and experiences it has in the applications it has been connected to or has been in communication with.