Metaverse Interaction Caching Using ML Complexity Scores

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Solution Overview

Problem

Existing metaverse environments face performance and stability issues due to delayed information caching, leading to service delays as large numbers of users interact, and current caching methods strain network and bandwidth resources.

Innovation Solution

A real-time dynamic caching platform using non-fungible tokens (NFTs) that employs a machine learning model to analyze user interactions and generate complexity scores, allowing for efficient caching of interaction information based on predefined caching rules, and continuously updates these rules as interactions evolve.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If information is cached only after user request, then network bandwidth is conserved, but service delays occur

Engineering Contradiction:

Improvenetwork bandwidth consumptionVSAvoidservice delay

Core Design Contradiction:

Loss of energyVSLoss of time

Solution Approach 1:

The system performs preliminary caching of interaction information before it is actually needed. The machine learning model predicts future information needs based on historical patterns and pre-caches relevant data, eliminating service delays while avoiding unnecessary bandwidth consumption through selective pre-caching based on prediction accuracy

Inventive Principle:

Principle #10Preliminary action

2Productivity

If real-time dynamic caching is implemented, then service performance is improved, but system complexity increases

Engineering Contradiction:

Improveservice performanceVSAvoidcaching system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The caching system operates autonomously using a machine learning model that automatically analyzes interaction patterns, generates complexity scores, and determines caching decisions without manual intervention. The system self-optimizes by continuously learning from new data, reducing the operational complexity burden despite the advanced algorithms employed

Inventive Principle:

Principle #25Self-service

3Productivity

If machine learning model continuously updates caching rules, then caching efficiency is optimized, but computational resources are consumed

Engineering Contradiction:

Improvecaching efficiencyVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The machine learning model updates caching rules periodically based on accumulated data rather than continuously processing every single interaction. This batch processing approach maintains caching efficiency by regularly incorporating new patterns while significantly reducing computational overhead compared to real-time continuous updates

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS12554639B2Real-time dynamic caching platform for metaverse environments using non-fungible tokens

Publication Date: 2026.02.17 BANK OF AMERICA CORP

AI summary

Aspects of the disclosure relate to a dynamic caching platform. The dynamic caching platform may train a machine learning model based on historical complexity score information. The dynamic caching platform may receive information streams from a client metaverse device and a metaverse host system. The dynamic caching platform may generate a complexity score based on the interaction information streams using the machine learning model. The dynamic caching platform may compare the complexity score to complexity thresholds. Based on the comparison, the dynamic caching platform may identify caching rules. The dynamic caching platform may cache interaction information based on the caching rules. The dynamic caching platform may update the complexity score using the machine learning model. The dynamic caching platform may update the caching rules based on the updated complexity score. The dynamic caching platform may cache interaction information based on the updated caching rules.