Electromagnetic Game Systems with Offline AI and RFID

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Solution Overview

Problem

Existing AI technologies heavily rely on cloud computing and continuous internet connectivity, leading to data privacy concerns, high operational costs, and accessibility barriers, especially in regions with limited or unreliable internet access, and lack multi-modal capabilities.

Innovation Solution

An offline AI engine with a multi-modal capability integrated with an embedded vector database, enabling local data processing and management, allowing seamless operation across diverse platforms and devices, including smartphones and edge computing devices, without constant internet access.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Power

If cloud computing and continuous internet connectivity are used, then AI processing power and model access are improved, but data privacy security deteriorates and operational costs increase

Engineering Contradiction:

ImproveAI processing powerVSAvoiddata privacy security

Core Design Contradiction:

PowerVSReliability

Solution Approach 1:

The patent segments the AI system into distributed edge devices that can independently run local AI models, separating the system from centralized cloud dependency. This allows processing power to be distributed across multiple devices while maintaining data privacy locally, resolving the contradiction between processing capability and data security.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces blockchain technology as an intermediary layer that enables secure peer-to-peer AI model sharing and verification without centralized control. This intermediary mechanism allows devices to access powerful AI models while maintaining data privacy through cryptographic verification and decentralized trust, eliminating the need to trade security for processing power.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If cloud-based AI services are used, then access to advanced AI models is improved, but accessibility in regions with limited internet connection deteriorates

Engineering Contradiction:

Improveaccess to AI modelsVSAvoidaccessibility in offline regions

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The patent divides the AI ecosystem into independent edge devices that can autonomously run AI models locally without requiring continuous cloud connectivity. This segmentation enables devices in regions with limited internet access to fully utilize AI capabilities offline, while still allowing optional cloud synchronization when connectivity is available.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements preliminary downloading and caching of AI models to local edge devices before offline operation is needed. This preliminary action ensures that advanced AI models are already available on-device, eliminating any accessibility barriers in regions with limited or no internet connection.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If multiple AI models are deployed across devices, then functional versatility is improved, but device complexity increases

Engineering Contradiction:

Improvefunctional versatilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent enables lightweight copying of AI models between devices through blockchain-verified model sharing. Instead of each device independently developing or hosting multiple complex models, devices can securely copy and verify models from other devices, achieving functional versatility while minimizing individual device complexity through shared model repositories.

Inventive Principle:

Principle #26Copying

4Reliability

If local AI processing is implemented, then data privacy security is improved, but operational costs increase due to device resource requirements

Engineering Contradiction:

Improvedata privacy securityVSAvoiddevice energy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent merges computational resources across multiple edge devices through the blockchain network, allowing devices to pool their processing power for running AI models. This distributed computing approach reduces the energy burden on individual devices while maintaining local data privacy, as the combined network resources handle complex processing tasks efficiently.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250278671A1Electromagnetic, physical game-systems with integrated ai and RFID technology

Publication Date: 2025.09.04 BITFORGE DYNAMICS LLC

AI summary

A method for operating artificial intelligence (AI) models on a device without internet connectivity is disclosed. The method involves creating an embedded vector database local to the device, which contains contextual information for the AI models. This database is utilized to automatically generate and engineer prompt inputs for the AI models, facilitating their operation in an offline environment. The invention allows for the effective use of AI models in scenarios where internet connectivity is unavailable, providing robust and contextually appropriate AI functionality.