Asset Tagging Platform Using AI for Authentication and Value Maximization
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Solution Overview
Problem
Existing asset management systems struggle to efficiently tag, connect, manage, authenticate, and monetize 'dumb' devices and assets that lack network access, leading to difficulties in organization, tracking, and maximizing the value of these assets.
Innovation Solution
A global marketplace and exchange platform that utilizes automatic identification and data capture technologies, such as QR codes, RFID tags, and NFC, to tag and manage assets, coupled with a smart scale that connects to a hub or directly to the Internet for real-time data transmission, and leverages Machine Learning, Deep Learning, and Artificial Intelligence for marketing and monetization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic identification and data capture technologies (QR codes, RFID tags, NFC) are used to tag and manage assets, then asset organization and tracking efficiency is improved, but device complexity increases
Solution Approach 1:
The system divides asset management into separate functional modules: tagging (QR codes, RFID tags, NFC), data capture (smart scales), authentication (biometric verification), and marketplace operations. Each module operates independently but integrates through the central platform, reducing overall system complexity while improving productivity.
Solution Approach 2:
The patent introduces a central marketplace platform as an intermediary that connects all asset management functions. This platform mediates between diverse asset types and users, handling authentication, tracking, and monetization through standardized interfaces, thereby improving efficiency without proportionally increasing complexity.
2Reliability
If real-time data transmission is implemented through smart scales connected to hub or Internet, then real-time monitoring capability is improved, but energy consumption increases
Solution Approach 1:
The smart scales transmit weight data periodically rather than continuously, adjusting transmission frequency based on operational needs. This allows real-time monitoring capability while significantly reducing energy consumption compared to continuous transmission, as the system only communicates when weight changes occur or at scheduled intervals.
Solution Approach 2:
The system uses passive RFID tags and NFC technology that do not require their own power sources, instead harvesting energy from reader devices. This enables real-time identification and tracking of assets without adding energy consumption to the tagged items themselves, maintaining reliability while minimizing energy use.
3Reliability
If biometric authentication methods (fingerprint, facial recognition, voice) are used to verify user identity, then authentication security is improved, but operation time increases
Solution Approach 1:
The system performs biometric authentication in advance during device setup or first login, storing encrypted biometric templates locally. Subsequent access requires only simple verification against stored data, maintaining high security while reducing authentication time to seconds for repeated access operations.
Solution Approach 2:
The system uses partial biometric verification for low-risk operations (such as viewing asset information) and requires full multi-factor authentication only for high-risk operations (such as transferring assets or making purchases). This selective approach maintains strong security where needed while minimizing authentication time for routine operations.
4Productivity
If machine learning and artificial intelligence are leveraged for marketing and monetization, then asset value maximization is improved, but computational resources required increases
Solution Approach 1:
The system applies machine learning algorithms selectively to high-value tasks such as predicting asset resale values, optimizing pricing strategies, and identifying investment opportunities. Routine operations use simpler rule-based approaches, reducing overall computational resource requirements while maintaining effective asset value maximization through targeted AI application.
Solution Approach 2:
The system creates simplified digital models of asset characteristics and market patterns using pre-trained machine learning models. These models can be deployed on edge devices or mobile applications, reducing the need for constant cloud computational resources while maintaining accurate predictions for asset valuation and monetization strategies.
Data Source
AI summary
Systems and methods for a global marketplace, authentication service, and exchange for asset tagging, weighing, measuring, authenticating, and management. Users connect any item to the platform by scanning a tag affixed to the item. Scanned items are registered to a user's account. The platform deploys Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in order to promote efficient and effective marketing and monetization of a user's registered items, in addition to providing organizational, repair, and maintenance services for any item tagged by a user.