Sensor Tag Asset Tracking for Non-Network Equipment Monitoring
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Solution Overview
Problem
Existing technologies struggle to effectively manage and monitor non-network enabled assets, such as manufacturing inventories, office equipment, and medical equipment, due to their lack of networking capabilities, making it difficult to organize, locate, secure, authenticate, share, manage, and generate intelligence from these assets.
Innovation Solution
A system utilizing machine learning and artificial intelligence, combined with environmental and location data, to tag, connect, and manage non-network enabled assets through an intelligent asset management platform that includes sensors to collect data and send alerts or instructions based on asset conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tags with sensors are attached to non-network enabled assets, then asset monitoring and data collection capabilities are improved, but device complexity and cost increase
Solution Approach 1:
The system divides the asset management functionality into separate components: simple tags with sensors attached to assets, intermediary readers that collect data from tags, and a centralized cloud platform that processes and analyzes data. This segmentation allows tags to remain simple while achieving sophisticated monitoring capabilities through the distributed system architecture.
Solution Approach 2:
The patent introduces readers as intermediary devices that bridge the gap between simple tags and the cloud platform. Readers collect data from multiple tags, handle communication protocols, and transmit aggregated data to the cloud, thereby simplifying the tag design while maintaining robust monitoring capabilities.
2Loss of information
If environmental sensors are integrated into tags, then environmental data collection is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic data collection and transmission cycles rather than continuous operation. Sensors collect environmental data at intervals, and tags transmit data to readers only when triggered by specific events or at scheduled times, significantly reducing energy consumption while maintaining adequate monitoring coverage.
Solution Approach 2:
The patent employs sensors that can operate in different modes or parameter settings based on environmental conditions. For example, sensors may adjust their measurement frequency, resolution, or activation thresholds dynamically to optimize between data quality and energy consumption based on the specific context.
3Loss of information
If AI and machine learning are implemented in the asset management platform, then intelligence generation and pattern recognition are improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary data processing, filtering, and aggregation at the edge devices (readers and tags) before transmitting data to the cloud platform. This preliminary action reduces the volume and complexity of data requiring AI processing, enabling faster analysis while maintaining intelligent insights.
Solution Approach 2:
The patent replaces complex centralized AI processing with a distributed architecture where simpler rule-based systems operate at the edge and more sophisticated AI/ML models run selectively in the cloud. This substitution optimizes the balance between computational power and processing speed by matching the complexity of analysis to the urgency and importance of specific tasks.
Data Source
AI summary
Systems, apparatuses, and methods for asset tagging and management. Users connect any asset to the platform by scanning a tag affixed to the asset. Tagged assets 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 management of a user's tagged assets, in addition to providing organizational, repair, and maintenance services for any tagged assets. The system incorporates location data and/or environmental data to manage the tagged assets.