Self-adaptive inventory tracking with automated ML updates
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Solution Overview
Problem
Existing asset tracking systems, particularly in retail and warehouse environments, face challenges in adaptively updating machine learning models due to frequent changes in infrastructure, requiring manual intervention for retraining and relabeling, which is time-consuming and inefficient.
Innovation Solution
The implementation of a self-adaptive system that automates the collection of labeled training data using indoor positioning systems (IPS) and RFID readers with WIFI and BLUETOOTH radios, allowing for unsupervised updates to machine learning applications, enabling online adaptiveness and reduced human effort in tracking fixtures and inventory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used for retraining and relabeling machine learning models, then model accuracy can be maintained, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system automatically collects labeled training data through indoor positioning systems and RFID readers, and performs unsupervised updates to machine learning models without requiring manual intervention. The system serves itself by autonomously maintaining model accuracy through automated data collection and retraining processes.
Solution Approach 2:
The system continuously collects and prepares labeled training data in advance through automated monitoring of asset locations and fixture movements. By having training data ready beforehand, the system can quickly retrain models when needed without time-consuming manual data collection and labeling processes.
2Adaptability or versatility
If frequent infrastructure changes are accommodated, then system adaptability improves, but manual retraining requirements increase
Solution Approach 1:
The system continuously monitors asset locations, fixture movements, and infrastructure changes through indoor positioning systems and RFID readers. This feedback loop automatically detects when infrastructure changes occur and triggers unsupervised retraining of machine learning models, enabling the system to adapt to frequent changes without increasing manual intervention.
Solution Approach 2:
The system dynamically adjusts to infrastructure changes by automatically detecting movements of fixtures and recalibrating location mappings in real-time. The machine learning models are continuously updated based on new data from the environment, allowing the system to remain adaptable while reducing operational complexity.
3Productivity
If automated data collection is implemented, then productivity improves, but system complexity increases
Solution Approach 1:
The system uses multi-functional devices that serve multiple purposes: indoor positioning systems not only track asset locations but also provide data for machine learning training, while RFID readers both identify assets and contribute to fixture movement detection. This consolidation reduces overall system complexity while maintaining high productivity through automated operations.
Data Source
AI summary
An embodiment of a semiconductor package apparatus may include technology to associate an asset to a fixture with a device positioned proximate to the fixture, and determine a location of the fixture based on a location of the device. Other embodiments are disclosed and claimed. Non-limiting example applications may include shipping, logistics, warehouse asset tracking, retail, etc.