Variable Ink Product Labels for Environmental Exposure Detection
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Solution Overview
Problem
Enterprise organizations face challenges in verifying if products have been exposed to adverse environmental conditions, which can lead to spoilage or deterioration, necessitating a reliable and sustainable method to monitor product conditions without electronic devices.
Innovation Solution
A system using variable inks on product labels that change color in response to environmental factors, combined with vision and olfactory sensors and machine learning models, to determine product status and alert users to potential issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electronic monitoring devices (IoT devices) are used to track environmental conditions, then measurement precision and reliability improve, but device complexity and environmental burden increase
Solution Approach 1:
The patent extracts the monitoring function from complex electronic IoT devices and embeds it directly into the product label through variable ink materials. The label itself becomes the sensor, eliminating separate electronic monitoring devices while maintaining environmental condition tracking capability.
Solution Approach 2:
The patent creates an analog copy of electronic monitoring data through visual color changes in variable ink. Instead of digital readings from electronic sensors, the system uses colorimetric responses that replicate environmental condition information in a visually detectable format.
2Reliability
If electronic monitoring devices are deployed, then reliability of condition verification improves, but environmental burden and maintenance requirements worsen
Solution Approach 1:
The patent employs disposable product labels with variable ink that require no maintenance, power, or recycling infrastructure. These labels are discarded with the product packaging, eliminating the environmental burden of maintaining electronic monitoring devices while providing reliable condition verification throughout the product lifecycle.
Solution Approach 2:
The variable ink labels autonomously monitor environmental conditions without requiring external power sources, data collection infrastructure, or maintenance. The labels self-verify product conditions through their colorimetric responses, eliminating the need for active electronic monitoring systems.
3Device complexity
If variable ink labels are used instead of electronic devices, then environmental burden and device complexity reduce, but measurement precision and automation extent may worsen
Solution Approach 1:
The patent introduces vision capturing devices and machine learning models as intermediaries that bridge the analog variable ink labels and digital automation systems. These intermediaries translate visual color information into automated condition assessments, maintaining high extent of automation while using simple variable ink labels.
Solution Approach 2:
The patent replaces electronic sensor systems with an optical detection system using vision capturing devices. Machine learning models process visual data from the variable ink labels, substituting electronic measurement mechanisms with optical and computational approaches that achieve comparable automation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides reliable condition monitoring of products, reducing environmental and maintenance burdens while ensuring product safety and sustainability by flagging improper products for review or action.
Implementation Method 1
the variable ink is a thermochromatic ink that changes color based on a temperature range
Implementation Method 2
the variable ink is a photochromic ink that changes colors based on temperatures exposed to sunlight
Data Source
AI summary
A method is provided, the method including obtaining one or more vision machine learning—artificial intelligence (ML-AI) models associated with a product and obtaining one or more images of a product label of the product. The product label indicates a variable ink that changes colors based on environmental aspects. The method also includes determining condition information indicating a status of the product label based on the one or more vision ML-AI models and the one or more images of the product label indicating the variable ink, and outputting an indicator indicating the status of the product label based on the condition information.