Alerion™ AI Classification Technology

Pushing past the limits of conventional modeling

Microtrace’s Alerion generative AI Classification technology goes far beyond limitations of conventional support vector machine (SVM) and neural network (NN) modeling. Alerion Classification is trained using artificial intelligence to recognize specific targets and has an additional, expansion class for previously unknown samples. Samples falling outside the trained classes are uniquely classified into the additional class until they are trained otherwise—which is vastly different than conventional modeling where unknown samples are forced into a trained class.  Since model creation and continuous development and optimization can be done using data collected from one or more sensors, a limitless number of internet of things (IOT) devices can be used for field or market surveillance to create a true smart network. None of this is possible with conventional SVM or NN modeling. 

Adaptive learning

Alerion technology’s ability to recognize samples from unknown classes, allow the Microtrace system to alert the user of their presence and specific groupings (numbers).  This allows for adaptive learning and improvement of the model over time:

Alerion Classification has uses in a wide range of industrial and life science settings

Originally developed for unique challenges posed by taggant signatures, the Alerion generative AI classification can be used to address a wide variety of commercial or research-data classification challenges. Possibilities are endless for this powerful technology—including diagnostics, production quality-control, mechanical equipment monitoring or personnel identification. Sample applications include scenarios involving different kinds of source data:

• Waveform (spectral, electrical, acoustic) target(s) with a plus or minus variation
• Multiple targets with varied ranges (ability to optimize individual target thresholds)
• Possibility of unknown classes in data collection (ability to detect previously unknown classes)
• Applications that can benefit from an early-warning system to detect drift or change in target(s), or a previously unknown class appearing similar to a known target class.
• Discrete data in which the combination of values is correlated with a particular condition or characteristic.

Alerion generative AI Classification overcomes conventional classification challenges for samples that have variation, change over time or contain new and unpredictable samples.

Conventional SVM and NN modeling, classification and identification strategies are unable to solve the unique challenges posed when characteristics of samples in known classes vary within a class, change over time or contain new and unpredictable samples. The challenge to classify taggant signatures of a secure taggant system is an example of this. Taggant signatures have natural variation, making it difficult for reliable detection when counterfeits and unknown competitors are in the mix.

The Alerion AI classification technology was developed to handle inherent and dynamic sample variation.  The Alerion AI classification technology uses cutting-edge AI chemometric methodologies. “Chemometrics” refers to using data-driven means for information extraction from chemical systems, including, for example, taggant signatures.

Having the unique ability to adjust target thresholds individually and automatically, the Alerion AI classification technology is highly accurate and automatically alerts you when new or previously unknown counterfeit or competitor signatures enter the market.  Class boundaries can be updated and new classes can be established as data is harvested from samples in the field.

Patent-pending technology for increased accuracy and intelligence

Taggant signatures are often identified with traditional chemometric techniques such as SVM or AI NN. These techniques work well in a controlled lab setting where all signatures are known with little variation; however, SVM and NN models are only able to classify samples into known classes that the system has previously recognized.

In other words, everything analyzed by SVM or NN models is classified based on inputs from only the known models. This creates a problem for taggant signatures and brand protection. New and previously unknown counterfeit signatures and other competitors will be misclassified as genuine, or as a known competitor and will go undetected as a new kind of class in the marketplace.

In stark contrast and advantageously, proprietary Alerion AI classification not only identifies known classes used to train the system, but then in a significant step beyond the capability of SVM or NN systems, the Alerion AI classification also recognizes samples in classes never encountered before, even sending out alerts when new/unknown signatures are detected. This can be done in combination with individually adjusting thresholds for each signature, something traditional SVM and NN methods cannot achieve.  In security applications, individually adjusting thresholds allows the Alerion classification technology to separate any samples that get close to an authentic spectral signature and exclude counterfeits, keeping your product secure.