Classification using artificial intelligence strategies that reconstruct data using compression and decompression transformations

A method for determining whether a sample is in a class, comprising the steps of:

• a) obtaining optical information from the sample;
• b) using the optical information to provide an input dataset that comprises information indicative of the spectral data characteristics associated with the sample;
• c) causing a computer processor to access an AI model stored in a computer memory and to use the AI model to carry out steps comprising transforming information comprising the input dataset to provide a reconstructed dataset, said transforming comprising compressing and decompressing a flow of data derived from the information comprising the input dataset, wherein a reconstruction error associated with the input data set and the reconstructed dataset is indicative of whether the sample is in the class;
• and
• d) using information comprising the reconstruction error to determine if the sample is in the class.

A method of making a system that determines information indicative of whether a sample is in a class, comprising the steps of:

• a) providing a training sample set comprising a plurality of training samples associated with the class;
• b) providing an input dataset for each of the training samples, wherein each input dataset characterizes a corresponding training sample of the training sample set;
• c) providing an artificial intelligence (AI) model that transforms the input dataset of each training sample into an associated reconstructed dataset, wherein the transforming comprises compressing a flow of data and decompressing or expanding a flow of data, and wherein a reconstruction error associated with each reconstructed dataset characterizes differences between the input dataset for each training sample and the associated reconstructed dataset;
• and
• d) using information comprising the input datasets, the reconstructed datasets, and the reconstructions errors to train the AI model such that the reconstruction errors are indicative the training samples are in the class.

A method of making a system that determines information indicative of whether a sample is in a class associated with an authentic taggant system, comprising the steps of:

• a) providing the authentic taggant system, wherein the authentic taggant system exhibits spectral characteristics associated with an authentic spectral signature;
• b) providing a plurality of training samples, wherein each training sample comprises the authentic taggant system, and wherein the authentic taggant system exhibits spectral characteristics associated with an authentic spectral signature;
• c) obtaining the spectral characteristics of the authentic spectral signature from each of the training samples;
• d) using the spectral characteristics obtained from the training samples to provide an input dataset for each of the training samples, wherein each of the input datasets comprises information indicative of the spectral characteristics exhibited by the authentic taggant system;
• c) providing an artificial intelligence (AI) model that compresses and decompresses a flow of data from each of the input datasets to provide an associated, reconstructed dataset, wherein a reconstruction error associated with each of the reconstructed data sets characterizes differences between each input dataset and the associated reconstructed dataset;
• and
• d) using information comprising the input datasets, the reconstructed datasets, and the reconstruction errors to train the AI model such that the reconstruction errors are indicative that the training samples are in the class.