Predictive System Based on a Semantically Trained Artificial Neural Network and ANN

A processor-based artificial neural network (ANN) comprising:

• an output level capable of generating a property vector in property space;
• an input layer;
• and
• a plurality of intermediate hidden layers separating the output level from the input layer, wherein the input layer and the hidden layers each contain a plurality of weight-adjustable neurons and where neural pathways are established between the plurality of neurons in the input layer to the output level based on weighting factors applied by neurons in the input layer and the hidden layers;
• wherein:
• the neural pathways and adjustable weights of the neurons are configured based on a training regime for the ANN that:
• subjects the ANN to a multiplicity of pairs of training data files sharing a common modality and where for each pair of training data files there are two independently derived separation distances, namely:
• a first independently derived separation distance that expresses a measure of relative distance between a first pair of training data files in semantic embedding space, where the first independently derived separation distance is obtained from natural language processing “NLP” of a semantic description of the nature of the data associated with each one of the first pair of training data files;
• and
• a second independently derived separation distance that expresses a measure of relative distance similarity between the first pair of training data files in property embedding space, where the second independently derived separation distance is a property distance derived from measurable qualities extracted from each one of the first pair of training data files, and
• wherein training of the ANN by a backpropagation process uses output vectors generated at the output level of the ANN from processing of said multiplicity of pairs to adjust weighting factors to adapt iteratively the ANN during training to converge distances of generated output vectors, in property embedding space, towards corresponding pairwise semantic distances in semantic space, and
• wherein the common modality relates to one of comparable:
• (i) video data files to video data files;
• or alternatively (ii) audio data files to audio data files;
• or alternatively (iii) static image files to static image files;
• or alternatively (iv) text files to text files.