Abstract: The AI-Based Neighbor Discovery Search Engine Apparatuses, Methods and Systems (“ANDSE”) transforms embedding neural network training request, object search request inputs via ANDSE components into embedding neural network response, object search response outputs. An embedding neural network training request associated with a set of context objects is obtained. Sample similarity evaluation metrics are determined. For each context object, a set of positive target samples that satisfy the sample similarity evaluation metrics for the respective context object is determined. For each context object and each positive target sample in the respective set of positive target samples, a training example comprising the respective context object and a positive target sample is added to a training set. Configuration parameters for an embedding neural network are determined. The embedding neural network is trained using training examples in the training set.

Abstract: The AI-Based Context Evaluation Engine Apparatuses, Methods and Systems (“ANDSE”) transforms embedding neural network training request, object search request, object evaluation request inputs via ANDSE components into embedding neural network response, object search response, object evaluation response outputs. Comparable context objects for a context object are determined. Relative values of the comparable context objects are calculated with regard to a benchmark object and used to calculate a relative value of the context object. The relative value is converted to a predicted price for the context object. Bid ask spreads for bid request objects are calculated. A spread win decision tree is constructed based on the calculated bid ask spreads and used to generate a spread win probability map for the context object. A spread is selected from the spread win probability map based on a desired winning bid confidence level and a bid price for the context object is calculated.

Abstract: The AI-Based Neighbor Discovery Search Engine Apparatuses, Methods and Systems (“ANDSE”) transforms embedding neural network training request, object search request inputs via ANDSE components into embedding neural network response, object search response outputs. An embedding neural network training request associated with a set of context objects is obtained. Sample similarity evaluation metrics are determined. For each context object, a set of positive target samples that satisfy the sample similarity evaluation metrics for the respective context object is determined. For each context object and each positive target sample in the respective set of positive target samples, a training example comprising the respective context object and a positive target sample is added to a training set. Configuration parameters for an embedding neural network are determined. The embedding neural network is trained using training examples in the training set.