Abstract: Tool detects anomalies in textual data and determines event sequences normality against broader data. Representative event samples are gathered for source, and a top-level wordpiece tokenizer is built. Tokenizer is serialized and stored. Additional samples are gathered, and encodings pulled from inputs via the tokenizer. For a given variable, the algorithm either polls encodings in groups of time steps or pads encodings up to these time steps. A square matrix of observations is created, whose basis is expanded with a random matrix and added dimensions. The basis is expanded via a random projection. The matrices are then passed to a variational autoencoder. To minimize information loss when sending encodings to compressed latent space, stochastic subgradient methods are used. Upon convergence, the trained model is saved. Observed errors are bootstrapped on the holdout set. If new events fall outside tolerances set via bootstrap series is declared anomalous.

Abstract: A system and method assists a dispatcher in responding to callers requesting medical transfers for a patient. A computer implemented medical transfer protocol includes interrogatories for a dispatcher to ask a caller to generate an appropriate medical transfer. The medical transfer protocol selects a medical vehicle with the appropriate equipment, medicinal inventory, and trained medical personnel.