Nuance Communications, Inc.

Neil D Barrett

Ravi Kumar Kondadadi

Brian William Delaney

John E Ortega, Jr

Nasr Madi

Oscar Ferrandez-Escamez

A neural network embodiment comprises an input layer, an output layer and a filter layer. Each unit of the filter layer receives a filter layer input from a single preceding unit via a respective filter layer input connection. Each filter layer input connection is coupled to a different single preceding unit. The filter layer incentivizes the neural network to learn to produce a target output from the output layer for a given input to the input layer while simultaneously learning weights for each filter layer input connection. The weights learned cause the filter layer to reduce a number of filter layer units that pass respective filter layer inputs as non-zero values. When applied as an initial internal layer between an input layer and an output layer, the filter layer incentivizes the neural network to learn which neural network input features to discard to produce the target output.

Neural network with embedded filter layer

A method, computer program product, and computing system for processing content concerning a plurality of patients using a CAC system to define one or more billing codes concerning a social habit status of one or more patients of the plurality of patients. The one or more billing codes concerning the social habit status of the one or more patients are provided to a user for review. Feedback is received from the user concerning the accuracy of the one or more billing codes. The feedback concerning the one or more billing codes is automatically processed to define one or more confidence scores. The CAC system is trained based, at least in part, upon the one or more confidence scores.

Automated analysis system and method

A method and system of removing noise from a dictionary using a weighted graph is presented. The method can include mapping, by a noise reducing agent executing on a processor, a plurality of dictionaries to a plurality of vertices of a graphical representation, wherein the plurality of vertices is connected by weighted edges representing noise. The plurality of dictionaries may further comprise a plurality of entries, wherein each entry further comprises a plurality of tokens. The method can include selecting a subset of the weighted edges, constructing an acyclic graphical representation from the selected subset of weighted edges, and determining an ordering based on the acyclic graphical representation. The selected subset of weighted edges may approximate a solution to the Maximum Acyclic Subgraph problem. The method can include removing noise from the plurality of dictionaries according to the determined ordering.

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Longueuil, Canada

Neil D Barrett