Analytical neural network intelligent interface machine learning method and system
AssigneeREMTCS Inc.
InventorsTommy Xaypanya, Richard E. Malinowski
Life of this patent
Estimating the term…
Abstract
A learning framework and methods of machine learning are disclosed. Specifically, an Analytical Neural Network Intelligent Interface (ANNII) is disclosed that includes the ability to analyze incoming data in substantially real-time and determine whether or not the data is statistically anomalous data. Learning models can then be updated depending upon whether or not the data is determined to be statistically anomalous data or not.
Claims20
A method, comprising:
- receiving a data input at a computer learning framework;
- decomposing the data input into elemental pieces;
- providing the elemental pieces of the data input to a statistical analysis layer where the elemental pieces are compared to one or more statistical models to determine if the data input corresponds to a statistically anomalous event;
- and
- at least one of marking the data input as statistically anomalous and updating the one or more statistical models.
A non-transitory computer-readable medium comprising processor-executable instructions that, when executed by a processor, perform a method, the method comprising:
- receiving a data input at a computer learning framework;
- decomposing the data input into elemental pieces;
- providing the elemental pieces of the data input to a statistical analysis layer where the elemental pieces are compared to one or more statistical models to determine if the data input corresponds to a statistically anomalous event;
- and
- at least one of marking the data input as statistically anomalous and updating the one or more statistical models.
A machine-learning system, comprising:
- a microprocessor configured to execute instructions stored in computer memory;
- and
- computer memory including:
- a computer learning framework that, when executed by the processor, is configured to receive a data input, decompose the data input into elemental pieces, provide the elemental pieces of the data input to a statistical analysis layer where the elemental pieces are compared to one or more statistical models to determine if the data input corresponds to a statistically anomalous event, and at least one of mark the data input as statistically anomalous and update the one or more statistical models.
Dependencies are inferred from the claim text.