US Patent Application 17729330. MACHINE LEARNING BASED MONITORING FOCUS ENGINE simplified abstract

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MACHINE LEARNING BASED MONITORING FOCUS ENGINE

Organization Name

Microsoft Technology Licensing, LLC


Inventor(s)

Kiran Rama of Bangalore (IN)


MACHINE LEARNING BASED MONITORING FOCUS ENGINE - A simplified explanation of the abstract

  • This abstract for appeared for US patent application number 17729330 Titled 'MACHINE LEARNING BASED MONITORING FOCUS ENGINE'

Simplified Explanation

This abstract describes a machine learning system that monitors computing systems to predict if they will continue running smoothly or if they will experience issues or failures. The system collects numeric and text data from the computing systems and uses this information to determine the likelihood of a particular state or condition. The text data is processed using word embedding techniques to generate embedded text features, which are then combined with numerical features and local characteristic information as inputs to a neural network. The neural network analyzes these inputs and provides an output that predicts the likelihood of the state being monitored.


Original Abstract Submitted

A machine learning based monitoring focus engine is provided. Numeric and text features are collected from a computing system(s) and are utilized to determine if the system(s) will continue to run without issues or failures. That is, external characteristic information is received that corresponds to a predicted likelihood of a state that is associated with a processing system, and textual and numerical portions of the external characteristic information are mapped to neural network inputs. Word embedding is performed on the textual portion to generate embedded text features, and a plurality of inputs are provided to the neural network, where the plurality of inputs includes at least embedded text features, numerical features based on the numerical portion, and local features based on local characteristic information. Accordingly, the predicted likelihood of the state is determined based at least on an output of the neural network from the plurality of inputs.

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