Abstract: The present disclosure describes a method, system, and apparatus for using a machine learning system to configure and optimize complex, distributed computer networks. The machine learning system receives an input related to a computer network and classifies the input using either a supervised learning approach or an unsupervised learning approach. From the classification of the input, the machine learning system builds a first training domain and determines a steady state network configuration for the computer network. After determining a steady state network configuration for the computer network, the machine learning system receives a plurality of inputs from one or more sensors or agents distributed throughout the computer network. The machine learning system compares the plurality of inputs to the steady state network configuration to detect a deviation from the first steady state network configuration.

Abstract: The present disclosure describes a method, system, and apparatus for using a machine learning system to configure and optimize complex, distributed computer networks. The machine learning system receives an input related to a computer network and classifies the input using either a supervised learning approach or an unsupervised learning approach. From the classification of the input, the machine learning system builds a first training domain and determines a steady state network configuration for the computer network. After determining a steady state network configuration for the computer network, the machine learning system receives a plurality of inputs from one or more sensors or agents distributed throughout the computer network. The machine learning system compares the plurality of inputs to the steady state network configuration to detect a deviation from the first steady state network configuration.

Abstract: The present disclosure describes a method, system, and apparatus for using a machine learning system to configure and optimize complex, distributed computer networks. The machine learning system receives an input related to a computer network and classifies the input using either a supervised learning approach or an unsupervised learning approach. From the classification of the input, the machine learning system builds a first training domain and determines a steady state network configuration for the computer network. After determining a steady state network configuration for the computer network, the machine learning system receives a plurality of inputs from one or more sensors or agents distributed throughout the computer network. The machine learning system compares the plurality of inputs to the steady state network configuration to detect a deviation from the first steady state network configuration.

Abstract: The present disclosure describes a method, system, and apparatus for using a machine learning system to configure and optimize complex, distributed computer networks. The machine learning system receives an input related to a computer network and classifies the input using either a supervised learning approach or an unsupervised learning approach. From the classification of the input, the machine learning system builds a first training domain and determines a steady state network configuration for the computer network. After determining a steady state network configuration for the computer network, the machine learning system receives a plurality of inputs from one or more sensors or agents distributed throughout the computer network. The machine learning system compares the plurality of inputs to the steady state network configuration to detect a deviation from the first steady state network configuration.

Abstract: The present disclosure describes a method, system, and apparatus for using a machine learning system to configure and optimize complex, distributed computer networks. The machine learning system receives an input related to a computer network and classifies the input using either a supervised learning approach or an unsupervised learning approach. From the classification of the input, the machine learning system builds a first training domain and determines a steady state network configuration for the computer network. After determining a steady state network configuration for the computer network, the machine learning system receives a plurality of inputs from one or more sensors or agents distributed throughout the computer network. The machine learning system compares the plurality of inputs to the steady state network configuration to detect a deviation from the first steady state network configuration.

Abstract: A lamp device for communicating moods between users having a base with a plurality of individually illuminatable elements extending from the base and two control units remote from the base for regulating the illumination of the elements. In a preferred embodiment, the illuminatable elements are in the form of five translucent flames of various heights containing two small electric bulbs at their bases for selectively producing colored illumination of each. Appropriate circuitry is disposed in the base member and connected to each of the external control units which may be used to activate the bulbs individually or collectively. The control units may each have five settings designed to supply power in a synchronized manner to the bulbs within the flame elements. Two persons involved in using the lamp for communication each operate a respective control unit which may be concealed, if desired, and by which a level of illumination may be selected to indicate, for instance, the level of interest of the user.

Abstract: A seating device is disclosed that offers comfort in use over extended periods of time and certain therapeutic effects as well as being readily portable and storable when not in use. The device is embodied in an inflatable seat that may be molded from thin, clear polyethylene/vinyl plastic material and formed with a flat thicker bottom surface, a concave seat area, a rising rear support, and a smaller frontal post section. The concave seat area is formed with an inherent forward tilt design which automatically aligns the spine of a user while the rear support provides additional comfort. The smaller frontal post section prevents forward sliding on and ride up of the rear of the seat. A valve is located in the bottom surface for inflating and deflating the seat and a snap strap may be provided for permanent closure after deflation and folding of the seat into itself.