Abstract: A method for training a system to specifically react on a specific input. The method can include defining a set of binary data structures, each representing a real-world component, item, or virtual object; storing each data structure as a binary pattern; creating uniquely identifiable copies of the data structures to represent individual instances of the components, items, or virtual objects; creating a virtual state space of the components, items, or virtual objects by grouping them as relevant for a specific situation; receiving an input to change a status or an attribute value of at least one of the components, items, or virtual objects; storing the received changes in a new version of the applicable data structure instance; analyzing similarities of the stored binary patterns related to a particular action performed; and if a matched binary pattern is identified, proposing at least one possible action related to the matched binary pattern.

Abstract: A method for training a system to specifically react on a specific input. The method can include defining a set of binary data structures, each representing a real-world component, item, or virtual object; storing each data structure as a binary pattern; creating uniquely identifiable copies of the data structures to represent individual instances of the components, items, or virtual objects; creating a virtual state space of the components, items, or virtual objects by grouping them as relevant for a specific situation; receiving an input to change a status or an attribute value of at least one of the components, items, or virtual objects; storing the received changes in a new version of the applicable data structure instance; analyzing similarities of the stored binary patterns related to a particular action performed; and if a matched binary pattern is identified, proposing at least one possible action related to the matched binary pattern.

Abstract: A method for training a system to specifically react on a specific input. The method can include defining a set of binary data structures, each representing a real-world component, item, or virtual object; storing each data structure as a binary pattern; creating uniquely identifiable copies of the data structures to represent individual instances of the components, items, or virtual objects; creating a virtual state space of the components, items, or virtual objects by grouping them as relevant for a specific situation; receiving an input to change a status or an attribute value of at least one of the components, items, or virtual objects; storing the received changes in a new version of the applicable data structure instance; analyzing similarities of the stored binary patterns related to a particular action performed; and if a matched binary pattern is identified, proposing at least one possible action related to the matched binary pattern.

Abstract: A method for training a system to specifically react on a specific input, comprising defining a set of binary data structures, whereas each data structure represents an abstract description of a certain type of a real-world component, item or a virtual object, comprising the features of said type of component, item or object per se, its possible states, its attributes, possible actions to be performed, and possible relationships between said components, items or virtual objects, storing each of the data structures directly as a binary pattern in a computer random access memory or a disk memory, creating uniquely identifiable copies of the binary data structures to represent an individual instance of said types of components, items or virtual objects, creating a virtual state space of said individual types of components, items or virtual objects by grouping them as relevant for the specific situation, receiving via keybord input, sensor devices or other technical means changes of status or attribute values of