Classification and relationship correlation learning engine for the automated management of complex and distributed networks

This invention describes a computer system that learns about complex data by identifying connections between different pieces of information. It extracts specific characteristics from data and determines if they influence each other, such as one changing in response to another. If a relationship is found, these characteristics are grouped into a training domain, with the claims specifically focusing on this initial learning and grouping process rather than broader network management.

Why it matters: Filed before the full maturity of automated machine learning (AutoML) and advanced causal inference techniques. The claims cover identifying feature relationships for training, a process now significantly more efficient and scalable for complex network data due to these advancements.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, machine learning algorithms

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