US 2019/0378010 A1 · UNSUPERVISED MACHINE LEARNING SYSTEM TO AUTOMATE FUNCTIONS ON A GRAPH STRUCTURE

Abstract

Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Claims20

Dependent claims

A system comprising:

a graph module configured to store and update a graph comprising nodes and edges, wherein each node represents an entity type, and wherein each edge represents a relationship between two nodes;
an unsupervised machine learning engine programmed to perform a decision-making process, the unsupervised machine learning engine further configured to:
train the decision-making process based on historical data;
and
output, based on the trained decision-making process, a feature vector;
a hotfile module configured to:
receive current event data associated with the graph;
determine, based on the feature vector and the trained decision-making process, an action to take with respect to the graph;
and
cause, a hotfile propagation engine, to execute the action.

7 more elements

An apparatus comprising:

one or more processors;
and
memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
determine data corresponding to one or more graph representations of a first plurality of entities, wherein the one or more graph representations indicate a plurality of relationships between at least two of the first plurality of entities, and wherein the one or more graph representations are unlabeled;
train, using the data corresponding to the one or more graph representations, an artificial neural network for machine learning executing on one or more computing devices, wherein the artificial neural network comprises a plurality of nodes, wherein the nodes are configured to process an input, and wherein the plurality of nodes are configured based on the one or more graph representations;
determine a first graph representation comprising a second plurality of entities;
determine a plurality of definitional functions corresponding to one or more of the second plurality of entities;
and
receive, from the artificial neural network and based on the first graph representation and the plurality of definitional functions, output indicating a modification to a hotfile.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application

16/006,559Filed 12 Jun 2018

Published

12 Dec 2019

Term ends

Not estimatedThe source records don’t support an estimate.

Classification17 codes

G06F 16/288
Relational databases: entity relationship models
G06N 3/084
Learning methods: backpropagation, e.g. using gradient descent
G06F 16/2291
Indexing; Data structures therefor; Storage structures: user-Defined Types; Storage management thereof
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks

Unsupervised machine learning system to automate functions on a graph structure

Life of this patent

Unsupervised machine learning system to automate functions on a graph structure

Life of this patent

Abstract

Claims20