US 2019/0378051 A1Published application

Machine learning system coupled to a graph structure detecting outlier patterns using graph scanning

AssigneeBank of America Corporation

InventorsCraig Douglas Widmann, Eren Kursun, Scott A. Sims, Dana M. Pusey-Conlin, Ronnie J. Morris, Margaret A. Payne, Joel Filliben, Lorraine C. Edkin

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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, wherein each entity is associated with one or more classifications, and wherein each edge represents a relationship between two entities;
one or more machine learning engines configured to perform a respective decision-making process, wherein each of the one or more machine learning engines is associated with at least one of the one or more classifications, and wherein each machine learning engine is further configured to:
train the respective decision-making process based on historical data associated with the one of the one or more classifications;
receive new data associated with the graph;
and
determine, based on the new data and using the trained respective decision-making process, hotfile parameters;
and
a hotfile propagation engine configured to:
determine, based on the hotfile parameters and historical hotfile data, an action to take with respect to a hotfile;
and
cause the action.

8 more elements

A method comprising:

determining 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 the first plurality of entities;
training, for a first entity type, a first artificial neural network for machine learning executing on one or more first computing devices, wherein the first artificial neural network comprises a plurality of nodes, and wherein the plurality of nodes are configured based on a first portion of the data corresponding to the first entity type;
training, for a second entity type, a second artificial neural network for machine learning executing on the one or more first computing devices, wherein the second artificial neural network comprises a second plurality of nodes, and wherein the second plurality of nodes are configured based on a second portion of the data corresponding to the second entity type;
determining a first graph representation comprising a second plurality of entities, wherein the second plurality of entities comprises a first entity corresponding to the first entity type and a second entity corresponding to the second entity type;
and
receiving, from the first artificial neural network and the second artificial neural network and based on the first graph representation, output indicating a modification to a hotfile.

3 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 the first plurality of entities;
train, for a first entity type, a first artificial neural network for machine learning executing on one or more first computing devices, wherein the first artificial neural network comprises a plurality of nodes, and wherein the plurality of nodes are configured based on a first portion of the data corresponding to the first entity type;
train, for a second entity type, a second artificial neural network for machine learning executing on the one or more first computing devices, wherein the second artificial neural network comprises a second plurality of nodes, and wherein the second plurality of nodes are configured based on a second portion of the data corresponding to the second entity type;
determine a first graph representation comprising a second plurality of entities, wherein the second plurality of entities comprises a first entity corresponding to the first entity type and a second entity corresponding to the second entity type;
and
receive, from the first artificial neural network and the second artificial neural network and based on the first graph representation, output indicating a modification to a hotfile.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/006,618Filed 12 Jun 2018
Published: 12 Dec 2019
Term ends: Not estimatedThe source records don’t support an estimate.

Classification18 codes

G06N 20/00
Machine learning
A63F 13/67
Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or…:…
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks

Family

No related US applications are recorded in the indexed filing history.

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