IP Library Granted Patent US 10,785,108
Granted Patent B1
US 10,785,108 · App. 16/014,688 · Granted Sep 22, 2020

Intelligent learning and management of a networked architecture

Inventor: Lawrence T. Belton, Jr. (Charlotte, NC)
Assignee: WELLS FARGO BANK, N.A.
H04L41/0823G06N20/00H04L12/2814H04L12/4675H04L41/04H04L41/0654H04L41/08H04L41/0803H04L41/085H04L41/0806H04L41/0876H04L41/16H04L41/5054H04L67/20H04L67/34
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 10,785,108
App. No.
16/014,688
Granted
Sep 22, 2020
Kind
B1
Abstract

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of intelligent learning and management of networked architectures. The innovation maps a networked architecture. The networked architecture includes elements having software elements and hardware elements interconnected in a common environment. The innovation determines data sources associated with the set of elements using the identifiers. The innovation compiles data associated with the set of elements into a knowledgebase. The innovation utilizes machine learning to analyze information from the data sources. The innovation determines a configuration for at least one element in the environment based on the mapping. The innovation executes the configuration based on the configuration.

Claims (57)

1. A method, comprising:

mapping a networked architecture, the networked architecture having a set of elements comprising software elements and hardware elements interconnected in a networked environment, wherein the mapping identifies a configuration of each element in the set of elements and wherein the configuration is a set of settings for each element in the set of elements;

determining a configuration change for at least one element in the set of elements in the networked environment based on the mapping, wherein determining the configuration change comprises:

determining identifiers for the set of elements of the networked architecture;

determining external data sources associated with the set of elements using the identifiers, wherein the data sources include remotely stored information associated with at least one element of the set of elements;

compiling data associated with the set of elements into a knowledgebase; and

machine learning information from the data sources to facilitate determining the configuration change;

determining an application ledger for a subset of elements of the set of elements, wherein the application ledger is a distributed ledger;

determining at least one unnecessary configuration based on the application ledger of the subset of elements; and

blocking the at least one unnecessary configuration from execution in the configuration change; and

automatically executing the configuration change for the at least one element.

2. The method of claim 1 , wherein the data sources is at least one of human behavior, previous configurations, failed configurations, internet database, or intranet database.

3. The method of claim 1 , comprising:

determining a known configuration for the set of elements from the learned information, wherein the known configuration is different from the mapped configuration; and

deploying the known configuration to the set of elements in the networked environment.

4. The method of claim 1 , comprising:

determining one or more sub-configurations for different elements in the set of elements in the networked environment;

compiling the sub-configurations into a batched configuration; and

deploying the batched configuration to the different elements in the set of elements in the networked environment.

5. The method of claim 1 , wherein the configuration includes at least one of an installation, an upgrade, a patch, or uninstallation.

6. A system, comprising:

a mapping component that maps a networked architecture, the networked architecture having a set of elements comprising software elements and hardware elements interconnected in a networked environment, wherein the mapping identifies a configuration of each element in the set of elements, wherein the mapping component comprises:

a scanning component that determines identifiers for each element in the set of elements of the networked architecture;

an information component that determines data sources associated with the set of elements using the identifiers;

a knowledgebase that compiles data associated with the set of elements; and

a learning component that analyzes information from the data to facilitate determining a configuration change;

a diagnosis component that determines a configuration change for at least one element in the set of elements in the networked environment based on the mapping, wherein the diagnosis component comprises:

a ledger component that determines an application ledger for a subset of elements of the set of elements, wherein the application ledger is a distributed ledger; and

an analysis component that:

determines an unnecessary configuration based on the application ledger of the subset of elements; and

blocks the unnecessary configuration from execution; and

an implementation component that executes the configuration change.

7. The system of claim 6 , wherein the data sources is at least one of human behavior, internet database, or intranet database.

8. The system of claim 6 , comprising:

wherein the diagnosis component determines a known configuration of at least one element in the set of elements from the analyzed information, wherein the known configuration is different from the mapped configuration; and

wherein the implementation component deploys the known configuration to the at least one element in the networked environment.

9. The system of claim 8 , comprising:

wherein the diagnosis component comprises:

a configuration component that:

determines one or more sub-configurations for different elements of the set of elements in the networked environment; and

compiles the one or more sub-configurations into a batched configuration; and

wherein the implementation component deploys the batched configuration to the different elements of the set of elements in the networked environment.

10. The system of claim 6 , wherein the configuration includes at least one of an installation, an upgrade, a patch, or uninstallation.

11. A non-transitory computer readable medium having instructions to control one or more processors configured to:

map a networked architecture, the networked architecture having a set of elements comprising software elements and hardware elements interconnected in a networked environment, wherein the mapping identifies a configuration of each element in the set of elements and wherein the configuration is a set of settings for each element in the set of elements;

determine a set of elements of the networked architecture, the set of elements having identifiers, wherein the determining identifies a configuration of each element in the set of elements;

determine data sources associated with the set of elements using the identifiers;

compile data associated with the set of elements into a knowledgebase;

machine learn information from the data sources to facilitate determining the configuration;

determine a configuration change for at least one element in the networked environment based on the machine learning, wherein determining the configuration change comprises:

determine an application ledger for a subset of elements of the set of elements, wherein the application ledger is a distributed ledger; determine unnecessary

configurations based on the application ledger of the subset of elements; and

block the unnecessary configuration from execution; and

automatically execute the configuration change on the set of elements.

12. The non-transitory computer readable medium of claim 11 , wherein the one or more processors are further configured to:

determine a failed execution of the configuration change; and

machine learn why the execution failed for execution of future configurations.

Assignments (2)
ADDRESS CHANGE Recorded Jun 2, 2025
From: WELLS FARGO BANK, N.A.
To: WELLS FARGO BANK, N.A.
Reel/Frame 071769/0158 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 21, 2019
From: BELTON, LAWRENCE T., JR.
To: WELLS FARGO BANK, N.A.
Reel/Frame 048656/0890 →
Cited By (1)
US 12,236,261