Data neural network system and method

• US 20170161609A1
• Filed: 12/28/2016
• Published: 06/08/2017
• Est. Priority Date: 04/06/2006
• Status: Active Grant

• Current Assignee: Samuel Bennett Wood, Samuel Frederick Wood
• Original Assignee: Samuel Bennett Wood, Samuel Frederick Wood
• Inventors: Wood, Samuel Frederick, Wood, Samuel Bennett
• Granted Patent: US 10,462,039 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 3/06 Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
• CPC Class Codes: H04L 45/04 Interdomain routing, e.g. hierarchical routing
• CPC Class Codes: H04L 45/08 Learning-based routing, e.g. using neural networks or artificial intelligence
• CPC Class Codes: H04L 45/121 by minimising delays
• CPC Class Codes: H04L 45/125 based on throughput or bandwidth
• CPC Class Codes: H04L 45/20 Hop count for routing purposes, e.g. TTL
• CPC Class Codes: H04L 45/28 using route fault recovery
• CPC Class Codes: H04L 45/302 Route determination based on requested QoS
• CPC Class Codes: H04L 45/50 using label swapping, e.g. multi-protocol label switch [MPLS]

Abstract

This application discloses a neural network that also functions as a packet data network using an MPLS-type label switching technology. The neural network uses its intelligence to build and manage label switched paths (LSPs) to transport user packets and solve complex mathematical problems. This architecture is well suited to interconnect large numbers of processors or computers into a neural network exhibiting advanced intelligence which can be used for complex activities such as managing the power grid. However, the methods taught here can be applied to other data networks including ad-hoc, mobile, Information Centric, Content Centric, Sensor, and traditional IP packet networks, cell or frame-switched networks, time-slot networks and the like.

Claims

1. A method of synthesizing a neural network from a communications network containing a plurality of interconnected nodes, where at least one node contains one or more transmitters and one or more receivers, and where each node corresponds to one or more neurons of the neural network comprising:

receiving at a first node a communications request from another node of the network;

initiating a time delay in response to the reception, where the time delay is used as a weight function for the corresponding neural network, and where the time delay is also used to influence the routing of communications through the network; and

transmitting at least a portion of the communications to at least a second node of the network to expose a possible path for use by the corresponding neural network, after the expiration of the time delay.

2. The method of claim 1 where the communications network is a radio communications network.

3. The method of claim 1 where the communications network is an optical communications network.

4. The method of claim 1 where the communications network is a packet data network.

5. The method of claim 4 where the packet data network is connection oriented.

6. A method of using an Information Centric Network to establish a connection between a user and one or more interested servers comprising:

receiving at a node of the Information Centric Network an information request containing a key word from a user;

using the request to initiate a hunt through the network for interested servers;

establishing a connection between the user and one or more interested servers based on the one or more servers accepting the information request; and

transmitting information pursuant to the request from the one or more servers to the user.