The first ANN is tasked with continuous deep learning of the network data flow and is strictly focused on data routing optimization.  This ANN is structured using each physical or virtual connection that is sending or receiving data as the collection of connected units or nodes that create artificial neurons.  The ANN utilizes deep learning technology to continuously output results to optimize data routing based on time results.  The ANN is continuously using deep learning technology to analyze internal results at the end device and shared ANN results from other end devices to optimize data routing based on time.  This ANN is also sharing its results to other end devices on the network for ANN input for their deep learning and optimization. Receives

The second ANN is tasked with continuous deep learning of the end devices, nodes (artificial neurons), and resources connected to the network and is strictly focused on spatially referencing of each.  This ANN is structured using the physical spatial position within the physical or virtual network topology of each physical or virtual connection that is sending or receiving data as the collection of connected units or nodes that create artificial neurons.  The ANN utilizes deep learning technology to continuously output results to optimize data routing based on spatial results.  The ANN is continuously using deep learning technology to analyze internal results and shared ANN results from other end devices, nodes (artificial neurons), and resources connected to the network to optimize data routing based on spatial results.  This ANN is also sharing its results to other end devices, nodes (artificial neurons), and resources connected to the network on the network for ANN input for their deep learning and optimization.

The third ANN is tasked with continuous deep learning using the output or results of the first and second ANN as its inputs and is strictly focused on the total data routing optimization of the entire artificial neural network topology.  This ANN is structured using its output as an integrated autonomous dynamic intelligent routing system for Named Data Networking.  The ANN utilizes deep learning technology to continuously output results to optimize total data routing optimization based on both time and spatial results of all end devices, nodes (artificial neurons), and resources connected to the network and is then interpolated for Named Data Networking networks at the network & transport layers of the OSI model.  The ANN is continuously using deep learning technology to analyze internal results and shared ANN results from other end devices, nodes (artificial neurons), and resources connected to the network to optimize total data routing of the entire artificial neural network topology based on time and spatial results.

This procedure uses each of the technologies as a combined procedure in order to process immutable transactions or interactions that contain contract logic.  The contract logic is based on real world contractual processes and constructs that typically form a contract.  The contract logic is created by using various predefined constructs that can be applied to the construction of the contract.  The predefined constructs can include a variety of prebuilt libraries of contractual processes, languages, and other resources that could be required in order to construct any type of contract.  The predefined constructs would be assembled in various programmatic languages and forms for compatibility and mobility.  The predefined constructs would allow for modifications to provide customized constructs for specific or specialized construct libraries.  Predefined constructs would be created using protocol buffers and similar remote procedure call (RPC) systems to form serialized data structures that can be used to generate source code in various programming languages using semantic-based compilation and/or code generators.  These predefined constructs would be able to perform inter-machine communication using binary wire format when deployed upon the network after being compiled.

The individual predefined constructs could be assembled into specific groups to create libraries that can then be used for development and creation of dynamic smart contracts.  These libraries could be part of a software development kit (SDK), integrated development environment (IDE), a software development environment, or could be compiled into specific programming language libraries for use in SDK’s, IDE’s, or software development environments.  Applications developed using SDK’s, IDE’s, or software development environments, that utilize the dynamic smart contract constructs could allow for user interface features to create contracts, deploy the contracts to a distributed ledger technology network, edit or modify deployed contracts prior to execution, administer contract ecosystems and networks, audit and test contracts prior to deployment, and delete or destroy contracts prior to execution.  Applications developed using SDK’s, IDE’s, or software development environments, that utilize the dynamic smart contract constructs could allow for user interface features to also develop specialized or custom functionality that is predefined as a plugin or similar for the development environment.  These plugins or similar for the development environment could allow for interfaces to other distributed ledger technologies, interfaces for other software and applications, and other features as developed for use within the application.  These applications with user interface features could allow for users to create contracts without any knowledge of any programming languages since the predefined constructs and associated libraries would be grouped in learned sequence combinations by the underlying dynamic smart contract constructs.

These learned sequence combinations would be formed using artificial neural network deep learning techniques and technology.  The techniques would include deep structured learning based on the predefined dynamic smart contract constructs themselves combined with hierarchical learning based on all possible combinations of the predefined dynamic smart contract constructs.  The two combined techniques would be used in a combined machine learning model.  The model could be used for the creation of predefined constructs themselves for use in contract building, modeling, auditing, testing, and other functions as they relate to creating the constructs.  The model could be used via the software development environment, SDK’s, or IDE’s, that are used for the development of dynamic smart contract software applications in order to model, audit, test, generate, compile, or provide other functionality as it relates to the application development.   The model could be used by end users for generating dynamic smart contracts via the applications user interface within an application for predictive building, suggestion on format, pre-built contracts or templates, error correction, and other features that include automation, security, auditing, administration, deployments, alerts, interfaces for other distributed ledger technologies, interfaces for other software and applications, and other features as developed for use within applications.

The creation of a dynamic smart contract that has been generated and deployed to a distributed ledger technology network would be initially pending approval for acceptance by all parties that are programmed into the contract such users, entities, machines, artificial neural networks, or similar and in any combination.  Each party would be authorized using distributed ledger technology systems for identification and credentials on that network to which the contract has been deployed.  The deployment of the contract could notify each party using the distributed ledger technology network functionality via an application user interface.  Each party could have features, including real time and collaborative, via an application user interface to edit or modify the contract with change tracking history, re-deploy after editing or modifying with party approval request, acceptance of edits or modifications, acceptance of contract and execution via distributed ledger technology identification credential signing, and other features as developed for use within applications to complete approval processing.  After each and all parties involved has accepted the contract the distributed ledger technology then executes the contract on the network.  The contract is now awaiting fulfillment via all programmed requirements.  The programmed requirements are the combination of various contract constructs that have been implemented within the contract during creation, deployment, and execution.  Contract fulfillment occurs when all contract constructs have been verified and/or authenticated as having met all criteria of each construct.  Construct verification and/or authentication could include being processed as an individual transaction or interaction, as a group of transactions or interactions, or any combination of construct processing to authenticate and verify the transaction(s) or interaction(s) in accordance with how the dynamic smart contract has been created, deployed, and executed.  This means that contract constructs could also be other dynamic smart contracts that have been created, deployed, executed, and fulfilled on a DLT network or other accessible DLT networks.  Constructs could also include other criteria after the execution that could allow for halting fulfillment based on security, safety, or any construct that is used for contract creation, deployment, execution, and fulfillment.  Dynamic smart contracts could allow for contracts to be changed in status from execution back to deployment and from deployment back to creation, so long as the contract constructs allow for this after execution and/or deployment.