Core Tech - RevoKind

A.I. Autonomous Communication System

block chain network solutions, virtual sim cards, blockchain, blockchain communication solutions, blockchain

Revo-Fi™

By using virtualization technology, a wireless network interface controller (WNIC) that is physically integrated or installed into devices or system on a chip (SoC) interfaces, Distributed Ledger Technology, and Named Data Networking, a communication protocol application can be used to provide multiple wireless connections for a single device. The virtualization technology creates software based virtual wireless network interface controllers (vWNIC) that use a shared physical WNIC or SoC interfaces, memory, and other various system resources as required for each connection. Each vWNIC uses the physical WNIC as a shared resource for wireless communications and can act as a wireless client, a wireless access point, or both simultaneously. Each vWNIC can connect independently to available wireless connections such as cellular networks, Wi-Fi Access Points, Hot Spots, and vWNIC Access Points. Each vWNIC can connect to wireless devices that are either connected to a wireless network or use an ad-hoc mode to provide connectivity via any connected network. Each vWNIC can use other physical WNIC’s to route data or for additional communications functionality such as cellular connections for internet connectivity. Applications can utilize the vWNIC’s to control wireless devices, transfer and share data, share bandwidth, and provide connectivity. Applications use the communication protocol for auto load balancing for bandwidth throughput based on user active actions.

Applications can utilize the vWNIC’s or SoC interfaces and Named Data Networking to interface with the distributed ledger technology (DLT) that is integrated into Named Data Networking. The DLT is integrated with Named Data Networking (NDN) and allows for applications to provide secure and immutable communications, identification, transactions, interactions, and application specific functionality that correlates to any data transfer of any type.

Multi-Connection Technology using Virtualization of Subscriber Identification Modules (SIM's)

RevoCol™

By utilizing two artificial neural network (ANN) instances that perform continuous deep learning, then applying the individual output results of each as an input to a third ANN that performs continuous deep learning, and then applying that output result to the network & transport layers of the OSI model, an artificially intelligent communication protocol is formed. This three (3) tiered ANN result is then integrated into Named Data Networking (NDN) to create a continuous self-configuring infrastructure-less communication network. The is accomplished by integrating the entire communication protocol stack at each end device, node (artificial neuron), and resource on the network. Each end device, node (artificial neuron), and resource can securely communicate and share ANN results. As a result, the entire network becomes decentralized and autonomous since all end devices, nodes (artificial neurons), and resources connected to the network become a collective ANN.

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.

A.I. Routing and Optimization for an Autonomous Network

RevoPN™

By utilizing distributed ledger technology (DLT) that validates a user and/or user authorized devices by way of biometrics and/or other means, uses DLT for access credentials to a network, and that is integrated with Named Data Networking (NDN), a private network protocol that is decentralized is established. The private network protocol allows for multiple secure connections simultaneously to applications, end devices, nodes (artificial neurons), and resources that are connected to the network. Each connection creates an encrypted communication channel.

A.I. Spacetime Neural Network Synchronization Protocol with Neural Cryptography

RNNSP™

By utilizing neural key exchange and network synchronization protocol with distributed ledger technology (DLT) that validates a user and/or user authorized devices by way of biometrics and/or other means, and that is integrated with Named Data Networking (NDN), an artificial neural network is created as an application at layer seven of the OSI model. This application provides end to end encrypted communications that creates symmetric keys using a computed hash algorithm and is then interpolated by Named Data Networking (NDN) for routing across the network. A multi-layer feedforward neural network, known as Tree Parity Machines (TPMs), is used within the process of computing the symmetric hash and synchronizing secure communications via encrypted keys. The inputs for the TPMs correlate to the DLT and use spatial and time dynamics within the computing of neural keys and symmetric hashes and is further encrypted using the NDN routing across the network. Using the TPMs, DLT, and NDN within the application creates secure broadcasting of communications of computed hashes that allow for multi-channel neural network synchronization simultaneously between multiple users and/or user authorized devices. The application can be used for point to point communications, point to multi-point communications, and multi-point to multi-point communications, or any combination simultaneously.

Distributed Ledger Technology with Global Scale Blockchain

Swarm Sync Protocol™

By utilizing RevoCol, RevoPN, RNNSP, and Named Data Networking (NDN) a consensus methodology for distributed ledger technology can be formed. By creating a communications network built using RevoCol, RevoPN, RNNSP, and NDN a procedure for processing immutable transactions or interactions can be constructed for various types of distributed ledger technologies.

This procedure uses each of the technologies as a combined protocol in order to process immutable transactions or interactions. These transactions or interactions can take place between any of the following on the communications network: users to users, users to machines, machines to machines, neural networks to neural networks, and any combination of those instances. Each user, end device, node (artificial neuron), and resource connected to the network would be identified and authenticated during the connection process via the RevoPN (private network). All data communications of each user, end device, node (artificial neuron), and resource connected to the network use RevoCol, RNNSP, and NDN. By leveraging the spatial neural network synchronization protocol of RNNSP a peer network functionality called swarms is used to create groups of users, end devices, nodes (artificial neurons), or resources that are connected to the network. These swarms are randomly generated by using the spatial, time, and total network topology optimization functions of RevoCol combined with the spatial neural network synchronization protocol that uses tree parity machine neurons. These swarms are then leveraged as part of a method to process transactions or interactions and provide consensus of those transactions or interactions that then become immutable records within a distributed ledger technology.

3-4 Million Transactions or Interactions per/second

Dynamic Smart Contracts™

By utilizing RevoCol, RevoPN, RNNSP, and Named Data Networking (NDN) to create a distributed ledger technology network, and using Swarm Synchronization Protocol for the consensus processing of immutable transaction or interactions, a dynamic smart contract apparatus for distributed ledger technology can be formed using predefined construct libraries of serialized structured data using protocol buffers and similar remote procedure call (RPC) systems. By creating this system for dynamic smart contract construction, the compiled and generated contract can be constructed for various types of distributed ledger technologies to include interoperability interfaces with existing technologies.

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.

Zero Programming - Easy to Build, Deploy, Edit, Administer, and Fulfill

Multi-One Payment™

By utilizing RevoCol, RevoPN, RNNSP, and Named Data Networking (NDN) to create a distributed ledger technology network, and using Swarm Synchronization Protocol for the consensus processing of immutable transaction or interactions of Dynamic Smart Contracts, a universal currency multiplex payment process apparatus for distributed ledger technology can be formed using predefined construct libraries of serialized structured data using protocol buffers and similar remote procedure call (RPC) systems. By creating this system for a universal currency multiplex payment process, the compiled and generated apparatus can be constructed for various types of distributed ledger technologies to include interoperability interfaces with existing technologies.

This procedure uses each of the technologies as a combined procedure in order to process immutable transactions that is agnostic of the currency and has multiplex functionality. The transactions are based on the contract logic of dynamic smart contracts that have real world contractual processes and constructs that typically form a contract with the constructs being specifically related to transactional logic and processes. The transaction contract logic is created by using various predefined constructs that can be applied to the construction of the transaction contract. The predefined constructs can include a variety of prebuilt libraries of transaction based contractual processes, languages, and other resources that could be required in order to construct any type of transaction 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 a dynamic smart contract’s plugin. The plugin could be used in a software development kit (SDK), integrated development environment (IDE), a software development environment, and 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 plugin could allow for user interface features to create transactional constructs for use in dynamic smart contracts. This plugin could allow for multiplex transaction constructs such as chained payments, split payments, custom payments, or any payment type construct combination while transacting with any type of currency and/or multiple currencies within a single transaction. The plugin could allow for an unlimited number of multiplex transaction constructs within a dynamic smart contract or a group of dynamic smart contracts where the multiplex payments are based the contract constructs and logic for execution and fulfillment. This plugin 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 transactional 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 transaction constructs themselves combined with hierarchical learning based on all possible combinations of the predefined dynamic smart contract transaction constructs. The two combined techniques would be used in a combined machine learning model. The model could be used for the creation of predefined transactional constructs themselves for use in contract building, modeling, auditing, testing, and other functions as they relate to creating the transaction 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 transactional 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.

Multiple Currencies in a Single Transaction with Various Payment Types

RevoXchange™

By utilizing RevoCol, RevoPN, RNNSP, and Named Data Networking (NDN) to create a distributed ledger technology network, and using Swarm Synchronization Protocol for the consensus processing of immutable transaction or interactions of Dynamic Smart Contracts, a universal currency multiplex exchange process apparatus for distributed ledger technology can be formed using predefined construct libraries of serialized structured data using protocol buffers and similar remote procedure call (RPC) systems. By creating this system for a universal currency multiplex exchange process, the compiled and generated apparatus can be constructed for various types of distributed ledger technologies to include interoperability interfaces with existing technologies.

This procedure uses each of the technologies as a combined procedure in order to process immutable exchanges that is agnostic of the currency and has multiplex functionality. The exchanges are based on the contract logic of dynamic smart contracts that have real world contractual processes and constructs that typically form a contract with the constructs being specifically related to exchange logic and processes. The exchange contract logic is created by using various predefined constructs that can be applied to the construction of the exchange contract. The predefined constructs can include a variety of prebuilt libraries of exchange based contractual processes, languages, and other resources that could be required in order to construct any type of exchange 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 a dynamic smart contract’s plugin. The plugin could be used in a software development kit (SDK), integrated development environment (IDE), a software development environment, and 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 plugin could allow for user interface features to create exchange constructs for use in dynamic smart contracts. This plugin could allow for multiplex exchange constructs such as chained exchanges, split exchanges, custom exchanges, or any exchange type construct combination while exchanging with any type of currency and/or multiple currencies within a single exchange. The plugin could allow for an unlimited number of multiplex exchange constructs within a dynamic smart contract or a group of dynamic smart contracts where the multiplex exchanges are based on the contract constructs and logic for execution and fulfillment. This plugin 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 exchange 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 exchange constructs themselves combined with hierarchical learning based on all possible combinations of the predefined dynamic smart contract exchange constructs. The two combined techniques would be used in a combined machine learning model. The model could be used for the creation of predefined exchange constructs themselves for use in contract building, modeling, auditing, testing, and other functions as they relate to creating the exchange 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 exchange 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.