Abstract

A system and method for dynamically creating distributed, self-adjusting and optimizing core network with machine learning is disclosed. The method includes receiving a request to access one or more services and establishing a secure real time communication session with one or more client devices and a set of service layers based on the received request. The method further includes determining one or more service parameters based on the received request and sending one or more handshake messages to each of the set of service layers. Further, the method includes determining one or more environmental parameters and determining best possible service layer capable of processing the received request by using a trained service based ML model. The method includes processing the request at the determined best possible service layer and terminating or transferring the secure real time communication session after the request is processed.

IPC Classes  ?

• H04L 12/00 - Data switching networks
• H04L 41/0246 - Exchanging or transporting network management information using the Internet; Embedding network management web servers in network elements; Web-services-based protocols
• H04L 41/08 - Configuration management of networks or network elements
• H04L 41/0823 - Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
• H04L 41/14 - Network analysis or design
• H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• H04L 41/5019 - Ensuring fulfilment of SLA

Abstract

The present disclosure describes an artificial intelligence (AI)/machine learning (ML) based distributed, hybrid, and multi-cloud software fabric-based system that unifies the communication infrastructure across hybrid and multi clouds. This mobile connectivity software fabric allows operators to modernize their networks to bring significant operational savings while rolling out new mobile services. This fabric can enable small independent networks and allow them to seamlessly connect with public networks, and it can enable network of networks while keeping the underlying compute and heterogeneity unified.

IPC Classes  ?

• H04L 67/51 - Discovery or management thereof, e.g. service location protocol [SLP] or web services

Abstract

The present disclosure describes solutions for dynamic network slicing including provisions to create, modify, and/or delete network slices in a de-centralized communication network including a plurality of central/regional/edge/far-edge locations across hybrid and multi-cloud environment referred to as edge server or edge location for providing service to the users. Network slicing enables multiple isolated and independent virtual (logical) networks to exist together. A plurality of virtual networks, i.e., slices, may be created using resources of the same physical network infrastructure.

IPC Classes  ?

• H04L 47/127 - Avoiding congestion; Recovering from congestion by using congestion prediction
• H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• H04L 47/125 - Avoiding congestion; Recovering from congestion by balancing the load, e.g. traffic engineering

Abstract

The present disclosure describes solutions for seamless enterprise network integration with operator networks. Enterprise networks can include full network resources to provide a complete, isolated network. The enterprise network can also host mobile network operator users with edge managers acting as routing agents. Moreover, when an enterprise moves outside of the enterprise network, the enterprise user can still access the enterprise network via an operator network without compromising security, privacy, and reliability. A neutral hosted core can be used as a routing agent to the enterprise network from one or more operator networks.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• H04L 67/141 - Setup of application sessions
• G06N 20/00 - Machine learning

Abstract

A system and method for autonomous data and signalling traffic management in a distributed infrastructure is disclosed. The method includes a distributed multi-cloud computing system with machine learning based intelligence across heterogenous computing platforms hosting mobile network functions, capable of leveraging AI based distribution across all the resources to creates autonomous network operations and intelligently work around any impairments. The method includes determining one or more service nodes by using a trained traffic management based ML model and establishing one or more cloud mesh links between the one or more service nodes at multiple levels of hierarchy based on the system, environment and network parameters and the current network demand. Further, the method includes processing the request by providing access of the one or more services hosted on the one or more external devices to the one or more electronic devices via the one or more cloud mesh links.

IPC Classes  ?

• H04L 47/2416 - Real-time traffic
• G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• G06N 20/00 - Machine learning
• H04L 9/40 - Network security protocols
• H04L 47/2483 - Traffic characterised by specific attributes, e.g. priority or QoS involving identification of individual flows
• H04L 47/60 - Queue scheduling implementing hierarchical scheduling

Abstract

A system and method for dynamically creating distributed, self-adjusting and optimizing core network with machine learning is disclosed. The method includes receiving a request to access one or more services and establishing a secure real time communication session with one or more client devices and a set of service layers based on the received request. The method further includes determining one or more service parameters based on the received request and sending one or more handshake messages to each of the set of service layers. Further, the method includes determining one or more environmental parameters and determining best possible service layer capable of processing the received request by using a trained service based ML model. The method includes processing the request at the determined best possible service layer and terminating or transferring the secure real time communication session after the request is processed.

IPC Classes  ?

• H04L 12/00 - Data switching networks
• H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• H04L 41/0246 - Exchanging or transporting network management information using the Internet; Embedding network management web servers in network elements; Web-services-based protocols
• H04L 41/14 - Network analysis or design
• H04L 41/0823 - Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
• H04L 41/5019 - Ensuring fulfilment of SLA
• H04L 41/08 - Configuration management of networks or network elements

Goods & Services

Downloadable cloud-computing software for telecom network functions namely, for providing 5G mobile broadband services to mobile network operators and enterprises

Goods & Services

Downloadable communication software for providing access to the Internet Wireless broadband communication services