WO2021226656A1 - Media distribution and management system and apparatus - Google Patents
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- WO2021226656A1 WO2021226656A1 PCT/AU2021/050426 AU2021050426W WO2021226656A1 WO 2021226656 A1 WO2021226656 A1 WO 2021226656A1 AU 2021050426 W AU2021050426 W AU 2021050426W WO 2021226656 A1 WO2021226656 A1 WO 2021226656A1
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Definitions
- the present invention relates to a media distribution and management system and more particularly but not exclusively, to such a system when implemented utilising a network termination unit (NTU) or an internet appliance which engages with internet infrastructure to deliver and control digital content including (but not limited to) streamed and downloaded digital content to digital devices including (but not limited to) television display units, video display units and the like.
- NTU network termination unit
- an internet appliance which engages with internet infrastructure to deliver and control digital content including (but not limited to) streamed and downloaded digital content to digital devices including (but not limited to) television display units, video display units and the like.
- ABR Adaptive Bit Rate
- the Internet is reaching its limits of scale, particularly the TCP/IP protocols and routing protocols based on them. Video has placed huge loads on the Internet that were unforeseen at the time of its invention.
- UCDN Unified Content Delivery Network system
- the Peer networks are SPAN-AI networks.
- the system comprising a hierarchical, hybrid adaptive AI driven networking technology (termed Secure Peer-Assisted Networking or SPAN-AI),that uses an AI-driven hybrid adaptive routing approach based on five key SPAN-AI sub systems: unified naming; unified discovery; hybrid adaptive routing; scalable pubsub; and embedded security; all of said five key SPAN-AI sub systems securely integrated and jointly optimized via a hierarchical, pluggable AI framework, with an associated simulation, training, and development pipeline that embeds AI agents with varying degrees of awareness and optimization capabilities at peer, edge, core or other network levels (hierarchies).
- SPAN-AI Secure Peer-Assisted Networking
- the system using a Unified Naming and Discovery (UND) System that i) maps mutable human readable names (e.g., domain names, content names) to immutable self- certifying content identifiers (CIDs), and ii) enables routing CIDs through both name-resolution and name based routing subsystems, by iii) prepending a name prefix to each CID.
- UND Unified Naming and Discovery
- the system using a Unified Naming and Discovery (UND) System that i) maps mutable human readable names (e.g., domain names, content names) to immutable self-certifying content identifiers (CIDs), and ii) enables routing CIDs through both name- resolution and name based routing subsystems, by iii) combining a name and a CID in such a way as to optimise routing and/or storage.
- mutable human readable names e.g., domain names, content names
- CIDs immutable self-certifying content identifiers
- the UND also combines IP DNS to ensure backwards compatibility.
- the system further employing an AI- driven universal discovery system which includes a key component, Ambient Intelligent Rendezvous (termed Aml- Rendezvous) which provides smart discovery, configuration, and self-organization services.
- AI-HARD AI-driven Hybrid Adaptive Routing Design
- said AI-HARD system composed of two subsystems: a storage-centric routing sub system; and a Delivery-centric routing subsystem; said sub systems combining the benefits of name-resolution-based routing (NRR) for scalable, available, accessible distributed storage, and the advantages of name-based routing (NBR) for fast, reliable content delivery.
- NBR name-resolution-based routing
- AI HARD also combines IP routing to provide backward compatibility.
- AI-HARD intelligent agents within SPAN- AI exploit predictive knowledge about network conditions and application requirements to adaptively choose most efficient routing policies from subsystems.
- the system includes both SPAN-AI's smart discovery service AmI-Rendezvous and IP name discovery i.e. DNS to provide backward compatibility.
- AI-HARD protocols, naming standards, conventions and methods are published to enable incorporation in existing as well as new routers thereby to enable inter- operation of existing IP networks with new SPAN-AI networks.
- the protocols, naming standards, conventions and methods include IP naming.
- the AI-HARD system interoperates with multiple storage and delivery networks.
- the storage and delivery networks may operate on a crypto token such as Filecoin or Blust.
- the SPAN-AI system utilising an AI- driven pub-sub system for asynchronous multi-party dissemination services that support: control plane dissemination of directory updates (names, discovery, configuration) and intelligence updates (optimization/control operations); as well as data plane dissemination for collaborative applications, e.g. for social networks, video conferencing, etc.
- SPAN-AI uses an AI-driven pub-sub system for asynchronous multi-party dissemination services that include communication between AI agents, naming services, discovery services.
- the AI-driven pub-sub system includes inter-operation with IP discovery services.
- the pub/sub system uses the AmI- Rendezvous service expanded with peer heartbeat and mesh health metrics and rankings for improved operation, intelligent discovery and configuration via a combination of awareness and control for: Peer/Local Intelligence; Edge/Swarm Intelligence; and Core/Global Intelligence.
- AmI-Rendezvous incorporates a pluggable interface for self-healing agents embedding AmI-Rendezvous clients into the pub/sub protocol e.g. an evolution of existing pubsub algorithms such as Gossipsub, PlumTree, HyParView .
- the SPAN-AI system incorporates security integrated at all levels.
- the SPAN-AI system uses machine learning and recognition to detect and manage security threats.
- Content is encrypted using DRM systems such as PlayReady before it is published to the system.
- Data packets are cryptographically signed by the publisher.
- Naming is rooted in self-sovereign identity, which can be defined as a lifetime portable digital identity that does not depend on any centralized authority.
- the system uses decentralized identifiers that provide: persistence, global resolvability, cryptographic verifiability, and decentralization.
- Names are self-certifying.
- the system is based on a hardware root of trust and secure boot.
- the system makes use of Web of Trust methods .
- the system makes use of Quantum encryption, i.e. encryption based on quantum state random number generators.
- the system orchestrating the adaptive operation of the routing and pub/sub systems via a family of pluggable, hierarchical (local/edge/global/other) AI agents that provide monitoring, prediction, optimization, and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.
- a family of pluggable, hierarchical (local/edge/global/other) AI agents that provide monitoring, prediction, optimization, and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.
- the system provides methods for pluggable AI agents to enable open, flexible innovation in the optimization and control of universal networks.
- the AI agents are exchangeable for crypto tokens such as Filecoin or Blust.
- the SPAN-AI system uses a simulation, training, and development pipeline that enables cloud-level replication of runtime environments, simulation, testing, and training of AI models and agents, that can then be plugged into peer/edge/core/other network nodes for real-time optimization and control.
- the system further includes a Self-Aware Mesh Simulator (termed SAMSim system), and wherein said SAMSim system is supported by distributed cloud hosting a big data lake of meshes with health metrics simulating and deploying AI models across an automated software engineering pipeline.
- SAMSim system Self-Aware Mesh Simulator
- a hierarchical hybrid adaptive Secure Peer- Assisted Networking System (termed SPAN-AI ),using a hierarchical AI driven approach under a unified secure content-addressable architecture which is based on five key SPAN-AI sub systems: unified naming; unified discovery; hybrid adaptive routing; scalable pubsub; and embedded security; all of said five key SPAN-AI sub systems securely integrated and jointly optimized via a hierarchical, pluggable AI framework, with an associated simulation, training, and development pipeline that embeds AI agents with varying degrees of awareness and optimization capabilities at peer, edge, or core or other network levels (hierarchies).
- SPAN-AI hierarchical hybrid adaptive Secure Peer- Assisted Networking System
- the system uses a Unified Naming and Discovery (UND) System that i) maps mutable human readable names (e.g., domain names, content names) to immutable self- certifying content identifiers (CIDs), and ii) enables routing CIDs through both name-resolution and name based routing subsystems, by iii) prepending a name prefix to each CID.
- UND Unified Naming and Discovery
- the system uses a Unified Naming and Discovery (UND) System that i) maps mutable human readable names (e.g., domain names, content names) to immutable self-certifying content identifiers (CIDs), and ii) enables routing CIDs through both name-resolution and name based routing subsystems, by iii) combining a name and CID in such a way as to optimise routing and/or storage.
- UND Unified Naming and Discovery
- the system further employs an AI-driven unified discovery system which includes a key component, Ambient Intelligent Rendezvous (termed AmI-Rendezvous) which provides smart discovery, configuration, and self-organization services.
- AI-driven unified discovery system which includes a key component, Ambient Intelligent Rendezvous (termed AmI-Rendezvous) which provides smart discovery, configuration, and self-organization services.
- the SPAN-AI system addressing routing at scale via an AI-driven Hybrid Adaptive Routing Design (termed AI-HARD system); said AI-HARD system composed of two subsystems: a storage-centric routing subsystem; and a Delivery-centric routing subsystem; said sub systems combining the benefits of name-resolution-based routing (NRR) for scalable, available, accessible distributed storage, and the advantages of name-based routing (NBR) for fast, reliable content delivery.
- NRR name-resolution-based routing
- NBR name-based routing
- AI-HARD intelligent agents within SPAN- AI exploit predictive knowledge about network conditions and application requirements to adaptively choose most efficient routing policies from subsystems.
- AI-HARD protocols are published to enable incorporation in existing as well as new routers thereby to ensure routing compatibility between all networks.
- the AI-HARD system interoperates with multiple storage and delivery networks.
- the storage and delivery networks may operate on a crypto token such as Filecoin or Blust.
- the SPAN-AI system utilising an AI- driven pub-sub system for asynchronous multi-party dissemination services that support control plane dissemination of: directory updates (names, discovery, configuration) and intelligence updates (optimization/control operations); as well as data plane dissemination for collaborative applications, e.g. for video conferencing , social networks, etc.
- SPAN-AI uses an AI-driven pub-sub system for asynchronous multi-party dissemination services that include communication between AI agents, naming services, discovery services.
- the pub/sub system uses the AmI- Rendezvous service expanded with peer heartbeat and mesh health metrics and rankings for improved operation, intelligent discovery and configuration via a combination of awareness and control for: Peer/Local Intelligence; Edge/Swarm Intelligence; Core/Global and other Intelligence.
- AmI-Rendezvous incorporates a pluggable interface for self-healing agents embedding AmI -Rendezvous clients into the pub/sub protocol e.g. an evolution of existing pubsub algorithms such as Gossipsub, PlumTree, HyParView .
- the SPAN-AI system incorporating security integrated at all levels.
- the SPAN-AI system using machine learning and recognition to detect and manage security threats .
- Content is encrypted using DRM systems such as PlayReady before it is published to the system.
- Data packets are cryptographically signed by the publisher.
- Naming is rooted in self-sovereign identity, which can be defined as a lifetime portable digital identity that does not depend on any centralized authority.
- the system uses decentralized identifiers that provide: persistence, global resolvability, cryptographic verifiability, and decentralization.
- Names are self-certifying.
- the system is based on a hardware root of trust and secure boot.
- the system makes use of Web of Trust methods .
- the system makes use of Quantum encryption, i.e. encryption based on quantum state random number generators.
- the SPAN-AI system orchestrating the adaptive operation of the routing and pub/sub systems via a family of pluggable, hierarchical (local/edge/global/other) AI agents that provide monitoring, prediction, optimization, and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.
- a family of pluggable, hierarchical (local/edge/global/other) AI agents that provide monitoring, prediction, optimization, and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.
- the system provides methods for pluggable AI agents to enable open, flexible innovation in the optimization and control of unified networks.
- the AI agents are exchangeable for a crypto token such as Filecoin or Blust.
- the SPAN-AI system uses a simulation, training, and development pipeline that enables cloud-level replication of runtime environments, simulation, testing, and training of AI models and agents, that can then be plugged into peer/edge/core/other network nodes for real-time optimization and control.
- the system further includes a Self-Aware Mesh Simulator (termed SAMSim system), and wherein said SAMSim system is supported by: distributed cloud hosting a big data lake of meshes with health metrics simulating and deploying AI models across an automated software engineering pipeline.
- SAMSim system Self-Aware Mesh Simulator
- a hierarchical hybrid adaptive Secure Peer-Assisted Networking System (termed SPAN-AI) ,using a hierarchical AI driven approach under a unified secure content-addressable architecture; said system comprising routing at scale via an AI-driven Hybrid Adaptive Routing Design (termed AI-HARD system )which is composed of two subsystems: a storage-centric routing subsystem; and a Delivery-centric routing subsystem; which combine the benefits of name-resolution-based routing (NRR) for scalable, available, accessible distributed storage, and name-based routing (NBR) for fast, reliable content delivery.
- NBR name-resolution-based routing
- the AI-HARD system interoperates with multiple storage and delivery networks.
- SPAN-AI for AI-driven Secure Peer- Assisted Networking
- SPAN-AI is a hybrid adaptive networking technology that provides global, scalable, secure, distributed content storage, computation, and delivery for any application and network environment.
- SPAN-AI recognizes the limitations of existing technologies, only suitable for specific applications at non-global scale, and uses an AI-driven hybrid routing approach to improve and adaptively combine best-fit features of existing solutions under a unified secure content- addressable architecture. We call this a Unified Content Delivery Network or UCDN.
- SPAN-AI is based on 5 key systems: unified naming; unified discovery; hybrid routing; scalable pubsub; and embedded security; all securely integrated and jointly optimized via a hierarchical, pluggable AI framework, with an associated simulation, training, and development pipeline that embeds AI agents with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.
- SPAN-AI uses a Unified Naming System that i) maps mutable human readable names (e.g., domain names, content names) to immutable self-certifying content identifiers (CIDs), and ii) enables routing CIDs through both name-resolution and name based routing subsystems by iii) prepending a name prefix to each CID.
- mutable human readable names e.g., domain names, content names
- CIDs immutable self-certifying content identifiers
- SPAN-AI uses a Unified Naming System that i) maps mutable human readable names (e.g., domain names, content names) to immutable self- certifying content identifiers (CIDs), and ii) enables routing CIDs through both name-resolution and name based routing subsystems by iii) combining a name prefix and CID in such a way as to optimise routing and/or storage.
- mutable human readable names e.g., domain names, content names
- CIDs immutable self- certifying content identifiers
- SPAN-AI uses a unified discovery system based on an Ambient Intelligent Rendezvous service, AmI- Rendezvous, designed to provide smart discovery and self- organizing services via a combination of hierarchical AI awareness and control agents: Peer/Local Intelligence; Edge/Swarm Intelligence; Core/Global Intelligence and Intelligence at other levels .
- AmI-Rendezvous includes peer heartbeat collection, mesh health metrics aggregation, peer rankings, peer discovery, and mesh self-configuration services.
- SPAN-AI addresses routing at scale via an AI-driven Hybrid Adaptive Routing Design (AI-HARD), composed of 2 subsystems: a storage-centric routing subsystem; and a Delivery-centric routing subsystem; aimed at combining the benefits of name-resolution-based routing (NRR) for scalable, available, accessible distributed storage, and the advantages of name-based routing (NBR) for fast, reliable content delivery.
- AI-HARD uses hierarchical AI agents to control and optimize the joint operation of NRR and NBR subsystems.
- AI-Hard can use AmI-Rendezvous for discovery and self-organization in highly dynamic scenarios.
- AI-HARD includes storage and delivery markets.
- SPAN-AI uses an AI-driven publish- subscribe (pub-sub) system for asynchronous multi-party dissemination services that support: control plane dissemination of directory updates (names, discovery, configuration) and intelligence updates (optimization/control operations) ; as well as data plane dissemination for collaborative applications, e.g. for video conferencing, social networks, etc.
- SPAN-AI pubsub uses AmI-Rendezvous for pubsub mesh discovery and self-organization, including a pluggable interface for self-healing agents into the pub/sub protocol which is an evolution of existing pubsub algorithms such as Gossipsub, PlumTree, HyParView.
- SPAN-AI incorporates security integrated at all levels.
- SPAN-AI uses machine learning and recognition to detect and manage security threats .
- Content can be encrypted using commercial DRM systems such as PlayReady before it is published to the system.
- Data packets can be cryptographically signed by the publisher.
- Naming is rooted in self-sovereign identity, which can be defined as a lifetime portable digital identity that does not depend on any centralized authority. It uses decentralized identifiers that provide: persistence, global resolvability, cryptographic verifiability, and decentralization. Names can also be self- certifying.
- a preferred embodiment is based on a hardware root of trust and secure boot.
- a further preferred embodiment may make use of Web of Trust methods.
- Quantum encryption i.e. encryption based on quantum state random number generators, may also be used.
- SPAN-AI orchestrates the adaptive operation of the routing and pub/sub systems via a family of pluggable, hierarchical (local/edge/global) AI agents that provide monitoring, prediction, optimization, and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.
- SPAN-AI provides a marketplace for pluggable AI agents to enable open, flexible innovation in the optimization and control of unified networks. This may be based on a crypto token such as Filecoin or Blust.
- SPAN-AI uses a simulation, training, and development pipeline that enables cloud-level replication of runtime environments, simulation, testing, and training of AI models and agents, that can then be plugged into peer/edge/core/other network nodes for real-time optimization and control.
- SPAN-AI includes a simulation pipeline, Self-Aware Mesh Simulator (SAMSim), supported by distributed cloud hosting a big data lake of meshes with health metrics simulating and deploying AI models across an automated software engineering pipeline.
- SAMSim Self-Aware Mesh Simulator
- a network appliance which receives digital content from a remote location; said appliance including decoding and recoding means by which digital content is downloaded, decoded then recoded for on-transmission to a digital device for consumption by a user.
- the digital content is recoded according to secure HDMI coding algorithms.
- said network appliance received said digital content according to criteria comprising one or more of : a. most needed packet b. fastest download speed c. least latency d. the network address from where the next digital bit or group of bits can most easily and efficiently be acquired in order to maintain real-time or near real-time delivery of digital content.
- a Web server which aggregates items of digital content for subsequent on forwarding according to a secure methodology of at least a portion of a copy of an item on request from a network appliance located at a remote location.
- said secure methodology comprises, obtaining and forwarding packets of data forming said digital content according to one or more of the following criteria: a. most needed packet b. fastest download speed c. least latency d. the network address from where the next digital bit or group of bits can most easily and efficiently be acquired in order to maintain real-time or near real-time delivery of digital content.
- a method of assembling an item of digital content comprising receiving at least a first portion of the item of digital content from an origin store of digital content located at a remote location.
- the method further includes obtaining and forwarding packets of data forming said item of digital content according to one or more of the following criteria: a. most needed packet b. fastest download speed c. least latency d. the network address from where the next digital bit or group of bits can most easily and efficiently be acquired in order to maintain real-time or near real-time delivery of digital content.
- a distributed system for delivery of digital content comprising at least one content aggregator in communication with an origin store; a plurality of network appliances; the aggregator receiving digital content in the form of items of content; the aggregator securing the digital content for distribution by the system; the origin store making available the digital content to said plurality of network appliances; each network appliance receiving specified items of content on request to said system by a said network appliance.
- said system communicates over the Internet.
- each network appliance operates according to secure peer assist criteria; said secure peer assist criteria enabling reception of at least portions of said item of content from others of said plurality of network appliances if said item of content has been previously downloaded to said others of said plurality of network appliances.
- the step of settling includes paying content owners and retailers for specified items of digital content according to complex rights and release window agreements .
- FIG. 1 is a block diagram of a system which combines and extends sub-systems to form a widely applicable, universally operable, highly scalable and efficient system for optimisation, management and operation of a Unified Content Delivery Network (UCDN)incorporating AI-driven Secure Peer- Assisted Networking (SPAN-AI), which is a hybrid adaptive networking technology that provides global, scalable, secure, distributed content storage, computation, and delivery for any application and network environment.
- SPAN-AI recognizes the limitations of existing technologies , only suitable for specific applications at non-global scale, and uses an AI- driven approach to improve and adaptively combine best-fit features of existing solutions under a unified secure content- addressable architecture. We call this a Unified Content
- Fig 1A is a system block diagram of the SPAN-AI system incorporating AI-HARD operable in the context of the UCDN system of Fig 1.
- Figure IB is a block diagram of an embodiment of UCDN being a network of one or more SPAN-AI networks operating via AI, routing or other interfaces.
- Figure 1C is a further embodiment of UCDN incorporating legacy networks which may be TCP/IP or other protocol networks.
- Figure 2A is a block diagram of a media distribution and management system comprising a first embodiment and the SPAN-AI components of figure 1A as a further embodiment (the SPAN-AI embodiment) of the present invention.
- Figure 2B is a block diagram of information flow through the network of figure 2A when publishing to the Name Resolution Routing (NRR) systems.
- NRR Name Resolution Routing
- FIG. 2C is a block diagram of information flow- through the network of Fig 2A via the Name Based Routing (NBR)systems.
- NBR Name Based Routing
- Figure 3 is a block diagram of a network appliance usable in conjunction with the system of Figure 2A and the associated routing methods
- Figure 3A is a block diagram of routing tables used in the SPAN-AI and A ⁇ HARD arrangement of Fig 1A
- Figure 4 is a video output view showing graphical structures that are utilised interactively with the modes of operational control of the appliance of Figure 3.
- Figures 5A through to 5F are video output views showing further graphical structures that are utilised interactively with the modes of operational control of the appliance of Figure 3.
- Figure 6 is a block diagram of a media distribution and management system in accordance with an implementation example .
- Figure 7 is a block diagram of the aggregator of the system of Figure 6.
- Figure 8 is a flow chart of service functions which give effect to the aggregator and origin store of the system of Figure 6.
- Figure 9 is a flow diagram of data packet sources and data packet flow which may contribute in whole or in part to delivery of digital content under the system of figure 6.
- Figure 10 is a conceptual diagram of the entire system of Figure 6 including a methodology for syndication.
- Figure 11 is a block diagram of an embodiment of the system of Figure 2A conceptualised from a user perspective.
- Figure 12 is a layout diagram of a processor module in accordance with a further embodiment of the present invention .
- Figure 13 illustrates diagrammatically some of the module functionality of the module of Figure 12.
- Figure 14 is a block diagram of data functionality operable with the further embodiment of Figure 12.
- Fig 15 is a screenshot of a menu screen output from the embodiment of Figure 12.
- Fig 16 is a screenshot of a menu selection screen output from the embodiment of Figure 12.
- Figure 17 is a screenshot of a selection screen interface for the embodiment of Figure 12.
- FIG. 2A there is illustrated a block diagram of a media distribution and management system 10 in accordance with a first preferred embodiment (and also incorporating SPAN-AI system components which define a further embodiment the SPAN-AI embodiment refer SPAN-AI embodiment description further in the specification).
- the system 10 includes an origin store 11 (sometimes termed a "Super PoP" in parts of this specification).
- the origin store 11 may be implemented as a single server or may itself be a network of servers. In particular commercial implementations, the servers may form part of a commercial partner content distribution network.
- the origin store 11 is in communication with various databases 12 which contain digital content 13 available for licensed use (usually, but not always , subject to negotiation of appropriate terms) .
- the origin store 11 receives the digital content 13 usually as "wrapped" content meaning that digital rights management (DRM) has been applied to the content.
- DRM digital rights management
- the origin store 11 makes this content 13 available to subscribers or purchasers by way of a network appliance 14.
- the network appliance 14 is located at or close to the point of consumption of digital content 13.
- the network appliance 14 can receive digital content 13 from the origin store 11 directly in accordance with communication protocols 15 commonly available when communicating via the Internet 16. Most commonly it is expected that communication will be via the Internet 16 but other structures can be contemplated which facilitate use of the protocols 15.
- the digital content 13 may be secured from the point of ingest to the network appliance 14 by use of one or more of the following security technologies and features: Secure ingest via Aspera to the Platform's secure environment
- the communication structures and algorithms programmed into the aggregator database 12 and the network appliances 14 are such that content 13 is initially obtained from the aggregator database 12 typically over the Internet 16 following an initialisation sequence which permits a given network appliance 14 access to and use of a specified item 17 of digital content 13. Again, but not necessarily always, permission will be subject to negotiation of commercial terms in advance of access being provided to the specified item 17.
- the appliance will output secure e.g. HDMI HDCP digital content to an audiovisual display device 18 such as a television set.
- an audiovisual display device 18 such as a television set.
- it can be streamed securely wirelessly or via Ethernet to other devices such as tablets and phones and TVs.
- game content may be played on the appliance or "side loaded” wirelessly or via Ethernet or some other method to gaming devices such as other gaming platforms.
- a feature of the present system 10 is that If another network appliance 14 negotiates and requests access to the same specified item 17 the content may be downloaded (or portions of it) from either the origin store 11 or the network appliance 14 which already has that specified item 17 stored on it.
- Routing information may be distributed and/or centralised and may be in the form of hash tables or other efficient database mechanisms. This detailed knowledge combined with control of network appliances 14 and routing is a form of software defined networking (SDN).
- SDN software defined networking
- network knowledge includes address information for all data packets that will form digital content 13 and, more particularly, at any one time address information for all data packets that form part of a specified item 17.
- This data packet address information may be stored in a database 40 as shown in figure 3 wherein each individual address, for example address AA of respective data packet 24 is linked to a location, location loci in this instance.
- the database 40 may be stored on or form part of the origin store 11 or it may be a separate server. In other instances it may be stored, at least partially, in memory 21 of the individual network appliances 14 in order to provide a distributed storage arrangement. It will be understood that over time there will become available a significant number of sources distributed over a wide area from which a specified item 17 may be downloaded (in whole or in part).
- telcos and ISPs As to which source to use may be determined in conjunction with telcos and ISPs in order to optimise use of their networks and minimise costs to consumers, the telcos and ISPs and the service operator. This may take the form of "unmetered content" agreements for Secure Peer assist traffic that remains within a network operator's domain.
- model can be used as to upon what basis the specified item is permitted to be downloaded or streamed to a specific network appliance 14.
- the model may be based upon "pay as you go” such as pay-per-view or rent or download to own.
- the combination of Super PoP CDN and Secure peer assist criteria ensures optimum delivery. Video packets are sourced from the best available location.
- the network of network appliance nodes provides the optimum network architecture: intelligence and storage at the furthest edge the network, i.e. the customer premises. This is reinforced by a master Super PoP to fill any gaps. This architecture ensures that we drive the user's connection at maximum bandwidth whilst minimising hierarchical network traffic and inter- network peering. Network protocols and parameters have been optimised based on experience.
- the Secure peer assist criteria and applications programs based on them are aware of and report network traffic at the SCTP, TCP/IP, UDP and video packet level.
- Each network appliance 14 forms an intelligent node in a mesh network. This may be sometimes described as grid computing or distributed cloud computing. We combine distributed and centralised routing information and intelligence down to the video packet level. This enables optimum management of the network with Software Defined Network like capability.
- Secure peer assist criteria permits formation of an entire ecosystem for video and game delivery management via the Internet.
- Each network appliance 14 monitors metrics and statistics at the network and video packet level, reporting traffic and video state in real time.
- QoS quality of service
- Secure peer assist criteria provides a very efficient method of video distribution via the Internet, minimizing network load and maximising network and customer viewing performance.
- Secure peer assist criteria may also be implemented in Consumer Electronics (CE) apps.
- CE Consumer Electronics
- Secure peer assist criteria 19 extends network reach beyond the edge, right to customers' homes. Secure peer assist criteria 19 may be architected to take advantage of the modern Internet: reasonably high customer premises tail speeds with fibre backhaul from the exchange. Secure peer assist criteria architecture uses the network of network appliance nodes which are each programmed with the secure peer assist criteria 19 combined with a Super PoP CDN architecture, to drive the user's connection at maximum capacity, thereby ensuring that content is delivered in the highest quality, without perceptible interruptions.
- the digital content 13 stored on the origin store 11 may be syndicated.
- the stored digital content 13 may be supplied as a store portal on anyone' s web site just like YouTube puts a portal on web sites.
- the participating site owner may choose a sub-catalogue of titles from a master catalogue that are relevant to their audience .
- the aggregator database 12 may include the following technologies in order to assist in applying appropriate security to the digital content 13 prior to delivery to the origin store 11:
- the Secure Peer Assist network is designed to be secure, hidden and not discoverable
- the Secure Peer assist network management system is protected by PKI and secure certificates
- Secure Peer assist is "invisible” to BitTorrent Networks and is not analogous in its protocols to such networks All Secure Peer assist protocols are standard Internet protocols or secure protocols with PKI security and verification
- All digital content 13 is encrypted with Microsoft PlayReady DRM and secured within the network appliance
- PlayReady DRM is implemented in the hardware of the appliance within its trusted execution environment (TEE)
- TEE trusted execution environment
- the appliance operating system is fully integrated with and utilises the hardware DRM to secure the media pipeline
- the appliance operating system may be Microsoft Windows
- PlayReady key management is completely separate from and additional to network appliance TEE security and key management .
- Key management and storage is performed within a secure application and environment on the appliance
- that secure key management system may utilise innovative secure enclave environments enabled by the processor architecture, instruction set, libraries, Application Programming Interfaces (APIs) and attestation services .
- APIs Application Programming Interfaces
- the network appliance 14 includes a processor or microprocessor 20 in communication with a memory 21.
- the microprocessor 20 is in communication with an input output device 22 by which signals can be sent to and received from an external digital device which preferably includes at least a visual display 23.
- the processor or microprocessor may include a graphics processing unit (GPU) or that GPU may be a separate processor, system or sub-system .
- the memory contains code including code corresponding to the secure peer assist criteria 19 which enables the processor 20 to effect various functions including sending and receiving digital content 13 over a network 25.
- the network 25 may include the Internet 16, local area networks 26 and wide area networks 27 all intercommunicating with each other.
- the digital content 13 will typically comprise a plurality of data packets 24 each of which comprises a header 24A and a payload 24B.
- the payload 24B comprises digital data which may more specifically he audio data, video data, game data or other data.
- the core function of the network appliance 14 is to controllably send and receive digital content 13 and to convert that digital content 13 locally into local signals 27 for driving an external digital device such as (but not limited to) audiovisual display device 18.
- a further function of the network appliance 14 is to permit a user to control the "purchasing” and “playing" of digital content received by or sent from the network appliance 14.
- the user experience and user interface are kept as simple as possible.
- user control is effected simply by moving a cursor left or right via a remote control device.
- These actions control extremely simple menus and displays of content on the screen. These may be homogenous or blended i.e. pure menu or pure content display or a mixture of both.
- the displays are arcs or circles to reflect the user experience and control via the remote control device.
- the display may be concentric arcs or circles of content "tiles" i.e. clean graphical images of the "cover" of the content title.
- these tiles may be in a grid formation.
- a menu of action items may be navigated left or right by clicking left or right.
- the menu may move correspondingly left and right under a selection graphical device such as a cursor box.
- the selection graphical device may move left or right.
- a menu item is selected by a simple single click. This may result in an action or in navigating deeper into the menu structure.
- Navigation "out” may be by double click.
- menu navigation items such as "back” or "cancel”. For navigation of large numbers of objects such as video libraries, these may be displayed in concentric arcs or rings or in a grid of tiles.
- the rings may be navigated "in” by clicks and "out” by double clicks and left and right by clicking left or right.
- Items, tiles, arcs or rings selected may be highlighted by increasing focus and/or size. Items, tiles, arcs or rings not currently selected may be reduced from focus by moving away from the centre of focus and/or "defocussing" the items or reducing them in size. This may give the effect of unselected items, tiles, arcs or rings moving "away” form the user and selected items, arcs or rings moving "toward” the user.
- control mechanisms such as rate or distance dependent actions.
- a small action may result in a slow, short movement of the menu or item .
- a larger action may result in a faster, longer movement of the menu or item.
- the rate of action may also determine the scale or nature of the menu action. This may be independent of distance of action or related.
- the user graphical display is very simple, clean, uncluttered and crisp to provide a feeling of simplicity and ease of use.
- a sequence of operation can be as follows: graphical structures 28 lie on a substantially vertically disposed arc are shown in figure 5A or maybe on a substantially horizontally disposed arc shown in figure 5B.
- a user manipulates the cursor 29 device to surround a chosen one of the graphical structures 28 for example to designate the "my movies" graphical structure.
- the user may then move the cursor through a series of, in this instance, movie selections to designate the "Capt. America" movie selections as shown in figure 5D.
- FIG. 5F shows details of a particular selection when the "Capt. America" graphical structure was shown highlighted by the cursor 29 (figure 5D in a purchase menu in the store context obtainable from syndicated webstore 41 -refer fig 6).
- this can be effected by control of a cursor 29 in the form of a rectangular-shaped border device in association with graphical structures 28 displayed on visual display 23, in this instance of audiovisual display device 18.
- the graphical structures 28 may lie on an arc or circular path.
- these controls may be "simulated" in a remote-control application on for example a smart phone connected wirelessly or via the Internet to the main network appliance 14 or a "satellite” network appliance 14 forming a home network.
- these controls may be embodied in a TV remote controller or a game controller.
- these controls may be duplicated on a smaller version of the network appliance 14 wirelessly connected to the main network appliance 14 or a "satellite" network appliance 14 forming a home network.
- these UI concepts permit streamlined control of operation of the network appliance 14 including most particularly selection of digital content 13 for viewing on the audio visual display device 18.
- Significant is the reflection of the physical user experience in the UI e.g. arcs for menus and images, concentric circles (or arcs) to show menus or titles, blending of menu and images, in one embodiment in circles and arcs.
- menu and images may be displayed in a grid of tiles .
- the network appliance 14 includes at least the following capabilities:
- remote functions e.g. seek, pause, rewind, fast forward, slow motion via apps or via appliance or smaller version of appliance wirelessly connected to "home” appliance
- Manage library including third party content Securely share content.
- Content will be DRM protected and a mechanism provided to purchase a key to unlock the content
- Embodiments of the network appliance 14 of the present invention comprise a device operating according to secure peer assist protocol 19 being a portable device for downloading, storing, streaming, playing and sharing high quality movies, games and TV on a TV or connected device. It combines secure peer assist criteria 19 technology and a content origin store 11 and a syndicated retail content web store 41 to provide the latest Hollywood and Indie movies, TV and games in true HD and UHD on a TV.
- Embodiments of the network appliance 14 address the key issue in OTT and IP TV delivery today: exponential growth of video traffic. In this instance the network appliance 14 provides the flexibility for a new generation of content owners who can choose what they want to watch, when they want to watch it and who and how they want to share it with in true High Definition and Ultra High Definition, all the time.
- Secure Peer assist network client in secure environment Play Movies, TV and games including "remote” functions e.g. seek, pause, rewind, fast forward, slow motion Microsoft PIayReady secure client
- Base model This is the base model with minimum 2TB disc and 128G SSD storage. It will be a fully functional peer in the Secure Peer assist network, enabling high quality download and streaming of movies and TV from the store 41. It will be controlled via the unit, via a phone or tablet app or via TV remote or keyboard, track pad or mouse.
- Base model with disk library This is the base unit with minimum 2TB 2.5 inch disk drive for storage of movies. It will be capable of storing 200-400 HD movies or 100 UHD movies, depending on encode size.
- SSD model with SSD library This is the base unit with 250G-2TB SSD hard drive. It will store 100 UHD movies, depending on encode size.
- [00181] Media hub and streaming: This will allow secure streaming of digital content to CE devices such as phones and tablets, and streaming of user's content to the TV.
- the network appliance 14 may be controlled by an app on a phone or tablet. This may be an Android or iOS app initially for iPhone and tablet. Other applications will be implemented in future. It may provide full remote control of all viewing functions, as well as the ability to purchase directly via the network appliance accessible store. [00183] It may optionally also remotely control the TV via USB or Bluetooth if equipped or via the network appliance 14.
- the system must be as low power as possible.
- the system may be powered by AC power pack.
- the system may be optionally battery powered.
- the system may run a secure, real time version of the Linux operating system or the Microsoft Windows operating system .
- the system architecture may be ARM Cortex A9 or later, including ARM TrustZone or it may be Intel Core architecture 6 th generation or later, including Secure Guard Extensions (SGX), Memory Protection Extensions (MPX), secure enclaves and hardware DRM.
- SGX Secure Guard Extensions
- MPX Memory Protection Extensions
- secure enclaves hardware DRM.
- all media files will be DRM encrypted.
- Preferred DRM are Microsoft PlayReady for movies, Ubisoft DRM or Fold Solid Shield for games but other studio approved DRM may be used including Adobe Access and Google Widevine.
- the system may provide a robust and long term solution where trusted applications are appended in the field over the lifetime of the device.
- the system may conform to the specification of a Trusted Execution Environment.
- the system may support trusted boot mode and trusted control of all I/O ports .
- the system may support Intel Secure Guard Extensions (SGX), Memory Protection Extensions (MPX), secure enclaves and hardware DRM
- the system may support secure attestation and sealing
- the system may support ARM Advanced System Architecture and Base Architecture platforms for digital rights management (DRM), with integration of the TrustZone Address Space Controller (TZASC) to protect areas of the RAM used to hold valuable content.
- DRM digital rights management
- TZASC TrustZone Address Space Controller
- the architecture may support integration of media accelerators, such as GPU, Video Engine and Display controller, all of which will require knowledge of the processor's security state.
- media accelerators such as GPU, Video Engine and Display controller
- the system may provide tamper protection and real time clock.
- the system may support secure hardware cryptographic acceleration to optimize DRM decoding speed.
- the system may support high assurance boot and recognition of digitally signed software.
- the system may support Secure JTAG-JTAG i.e. use is restricted (in the No-Debug level) unless a secret- key challenge/response protocol is successfully executed.
- example 1 in preferred forms will support digital rights management (DRM).
- DRM digital rights management
- Microsoft PlayReady is preferred for movies and TV and Ubisoft DRM or Fold Solid Shield for games initially.
- Other studio approved DRM e.g. Adobe Access and Google Widevine are alternatives.
- Hardware & O/S is preferred for movies and TV and Ubisoft DRM or Fold Solid Shield for games initially.
- Other studio approved DRM e.g. Adobe Access and Google Widevine are alternatives.
- Hardware & O/S hardware & O/S
- Preferred forms of criteria for receipt of data packets at the network appliances operate according to one or more of the following, alone or in combination: a. most needed packet b. fastest download,speed c. least latency d. the network address from where the next digital bit or group of bits can most easily and efficiently be acquired in order to maintain real-time or near real-time delivery of digital content.
- digital content and more particularly specified items of digital content are DRM 'wrapped', delivered to the network appliances and decoded at the network appliances utilising the Microsoft PlayReady infrastructure.
- DRM 'wrapped' delivered to the network appliances and decoded at the network appliances utilising the Microsoft PlayReady infrastructure.
- FIG 11 With reference to figure 11 there is illustrated the system 10 conceptualised from a user perspective.
- the system enhances the experience for all stakeholders by providing confidence in the security of the digital data to the originators and rights owners of the digital data whilst also providing a wide array of digital content for the selection of the user 42 all delivered in a controlled and timely manner such that both substantially real-time streaming as well as data download are available over a wide range of Internet connections.
- UHD ultra high definition
- Typical UHD movies operate according to MPEG4 standards such as H.264 (so called HD definition typically operating at 1080 pixels or lines down the screen) and H.265 (so called 4K or UHD definition operating at 2160 lines or pixels down the screen).
- a typical file for such a movie may be of the order of 15-20 GB in size.
- the "secure peer assist" arrangement described in earlier embodiments is enabled on a Windows/Intel platform.
- TPM trusted platform module
- the TPM may be embodied in the processor 113 or an associated system module.
- the trusted platform module 112 includes a unique identifier 115, a certificate for encryption and decryption 116 and secure boot code 117.
- the trusted platform module 112 implements Trusted Computing Group architecture in this instance on hardware which is part of the TXT platform available from Intel Corporation providing a Trusted Execution Environment (TEE) incorporating Intel Secure Guard Extensions (SGX), Memory Protection Extensions (MPX), secure enclaves and hardware DRM
- TEE Trusted Execution Environment
- SGX Intel Secure Guard Extensions
- MPX Memory Protection Extensions
- secure enclaves and hardware DRM
- the processor supports Intel Secure Guard Extensions (SGX), Memory Protection Extensions (MPX), secure enclaves and hardware DRM
- DRM is implemented utilising the Microsoft PlayReady environment.
- UHD 4K content will play if and only if:
- a hardware DRM environment is detected [00212] that environment is within a trusted execution environment and
- the trusted platform module 112 permits the processor 113 to enter into a trusted running state.
- a preferred operating system loaded into memory 114 for execution by a processor 113 is the Microsoft Windows 10 operating system or a later version.
- the processor 113 and memory 114 may optionally execute a virtual machine 118 within the Intel architecture environment .
- the virtual machine 118 permits direct hardware access by an operating system such as the Windows 10 operating system whilst operating within a highly secure environment .
- a movie file 119 downloaded to memory 114 utilising the secure peer assist arrangement of previous embodiments may be processed and the video stream decoded via hardware DRM and an HDCP Level Shifter Protocol Converter (LSPCON) chip 120 for secure output via HDMI,
- LSPCON HDCP Level Shifter Protocol Converter
- the video stream may be securely routed to a secure GPU 120A for secure output via HDMI.
- the movie file 119 assembled potentially from many sources preferably in the form of secure peer assist platforms 122 is processed by the components including optionally virtual machine 118 operating within a Windows 10 environment utilising hardware DRM providing a highly secure output stream 119A which is processed by a converter chip 120 (preferably an HDCP 2.2 LSPCON chip) in order to output a secure video stream 119C displayable on ultra high resolution display device 121.
- a converter chip 120 preferably an HDCP 2.2 LSPCON chip
- the trusted execution environment and stream 119 ⁇ is secured via data 119B provided from independent security support and attestation servers 123 as illustrated in Figure
- Fig 15 is a screenshot of a menu screen output to screen 121 by which a user may select a movie for watching on the display 121.
- Fig 16 is a screenshot of a menu selection screen by which a user may select a movie for watching on the display 121 utilising a scrolling arrangement.
- Figure 17 is a screenshot of a selector screen arrangement .
- a user may make use of associative technology which clusters items for selection in accordance with predetermined criteria.
- associative technology which clusters items for selection in accordance with predetermined criteria.
- An example of such a system is described in US 2014/0330841 the description, claims and drawings of which are incorporated here by cross- reference .
- a correlation algorithm is applied between items belonging to a finite set of items wherein each item has an associated visual indicia and at least a set of attributes that are common to every other item belonging to said finite set of items to facilitate discovery of said items within said finite set.
- Secure Peer Assist may be "inserted" in or integrated with Adaptive Bit Rate protocols in order to utilise the extensive existing assets and resources that use adaptive bit rate. This may be by direct integration or via an Application Programming Interface (API).
- Secure Peer Assist would be responsible for network communication and would interface to Adaptive Bit Rate resources such as media servers , video encoders and se gmenters/packagers, Digital Rights Management systems, key management systems , content distribution networks, video players , browsers, client applications etc.
- Secure Peer Assist would manage timely delivery of video and other content packets . To the adaptive bit rate protocol it would appear as an optimum single fixed rate stream . In effect this would convert adaptive bit rate into progressive download or optimum fixed rate streaming, depending on available user bandwidth.
- Secure Peer Assist would be integrated with Dynamic Adaptive Streaming over HTTP (DASH), also known as MPEG-DASH, with Common Encryption and Encrypted Media Extensions (EME).
- DASH Dynamic Adaptive Streaming over HTTP
- EME Common Encryption and Encrypted Media Extensions
- a proposed name for this arrangement would be DSPASH (Dynamic Secure Peer Assist over HTTP) .
- This preferred embodiment would be integrated with an HTML5 browser supporting Media Source Extensions . This would provide a standardised implementation, capable of the most efficient implementation across a multiplicity of consumer devices.
- a further preferred embodiment would use Microsoft PlayReady DRM and the Microsoft Edge HTML5 browser on the above described preferred embodiment of an Intel processor hardware platform implementing PlayReady in hardware under the tightly integrated Microsoft Windows 10 (or later) operating system.
- the network appliance may be implemented as stand- alone hardware units or multiple connected units programmed with the secure peer assist criteria described above.
- the secure peer assist criteria may be made available for programming into other devices such as smart phones, game controllers, smart TVs and the like.
- Server based devices can be used to implement the aggregator 12 and the origin store 11.
- FIG 1A With reference to figure 1A there is shown a block diagram of a SPAN-AI embodiment of a media distribution and management system 10 as shown in figure 2A.
- the SPAN-AI embodiment includes sub-systems which form a widely applicable, universally operable, highly scalable and efficient system for optimisation, management and operation of a Unified Content Delivery Network (UCDN)- as shown in figure 1 - incorporating AI-driven Secure Peer-Assisted Networking
- UCDN Unified Content Delivery Network
- SPAN-AI which is a hybrid adaptive networking technology that provides global, scalable, secure, distributed content storage, computation, and delivery for any application and network environment.
- SPAN-AI recognizes the limitations of existing technologies, only suitable for specific applications at non-global scale, and uses an AI-driven hybrid routing approach to improve and adaptively combine best-fit features of existing solutions under a unified secure content- addressable architecture . We call this a Unified Content Delivery Network or UCDN.
- UCDN creates a global network of inter-operable peer networks, thereby eliminating the problems associated to date with the "network of networks" approach.
- UCDN does that via open standards, interfaces, protocols, methods enabling any network to inter-operate with any other. These include but are not limited to AI and routing standards, interfaces, protocols, methods.
- the initial embodiments with reference to figure 2A teach a hybrid ecosystem of peer to peer streaming and download, combining semi -centralised (cloud) media distribution and management servers and super-pops (points of presence) with distributed, [self-organising], intelligent edge nodes in a mesh network forming a [content based] distributed storage network for optimal distribution of encrypted media content via the Internet utilising [centralised and distributed] network knowledge to provide comprehensive guality of service (QoS) monitoring, control and optimisation of the entire network.
- QoS quality of service
- SPAN Secure Peer Assist
- SPAN-AI Secure Peer Assist Network
- SPAN-AI-HARD Hybrid Adaptive Routing Design
- routing information may be distributed and/or centralised and may be in the form of hash tables or other efficient database mechanisms.
- This detailed knowledge combined with control of network appliances 14 and routing is a form of software defined networking (SDN).
- network knowledge includes address information for all data packets that will form digital content 13 and, more particularly, at any one time address information for all data packets that form part of a specified item 17.
- This data packet address information may be stored in a database 40 as shown in figure 3 wherein each individual address, for example address AA of respective data packet 24 is linked to a location, location loci in this instance.
- the database 40 may be stored on or form part of the origin store 11 or it may be a separate server. In other instances, it may be stored, at least partially, in memory 21 of the individual network appliances 14 in order to provide a distributed storage arrangement. It will be understood that over time there will become available a significant number of sources distributed over a wide area from which a specified item 17 may be downloaded (in whole or in part).
- the Secure Peer Assist criteria and applications programs based on them are aware of and report network traffic at the SCTP, TCP/IP, UDP and video packet level.
- Each network appliance 14 forms an intelligent node in a mesh network. This may be sometimes described as grid computing or distributed cloud computing. We combine distributed and centralised routing information and intelligence down to the video packet level. This enables optimum management of the network with Software Defined Network like capability.
- Secure peer assist criteria permit formation of an entire ecosystem for video and game delivery management via the Internet.
- Each network appliance 14 monitors metrics and statistics at the network and video packet level, reporting traffic and video state in real time.
- QoS quality of service
- Secure Peer Assist criteria provides a very efficient method of video distribution via the Internet, minimizing network load and maximising network and customer viewing performance.
- Secure Peer Assist criteria may also be implemented in Consumer Electronics (CE) apps.
- CE Consumer Electronics
- SPAC Secure Peer Assist criteria
- the initial embodiments with reference to figure 2A describes a system for management and optimisation of telecommunications networks .
- it is a system for management and optimisation of the Internet, telecommunications carriage networks and Content Delivery Networks (CDNs).
- CDNs Content Delivery Networks
- the SPAN-AI embodiment and the UCDN embodiment build on the SPAN system - components of which are shown in figure 2A (in addition to the SPAN components).
- the additions comprising the SPAN-AI embodiment and the UCDN embodiment teach the methods of a Unified Content Delivery Network (UCDN), incorporating and extending the methods of the distributed storage network of the SPAN system thereby unifying and optimising all Internet, telecommunications carriage networks and Content Delivery Networks (CDNs) into a single, unified, optimised network.
- UCDN Unified Content Delivery Network
- CDNs Content Delivery Networks
- this is done by state-of-the-art methods of Secure Peer Assist Network (SPAN) machine learning and Artificial Intelligence Hybrid Adaptive Network Design, or SPAN-AI-HARD.
- SPAN-AI-HARD Artificial Intelligence Hybrid Adaptive Network Design
- the present invention combines and extends these sub-systems to form a widely applicable, universally operable, highly scalable and efficient system for optimisation, management and operation of a Unified Content Delivery Network (UCDN)incorporating AI-driven Secure Peer-Assisted Networking (SPAN-AI), which is a hybrid adaptive networking technology that provides global, scalable, secure, distributed content storage, computation, and delivery for any application and network environment.
- SPAN-AI recognizes the limitations of existing technologies, only suitable for specific applications at non-global scale, and uses an AI-driven approach to improve and adaptively combine best-fit features of existing solutions under a unified secure content-addressable architecture. We call this a Unified Content Delivery Network or UCDN.
- UCDN creates a global network of inter-operable peer networks, thereby eliminating the problems associated to date with the "network of networks" approach.
- UCDN does that via open standards, interfaces, protocols, methods enabling any network to inter-operate with any other. These include but are not limited to AI and routing standards, interfaces, protocols, methods.
- UCDN creates a global network of inter- operable peer networks, thereby eliminating the problems associated to date with the "network of networks” approach.
- UCDN does that via open standards, interfaces, protocols, methods enabling any network to inter-operate with any other. These include but are not limited to AI and routing standards, interfaces, protocols, methods.
- a UCDN is formed from a network of one or more inter-operable peer networks.
- the UCDN network may comprise peer networks in the form of SPAN_AI networks.
- this may be a network of one or more SPAN-AI networks inter-operating via AI, routing or other interfaces (see Fig IB).
- Any network may be transformed into a SPAN-AI network simply by the "injection” (distribution of containerized micro services or applications) of SPAN-AI agents into the network and the incorporation of SPAN-AI intelligent hybrid adaptive routing (AI-HARD) and a SPAN-AI global optimising AI into the network.
- injection distributed of containerized micro services or applications
- SPAN-AI agents distributed of containerized micro services or applications
- SPAN-AI intelligent hybrid adaptive routing AI-HARD
- SPAN-AI global optimising AI into the network.
- any network may be interconnected to form a UCDN by connection to compatible open standard interfaces, protocols or methods (APIs) of a SPAN-AI network to retain compatibility and communication with "legacy” networks (see Fig 1C).
- APIs open standard interfaces, protocols or methods
- SPAN-AI network to retain compatibility and communication with "legacy” networks (see Fig 1C).
- these networks would be transformed into SPAN-AI networks.
- a minimal embodiment of a SPAN-AI network comprises a network of self-organising peers and agents incorporating AI-HARD intelligent hybrid adaptive routing with a global optimising AI. Other embodiments may include any additional capability or function.
- the core SPAN-AI systems are:
- AI-HARD Hybrid Adaptive Routing Design
- Storage-centric routing subsystem and Delivery- centric routing subsystem; combining the benefits of name-resolution-based routing (NRR) for scalable, available, accessible distributed storage, and the advantages of name-based routing (NBR) for fast, reliable content delivery.
- NBR name-resolution-based routing
- AI HARD also combines IP routing to provide backward compatibility .
- AI-HARD intelligent agents within SPAN-AI exploit predictive knowledge about network conditions and application requirements to adaptively choose the most efficient routing policies from subsystems.
- SPAN-AI's unified naming and discovery system i) maps mutable human-readable names (e.g., domain names, content names) to immutable self-certifying content identifiers (CIDs), and ii) enables routing CIDs through both NRR and NBR subsystems by iii) prepending a name prefix to each CID .
- UND also combines IP DNS to ensure backwards compatibility .
- SPAN-AI's unified naming and discovery system i) maps mutable human- readable names (e.g., domain names, content names ) to immutable self-certifying content identifiers (CIDs), and ii) enables routing CIDs through both NRR and NBR subsystems by iii) combining a name prefix and CID in such a way as to optimise routing and/or storage.
- UND also combines IP DNS to ensure backwards compatibility.
- UND discovery includes both SPAN-AI's smart discovery service AmI-Rendezvous and IP name discovery i.e. DNS.
- SPAN-AI uses an AI-driven pub-sub system for asynchronous multi-party dissemination services. This includes communication between AI agents, naming services, and discovery services. This also includes inter-operation with IP discovery services.
- SPAN-AI provides optimisation at the global level by "rolling up" data from hierarchical AI agents with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels. This provides a global view and allows global optimisation.
- the data and protocols make use of a formal logic ontology and semantics to describe the SPAN-AI system.
- SPAN-AI orchestrates the adaptive operation of the routing and pub/ sub systems via a family of pluggable, hierarchical (local/edge/global/other) AI agents that provide monitoring, prediction, optimization, and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.
- the swarm intelligence enables other swarms to join and be part of the network.
- Networking is a hybrid adaptive networking technology aimed at providing global, scalable, secure, distributed content storage, computation , and delivery for any application and network environment.
- SPAN-AI recognizes the limitations of existing technologies , only suitable for specific applications at non- global scale, and leverages an AI-driven approach and hybrid adaptive routing to improve and adaptively combine best-fit features of existing solutions under a unified secure content- addressable architecture. We call this a Unified Content Delivery Network or UCDN.
- Peer any hardware or software apparatus with a similar or comparable general or specific purpose in whole or part .
- P2P Peer-to-Peer.
- Agent a software application with varying degrees of awareness, communication, optimization, learning, reporting, self-organising or other capabilities distributed to and/or running on any network appliance (computer, consumer electronics device, router, switch, server, etc) at peer, edge, core, or other network levels; a virtual network service or application.
- I/F interface.
- the method is open and standardised in which case the interface may be known as an Application Programming Interface or API.
- Peer Network any network with a similar or comparable general or specific purpose in whole or part.
- IP Internet Protocol
- TCP Transport Control Protocol
- SPAN Secure Peer-Assisted Networking
- AI Artificial Intelligence
- HARD Hybrid Adaptive Routing Design
- SAMSim Self-Aware Mesh Simulator
- CID Content Identifier
- IPFS Inter-Planetary File System
- IPLD Inter-Planetary Linked Data
- IPNS Inter-Planetary Name System
- DNSLink protocol that uses DNS text records to link domain names to IPFS addresses or CIDs
- NDNS Domain Name System for Named Data Networking
- mDNS multicast DNS
- Pub/Sub Publish/Subscribe
- libp2p a location independent modular network stack. Part of IPFS.
- NRR Name Resolution based Routing
- NBR Name Based Routing
- NDN Named Data Networking
- NBN either Name Based Networking or National Broadband Network in Australia
- DHT Distributed Hash Table
- DRM Digital Rights Management
- VoD Video on Demand
- ISP Internet Service Provider
- CDN Content Distribution Network
- testlab & testground IPFS test frameworks
- NRT Near Real Time or Non-Real Time
- telco telecommunications company
- Node a vertex of a graph network model; the joining point of graph edges;
- Edge network edge (1-2 hops away from the end-user device) ; or the connection between nodes in a graph;
- Graph mathematical model used to represent communication networks, data organization, computational devices, the flow of computation or communication, etc.
- SPAN-AI for AI-driven Secure Peer-Assisted Networking, is a hybrid adaptive networking technology that provides global, scalable, secure, distributed content storage, computation, and delivery for any application and network environment.
- SPAN-AI recognizes the limitations of existing technologies, which are only suitable for specific applications at non-global scale, SPAN-AI uses an AI-driven approach to improve and adaptively combine best-fit features of existing solutions under a unified secure content- addressable architecture. We call this a Unified Content
- SPAN-AI is based on 5 key systems: unified naming; unified discovery; hybrid routing; scalable pubsub; and embedded security; all securely integrated and jointly optimized via a hierarchical, pluggable AI framework with an associated simulation, training, and development pipeline that embeds AI agents with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.
- SPAN-AI uses a Unified Naming and Discovery System that i) maps mutable human readable names (e.g., domain names, content names) to immutable self-certifying content identifiers (CIDs), and ii) enables routing CIDs through both name-resolution and name based routing subsystems by iii) prepending a name prefix to each CID or iv) combining a name with a CID in such a way as to optimise routing and/or storage .
- mutable human readable names e.g., domain names, content names
- CIDs immutable self-certifying content identifiers
- Unified Naming System a SPAN-AI is content-addressable b .
- Content items or blocks are identified via immutable self-certifying content identifiers (CIDs), as in IPFS c .
- CIDs immutable self-certifying content identifiers
- a global, distributed naming directory service is used to map mutable human-readable names/links to immutable CIDs i. Initially, IPNS and/or DNSLink will be used ii . Extensions include the use of NDNS d.
- CIDs are then resolved (CID-provider mapping and provider-requester path formation) via a hybrid adaptive routing system (system 3) i. Name -resolution based routing, i.e., querying a (multilevel) DHT ii.
- Name based routing i.e., hop-by-hop forwarding of an interest packet with a prefix (e.g., SPAN/ ⁇ CID>)
- Extensions include hierarchical names and name-based routing for name-CID mapping f.
- SPAN-AI intelligence determines where to host distributed naming services (see SPAN-AI Intelligence section) g ⁇ SPAN-AI pub/sub system is used for scalable, fast dissemination of naming updates (see Scalable Pub/Sub system)
- the Unified Naming System may also use JSON updates in a Conflict-free Replicated Data Type (CRDT) with cryptographic key value pairs. These may be structured in DHTs, Merkle Trees, simple blockchains or other efficient distributed data structures.
- CRDT Conflict-free Replicated Data Type
- SPAN-AI employs an AI -driven unified discovery system, whose key component, Ambient Intelligent Rendezvous (AmI-Rendezvous), provides smart discovery, configuration, and self-organization services.
- AmI-Rendezvous Ambient Intelligent Rendezvous
- Unified Discovery System (AmI-Rendezvous) a . Provides smart discovery, configuration, and self-healing services i . Bootstrap nodes , discover peers, maintain DHTs and pub/sub overlays b . Combines peer-level self-healing intelligence and edge- level smart discovery (see AmI-Rendezvous operation in SPAN-AI intelligence section) c. SPAN-AI intelligence determines where to host distributed AmI-Rendezvous services (see SPAN-AI intelligence section) i. AmI -Rendezvous is ideally co-hosted with edge-level naming and intelligence services
- SPAN-AI addresses routing at scale via an AI-driven Hybrid Adaptive Routing Design (AI-HARD) , composed of 2 subsystems, aimed at combining the benefits of name- resolution-based routing (NRR) for scalable, available, accessible distributed storage, and the advantages of name- based routing (NBR) for fast, reliable content delivery.
- AI- HARD includes storage and delivery markets.
- AI-HARD Hybrid Adaptive Routing Design
- Storage-centric routing subsystem a Main goal is persistent data availability (all content should be reachable) and relatively fast content access ( ⁇ 1 sec) b .
- Each DHT only involves content related to a given topic iv. Heterogeneous layers 1)Each DHT involves nodes that are both nearby and share similar interests c. Intelligent content placement with adaptive replication level i. Content is replicated according to learned interest /popularity and network connectivity/stability (more replication under high churn/instability) ii. Replication level is optimized to guarantee lookup+del ivery latency requirements of "storage- centric" applications. Additional in-network caching is provided for delivery-centric applications (see subsystem 3.2) d . Multi-level DHT structure and associated parameters
- Multi-level DHT and smart content replication solutions allow maximizing the number of queries resolved locally In order to provide efficient, scalable, persistent content access f .
- 3.2 Delivery- centric routing subsystem a. Main goal is fast content delivery ( ⁇ 100 ms) b. Name-based routing (NBR) for fast lookup and delivery (e.g., NDN) i . Dataplane-aware symmetric Interest-Data packet forwarding ii. In-network caching iii . Native multicast and mobility support iv. In-network load balancing c. Integrated with NRR subsystem via common unified name directory service (system 1) d. Only used for applications with real-time requirements
- Storage-centric i. Publishers may choose and pay for suitable storage metrics (reliability, duplication, dispersion, persistence, etc.) in a market such as Filecoin ii .
- SPAN-AI supports multiple storage markets and technology platforms and unites them into a Unified Content Storage and Delivery Network. This may include storage markets and platforms such as blockchain.
- Delivery-centric i. Publishers may choose and pay for suitable delivery metrics (resolution, bit rate, delay, etc.) in a market similar to Filecoin ii.
- Distribution providers may bid for delivery in the same market or rely on SPAN-AI and AI-HARD to choose the most efficient path, thereby incentivizing efficiency iii.
- Consumers may choose which distributor or distributors they wish to use if, for example, they have come to an arrangement with any distributor. Consumers are free to choose if and who they make arrangements with, or they may contribute to and be rewarded by a common pool or pools.
- Distribution preferences may be expressed by consumers in the name request. For example:
- SPAN-AI and AI-HARD will choose the most efficient path, once again incentivizing efficiency . vi . If the consumer or publisher does not specify distribution preferences, SPAN-AI and AI-HARD choose the most efficient path. vii . If a consumer's or publisher's chosen distributor or distributors is/are not the most efficient in any routing case, SPAN-AI and AI-HARD will choose the most efficient path and inform all interested parties of the decision to allow them to optimize efficiency, viii.
- SPAN-AI Payment for distribution is calculated and made by a settlement system or systems, informed by the SPAN-AI and AI-HARD routing system, in a similar manner to how telephony call settlement is performed today, ix.
- Teen may contribute resources and be rewarded for that contribution, providing a free market for telecomm's services.
- SPAN-AI monitors and maintains the security and health of the network. Non-performing resources will be removed.
- SPAN-AI is designed to work and meet QoS levels on both commercial and telco grade resources. QoS metrics and cost will determine the resources used and vice versa.
- SPAN-AI uses an AI-driven pub-sub system for asynchronous multi-party dissemination services that support control plane dissemination: directory updates (names, discovery, configuration) and intelligence updates
- Scalable Pub/Sub System a . Fast, scalable, asynchronous, multi-party dissemination services b . Pubsub for control i. Name directory updates ii . Discovery updates (new peers, new services) iii . Configuration updates (new roles, new memberships) iv . Intelligence updates (optimization/ control commands, e.g., resource allocation, storage, and routing decisions) c . Pubsub for data 1 . Collaborative media apps ii . Live streaming d.
- the pub/sub system uses an evolution of existing pubsub algorithms such as Gosslpsub, PlumTree, HyParViewthat uses the AmI-Rendezvous service for improved operation i.
- Ami-Rendezvous smart discovery and self-healing improves scalability and churn-resilience with little impact on routing scheme, other than tuning overlay degree, fanout, and probability weights.
- Embedded plugins for self-healing and smart discovery strengthen peer discovery, activation, and lifecycle maintenance of overlay:
- the pub/sub system is embedded with pluggable metrics, actuators, and triage for smart discovery
- SPAN-AI incorporates security integrated at all levels. SPAN-AI uses machine learning and recognition to detect and manage security threats. Content can be encrypted using commercial DRM systems such as PlayReady before it is published to the system. Data packets can be cryptographically signed by the publisher. Naming is rooted in self-sovereign identity, which can be defined as a lifetime portable digital identity that does not depend on any centralized authority. It uses decentralized identifiers that provide: persistence, global resolvability, cryptographic verifiability, and decentralization. Names can also be self certifying. A preferred embodiment is based on a hardware root of trust and secure boot. A further preferred embodiment may make use of Web of Trust methods. Quantum encryption, i.e. encryption based on quantum state random number generators, may also be used .
- SPAN-AI orchestrates the adaptive operation of the routing and pub/sub systems via a family of pluggable, hierarchical (local/edge/global/other) AI agents that provide monitoring, prediction, optimization, and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.
- SPAN-AI uses a simulation, training, and development pipeline that enables cloud-level replication of runtime environments, simulation, testing, and training of AI models, that can then be plugged into peer/edge/core/other network nodes for real-time optimization and control.
- SPAN-AI provides a marketplace for pluggable AI agents to enable open, flexible innovation in the optimization and control of universal networks. This may be based on a crypto token such as Filecoin or Blust.
- Hierarchical AI a. Hybrid local/global ⁇ optimization and control i . Combine fast local reactive self-organization with slower global/hierarchical proactive guidance/supervision and backup support b. Hierarchical intelligence i. Local intelligence at peer level
- Highest capability nodes e.g., stable peers, cloud nodes, ISP core, CDN PoP
- Application and network awareness i. Global predictive knowledge of users' consumption/production patterns, application requirements, network conditions (including overlay mesh health), and available resources for proactive optimization ii . Complemented by local situational awareness (of peer/mesh/network conditions) for reactive control and resilience to unpredictable change iii. Exploit metadata in content requests (e.g., delivery deadlines) d. Objectives & Principles i. No single point of failure in runtime agents/services
- Algorithms determine a) Service roles: DHT routing, name-based routing, storage, caching, discovery, monitoring, information mediation/decisioning b) Intelligence capability: reactive /proactive, local /global view, learning /observing, heuristic/ optimization
- Network architecture level peers , gateways, servers , switches, routers, application servers, etc.
- Trust, security, and stability level ii. Resource allocation
- endpoint nodes will tend to have more resources allocated to storage-centric subsystem
- AmI-Rendezvous operation (intelligent discovery and configuration)
- Ambient Intelligence refers to a combination of awareness and control for: i. Peer/Local Intelligence: Embedded actuators in pub/sub peers control probability weights, degree, and fanout of mesh. Observers of p2p pub/sub messaging compile metrics from neighbour subscriptions and events to infer health (e.g., hop-count, reliability, latency, load-balance) . Self-healing strategies can be as simple as filtering.
- Edge/Swarm Intelligence Aral health classification decisions (scoring, ranking) for peers and p2p overlay meshes are derived by a basic reinforcement learning model . iii.
- Core/Global Intelligence Maintain aggregated usage predictions and mesh/network conditions . Determine placement of rendezvous servers.
- AmI -Rendezvous service builds on state-of-the-art rendezvous services such as libp2p Rendezvous, which supports periodic peer re-registration, discovery, bootstrap, expanded with peer heartbeat and mesh health metrics and rankings.
- a pluggable interface for self-healing agents embeds AmI- Rendezvous clients into pub/sub i. Embeds pluggable metrics, actuators, and triage for smart discovery ii.
- a periodic heartbeat disseminates mesh-health metrics and change deltas to AmI-Rendezvous and SPAN-AI data lake. iii. Registration and re-registration is extended to exchange whole mesh snapshots
- a pluggable interface integrates smart discovery at rendezvous points i. AmI -Rendezvous server and messaging builds on libp2p
- Rendezvous service with metrics collection expanded with time series based monitoring systems such as Prometheus, InflUxDB.
- iii. Discovery is extended with peer rankings.
- Additional features include discovery records, federation and caching, adaptive control, metrics/actuator reuse, topic specific/device specific metrics.
- Further embedded intelligence and adaptive control of simulations and development pipeline by integrating with AI- HARD solution to support: i. hybrid P2P routing via rendezvous bypass to satisfy deadlines or optimise overlay breadth, ii. rendezvous & information mediator role assignment/placement , iii. partitioning by DHT topological layers, hints from naming enhancements or global awareness, iv.
- SPAN-AI uses a simulation, training, and development pipeline that enables cloud-level replication of runtime environments, simulation, testing, and training of AI models and agents, that can then be plugged into peer/edge/core/other network nodes for real-time optimization and control.
- SPAN-AI's simulator Self-Aware Mesh Simulator (SAMSim)
- SAMSim Self-Aware Mesh Simulator
- Simulator Intelligence uses SPAN-AI intelligence for adaptive scalability, including guiding placement of AmI-Rendezvous servers, scheduling and supporting data exchange between federated rendezvous servers and data lake.
- Test frameworks are used to prototype SAMSim, especially agile container infrastructure (e.g. HashiCorp Nomad and Consul for orchestration, Prometheus and InfluxDB for metrics) .
- AI developers use mesh-health metrics to test, train, sample simulations and reward reinforcement learning.
- Mesh health metrics and actuators frame a reinforcement learning problem.
- Metrics and actuators are refined with rules elicitation and management in data lake.
- Agents are refined from rules sets, metrics, and actuators, into:
- Additional simulators include NS3 for network events, RealPeer and ProtoSim for refinement model, PlanetSim for intelligent swarming, PEERFACTSIM.kom for DHT routing. f. Additional simulator & pipeline features include customisable API standards, simulator layering, topology graph export formats and failure simulation.
- Pub/Sub Simulation a. Supports essential scaffolding to engineer various trial agents by evaluating and integrating capabilities from the test framework and third party p2p simulators (e.g. PeerSim, D-P2P-Sim) .
- the agent engineering and assurance environment i. Initially focuses on ensuring latency in scalable, resilient pubsub meshes. ii . Supports simulation and iterative development of
- the SPAN-AI embodiment may include a Distributed Origin Store, Publishing and Distribution System using SPAN-AI wherein the Distributed video origin store and distribution service comprise the steps of :
- Ingest video encode; package; encrypt; sign
- This use embodiment allows a user to subscribe to a video using universal pub/sub system.
- NBR network for real time (live) streaming and/or NRR network for near/non real time distribution.
- SPAN-AI is for the distribution of video
- SPAN-AI has been designed to be a Unified Content Distribution Network (UCDN) for ANY type of content.
- UCDN Unified Content Distribution Network
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AU2021270052A AU2021270052A1 (en) | 2020-05-09 | 2021-05-07 | Media distribution and management system and apparatus |
US17/603,673 US12041297B2 (en) | 2014-11-04 | 2021-05-07 | Media distribution and management system and apparatus |
JP2022567832A JP2023525295A (en) | 2020-05-09 | 2021-05-07 | Media delivery management system and device |
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CN117149782B (en) * | 2023-11-01 | 2024-02-13 | 北京中兴正远科技有限公司 | CRC networking management method and system based on big data analysis |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060013219A1 (en) * | 2004-06-29 | 2006-01-19 | Neilson Brian R | Peer-to-peer data backup and data access tool |
US7783777B1 (en) * | 2003-09-09 | 2010-08-24 | Oracle America, Inc. | Peer-to-peer content sharing/distribution networks |
CN102196314A (en) * | 2011-03-28 | 2011-09-21 | 苏州汉辰数字多媒体有限公司 | System and method for realizing streaming media transmission by using peer-to-peer (P2P) set-top box |
US20120317655A1 (en) * | 2011-06-10 | 2012-12-13 | Futurewei Technologies, Inc. | Method for Flexible Data Protection with Dynamically Authorized Data Receivers in a Content Network or in Cloud Storage and Content Delivery Services |
WO2016070224A1 (en) * | 2014-11-04 | 2016-05-12 | Gt Systems Pty Ltd | Media distribution & management system & apparatus |
CN105763438B (en) * | 2016-04-29 | 2018-08-28 | 清华大学 | A kind of content distribution method based on software defined network Yu name route technology |
US20190319852A1 (en) * | 2018-04-13 | 2019-10-17 | Centurylink Intellectual Property Llc | Method and System for Implementing Intelligent Network Services Automation |
CN111125539A (en) * | 2019-12-31 | 2020-05-08 | 武汉市烽视威科技有限公司 | CDN harmful information blocking method and system based on artificial intelligence |
AU2019203053B2 (en) * | 2012-12-07 | 2020-07-09 | Adeia Media Holdings Llc | Peer-to-Peer Content Delivery Network, Method, and Manager |
-
2021
- 2021-05-07 WO PCT/AU2021/050426 patent/WO2021226656A1/en unknown
- 2021-05-07 AU AU2021270052A patent/AU2021270052A1/en active Pending
- 2021-05-07 CN CN202180048606.0A patent/CN116076076A/en active Pending
- 2021-05-07 EP EP21805127.4A patent/EP4147426A4/en active Pending
- 2021-05-07 JP JP2022567832A patent/JP2023525295A/en active Pending
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7783777B1 (en) * | 2003-09-09 | 2010-08-24 | Oracle America, Inc. | Peer-to-peer content sharing/distribution networks |
US20060013219A1 (en) * | 2004-06-29 | 2006-01-19 | Neilson Brian R | Peer-to-peer data backup and data access tool |
CN102196314A (en) * | 2011-03-28 | 2011-09-21 | 苏州汉辰数字多媒体有限公司 | System and method for realizing streaming media transmission by using peer-to-peer (P2P) set-top box |
US20120317655A1 (en) * | 2011-06-10 | 2012-12-13 | Futurewei Technologies, Inc. | Method for Flexible Data Protection with Dynamically Authorized Data Receivers in a Content Network or in Cloud Storage and Content Delivery Services |
AU2019203053B2 (en) * | 2012-12-07 | 2020-07-09 | Adeia Media Holdings Llc | Peer-to-Peer Content Delivery Network, Method, and Manager |
WO2016070224A1 (en) * | 2014-11-04 | 2016-05-12 | Gt Systems Pty Ltd | Media distribution & management system & apparatus |
CN105763438B (en) * | 2016-04-29 | 2018-08-28 | 清华大学 | A kind of content distribution method based on software defined network Yu name route technology |
US20190319852A1 (en) * | 2018-04-13 | 2019-10-17 | Centurylink Intellectual Property Llc | Method and System for Implementing Intelligent Network Services Automation |
CN111125539A (en) * | 2019-12-31 | 2020-05-08 | 武汉市烽视威科技有限公司 | CDN harmful information blocking method and system based on artificial intelligence |
Non-Patent Citations (2)
Title |
---|
ALUBABY R., SAMAN M., HASSAN S., HABBAL A.: "Review of name resolution and data routing for information centric networking", 4TH INTERNATIONAL CONFERENCE ON INTERNET APPLICATIONS, PROTOCOLS, AND SERVICES (NETAPPS2015), 3 December 2015 (2015-12-03), pages 48 - 53, XP055872181, Retrieved from the Internet <URL:https://www.researchgate.net/publication/289433441_Review_of_Name_Resolution_and_Data_Routing_for_Information_Centric_Networking> [retrieved on 20211213] * |
See also references of EP4147426A4 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWI831662B (en) * | 2022-04-06 | 2024-02-01 | 聯發科技股份有限公司 | Artificial intelligence (ai) security apparatus |
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