Media distribution and management system and apparatus

Abstract

A unified content delivery network system (UCDN) system formed by one or more networks of interoperable peer-to-peer networks. A hierarchical hybrid adaptive secure peer-assisted network system (referred to as SPAN-AI) using a hierarchical AI-driven approach under a unified secure content addressable architecture, the system is based on five key SPAN-AI subsystems: unified naming; unified discovery; hybrid adaptive routing; scalable publish subscribe; and embedded security; all five key SPAN-AI subsystems are securely integrated and jointly optimized via a hierarchical pluggable AI framework with associated simulation, training, and development pipelines embedded in AI agents with varying degrees of awareness and optimization capabilities at the peer-to-peer, edge, core, or other network levels (hierarchy).

Description

Technical Field

[0001] This invention relates to a media distribution and management system, and more specifically, but not exclusively, to such a system implemented using a network terminal unit (NTU) or internet device, which is combined with an internet infrastructure to deliver digital content (including, but not limited to, streaming and downloaded digital content) to and control digital devices (including, but not limited to, television display units, video display units, etc.). Background Technology

[0002] Certain forms of content receiving and viewing devices are available to consumers. These devices include television “set-top boxes” provided by media distribution companies. In Australia, well-known versions include Fox set-top boxes and Odus set-top boxes. These devices are limited to receiving and delivering content that is typically delivered via cable.

[0003] Also known are certain forms of “Internet devices” that typically receive digital content over the Internet in order to deliver it (usually via streaming) to television display units, etc. The “Apple TV” device is an example of such a device currently in use in Australia.

[0004] It is also known to stream digital content over the Internet to personal computers seeking assistance from file-sharing services such as BitTorrent. Such services and their protocols are highly insecure, unsuitable for streaming, typically require long times to start playback, and are not conducive to features such as “jumping” to a specific point in the content.

[0005] The problem with the devices and mechanisms currently used to receive and deliver digital content is that the upload and download speeds of the current internet infrastructure are variable, and it may be difficult (and in some cases impossible, especially for consumers in home environments) to reliably receive real-time or near real-time content, especially HD and UHD content or large file content on demand.

[0006] Many (if not all) current internet video distribution systems use adaptive bitrate (ABR) technology to overcome the problems of video-on-demand distribution over the internet. However, ABR reduces bitrate and resolution, and degrades the user experience.

[0007] Individually, and in some cases additionally, the choice of content available to consumers is limited by the proprietary nature of the device.

[0008] Furthermore, the current mechanisms for local control of content and its delivery and display are neither intuitive nor "user-friendly".

[0009] The internet is reaching its scalability limits, particularly the TCP / IP protocol and the routing protocols based on it. Video has already placed a tremendous load on the internet, something unforeseen when it was invented.

[0010] After decades of centralization in hyperscale data centers, networks are beginning to push back to the "edge." But there are some subtle yet impressive problems in this process.

[0011] Emerging applications such as industrial automation, machine vision, AR, 5G, and other future applications will place a greater load on global networks and the Internet.

[0012] Telecom companies, CDNs, and ISPs worldwide are scrambling to catch up, but no single network can solve these problems. It requires new methods that can seamlessly interoperate and scale for the foreseeable future.

[0013] The purpose of this invention is to solve or at least improve some of the above-mentioned disadvantages, or to provide useful alternatives.

[0014] Notes

[0015] The term “including” (and its grammatical variations) is used in this specification in an inclusive sense as to “have” or “include”, rather than in an exclusive sense as to “consist of only”.

[0016] The above discussion of the prior art in the background of this invention does not acknowledge that any information discussed herein is part of the prior art that can be cited or of common knowledge to those skilled in the art in any country. Summary of the Invention

[0017] Therefore, in a broad form of the invention, a Unified Content Delivery Network (UCDN) system is provided, which is formed by a network of one or more interoperable peer-to-peer networks.

[0018] Preferably, the peer-to-peer network is the SPAN-AI network.

[0019] Preferably, the system includes a hierarchical hybrid adaptive AI-driven networking technology (referred to as Secure Peer-to-Peer Assisted Networking or SPAN-AI), which uses an AI-driven hybrid adaptive routing approach based on five key SPAN-AI subsystems: unified naming; unified discovery; hybrid adaptive routing; scalable publish-subscribe; and embedded security. All five key SPAN-AI subsystems are securely integrated and jointly optimized via a hierarchical pluggable AI framework and have associated simulation, training, and development pipelines embedded in AI agents with varying degrees of perception and optimization capabilities at peer, edge, core, or other network levels (layers).

[0020] Preferably, the system uses a unified naming and discovery (UND) system, which i) maps variable human-readable names (e.g., domain names, content names) to immutable self-certifying content identifiers (CIDs), and ii) routes CIDs by adding a name prefix to each CID through both a name-based routing subsystem and a name-based routing subsystem.

[0021] In another preferred embodiment, the system uses a unified naming and discovery (UND) system that i) maps variable, human-readable names (e.g., domain names, content names) to immutable, self-certifying content identifiers (CIDs), and ii) combines names and CIDs in a way that optimizes routing and / or storage to route CIDs through both a name-based routing subsystem and a name-based routing subsystem.

[0022] Preferably, UND also incorporates IP DNS to ensure backward compatibility.

[0023] Preferably, the system also employs an AI-driven general discovery system, which includes key component environment intelligent convergence (referred to as AmI convergence), providing intelligent discovery, configuration, and self-organizing services.

[0024] Preferably, the SPAN-AI system employs large-scale addressing routing via an AI-driven hybrid adaptive routing design (referred to as the AI-HARD system); the AI-HARD system comprises two subsystems: a storage-centric routing subsystem and a delivery-centric routing subsystem; these subsystems combine the benefits of Name Resolution-based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name-based Routing (NBR) for fast and reliable content delivery.

[0025] Preferably, AI HARD also incorporates IP routing to provide backward compatibility.

[0026] Preferably, the AI-HARD intelligent agent within SPAN-AI utilizes predictive knowledge about network conditions and application requirements to adaptively select the most effective routing strategy from the subsystem.

[0027] Preferably, the system includes SPAN-AI's intelligent discovery service AmI convergence and IP name discovery, i.e., DNS, to provide backward compatibility.

[0028] Preferably, the AI-HARD protocol, naming standards, conventions, and methods are published to enable inclusion in existing and new routers, thereby enabling existing IP networks to interoperate with new SPAN-AI networks.

[0029] Preferably, the protocol, naming standard, convention, and method include IP naming.

[0030] Preferably, the AI-HARD system interoperates with multiple storage and delivery networks.

[0031] Preferably, the storage and delivery network is capable of operating on cryptographic tokens such as Filecoin or Blust.

[0032] Preferably, the SPAN-AI system utilizes an AI-driven publish-subscribe system to provide asynchronous multi-party propagation services. These services support: control plane propagation for directory updates (name, discovery, configuration) and smart updates (optimization / control operations); and data plane propagation for collaborative applications, such as social networks and video conferencing.

[0033] Preferably, SPAN-AI uses an AI-driven publish-subscribe system for asynchronous multi-party propagation services, which include communication between AI agents, naming services, and discovery services.

[0034] Preferably, the AI-driven publish-subscribe system includes interoperability with an IP discovery service.

[0035] Preferably, the publish / subscribe system uses the AmI rendezvous service, extended by peer heartbeats and grid health metrics and rankings, to improve operation, intelligence discovery, and configuration via a combination of perception and control for peer / local intelligence, edge / swarm intelligence, and core / global intelligence.

[0036] Preferably, AmI rendezvous includes a pluggable interface for a self-healing agent that embeds the AmI rendezvous client into a publish / subscribe protocol, such as an evolution of existing publish / subscribe algorithms like Gossipsub, PlumTree, and HyParView.

[0037] Preferably, the SPAN-AI system includes security integrated at all levels.

[0038] Preferably, the SPAN-AI system uses machine learning and identification to detect and manage security threats.

[0039] Preferably, the content is encrypted using a DRM system such as PlayReady before being published to the system.

[0040] Preferably, the data packets are cryptographically signed by the publisher.

[0041] Preferably, the naming is rooted in self-sovereign identity, which can be defined as a lifelong portable digital identity that does not depend on any centralized authority.

[0042] Preferably, the system uses a decentralized identifier that provides: persistence, global resolvability, cryptographic verifiability, and decentralization.

[0043] Preferably, the name is self-certified.

[0044] Preferably, the system is based on hardware root of trust and secure boot.

[0045] Preferably, the system utilizes a trust network method.

[0046] Preferably, the system utilizes quantum encryption, that is, encryption based on a quantum state random number generator.

[0047] Preferably, the system coordinates the adaptive operation of the routing and publish / subscribe system via a series of pluggable, hierarchical (local / edge / global / other) AI agents, which provide monitoring, prediction, optimization and control services with varying degrees of perception and optimization capabilities at peer, edge, core and other network levels.

[0048] Preferably, the system provides a method for pluggable AI agents to enable open, flexible innovation in the optimization and control of general networks.

[0049] Preferably, the AI ​​agent is capable of exchanging cryptographic tokens such as Filecoin or Blust.

[0050] Preferably, the SPAN-AI system uses a simulation, training, and development pipeline that enables cloud-level replication of the runtime environment, simulation, testing, and training of AI models and agents, which can then be plugged into peer / edge / core / other network nodes for real-time optimization and control.

[0051] Preferably, the system also includes a self-aware grid simulator (referred to as the SAMSim system), wherein the SAMSim system is supported by: a distributed cloud-hosted big data grid lake with health metrics; and the simulation and deployment of AI models across an automated software engineering pipeline.

[0052] According to another broad form of the invention, a layered hybrid adaptive secure peer-to-peer assisted networking system (referred to as SPAN-AI) using a layered AI-driven approach is provided under a unified secure content addressable architecture. SPAN-AI is based on five key SPAN-AI subsystems: unified naming; unified discovery; hybrid adaptive routing; scalable publish / subscribe; and embedded security. All five key SPAN-AI subsystems are securely integrated and jointly optimized via a layered pluggable AI framework and have associated simulation, training, and development pipelines embedded in AI agents with varying degrees of perception and optimization capabilities at peer, edge, core, or other network level layers.

[0053] Preferably, the system uses a unified naming and discovery (UND) system, which i) maps variable human-readable names (e.g., domain names, content names) to immutable self-certifying content identifiers (CIDs), and ii) routes CIDs by adding a name prefix to each CID through both a name-based routing subsystem and a name-based routing subsystem.

[0054] In another preferred embodiment, the system uses a unified naming and discovery (UND) system that i) maps variable, human-readable names (e.g., domain names, content names) to immutable, self-certifying content identifiers (CIDs), and ii) combines names and CIDs in a way that optimizes routing and / or storage to route CIDs through both a name-based routing subsystem and a name-based routing subsystem.

[0055] Preferably, the system also employs an AI-driven unified discovery system, which includes key component environment intelligent convergence (referred to as AmI convergence) to provide intelligent discovery, configuration, and self-organizing services.

[0056] Preferably, the SPAN-AI system employs large-scale addressing routing via an AI-driven hybrid adaptive routing design (referred to as the AI-HARD system); the AI-HARD system comprises two subsystems: a storage-centric routing subsystem and a delivery-centric routing subsystem; these subsystems combine the benefits of Name Resolution-based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name-based Routing (NBR) for fast and reliable content delivery.

[0057] Preferably, the AI-HARD intelligent agent within SPAN-AI utilizes predictive knowledge about network conditions and application requirements to adaptively select the most effective routing strategy from the subsystem.

[0058] Preferably, the AI-HARD protocol is published so that it can be included in existing and new routers, thereby ensuring routing compatibility between all networks.

[0059] Preferably, the AI-HARD system interoperates with multiple storage and delivery networks.

[0060] Preferably, the storage and delivery network is capable of operating on cryptographic tokens such as Filecoin or Blust.

[0061] Preferably, the SPAN-AI system utilizes an AI-driven publish-subscribe system to provide asynchronous multi-party propagation services. These services support: control plane propagation for directory updates (name, discovery, configuration) and smart updates (optimization / control operations); and data plane propagation for collaborative applications, such as video conferencing and social networks.

[0062] Preferably, SPAN-AI uses an AI-driven publish-subscribe system for asynchronous multi-party propagation services, which include communication between AI agents, naming services, and discovery services.

[0063] Preferably, the publish / subscribe system uses the AmI rendezvous service, extended by peer heartbeats and grid health metrics and rankings, to improve operation, intelligence discovery, and configuration via a combination of perception and control for peer / local intelligence, edge / swarm intelligence, core / global intelligence, and other intelligences.

[0064] Preferably, AmI rendezvous includes a pluggable interface for a self-healing agent that embeds the AmI rendezvous client into a publish / subscribe protocol, such as an evolution of existing publish / subscribe algorithms like Gossipsub, PlumTree, and HyParView.

[0065] Preferably, the SPAN-AI system includes security integrated at all levels.

[0066] Preferably, the SPAN-AI system uses machine learning and identification to detect and manage security threats.

[0067] Preferably, the content is encrypted using a DRM system such as PlayReady before being published to the system.

[0068] Preferably, the data packets are cryptographically signed by the publisher.

[0069] Preferably, the naming is rooted in self-sovereign identity, which can be defined as a lifelong portable digital identity that does not depend on any centralized authority.

[0070] Preferably, the system uses a decentralized identifier that provides: persistence, global resolvability, cryptographic verifiability, and decentralization.

[0071] Preferably, the name is self-certified.

[0072] Preferably, the system is based on hardware root of trust and secure boot.

[0073] Preferably, the system utilizes a trust network method.

[0074] Preferably, the system utilizes quantum encryption, that is, encryption based on a quantum state random number generator.

[0075] Preferably, the SPAN-AI system coordinates the adaptive operation of the routing and publish / subscribe system via a series of pluggable, hierarchical (local / edge / global / other) AI agents, which provide monitoring, prediction, optimization and control services with varying degrees of perception and optimization capabilities at peer, edge, core and other network levels.

[0076] Preferably, the system provides a method for pluggable AI agents to enable open, flexible innovation in the optimization and control of a unified network.

[0077] Preferably, the AI ​​agent is capable of exchanging cryptographic tokens such as Filecoin or Blust.

[0078] Preferably, the SPAN-AI system uses a simulation, training, and development pipeline that enables cloud-level replication of the runtime environment, simulation, testing, and training of AI models and agents, which can then be plugged into peer / edge / core / other network nodes for real-time optimization and control.

[0079] Preferably, the system further includes a self-aware grid simulator (referred to as the SAMSim system), wherein the SAMSim system is supported by: a distributed cloud-hosted big data grid lake with health metrics; and the simulation and deployment of AI models across an automated software engineering pipeline.

[0080] According to another broad form of the invention, a hierarchical hybrid adaptive secure peer-to-peer assisted networking system (referred to as SPAN-AI) using a hierarchical AI-driven approach is provided under a unified secure content addressable architecture; the system includes large-scale routing via an AI-driven hybrid adaptive routing design called AI-HARD system, which consists of two subsystems: a storage-centric routing subsystem and a delivery-centric routing subsystem; the two subsystems combine the benefits of name-resolved routing (NRR) for scalable, available, and accessible distributed storage with the advantages of name-based routing (NBR) for fast and reliable content delivery.

[0081] Preferably, the AI-HARD system interoperates with multiple storage and delivery networks.

[0082] Therefore, in another broad form of the invention, SPAN-AI, a hybrid adaptive networking technology for AI-driven secure peer-to-peer assisted networking, is provided. This technology provides global, scalable, secure, and distributed content storage, computation, and delivery for any application and network environment. Recognizing the limitations of existing technologies, which are only suitable for specific applications at a non-global scale, SPAN-AI improves and adaptively combines the best features of existing solutions within a unified secure content addressable architecture using an AI-driven hybrid routing approach. We refer to this as a unified content delivery network or UCDN. SPAN-AI is based on five key systems: unified naming; unified discovery; hybrid routing; scalable publish / subscribe; and embedded security. All are securely integrated and jointly optimized via a layered, pluggable AI framework, with associated simulation, training, and development pipelines embedded in AI agents at peer, edge, core, or other network levels with varying degrees of awareness and optimization capabilities.

[0083] Preferably, SPAN-AI uses a unified naming system that i) maps variable, human-readable names (e.g., domain names, content names) to immutable, self-certifying content identifiers (CIDs), and ii) routes CIDs by adding a name prefix to each CID through both a name-based routing subsystem and a name-based routing subsystem.

[0084] In another preferred embodiment, SPAN-AI uses a unified naming system that i) maps variable, human-readable names (e.g., domain names, content names) to immutable, self-certified content identifiers (CIDs), and ii) combines name prefixes and CIDs in a way that optimizes routing and / or storage, thereby enabling routing of CIDs through both a name-based routing subsystem and a name-based routing subsystem.

[0085] Preferably, SPAN-AI uses a unified discovery system based on the Environmental Intelligence Meetup (AmI) service, which is designed to provide intelligent discovery and self-organizing services through a combination of hierarchical AI perception and control agent peer / local intelligence, edge / swarm intelligence, core / global intelligence, and other levels of intelligence. AmI Meetup includes peer heartbeat collection, grid health metric aggregation, peer ranking, peer discovery, and grid self-configuration services.

[0086] Preferably, SPAN-AI utilizes AI-Driven Hybrid Adaptive Routing Design (AI-HARD) for large-scale addressing routing. AI-HARD consists of two subsystems: a storage-centric routing subsystem and a delivery-centric routing subsystem. It aims to combine the benefits of Name Resolution-based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name-Based Routing (NBR) for fast and reliable content delivery. AI-HARD uses a hierarchical AI agent to control and optimize the joint operation of the NRR and NBR subsystems. AI-HARD can utilize AmI convergence for discovery and self-organization in highly dynamic scenarios. AI-HARD encompasses both storage and delivery markets.

[0087] Preferably, SPAN-AI utilizes an AI-driven publish-subscribe (publish-subscribe) system for asynchronous multi-party propagation services. These services support: control plane propagation for directory updates (name, discovery, configuration) and intelligent updates (optimization / control operations); and data plane propagation for collaborative applications, such as video conferencing and social networks. SPAN-AI publish-subscribe uses AmI convergence for publish-subscribe mesh discovery and self-organization, including a pluggable interface to a self-healing agent within the publish / subscribe protocol, an evolution of existing publish-subscribe algorithms such as Gossipsub, PlumTree, and HyParView.

[0088] Preferably, SPAN-AI incorporates security at all levels of integration. SPAN-AI uses machine learning and identification to detect and manage security threats. Content can be encrypted using a commercial DRM system such as PlayReady before being published to the system. Data packets can be cryptographically signed by the publisher. Naming is rooted in a self-sovereign identity, which can be defined as a lifelong, portable digital identity that does not rely on any centralized authority. It uses a decentralized identifier, which provides: persistence, global resolvability, cryptographic verifiability, and decentralization. The name can also be self-certified. A preferred embodiment is based on a hardware root of trust and secure boot. Another preferred embodiment can utilize a trust network approach. Quantum cryptography, i.e., encryption based on a quantum state random number generator, can also be used.

[0089] Preferably, SPAN-AI coordinates the adaptive operation of routing and publish / subscribe systems through a series of pluggable, hierarchical (local / edge / global) AI agents. The AI ​​agents provide monitoring, prediction, optimization, and control services with varying degrees of perception and optimization capabilities at peer, edge, core, and other network levels.

[0090] Preferably, SPAN-AI provides a marketplace for pluggable AI agents to enable open, flexible innovation in the optimization and control of a unified network. This can be based on cryptographic tokens such as Filecoin or Blust.

[0091] Preferably, SPAN-AI uses a simulation, training, and development pipeline that enables cloud-level replication of the runtime environment, simulation, testing, and training of AI models and agents, which can then be plugged into peer / edge / core / other network nodes for real-time optimization and control.

[0092] Preferably, SPAN-AI includes a simulation pipeline, a self-aware grid simulator (SAMSim), which is supported by: a distributed cloud-hosted big data grid lake with health metrics; and by simulating and deploying AI models across an automated software engineering pipeline.

[0093] Therefore, in another broad form of the invention, a network device is provided for receiving digital content from a remote location; the device includes decoding and re-encoding components by which the digital content is downloaded, decoded, and then re-encoded for transmission to a digital device for user use.

[0094] Preferably, the digital content is re-encoded according to the secure HDMI encoding algorithm.

[0095] Preferably, the network device receives the digital content according to one or more of the following criteria:

[0096] a. The most needed group

[0097] b. Fastest download speed

[0098] c. Minimal delay

[0099] d. A network address from which the next digital bit or group of bits can be obtained most easily and efficiently in order to maintain the real-time or near real-time delivery of digital content.

[0100] In another broad form of the invention, a web server is provided that aggregates items of digital content so as to subsequently forward at least a portion of copies of the items according to a secure method upon request from a network device located at a remote location.

[0101] Preferably, the security method includes: obtaining and forwarding data packets that form the digital content according to one or more of the following criteria:

[0102] a. The most needed group

[0103] b. Fastest download speed

[0104] c. Minimal delay

[0105] d. A network address from which the next digital bit or group of bits can be obtained most easily and efficiently in order to maintain the real-time or near real-time delivery of digital content.

[0106] In yet another broad form of the invention, a method for assembling a project of digital content is provided; the method includes receiving at least a first portion of the project of digital content from a source store of digital content located at a remote location.

[0107] Preferably, the method further includes: obtaining and forwarding data packets of the items forming the digital content according to one or more of the following criteria:

[0108] a. The most needed group

[0109] b. Fastest download speed

[0110] c. Minimal delay

[0111] d. A network address from which the next digital bit or group of bits can be obtained most easily and efficiently in order to maintain the real-time or near real-time delivery of digital content.

[0112] In another broad form of the invention, a distributed system for delivering digital content is provided; the system includes at least one content aggregator communicating with an origin store; a plurality of network devices; the aggregator receiving digital content in the form of content items; the aggregator protecting the digital content for distribution by the system; the origin store providing the digital content to the plurality of network devices; and each network device receiving a specified content item according to a request from the network device to the system.

[0113] Preferably, the system communicates via the Internet.

[0114] Preferably, each network device operates according to a security peering aid standard; if the content item has previously been downloaded to other network devices among the plurality of network devices, the security peering aid standard enables the reception of at least a portion of the content item from the other network devices among the plurality of network devices.

[0115] In another broad form of the invention, a system is provided for ingesting, aggregating, curating, managing, publishing, searching, selling, distributing, and settling the purchase of digital content; said system operates according to the methods described above.

[0116] Preferably, the settlement steps include: paying the content owner and retailer a fee for the designated digital content item in accordance with the composite copyright and distribution window agreement.

[0117] In yet another broad form of the invention, a method is provided that combines the above-described systems, thereby allowing multiple internet retailers to sell digital content transmitted according to the above-described method. Attached Figure Description

[0118] It should be understood that the following figures represent specific aspects of the invention and are not intended to be exhaustive or complete, individually or collectively. Specifically, it should be understood that in the system or block diagram, any system or subsystem may be functionally, logically, or physically connected to or through any other system or subsystem, regardless of whether the connection is transformed.

[0119] Embodiments of the present invention will now be described with reference to the accompanying drawings, in which:

[0120] Figure 1A This is a system block diagram. This system combines and extends subsystems to form a widely applicable, universally operable, highly scalable, and efficient system for optimizing, managing, and operating a Unified Content Delivery Network (UCDN). This UCDN includes AI-Driven Secure Peer-Assisted Networking (SPAN-AI), a hybrid adaptive networking technology that provides global, scalable, secure, and 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 a non-global scale, and uses an AI-driven approach to improve and adaptively combine the best features of existing solutions within a unified secure content addressable architecture. We refer to this as a Unified Content Delivery Network or UCDN.

[0121] Figure 1B It includes what can be found Figure 1A System block diagram of AI-HARD SPAN-AI system operating in the context of UCDN system.

[0122] Figure 1C This is a block diagram of an embodiment of UCDN as a network operating via AI, routing, or other interfaces of one or more SPAN-AI networks.

[0123] Figure 1D It is another embodiment of UCDN that includes a traditional network, which may be a TCP / IP or other protocol network.

[0124] Figure 2A It includes the first embodiment and another embodiment of the present invention (SPAN-AI embodiment). Figure 1B A block diagram of the media distribution and management system for the SPAN-AI component.

[0125] Figure 2B It is when published to the Name Resolution Routing (NRR) system via Figure 2A A diagram of the information flow in a network.

[0126] Figure 2C It is via a name-based routing (NBR) system. Figure 2A A diagram of the information flow in a network.

[0127] Figure 3A Is it possible to... Figure 2A A block diagram showing the network devices used in combination with the associated routing methods in the system.

[0128] Figure 3B Is Figure 1B A block diagram of the routing tables used in the SPAN-AI and AI HARD layouts.

[0129] Figure 4 It shows the relationship with Figure 3A The device's operation and control mode uses a graphical structure for video output view.

[0130] Figures 5A to 5F It shows the relationship with Figure 3A The device's operation control mode interacts with other graphical structures used for video output view.

[0131] Figure 6 It is a block diagram of a media distribution and management system based on an implementation example.

[0132] Figure 7 yes Figure 6 A block diagram of the aggregator in the system.

[0133] Figure 8 Is to make Figure 6 The flowchart shows the service functions of the system's aggregator and source store.

[0134] Figure 9 It is possible Figure 6 A flowchart of data packet sources and data packet flows that, in whole or in part, contribute to the delivery of digital content within a system.

[0135] Figure 10 It includes methods used for aggregation. Figure 6 A conceptual diagram of the entire system.

[0136] Figure 11 Conceptualized from the user's perspective Figure 2A A block diagram of an embodiment of the system.

[0137] Figure 12 This is a layout diagram of a processor module according to another embodiment of the present invention.

[0138] Figure 13 schematically shown Figure 12 Some module functions of the module.

[0139] Figure 14 Is it possible to... Figure 12 A block diagram of the data function operation in another embodiment.

[0140] Figure 15 From Figure 12 A screenshot of the menu screen output by the embodiment.

[0141] Figure 16 From Figure 12 A screenshot of the menu selection screen output by the embodiment.

[0142] Figure 17 It is aimed at Figure 12 A screenshot of the selection screen interface of an embodiment. Detailed Implementation

[0143] First preferred embodiment

[0144] refer to Figure 2A A block diagram of a media distribution and management system 10 according to a first preferred embodiment is shown (and also includes a SPAN-AI system component, which defines another embodiment – ​​the SPAN-AI embodiment – ​​which is further described in the specification with reference to the SPAN-AI embodiment).

[0145] In this example, system 10 includes a source store 11 (sometimes referred to as a "super PoP" in this specification). Source store 11 can be implemented as a single server or as a network of servers. In a specific commercial implementation, the server may form part of a content distribution network of commercial partners. Source store 11 communicates with various databases 12 containing licensed digital content 13 (usually, but not always, subject to negotiation of appropriate terms). Source store 11 receives digital content 13 typically as "packaged" content, meaning that digital rights management (DRM) has already been applied to the content.

[0146] Source store 11 makes the content 13 available to subscribers or buyers via network device 14. Network device 14 is located at or near the point of consumption of digital content 13.

[0147] According to an embodiment of the invention, network device 14 can receive digital content directly from source store 11 according to communication protocol 15 typically available when communicating via Internet 16. Most commonly, communication is expected to take place via Internet 16, but other structures that facilitate the use of protocol 15 may also be considered. Digital content 13 can be protected from the ingestion point to network device 14 by using one or more of the following security technologies and features:

[0148] - Securely capture the platform's security environment via Aspera

[0149] - All master assets are stored and processed within the platform's approved secure environment.

[0150] i. Mezzanine storage in platform secure storage

[0151] ii. Transcoding on the platform's secure transcoding field

[0152] iii. Platform Security: DRM Packaging on Microsoft PlayReady Servers

[0153] iv. Transfer DRM-wrapped assets from the platform to the EdgeCast source store

[11]

[0154] - Another embodiment includes separate secure ingestion, mezzanine storage, transcoding, and DRM packaging on and within facilities approved by the Motion Picture Association of America.

[0155] - All copyrights and rights are managed by the platform and Microsoft PlayReady.

[0156] - Distributed via the EdgeCast network with SuperPoP and Secure Peer Assist™ to network device hardware Trusted Execution Environment (TEE) with a secure PlayReady client.

[0157] - The system is designed for multi-layered security from the ground up.

[0158] - Network device security DRM transcoding in TEE to protect the HDMI HDCP connection to the TV.

[0159] Network access for network devices is protected by Public Key Infrastructure (PKI) security and certificates.

[0160] The communication structures and algorithms programmed into the aggregator database 12 and network device 14 enable content 13 to be initially obtained from the aggregator database 12 via the Internet 16 after an initialization sequence that allows a given network device 14 to access and use a designated item 17 of digital content 13. Similarly, but not always, a license will be subject to negotiation of commercial terms before granting access to the designated item 17.

[0161] Once all or part of the designated item 17 has been downloaded to the given network device 14, it can be "played" by that device. In most cases, the device will securely output digital content, such as HDMI HDCP, to an audiovisual display device 18, such as a television. In other embodiments, it can be securely streamed wirelessly or via Ethernet to other devices, such as tablets, phones, and televisions. In other cases, it can be game content played on the device, or "sideloaded" wirelessly or via Ethernet or some other method to a gaming device, such as another gaming platform.

[0162] The feature of this system 10 is that if another network device 14 negotiates and requests access to the same designated item 17, the content (or a portion thereof) can be downloaded from the source store 11 or the network device 14 on which the designated item 17 is already stored.

[0163] The choice of which source to use will be determined based on network knowledge and Security Peer Support Standard 19, which includes:

[0164] a. The most needed group

[0165] b. Fastest download speed

[0166] c. Minimal delay

[0167] d. A network address from which the next digital bit or group of bits can be obtained most easily and efficiently in order to maintain the real-time or near real-time delivery of digital content.

[0168] Routing information can be distributed and / or centralized, and can be in the form of hash tables or other efficient database mechanisms. This detailed knowledge, combined with the control of network device 14 and routing, is a form of Software-Defined Networking (SDN).

[0169] Specifically, "network knowledge" includes address information for all data packets that will form digital content 13, and more specifically, it includes address information for all data packets that form part of the specified item 17 at any given time. This data packet address information can be stored in, for example... Figure 3AIn the database 40 shown, each individual address (e.g., address AA of the corresponding data packet 24) is linked to a location, in this instance, location loc1.

[0170] Database 40 may be stored on or as part of source store 11, or it may be a separate server. In other instances, it may be stored at least partially in the storage 21 of the various network devices 14 to provide a distributed storage arrangement. It should be understood that over time, a large number of sources distributed across a wide area will become available from which the specified item 17 (in whole or in part) can be downloaded.

[0171] The choice of which source to use can be determined in conjunction with the telecommunications company and ISP to optimize network usage and minimize costs for consumers, telecommunications companies, ISPs, and service providers. For secure peer-to-peer ancillary services retained within the network operator's domain, this can take the form of an "unmetered content" agreement.

[0172] Different commercial models can be used to determine on what basis a specified item is allowed to be downloaded or streamed to a particular network device.14 For example, the model could be based on "pay-as-you-go," such as pay-per-use, rental, or download ownership.

[0173] In an alternative, it can be based on a subscription model.

[0174] An example of network device 14 will be described in more detail below; however, it should be understood that the processor programmed to provide the above functions can be located in a smartphone, smart TV, or game controller – it is not required to be limited to a specific, standalone dedicated network device 14.

[0175] The combination of Super PoP CDN and secure peering auxiliary standards ensures optimal delivery. Video packets originate from the best available locations. The network of network device nodes provides the optimal network architecture: intelligence and storage at the network's outermost edge (i.e., customer premises). This is reinforced by the main Super PoP to fill any gaps. This architecture ensures we drive user connections with maximum bandwidth while minimizing hierarchical network traffic and inter-network peering. Network protocols and parameters have been optimized empirically.

[0176] Secure peer-to-peer auxiliary standards and applications based on them detect and report network traffic at the SCTP, TCP / IP, UDP, and video packet levels. Each of the 14 network devices forms an intelligent node in the mesh network. This can sometimes be described as grid computing or distributed cloud computing. We combine distributed and centralized routing information and intelligence down to the video packet level. This enables optimal network management through capabilities similar to software-defined networking.

[0177] The Secure Peer-to-Peer (SPPP) auxiliary standard allows for the formation of a complete ecosystem for managing video and game delivery over the Internet. Each network device monitors metrics and statistics at the network and video packet levels, reporting service and video status in real time. Combined with a video asset management and distribution platform and a Super PoP CDN, it provides comprehensive Quality of Service (QoS) monitoring and control across the entire network. The SPPP auxiliary standard offers a highly efficient method for video distribution over the Internet, minimizing network load and maximizing network and client viewing performance. The SPPP auxiliary standard can also be implemented in consumer electronics (CE) applications.

[0178] Secure Peer-to-Peer Supplemental Standard 19 extends network coverage beyond the edge, reaching customers' homes. Secure Peer-to-Peer Supplemental Standard 19 can be designed to leverage the modern internet: relatively high customer premises tailspeeds and fiber optic backhaul from the switch. The Secure Peer-to-Peer Supplemental Standard architecture uses a network of network device nodes, all programmed with Secure Peer-to-Peer Supplemental Standard 19, combined with a Super PoP CDN architecture to drive user connections at maximum capacity, thereby ensuring content delivery at the highest quality without perceptible interruptions.

[0179] In a preferred form, digital content 13 stored on source store 11 can be aggregated. For example, the stored digital content 13 can be offered as a storage portal on anyone's website, just as YouTube places a portal on a website. Participating site owners can select subdirectories with titles relevant to their audience from the main directory.

[0180] The aggregator database 12 may include the following technologies to assist in applying appropriate security to the digital content 13 before delivery to the source store 11:

[0181] - Designed for layered security from the ground up.

[0182] - Secure peer-to-peer auxiliary networks are designed to be secure, hidden, and undetectable.

[0183] - The secure peer-to-peer auxiliary network management system is protected by PKI and security certificates.

[0184] - Secure peer-to-peer assistance is "invisible" to the BitTorrent network and is dissimilar to such a network in its protocol.

[0185] - All secure peer-to-peer auxiliary protocols are standard Internet Protocol (IP) protocols or secure protocols with PKI security and authentication.

[0186] - All digital content is encrypted using Microsoft PlayReady DRM and protected within the network device TEE.

[0187] PlayReady DRM is implemented in the device hardware within its Trusted Execution Environment (TEE).

[0188] - The device operating system is fully integrated with the hardware DRM and utilizes this hardware DRM to protect the media pipeline.

[0189] - In one embodiment, the device operating system may be Microsoft Windows.

[0190] PlayReady key management is completely separate from network device TEE security and key management, and is attached to network device TEE security and key management.

[0191] - PlayReady DRM and decryption are protected by the network device TEE.

[0192] - Key management and storage for secure applications and environments executed on the device

[0193] - In one embodiment, the secure key management system can leverage an innovative secure enclave environment enabled by processor architecture, instruction set, libraries, application programming interfaces (APIs), and proof services.

[0194] user interface

[0195] refer to Figure 3A The network device 14 and the example visual display device 18 are shown in more detail.

[0196] In this example, network device 14 includes a processor or microprocessor 20 that communicates with memory 21. The microprocessor 20 communicates with input / output device 22, through which it can send signals to and receive signals from external digital devices, which preferably include at least a visual display 23. The processor or microprocessor may include a graphics processing unit (GPU), or the GPU may be a separate processor, system, or subsystem.

[0197] The memory contains code, including code corresponding to the Secure Peer-to-Peer Auxiliary Standard 19, which enables the processor 20 to perform various functions, including sending and receiving digital content 13 via network 25. Network 25 may include interconnected Internet 16, local area network 26, and wide area network 27.

[0198] Digital content 13 typically includes multiple data packets 24, each of which includes a header 24A and a payload 24B.

[0199] Payload 24B includes digital data, which may more specifically be audio data, video data, game data, or other data.

[0200] It is important to note that packet 24 may not arrive at device 14 in sequence. In a typical scenario, different packets will arrive from different sources (for this, please refer to...). Figure 9 and Figure 11 ).

[0201] The core function of network device 14 is to controllably send and receive digital content 13 and convert the digital content 13 locally into local signals 27 for use in driving external digital devices, such as (but not limited to) audiovisual display devices 18.

[0202] Another function of network device 14 is to allow users to control the “purchase” and “play” of digital content received by or sent from network device 14.

[0203] In the preferred form, the user experience and user interface are kept as simple as possible. In the simplest form, user control is achieved simply by moving the cursor left or right on a remote control device. These actions control extremely simple menus and on-screen content display. These can be homogeneous or hybrid, i.e., pure menus or pure content display, or a combination of both. In a preferred form, the display is an arc or circle to reflect the user experience and control via the remote control device. In cases where there are many items to display (such as a large content library), the display can be concentric arcs or circles of content "tiles," i.e., clean graphic images of the "covers" of content titles. In another embodiment, these tiles can be in the form of a grid.

[0204] Menu navigation is achieved through a simple combination of "left" and "right" navigation. Simply put, you can navigate to the left or right of a menu item by clicking left or right. In one example, the menu can be moved left or right accordingly with the selection of a graphics device, such as the cursor box. In another example, selecting a graphics device can move left or right. Once highlighted, a menu item can be selected with a single click. This can result in an action or navigation into a deeper menu structure. Navigating "exit" can be done by double-clicking. Alternatively, menu navigation items such as "back" or "cancel" can exist. For navigation of large numbers of objects (such as a video library), these objects can be displayed as concentric arcs or rings, or as a tile grid. Rings can be navigated to "enter" by clicking and to "exit" by double-clicking, and to the left or right by clicking left or right. Selected items, tiles, arcs, or rings can be highlighted by increasing their focus and / or size. Unselected items, tiles, arcs, or rings can be removed from focus by moving them away from the focus center and / or by "defocusing" or reducing their size. This can produce the effect of unselected items, tiles, arcs, or rings moving "away" from the user, while selected items, arcs, or rings move "toward" the user.

[0205] More complex uses can be supported by control mechanisms such as rate- or distance-related actions. Small actions can cause menus or items to move slowly and briefly. Larger actions can cause menus or items to move faster and for a longer period. Similarly, the rate of action can determine the scale or nature of a menu action. This can be independent of or related to the distance of the action.

[0206] In the preferred form, the user graphic display is very simple, clean, neat, and clear, thus providing a simple and easy-to-use feel.

[0207] For example, refer to Figures 5A to 5F The operation sequence can be as follows: Graphical structure 28 is located in Figure 5A The arc shown is basically vertically positioned, or it may be located on... Figure 5B The arc is set horizontally as shown. The user manipulates the cursor 29 device to surround one of the selected graphic structures 28, for example, to specify the "My Movies" graphic structure.

[0208] Then, the user can move the cursor over a series of movie selections in the instance to specify the "Capt. America" ​​movie selection, such as... Figure 5D As shown.

[0209] Users can "exit" the current menu item at any time to move up one level. Figure 5E The series of graphic structures shown in Figure 28. Figure 5FThis shows details of a specific selection when the “Capt. America” graphic structure is highlighted by cursor 29 (available in the purchase menu within the store context of the United Online Store 41). Figure 5D –See also Figure 6 ).

[0210] In another form, this can be achieved by controlling a cursor 29 in the form of a rectangular boundary device in association with a graphic structure 28 displayed on a visual display 23 (audiovisual display device 18 in this example).

[0211] In a particular form, the graphic structure 28 can be located on an arc or circular path.

[0212] In one form, these controls can be "simulated" in a remote control application, for example, wirelessly or via the Internet, on a smartphone connected to the main network device 14 or on a "satellite" network device 14 forming a home network.

[0213] In another form, these controls can be implemented in a TV remote or game controller.

[0214] In another form, these controls can be replicated on a smaller version of the network device 14 that is wirelessly connected to the main network device 14 or on a “satellite” network device 14 that forms a home network.

[0215] like Figure 6 and Figure 7 As illustrated, these UI concepts allow for streamlined control of the operation of network device 14 (most specifically including selecting digital content 13) for viewing on audiovisual display device 18. It is important that the UI (e.g., arcs for menus and images, concentric circles (or arcs) for displaying menus or titles, and blending of menus and images) reflects the physical user experience, in one embodiment being circles and arcs. In another embodiment, menus and images may be displayed in a tile grid.

[0216] First example of implementation

[0217] In a preferred embodiment, network device 14 includes at least the following capabilities:

[0218] - Connect to the internet via WiFi or Ethernet cable

[0219] - Connect to TV via HDMI or WiFi interface

[0220] - Connect via USB or HDMI for TV control

[0221] - Connect to other devices, such as tablets and PCs, via WiFi or Ethernet.

[0222] - Secure peer-to-peer auxiliary standard network client

[0223] - Microsoft PlayReady Security Client

[0224] Trusted Execution Environment

[0225] - Play movies, TV shows and games

[0226] - "Remote" functions, such as finding, pausing, rewinding, fast forwarding, and slow motion, can be performed via an app or via a smaller version of a device connected wirelessly to the "home" device.

[0227] - Remote control via TV, game controller, keyboard, touchpad, or mouse

[0228] - Stream, download, and store all content (with large storage options).

[0229] - Load the game onto other gaming platforms, tablets, and phones.

[0230] - HD and UHD (“4K”)

[0231] - Manage libraries that include third-party content

[0232] - Share content securely. Content will be DRM protected, and a mechanism will be provided to unlock the content by purchasing a key.

[0233] - Record metrics / statistics and send them to the management system

[0234] - Monitor and manage content behavior and performance

[0235] - Monitor and manage network behavior and performance

[0236] - Media Center

[0237] The overall topology of the example system can be as follows: Figure 6 , Figure 7 and Figure 8 As shown, it has the following functional specifications:

[0238] Function Description

[0239] Embodiments of the network device 14 of the present invention include a device operating according to a Secure Peer-to-Peer Assistance Protocol 19, which is a portable device for downloading, storing, streaming, playing, and sharing high-quality movies, games, and television programs on a television or connected device. This device combines Secure Peer-to-Peer Assistance Protocol 19 technology with Content Source Store 11 and Unified Retail Content Online Store 41 to provide the latest Hollywood and independent movies, television programs, and games on television in true HD and UHD. Embodiments of network device 14 address a key issue in today's OTT and IPTV delivery: the exponential growth of video services. In this instance, network device 14 provides flexibility for a new generation of content owners who can choose what they want to watch, when they want to watch it, and with whom and how they want to share it, in true HD and UHD quality.

[0240] Function:

[0241] Connect to the internet via WiFi a, b, g, n, ac or Ethernet cable to download and stream movies and TV shows from the GT TV Store.

[0242] - Bluetooth option, such as connecting to a TV or other devices.

[0243] - Connect to the TV via HDMI v 2.0a and HDCP 2.2 interfaces or higher versions as the standard evolves.

[0244] - Full HD 1080p60, UHD (4K UHDTV 2160p 3840×2160) and HDCP secure HDMI connection or optical connection to a high-quality audio system that supports high-quality audio (e.g., Dolby 5.1 or 7.1).

[0245] - Supports a wide range of video coding standards, including all H.264 codecs.

[0246] - H.265 HEVC and VP9, ​​as well as the Open Media Codecs Alliance

[0247] - 3 x USB ports for connecting other devices, peripherals, and TV controls.

[0248] - PSU for power supply

[0249] - Connect to other devices, such as phones, tablets, and PCs, via WiFi or Ethernet.

[0250] - Initial remote control and store purchase

[0251] - Streaming via Miracast and DRM

[0252] - Secure peer-to-peer auxiliary network clients in a secure environment

[0253] - Play movies, TV shows, and games, including "remote" features such as find, pause, rewind, fast forward, and slow motion.

[0254] - Microsoft PlayReady Security Client

[0255] - Load the game side onto other gaming platforms, tablets, and phones via Ethernet, WiFi, or USB (future version).

[0256] - Stream, download, and store all content (with large storage options).

[0257] - HD and UHD

[0258] - Manage libraries that include third-party content

[0259] - Share content securely

[0260] - Record metrics / statistics and send them to the management system for content behavior and performance, and network behavior and performance.

[0261] - Media Center

[0262] - Universal Plug and Play (UPnP)

[0263] model

[0264] All models will be designed for a single housing to minimize production costs. This will be a device with a highly aesthetic form and functionality, featuring a simple and innovative human-machine interface. It will be designed to appeal to the ultra-early adopter market, but will also attract the mainstream market. It is extremely easy to use.

[0265] Basic Model: This is the base model with a minimum 2TB hard drive and 128GB SSD storage. It will be a fully functional peer in a secure peer-to-peer auxiliary network, capable of downloading and streaming movies and TV shows in high quality from Store 41. It will be controlled via unit, via phone or tablet app, or via TV remote or keyboard, touchpad, or mouse.

[0266] Basic model with disk library: This is a basic unit with at least 2TB of 2.5-inch disk drives for storing movies. It will be able to store 200 to 400 HD movies or 100 UHD movies, depending on the encoding size.

[0267] SSD models with SSD libraries: These are basic units with SSD hard drives ranging from 250GB to 2TB. They can store up to 100 UHD movies, depending on the encoding size.

[0268] Media Center and Streaming: This will allow secure streaming of digital content to CE devices such as phones and tablets, as well as streaming user content to televisions.

[0269] Network device control applications

[0270] Network device 14 can be controlled by an app on a phone or tablet. This could initially be an Android or iOS app for iPhones and tablets. Other apps will be implemented in the future. It can provide full remote control of all viewing functions, as well as the ability to make purchases directly through the network device's access to a store.

[0271] It can also optionally control the TV remotely via USB or Bluetooth (if equipped) or via network device 14.

[0272] power supply

[0273] In the preferred embodiment, the system must consume as little power as possible. The system can be powered by an AC power supply. Alternatively, the system can be powered by batteries.

[0274] operating system

[0275] The system can run a secure, real-time version of the Linux operating system or the Microsoft Windows operating system.

[0276] Architecture

[0277] In the Example 1 system, the system architecture can be ARM Cortex A9 or later, including ARM TrustZone, or it can be a 6th generation or later Intel Core architecture, including Security Protection Extension (SGX), Memory Protection Extension (MPX), Security Zones, and Hardware DRM.

[0278] Security

[0279] In Example 1 system, all media files will be DRM encrypted. Preferred DRM options are Microsoft PlayReady for movies, Ubisoft DRM for games, or Tages Solid Shield, but other studio-approved DRMs, including Adobe Access and Google Widevine, can also be used. The system provides a robust and long-term solution where trusted applications are attached to the field throughout the device's entire lifecycle. The system can conform to Trusted Execution Environment (TEX) specifications. The system can support trusted boot modes and trusted control on all I / O ports.

[0280] The system can support Intel Security Protection Extensions (SGX), Memory Protection Extensions (MPX), Security Zones, and Hardware DRM.

[0281] The system can support security verification and sealing.

[0282] The system can support ARM advanced system architecture and infrastructure platforms for digital rights management (DRM) and integrates the TrustZone address space controller (TZASC) to protect RAM areas used to store valuable content.

[0283] The architecture can support the integration of media accelerators, such as GPUs, video engines, and display controllers, all of which require knowledge of the processor's secure state.

[0284] The system can provide tamper protection and a real-time clock.

[0285] The system supports secure hardware encryption acceleration to optimize DRM decoding speed. The system also supports high-security startup and recognition of digital signature software.

[0286] The system can support secure JTAG-JTAG, meaning its use is limited (at the no-debug level) unless the key challenge / response protocol is successfully executed.

[0287] DRM

[0288] The preferred form of Example 1's system will support Digital Rights Management (DRM). Microsoft PlayReady was originally preferred for movies and television, while Ubisoft DRM or Tages Solid Shield was originally preferred for games. Other studio-approved DRMs (such as Adobe Access and Google Widevine) are alternatives.

[0289] Hardware and O / S

[0290] - Current hardware and O / S specifications

[0291] - I / O ports / antenna

[0292] AC power adapter

[0293] - 3×USB 2.0

[0294] - 1×1000Mb Ethernet

[0295] - HDMI 2.0a connector

[0296] - WiFi a, b, g, n, ac

[0297] - HiFi audio and visuals or HDMI

[0298] - Large capacity storage

[0299] - At least a 2 TB 2.5-inch hard drive,

[0300] - At least 128GB to 1TB SSD

[0301] refer to Figure 14 The diagram shows a conceptual flowchart for aggregating digital content 13.

[0302] As an overview, the system of Example 1 is described, and preferably via reference Figure 3A The network device type 14 described is used to implement this.

[0303] The preferred form of the standard for receiving data packets at network devices operates individually or in combination according to one or more of the following:

[0304] a. The most needed group

[0305] b. Fastest download speed

[0306] c. Minimal delay

[0307] d. A network address from which the next digital bit or group of bits can be obtained most easily and efficiently in order to maintain the real-time or near real-time delivery of digital content.

[0308] Preferably, the digital content and more specifically, designated items of digital content are "wrapped" by DRM, delivered to the network device, and decoded at the network device using the Microsoft PlayReady infrastructure.

[0309] refer to Figure 11 This illustrates a system 10 conceptualized from the user's perspective.

[0310] Broadly speaking, in this instance there exists a “super PoP” that combines an aggregator database 12, a source store 11, and a data packet address database / network management server 40. This super PoP, in conjunction with a distributed network device 14 and preferably using the Internet as the primary communication channel, coordinates the efficient and timely delivery of data packets 24 (forming designated items 17 of digital content 13), thereby allowing various digital content to be delivered securely and promptly to users 42.

[0311] The system enhances the experience for all stakeholders by providing digital data initiators and copyright holders with confidence in the security of digital data, while also offering users a wide selection of digital content, all delivered in a controlled and timely manner, enabling virtually real-time streaming and data downloads via widespread internet connectivity.

[0312] Another preferred embodiment

[0313] refer to Figure 12 , Figure 13 and Figure 14 This illustrates the basic platform and functional implementation of another embodiment of the present invention, which can be implemented using Intel-branded chipsets and Microsoft Windows-branded software modules.

[0314] It is understood that, for at least some embodiments of the invention, it would be advantageous to operate under a highly secure condition, thereby enabling the processing of potentially valuable software such as ultra-high-definition (UHD) movies without concern for leakage or unauthorized use.

[0315] Typical UHD movies operate according to MPEG4 standards, such as H.264 (the so-called HD resolution, which typically operates at 1080 pixels or lines on the screen) and H.265 (the so-called 4K or UHD resolution, which operates at 2160 lines or pixels on the screen). The typical file size of such a movie can be 15 to 20 GB. In another preferred embodiment, the "secure peer assistance" arrangement described in the preceding embodiments is enabled on the Windows / Intel platform.

[0316] refer to Figure 12 The diagram shows a circuit board 111 on which at least a Trusted Platform Module (TPM) 112, which communicates with a processor 113 and a memory 114, is mounted. Alternatively, the TPM may be embodied in the processor 113 or an associated system module.

[0317] The Trusted Platform Module 112 includes a unique identifier 115, a certificate 116 for encryption and decryption, and a secure boot code 117.

[0318] In this instance, the Trusted Platform Module 112 implements a Trusted Computing Group architecture on hardware, which is part of the TXT platform available from Intel Corporation and provides a Trusted Execution Environment (TEE) that includes Intel Security Protection Extensions (SGX), Memory Protection Extensions (MPX), Security Zones, and Hardware DRM.

[0319] In a preferred arrangement where the TPM is incorporated into the processor or associated module, the processor supports Intel Security Protection Extensions (SGX), Memory Protection Extensions (MPX), Security Zones, and Hardware DRM.

[0320] In the preferred configuration, DRM is implemented using the Microsoft PlayReady environment. In this setup, UHD 4K content will be played if and only if the following conditions are met:

[0321] Hardware DRM environment detected

[0322] This environment is located within a trusted execution environment, and

[0323] All video outputs are implemented using the preferred output protocol, which in a specific preferred instance is HDCP 2.2.

[0324] During operation, the Trusted Platform Module 112 allows the processor 113 to enter a trusted operating state.

[0325] The preferred operating system loaded into memory 114 for execution by processor 113 is Microsoft Windows 10 or later.

[0326] refer to Figure 13 Processor 113 and memory 114 may optionally execute virtual machine 118 within an Intel architecture environment. Virtual machine 118 allows an operating system (such as Windows 10) to directly access hardware while operating in a highly secure environment. Movie files 119 downloaded to memory 114 using the secure peer-assisted arrangement of the previous embodiment can be processed, and the video stream is decoded via hardware DRM and HDCP level shifter protocol converter (LSPCON) chip 120 for secure output, preferably securely, to ultra-high resolution display device 121 via HDMI, DisplayPort, or Thunderbolt connection.

[0327] Alternatively, the video stream can be securely routed to the Secure GPU 120A for secure output via HDMI.

[0328] refer to Figure 14The diagram schematically illustrates the data flow on platform 111. Movie files 119, preferably assembled from numerous sources in the form of a secure peer-to-peer auxiliary platform 122, are processed by a component (optionally including a virtual machine 118 operating within a Windows 10 environment) using hardware DRM, which provides a highly secure output stream 119A. This output stream 119A is processed by a converter chip 120 (preferably an HDCP 2.2 LSPCON chip) to output a secure video stream 119C that can be displayed on an ultra-high resolution display device 121.

[0329] The trusted execution environment and stream 119A are protected via data 119B provided from an independent security support and verification server 123, such as Figure 13 As shown.

[0330] The end result is an output stream 119C to the ultra-high resolution display device 121, which is decoded in real time while maintaining a high level of security, thereby allowing the display of very high resolution video files, such as UHD 4K definition movie files that conform to the specifications of the Motion Picture Labs and Motion Picture Association of America, as well as the specifications of high-value content for individual studios and content owners, in virtually real time.

[0331] Figure 15 This is a screenshot of the menu screen output to screen 121, through which the user can select the movie to watch on monitor 121.

[0332] Figure 16 This is a screenshot of the menu selection screen, through which users can use scrolling to select the movie to watch on monitor 121.

[0333] Figure 17 This is a screenshot of the selector screen layout.

[0334] In a particular form, users can utilize association techniques that cluster items according to predetermined criteria for selection. An example of such a system is described in US 2014 / 0330841, the specification, claims, and drawings of which are incorporated herein by cross-reference. In this particular form, the association algorithm is applied among items belonging to a finite set of items, where each item has associated visual markers and at least one set of attributes that are common to every other item belonging to the finite set, thereby facilitating the discovery of the items within the finite set.

[0335] In certain forms, rating systems are used to quantify the degree of relevance.

[0336] In another preferred embodiment, a secure peer-to-peer assistant can be "plugged in" into or integrated with an adaptive bitrate protocol to leverage a wide range of existing assets and resources that use adaptive bitrates. This can be achieved through direct integration or via an application programming interface (API). The secure peer-to-peer assistant will handle network communications and interface with adaptive bitrate resources such as media servers, video encoders and segmenters / packets, digital rights management systems, key management systems, content delivery networks, video players, browsers, client applications, etc. The secure peer-to-peer assistant will manage the timely delivery of video and other content packets. For adaptive bitrate protocols, this will behave as an optimal single fixed-rate stream. In practice, this translates to progressive downloading or optimal fixed-rate streaming, depending on the available user bandwidth.

[0337] In another preferred embodiment, secure peer assistance will be integrated with HTTP-based Dynamic Adaptive Streaming (DASH) (also known as MPEG-DASH), and with Universal Encryption and Encrypted Media Extensions (EME). The proposed name for this arrangement would be DSPASH (HTTP-based Dynamic Secure Peer Assist). This preferred embodiment will be integrated with HTML5 browsers that support media source extensions. This will provide a standardized implementation that can be implemented most efficiently across a variety of consumer devices.

[0338] Another preferred embodiment will use Microsoft PlayReady DRM and the Microsoft Edge HTML5 browser on the above-described preferred embodiment of the Intel processor hardware platform, which implements PlayReady in hardware under a tightly integrated Microsoft Windows 10 (or later) operating system.

[0339] Industrial applicability

[0340] Network devices can be implemented as standalone hardware units or as multiple connected units programmed with the aforementioned Security Peer-to-Peer Auxiliary Standard. Alternatively, the Security Peer-to-Peer Auxiliary Standard can be used to program other devices, such as smartphones, game controllers, and smart TVs.

[0341] Server-based devices can be used to implement aggregator 12 and source store 11.

[0342] Introduction to SPAN-AI and UCDN implementations of media distribution and management systems

[0343] refer to Figure 1B This shows, as Figure 2AThe diagram shows a block diagram of a SPAN-AI embodiment of a media distribution and management system 10. The SPAN-AI embodiment includes subsystems that form a widely applicable, universally operable, highly scalable, and efficient system for optimizing, managing, and operating a unified content delivery network (UCDN) (such as...). Figure 1A As shown, this UCDN includes AI-Driven Secure Peer-Assisted Networking (SPAN-AI), a hybrid adaptive networking technology that provides global, scalable, secure, distributed content storage, compute, and delivery for any application and network environment. Recognizing the limitations of existing technologies, which are only suitable for specific applications at a non-global scale, SPAN-AI uses an AI-driven hybrid routing approach to improve and adaptively combine the best features of existing solutions within a unified secure content addressable architecture. We refer to this as a Unified Content Delivery Network or UCDN.

[0344] UCDN creates a global network of interoperable peer-to-peer networks, thereby eliminating the problems associated with the "network of networks" approach to date. UCDN does this through open standards, interfaces, protocols, and methods that enable any network to interoperate with any other network. These open standards, interfaces, protocols, and methods include, but are not limited to, AI and routing standards, interfaces, protocols, and methods. Background Technology

[0346] refer to Figure 2A The initial implementation teaches a hybrid ecosystem of peer-to-peer streaming and downloading, combining semi-centralized (cloud) media distribution and management servers and superpop (inbound points) with distributed, self-organizing intelligent edge nodes in a mesh network to form a content-based distributed storage network. This leverages centralized and distributed network knowledge for optimal distribution of encrypted media content over the Internet, providing comprehensive Quality of Service (QoS) monitoring, control, and optimization across the entire network. This system, referred to above as Secure Peer-to-Peer Assisted (SPA), is now known and will be referred to as Secure Peer-to-Peer Assisted Network or SPAN; with the inclusion of AI, it is called SPAN-AI, and with the inclusion of a hybrid adaptive routing design, it is called SPAN-AI-HARD.

[0347] refer to Figure 2A The initial implementation also teaches that routing information can be distributed and / or centralized, and can be in the form of hash tables or other efficient database mechanisms. This detailed knowledge, combined with the control of network device 14 and routing, is a form of Software-Defined Networking (SDN).

[0348] It also teaches; specifically, "network knowledge" includes address information of all data packets that will form digital content 13, and more specifically, address information of all data packets that form part of a specified item 17 at any given time. This data packet address information can be stored in, for example... Figure 3A In the database 40 shown, each individual address (e.g., address AA of the corresponding data packet 24) is linked to a location, in this instance, location loci.

[0349] Database 40 may be stored on or as part of source store 11, or it may be a separate server. In other instances, it may be stored at least partially in the storage 21 of the various network devices 14 to provide a distributed storage arrangement. It should be understood that over time, a large number of sources distributed across a wide area will become available from which the specified item 17 (in whole or in part) can be downloaded.

[0350] Secure peer-to-peer auxiliary standards and applications based on them detect and report network traffic at the SCTP, TCP / IP, UDP, and video packet levels. Each of the 14 network devices forms an intelligent node in the mesh network. This can sometimes be described as grid computing or distributed cloud computing. We combine distributed and centralized routing information and intelligence down to the video packet level. This enables optimal network management through capabilities similar to software-defined networking.

[0351] The Secure Peer-to-Peer (SPPP) auxiliary standard allows for the formation of a complete ecosystem for managing video and game delivery over the Internet. Each network device monitors metrics and statistics at the network and video packet levels, reporting service and video status in real time. Combined with a video asset management and distribution platform and a Super PoP CDN, it provides comprehensive Quality of Service (QoS) monitoring and control across the entire network. The SPPP auxiliary standard offers a highly efficient method for video distribution over the Internet, minimizing network load and maximizing network and client viewing performance. The SPPP auxiliary standard can also be implemented in consumer electronics (CE) applications.

[0352] The Secure Peer Supplemental Standard (SPAC) 19 extends network coverage beyond the edge, reaching right into customers' homes.

[0353] refer to Figure 2A The initial embodiments describe a system for managing and optimizing telecommunications networks. In a preferred embodiment, it is a system for managing and optimizing the Internet, telecommunications carrier networks, and content delivery networks (CDNs). The SPAN-AI and UCDN embodiments are built on a SPAN system, whose components include... Figure 2AAs shown (excluding the SPAN component). Ultimately, the method of additionally teaching a unified content delivery network (UCDN) including SPAN-AI and UCDN embodiments, and the method of incorporating and extending the distributed storage network of the SPAN system, thereby unifying and optimizing all Internet, telecom carrier networks, and content delivery networks (CDNs) into a single, unified, optimized network. In a preferred embodiment, this is accomplished through state-of-the-art methods of Secure Peer-Assisted Network (SPAN) machine learning and artificial intelligence hybrid adaptive network design or SPAN-AI-HARD.

[0354] There are other schemes and projects that define the current level of technology in conjunction with the SPAN system. These include the Named Data Network project. i , ii Information-centric networking iii , iv and IPFS v These other solutions are not complete solutions. They effectively form subsystems of a general scalable solution (which is an implementation of UCDN). Each has its own limitations, particularly limitations in applicability and scalability. UCDN, which includes SPAN-AI and AI HARD, overcomes these limitations.

[0355] Overview of SPAN-AI and UCDN Implementations

[0356] This invention combines and extends these subsystems to form a widely applicable, universally operable, highly scalable, and efficient system for optimizing, managing, and operating a Unified Content Delivery Network (UCDN). This UCDN incorporates AI-Driven Secure Peer-Assisted Networking (SPAN-AI), a hybrid adaptive networking technology that provides global, scalable, secure, and distributed content storage, computation, and delivery for any application and network environment. Recognizing the limitations of existing technologies, which are only suitable for specific applications at a non-global scale, SPAN-AI uses an AI-driven approach to improve and adaptively combine the best features of existing solutions within a unified secure content addressable architecture. We refer to this as a Unified Content Delivery Network or UCDN.

[0357] UCDN creates a global network of interoperable peer-to-peer networks, thereby eliminating the problems previously associated with the "network of networks" approach. UCDN achieves this through open standards, interfaces, protocols, and methods that enable any network to interoperate with any other network. These open standards, interfaces, protocols, and methods include, but are not limited to, AI and routing standards, interfaces, protocols, and methods.

[0358] UCDN consists of a network of one or more interoperable peer-to-peer networks.

[0359] UCDN networks can include peer-to-peer networks in the form of SPAN_AI networks.

[0360] These become interoperable by using open standards, interfaces, protocols, or methods. In a preferred embodiment, this could be a network of one or more SPAN-AI networks interoperable via AI, routing, or other interfaces (see [link to SPAN-AI network]). Figure 1C ).

[0361] Any network can be converted into a SPAN-AI network simply by "injecting" the SPAN-AI agent (distributing containerized microservices or applications) into the network and including SPAN-AI Smart Hybrid Adaptive Routing (AI-HARD) and SPAN-AI Global Optimization AI into the network.

[0362] Alternatively, any network can interconnect to form a UCDN by connecting to a compatible open standard, interface, protocol, or method (API) of the SPAN-AI network, in order to maintain compatibility with "legacy" networks (see [link to UCDN]). Figure 1D Compatibility and communication between these networks. Preferably, these networks will be converted to SPAN-AI networks.

[0363] The minimum embodiment of the SPAN-AI network includes a network of self-organizing peers and agents comprising AI-HARD intelligent hybrid adaptive routing and globally optimized AI. Other embodiments may include any additional capabilities or functionalities.

[0364] The core SPAN-AI system is:

[0365] routing

[0366] SPAN-AI's routing protocol, AI-HARD (Hybrid Adaptive Routing Design), consists of two subsystems: a storage-centric routing subsystem and a delivery-centric routing subsystem. It combines the benefits of Name Resolution-based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name-based Routing (NBR) for fast and reliable content delivery. AI HARD also incorporates IP routing for backward compatibility.

[0367] The AI-HARD intelligent agent within SPAN-AI utilizes predictive knowledge about network conditions and application requirements to adaptively select the most effective routing strategy from subsystems.

[0368] The release of the AI-HARD protocol made it possible to include it in both existing and new routers. This ensures routing compatibility across all networks.

[0369] Naming and Discovery

[0370] SPAN-AI's Unified Naming and Discovery System (UND) i) maps mutable, human-readable names (e.g., domain names, content names) to immutable, self-certifying content identifiers (CIDs), and ii) routes CIDs by prefixing each CID with a name, through both the NRR and NBR subsystems. UND also incorporates IP DNS to ensure backward compatibility.

[0371] In another preferred embodiment, SPAN-AI's Unified Naming and Discovery System (UND) i) maps variable, human-readable names (e.g., domain names, content names) to immutable, self-certifying content identifiers (CIDs), and ii) combines name prefixes and CIDs in a way that optimizes routing and / or storage, with both routing CIDs implemented through the NRR and NBR subsystems. UND also combines IP DNS to ensure backward compatibility.

[0372] UND discovery includes SPAN-AI's intelligent discovery service AmI convergence and IP name discovery, i.e., DNS.

[0373] The release of UND naming standards and conventions enables interoperability between existing IP networks and the new SPAN-AI network. The conventions include IP naming, such as DNS.

[0374] Publish-Subscribe

[0375] SPAN-AI uses an AI-driven publish-subscribe system for asynchronous multi-party propagation services. This includes communication between the AI ​​agent, naming service, and discovery service. It also includes interoperability with the IP discovery service.

[0376] Global AI optimization

[0377] SPAN-AI provides global-level optimization by "aggregating" 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 for global optimization. Data and protocols utilize formal logic ontology and semantics to describe the SPAN-AI system.

[0378] The release of open interfaces enables AIs from other networks to communicate with SPAN-AI and to communicate between them. These interfaces will leverage SPAN-AI semantics and ontology. They can also utilize open software-defined networking (SDN) standards such as OpenFlow.

[0379] Self-organizing swarm intelligence

[0380] SPAN-AI coordinates the adaptive operation of routing and publish / subscribe systems through a series of pluggable, hierarchical (local / edge / global / other) AI agents. The AI ​​agents provide monitoring, prediction, optimization, and control services with varying degrees of perception and optimization capabilities at peer, edge, core, and other network levels.

[0381] These agents use distributed control and machine learning models to form self-organizing swarms to optimize behavior at both local and edge levels, thereby providing adaptability and resilience to dynamic events such as large-scale churn.

[0382] Swarm intelligence enables other groups to join the network and become part of it.

[0383] SPAN-AI, a hybrid adaptive networking technology for AI-driven secure peer-to-peer assisted networking, is designed to provide global, scalable, secure, and distributed content storage, computing, and delivery for any application and network environment.

[0384] SPAN-AI recognizes the limitations of existing technologies that are only suitable for specific applications at a non-global scale, and leverages AI-driven approaches and hybrid adaptive routing to improve and adaptively combine the best features of existing solutions within a unified secure content addressable architecture. We call this a unified content delivery network, or UCDN.

[0385] Glossary:

[0386] Peer: Any hardware or software device, in whole or in part, that has a similar or equivalent general or specific purpose.

[0387] P2P: peer-to-peer.

[0388] Agent: A software application distributed at the peer, edge, core, or other network levels to and / or running on any network device (computer, consumer electronics, router, switch, server, etc.) with varying degrees of perception, communication, optimization, learning, reporting, self-organization, or other capabilities; a virtual network service or application. This could be an AI agent; an application running on a virtual peer, i.e., an operating system running in a virtual environment; a network service; etc.

[0389] I / F: Interface. A method for connecting software or hardware applications to each other or to communicate with people. In a preferred embodiment, the method is open and standardized, in which case the interface may be referred to as an application programming interface or API.

[0390] Peer-to-peer network: any network, in whole or in part, that has a similar or comparable general or specific purpose.

[0391] IP: Internet Protocol; the "slim" routing protocol of the original and current Internet.

[0392] TCP: Transmission Control Protocol

[0393] SPAN: Secure Peer-Assisted Networking

[0394] AI: Artificial Intelligence

[0395] ML: Machine Learning

[0396] AmI: Environmental Intelligence

[0397] HARD: Hybrid Adaptive Routing Design

[0398] SAMSim: Self-Aware Mesh Simulator

[0399] CID: Content Identifier

[0400] IPFS: InterPlanetary File System

[0401] IPLD: Interplanetary Link Data

[0402] IPNS: Interplanetary Name System

[0403] DNS: Domain Name System

[0404] DNSLink: A protocol that uses DNS text records to link domain names to IPFS addresses or CIDs.

[0405] NDNS: Domain Name System for naming data networking.

[0406] mDNS: Multicast DNS

[0407] Pub / Sub: Publish / Subscribe

[0408] libp2p: A location-independent modular network stack. Part of IPFS.

[0409] NRR: Name Resolution-Based Routing

[0410] NBR: Name-based routing

[0411] NDN: Named Data Networking

[0412] NBN: Australia's name-based network or national broadband network

[0413] DHT: Distributed Hash Table

[0414] DRM: Digital Rights Management

[0415] VoD: Video on Demand

[0416] ISP: Internet Service Provider

[0417] CDN: Content Delivery Network

[0418] Point of Access (PoP): Point of Access

[0419] FIL: Abbreviation for Filecoin Crypto Token Transactions

[0420] testlab & testground: IPFS testing framework

[0421] PoC: Proof of Concept

[0422] MVP: Minimum Viable Product

[0423] NRT: Near Real-Time or Non-Real-Time

[0424] ISO: International Organization for Standardization

[0425] QoS: Quality of Service

[0426] telecomm's

[0427] telco: telecommunications company

[0428] Node: A vertex in a graph network model; a connection point at the edge of a graph;

[0429] Edge: Network edge (1-2 hops from the end-user device); or connection between nodes in a graph;

[0430] Graphics: Mathematical models used to represent communication networks, data organization, computing devices, computation or communication flows, etc.

[0431] UND: Unified Naming and Discovery / Directory System / Services

[0432] introduction

[0433] SPAN-AI, designed for AI-driven secure peer-to-peer assisted networking, is a hybrid adaptive networking technology that provides global, scalable, secure, and distributed content storage, computing, and delivery for any application and network environment.

[0434] SPAN-AI recognizes the limitations of existing technologies that are only suitable for specific applications at a non-global scale. SPAN-AI uses an AI-driven approach to improve and adaptively combine the best features of existing solutions within a unified secure content addressable architecture. We call this a unified content delivery network, or UCDN.

[0435] SPAN-AI is based on five key systems: unified naming; unified discovery; hybrid routing; scalable publish / subscribe; and embedded security. All of these are securely integrated and jointly optimized via a layered, pluggable AI framework, with associated simulation, training, and development pipelines embedded in AI agents at peer, edge, core, or other network levels with varying degrees of awareness and optimization capabilities.

[0436] SPAN-AI architecture

[0437] 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) routes CIDs through a name resolution-based and name-based routing subsystem by iii) prefixing each CID with a name or iv) combining the name with the CID in a way that optimizes routing and / or storage.

[0438] 1. Unified naming system

[0439] a. SPAN-AI is content-addressable.

[0440] b. Content items or blocks are identified by immutable, self-certified content identifiers (CIDs), such as in IPFS.

[0441] c. A globally distributed naming directory service is used to map mutable, human-readable names / links to immutable CIDs.

[0442] i. Initially, IPNS and / or DNSLink will be used.

[0443] ii. Extensions including the use of NDNS

[0444] d. Then, the CID (CID-provider mapping and provider-requester path formation) is resolved via the hybrid adaptive routing system (System 3).

[0445] i. Name resolution-based routing, i.e., querying (multi-level) DHT

[0446] ii. Name-based routing, i.e., routing with a prefix (e.g., SPAN / <cid>) hop-by-hop forwarding of interest groups

[0447] e. Extensions include hierarchical naming for name-CID mapping and name-based routing.

[0448] f. SPAN-AI intelligently determines the location of the hosted distributed naming service (see the SPAN-AI section).

[0449] g. The SPAN-AI publish / subscribe system is used for scalable, rapid propagation of named updates (see Scalable Publish / Subscribe Systems).

[0450] The unified naming system can also use JSON updates with conflict-free replicated data types (CRDTs) that have cryptographic key-value pairs. These can be built on DHTs, Merkle trees, simple blockchains, or other efficient distributed data structures.

[0451] SPAN-AI employs an AI-driven unified discovery system, whose key component, Intelligent Environment Meetup (AmI Meetup), provides intelligent discovery, configuration, and self-organizing services.

[0452] 2. Unified Discovery System (AmI Reunification)

[0453] a. Provide intelligent discovery, configuration, and self-healing services.

[0454] i. Bootstrapping nodes, discovering peers, maintaining DHT and publish / subscribe coverage

[0455] b. Combine peer-level self-healing intelligence and edge-level intelligence discovery (see AmI convergence operation in the SPAN-AI intelligence section).

[0456] c. SPAN-AI intelligently determines the location of the hosted distributed AmI rendezvous service (see the SPAN-AI intelligent section).

[0457] i. AMI will be reasonably co-managed with edge-level naming and intelligent services.

[0458] ii. Peers register during initialization after performing mDNS and DHT discovery.

[0459] iii. Data partitioning and service placement can be guided by naming conventions.

[0460] SPAN-AI offers massive addressing routing via AI-Driven Hybrid Adaptive Routing Design (AI-HARD). AI-HARD consists of two subsystems designed to combine the benefits of Name Resolution-based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name-Based Routing (NBR) for fast and reliable content delivery. AI-HARD covers both the storage and delivery markets.

[0461] 3. AI-Driven Hybrid Adaptive Routing Design (AI-HARD)

[0462] 3.1 Storage-centric routing subsystem

[0463] a. The primary goals are persistent data availability (all content should be reachable) and relatively fast content access (<1 second).

[0464] b. Name Resolution-Based Routing (NRR) included via the new parameterized multi-level DHT

[0465] i. Understand information about user needs (e.g., content popularity, delivery deadlines) and network / topology (e.g., hop distance, latency, load) to create multiple fast lookup layers of finite size.

[0466] ii. Topology layer

[0467] 1) Each DHT only involves topologically neighboring nodes.

[0468] 2) Local, regional, and national DHT

[0469] iii. Theme Layer

[0470] 1) Each DHT only involves content related to a given topic.

[0471] iv. Heterogeneous layer

[0472] 1) Each DHT involves nearby nodes that share similar interests.

[0473] c. Intelligent content placement with adaptive copy level

[0474] i. Duplicate content based on perceived interest / popularity and network connectivity / stability (duplicate more when there is high churn / instability).

[0475] ii. Optimize replication levels to ensure lookup and delivery latency requirements for storage-centric applications. Provide additional in-network caching for delivery-centric applications (see subsystem 3.2).

[0476] d. The multi-level DHT structure and associated parameters (layers, participating nodes, bucket size, concurrency factor) and content replication are dynamically adjusted based on AI-driven optimization and distributed control algorithms (see AI-HARD operation in SPAN-AI Intelligence).

[0477] e. Multi-level DHT and intelligent content replication solutions allow for maximizing the number of queries resolved locally, thereby providing efficient, scalable, and persistent content access.

[0478] f. Content-level (as opposed to block-level) forwarding status to further improve scalability.

[0479] g. Integration with the name resolution-based subsystem via the universal unified name directory service (System 1).

[0480] 3.2 Delivery-Centric Routing Subsystem

[0481] a. The primary goal is rapid content delivery (<100ms).

[0482] b. Name-based routing (NBR) for fast lookup and delivery (e.g., NDN)

[0483] i. Data plane perception symmetric interest data packet forwarding

[0484] ii. Network caching

[0485] iii. Local multicast and mobility support

[0486] iv. Intra-network load balancing

[0487] c. Integration with the NRR subsystem via the Universal Uniform Name Directory Service (System 1)

[0488] d. For applications with real-time requirements only (e.g., video streaming)

[0489] i. Reserved for applications that can benefit from faster and more efficient application-level aware (as opposed to network-level) content distribution.

[0490] ii. Significantly reduce forwarding status (NRR subsystem handles applications with non-real-time requirements)

[0491] iii. Allow block-level forwarding to be maintained to take advantage of path diversity and further accelerate content delivery.

[0492] 3.3 Market Enablement

[0493] a. Storage-centric

[0494] i. Publishers can choose appropriate storage metrics (reliability, repeatability, decentralization, durability, etc.) in marketplaces such as Filecoin and pay for them.

[0495] ii. SPAN-AI supports multiple storage markets and technology platforms and unites them into a unified content storage and delivery network. This can include storage markets and platforms such as blockchain.

[0496] b. Delivery-centric

[0497] i. Publishers can choose appropriate delivery metrics (resolution, bit rate, latency, etc.) and pay for them in a marketplace similar to Filecoin.

[0498] ii. Distribution providers (telecom companies, ISPs, CDNs, etc.) can bid for delivery in the same market or rely on SPAN-AI and AI-HARD to select the most efficient path, thereby incentivizing efficiency.

[0499] iii. Consumers can choose one or more distributors they wish to use, for example, if they have already entered into an agreement with any distributor. Consumers are free to choose whether and with whom to enter into an agreement, or they can contribute to one or more public pools and receive rewards from them.

[0500] iv. Consumers can express distribution preferences in their name requests. For example: SPAN: / / warnerbros / batman / directorscut / 4K / <myaddress> / telstra / akamai / (The actual name and order may vary due to naming and routing considerations)

[0501] v. Publishers can choose a default delivery partner. In the event of a conflict, SPAN-AI and AI-HARD will select the most efficient path, further incentivizing efficiency.

[0502] vi. If the consumer or publisher does not specify distribution preferences, SPAN-AI and AI-HARD will choose the most efficient path.

[0503] vii. If one or more distributors selected by a consumer or publisher are not the most efficient under any routing conditions, SPAN-AI and AI-HARD will select the most efficient path and notify all relevant parties of the decision to allow them to optimize for efficiency.

[0504] viii. Payment distribution is calculated and completed by one or more settlement systems, and is notified by the SPAN-AI and AI-HARD routing systems in a manner similar to how telephone call settlement is performed today.

[0505] ix. Anyone can contribute resources and be rewarded for that contribution, thus providing a free market for telecommunications services. SPAN-AI monitors and maintains the security and health of the network. Bad resources will be removed. SPAN-AI is designed to work on commercial-grade and telecom-grade resources and meet QoS levels. QoS metrics and costs will determine the resources used and vice versa.

[0506] The SPAN-AI system uses an AI-driven publish-subscribe system for asynchronous multi-party propagation services. This asynchronous multi-party propagation service supports control plane propagation: directory updates (name, discovery, configuration) and smart updates (optimization / control operations); as well as data plane propagation: collaborative applications, live streaming, etc.

[0507] 4. Scalable publish / subscribe system

[0508] a. Fast, scalable, asynchronous, and multi-party propagation service

[0509] b. Publish-subscribe for control

[0510] i. Name directory update

[0511] ii. Discover updates (new peers, new services)

[0512] iii. Configuration Update (New Roles, New Member Qualifications)

[0513] iv. Intelligent updates (optimization / control commands, such as resource allocation, storage, and routing decisions)

[0514] c. Used for data publishing and subscription

[0515] i. Collaborative Media Applications

[0516] ii. Live Streaming

[0517] d. The publish / subscribe system uses an evolution of existing publish / subscribe algorithms (such as Gossipsub, PlumTree, HyParView) and uses the AmI rendezvous service to improve operation.

[0518] i and AmI combine intelligent discovery and self-healing to improve scalability and churn resilience, and have almost no impact on routing schemes except for adjusting coverage, fan-out, and probability weights.

[0519] ii. Embedded plug-ins for self-healing and intelligent discovery enhance peer discovery, activation, and lifecycle maintenance:

[0520] 1) The publish / subscribe system embeds pluggable metrics, actuators, and classifications to enable intelligent discovery.

[0521] 2) Periodic heartbeats propagate grid health metrics to AmI convergence.

[0522] SPAN-AI incorporates security at all levels of integration. SPAN-AI uses machine learning and identification to detect and manage security threats. Content can be encrypted using a commercial DRM system such as PlayReady before being published to the system. Data packets can be cryptographically signed by the publisher. Naming is rooted in a self-sovereign identity, which can be defined as a lifelong, portable digital identity that does not rely on any centralized authority. It uses a decentralized identifier, which provides: persistence, global resolvability, cryptographic verifiability, and decentralization. The name can also be self-certified. A preferred embodiment is based on a hardware root of trust and secure boot. Another preferred embodiment can utilize a trust network approach. Quantum cryptography, i.e., encryption based on a quantum state random number generator, can also be used.

[0523] 5. Overall Security Architecture and Preferred Implementation

[0524] a. Machine learning and identification to detect and manage security threats.

[0525] b. Encrypted block signature

[0526] c. Encryption and DRM

[0527] d. Hardware root of trust

[0528] e. Safe Start

[0529] f. Decentralized identifiers. This can be achieved using a trust network approach.

[0530] g. Sovereign identity

[0531] h. Naming rooted in sovereign identity

[0532] i. Use a self-certified name with a CID prefix.

[0533] j. Quantum encryption

[0534] SPAN-AI

[0535] SPAN-AI coordinates the adaptive operation of routing and publish / subscribe systems through a series of pluggable, hierarchical (local / edge / global / other) AI agents. The AI ​​agents provide monitoring, prediction, optimization, and control services with varying degrees of perception and optimization capabilities at peer, edge, core, and other network levels.

[0536] SPAN-AI uses a simulation, training, and development pipeline that enables cloud-level replication of the runtime environment, simulation, testing, and training of AI models, which can then be plugged into peer / edge / core / other network nodes for real-time optimization and control.

[0537] SPAN-AI provides a marketplace for pluggable AI agents, enabling open, flexible innovation in the optimization and control of general-purpose networks. This can be based on cryptographic tokens such as Filecoin or Blust.

[0538] 1. Layered AI

[0539] a. Hybrid local / global optimization and control

[0540] i. Combine rapid local reactive self-organization with slower global / hierarchical proactive guidance / supervision and backup support.

[0541] b. Hierarchical intelligence

[0542] i. Local intelligence at the same level

[0543] 1) Local surveillance and rapid response for basic survival operations and self-organization.

[0544] 2) Quick and simple rules (e.g., filters, thresholds)

[0545] 3) Nodes with limited capabilities

[0546] ii. Edge-level environmental / swarm intelligence

[0547] 1) Supports higher-level services such as discovery, bootstrapping, configuration, resource allocation, role assignment, storage decision-making, routing hints, publish / subscribe membership, and naming.

[0548] 2) Ideal setting for ML models

[0549] 3) Nodes with higher capabilities, trust, and stability

[0550] iii. Core-level global intelligence

[0551] 1) Global view optimization

[0552] 2) Train the M-model to be pushed down to the edge

[0553] 3) AI simulation

[0554] 4) Highest capacity nodes (e.g., stable peers, cloud nodes, ISP cores, CDN PoP)

[0555] c. Application and Network Awareness

[0556] i. Global predictive knowledge of user consumption / production patterns, application requirements, network conditions (including coverage grid health), and available resources for proactive optimization.

[0557] ii. Supplemented by local situational awareness (peer-to-peer / grid / network conditions) to achieve reactive control and resilience against unpredictable changes.

[0558] iii. Utilize metadata in the content request (e.g., delivery deadlines).

[0559] d. Goals and Principles

[0560] i. There is no single point of failure in the runtime agent / service (e.g., AMI rendezvous).

[0561] ii. Metrics / Actuator Ontology for Data-Driven Innovation in Reinforcement Learning

[0562] iii. An open framework with a pluggable programming API

[0563] iv. AI agent reuse and market empowerment

[0564] v. Enable innovative developers to reuse AI agents / components.

[0565] vi. It can interoperate with existing telecommunications companies, ISPs, CDNs, and internet networks. This can be achieved using existing standards (such as OpenFlow) or new AI interoperability standards evolved from the SPAN-AI ontology.

[0566] vii, thereby forming an interoperable intelligent network mesh that operates as a single unified content delivery network without a single point of failure.

[0567] 2. AI-HARD Operation (Intelligent Hybrid Adaptive Routing)

[0568] a. AI-HARD allows the network to be operated adaptively as a full p2p overlay, a full network-level mesh, or anything in between.

[0569] b. Optimized role assignment and resource allocation

[0570] i. Heterogeneous proxies / roles

[0571] 1) Algorithm determination

[0572] a) Service Roles: DHT Routing, Name-Based Routing, Storage, Caching, Discovery, Monitoring, Information Mediation / Decision Making

[0573] b) Intelligent capabilities: reactive / proactive, local / global view, learning / observation, heuristic / optimization

[0574] 2) Based on

[0575] a) Network architecture levels: peers, gateways, servers, switches, routers, application servers, etc.

[0576] b) Trust, security, and stability levels

[0577] ii. Resource allocation

[0578] 1) Algorithm determination

[0579] a) Allocate CPU, memory, disk, and upload / download bandwidth resources to each subsystem within each agent.

[0580] 2) Based on

[0581] a) Service Role

[0582] b) Intelligent capabilities

[0583] c) Network architecture level

[0584] d) Trust, security, and stability levels

[0585] 3) More stable endpoint nodes tend to allocate more resources to storage-centric subsystems.

[0586] 4) In less stable network nodes tend to allocate more resources to delivery-centric subsystems.

[0587] 5) Relax the replication level in DHT, as name-based routing is effective for delivery-centric applications.

[0588] 6) Relax name-based routing forwarding state, as DHT processes content with more lenient delivery requirements.

[0589] iii. Incentivize resource contributions through FIL-type markets (see Market Empowerment section)

[0590] iv. Primarily provided by environment and global intelligent nodes with edge / core level views / capabilities

[0591] v. Supplemented by the ability to assign self-roles and allocate resources

[0592] c. Optimized long-term placement and short-term caching

[0593] i. Distributed cloud network flow algorithms for long-term placement in distributed, storage-centric subsystems.

[0594] ii. Probabilistic local caching strategies for delivery-centric subsystems

[0595] d. Adaptive routing based on name resolution and names

[0596] i. AI-based optimization and self-organizing methods determine how the application will split requests into two subsystems.

[0597] 1) Requests with lenient delivery deadlines and forecasted or pre-established requests with tight delivery deadlines can be processed through a "slower" but persistent and scalable DHT subsystem.

[0598] 2) Requests with tight delivery deadlines are processed via a fast name-based routing subsystem.

[0599] 3) Requests for pre-planned market incentives via FIL (e.g., for large launches - see the Market Empowerment section).

[0600] ii. DHT routing operations

[0601] 1) Cardmillia-type protocol, but on improved multi-stage DHT

[0602] 2) Adaptive DHT parameter optimization (nodes, blocks, replication factor, concurrency factor)

[0603] 3) Used for DHT maintenance and self-organized AmI rendezvous under high churn conditions (see AmI rendezvous operation)

[0604] iii. Name-based routing operations

[0605] 1) NDN-type method, but the forwarding state is reduced due to the cooperation of the DHT subsystem.

[0606] 3. AMI rendezvous operation (intelligent discovery and configuration)

[0607] a. Ambient intelligence (AmI) refers to the combination of sensing and control used for the following:

[0608] i. Peer / Local Intelligence: Embedded actuators in publish / subscribe peers control the probability weights, degrees, and fan-out of the mesh. Observers in p2p publish / subscribe messaging compile metrics from neighbor subscriptions and events to infer health (e.g., hop count, reliability, latency, load balancing). Self-healing strategies can be as simple as filtering.

[0609] ii. Edge / Swarm Intelligence: AmI health classification decisions (scores, rankings) for peers and p2p overlay grids are derived through a basic reinforcement learning model.

[0610] iii. Core / Global Intelligence: Maintain aggregated usage predictions and grid / network conditions. Determine the placement of rendezvous servers.

[0611] b. The AmI rendezvous service is built on top of state-of-the-art rendezvous services, such as libp2p rendezvous, which supports periodic peer re-registration, discovery, and bootstrapping, and is extended by peer heartbeats and grid health metrics and rankings.

[0612] c. A pluggable interface for the self-healing agent embeds the AMI rendezvous client into the publish / subscribe mechanism.

[0613] i. Embedding pluggable metrics, actuators, and classifications to enable intelligent discovery

[0614] ii. Periodic heartbeats propagate grid health metrics and change increments to AmI convergence and SPAN-AI data lake.

[0615] iii. Expand registration and re-registration to exchange full-grid snapshots.

[0616] d. Pluggable interface with intelligent discovery at the rendezvous point

[0617] i. The AmI rendezvous server and message delivery are built on top of the libp2p rendezvous service, where metric collection is extended by time-series-based monitoring systems such as Prometheus and InfluxDB.

[0618] ii. Integrate reinforcement learning agents for grid health classification.

[0619] iii. Discovery of expansion through peer ranking.

[0620] e. Additional features include discovery records, federation and caching, adaptive control, metric / actuator reuse, and subject-specific / device-specific metrics.

[0621] f. Simulate and develop further embedded intelligence and adaptive control for pipelines, through integration with AI-HARD solutions, to support:

[0622] i. Use hybrid P2P routes via convergence bypasses to meet deadlines or optimize coverage width.

[0623] ii. Assignment / placement of the role of meeting & information mediator

[0624] iii. Hints from naming enhancement or global awareness, derived from partitioning of the DHT topology layer.

[0625] iv. A coarse-grained adaptive control activation plugin from the plugin series, used for intelligent discovery / repair of DHT and NDN routes.

[0626] g. Supports different mesh types (NBR, DHT-Cadmillia) and guarantee requirement metrics (security, trust, integrity, efficiency, reliability, stability, latency).

[0627] SPAN-AI simulator

[0628] SPAN-AI uses a simulation, training, and development pipeline that enables cloud-level replication of the runtime environment, simulation, testing, and training of AI models and agents, which can then be plugged into peer / edge / core / other network nodes for real-time optimization and control.

[0629] SPAN-AI's simulator, the Self-Aware Mesh Simulator (SAMSim), is supported in the following ways:

[0630] - Distributed cloud-hosted big data grid lake with health metrics

[0631] - Simulate and deploy AI models across an automated software engineering pipeline

[0632] 1. Simulator intelligence

[0633] a. The testing tool uses SPAN-AI intelligence to achieve adaptive scalability, including guiding the placement and scheduling of AmI rendezvous servers and supporting data exchange between the rendezvous servers and the data lake.

[0634] b. Testing frameworks are used for prototyping SAMSim, particularly agile container infrastructures (e.g., HashiCorp Noma and Consul for orchestration, Prometheus and InfluxDB for metrics).

[0635] c. AI developers use grid health metrics to test, train, sample simulations, and reward reinforcement learning.

[0636] i. The grid health metric and the actuator constitute a reinforcement learning problem.

[0637] ii. Metrics and executors are refined through rule acquisition and management in the data lake.

[0638] iii. The agent is refined from rule sets, metrics, and executors into:

[0639] 1) Imitation controls and protocol plugins for abstract simulators (e.g., PeerSim, agent-based emulation, TestGround topology simulator, existing Gerbil simulator)

[0640] 2) Prototype plugins for specific embedded simulations (borrowed from D-P2P-Sim, RealPeer, ProtoSim) are distributed in cloud providers such as Amazon Web Services (AWS) or Digital Ocean Cloud (DO-Cloud).

[0641] iv. Integrate data design tools to prepare and quality control datasets.

[0642] d. Simulator and pipeline characteristics:

[0643] i. Primarily derived from integrated testing frameworks and integrated P2P simulators.

[0644] ii. Includes multithreading, execution controls / runners, reality stubs and network models, agile container infrastructure, metrics and visualization.

[0645] e. Additional simulators include NS3 for network events, RealPeer and ProtoSim for refining models, PlanetSim for intelligent clustering, and PEERFACTSIM.kom for DHT routing.

[0646] f. Additional simulator and pipeline features include customizable API standards, simulator layering, topology export format, and fault simulation.

[0647] 2. Publish / Subscribe Simulation

[0648] a. Support key scaffolding for designing various experimental agents by evaluating and integrating capabilities from testing frameworks and third-party p2p simulators (e.g., PeerSim, D-P2P-Sim).

[0649] b. Agency engineering and guaranteed environment:

[0650] i. Initially, the focus was on ensuring latency in a scalable, elastic publish-subscribe grid.

[0651] ii. Supports the following simulation and iterative development

[0652] 1) Health metrics compilation, classification for intelligent discovery during broadcasts and chatter, and weighted probabilities that trigger actuators to filter or limit grid coverage.

[0653] 2) Message transmission used for AmI rendezvous registration, heartbeat, and smart discovery.

[0654] 3) Messaging for publish / subscribe bootstrapping, subscribe / publish, migration / cleanup.

[0655] iii. Supports the reuse of grid health datasets and health metrics, as well as the repair of executor rules, across empirical simulation experiments. This reuse can be extended in future implementations.

[0656] In use

[0657] SPAN-AI Preferred Implementation Examples and Use Cases

[0658] In a particular embodiment, the SPAN-AI embodiment may include a distributed source store, publishing, and distribution system using SPAN-AI, wherein the distributed video source store and distribution service includes the following steps:

[0659] 1. Video capture: encoding; packaging; encryption; signing

[0660] 2. Assign a unified name

[0661] 3. Publish to distributed storage networks (NRR and DHT) with storage metrics.

[0662] 4. Publish in a general publish / subscribe system with distribution QoS metrics.

[0663] This use case allows users to subscribe to videos using a common publish / subscribe system.

[0664] It also allows publishers to distribute video using NBR networks for real-time (live) streaming and / or NRR networks for near / non-real-time distribution.

[0665] SPAN AI for gaming

[0666] It will be understood that while the preferred embodiment of SPAN-AI is for video distribution, SPAN-AI has been designed for use as a unified content delivery network (UCDN) for any type of content. This includes, but is not limited to: game streaming (distributed or from a "server" or from a consumer's device); distributed game execution; social media; websites; blogs; e-commerce; medical applications such as MRI, X-rays, remote diagnostics, etc.; simulations; command and control; etc.< / myaddress> < / cid>

Claims

1. A Unified Content Delivery Network (UCDN) system, said UCDN system comprising a network of one or more interoperable peer-to-peer networks. in, The UCDN system includes a hierarchical hybrid adaptive AI-driven secure peer-to-peer assisted networking (SPAN-AI) system. The SPAN-AI system uses an AI-driven hybrid adaptive routing method based on five key SPAN-AI subsystems: a unified naming subsystem; a unified discovery subsystem; and a hybrid adaptive routing subsystem. Scalable publish-subscribe subsystem; And embedded security subsystems; All five key SPAN-AI subsystems are securely integrated and jointly optimized via a hierarchical pluggable AI framework, with associated simulation, training, and development pipelines embedded in AI agents at peer, edge, core, or other network levels / layers with varying degrees of perception and optimization capabilities. The SPAN-AI system uses a unified naming and discovery (UND) system, which maps variable, human-readable names to immutable, self-certifying content identifiers (CIDs). The UND system routes CIDs by adding a name prefix to each CID or by combining the name and CID in a way that optimizes routing and / or storage. The UND system includes the unified naming subsystem and the unified discovery subsystem.

2. The UCDN system according to claim 1, wherein, The peer-to-peer network is the SPAN-AI network.

3. In the UCDN system according to claim 1, the variable human-readable name includes at least one of a domain name and a content name.

4. The UCDN system according to claim 1 or claim 2, wherein, The UND also incorporates IP DNS to ensure backward compatibility.

5. The UCDN system according to claim 1 or claim 2 further employs an AI-driven general discovery system, wherein the AI-driven general discovery system includes key component environment intelligent convergence, referred to as AmI convergence, which provides intelligent discovery, configuration and self-organization services.

6. The UCDN system according to claim 1 or 2, wherein the SPAN-AI system employs a large-scale addressing routing design via an AI-driven hybrid adaptive routing system called AI-HARD; the AI-HARD system comprises two subsystems: A storage-centric routing subsystem; And a delivery-centric routing subsystem; the subsystem combines the benefits of Name Resolution-based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name-based Routing (NBR) for fast and reliable content delivery.

7. The UCDN system according to claim 6, wherein, AI-HARD also incorporates IP routing to provide backward compatibility.

8. The UCDN system according to claim 6, wherein, The AI-HARD intelligent agent within SPAN-AI utilizes predictive knowledge about network conditions and application requirements to adaptively select the most effective routing strategy from subsystems.

9. The UCDN system according to claim 3, wherein, UND discovery includes both SPAN-AI's intelligent discovery service AmI convergence and IP name discovery, i.e., DNS, to provide backward compatibility.

10. The UCDN system according to claim 6, wherein, The AI-HARD protocol, naming standards, conventions, and methods are released to enable inclusion in existing and new routers, thereby enabling existing IP networks to interoperate with new SPAN-AI networks.

11. The UCDN system according to claim 10, wherein, The AI-HARD protocol, naming standards, conventions, and methods include IP naming.

12. The UCDN system according to claim 6, wherein, The AI-HARD system interoperates with multiple storage and delivery networks.

13. The UCDN system according to claim 12, wherein, The storage and delivery network can operate on cryptographic tokens such as Filecoin or Blust.

14. The UCDN system according to claim 2, wherein the SPAN-AI system utilizes an AI-driven publish-subscribe system to perform asynchronous multi-party propagation services, the asynchronous multi-party propagation services supporting: control plane propagation involving directory updates related to names, discovery, and configuration, and intelligent updates involving optimization / control operations; and data plane propagation for collaborative applications, such as social networks and video conferencing.

15. The UCDN system according to claim 2, wherein, SPAN-AI uses an AI-driven publish-subscribe system for asynchronous multi-party propagation services, which include communication between AI agents, naming services, and discovery services.

16. The UCDN system according to claim 15, wherein, The AI-driven publish-subscribe system includes interoperability with the IP discovery service.

17. The UCDN system according to claim 16, wherein, The AI-driven publish-subscribe system uses AmI rendezvous services expanded through peer heartbeats and grid health metrics and rankings to improve operations, intelligent discovery, and configuration via a combination of perception and control for peer / local intelligence, edge / swarm intelligence, and core / global intelligence.

18. The UCDN system according to claim 9, wherein, AmI Reunion includes a pluggable interface for a self-healing agent that embeds the AmI Reunion client into a publish / subscribe protocol that includes evolutions of existing publish / subscribe algorithms, including Gossipsub, PlumTree, and HyParView.

19. The UCDN system of claim 2, wherein the SPAN-AI system includes security integrated at all levels.

20. The UCDN system of claim 2, wherein the SPAN-AI system uses machine learning and identification to detect and manage security threats.

21. The UCDN system according to claim 1, wherein, The content is encrypted using a DRM system such as PlayReady before being published to the SPAN-AI system.

22. The UCDN system according to claim 1, wherein, The data packets are cryptographically signed by the publisher.

23. The UCDN system according to claim 1, wherein, The naming is rooted in self-sovereign identity, which can be defined as a lifelong, portable digital identity that does not depend on any centralized authority.

24. The UCDN system according to claim 1, wherein the UCDN system uses a decentralized identifier, the decentralized identifier providing: persistence, global resolvability, cryptographic verifiability and decentralization.

25. The UCDN system according to claim 1, wherein, The name is self-certified.

26. The UCDN system according to claim 1, wherein the UCDN system is based on hardware root of trust and secure boot.

27. The UCDN system according to claim 1, wherein the UCDN system utilizes a trust network method.

28. The UCDN system according to claim 1, wherein the UCDN system utilizes quantum encryption, i.e. encryption based on a quantum state random number generator.

29. The UCDN system of claim 1, wherein the SPAN-AI system coordinates the adaptive operation of the routing and publish / subscribe system via a series of pluggable, hierarchical AI agents that coordinate local / edge / global operations, the AI ​​agents providing monitoring, prediction, optimization, and control services with varying degrees of awareness and optimization capabilities at peer, edge, core, and other network levels.

30. The UCDN system according to claim 1, wherein the UCDN system provides a market for pluggable AI agents to enable open, flexible innovation in the optimization and control of general networks.

31. The UCDN system according to claim 1, wherein, The AI ​​agent is capable of exchanging crypto tokens such as Filecoin or Blust.

32. The UCDN system according to claim 2, wherein, The SPAN-AI system uses a simulation, training, and development pipeline that enables cloud-level replication of the runtime environment, simulation, testing, and training of AI models and agents, which can then be plugged into peer / edge / core / other network nodes for real-time optimization and control.

33. The UCDN system according to claim 1 further includes a self-sensing mesh simulator, referred to as the SAMSim system, and wherein, The SAMSim system is supported by: a distributed cloud-hosted big data grid lake with health metrics; and the simulation and deployment of AI models across an automated software engineering pipeline.

34. A layered hybrid adaptive AI-driven secure peer-assisted networking (SPAN-AI) system using a layered AI-driven approach under a unified secure content addressable architecture, the SPAN-AI system being based on five key SPAN-AI subsystems: a unified naming subsystem; Unified discovery subsystem; Hybrid adaptive routing subsystem; Scalable publish-subscribe subsystem; And embedded security subsystems; All five key SPAN-AI subsystems are securely integrated and jointly optimized via a layered, pluggable AI framework, and have associated simulation, training, and development pipelines embedded in AI agents with varying degrees of awareness and optimization capabilities at peer, edge, core, or other network levels / layers. The SPAN-AI systems utilize a unified naming and discovery (UND) system that maps mutable, human-readable names to immutable, self-certifying content identifiers (CIDs). Routing of CIDs is achieved through both a name-based routing subsystem and a name-based routing subsystem by prefixing each CID with a name or combining names and CIDs in a way that optimizes routing and / or storage. The UND system includes the unified naming subsystem and the unified discovery subsystem.

35. The SPAN-AI system of claim 34, wherein the variable human-readable name includes at least one of a domain name and a content name.

36. The SPAN-AI system according to claim 34 or claim 35 further employs an AI-driven unified discovery system, wherein the AI-driven unified discovery system includes key component environment intelligent convergence, referred to as AmI convergence, which provides intelligent discovery, configuration and self-organization services.

37. The SPAN-AI system according to claim 34 or claim 35, wherein the SPAN-AI system is a large-scale addressing route designed via an AI-driven hybrid adaptive routing system called AI-HARD; the AI-HARD system comprises two subsystems: A storage-centric routing subsystem; And a delivery-centric routing subsystem; the subsystem combines the benefits of Name Resolution-based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name-based Routing (NBR) for fast and reliable content delivery.

38. The SPAN-AI system according to claim 37, wherein, The AI-HARD intelligent agent within SPAN-AI utilizes predictive knowledge about network conditions and application requirements to adaptively select the most effective routing strategy from subsystems.

39. The SPAN-AI system according to claim 37, wherein, The AI-HARD protocol was released to enable its inclusion in existing and new routers, thereby ensuring routing compatibility across all networks.

40. The SPAN-AI system according to claim 37, wherein, The AI-HARD system interoperates with multiple storage and delivery networks.

41. The SPAN-AI system according to claim 40, wherein, The storage and delivery network can operate on cryptographic tokens such as Filecoin or Blust.

42. The SPAN-AI system according to claim 36, wherein the SPAN-AI system utilizes an AI-driven publish-subscribe system to perform asynchronous multi-party propagation services, the asynchronous multi-party propagation services supporting: control plane propagation involving directory updates related to names, discovery, and configuration, and intelligent updates involving optimization / control operations; and data plane propagation for collaborative applications, such as video conferencing and social networks.

43. The SPAN-AI system according to claim 34, wherein, SPAN-AI uses an AI-driven publish-subscribe system for asynchronous multi-party propagation services, which include communication between AI agents, naming services, and discovery services.

44. The SPAN-AI system according to claim 42, wherein, The AI-driven publish-subscribe system uses AmI rendezvous services expanded through peer heartbeats and grid health metrics and rankings to improve operations, intelligent discovery, and configuration via a combination of perception and control for peer / local intelligence, edge / swarm intelligence, and core / global intelligence.

45. The SPAN-AI system according to claim 42, wherein, AmI Reunion includes a pluggable interface for a self-healing agent that embeds the AmI Reunion client into a publish / subscribe protocol that includes evolutions of existing publish / subscribe algorithms, including Gossipsub, PlumTree, and HyParView.

46. ​​The SPAN-AI system of claim 34, wherein the SPAN-AI system includes security integrated at all levels.

47. The SPAN-AI system of claim 34, wherein the SPAN-AI system uses machine learning and identification to detect and manage security threats.

48. The SPAN-AI system according to claim 34, wherein, The content is encrypted using a DRM system such as PlayReady before being published to the SPAN-AI system.

49. The SPAN-AI system according to claim 34, wherein, The data packets are cryptographically signed by the publisher.

50. The SPAN-AI system according to claim 34, wherein, The naming is rooted in self-sovereign identity, which can be defined as a lifelong, portable digital identity that does not depend on any centralized authority.

51. The SPAN-AI system of claim 34, wherein the SPAN-AI system uses a decentralized identifier that provides: persistence, global resolvability, cryptographic verifiability and decentralization.

52. The SPAN-AI system according to claim 34, wherein, The name is self-certified.

53. The SPAN-AI system according to claim 34, wherein the SPAN-AI system is based on hardware root of trust and secure boot.

54. The SPAN-AI system of claim 34, wherein the SPAN-AI system utilizes a trust network method.

55. The SPAN-AI system according to claim 34, wherein the SPAN-AI system utilizes quantum encryption, i.e. encryption based on a quantum state random number generator.

56. The SPAN-AI system of claim 34, wherein the SPAN-AI system coordinates the adaptive operation of the routing and publish / subscribe system via a series of pluggable, hierarchical AI agents that coordinate local / edge / global operations, the AI ​​agents providing monitoring, prediction, optimization, and control services with varying degrees of awareness and optimization capabilities at peer, edge, core, and other network levels.

57. The SPAN-AI system of claim 34, wherein the SPAN-AI system provides a market for pluggable AI agents to enable open, flexible innovation in the optimization and control of a unified network.

58. The SPAN-AI system according to claim 56, wherein, The AI ​​agent is capable of exchanging crypto tokens such as Filecoin or Blust.

59. The SPAN-AI system according to claim 34, wherein, The SPAN-AI system uses a simulation, training, and development pipeline that enables cloud-level replication of the runtime environment, simulation, testing, and training of AI models and agents, which can then be plugged into peer / edge / core / other network nodes for real-time optimization and control.

60. The SPAN-AI system of claim 34 further includes a self-sensing mesh simulator, referred to as the SAMSim system, and wherein, The SAMSim system is supported by a distributed, cloud-hosted big data grid lake with health metrics. And to simulate and deploy AI models across automated software engineering pipelines.

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