Systems, methods, and apparatuses for intelligent network automation

Abstract

A system for intelligent network automation that includes at least one processor and at least one memory device. The at least one memory device stores computer-readable instructions that, when loaded into the at least one processor, causes the at least one processor to: discover one or more computing devices in electronic communication with a computer network; identify one or more candidate parameters for the one or more discovered computing devices; select, from the one or more candidate parameters, one or more golden parameters; generate, based at least in part on one or more golden parameters, a golden configuration; and transmit the golden configuration.

Description

PATENT Attorney Docket No. NETB-OOOl-WO SYSTEMS, METHODS, AND APPARATUSES FORINTELLIGENT NETWORK AUTOMATION CROSS-REFERENCES TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. provisional patent application no.63 / 721,088 (Attorney Docket No. NETB-0001-P01), filed on November 15, 2024, and entitled “SYSTEMS, METHODS, AND APPARATUSES FOR INTELLIGENT NETWORK AUTOMATION”.

[0002] The foregoing patent application is incorporated herein by reference in its entirety for all purposes.BACKGROUND

[0003] Modern computer networks have become increasingly complex in both scale and architecture. A typical enterprise or service provider network may include a wide variety of interconnected devices such as routers, switches, firewalls, servers, load balancers, and end-user devices. These devices often originate from different vendors and may operate using distinct communication protocols, management interfaces, and configuration standards. As a result, the overall network environment is highly heterogeneous and dynamic.

[0004] In practice, documentation regarding the architecture and configuration of such networks is frequently incomplete, outdated, or entirely absent. The documentation problem is exacerbated by turnover among network engineering staff, where the departure of experienced personnel often results in the loss of critical institutional knowledge. Without accurate and up-to-date documentation, understanding the topology, dependencies, and operational characteristics of a given network becomes difficult.

[0005] The lack of reliable network architecture documentation presents significant challenges when troubleshooting network issues. Outages, degraded performance, dropped connections, and nonfunctional services can arise from a wide range of causes, including misconfigurations, hardware failures, or protocol incompatibilities. In the absence of clear architectural visibility, diagnosing and resolving such issues often requires substantial time and effort, leading to prolonged service disruptions and increased operational costs.

[0006] Moreover, the expertise required to efficiently troubleshoot complex networks is typically acquired through years of hands-on experience. Senior network engineers often develop an intuitive understanding of network behavior and failure modes that is difficult to capture in written form. Transferring this knowledge to junior engineers is a time-consuming process, and the lack ofPATENT Attorney Docket No. NETB-OOOl-WO systematic tools or documentation further hinders the development of troubleshooting proficiency among less experienced personnel.SUMMARY

[0007] Disclosed herein are systems, apparatuses, and methods thereof for intelligent network automation. Embodiments of the current disclosure provide for the discovery, identification, and / or generation of golden configurations, golden parameters, golden intents, and / or golden features, which further provide for the identification of network configurations and mitigation of network drift, e.g., the tendency for network device configuration to alter / change over time away from a desired standard, e.g., a golden configuration. Aspects of the current disclosure provide for the discovery of a network’s current golden configuration through use of intelligent artificial models, command discovery processes, and credential databases, which, in turn, eases and / or simplifies the workload on network engineers and / or maintainers. Embodiments of the current disclosure are useful for managing networks with a high maintainer churn rate and / or networks having poor documentation, e.g., a lack of network maps, device property settings, and / or the like.

[0008] For example, embodiments of the current disclosure provide for an apparatus for intelligent network automation. The apparatus includes: an input command processing circuit, an orchestration circuit, an execution engine circuit, and a golden configuration provisioning circuit. The input command processing circuit is structured to interpret one or more input command values corresponding to a request to generate golden configuration data for at least one computing device in electronic communication with a computer network. The orchestration circuit is structured to obtain, in response to the one or more input command values, network context data corresponding to a feature of at least one of: the at least one computing device, or the computer network. The orchestration circuit is further structured to: generate, in response to the network context data, at least one artificial intelligence prompt value; transmit the network context data and the at least one artificial intelligence prompt value to an artificial intelligence circuit; receive, from the artificial intelligence circuit in response to the at least one artificial intelligence prompt value, network management instruction data; and generate, based at least in part on the network management instruction data, one or more network operation command values. The execution engine circuit is structured to: execute the one or more network operation command values; and interpret result data responsive to the one or more network operation command values. The orchestration circuit is further structured to generate, based at least in part on the result data, the golden configuration data. The golden configuration provisioning circuit is structured to transmit the golden configuration data.PATENT Attorney Docket No. NETB-0001-WO

[0009] Other embodiments of the current disclosure provide for a method for intelligent network automation. The method includes: interpreting one or more input command values corresponding to a request to generate golden configuration data for at least one computing device in electronic communication with a computer network; obtaining, in response to the one or more input command values, network context data corresponding to a feature of at least one of: the at least one computing device, or the computer network; and generating, in response to the network context data, at least one artificial intelligence prompt value. The method further includes: transmitting the network context data and the at least one artificial intelligence prompt value to an artificial intelligence model; receiving, from the artificial intelligence model in response to the at least one artificial intelligence prompt value, network management instruction data; generating, based at least in part on the network management instruction data, one or more network operation command values; executing the one or more network operation command values; and interpreting result data responsive to the one or more network operation command values. The method further includes: generating, based at least in part on the result data, the golden configuration data; and transmitting the golden configuration data.

[0010] Further embodiments of the current disclosure provide for a non-transitory computer-readable medium. The non-transitory computer-readable medium stores instructions that, when loaded into at least one processor, causes the at least one processor to: interpret one or more input command values corresponding to a request to generate golden configuration data for at least one computing device in electronic communication with a computer network; obtain, in response to the one or more input command values, network context data corresponding to a feature of at least one of: the at least one computing device, or the computer network; and generate, in response to the network context data, at least one artificial intelligence prompt value. The stored instructions further cause the at least one processor to: transmit the network context data and the at least one artificial intelligence prompt value to an artificial intelligence model; receive, from the artificial intelligence model in response to the at least one artificial intelligence prompt value, network management instruction data; generate, based at least in part on the network management instruction data, one or more network operation command values; and execute the one or more network operation command values. The stored instructions further cause the at least one processor to: interpret result data responsive to the one or more network operation command values; generate, based at least in part on the result data, the golden configuration data; and transmit the golden configuration data.

[0011] Yet further embodiments of the current disclosure provide for another apparatus for intelligent network automation. The apparatus includes: at least one processor, and at least one memory device. The at least one memory device stores computer-readable instructions that, when loaded into at least one processor, causes the at least one processor to: discover one or morePATENT Attorney Docket No. NETB-0001-WO computing devices in electronic communication with a computer network; determine one or more eigen variables for the one or more discovered computing devices; generate, based at least in part on one or more eigen variables, a golden feature; and validate the golden feature against a golden configuration based at least in part on a golden intent.

[0012] Yet further embodiments of the current disclosure provide for another method for intelligent network automation. The method includes: discovering one or more computing devices in electronic communication with a computer network; determining one or more eigen variables for the one or more discovered computing devices; generating, based at least in part on one or more eigen variables, a golden feature; and validating the golden feature against a golden configuration based at least in part on a golden intent.

[0013] Still yet further embodiments of the current disclosure provide for another apparatus for intelligent network automation. The apparatus includes: at least one processor, and at least one memory device. The at least one memory device stores computer-readable instructions that, when loaded into the at least one processor, causes the at least one processor to: discover one or more computing devices in electronic communication with a computer network; identify one or more candidate parameters for the one or more discovered computing devices; select, from the one or more candidate parameters, one or more golden parameters; generate, based at least in part on one or more golden parameters, a golden configuration; and transmit the golden configuration.

[0014] Yet further embodiments of the current disclosure provide for another method for intelligent network automation. The method includes: discovering one or more computing devices in electronic communication with a network; identifying one or more candidate parameters for the one or more discovered computing devices; selecting, from the one or more candidate parameters, one or more golden parameters; generating, based at least in part on one or more golden parameters, a golden configuration; and transmitting the golden configuration.

[0015] These and other systems, apparatuses, methods, objects, features, and advantages of the present disclosure will be apparent to those skilled in the art from the following detailed description of the preferred embodiment and the drawings.

[0016] All documents mentioned herein are hereby incorporated in their entirety by reference.References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the text. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context.PATENT Attorney Docket No. NETB-OOOl-WO BRIEF DESCRIPTION OF THE FIGURES

[0017] The disclosure and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:

[0018] Fig. 1 depicts a schematic diagram of a network environment incorporating a system for intelligent network automation, in accordance with embodiments of the current disclosure;

[0019] Fig. 2 depicts an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0020] Fig. 3 depicts a block diagram of an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0021] Fig. 4 depicts a block diagram of a circuit of an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0022] Fig. 5 depicts a block diagram of a circuit of an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0023] Fig. 6 depicts a graphical user interface (GUI) for intelligent network automation, in accordance with embodiments of the current disclosure;

[0024] Fig. 7 depicts a block diagram of a circuit of an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0025] Fig. 8 depicts a block diagram of a circuit of an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0026] Fig. 9 depicts a block diagram of a system for intelligent network automation, in accordance with embodiments of the current disclosure;

[0027] Fig. 10 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0028] Fig. 11 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0029] Fig. 12 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0030] Fig. 13 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0031] Fig. 14 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0032] Fig. 15 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;PATENT Attorney Docket No. NETB-0001-WO

[0033] Fig. 16 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0034] Fig. 17 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0035] Fig. 18 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0036] Fig. 19 depicts a block diagram of a circuit of an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0037] Fig. 20 depicts a block diagram of data structures for intelligent network automation, in accordance with embodiments of the current disclosure;

[0038] Fig. 21 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0039] Fig. 22 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0040] Fig. 23 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0041] Fig. 24 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0042] Fig. 25 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0043] Fig. 26 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0044] Fig. 27 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0045] Fig. 28 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0046] Fig. 29 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0047] Fig. 30 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0048] Fig. 31 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0049] Fig. 32 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;PATENT Attorney Docket No. NETB-OOOl-WO

[0050] Fig. 33 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0051] Fig. 34 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0052] Fig. 35 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0053] Fig. 36 depicts a block diagram of a data structure for intelligent network automation, in accordance with embodiments of the current disclosure;

[0054] Fig. 37 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0055] Fig. 38 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0056] Fig. 39 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0057] Fig. 40 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0058] Fig. 41 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0059] Fig. 42 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0060] Fig. 43 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0061] Fig. 44 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0062] Fig. 45 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0063] Fig. 46 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0064] Fig. 47 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0065] Fig. 48 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0066] Fig. 49 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;PATENT Attorney Docket No. NETB-0001-WO

[0067] Fig. 50 is a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0068] Fig. 51 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0069] Fig. 52 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0070] Fig. 53 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0071] Fig. 54 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0072] Fig. 55 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0073] Fig. 56 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0074] Fig. 57 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0075] Fig. 58 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0076] Fig. 59 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0077] Fig. 60 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0078] Fig. 61 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0079] Fig. 62 depicts a process flow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0080] Fig. 63 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0081] Fig. 64 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0082] Fig. 65 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0083] Fig. 67 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;PATENT Attorney Docket No. NETB-0001-WO

[0084] Fig. 68 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0085] Fig. 69 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0086] Fig. 70 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0087] Fig. 71 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0088] Fig. 72 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0089] Fig. 73 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0090] Fig. 74 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0091] Fig. 75 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0092] Fig. 76 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0093] Fig. 77 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0094] Fig. 78 depicts aspects of a workflow for a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0095] Fig. 79 depicts an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0096] Fig. 80 depicts an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0097] Fig. 81 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0098] Fig. 82 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0099] Fig. 83 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0100] Fig. 84 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;PATENT Attorney Docket No. NETB-0001-WO

[0101] Fig. 85 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure:

[0102] Fig. 86 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0103] Fig. 87 depicts an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0104] Fig. 88 depicts an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0105] Fig. 89 depicts an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0106] Fig. 90 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0107] Fig. 91 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0108] Fig. 92 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0109] Fig. 93 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0110] Fig. 94 depicts an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0111] Fig. 95 depicts an apparatus for intelligent network automation, in accordance with embodiments of the current disclosure;

[0112] Fig. 96 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0113] Fig. 97 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0114] Fig. 98 depicts a flowchart for a method for intelligent network automation, in accordance with embodiments of the current disclosure;

[0115] Fig. 99 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0116] Fig. 100 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0117] Fig. 101 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;PATENT Attorney Docket No. NETB-OOOl-WO

[0118] Fig. 102 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure:

[0119] Fig. 103 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0120] Fig. 104 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure:

[0121] Fig. 105 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0122] Fig. 106 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0123] Fig. 107 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0124] Fig. 108 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0125] Fig. 109 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0126] Fig. 110 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0127] Fig. 111 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0128] Fig. 112 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0129] Fig. 113 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure:

[0130] Fig. 114 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0131] Fig. 115 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure:

[0132] Fig. 116 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure;

[0133] Fig. 117 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure:

[0134] Fig. 118 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure; andPATENT Attorney Docket No. NETB-OOOl-WO

[0135] Fig. 119 depicts a GUI for intelligent network automation, in accordance with embodiments of the current disclosure.DETAILED DESCRIPTION

[0136] Many businesses rely on computer networks to function properly. Managing and troubleshooting computer networks are often complex tasks. For example, a typical corporate network will often contain different device types, e.g., switches, firewalls, routers, traffic shapers, virtual private network gateways, and / or the like, made by different vendors and / or running different operating systems. Many networks will often include complex communication links spanning continents and / or across the globe traversing complex physical and / or virtual network topologies.

[0137] Accordingly, it is typical for configuring and / or troubleshooting computer networks to involve knowledge of hundreds and / or thousands of different devices, operating systems, shell scripts, and / or applications. It is often the case, however, that documentation (device commands, network maps, troubleshooting solutions / processes, etc.) available to network managers is of limited and / or poorly documented quality. Thus, troubleshooting a network outage on a corporate or other network can be overwhelming for network engineers.

[0138] Many network engineers learn troubleshooting through reading a manufacturer's manual or a company’s internal documentation. However, the effectiveness of such documentation varies. For instance, the troubleshooting knowledge captured in a document is usually only helpful if the information is accurate and the user correctly identifies the problem. As such, many companies conduct extensive training for network engineers which is time-consuming and / or expensive.

[0139] Moreover, the conventional way of network troubleshooting requires a network professional to manually run a set of standard commands and processes for each device that may potentially be involved with the network issue. However, it often takes years of practice to become familiar with those commands, along with each of their parameters. Additionally, complicated troubleshooting methodologies are often hard to share and transfer. Therefore, even though a similar network problem may happen repeatedly, each troubleshooting instance may still start from scratch. Moreover, networks are continuing to grow in complexity and, as such, it is increasingly difficult to manage computer networks efficiently with traditional methods and tools. This increasing complexity often results in increased network outages and / or difficulty in troubleshooting a given network outage. Network outages, however, can be costly to a company in terms of employee downtime, sales downtime, and / or the company’s reputation.

[0140] Disclosed herein are systems, methods, and apparatuses for automating network management and troubleshooting. For example, some embodiments of the current disclosure providePATENT Attorney Docket No. NETB-0001-WO for a user with limited knowledge of devices and / or command line interfaces to generate a series of commands structured to identify a common configuration, also referred to and described herein in further detail, as a golden configuration, for one or more network devices in instances where the common configuration is unknown.

[0141] For example, embodiments of the current disclosure provide for an improved humanmachine interface for querying the configuration state and / or the network state (e.g., the running memory state of devices at all layers of the Open Systems Interconnection (OSI) model and / or the TCP / IP stack equivalent layers).

[0142] Embodiments of the current disclosure also provide for a system for network management. The system includes an interface configured to receive natural language input; an orchestration agent; a context management system; and an execution engine. The orchestration agent may be implemented using a large language model and be configured to: receive the natural language input and session context, identify requested network management operations, and generate text representations of API calls to perform the operations. The context management system may be configured to provide at least one of: information about available network management commands, current session state including network maps and devices, or a command line interface dictionary including vendor-specific commands and descriptions. The execution engine may be configured to validate and execute the API calls and return results to the interface.

[0143] In certain aspects, the context management system maintains a vector database storing command templates and calculates similarity between natural language input and stored command descriptions to identify appropriate commands. In certain aspects, the orchestration agent is configured to process stored action plans including predefined sequences of network operations described in natural language. In certain aspects, the command line interface dictionary includes: vendor-specific commands for different device types, natural language descriptions of the commands' purposes, and sample command outputs for parsing results. In certain aspects, the orchestration agent is configured to maintain conversation context within a session while enforcing token limits. In certain aspects, the system is configured to schedule repeated execution of identified network management operations and display results in automatically updating dashboards. In certain aspects, the orchestration agent generates sequences of API calls for multi-step operations while maintaining dependencies between operations.

[0144] Additional embodiments of the current disclosure also provide for a method for network management. The method includes: receiving natural language input through an interface; and providing the natural language input and session context to an orchestration agent implemented using a large language model. The context includes at least one of: information about available networkPATENT Attorney Docket No. NETB-0001-WO management commands, current session state, or a command line interface dictionary including vendor-specific commands and descriptions. The method further includes identifying, by the orchestration agent, requested network management operations; generating, by the orchestration agent, text representations of API calls to perform the operations; validating and executing the API calls; and returning results to the interface.

[0145] Yet further embodiments of the current disclosure provide for a system for network management. The system includes at least one processor and a memory device. The memory device stores computer-readable instractions (e.g., an application) that, when loaded into the at least one processor, causes the at least one processor to: interpret a first user command; in response to the first user command, display a portion of a configuration file of a network computing device; interpret a second user command; in response to the second user command, select a configuration setting within the portion of the configuration file; interpret a third user command; in response to the third user command, identify one or more groups of network computing devices based at least in part on the selected configuration setting; interpret a fourth user command; in response to the fourth user command, select a common configuration setting of one of the one or more groups of network computing devices as a golden configuration; and at least one of transmit the golden configuration or store the golden configuration in a database.

[0146] These and other systems, methods, objects, features, and advantages of the present disclosure will be apparent to those skilled in the art from the following detailed description of the preferred embodiment and the drawings.

[0147] All documents mentioned herein are hereby incorporated in their entirety by reference. References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the text. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context.

[0148] For the purposes of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiments illustrated in the drawings and described in the following written specification. It is understood that no limitation to the scope of the disclosure is thereby intended. It is further understood that the present disclosure includes any alterations and modifications to the illustrated embodiments and includes further applications of the principles disclosed herein as would normally occur to one skilled in the art to which this disclosure pertains.

[0149] Accordingly, referring to Fig. 1, a system 100 for intelligent network automation is shown in the context of a computer network environment 110. The computer network environment 110 may include one or more network sites 112, 114, 116, 118, remote devices 120, and / or any other sub-PATENT Attorney Docket No. NETB-OOOl-WO computer network environments having one or more networkable computing devices (also referred to herein as “computing devices”, “network devices”, “computing network devices”, “devices”, and / or the like) that can electronically communicate with each other via a computer network (also referred to herein as simply a “network”). A computer network may include one or more local area networks (LANS) 122, 124, 126, and 128, connected via one or more wide area networks (WANS) 130, e.g., the Internet. Nonlimiting examples of computing devices include workstations 132, servers 134, routers 136, and / or firewalls 138. Further nonlimiting examples of network devices may include switches, wireless access points, network bridges, gateways, load balancers, network hubs, network controllers, virtualized network appliances, edge devices, network-attached storage (NAS) units, loT devices, and network monitoring tools. These devices may operate individually or in combination to facilitate data transmission, routing, security enforcement, traffic optimization, and connectivity within the computer network environment 110.

[0150] In embodiments, the network sites 112, 114, 116, and / or 118 may correspond to distinct physical and / or logical locations within and / or across organizations. Nonlimiting examples include multiple offices of a single business, regional branches, government departments, educational institutions, and / or data centers. In some aspects, the network sites 112, 114, 116, and / or 118 may represent networks owned by separate entities but managed by a common contractor and / or service provider, and / or collaborative environments where independent organizations share infrastructure under a unified management framework. Additionally, the network sites 112, 114, 116, and / or 118 may include cloud-hosted environments, virtual private networks (VPNs), hybrid networks combining on-premises and cloud resources, and temporary or project-based networks established for specific initiatives.

[0151] Non-limiting industry-specific examples of network sites may include healthcare networks connecting hospitals and clinics, financial institution networks linking banking branches and trading platforms, manufacturing plant networks integrating operational technology (OT) and IT systems, retail networks spanning point-of-sale systems and inventory management, and energy sector networks managing distributed power generation and smart grids. Each network site 112, 114, 116, and / or 118 may operate autonomously while maintaining secure connectivity and interoperability with other sites through the computer network environment 110.

[0152] As shown in Fig. 1, in embodiments, the system 100 for intelligent network automation may be formed from one or more computing devices which may be physical and / or virtualized. Embodiments of the system 100 may be disposed in a single network site, e.g., 118, and / or distributed across multiple network sites.PATENT Attorney Docket No. NETB-0001-WO

[0153] In embodiments, the system 100 may provide a wide range of network management functions to one or more network sites 112, 114, 116, and / or 118. Nonlimiting examples of such functions may include generating and / or maintaining accurate network topology maps, computing device configuration settings, setting and / or verifying permissions and / or network credentials, and / or automating device provisioning and / or deprovisioning. Additional functions may include monitoring and / or analyzing network traffic, detecting and / or mitigating security threats, applying and / or updating firmware and / or software patches, managing and / or optimizing bandwidth allocation, enforcing and / or auditing compliance policies, and / or orchestrating failover and / or disaster recovery procedures.

[0154] In some aspects, the system 100 may also perform centralized logging and / or event correlation, alerting and / or reporting on performance metrics, automating backup and / or restore operations, and / or integrating with third-party management platforms and / or cloud services. These functions may be executed dynamically and / or adaptively to maintain optimal network performance, security, and reliability across the computer network environment 110.

[0155] Fig. 2 depicts a non-limiting embodiment of a computing device 200 of the network environment 110 (Fig. 1) and / or the system 100 for intelligent network automation, disclosed herein. The apparatus 200 may include at least one processor 210, and at least one memory device 212 that stores computer-readable instructions 214, e.g., an application, script file, and / or the like, that, when loaded into the at least one processor 210, causes the at least one processor 210 to perform one of more of the methods and / or processes disclosed herein. Embodiments of the apparatus 200 may form part of a single computing device and / or be distributed across multiple computing devices which, in turn, may be disposed at a single site 112, 114, 116, 118, and / or 120, and / or disposed across multiple sites 112, 114, 116, 118, and / or 120. Embodiments of the apparatus 200 may also be virtualized.

[0156] Fig. 3 depicts a schematic diagram of the system 100 for intelligent network automation, in accordance with embodiments of the current disclosure. As explained in greater detail herein, embodiments of the system 100 may provide for a management application executing on a management computing device inside of a computer network. The management application may have administrative rights to one or more known network computing devices on the computer network, such as a gateway device. The management application may make remote calls, e.g., via ssh, to the gateway device to retrieve a routing table, e.g., a container database (CDB) table. The management application may parse the routing table to discover one or more additional network computing devices within the computer network. The management application may then make further remote calls to the one or more additional computing network devices to retrieve morePATENT Attorney Docket No. NETB-OOOl-WO routing tables which it may parse to discover yet further network computing devices within the network, where the foregoing process is repeated until no new network computing devices are discovered.

[0157] Accordingly, as shown in Fig. 3, embodiments of the apparatus 100 may include: an interface circuit 310, an orchestration circuit 312, a network state circuit 314, an artificial intelligence management circuit 316, an artificial intelligence interface circuit 318, a command identifier circuit 320, a vector database 322, a command database 324, a configuration database 326, a network monitoring and validation circuit 328, an execution engine 330, a network intent database 332, a diagnosis circuit 334, a remediation circuit 336, a golden engineering circuit 338, an action plan management circuit 340, an action plan database 341, a troubleshooting library database 342, a draw path circuit 344, and / or a mapping circuit 346, an IP lookup circuit 348, a neighbor lookup circuit 350, a draw device circuit 352, a draw IP circuit 354, a DNS lookup circuit 356, a device property circuit 358, an automation data table (ADT) lookup circuit 360, and / or other types of specialized circuits structured to perform the processes and / or method disclosed herein.

[0158] The IP lookup circuit 348 is structured to obtain the IP address of one or more computing devices based at least in part on one or more computing device properties. Non-limiting examples of computing device properties include: name (domain and / or device), role (e.g., firewall, switch, router, mail server, NTP server, etc.), manufacturer, location, and / or any other type of property that can be used to identify a computing device and / or group of computing devices. In one non-limiting example, the system 100 may include a One-IP Table, e.g., a data structure that serves as a single source of troth for all IP-related information across a network. In such an example, the IP lookup circuit 348 may be structured to return L3 and L2 gateway devices when provided an IP listed as an end system on the One-IP Table but does not have a configured device interface (e.g., a network interface). Further nonlimiting examples of a One-IP Table include a data structure that is dynamically generated and / or continuously updated within a network management system, configured to consolidate and / or correlate information pertaining to individual IP addresses across a multi-layered network topology. Embodiments of the One-IP Table may provide a unified repository that maps each IP address (of a network) to its associated network attributes, including but not limited to: MAC address, switch port, VLAN identifier, device interface, DNS name, vendor information, data source origin, and / or the like. The One-IP Table may be constructed through automated network discovery processes and / or parsing of device configuration and / or operational data, enabling real-time (or near real-time) resolution of IP-to-device relationships across physical, virtual, and / or cloud-based infrastructures.PATENT Attorney Docket No. NETB-0001-WO

[0159] The neighbor lookup circuit 350 is structured to identify one or more computing devices that are within a certain number of network hops (e.g., 1, 2, 3, 4, etc.) from a provided computing device. In a nonlimiting example, the neighbor lookup circuit 350 may retrieve a list of neighboring computing devices for a given device, including connected interfaces and neighbor types.Embodiments of the network lookup circuit may be used in conjunction with and / or incorporated into one or more of the other various circuits herein, e.g., the draw IP circuit 354, draw path circuit 344, and / or other circuits that perform functions based at least in part on computing devices related by network and / or physical location.

[0160] The draw device circuit 352 is structured to depict a computing device along with its various properties, e.g., interfaces, neighbors, and / or the like. For example, one or more of the mapping and / or topology features disclosed herein may utilize aspects of the draw device circuit 352 to show computing devices on a graphical user interface.

[0161] The draw IP circuit 354 is structured to identify and visually represent devices that are associated with a specific network address (e.g., an IP address) on one or more graphical user interfaces. These interfaces may include various types of network maps or topological diagrams as disclosed herein. The circuit operates by retrieving device information rendering corresponding graphical elements (e.g., nodes, icons, or labels) on the selected map or topology, thereby enabling users to visualize the spatial or logical arrangement of networked devices.

[0162] The DNS lookup circuit 356 is structured to translate hostname to IP address (and / or vice-versa) using DNS services (e.g., local and / or external).

[0163] The device property circuit 358 is structured to access and retrieve a computing device’s properties from a database (e.g., the configuration database 326). In embodiments, the device property circuit may get the properties for one or more devices (and / or visible interfaces) depicted on a map shown in a graphical user interface (e.g., embodiments of the device property circuit 358 may provide for a user to retrieve a device’s information by clicking on a representation of the device on a network map).

[0164] The ADT lookup circuit 360 is structured to translate data across / between fields inside an ADT. In embodiments, the ADT lookup circuit 360 extracts data from an ADT table to answer a user’s queries.

[0165] The draw path circuit 344 is structured to draw a map / topology of a network path between two computing devices on a graphical user interface. In embodiments, the draw path circuit 344 may show every node / hop along the path and / or may only show certain types of nodes / hops based on defined criteria (e.g., show only firewalls, show only routes, show all L2 and L3 switches insidePATENT Attorney Docket No. NETB-0001-WO external gateways, etc.). In some instances, the draw path circuit 344 may abstract or combine nodes into one simplified graphical representation.

[0166] The mapping circuit 346 is structured to facilitate mapping of a specified portion of a network (which can include the full network). For example, in a nonlimiting embodiment, the mapping circuit 346 may take in a list of computing devices and associated network routes and / or path information and draw a graphical representation (e.g., a topology map) of the computing devices and their connection paths. Embodiments of the mapping circuit 346 may use aspects of the draw path circuit 344.

[0167] Embodiments of the disclosure may also interact with an artificial intelligence model which may be provided via an artificial intelligence circuit 362. The artificial intelligence circuit 362 is depicted in Fig. 3 in dashed lines to represent that the artificial intelligence circuit 362 may be disposed apart from or incorporated into the system 100. A nonlimiting example of the artificial intelligence circuit 362 being disposed apart from the system 100 may be a scenario where the artificial intelligence model is owned and / or operated by an entity other than an entity that owns and / or operates the system 100. A nonlimiting example of the artificial intelligence circuit 362 being incorporated into the system 100 may be a scenario where the artificial intelligence model and system 100 are owned and / or operated by a same entity.

[0168] In embodiments, the artificial intelligence circuit 362 includes a neural network (e.g., a large language model (LLM)). In embodiments, the artificial intelligence circuit 362 may include any suitable model and may include multimodal models capable of processing and integrating heterogeneous data types such as natural language, structured metadata, graphical representations, and topological layouts. These models may be trained to correlate textual inputs (e.g., device identifiers, configuration data, or diagnostic logs) with visual or spatial representations of network environments, enabling more context-aware reasoning, visualization, and interaction. The interface circuit 310 is structured to provide for machine-to-human and human-to-machine communications. For example, the interface circuit 310 may be leveraged by one or more of the other circuits disclosed herein to: display (and / or provide data to be displayed on) a graphical user interface, play (and / or provide data to play) one or more sounds on an auditory device (e.g., a speaker), and / or to capture / record / convert sounds into machine-readable form (e.g., a sound capture from a microphone). In embodiments, the interface circuit 310 may provide for the user to configure one or more settings of the system 100, to include settings for the various circuits disclosed herein. It is to be understood, however, that embodiments of the circuits disclosed herein may not need configuration prior to use.PATENT Attorney Docket No. NETB-OOOl-WO

[0169] Referring to Fig. 4, the orchestration circuit 312 is structured to coordinate the actions and / or processes performed by one or more of the other circuits disclosed herein to achieve an objective, e.g., determining a golden configuration when the state and / or topology of a scoped network (which may include LAN and / or WAN components / sectors) is unknown. For example, the orchestration circuit 312 may interact with the artificial intelligence circuit 362 (e.g., via the artificial intelligence management circuit 316) to discover network devices and / or topologies and generate corresponding golden configurations. In embodiments, the orchestration circuit 312 may include a context management circuit 410, a command planning circuit 412, a command validation circuit 416, an artificial intelligence command / prompt circuit 418, and / or an automation management circuit 420.

[0170] In embodiments, the orchestration circuit 312 may delegate tasks (e.g., user queries, requests, and / or other instructions) to one or more of the other various circuits disclosed herein. For example, the orchestration circuit 312 may delegate mapping functions to the draw device circuit 352, draw IP circuit 354, and / or the like. The orchestration circuit 312 may also request additional information from users when user input is insufficient.

[0171] The orchestration circuit 312 may orchestrate / coordinate automations (as disclosed herein) by leveraging various tools and / or circuits to answer a user’s questions. As will be appreciated, the orchestration circuit 312 may provide one or more of the following functions: answer a user question based on a pre-defined flow based on an action plan (as disclosed herein); perform reasoning / analysis on results returned from one or more of the various circuits disclosed herein, to include the artificial intelligence circuit 362, and determining a next step for a particular problem, e.g., network troubleshooting; integration of results from different circuits, which may be formatted and returned to a user in Markdown format for a conversational user interface (e.g., a chat dialogue box and / or a voice communication interface); and / or tracking and / or analysis of user inputs, which may be across multiple users and / or instances of an embodiment of the system 100 (e.g., collective analysis).

[0172] The context management circuit 410 may include a context hierarchy circuit 422 and / or a context prioritization circuit 424. Embodiments of the context management circuit 410 may maintain several distinct categories of context data / information. A command context provides the artificial intelligence circuit 362 with information about available network operations, including CLI commands, their purposes, and their expected outputs. This context may include detailed command descriptions in natural language format that enable the artificial intelligence circuit 362 to match user requests with appropriate operations. For example, a command context entry might specify that "show interface errors" is used to "display error statistics for network interfaces including input errors, output errors, and other error conditions," along with sample command output demonstrating the expected format and key data fields.PATENT Attorney Docket No. NETB-0001-WO

[0173] Context data may include data structured to track the current state of network devices relevant to the user's session. This includes information about device types, their capabilities, and their current configuration state. The device context enables the artificial intelligence circuit 362 to understand which commands are applicable to specific devices and how commands should be modified based on device vendor or type. For example, when a user references a device, the orchestration circuit 312 may provide the artificial intelligence circuit 362 with context about that device's vendor, model, and supported command set.

[0174] Context data may include session context concerning the current state of the user's interaction, including any active network maps, recently executed commands, and relevant results. This enables the artificial intelligence circuit 362 to understand references to previous operations and maintain continuity across multiple related requests. For example, if a user requests information about "this interface" after examining interface statistics, the session context enables the artificial intelligence circuit 362 to understand which specific interface is being referenced.

[0175] In embodiments, the context hierarchy circuit 422 and / or context prioritization circuit 424 may implement context prioritization mechanisms (e.g., to manage the total amount of context provided to the language model while staying within token limits).

[0176] The context hierarchy circuit 422 may also maintain a context hierarchy that enables efficient context updates and modifications. For example, when new commands and / or capabilities are added to the system 100, they can be integrated into the appropriate context categories without requiring changes to other parts of the system 100.

[0177] The automation management circuit 420 may be structured to execute pre-defined automation tasks, optionally with unique descriptions. A user may run an automation task on demand and / or schedule the task by specifying one or more preference in an input to the system 100. In embodiments, the automation management circuit 420 may use task results as a data source for generating a review dashboard on a graphical user interface.

[0178] Referring to Fig. 5, the network state circuit 314 is structured to obtain the current state of a network and / or its corresponding computing devices. For example, the network state circuit 314 may include an exploration circuit 510, a polling circuit 518, a digital twin management circuit 520 that accesses a digital twin database 522, and / or a mapping circuit 524.

[0179] The digital twin management circuit 520 may access digital twins of the computing devices of a network stored in the digital twin database 522. In embodiments, the digital twin management circuit 520 may pull configuration files from the one or more network computing devices and generate digital twins, which may be stored in the database 522 for future reference. Digital twins may include the configuration settings for a network computing device and / or data capturing thePATENT Attorney Docket No. NETB-0001-WO local topology of the network computing device, e.g., which of its interfaces are connected to which other network computing devices.

[0180] As illustrated in Fig. 6 embodiments of the current disclosure may provide for the ability to map a network and / or to generate a visual and / or text description of the network map 600. The map 600 may show one or more nodes 610 (e.g., computing devices). The map 600 may be generated via the draw IP circuit 354, draw device circuit 352, mapping circuit 524, and / or any other circuit disclosed herein as providing mapping capabilities, which may utilize an application programming interface (API) dictionary 710 and / or a command line interface (CLI) dictionary 712 (Fig. 7) provided by the command database 324. Maps may include arrow and / or other symbols to show directional communications. Boxes and / or other container type object may be used to show related devices (e.g., golden features). In embodiments, a map may show one or more paths corresponding to one or more network services provided by the network and / or its computing devices.

[0181] Referring back to Fig. 3, the artificial intelligence management circuit 316 is structured to manage one or more parameters governing interactions with the artificial intelligence circuit 362. For example, in embodiments, the artificial intelligence management circuit 316 may provide for a user to specify an input type (e.g., comma-separated values (CSV) file, image file, xml file, and / or the like) for the artificial intelligence circuit 362 to improve the artificial intelligent circuit’s 362 ability to reason and / or respond more precisely to prompts from a user, the orchestration circuit 312, and / or any other circuit disclosed herein. For example, if a user knows what action and / or objective they want to do and / or achieve, and / or what automation they want to execute, the artificial intelligence management circuit may provide for the user (or circuit) to tag prompts to the artificial intelligence circuit 362 with a type. Embodiments of the artificial intelligence management circuit 316 may provide for a graphical user interface that provides a rich text feature for a user to enter a multi-step prompt and / or to provide for better organization of complex user input to the system 100.

[0182] Referring to Fig. 8, the artificial intelligence interface circuit 318 is structured to facilitate electronic communications between the artificial intelligence circuit / model 362 (Fig. 3) and one or more of the other circuits disclosed herein. For example, the artificial intelligence interface circuit 318 may include a text processing circuit 810 structured to convert and / or condition data received from the orchestration circuit 312 into a form understood and / or processable by the artificial intelligence circuit 362 or vice-versa. In embodiments, the text processing circuit 810 may convert text received via the interface circuit 310 into a form understood and / or processable by the artificial intelligence circuit 362 and / or vice-versa. The artificial intelligence interface circuit 318 may include a voice processing circuit 812 structured to convert data received from the artificial intelligence circuit 362 and / or the orchestration circuit 312 into sounds understood by a human user.PATENT Attorney Docket No. NETB-0001-WO

[0183] In embodiments, the artificial intelligence interface circuit 318 may provide a conversational interface (e.g., a chat dialogue box) for a user to ask natural language questions of the artificial intelligence circuit. As will be appreciated, in embodiments, conversational interface may use one or more features on the interface circuit 310, text processing circuit 810, voice processing circuit 812, orchestration circuit 312, and / or any other circuit disclosed herein. For example, a user's text input (a question and / or command) may enter the system 100 via the interface circuit 310, be sent to the text processing circuit 810, then passed to the orchestration circuit 312. The orchestration circuit 312 may then coordinate one or more of the other various circuits disclosed herein to interact with the artificial intelligence circuit 362 via prompting the artificial intelligence interface circuit 362 to respond to the user’s text input or in some cases, input in other modalities such as images, network topology descriptions, graphs, etc. The response(s) from the artificial intelligence circuit 362 to the prompts may be received by the artificial intelligence interface circuit 318, passed to the orchestration circuit 312 for processing, where results of the orchestration circuit’s processing 312 may be transmitted to the user via the interface circuit 310.

[0184] Fig. 9 depicts a functional block diagram of the features of an embodiment of the system 100 provided by, at least in part, one or more of the interface circuit 310, artificial intelligence management circuit 316, artificial intelligence interface circuit 318, artificial intelligence circuit 362, and / or orchestration circuit 312. It is to be appreciated that one or more of the features depicted in Fig. 9 may also be provided by one or more of the other circuits disclosed herein, e.g., the mapping circuit 524, polling circuit 518, draw device circuit 352, etc.

[0185] As shown in Fig. 9, the example system 100 provides for user input 910, searching / querying 912, an automation orchestration agent 14 (also referred to herein as “orchestration agent’’), an executor 916, other tools 918, a command finder 920, and an artificial intelligence model 922. The user input 910 may take the form of natural language text and / or voice questions and / or comments. The user input 910 may be acquired / obtained via a graphical user interface, which may have one or more input prompts. Searching 912 may provide for the system to identify computing devices relevant to the user input and / or to a task performed by the orchestration agent 914 and / or artificial intelligence model 922. The executor 916 may provide for the execution of one or more commands, which may be directly invoked by the user input, the artificial intelligence model 922, and / or the orchestration agent 914. The commands may be executed against a network and / or against one or more computing devices on the network. The other tools 918 may include mapping functionality, DNS resolution, device property retrieval, automation generation guides, software version updating and tacking tools, and / or the like. The command finder 920 may provide for the identification and presentation of available network and computing device commands. ThePATENT Attorney Docket No. NETB-0001-WO artificial intelligence model 912 provides for deep learning and / or processing of prompts generated by the user input 900 and / or the orchestration agent 914. The orchestration agent 914 may coordinate the user’s interaction with the other depicted functional blocks, e.g., searching 912, the executor 916, the other tools 918, the command finder 920, and / or the artificial intelligence model 922. In embodiments, the orchestration agent 914 may form part of and / or be embodied by the orchestration circuit 312; and / or the artificial intelligence model 922 may fonn part of and / or be embodied by the artificial intelligence circuit 362. In embodiments, the orchestration circuit 312 implements prompt engineering techniques that guide the artificial intelligence circuit 362 in processing network management requests. These prompts provide the model with structured frameworks for breaking down complex operations, selecting appropriate tools, and formatting responses. These prompts are designed to work with any artificial intelligence model 922 that meets basic capability requirements, allowing the system 100 to adapt to new or improved models as they become available.

[0186] As will be appreciated, embodiments of the current disclosure may provide for an improved human-machine interface for querying the status of one or more network computing devices and / or for troubleshooting network outages. For example, embodiments of the orchestration circuit 312 may provide for users to ask questions in natural language for intuitive problem resolution. The orchestration circuit 312 can orchestrate automations, chaining different actions together through reasoning. Embodiments may also return diagnosis results in natural language and / or other preferred formats, such as a table or dashboard.

[0187] Embodiments of the system 100 provide a natural language interface for executing network operations through an Al-powered chatbot interface (e.g., artificial intelligence interface circuit 318, orchestration circuit 312, artificial intelligence circuit 362, etc.). Embodiments of the natural language interface may provide for a user to have a natural language conversation, issuing interactive commands to the model.

[0188] In operation, according to an embodiment of the current disclosure, when a user submits a natural language request, the system 100 enriches (e.g., appends text, expands parts, injects additional info, etc.) the request with relevant context (e.g., context data) before sending it to the artificial intelligence model 922. This context may include information about available network management tools, current network state, device information, and operational constraints. For example, when processing a request to "show interface errors," the system 100 may provide the artificial intelligence model 922 with context about the current network map, available CLI commands, and output parsing capabilities, enabling the model to formulate an appropriate response despite having no specific network management training.PATENT Attorney Docket No. NETB-0001-WO

[0189] Thus, and as will be appreciated, embodiments of the interface enable network operators to perform complex network tasks using conversational language rather than requiring knowledge of specific command syntax or system interfaces.

[0190] The interface may support progressive disclosure of information, allowing users to start with high-level queries and drill down into specific details through natural conversation. For instance, after displaying interface statistics, users can ask follow-up questions about specific metrics, request historical comparisons, or initiate automated monitoring of identified issues. The system 100 may also maintain appropriate context (e.g., context data) throughout these interactions while enforcing security policies and access controls.

[0191] Results may be presented in multiple formats depending on the nature of the query and the type of data being displayed. As explained in greater detail herein, the system 100 may generate tabular displays of command outputs, visual network maps, comparison dashboards, and natural language summaries of findings. Users can interact with these displays through continued natural language conversation, requesting additional details or initiating related operations.

[0192] Accordingly, Fig. 10 shows an embodiment in which the orchestration agent 914 presents as a conversational chatbot via a conversational user interface 1000 with a text input box 1010 and conversational stream window / panel / dialogue box 1012. The chatbot interface processes and executes multi-step network operations through natural conversation. For example, when a user requests to "draw a device with IP address 192.168.1.1 and show its neighbors," the system interprets this as a series of operations: first locating the device, then creating a network map visualization, and finally expanding the map to show connected devices. Embodiments of the system 100 may retain this context throughout a session, enabling follow-up queries about the displayed devices without requiring explicit reference to previously established context.

[0193] Non-limiting examples of user input to the chatbot interface include: “show source data on map”, which may cause the orchestration agent 914 to display CLI commands and / or parser results as a data view and / or the status code of a network intent result on the map; “dashboard”, which may cause the orchestration agent 914 to show and / or provide access to a map dashboard; “hamburger menu”, which renders the user interface in hamburger menu form (e.g., a type of GUI structured for user on a mobile device that has sliding panels that selectively show and hide various menu items and / or objects); “reset bot session”, which may clear the current chat session with the orchestration agent 914; “manage saved input”, which may provide for a user to save their input(s) in a private or shared location (e.g., a folder 1110) (as shown in Fig. 11). The chatbot interface, as disclosed herein, may support various categories of network operations, including but not limited to: topology mapping operations, where users can request network visualizations using natural languagePATENT Attorney Docket No. NETB-0001-WO descriptions; data retrieval operations, where users can request specific information about network devices or configurations; comparison operations, where users can compare current network state against baseline configurations; and automated assessment operations, where users can initiate predefined network analysis procedures, and the like.

[0194] In operation, when a user inputs a natural language query (e.g., a natural language command), the system passes the query to an orchestration agent. The orchestration agent may be implemented using a large language model. This agent analyzes the query along with relevant context. In embodiments, the content may include the current network map state, device information, conversation history, and the like. The agent then determines the appropriate sequence of operations needed to fulfill the request. For example, if a user asks "show me interface errors on Device A and compare with baseline," the system recognizes this as a command execution operation followed by a comparison operation.

[0195] As illustrated in Fig. 12, embodiments of the orchestration agent 914 may provide graphical user interface(s) 1200 and 1210 for management of network intents (disclosed in greater detail elsewhere herein) and / or templates 1212 thereof. In embodiments, a user’s query may be handled using a network intent template, wherein a seed network intent will be replicated based on an input computing device, a macro variable, and / or a task variable. The replicated network intent may then retrieve data by accessing the input device and return a status code after execution. In embodiments, the generation and / or application of network intents may be automated. In embodiments, a user must generate and / or add a network intent (or template) to the network intent database 332 for the orchestration agent 914 to have access to the network intent (or template). In embodiments, a user may need to specify, via the artificial intelligence management circuit 316, which network intents and / or templates the orchestration agent 914 has access to.

[0196] Embodiments of the current disclosure may also provide an interface (e.g., GUI 1300 in Fig. 13) for a user to define a description 1310 of a network intent (NIT) for the artificial intelligence circuit 362 to understand the NIT’s purposes and / or category.

[0197] Embodiments of the current disclosure may also provide an interface (e.g., GUI 1400 in Fig. 14) for a user to define a description for input variables 1410 (macro variable or / and task variable), an output sample 1412 for artificial intelligence circuit 362 to understand its purpose, and / or prompt for IntelliSense.

[0198] As shown in Fig. 15, the artificial intelligence management circuit 316 may provide for an interface (e.g., 1500 in Fig. 15) to an ADT 1510 for a user to specify an intent column 1512 (listing network intents) as an automation tool. In such embodiments, when a user’squestion / query / command can be addressed by an intent column, the orchestration agent 914 willPATENT Attorney Docket No. NETB-0001-WQ search the intent column for an appropriate network intent. Embodiments of the system may also provide an interface (e.g., 1600 in Fig. 16) for defining descriptions 1610 of an ADT and the intent column 1612 for the artificial intelligence circuit 362 to understand their purposes and, optionally, select a category to store the ADT. An interface (e.g., GUI 1700 in Fig. 17) may provide for a user to define description(s) for input variables 1710, an output sample 1712 for the artificial intelligence circuit 362 to understand its purpose, and / or prompt for IntelliSense.

[0199] Referring back to Fig. 3, the command identifier circuit 320 may be structured to identify one or more commands available for execution on a network and / or on one or more computing devices on a network. As such the command identifier 320 may access (e.g., query) the command database 324 which may include the API dictionary 710 (Fig. 7) and / or the CEI dictionary 712 (Fig.7) and / or the vector database 322.

[0200] The API dictionary 710 maintains a library of API templates (and / or other commands) that the orchestration circuit 312 and / or artificial intelligence circuit 362 can reference when generating call representations. These templates may define required parameters and format(s) for different types of network and / or device operations. The artificial intelligence circuit 362 can use these templates, combined with an understanding of the user's request (e.g., via provided context data), to generate appropriate API call (and / or other command) representations. The templates may specify the required fields for each type of operation, including operation type, target devices, commands to execute, parsing requirements, and any additional parameters needed for the operation.

[0201] For multi-step operations, the orchestration circuit 312 and / or the artificial intelligence circuit 362 can generate sequences of API calls (and / or other commands) that work together to accomplish the desired task. Each call in the sequence may include the necessary parameters and dependencies from previous operations. Embodiments of the system 100 may track these dependencies to ensure proper execution order and / or data flow between operations. For example, when comparing current data with baseline data, the system 100 may generate separate calls for retrieving each dataset and a subsequent call for performing the comparison, with appropriate references to the results of previous operations.

[0202] The CEI dictionary 712 implements a specialized dictionary architecture for managing CEI operations across multiple network device vendors. This dictionary system may interact with and / or include the vector database 322 (Fig. 3) which stores vendor-specific command templates, command descriptions, and expected output formats. When processing user requests, the system 100 can leverage CEI dictionary 712 to determine and / or execute the appropriate vendor-specific commands.

[0203] Embodiments of the CLI dictionary 712 maintain entries for each device type, where each entry includes the command syntax, a natural language description of the command's purpose, andPATENT Attorney Docket No. NETB-OOOl-WO sample command output data. This structure enables the orchestration circuit 312 and / or artificial intelligence circuit 362 to match natural language queries to appropriate vendor-specific commands. For example, when a user requests information about Border Gateway Protocol (BGP) neighbors, the orchestration circuit 312 and / or artificial intelligence circuit 362 may consult the CLI dictionary 712 to identify the correct command syntax for each vendor's implementation (e.g., using different commands for Cisco devices versus Palo Alto firewalls or other network devices).

[0204] Fig. 18 depicts a non-limiting example of the CLI dictionary 712 shown in a GUI 1800. The CLI dictionary 712 may be dynamically searchable for a related CLI command based on a user’s input. CLI commands may be sorted by device type in the CLI dictionary 712. To answer a query regarding computing devices made by different vendors, embodiments of the system 100 may issue, select, and / or execute appropriate commands for / to each device based on the CLI dictionary 712, which, in turn, may ensure the correct commands are issued to each vendor’ s device. Embodiments of the system 100 may provide for users to add CLI commands and / or parsers into the CLI dictionary 712. In certain aspects, during a system discovery task, the system 100 pre -defines CLI commands and generates key fields for major vendors in the CLI dictionary 712. The CLI dictionary can be updated via one or more servers external to the system 100.

[0205] In a nonlimiting scenario, the artificial intelligence circuit 362 may detect that a user wants to analyze a section of a config file (for a computing device), the command identifier circuit 320 searches a configuration parser in the config dictionary 712. If no configuration parser is applied in the dictionary 712, the orchestration agent 914 will retrieve raw data (e.g., raw configuration file data). If a configuration parser is found, the artificial intelligence circuit 362 will parse the value and retrieve the raw data.

[0206] Embodiments of the current disclosure may employ a similarity matching algorithm to calculate the relationship between user requests and command descriptions stored in the command database 324. For example, when a user submits a query, the artificial intelligence circuit 362 may analyze the request and match it against the command descriptions in the vector database 322. The orchestration circuit 312 and / or the artificial intelligence circuit 362 may then select the most appropriate command based on the device type and the nature of the request. For example, if a user asks to "show routing table" for a specific device, the orchestration circuit 312 and / or the artificial intelligence circuit 362 identifies the device type and retrieves the corresponding vendor-specific command from the dictionary appropriate dictionary / database.

[0207] To facilitate accurate command selection, embodiments of the system 100 may maintain sample output data for each command. This sample data serves multiple purposes: 1) it helps the system understand the expected output format, 2) enables the extraction of relevant information fromPATENT Attorney Docket No. NETB-OOOl-WO command results, and 3) assists in generating field definitions for parsing command outputs. The artificial intelligence circuit 362 may process the sample data to automatically generate these field definitions, eliminating the need for manual parsing rule creation.

[0208] When new device types and / or commands need to be supported, the dictionaries and / or databases, disclosed herein, can be expanded by adding new entries with appropriate command syntax, descriptions, and sample outputs. Embodiments of the system 100 may then automatically incorporate these new entries into the orchestration circuit’s 312 and / or the artificial intelligence circuit’s 362 command selection and / or parsing processes without requiring modifications to the core system architecture.

[0209] Through this dictionary -based approach, embodiments of the system 100 provide consistent functionality across diverse network environments while accommodating the specific command syntax and output formats of different vendor implementations. This enables users to interact with multi-vendor networks using natural language queries without needing to know the specific command syntax for each vendor's devices.

[0210] Configuration parsers may be sorted by device type in the configuration database 326 (Fig.3). A configuration parser may have a corresponding description that can be matched against a user’s input. As will be appreciated, embodiments of the current disclosure provide for a user to add, remove, and / or modify configuration parsers from the configuration database 326.

[0211] The vector database 322 may include command templates, descriptions, and expected outputs for one or more network and / or device commands and may serve as an external knowledge base for the artificial intelligence model 922. In embodiments, the vector database 322 may include and / or otherwise be integrated with the command database 324. Rather than encoding this information into the artificial intelligence model 922 itself, the embodiments of the system 100 provide data from the vector database 322 as context during query processing I prompting. As will be appreciated, this approach allows embodiments of the system 100 to update network management capabilities (e.g., such as adding support for new vendor commands and / or management functions) without requiring any changes to the underlying artificial intelligence model 922.

[0212] The configuration database 326 may store configuration data for one or more computing devices. Non-limiting examples of configuration data include XML files, Linux-style configuration files, proprietary configuration file formats, comma-separated values files, database records, and / or any other suitable data objects for storing configuration data. Non-limiting examples of configuration data include: network interface settings, DNS settings, user interface parameters, application and / or role specific parameters (e.g., firewall rules, routing tables, etc.), and / or any otherPATENT Attorney Docket No. NETB-0001-WO type of setting for an application that is typically not set programmatically at compile-time (e.g., variables that are read-in during an application’s loading and / or during execution).

[0213] The network monitoring and validation circuit 328 is structured to provide for one or more features for monitoring for device configuration changes, and / or for verifying device configuration settings. For example, embodiments of the network monitoring and validation circuit 328 may provide for the application of network intents that validate a golden feature against a golden configuration, as disclosed in greater detail elsewhere herein. A verification and / or validation of a golden intent, golden configuration rule, a golden configuration, and / or a device may involve retrieving a device’s current configuration and comparing it to a golden configuration. As will be appreciated, this concept can be expanded to golden features where the one or more devices in the golden feature can be compared to one or more golden rules.

[0214] In embodiments, the network monitoring and validation circuit 328 may also implement / perform validation checks on generated API call representations before execution, including syntax validation to ensure proper formatting, parameter validation to ensure all required fields are present, permission validation to ensure the requested operations are allowed, and resource validation to ensure the operations can be executed.

[0215] The execution engine 330 is structured to execute one or more command values (e.g., network command and / or computing device commands), such as those stored in the command database 324. In embodiments, the execution engine 330 may be directed by the orchestration circuit 312 (e.g., the orchestration circuit 312 may pass a list of commands for the execution engine 330 to execute against one or more computing devices and / or a network, and / or the execution engine 330 may pass the results of the one or more commands back to the orchestration circuit 312). The execution engine 330 may execute the commands in order of priority (e.g., the commands may be associated with a priority level).

[0216] The network intent database 332 stores network intents (and / or templates thereof), as disclosed in greater detail elsewhere herein. Network intents may represent predefined automated procedures that perform specific network management functions, such as monitoring interface errors or assessing security compliance.

[0217] The diagnosis circuit 334 may be structured to receive network context data (e.g., device states, configurations, command results) and determine one or more possible (and / or actual) computing device and / or network issues. In embodiments, the diagnosis circuit 334 may be directed by the orchestration circuit 312, and / or interact with the artificial intelligence model 922 (and / or the artificial intelligence circuit 362) via the artificial intelligence interface circuit 318.PATENT Attorney Docket No. NETB-0001-WO

[0218] Referring to Figs. 19 and 20, the golden engineering circuit 338 is structured to facilitate the defining, generation, identification, management of golden configurations 2010, golden configuration templates 2012, golden features 2014, and / or golden intents 2016. Embodiments of the golden engineering circuit 338 may include a golden configuration management circuit 1910, a configuration parsing circuit 1912, a golden configuration browsing circuit 1914, a golden feature management circuit 1916, and / or a golden intent management circuit 1918.

[0219] Non-limiting examples of golden configurations 2010, as disclosed herein, include data objects that include / group one or more golden parameters 2020 (e.g., device and / or network configuration settings common and / or shared between two or more network computing devices, e.g., routers, switches, firewalls, and / or the like). In other words, a golden configuration 2010 may represent a common configuration between two or more computing devices. A golden configuration 2010 may be common between similar devices (e.g., a routing table for a group of backbone routers made by the same provider and / or running the same operating system, etc.), and / or common between disparate network computing devices (e.g., a default TCP / IP gateway and / or DNS server common between a switch and a mail exchanger, etc.). In embodiments, there may be a level of tolerance in variation in the commonalities of configuration files that make up a golden configuration. The level of tolerance may be defined by a user. In embodiments, golden parameters may provide for the level of tolerance (e.g. variance) within a golden configuration, e.g., a range of acceptable NTP servers.

[0220] In embodiments, tolerance may be quantitatively and / or qualitatively measured per golden parameter 2020 and / or evaluated against candidate device configurations using one or more comparison schemes. By way of nonlimiting examples: (i) range / interval thresholds may be specified for numeric fields (e.g., CPU utilization < 80%, NTP time offset within ±200 ms, MTU 6 [1500, 9000]); (ii) relative / percentage variance may be applied where absolute values differ across devices (e.g., interface queue depth within ±10% of golden value); (iii) enumerations and allow-lists may define acceptable discrete values (e.g., permitted DNS servers 6 { 10.0.0.10, 10.0.0.11 }); (iv) pattern / format constraints (e.g., regular expressions for hostname schema, certificate patterns) may allow controlled variability while enforcing structure; (v) set similarity metrics be used for unordered collections such as access-lists, BGP neighbor sets, or enabled services; (vi) string distance (e.g., Hamming distance < K) may be used for near-matching textual fields; (vii) schema-level validation may enforce presence / absence, type, and dependency constraints (e.g., if feature X enabled, parameter Y must be set within [a, b]); and (x) weighted scoring may combine multiple perparameter tolerances into a composite compliance score, where each golden parameter 2020 includes a weight and tolerance descriptor (e.g., {type: range, min:..., max:..., weight:...}), and compliance is satisfied when the weighted score exceeds a threshold (e.g., > 0.95).PATENT Attorney Docket No. NETB-0001-WO

[0221] In further embodiments, tolerance may be derived using statistical baselines computed over a cohort of devices (e.g., golden value = median; tolerance = ± one interquartile range or ± two standard deviations), optionally stratified by device class, version, role, etc. Evaluation may be performed by a compliance engine that (a) normalizes candidate configurations, (b) aligns fields to corresponding golden parameters 2020, (c) applies the specified tolerance functions, and (d) emits a per-parameter pass / fail and / or deviation score, along with remediation guidance.

[0222] As is to be understood, ensuring that network computing devices adhere to a golden configuration 2010 reduces the likelihood of network and / or device errors. For example, in embodiments, a golden configuration 2010 may not be a pre-defined / documented configuration but rather a capture of the running configurations of several devices, where the running configuration has an acceptable level of network functionality. As will be appreciated, identifying the golden configuration 2010 of an operational network with no prior documentation lowers the risk of causing a network issue (e.g., an outage and / or reduction in service) when modifying the network (e.g., adding, removing, and / or updating network computing devices). For example, ensuring that a new router conforms to a golden configuration prior to becoming operational on the network increases the odds that the router can be added without causing a network outage as the golden configuration is known to be safe (or relatively safe) on the existing routers in the network.

[0223] A non-limiting example of a golden configuration template 2012, as disclosed herein, includes a data object that serves as a basis for making a golden configuration 2010. As such, embodiments of the current disclosure may discuss the generation, modification, and / or use of golden configurations 2010 in the context of golden configuration templates 2012. Thus, the features of golden configuration templates 2012 disclosed herein may, in embodiments, be equally applicable to golden configurations 2010, or vice-versa

[0224] The golden engineering circuit 338 is structured to facilitate defining, discovering, managing, and / or verifying a network’s design and / or topology via golden configurations, golden configuration templates, golden features, and / or golden intents - in other words, the golden engineering circuit 338 provides for the discovery and management of a network’s configuration. Thus, embodiments of the golden engineering circuit 338 ensure optimal network operations and enhance security.

[0225] The golden configuration management circuit 1910 may provide for a graphical user interface that facilitates maintaining a consistent network design and prevents / mitigates configuration drifts (e.g., the tendency of device configurations to change over time), which, in turn, avoids / mitigates security vulnerabilities, compliance issues, and / or outages across the network. As explained in greater detail elsewhere herein, embodiments of the golden engineering managementPATENT Attorney Docket No. NETB-OOOl-WO circuit 1910 provide for the discovery, identification, and / or defining of golden configurations 2010 via a reverse engineering process and / or forward engineering process.

[0226] Golden parameters 2020, as disclosed herein, include device and / or network configuration settings common and / or shared between two or more network computing devices (e.g., routers, switches, firewalls, and / or the like). Golden parameters 2020 may be common between similar devices (e.g., a routing table for a group of backbone routers made by the same provider and / or running the same operating system, etc.), and / or common between disparate network computing devices (e.g., a default TCP / IP gateway and / or DNS server common between a switch and a mail exchanger, etc.).

[0227] Non-limiting examples of golden parameters 2020 include: network settings: (e.g., IPv4 and IPv6 address settings, subnet mask configuration, default gateway settings, DNS server settings, DHCP lease parameters, VLAN identifiers, static route configurations, firewall rule definitions, NAT mappings, VPN tunnel configurations, proxy server settings, etc.); wireless settings: (e.g., SSID configuration, encryption protocol selection, channel assignment, frequency band settings, transmit power levels, beamforming options, MU-MIMO configuration, roaming aggressiveness settings, MAC address filtering policies, hidden SSID settings, etc.): device performance settings: (e.g., CPU clock speed settings, GPU frequency adjustments, memory allocation parameters, power-saving mode configurations, thermal throttling thresholds, fan speed profiles, battery optimization settings, sleep and hibernate timers, etc.); security settings: (e.g., authentication method selection, encryption key management, access control list configurations, intrusion detection and prevention settings, certificate management policies, secure boot settings, password complexity requirements, biometric authentication options, etc.); application and service settings: (e.g., API endpoint configurations, request timeout parameters, retry interval settings, logging level definitions, caching parameters, load balancing algorithms, session persistence settings, compression options, etc.); Quality of Service (QoS) settings: (e.g., traffic prioritization rules, bandwidth reservation parameters, latency thresholds, jitter control settings, packet loss tolerance levels, traffic shaping policies, policing configurations, etc.); protocol settings: (e.g., TCP window size parameters, UDP buffer size settings, MTU configurations, keep-alive interval settings, retransmission timeout parameters, congestion control algorithm selection, SSL / TLS version settings, HTTP protocol enablement options, etc.); storage settings: (e.g., RAID level configurations, disk partitioning schemes, file system type selection, block size settings, write caching options, snapshot scheduling parameters, replication settings, encryption-at-rest configurations, etc.); display and interface settings: (e.g., screen resolution settings, refresh rate configurations, color depth parameters, brightness and contrast adjustments, UI scaling options, accessibility feature settings, etc.); update and maintenance settings:PATENT Attorney Docket No. NETB-0001-WO (e.g., firmware update schedules, patch management policies, rollback options, auto-update enablement, backup frequency settings, restore point configurations, etc.); and / or any other type of device and / or network setting.

[0228] Fig. 21 shows a GUI 2100 for managing golden parameters 2020, in accordance with embodiments of the current disclosure. In embodiments, a user may use the GUI 2100 to define one or more golden parameters 2020 via base parameters 2112, configuration parameters 2111, and / or golden configuration templates 2012. A base parameter 2112, as used herein, refers to a variable whose value is conditionally assigned based on a device grouping, typically basic essential parameters, and / or usually used for key values and device essential attributes, such as region, contact, etc. Use of base parameters may improve the universality I applicability of a golden configuration 2010 (e.g., a golden configuration template based on base parameters may fit a wider variety of devices and / or network scenarios). A configuration parameter, as used herein, refers to a device and / or network parameter that is typically used for configlet standards.

[0229] GUI 2100 may provide for a golden parameter 2020 to be defined manually by entering one or more values and / or conditions for each value (e.g., whether a target device belongs to a device group). The golden parameter 2020 may then be incorporated into a golden configuration template 2012.

[0230] GUI 2100 may provide for the discovery, identification, and / or generation of golden parameters 2020 via a reverse engineering process. The reverse engineering process may help to discover the different values of a golden parameter 2020, where a user can view and select the different values for inclusion in a golden parameter 2020. Such embodiments may utilize an IP helper function. Non-limiting examples of an IP helper include a software -implemented functions and / or module configured to facilitate operations involving IP addressing within a network management and / or automation system. In some embodiments, an IP helper is operable to perform one or more tasks including, but not limited to, converting IP addresses between different formats (e.g., numeric and string representations), resolving IP addresses to corresponding hostnames, determining subnet information based on an IP address, mapping IP addresses to associated network interfaces, and validating IP address configurations against predefined policies. An IP helper may further support operations such as calculating broadcast addresses, identifying overlapping subnets, and generating IP ranges for allocation or scanning. In certain aspects, an IP helper may be invoked by automation workflows, scripts, or intent-based modules to streamline network troubleshooting, configuration validation, and compliance enforcement. Non-limiting examples of IP helper functionality include IP-to-hostname resolution, subnet mask derivation, IP range generation, interface association, and / or the like.PATENT Attorney Docket No. NETB-0001-WO

[0231] In embodiments, the IP Helper address can vary depending on the region (e.g., if the device group is “AMERICA region”, the IP Helper address may be set to 172.16.131.2; if the device group is “EMEA region”, the IP Helper address may be set to 172.16.101.2; etc.). As will be appreciated, this base parameter may be useful in scenarios where conditional value assignments are required when building golden configuration templates 2012.

[0232] Fig. 22 shows an embodiment of the GUI 2100 where configuration parameters 2111 are used to define, identify, generate, and / or discover golden parameters 2020. The GUI 2100 may provide for input variables to be entered / created where the value can be passed from a parser variable when creating golden config templates 2012. The input variable can be used in value definition and / or when defining a golden parameter 2020 via criteria condition(s).

[0233] As shown in Fig. 23, GUI 2100 may provide for a condition 2310 to be defined using a device group 2312 and criteria 2314 (which may be defined using base parameter / input variable(s)).

[0234] Fig. 24 illustrates a GUI 2400 (which may be generated by the golden feature management circuit 1916) for generating, defining, and / or identifying golden features 2014 for a network. Nonlimiting examples of golden features 2014, as disclosed herein, include data objects / constructs that model design assets (often critical ones) for a network, and may be used as the base for building an intent automation (e.g., a golden intent). For example, in embodiments, a golden feature 2014 example of may include one or more computing devices 2022.

[0235] The GUI 2400 may provide for a user to define a set of eigen variable-based golden features 2014 according to the network technologies operating within their network, e.g., BGP, HSRP, multicasting, etc. Such golden features may categorize the network's devices based on various technical configuration characteristics and / or generate calculated feature instances to be used by a golden intent 2016. For example, in embodiments, eigen variables can be used to identify a feature instance (including a golden feature instance). Eigen variables, as used herein, include variables and / or network configuration parameters that can be used to group network computing devices together, e.g., pairs of failover devices. Nonlimiting examples of eigen variables include: protocol-specific identifiers (e.g., a group identifier; a virtual internet protocol address, etc.); device attributes (e.g., a host name; a device type; a location, etc.); interface-level attributes (e.g., a local interface name; a neighbor interface name; an interface internet protocol address, etc.); a neighbor relationship attribute (e.g., a neighbor device name; a neighbor device internet protocol address; a layer 2 neighbor; or a layer 3 neighbor, etc.); configuration parameters (e.g., a routing protocol setting; a network time protocol server address; a simple network management protocol community string, any golden parameter disclosed herein, etc.); system data variables (e.g., a region; a role, etc.); and / or the like.PATENT Attorney Docket No. NETB-0001-WO

[0236] The golden feature management circuit 1916, via one or more GUIs (e.g., GUI 2400) may provide for managing and maintaining golden features 2014 (e.g., defining, executing and / or utilizing, debugging, and publishing of golden features 2014). In a nonlimiting scenario demonstrating the creation, management, and use of a golden feature 2014, the golden management feature circuit 1916 retrieves all values of a defined eigen variable and combines them to form an “eigen value”. The golden feature management circuit 2016 then groups computing devices by their eigen values so that devices with the same eigen value are assigned to the same group (eigen group), which creates one feature instance. As will be understood, one device may have several eigen values, which means one device may belong to several feature instances. For example, one device can have multiple HSRP groups, which can be identified by Group_ID and Virtual_IP as an HRSP feature instance.

[0237] Moving to Fig. 25, after creating a new golden feature, the GUI 2400 may show one or more show nodes 2501 for managing the feature (e.g., “This Feature” 2510, “Defined Eigen” 2512, “Calculate Feature Instance” 2514, and “Define Role” 2516). The GUI 2400 may provide a folder tree structure 2518 to manage all golden features 2014 available to the system 100. The tree structure 2518 may provide for operations such as adding, deleting, modifying, and / or querying available golden features 2014. In embodiments, all available golden features 2014 may be shared (e.g., private golden features 2014 may be prohibited).

[0238] Continuing with the scenario, and turning to Fig. 26, “This Feature” 2510 provides for the creation of a golden feature by specifying a name 2610 and description 2612 and selecting a one or more target devices 2614. “This Feature” 2510 may also provide for viewing associated golden intents 2616 (2016 in Fig. 20) and / or operation history 2618. For the one or more target devices 2614, the GUI 2400 may provide for the selection of a device group and / or all network options from a dropdown, where the option corresponds to the network feature intended to be decoded (e.g., all networks should be selected for network features configured on all devices (e.g., SNMP, Password, Telnet / SSH, etc.); whereas a device group (e.g., HRSP device) should be selected where a network feature is configured on a specific subset of devices). As will be understood, use of a device group often provides easier maintenance and / or better performance. The GUI 2400 may also prevent multiple users from editing a feature simultaneously. As such, the system 100 may enforce controls and provide prompts based on existing user rights (e.g., read, write, edit, etc.) for specific resources.

[0239] Moving to Fig. 27, “Define Eigen” 2512 provides for the defining of eigen variables (as disclosed herein) variables used for eigen calculations and / or other conditions. Each eigen value may create one (1) feature instance, and each feature instance may be assigned to one or more relatedPATENT Attorney Docket No. NETB-0001-WQ devices. Eigen variables may be added via use of: 1) parser variables 2710 from a CLI and / or saved configuration; and / or 2) system data (e.g., a distributed graph repository (GDR)).

[0240] As shown in Fig. 28, when adding eigen variables via parser variables 2710, a user can select a device, enter a CLI command and / or select a configuration 2810, and then retrieve the corresponding device property data. After retrieving the device property data, the user can choose a matched parser from the dropdown menu 2812 or create a new parser. Referring to Fig. 29, users can have a text view 2910 or variable view 2912, and add 2914 a variable to the eigen variables. Embodiments of the system 100 may provide built-in functions to define compound variables to convert predefined variables in terms of variable types, units, values, etc. Thus, users can create compound variables from selected variables.

[0241] As shown in Fig. 30, when adding eigen variables from system data 3010, a user can select variables based on device properties 3012 (from the system data 3010 such as hostname and site). As shown in Fig. 31, selected eigen variables 3110 can be grouped 3112. As stated elsewhere herein, eigen values can be used to group devices with similar characteristics into a single group 3112. As will be understood, however, a different method may be needed to group a device with its layer-2 neighbors. For example, a layer-2 group may need to be defined via the following variables / properties: “this device”, “local interface”, “neighbor device”, and / or “neighbor interface”.

[0242] Referring to Fig. 32, “Calculate Feature Instance” 2514 provides for selection of a subset of test devices 3210 for computing and debugging the feature instance (e.g., “HSRP Feature” 3212). This process may be based at least in part on one or more of the eigen variable 3110 and / settings configured in “Define Eigen” 2512. By choosing a small set of test devices 3210, a user can focus on testing and refining the feature instance, thereby ensuring its accuracy before applying it across the network. The GUI 2400 may further provide for the setting of a minimal device count 3214 that controls the minimum number of devices required for each feature instance. In embodiments, the range for the minimal device count 3214 may be between about one (1) to about a thousand (1000), and / or the default value may be one (1). In embodiments, a feature instance will only be generated if it contains more than the minimal device count 3214. The GUI 2400 may further provide for the selection of test device(s) 3216, which may (optionally) have a default value of “All Devices in Scope”, which includes all the devices specified in the target device setting 2614 of “This Feature” 2510. Embodiments of the current disclosure may calculate the feature instance using live network data or from a selected data source.

[0243] As shown in Fig. 33, the results 3310 may be displayed with three fixed columns: “Device” 3312, “Eigen Value” 3314, and “Device Count” 3316. Each row may represent a feature instance.PATENT Attorney Docket No. NETB-OOOl-WO each unique eigen value may generate a row, and devices with the same eigen values may be grouped into the same row.

[0244] Referring to Fig. 34, under “Define Role” 2516, one or more roles 3410 can be defined for devices in a feature instance. For example, for the HRSP feature 3212, users can define two roles: “Active Device” 3412 and “Standby Device” 3414. In embodiments, the “All Devices” 3412 may be the default role, which includes all the devices associated with the feature instances calculated under “Calculate Feature Instance” 2514. Embodiments of the system 100 may also select a first device (in a device type) as a representative device.

[0245] Fig. 35 shows the adding of a new role via the GUI 2400, in which a user can enter a name 3510 and define a condition 3512 for the role. Embodiments of the GUI 2400 may also provide a “calculate all roles” feature 3514 that finds and lists all roles for each feature instance. Embodiments of the GUI 2400 may also provide for a user to select different device roles and view the corresponding definitions and feature instance calculation results. The GUI 2400 may also provide the user to select / set a representative device number 3516 to set specific number of representative devices. In embodiments, the GUI 2400 may provide an indication (e.g., depiction of a star on the “calculate all roles” feature 3514) after a role definition is created and / or modified as a reminder to the user to recalculate the roles.

[0246] Thus, as shown by the preceding scenario, embodiments of GUI 2400 provide for: the retrieval of all values of eigen variables; the combining of eigen variable values to form a specified eigen value; the grouping of devices by eigen value; the creation of a feature instance for an eigen group (having a device count may exceed a defined minimum value); and the display of calculated feature instances.

[0247] Once a golden feature is fully configured and / or debugged, the golden feature may be published (e.g., made available for use in the various components of the system 100) so that the golden feature can be used and / or accessed by and / or with golden intents 2016. Embodiments of the current disclosure may also provide for an indication (e.g., a displayed on a “Publish” button in a GUI) under the following conditions: 1) a user has clicked the Run button on all devices in scope and the result is not empty; and / or 2) a user has used a calculate role function under “Define Role” 2516. The indicator may be structured to remind the user to publish (and / or republish) a feature to update the results. Embodiments of the system 100 may also generate a feature ADT data structure 3610 (Fig. 36) upon publication of a golden feature, where the ADT data structure 3610 includes columns 3612 (Fig. 36) for intents. A further nonlimiting example of an ADT data structure 3610 is shown inPATENT Attorney Docket No. NETB-OOOl-WO

[0248] Referring to Fig. 38, embodiments of the golden feature management circuit 1916 (Fig. 19) and / or golden engineering circuit 338 (Fig. 19) may provide for the identification and / or management of golden neighbors, where a golden feature can be used as a golden neighbor. As used herein, a golden neighbor refers to a relationship (e.g., within a certain number of network hops) between one or more devices and / or features (and their corresponding devices). In embodiments, a golden neighbor may have a relationship between "this" and "neighbor." For example, as shown in the example GUI 3800, all the devices of the "HSRP" feature 3810 use each device as "this" and its HSRP neighbor device as the "neighbor. "

[0249] As stated herein, the golden engineering circuit 338 (Figs. 3 and 19) may include a golden intent management circuit 1918 (Fig. 19) structured to provide for the management of golden intents 2016 (Fig. 20). Accordingly, Fig. 39 shows a nonlimiting example of a GUI 3900 that may be generated by the golden intent management circuit 1918 for creating, deleting, and / or managing golden intents

[0250] As used herein, a nonlimiting example of a golden intent 2016 includes a structured automation construct that defines the ideal operational state and expected behavior of a specific network feature or service. As explained in greater detail herein, golden intents can simplify the creation process of member intents for golden features so that users do not need to learn multiple conceptions and complex logic of NIT and ADT, reducing the learning curve. A golden intent 2016 may include several components: 1) the scope of the feature being modeled 2024; 2) the logical roles and / or relationships of participating devices 2026; 3) validation rules that specify conditions for correct implementation 2028; 4) execution logic that governs automated workflows for assessment, diagnostics, and remediation 2030; and / or 5) one or more member intents 2032.

[0251] For example, a golden intent for a Border Gateway Protocol (BGP) feature might define roles such as “primary router’’ and / or “peer router,’’ include rules to verify that neighbor sessions are established and route advertisements comply with policy, and / or incorporate logic to check live routing tables against these rules. Similarly, a golden intent for a VPN service could specify hub and spoke roles, confirm encryption settings, and / or validate tunnel health. In software, a golden intent 2016 may be implemented as a structured template and / or an object that stores the components 2024, 2026, 2028, and / or 2030, along with associated scripts and / or APIs to query live network data and compare it against a golden configuration 2010 (Fig. 20). Once deployed, a golden intent 2016 may act as a benchmark for continuous and / or periodic validation, enabling preventive automations to detect and / or resolve network and / or device issues, often before such issues impact service. Golden intents 2016 may also be used to identify configuration drift, and / or enforce compliance across dynamic and / or complex network environments. As will be appreciated, the use of golden intents, asPATENT Attorney Docket No. NETB-OOOl-WO disclosed herein, can shift network management toward an intent-based paradigm, where the actual state of the network is constantly measured and / or maintained against a predefined, automated representation of the desired state.

[0252] Member intents 2032, as used herein, may be subordinate automation constructs that represent specific instances and / or components of a broader intent-based model (e.g., a golden intent 2016). Member intents 2032 may include the detailed configuration, operational parameters, and / or validation logic for a single device and / or role within the scope of the larger golden intent 2016. In embodiments, each member intent 2032 of a golden intent 2016 may define the responsibilities and / or expected state of an assigned device and / or element, including role-specific attributes, compliance rules, and / or diagnostic checks. For example, in a BGP routing scenario, a member intent 2032 for a peer router might specify neighbor IP addresses, authentication settings, and / or route advertisement policies, along with validation steps to confirm session establishment and prefix exchange. Similarly, in a VPN feature, a member intent 2032 for a spoke device could define tunnel endpoints, encryption parameters, and health checks for connectivity. In software, member intents 2032 may be implemented as structured objects linked to their parent golden intents 2016, where the member intents 2032 store device-specific definitions, associated scripts, and / or APIs to query and validate live network and / or device data.

[0253] Embodiments of the GUI 3900 (Fig. 39) are designed to simplify the creation of member intents 2032 for related feature instances, reducing learning costs. In embodiments, after discovering features configured on a device in a network and generating a feature instance table based on the feature discovery function, the GUI 3900 provides for a user to associate one or more golden intents 2016 for each device list and feature for diagnosis analysis. Furthermore, golden intents 2016 and / or the GUI 3900 can be integrated with automation frameworks (AFs), to include preventative automation frameworks (PAFs), and / or triggered automation frameworks (TAFs), and / or the artificial intelligence circuit 362.

[0254] In embodiments, the GUI 3900 may include four (4) areas / portions: a golden intent manager 3910; a header 3912; a flowchart 3914; and a property pane 3916. The golden intent manager 3910 is structured to allow users to manage golden intents. The header 3912 displays the data at the golden intent level and / or provides the major intent-level operations. The header 3912 may also provide for the user to switch the GUI 3900 between three modes: “Define” 3918, “Build” 3920, and “Publish” 3922. The flowchart 3914 may provide for a user to generate, define, and / or complete an entire golden intent definition.

[0255] The property pane 3916 may provide for a user to select different nodes (e.g., devices), where the corresponding device information or configuration interface can be displayed (e.g., on thePATENT Attorney Docket No. NETB-OOOl-WO property pane 3916 itself). In embodiments, the GUI 3900 may guide a user through a workflow for creating and / or managing a golden intent 2016 having the following portions: “define golden intent”, “build golden intent”, and / or “publish golden intent”, respectively corresponding to the “Define” 3918, “Build” 3920, and “Publish” 3922 modes.

[0256] In the “Define” mode 3918, the GUI 3900 provides for a user to complete a golden intent definition by clicking on nodes 3924 (which extend) and defining properties. Nonlimiting examples of nodes 3924 for use in building a golden intent are shown in Fig. 40 with corresponding descriptions in Table 1.Table 1>PATENT Attorney Docket No. NETB-0001-WO

[0257] In embodiments, GUI 3900 may provide for a user to start with a feature and extend it (e.g., modify, specify, and / or add onto the node) with a device role. In such embodiments, the feature node 4010 provides for a user to select and switch features and view the current feature’s properties. For example, selecting a feature for an empty node may cause the feature’s details to be displayed on the property pane 3916 (Fig. 39) with (optionally) a summary of the device roles and the total number of devices and / or instances (of the selected feature). The user may then select device roles defined in the feature for use in the current golden intent 2016.

[0258] Fig. 41 depicts an embodiment of the GUI 3900 where a device role can be extended with a parser, golden configuration check, and / or a neighbor device. As shown, while in a device role node screen 4110, a user can extend the following actions: “parser” 4112, “Golden Configuration Check” 4114, and “Neighbor Device” 4116.

[0259] Figs. 42, 43, and 44 depict a “parser” action extension 4112 for a node 4210, where the GUI 3900 depicts a listing of matched parsers 4310 for the node 4210. Selection of a matched parser (e.g., parser 4312) reveals and / or determines the command 4314 for retrieving a particular variable 4316. The GUI 3900 may also provide for a user to define one or more settings on the property pane 3916 (Fig. 39) (e.g., macro variable) and assign values. One or more variables in the selected parser may be made available on a subsequent diagnosis node.

[0260] Figs. 45, 46, and 47 depict a “Golden Configuration Check” action extension 4114 for node 4510, where GUI 3900 depicts a listing of all available golden rules 4512 for user selection.Selection of a listed golden configuration rule 4514 may update the property pane 3916 to display properties of the selected golden configuration rule 4514 and / or options 4610 for performing a golden configuration check of the selected golden configuration rule 4514 against the node 4510.PATENT Attorney Docket No. NETB-0001-WO Results 4710 of the golden configuration check may be displayed in a diagnosis window 4712 opened by extending a diagnosis node. Embodiments may also provide for checking of the golden configuration and / or golden configuration template.

[0261] Figs. 48 and 49 depict embodiments of the GUI 3900 where device roles defined in a feature are independent of each other and / or have no network relationship. In such a scenario, a user can extend neighbor devices from a device role node 4810. Neighbor devices may be extended based on scope 4812 (e.g., IPv4 L3 Neighbor / IPv6 L3 Neighbor / L2 Neighbor - extend all the neighbor devices based on topology; Golden Neighbor - dynamically find neighbors depending on the structure of the golden table, if a user has specified a golden neighbor table); Command Table -refer to the table variables parsed from CLI commands; Filter Neighbor Device - determine which column in the table is ‘This’ and which column is ‘Neighbor’ (when Golden Neighbor is selected)).

[0262] Fig. 50 depicts a nonlimiting embodiment of a GUI 5000, generated by the golden configuration browsing circuit 1914 (Fig. 19) for browsing golden configurations 2010 and / or golden configuration templates 2012. The GUI 5000 allows a user to verify a golden rule 5010 and see the results 5012 of golden configurations. Embodiments of the GUI 5000 may depict one or more of the following: rule name 5014 (the name of the currently selected golden configuration rule); apply-to-device 5016 (the collection of all devices from one or more applicable golden configuration templates); compliance 5018 (information on compliance for a golden configuration template after calculation); verify 5020 (calculations performed for the selected golden rule based on baseline configuration files); “golden configuration” 5022 (the number of golden configuration templates included in the currently selected golden configuration rule); and / or alerts 5024 (information about the number of generated alerts). Embodiments of the GUI 5000 may also provide one or more of the following: an alert tab 5026 (displays more detailed information regarding alerts related to a matched golden configuration for a device instance, where, in embodiments, if a device instance matches multiple golden configuration templates, each alert for each golden configuration template will be counted separately); a filter (provides for filtering of the results 5012 based on success and alert severity); and / or muted alerts (e.g., a representation of entries configured with muted alerts).

[0263] Referring again to Fig. 19, embodiments of the golden engineering circuit 338 enable users to define, identify, and / or discover a network’s design and / or topology and generate corresponding golden configurations by forward and / or reverse engineering processes. As will be appreciated, and as explained in greater detail herein, embodiments of the golden engineering circuit 338 improve the network arts by automating and / or simplifying the discovery and / or documentation of a network’ s configuration and / or topology, which traditionally take network engineers days, weeks, months, and (in some cases) years to perform. For example, embodiments of the golden engineering circuit 338PATENT Attorney Docket No. NETB-0001-WO may create thousands of automations without requiring coding (at the time of discovering and documenting a network’s design) by a network engineer. Accordingly, embodiments of the golden configuration management circuit 1910 may include a forward engineering circuit 1920 and a reverse engineering circuit 1922.

[0264] Illustrated in Figs. 51 and 52 are aspects of an interactive workflow 5100 and corresponding GUI 5200 provided by the golden engineering circuit 338 for discovering, defining, and / or managing golden configurations 2010, in accordance with embodiments of the current disclosure. Embodiments of the GUI 5200 may represent a segment of configlet representing a standard for network design. The interactive workflow 5100 may include the following nodes / processes: “This Rule” 5110; “Configuration Parser” 5112; “Discover” 5114; and / or “Alert” 5116.

[0265] As the first node in the interactive workflow 5100, “This Rule” 5110 provides for a user to define the basic information for a golden configuration rule, e.g., name the name 5210, description 5212, etc. The GUI 5200 may also provide for a user to modify success 5214 and / or alert messages 5216 using an advanced Settings option. Golden configuration calculations may be triggered by a change analysis (CA) feature provided in advanced settings via an optional checkbox 5218. In embodiments, the CA feature is applied to the devices within the scope of an apply-to-device range. For example, when device configuration files are changed and updated to the current baseline, a configuration files' verification event will be triggered, with the results of the CA viewable in the golden configuration browser GUI 5000 (Fig. 50).

[0266] The “configuration parser” node 5112, as shown in Fig. 53, provides for the selection of a configuration parser 5310 and corresponding variable 5312 for use. Users can create a new configuration and / or select an existing configuration parser from the library.

[0267] Fig. 54 illustrates a GUI 5400 for creating a configuration parser. In embodiments of the GUI 5400, configuration information 5410 appears by default, and CLI commands cannot be executed. Drop-down menus 5414, 5416, 5418 may provide for adding / selecting a device and / or retrieving the data with an (optional) default cached data selection. With the foregoing settings set, a user may then proceed to parse the variables from the sample data 5410.

[0268] As shown in Fig. 55, once a parser is chosen 5510, it will be added to the interactive workflow 5100 with the corresponding variables (e.g., single variables 5514 and / or table variables 5516), where the variables can be selected for use with / for selection of a target configuration 5518 (for comparing with a golden config template); defining compliance / violation messages 5520; filter of configuration instances; and / or setting values for the input variables of golden parameters. The GUI 5200 may provide for selection of one of the variables as a target configuration 5518 forPATENT Attorney Docket No. NETB-OOOl-WO subsequent golden configuration definitions, calculations, and / or verifications. The selected variables may be either a single-value variable 5514 (e.g., for device-level configurations) and / or a table column variable 5516 (e.g., for instance-level config). If a configuration variable is defined at an instance level via a table column variable 5516, the target configuration may have multiple instances.

[0269] Fig. 56 depicts a portion 5600 of GUI 5200 having filled fields showing instances. A unique identifier may be set for the instance information using an instance key 5610. The instance key 5610 may define the representation and unique identification of each instance-level configuration. The instance key 5610 may be (optionally) inherited from the parser.

[0270] Referring to Fig. 57, the “Discover” node 5114 of the interactive workflow 5100 may provide for the discover and / or definition of a golden configuration 2010 via a forward engineer process (provided by the forward engineering circuit 1920 (Fig. 19)) and / or a reverse engineering process (provided by the reverse engineering circuit 1922 (Fig. 19)).

[0271] The “Alert” node 5116 (Fig. 51) may provide for viewing of alert information generated by a golden configuration for the selected / specified device scope.

[0272] Fig. 57 depicts an embodiment of the forward engineering process, which finds / discovers a golden configuration 2010 (Fig. 20) using a golden configuration template 2012 (Fig. 20). The forward engineering process is ideal for scenarios where a user has a golden configuration template 2012 and knows the target devices to which it should be applied. In other words, forward engineering, as used herein, is suitable for instances where an ideal and / or golden configuration 2010 for a device and / or network is known. Users may manually create the golden configuration template 2012 using static configurations, parser variables, and / or golden parameters 2020. A user should (but may not always) specify the target devices for the golden configuration template 2012 when performing the forwarding engineering process. Users may also define the applicable configuration instances for the golden configuration template 2012 by setting specific criteria. The components (subprocesses) of forward engineering a golden configuration follow.

[0273] With reference to Fig. 57, reference devices are selected 5710 and / or added (optionally). A device configuration is retrieved 5712 from a current baseline (e.g., a digital twin). In embodiments, multiple instances of the retrieved configuration are retrieved from a device and listed in the instance column 5714, allowing users to switch between instances.

[0274] Fig. 58 depicts a GUI 5800 (which may form part of the GUI 5200) that provides for the defining and testing of a golden configuration (e.g., configuration settings and target devices), where Figs. 59 and 60 depict first 5810 and second 5820 portions, respectively, of the GUI 5800. A user may select and / or edit a golden configuration template (e.g., via portion 5810) as a starting point forPATENT Attorney Docket No. NETB-0001-WO defining the golden configuration. For example, a user may add references to a parser variable (e.g., <$name>') (which may be from a currently selected configuration parser) and / or golden parameter (e.g., $$name) 5910. The GUI 5800 may also provide for the defining of verification settings.Embodiments of the GUI 5800 may provide for a user to define match pattern rules 5912 (e.g., defined patterns for comparing the target configuration against the golden configuration template, which may be functionality the same as in a network intent), messages (and corresponding severities), select devices for application 5914, and / or instance condition(s) for the golden config verification.

[0275] The referenced golden parameter 5910 may be a value identified through a parameter value defined based on a match condition. The user may set the golden parameter field to a base parameter value or a configuration parameter value.

[0276] In embodiments, the portion 5820 may provide for the user to define alert messages 6010, success messages 6012, applicable device groups 6014, and / or applicable instance conditions 6016 for the current golden configuration template. Alert messages and success messages may include variables, such as the parser variable, golden parameter information, ADT table variables, and / or match pattern return values. In embodiments, the golden configuration may be set to only the instances having one or more specified matching conditions and / or properties.

[0277] After defining the golden configuration, the GUI 5800 may provide for the user to select one or more devices and test and verify the golden configuration against them. Results of the test(s) may be displayed for evaluation by the user. In embodiments, the GUI 5800 may provide for a user to select a standard set of devices, and / or to select all devices in scope (e.g., all devices within the apply to device scope for the golden configuration). In embodiments, testing of the golden configuration template involves verifying data from the current baseline(s) against the test devices. Test results may display i nformation in the form of X alerts on y Devices along with a timestamp. Instances set with multiple table columns as the instance key may be displayed as (value 1, value2). Template parameters (e.g., values of the current variable used in the golden template, including the parser variables and / or golden parameters) may also be displayed in conjunction with the test results. In embodiments, the test results may display the comparison between the target variable of a selected device and the golden configuration computed for the device using the match patterns (as disclosed herein). The GUI 5800 may also depict an execution log showing instances that did not match filter conditions and / or corresponding messages to facilitate troubleshooting of if the golden configuration appears to be misconfigured.

[0278] Fig. 61 depicts a transition of the GUI 5200 (Fig. 52) from a first instance 6110 to a second instance 6112 showing the addition of the golden configuration generated via GUI 5800 (after beingPATENT Attorney Docket No. NETB-0001-WO successfully tested), where the golden configuration is absent (indicated by arrow 6114) from instance 6110 and added / adopted / present (indicated by arrow 6116) in instance 6112. As shown in instance 6112, the golden configuration’s name and / or alerts generated from the last verification operation (e.g., test) may be displayed upon adoption / addition of the golden configuration into the interactive workflow 5100.

[0279] Fig. 62 depicts a nonlimiting embodiment of a process flow 6210 for the reverse engineering process (provided by the reverse engineering circuit 1922 (Fig. 19). At a high level, the process flow 6210 includes: defining a configuration parser 6212; discovering golden parameters 6214; defining a golden configuration 6216; and monitoring rule violations 6218. Defining the configuration parser 6212 may involve using a visual parser (and / or other parsing tool) to parse a target configuration and parameters. Discovering the golden parameters 6214 may involve using a reverse engineering tool (e.g., the reverse engineering circuit 1922) to discover instances of parameters and save / form them into a golden parameter(s). Defining the golden configuration 6216 may involve defining a configuration rule using the golden parameter(s). Monitoring rule violations 6218 may involve continuously running rules to identify violations.

[0280] Fig. 63 depicts the GUI 5200 configured for the reverse engineering process, which provides for a user to calculate the golden configuration from a batch of devices (e.g., identifying the candidate of the golden information). Values for a target-configuration (or normalized-configuration variables) may be calculated the specified set / batch of devices, where the results are aggregated to identify the number of distinct values and the corresponding device count for each value. The values may then be sorted in descending order based on the number of devices. Configurations with a device count exceeding a pre-configured threshold, M, may be designated as candidate golden configuration templates. In embodiments, only the top N configurations determined (optionally) by another pre-configured threshold may be selected as candidates. Users can then review these candidates and choose one of the candidates as the final golden configuration template.

[0281] Accordingly, the GUI 5200 may include an add devices interface 6310 that provides for the selection of devices that need to comply with a current / selected golden configuration rule defined under the “This Rule” node 5110. The GUI 5200 may provide for a user to choose 6312 to select the configuration variable to discover the instance, otherwise, the target configuration variable may be directly used for performing calculation(s) 6314. In embodiments, users can modify the calculation(s) 6314 settings, as shown in Fig. 64, (e.g., the user can set a minimal device in each golden configuration 6410, a maximum number of golden configurations 6412, instance conditions settings 6414 for the calculation, and / or the like).PATENT Attorney Docket No. NETB-0001-WO

[0282] Moving to Fig. 65, upon matching the number of devices / instances conditions, the GUI 5200 may display devices instances 6510, options to define and test a golden configuration 6512, options to add to a golden configuration 6514. In embodiments, defining and testing 6512 may be performed in a manner similar to the one disclosed herein with respect to the forward engineering process. The add to golden options 6514 provide for candidate golden configuration information (e.g., candidate golden parameters) to be moved / promoted into the golden configuration.

[0283] Embodiments of the GUI 5200 may further provide for a user to update a device scope setting (e.g., recalculation of the latest golden information based on the most recent configuration). For example, if the golden information does not completely match a calculated device scope, the GUI 5200 may provide for the updating of the device scope.

[0284] As shown in Fig. 66, embodiments of the GUI 5200 may provide for batch addition (represented by checkboxes 6601) of candidate golden configurations to an adopted golden configuration template. For example, a user may add multiple candidate golden config templates to an adopted golden configuration template at once by selecting multiple checkboxes provided on a pane within the GUI 5200. In such embodiments, when adding multiple candidate configurations to the golden configuration, the embodiments of the system 100 may check for the following: name conflicts (e.g., if two candidate configurations have identical names, the GUI 5200 may notify the user and forego adding the offending candidate configurations); existing golden configuration template (e.g., the system 100 may provide options for handling a golden configuration template that already exists; available options include: appending the new information to the existing golden configuration, overwriting the existing golden configuration with the new one, cancelling the addition process, and selectively adding items to a golden parameter.

[0285] Figs. 66 and 67 depict two instances 6610 and 6710 of a window of the GUI 5200 for selecting candidate golden configuration templates and adding golden parameters via a selection box 6612 (shown in both Fig. 66 and in Fig. 67). Instance 6610 of the window is shown when a user desires to add golden parameters to a device group (e.g., check box 6614); whereas instance 6710 is shown when a user does not want to add the golden parameters to a device group (e.g., “unchecked” box 6712).

[0286] Fig. 68 depicts a scenario where a user can select multiple golden configuration items for a NTP server as golden parameters, where the user can then define criteria 6810 to match against actual NTP server configurations.

[0287] Fig. 69 depicts a scenario where a user creates a new device group 6910 with the calculated devices for each corresponding configuration variable 6912. The newly created device groups 6910 appear along with default names.PATENT Attorney Docket No. NETB-0001-WQ

[0288] Accordingly, and as disclosed herein, a non-limiting embodiment of the current disclosure includes system 100 for identifying a golden configuration via reverse engineering. Nonlimiting examples of reverse engineering include retrieving configuration files from multiple related network computing devices and finding commonalities. For example, in embodiments, the system includes at least one processor and a memory device. The memory device stores an application that, when loaded into the at least one processor, causes the at least one processor to: interpret a first user command; and, in response to the first user command, display a portion of a configuration file of a network computing device. The application may further cause the at least one processor to: interpret a second user command; and, in response to the second user command, select a configuration setting within the portion of the configuration file. The application may further cause the at least one processor to interpret a third user command: and, in response to the third user command, identify one or more groups of network computing devices based at least in part on the selected configuration setting. The application may further cause the at least one processor to interpret a fourth user command; in response to the fourth user command, select a common configuration setting of one of the one or more groups of network computing devices as a golden configuration; and at least one of transmit the golden configuration or store the golden configuration in a database.

[0289] Embodiments of the current disclosure also provide for the automation and / or simplification of preventing, mitigating, and / or resolving network issues / problems (e.g., loss and / or degradation of network services). Such embodiments utilize action plans to handle (and / or assist network engineers and technicians) in handling / resolving network issues. Accordingly, the action plan management circuit 340 (Fig. 3) stores actions plans. An action plan 7000, as disclosed herein and as shown in GUI 7010 in Fig. 70, may include sequences of predefined steps 7012 described in natural language, which the orchestration circuit 312 (Fig. 3) and / or the artificial intelligence circuit 362 (Fig. 3) can interpret and / or execute via the execution engine 330 (Fig. 3). An action plan may include combinations of commands, data gathering operations, comparison operations, and / or analysis steps. An action plan, as disclosed herein, may also store and / or identify, store operations for troubleshooting and / or resolving network issues.

[0290] In embodiments, a user can invoke stored operations (e.g., include action plans and / or network intents) through natural language requests in multiple ways. The user may directly reference a stored operation by name, such as requesting execution of a specific security assessment action plan. Alternatively, a user may describe their desired outcome in natural language, allowing the system 100 to match the request with appropriate stored operations. The orchestration circuit 312, aided by the artificial intelligence circuit 362, may determine whether to execute a stored operation and / or to perform a new sequence of actions based on the user's request.PATENT Attorney Docket No. NETB-0001-WQ

[0291] In embodiments, when a user invokes a stored operation, the system 100 may process the request through several stages. For example, as a non-limiting example, the orchestration circuit 312 first identifies the referenced operation and retrieves its definition. For action plans, this may include the natural language description of required steps. For network intents, this may include the defined automation parameters and / or execution requirements. Next, the orchestration circuit 312 and / or the artificial intelligence model 362 generates appropriate API calls to execute the operation, maintaining the same validation and security controls used for direct commands.

[0292] Embodiments of the system 100 may support parameterization of stored operations, allowing users to specify variables through natural language. For example, a stored security assessment action plan may accept target devices as parameters, allowing users to specify which devices to assess through their natural language request. The artificial intelligence model then interprets these parameters from the user's request and incorporates them into the generated API calls.

[0293] Stored operations may be scheduled for repeated execution through the natural language interface. For example, a user can specify execution schedules using natural language, such as requesting an operation to run periodically or at specific times. The system 100 maintains these schedules and / or executes the operations accordingly, storing results for later review and comparison.

[0294] Results from stored operations can be displayed in various formats, including dashboards that update automatically with new execution results. Users can interact with these results through natural language queries, requesting additional details or initiating follow-up operations.

[0295] In complex troubleshooting cases, the orchestration agent’s 914 response might not adequately address the problem due to lack of network knowledge (e.g., context data). To automate the diagnosis, users can leverage the action plans to describe the troubleshooting steps in natural language, allowing the orchestration agent 914 to follow the steps to identify potential root causes of the network issue.

[0296] As will be appreciated, embodiments of the system 100 that provide action plans, as disclosed herein, improve the network management arts by providing an easy way for users to translate human knowledge into actionable steps, convert human network knowledge into automated troubleshooting, and / or customize the orchestration agent’s 914 response.

[0297] Fig. 71 depicts a series of GUIs 7110, 7112, and 7114 showing a non-limiting scenario in which a user chats with the orchestration agent 914 via a chat portal 7116 to generate an action plan 7000. As will be understood, GUI 7110 depicts the action plan 7000 and GUI 7114 depicts responses 7118 from the orchestration agent 914 (Fig. 9). Fig. 72 depicts two instances 7210 andPATENT Attorney Docket No. NETB-OOOl-WO 7212 of GUI 7110, where instance 7210 provides for a user to tag a device, automation, CLI, and / or or even another action plan in plain text 7214, and instance 7212 provides for a user to define a corresponding description, category, and / or input variable 7216 to help the orchestration agent 914 (Fig. 9) extract input variables from user input (e.g., the text 7214). Embodiments of the instance 7210 may provide for a user to create categories to organize action plans and / or automations. The instance 7210 may also provide for the user to define the data source for the CLI tool, automation tool, and / or NIT replication. For example, the instance 7210 may provide for users to specify the data that the CLI tool will use as the baseline for comparison. As shown in Fig. 73, the instance 7210 may provide for a user to create categories 7310 to organize action plan and / or automations. Embodiments of the instance 7210 may also provide for searching of action plans, either by a user or by the orchestration circuit 914, where matched action plans may be sent to the artificial intelligence circuit 362 (optionally) along with the search criteria (input) used to identify the matched action plans.

[0298] Referring again to Fig. 3, the remediation circuit 336 (Fig. 3) is structured to facilitate repair and / or mitigation actions upon the discovery of noncompliant devices (e.g., devices that violate a golden rule) and / or encountering a network issue, as disclosed herein. The remediation circuit 336 may perform one or more remediation actions (to address a network issue) which may involve use of network intents, golden configurations, and / or action plans, as disclosed herein.

[0299] For example, with reference to Fig. 74, embodiments of the GUI 5200 under the “This Rule” node 5110 may utilize one or more aspects of the remediation circuit 336 (Fig. 3) to set up auto-remediation settings 7410. Such settings 7410 may execute a golden intent that runs a golden configuration against a golden feature (a group of one or more devices) and / or against configuration type on a device. After evaluating the differences / discrepancies, the user can create configlets to resolve detected issues. Users may also set a default intent template 7412 to remediate devices.

[0300] Referring now to Fig. 75, embodiments of the system 100 (Fig. 1 and Fig. 3) may provide for a GUI 7500 that integrates a golden configuration pane 7512 with a network map pane 7514. Embodiments of the GUI 7500 may show and / or provide access to: summary information 7516; verification and / or reverification of a golden configuration rules 7518; summary information for a specific device 7520; configuration lines 7522; instances of applied golden configuration rules 7524; and / or a window 7526 for checking a golden configuration against a golden configuration. The summary information 7516 may include the count of matched devices, the count of golden configurations rules matched to the devices, and / or the count of alerts / compliances from golden configuration checks. Verification and / or reverification of a golden configuration rules 7518 may be manually performed against one or more of the golden configuration rules via batching to get thePATENT Attorney Docket No. NETB-0001-WO latest / current results. Summary infomation for a specified device 7520 may include the statistical data for a specified device, including the number of golden configuration rules applied to the device, the count of alerts and / or the count of compliances generated by the device against the golden configuration rules. The configuration lines 7522 may depict the full configuration of the selected device. The instances of the applied golden configuration rules 7524 depicts configuration rules matching the device and may be identified by unit of instance. A ‘Golden Config Rule Check Note’ and / or other types of indicators may be used to indicate compliance status. For example, embodiments of the current disclosure may use a color-coded scheme to indicate the golden configuration check results (alert / compliance) (e.g., green to show compliance, red to show a violation, yellow to show unknown, etc.). Window 7526 may display the details of golden configuration check results for each configuration instance, and provide visual verification of the current golden rule.

[0301] In embodiments, golden configuration check results shown in the golden configuration pane 7512 may be generated from one or more of the following sources: golden configuration checks triggered by a change analysis event, and / or users manually verifying the golden configuration rule. Embodiments of the golden configuration pane 7512 may also support adding additional devices (to view related golden configuration check results) 7610 (Fig. 76). In embodiments, the summary information 7516 may be updated accordingly. In embodiments, the data in the golden configuration pane 7512 may be manually refreshable in real-time (or near real-time), and / or the golden configuration rules may also be verified and / or reverified via batch processing as shown in Fig. 77; and, as shown in Fig. 78, users may be able to view the golden configuration check results for one matched device at a time, where the GUI 7500 may further provide for the selection of a device, the filtering of results by alert and / or compliance, comparison the data, etc.

[0302] As will be understood, embodiments of the current disclosure may include one or more components, circuits, and / or features of the foregoing examples (e.g., the components of system 100 shown in Fig. 3). It will be further understood that embodiments of the circuits disclosed herein may include one or more aspects of and / or perform one or more features of the other circuits disclosed herein. Further still, embodiments of the various circuits disclosed herein may be embodied by and / or form part of the apparatus 200 (Fig. 2).

[0303] Accordingly, referring to Fig. 79, an apparatus 7900 for network management is shown. The apparatus 7900 may be embodied by and / or form part of apparatus 200 (Fig. 2) and / or any other computing device disclosed herein.

[0304] The apparatus 7900 may include an input command processing circuit 7912, an orchestration circuit 7914, an execution engine 7916, and / or a golden configuration data provisioningPATENT Attorney Docket No. NETB-0001-WO circuit 7918. The input command processing circuit 7912 is structured to interpret one or more input command values 7920 corresponding to a request to generate golden configuration data 7922 for at least one computing device (e.g., 134, 132, 136, 138 (Fig. 1)) in electronic communication with a computer network (e.g., 122, 124, 126, 130 (Fig. 1)). The orchestration circuit 7914 is structured to obtain, in response to the one or more input command values 7920, network context data 7924 corresponding to a feature of at least one of: the at least one computing device, or the computer network. The orchestration circuit 7914 is further structured to generate, in response to the network context data 7924, at least one artificial intelligence prompt value 7926; and transmit the network context data 7924 and the at least one artificial intelligence prompt value 7926 to an artificial intelligence circuit 362 (Fig. 3). The orchestration circuit 7914 is further structured to receive, from the artificial intelligence circuit 362 in response to the at least one artificial intelligence prompt value 7926, network management instruction data 7928. The orchestration circuit 7914 is further structured to generate, based at least in part on the network management instruction data 7928, one or more network operation command values 7930. The execution engine circuit 7916 is structured to: execute the one or more network operation command values 7930; and interpret result data 7932 responsive to the one or more network operation command values 7930. The orchestration circuit 7914 is further structured to generate, based at least in part on the result data 7932, the golden configuration data 7922. The golden configuration data provisioning circuit 7918 is structured to transmit the golden configuration data 7922.

[0305] Further aspects of embodiments of the apparatus 7900 are shown in Fig. 80. For example, in embodiments, the apparatus 7900 may include a golden engineering circuit 8010 (also shown as 338 (Fig. 3) (structured to generate a graphical user interface 8012 that provides a plurality of input prompts 8014 structured to guide a user through a golden configuration discovery process. The golden engineering circuit 8010 is also structured to generate, in response to one or more user interactions with the plurality of input prompts 8014, the one or more input command values 7920.

[0306] In embodiments, the golden engineering circuit 8010 includes a forward engineering circuit 1920 (Fig. 19) structured to generate the plurality of input prompts 8014. In such embodiments, the golden configuration discovery process is forward engineered and the plurality of input prompts 8014 may be further structured to present one or more selectable golden configuration templates 2012 (Fig. 20), where at least one of the one or more input command values 7920 corresponds to a selected golden configuration template of the one or more selectable golden configuration templates.

[0307] The plurality of input prompts 8014 may be further structured to present one or more selectable reference computing devices, where at least one of the one or more input command values 7920 corresponds to a selected reference computing device of the one or more selectable referencePATENT Attorney Docket No. NETB-OOOl-WO computing devices, and the at least one computing device for the request to generate golden configuration data 7922 corresponds to the selected reference computing device.

[0308] In embodiments, the golden engineering circuit 8010 includes a reverse engineering circuit 1 22 (Fig. 19) structured to generate the plurality of input prompts 8014. In such embodiments, the golden configuration discovery process is reverse engineered, and the reverse engineering circuit 1922 may include a discovery circuit 1924 (Fig. 19) structured to generate one or more discovery command values structured to identify an architecture of the computer network via directing the orchestration circuit 7914 to iteratively discover one or more computing devices in electronic communication with the computer network. In such embodiments, the graphical user interface 8012 may be structured to: display the one or more discovered computing devices; and provide for a user selection of at least one of the one or more discovered computing devices, where the at least one computing device for the request to generate the golden configuration data 7922 corresponds to a user selected at least one of the one or more discovered computing devices. In embodiments, the discovery circuit 1924 may be structured to utilize types of connections and / or topology information, such as Access Control Lists (ACLs), etc., to determine what other network computing devices a particular network computing device is connected to.

[0309] In embodiments, at least one of the plurality of input prompts 8014 is structured to obtain one or more golden parameters; and the one or more discovered computing devices satisfy the one or more golden parameters.

[0310] The graphical user interface 8012 may be further structured to define a golden configuration template 2012 (Fig. 20) based at least in part on the one or more golden parameters, where the orchestration circuit 7914 may be further structured to generate the golden configuration data 7922 based at least in part on the golden configuration template.

[0311] In embodiments, the golden engineering circuit 8010 includes a golden feature management circuit 1916 (Fig. 19) structured to generate a golden feature 2014 (Fig. 20) that includes the one or more discovered computing devices.

[0312] The apparatus 7900 may further include a network monitoring and validation circuit 8016 structured to: validate the golden feature 2014 against the golden configuration data 7922; and generate an indication 8018 that the one or more discovered computing devices of the golden feature 2014 satisfy the golden configuration data 7922. In embodiments, validation of the golden feature 2014 against the golden configuration data 7922 is based at least in part on a golden intent 2016 (Fig.20).

[0313] Shown in Fig. 81 is a method 8100 for network management, in accordance with embodiments of the current disclosure. The method 8100 may be performed by the apparatus 7900PATENT Attorney Docket No. NETB-0001-WO (Fig. 79), apparatus 200 (Fig. 2), system 100 (Figs. 1 and 3), and / or any other computing device disclosed herein. The method 8100 includes: interpreting one or more input command values corresponding to a request to generate golden configuration data for at least one computing device in electronic communication with a computer network 8110; and obtaining, in response to the one or more input command values, network context data corresponding to a feature of at least one of: the at least one computing device, or the computer network 8112. The method 8100 further includes generating, in response to the network context data, at least one artificial intelligence prompt value 8114; and transmitting the network context data and the at least one artificial intelligence prompt value to an artificial intelligence model 8116. The method 8100 further includes: receiving, from the artificial intelligence model in response to the at least one artificial intelligence prompt value, network management instruction data 8118; and generating, based at least in part on the network management instruction data, one or more network operation command values 8120. The method 8100 further includes executing the one or more network operation command values 8122; interpreting result data responsive to the one or more network operation command values 8124; generating, based at least in part on the result data, the golden configuration data 8126: and transmitting the golden configuration data 8128.

[0314] Further aspects of the method 8100 are shown in Fig. 82. For example, in embodiments, the method 8100 further includes generating a graphical user interface that provides a plurality of input prompts structured to guide a user through a golden configuration discovery process 8210; and generating, in response to one or more user interactions with the plurality of input prompts, the one or more input command values 8212.

[0315] As illustrated in Fig. 83, when the golden configuration discovery process is forward engineered, embodiments of the method 8100 further include presenting, via the plurality of input prompts, one or more selectable golden configuration templates 8310. In such embodiments, at least one of the one or more input command values may correspond to a selected golden configuration template of the one or more selectable golden configuration templates.

[0316] The method 8100 may further include presenting, via the plurality of input prompts, one or more selectable reference computing devices 8312. In such embodiments, at least one of the one or more input command values corresponds to a selected reference computing device of the one or more selectable reference computing devices; and the at least one computing device for the request to generate golden configuration data corresponds to the selected reference computing device.

[0317] As illustrated in Fig. 84, when the golden configuration discovery process is reverse engineered, the method 8100 may further include generating one or more discovery command values 8410; identifying, based at least in part on the one or more discovery command values, anPATENT Attorney Docket No. NETB-0001-WO architecture of the computer network via iteratively discovering one or more computing devices in electronic communication with the computer network 8412; and displaying the one or more discovered computing devices 8414. The method 8100 may further include: providing for a user selection of at least one of the one or more discovered computing devices 8416; and interpreting at least one of the one or more user interactions with the plurality of input prompts as a selection of the at least one of the one or more discovered computing devices 8418. In such embodiments, the at least one computing device for the request to generate the golden configuration data corresponds to the user selected at least one of the one or more discovered computing devices.

[0318] As illustrated in Fig. 85, embodiments of the method 8100 may also provide for a hybrid approach between forward engineering and reverse engineering a golden configuration. In such embodiments, the method 8100 may include obtaining, via at least one of the plurality of input prompts, one or more golden parameters 8510, wherein the one or more discovered computing devices satisfy the one or more golden parameters. The method 8100 may further include defining, via the graphical user interface, a golden configuration template based at least in part on the one or more golden parameters 8512, where generating the golden configuration data is based at least in part on the golden configuration template.

[0319] As illustrated in Fig. 86, embodiments of the method 8100 may also provide for identification and remediation of non-compliant devices, e.g., devices that violate a golden configuration. Accordingly, the method 8100 may include generating a golden feature that includes the one or more discovered computing devices 8610; validating the golden feature against the golden configuration data 8612; and generating an indication that the one or more discovered computing devices of the golden feature satisfy the golden configuration data 8614. As will be appreciated, validating the golden feature against the golden configuration data may be based at least in part on a golden intent.

[0320] Referring to Fig. 87, another apparatus 8700 for network management includes: a discovery circuit 8710 (also shown as 1924 in Fig. 19), an eigen circuit 8712, a golden feature generation circuit 8714, and a validation circuit 8716. The apparatus may be embodied and / or form part of apparatus 200 (Fig. 2), system 100 (Figs. 1 and 3), and / or any other computing device disclosed herein.

[0321] The discovery circuit 8710 is structured to discover one or more computing devices in electronic communication with a computer network.

[0322] The eigen circuit 8712 is structured to determine one or more eigen variables for the one or more discovered computing devices. Discovery of the one or more computing devices may be based at least in part on network context data 8718.PATENT Attorney Docket No. NETB-0001-WQ

[0323] The golden feature generation circuit 8714 is structured to generate, based at least in part on one or more eigen variables 8720, a golden feature 2014 (also shown in Fig. 20). In embodiments, the golden feature generation circuit 8714 may be similar in function and / or form part of the golden feature management circuit 1916 (Fig. 19).

[0324] The validation circuit 8716 is structured to validate the golden feature 2014 against a golden configuration 2010 (also shown in Fig. 20) based at least in part on a golden intent 2016 (also shown in Fig. 20).

[0325] Further aspects of embodiments of the apparatus 8700 are shown in Fig. 88. For example, embodiment of the apparatus 8700 may include a remediation circuit 8810, where the validation circuit 8716 is further structured to identify, based at least in part on validation of the golden feature 2014 against the golden configuration 2010, one or more computing devices 8812 of the golden feature 2014 that deviate from the golden configuration 2010; and the remediation circuit 8810 is structured to adjust, via one or more adjustment command values 8814, the one or more computing devices 8812 of the golden feature 2014 to satisfy the golden configuration 2010.

[0326] Fig. 89 depicts still further aspects of embodiments of apparatus 8700. For example, in embodiments, the apparatus 8700 may include a golden configuration generation circuit 8910 structured to generate the golden configuration 2010 via a forward engineering process.

[0327] Embodiments of the apparatus 8700 may further include a golden parameter processing circuit 8912 structured to interpret a user defined golden parameter 8914, where the golden configuration generation circuit 8910 is further structured to generate, based at least in part on the user defined golden parameter 8914, the golden configuration 2010.

[0328] In embodiments, the golden configuration generation circuit 8910 may employ a reverse engineering process to generate the golden configuration. In such embodiments, the discovery circuit 8710 may generate, based at least in part on a user input value 8916, a golden parameter 8918 for at least one of the one or more discovered computing devices, wherein the golden configuration 2010 is based at least in part on the golden parameter 8918.

[0329] The discovery circuit 8710 may be further structured to discover available commands, e.g., one or more network commands 8920, and / or one or more device commands 8922. In such embodiments, the golden configuration generation circuit 8910 may be further structured to: generate, in response to the available commands 8920 and the one or more device commands 8922, one or more artificial intelligence prompt values 8924; transmit the one or more artificial intelligence prompt values 8924 to an artificial intelligence circuit, e.g., 362 (Fig. 3); and interpret network management instruction data 8926 generated by the artificial intelligence circuit 362 in response to the one or more artificial intelligence prompt values 8924.PATENT Attorney Docket No. NETB-0001-WO

[0330] The apparatus 8700 may further include an execution engine circuit 8928 (also shown as 30 in Fig. 3) structured to execute one or more network operation command values 8930 based at least in part on the network management instruction data 8926 and the discovered available commands 8920 and / or 8922. In such embodiments, discovery of the one or more computing devices in electronic communication with the computer network may be based at least in part on the execution of the one or more network operation command values 8930.

[0331] The discovery circuit 8710 may be further structured to discover the available command 8920 and / or 8922 via querying 8932 a command database 324 (Figs. 3 and 7).

[0332] Fig. 90 depicts another method 9000 for network management, in accordance with embodiments of the current disclosure. The method 9000 may be performed via apparatus 8700 (Fig. 87), apparatus 200 (Fig. 2), system 100 (Figs. 1 and 3), and / or any other computing device disclosed herein.

[0333] The method 9000 includes: discovering one or more computing devices in electronic communication with a computer network 9010; determining one or more eigen variables for the one or more discovered computing devices 9012; generating, based at least in part on one or more eigen variables, a golden feature 9014; and validating the golden feature against a golden configuration based at least in part on a golden intent 9016.

[0334] Fig. 91 depicts further aspects of embodiments of the method 9000. For example, in embodiments, the method 9000 may further include identifying, based at least in part on validation of the golden feature against the golden configuration, one or more computing devices of the golden feature that deviate from the golden configuration 9110; and adjusting the one or more computing devices of the golden feature to satisfy the golden configuration 9112.

[0335] As further shown in Fig. 91, the method 9000 may further include generating the golden configuration via a forward engineering process 9114. As such, the method 9000 may include: interpreting a user defined golden parameter 9116; where generating the golden configuration via the forward engineering process 9114 is based at least in part on the user defined golden parameter.

[0336] As shown in Fig. 92, embodiments of the method 9000 may generate the golden configuration via a reverse engineering process. Accordingly, in such embodiments, the method 9000 may include: generating, based at least in part on a user input value, a golden parameter for at least one of the one or more discovered computing devices 9210. In such embodiments, the golden configuration may be generated 9212 based at least in part on the golden parameter.

[0337] The method 9000 may further include: discovering one or more available commands including at least one of: one or more network commands; or one or more device commands 9214; generating, in response to the available commands, one or more artificial intelligence prompt valuesPATENT Attorney Docket No. NETB-0001-WQ 9216; transmitting the one or more artificial intelligence prompt values to an artificial intelligence circuit 9218; and interpreting, network management instruction data generated by the artificial intelligence circuit in response to the one or more artificial intelligence prompt values 9220. The method 9000 may further include executing one or more network operation command values based at least in part on the network management instruction data and the discovered one or more available commands 9222, where discovery of the one or more computing devices in electronic communication with the computer network 9010 may be based at least in part on the execution of the one or more network operation command values.

[0338] As shown in Fig. 93, the method 9000 may include querying a command database 9310, where the one or more available commands are discovered based at least in part on results of the query.

[0339] Fig. 94 depicts another apparatus 9400 for managing a network. The apparatus 9400 may be embodied and / or form part of apparatus 200 (Fig. 2), system 100 (Figs. 1 and 3), and / or any other computing device disclosed herein. The apparatus 9400 includes a discovery circuit 9410 (also shown as 1924 in Fig. 19), a candidate parameter identification circuit 9412, a golden parameter selection circuit 9414, a golden configuration generation circuit 9416, and / or a golden configuration provisioning circuit 9418.

[0340] The discovery circuit 9410 is structured to discover one or more computing devices 9420 in electronic communication with a computer network. The candidate parameter identification circuit 9412 is structured to identify one or more candidate parameters 9422 for the one or more discovered computing devices. The golden parameter selection circuit 9414 is structured to select, from the one or more candidate parameters, one or more golden parameters 9424. The golden configuration generation circuit 9416 is structured to generate, based at least in part on the one or more golden parameters, a golden configuration 9426. The golden configuration provisioning circuit 9418 is structured to transmit the golden configuration 9426.

[0341] Fig. 95 depicts further aspects of the apparatus 9500. For example, in embodiments, discovery circuit 9420 may be further structured to obtain network context data 9510 corresponding to at least one of: the one or more computing devices 9420, or the computer network, and the golden configuration generation circuit 9416 may be further structured to generate, via an artificial intelligence model 922 (Fig. 9) in response to the network context data 9510; one or more network operation command values 9512. In such embodiments, the discovery of the one or more computing devices 9420 may be based at least in part on the one or more network operation command values 9512.PATENT Attorney Docket No. NETB-0001-WO

[0342] In embodiments, the apparatus 9400 may include a mapping circuit 9514 structured to generate network map data 9516 structured to depict a network map of the one or more computing devices. In such embodiments, the selection of the one or more golden parameters via the golden parameter selection circuit 9514 may be based at least in part on the depiction of the network map.

[0343] In embodiments, the apparatus 9400 may include: a validation circuit 9518 and a remediation circuit 9520. The validation circuit 9518 is structured to identify one more deviating computing devices 9522 that deviate from the golden configuration 9426; and the remediation circuit 9520 is structured to adjust, via one or more adjustment command values 9524 the one or more deviating computing devices 9522 to satisfy the golden configuration 9426.

[0344] In embodiments, the one or more deviating computing devices 9522 form part of a golden feature 2014 (Fig. 20), where the validation circuit 9518 is structured to validate the golden feature 2014 against the golden configuration 9426, and the identification of the one or more deviating devices 9522 by the validation circuit 9518 is based at least in part on the validation of the golden feature 2014 against the golden configuration 9426.

[0345] In embodiments, the discovery circuit 9420 may be structured to query 9526 a command database 324 (Fig. 3) and generate a listing 9528 of one or more available commands. The discovery circuit 9410 may be further structured to query 9530 an action plan database 341 (Fig. 3) and generate a listing of action plans 9532. In such embodiments, the golden configuration generation circuit 9416 may be further structured to: transmit the listing 9528 of one or more available commands and the listing 9532 of action plans to an artificial intelligence model 922 (Fig. 9). The golden configuration circuit 9416 may be further structured to: interpret, from the artificial intelligence model 922, network management instruction data 9534; and generate, based at least in part on the network management instruction data 9534, the one or more network operation command values 9512, where the adjustment, e.g., adjustment values 9524, to the one or more deviating computing devices 9522 to satisfy the golden configuration 9426 is based at least in part on the network operation command values 9512.

[0346] Illustrated in Fig. 96 is another method 9600 for managing a network. The method 9600 may be performed via apparatus 9400 (Fig. 94), apparatus 200 (Fig. 2), system 100 (Figs. 1 and 3), and / or any other computing device disclosed herein. The method 9600 includes: discovering one or more computing devices in electronic communication with a network 9610; identifying one or more candidate parameters for the one or more discovered computing devices 9612; selecting, from the one or more candidate parameters, one or more golden parameters 9614; generating, based at least in part on one or more golden parameters, a golden configuration 9616; and transmitting the golden configuration 9618.PATENT Attorney Docket No. NETB-0001-WO

[0347] Fig. 97 depicts further aspects of embodiments of the method 9600. For example, the method 9600 may further include: obtaining network context data corresponding to at least one of: the one or more computing devices, or the computer network 9710; and generating, in response to the network context data and using an artificial intelligence model; one or more network operation command values 9712. In such embodiments, discovering the one or more computing devices in electronic communication with a computer network 9610 may be based at least in part on the one or more network operation command values.

[0348] In embodiments, the method 9600 further includes generating network map data structured to depict a network map of the one or more computing devices 9714. In such embodiments, selecting the one or more golden parameters 9614 may be based at least in part on the depiction of the network map.

[0349] The method 9600 may include: identifying one more deviating computing devices that deviate from the golden configuration 9716; and adjusting the one or more deviating computing devices to satisfy the golden configuration 9718.

[0350] In embodiments, the one or more deviating computing devices form part of a golden feature, where, in such embodiments, the method 9600 may further include validating the golden feature against the golden configuration 9720. Identifying the one or more deviating computing devices 9716 may be based at least in part on validating the golden feature against the golden configuration 9720.

[0351] As shown in Fig. 98, in embodiments, the method 9600 may further include: generating a listing of one or more available commands based at least in part on a command database 9810; generating a listing of action plans based at least in part on an action plan database 9812; transmitting the listing of one or more available commands and the listing of action plans to an artificial intelligence model 9814; interpreting, from the artificial intelligence model, network management instruction data 9816; and generating, based at least in part on the network management instruction data, one or more network operation command values 9818. In such embodiments, adjusting the one or more deviating computing devices to satisfy the golden configuration 9718 may be based at least in part on the one or more network operation command values.

[0352] As described herein, machine learning models (e.g., the artificial intelligence model 922 (Fig. 9)) may be trained using supervised learning or unsupervised learning. In supervised learning, a model is generated using a set of labeled examples, where each example has corresponding target label(s). In unsupervised learning, the model is generated using unlabeled examples. The collection of examples constructs a dataset, usually referred to as a training dataset. During training, a model is generated using this training data to leam the relationship between examples in the dataset. ThePATENT Attorney Docket No. NETB-0001-WO training process may include various phases such as: data collection, preprocessing, feature extraction, model training, model evaluation, and model fine-tuning. The data collection phase may include collecting a representative dataset, typically from multiple users, that covers the range of possible scenarios and positions. The preprocessing phase may include cleaning and preparing the examples in the dataset and may include filtering, normalization, and segmentation. The feature extraction phase may include extracting relevant features from examples to capture relevant information for the task. The model training phase may include training a machine learning model on the preprocessed and feature-extracted data. Models may include support vector machines (SVMs), artificial neural networks (ANNs), decision trees, and the like for supervised learning, or autoencoders, Hopfield, restricted Boltzmann machine (RBM), deep belief. Generative Adversarial Networks (GAN), or other networks, or clustering for unsupervised learning. The model evaluation phase may include evaluating the performance of the trained model on a separate validation dataset to ensure that it generalizes well to new and unseen examples. The model fine-tuning may include refining a model by adjusting its parameters, changing the features used, or using a different machine-learning algorithm, based on the results of the evaluation. The process may be iterated until the performance of the model on the validation dataset is satisfactory and the trained model can then be used to make predictions.

[0353] In embodiments, trained models may be periodically fine-tuned for specific user groups, applications, and / or tasks. Fine-tuning of an existing model may improve the performance of the model for an application while avoiding completely retraining the model for the application.

[0354] In embodiments, fine-tuning a machine learning model may involve adjusting its hyperparameters or architecture to improve its performance for a particular user group or application. The process of fine-tuning may be performed after initial training and evaluation of the model, and it can involve one or more hyperparameter tuning and architectural methods.

[0355] Hyperparameter tuning includes adjusting the values of the model's hyperparameters, such as learning rate, regularization strength, or the number of hidden units. This can be done using methods such as grid search, random search, or Bayesian optimization. Architecture modification may include modifying the structure of the model, such as adding or removing layers, changing the activation functions, or altering the connections between neurons, to improve its performance.

[0356] Online training of machine learning models includes a process of updating the model as new examples become available, allowing it to adapt to changes in the data distribution over time. In online training, the model is trained incrementally as new data becomes available, allowing it to adapt to changes in the data distribution over time. Online training can also be useful for user groupsPATENT Attorney Docket No. NETB-0001-WO that have changing usage habits of the stimulation device, allowing the models to be updated in almost real-time.

[0357] In embodiments, online training may include adaptive filtering. In adaptive filtering, a machine learning model is trained online to learn the underlying structure of the new examples and remove noise or artifacts from the examples.

[0358] The methods (e.g., 8100, 9000, 9600, etc.), apparatuses (e.g., 7900, 8700, 9400, etc.), and / or systems (e.g., 100) described herein may be deployed in part or in whole through a machine (e.g., apparatus 200) having a computer, computing device, processor, circuit, and / or server that executes computer-readable instructions (e.g., application 214), program codes, instructions, and / or includes hardware configured to functionally execute one or more operations of the methods (e.g., 8100, 9000, 9600, etc.), apparatuses (e.g., 200, 7900, 8700, 9400, etc.), and / or systems (e.g., 100) disclosed herein. The terms computer, computing device, processor, circuit, and / or server, as utilized herein, should be understood broadly.

[0359] Any one or more of the terms computer, computing device, processor, circuit, and / or server include a computer of any type, capable to access instructions stored in communication thereto such as upon a non-transient computer-readable medium, whereupon the computer performs operations of systems or methods described herein upon executing the instructions. In certain embodiments, such instructions themselves include a computer, computing device, processor, circuit, and / or server. Additionally or alternatively, a computer, computing device, processor, circuit, and / or server may be a separate hardware device, one or more computing resources distributed across hardware devices, and / or may include such aspects as logical circuits, embedded circuits, sensors, actuators, input and / or output devices, network and / or communication resources, memory resources of any type, processing resources of any type, and / or hardware devices configured to be responsive to determined conditions to functionally execute one or more operations of systems and methods herein.

[0360] Network and / or communication resources include, without limitation, local area network, wide area network, wireless, internet, or any other known communication resources and protocols. Example and non-limiting hardware, computers, computing devices, processors, circuits, and / or servers include, without limitation, a general purpose computer, a server, an embedded computer, a mobile device, a virtual machine, and / or an emulated version of one or more of these. Example and non-limiting hardware, computers, computing devices, processors, circuits, and / or servers may be physical, logical, or virtual. A computer, computing device, processor, circuit, and / or server may be: a distributed resource included as an aspect of several devices; and / or included as an interoperable set of resources to perform described functions of the computer, computing device, processor, circuit, and / or server, such that the distributed resources function together to perform the operationsPATENT Attorney Docket No. NETB-0001-WO of the computer, computing device, processor, circuit, and / or server. In certain embodiments, each computer, computing device, processor, circuit, and / or server may be on separate hardware, and / or one or more hardware devices may include aspects of more than one computer, computing device, processor, circuit, and / or server, for example as separately executable instructions stored on the hardware device, and / or as logically partitioned aspects of a set of executable instructions, with some aspects of the hardware device including a part of a first computer, computing device, processor, circuit, and / or server, and some aspects of the hardware device including a part of a second computer, computing device, processor, circuit, and / or server.

[0361] A computer, computing device, processor, circuit, and / or server may be part of a server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like. The processor may be or include a signal processor, digital processor, embedded processor, microprocessor or any variant such as a co-processor (math co-processor, graphic co-processor, communication co-processor and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more threads. The thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor may include memory that stores methods, codes, instructions and programs as described herein and elsewhere. The processor may access a storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

[0362] A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).

[0363] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer readable instructions on a server, client, firewall, gateway, hub,PATENT Attorney Docket No. NETB-0001-WO router, or other such computer and / or networking hardware. The computer readable instructions may be associated with a server that may include a file server, print server, domain server, internet server, intranet server and other variants such as secondary server, host server, distributed server and the like. The server may include one or more of memories, processors, computer readable transitory and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

[0364] The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of instructions across the network. The networking of some or all of these devices may facilitate parallel processing of program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the server through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.

[0365] The methods, program code, instructions, and / or programs may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client and the like. The client may include one or more of memories, processors, computer readable transitory and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, program code, instructions, and / or programs as described herein and elsewhere may be executed by the client. In addition, other devices utilized for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.

[0366] The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of methods, program code, instructions, and / or programs across the network. The networking ofPATENT Attorney Docket No. NETB-0001-WO some or all of these devices may facilitate parallel processing of methods, program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the client through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.

[0367] The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules, and / or components as known in the art. The computing and / or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM and the like. The methods, program code, instructions, and / or programs described herein and elsewhere may be executed by one or more of the network infrastructural elements.

[0368] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on a cellular network having multiple cells. The cellular network may either be frequency division multiple access (FDMA) network or code division multiple access (CDMA) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like.

[0369] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players, and the like. These mobile devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute methods, program code, instructions, and / or programs stored thereon.Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute methods, program code, instructions, and / or programs. The mobile devices may communicate on a peer to peer network, mesh network, or other communications network. The methods, program code, instructions, and / or programs may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may storePATENT Attorney Docket No. NETB-0001-WO methods, program code, instructions, and / or programs executed by the computing devices associated with the base station.

[0370] The methods, program code, instructions, and / or programs may be stored and / or accessed on machine readable transitory and / or non-transitory media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time; semiconductor storage known as random access memory (RAM); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read / write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.

[0371] Certain operations described herein include interpreting, receiving, and / or determining one or more values, parameters, inputs, data, or other information. Operations including interpreting, receiving, and / or determining any value parameter, input, data, and / or other information include, without limitation: receiving data via a user input; receiving data over a network of any type; reading a data value from a memory location in communication with the receiving device; utilizing a default value as a received data value; estimating, calculating, or deriving a data value based on other information available to the receiving device; and / or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation, and later updated by a second operation, as part of the receiving a data value. For example, when communications are down, intermittent, or interrupted, a first operation to interpret, receive, and / or determine a data value may be performed, and when communications are restored an updated operation to interpret, receive, and / or determine the data value may be performed.

[0372] Certain logical groupings of operations herein, for example methods or procedures of the current disclosure, are provided to illustrate aspects of the present disclosure. Operations described herein are schematically described and / or depicted, and operations may be combined, divided, reordered, added, or removed in a manner consistent with the disclosure herein. It is understood that the context of an operational description may require an ordering for one or more operations, and / or an order for one or more operations may be explicitly disclosed, but the order of operations should be understood broadly, where any equivalent grouping of operations to provide an equivalent outcome of operations is specifically contemplated herein. For example, if a value is used in one operationalPATENT Attorney Docket No. NETB-OOOl-WO step, the determining of the value may be required before that operational step in certain contexts (e.g. where the time delay of data for an operation to achieve a certain effect is important), but may not be required before that operation step in other contexts (e.g. where usage of the value from a previous execution cycle of the operations would be sufficient for those purposes). Accordingly, in certain embodiments an order of operations and grouping of operations as described is explicitly contemplated herein, and in certain embodiments re-ordering, subdivision, and / or different grouping of operations is explicitly contemplated herein.

[0373] The methods and systems described herein may transform physical and / or or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another.

[0374] The elements described and depicted herein, including in flow charts, block diagrams, and / or operational descriptions, depict and / or describe specific example arrangements of elements for purposes of illustration. However, the depicted and / or described elements, the functions thereof, and / or arrangements of these, may be implemented on machines, such as through computer executable transitory and / or non-transitory media having a processor capable of executing program instructions stored thereon, and / or as logical circuits or hardware arrangements. Example arrangements of programming instructions include at least: monolithic structure of instructions; standalone modules of instructions for elements or portions thereof; and / or as modules of instructions that employ external routines, code, services, and so forth; and / or any combination of these, and all such implementations are contemplated to be within the scope of embodiments of the present disclosure Examples of such machines include, without limitation, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements described and / or depicted herein, and / or any other logical components, may be implemented on a machine capable of executing program instructions. Thus, while the foregoing flow charts, block diagrams, and / or operational descriptions set forth functional aspects of the disclosed systems, any arrangement of program instructions implementing these functional aspects are contemplated herein. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein.Additionally, any steps or operations may be divided and / or combined in any manner providing similar functionality to the described operations. All such variations and modifications are contemplated in the present disclosure. The methods and / or processes described above, and stepsPATENT Attorney Docket No. NETB-0001-WO thereof, may be implemented in hardware, program code, instructions, and / or programs or any combination of hardware and methods, program code, instructions, and / or programs suitable for a particular application. Example hardware includes a dedicated computing device or specific computing device, a particular aspect or component of a specific computing device, and / or an arrangement of hardware components and / or logical circuits to perform one or more of the operations of a method and / or system. The processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and / or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine readable medium.

[0375] The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and computer readable instructions, or any other machine capable of executing program instructions.

[0376] Thus, in one aspect, each method described above and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and / or computer-readable instructions described above. All such pennutations and combinations are contemplated in embodiments of the present disclosure.

[0377] One non-limiting use case of the current disclosure is to provide for the ability to identify a common configuration, e.g., a golden configuration, for a group of network devices when prior documentation does not exist and / or is lacking.

[0378] Another non-limiting use case of the current disclosure is to provide for the automatic generation of command line interface commands (for viewing network configurations on networkPATENT Attorney Docket No. NETB-OOOl-WO computing devices) using a user interface where the user does not need to know the underlying command line interface commands.

[0379] Another non-limiting use case of the current disclosure is to provide for a simplified human-machine interface where a user can use natural language to obtain reports and / or dashboard depicting configurations and / or network states for one or more network computing devices.

[0380] Fig. 99 depicts another nonlimiting use case of the current disclosure for mapping a network. In such a scenario, the system 100 may generate a GUI 9900 having a chatbot type interface 9910 with a command prompt 9912 and a map pane 9 14. The user may enter a command 9916 such as “issue show ip route 10.8.3.130 on US-SMF-R1, and find the IP of next hop device based on result,’’ and the orchestration agent 914 (Fig. 9) may proceed to gather the appropriate context data and interact with the artificial intelligence circuit 362 (Fig. 3) in an iterative manner to execute one or more CLI commands and generate (via a mapping circuit 524 (Fig. 5)) the requested map 9918. As shown in Fig. 100, the user may enter a command to ask the chatbot to “Draw an arrow from [a currently selected device] to the next hop”, where the orchestration agent 914 may insert arrow 10010.

[0381] Fig. 101 shows another nonlimiting use case where a user asks 10110 the chatbot to do a step-by-step analysis of a multiple hop path, where the orchestration agent 914 performs multiple CLI execution cycles and then displays the results 10132 in the interface 9910 and updates the map 9918 as shown in Fig. 102. Fig. 103 depicts a time forward (represented by arrow 10310) view of the user iteratively interacting with the chatbot to discover nodes on a network one step at a time, where the user issues a first command 10312, the chatbot responds 10414, the user issues a second command 10316, and the chatbot responds 10318 and updates the map 9918 to show the newly discovered node. The user can repeat the interactive process with the chatbot to discover an entire path between two different devices. The user may then save the sequence of commands into an action plan, as disclosed herein, for future use.

[0382] Figs. 104 and 105 depicts another nonlimiting use case where a user works with the chatbot to schedule a periodic automation to ping a certain IP from a device every two (2) minutes for five (5) times to ensure the device is stable. The user may enter a command 10410 “run automation Ping to 10.8.1.25 from CA-TOR-R1 every 2 minutes for 5 times, then display results in a dashboard.” The chatbot may provide confirmation 10412 of the creation of the requested automation, where the user can click the confirmation 10412 to view details 10416 of the automation and / or to view and / or specific applicable network intents. The Al may then perform the automation and generate the requested dashboard 10510.PATENT Attorney Docket No. NETB-OOOl-WO

[0383] Fig. 106 shows another nonlimiting use case of the current disclosure where a user interacts with a chatbot to define an action plan 10610 (having steps 10612, as disclosed herein) to troubleshoot an unreachable host issue. As shown in Fig. 107, the user may define a match condition 10710 (e.g., “unreachable”) specifying the conditions for when the action plan may be useful and / or triggered. As shown in Figs. 108 and 109, after creation of the action plan 10610, the chatbot may display the action plan 10610 when prompted by the user and, upon the user’s selection 10810 of the action plan 10610, the orchestration agent 914 (Fig. 9) proceeds to execute 10910 the action plan 10610 and displays the results 10912.

[0384] Figs. 110 and 111 depict another nonlimiting use case of the current disclosure, in which a user extends a parser in the context of a network issue diagnosis. As will be understood, Fig. 111 depicts a portion 11100 of a GUI 11000 shown in Fig. 110. The user may begin by selecting a node 11010, then selecting an option to make a new action (e.g., an automation) and further specifying the action type 11012 as a “Diagnosis”. The user may then proceed to define diagnosis logic steps 11014 (e.g., steps) for one or more selected parser variables. The user may also view a current baseline window 11016 and / or, as best seen in Fig. Ill, define and / or add golden checks 11110. When defining a golden check, the user may choose either single variable or a table variable.Multiple golden checks 11100 can be added, and each golden check 11100 may be regarded as an independent diagnosis. The user may also select the variable scope, which can be set to all the variables in the golden parser, where the GUI 11000 may show the available actions for selected parsers. Table 2 shows a mapping of available diagnostic types to single and table variables.Table 2PATENT Attorney Docket No. NETB-OOOl-WO

[0385] Fig. 112 depicts another nonlimiting use case of the system 100 (Fig. 1 and 3) where a user extends a parser with a compound table within a GUI 11200. The user may click on a node 11210, then make a selection to add a parse node action 11212 and specify the action type 11214 as a compound table. Embodiments of the GUI 11200 may also provide for the user to add compound table nodes from a golden parser node. In embodiments, a compound table may support four types of actions / nodes: Merge Table 11216, Sub Table 11218, Append Table 11220, and Neighbor Join 1 1222.

[0386] Fig. 113 depicts a GUI 11300 for executing / performing the Merge Table action 11216 which merges multiple command tables using specific keys. Supported merge types include: full join, inner join, and left join. Merging may also occur between multiple parsers under the same device role and / or between current and parent device roles.

[0387] Fig. 114 depicts a GUI 11400 for executing / performing the Neighbor Join action 11222, which merges two parser tables and one global neighbor table based on specified mapping rules. Merging can occur between multiple parsers under the same device role or between the current device role and the parent device role.

[0388] Fig. 115 depicts a GUI 11500 for executing / performing the Sub Table action 11218, which filters a selected table to obtain a subset table for subsequent diagnosis.

[0389] Fig. 116 depicts a GUI 11600 for executing / performing the Append Table action 11220, which selects two tables and appends their data according to specified rules.PATENT Attorney Docket No. NETB-0001-WO

[0390] Referring again to Fig. 112, the GUI 11200 may provide for further actions. For example, a user may be able to extend one of more of the following more types of nodes: “External Intent’’ (which provides for the configuration of the matching relationship with the device role, assigns a value to the macro variable if needed, and defines related replication settings); and / or “Follow-up Diagnosis” (which provides for user selection of a network intent and device role, the system 100 (Fig. 1 and 3), based on the selected network intent, generates a device section and a device diagnosis for all devices in the selected device role).

[0391] In another nonlimiting use case of the system 100 (Fig. 1 and Fig. 3), a user may build a golden intent from a feature instance. Predefined logic in a golden intent may be used to clone member intents for each of a plurality of feature instances, utilizing the devices within each instance. The member intents may have been previously saved in a staging environment and subsequently published to a production environment. Cloned intents of a golden intent (e.g., for IAF, TAF, and PAF) may also be published. In embodiments, after a golden intent is published, the golden intent is shown in the production environment where users can view the feature details and the related golden intent for each feature instance (also referred to as a feature ADT).

[0392] In yet another nonlimiting use case of the system 100 (Figs. 1 and 3), a user may schedule golden intents as part of a PAF. The golden intents may be scheduled in batches and / or independently. Such automations may also execute one or more member intents of a golden intent, as opposed to executing the entire parent golden intent. The system 100 may provide a GUI and / or other type of interface for displaying and / or managing the schedules, where golden intents may be grouped by feature. The interface may also display other tasks related to the scheduled golden intents.

[0393] Fig. 117 depicts yet another nonlimiting use case of the system 100 (Figs. 1 and 3) where a GUI 11700 shows a diagnosis tree 11710 generated in response to executing one or more golden checks.

[0394] Figs. 118 and 119 depict yet a further nonlimiting embodiment of a GUI 11800 that may be generated by the system 100 (Figs. 1 and 3) after executing one or more scheduled golden intents. The GUI 11800 may show diagnosis results 11810 and / or provide for filtering of the results based on member intents and / or devices. As will be appreciated, embodiments of the GUI 11800 may provide for use of the golden intents to display a golden automation of map devices, view golden intent results, and / or build a summary dashboard based on the golden automation map. Embodiments of the GUI 11800 may provide for: filtering of the feature instances and member intents: viewing of a timeline depicting historical results of intents executions; viewing of features and / or intent details and / or other related information; selection and execution of intents, with the execution results readilyPATENT Attorney Docket No. NETB-OOOl-WO viewable; encapsulation of an intent (e.g., a golden check that runs and generates a diagnosis tree); a list of related eigen variables and their values; and / or a list of devices their roles. Embodiments of the GUI 11800 may display the results of executed intents on a network topology map.

[0395] Accordingly, and as is to be appreciated, embodiments of the systems and methods described herein improve the network management art by enabling the use of general-purpose large language models (LLMs) for specialized network management operations without requiring domainspecific model training or fine-tuning. Rather than utilizing a custom-trained model for network operations, embodiments of the system implement an architectural framework that adapts any suitable LLM to perform network management tasks through carefully structured prompts, contextual information, and specialized tools.

[0396] As is to be further appreciated, embodiments of the orchestration circuit 312 disclosed herein improve the network management arts by identifying the appropriate network operations to perform; then, rather than directly executing these operations, the artificial intelligence circuit 362 generates a textual representation of any required API calls (or other commands) in a predetermined format. This text-based intermediate step provides several advantages (e.g.: it allows for validation before execution; enables consistent handling of complex multi-step operations; and maintains a clear audit trail of system actions.

[0397] As also shown herein, this adaptability is provided through a layered architecture that separates the general language understanding capabilities of the artificial intelligence model 922 (e.g., a LLM) from the specific network management operations (e.g., as depicted in Figs. 1, 3, and 9 and discussed in the corresponding text within this disclosure). For example, the orchestration circuit 312 provides the LLM with context and operational frameworks through structured prompts, while specialized tools (e.g., the command identifier circuit 320, IP lookup circuit 348, execution engine 330, etc.) handle the actual execution of network operations. This architecture allows embodiments of the system 100 to leverage different LLM providers or switch between models without requiring modifications to the core network management functionality.

[0398] Further, embodiments of the system 100, disclosed herein, improve the network management arts by enabling execution of predefined network operations through a natural language interface by maintaining libraries of stored actions and / or intents that can be referenced and executed through conversational commands. These stored procedures range from simple command sequences to complex multi-step operations, thus, allowing users to invoke sophisticated network management tasks through natural language requests.

[0399] While the disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readilyPATENT Attorney Docket No. NETB-OOOl-WO apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not to be limited by the foregoing examples, but is to be understood in the broadest sense allowable by law.

Claims

PATENT Attorney Docket No. NETB-OOOl-WO CLAIMSWhat is Claimed Is:

1. An apparatus for network management comprising:an input command processing circuit structured to interpret one or more input command values corresponding to a request to generate golden configuration data for at least one computing device in electronic communication with a computer network;an execution engine circuit structured to:execute the one or more input command values; andinterpret result data responsive to the one or more input command values;an orchestration circuit structured to generate, based at least in part on the result data, the golden configuration data; anda golden configuration provisioning circuit structured to transmit the golden configuration data.

2. The apparatus of claim 1 further comprising:a golden engineering circuit structured to:generate a graphical user interface that provides a plurality of input prompts structured to guide a user through a golden configuration discovery process; and generate, in response to one or more user interactions with the plurality of input prompts, the one or more input command values.

3. The apparatus of claim 2, wherein the golden engineering circuit comprises:a forward engineering circuit structured to generate the plurality of input prompts, wherein the golden configuration discovery process is forward engineered.

4. The apparatus of claim 3, wherein:the plurality of input prompts is further structured to present one or more selectable golden configuration templates; andat least one of the one or more input command values corresponds to a selected golden configuration template of the one or more selectable golden configuration templates.

5. The apparatus of claim 4, wherein:the plurality of input prompts is further structured to present one or more selectable reference computing devices;at least one of the one or more input command values corresponds to a selected reference computing device of the one or more selectable reference computing devices; andPATENT Attorney Docket No. NETB-OOOl-WO the at least one computing device for the request to generate golden configuration data corresponds to the selected reference computing device.

6. The apparatus of claim 2, wherein the golden engineering circuit comprises:a reverse engineering circuit structured to generate the plurality of input prompts, wherein the golden configuration discovery process is reverse engineered.

7. The apparatus of claim 6, wherein:the reverse engineering circuit comprises:a discovery circuit structured to:generate one or more discovery command values structured to identify an architecture of the computer network via directing the orchestration circuit to iteratively discover one or more computing devices in electronic communication with the computer network;wherein:the graphical user interface is structured to:display the one or more discovered computing devices; andprovide for a user selection of at least one of the one or more discovered computing devices; andthe at least one computing device for the request to generate the golden configuration data corresponds to a user selected at least one of the one or more discovered computing devices.

8. The apparatus of claim 7, wherein:at least one of the plurality of input prompts is structured to obtain one or more golden parameters; andthe one or more discovered computing devices satisfy the one or more golden parameters.

9. The apparatus of claim 8, wherein:the graphical user interface is further structured to define a golden configuration template based at least in part on the one or more golden parameters; andthe orchestration circuit is further structured to generate the golden configuration data based at least in part on the golden configuration template.

10. The apparatus of claim 7, wherein:PATENT Attorney Docket No. NETB-OOOl-WO the golden engineering circuit further comprises a golden feature management circuit structured to generate a golden feature that comprises the one or more discovered computing devices; andthe apparatus further comprises a network monitoring and validation circuit structured to: validate the golden feature against the golden configuration data; andgenerate an indication that the one or more discovered computing devices of the golden feature satisfy the golden configuration data.

11. The apparatus of claim 10, wherein validation of the golden feature against the golden configuration data is based at least in part on a golden intent.

12. A method for network management comprising:interpreting one or more input command values corresponding to a request to generate golden configuration data for at least one computing device in electronic communication with a computer network;executing the one or more input command values;interpreting result data responsive to the one or more input command values; generating, based at least in part on the result data, the golden configuration data; and transmitting the golden configuration data.

13. The method of claim 12 further comprising:generating a graphical user interface that provides a plurality of input prompts structured to guide a user through a golden configuration discovery process; andgenerating, in response to one or more user interactions with the plurality of input prompts, the one or more input command values.

14. The method of claim 13, wherein the golden configuration discovery process is forward engineered.

15. The method of claim 14 further comprising:presenting, via the plurality of input prompts, one or more selectable golden configuration templates;wherein at least one of the one or more input command values corresponds to a selected golden configuration template of the one or more selectable golden configuration templates.

16. The method of claim 15 further comprising:presenting, via the plurality of input prompts, one or more selectable reference computing devices;PATENT Attorney Docket No. NETB-OOOl-WO wherein:at least one of the one or more input command values corresponds to a selected reference computing device of the one or more selectable reference computing devices; and the at least one computing device for the request to generate golden configuration data corresponds to the selected reference computing device.

17. The method of claim 13, wherein the golden configuration discovery process is reverse engineered.

18. The method of claim 17 further comprising:generating one or more discovery command values;identifying, based at least in part on the one or more discovery command values, an architecture of the computer network via iteratively discovering one or more computing devices in electronic communication with the computer network;displaying the one or more discovered computing devices;providing for a user selection of at least one of the one or more discovered computing devices; andinterpreting at least one of the one or more user interactions with the plurality of input prompts as a selection of the at least one of the one or more discovered computing devices;wherein the at least one computing device for the request to generate the golden configuration data corresponds to the user selected at least one of the one or more discovered computing devices.

19. The method of claim 18 further comprising:obtaining, via at least one of the plurality of input prompts, one or more golden parameters; wherein the one or more discovered computing devices satisfy the one or more golden parameters.

20. The method of claim 19 further comprising:defining, via the graphical user interface, a golden configuration template based at least in part on the one or more golden parameters;wherein generating the golden configuration data is based at least in part on the golden configuration template.

21. The method of claim 18 further comprising:generating a golden feature that comprises the one or more discovered computing devices; validating the golden feature against the golden configuration data; andPATENT Attorney Docket No. NETB-OOOl-WO generating an indication that the one or more discovered computing devices of the golden feature satisfy the golden configuration data.

22. The method of claim 21, wherein validating the golden feature against the golden configuration data is based at least in part on a golden intent.

23. A non-transitory computer-readable medium storing instructions that, when loaded into at least one processor, causes the at least one processor to:interpret one or more input command values corresponding to a request to generate golden configuration data for at least one computing device in electronic communication with a computer network;execute the one or more input command values;interpret result data responsive to the one or more input command values;generate, based at least in part on the result data, the golden configuration data; and transmit the golden configuration data.

24. The non-transitory computer-readable medium of claim 23, wherein the stored instructions further cause the at least one processor to:generate a graphical user interface that provides a plurality of input prompts structured to guide a user through a golden configuration discovery process; andgenerate, in response to one or more user interactions with the plurality of input prompts, the one or more input command values.

25. The non-transitory computer-readable medium of claim 24, wherein the golden configuration discovery process is forward engineered.

26. The non-transitory computer-readable medium of claim 25, wherein the stored instructions further cause the at least one processor to:present, via the plurality of input prompts, one or more selectable golden configuration templates;wherein at least one of the one or more input command values corresponds to a selected golden configuration template of the one or more selectable golden configuration templates.

27. The non-transitory computer-readable medium of claim 26, wherein the stored instructions further cause the at least one processor to:present, via the plurality of input prompts, one or more selectable reference computing devices;wherein:PATENT Attorney Docket No. NETB-OOOl-WO at least one of the one or more input command values corresponds to a selected reference computing device of the one or more selectable reference computing devices; and the at least one computing device for the request to generate golden configuration data corresponds to the selected reference computing device.

28. The non-transitory computer-readable medium of claim 24, wherein the golden configuration discovery process is reverse engineered.

29. The non-transitory computer-readable medium of claim 28, wherein the stored instructions further cause the at least one processor to:generate one or more discovery command values;identify, based at least in part on the one or more discovery command values, an architecture of the computer network via iteratively discovering one or more computing devices in electronic communication with the computer network;display the one or more discovered computing devices;provide for a user selection of at least one of the one or more discovered computing devices; andinterpret at least one of the one or more user interactions with the plurality of input prompts as a selection of the at least one of the one or more discovered computing devices;wherein the at least one computing device for the request to generate the golden configuration data corresponds to the user selected at least one of the one or more discovered computing devices.

30. The non-transitory computer-readable medium of claim 29, wherein the stored instructions further cause the at least one processor to:obtain, via at least one of the plurality of input prompts, one or more golden parameters; wherein the one or more discovered computing devices satisfy the one or more golden parameters.

31. The non-transitory computer-readable medium of claim 30, wherein the stored instructions further cause the at least one processor to:define, via the graphical user interface, a golden configuration template based at least in part on the one or more golden parameters;wherein the golden configuration data is generated based at least in part on the golden configuration template.

32. The non-transitory computer-readable medium of claim 29, wherein the stored instructions further cause the at least one processor to:PATENT Attorney Docket No. NETB-OOOl-WO generate a golden feature that comprises the one or more discovered computing devices; validate the golden feature against the golden configuration data; andgenerate an indication that the one or more discovered computing devices of the golden feature satisfy the golden configuration data.

33. The non-transitory computer-readable medium of claim 32, wherein validation of the golden feature against the golden configuration data is based at least in part on a golden intent.

34. An apparatus comprising:at least one processor; andat least one memory device that stores computer-readable instructions that, when loaded into at least one processor, causes the at least one processor to:discover one or more computing devices in electronic communication with a computer network;determine one or more eigen variables for the one or more discovered computing devices;generate, based at least in part on one or more eigen variables, a golden feature; and validate the golden feature against a golden configuration based at least in part on a golden intent.

35. The apparatus of claim 34, wherein the computer-readable instructions further cause the at least one processor to:identify, based at least in part on validation of the golden feature against the golden configuration, one or more computing devices of the golden feature that deviate from the golden configuration; andadjust the one or more computing devices of the golden feature to satisfy the golden configuration.

36. The apparatus of claim 34, wherein the computer-readable instructions further cause the at least one processor to:generate the golden configuration via a forward engineering process.

37. The apparatus of claim 36, wherein the computer-readable instructions further cause the at least one processor to:interpret a user defined golden parameter; andgenerate, based at least in part on the user defined golden parameter, the goldenconfiguration.PATENT Attorney Docket No. NETB-0001-WO 38. The apparatus of claim 35, wherein the computer-readable instructions further cause the at least one processor to:generate the golden configuration via a reverse engineering process.

39. The apparatus of claim 38 wherein the computer-readable instructions further cause the at least one processor to:generate, based at least in part on a user input value, a golden parameter for at least one of the one or more discovered computing devices, wherein the golden configuration is based at least in part on the golden parameter.

40. The apparatus of claim 34, wherein the one or more eigen variables comprise at least one of: a protocol-specific identifier;a device attribute;an interface-level attribute;a neighbor relationship attribute;a configuration parameter;a system data variable; ora composite variable.

41. The apparatus of claim 40, wherein the one or more eigen variables comprise the protocolspecific identifier, wherein the protocol-specific identifier is at least one of:a group identifier; ora virtual internet protocol address.

42. The apparatus of claim 40, wherein the one or more eigen variables comprise the device attribute, wherein the device attribute comprises at least one of:a host name;a device type; ora location.

43. The apparatus of claim 40, wherein the one or more eigen variables comprise the interface-level attribute, wherein the interface-level attribute comprises at least one of:a local interface name:a neighbor interface name; oran interface internet protocol address.

44. The apparatus of claim 40, wherein the one or more eigen variables comprise the neighbor relationship attribute, wherein the neighbor relationship attribute comprises at least one of:PATENT Attorney Docket No. NETB-OOOl-WO a neighbor device name;a neighbor device internet protocol address;a layer 2 neighbor; ora layer 3 neighbor.

45. The apparatus of claim 40, wherein the one or more eigen variables comprise the configuration parameter, wherein the configuration parameter comprises at least one of:a routing protocol setting;a network time protocol server address; ora simple network management protocol community string.

46. The apparatus of claim 40, wherein the one or more eigen variables comprise the system data variable, wherein the system data variable comprises at least one of:a region; ora role.

47. A method comprising:discovering one or more computing devices in electronic communication with a computer network;determining one or more eigen variables for the one or more discovered computing devices; generating, based at least in part on one or more eigen variables, a golden feature; and validating the golden feature against a golden configuration based at least in part on a golden intent.

48. The method of claim 47 further comprising:identifying, based at least in part on validation of the golden feature against the golden configuration, one or more computing devices of the golden feature that deviate from the golden configuration; andadjusting the one or more computing devices of the golden feature to satisfy the golden configuration.

49. The method of claim 47 further comprising:generating the golden configuration via a forward engineering process.

50. The method of claim 48 further comprising:interpreting a user defined golden parameter; andgenerating, based at least in part on the user defined golden parameter, the golden configuration.PATENT Attorney Docket No. NETB-OOOl-WO 51. The method of claim 47 further comprising:generating the golden configuration via a reverse engineering process.

52. The method of claim 51 further comprising:generating, based at least in part on a user input value, a golden parameter for at least one of the one or more discovered computing devices, wherein the golden configuration is based at least in part on the golden parameter.

53. The method of claim 47, wherein the one or more eigen variables comprise at least one of: a protocol-specific identifier;a device attribute;an interface-level attribute:a neighbor relationship attribute;a configuration parameter;a system data variable; ora composite variable.

54. The method of claim 53, wherein the one or more eigen variables comprise the protocol-specific identifier, wherein the protocol-specific identifier is at least one of:a group identifier; ora virtual internet protocol address.

55. The method of claim 53, wherein the one or more eigen variables comprise the device attribute, wherein the device attribute comprises at least one of:a host name;a device type; ora location.

56. The method of claim 53, wherein the one or more eigen variables comprise the interface-level attribute, wherein the interface-level attribute comprises at least one of:a local interface name;a neighbor interface name; oran interface internet protocol address.

57. The method of claim 53, wherein the one or more eigen variables comprise the neighbor relationship attribute, wherein the neighbor relationship attribute comprises at least one of:a neighbor device name;a neighbor device internet protocol address;PATENT Attorney Docket No. NETB-OOOl-WO a layer 2 neighbor; ora layer 3 neighbor.

58. The method of claim 53, wherein the one or more eigen variables comprise the configuration parameter, wherein the configuration parameter comprises at least one of:a routing protocol setting;a network time protocol server address; ora simple network management protocol community string.

59. The method of claim 53, wherein the one or more eigen variables comprise the system data variable, wherein the system data variable comprises at least one of:a region; ora role.

60. An apparatus comprising:at least one processor; andat least one memory device that stores computer-readable instructions that, when loaded into the at least one processor, causes the at least one processor to:discover one or more computing devices in electronic communication with a computer network;identify one or more candidate parameters for the one or more discovered computing devices; select, from the one or more candidate parameters, one or more golden parameters; generate, based at least in part on the one or more golden parameters, a golden configuration; andtransmit the golden configuration.

61. The apparatus of claim 60, wherein the stored computer-readable instructions further cause the at least one processor to:obtain network context data corresponding to at least one of:the one or more computing devices, orthe computer network; andgenerate, via an artificial intelligence model in response to the network context data; one or more network operation command values;wherein the discovery of the one or more computing devices in electronic communication with a computer network is based at least in part on the one or more network operation command values.PATENT Attorney Docket No. NETB-OOOl-WO 62. The apparatus of claim 61, wherein the artificial intelligence model comprises a neural model.

63. The apparatus of claim 62, wherein the neural model forms part of a large language model.

64. The apparatus of claim 60, wherein the stored computer-readable instructions further cause the at least one processor to:generate network map data structured to depict a network map of the one or more computing devices;wherein the selection of the one or more golden parameters is based at least in part on the depiction of the network map.

65. The apparatus of claim 60, wherein the stored computer-readable instructions further cause the at least one processor to:identify one more deviating computing devices that deviate from the golden configuration; andadjust the one or more deviating computing devices to satisfy the golden configuration.

66. The apparatus of claim 65, wherein:the one or more deviating computing devices form part of a golden feature;the stored instructions further cause the at least one processor to validate the golden feature against the golden configuration; andthe identification of the one or more deviating devices is based at least in part on the validation of the golden feature against the golden configuration.

67. A method comprising:discovering one or more computing devices in electronic communication with a network; identifying one or more candidate parameters for the one or more discovered computing devices;selecting, from the one or more candidate parameters, one or more golden parameters; generating, based at least in part on the one or more golden parameters, a golden configuration; andtransmitting the golden configuration.

68. The method of claim 67 further comprising:obtaining network context data corresponding to at least one of:the one or more computing devices, orthe computer network; andPATENT Attorney Docket No. NETB-OOOl-WO generating, in response to the network context data and using an artificial intelligence model; one or more network operation command values;wherein discovering the one or more computing devices in electronic communication with a computer network is based at least in part on the one or more network operation command values.

69. The method of claim 68, wherein the artificial intelligence model comprises a neural network.

70. The method of claim 69, wherein the neural network forms part of a large language model.

71. The method of claim 67 further comprising:generating network map data structured to depict a network map of the one or more computing devices;wherein selecting the one or more golden parameters is based at least in part on the depiction of the network map.

72. The method of claim 67 further comprising:identifying one more deviating computing devices that deviate from the golden configuration; andadjusting the one or more deviating computing devices to satisfy the golden configuration.

73. The method of claim 72, wherein:the one or more deviating computing devices form part of a golden feature;the method further comprises validating the golden feature against the golden configuration; andidentifying the one or more deviating computing devices is based at least in part on validating the golden feature against the golden configuration.

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