Intent driven and intelligent network service and network slice management

Abstract

Provided are apparatus, method, and device for intent driven and intelligent network service and network slice management. The method may include receiving, by an intelligent fulfillment / intelligent controller framework, at least one of a user intent or service profile originating from an Application Protocol Interface (API) gateway. After receiving the user intent, the intelligent fulfillment / intelligent controller framework may generate, based on at least one of the user intent or the service profile, a network slice instance (NSI) design and a network subnet slice instance (NSSI) design. The method may also include validating, by the intelligent fulfillment / intelligent controller framework, the generated NSI design and the NSSI design. After performing the validation, the intelligent fulfillment / intelligent controller framework may send an NSI allocation request to a network slice management function (NSMF) based on the validated NSI design and the NSSI design.

Description

PCT / US25 / 31594 30 May 2025 (30.05.2025)INTENT DRIVEN AND INTELLIGENT NETWORK SERVICE AND NETWORK SLICE MANAGEMENTFIELD

[0001] The present disclosure relates to intent driven and intelligent network service and network slice management.CROSS REFERENCE TO RELATED APPLICATIONS

[0002] The current application claims priority from Singapore Patent Application No. 10202403724W, the disclosures of which are incorporated herein by reference in their entiretiesBACKGROUND

[0003] In the related art, network slicing may be performed in telecommunications networks (such as 5G) in order to partition networks into multiple logical networks (commonly referred to as “network slices”). Network slices may have a topology which may need to be designed, and network slices may need to be commissioned prior to service.

[0004] FIG. 1 illustrates a lifecycle diagram of a network slice instance according to the related art.

[0005] Referring to FIG. 1 , in the preparation phase, a network slice instance does not exist. The preparation phase may include network slice design, network slice capacity planning, onboarding, and evaluation of the network functions, preparing the network environment and other necessary preparations required to be done before the creation of a network slice instance.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0006] In the commissioning phase, network slice subnets are instantiated and the corresponding resources deployed, provisioned and allocated in the Radio Access Network (RAN), Core Network and Transport Network but whose administrative state is locked. The creation of a network slice instance may include creation and / or modification of existing or new network slice instance constituents.

[0007] In the operation phase, the network slice is delivering a service, or it is the service (in As a Service business model), and actions such as Slice Activation, De-Activation, Modification, Supervision and Reporting may be performed.

[0008] In the decommissioning phase, a network slice may be decommissioned from the associated service or as it is (in As a Service business model), including decommissioning of nonshared constituents, if required, and removing the network slice instance specific configuration, if required, from the shared constituents. After the decommissioning phase, the network slice instance is terminated and does not exist anymore.

[0009] In the related art, a typical procedure / workflow for designing and implementing the network slice (in the preparation and commissioning phases) may include steps of: S101 Providing the slice requirements; SI 02 Designing the slice topology based on the provided slice requirements, S103 Receiving / previewing a design result, S104 Providing a service profile, and S105 Slice Creation based on Slice topology design.

[0010] In the related art, network slice management functions (N SMF) and network subnet slice management functions (NS SMF) are main hosts of network slicing management functions which automatically convert operator input service profile into slice configurations and KPIs based on conventional scripted business logics.PCT / US25 / 31594 30 May 2025 (30.05.2025)SUMMARY

[0011] NSMF and NSSMF in the related art lack the intent driven and intelligent automated approach to address issues with large scale edge deployment with highly diversified operator requirements. There is a need for intent-driven and intelligent network Operations Administration Maintenance (0AM) and network slice management framework, however such features are not considered by the related art.

[0012] Provided are apparatus, method, and device for intent driven and intelligent network service and network slice management. The method may include receiving, by an intelligent fulfillment / intelligent controller framework or fApp, at least one of a user intent or service profile originating from an Application Protocol Interface (API) gateway. After receiving the user intent, the intelligent fulfillment I intelligent controller framework or fApp may generate, based on at least one of the user intent or the service profile, a network slice instance (NSI) design and a network subnet slice instance (NSSI) design. The method may further include validating, by the intelligent fulfillment / intelligent controller framework or fApp, the generated NSI design and the NSSI design. After performing the validation, the intelligent fulfillment / intelligent controller framework or fApp may send an NSI allocation request to a network slice management function (NSMF) based on the validated NSI design and the NSSI design.

[0013] According to example embodiments, a system including an intelligent fulfillment / intelligent controller function; an fApp; an Application Protocol Interface (API) gateway; and a network slice management function (NSMF) may be provided. The intelligent fulfillment / intelligent controller function or fApp may be configured to: receive at least one of a user intent or service profile originating from the API gateway; generate, based on the at least one of the userPCT / US25 / 31594 30 May 2025 (30.05.2025) intent or the service profile, a network slice instance (NSI) design and a network subnet slice instance (NSSI) design; validate the NSI design and the NSSI design; and send an NSI allocation request to the NSMF based on the validated NSI design and the NSSI design.

[0014] According to example embodiments, a non-transitory computer-readable recording medium having recorded thereon instructions executable to perform a method may be provided, the method including receiving, by a at least one of intelligent fulfillment I intelligent controller function or fApp, at least one of a user intent or service profile originating from an Application Protocol Interface (API) gateway; generating, by the intelligent fulfillment I intelligent controller function or fApp based on the at least one of the user intent or the service profile, a network slice instance (NSI) design and a network subnet slice instance (NSSI) design; validating, by the intelligent fulfillment / intelligent controller function or fApp, the NSI design and the NSSI design; and sending, by the intelligent fulfillment / intelligent controller function or fApp, an NSI allocation request to a network slice management function (NSMF) based on the validated NSI design and the NSSI design.

[0015] Additional aspects will be set forth in part in the description that follows and, in part, will be apparent from the description, or may be realized by practice of the presented embodiments of the disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Features, aspects, and advantages of embodiments of the disclosure will be described below with reference to the accompanying drawings, in which like reference numerals denote like elements, and wherein:PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0017] FIG. 1 illustrates a illustrates a lifecycle diagram of a network slice instance according to the related art;

[0018] FIG. 2 illustrates an example system architecture in function view, according to one or more example embodiments;

[0019] FIG. 3 illustrates an example system architecture in service view, according to one or more example embodiments;

[0020] FIG. 4 illustrates a callflow diagram for a full framework-based approach for network slice instance design;

[0021] FIG. 5 illustrates a callflow diagram for an fApp-based approach for network slice instance design;

[0022] FIG. 6 illustrates an example implementation of the intelligent fulfdlment / intelligent controller framework in Open RAN (O-RAN) architecture;

[0023] FIG. 7 illustrates an example method for intent interpretation and network slice instance design;

[0024] FIG. 8 illustrates a diagram of example components of a device for implementing one or more example embodiments; and

[0025] FIG. 9 illustrates a diagram of an example of implementation environment in which systems and / or method, described herein, may be implemented.DETAILED DESCRIPTION

[0026] The following detailed description of example embodiments refers to the accompanying drawings. The present disclosure provides illustrations and descriptions but is not intended to be exhaustive or to limit the implementations to the precise form disclosed.PCT / US25 / 31594 30 May 2025 (30.05.2025)Modifications and variations are possible considering the present disclosure or may be acquired from practice of the implementations. Further, one or more features or components of one embodiment may be incorporated into or combined with another embodiment (or one or more features of another embodiment). Additionally, the flowchart and description of operations provided below relate to at least one of the embodiments in the present disclosure. It should be noted that it is possible to make other embodiments that do not exactly match the flowchart and its description. It is understood that in other embodiments one or more operations may be omitted, one or more operations may be added, one or more operations may be performed simultaneously (at least in part). Further, the order of one or more operations may be switched, as long as these modifications may not affect the resulting scope of the present disclosure.

[0027] It will be apparent that systems and / or methods, described herein, may be implemented in different forms of hardware, software, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods should not limit their implementations. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code. It is understood that software and hardware may be designed to implement the systems and / or methods based on the description herein.

[0028] Even though particular combinations of features are recited in the claims and / or disclosed in the specification, the combinations are not intended to limit the disclosure of implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. Even if a dependent claim directly depends onPCT / US25 / 31594 30 May 2025 (30.05.2025) only one claim, the present disclosure may indicate that the dependent claim is dependent on other claims in the claim set.

[0029] No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” (in other words, nouns not mentioned in the plural) are intended to include one or more items, and may be used interchangeably with “one or more.” Also, as used herein, the terms “has,” “have,” “having,” “include,” “including,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Furthermore, expressions such as “at least one of [A] and [B],” “[A] and / or [B],” or “at least one of [A] or [B]” are to be understood as including only A, only B, or both A and B. Further still, where only one item is intended, the term “one” or similar language is used.

[0030] Expressions such as “at least one processor,” where configured to implement a plurality of operations, execute a plurality of instructions, etc., are to be understood as a single processor implementing the plurality of operations, etc., or each of plural processors implementing at least some (but not necessarily all) of the plurality of operations, etc.

[0031] Reference throughout this specification to “one embodiment,” “an embodiment,” “non-limiting exemplary embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the indicated embodiment is included in at least one embodiment of the present solution. Thus, the phrases “in one embodiment”, “in an embodiment,” “in one non-limiting exemplary embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0032] Further, the described features, advantages, and characteristics of the present disclosure may be combined in any suitable manner in one or more example embodiments. One skilled in the relevant art will recognize, considering the description herein, that the present disclosure can be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the present disclosure.

[0033] The foregoing disclosure provides illustration and description but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible considering the above disclosure or may be acquired from practice of the implementations.

[0034] It shall be noted that, descriptions of example embodiments of the present disclosure may include terms and names defined in one or more standard organizations, such as the 3rd Generation Partnership Project (3GPP) standard organization, the European Telecommunications Standards Institute (ETSI) standard organization, the Open Radio Access Network (O-RAN) Alliance standard organization, and the like. For instance, the terms “NSMF”, “NSSMF”, “LCM”, “service profile”, “subnet profile”, and the like, as well as the associated features and operations, are to be interpreted as consistent with those specified in one or more technical specifications, unless described otherwise.

[0035] Further, although some embodiments of the present disclosure may be described herein with reference specific components of 5G system, it can be understood that the scope of the present disclosure should not be limited thereto. Specifically, example embodiments of the present disclosure may also apply to any suitable network elements in any suitable telecommunicationPCT / US25 / 31594 30 May 2025 (30.05.2025) system, such as a 4G LTE system, a 6G system, and the like, without departing from the scope of the present disclosure.

[0036] According to example embodiments, intelligent operations administration management (0AM) and network slicing management solutions may be provided, which may add an intelligence layer / framework with respect to existing management architecture.

[0037] Intelligent fulfillment / intelligent controller and controller framework according to example embodiments may enable a third-party (relative to the vendor) fApp to design and configure the network slicing topology, and configurations automatically based on the operator’s intents and requirements, thereby enabling intelligent 0AM decisions which leverage AI / ML solutions, digital twin, and large language models (LLM). Furthermore, said framework may provide assistant functions to fApp’s such as an AI / ML workflow function, DME, SME, digital twin function, LLM function, etc.)

[0038] Based on the above embodiments, for example, intent-driven network 0AM and network slicing may be enabled for large scale edge deployment, since highly diversified and sitespecific requirements may be achieved. Further, network operators may be able to write highly customized applications to optimize their network and slices according to their specific business needs. Supply chains for operators may also be diversified, since advanced application solutions may be provided for network 0AM and network slicing management and optimization. Service introduction / deployment can be faster, and service demand may have more agile adaptation and be more adaptive to granular market segmentations. Autonomy and optimization may also be improved.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0039] It is contemplated that features, advantages, and significances of example embodiments described hereinabove are merely a portion of the present disclosure, and are not intended to be exhaustive or to limit the scope of the present disclosure.

[0040] Further descriptions of the features, components, configuration, operations, and implementations of the system of the present disclosure, according to one or more embodiments, are provided in the following.Example System Architecture

[0041] FIG. 2 illustrates an example system architecture in function view, according to one or more example embodiments. As illustrated in FIG. 2, the system architecture may include at least management system 200, which may include API gateway (GW) 210, intelligent fulfillment or intelligent controller function 220, and intelligent fulfillment or intelligent controller framework 230. Management system 200 may be responsible for managing Radio Access Network (RAN) network elements (NE) / network functions (NF’s) 240 and core network (CN) NE / NF’s 250, which may operate or interact with cloud 260.

[0042] Management system 200 may include gateway 201 configured to provide communication with an external service interface (e.g., management data analytics functions such as MDAF in 3 GPP, MWDAF in 5G standards, etc.) Management system 200 may include, but are not necessarily limited to, intent management 202 (e.g., for interpreting intent), policy management 203, network slice management function (NSMF) 204, network subnet slice management function (NSSMF) 205, network function management function (NFMF) 206, and orchestrator 207. Management system 200 may be implemented, for example, by OSS / BSS / SOM / CSMF / SMO / Non-RT RIC.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0043] According to example embodiments, API GW 210 may allow for receiving of external user input to provide intent or service profdes, and optionally service / slice design rules, which may be in the form of a user API.

[0044] Intelligent fulfillment / intelligent controller functions 220 may implement a plurality of fApp’s 221 (e.g., 221-2, 221-2, . . . 221-n). Intelligent fulfillment I intelligent controller function framework 230 may provide standardized API’s and SDK’s to fApps 221 such that 3rdparty applications may be onboarded to the system for intelligent and intent-driven network Operations Administration and Management (0AM) and network slicing management. Intelligent fulfillment I intelligent controller function framework 230 may provide function / services to fApp’ s 221 including, but not necessarily limited to the following:

[0045] AI / ML workflow 231 may provide AI / ML model related services to fApp’s 221 such as model registration discovery, storage, training, and inference, as well as other AI / ML related services.

[0046] Conflict mitigation 232 may provide conflict detection and mitigation services to the fApp’s to detect and resolve conflicts among different fApp’s 221.

[0047] Data Management and Exposure (DME) function / service 233 may handle data distribution and sharing among the fApps 221.

[0048] Service Management and Exposure (SME) function / service 234 may handle application service registration and exposure to / from the fApps 221.

[0049] Network Digital Twin function / service 235 may provide digital twin services to fApps 221 (e.g. verify or evaluate fApps decisions and provide performance feedback).PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0050] Network Slice Design function / service 236 may provide network slice design services to fApps 221 (e.g. derive the Network Slice Instance (NSI) and Network Subnet Slice Instance (NSSI) topology and configuration parameters based on input service / slice profiles or intent).

[0051] Large Language Model (LLM) function / service 237 may provide large language model services to fApps 221 (e.g. convert user input intent in natural language into standardized service and slice profile templates and design rules).

[0052] Design Rule Management function / service 238 may receive, store and manage user slice and service design rules and provide rule access to fApps 221. The user design rules may include, for example, slice profile parameter to RAN configuration parameter mapping rules, user manual NSI design rules, user manual NSSI design rules, and design policies: e.g. maximum reuse, maximum performance, maximum energy saving.

[0053] An example use-case of the system architecture may include deploying a new service or a new slice, or modify existing service or slice. The user may provide high-level intent or service profiles along with optional user specified service / slice design rules via API GW 210. The provided intent / service profile / design rules are sent to the Intelligent Fulfilment / Controller Function Framework 230 and then made available to the authorized fApp’s 221 (e.g., via the fApp API / SDK). Based on this input information, either the Intelligent Fulfilment / Controller Function Framework 230 or the fApp 221 can make intelligent decisions to design, deploy, and configure the new service or slice or modify the existing service or slice, assure service level (SLS) specifications and network performance and efficiency optimisations. The service or slice or network configuration parameters are derived and committed to the RAN NE / NF’s 240, CorePCT / US25 / 31594 30 May 2025 (30.05.2025)Network (CN) NE / NF’s 250 and cloud 260 automatically such that the user input intent / service profdes / design rules are fulfilled.

[0054] FIG. 3 illustrates an example system architecture in service view, according to one or more example embodiments. Elements in management system 200 may be similar to the functions implemented in the example system architecture in function view as illustrated in FIG.2, however the functions are instead implemented as services, and may either consume or produce services, as illustrated in FIG. 3.

[0055] For example, an external service may either produce / consume services to interact with management system 200.

[0056] Intent management function 202, policy management function 203, NSSMF 205, NSMF 204, NFMF 206, orchestration function 206 may interact on a bus to consume and produce services relative to RAN management 240, CN management 250, and Cloud management 260. They may also interact with intent fulfillment / controller framework 230 via said bus. fApp service API / SDK may be used by intent fulfillment / controller framework 230 in order to interact with fApp’s 221 (221-1, 221-2, ... 221-n).

[0057] FIG. 4 illustrates a callflow diagram for a full framework-based approach for network slice instance design. API gateway 400, fApp 410, intelligent fulfillment / intelligent controller framework 420, NSMF 430, NSSMF 440, NFMF 450, orchestrator 460, RAN 470, and CN 480 may be provided.

[0058] At step 1, the API GW 400 may send a user intent or service profile (optionally including user design rules) to intelligent fulfillment / intelligent controller framework 420.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0059] At step 2, the intelligent fulfillment / intelligent controller framework 420 may forward what was received in step 1 to fApp 410.

[0060] At step 3a, in the case where a framework function is used, the intent may be interpreted by an Large Language Model (LLM) implemented as a framework function in intelligent fulfillment I intelligent controller framework 420.

[0061] At step 3b, in the case where a framework function is not used, fApp 410 interprets the intent received in step 2.

[0062] At step 4, fApp 410 may validate the intent interpretation.

[0063] Steps 5-6 are performed to continuously design, and evaluate until the intent validated from step 4 is fulfilled.

[0064] At step 5a, in the case where a framework function is used, network slice design may be performed using a network slice design function implemented as a framework function in intelligent fulfillment / intelligent controller framework 420.

[0065] At step 5b, in the case where a framework function is not used, fApp 410 performs the network slice design.

[0066] At step 6a, in the case where a framework function is used, the network slice design as generated in step 5 is validated by a validation function (e.g., a digital twin (DT) function) implemented in intelligent fulfillment I intelligent controller framework 420.

[0067] At step 6b, in the case where a framework function is not used, fApp 410 validates or evaluates the design.

[0068] At step 7, fApp 410 may send an instruction to perform NSI allocation (based on the input service profile, NSI and NSSI design guidance) to intelligent fulfillment / intelligentPCT / US25 / 31594 30 May 2025 (30.05.2025) controller framework 420, which may forward it to NSMF 430, and NSMF 430 may forward the allocation instruction to NSSMF 440.

[0069] At step 8, the NSSMF 440, NFMF 450, orchestrator 460, RAN 470, and CN 480 may perform the instantiation or modification of the RAN / CN NF’ s,. VNF’ s, CNF’ s, and configure them accordingly.

[0070] At step 9, PM metrics and KPI collection may be performed by intelligent fulfillment / intelligent controller framework 420 for NSI / NSSI’s which were instantiated in step 8.

[0071] At step 10, performance feedback validation / evaluation may be performed by intelligent fulfillment / intelligent controller framework 420.

[0072] At step 11, the PM metrics and KPI collected may be forward to fApp 410.

[0073] At step 12, the fApp 410 may also do performance feedback validation / evaluation

[0074] FIG. 5 illustrates a callflow diagram for an fApp-based approach for network slice instance design. The callflow in FIG. 5 shares similarities with the example callflow illustrated in FIG. 4, however instead of the functions being implemented in a framework-based approach, they are instead delegated into separate fApp’s (fApp’s 410-1, 410-2, and 410-3).

[0075] At step 1, API GW 400 may send the user intent or service profile (optionally including the user design rules) to intelligent fulfillment I intelligent controller framework 420.

[0076] At step 2, fApp 1 410-1 may be responsible for interpreting the user intent. fApp 1 410-1 may, for example, implement an LLM function / service.

[0077] At step 3 , fApp 1410-1 may return the intent interpretation result back to intelligent fulfillment / intelligent controller framework 420.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0078] At step 4, service profiles which were received in step 1 may be sent to fApp 2410- 2 by intelligent fulfillment I intelligent controller framework 420. fApp 2 410-2 may be implementing a NSI design function.

[0079] At step 5, the NSI design result may be returned by fApp 2 410-2 to intelligent fulfillment I intelligent controller framework 420.

[0080] At step 6, slice profiles may be sent by intelligent fulfillment / intelligent controller framework 420 to fApp 3 410-3, which may be implementing a NSSI design function. It should be appreciated, for example, the slice profiles may have been generated by intelligent fulfillment I intelligent controller framework 420 based on the user intent or service profile received in step 1.

[0081] At step 7, the NSSI design may be sent from fApp 3 410-3 back to intelligent fulfillment / intelligent controller framework 420.

[0082] At step 8, validation or evaluation of the NSI design and NSSI design may be performed using, for example, a digital twin (DT) function

[0083] At step 9, NSI allocation may be performed (e.g., an instruction may be sent from intelligent fulfillment I intelligent controller framework 420 to NSMF 430, and forwarded by NSMF 430 to NSSMF 440). This may be based on the input service profile, and the NSI and NSSI design guidance generated in previous steps.

[0084] At step 10, he NSSMF 440, NFMF 450, orchestrator 460, RAN 470, and CN 480 may perform the instantiation or modification of the RAN / CN NF’ s,. VNF’ s, CNF’ s, and configure them accordingly.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0085] At step 11, PM metrics and KPI collection may be performed by intelligent fulfillment / intelligent controller framework 420.

[0086] At step 12, performance feedback validation / evaluation may be performed by intelligent fulfillment / intelligent controller framework 420 based on the PM metrics and KPI collected in step 11.

[0087] FIG. 6 illustrates an example implementation of the intelligent fulfillment / intelligent controller framework in Open RAN (O-RAN) architecture.

[0088] RAN functions in the O-RAN architecture may be controlled and optimized by a RAN Intelligent Controller (RIC). The RIC may be a software-defined component that implements modular applications to facilitate the multivendor operability required in the O-RAN system, as well as to automate and optimize RAN operations. The RIC may be divided into two types: a non- real-time RIC (Non-RT RIC) and a near-real-time RIC (Near-RT RIC).

[0089] The embodiment illustrated in FIG. 6 exemplifies how a Service Management and Orchestration (SMO) implemented in O-RAN may use the intelligent fulfillment I intelligent controller controller / function described with reference to FIG. 2-5 above.

[0090] Intelligent fulfillment / intelligent controller controller / function End-to-End 670 may be a cross-domain function responsible for providing intent (intent driven mode), slice profiles (profile driven mode) and NSSI design guidance (direct commission mode) to SMO 600, particularly with respect to CN NE’s / NF’s 680 and CN cloud 690, in order to bridge between the CN and RAN domains.

[0091] The intelligent fulfillment / intelligent controller function, with respect to the RAN domain, may be implemented in the Non-RT RIC 620 / 630. fApp’s may be implemented as rApp’sPCT / US25 / 31594 30 May 2025 (30.05.2025)621 (621-1, 621-2, . . . 621-3). Noticeably, an Al policy manager 639 may also be included as part of IFF framework 630 to interact with the near-RT RIC 650 over the Al interface.

[0092] Orchestration functions may be performed by NFO / FOCOM 607 with respect to O-Cloud 660 over the 02 interface, and RAN 0AM 606 with respect to RAN NE’s / NF’s 640 over the Ol / O-FH interface.

[0093] Other functions / services may be similar to their counterparts described with respect to FIG. 2 and 3, with the primary difference that they may be implemented as an rApp in the non- RT RIC rather than as an fApp.

[0094] FIG. 7 illustrates an example method 700 for intent interpretation and network slice instance design. Example method 700 may be implemented, for example, by an intelligent fulfillment / intelligent controller framework (may be, for example, a function / controller).

[0095] At operation S710, the user intent or service profile originating from the API gateway may be receive by the intelligent fulfillment / intelligent controller framework. According to embodiments, the user intent may be interpreted by an LLM implemented as a function in the intelligent fulfillment / intelligent controller framework, or an fApp.

[0096] At operation S720, the NSI and NSSI design may be generated based on the user intent / service profile received in operation S710. The NSI design and the NSSI design may be generated based on the interpreted user intent., and may be generated based on a slice design function either implemented in the framework or an fApp.

[0097] At operation S730, the NSI design and NSSI design may be validated. The validation may be performed, for example, based on a digital twin function implemented in an intelligent fulfillment / intelligent controller framework, or an fApp.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0098] At operation S740, an instruction for NSI allocation request may be sent to the NSMF based on the validated NSI and NSSI design. Upon receiving the request, the NSMF may be configured to send an NSSI allocation request to an NSSMF. The NSSMF may be configured to instantiate / modify / configure the NF, VNF, or CNF based on the NSSI design.

[0099] According to embodiments, the method 700 may also include collecting PM metrics and KPI, and generating a performance feedback based on the collected PM metrics KPI. This may be performed either by the framework itself, or in an fApp.

[0100] Various Aspects of Embodiments

[0101] Based on the above embodiments, for example, intent-driven network 0AM and network slicing may be enabled for large scale edge deployment, since highly diversified and sitespecific requirements may be achieved. Further, network operators may be able to write highly customized applications to optimize their network and slices according to their specific business needs. Supply chains for operators may also be diversified, since advanced application solutions may be provided for network 0AM and network slicing management and optimization. Service introduction / deployment can be faster, and service demand may have more agile adaptation and be more adaptive to granular market segmentations. Autonomy and optimization may also be improved.

[0102] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations.

[0103] Some embodiments may relate to a system, a method, and / or a computer readablePCT / US25 / 31594 30 May 2025 (30.05.2025) medium at any possible technical detail level of integration. Further, one or more of the above components described above may be implemented as instructions stored on a computer readable medium and executable by at least one processor (and / or may include at least one processor). The computer readable medium may include a computer-readable non-transitory storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out operations.

[0104] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0105] Computer readable program instructions described herein can be downloaded toPCT / US25 / 31594 30 May 2025 (30.05.2025) respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0106] Computer readable program code / instructions for carrying out operations may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a standalone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), orPCT / US25 / 31594 30 May 2025 (30.05.2025) programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects or operations.

[0107] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks.

[0108] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0109] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer readable media according to various embodiments. In this regard, each block in the flowchart orPCT / US25 / 31594 30 May 2025 (30.05.2025) block diagrams may represent a microservice(s) module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). The method, computer system, and computer readable medium may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in the Figures. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed concurrently or substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0110] It will be apparent that systems and / or methods, described herein, may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and / or methods were described herein without reference to specific software code-it being understood that software and hardware may be designed to implement the systems and / or methods based on the description herein.

[0111] One or more components of the system of the example embodiments (e g., fApp etc ), as well as the operations associated therewith (e.g., one or more operations in FIG. 7, etc.), may be implemented in one or more systems, devices, or hardware components, such as one orPCT / US25 / 31594 30 May 2025 (30.05.2025) more servers, and the like. In the following, descriptions of a device in which the systems or components of the example embodiments may be implemented are provided. It is contemplated that one or more operations or methods described above with reference to FIG. 2 to FIG. 7 may be performed by the device. For instance, the one or more operations or methods may be performed by at least one processor of the device upon executing machine-readable instructions or computer- readable instructions stored in a memory or a storage component of the device.

[0112] FIG. 8 illustrates an embodiment of a device 800 for implementing one or more example embodiments. As shown in FIG. 8, the device 800 includes a processor 810, a memory 820, a storage component 830, an input component 840, an output component 850, a communication interface 860, and a bus 870.

[0113] The processor 810, as used herein, means any type of computational circuit that may comprise hardware elements and software elements. The processor 810 may be embodied as a multi-core processor, a single core processor, or a combination of one or more multi-core processors and one or more single core processors, a distributed processing system, or the like. The processor 810 may be a Central Processing Unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), an application-specific integrated circuit (ASIC), or another type of processing component.

[0114] Memory 820 includes a non-transitory computer readable medium. Memory 820 includes a random-access memory (RAM), a read only memory (ROM), and / or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and / or an optical memory) that stores information and / or instructions for use by processor 810. The memory 820 comprises machine-readable instructions which are executable by the processor 810. ThesePCT / US25 / 31594 30 May 2025 (30.05.2025) machine-readable instructions when executed by the processor 810 causes the processor 810 to perform one or more method steps of an embodiment described herein.

[0115] Storage component 830 stores information and / or software related to the operation and use of the device 800. For example, storage component 830 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, and / or a solid-state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer-readable medium, along with a corresponding drive.

[0116] Input component 840 is configured to receive information, such as user input. For example, the input component 840 may include, but not be limited to, a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and / or a microphone. Additionally, or alternatively, the input component 840 may include a sensor for sensing information (e.g., a global positioning system (GPS), an accelerometer, a gyroscope, and / or an actuator).

[0117] Output component 850 is configured to provide output information from the device 800. For example, the output component 850 may be, but not limited to, a display, a speaker, an instruction device to an external device, and / or one or more light-emitting diodes (LEDs).

[0118] Communication interface 860 is an interface that provides a communication connection to other devices, such as external devices and internal devices. The connection by the communication interface 860 can be a wired connection, a wireless connection, or a combination of wired and wireless connections, and can be a direct connection or an indirect connection via a communication network that exists between the device 800 and other devices. In other words, the standard of the communication interface 860 is not limited.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0119] The bus 870 acts as an interconnect between the processor 810, the memory 820, the storage component 830, the input component 840, the output component 850, and the communication interface 860 of the device 800. The bus 870 may include a wired interconnection or a wireless interconnection.

[0120] The number and arrangement of components shown in FIG. 8 are provided as an example. In practice, device 800 may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. 8. Additionally, or alternatively, a set of components (e.g., one or more components) of device 800 may perform one or more functions described as being performed by another set of components of device 800. Further, one or more method steps described in any of the embodiments may be performed utilizing a plurality of device 800 in communication with one another.

[0121] Further, according to example embodiments, the device 800 may include one or more elements from the system architecture described above in relation to FIG. 1. For example, the device 800 may include the network management system configured to implement the fApp.

[0122] In the present disclosure, specific tasks may be performed using AI / ML (Artificial Intelligence / Machine Learning) models. An AI / ML model is a model generated using one or more Al technologies, one or more ML algorithm or both, and generates output data based on input data. This output data is used to perform tasks. Tasks performed using AI / ML models include those generally referred to as intellectual tasks, such as classification, prediction, natural language processing, etc.

[0123] Although Al and ML are explained separately, ML is a technology included in Al. In ML, instead of being explicitly programmed for a specific task, systems can improve theirPCT / US25 / 31594 30 May 2025 (30.05.2025) performance overtime by identifying patterns and making inferences from training data. Typically, the generation of ML models includes data collection, model training, and model inference. Data collection involves gathering and preprocessing data to be used for training and inference. Model training involves developing and validating models using the collected data. Model inference involves applying the trained models to new data to generate new output data and perform tasks.

[0124] Machine learning includes various types of learning methods such as supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, self-supervised learning, transductive learning, transfer learning, meta learning, and the like. These types of learning methods can be appropriately selected according to the embodiments. Unless otherwise specified, the application of types not mentioned in this description is not precluded. Additionally, the structure of ML models may vary depending on the embodiments and learning methods, and is not limited to the methods disclosed. Furthermore, ML includes deep learning, which uses models that include neural networks. Deep learning models may include, for example, deep neural networks (DNNs), convolutional neural networks (CNNs), etc.

[0125] It should be noted that the AI / ML models presented hereinafter are examples and are not limited to the illustrated AI / ML models. They can be modified or altered by using different Al or ML algorithms. The configuration of the neural network is not limited to the configuration disclosed in the present disclosure and can be modified.

[0126] FIG. 9 is a diagram of an example of implementation environment 900 in which systems and / or method, described herein, may be implemented. The implementation environment 900 includes a UE (User equipment) 910, a service environment 920, and a network 930. The service environment 920 include one or more sub-environments 921. To illustrate this, FIG. 9PCT / US25 / 31594 30 May 2025 (30.05.2025) shows, for convenience, examples of a 1st sub-environment 921-1, a 2nd sub-environment 921-2, and an N-th sub-environment 921-N (where N is any natural number).

[0127] The UE 910 is connected to the network 930, and the network 930 is connected to the service environment 920. The connections may be wired, wireless, or a combination of both wired and wireless. The UE 910 and the service environment 920 are connected via the network 930.

[0128] The UE 910 is a device that communicates with the service environment 920. The UE 910 receives information from the service environment 920 and / or sends information to the service environment 920. Also, the UE 910 may generate and / or store information to be transmitted, as necessary. Also, the UE 910 may store and / or process information that is received, as necessary.

[0129] The example figure 9 refers to the “UE”. However, it should be understood by those skilled in the art that general terms such as “user device,” “terminal,” “terminal device,” “communication device,” and “communication terminal” can be used interchangeably with the term “UE.”

[0130] For example, the UE 910 may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smart phone, a radiotelephone, etc.), a wearable device (e.g., a pair of smart glasses or a smart watch), or a similar device.

[0131] The service environment 920 is an environment that communicates with the UE 910 to provide one or more services. The service environment 920 receives information from the UE 910 and / or sends information to the UE 910. Also, the service environment 920 may generatePCT / US25 / 31594 30 May 2025 (30.05.2025) and / or store information to be transmitted, as necessary. Also, the service environment 920 may store and / or process information that is received, as necessary. For example, the service environment 920 may provide computing resources as one of the services. It should be noted that the service is not limited to being provided to the UE; it may also be provided to devices other than the UE. For example, based on communication from the UE, the service may perform processes such as anomaly detection or traffic analysis and notify the results to a predetermined destination.

[0132] The example figure 9 refers to the “service environment”. The term "service environment" is used to refer to the broader context within which services operate. For example, cloud environments, platforms, computing systems, network systems, and cloud systems generally represent the environments in which services are conducted, and these are included within the "service environment." However, the "service environment" is not limited to these examples. Additionally, the specific types of environments within the "service environment" are not restricted. For instance, cloud environments and cloud systems can be categorized as private cloud, public cloud, hybrid cloud, or multi-cloud, all of which are included within the "service environment.”

[0133] The one or more services provided by the service environment 920 is not specifically limited and can be adjusted according to the embodiments. For example, the services may include a service that provides information to the UE 910, a service that stores information from the UE 910, or a service that performs processing based on information from the UE 910 and returns the results of the processing.

[0134] In an embodiment, the Service Environments 920 may also provide computing resources as the service. The computing resources can be hardware resources and / or softwarePCT / US25 / 31594 30 May 2025 (30.05.2025) resources. For example, applications, processors, memory, and storage can be included in the provided computing resources. Each computing resource can communicate with other computing resources via wired connections, wireless connections, or a combination of wired and wireless connections.

[0135] The provided computing resources can be actual resources (also referred to as physical resources) and / or virtual resources. Furthermore, means of virtualization for virtual resources can be selected as appropriate. That is, in this disclosure, the use of adjectives such as "Virtual" or "Virtualized" to describe names does not imply that they are virtualized by a specific means of virtualization. For example, “virtual machine” refers to software that operates like an actual computer, realized through means of virtualization, and it is not intended to exclude those realized by specific means of virtualization such as Hypervisors or Containers. Conversely, when means of virtualization such as Hypervisors or containers are mentioned in this disclosure, it is merely cited as a general method of implementation. It should also be interpreted that embodiments implemented with other virtualization means are also disclosed. Also, the services may also be provided using resources virtualized by different means.

[0136] The service environment 920 includes one or more devices, such as servers and network devices, which provide services or perform processes. The placement of these devices within the service environment 920 can be determined as appropriate. Additionally, if the service environment 920 includes one or more sub-environments 921, the placement of devices can be determined based on predetermined policies for each sub-environment 921. For example, devices related to the first service may be placed in the 1st sub-environment 921-1, and devices related to the second service may be placed in the 2nd sub-environment 921-2. In another example, devicesPCT / US25 / 31594 30 May 2025 (30.05.2025) expected to have a higher load than a predetermined threshold may be placed in the 1st subenvironment 921-1, while devices expected to have a lower load than the predetermined threshold may be placed in the 2nd sub-environment 921-2. In this way, specific devices can be placed in specific sub -environments 921. Conversely, each sub-environment 921 can be specialized for a particular purpose.

[0137] In an embodiment, all processes executed in a single service may run within a single service environment, or in multiple service environments. Multiple processes executed in a single service could be provided by different service environments.

[0138] The network 930 is a network that exchanges information between the UE 910 and the service environment 920. The network 930 includes one or more wired and / or wireless networks.

[0139] For example, the network 930 may include a cellular network (e.g., a fifth generation (5G) network, a long-term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e g., the Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, or the like, a non-terrestrial network (NTN), and / or a combination of these or other types of networks.

[0140] The network 930 can be a part of a network. For example, in a 5G network that includes a RAN, a transport network, and a core network, the network 930 can be at least one of the RAN, the transport network, or the core network. For example, the service environment 920PCT / US25 / 31594 30 May 2025 (30.05.2025) could be in the core network, in which case the network 930 could correspond to a network that is a combination of a RAN and a transport network and is part of the 5G network.

[0141] The number and arrangement of devices and networks shown in FIG. 9 are provided as an example. It should be understood that any changes that may be implemented by those skilled in the art, such as the addition or rearrangement of well-known devices or networks at the time of implementation, are included in this disclosure.

[0142] Various further respective aspects and features of embodiments of the present disclosure may be defined by the following items:Item [1]: A method including: receiving, by a at least one of intelligent fulfillment I intelligent controller function or fApp, at least one of a user intent or service profile originating from an Application Protocol Interface (API) gateway; generating, by the intelligent fulfillment / intelligent controller function or fApp based on the at least one of the user intent or the service profile, a network slice instance (NSI) design and a network subnet slice instance (NS SI) design; validating, by the intelligent fulfillment / intelligent controller function or fApp, the NSI design and the NS SI design; and sending, by the intelligent fulfillment / intelligent controller function or fApp, an NSI allocation request to a network slice management function (NSMF) based on the validated NSI design and the NS SI design.Item [2]: The method according to Item [1], wherein the at least one user intent is interpreted by a Large Language Model (LLM) implemented as a function in the intelligent fulfillment / intelligent controller framework or the fApp, and the NSI design and the NSSI design are generated based on the interpreted user intent.PCT / US25 / 31594 30 May 2025 (30.05.2025)Item [3]: The method according to any one of Items [l]-[2], wherein the NSI design and the NS SI design are generated based on a slice design function configured in the intelligent fulfillment / intelligent controller framework or the fApp.Item [4]: The method according to any one of Items [l]-[3], wherein the NSI design and the NSSI design are validated based on a digital twin function configured in the intelligent fulfillment / intelligent controller framework or the fApp.Item [5]: The method according to any one of Items [l]-[4], wherein upon receiving the NSI allocation request, the NSMF is configured to send an NSSI allocation request to a network subnet slice management function (NSSMF).Item [6]: The method according to any one of Items [l]-[5], wherein the NSSMF is configured to instantiate, modify, or configure one of a network function (NF), a network function deployment (NF Deployment), a virtualized network function (VNF), a cloud-native / containerized network function (CNF), or cloud infrastructure resources based on the NSSI design.Item [7]: The method according to any one of Items [l]-[6], further including: collecting, by the intelligent fulfillment / intelligent controller function or fApp, performance management (PM) metrics and key performance indicators (KPI); andPCT / US25 / 31594 30 May 2025 (30.05.2025) generating, by the intelligent fulfillment / intelligent controller function or fApp, at least one of service level specification (SLS) assurance, network performance and efficiency optimization command to one of the NF(s), the NF Deployments, the VNF, the CNF or the cloud infrastructure resources, based on the collected PM metrics and KPI.Item [8]: The method according to any one of Items [l]-[7], further including: receiving, by the intelligent fulfillment / intelligent controller function or fApp, at least one design rule for the NSI design and the NSSI design from the API gateway, wherein the NSI design and the NSSI design are generated based on the at least one design rule.Item [9]: The method according to any one of Items [l]-[8], further including: providing, by the intelligent fulfillment / intelligent controller function, one or more assistant functions / services to the fApp including a conflict mitigation function / service, an Artificial Intelligence (AI) / Machine Learning (ML) model related function / service, a Data Management and Exposure (DME) function / service, and a Service Management and Exposure (SME) function / service.Item

[0010] : A system including an intelligent fulfillment I intelligent controller function; an fApp; an Application Protocol Interface (API) gateway; and a network slice management function (NSMF), wherein the intelligent fulfillment / intelligent controller function or f pp is configured to: receive at least one of a user intent or service profile originating from the API gateway; generate, based on the at least one of the user intent or the service profile, a network slice instance (NSI)PCT / US25 / 31594 30 May 2025 (30.05.2025) design and a network subnet slice instance (NSSI) design; validate the NSI design and the NSSI design; and send an NSI allocation request to the NSMF based on the validated NSI design and the NSSI design.Item

[0011] : The system according to Item

[0010] : wherein the at least one user intent is interpreted by a Large Language Model (LLM) implemented as a function in the intelligent fulfillment / intelligent controller framework or fApp, and the NSI design and the NSSI design are generated based on the interpreted user intent.Item

[0012] : The system according to any one of Items

[0010] -[l 1], wherein the NSI design and the NSSI design are generated based on a slice design function configured in the intelligent fulfillment / intelligent controller framework or an fApp.Item

[0013] : The system according to any one of Items

[0010] -

[0012] , wherein the NSI design and the NSSI design are validated based on a digital twin function configured in the intelligent fulfillment / intelligent controller framework or an fApp.Item

[0014] : The system according to any one ofltems

[0010] -

[0013] , further including a network subnet slice management function (NSSMF), wherein upon receiving the NSI allocation request, the NSMF is configured to send an NSSI allocation request to the NSSMF.PCT / US25 / 31594 30 May 2025 (30.05.2025)Item

[0015] : The system according to any one of Items

[0010] -

[0014] , wherein the NSSMF is configured to instantiate, modify, or configure one of a network function (NF), a network function deployment (NF Deployment), a virtualized network function (VNF), a cloud-native / containerized network function (CNF), or cloud infrastructure resources based on the NS SI design.Item

[0016] : The system according to any one of Items

[0010] -

[0015] , wherein the intelligent fulfillment I intelligent controller function or fApp is configured to: collect performance management (PM) metrics and key performance indicators (KPI); and generate at least one of service level specification (SLS) assurance, network performance and efficiency optimization command to one of the NF(s), the NF Deployments, the VNF, the CNF or the cloud infrastructure resources, based on the collected PM metrics and KPI.Item

[0017] : The system according to any one of Items

[0010] -

[0016] , wherein the intelligent fulfillment I intelligent controller function or fApp is configured to: receive at least one design rule for the NSI design and the NSSI design from the API gateway, wherein the NSI design and the NSSI design are generated based on the at least one design rule.Item

[0018] : The system according to any one of Items

[0010] -

[0017] , wherein the intelligent fulfillment I intelligent controller function or fApp is configured to provide one or more assistant functions / services to the fApp including a conflict mitigation function / service, an Artificial Intelligence (AI) / Machine Learning (ML) model related function / service, a Data Management andPCT / US25 / 31594 30 May 2025 (30.05.2025)Exposure (DME) function / service, and a Service Management and Exposure (SME) function / service.Item

[0019] : A non-transitory computer-readable recording medium having recorded thereon instructions executable to perform a method including: receiving, by a at least one of intelligent fulfillment / intelligent controller function or f pp, at least one of a user intent or service profile originating from an Application Protocol Interface (API) gateway; generating, by the intelligent fulfillment / intelligent controller function or fApp based on the at least one of the user intent or the service profile, a network slice instance (NSI) design and a network subnet slice instance (NSSI) design; validating, by the intelligent fulfillment I intelligent controller function or fApp, the NSI design and the NSSI design; and sending, by the intelligent fulfillment / intelligent controller function or fApp, an NSI allocation request to a network slice management function (NSMF) based on the validated NSI design and the NSSI design.Item

[0020] : The non-transitory computer-readable recording medium according to Item

[0019] , wherein the at least one user intent is interpreted by a Large Language Model (LLM) implemented as a function in the intelligent fulfillment / intelligent controller framework or the fApp, and the NSI design and the NSSI design are generated based on the interpreted user intent.

[0143] It is understood that numerous modifications and variations of the present disclosure are possible in light of the above teachings. It will be apparent that within the scope ofPCT / US25 / 31594 30 May 2025 (30.05.2025) the appended clauses, the present disclosures may be practiced otherwise than as specifically described herein.

[0144] 0-RAN and 3GPP standardized network slicing architectures and service procedures which may be referred to may include: 3GPP TS 28.533, 3GPP TS 28.530, 3GPP TS 28.531, and 3GPP TS 28.541.

[0145] 0-RAN.WG1. Slicing-Architecture:

[0146] Detailed Description:

[0147] Example Problems Addressed by Example Embodiments:

[0148] In 3GPP network slicing LCM architecture and workflow, currently NSMF andNSSMF are main hosts of network slicing management functions which automatically convert operator input service profile into slice configurations based on conventional scripted business logics. As more edge and industrial applications emerge, intent driven and more intelligent automation is needed to tackle the challenges from large scale edge deployment with highly diversified operator requirements. Intelligent network 0AM and network slice management framework is urgently needed but the solution has never been studied in pre-art.

[0149] Example Solutions Achieved by Example Embodiments:

[0150] Introduce intelligent 0AM and network slicing management solution which adds an intelligence layer on top of exiting management architecture. Introduce an intelligent fulfillment and controller framework that enables a 3rd party fApp to design and configure the network slicing topology and configurations automatically based on operator’s intents and requirements, and make intelligent 0AM decisions leveraging AIML, digital twin and large language models. IntroducePCT / US25 / 31594 30 May 2025 (30.05.2025) an intelligent fulfillment and controller framework providing assistant functions to fApps, e.g. AI / ML workflow function, DME, SME, digital twin function, large language model function etc.

[0151] Example Benefits / Advantages Achieved by Example Embodiments:

[0152] Enable intent driven network 0AM and network slicing for large scale edge deployment with highly diversified site-specific requirements. Enable operators to write highly customized applications to optimize their network and slices according to their specific business need. Enable diversified supply chain to operators for advanced application solutions for network 0AM and network slicing management and optimization. Maximize operator revenue with faster new service introduction, agile service demand adaption and more adaptive to granular market segmentations. Minimize CAPEX and OPEX through higher level of autonomy and optimization.

[0153] An Intelligent and Intent-driven Network 0AM and Network Slice Management System which contains an Intelligent Fulfillment / Controller Function may be provided. The Intelligent Fulfillment / Controller Function may include: A Intelligent Fulfillment / Controller Framework and fApps.

[0154] The Intelligent Fulfillment / Controller Framework provide standardized APIs and SDK to fApps such that 3rd party application can be onboarded to the system for intelligent and intent-driven network 0AM and network slicing management. The Intelligent Fulfillment / Controller Framework provides assistant functions / services to fApps which includes but not limited to:

[0155] AI / ML Workflow function / service: provide AI / ML model related services to fApps such as model registration, discovery, storage, training and inference etcPCT / US25 / 31594 30 May 2025 (30.05.2025)

[0156] Conflict Mitigation function / service: provide conflict detection and mitigation services to the fApps to detect and resolve conflicts among different fApps.

[0157] Network Digital Twin function / service: provide digital twin services to fApps, e.g. verify or evaluate fApps decisions and provide performance feedback.

[0158] Network Slice Design function / service: provide network slice design services to fApps, e.g. derive the NSI and NSSI topology and configuration parameters based on input service / slice profiles or intent.

[0159] Large Language Model (LLM) function / service: provide large language model services to fApps, e.g. convert user input intent in natural language into standardized service and slice profile templates and design rules.

[0160] Design Rule Management function / service: receive, store and manage user slice and service design rules and provide rule access to fApps. The user design rules may include:

[0161] 1. slice profile parameter to RAN configuration parameter mapping rules

[0162] 2. user manual NSI design rules

[0163] 3. user manual NSSI design rules

[0164] 4. design policies: e.g., maximum reuse, maximum performance, maximum energy savings, etc.

[0165] Data Management and Exposure (DME) function / service: handles data distribution and sharing among the fApps.

[0166] Service Management and Exposure (SME) function / service: handles application service registration and exposure to / from the fApps.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0167] The Intelligent and Intent-driven Network 0AM and Network Slice Management System also includes: Intent Management Function, Policy Management Function, NSMF, NSSMF, NFMF, Orchestrator, API Gate Way (GW) to user , and API Gate Way (GW) to external systems e g. MDAF, NWDAF.

[0168] In order to deploy a new service or a new slice, or modify existing service or slice, user provide high-level intent or service profiles along with optional user specified service / slice design rules via the user API GW. The provided intent / service profile / design rules are sent to the Intelligent Fulfilment / Controller Framework and then made available to the authorized fApps. Based on this input information, either the Intelligent Fulfilment / Controller Framework or the fApp can make intelligent decisions to design, deploy, and configure the new service or slice or modify the existing service or slice. The service or slice configuration parameters are derived and committed to the RAN, CN and cloud automatically such that the user input intent / service profiles / design rules are fulfilled.

[0169] Interactions among the key elements in the Intelligent and Intent-driven Network 0AM and Network Slice Management System to automatically design and commit network services and slices to fulfill user’s intent or service profiles or design rules may be provided. The main control loop may be driven by a 3rd party fApp. The main control loop may be driven by the Intelligent Fulfilment / Controller Framework. As introduced above, both the Intelligent Fulfilment / Controller Framework and the fApp can take the intelligent control.

[0170] A service-based architectural view of the Intelligent and Intent-driven Network 0AM and Network Slice Management System may be provided.PCT / US25 / 31594 30 May 2025 (30.05.2025)

[0171] A mapping of the system architecture to 0-RAN architecture may be provided where: the same functionality of the Intelligent Fulfilment / Controller Function can be provided by Non-RT RIC for RAN domain network 0AM and network slice management, the same functionality of fApps can be provided by 0-RAN rApps for RAN domain network 0AM and network slice management, and the same functionality of Intelligent Fulfilment / Controller Framework can be provided by Non-RT RIC framework.

[0172] The end-to-end cross domain (RAN and CN) Intelligent Fulfilment / Controller Function connect to the RAN domain management, 0-RAN SMO / Non-RT RIC, through external API GW and provide intent / slice profiles / slice subnet design rules input to the 0-RAN domain for further actions related to RAN domain network 0AM and network slice management.

[0173] The 0-RAN domain further derives control actions based on intent input (intent driven), or based on slice profiles input (profile driven), or based on slice subnet design rules input (direct commission).

Claims

PCT / US25 / 31594 30 May 2025 (30.05.2025)What is claimed is:

1. A method comprising: receiving, by at least one of intelligent fulfillment / intelligent controller function or fApp, at least one of a user intent or service profile originating from an Application Protocol Interface (API) gateway; generating, by the intelligent fulfillment I intelligent controller function or fApp based on the at least one of the user intent or the service profile, a network slice instance (NSI) design and a network subnet slice instance (NSSI) design; validating, by the intelligent fulfillment / intelligent controller function or fApp, the NSI design and the NSSI design; and sending, by the intelligent fulfillment I intelligent controller function or fApp, an NSI allocation request to a network slice management function (NSMF) based on the validated NSI design and the NSSI design.

2. The method as claimed in claim 1, wherein the at least one user intent is interpreted by a Large Language Model (LLM) implemented as a function in the intelligent fulfillment I intelligent controller framework or the fApp, and the NSI design and the NSSI design are generated based on the interpreted user intent.PCT / US25 / 31594 30 May 2025 (30.05.2025)3. The method as claimed in claim 1, wherein the NSI design and the NS SI design are generated based on a slice design function configured in the intelligent fulfillment / intelligent controller framework or the fApp.

4. The method as claimed in claim 3, wherein the NSI design and the NS SI design are validated based on a digital twin function configured in the intelligent fulfillment / intelligent controller framework or the fApp.

5. The method as claimed in claim 1, wherein upon receiving the NSI allocation request, the NSMF is configured to send an NSSI allocation request to a network subnet slice management function (NSSMF).

6. The method as claimed in claim 5, wherein the NSSMF is configured to instantiate, modify, or configure one of a network function (NF), a network function deployment (NF Deployment), a virtualized network function (VNF), a cloud-native / containerized network function (CNF), or cloud infrastructure resources based on the NSSI design.

7. The method as claimed in claim 6, further comprising: collecting, by the intelligent fulfillment I intelligent controller function or fApp, performance management (PM) metrics and key performance indicators (KPI); and generating, by the intelligent fulfillment / intelligent controller function or fApp, at least one of service level specification (SLS) assurance, network performance and efficiencyPCT / US25 / 31594 30 May 2025 (30.05.2025) optimization command to one of the NF(s), the NF Deployments, the VNF, the CNF or the cloud infrastructure resources, based on the collected PM metrics and KPI.

8. The method as claimed in claim 1, further comprising: receiving, by the intelligent fulfillment I intelligent controller function or fApp, at least one design rule for the NSI design and the NSSI design from the API gateway, wherein the NSI design and the NSSI design are generated based on the at least one design rule.

9. The method as claimed in claim 1, further comprising: providing, by the intelligent fulfillment I intelligent controller function, one or more assistant functions / services to the fApp comprising a conflict mitigation function / service, an Artificial Intelligence (AI) / Machine Learning (ML) model related function / service, a Data Management and Exposure (DME) function / service, and a Service Management and Exposure (SME) function / service.

10. A system comprising: an intelligent fulfillment / intelligent controller function; an fApp; an Application Protocol Interface (API) gateway; and a network slice management function (NSMF), wherein the intelligent fulfillment / intelligent controller function or fApp is configured to: receive at least one of a user intent or service profile originating from the API gateway;PCT / US25 / 31594 30 May 2025 (30.05.2025) generate, based on the at least one of the user intent or the service profile, a network slice instance (NSI) design and a network subnet slice instance (NS SI) design; validate the NSI design and the NSSI design; and send an NSI allocation request to the NSMF based on the validated NSI design and the NSSI design.

11. The system as claimed in claim 10, wherein the at least one user intent is interpreted by a Large Language Model (LLM) implemented as a function in the intelligent fulfillment / intelligent controller framework or fApp, and the NSI design and the NSSI design are generated based on the interpreted user intent.

12. The system as claimed in claim 10, wherein the NSI design and the NSSI design are generated based on a slice design function configured in the intelligent fulfillment / intelligent controller framework or an fApp.

13. The system as claimed in claim 10, wherein the NSI design and the NSSI design are validated based on a digital twin function configured in the intelligent fulfillment / intelligent controller framework or an fApp.

14. The system as claimed in claim 10, further comprising a network subnet slice management function (NSSMF), wherein upon receiving the NSI allocation request, the NSMF is configured to send an NSSI allocation request to the NSSMF.PCT / US25 / 31594 30 May 2025 (30.05.2025)15. The system as claimed in claim 14, wherein the NSSMF is configured to instantiate, modify, or configure one of a network function (NF), a network function deployment (NF Deployment), a virtualized network function (VNF), a cloud-native / containerized network function (CNF), or cloud infrastructure resources based on the NSSI design.

16. The system as claimed in claim 15, wherein the intelligent fulfillment / intelligent controller function or fApp is configured to: collect performance management (PM) metrics and key performance indicators (KPI); and generate at least one of service level specification (SLS) assurance, network performance and efficiency optimization command to one of the NF(s), the NF Deployments, the VNF, the CNF or the cloud infrastructure resources, based on the collected PM metrics and KPI.

17. The system as claimed in claim 10, wherein the intelligent fulfillment I intelligent controller function or fApp is configured to: receive at least one design rule for the NSI design and the NSSI design from the API gateway, wherein the NSI design and the NSSI design are generated based on the at least one design rule.

18. The system as claimed in claim 10, wherein the intelligent fulfillment / intelligent controller function or fApp is configured to provide one or more assistant functions / services to the fApp comprising a conflict mitigation function / service, an Artificial Intelligence (AI) / MachinePCT / US25 / 31594 30 May 2025 (30.05.2025)Learning (ML) model related function / service, a Data Management and Exposure (DME) function / service, and a Service Management and Exposure (SME) function / service.

19. A non-transitory computer-readable recording medium having recorded thereon instructions executable to perform a method comprising: receiving, by a at least one of intelligent fulfillment / intelligent controller function or fApp, at least one of a user intent or service profile originating from an Application Protocol Interface (API) gateway; generating, by the intelligent fulfillment I intelligent controller function or fApp based on the at least one of the user intent or the service profile, a network slice instance (NSI) design and a network subnet slice instance (NSSI) design; validating, by the intelligent fulfillment / intelligent controller function or fApp, the NSI design and the NSSI design; and sending, by the intelligent fulfillment I intelligent controller function or fApp, an NSI allocation request to a network slice management function (NSMF) based on the validated NSI design and the NSSI design.

20. The non-transitory computer-readable recording medium as claimed in claim 19, wherein the at least one user intent is interpreted by a Large Language Model (LLM) implemented as a function in the intelligent fulfillment / intelligent controller framework or the fApp, and the NSI design and the NSSI design are generated based on the interpreted user intent.

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