DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in responsive to communication(s): original application filed on 03/17/2023, said application claims a priority filing date of 03/25/2022. Claims 1-18 are pending. Claims 1, 5, 9, and 13 are independent.
Drawings
The drawings are objected to because (1) FIG. 5 (e.g., readMOI(List(MLTrainingJobs))) is inconsistent with description of FIG. 5 in ¶ [0021] that "FIG. 5 illustrates an example of modifying the characteristics of one or more ongoing or completed MLTrainingJobs …"; (2) FIG. 6 (e.g., modifyMOIAttributes(List(MLTrainingJobs), attributes)) is inconsistent with description of FIG. 6 in ¶ [0022] that "FIG. 6 illustrates an example of reading the characteristics of one or more ongoing or completed MLTrainingJobs …"; (3) FIG. 14 (e.g., readMOI(List(MLTrainingReporting))) is inconsistent with description of FIG. 14 in ¶ [0030] that "FIG. 14 illustrates an example of reading the characteristics of one or more ongoing or completed MLTrainingJobs …"; (4) FIG. 15 (e.g., modifyMOIAttributes(List(MLTrainingReporting), attributes)) is inconsistent with description of FIG. 15 in ¶ [0031] that "FIG. 15 illustrates an example of reading the characteristics of one or more ongoing or completed MLTrainingJobs …"; (5) FIG. 16 (e.g., deleteMOI (List(MLTrainingReportingID))) is inconsistent with description of FIG. 16 in ¶ [0032] that "FIG. 16 illustrates an example of deleting the characteristics of one or more ongoing or completed MLTrainingJobs …"; (6) FIG. 17 (e.g., readMOI (List(MLTrainingReportingRequests))) is inconsistent with description of FIG. 17 in ¶ [0033] that "FIG. 17 illustrates an example of reading the characteristics of one or more ongoing or completed MLTrainingJobs …"; (7) FIG. 18 (e.g., modifyMOIAttributes(List(MLTrainingReportingRequestsID), attributes)) is inconsistent with description of FIG. 18 in ¶ [0034] that "FIG. 18 illustrates an example of modifying the characteristics of one or more ongoing or completed MLTrainingJobs …"; and (8) FIG. 19 (e.g., deleteMOI (List(MLTrainingReportingRequestsID))) is inconsistent with description of FIG. 19 in ¶ [0035] that "FIG. 19 illustrates an example of deleting the characteristics of one or more ongoing or completed MLTrainingJobs …". Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
in ¶ [0021], "FIG. 5 illustrates an example of modifying …" appears to be "FIG. 5 illustrates an example of reading …";
in ¶ [0022], "FIG. 6 illustrates an example of reading …" appears to be "FIG. 5 illustrates an example of modifying …";
in ¶ [0031], "FIG. 15 illustrates an example of reading …" appears to be "FIG. 15 illustrates an example of modifying …";.
Appropriate correction is required.
Claim Objections
Claims 3-4, 7-8, and 10-16 are objected to because of the following informalities:
in Claim 3, lines 5-6, "… transmitting … a notification to the consumer of the list of modified machine learning training jobs identifiers" appears to be "… transmitting … a notification to the consumer indicating the list of modified machine learning training jobs identifiers" according to FIG. 6 and Claim 1;
in Claim 4, lines 5-6, "… transmitting … a notification to the consumer of the list of deleted machine learning training jobs identifiers" appears to be "… transmitting … a notification to the consumer indicating the list of deleted machine learning training jobs identifiers" according to FIG. 7 and Claim 1;
in Claim 7, lines 5-6, "… receiving … a notification from the machine learning training function of the list of modified machine learning training jobs identifiers" appears to be "… receiving … a notification from the machine learning training function indicating the list of modified machine learning training jobs identifiers" according to FIG. 6 and Claim 5;
in Claim 8, lines 5-6, "… receiving … a notification from the machine learning training function of the list of deleted machine learning training jobs identifiers" appears to be "… receiving … a notification from the machine learning training function indicating the list of deleted machine learning training jobs identifiers" according to FIG. 7 and Claim 5;
in Claims 10-12, lines 1-2, "… wherein the at least one memory and computer program code are further configured …" appears to be "… wherein the at least one memory and the computer program code are further configured …";
in Claim 11, lines 6-7, "… transmitting a notification to the consumer of the list of modified machine learning training jobs identifiers" appears to be "… transmitting a notification to the consumer indicating the list of modified machine learning training jobs identifiers" according to FIG. 6 and Claim 9;
in Claim 12, lines 6-7, "… transmitting a notification to the consumer of the list of deleted machine learning training jobs identifiers" appears to be "… transmitting a notification to the consumer indicating the list of deleted machine learning training jobs identifiers" according to FIG. 7 and Claim 9;
in Claim 13, line 8, "… receiving a notification form the machine learning training function …" appears to be "… receiving a notification from the machine learning training function …" according to Claim 5;
in Claims 14-16, lines 1-2, "… wherein the at least one memory and computer program code are further configured …" appears to be "… wherein the at least one memory and the computer program code are further configured …";
in Claim 15, lines 7-8, "… receiving … a notification from the machine learning training function of the list of modified machine learning training jobs identifiers" appears to be "… receiving … a notification from the machine learning training function indicating the list of modified machine learning training jobs identifiers" according to FIG. 6 and Claim 13;
in Claim 16, lines 7-8, "… receiving … a notification from the machine learning training function of the list of deleted machine learning training jobs identifiers" appears to be "… receiving … a notification from the machine learning training function indicating the list of deleted machine learning training jobs identifiers" according to FIG. 7 and Claim 13.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 6, 10, and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recite the limitation "… receiving … a request from the consumer to read a managed object instance associated with a list of machine learning training jobs; and transmitting … a list of machine learning training jobs identifiers to the consumer" in lines 2-6, which rendering these claims indefinite because it is unclear whether "a list of machine learning training jobs identifiers" are identifiers for the same or different "list of machine learning training jobs" associated with a "managed object instance". For examination purpose, "… receiving … a request from the consumer to read a managed object instance associated with a list of machine learning training jobs; and transmitting … identifiers associated with the list of machine learning training jobs to the consumer" or "… receiving … a request from the consumer to read a managed object instance associated with a list of machine learning training jobs identifiers; and transmitting … the list of machine learning training jobs identifiers to the consumer" according to Claims 3 and 4.
Claim 6 recite the limitation "… transmitting … a request to the machine learning training function to read a managed object instance associated with a list of machine learning training jobs; and receiving … a list of machine learning training jobs identifiers from the machine learning training function" in lines 2-6, which rendering these claims indefinite because it is unclear whether "a list of machine learning training jobs identifiers" are identifiers for the same or different "list of machine learning training jobs" associated with a "managed object instance". For examination purpose, "… transmitting … a request to the machine learning training function to read a managed object instance associated with a list of machine learning training jobs; and receiving … identifiers associated with the list of machine learning training jobs from the machine learning training function" or "… transmitting … a request to the machine learning training function to read a managed object instance associated with a list of machine learning training jobs identifiers; and receiving … the list of machine learning training jobs identifiers from the machine learning training function" according to Claims 7 and 8.
Claim 10 recite the limitation "… receiving a request from the consumer to read a managed object instance associated with a list of machine learning training jobs; and transmitting a list of machine learning training jobs identifiers to the consumer" in lines 4-6, which rendering these claims indefinite because it is unclear whether "a list of machine learning training jobs identifiers" are identifiers for the same or different "list of machine learning training jobs" associated with a "managed object instance". For examination purpose, "… receiving a request from the consumer to read a managed object instance associated with a list of machine learning training jobs; and transmitting identifiers associated with the list of machine learning training jobs to the consumer" or "… receiving a request from the consumer to read a managed object instance associated with a list of machine learning training jobs identifiers; and transmitting the list of machine learning training jobs identifiers to the consumer" according to Claims 11 and 12.
Claim 14 recite the limitation "… transmitting … a request to the machine learning training function to read a managed object instance associated with a list of machine learning training jobs; and receiving … a list of machine learning training jobs identifiers from the machine learning training function" in lines 4-8, which rendering these claims indefinite because it is unclear whether "a list of machine learning training jobs identifiers" are identifiers for the same or different "list of machine learning training jobs" associated with a "managed object instance". For examination purpose, "… transmitting … a request to the machine learning training function to read a managed object instance associated with a list of machine learning training jobs; and receiving … identifiers associated with the list of machine learning training jobs from the machine learning training function" or "… transmitting … a request to the machine learning training function to read a managed object instance associated with a list of machine learning training jobs identifiers; and receiving … the list of machine learning training jobs identifiers from the machine learning training function" according to Claims 15 and 16.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2022/0108214 A1, filed on 08/13/2021), hereinafter Lee in view of DIRAC et al. (US 2015/0379424 A1, pub. date: 12/31/2015), hereinafter DIRAC.
Independent Claims 1 and 9
Lee discloses a method, comprising: receiving, by a machine learning training function, a request to instantiate a machine learning training job from a consumer; instantiating, by the machine learning training function, the requested machine learning training job (Lee, ¶¶ [0066]-[0102] with FIG. 1: a network data analytics function (NWDAF) device 101 may use a mechanism and interface specified with respect to a 5G core (5GC) network; the NWDAF device 101 may interact with different entities for different purposes: (a) data collection based on event subscriptions provided by an access and mobility management function (AMF), a session management function (SMF), a policy control function (PCF), unified data management (UDM), AF (directly or through NEF) and OAM (Operations, administration and management); (b) analytics and data collection using a data collection coordination function (DCCF); (c) information search from a datastore (for example, UDR through UDM for subscription interest-related information); (d) information storage and search using an analytics data repository function (ADRF); (e) analytics and data collection using a messaging framework adapter function (MF AF); (f) search of information on a network function (NF) (for example, using a network repository function (NRF) for NF-related information): (g) on-demand provision of analytics for a consumer; and (h) mass data provision to a consumer; a single instance or multiple instances of the NWDAF device 101 may be deployed in a PLMN (Public Land Mobile Network); when multiple NWDAF instances are deployed, an architecture may support deploying the NWDAF device 101 as a central NF device 102, a distributed NF collection, or a combination of the two; when the multiple NWDAF instances are deployed, the NWDAF device 101 may serve as an aggregation point (that is, an NWDAF device that performs aggregation of analytics, aggregation of a machine learning (ML) model of an untrained initial model or aggregation of a trained model); in addition, the NWDAF device 101 may generate aggregate analytics (per Analytic ID) by collecting analytics information from another NWDAF device capable of having another service area, or may perform federation learning or aggregation training (per Analytic ID) by training an ML model on each of the NWDAF devices; an instance of the NWDAF device 101 may be deployed with a 5GC NF; the NWDAF device 101 may provide analytics for the 5GC NF and the OAM, which may be disassembled as follows: (i) Analytics Logical Function (AnLF): (a) the NWDAF device 101 that performs an AnLF may perform inference and derive analytics information (that is, derive a statistic and/or prediction in response to an analytics consumer request); and (b) in addition, the NWDAF device 101 that performs the AnLF may expose an analytics service for network data (for example, Nnwdaf_AnalyticsSubscription or Nnwdaf_Analyticsinfo); and (ii) Model Training Logical Function (MTLF): (a) the NWDAF device 101 that performs an MTLF may train an ML model and expose a new training service (for example, provision of an untrained initial version of model or a trained model); the NWDAF device 101 may include each of the MTLF and the AnLF, or may support both functions; the NWDAF device 101 that performs the AnLF may set an ID and an Analytic ID of an NWDAF device that performs the MTLF so as to search for a trained ML model; the NWDAF device 101 that performs the AnLF may search for the NWDAF device that performs the MTLF using an MTLF ID; the NWDAF device 101 that performs the MTLF may discover and select an NWDAF device that supports the MTLF for federation learning; the NWDAF device 101 may perform local training and global training in federation learning; the NWDAF device 101 may generate a new model without input data related to the abnormal UE list during the observed time window and/or generate an analytics result for network data, and then may transmit the new model or the network data to the subscribed NWDAF device 101 or update the new model or the network data; the NF device 102 may include any one of the MTLF and the AnLF capable of providing a service operation (e.g., an analytic exposure operation, an ML model provisioning operation, or an ML model training operation) required for a type of analytics required, or an instance of each NWDAF device 101 may provide the following information so as to assist in searching for and selecting an instance of the NWDAF device 101 including both the MTLF and the AnLF; the NF device 102 that needs to search for an NWDAF instance that provides support for some specific service operations for a specific type of analytics may query the NRF with respect to the NWDAF device 101 that supports a required service operation and a required Analytic ID; the NWDAF device 101 that performs the MTLF may register an ML model provisioning service and a training service (that is, Nnwdaf_MLModelProvision, Nnwdaf_MLModelInfo, Nnwdaf_MLModelUpdate, Nnwdaf_MLModelTraining, and Nnwdaf_MLModelTraininginfo) when an ML model is providable and trainable with respect to the Analytic ID; the 5GC NF and the OAM, which are consumers, may determine how to use data analytics provided by the NWDAF device 101; interaction between the 5GC NF(s) and the NWDAF device 101 may occur within the PLMN; ¶¶ [0103]-[0109] with FIG. 2: allow an NWDAF device 201 including an AnLF to use a provisioning service operation and a training service operation with respect to an ML model of an untrained initial model or a trained ML model in another NWDAF device 202 that supports an MTLF; the AnLF and the MTLF may be defined as follows: (i) AnLF: the NWDAF device 101 including the AnLF may perform inference, derive analytics information (i.e., derive a statistic and/or prediction in response to an analytics consumer request), and expose an analytics service (e.g., Nwdaf_AnalyticsSubscription or Nnwdaf_Analyticsinfo); (ii) MTLF: the NWDAF device 101 including the MTLF may train an ML model and expose a new training service (e.g., trained model provision and model training); the AnLF may support Nnwdaf_Analyticsinfo (data analytics information) or Nnwdaf_AnalyticsSubscription (analytics subscription) service; the MTLF may support services such as Nnwdaf_MLModelProvision (ML model provisioning), Nnwdaf_MLModelInfo (ML model information request), Nnwdaf_MLModelUpdate (ML model update), Nnwdaf_MLModelTraining (ML model training), and Nnwdaf_MLModelTraininginfo (ML model training information); an Nnwdaf interface may be used to request and subscribe to a provisioning service for an untrained initial version of ML model or a trained ML model in an NWDAF; in addition, the Nnwdaf interface may be used by the NWDAF device 101 that supports the MTLF to request and subscribe to an ML model training service for model learning and cooperative learning; ¶¶ [0110]-[0113] with FIG. 3: an operation of an NWDAF device that performs an MTLF is described in FIG. 3(a); the NWDAF device may receive an initial version of ML model from a model provisioning server (operator), a model provisioning server (3rd party), or another NWDAF device that performs the MTLF; then, after training the initial version of ML model, the NWDAF device may provide the trained ML model to an NWDAF device that performs an AnLF or MTLF through an Nnwdaf_MLModelProvision service (model provisioning service) or Nnwdaf_MLModelInfo service (model information service); in addition, for update of the ML model, the NWDAF device may use an Nnwdaf_MLModelUpdate, Nnwdaf_MLModelTraining, or Nnwdaf_MLModelTraininginfo service; the NWDAF device illustrated in FIG. 3(b) may collect data from a DCCF device and a data source (NF device or ADRF device), and receive an ML model from an NWDAF device that performs an MTLF; then, the NWDAF device that performs the AnLF may analyze the collected data using the ML model; a data analytics result may be provided to a consumer NF device in a statistical or predictive manner; ¶¶ [0114]-[0156] with FIG. 4: an NWDAF device 401 (service consumer) may use an NWDAF search principle to select an NWDAF device that supports requested analytics information, required analytics function, and/or requested ML model information; in order to search for an NWDAF device that supports an AnLF or an NWDAF device that supports an MTLF using an NRF, the following conditions may need to be satisfied: when (i) an ML model to be provided/trained is related to UE(s) and (ii) an NWDAF service consumer (other than an NWDAF) is not capable of providing an area of interest for the requested ML model to be provided/trained, the NWDAF device 401 may select an NWDAF device 403 having a large service area from candidate NWDAF devices 403; e.g., in response to discovery, when the NWDAF device 401 receives NWDAF device(s) 403 having an aggregation capability (e.g., an ML model aggregation capability and an ML model update capability), the NWDAF device 401 may preferably select the NWDAF device 403 having an aggregation capability (for example, an ML model aggregation capability and an ML model update capability) with a large serving area; when the NWDAF device 401 is not capable of providing an ML model to be provided/trained for requested UE(s) (e.g., an NWDAF providing another service area), the NWDAF device 401 may reject a subscription or request for provision of the ML model to be provided/trained, or determine an AMF that serves an UE as specified; in order to request UE location information from the AMF and discover another target NWDAF that serves a region where the UE(s) is located, the NWDAF device 401 may query the NRF device 402 with a tracking area where the UE is located; during discovery of the NWDAF device 403 that supports the MTLF, the NRF device 402 may return instances of one or more candidate NWDAF devices 403 to an NF consumer, and an instance of each candidate NWDAF device 403 may include analytics filter information on an ML model trained for each Analytic ID; a selection function of the NWDAF device 403 that supports an MTLF of the NF consumer may select an NWDAF instance based an instance of the NWDAF device 403 that supports an available MTLF; for NWDAF selection, the NF consumer may consider at least one of the following items: a supportable service for each Analytic ID (e.g., an ML model provision/training service) or NWDAF service region information, that is, a list of TAIs to which an NWDAF may provide analytics, ML model provision, ML model training, and/or data; when selecting the NWDAF device 103 that supports an MTLF for ML model provisioning and model training, the NWDAF device 401 may consider the following additional factors: (a) An analytics filter for ML model trained for each Analytic ID; and (b) an ML model aggregation capability; ¶¶ [0135] and [0157]-[0209] with operation 1 in FIG. 5: the NWDAF device 501 that performs an AnLF, which is a service consumer, may subscribe or unsubscribe to the NWDAF device 502 that performs an MTLF; the NWDAF device 501 may simultaneously be a consumer of services provided by other NWDAF(s) and a provider of services to other NWDAF device(s) 502; in operation 1, the NWDAF device 501, which is a service consumer, may invoke a subscription service operation for provisioning of an ML model (Nnwdaf_MLModelProvision_Subscribe) or an unsubscription service operation for provisioning of the ML model (Nnwdaf_MLModelProvision_Unsubscribe) to subscribe, modify, or unsubscribe an ML model trained in the NWDAF device 502 that supports an MTLF connected to an Analytic ID; a parameter used by the NWDAF device 501 may include at least one of (i) an Analytic ID, (ii) S-NSSAI (Subscribed Network Slice Selection Assistance Information), (iii) a target area of interest, (iv) an application ID, (v) a target UE, (vi) an ML model target period, (vii) an expiry time, and (viii) ML model information including at least one of an ML model file address, an ML model file, a model ID, and a model version; when a subscription to the trained ML model connected to the Analytic ID is received, the NWDAF device 502 including the MTLF may perform the following process: determine (i) whether an existing ML model is available for subscription, or (ii) whether to trigger additional training for the existing ML model with respect to subscription; the NWDAF device 502 that performs the MTLF may determine that additional training is required for an already subscribed ML model; when the NWDAF device 502 that performs the MTLF determines that additional training is required for the already subscribed ML model, the NWDAF device 502 may collect data required for training of the ML model from an NF device, DCCF device, or OAM device; ¶¶ [0210]-[0216] with operation 1 in FIG. 6: an NWDAF service consumer, that is, an NWDAF device 601 may request an NWDAF device 602 including MTLF ML model information using an Nnwdaf_MLModelInfo service; the NWDAF device 601 (e.g., NWDAF(MTLF+AnLF)) may simultaneously be a consumer of a service provided by another NWDAF device 602 and a provider of this service to other NWDAF(s); in operation 1, the NWDAF device 601 that supports an AnLF may invoke an ML model information request service operation (Nnwdaf_MLMoldelInfo_Request) to request ML model(s) connected to an Analytic ID from the NWDAF device 602 that supports an MTLF; a parameter used when the NWDAF device 601, which is an NWDAF service consumer, invokes an information request service operation, may include at least one of an Analytic ID, S-NSSAI, a target area of interest, an application ID, a target UE, an ML model target period, and an expiry time; when a request for ML model information for Analytics is received, the NWDAF device 602 that performs the MTLF may perform the following process: (i) determine whether an existing trained ML model is available for the request, or (ii) determine whether an additional training trigger for the existing trained ML model is required for the request; when the NWDAF device 602 that performs the MTLF determines that additional training is required for an already requested ML model, the NWDAF may start collecting data from an NF device, DCCF device, or OAM device required for ML model training; ¶¶ [0218]-[0222] with operation 3 in FIG. 7: in operation 3, the NWDAF device 701 may invoke, from the NWDAF device 702, a subscription service operation for provisioning of the ML model (Nnwdaf_MLModelProvision_Subscribe); at this time, a subscription ID included in the subscription service operation may be the same as a subscription ID used in operation 1; i.e., when invoking the subscription service operation of operation 3 again, the NWDAF device 701 may include a parameter same as that included when previously invoking the subscription service operation for provisioning of the ML model to request a new ML model different from a previous one or re-request an ML model previously provided through a subscription or request process from the NWDAF device 702; at this time, the NWDAF device 701 may incorporate an alternative ML model flag into the subscription service operation for provisioning of the ML model in operation 3 to request a new ML model different from a previous one or re-request a previously provided ML model from the NWDAF device 702; ¶¶ [0224]-[0247] with FIG. 8: the NWADF device 801 may perform local training in federation learning, and the NWDAF device 803 may perform global training in federation learning; the NWDAF device 801 (service consumer) may search for and select the NWDAF device 803 that provides requested analytics information, a required analytics function and/or a requested ML model, and supports ML model training; when model training is related to NF(s)/UE(s) and an NWDAF service consumer (NWDAF device 803) is not capable of providing an area of interest for requested model training, the NWDAF device 801 may select the NWDAF device 803 having a large service area from candidate NWDAF devices 803; e.g., in response to discovery, when the NWDAF device 801 receives the NWDAF device(s) 803 having an ML model update capability, the NWDAF device 801 may preferably select the NWDAF device 803 having a large serving area and an ML model update capability; in order to search for an NWDAF registered in a UDM with respect to a given UE, the NWDAF device 801 or other NWDAFs interested in providing and training UE-related data or an ML model may make a query to a UDM device to search for an instance of the NWDAF device 803 that is already providing a service to the UE; in order to search for the NWDAF device 803 that performs the MTLF, the NWDAF device 801 that performs the MTLF may include at least one of analytics filter information, a trainable and providable ML model ID, an ML model version, and an ML model aggregation capability with respect to an ML model that is trained per Analytic ID in response to a registration request for the NRF device 802; in operation 1, the NWDAF device 803 may invoke, from the NRF device 802, a registration service operation (Nnrf_NFManagement_NFRegister request) for the NWDAF device 803; in operation 2, the NRF device 802 may store a profile of an NF device of the NWDAF device 803; in operation 4, the NWDAF device 801 may invoke, from the NRF device 802, a request service operation (Nnrf_NFDiscovery_Request) for searching for the NWDAF device 803; in operation 7, the NWDAF device 801 may select an NWDAF device that performs an MTLF; ¶¶ [0248]-[00271] with FIG. 9: the NWDAF device 901 may perform local training in federation learning, and the NWDAF device 903 may perform global training in federation learning; the NWDAF device 903 (service consumer) may use an NWDAF search principle to select the NWDAF device 901 that supports requested analytics information, required analytics function, and/or requested ML model training; the NWDAF device 903 may support at least one of an ML model training service (Nnwdaf_MLModelTraining) or an ML model training information service (Nnwdaf_MLModelTraininginfo) to search for the NWDAF device 901; when an ML model to be trained is related to NF(s)/UE(s) and an NWDAF service consumer (other than an NWDAF) is not capable of providing an area of interest for requested ML model training, the NWDAF device 903 may select an NWDAF having a large service area from candidate NWDAFs; e.g., in response to discovery, when the NWDAF device 903 receives NWDAF(s) 901 having an ML model aggregation/update capability, the NWDAF device 903 may preferably select the NWDAF device 901 having an ML model aggregation/update capability for a large serving area; in order to search for the NWDAF device 901 that supports the MTLF, the NWDAF device 901 that supports the MTLF may include at least one of analytics filter information and a service providable for model training (i.e., an Nnwdaf_MLModelTraining service or an Nnwdaf_MLMode1Traininginfo service) with respect to an ML model that is trainable per Analytic ID in response to a registration request for the NRF device 902; in operation 1, the NWDAF device 901 may invoke, from the NRF device 902, a registration service operation (Nnrf_NFManagement_NFRegister request) for the NWDAF device 901; at this time, the registration service operation may include at least one of a list of supported Analytic IDs, per supported service (e.g., an Nnwdaf_MLModelTraining service or an Nnwdaf_MLModelTraininginfo service), a serving area where an ML model is provided, S-NSSAI, ML model information including at least one of an ML model file address, an ML model file, a model ID, and a model version, and a federation learning capability (ML model training capability); in operation 2, the NRF device 902 may store a profile of an NF device of the NWDAF device 901; in operation 4, the NWDAF device 903 may invoke, from the NRF device 902, a request service operation (Nnrf_NFDiscovery_Request) for searching for the NWDAF device 901; at this time, the request service operation may include at least one of an Analytic ID, per supported service (e.g., an Nnwdaf_MLModelTraining service or an Nnwdaf_MLModelTraininginfo service), a serving area where an ML model is provided, S-NSSAI, ML model information including at least one of an ML model file address, an ML model file, a model ID, and a model version, and a federation learning capability (for example, an ML model training capability); in operation 7, the NWDAF device 903 may select at least one NWDAF device 901 capable of learning a local ML model that performs the MTLF; ¶¶ [0272]-[0289] with operation 1 in FIG. 10: the NWDAF device 1001 may perform local training in federation learning, and the NWDAF device 1002 may perform global training in federation learning; the NWDAF device 1001 may be configured locally with ID(s) and Analytic ID(s) of an NWDAF device that performs an MTLF to search for an ML model of an untrained initial model or a trained ML model; the NWDAF device 1001 that performs an MTLF, which is a service consumer, may be used to subscribe or unsubscribe to the NWDAF device 1002 that performs the MTLF; the NWDAF device 1001 may simultaneously be a consumer of this service provided by other NWDAF(s) and a provider of this service to other NWDAF device(s) 1002; in operation 1, the NWDAF device 1001, which is a service consumer, may invoke a subscription service operation for provisioning of an ML model (Nnwdaf_MLModelProvision_Subscribe) or an unsubscription service operation for provisioning of the ML model (Nnwdaf_MLModelProvision_Unsubscribe) to subscribe, modify, or unsubscribe an ML model of an untrained initial model or a trained ML model connected to an Analytic ID; when a subscription to the ML model of the untrained initial model or the trained ML model connected to the Analytic ID is received, the NWDAF device 1002 including an MTLF may perform the following process: determine (i) whether an existing trained ML model is available for subscription or (ii) whether to trigger additional training for the existing trained ML model with respect to subscription; the NWDAF device 1002 that performs the MTLF may determine that additional training is required for the existing ML model; when the NWDAF device 1002 determines that additional training is required, the NWDAF device 1002 may collect data required for training of the ML model from an NF device, DCCF device, or OAM device; ¶¶ [0290]-[0298] with operation 1 in FIG. 6: the NWDAF device 1101 may perform local training in federation learning, and the NWDAF device 1102 may perform global training in federation learning; an NWDAF service consumer, that is, an NWDAF device 1101 may request an NWDAF device 1102 including ML model information using an Nnwdaf_MLModelInfo service operation; the NWDAF device 1101 (e.g., NWDAF(MTLF+AnLF)) may simultaneously be a consumer of a service provided by another NWDAF device 1102 and a provider of this service to other NWDAF(s); in operation 1, the NWDAF device 1101 may invoke an ML model information request service operation (Nnwdaf_MLMoldelInfo_Request) to request ML model(s) connected to an Analytic ID; when a request for ML model information for analytics is received, the NWDAF device 1102 that performs the MTLF may perform the following process: (i) determine whether an existing trained ML model is available for the request, or (ii) determine whether an additional training trigger for the existing trained ML model is required for the request; when the NWDAF device 1102 that performs the MTLF determines that additional training is required, this NWDAF may start collecting data from an NF device, DCCF device, or OAM device required for ML model training; ¶¶ [0299]-[0305] with FIGS. 10 and 12: operations 1 and 2 may be the same as operation 1 and 2 described with reference to FIG. 10; in operation 3, the NWDAF device 1201 may locally train the ML model; in operation 4, the NWDAF device 1201 may invoke an ML model update notification service operation (Nnwdaf_MLModelUpdate_Notify) from the NWDAF device 1202 that performs a global update; in operation 5, the NWDAF device 1202 may globally update the ML model; here, globally updating the ML model may mean aggregating, by each of a plurality of NWDAF devices 1202, the locally trained ML model, and then changing the ML model by reflecting a gradient of the ML model expressed as a polynomial; ¶¶ [0306]-[0310] with FIGS. 11 and 13: operations 1 and 2 may be the same as operations 1 and 2 described with reference to FIG. 11; in operation 3, an NWDAF device 1301 may locally train the ML model; in operation 4, the NWDAF device 1301 may invoke an ML model update notification service operation (Nnwdaf_MLModelUpdate_Notify) from an NWDAF device 1302 that performs a global update; in operation 5, the NWDAF device 1302 may globally update the ML model; ¶¶ [0311]-[0317] with FIGS.9 and 14: the NWDAF device 1401 may perform local training in federation learning, and the NWDAF device 1402 may perform global training in federation learning; in operation 1 of FIG. 14, the NWDAF device 1401, the NRF device 1402, and the NWDAF device 1403 may be the same as the discovery process of the NWDAF device 903 that performs the MTLF previously described with reference to FIG. 9; in operation 2, the NWDAF device 1403 may invoke, from the NWDAF device 1401, an ML model training subscription service operation (Nnwdaf_MLModelTraining_Subscribe); the training subscription service operation may include at least one of (i) an Analytic ID, (ii) S-NSSAI, (iii) a target area of interest, (iv) an application ID, (v) a target UE, (vi) an ML model target period, (vii) an expiry time, and (viii) ML model information including at least one of an ML model file address, an ML model file, a model ID, and a model version; in addition, the training subscription service operation may further include at least one of a description of a requested parameter for ML model update and a description of a budget for an update reporting time (e.g., a target reporting time); in operation 3, the NWDAF device 1401 may locally train an ML model; in operation 4, the NWDAF device 1401 may invoke an ML model training notification service operation (Nnwdaf_MLModelTraining_Notify) from the NWDAF device 1402 that performs a global update; in operation 5, the NWDAF device 1402 may globally update the ML model; ¶¶ [0318]-[0324] with FIGS. 9 and 15: the NWDAF device 1501 may perform local training in federation learning, and the NWDAF device 1502 may perform global training in federation learning; in operation 1 of FIG. 15, the NWDAF device 1501, the NRF device 1502, and the NWDAF device 1503 may be the same as the discovery process of the NWDAF device 903 that performs the MTLF previously described with reference to FIG. 9; in operation 2, an NWDAF device 1503 may invoke, from the NWDAF device 1502, an ML model training request service operation (Nnwdaf_MLModelTraininginfo_request); at this time, the training request service operation may include at least one of (i) an Analytic ID, (ii) S-NSSAI, (iii) a target area of interest, (iv) an application ID, (v) a target UE, (vi) an ML model target period, (vii) an expiry time, and (viii) ML model information including at least one of an ML model file address, an ML model file, a model ID, and a model version.; in addition, the training request service operation may further include at least one of a description of a requested parameter for ML model update and a description of a budget for an update reporting time (e.g., a top-k gradient, a threshold for sparsification of gradient, and the like); in operation 3, the NWDAF device 1501 may locally train an ML model; in operation 4, the NWDAF device 1501 may invoke an ML model training request response service operation (Nnwdaf_MLModelTraininginfo_request_response) from an NWDAF device 1502 that performs global training; in operation 5, the NWDAF device 1502 may globally update the ML model; ¶¶ [0325]-[0345] with Table 1: an Nnwdaf_MLModelTraining service operation or an Nnwdaf_MLModelTraininginfo service operation may need to include the following input and output: (i) Input: an Analytic ID, an expiry time, and ML model information (an ML model file, a model ID, and a model version); and (ii) Output: an Analytic ID, a requested parameter for ML model update (for example, a gradient), a time stamp, an ID and a version of an ML model targeted for training, and a training area (e.g., a list of TAs targeted for training, and the like); ¶¶ [0346]-[0359] with FIGS. 16-17: in operation 1, an NWDAF device 1 1601, which is an ML model provider, may register a function of providing an untrained initial version of model or a trained model (that is, an "MLModelProvision service operation" with a list of supported Analytic IDs) with an NRF device 1603 as part of a profile; in operation 2, an NRF device 1603 may store an NWDAF profile of the NWDAF device 1 1601; in operation 4, an NWDAF device 2 1602 may invoke, from the NRF device 1603, a discovery request service operation including a service parameter list (e.g., Analytic ID, and the like) so as to search for the NWDAF device 11601 that provides an "MLModelProvision service"; in operation 6, the consumer NWDAF device 2 1602 may invoke, from the NWDAF device 1 1601, a request service operation or subscription service operation of the "MLModelProvision service" using an instance of the searched provider NWDAF device 1 1601; in operation 7, the NWDAF device 1 1601 may invoke, from the NWDAF device 2 1602, a request response service operation or subscription notification service operation including a model parameter for an untrained initial version of model or a trained model; in operation 8, when the NWDAF device 2 1602 is capable of training an ML model, the NWDAF device 2 1602 may locally train the model and model parameter; in operation 9, the NWADF device 2 1602 may locally evaluate the ML model after training the ML model; in operation 10, when a subscription to the ML model is performed, the NWDAF device 2 1602 may invoke, from the NWDAF device 1 1601, an ML model update notification service operation (Nnwdaf_MLModelUpdate_Notify) to transmit information on the locally trained ML model; in operation 11, the NWDAF device 1 1601 may aggregate the trained ML model transmitted from the NWDAF device 2 1602 to update the ML model based on a globally trained ML model; in operation 11, the NWDAF device 1 1601 may evaluate the ML model); and
transmitting, by the machine learning training function, a notification to the consumer indicating that the machine learning training job has been completed (Lee, ¶¶ [0088] and [0090] with FIG. 1; the NWDAF device 101 may generate a new model without input data related to the abnormal UE list during the observed time window and/or generate an analytics result for network data, and then may transmit the new model or the network data to the subscribed NWDAF device 101 or update the new model or the network data; ¶¶ [0162]-[0209] with FIG. 5: a notification may be received from the NWDAF device 502 that performs the MTLF using an ML model provisioning service (Nnwdaf_MLModelProvision); ML model information received through the notification may be used to output analytics from the NWDAF device 501 that performs the AnLF; the provisioning service for the ML model may be used to modify an existing ML model subscription in the NWDAF device 501; the NWDAF device 501 may simultaneously be a consumer of services provided by other NWDAF(s) and a provider of services to other NWDAF device(s) 502; in operation 2, when the NWDAF device 501 subscribes to the trained ML model(s) connected to the Analytic ID(s), the NWDAF device 502 that performs the MTLF may invoke a notification service operation for provisioning of the ML model (Nnwdaf_MLModelProvision_Notify) to transmit trained ML model information (e.g., a file address of the trained ML model) to the NWDAF device 501; the NWDAF device 502 that performs the MTLF may invoke an Nnwdaf_MLModelProvision_Notify service operation to notify an available retrained ML model when the NWDAF device 502 determines that retraining is required for a previously provided trained ML model; when a process of operation 1 is performed for subscription modification (i.e., including a subscription correlation ID), the NWDAF device 502 that performs the MTLF may invoke the notification service operation for provisioning of the ML model (Nnwdaf_MLModelProvision_Notify) to provide a new learned ML model different from that previously provided or provide a relearned ML model; <Nnwdaf_MLModelProvision Service – ML Model Provisioning Service> a service description: this service may allow a consumer to be notified when an ML model corresponding to a subscription parameter becomes available; an NWDAF may notify ML model information to a consumer instance subscribed to a specific NWDAF service; ¶ [0217] with operation 2 in FIG. 6: in operation 2, invoke an ML model information request response service operation (Nnwdaf_MLModelInfo_Request_response) to respond to the NWDAF device 601 (service consumer) with ML model information (including an ML model file address); the NWDAF device 103 that performs the MTLF may invoke an ML model information request response service operation including at least one of (i) ML model information, (ii) a validity period, and (iii) a spatial validity; at this time, the ML model information may include at least one of an ML model file address, an ML model file, a model ID, and a model version; ¶¶ [0223] with operation 4 in FIG. 7: in operation 4, the NWDAF device 702 may invoke, from an NWDAF device, a notification service operation for provisioning of the ML model (Nnwdaf_MLModelProvision_Notify); at this time, the notification service operation may include at least one of ML model information different from the ML model provided in operation 1 (e.g., including at least one of an ML model file, an ML model file address, a model version, or a model ID), a validity period, and a spatial validity; ¶¶ [0240]-[0247] with FIG. 8: during discovery of the NWDAF device 803 that performs the MTLF, the NRF device 802 may return instances of one or more candidate NWDAF devices 803 to an NF consumer, and an instance of each candidate NWDAF device 803 may include analytics filter information on an ML model of an initial model that is untrained or an ML Model that is trained for each Analytic ID; in operation 3, the NRF device 802 may invoke, from the NWDAF device 803, a registration response service operation (Nnrf_NFManagement_NFRegister_response); in operation 5, the NRF device 802 may invoke, from the NWDAF device 801, a discovery request response service operation (Nnrf_NFDiscovery_Request_response); here, the response service operation may include a list and an address of an instance ID of the NWDAF device 803; ¶¶ [0264]-[0271] with FIG. 9: during discovery of the NWDAF device 901 that supports the MTLF, the NRF device 902 may return instances of one or more candidate NWDAF devices 901 to an NF consumer, and an instance of each candidate NWDAF device 901 may include analytics filter information for an ML model that is trainable for each Analytic ID; in operation 3, the NRF device 902 may invoke an Nnrf_NFManagement_NFRegister response from the NWDAF device 901; in operation 5, the NRF device 902 may invoke, from the NWDAF device 903, a response service operation (Nnrf_NFDiscovery_Request_response); here, the response service oper