CTFR 18/122,909 CTFR 94198 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Amendment This office action is in response to the amendment filed on 04/13/202. Claims remain pending in the application. Claims 1 and 9 are independent. Drawings Applicant's amendment to specification corrects some of previous objections; therefore, some of previous objections are withdrawn. The remaining objections are shown below. The drawings are objected to because (1) FIG. 6 (e.g., modify MOIAttributes(List(MLTrainingJobs), attributes)) is inconsistent with description of FIG. 6 in ¶ [0022] that " FIG. 6 illustrates an example of a procedure for reading one or more ongoing or completed MLTrainingJobs … " ; (2) FIG. 14 (e.g., readMOI(List( MLTrainingReporting ))) is inconsistent with description of FIG. 14 in ¶ [0030] that " FIG. 14 illustrates an example of a procedure for reading one or more ongoing or completed MLTrainingJobs … " ; (3) FIG. 15 (e.g., modifyMOIAttributes(List( MLTrainingReporting ), attributes)) is inconsistent with description of FIG. 15 in ¶ [0031] that " FIG. 15 illustrates an example of a procedure for modifying attributes of one or more ongoing or completed MLTrainingJobs … " ; (4) FIG. 16 (e.g., deleteMOI (List( MLTrainingReportingID ))) is inconsistent with description of FIG. 16 in ¶ [0032] that " FIG. 16 illustrates an example of a procedure for deleting one or more ongoing or completed MLTrainingJobs … " ; (5) FIG. 17 (e.g., readMOI (List( MLTrainingReportingRequests ))) is inconsistent with description of FIG. 17 in ¶ [0033] that " FIG. 17 illustrates an example of a procedure for reading one or more ongoing or completed MLTrainingJobs … " ; (6) 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 attributes of one or more ongoing or completed MLTrainingJobs … " ; and (7) FIG. 19 (e.g., deleteMOI (List( MLTrainingReportingRequestsID ))) is inconsistent with description of FIG. 19 in ¶ [0035] that " FIG. 19 illustrates an example of a procedure for 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. See also Specification Objections for recommendation to resolve Drawing issues . Specification Applicant's amendment to specification corrects some of previous objections; therefore, some of previous objections are withdrawn. The remaining objections with recommendations for resolving Drawing issues are shown below. 07-29 AIA The disclosure is objected to because of the following informalities: in ¶ [0022], " FIG. 6 illustrates an example of a procedure for reading … " appears to be " FIG. 6 illustrates an example of a procedure for modifying … "; in ¶ [0030], " FIG. 14 illustrates an example of a procedure for reading one or more ongoing or completed MLTrainingJobs … " appears to be " FIG. 14 illustrates an example of a procedure for reading one or more ongoing or completed MLTrainingReporting instances … " according to ¶ [0087]; in ¶ [0031], " FIG. 15 illustrates an example of a procedure for modifying attributes of one or more ongoing or completed MLTrainingJobs … " appears to be " FIG. 15 illustrates an example of a procedure for modifying attributes of one or more ongoing or completed MLTrainingReporting instances … " according to ¶ [0087]; in ¶ [0032], " FIG. 16 illustrates an example of a procedure for deleting one or more ongoing or completed MLTrainingJobs … " appears to be " FIG. 16 illustrates an example of a procedure for deleting one or more ongoing or completed MLTrainingReporting instances … " according to ¶ [0087]; in ¶ [0033], " FIG. 17 illustrates an example of a procedure for reading one or more ongoing or completed MLTrainingJobs … " appears to be " FIG. 17 illustrates an example of a procedure for reading one or more ongoing or completed MLTrainingReportingRequests … " according to ¶ [0088]; in ¶ [0034], " FIG. 18 illustrates an example of modifying attributes of one or more ongoing or completed MLTrainingJobs … " appears to be " FIG. 18 illustrates an example of modifying attributes of one or more ongoing or completed MLTrainingReportingRequests … " according to ¶ [0088]; in ¶ [0035], " FIG. 19 illustrates an example of a procedure for deleting the characteristics of one or more ongoing or completed MLTrainingJobs … " appears to be " FIG. 19 illustrates an example of a procedure for deleting the characteristics of one or more ongoing or completed MLTrainingReportingRequests … " according to ¶ [0088] . Appropriate correction is required. Claim Objections Applicant's amendment to claims corrects previous objections; therefore, the previous objections are withdrawn. Applicant's amendment to claims also raises the following new objections. 07-29-01 AIA Claim s 4 and 9 are objected to because of the following informalities: in Claim 4, lines 6-8 , " … transmitting … a notification deleted machine learning training jobs identifiers indicating that … to be deleted " appears to be " … transmitting … a notification indicating that … have been deleted " according to Claim 12; in Claim 9, lines 3-6 , " … at least one memory including computer program code of a machine learning training function , cause the apparatus to perform operations … " appears to be " … at least one memory including computer program code of a machine learning training function causing the apparatus to perform operations … " . Appropriate correction is required. Claim Rejections - 35 USC § 112 Applicant's amendment to claims corrects previous rejections; therefore, the previous rejections are withdrawn. Applicant's amendment to claims also raises the following new rejections. 07-30-02 AIA 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. 07-34-01 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 3 recite the limitation " … receiving … a request to modify attributes of managed object instances representing ongoing or completed machine learning training jobs , the request comprising a list of identifiers of the ongoing or completed machine learning training jobs whose attributes are to be modified and attributes for t he ongoing or completed machine learning training jobs that are to be modified … a notification indicating that the attributes of the ongoing or completed machine learning training jobs identified in the list have been modified " in lines 2-, which rendering these claims indefinite because (1) i. Claims 4 and 12 recites the limitation " … to delete managed object instances representing machine learning training jobs , the request comprising a list of identifiers of the ongoing or completed machine learning training requests that are to be deleted … indicating that the ongoing or completed machine learning training jobs identified in the list that have been deleted " in lines 3-. There is insufficient antecedent basis for the limitations " the ongoing or completed machine learning training requests " and " the ongoing or completed machine learning training jobs " in the claim. For examination purposes, " … to delete managed object instances representing ongoing or completed machine learning training jobs , the request comprising a list of identifiers of the ongoing or completed machine learning training jobs that are to be deleted … indicating that the ongoing or completed machine learning training jobs identified in the list that have been deleted " is considered (see also Claim Objections to Claim 4). Claim 11 recite the limitation " … receiving … a request to modify attributes of managed object instances representing ongoing or completed machine learning training jobs , the request comprising a list of identifiers of the ongoing or completed machine learning training jobs whose attributes are to be modified and attributes for the ongoing or completed machine learning training requests that are to be modified … a notification indicating that the ongoing or completed machine learning training jobs identified in the list have been modified " in lines 4-12, which rendering these claims indefinite because (1) it is unclear whether three instances of " attributes " recited here are the same or different; (2) there is insufficient antecedent basis for the limitation " the ongoing or completed machine learning training requests "; and (3) it is unclear which are modified, " attributes of managed object instances representing ongoing or completed machine learning training jobs " or " the ongoing or completed machine learning training jobs "? Clarification is required. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-4 and 9-12 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 and Yue et al. (US 2025/0119715 A1, priority date: 01/13/2022), hereinafter Yue . Independent Claims 1 and 9 Lee discloses a method, comprising: receiving, by a machine learning training function of a communication network, from a consumer of the machine learning training function, a request to instantiate a managed object instance (MOI) representing a machine learning training job for training a machine learning model, wherein the request comprises a plurality of attributes to be used to instantiate the MOI, the plurality of attributes including a first attribute comprising an identifier of the machine learning training job, a second attribute comprising a machine learning model identifier that identifies the machine learning model, a third attribute comprising an indication of a termination condition for terminating the machine learning training job, and a fourth attribute comprising an indication of a level of success of the machine learning training job ; instantiating, by the machine learning training function, the MOI representing the machine learning training job, based on the plurality of attributes comprised in the machine learning training request (Lee, ¶¶ [0005]-[0035]: a registration request service operation with an NF (network function) may include at least one of (i) a list of Analytic IDs, (ii) a supported service, (iii) a serving area, (iv) S-NSSAI (subscribed network slice selection assistance information), and (v) ML model information including at least one of an ML model file address, an ML model file, a model ID, and a model version; the selecting of the NWDAF (network data analytics function) instance may include selecting an NWDAF instance based on at least one of (i) S-NSSAI, (ii) an Analytic ID, (iii) a supported service, (iv) NWDAF serving area information, (v) NWDAF location information, (vi) an NF type of a data source, (vii) an NF Set ID of the data source, (viii) a supported analytics delay, and (ix) an NWDAF capability; the first NWDAF device may be configured to perform local training of federation learning, and support the MTLF, and the second NWDAF device may be configured to perform global training of federation learning, and support the MTLF (a model training logical function); the registration request service operation may include at least one of a list of Analytic IDs, a supported service, a serving area, 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 an ML model training capability or an ML model update capability; invoking, from a second NWDAF device that performs an MTLF, a subscription service operation for provisioning of the ML model; and invoking, from the second NWDAF device, a notification service operation for the subscription service; the 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 user equipment (UE), (v) an ML model target period, and (vi) an expiry time; the 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, (v) an ML model target period, (vi) an expiry time, and (vii) ML model information including at least one of an ML model file address, an ML model file, a model ID, and a model version; the notification service operation may include at least one of (i) ML model information including at least one of an ML model file address, an ML model file, a model ID, and a model version, (ii) a validity period, and (iii) a spatial validity; invoking a subscription service operation for provisioning of a second ML model after a subscription for provisioning of a first ML model is completed, and the subscription service operation may include a subscription ID same as a subscription ID for the first ML model; the 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, (v)an ML model target period, (vi) an expiry time, (vii) 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 (viii) an alternative ML model flag; ¶¶ [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 (3 rd 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]-[0271] 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, to the consumer, a notification indicating that the MOI representing the machine learning training job has been instantiated 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 operation may include a list and an address of an instance ID of the NWDAF device 901; ¶¶ [0272]-[0289] with operation 2 in FIG. 10: the NWDAF device 1001 may receive a notification from the NWDAF device 1002 that performs the MTLF using an ML model provisioning service (Nnwdaf_MLModelProvision) with respect to ML model information in related analytics; the ML model information received through the notification may be used by the NWDAF device 1001 to train the ML model ; the provisioning service may be also used by the NWDAF device 1001 to modify an existing ML model subscription; 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 2, when the NWDAF device 1001 subscribes to the ML model of the untrained initial model or the trained ML model(s) connected to the Analytic ID(s), the NWDAF device 1002 may invoke an Nnwdaf_MLModelProvision_Notify service operation including information on the ML model of the untrained initial model or information on the trained ML model to transmit a file address of the ML model of the untrained initial model or the trained ML model; the NWDAF device 1002 that performs the MTLF may invoke a notification service operation for provisioning of the ML model ( Nnwdaf_MLModelProvision_Notify ) to notify an available retrained ML model when the NWDAF device 1002 determines that retraining is required for an ML model of a previously provided untrained initial model or a trained ML model; when a process of operation 1 is performed for subscription modification (i.e., including a subscription correlation ID), the NWDAF device 1002 that performs the MTLF may provide a new learned ML mode l different from that previously provided, or may provide a relearned ML model by invoking the Nnwdaf_MLModelProvision_Notify service operation; ¶ [0298] with operation 2 in FIG. 11: in operation 2, the NWDAF device 1102 that performs the MTLF may invoke an ML model information request response service operation (Nnwdaf_MLModelInfo_Request_response) for an ML information request service operation (Nnwdaf_MLMode1Info_Request) to respond to the NWDAF device 1101 (service consumer), including at least one of ML model information, a validity period, and a spatial validity. ML model information that may be provided by an NWDAF that performs the MTLF may include at least one of an ML model file address, an ML model file, a model ID, and a model version; ¶¶ [0299]-[0305] with FIG. 12: 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 6, the NWDAF device 1202 may invoke, from the NWDAF device 1201, a notification service operation for provisioning of the ML model; at this time, the notification service operation may include an updated model; ¶¶ [0311]-[0317] with FIG. 14: 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; the training notification service operation may include at least one of (i) an Analytic ID, (ii) a requested parameter for ML model update (e.g., a gradient), (iii) a time stamp, (iv) 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 (v) a training area (for example, a list of TAs targeted for training, and the like); ¶¶ [0318]-[0324] with FIGS. 9 and 15: 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; the training response service operation may include at least one of an Analytic ID, a requested parameter for ML model update (e.g., a gradient), a time stamp, a training area, an ML model ID, and an ML model version; ¶¶ [0325]-[0345] with Table 1: the Nnwdaf_MLModelUpdate_Notify service operation may need to include the following input and output: (i) Input: an Analytic ID, a model update request parameter (e.g., a gradient), an update time stamp, a target model ID, and an update version; and (ii) Output: display of success or failure; when the ML model provider NWDAF provides a model to a consumer NWDAF by invoking Nnwdaf_ModelProvision_Notify, the provider NWDAF may (implicitly) subscribe to an Nnwdaf_ModelUpdate service operation of the consumer NWDAF to obtain a result of a locally updated model parameter when the consumer NWDAF is capable of training the model; ¶¶ [0346]-[0359] with FIGS. 16-17: in operation 3, the NRF device 1603 may invoke, from the NWDAF device 1 1601, a registration response service operation; in operation 5, the NRF device 1603 may invoke, from the NWDAF device 2 1602, a discovery request response service operation including an instance of the NWDAF device 11601 that provides the "MLModelProvision service"; 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 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 13, the NWDAF device 1 1601 may transmit the updated ML model to the NWDAF device 2 1602 through a notification service operation for provisioning of the ML model); Lee further discloses an apparatus (Lee, ¶ [0379] with FIG. 1: NWDAF device 101; ¶ [0382]: data processing apparatus), comprising: at least one processor (Lee, ¶ [0380]: at least one digital signal processor (DSP), a processor, a controller, an application-specific integrated circuit (ASIC), a programmable logic element, such as a field programmable gate array (FPGA), other electronic devices, or combinations thereof; ¶ [0383]: processors suitable for processing of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer); and at least one memory including computer program code (Lee, ¶ [0383]: one or more memory devices for storing instructions and data; one or more mass storage devices for storing data), wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform a method described above (Lee, ¶¶ [0382]-[0383]: various techniques described herein may be implemented in digital electronic circuitry, computer hardware, firmware, software, or combinations thereof; a computer program may be deployed to be processed on one computer or multiple computers at one site or distributed across multiple sites and interconnected by a communication network; at least one processor for executing instructions). Lee fails to explicitly disclose wherein (1) a third attribute comprising an indication of a termination condition for terminating the machine learning training job , and a fourth attribute comprising an indication of a level of success of the machine learning training job ; and (2) transmitting a notification indicating that the machine learning training job has been instantiated . DIRAC teaches a system and a method for providing machine learning service (DIRAC, ¶ [0024]), wherein transmitting a notification indicating that the machine learning training job has been instantiated (DIRAC, ¶¶ [0024]-[0031]: a customizable, easy-to-use machine learning service (MLS) designed to support large numbers of users and a wide variety of algorithms and problem sizes; allow non-experts to rely on default settings or parameters for various aspects of the procedures used for building, training and using machine learning models, where the defaults are derived from the accumulated experience of other practitioners addressing similar types of machine learning problems; at the same time, expert users may customize the parameters or settings they wish to use for various types of machine learning tasks, such as input record handling, feature processing, model building, execution and evaluation; in addition to or instead of using predefined libraries implementing various types of machine learning tasks, MLS clients may be able to extend the built-in capabilities of the service, e.g., by registering their own customized functions with the service; the term "MLS control plane" may be used herein to refer to a collection of hardware and/or software entities that are responsible for implementing various types of machine learning functionality on behalf of clients of the MLS; the term "MLS data plane" may refer to the pathways and resources used for the processing, transfer, and storage of the input data used for client-requested operations, as well as the processing, transfer and storage of output data produced as a result of client-requested operations; a number of different types of entities related to machine learning tasks may be generated , modified, read, executed, and/or queried/searched via MLS programmatic interfaces; the MLS programmatic interfaces may enable users to submit respective requests for several related tasks of a given machine learning workflow, such as tasks for extracting records from data sources, generating statistics on the records, feature processing, model training , prediction, and so on; a queue of job objects may be used for storing internal representations of requested tasks ; the term "task", as used herein, refers to a set of logical operations corresponding to a given request from a client, while the term "job" refers to the internal representation of a task within the MLS; the MLS may be responsible for ensuring that the dependencies of a given job have been met before the corresponding operations are initiated; the MLS may also be responsible for generating a processing plan for each job, identifying the appropriate set of resources (e.g., CPUs/cores, storage or memory) for the plan, scheduling the execution of the plan, gathering results, providing/saving the results in an appropriate destination, and at least in some cases for providing status updates or responses to the requesting clients; not all client API requests may be implemented using jobs – e.g., a relatively short or lightweight task may be performed synchronously with respect to the corresponding request, without incurring the overhead of job creation and asynchronous job scheduling; the APIs implemented by the MLS may allow clients to submit requests to create, query the attributes of, read, update/modify, search, or delete an instance of at least some of the various entity types supported; e.g., for the entity type "DataSource", respective APIs similar to " create DataSource", "describeDataSource" (to obtain the values of attributes of the data source), " update DataSource", "searchForDataSource", and "deleteDataSource" may be supported by the MLS; a similar set of APIs may be supported for recipes, model s, and so on; some entity types may also have APIs for executing or running the entities, such as " execute Model" or "executeRecipe"; some machine learning models may be created and trained, e.g., by a group of model developers or data scientists using the MLS APIs, and then published for use by another community of users; an alias may comprise an immutable name and a pointer to a model (i.e., an internal identifier generated for the model by the MLS) that has already been created and stored in an MLS artifact repository; members of a business analyst group may be allowed to run the model using its alias name, but may not be allowed to change the pointer, while model developers may be allowed to modify the pointer and/or modify the underlying model; the model developers may continue to experiment with various algorithms, parameters and/or input data sets to obtain improved versions of the underlying model, and may be able to change the pointer to point to an enhanced version to improve the quality of predictions obtained by the business analysts; as part of developing the processing plan for a job, the MLS may select a workload distribution strategy for the job; after the workload strategy is selected, the actual set of resources to be used may be identified in accordance with the strategy, and the job's operations may be scheduled on the identified resources; a pool of compute servers and/or storage servers may be pre-configured for the MLS, and the resources for a given job may be selected from such a pool; ¶¶ [0032]-[0036] with FIG. 1: the MLS may implement a set of programmatic interfaces 161 (e.g., APIs, command-line tools, web pages, or standalone GUIs) that can be used by clients 164 (e.g., hardware or software entities owned by or assigned to customers of the MLS) to submit requests 111 for a variety of machine learning tasks or operations; the administrative or control plane portion of the MLS may include MLS request handler 180, which accepts the client requests 111 and inserts corresponding job objects into MLS job queue 142, as indicated by arrow 112; i.e., job objects (e.g., model training job objects) corresponding to client requests 111 are instantiated into MLS job queue 142 in response to requests 111 submitted by clients 164 ; the control plane of the MLS may comprise a plurality of components (including the request handler, workload distribution strategy selectors, one or more job schedulers, metrics collectors, and modules that act as interfaces with other services) which may also be referred to collectively as the MLS manager; each job object may indicate one or more operations that are to be performed as a result of the invocation of a programmatic interface 161, and the scheduling of a given job may in some cases depend upon the successful completion of at least a subset of the operations of an earlier-generated job; job queue 142 may be managed as a first-in-first-out (FIFO) queue, with the further constraint that the dependency requirements of a given job must have been met in order for that job to be removed from the queue; asynchronously with respect to the submission of the requests 111, the next job whose dependency requirements have been met may be removed from job queue 142 as indicated by arrow 113, and a processing plan comprising a workload distribution strategy may be identified for it; the workload distribution strategy layer 175 may determine the manner in which the lower level operations of the job are to be distributed among one or more compute servers (e.g., servers selected from pool 185), and/or the manner in which the data analyzed or manipulated for the job is to be distributed among one or more storage devices or servers; results of some jobs may be stored as MLS artifacts within repository 120 as indicated by arrow 142; some relatively simple types of client requests 111 may result in the immediate generation, retrieval, storage, or modification of corresponding artifacts within MLS artifact repository 120 by the MLS request handler 180 (as indicated by arrow 141); thus, the insertion of a job object in job queue 142 may not be required for all types of client requests; clients 164maybeabletoview at least a subset of the artifacts stored in repository 120, e.g., by issuing read requests 118 via programmatic interfaces 161; a client request 111 may indicate one or more parameters that may be used by the MLS to perform the operations, such as a data source definition 150, a feature processing transformation recipe 152, or parameters 154 to be used for a particular machine learning algorithm; the output 116 of the feature processing transformations may in tum be used as input for a selected machine learning algorithm 166, which may be executed in accordance with algorithm parameters 154 using yet another set of resources from pool 185; a wide variety of machine learning algorithms may be supported natively by the MLS libraries, including for example random forest algorithms, neural network algorithms, stochastic gradient descent algorithms, and the like; the MLS may maintain knowledge base 122 containing information on best practices for various machine learning tasks; entries may be added into the best practices KB 122 by various control-plane components of the MLS, e.g., based on metrics collected from server pools 185, feedback provided by clients 164, and so on; clients 164 may be able to search for and retrieve KB entries via programmatic interfaces 161, as indicated by arrow 117, and may use the information contained in the entries to select parameters (such as specific recipes or algorithms to be used) for their request submissions; ¶¶ [0037]-[0041] with FIG. 2: the MLS utilizes storage service 202, computing service 258, and database service 255 of provider network 202; MLS gateway 222 may be established to receive client requests 210 submitted over external network 206 (such as portions of the Internet) by clients 164; MLS customers may be provided an SDK (software development kit) 204 for local installation at client computing devices, and the requests 210 may be submitted from within programs written in conformance with the SDK; a client may also or instead access MLS functions from a compute server 262 of computing service 262 that has been allocated to the client; in response to at least some client requests 210, the MLS request handler 180 may generate and store corresponding job objects within a job queue 142; the job queue 142 may itself be represented by a database object (e.g., a table) stored at database service 255; a job scheduler 272 may retrieve a job from queue 142, e.g., after checking that the job's dependency requirements have been met, and identify one or more servers 262 from computing service 258 to execute the job's computational operations; ¶¶ [0046]-[0048] with FIG. 4: the MLS control plane may be responsible for generating processing plans corresponding to each of the job objects generated in response to client requests; for each processing plan, a corresponding set of resources may then have to be identified to execute the plan, e.g., based on the workload distribution strategy selected for the plan, the available resources, and so on; MLS job queue 142 comprises five jobs, each corresponding to the invocation of a respective API by a client; corresponding to job J1, an input data cleansing plan 422 may be generated, and the plan may be executed using resource set RS1; corresponding to job J2, a statistics generation plan 424 may be generated, and subsequently executed on resource set RS2; a recipe-based feature processing plan 426 corresponding to job J3 (and APB) may be generated, and executed on resource set RS3; Job J4 may result in the generation of a model training plan 428 (which may in tum involve several iterations of training , e.g., with different sets of parameters); the model training may be performed using resource set RS4; model execution plan 430 may correspond to job JS (resulting from the client's invocation of API5), and the model may eventually be executed using resource set RS5; the same set of resources (or an overlapping set of resources) may be used for performing several or all of a client's jobs – e.g., the resource sets RS1-RS5 may not necessarily differ from one another; ¶¶ [0055]-[0061] with FIG. 6: MLS artifacts 601 may include, among others, data sources 602, statistics 603, feature processing recipes 606, model predictions 608, evaluations 610, modifiable or in-development models 630, and published models or aliases 640; the MLS may generate a respective; unique identifier for each instance of at least some of the types of artifacts shown and provide the identifiers to the clients; the identifiers may subsequently be used by clients to refer to the artifact (e.g., in subsequent API calls, in status queries, and so on); at least two types of artifacts representing machine learning models or predictors may be generated and stored; the process of developing and refining a model may take a long time, as the developer may try to improve the accuracy of the predictions using a variety of data sets and a variety of parameters; the artifacts representing models may belong to one of two categories modifiable models 630, and published models or aliases 640; the phrase "publishing a model" refers to making a particular version of a model executable by a set of users by reference to an alias name or identifier; nonexpert users 678 may be granted read and execute permissions to the aliases, while model developers 676 may also be allowed to modify models 630 (and/or the pointers of the aliases 640); after model developers 676 improve the accuracy and/or performance characteristics of a newer version of a model 630 relative to an older version for which an alias 640 has been created, they may switch the pointer of the alias so that it now points to the improved version; submit a query to learn when the underlying model was last changed, or may be notified when they request an execution of an alias that the underlying model has been changes since the last execution; the MLS may support recurring scheduling of related jobs; e.g. a client may create an artifact such as a model, and may want that same model to be re- trained and/or re-executed for different input data sets (e.g., using the same configuration of resources for each of the training or prediction iterations) at specified points in time; a respective job may be placed in the MLS job queue for each recurring training or execution iteration; in addition to the artifact types shown in FIG. 6, pipeline artifacts may be stored in the MLS artifact repository with each instance of a pipeline artifact representing a named set of recurring operations requested via such APIs; ¶¶ [0067]-[0071] with FIGS. 9a-9b: in element 901 of FIG. 9a, the MLS may receive a request from a client via a programmatic interface (such as an API, a command-line tool, a web page, or a custom GUI) to perform a particular operation on an entity belonging to a set of supported entity types of the MLS; the entity types may include, e.g., data sources, statistics, feature processing recipes, models , aliases, predictions, and/or evaluations; the operations requested may include, e.g., create , read (or describe the attributes of), modify/update attributes, execute, search, or delete operations; the request may next be validated in accordance with various rules or policies of the MLS (element 904); if the request passes the validation checks, a decision may be made as to whether a job object is to be created for the request; if an analysis of the request indicates that a job is required (as detected in element 907), a job object may be generated, indicating the nature of the lower-level operations to be performed at the MLS as well as any dependencies on other jobs, and the job object may be placed in a queue (element 913); the requesting client may be notified that the request has been accepted for execution (e.g., by indicating to the client that a job has been queued for later execution); i.e., when a job object (e.g., model training job object) is instantiated into a queue, the client requesting a machine learning task associated with the job object is notified ; if the job does not have any dependencies that have yet to be met, and meets other criteria for immediate or in-line execution (as also determined in element 907), the requested operation may be performed without creating a job object (element 910) and the results may optionally be provided to the requesting client; operations corresponding to elements 901-913 may be performed for each request that is received via the MLS programmatic interface; at some point after a particular job Jk is placed in the queue, Jk may be identified (e.g., by a job scheduler component of the MLS control plane) as the next job to be implemented (element 951 of FIG. 9b); to identify the next job to be implemented, the scheduler may, e.g., start from the head of the queue (the earliest-inserted job that has not yet been executed) and search for jobs whose dependencies (if any are specified) have been met; in element 952 of FIG. 9b, one or more types of validation checks may be performed on the job Jk identified in element 951; a workload distribution strategy and processing plan may be identified for Jk – e.g., the number of processing passes or phases to be used, the degree of parallelism to be used, an iterative convergence criterion to be used for completing Jk (element 954); in accordance with the selected distribution strategy and processing plan, a set of resources may be identified for Jk (element 957); JK's operations may then be performed on the identified resources (element 960), and the client on whose behalf Jk was created may optionally be notified when the operations complete (or in the event of a failure that prevents completion of the operations); ¶¶ [0072]-[0078] with FIGS. 10a-10b: Given the asynchronous manner in which client requests are handled, clients may sometimes end up submitting the same request multiple times; such multiple submissions may occur because the client is unaware whether the previous submission was accepted or not (e.g., because the client failed to notice an indication that the previous submission was accepted, or because such an indication was lost); a duplicate request may be received because the client has assumed that since the expected results of completing the requested task have not been provided for a long time, the previous request must have failed; in order to avoid such problematic scenarios, in at least one embodiment one or more of the programmatic interfaces supported by the MLS may be designed to be idempotent, such that the re-submission of a duplicate request by the same client does not have negative consequences; as shown in element 1001, a request to create a new instance of an entity type ET1 may be received from a client C1 at the MLS via a programmatic interface such as a particular APL; the MLS may generate a representation IPR1 of the input parameters included in the client's invocation of the programmatic interface (element 1004); the MLS may check, e.g., via a lookup in the artifact repository, whether an instance of entity type ET1, with instance identifier ID1 and client identifier Cl already exists in the repository; if no such instance is found (as detected in element 1007), a new instance of type ET1 with the identifier ID1, input parameter representation IPR1 and client identifier C1 may be inserted into the repository (element 1007); a success response to the client's request (element 1016) may be generated ; if, in operations corresponding to element 1007, a pre-existing instance with the same instance identifier ID1 and client identifier C1 is found in the repository, the MLS may check whether the input parameter representation of the pre-existing instance also matches IPR1 (element 1013); if the input parameter representations also match, the MLS may assume that the client's request is a (harmless) duplicate, and no new work needs to be performed; accordingly, the MLS may also indicate success to the client (either explicitly or implicitly) if such a duplicate request is found (element 1016); thus, if the client had inadvertently resubmitted the same request, the creation of a new job object and the associated resource usage may be avoided; if the client request is found to be an exact duplicate of an earlier request using the methodology described, an indication may be provided to the client that the request, while not being designated as an error, was in fact identified as a duplicate; if the input parameter representation of the pre-existing instance does not match that of the client's request, an error message may be returned to the client (element 1019), e.g., indicating that there is a pre-existing instance of the same entity type ET1 with the same identifier; as shown in element 1051, at least some of the artifacts (such as recipes and models) generated at the MLS as a result of client requests may be classified into groups based on problem domains-e. g., some artifacts may be used for financial analysis, others for computer vision applications, others for bioinformatics, and so on; the MLS control plane may comprise a set of monitoring agents that collect performance and other metrics from the resources used for the various phases of machine learning operations (element 1054); based on the collected metrics and/or feedback, respective sets of best practices for various phases of machine learning workflows may be identified (element 1057); representations or summaries of the best practices identified may be stored in a knowledge base of the MLS; access (e.g., via a browser or a search tool) to the knowledge base may be provided to MLS users (element 1060)). Lee and DIRAC are analogous art because they are from the same field of endeavor, a system and a method for providing machine learning service. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to apply the teaching of DIRAC to Lee. Motivation for doing so would provide a proper feedback for a requesting client to prevent resubmitting the same request multiple times and avoid their . Lee in view of DIRAC fails to explicitly disclose wherein a third attribute comprising an indication of a termination condition for terminating the machine learning training job , and a fourth attribute comprising an indication of a level of success of the machine learning training job . Yue teaches a system and a method relating to control of Machine Learning model provisioning and training (Yue, ¶ [0001]), wherein a third attribute comprising an indication of a termination condition for terminating the machine learning training job , and a fourth attribute comprising an indication of a level of success of the machine learning training job (Yue, ¶¶ [0064]-[0096] with FIG. 7: capability of supporting consumer control, e.g., start, modify, restart, and terminate ML model training process according to the interactions with the consumer (AF/NF/OAM); at step 2, the consumer (AF/NF/OAM) starts a subscription to the NWDAF by invoking an Nnwdaf_MLModelProvision_Subscribe service operation with consumer request; in addition to other parameters, the following parameters may also be included in the consumer request: time window for confirming response from NWDAF in preparation phase; time window for intermediate results report from NWDAF; time window for final outputs from NWDAF; accuracy level could be achieved by the trained model ; information about consumer (e.g., Vendor ID, etc.); learning architecture, e.g., DML/FL framework, etc.; output strategy for intermediate results reporting during the ML model training process, e.g., binary output strategy or gradient output strategy; information about an initial model provider (e.g., the provider is internal or external to the consumer, or other vendors); initial ML model structure; in the consumer request, the ML model structure can be provided in any of the following formats: if the subscription is terminated, ML model ID and ML model file address, e.g., URL; if the subscription is continuing, an indication of update based on the current ML model; indication of online or offline learning; data source, e.g., data type (e.g., the type of data used for inference and/or training), NF ID(s), information of data provider (e.g., Analytics Data Repository Function, or ADRF, etc.); if the estimated time does not match to the requirement from the consumer , the consumer (AF/NF/OAM) sends a request to terminate DML/FL (e.g., sends Nnwdaf_ML_Terminate request or an Nnwdaf_MLModelProvision_Unsubscribe request to the NWDAF); ¶¶ [0097]-[0126] with FIG. 8: begin initial DML/FL parameter provisioning to all the selected Client NWDAF(s) with a requirement on a time window for local ML model reporting; judge the training status (e.g., whether the current trained ML model could satisfy the accuracy requirement or not , whether it has converged or not , the remaining time to complete training, etc.); update the training status (judged in step 2) to the consumer (AF /NF/OAM) periodically (one or multiple rounds of training), or dynamically (e.g., when a predetermined status required by the consumer is achieved (such as accuracy that can be achieved by the trained ML model ), etc.), according to the prior communicated output strategy for intermediate results reporting; the consumer (AF/NF/OAM) decides whether to continue or not based on the training status ; e.g., the consumer (AF/NF/OAM) judges whether the time and accuracy requirements can be satisfied by the current trained ML model (or ML model parameters) provided by the NWDAF (Server NWDAF); in the intermediate results report to the consumer (AF/NF/OAM), the following information may be included: an indicator of the accuracy that could be achieved by the current trained ML model ; an indication of whether the training status is converged or not ; and the remaining time or effort required to complete training, or the remaining unfinished percentage of training tasks; the consumer (AF/NF/OAM) sends a terminate request to the NWDAF (Server NWDAF) if its requirements can be satisfied based on the current ML model (or ML model parameters); if it receives a termination request from the consumer (AF/NF/OAM), the NWDAF (Server NWDAF) sends a terminate request to the Client NWDAF(s); as indicated to the right in FIG. 8, steps 2-6 should be repeated until a relevant ML model training termination condition is satisfied (e.g., the maximum number of iterations , or the result of loss function is lower than a threshold , or termination request is received ); provide the required trained ML model (or ML model meta data, ML model weights, etc.) to the consumer (AF/NF/OAM); the following information may be contained in the outputs: the trained ML model (or ML model meta data, ML model weights, etc.); an indication of the accuracy level that could be achieved by the trained ML model ; the accuracy level could be provided, e.g., by the ML model meta data, or in a separate method, etc.; the learning architecture used for training the ML model, e.g., DML/FL, etc.; whether the ML model is obtained by a training or a retraining process, and the corresponding initial ML model; if it is a training process, the initial ML model information is provided in step 2 of the consumer trigger and control of ML model provisioning process described above, e.g., by ML model ID or ML model file address (e.g., URL); if it is a retraining process, the initial ML model is the ML model used for starting the retraining; this ML model may be recorded during the training process; the trained ML model is obtained by online or offline learning; the actual data source used for training the ML model; as indicated to the left in FIG. 8, steps 2-7 could be repeated for retraining until the service required by the consumer (AF/NF/OAM) is satisfied ; ¶ [0140]: provide for ML model consumer to control both the provisioning of an ML model, and its training execution process; this allows the ML model consumer to specify numerous parameters, such as the learning architecture (e.g., DML/FL); the type of data used for training (e.g., online or offline); time windows for critical aspects of the ML model provisioning and training (e.g., for confirming response from NWDAF in preparation phase, for intermediate results reporting from NWDAF, and for final outputs from NWDAF); an accuracy level the trained ML model can achieve ; an output strategy for intermediate results reported during training; information about an initial ML model to be used; and the like; furthermore, during training execution, the ML model consumer can specify that the NWDAF report various parameters related to the ML model and its training process, and the ML model can terminate the ML model provisioning and/or training process at several points, if the reported parameters do not match requirements of the ML model consumer ). Le and Yue are analogous art because they are from the same field of endeavor, a system and a method relating to control of Machine Learning model provisioning and training. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to apply the teaching of Yue to Lee in view of DIRAC. Motivation for doing so would provide . Claims 2 and 10 Lee in view of DIRAC and Yue discloses all the elements as stated in Claims 1 and 9 respectively and further discloses receiving, by the machine learning training function, from the consumer, a request to read managed object instances representing ongoing or completed machine learning training jobs, the request comprising a list of the ongoing or completed machine learning training jobs; and transmitting, by the machine learning training function to the consumer, identifiers of the ongoing or completed machine learning training jobs and characteristics of the ongoing or completed machine learning training jobs (Lee, ¶¶ [0094]-[0095] with FIG. 1: 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; ¶¶ [0241]-[0247] with FIG. 8: 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; at this time, the request service operation may include at least one of (i) a list of supported Analytic IDs, (ii) a service supported by the NWDAF device 803 (e.g., an Nnwdaf_MLModelProvision service and an Nnwdaf_MLModelInfo service), (iii) a serving area where an ML model is provided, (iv) S-NSSAI, (v) 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 (vi) a federation learning capability (aggregation capability for a result of training an ML model (e.g., an ML model update capability)); 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; ¶¶ [0265]-[0271] with FIG. 9: 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); 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 (e.g., an ML model training capability); the NRF device 902 may invoke, from the NWDAF device 903, a 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 901; ¶¶ [0325]-[0345] with Table 1: an Nnwdaf_MLModelProvision service operation or an Nnwdaf_MLModelInfo service operation may include the following input and output: (i) Input: an analytic ID or configured ML model information (e.g., specific purpose or pre-configured ML model ID) (if available); and (ii) Output: (a) a description of a corresponding ML model with a model parameter with respect to each requested Analytic ID, wherein the description of the ML model with the model parameter may include at least one of an ML model type (e.g., a neural network), an ML model structure (e.g., a weight matrix describing a weight), and a connection and hierarchy for each node of a neural network; (b) a model ID and model version, wherein the model ID and model version may be local information rather than global unique information of an ML model provider NWDAF; and (c) optional output: a description of a requested parameter for model update and a description of a budget for an update reporting time, wherein the description of the requested parameter for model update may include a requested parameter (e.g., a gradient and a specific method for specifying a gradient (e.g., a top-k gradient, a threshold for sparsation of gradient, and the like); an Nnwdaf_MLModelTraining service operation or an Nnwdaf_MLModelTraininginfo service operation may need to include the following input and output: (i) Input: (a) an Analytic ID, an expiry time, and ML model information (an ML model file, a model ID, and a model version); and (b) optional input: a description of a requested parameter for model update and a description of a budget for an update reporting time, wherein the description of the requested parameter for model update may include a requested parameter (e.g., a gradient and a specific method for specifying a gradient (e.g., a top-k gradient, a threshold for sparsation of gradient, and the like), S-NSSAI, a target area of interest, an application ID, a target UE, and an ML model target period; 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)) (DIRAC, ¶¶ [0026]-[0029]: a number of different types of entities related to machine learning tasks may be generated, modified, read , executed, and/or queried / searched via MLS programmatic interfaces; the MLS programmatic interfaces may enable users to submit respective requests for several related tasks of a given machine learning workflow, such as tasks for extracting records from data sources, generating statistics on the records, feature processing, model training , prediction, and so on; the APIs implemented by the MLS may allow clients to submit requests to create, query the attributes of, read , update/modify, search, or delete an instance of at least some of the various entity types supported; ¶ [0034] with FIG. 1: clients 164 may be able to view at least a subset of the artifacts stored in repository 120, e.g., by issuing read requests 118 via programmatic interfaces 161; ¶¶ [0046]-[0048] and [0051] with FIGS. 4-5: the MLS control plane may be responsible for generating processing plans corresponding to each of the job objects generated in response to client requests; for each processing plan, a corresponding set of resources may then have to be identified to execute the plan, e.g., based on the workload distribution strategy selected for the plan, the available resources, and so on; Job J4 (i.e., job identifier) may result in the generation of a model training plan 428 (which may in tum involve several iterations of training, e.g., with different sets of parameters); the model training may be performed using resource set RS4; model execution plan 430 may correspond to job JS (resulting from the client's invocation of APIS), and the model may eventually be executed using resource set RSS; ¶¶ [0067]-[0069] with FIGS. 9a-9b: in element 901 of FIG. 9a, the MLS may receive a request from a client via a programmatic interface (such as an API, a command-line tool, a web page, or a custom GUI) to perform a particular operation on an entity belonging to a set of supported entity types of the MLS; the operations requested may include, e.g., create, read (or describe the attributes of), modify/update attributes, execute, search , or delete operations; the requesting client may be notified that the request has been accepted for execution (e.g., by indicating to the client that a job has been queued for later execution); at some point after a particular job Jk is placed in the queue, Jk may be identified (e.g., by a job scheduler component of the MLS control plane) as the next job to be implemented (element 951 of FIG. 9b); to identify the next job to be implemented, the scheduler may, e.g., start from the head of the queue (the earliest-inserted job that has not yet been executed) and search for jobs whose dependencies (if any are specified) have been met; ¶ [0074] with FIG. 10a: a success response to the client's request (element 1016) may be generated; ¶ [0120]: the particular operation comprises one or more of: (a) a creation of the instance, (b) a read operation to obtain respective values of one or more attributes of the instance, (c) a modification of an attribute of the instance, (d) a deletion of the instance, (e) a search operation, or (f) an execute operation). Claims 3 and 11 Lee in view of DIRAC and Yue discloses all the elements as stated in Claims 1 and 9 respectively and further discloses receiving, by the machine learning training function, from the consumer, a request to modify attributes of managed object instances representing ongoing or completed machine learning training jobs the request comprising a list of identifiers of the ongoing or completed machine learning training jobs whose attributes are to be modified and attributes for the ongoing or completed machine learning training jobs that are to be modified; and transmitting, by the machine learning training function, to the consumer, a notification indicating that the attributes of the ongoing or completed machine learning training jobs identified in the list have been modified (Lee, ¶¶ [0023]-[0024]: locally training the ML model, invoking, from the second NWDAF device, an ML model update notification service operation, and invoking, from the second NWDAF device that globally updates the ML model, a notification service operation for provisioning of the ML model; locally training the ML model, and invoking, from the second NWDAF device that globally updates the ML model, an ML model update notification service operation; ¶¶ [0090]-[0095] 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; 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; ¶ [0111] with FIG. 3: for update of the ML model, the NWDAF device may use an Nnwdaf_MLModelUpdate , Nnwdaf_MLModelTraining, or Nnwdaf_MLModelTraininginfo service; ¶¶ [0157]-[0209] with FIG. 5: the provisioning service for the ML model may be used to modify an existing ML model subscription in the NWDAF device 501; 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, (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 invocation of a service operation of the NWDAF device 501 is for subscription modification or unsubscription, the NWDAF device 501 may include an identifier (subscription correlation ID) to be modified in invocation of a subscription service operation for provisioning of the ML model (Nnwdaf_MLModelProvision_Subscribe); when a process of operation 1 is performed for subscription modification (that is, 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; a list of Analytic IDs used to identify analytics for which an ML model is used; when a subscription is accepted by an NWDAF, a consumer NF device receives an identifier (subscription correlation ID) from the NWDAF so as to additionally manage ( modify and delete ) this subscription; a modification to an ML model subscription may be performed by the NWDAF based on an operator policy and a configuration; a model for analyzing a subscription correlation ID (when modifying an ML model subscription); ¶¶ [0274]-[0289] with FIG. 10: 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 invocation of a service operation of the NWDAF device 1001 is for subscription modification or unsubscription, the NWDAF device 1001 may include an identifier (subscription correlation ID) to be modified in invocation of Nnwdaf_MLModelProvision_Subscribe; when a process of operation 1 is performed for subscription modification (that is, including a subscription correlation ID), the NWDAF device 1002 that performs the MTLF may provide a new learned ML model different from that previously provided, or may provide a relearned ML model by invoking the Nnwdaf_MLModelProvision_Notify service operation; ¶¶ [0299]-[0305] with FIG. 12: 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; the notification service operation may include at least one of (i) an Analytic ID, (ii) a requested parameter for ML model update (e.g., a gradient), (iii) a time stamp, (iv) 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 (v) a training area (for example, a list of target areas (TAs) targeted for training, and the like); in operation 5, the NWDAF device 1202 may globally update the ML model, wherein 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 FIG. 13: 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; the update notification service operation may include at least one of (i) an Analytic ID, (ii) a requested parameter for ML model update (e.g., a gradient), (iii) a time stamp, (iv) 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 (v) a training area (e.g., a list of TAs targeted for training, and the like); in operation 5, the NWDAF device 1302 may globally update the ML model; ¶¶ [0311]-[0317] with FIG. 14: 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; the training notification service operation may include at least one of (i) an Analytic ID, (ii) a requested parameter for ML model update (for example, a gradient), (iii) a time stamp, (iv) 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 (v) a training area (for example, a list of TAs targeted for training, and the like); in operation 5, the NWDAF device 1402 may globally update the ML model; ¶¶ [0318]-[0324] with FIG. 15: 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; the training response service operation may include at least one of an Analytic ID, a requested parameter for ML model update (e.g., a gradient), a time stamp, a training area, an ML model ID, and an ML model version; in operation 5, the NWDAF device 1502 may globally update the ML model; ¶¶ [0325]-[0345] with Table 1: the Nnwdaf_MLModelUpdate_Notify service operation may need to include the following input and output: (i) Input: (a) an Analytic ID, a model update request parameter (e.g., a gradient), an update time stamp, a target model ID, and an update version; and (b) optional input: evaluation of an updated model if available; and (ii) Output: [0338] display of success or failure; when the ML model provider NWDAF provides a model to a consumer NWDAF by invoking Nnwdaf_ModelProvision_Notify, the provider NWDAF may (implicitly) subscribe to an Nnwdaf_ModelUpdate service operation of the consumer NWDAF to obtain a result of a locally updated model parameter when the consumer NWDAF is capable of training the model; ¶¶ [0356]-[0359] with FIG. 17: 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 12, the NWDAF device 1 1601 may transmit the updated ML model to the NWDAF device 2 1602 through a notification service operation for provisioning of the ML model; ¶ [0379] with FIG. 1: the NWDAF device 101 may update an analytics model used when generating the analytics information of the first network data (for example, optimizing or additionally training the analytics model)) (DIRAC, ¶¶ [0026]-[0030]: a number of different types of entities related to machine learning tasks may be generated, modified , read, executed, and/or queried/searched via MLS programmatic interfaces; the MLS programmatic interfaces may enable users to submit respective requests for several related tasks of a given machine learning workflow, such as tasks for extracting records from data sources, generating statistics on the records, feature processing, model training , prediction, and so on; the MLS may also be responsible for generating a processing plan for each job, identifying the appropriate set of resources (e.g., CPUs/cores, storage or memory) for the plan, scheduling the execution of the plan, gathering results, providing/saving the results in an appropriate destination, and at least in some cases for providing status updates or responses to the requesting clients; the APIs implemented by the MLS may allow clients to submit requests to create, query the attributes of, read, update / modify , search, or delete an instance of at least some of the various entity types supported; model developers may be allowed to modify the pointer and/or modify the underlying model; ¶ [0034] with FIG. 1: some relatively simple types of client requests 111 may result in the immediate generation, retrieval, storage, or modification of corresponding artifacts within MLS artifact repository 120 by the MLS request handler 180 (as indicated by arrow 141); ¶ [0059] with FIG. 6: model developers 676 may also be allowed to modify models 630 (and/or the pointers of the aliases 640); submit a query to learn when the underlying model was last changed, or may be notified when they request an execution of an alias that the underlying model has been changes since the last execution; ¶¶ [0067]-[0069] with FIG. 9a: in element 901 of FIG. 9a, the MLS may receive a request from a client via a programmatic interface (such as an API, a command-line tool, a web page, or a custom GUI) to perform a particular operation on an entity belonging to a set of supported entity types of the MLS; the operations requested may include, e.g., create, read (or describe the attributes of), modify / update attributes, execute, search, or delete operations; the requesting client may be notified that the request has been accepted for execution (e.g., by indicating to the client that a job has been queued for later execution); ¶ [0074] with FIG. 10a: a success response to the client's request (element 1016) may be generated; ¶ [0120]: the particular operation comprises one or more of: (a) a creation of the instance, (b) a read operation to obtain respective values of one or more attributes of the instance, (c) a modification of an attribute of the instance, (d) a deletion of the instance, (e) a search operation, or (f) an execute operation). Claims 4 and 12 Lee in view of DIRAC and Yue discloses all the elements as stated in Claims 1 and 9 respectively and further discloses r eceiving, by the machine learning training function, from the consumer, a request to delete managed object instances representing machine learning training jobs, the request comprising a list of identifiers of the ongoing or completed machine learning training requests that are to be deleted; and transmitting, by the machine learning training function, to the consumer, a notification indicating that the ongoing or completed machine learning training jobs identified in the list that have been deleted (Lee, ¶¶ [0023]-[0024]: locally training the ML model, invoking, from the second NWDAF device, an ML model update notification service operation, and invoking, from the second NWDAF device that globally updates the ML model, a notification service operation for provisioning of the ML model; locally training the ML model, and invoking, from the second NWDAF device that globally updates the ML model, an ML model update notification service operation; ¶¶ [0090]-[0095] 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; 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; ¶ [0111] with FIG. 3: for update of the ML model, the NWDAF device may use an Nnwdaf_MLModelUpdate , Nnwdaf_MLModelTraining, or Nnwdaf_MLModelTraininginfo service; ¶¶ [0157]-[0209] with FIG. 5: the provisioning service for the ML model may be used to modify an existing ML model subscription in the NWDAF device 501; 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, (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 invocation of a service operation of the NWDAF device 501 is for subscription modification or unsubscription, the NWDAF device 501 may include an identifier (subscription correlation ID) to be modified in invocation of a subscription service operation for provisioning of the ML model (Nnwdaf_MLModelProvision_Subscribe); when a process of operation 1 is performed for subscription modification (that is, 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; a list of Analytic IDs used to identify analytics for which an ML model is used; when a subscription is accepted by an NWDAF, a consumer NF device receives an identifier (subscription correlation ID) from the NWDAF so as to additionally manage ( modify and delete ) this subscription; a modification to an ML model subscription may be performed by the NWDAF based on an operator policy and a configuration; a model for analyzing a subscription correlation ID (when modifying an ML model subscription); ¶¶ [0274]-[0289] with FIG. 10: 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 invocation of a service operation of the NWDAF device 1001 is for subscription modification or unsubscription, the NWDAF device 1001 may include an identifier (subscription correlation ID) to be modified in invocation of Nnwdaf_MLModelProvision_Subscribe; when a process of operation 1 is performed for subscription modification (that is, including a subscription correlation ID), the NWDAF device 1002 that performs the MTLF may provide a new learned ML model different from that previously provided, or may provide a relearned ML model by invoking the Nnwdaf_MLModelProvision_Notify service operation; ¶¶ [0299]-[0305] with FIG. 12: 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; the notification service operation may include at least one of (i) an Analytic ID, (ii) a requested parameter for ML model update (e.g., a gradient), (iii) a time stamp, (iv) 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 (v) a training area (for example, a list of target areas (TAs) targeted for training, and the like); in operation 5, the NWDAF device 1202 may globally update the ML model, wherein 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 FIG. 13: 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; the update notification service operation may include at least one of (i) an Analytic ID, (ii) a requested parameter for ML model update (e.g., a gradient), (iii) a time stamp, (iv) 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 (v) a training area (e.g., a list of TAs targeted for training, and the like); in operation 5, the NWDAF device 1302 may globally update the ML model; ¶¶ [0311]-[0317] with FIG. 14: 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; the training notification service operation may include at least one of (i) an Analytic ID, (ii) a requested parameter for ML model update (for example, a gradient), (iii) a time stamp, (iv) 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 (v) a training area (for example, a list of TAs targeted for training, and the like); in operation 5, the NWDAF device 1402 may globally update the ML model; ¶¶ [0318]-[0324] with FIG. 15: 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; the training response service operation may include at least one of an Analytic ID, a requested parameter for ML model update (e.g., a gradient), a time stamp, a training area, an ML model ID, and an ML model version; in operation 5, the NWDAF device 1502 may globally update the ML model; ¶¶ [0325]-[0345] with Table 1: the Nnwdaf_MLModelUpdate_Notify service operation may need to include the following input and output: (i) Input: (a) an Analytic ID, a model update request parameter (e.g., a gradient), an update time stamp, a target model ID, and an update version; and (b) optional input: evaluation of an updated model if available; and (ii) Output: [0338] display of success or failure; when the ML model provider NWDAF provides a model to a consumer NWDAF by invoking Nnwdaf_ModelProvision_Notify, the provider NWDAF may (implicitly) subscribe to an Nnwdaf_ModelUpdate service operation of the consumer NWDAF to obtain a result of a locally updated model parameter when the consumer NWDAF is capable of training the model; ¶¶ [0356]-[0359] with FIG. 17: 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 12, the NWDAF device 1 1601 may transmit the updated ML model to the NWDAF device 2 1602 through a notification service operation for provisioning of the ML model; ¶ [0379] with FIG. 1: the NWDAF device 101 may update an analytics model used when generating the analytics information of the first network data (for example, optimizing or additionally training the analytics model)) (DIRAC, ¶¶ [0026]-[0030]: a number of different types of entities related to machine learning tasks may be generated, modified , read, executed, and/or queried/searched via MLS programmatic interfaces; the MLS programmatic interfaces may enable users to submit respective requests for several related tasks of a given machine learning workflow, such as tasks for extracting records from data sources, generating statistics on the records, feature processing, model training , prediction, and so on; the MLS may also be responsible for generating a processing plan for each job, identifying the appropriate set of resources (e.g., CPUs/cores, storage or memory) for the plan, scheduling the execution of the plan, gathering results, providing/saving the results in an appropriate destination, and at least in some cases for providing status updates or responses to the requesting clients; the APIs implemented by the MLS may allow clients to submit requests to create, query the attributes of, read, update / modify , search, or delete an instance of at least some of the various entity types supported; model developers may be allowed to modify the pointer and/or modify the underlying model; ¶ [0034] with FIG. 1: some relatively simple types of client requests 111 may result in the immediate generation, retrieval, storage, or modification of corresponding artifacts within MLS artifact repository 120 by the MLS request handler 180 (as indicated by arrow 141); ¶ [0059] with FIG. 6: model developers 676 may also be allowed to modify models 630 (and/or the pointers of the aliases 640); submit a query to learn when the underlying model was last changed, or may be notified when they request an execution of an alias that the underlying model has been changes since the last execution; ¶¶ [0067]-[0069] with FIG. 9a: in element 901 of FIG. 9a, the MLS may receive a request from a client via a programmatic interface (such as an API, a command-line tool, a web page, or a custom GUI) to perform a particular operation on an entity belonging to a set of supported entity types of the MLS; the operations requested may include, e.g., create, read (or describe the attributes of), modify / update attributes, execute, search, or delete operations; the requesting client may be notified that the request has been accepted for execution (e.g., by indicating to the client that a job has been queued for later execution); ¶ [0073]: although idempotency may be especially useful for programmatic interfaces that involve creation of artifacts such as data sources and models, idempotent interfaces may also be supported for other types of operations (e.g., deletes or executes); ¶ [0074] with FIG. 10a: a success response to the client's request (element 1016) may be generated; ¶ [0120]: the particular operation comprises one or more of: (a) a creation of the instance, (b) a read operation to obtain respective values of one or more attributes of the instance, (c) a modification of an attribute of the instance, (d) a deletion of the instance, (e) a search operation, or (f) an execute operation) . Response to Arguments Applicant’s arguments filed on 04/13/2026 with respect to Claims 1 and 9 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Browne et al . (US 2018/0357152 A1, pub. date: 12/13/2018) discloses in ¶ [0082], [0138], and [0168] with FIG. 2 that (1) the training data source 219 can send the training data to the instructor module 324 upon the request; (2) the instructor module 324 can subsequently instruct the learner module 328 on training the Al object with pedagogical software programming language based curricula for training the concepts into the Al objects; (3) the instructor module 324 can decide what pedagogical software programming language based concepts and streams should be actively trained in a mental model; (4) the instructor module 324 can know what are the terminating conditions for training the concepts based on user criteria and/or known best practices; (5) based on constraints or other statements in the code , such as Inkling TM , eventually the learner knows when to stop the reiterative loop of sending data between the data source, training with that data, making predictions, adjusting coefficient weights, and getting better accuracy with the trained Al model; in the end, the criteria of the trainings are achieved; and (6) the training process continues until the model achieves a desired level of accuracy on the training data. Schmidt et al. (US 2021/0224585 A1, pub. date: 07/22/2021) discloses in ¶¶ [0048]-[0054] and [0071] with Table 1 that (1) HAMLET can be provided as an Auto ML platform with the following characteristics in order to find the best possible model for a given ML task; (2) Table 1 below shows terms and definitions used in herein to describe HAMLET; (3) stop criterion: a user specified criterion telling HAMLET when to stop AutoML, which can be in the form of a time budget, a minimum required performance threshold (related to the loss function) for the user's application, or lack of improvement (e.g. a period of time or number of main loop iterations) during which HAMLET's tuners did not improve in performance anymore; and (4) the dispatcher receives a machine learning task referring a dataset, a description of the dataset, a loss function, a stopping criterion and the type of machine learning problem the task is about (regression, classification or clustering). Schmidt further discloses in ¶ [0107] with Algorithms 1.2-1.3 that (1) the Master chooses an arm based on objective: it can either choose # the arm which is expected to have a user defined desired accuracy first (op=1), or it can choose the arm which is expected to have the highest score in the remaining amount of time (op=2); and (2) check whether all Budget is used and whether the Desired accuracy is already reached. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HWEI-MIN LU whose telephone number is (313)446-4913. The examiner can normally be reached Mon - Fri: 9:00 AM - 6:00 PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela D. Reyes can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HWEI-MIN LU/Primary Examiner, Art Unit 2142 Application/Control Number: 18/122,909 Page 2 Art Unit: 2142 Application/Control Number: 18/122,909 Page 3 Art Unit: 2142 Application/Control Number: 18/122,909 Page 4 Art Unit: 2142 Application/Control Number: 18/122,909 Page 5 Art Unit: 2142 Application/Control Number: 18/122,909 Page 6 Art Unit: 2142 Application/Control Number: 18/122,909 Page 7 Art Unit: 2142 Application/Control Number: 18/122,909 Page 8 Art Unit: 2142 Application/Control Number: 18/122,909 Page 9 Art Unit: 2142 Application/Control Number: 18/122,909 Page 10 Art Unit: 2142 Application/Control Number: 18/122,909 Page 11 Art Unit: 2142 Application/Control Number: 18/122,909 Page 12 Art Unit: 2142 Application/Control Number: 18/122,909 Page 13 Art Unit: 2142 Application/Control Number: 18/122,909 Page 14 Art Unit: 2142 Application/Control Number: 18/122,909 Page 15 Art Unit: 2142 Application/Control Number: 18/122,909 Page 16 Art Unit: 2142 Application/Control Number: 18/122,909 Page 17 Art Unit: 2142 Application/Control Number: 18/122,909 Page 18 Art Unit: 2142 Application/Control Number: 18/122,909 Page 19 Art Unit: 2142 Application/Control Number: 18/122,909 Page 20 Art Unit: 2142 Application/Control Number: 18/122,909 Page 21 Art Unit: 2142 Application/Control Number: 18/122,909 Page 22 Art Unit: 2142 Application/Control Number: 18/122,909 Page 23 Art Unit: 2142 Application/Control Number: 18/122,909 Page 24 Art Unit: 2142 Application/Control Number: 18/122,909 Page 25 Art Unit: 2142 Application/Control Number: 18/122,909 Page 26 Art Unit: 2142 Application/Control Number: 18/122,909 Page 27 Art Unit: 2142 Application/Control Number: 18/122,909 Page 28 Art Unit: 2142 Application/Control Number: 18/122,909 Page 29 Art Unit: 2142 Application/Control Number: 18/122,909 Page 30 Art Unit: 2142 Application/Control Number: 18/122,909 Page 31 Art Unit: 2142 Application/Control Number: 18/122,909 Page 32 Art Unit: 2142 Application/Control Number: 18/122,909 Page 33 Art Unit: 2142 Application/Control Number: 18/122,909 Page 34 Art Unit: 2142 Application/Control Number: 18/122,909 Page 35 Art Unit: 2142 Application/Control Number: 18/122,909 Page 36 Art Unit: 2142 Application/Control Number: 18/122,909 Page 37 Art Unit: 2142 Application/Control Number: 18/122,909 Page 38 Art Unit: 2142 Application/Control Number: 18/122,909 Page 39 Art Unit: 2142 Application/Control Number: 18/122,909 Page 40 Art Unit: 2142 Application/Control Number: 18/122,909 Page 41 Art Unit: 2142 Application/Control Number: 18/122,909 Page 42 Art Unit: 2142 Application/Control Number: 18/122,909 Page 43 Art Unit: 2142 Application/Control Number: 18/122,909 Page 44 Art Unit: 2142 Application/Control Number: 18/122,909 Page 45 Art Unit: 2142 Application/Control Number: 18/122,909 Page 46 Art Unit: 2142 Application/Control Number: 18/122,909 Page 47 Art Unit: 2142 Application/Control Number: 18/122,909 Page 48 Art Unit: 2142 Application/Control Number: 18/122,909 Page 49 Art Unit: 2142 Application/Control Number: 18/122,909 Page 50 Art Unit: 2142 Application/Control Number: 18/122,909 Page 51 Art Unit: 2142 Application/Control Number: 18/122,909 Page 52 Art Unit: 2142 Application/Control Number: 18/122,909 Page 53 Art Unit: 2142 Application/Control Number: 18/122,909 Page 54 Art Unit: 2142 Application/Control Number: 18/122,909 Page 55 Art Unit: 2142 Application/Control Number: 18/122,909 Page 56 Art Unit: 2142 Application/Control Number: 18/122,909 Page 57 Art Unit: 2142 Application/Control Number: 18/122,909 Page 58 Art Unit: 2142 Application/Control Number: 18/122,909 Page 59 Art Unit: 2142 Application/Control Number: 18/122,909 Page 60 Art Unit: 2142 Application/Control Number: 18/122,909 Page 61 Art Unit: 2142 Application/Control Number: 18/122,909 Page 62 Art Unit: 2142 Application/Control Number: 18/122,909 Page 63 Art Unit: 2142 Application/Control Number: 18/122,909 Page 64 Art Unit: 2142 Application/Control Number: 18/122,909 Page 65 Art Unit: 2142 Application/Control Number: 18/122,909 Page 66 Art Unit: 2142 Application/Control Number: 18/122,909 Page 67 Art Unit: 2142 Application/Control Number: 18/122,909 Page 68 Art Unit: 2142 Application/Control Number: 18/122,909 Page 69 Art Unit: 2142 Application/Control Number: 18/122,909 Page 70 Art Unit: 2142 Application/Control Number: 18/122,909 Page 71 Art Unit: 2142 Application/Control Number: 18/122,909 Page 72 Art Unit: 2142
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