CTNF 18/527,802 CTNF 101802 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. This action is responsive to the application filed 12/04/2023. Claims 1-16 are presented for examination. Priority Applicant’s claim for the benefit of a prior filed application KR10-2023-0018301, filed 02/10/2023, is acknowledged. Information Disclosure Statement The information disclosure statements (IDS) submitted 12/04/2023 & 01/20/2026 have been considered by the examiner. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 Step 1 : The claim recites “An operating method of a provider providing a machine learning (ML) model, the operating method comprising:”; therefore, it is directed to the statutory category of a process. Step 2A Prong 1 : The claim recites, inter alia: providing a first ML model to a consumer : These limitations recite a mentally performable process with the aid of pen and paper of using judgement to provide a ML model to a consumer. based on an assessment result of the first ML model, providing a second ML model to the consumer : These limitations recite a mentally performable process with the aid of pen and paper of using observation and judgement to provide a second ML model to a consumer based on the assessment of the first ML model. Thus, the claim recites a judicial exception. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: An operating method of a provider providing a machine learning (ML) model, the operating method comprising : These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. an operating method of a provider, to a particular technological environment or field of use, e.g. used to provide a machine learning (ML) model. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application. assessing the first ML model by collecting data from data sources : These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application. Step 2B : The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment and include insignificant extra-solution activity of data gathering recited by “ assessing the first ML model by collecting data from data sources ” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05. Claim 2 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas as the judicial exception of claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: wherein the provider comprises a network data analytics function (NWDAF) comprising a model training logical function (MTLF), and the consumer comprises an NWDAF comprising an analytics logical function (AnLF) : These additional elements are recited at a high level of generality and amount to invoking computers or other machinery merely as a tool to apply the underlying judicial exception similar to a commonplace business method or mathematical algorithm being applied on a general purpose computer per MPEP § 2106.05(f)(2)(i). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application. Step 2B : The additional elements from Step 2A Prong 2 include invoking generic computer components to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05. Claim 3 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas as the judicial exception of claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information on analytics generated by the first or second ML model and information on analytics accuracy of the first or second ML model : assessing the first ML model by collecting data from data sources : These additional elements amount to insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated per MPEP § 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application. Step 2B : The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “ registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information on analytics generated by the first or second ML model and information on analytics accuracy of the first or second ML model : assessing the first ML model by collecting data from data sources ” which are well-understood routine and conventional activities similar to receiving or transmitting data over a network per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05. Claim 4 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas as the judicial exception of claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: wherein the assessing comprises monitoring accuracy of the first ML model by collecting data from a data source network function (NF) and a data collection coordination function (DCCF) : These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application. Step 2B : The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “ wherein the assessing comprises monitoring accuracy of the first ML model by collecting data from a data source network function (NF) and a data collection coordination function (DCCF) ” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05. Claim 5 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas as the judicial exception of claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: wherein the assessing comprises monitoring accuracy of the first ML model by collecting data of an analytics data repository function (ADRF) : These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g). using at least one of an ADRF identifier (ID) and DataSetTag: These additional elements are recited at a high level of generality and amount to invoking computers or other machinery merely as a tool to apply the underlying judicial exception similar to a commonplace business method or mathematical algorithm being applied on a general purpose computer per MPEP § 2106.05(f)(2)(i). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application. Step 2B : The additional elements from Step 2A Prong 2 include invoking generic computer components to apply the underlying judicial exception and insignificant extra-solution activity of data gathering recited by “ wherein the assessing comprises monitoring accuracy of the first ML model by collecting data of an analytics data repository function (ADRF) ” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05. Claim 6 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas as the judicial exception of claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: wherein the assessing comprises monitoring accuracy of the first ML model by collecting fault prediction analytics data from an MDAS (Management Data Analytics Service): These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application. Step 2B : The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “ wherein the assessing comprises monitoring accuracy of the first ML model by collecting fault prediction analytics data from an MDAS (Management Data Analytics Service) ” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05. Claim 7 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas as the judicial exception of claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: wherein the assessing comprises subscribing to a unified data management (UDM) to receive a notification of modification in subscription data for a target of the first ML model by invoking an Nudm_SDM_Subscribe service operation, and wherein the UDM subscribes to a unified data repository (UDR) to receive a notification of user equipment (UE) subscription data by invoking an Nudr_DM_Subscribe service operation: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application. Step 2B : The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “ wherein the assessing comprises subscribing to a unified data management (UDM) to receive a notification of modification in subscription data for a target of the first ML model by invoking an Nudm_SDM_Subscribe service operation, and wherein the UDM subscribes to a unified data repository (UDR) to receive a notification of user equipment (UE) subscription data by invoking an Nudr_DM_Subscribe service operation ” which are well-understood routine and conventional activities similar to receiving or transmitting data over a network per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05. Claim 8 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the abstract ideas as the judicial exception of claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: wherein the second ML model comprises a newly generated ML model or a retrained first ML model based on the collected data from the data sources : These additional elements are recited at a high level of generality and amount to invoking computers or other machinery merely as a tool to apply the underlying judicial exception. See MPEP § 2106.05(f). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application. Step 2B : The additional elements from Step 2A Prong 2 include invoking generic computer components to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05. Claim 9-16 Step 1 : These claims are directed to “A server apparatus providing a machine learning (ML) model, the server apparatus comprising:”; therefore, it is directed to the statutory category of a machine. Step 2A Prong 1 : Claims 9-16 recite the same judicial exception as Claims 1-8, respectively. Step 2A Prong 2 : The judicial exception recited in these claims are not integrated into a practical application. The analysis at this step for 9-16 mirrors that of Claims 1-8, respectively. Step 2B : The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception for these claims. The analysis at this step for Claims 9-16 mirrors that of Claims 1-8, respectively. Claim Rejections - 35 USC § 102 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1-2, 4, 8-10, 12, & 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Papageorgiou et al. (US 2023/0060071 A1, filed 08/19/2022), hereinafter Papageorgiou . Regarding independent claim 1, Papageorgiou teaches an operating method of a provider providing a machine learning (ML) model, the operating method comprising: providing a first ML model to a consumer ([Fig. 4, 0089-0092] discusses providing a machine learning model to a consumer); assessing the first ML model by collecting data from data sources ([Fig. 5, 0010] discusses collecting data in order to gauge the accuracy of the ML model); and based on an assessment result of the first ML model, providing a second ML model to the consumer ([Fig. 5, 0089-0092] discusses providing a new ML model to be provisioned, triggering training of a new ML model). Regarding dependent claim 2, Papageorgiou teaches the claimed invention as claimed in claim 1, including wherein the provider comprises a network data analytics function (NWDAF) comprising a model training logical function (MTLF), and the consumer comprises an NWDAF comprising an analytics logical function (AnLF) ([0004] discusses the provider comprising a NWDAF further comprising a MTLF; and the consumer comprises an NWDAF further comprising an AnLF). Regarding dependent claim 4, Papageorgiou teaches the claimed invention as claimed in claim 1, including wherein the assessing comprises monitoring accuracy of the first ML model by collecting data from a data source network function (NF) and a data collection coordination function (DCCF) ([Fig. 4, 0010] discusses collecting data from NFs, DCCFs to monitor accuracy). Regarding dependent claim 8, Papageorgiou teaches the claimed invention as claimed in claim 1, including wherein the second ML model comprises a newly generated ML model or a retrained first ML model based on the collected data from the data sources ([Fig. 5, 0089-0092] discusses generating a new ML model or retrained first ML model based on the collected data). Regarding claims 9-10, 12, & 16, claims 9-10, 12, & 16 are machine claims that are substantially the same as the process of claims 1-2, 4, & 8. Therefore, claims 9-10, 12, & 16 are rejected for the same reasons as claims 1-2, 4, & 8 . 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 3, 5-7, 11, & 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Papageorgiou et al. (US 2023/0060071 A1, filed 08/19/2022), hereafter Papageorgiou as applied in claims 1 & 9 above, and further in view of 3GPP ("5G; Architecture enhancements for 5G System (5GS) to support network data analytics services", 3GPP TS 23.288 version 17.4.0 Release 17, ETSI) (Year: 2022), hereafter 3GPP . Regarding dependent claim 3, Papageorgiou teaches the claimed invention as claimed in claim 1, including generating analytics related to the accuracy of the first or second ML model ([Fig. 5, 0010] discusses collecting data in order to gauge the accuracy of the ML model). Papageorgiou does not explicitly teach registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information on analytics generated by the first or second ML model and information on analytics accuracy of the first or second ML model . However, in the same field of endeavor, 3GPP’s publicly released specification teaches a method to register use of a ML model, wherein the registering indicates the capability to transmit a least one of analytics feedback information and information on analytics accuracy ([Figure 6.2A1-1] discusses registering a new ML model of the consumer; [7.2.2] discusses capabilities to transmit analytics information generated by the ML model and also the accuracy of the analytics). Because Papageorgiou teaches generating analytics related to the accuracy of the first or second ML model, and 3GPP teaches registering a use of the ML model of the consumer, transmitting analytics feedback information generated by the first of second ML model, and information on analytics accuracy of the first or second ML model, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information and information on analytics accuracy as taught by 3GPP into Papageorgiou’s method, with a reasonable expectation of success, to teach registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information on analytics generated by the first or second ML model and information on analytics accuracy of the first or second ML model . This combination would have been motivated by the desire such that the ML Model Information is used by an NWDAF containing AnLF to derive analytics (3GPP [6.2A.1]). Regarding dependent claim 5, the combination of Papageorgiou and 3GPP teaches the claimed invention as claimed in claim 1, including wherein the assessing comprises monitoring accuracy of the first ML model by collecting data of an analytics data repository function (ADRF) using at least one of an ADRF identifier (ID) and DataSetTag (3GPP [Figure 6.2B.2] discusses storing and retrieving data from an ADRF by invoking an identifier (ADRF ID); 3GPP [10.2.6] discusses the retrieval operation uses “Analytics Specification” and “Data Specification”, which operates as the DataSetTag parameter). Regarding dependent claim 6, the combination of Papageorgiou and 3GPP teaches the claimed invention as claimed in claim 1, including wherein the assessing comprises monitoring accuracy of the first ML model by collecting fault prediction analytics data from an MDAS (Management Data Analytics Service) (3GPP [Figure 6.2.3] discusses collecting fault analytics using MDAS, and these fault analytics relate to performance assurance; 3GPP [Fig. 6.2.3.2-1] discusses the notification and subscription mechanism for collecting management data that includes fault and performance data). Regarding dependent claim 7, the combination of Papageorgiou and 3GPP teaches the claimed invention as claimed in claim 1, including wherein the assessing comprises subscribing to a unified data management (UDM) to receive a notification of modification in subscription data for a target of the first ML model by invoking an Nudm_SDM_Subscribe service operation, and wherein the UDM subscribes to a unified data repository (UDR) to receive a notification of user equipment (UE) subscription data by invoking an Nudr_DM_Subscribe service operation (3GPP [Figure 6.2.9] discusses invoking a Nudm_SDM_Subscribe operation to subscribe to modification updates; 3GPP [6.2.2.2] discusses the NWDAF subscribes to UDM to receive a notification of modification in subscription data; 3GPP [Figure 6.1C.2] discusses the NWDAF registering into UDM to receive a notification of UE related data and does so by invoking indicator Nudm_UECM_Registration and thus, the subscription operates in a UDR to receive a notification of user equipment subscription data by invoking an Nudr_DM_Subscribe service operation). Regarding claims 11, & 13-15, claims 11, & 13-15 are machine claims that are substantially the same as the process of claims 3 & 5-7. Therefore, claims 11, & 13-15 are rejected for the same reasons as claims 3 & 5-7 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kim et al. (US 20230254719 A1, filed 02/02/2023) ([Abstract] A method of performing federated learning by a first network data analytic function (NWDAF) is provided. The method includes determining whether to perform a federated learning based on an analytics request message from a network function (NF), receiving, from a network repository function (NRF), information regarding a second NWDAF capable of participating in the federated learning, generating a federated learning group, transmitting, to the second NWDAF, a federated learning join request message based on the information regarding the second NWDAF, and receiving, from the second NWDAF, a federated learning join response message including information regarding whether to participate in the federated learning group). Any inquiry concerning this communication or earlier communications from the examiner should be directed to RILEY S ACOSTA whose telephone number is (571)272-8714. The examiner can normally be reached Monday-Thursday 6am-4pm. 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, Jennifer N Welch can be reached at (571)272-7212. 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. /RILEY S ACOSTA/Examiner, Art Unit 2143 /JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143 Application/Control Number: 18/527,802 Page 2 Art Unit: 2143 Application/Control Number: 18/527,802 Page 3 Art Unit: 2143 Application/Control Number: 18/527,802 Page 4 Art Unit: 2143 Application/Control Number: 18/527,802 Page 5 Art Unit: 2143 Application/Control Number: 18/527,802 Page 6 Art Unit: 2143 Application/Control Number: 18/527,802 Page 7 Art Unit: 2143 Application/Control Number: 18/527,802 Page 8 Art Unit: 2143 Application/Control Number: 18/527,802 Page 9 Art Unit: 2143 Application/Control Number: 18/527,802 Page 10 Art Unit: 2143 Application/Control Number: 18/527,802 Page 11 Art Unit: 2143 Application/Control Number: 18/527,802 Page 12 Art Unit: 2143 Application/Control Number: 18/527,802 Page 13 Art Unit: 2143 Application/Control Number: 18/527,802 Page 14 Art Unit: 2143 Application/Control Number: 18/527,802 Page 15 Art Unit: 2143 Application/Control Number: 18/527,802 Page 16 Art Unit: 2143
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