CTNF 18/998,697 CTNF 79365 Detailed Action 1. This Office Action is responsive to the Preliminary Amendment filed 01/27/2025. Claims 3, 5, 6, 9-11, 14, and 16-17 have been amended. Claims 18-24 have been cancelled. Claims 1-17 and are presented for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 2. Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Information Disclosure Statement 3. The information disclosure statement (IDS) submitted on 03/04/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 4. 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 5. Claim s 1-5, 7-10 and 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over LIM et al., “Federated Learning in Mobile Edge Networks”, arXiv, February 28, 2020, pp. 1-34, in view of ALABBASI et al. (WO 2021/123139 A1), hereinafter “LIM” and “ALABBASI” . 6. As to claim 1, LIM teaches an apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: applying, by a server network function, an iterative distributed machine learning model training process between the server network function and at least one local network function ( Fig. 4, page 5, Section B : a Federated Learning process is carried out between a model owner server and a plurality of data owners for collaboratively training an ML model) , the server network function initially providing the at least one local network function with an initial global machine learning model ( Fig. 4, page 6, Step 1 : an initialized global ML model is broadcast from the model owner server to the plurality of data owner participants) to be trained by the at least one local network function by respective local data of the at least one local network function ( Fig. 4, page 6, Step 2 : local model parameters are updated based on the initialized global ML model and local data by the plurality of data owner participants) ; obtaining, from each local network function of the at least local network function, new parameter data associated with a trained initial global machine learning model trained by each local network function in its latest iteration ( Fig. 4, page 6, Step 2 : the updated local model parameters are subsequently sent to the model owner server by the plurality of data owner participants) ; updating the initial global machine learning model at least partly based on the new parameter data obtained from the at least one local network function ( Fig. 4, page 6, Step 3 : the initial global ML model is updated based on aggregating the local ML models received from the plurality of data owner participants) . LIM does not explicitly disclose “generating, at least partly based on the updated initial global machine learning model, feedback data to the at least one local network function, the feedback data of a local network function reflecting individual performance of the local network function in its latest iteration; and transmitting the feedback data to the at least one local network function”. In an analogous art, ALABBASI teaches “generating, at least partly based on the updated initial global machine learning model, feedback data to the at least one local network function, the feedback data of a local network function reflecting individual performance of the local network function in its latest iteration; and transmitting the feedback data to the at least one local network function” ( Fig. 3 and [0065-0075] : a cascaded federated machine learning process carried out between a server and a plurality of client devices, wherein a global ML model maintained by the server is generated by aggregating a plurality local ML models trained and transmitted from the plurality of client devices to the server. This mechanism of providing feedback from the server to the plurality of client devices may improve the performance of the training of the local ML models at the plurality of client devices) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of LIM and ALABBASI to achieve the claimed invention to offer significant advantages in accuracy, performance, and user experience, driven by the ability to learn from new data, patterns and behaviors. 7. As to claim 2, LIM-ALABBASI teaches the apparatus according to claim 1, wherein the instructions, when executed by the at least one processor, cause the apparatus to perform: transmitting to each local network function of the least one network function an initialization message to start the iterative distributed machine learning model training process ( LIM, Fig. 4, page 6, Step 2 : local model parameters are updated based on the initialized global ML model and local data by the plurality of data owner participants) , the initialization message transmitted to a local network function comprising encryption parameter data defined for the local network function for encrypting the parameter data sent by the local network function; and decrypting the new parameter data obtained from a local network function using the encryption parameter data associated with the local network function ( LIM, page 21, Section 4) Encryption-based Solutions : the participants’ trained parameters will be encrypted using the homomorphic encryption technique before they are sent to the server) . 8. As to claim 3, LIM-ALABASI teaches the apparatus according to claim 1, wherein the instructions, when executed by the at least one processor, cause the apparatus to perform: generating after each iteration, at least partly based on individual behaviour of the at least one local network function, weight data associated with the at least one local network function, the weight data providing a weight value for each local network function of the at least one local network function for weighing the new parameter data associated with the local network function ( LIM, page 12, Section C: Importance-based Updating : If the loss value of the current iteration is smaller than the preceding iteration, this implies that current training gradients and model parameters are important for training loss minimalization and thus, their respective hidden weights are assigned to the server for parameter update. If not, the loss increases as compared to the previous iteration, other parameters are selected to be updated based on their hidden weight values) . 9. As to claim 4, LIM-ALABBASHI teaches the apparatus according to claim 3, wherein the instructions, when executed by the at least one processor, cause the apparatus to perform: aggregating the new parameter data obtained from the at least one local network function using weighted federated averaging and the current weight data associated with the at least one local network function ( LIM, page 13, Section IV. Resource Allocation : the server has to aggregate parameter updates from all participating devices in the round before taking a weighted average of the models) . 10. As to claim 5, LIM-ALABBASI teaches the apparatus according to any of claims 1, wherein the instructions, when executed by the at least one processor, cause the apparatus to perform: receiving an evaluation result from each local network function of the at least one local network function, the evaluation result indicating performance of the local network function on its local data; and using the evaluation results in generating the feedback data ( LIM, page 18 : The simulation results show that the proposed scheme can significantly improve the accuracy of the FL model since unreliable workers are detected and not selected for FL training; and page 20 : the accuracy of prediction model when the participants agree to share 10% and 1% of their parameters are respectively 99.14% and 98.71%, compared with 99.17% for the centralized solution when the server has full data to train) . 11. As to claims 7-10, claims 7-10 are apparatus claims that recite similar limitations as of apparatus claims 1-5, therefore, they are rejected under the same rationale. 13. As to claims 12-16, claims 12-16 are corresponding method claims that recite similar limitations as of method claims 1-5, therefore, they are rejected under the same rationale . 07-21-aia AIA 14. Claim s 6, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over LIM-ALABBASI, in view of LEE et al. (US 2022/0108214 A1), hereinafter “LEE” . 15. As to claim 6, LIM-ALABBASI teaches the apparatus according to claim1, but does not explicitly disclose “the server network function comprises a network data analytics function”. In an analogous art, LEE discloses that the NWDAF device 101 may perform an Analytics Logical Function (AnLF) and provide the analytics information of the first network data to the consumer NF device 102 in response to the network data analytics request received from the consumer NF device 102 (Fig. 1, [0078] and [0268]) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of LIM-ALABBSI and LEE to achieve the claimed invention to allow the system to evaluate an analytics model for a specific use case or for a particular consumer NF device. 16. As to claim 11, claim 11 is an apparatus claim that recites similar limitations as of apparatus claim 6, therefore, it is rejected under the same rationale. 17. As to claim 17, claim 17 is a corresponding method claim that recites similar limitations as of apparatus claim 6, therefore, it is rejected under the same rationale. 18. Further references of interest are cited on Form PTO-892, which is an attachment to this Office Action. 19. A shortened statutory period for reply to this action is set to expire THREE (3) months from the mailing date of this communication. See 37 CFR 1.134. Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUANG N NGUYEN whose telephone number is (571) 272-3886. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KAMAL B. DIVECHA, can be reached at (571) 272-5863. The fax phone number for the organization is (571) 273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /QUANG N NGUYEN/Primary Examiner, Art Unit 2441 Application/Control Number: 18/998,697 Page 2 Art Unit: 2441 Application/Control Number: 18/998,697 Page 3 Art Unit: 2441 Application/Control Number: 18/998,697 Page 4 Art Unit: 2441 Application/Control Number: 18/998,697 Page 5 Art Unit: 2441 Application/Control Number: 18/998,697 Page 6 Art Unit: 2441 Application/Control Number: 18/998,697 Page 7 Art Unit: 2441 Application/Control Number: 18/998,697 Page 8 Art Unit: 2441
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