DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to the application filed on September 19 th , 2023 . Claims 1- 15 are pending in the case. Claims 1, 8, and 15 are independent claims. Priority Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Specification The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The disclosure is objected to because of the following informalities: “ a a local data set size ” in ¶21, ¶135, and ¶195 should be “ a local data set size ” . Appropriate correction is required. Information Disclosure Statement The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, “ the list may not be incorporated into the specification but must be submitted in a separate paper. ” Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered. Such references include Abdellatif et al. in ¶163. Claim Rejections - 35 USC § 101 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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claim SEQ claimNum 1 : Step 1 : Claim SEQ claimNum \c 1 is directed to [a] n apparatus , therefore it falls under the statuary category of a machine. Step 2A Prong 1 : The claim recites, in part: “ assign a score to the distributed node ” this encompasses the mental assigning of a score based on observed local dataset information. “ determine whether the distributed node is a potential malicious distributed node based on the local dataset information ” this encompasses the mental determination that an observed distributed node is a potential malicious distributed node based on the local dataset information. “ determine whether to select the distributed node for training a local model for managing a network in a federated learning mechanism based on the score assigned to the distributed node ” this encompasses the mental determination of whether to select an observed node based on a score assigned to the node. “ [determine] … whether the distributed node is a potential malicious distributed node ” this encompasses the mental determination of whether an observed node is potentially malicious. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ receive, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node ”, “ send, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Furthermore, “ receive, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node ”, “ send, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec , 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible. Regarding claim SEQ claimNum 2 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ the local dataset information comprises at least one of: a local dataset size, a local dataset statistics or a local dataset bias metrics ” a continuation of the abstract idea identified in the parent claim. Step 2A Prong 2 : The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more. Regarding claim SEQ claimNum 3 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ select the distributed node ” this encompasses the mental selection of an observed node. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ t raining the model for managing the network ” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “ end, to the distributed node, an indication that the distributed node has been selected for training the local model for managing the network ”, “ receive, from the distributed node, locally trained model parameters ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Step 2B : The additional element “ t raining the model for managing the network ” , taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Furthermore, “ end, to the distributed node, an indication that the distributed node has been selected for training the local model for managing the network ”, “ receive, from the distributed node, locally trained model parameters ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec , 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible. Regarding claim SEQ claimNum 4 , the rejection of claim 3 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ generate aggregated model parameters based on the locally trained model parameters ” this encompasses the mental creation of aggregated model parameters based on an observed locally trained model parameter. Step 2A Prong 2 : The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more. Regarding claim SEQ claimNum 5 , the rejection of claim 3 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ cluster the distributed node with another distributed node based on the score assigned to the distributed node ” this encompasses the mental clustering of an observed node with another observed node based on an observed score assigned to a node. “ generate cluster specific aggregated model parameters based on the…parameters ” this encompasses the mental creation of cluster specific aggregated model parameters based on an observed locally trained model parameter. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ locally trained model ” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible. Regarding claim SEQ claimNum 6 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : The claim recites, in part: “ determine to not select the distributed node ” this encompasses the mental determination to not select an observed distributed node for training a model for managing a network. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ training the model for managing the network ” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “ send, to the distributed node, an indication that the distributed node has not been selected for training the model for managing the network ” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Step 2B : The additional element “ training the model for managing the network ” , taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Furthermore, “ send, to the distributed node, an indication that the distributed node has not been selected for training the model for managing the network ” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec , 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible. Regarding claim SEQ claimNum 7 , the rejection of claim 1 is incorporated and further : Step 2A Prong 1 : a continuation of the abstract idea identified in the parent claim. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ send, to the distributed node, global dataset information characterizing the local dataset in relation to a global dataset and/or a global dataset and/or send a score assigned to the distributed node ”, “ receive updated local dataset information comprising characteristics of an updated local dataset of the distributed node ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Furthermore, “ send, to the distributed node, global dataset information characterizing the local dataset in relation to a global dataset and/or a global dataset and/or send a score assigned to the distributed node ”, “ receive updated local dataset information comprising characteristics of an updated local dataset of the distributed node ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec , 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible. Regarding claim SEQ claimNum 8 : Step 1 : Claim SEQ claimNum \c 8 is directed to [a] method , therefore it falls under the statuary category of a process. Step 2A Prong 1 : The claim recites, in part: “ assigning a score to the distributed node ” this encompasses the mental assigning of a score based on observed local dataset information. “ determining whether the distributed node is a potential malicious distributed node based on the local dataset information ” this encompasses the mental determination that an observed distributed node is a potential malicious distributed node based on the local dataset information. “ determining whether to select the distributed node for training a local model for managing a network in a federated learning mechanism based on the score assigned to the distributed node ” this encompasses the mental determination of whether to select an observed node based on a score assigned to the node. “[determine] …whether the distributed node is a potential malicious distributed node ” this encompasses the mental determination of whether an observed node is potentially malicious. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ receiving, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node ”, “ sending, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Furthermore, “ receiving, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node ”, “ sending, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec , 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible. Regarding claims 9-14: The rejection of claim 8 is further incorporated, the rejection of claims 2-7 are applicable to claims 9-14, respectively. Regarding claim 15: Step 1 : Claim 15 is directed to [a] non-transitory computer readable medium , therefore it falls under the statuary category of a manufacture. Step 2A Prong 1 : The claim recites, in part: “ assigning a score to the distributed node ” this encompasses the mental assigning of a score based on observed local dataset information. “ determining whether the distributed node is a potential malicious distributed node based on the local dataset information ” this encompasses the mental determination that an observed distributed node is a potential malicious distributed node based on the local dataset information. “ determining whether to select the distributed node for training a local model for managing a network in a federated learning mechanism based on the score assigned to the distributed node ” this encompasses the mental determination of whether to select an observed node based on a score assigned to the node. “[determine] …whether the distributed node is a potential malicious distributed node ” this encompasses the mental determination of whether an observed node is potentially malicious. Step 2A Prong 2 : The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “ receiving, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node ”, “ sending, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Step 2B : The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Furthermore, “ receiving, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node ”, “ sending, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism ” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec , 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible. Claim Rejections - 35 USC § 102 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 – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale , or otherwise available to the public before the effective filing date of the claimed invention. Claims 1-5, 7-12 and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kang et al. ( “ Reliable Federated Learning for Mobile Networks ” , Kang et al., 14 Oct 2019) ( hereinafter “ Kang ” ). Regarding claim SEQ claimMapNum 1 : Kang teaches [a]n apparatus comprising at least one processor and at least one memory storing instructions (Kang, page 1, col 1, section A, ¶1 “ The central aggregator, e.g., a central server, collects all the local updates and calculates the average value of these local updates as a new global model. ” A central server can be considered an apparatus comprising a processor and memory ) that, when executed by the at least one processor, cause the apparatus at least to: receive, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node (Kang, page 4, col 1, section B, ¶2 “ Step 1: Task publishment: Federated learning tasks from task publishers are first broadcast with specific data requirements (e.g., data sizes, types and time range). Mobile devices, that want to join one task and also satisfy the specific data requirements, will send a joining request with identity and data resource information back to one task publisher. ” ) ; assign a score to the distributed node (Kang, page 4, col 1, section B, ¶3 “ The task publisher starts to select its workers from the worker candidates according to their reputation values calculated by the subjective logic model in Section IV. ” Here, the reputation values can be considered the assigned scores ) and/or determine whether the distributed node is a potential malicious distributed node based on the local dataset information; determine whether to select the distributed node for training a local model for managing a network in a federated learning mechanism (Kang, page 1, col 2, ¶3 “ In this article, we propose that reputation can be used to provide solutions to select reliable and trusted workers for the federated learning tasks. ” ) based on the score assigned to the distributed node and/or whether the distributed node is a potential malicious distributed node (Kang, page 5, col 1, ¶1 “ Lastly, with the help of the reputation blockchain, the task publishers are able to select high-reputation workers for federated learning tasks. ” It is noted the claim recites alternative language, and Kang teaches at least one of the alternatives. ) ; and send, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism (Kang, page 4, col 2, ¶2 “ This initial SGD model is received by selected workers and the workers collaboratively train the global model by using their own local data. ” Here, the sending of the model for training to the selected worked can be considered an indication as to whether the distributed node has been selected for training ) . Regarding claim SEQ claimMapNum 2 : Kang teaches [t]he apparatus of claim 1, wherein the local dataset information comprises at least one of: a local dataset size, a local dataset statistics or a local dataset bias metrics (Kang, page 4, col 1, section B, ¶2 “ Step 1: Task publishment: Federated learning tasks from task publishers are first broadcast with specific data requirements (e.g., data sizes, types and time range ” It is noted the claim recites alternative language, and Kang teaches at least one of the alternatives. ) . Regarding claim SEQ claimMapNum 3 : Kang teaches [t]he apparatus of claim 1, wherein the apparatus is further caused to: select the distributed node for training the model for managing the network (Kang, page 4, col 1, section B, ¶3 “ The worker candidates with reputation values above a threshold can be selected as the workers. ” ) ; send, to the distributed node, an indication that the distributed node has been selected for training the local model for managing the network (Kang, page 4, col 2, ¶2 “ This initial SGD model is received by selected workers and the workers collaboratively train the global model by using their own local data. ” Here, the sending of the model for training to the selected worked can be considered an indication as to whether the distributed node has been selected for training ) ; and receive, from the distributed node, locally trained model parameters (Kang, page 4, col 2, ¶2 “ The workers generate local model updates and the corresponding local computation time and upload these information to the task publisher. ” ) . Regarding claim SEQ claimMapNum 4 : Kang teaches [t]he apparatus of claim 3, wherein the apparatus is further caused to: generate aggregated model parameters based on the locally trained model parameters (Kang, page 4, col 2, ¶3 “ Then, the task publisher generates a new global model by calculating the average of the rest of local model updates. ” ) . Regarding claim SEQ claimMapNum 5 : Kang teaches [t]he apparatus of claim 3, wherein the apparatus is further caused to: cluster the distributed node with another distributed node based on the score assigned to the distributed node (Kang, page 4, col 1, section B, ¶3 “ The worker candidates with reputation values above a threshold can be selected as the workers. ” here, the workers selected above the threshold can be considered clustering distributed nodes based on their scores ) ; and generate cluster specific aggregated model parameters based on the locally trained model parameters (Kang, page 2, col 1, Section A, ¶1 “ Every mobile device computes a local update, for example via a distributed Stochastic Gradient Descent (SGD) algorithm, and uploads the local update, i.e., weight parameters of current global model, to a central aggregator. ” here, the aggregation of the selected clients can be considered an aggregation of the cluster ) . Regarding claim 7 : Kang teaches [t]he apparatus of claim 1, wherein the apparatus is further caused to: send, to the distributed node, global dataset information characterizing the local dataset in relation to a global dataset and/or a global dataset and/or send a score assigned to the distributed node (Kang, page 4, col 1, section B, ¶2 “ Step 1: Task publishment: Federated learning tasks from task publishers are first broadcast with specific data requirements (e.g., data sizes, types and time range ” It is noted the claim recites alternative language, and Kang teaches at least one of the alternatives. ) ; and receive updated local dataset information comprising characteristics of an updated local dataset of the distributed node (Kang, page 4, col 2, ¶2 “ This initial SGD model is received by selected workers and the workers collaboratively train the global model by using their own local data. The workers generate local model updates and the corresponding local computation time and upload these information to the task publisher. ” ) . Regarding claim 8 : Kang teaches [a] method comprising: receiving, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node (Kang, page 4, col 1, section B, ¶2 “ Step 1: Task publishment: Federated learning tasks from task publishers are first broadcast with specific data requirements (e.g., data sizes, types and time range). Mobile devices, that want to join one task and also satisfy the specific data requirements, will send a joining request with identity and data resource information back to one task publisher. ” ) ; assigning a score to the distributed node (Kang, page 4, col 1, section B, ¶3 “ The task publisher starts to select its workers from the worker candidates according to their reputation values calculated by the subjective logic model in Section IV. ” Here, the reputation values can be considered the assigned scores ) and/or determine whether the distributed node is a potential malicious distributed node based on the local dataset information; determining whether to select the distributed node for training a local model for managing a network in a federated learning mechanism (Kang, page 1, col 2, ¶3 “ In this article, we propose that reputation can be used to provide solutions to select reliable and trusted workers for the federated learning tasks. ” ) based on the score assigned to the distributed node and/or whether the distributed node is a potential malicious distributed node (Kang, page 5, col 1, ¶1 “ Lastly, with the help of the reputation blockchain, the task publishers are able to select high-reputation workers for federated learning tasks. ” It is noted the claim recites alternative language, and Kang teaches at least one of the alternatives. ) ; and sending, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism (Kang, page 4, col 2, ¶2 “ This initial SGD model is received by selected workers and the workers collaboratively train the global model by using their own local data. ” Here, the sending of the model for training to the selected worked can be considered an indication as to whether the distributed node has been selected for training ) . Regarding claims 9-12 and14: Claims 9-12 and14 are rejected under the same rationale as claims 2-5 and 7, respectively. Regarding claim 15: Kang teaches [a] non-transitory computer readable medium comprising computer executable instructions which when run on one or more processors perform (Kang, page 1, col 1, section A, ¶1 “ The central aggregator, e.g., a central server, collects all the local updates and calculates the average value of these local updates as a new global model. ” A central server can be considered to have a non-transitory computer readable medium and a processor ) : receiving, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node (Kang, page 4, col 1, section B, ¶2 “ Step 1: Task publishment: Federated learning tasks from task publishers are first broadcast with specific data requirements (e.g., data sizes, types and time range). Mobile devices, that want to join one task and also satisfy the specific data requirements, will send a joining request with identity and data resource information back to one task publisher. ” ) ; assigning a score to the distributed node (Kang, page 4, col 1, section B, ¶3 “ The task publisher starts to select its workers from the worker candidates according to their reputation values calculated by the subjective logic model in Section IV. ” Here, the reputation values can be considered the assigned scores ) and/or determine whether the distributed node is a potential malicious distributed node based on the local dataset information; determining whether to select the distributed node for training a local model for managing a network in a federated learning mechanism (Kang, page 1, col 2, ¶3 “ In this article, we propose that reputation can be used to provide solutions to select reliable and trusted workers for the federated learning tasks. ” ) based on the score assigned to the distributed node and/or whether the distributed node is a potential malicious distributed node (Kang, page 5, col 1, ¶1 “ Lastly, with the help of the reputation blockchain, the task publishers are able to select high-reputation workers for federated learning tasks. ” It is noted the claim recites alternative language, and Kang teaches at least one of the alternatives. ) ; and sending, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism (Kang, page 4, col 2, ¶2 “ This initial SGD model is received by selected workers and the workers collaboratively train the global model by using their own local data. ” Here, the sending of the model for training to the selected worked can be considered an indication as to whether the distributed node has been selected for training ) . Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kang in view of Wen et al. ( “ Federated Learning powered by NVIDIA Clara ” , Wen et al., 1 December 2019) ( hereinafter “ Wen ” ) Regarding claim 6 : Kang teaches [t]he apparatus of claim 1, wherein the apparatus is further caused to: determine to not select the distributed node for training the model for managing the network (Kang, page 4, col 1, section B, ¶3 “ The worker candidates with reputation values above a threshold can be selected as the workers. ” workers that fall below the threshold can be considered not selected ) ; and Kang does not teach “ send, to the distributed node, an indication that the distributed node has not been selected for training the model for managing the network ” However, Wen teaches send, to the distributed node, an indication that the distributed node has not been selected for training the model for managing the network (Wen, page 4, ¶1 “ The server will check the credentials of the client and perform the authentication process to validate the client. If the authentication is successful, the server sends a Federated Learning token back to the client for use in the following client-server communication. If the client can not be authenticated, it sends an authentication rejection. ” ) . Kang and Wen are analogous art because both references concern methods for federated learning . Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kang’s federated learning system to incorporate the authentication and notification of not being selected taught by Wen . The motivation for doing so would have been to allow developers to bring their own models and control training, as stated in Wen “ The configurable MMAR ( M edical M odel AR chive ) feature of Clara Train SDK makes it possible for developers to bring their own models and components to perform Federated Learning and also have control over whether the local training is run on a single GPU or multiple GPUs. ” Regarding claim 13: Claim 13 is rejected under the same rationale as claim 6. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Huang et al. ( “ An Exploratory Analysis on Users’ Contributions in Federated Learning ” , Huang et al., 13 Nov 2020 ) discloses two contrasting perspectives: incentive mechanisms to measure the contribution of local models by honest users, and malicious users to deliberately degrade the overall model . Singhal et al. ( US 2022 / 0398500 A1 ) discloses m ethods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model having a set of local model parameters and a set of global model parameters under a partially local federated learning framework. One of the methods include maintaining local data and data defining the local model parameters; receiving data defining current values of the global model parameters; determining, based on the local data, the local model parameters, and the current values of the global model parameters, current values of the local model parameters; determining, based on the local data, the current values of the local model parameters, and the current values of the global model parameters, updated values of the global model parameters; generating, based on the updated values of the global model parameters, parameter update data defining an update to the global model parameters; and transmitting the parameter update data . Lee et al. ( US 2022 / 0158888 A1 ) discloses a method of removing, by a server, an abnormal client in federated learning. A method of removing, by a server, an abnormal client in federated learning may include receiving, from a user equipment (UE), first weight values trained in a first local model, generating a first client model based on the first weight values, validating the first client model by using a validation data set in order to determine whether the first client model is legitimate, and removing the first weight values based on the first client model not being legitimate. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Enter examiner's name" \* MERGEFORMAT JACOB Z SUSSMAN MOSS whose telephone number is (571) 272-1579 . The examiner can normally be reached FILLIN "Work schedule?" \* MERGEFORMAT Monday - Friday, 9 a.m. - 5 p.m. ET . 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, FILLIN "SPE Name?" \* MERGEFORMAT Kakali Chaki can be reached on FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-3719 . 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. /J.S.M./ Examiner, Art Unit 2122 /KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122