DETAILED ACTION 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 Preliminary Amendment filed on 8 / 28 /20 23. Claims 1- 2, 4, 7-8, 10, 13-21, 23, 25-27, and 29 are pending in the case. Claims 1, 14 , and 25 are independent claims. Drawings New corrected drawings in compliance with 37 C.F.R. § 1.121(d) are required in this application because FILLIN "Enter the appropriate information " \* MERGEFORMAT portions of figure s 6A-11C are too blurry to be readable . Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance. INFORMATION ON HOW TO EFFECT DRAWING CHANGES Replacement Drawing Sheets Drawing changes must be made by presenting replacement sheets which incorporate the desired changes and which comply with 37 C . F.R. § 1.84 . An explanation of the changes made must be presented either in the drawing amendments section, or remarks, section of the amendment paper. 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 C . F.R. § 1.121 (d). A replacement sheet must 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 the amended drawing(s) must not be labeled as “amended.” If the changes to the drawing figure(s) are not accepted by the examiner, applicant will be notified of any required corrective action in the next Office action. No further drawing submission will be required, unless applicant is notified. Identifying indicia, if provided, should include the title of the invention, inventor’s name, and application number, or docket number (if any) if an application number has not been assigned to the application. If this information is provided, it must be placed on the front of each sheet and within the top margin. Annotated Drawing Sheets A marked-up copy of any amended drawing figure, including annotations indicating the changes made, may be submitted or required by the examiner. The annotated drawing sheet(s) must be clearly labeled as “Annotated Sheet” and must be presented in the amendment or remarks section that explains the change(s) to the drawings. Timing of Corrections Applicant is required to submit acceptable corrected drawings within the time period set in the Office action. See 37 C . F.R. § 1.85(a). Failure to take corrective action within the set period will result in ABANDONMENT of the application. If corrected drawings are required in a Notice of Allowability (PTOL-37), the new drawings MUST be filed within the THREE MONTH shortened statutory period set for reply in the “Notice of Allowability.” Extensions of time may NOT be obtained under the provisions of 37 C . F.R. § 1.136 for filing the corrected drawings after the mailing of a Notice of Allowability. Claim Interpretation Claim 1 recites “so that the indicated federated learning nodes that are able to participate in federated learning are able to perform data processing based on federated learning . ” Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. MPEP § 2111.04. Claim 4 recites “so that federated learning participant nodes are able to optimize model parameters based on the model blocks . ” Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. MPEP § 2111.04. Claim Rejections - 35 U.S.C. § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 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 1-2, 4, 7-8, 10, 13-21, 23, 25-27, and 29 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Zhang et al. ( US 2022/0044162 A1 , hereinafter Zhang ). As to independent claim 1 , Zhang discloses an electronic device for blockchain-based federated learning, comprising a processing circuit configured to: acquire first federated learning related information from a federated learning node (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3 ) , cause verifying whether the federated learning node is able to participate in federated learning based on the first federated learning related information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12 ) through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13 ) , and notify the federated learning side of indication information indicating federated learning nodes that are able to participate in federated learning, so that the indicated federated learning nodes that are able to participate in federated learning are able to perform data processing based on federated learning (“ A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate ,” paragraph 0059 lines 13-16 ). As to dependent claim 2 , Zhang further discloses a device wherein the processing circuit is further configured to : cause verifying whether the first federated learning related information meets a federated learning requirement (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) ; and confirm that the federated learning node is able to participate in federated learning under the condition that the first federated learning related information meets the federated learning requirement (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16 ). As to dependent claim 4 , Zhang further discloses a device wherein the processing circuit is further configured to acquire model parameters from federated learning participant nodes on the federated learning side (“At block 430, one or more local model updates may be obtained from the clients participating in the training of the global machine-learning model,” paragraph 0060 lines 1-3 ), cause generation of model blocks based on the model parameters from the federated learning participant nodes (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3 ) through blockchain (“ At operations 316, metadata of the masked local model updates may be published to the blockchain 306 ,” paragraph 0053 lines 1-2 ), and notify the generation of model blocks to the federated learning side, so that federated learning participant nodes are able to optimize model parameters based on the model blocks (“At operations 208, metadata associated with the global machine-learning model of the central server 202 may be published to the blockchain 206,” paragraph 0039 lines 1-3) , wherein the model blocks comprise one of sub model blocks and a global model block generated based on the sub model blocks (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) . As to dependent claim 7 , Zhang further discloses a device wherein the processing circuit is further configured to : generate sub-model blocks based on model parameters from federated learning participant nodes (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) through blockchain (“At operations 316, metadata of the masked local model updates may be published to the blockchain 306,” paragraph 0053 lines 1-2) , and cause aggregation of the generated sub-model blocks through a blockchain technique to generate a global model block (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) . As to dependent claim 8 , Zhang further discloses a device wherein the processing circuit is further configured to : acquire third (“one or more training rounds of the global machine-learning model,” paragraph 0014 lines 8-9 ) federated learning related information from the federated learning nodes (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3) , cause verifying whether to initiate execution of federated learning through blockchain based on the third federated learning related information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13), and transmit at least one part of the third federated learning related information to the federated learning side (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16 ) , in a case that it is verified that federated learning through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) is able to be initiated (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) , wherein th e third federated learning related information is at least partially the same type as information contained in the first federated learning related information (“ one or more training rounds of the global machine-learning model ,” paragraph 0014 lines 8-9 ). As to dependent claim 10 , Zhang further discloses a device wherein the first federated learning related information includes at least one of identity information, data metadata information, model parameter information and model metadata information of the federated learning node, wherein, the data metadata information includes at least one of data attribute information, data structure information and data distribution information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) ; and/or the model parameter information includes at least one of model type, model weight and model gradient (“the clients 110 may publish metadata to the blockchain 130 in which the published metadata describes the local datasets of the clients 110 and/or the clients 110 themselves,” paragraph 0020 lines 3-6) ; and/or the model metadata information includes at least one of an identifier of a federated learning participant node, a model type, the amount of local training samples, local model training accuracy, and the federated learning participation status information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) . As to dependent claim 13 , Zhang further discloses a device wherein at least one of the federated learning nodes belongs to a node in the blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) . As to independent claim 14 , Zhang discloses an electronic device for blockchain-based federated learning, comprising a processing circuit configured to : transmit first federated learning related information (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3) to a blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) , acquire indication information from the blockchain side indicating whether a federated learning node associated with the electronic device is able to participate in federated learning, wherein the indication information is generated by verifying the first federated learning related information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) ; and under the condition that it is determined that the federated learning node associated with the electronic device is able to participate in federated learning based on the indication information (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16 ) , cause data processing by federated learning nodes on the federated learning s ide that are able to participate in the federated learning in combination based on federated learning (“ At block 430, one or more local model updates may be obtained from the clients participating in the training of the global machine-learning model ,” paragraph 0060 lines 1-3 ). As to dependent claim 15 , Zhang further discloses a device wherein the processing circuit is further configured to : acquire global model parameters, which are generated based on model parameters of federated learning nodes on the federated learning side that are able to participate in federated learning (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) ; and perform model optimization based on the global model parameters (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) . As to dependent claim 16 , Zhang further discloses a device wherein the processing circuit is further configured to : acquire model parameters of federated learning nodes on the federated learning side that are able to participate in federated learning (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) , generate global model parameters by aggregating the model parameters of federated learning nodes (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) , and transmit the global model parameters to each federated learning node (“ At operations 208, metadata associated with the global machine-learning model of the central server 202 may be published to the blockchain 206 ,” paragraph 0039 lines 1-3 ). As to dependent claim 17 , Zhang further discloses a device wherein the processing circuit is further configured to : transmit model parameters acquired by the federated learning node through local model training (“At operations 316, metadata of the masked local model updates may be published to the blockchain 306,” paragraph 0053 lines 1-2) , and acquire a global model block, which is generated based on model parameters of federated learning nodes on the federated learning side that are able to participate in the federated learning through blockchain (“At operations 208, metadata associated with the global machine-learning model of the central server 202 may be published to the blockchain 206,” paragraph 0039 lines 1-3) ; and perform local model optimization based on the global model block (“ The central server 202 may send data associated with the global machine-learning model to the clients 204 capable of performing the training tasks at operations 216 such that the respective local dataset corresponding to each of the clients 204 may be used to train the global machine-learning model at operations 214 ,” paragraph 0042 lines 5-11 ). As to dependent claim 18 , Zhang further discloses a device wherein the processing circuit is further configured to : acquire sub-model blocks generated based on model parameters of federated learning nodes (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) through blockchain (“At operations 316, metadata of the masked local model updates may be published to the blockchain 306,” paragraph 0053 lines 1-2) ; and aggregate the acquired sub-model blocks into a global model block (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) . As to dependent claim 19 , Zhang further discloses a device wherein the processing circuit is further configured to: distribute the global model block to other federated learning nodes that are able to participate in federated learning (“The central server 202 may send data associated with the global machine-learning model to the clients 204 capable of performing the training tasks at operations 216 such that the respective local dataset corresponding to each of the clients 204 may be used to train the global machine-learning model at operations 214,” paragraph 0042 lines 5-11) . As to dependent claim 20 , Zhang further discloses a device wherein the processing circuit is further configured to : acquire second (“one or more training rounds of the global machine-learning model,” paragraph 0014 lines 8-9 ) federated learning related information (“ I n response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5), verify whether local federated learning related information matches the acquired second federated learning related information (“In response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5); and in the case of matching, determine that the federated learning node associated with the electronic device intends to participate in federated learning (“In response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5) . As to dependent claim 21 , Zhang further discloses a device wherein the processing circuit is further configured to : transmit a federated learning initiation request to the blockchain side, wherein the federated learning initiation request includes third federated learning related information (“In response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5) ; and acquire information indicating whether the blockchain-based federated learning is allowed to be initiated, wherein the information is generated by verifying the third federated learning related information through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) . As to dependent claim 23 , Zhang further discloses a device wherein the processing circuit is configured to : verify whether model parameters meet a specific convergence condition (“determining the updated global machine-learning model may indicate an end of a given training round,” paragraph 0046 lines 7-9), and perform the blockchain-based federated learning iteratively, if the model parameters cannot meet the specific convergence condition (“ the central server 202 may include a threshold number of training rounds (e.g., a minimum number of training rounds), and the operations 200 may be repeated until the threshold number of training rounds is satisfied ,” paragraph 0046 lines 16-20 ). As to independent claim 25 , Zhang discloses a method of blockchain-based federated learning, which is executed in a blockchain-based federated learning system including a federated learning side and a blockchain side, the method comprises : transmitting first federated learning related information associated with federated learning nodes from the federated learning side (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3) to the blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) , receiving the first federated learning related information (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3) , and verifying whether the federated learning nodes are able to participate in federated learning based on the first federated learning related information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) through blockchain, by the blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) , notifying the federated learning side of indication information indicating federated learning participant nodes that are able to participate in federated learning (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16 ) , by the blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) , determining, by the federated learning side, federated learning participant nodes based on the indication information (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16 ) , and performing data processing by the federated learning nodes on the federated learning side that able to participate in federated learning based on federated learning (“At block 430, one or more local model updates may be obtained from the clients participating in the training of the global machine-learning model,” paragraph 0060 lines 1-3 ). As to dependent claim 26 , Zhang further discloses a method comprising : transmitting third (“one or more training rounds of the global machine-learning model,” paragraph 0014 lines 8-9) federated learning related information by a federated learning node on the federated learning side to the blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) , verifying, by the blockchain side, whether the federated learning is allowed to initiate based on the third federated learning related information, and transmitting second federated learning related information to the federated learning side if the federated learning is allowed to initiate (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) , wherein the second federated learning related information is at least a part of the third federated learning related information (“one or more training rounds of the global machine-learning model,” paragraph 0014 lines 8-9) , judging, by each federated learning node on the federated learning side, whether the federated learning node intends to participate in federated learning based on the second federated learning related information (“In response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5) . As to dependent claim 27 , Zhang further discloses a method comprising : transmitting model parameters of each federated learning node that is allowed to participate in federated learning by the federated learning side (“At block 430, one or more local model updates may be obtained from the clients participating in the training of the global machine-learning model,” paragraph 0060 lines 1-3) to the blockchain side (“At operations 316, metadata of the masked local model updates may be published to the blockchain 306,” paragraph 0053 lines 1-2) , generating a model block by the blockchain side based on the model parameters of each federated learning node (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) , notifying the federated learning side of the generation of the model block by the blockchain side (“At operations 208, metadata associated with the global machine-learning model of the central server 202 may be published to the blockchain 206,” paragraph 0039 lines 1-3) ; and performing local model optimization by each federated learning node on the federated learning side based on the model block (“The central server 202 may send data associated with the global machine-learning model to the clients 204 capable of performing the training tasks at operations 216 such that the respective local dataset corresponding to each of the clients 204 may be used to train the global machine-learning model at operations 214,” paragraph 0042 lines 5-11 ) , wherein the model block comprises one of sub-model blocks and a global model block generated based on the sub model blocks (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) . As to dependent claim 29 , Zhang further discloses a method comprising : generating global model parameters on the federated learning side based on model parameters of federated learning nodes on the federated learning side that are allowed to participate in federated learning (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) , and performing local model optimization by each federated learning node on the federated learning side based on the global model parameters (“The central server 202 may send data associated with the global machine-learning model to the clients 204 capable of performing the training tasks at operations 216 such that the respective local dataset corresponding to each of the clients 204 may be used to train the global machine-learning model at operations 214,” paragraph 0042 lines 5-11 ) . Conclusion The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure: US 2021 / 0067339 A1 disclosing blockchain-based federated learning with node restriction Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). In the interests of compact prosecution, Applicant is invited to contact the e xaminer via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://www.uspto.gov/patent/laws-and-regulations/interview-practice. Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e-mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryan Barrett whose telephone number is 571 270 3311. The examiner can normally be reached 9:00am to 5:30pm. 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 Michelle Bechtold can be reached at 571 431 0762 . 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. /Ryan Barrett/ Primary Examiner, Art Unit 2148