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 .
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 1-20 is/are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Regarding claims 1-20,
Step 1: Applying step 1, the preamble of claims 1-10 recites a method, which falls within the statutory category of a process. The preamble of claims 11-19 recites an apparatus, which falls within the statutory category of an apparatus. The preamble of claim 20 recites a non-transitory computer-readable medium, which falls within the statutory category of a manufacture.
Regarding claim 1,
Step 2A – Prong One: Claim 1 recites:
A method comprising:
obtaining, by a controller for a federated learning system, state information from a plurality of nodes in the federated learning system, the plurality of nodes being arranged in a topology to implement federated learning with local datasets that are separately maintained at individual ones of the plurality of nodes, the topology indicative of how the plurality of nodes are connected to perform the federated learning;
determining, by the controller and based on the state information, an adjustment to [[a]] the topology of the plurality of nodes;
selecting, by the controller, one or more nodes from among the plurality of nodes affected by the adjustment; and
sending, by the controller, instructions to the one or more nodes, to implement the adjustment to the topology of the plurality of nodes.
The broadest reasonable interpretation, in light of the Specification, of the bolded limitations above amounts to a mental process that can practically be performed in the human mind with or without the physical aids of a pen and paper. A human could use observation, evaluation, and judgement to determine, based on state information, an adjustment to a topology of a plurality of nodes, as supported by the Specification at Pg. 20, Lines 7-10 (“the user may manipulate a graphical user interface (GUI) on which the current topology of the federated learning system is displayed”, thus the user may determine an adjustment to the current topology). Step 2A – Prong One (Yes).
Step 2A – Prong Two: The additional element of claim 1 regarding “obtaining, by a controller for a federated learning system, state information from a plurality of nodes in the federated learning system, the plurality of nodes being arranged in a topology to implement federated learning with local datasets that are separately maintained at individual ones of the plurality of nodes, the topology indicative of how the plurality of nodes are connected to perform the federated learning;” and “selecting, by the controller, one or more nodes from among the plurality of nodes affected by the adjustment;” amount to insignificant extra-solution activity that amounts to no more than mere data gathering (See MPEP 2106.05(g)). The additional elements of claim 1 regarding “by the controller” and “sending, by the controller, instructions to the one or more nodes, to implement the adjustment to the topology of the plurality of nodes” are mere instructions to apply the abstract idea on a generic computer (See MPEP 2106.05(f)).
Even when viewed in combination, the additional element does not integrate the judicial exception into a practical application. Step 2A – Prong Two (No).
Step 2B: The additional element of claim 1 regarding “obtaining, by a controller for a federated learning system, state information from a plurality of nodes in the federated learning system, the plurality of nodes being arranged in a topology to implement federated learning with local datasets that are separately maintained at individual ones of the plurality of nodes, the topology indicative of how the plurality of nodes are connected to perform the federated learning;” and “selecting, by the controller, one or more nodes from among the plurality of nodes affected by the adjustment;” amount to insignificant extra-solution activity that amounts to no more than mere data gathering (See MPEP 2106.05(g)). Data gathering is a well-understood, routine conventional activity as recognized by the courts (See MPEP 2106.05(d)(II)). The additional elements of claim 1 regarding “by the controller” and “sending, by the controller, instructions to the one or more nodes, to implement the adjustment to the topology of the plurality of nodes” are mere instructions to apply the abstract idea on a generic computer (See MPEP 2106.05(f)). The computer is recited at a high level of generality and imposes no meaningful limitations on the claim.
Even when viewed in combination, the additional element does not amount to significantly more than the judicial exception. Step 2B (No).
Claim 1 is ineligible.
Regarding claims 11 and 20,
Claims 11 and 20 incorporate substantively all the limitations of claim 1 in an apparatus and non-transitory computer-readable medium, and are rejected on the same grounds as above.
The processors, memory, and network interfaces recited in these claims are also generic computing components.
Claims 11 and 20 are ineligible.
Dependent claims:
Claims 2-3, 9-10, 12-13, and 19: These claims recite further instructions to apply the judicial exception on a generic computer (See MPEP 2106.05(f)). The computer is recited at a high level of generality and imposes no meaningful limitations on the claims. These claims are ineligible.
Claims 4-5, 8, 14-15, and 18: These claims recite further insignificant extra-solution activities that amount to no more than mere data gathering (See MPEP 2106.05(g)). Data gathering is well-understood, routine conventional activity as recognized by the courts (See MPEP 2106.05(d)(II)). These claims are ineligible.
Claims 6-7 and 16-17: These claims recite further abstract ideas (mental processes). There are no additional elements in these claims that integrate the judicial exception into a practical application and no additional elements in these claims that would amount to significantly more than the judicial exception. These claims are ineligible.
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.
Claim(s) 1-9 and 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Surya et al. (NPL: Dynamic Resource Allocation for Distributed TensorFlow Training in Kubernetes Cluster, published Nov. 2019, hereinafter “Surya”) in view of Tang et al. (NPL: GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication, date of publication Dec. 2022, hereinafter “Tang”).
Regarding claim 1, Surya teaches a method comprising:
obtaining, by a controller for a federated learning system, state information from a plurality of nodes in the federated learning system, the plurality of nodes being arranged in a topology (Surya, Pg. 2 Col. 2 Last Paragraph and Figs. 1 & 5 – “The dynamic resource allocation is initiated by the training job requesting a resource check. This is done after training job finishes its epoch (and save its temporary trained model to persistent storage). Upon receiving the request, Config Manager gathers information about the cluster’s resources and decides if there are free resources.” – teaches obtaining, by a controller for a federated learning system, state information from a plurality of nodes in the federated learning system (federated learning system sends resource check to config manager, config manager gathers information about resources), the plurality of nodes being arranged in a topology (Fig. 1 shows the plurality of nodes being arranged in a topology));
determining, by the controller and based on the state information, an adjustment to [[a]] the topology of the plurality of nodes (Surya, Pg. 3 Col. 1 Last Paragraph to Pg. 3 Col. 2 First Paragraph and Fig. 5 – “The value of current_number_of_ps_node and current_number_of_worker_node represents the current number of ps and worker nodes respectively. These values are required when training job requests resource check because Config Manager does not know any information about the training job, i.e. the current number of ps and worker nodes. The value of threshold is a percentage (0 – 100), which represents the cluster’s resources utilization. This value is used to evaluate whether there are free resources in the cluster. If the current cluster’s resources utilization is under the threshold value, then Config Manager decides that there are free resources, thus it adds ps and worker nodes to training job.” – teaches determining, by the controller (Config Manager) and based on the state information (based on current number of ps and worker nodes, resource utilization), an adjustment to a topology of the plurality of nodes (adds ps and worker nodes to training job));
selecting, by the controller, one or more nodes from among the plurality of nodes affected by the adjustment (Surya, Fig. 5 and Table 1 – teaches selecting, by the controller (Config Manager), one or more nodes from among the plurality of nodes affected by the adjustment (calculates additional nodes to be added to the training job when configuration is determined to not meet threshold requirement)); and
sending, by the controller, instructions to the one or more nodes, to implement the adjustment to the topology of the plurality of nodes (Surya, Fig. 5 and Table 1 – teaches sending, by the controller (Config Manager), instructions to the one or more nodes, to implement adjustment to the topology of the plurality of nodes (calculates additional nodes to be added to the training job, creates new training configuration, and applies new configuration to training)).
Surya fails to explicitly teach the plurality of nodes being arranged in a topology to implement federated learning with local datasets that are separately maintained at individual ones of the plurality of nodes, the topology indicative of how the plurality of nodes are connected to perform federated learning.
However, analogous to the field of the claimed invention, Tang teaches:
the plurality of nodes being arranged in a topology to implement federated learning with local datasets that are separately maintained at individual ones of the plurality of nodes, the topology indicative of how the plurality of nodes are connected to perform federated learning (Tang, Fig. 1 description – “The system architecture of our GossipFL contains two roles: Coordinator and Client. The Coordinator is a central server that manages the training process, and the clients are the clients that iteratively train the model with local data.” Section 3.3 Paragraph 1 – “A client in GossipFL could be a mobile device or a user who collaborates with other clients. Each client trains a single model with its local data using mini-batch stochastic gradient descent (SGD).” – teaches the plurality of nodes being arranged in a topology to implement federated learning (Fig. 1 (right) shows the nodes, or clients, arranged in a topology to implement federated learning) with local datasets that are separately maintained at individual ones of the plurality of nodes (clients train a single model with its local data, thus local datasets are separately maintained at individual ones of the plurality of nodes or clients), the topology indicative of how the plurality of nodes are connect to perform federated learning (Fig. 1 (right) shows the clients arranged in a topology, where the topology is indicative of how the plurality of clients are connected to perform federated learning));
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the local datasets separately maintained at individual nodes of the topology of Tang to the plurality of nodes, controller, and adjustments of Surya. Doing so would enable a federated learning framework to train machine learning models from multiple clients without sharing data to preserve data privacy (Tang, Introduction).
Claims 11 and 20 incorporate substantively all the limitations of claim 1 in an apparatus and non-transitory computer-readable medium and are rejected on the same grounds as above. Surya teaches the network interfaces; processor coupled to the one or more network interfaces; and memory configured to store instructions executable by the processor; and a non-transitory computer-readable medium storing program instructions (Surya, Table 2 and Diagram 1 – teaches machine specification, network specification, and evaluation of memory usage).
Regarding claim 2, the combination of Surya and Tang teaches the method as in claim 1,
wherein the instructions sent to a particular node of the one or more nodes changes its role from among: a training role, an intermediate aggregation role, or a global aggregation role (Tang, Section 2.3 Paragraph 2 – “The procedure of the randomized gossip algorithm can be formulated as Eq. (2) where Wt .elementOf. W Rnxn is called Random Gossip Matrix.It is a doubly stochastic matrix generated at tth iteration, satisfying 1ntWt = 1tn and Wt1n = 1n, indicating how clients communicate and do average with others and 1n = [1 1...1 1]T .elementOf. Rn. The Wt,i,j > 0 means that client i communicates with client j at iteration t. And E(Wt,i,j) > 0 indicates that client i and client j have a probability of exchanging information at some iterations.” and in Section 3.3 Paragraph 2 – “(W1) Using the same seed from the Coordinator, the client generates the sparsification mask mt (line 5) to obtain a sparsified model... (W2) Each client sends/receives the sparsified model to/from its peer and conducts a weighted average based on the matrix Wt (line 7). (W3) Then, the client com bines the exchanged part of the model with its un exchanged part (decided by :mt =1n - mt) of the model together (line 8). (W4) Each client samples the local data to conduct SGD updates (line 9), in which gt is the learning rate. (W5) Finally, each client sends its bandwidth4 and training information to the Coordinator” – wherein the instructions sent to a particular node of the one or more nodes (clients receive seed and instructions on which other clients to communicate with from Coordinator, as in Section 3.2 Paragraph 2) changes its role from among: a training role, an intermediate aggregation role, or a global aggregation role (clients are randomly selected to exchange information at some iterations, thus instructions received from Coordinator change role of clients from among a training role or an intermediate aggregation role)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the sending of instructions that change node roles of Tang to further modify the federated learning system, ps nodes, and worker nodes of Surya and Tang in order to change the roles of nodes within the federated learning system. Doing so would reduce the communication traffic on clients and achieves better utilization of the bandwidth resources (Tang, Introduction).
Claim 12 is similar to claim 1, hence similarly rejected.
Regarding claim 3, the combination of Surya and Tang teaches the method as in claim 1,
wherein the adjustment to the topology of the plurality of nodes changes the topology from among any of a set of topologies comprising one or more of: a hierarchical topology, a centralized topology, a hybrid topology, or a distributed hierarchy (Surya, Fig. 4 and Fig. 5 – teaches wherein the adjustment to the topology (configuration) of the plurality of nodes changes the topology from among any of a set of topologies comprising one or more of a hierarchical topology, a centralized topology, a hybrid topology, or a distributed hierarchy (teaches adding one or more parameter and worker nodes to a distributed hierarchy topology, thus changes from the original distributed hierarchy to an updated distributed hierarchy)).
Claim 13 is similar to claim 3, hence similarly rejected.
Regarding claim 4, the combination of Surya and Tang teaches the method as in claim 1,
wherein the state information from a node in the plurality of nodes is indicative of one or more system or network performance metrics associated with that node (Surya, Table 1 and Pg. 3 Col. 1 Last Paragraph to Pg. 3 Col. 2 First Paragraph – “The value of threshold is a percentage (0 – 100), which represents the cluster’s resources utilization. This value is used to evaluate whether there are free resources in the cluster. If the current cluster’s resources utilization is under the threshold value, then Config Manager decides that there are free resources, thus it adds ps and worker nodes to training job.” – teaches wherein the state information from a node in the plurality of nodes is indicative of one or more system or network performance metrics associated with that node (threshold represents cluster’s resource utilization, which is used to determine if there are free resources in the cluster of nodes, thus state information from the nodes is indicative of one or more system performance metrics such as resource utilization)).
Claim 14 is similar to claim 4, hence similarly rejected.
Regarding claim 5, the combination of Surya and Tang teaches the method as in claim 1,
wherein the state information from a node in the plurality of nodes is indicative of one or more performance metrics associated with a training job assigned to that node (Surya, Table 1 and Pg. 3 Col. 1 Last Paragraph to Pg. 3 Col. 2 First Paragraph – “The value of threshold is a percentage (0 – 100), which represents the cluster’s resources utilization. This value is used to evaluate whether there are free resources in the cluster. If the current cluster’s resources utilization is under the threshold value, then Config Manager decides that there are free resources, thus it adds ps and worker nodes to training job.” – teaches wherein the state information from a node in the plurality of nodes is indicative of one or more system or network performance metrics associated with a training job assigned to that node (threshold represents cluster of nodes resource utilization, which is used to determine if there are free resources in the cluster of nodes and adds nodes to training job, thus state information is indicative of one or more performance metrics such as resource utilization associated with the training job)).
Claim 15 is similar to claim 5, hence similarly rejected.
Regarding claim 6, the combination of Surya and Tang teaches the method as in claim 1,
wherein determining the adjustment to the topology of the plurality of nodes comprises: identifying, by the controller and based on the state information, a bottleneck in the federated learning system (Surya, Fig. 5 ,Table 1, and Pg. 3 Col. 1 Last Paragraph to Pg. 3 Col. 2 First Paragraph – “The value of threshold is a percentage (0 – 100), which represents the cluster’s resources utilization. This value is used to evaluate whether there are free resources in the cluster. If the current cluster’s resources utilization is under the threshold value, then Config Manager decides that there are free resources, thus it adds ps and worker nodes to training job.” – teaches wherein the determining the adjustment to the topology of the plurality of nodes comprises identifying, by the controller and based on the state information (based on Config Manager and configuration), a bottleneck in the federated learning system (identifies that there are free resources not being utilized in the system and adds node to use up free resource, thus identifying that resource utilization is a bottleneck)).
Claim 16 is similar to claim 6, hence similarly rejected.
Regarding claim 7, the combination of Surya and Tang teaches the method as in claim 1,
wherein determining the adjustment to the topology of the plurality of nodes comprises: identifying a presence of a condition in the federated learning system from the state information (Surya, Fig. 5 ,Table 1, and Pg. 3 Col. 1 Last Paragraph to Pg. 3 Col. 2 First Paragraph – “The value of threshold is a percentage (0 – 100), which represents the cluster’s resources utilization. This value is used to evaluate whether there are free resources in the cluster. If the current cluster’s resources utilization is under the threshold value, then Config Manager decides that there are free resources, thus it adds ps and worker nodes to training job.” – teaches wherein the determining the adjustment to the topology of the plurality of nodes comprises identifying a presence of a condition in the federated learning system from the state information (determines cluster’s resource utilization is under a threshold value)); and
performing a lookup of the adjustment to the topology of the plurality of nodes based on the condition identified from the state information (Surya, Table 1 and Pg. 3 Col. 2 Last Paragraph – “As an example to the algorithm, consider a cluster whose current memory utilization is 50% of the total memory size, and user sets the value of threshold, ratio, and minimum_added_node to be 80%, 1:1, and 4 respectively. When the training job finishes its epoch, it initiates an API call to Config Manager, current_number_of_ps_node, providing the current_number_of_worker_node, and other parameters required. For example, current_number_of_ps_node the value of and current_number_of_worker_node are 1 and 1 respectively. After the API call is received, Config Manager gets information about the cluster’s resources, i.e. memory” – teaches performing a lookup (performs API call providing parameters required) of the adjustment to the topology of the plurality of nodes (API call to config manager returns number of ps and worker nodes to add to configuration) based on condition identified from state information (gets information about memory utilization, current number of ps and worker nodes, threshold, ratio, etc. and performs call to lookup adjustment to configuration)).
Claim 17 is similar to claim 7, hence similarly rejected.
Regarding claim 8, the combination of Surya and Tang teaches the method as in claim 1,
wherein the local datasets are not shared among the plurality of nodes (Tang, Fig. 1 description – “The system architecture of our GossipFL contains two roles: Coordinator and Client. The Coordinator is a central server that manages the training process, and the clients are the clients that iteratively train the model with local data. The clients train the model and exchange the sparsified model with their peers (e.g., W1 or W2) that are determined by the Coordinator.” and in Section 3.3 Paragraph 1 – “A client in GossipFL could be a mobile device or a user who collaborates with other clients. Each client trains a single model with its local data using mini-batch stochastic gradient descent (SGD).” – teaches wherein the local datasets are not shared among the plurality of nodes (clients train single model with local data, only sparsified model is shared, thus teaching wherein the local datasets are not shared among the plurality of nodes or clients)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the nodes with local datasets that are not shared among the plurality of nodes of Tang to further modify the plurality of nodes, controller, and adjustments of Surya and Tang. Doing so would enable a federated learning framework to train machine learning models from multiple clients without sharing data to preserve data privacy (Tang, Introduction).
Claim 18 is similar to claim 8, hence similarly rejected.
Regarding claim 9, the combination of Surya and Tang teaches the method as in claim 1,
wherein the adjustment to the topology comprises adding an intermediate node to the federated learning system that generates an intermediate model that aggregates models trained by the one or more nodes (Surya, Fig. 5, Table 1, and Pg. 1 Col. 2 Paragraph 3 – “During the training, the model is partitioned among several ps nodes while training data is partitioned among several worker nodes. The role of the ps node is to store the model’s parameters, while the role of worker node is to fetch parameters from ps node, compute new value of those parameters, and update the value stored by ps node.” – teaches wherein the adjustment to the topology comprises adding an intermediate node to the federated learning system (adding ps node to configuration, as in Table 1) that generates an intermediate model that aggregates model trained by the one or more nodes (ps node stores parameter updates generated by the one or more worker nodes, thus aggregating models trained by the one or more nodes)).
Claim 19 is similar to claim 9, hence similarly rejected.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Surya and Tang, as applied to claims 1 above, and further in view of Cao et al. (NPL: Layered Model Aggregation based Federated Learning in Mobile Edge Networks, published Nov. 2021, hereinafter “Cao”).
Regarding claim 10, the combination of Surya and Tang teaches the method as in claim 1.
The combination of Surya and Tang fails to explicitly teach wherein the adjustment to the topology comprises grouping the one or more nodes based on a similarity between their network bandwidths.
However, analogous to the field of the claimed invention, Cao teaches:
wherein the adjustment to the topology comprises grouping the one or more nodes based on a similarity between their network bandwidths (Cao, Pg. 3 Col. 1 Paragraph 5 and Algorithm 2 – “During different rounds of model aggregation layer by layer, the topology of edge nodes in federated learning can be changed according to the CPU usage in each node and network bandwidth usage between them” – teaches wherein the adjustment to the topology comprises grouping the one or more nodes based on a similarity between their network bandwidths (topology of nodes can be changed according to network bandwidth usage between nodes)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the grouping of nodes based on a similarity between network bandwidths of Cao to the federated learning system and topology adjustments of Surya and Tang. Doing so modifies the topology of the edge nodes based on the real-time bandwidth occupancy of the links between them (Cao, Section 2B) and reduces the uplink bandwidth usage (Cao, Introduction).
Response to Arguments
Applicant's arguments, see pp. 1 of Remarks, filed 21 May 2026, with respect to the rejections of claim(s) 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues that the amendments render the 35 U.S.C. 101 rejection moot. Examiner respectfully disagrees. As explained in the 35 U.S.C. 101 rejection above, the limitation regarding “determining, based on state information, an adjustment to [[a]] the topology of the plurality of nodes” amounts to a mental process that can practically be performed in the human mind with or without the physical aids of a pen and paper. A human could use observation, evaluation, and judgement to determine, based on state information, an adjustment to a topology of a plurality of nodes. The Specification at Pg. 20, Lines 7-10 states “In turn, the developer may request a topology change to the federated system by interacting with the user interface. For instance, the user may manipulate a graphical user interface (GUI) on which the current topology of the federated learning system is displayed. Example actions supported by such a GUI may include, by are not limited to: …”, thus the developer or user may determine, based on state information, an adjustment to the topology of the plurality of nodes. The additional elements of the claim amount mere data gathering (See MPEP 2106.05(g)) and instructions to apply the judicial exception on a generic computer (See MPEP 2106.05(f)). Data gathering is a well-understood, routine conventional activity (See MPEP 2106.05(d)(II)). Even when viewed in combination, the additional elements of the claim fail to integrate the judicial exception into a practical application and fail to amount to significantly more than the judicial exception.
Applicant’s arguments, see pp. 1-4 of Remarks, filed 21 May 2026, with respect to the rejection(s) of claim(s) 1, 11, and 20 under 35 U.S.C. 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 35 U.S.C. 103 over Surya in view of Tang. Surya teaches the limitations of claim 1 regarding “obtaining, by a controller for a federated learning system, state information…”, “determining, by the controller… an adjustment to the topology of the plurality of nodes”, “selecting, by the controller, one or more nodes…”, and “sending, by the controller, instructions… to implement the adjustment to the topology of the plurality of nodes”. Tang teaches the amended limitations of claim 1 regarding “the plurality of nodes being arranged in a topology to implement federated learning with local datasets that are separately maintained… the topology indicative of how the plurality of nodes are connected…”.
Applicant argues on pp. 2 of Remarks that Surya fails to teach “determining, by the controller and based on the state information, an adjustment to the topology of the plurality of nodes”. Examiner respectfully disagrees. Surya at Pg. 3 Col. 1 Last Paragraph to Pg. 3 Col. 2 First Paragraph – “If the current cluster’s resources utilization is under the threshold value, then Config Manager decides that there are free resources, thus it adds ps and worker nodes to training job” – teaches determining if current cluster resource utilization is under a threshold value, and adds nodes to the topology of the plurality of nodes, which is an adjustment to the topology. Thus, Surya teaches, under broadest reasonable interpretation, determining, by the controller (Config Manager) and based on the state information (based on current number of ps and worker nodes, resource utilization), an adjustment to a topology of the plurality of nodes (adds ps and worker nodes to plurality of nodes).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Behera et al. (US Pub. No. 2022/0245528, published Aug. 2022) teaches systems and methods for federated learning using peer-to-peer networks. Teaches adjusting topologies of a plurality of nodes by modifying the roles of nodes within the topology, such as selecting candidate nodes to become leader nodes of a topology. Teaches wherein the nodes train local models on local datasets without sharing data.
Zhu et al. (US Pub. No. 2022/0114475, published April 2022) teaches systems and methods for decentralized federated learning, where each client trains a local model on local data that is not shared among the plurality of clients. Teaches wherein the topology may be a mesh, ring, or other varying topologies. Clients of the topology may also be responsible for defining local topology.
Marfoq et al. (NPL: Throughput-Optimal Topology Design for Cross-Silo Federated Learning, published 2020) teaches methods for federated learning with topology design optimization. Teaches adjustment of a topology of a plurality of nodes to find a topology with the largest throughput.
Huang et al. (NPL: Accelerating Federated Edge Learning via Topology Optimization, published April 2022) teaches a federated edge learning system with a topology optimization scheme to improve communication-and-computation efficiency. Teaches joint optimization of aggregation topology and computing speed. Teaches wherein clients train a model using local datasets that are not shared among the plurality of clients, and an edge server, or controller, determines communication and computation parameters.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LOUIS CHRISTOPHER NYE/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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