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 in response to communications filed on 06/25/2026.
Claims 2-24, 26-31, 33, 40-41, 47, 52, and 55-56 have been canceled.
Claims 1, 25, 32, 34-39, 42-46, 48-51, and 53-54 are pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Response to Arguments
Applicant’s arguments with respect to claims 1 and 32 and associated dependent claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. See Okanohara et al. (US 20160217387 A1) below.
Applicant's arguments with respect to claim 25 have been fully considered but they are not persuasive. Applicant argues in substance that the references allegedly do not teach the newly amended features. However, examiner respectfully disagrees. It is noted that “performance counter” is specifically not defined in the claims. As understood by examiner, “performance counter” merely refers to an entity that measures a particular performance of a system, such as load processing, data updating degree, CPU usage, etc. This is taught by Yang, which recites “judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing [i.e. performance counter] criterion, a data updating degree [i.e. performance counter] criterion, and other criteria or a combination of present criteria… preset criteria according to its own hardware configuration and processing capabilities [i.e. performance counters]” (e.g. in paragraphs 71-73) and “election cycle can change, such as based on conditions (or predictions thereof) that may affect the reliability of leaders, including…hardware issues… no longer being adequate for performing the operations required of a leader node… judge whether its current node state meets the preset criteria of a leader node… preset criteria according to its own hardware configuration and processing capabilities… when the usage of the CPU does not exceed 80% [i.e. performance counter], it may be considered that the node meets the preset criteria” (e.g. in paragraphs 42, 71-73 and 81). As such, applicant’s arguments are not persuasive.
Applicant’s arguments with respect to claim 48 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. See Shih (US 20180267539 A1) below.
Claim Rejections - 35 USC § 103
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 32, 34-35, 37-39, 42-44, 49-51, and 53-54 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 20180018198 A1) in view of Prakash et al. (US 20190138934 A1) and Okanohara et al. (US 20160217387 A1).
As per independent claim 1, Yang teaches a method for dynamically configuring a network comprising a plurality of computing devices for a system, the method performed by a computing device communicatively coupled to the network (e.g. in paragraphs 4, 71, and 104, “multiple processing devices included in a distributed system… connected to the network 1130 in a distributed environment”) and comprising:
dynamically identifying a predicted change in a state of a leader computing device of a geographic region, the predicted change in the state being based on a monitored status of the leader computing device (e.g. in paragraphs 42, 71-73 and 81, and 138, “election cycle can change, such as based on conditions (or predictions thereof) that may affect the reliability of leaders, including…hardware issues… no longer being adequate for performing the operations required of a leader node… judge whether its current node state meets the preset criteria of a leader node… preset criteria according to its own hardware configuration and processing capabilities… multiple computers that are located at one site”), wherein the leader computing device comprises one of a server computing device and a client computing device and wherein the plurality of computing devices comprise server computing devices and/or client computing devices (e.g. in paragraphs 4, 100, and 104, “multiple processing devices included in a distributed system… serve in a role in a computer system as a client, network component, a server”);
determining whether the change in the state of the leader computing device triggers a new leader computing device to be selected (e.g. in paragraphs 32 and 83, “when there is already a determined leader node among all nodes of the distributed system, and the leader node determines that the leader node itself does not meet a preset criteria, the leader node screens out…a node that meets the preset criteria among other nodes to serve as a successor node”);
initiating a new leader election among the plurality of computing devices, within the geographic region, responsive to determining that the change in the state of the leader computing device triggers the new leader computing device to be selected (e.g. in paragraphs 83 and 138, “Screening out a successor node can include, for example, the leader node sending a query request to other nodes to query node information of other nodes and selecting a successor node according to the node information received in response to the queries… multiple computers that are located at one site”); and
receiving an identification of the new leader computing device for the geographic region based on the new leader election (e.g. in paragraphs 83 and 138, “selecting a successor node according to the node information received in response to the queries… multiple computers that are located at one site”),
but does not specifically teach the plurality of computing devices configured to perform training of a machine learning model and the leader computing device responsible for the machine learning model of a geographic region.
However, Prakash teaches a plurality of computing devices configured to perform training of a machine learning model (e.g. in paragraphs 24, 30, 35, and 38-40, “the master node obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity. The computed full gradient is then sent back to the edge compute nodes to further refine the ML model until the ML model converge… learning takes place by a federation of client compute nodes”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yang to include the teachings of Prakash because one of ordinary skill in the art would have recognized the benefit of allowing tasks/services in well-known networks to be performed optimally, with privacy, and/or with improved reliably, and appropriately reconfiguring networks where nodes can move,
but does not specifically teach the leader computing device responsible for the machine learning model of a geographic region.
However, Okanohara teaches a leader computing device responsible for a machine learning model of a geographic region (e.g. in paragraphs 16, 18, 25, and 27*28, “Group membership may be based on geographic proximity between edge devices 100. A leader election module 112 determines which edge device 100 should act as a leader for a group of edge devices… the edge devices 100 may maintain communication with known edge devices 100, and make new connections with newly available edge devices 100, for example, that have be moved within range for communication… mixed model is distributed to the group (block 410). For example, the leader 302a sends a mixed model 206 to edge devices 100 to update local models 206”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Okanohara because one of ordinary skill in the art would have recognized the benefit of facilitating updating of models of a group of available nodes.
Claim 32 is the device claim corresponding to the method claim 1 and is rejected under the same reasons set forth, and the combination further teaches the computing device comprising a processor and a memory storing instructions that, when executed by the processor, cause the computing device to be configured to perform operations (e.g. Yang, in paragraphs 4, 12, 71, and 104, “multiple processing devices included in a distributed system… a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method/the instructions stored on the non-transitory, computer-readable medium… connected to the network 1130 in a distributed environment”) and wherein the machine learning model is part of a vehicle distributed learning system in a geographic area and the leader computing device is a leader computing device associated with a vehicle and wherein dynamically identifying the change in the state of the leader computing device comprises detecting that the vehicle is leaving the geographic area (e.g. Yang, in paragraphs 4, 32, 42, 71-73, 81, 83, and 138, “multiple processing devices included in a distributed system… no longer being adequate for performing the operations required of a leader node… judge whether its current node state meets the preset criteria of a leader node… multiple computers that are located at one site”; Prakash, in paragraphs 24, 30, 35, 38-40, 55, 259, and 274, “sent back to the edge compute nodes to further refine the ML model” and “compute nodes…may comprise…vehicle-embedded systems or a vehicle-to-everything (V2X) devices… moving vehicles… As the transient devices 1512, 1514, and 1516, leave the vicinity of the fog 1520, it may reconfigure itself to eliminate those IoT devices 1504 from the network… a particular area or geographic feature”).
As per claim 34, the rejection of claim 32 is incorporated and the combination further teaches wherein in determining whether the change in the state of the leader computing device triggers a new leader computing device to be selected, the computing device is configured to perform operations comprising determining whether the change in the state of the leader computing device triggers a new leader computing device to be selected based on at least one performance counter (e.g. Yang, in paragraphs 71-73, “Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… preset criteria according to its own hardware configuration and processing capabilities… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria”).
As per claim 35, the rejection of claim 34 is incorporated and the combination further teaches responsive to initiating the new leader election, transmitting a leader candidate request message to each candidate computing device that may be the new leader computing device (e.g. Yang, in paragraph 83, “sending a query request to other nodes to query node information of other nodes”); receiving, via the network, a response from at least one candidate computing device to the leader candidate request message indicating the at least one candidate computing device can be the new leader computing device, wherein receiving the identification of the new leader computing device based on the initiating of the new leader election comprises selecting the new leader computing device based on the response from the at least one candidate computing device (e.g. Yang, in paragraph 83, “identifying the node that, for example, is determined among the other nodes at being capable of meeting the preset criteria of a leader node…in response to the queries”); transmitting, via the network, an acceptance request to the new leader computing device selected (e.g. Yang, in paragraph 84, “leader node sends a voting request for the successor node”); and receiving, via the network, a response from the new leader computing device accepting to be the new leader computing device (e.g. Yang, in paragraph 84, “successor node can also send a notification message to each node, such as to notify each node that the successor node has become the new leader node”).
As per claim 37, the rejection of claim 34 is incorporated and the combination further teaches wherein the at least one performance counter comprises a plurality of performance counters (e.g. Yang, in paragraphs 71-73, “Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria”) and determining whether the change in the state of the leader computing device triggers a new leader computing device to be selected further comprises: monitoring the plurality of performance counters of the leader computing device to determine whether a change in at least one of the plurality of performance counters raises above a threshold and responsive to determining the change raises above the threshold, determining that the change in the state of the leader computing device triggers a new leader computing device to be selected (e.g. Yang, in paragraphs 71-73 and 81, “judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… preset criteria according to its own hardware configuration and processing capabilities… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria [i.e. if raises above 80% threshold, then does not meet criteria]… leader node to stop meeting the preset criteria. In this case, a new leader node can be selected”).
As per claim 38, the rejection of claim 37 is incorporated and the combination further teaches wherein in monitoring the plurality of performance counters of the leader computing device to determine whether a change in at least one of a plurality of performance counters raises above a threshold, the computing device is configured to perform operations further comprising monitoring the plurality of performance counters of the leader computing device to determine whether a change in a key performance index rises above a key performance index threshold (e.g. Yang, in paragraphs 71-73 and 81, “judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion [i.e. key performance index], and other criteria or a combination of present criteria… preset criteria according to its own hardware configuration and processing capabilities… when the usage of the CPU [i.e. key performance index] does not exceed 80%, it may be considered that the node meets the preset criteria [i.e. if raises above 80% threshold, then does not meet criteria]… leader node to stop meeting the preset criteria. In this case, a new leader node can be selected”).
As per claim 39, the rejection of claim 32 is incorporated and the combination further teaches wherein the machine learning model is part of a federated learning system and in detecting the change in the state of the leader computing device, the computing device is configured to perform operations further comprising detecting the change in the state of the leader computing device in the federated learning system that affects current performance or future performance of the leader computing device (e.g. Yang, in paragraphs 71-73 and 81, “no longer being adequate for performing the operations required of a leader node… judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria… leader node to stop meeting the preset criteria. In this case, a new leader node can be selected”; Prakash, in paragraphs 24, 30, 35, and 38-40, “the master node obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity. The computed full gradient is then sent back to the edge compute nodes to further refine the ML model until the ML model converge… learning takes place by a federation of client compute nodes”).
As per claim 42, the rejection of claim 32 is incorporated and the combination further teaches wherein the computing device is configured to perform operations further comprising: monitoring a condition of the leader computing device to dynamically identify the change in the state of the leader computing device (e.g. Yang, in paragraphs 71-73 and 81, “judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria… leader node to stop meeting the preset criteria. In this case, a new leader node can be selected”).
As per claim 43, the rejection of claim 42 is incorporated and the combination further teaches wherein in monitoring the condition of the leader computing device to dynamically identify the change in the state of the leader computing device, the computing device is configured to perform operations comprising monitoring at least one of a predicted performance level of the leader computing device, a current performance level of the leader computing device, and a loss in power at a site where the leader computing device is located (e.g. Yang, in paragraphs 71-73 and 81, “judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria”).
As per claim 44, the rejection of claim 42 is incorporated and the combination further teaches wherein in monitoring the condition of the leader computing device to dynamically identify the change in the state of the leader computing device, the computing device is configured to perform operations further comprising monitoring the condition of the leader computing device to detect the change in the state of the leader computing device without sharing results of the monitoring to other computing devices in the plurality of computing devices (e.g. Yang, in paragraphs 71-73, 81, and 83, “judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… leader node to stop meeting the preset criteria. In this case, a new leader node can be selected… the leader node sending a query request to other nodes to query node information of other nodes and selecting a successor node according to the node information received in response to the queries”, i.e. without sharing results of monitoring to other nodes).
As per claim 49, the rejection of claim 32 is incorporated and the combination further teaches wherein the machine learning model is part of an Internet of things (IoT) learning system and in dynamically identifying the change in the state of the leader computing device, the computing device is configured to perform operations comprising detecting the change in the state of the leader computing device in the IoT learning system that affects current performance or future performance of the leader computing device (e.g. Yang, in paragraphs 71-73 and 81, “no longer being adequate for performing the operations required of a leader node… judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria… leader node to stop meeting the preset criteria. In this case, a new leader node can be selected”; Prakash, in paragraphs 42, 53, 56, 261, and 271, “a MEC system 200 may execute machine learning (ML) functionalities, namely training process (model) β, and distributes different computational tasks β.sub.1, β.sub.2, β.sub.3, β.sub.d-1, and β.sub.d (where d is a number) of the training process (model) β to different heterogeneous compute nodes, including UE… IoT UE(s)”).
As per claim 50, the rejection of claim 49 is incorporated and the combination further teaches wherein the IoT learning system comprises one of a massive machine type communication (mMTC) learning system or a critical machine type communication (cMTC) learning system and in dynamically identifying the change in the state of the leader computing device, the computing device is configured to perform operations comprising dynamically identifying the change in the state of the leader computing device in the one of the mMTC learning system or the cMTC learning system that affects current performance or future performance of the leader computing device (e.g. Yang, in paragraphs 71-73 and 81, “no longer being adequate for performing the operations required of a leader node… judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria… leader node to stop meeting the preset criteria. In this case, a new leader node can be selected”; Prakash, in paragraphs 42, 53, 56, 261, and 271, “a MEC system 200 may execute machine learning (ML) functionalities, namely training process (model) β, and distributes different computational tasks β.sub.1, β.sub.2, β.sub.3, β.sub.d-1, and β.sub.d (where d is a number) of the training process (model) β to different heterogeneous compute nodes, including UE… IoT UE(s)… IoT UE 101x can utilize technologies such as M2M or MTC for exchanging data… massively interconnected network wherein a number of IoT devices are in communications with each other”).
As per claim 51, the rejection of claim 1 is incorporated and the combination further teaches wherein the machine learning model is part of a vehicle distributed learning system in a geographic area and the leader computing device is a leader computing device associated with a vehicle and wherein dynamically identifying the change in the state of the leader computing device comprises detecting that the vehicle is leaving the geographic area (e.g. Yang, in paragraphs 4, 32, 42, 71-73, 81, 83, and 138, “multiple processing devices included in a distributed system… no longer being adequate for performing the operations required of a leader node… judge whether its current node state meets the preset criteria of a leader node… multiple computers that are located at one site”; Prakash, in paragraphs 24, 30, 35, 38-40, 55, 259, and 274, “sent back to the edge compute nodes to further refine the ML model” and “compute nodes…may comprise…vehicle-embedded systems or a vehicle-to-everything (V2X) devices… moving vehicles… As the transient devices 1512, 1514, and 1516, leave the vicinity of the fog 1520, it may reconfigure itself to eliminate those IoT devices 1504 from the network… a particular area or geographic feature”).
As per claim 53, the rejection of claim 32 is incorporated and the combination further teaches wherein the computing device is configured to perform operations further comprising: receiving an indication to be the new leader computing device (e.g. Yang, in paragraph 84, “voting node can send the vote information to the successor node”); receiving a latest version of the machine learning model from a current leader computing device (e.g. Prakash, in paragraph 24, “master compute node updates the reference model, and then communicates the updated reference model to all worker compute node”); and performing leader computing device operations (e.g. Yang, in paragraph 89, “electing leaders and successors can help to ensure that a currently-elected leader node is in a high-performance state for processing one or more specified services, and this can improve the service processing efficiency of the whole distributed system overall”).
As per claim 54, the rejection of claim 32 is incorporated and the combination further teaches receiving an indication to be the new leader computing device (e.g. Yang, in paragraph 84, “voting node can send the vote information to the successor node”); requesting a latest version of the machine learning model from at least one non-leader computing device (e.g. Okanohara, in paragraphs 16 and 27, “a machine learning model… a leader 302a requests local models 206 from edge devices 100 and mixes received local models”); and performing leader computing device operations (e.g. Yang, in paragraph 89, “electing leaders and successors can help to ensure that a currently-elected leader node is in a high-performance state for processing one or more specified services, and this can improve the service processing efficiency of the whole distributed system overall”).
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 20180018198 A1) in view of Prakash et al. (US 20190138934 A1).
As per independent claim 25, Yang teaches a method performed by a computing device in a plurality of computing devices for selecting a new leader computing device for operationally controlling a system in a network (e.g. in paragraphs 4 and 104, “multiple processing devices included in a distributed system… connected to the network 1130 in a distributed environment”), the method comprising:
monitoring a plurality of performance counters of a leader computing device (e.g. Yang, in paragraphs 71-73, “Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… preset criteria according to its own hardware configuration and processing capabilities… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria”);
dynamically identifying a change in a state of the leader computing device based on at least one performance counter of the plurality of performance counters (e.g. in paragraphs 42, 71-73 and 81, “election cycle can change, such as based on conditions (or predictions thereof) that may affect the reliability of leaders, including…hardware issues… no longer being adequate for performing the operations required of a leader node… judge whether its current node state meets the preset criteria of a leader node… preset criteria according to its own hardware configuration and processing capabilities… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria”);
determining whether the change in the state of the leader computing device triggers a new leader computing device to be selected based on whether a change in the at least one performance counter raises above a threshold (e.g. Yang, in paragraphs 71-73 and 81, “judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… preset criteria according to its own hardware configuration and processing capabilities… when the usage of the CPU does not exceed 80%, it may be considered that the node meets the preset criteria [i.e. if raises above 80% threshold, then does not meet criteria]… leader node to stop meeting the preset criteria. In this case, a new leader node can be selected”);
initiating a new leader election among the plurality of computing devices responsive to determining the change in the state of the leader computing device triggers a new leader computing device to be selected (e.g. in paragraphs 71-73 and 83, “Screening out a successor node can include, for example, the leader node sending a query request to other nodes to query node information of other nodes and selecting a successor node according to the node information received in response to the queries”); and
receiving an identification of the new leader computing device based on the initiating of the new leader election (e.g. in paragraph 83, “selecting a successor node according to the node information received in response to the queries”),
but does not specifically teach computing device for operationally controlling a machine learning model in a telecommunications network.
However, Prakash teaches a computing device(s) for operationally controlling a machine learning model in a telecommunications network (e.g. in paragraphs 24, 30, 35, 38-40, 55, and 57, “the master node obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity. The computed full gradient is then sent back to the edge compute nodes to further refine the ML model until the ML model converge… learning takes place by a federation of client compute nodes… smartphone… 5G RAN… LTE or 4G system… cellular communications” and figure 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yang to include the teachings of Prakash because one of ordinary skill in the art would have recognized the benefit of allowing tasks/services in well-known networks to be performed optimally, with privacy, and/or with improved reliably, and appropriately reconfiguring networks where nodes can move.
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 20180018198 A1) in view of Prakash et al. (US 20190138934 A1) and Okanohara et al. (US 20160217387 A1) as applied above, and further in view of Manamohan et al. (US 20200272934 A1).
As per claim 36, the rejection of claim 35 is incorporated and the combination further teaches wherein the computing device is the leader computing device, wherein the leader computing device is configured to perform operations further comprising: transmitting a latest version of the machine learning model to the new leader computing device (e.g. Prakash, in paragraph 24, “master compute node updates the reference model, and then communicates the updated reference model to all worker compute node”; Yang, in paragraph 84, “the new leader node”) and withdrawing from acting as the leader computing device (e.g. Yang, in paragraphs 71-73, 81, and 87, “voting request sent by the leader node can include a node identifier of the successor node”), but does not specifically teach responsive to transmitting the latest version, withdrawing. However, Manamohan teaches responsive to transmitting a latest version, withdrawing from acting as a leader node (e.g. in paragraphs 19, 38, 40, 59, and 62, “distributed ledger 42 may store blocks that indicate a state of a node 10e relating to its machine learning during an iteration… distributed ledger 42 may store a current and historic state of a model 44… merged training parameters 15g that have been generated from the most recent iteration of model building… the master node 10g indicating that it has completed the merge, also releases its status as master node for the iteration”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Manamohan because one of ordinary skill in the art would have recognized the benefit of facilitating consistency and/or seamless transitioning.
Claim 45 is rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 20180018198 A1) in view of Prakash et al. (US 20190138934 A1) and Okanohara et al. (US 20160217387 A1) as applied above, and further in view of Kancharia et al. (US 10474497 B1).
As per claim 45, the rejection of claim 42 is incorporated and the combination further teaches wherein in dynamically identifying the change in the state of the leader computing device, the computing device is configured to perform operations further comprising determining a change of the leader computing device (e.g. Yang, in paragraphs 71-73 and 81, “no longer being adequate for performing the operations required of a leader node… judge whether its current node state meets the preset criteria of a leader node… Preset criteria of a leader node can include, but are not limited to, a load processing criterion, a data updating degree criterion, and other criteria or a combination of present criteria… leader node to stop meeting the preset criteria. In this case, a new leader node can be selected”), but does not specifically teach the change in a software version. However, Kancharia teaches a change in a software version (e.g. in column 5 line 39 – column 6 line 33 and column 8 lines 25-43, “a heartbeat message may include information relating to selecting a leader computing node to process a particular set of jobs… a version of software installed on the computing node”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Kancharia because one of ordinary skill in the art would have recognized the benefit of determining relevant processing features of the node.
Claim 46 is rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 20180018198 A1) in view of Prakash et al. (US 20190138934 A1) and Okanohara et al. (US 20160217387 A1) as applied above, and further in view of Tanada et al. (US 20030124979 A1).
As per claim 46, the rejection of claim 42 is incorporated, but the combination does not specifically teach wherein in dynamically identifying the change in the state of the leader computing device further comprises determining that the leader computing device is operating on battery power and responsive to operating on battery power, withdrawing from participating in the machine learning model. However, the combination teaches a network including a machine learning model (e.g. Prakash in paragraphs 24, 30, 35, and 38-40, “the master node obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity. The computed full gradient is then sent back to the edge compute nodes to further refine the ML model until the ML model converge… learning takes place by a federation of client compute nodes”) and Tanada dynamically identifying a change in a state of a leader computing device in a network comprising determining that the leader computing device is operating on battery power and responsive to operating on battery power, withdrawing from participating in the network (e.g. in paragraphs 14, 16-17, 29, and 33, “in the network formed with the terminal subject to switching as the new master terminal, some terminals [which can include the leader before switching] may come to fall outside of the communication range of the new master terminal, and thus communication cannot be performed among the same terminals as those in the network prior to switching [i.e. withdrawing from participating in the network]… measuring the remaining battery power of the master terminal itself… switching to a communication mode in which the radio communication device subject to switching is set to operate as a new master terminal… disconnection detection means for detecting the disconnection of the master terminal from the network following reception of the substitute designation information and network configuration information”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Tanada because one of ordinary skill in the art would have recognized the benefit of determining relevant processing features of the node and/or adapting the network based on relevant features.
Claim 48 is rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 20180018198 A1) in view of Prakash et al. (US 20190138934 A1) and Okanohara et al. (US 20160217387 A1) as applied above, and further in view of Shih (US 20180267539 A1).
As per claim 48, the rejection of claim 32 is incorporated, but does not specifically teach, as a whole, maintaining information stored in a local copy of a distributed ledger; receiving a new entry identifying the new leader computing device; and updating the local copy of the distributed ledger with the received new entry. However, Shih teaches maintaining information stored in a local copy of a distributed ledger, receiving a new entry identifying a new leader computing device, and updating the local copy of the distributed ledger with the received new entry (e.g. in paragraphs 26 and 57, “initiates communication…where a new leader may be elected… Ledgers keep track of all transactions and the respective nodes of each ledger network have a copy of an updated ledger… After the leader is elected upon waiting the allotted transaction time, the transaction is written to the ledger”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Shih because one of ordinary skill in the art would have recognized the benefit of allowing transactions to be tracked.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Critchley et al. (US 20100262717 A1) teaches “new node establishes a partnership with the discovered node and uses that partnership to learn about the presence of other new nodes participating in the federation infrastructure… routing consistency and ring consistency are applicable with respect to leader election (i.e. electing a primary node)” (e.g. in paragraphs 116 and 507).
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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/W.W/Examiner, Art Unit 2144 09/05/2026
/T.T.K/Supervisory Patent Examiner, Art Unit 2144
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