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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/26/2026 has been entered.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Duan US 2024/0281719 in view Wang et al. (Wang) US 2022/0269976 and BahenaTapia et al. (BahenaTapia) US 20200364607
In regard to claim 1, Duan disclose A device, comprising: (Fig. 1, [0106] [0204] device 102)
a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: (Fig. 1,[0106]-[0108] [0204]-[0208] a memory, a processor and program)
providing a model to a group of nodes of a communication network, (Fig. 1, [0202]-[0208] providing by a first network function entity a global ML model to one or more UEs through an application )
receiving, from a first node of the group of nodes, updated model parameters, wherein the updated model parameters are generated by the first node via training of a local model utilizing the model and local data accessible to the first node resulting in local models, wherein the device does not receive the local data; (Fig. 1, [0004][0201]-[0208][0216] retraining the local ML model based on local training data from user equipment 101 and send local model parameters of the trained local ML model from user equipment 101 to the first network function entity 102, while protecting the privacy of the local user data of the UE)
generating an updated model based on the updated model parameters, (Fig. 1, [0201]-[0208] updating the global ML model based on the received local model parameters and updated the local ML model)
storing at least one of the updated model or the updated model parameters in a blockchain database; ([0277]-[0278] [0304]-[0315] the model or the global model parameters are stored at a database, Note: the information stored at a blockchain database is an implementation choice, but not an invention) and
providing the updated model to the group of nodes of the network. ([0201]-[0208] UEs obtain information on a second model file of the updated global ML model from 102 and update the local ML model)
But Duan fail to explicitly disclose “the model being associated with determining a configuration of the communication network for at least one of distribution or usage of qubits; the updated model being associated with determining the configuration of the network for at least one of distribution or usage of qubits;”
Wang disclose the model being associated with determining a configuration of the communication network for at least one of distribution or usage of qubits; the updated model being associated with determining the configuration of the network for at least one of distribution or usage of qubits; ([0112] [0170]-[0180] [0280] [0346] the ML model related to network configuration such as distribute entangled qubits, quantum statistics, etc. )
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
But Duan and Wang disclose fail to explicitly disclose “and wherein the updated model parameters are generated in response to identifying an occurrence of a first network event comprising an abnormal change in demand and/or capacity of the communication network; upon an occurrence of a second abnormal network event similar to or the same as the first network event;”
BahenaTapia disclose and wherein the updated model parameters are generated in response to identifying an occurrence of a first network event comprising an abnormal change in demand and/or capacity of the communication network; ([0095]–[0105] [0131]-[0133] when the abnormal resource demand increase or decrease is identified, it triggers a responsive operation, such as resource configurations are updated)
upon an occurrence of a second abnormal network event similar to or the same as the first network event; ([0028] [0029] ([0095]–[0105] [0131]-[0133] identify the occurrence of the abnormal event based on the specific recurring pattern or behavior with threshold to identify the similar event)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate BahenaTapia‘s anomaly detection model into Wang and Duan’s invention as they are related to the same field endeavor of model training and learning related to system monitoring using ML. The motivation to combine these arts, as proposed above, at least because BahenaTapia‘s anomaly detection model would help to provide action trigger condition related ML update into Wang and Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing action trigger condition related ML update would facilitate model training.
In regard to claim 2, Duan and Wang, BahenaTapia disclose The device of claim 1,
But Duan, Kim fail to explicitly disclose “wherein the determining the configuration of the communication network comprises determining network devices for distribution of entangled qubits of the qubits.”
Wang disclose wherein the determining the configuration of the communication network comprises determining network devices for distribution of entangled qubits of the qubits.
([0004]-[0006] [0112] [0170]-[0180] [0280] [0346] determine the quantum path between the nodes to distribute the entangled qubits, quantum statistics, etc. )
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 3, Duan and Wang, BahenaTapia disclose The device of claim 2,
But Duan, BahenaTapia fail to explicitly disclose “wherein at least a portion of the entangled qubits of the network devices operate as reserve distributed compute capacity that is accessible when an event threshold is satisfied.”
Wang disclose wherein at least a portion of the entangled qubits of the network devices operate as reserve distributed compute capacity that is accessible when an event threshold is satisfied. ([0156]-[0175][0179]-[0183] [0191]-[0222] [0343]-[0347] [0388]-[0389] trigger to solicit quantum capabilities, for example, if the fidelities become below a threshold, the entanglement distillation maybe be employed to the one or more entangled pairs by consuming and /or sacrificing other entangled pairs and “may not operate on and/or consume one or more of the entangled pairs (e.g. all the entangled pairs) to have one pair with fidelity greater than “minFidelity” so the quantum capability is reserved)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 4, Duan and Wang, BahenaTapia disclose The device of claim 1,
Duan disclose wherein the local model employ quantum federated reinforcement learning. ([0004] [0169] [0200]-[0220] [0387] the local model is a federated reinforcement learning and the cycle of generating the local model is repeated several times, and a better model for the network analysis is finally obtained)
In regard to claim 5, Duan and Wang, BahenaTapia disclose The device of claim 1,
But Duan, BahenaTapia fail to explicitly disclose “wherein the local model employ quantum computing of graph adjacency matrices.”
Wang disclose wherein the local model employ quantum computing of graph adjacency matrices. (Fig. 2, ([0112] [0149][0170]-[0180] [0201]-[0216] [0280] [0346] the ML model related to network configuration of quantum computing such as distribute entangled qubits, quantum statistics, etc. and report network neighbor and topology information to QNM)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 6, Duan and Wang, BahenaTapia disclose The device of claim 1,
Duan disclose wherein the local data is not shared with other nodes of the group of nodes. (Fig. 1, [0004] [0201]-[0208] [0216] retraining the local ML model based on local training data from UEs and send local model parameters of the trained local ML model to the first network function entity 102, while protecting the privacy of the local user data of the UEs)
In regard to claim 7, Duan and Wang, BahenaTapia disclose The device of claim 1,
But Duan, BahenaTapia fail to explicitly disclose “wherein a quantum-classical graph blockchain database stores one of Smart contract ledgers, network topology, network performance parameters or a combination thereof, which is accessible to the first node of the group of nodes.”
Wang disclose wherein a quantum-classical graph blockchain database stores one of Smart Contract ledgers, network Topology, network performance parameters or a combination thereof, which is accessible to the first node of the group of nodes. ([0187]-[0192] ([0199]-[0216] third party database store network neighbor and topology information etc. and accessible to the node)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 8, Duan and Wang, BahenaTapia disclose The device of claim 1,
But Duan and BahenaTapia fail to explicitly disclose “wherein at least one of the updated model or the local model employs a quantum-classical graph neural network to estimate network capacity metrics for a network topology, routing, traffic measurements, qubit memory, and optimized qubit bandwidth storage of the communication network, wherein normalized quantum key performance indicators (QKPIs) for a target network and QKPIs from other networks with selected topology and performance metrics are utilized as model training data for the quantum-classical graph neural network.”
Wang disclose wherein at least one of the updated model or the local models employs quantum-classical graph neural network to estimate network capacity metrics for a network topology, routing, traffic measurements, qubit memory, and optimized qubit bandwidth storage of the communication network, wherein normalized quantum key performance indicators (QKPIs) for a target network and QKPIs from other networks with selected topology and performance metrics are utilized as model training data for the quantum-classical graph neural network.([0067] [0094]-[0108] [0140] [0156] [0177]-[0189] [0268]-[0277] [0388]-[0389] the ML model estimate the network capacity metric ( resource/capabilities, fidelity, etc.) for the network topology, routing, traffic (QOS), quantum memory and bandwidth of the network and performance data for the node and metric data from other networks (QNRs/QNTs) are used as training data)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 9, Duan and Wang, BahenaTapia disclose The device of claim 1,
But Duan, BahenaTapia fail to explicitly disclose “wherein the operations include executing superdense coding quantum communications protocols for communicating classical bits of information by only transmitting a smaller number of qubits between the device and a quantum-classical network element.”
Wang disclose wherein the operations include executing superdense coding quantum communications protocols for communicating classical bits of information by only transmitting a smaller number of qubits between the device and a quantum-classical network element. ([0064]-[0066] [0090]-[0096] [0144]-[0150] using quantum superdense coding communications protocol for communicating classical bits of information by transmitting one or more qubits between the nodes)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 10, Duan and Wang, BahenaTapia disclose The device of claim 1,
Duan and BahenaTapia fail to explicitly disclose “wherein the local data includes bandwidth storage, time, distance, and latency, and wherein the model and the updated model employs explainable machine learning.”
Wang disclose wherein the local data includes bandwidth storage, time, distance, and latency, and wherein the model and the updated model employs explainable machine learning. ([0004]-[0006] [0045]-[0055] [0067][0094]-[0096] [0154] [0179] [0287]-[0288] the data may have QOS requirements, latency, bandwidth, distance, etc. and the using ML and AI on the reported data so the QNM with the intelligence to be able to select best paths)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 11, Duan and Wang, BahenaTapia disclose The device of claim 1,
But Duan and BahenaTapia fail to explicitly disclose “wherein one or more nodes of the group of nodes produce local location tokens and user equipment (UE) applications produce trusted identity tokens.”
Wang disclose wherein the one or more nodes of the group of nodes produce local location tokens and user equipment (UE) applications produce trusted identity tokens. ([0077] [0150]-[0157] provide location information by the node and application generate QKD quantum key distribution)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 12, Duan and Wang, BahenaTapia disclose The device of claim 1,
But Duan and BahenaTapia fail to explicitly disclose “wherein the determining the configuration of the communication network comprises adjusting network traffic based on stored capacity and network topology between low latency channels sharing Bell State pairs and alternate path high latency channels.”
Wang disclose wherein the determining the configuration of the communication network comprises adjusting network traffic based on stored capacity and network topology between low latency channels sharing Bell State pairs and alternate path high latency channels. (Fig. 4, [0067] [0090]-[0106] [0138]-[0143] [0151] [0179]-[0183] [0256]-[0277] the network traffic can be routed and controlled based on the bandwidth and topology between low latency channels sharing Bell State pairs and other path with high latency based on latency requirement)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia and Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 13, Duan and Wang, BahenaTapia disclose The device of claim 1,
But Duan and BahenaTapia fail to explicitly disclose “wherein the determining the configuration of the communication network comprises identifying one of network elements, aerial devices, user devices or a combination thereof that can operate as network devices for distribution of entangled qubits of the qubits.”
Wang disclose wherein the determining the configuration of the network comprises identifying one of communication network elements, aerial devices, user devices or a combination thereof that can operate as network devices for distribution of entangled qubits of the qubits. ([0171]-[0179] trigger the quantum link layer of a node to distribute the one or more entangled qubit to other quantum network node) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 14, Duan disclose A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor of a network node, facilitate performance of operations, the operations comprising: (Fig. 1, [0060]-[0061] [0106]-[0108] [0204]-[0208] a memory, a processor and program, UE)
receiving a model from a global node of a communication network, (Fig. 1, [0202]-[0208] providing by a first network function entity a global ML model to one or more UEs through an application )
obtaining local data associated with a portion of the communication network that is associated with the network node; (Fig. 1, [0062][0063] [0201]-[0208] determine the local training data related to a target application from the UE)
training a local model utilizing the model and the local data; (Fig. 1, [0062][0063] [0201]-[0208] training the local ML model based on local training data from UEs and the received global model)
determining updated model parameters according to the training; (Fig. 1, [0201]-[0208] determine the local model parameters of the trained local ML model according to the training)
providing the updated model parameters to the global node, wherein the global node generates an updated model based on the updated model parameters, and
receiving the updated model from the global node. (Fig. 1, [0201]-[0208] send local model parameters of the trained local ML model to the first network function entity 102, and the UE obtain information on the second model file of the updated global ML model from 102)
But Duan fail to explicitly disclose “the model being associated with determining a configuration of the communication network for qubits; the updated model being associated with determining the configuration of the communication network for the qubits;”
Wang disclose the model being associated with determining a configuration of the communication network for qubits; the updated model being associated with determining the configuration of the communication network for the qubits; ([0112] [0170]-[0180] [0280] [0346] the ML model related to network configuration such as distribute entangled qubits, quantum statistics, etc. )
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
But Duan and Wang fail to explicitly disclose “training the model in response to identifying a first occurrence of a network emergency condition comprising an abnormal change in demand and/or capacity of the communication network; based upon an occurrence of a second occurrence of a network emergency condition substantially similar to or the same as the first occurrence;”
BahenaTapia disclose training the model in response to identifying a first occurrence of a network emergency condition comprising an abnormal change in demand and/or capacity of the communication network; ([0095]–[0105] [0131]-[0133] training the model when the abnormal resource demand increase or decrease is identified, it triggers a responsive operation, such as resource configurations are updated)
based upon an occurrence of a second occurrence of a network emergency condition substantially similar to or the same as the first occurrence; ([0028] [0029] ([0095]–[0105] [0131]-[0133] identify the occurrence of the abnormal event based on the specific recurring pattern or behavior with threshold to identify the similar event)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate BahenaTapia‘s anomaly detection model into Wang and Duan’s invention as they are related to the same field endeavor of model training and learning related to system monitoring using ML. The motivation to combine these arts, as proposed above, at least because BahenaTapia‘s anomaly detection model would help to provide action trigger condition related ML update into Wang and Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing action trigger condition related ML update would facilitate model training.
In regard to claims 15-17, claims 15-17 are medium claims corresponding to the device claims 1+2, 3, 11 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1+2, 3, 11.
In regard to claim 18, Duan and Wang, BahenaTapia disclose The non-transitory machine-readable medium of claim 14,
But Duan, BahenaTapia fail to explicitly disclose “wherein the operations further comprise applying a quantum-classical graph neural network quantum machine learning process to determine low hybrid classical/quantum network utilization and distributing entangled qubits to network devices based on node resource capacity.”
Wang disclose wherein the operations further comprise applying a quantum-classical graph neural network quantum machine learning process to determine low hybrid classical/quantum network utilization and distributing entangled qubits to network devices based on node resource capacity. ([0067] [0090]-[0106] [0138]-[0143] [0151] [0171]-[0183] [0256]-[0277] [281]-[302] the network traffic can be routed and controlled based on the bandwidth and topology based on capabilities and trigger the quantum link layer of a node to distribute the one or more entangled qubit to other quantum network node based on the available resource)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into BahenaTapia, Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into BahenaTapia, Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
In regard to claim 19, Duan disclose A method, comprising: (Fig. 1, [0005]-[0010] [0106] [0204] method)
providing, by a global node, a model to a group of nodes of a communication network, (Fig. 1, [0202]-[0208] providing by a first network function entity a global ML model to one or more UEs through an application )
receiving, by the global node from one or more nodes of the group of nodes, updated model parameters, wherein the updated model parameters are generated by each of the one or more nodes via training of a local model utilizing the model and local data accessible to the particular node of the one or more nodes resulting in local nodes;
(Fig. 1, [0201]-[0208] retraining the local ML model based on local training data from UEs and send local model parameters of the trained local ML model to the first network function entity 102, while protecting the privacy of the local user data of the UEs)
generating, by the global node, an updated model based on the updated model parameters, (Fig. 1, [0201]-[0208] updating the global ML model based on the received local model parameters and updated the local ML model) and
providing, by the global node, the updated model to the group of nodes of the communication network, ([0201]-[0208] UEs obtain information on a second model file of the updated global ML model from 102 and update the local ML model)
But Duan fail to explicitly disclose “the model being associated with determining a configuration of the communication network for qubits; the updated model being associated with determining the configuration of the communication network for the qubits; wherein the updated model facilitates managing distribution and usage of entangled qubit storage in devices of the communication network as reserve hybrid quantum-classical network capacity for bandwidth and computing.”
Wang disclose the model being associated with determining a configuration of the communication network for qubits; the updated model being associated with determining the configuration of the communication network for the qubits; wherein the updated model facilitates managing distribution and usage of entangled qubit storage in devices of the communication network as reserve hybrid quantum-classical network capacity for bandwidth and computing. ([0055] [0089]-[0096] [0112] [0156] [0170]-[0187] [0280]-[0287] [0326] [0346] the ML model related to network configuration such as distribute entangled qubits, quantum statistics, etc. and helpful to manage quantum capabilities of the one or more quantum nodes, distributing entangled qubits, usage of entangled qubit, etc. stored at the network and to support limited bandwidth)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wang‘s managing quantum networks into Duan’s invention as they are related to the same field endeavor of model training and learning related to network management. The motivation to combine these arts, as proposed above, at least because Wang‘s managing quantum networks using ML would help to provide more quantum network related ML into Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more quantum network related ML would help to manage the quantum capabilities of the quantum nodes within the quantum network.
But Duan and Wang disclose fail to explicitly disclose “wherein the updated model parameters are generated in response to determining a first network emergency event comprising an abnormal change in demand and/or capacity of the communication network; when a second network emergency event similar to or the same as the first network emergency event occurs; during the second network emergency event.”
BahenaTapia disclose wherein the updated model parameters are generated in response to determining a first network emergency event comprising an abnormal change in demand and/or capacity of the communication network; ([0095]–[0105] [0131]-[0133] when the abnormal resource demand increase or decrease is identified, it triggers a responsive operation, such as resource configurations are updated)
when a second network emergency event similar to or the same as the first network emergency event occurs; during the second network emergency event. ([0028] [0029] ([0095]–[0105] [0131]-[0133] identify the occurrence of the abnormal event based on the specific recurring pattern or behavior with threshold to identify the similar event)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate BahenaTapia‘s anomaly detection model into Wang and Duan’s invention as they are related to the same field endeavor of model training and learning related to system monitoring using ML. The motivation to combine these arts, as proposed above, at least because BahenaTapia‘s anomaly detection model would help to provide action trigger condition related ML update into Wang and Duan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing action trigger condition related ML update would facilitate model training.
In regard to claim 20, claim 20 is a method claim corresponding to the device claims 1+6+13 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1+6+13.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 filed on 2/26/2026 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20170186307 A1 2017-06-29 KIM
PERSONAL PROTECTION SERVICE SYSTEM AND METHOD
KIM disclose a personal protection service and method, comprise: a user terminal requesting a personal protection service by transmitting basic user information, a situation setting message, and etc., when an emergency arises; a personal protection service server connecting to the user terminal through a mobile communication network to provide an Individual Safety Guard service; and a terminal of an institution connected to the personal protection service server and a social safety network. When an emergency arises, a user who encounters danger presses an emergency button on a terminal to immediately request to the personal protection service server that the emergency be transmitted to the terminal of a guardian or an institute linked to the social safety network to respond within a short period of time, and the location of the user terminal requesting rescue is tracked so that safety personnel can be immediately dispatched to provide safety… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143