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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mahalank et al. (Mahalank) US 2021/0111985 in view of LV CN113254159, Suzuki et al. (Suzuki) US 2013/0239183 and Dzhulgakov et al. (Dzhulgakov) US 2019/0073580
In regard to claim 1, Mahalank disclose A graph network processing method, for use in a master, wherein the master, a first worker and a second worker in a distributed environment, ([0011]-[0034] leader node and worker nodes are in a distributed env. to distributing NF topology information among worker nodes. Note: please use functional language to describe the invention, intended use language do not have much patent weight, such as “for use”)
and the master, the first worker and the second worker respectively store information of some of nodes of the graph neural network, wherein the graph neural network method comprises: ([0012] [0034][0050]-[0054][0061] Fig. 5 leader SCP store the NF topology information from the worker nodes 242, and worker nodes store NF topology information in the memory 256)
receiving a first request sent from the first worker and a second request sent from the second worker, wherein the first worker sends the first request to the master to obtain at least an attribute of a first requested node, and the second worker sends the second request to the master to obtain at least an attribute of a second requested node; (Fig. 3, [0053]-[0058] receive request from the worker nodes to the leader which include the topology of the nodes, the master node send the worker nodes with the topology information (change in NF topology, etc.) of the nodes in topology connected with the leader node) wherein the first requested node and the second requested node are selected from the nodes of the graph neural network; (Fig. 3, [0053]-[0058] the nodes are belong to the NF topology)
But Mahalank fail to explicitly disclose “determining whether the first requested node and the second requested node are the same nodes and generating a determination result accordingly”
LV disclose determining whether the first requested node and the second requested node are the same nodes and generating a determination result accordingly;
(“under the condition that S2 and S1 are not the same service node, representing that there is need to perform the migration of the state service, namely discovery service migration. Therefore, entering the subsequent service migration process.”
“In summary, the migration method of state service provided by the embodiment of the invention, the first stage by judging whether the first service node and the second service node is the same node to finish the service migration discovery; … and the service calling party, namely the service side sends the response of the service request; The invention claims a migration method of the state service of the service node and the service migration of the service node in the distributed service scheduling architecture globally unique determining service migration, …by sending the response of the service request to the service calling party,...” The reference discloses the judgement that the first service node and the second service node are the same and corresponding action based on the result)
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 LV’s method of transmission control into Mahalank’s invention as they are related to the same field endeavor of network information transmission. The motivation to combine these arts, as proposed above, at least because LV’s transmission control based on the service nodes are the same or not would help to provide more transmission control method into Mahalank’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 controlling network mitigation based on the service node conditions would facilitate information distribution in the network.
But Mahalank and LV fail to explicitly disclose “and selectively performing broadcast or unicast to the first worker and the second worker, at least based on the determination result.”
Suzuki disclose determining selectively performing broadcast or unicast to the first worker and the second worker, at least based on the determination result. ([0027] [0035] [0042]-[0067] Fig. 5, broadcast or unicast the IP addresses (which represent nodes) can be determined based on the nodes are in the same sub-net or different sub-net. If the nodes are in the same sub-net which represent the same node condition.
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 Suzuki’s method of transmission control into LV and Mahalank’s invention as they are related to the same field endeavor of network information transmission. The motivation to combine these arts, as proposed above, at least because Suzuki’s transmission control using broadcast or unicast based on network configuration would help to provide more transmission control method into LV and Mahalank’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 using broadcast or unicast to transit information based on network configuration would facilitate information distribution in the network.
But Mahalank and Suzuki, LV fail to explicitly disclose “a graph neural network processing method, wherein the master, the first worker and the second worker train the graph neural network,”
Dzhulgakov disclose a graph neural network processing method, wherein the master, the first worker and the second worker train the graph neural network, ([0029]-[0049] [0056]-[0060][0124]-[0139] training a NN with master, worker nodes. Note: please use functional language to describe the invention, intended use language do not have much patent weight, such as “for use” and please further define the steps in “train”ing the graph neural network, call to discuss if necessary)
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 Dzhulgakov’s method of training network into LV, Suzuki and Mahalank’s invention as they are related to the same field endeavor of network information transmission. The motivation to combine these arts, as proposed above, at least because Dzhulgakov’s training network would help to reduce the communication cost and resources into LV, Suzuki and Mahalank’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 reducing the communication cost and resources by training the network would improve efficiency of the communication of the network nodes.
In regard to claim 2, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network processing method of claim 1, the rejection in incorporated herein.
Mahalank disclose wherein the graph neural network comprises a first node, ([0011]-[0034] leader node and worker nodes are in a distributed env. to distributing NF topology information among worker nodes, a first node can be one of the worker node) wherein the first requested node and the second requested node are both the first node, (Fig. 3, [0053]-[0058] receive request from the worker nodes to the leader which implicitly disclose to request the topology of the nodes which includes the first node, the master node send the worker nodes with the topology information (change in NF topology, etc.) of the nodes in topology connected with the leader node)
But Mahalank and Dzhulgakov fail to explicitly disclose “wherein the step of determining whether the first requested node and the second requested node are the same nodes, and generating the determination result accordingly comprises: and the step of selectively performing broadcast or unicast to the first worker and the second worker at least based on the determination result comprises;”
Suzuki disclose wherein the step of determining whether the first requested node and the second requested node are the same nodes, and generating the determination result accordingly comprises: and the step of selectively performing broadcast or unicast to the first worker and the second worker at least based on the determination result comprises;”
Suzuki disclose wherein the step of determining whether the first requested node and the second requested node are the same nodes, and generating the determination result accordingly comprises: and the step of selectively performing broadcast or unicast to the first worker and the second worker at least based on the determination result comprises; ([0027] [0035] [0042]-[0067] Fig. 5, broadcast or unicast the IP addresses (which represent nodes) can be determined based on the nodes are in the same sub-net or different sub-net. If the nodes are in the same sub-net, they are the same nodes, if the nodes are in different sub-net, they are different nodes, broadcast or unicast based on a condition) performing broadcast to the first worker and the second worker, wherein a broadcast content includes an attribute of the first node. ([0027] [0035] [0042]-[0067] Fig. 5, broadcast or unicast the IP addresses (which represent nodes) can be determined based on the nodes are in the same sub-net or different sub-net. If the nodes are in the same sub-net, they are the same nodes, if the nodes are in different sub-net, they are different nodes. broadcast the node information)
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 Suzuki’s method of transmission control into Dzhulgakov and Mahalank’s invention as they are related to the same field endeavor of network information transmission. The motivation to combine these arts, as proposed above, at least because Suzuki’s transmission control using broadcast or unicast based on network configuration would help to provide more transmission control method into Dzhulgakov and Mahalank’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 using broadcast or unicast to transit information based on network configuration would facilitate information distribution in the network.
But Mahalank, Suzuki and Dzhulgakov fail to explicitly disclose “determining that the first requested node and the second requested node are both the first node;”
LV disclose determining that the first requested node and the second requested node are both the first node; (“In summary, the migration method of state service provided by the embodiment of the invention, the first stage by judging whether the first service node and the second service node is the same node to finish the service migration discovery; … and the service calling party, namely the service side sends the response of the service request; The invention claims a migration method of the state service of the service node and the service migration of the service node in the distributed service scheduling architecture globally unique determining service migration, …by sending the response of the service request to the service calling party,...” The reference discloses the judgement that the first service node and the second service node are the same 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 LV’s method of transmission control into Suzuki, Dzhulgakov and Mahalank’s invention as they are related to the same field endeavor of network information transmission. The motivation to combine these arts, as proposed above, at least because LV’s transmission control based on the service nodes are the same or not would help to provide more transmission control method into Suzuki, Dzhulgakov, Mahalank’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 controlling network mitigation based on the service node conditions would facilitate information distribution in the network.
In regard to claim 3, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network processing method of claim 1, the rejection in incorporated herein.
Mahalank disclose wherein the master records a storage situation of attributes of a plurality of nodes in the graph neural network in the first worker and the second worker. ([0012] [0034][0050]-[0054][0061] Fig. 5 leader SCP store the NF topology information from the worker nodes 242, and worker nodes store NF topology information in the memory 256)
In regard to claim 4, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network processing method of claim 3, the rejection in incorporated herein.
Mahalank disclose wherein the graph neural network comprises a first node and a second node, wherein the first requested node is the first node, and the second requested node is the second node, (Fig. 3, [0053]-[0058] the network includes multiple worker nodes receive request from the worker nodes to the leader, the master node send the worker nodes with the topology information (or change of topology, etc.) of the nodes in topology connected with the leader node, the requesting worker nodes request the master node to send the response back to the requesters (same node))
But Mahalank, LV and Dzhulgakov fail to explicitly disclose “wherein the step of determining whether the first requested node and the second requested node are the same nodes, and generating the determination result accordingly comprises: determining that the first requested node and the second requested node are not the same nodes; and the step of selectively performing broadcast or unicast to the first worker and the second worker at least based on the determination result comprises: selectively performing broadcast or unicast to the first worker and the second worker based on a storage situation of attributes of a plurality of nodes in the graph neural network in the first worker and the second worker.”
Suzuki disclose wherein the step of determining whether the first requested node and the second requested node are the same nodes, and generating the determination result accordingly comprises: (Fig. 5, [0042]-[0059] broadcast or unicast the IP addresses (which represent nodes) can be determined based on the nodes are in the same sub-net or different sub-net. If the nodes are in the same sub-net, they are the same nodes, if the nodes are in different sub-net, they are different nodes.)
determining that the first requested node and the second requested node are not the same nodes; ( [0027] [0035] [0042]-[0067] determine based on the destination identified in the destination information the nodes are in different sub-net. if the nodes are in different sub-net, they are different nodes.)
and the step of selectively performing broadcast or unicast to the first worker and the second worker at least based on the determination result comprises: selectively performing broadcast or unicast to the first worker and the second worker based on a storage situation of attributes of a plurality of nodes in the graph neural network in the first worker and the second worker. ([0027] [0035] [0042]-[0067] Fig. 5, broadcast or unicast the IP addresses (which represent nodes) can be determined based on the nodes are in the same sub-net or different sub-net. If the nodes are in the same sub-net, they are the same nodes, if the nodes are in different sub-net, they are different nodes and it is based on the IP addresses of the nodes to determine)
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 Suzuki’s method of transmission control into LV, Dzhulgakov and Mahalank’s invention as they are related to the same field endeavor of network information transmission. The motivation to combine these arts, as proposed above, at least because Suzuki’s transmission control using broadcast or unicast based on network configuration would help to provide more transmission control method into LV, Dzhulgakov and Mahalank’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 using broadcast or unicast to transit information based on network configuration would facilitate information distribution in the network.
In regard to claim 5, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network processing method of claim 4, the rejection in incorporated herein.
Mahalank disclose wherein the first worker stores an attribute of the second node, and the second worker stores an attribute of the first node, ([0012] [0034][0050]-[0054][0061] Fig. 5 leader SCP store the NF topology information from the worker nodes 242, and worker nodes store NF topology information in the memory 256 which include all nodes in the topology)
notify to the first worker and the second worker, wherein a broadcast content includes a summation result of the attribute of the first node and the attribute of the second node. (Fig. 3, [0053]-[0058] the master node send the worker nodes with the topology information (change in NF topology, etc.) of the nodes in topology connected with the leader node)
But Mahalank, LV and Dzhulgakov fail to explicitly disclose “the step of selectively performing broadcast or unicast to the first worker and the second worker based on the storage situation of attributes of the plurality of nodes in the graph neural network in the first worker and the second worker comprises: performing broadcast to the first worker and the second worker.”
Suzuki disclose the step of selectively performing broadcast or unicast to the first worker and the second worker based on the storage situation of attributes of the plurality of nodes in the graph neural network in the first worker and the second worker comprises: performing broadcast to the first worker and the second worker. ([0027] [0035] [0042]-[0067] Fig. 5, broadcast or unicast the IP addresses (which represent nodes) can be determined based on the nodes are in the same sub-net or different sub-net. If the nodes are in the same sub-net, they are the same nodes, if the nodes are in different sub-net, they are different nodes and it is based on the IP addresses of the nodes to determine)
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 Suzuki’s method of transmission control into LV, Dzhulgakov and Mahalank’s invention as they are related to the same field endeavor of network information transmission. The motivation to combine these arts, as proposed above, at least because Suzuki’s transmission control using broadcast or unicast based on network configuration would help to provide more transmission control method into LV, Dzhulgakov and Mahalank’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 using broadcast or unicast to transit information based on network configuration would facilitate information distribution in the network.
In regard to claim 6, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network processing method of claim 4, the rejection in incorporated herein.
Mahalank disclose wherein the first worker does not store the attribute of the second node, and the second worker does not store the attribute of the first node, (Fig. 3, [0011]-[0024] [0034] [0053]-[0058] receive request from the worker nodes to the leader, the master node send the worker nodes with the topology information (change in NF topology, etc.) of the nodes in topology connected with the leader node after the worker nodes registered to the leader nodes, before that there is no nodes information stored ) and the step of selectively performing broadcast or unicast to the first worker and the second worker based on the storage situation of attributes of the plurality of nodes in the graph neural network in the first worker and the second worker comprises: performing a first unicast to send the attribute of the first node to the first worker, and performing a second unicast to send the attribute of the second node to the second worker. (Fig. 3, [0011]-[0024] [0034] [0047] [0053]-[0058] the master node send the worker nodes with the topology information (change in NF topology, etc.) of all nodes in topology connected with the leader node through unicast, such as MPTCP protocol which is a unicast)
In regard to claim 7, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network processing method of claim 4, the rejection in incorporated herein.
Mahalank disclose wherein the first worker stores the attribute of the second node, and the second worker does not store the attribute of the first node, (Fig. 3, [0011]-[0024] [0034] [0053]-[0058] receive request from the worker nodes to the leader, the master node send the worker nodes with the topology information (change in NF topology, etc.) of the nodes in topology connected with the leader node) after the worker nodes registered to the leader nodes, after the first worker node registered, the information of the second node is stored, but the if the first node is not registered, the first node information is not stored) and the step of selectively performing broadcast or unicast to the first worker and the second worker based on the storage situation of attributes of the plurality of nodes in the graph neural network in the first worker and the second worker comprises:
performing a first unicast to send the attribute of the first node to the first worker, and performing a second unicast to send the attribute of the second node to the second worker. (Fig. 3, [0011]-[0024] [0034] [0047] [0053]-[0058] the master node send the worker nodes with the topology information (change in NF topology, etc.) of all nodes in topology connected with the leader node through unicast, such as MPTCP protocol which is a unicast based on the request which is different request and different response)
In regard to claim 8, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network processing method of claim 6, the rejection in incorporated herein.
Mahalank disclose wherein the first unicast and the second unicast are not performed simultaneously. (Fig. 3, [0011]-[0024] [0034] [0047] [0053]-[0058] the master node send the worker nodes with the topology information (change in NF topology, etc.) of all nodes in topology connected with the leader node through unicast, such as MPTCP protocol which is a unicast protocol based on the request and the requssts are different requests with different responses)
In regard to claims, claims 9-15 are graph NN machine claims corresponding to the method claims 1-3, 4+5, 6-8 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-3, 4+5, 6-8.
In regard to claim 16, claim 16 is a graph NN system claim corresponding to the method claim 9 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 9.
In regard to claim 17, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network system of claim 16, the rejection in incorporated herein.
Mahalank disclose further comprising: a second machine, comprising a second graph neural network machine for use as the first worker, (Fig. 5, [0035] multiple computing platforms or devices) wherein the second graph neural network machine comprises: an input storage device, (Fig. 5, [0061]-[0062] memory) configured to store a broadcast content sent from the master, (Fig. 5, [0061]-[0062] with NF topology information distribution) wherein the first worker sends the first request to the master to obtain at least an attribute of the first requested node, and the first worker stores the attribute of the second node; Fig. 3, [0053]-[0058] receive request from the worker nodes to the leader, the master node send the worker nodes with the topology information (change in NF topology, etc.) of the nodes in topology connected with the leader node and the topology information is stored at memory) and a controller, coupled to the input storage device, wherein the controller subtracts the attribute of the second node from the broadcast content to obtain the attribute of the first node. (Fig. 4, 5, [0051]-[0062] processor and couple with the memory, the node information of the other node is retrieved form the change of topology information received for example, the second node is deregistered, then second node information need to be removed from the topology and the first node information need to be updated if connected with second node before, Note: please further clarify the process to help move forward the prosecution, and please make the claim set the same as 1 and 9)
In regard to claim 18, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network system of claim 17, the rejection in incorporated herein.
Mahalank disclose wherein the first machine and the second machine are arranged in a same device or different devices. (Fig. 5, [0035] multiple computing platforms or devices or the single device)
In regard to claim 19, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network system of claim 18, the rejection in incorporated herein.
Mahalank disclose wherein the first machine and the second machine are two different cores in a same device or different devices. (Fig. 5, [0035] multiple computing platforms or devices or the single device)
In regard to claim 20, Mahalank, LV, Suzuki and Dzhulgakov disclose The graph neural network system of claim 18, the rejection in incorporated herein.
Mahalank disclose wherein the first machine and the second machine run two different processes on a same device or different devices. (Fig. 2,4, 5, [0035] [0050]-[0051] multiple computing platforms or devices or the single device run at different regions)
Response to Arguments
Applicant’s arguments with respect to claims 1-20 filed on 7/2/2025 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.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US20220230053 A1 2022-07-21 BETTHAUSER et al.
GRAPH NEURAL NETWORK FOR SIGNAL PROCESSING
BETTHAUSER et al. disclose Creating a machine learning graph neural network configured to process signals. A method includes identifying a plurality of machine learning graphs where each of the machine learning graphs are for different types of data. The method further includes receiving input identifying shared content of different machine learning graph nodes from different graphs in the plurality of machine learning graphs. The method further includes creating a combined machine learning graph neural network, configured to process signals, using the plurality of machine learning graphs based on the shared content, the combined machine learning graph neural network comprising nodes corresponding to nodes in the plurality of machine learning graphs such that output from the combined machine learning graph neural network comprises outputs generated based on relationships of nodes in the combined machine learning graph corresponding to nodes in different machine learning graphs in the plurality of machine learning graphs… see abstract.
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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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143