DETAILED ACTION
Notice of 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 .
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.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 28 Apr 2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11 July 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Regarding unamended claim 17, the Reply contends “Dechene does not show or suggest simulating a change and then implementing the simulated change on the network.” Reply, 14 (emphasis in original). Specifically, the Reply finds that the “implementation’ cited by the examiner is merely deployment of a trained model to a control service that will determine future routing for all the system nodes.” Ibid. This argument was addressed on page 3 of the Final Rejection. Additionally, this argument implies that the deployment of Dechene’s trained model will result in no changes to the network. The Examiner believes this implication is unreasonable. The purpose of the invention of Dechene is to train a model “such that it can optimize decisions in real-world scenarios.” Dechene, ¶83. The optimization of future routing for all system nodes would result in an implemented “change” in the actual network.
Regarding unamended claim 17, the Reply also finds “the Dechene simulation is not responsive to resolve an actual problem detected on network.” Reply, 14 (emphasis in original). It is unclear how this alleged failure of Dechene relates to the claimed invention. A resolution of a network problem is not required in claim 17.
Regarding claim 9, the Reply finds Dechene seeks to “scale down the Dechene simulated network, not enhance it.” Reply, 13 (emphasis in original). As a result, the Reply contends the software control system that is trained in Dechene “cannot implement” allocating additional computing resources, as required by claim 8. Ibid. This argument fails to consider the “positive conditions” that may be introduced when training the AI model. Examples of a positive condition are “introducing a new host or network node, improvement to throughput, or an otherwise favorable change to network conditions.” Dechene, ¶105. As a result, the Examiner does not find the simulation of Dechene being limited to only removing or scaling down the network elements in the digital twin.
Regarding claim 9, the Reply also “points out that the cited portions of Griffiths merely describe representing a network element as a virtual network entry in a database” and “does not show or suggest implementing a simulated change that allocates additional hardware resources to a target node.” Reply, 13. The Examiner disagrees with this description of Griffiths. In particular, Griffiths is not limited to only virtual simulations of network elements, but also communicates with actual network elements. Griffiths, ¶¶32, 40. The simulations in Griffiths was noted in the Final Rejection to demonstrate its similarity to the claimed invention, which also utilizes simulations.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because they are directed to a product that does not have a physical or tangible form. Specifically, the “AI network traffic simulator comprising computer executable instructions” is software per se.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 5, 7, 10, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Filsfils (US 20200204469) in view of Dechene (US 20220245462) and further in view of Moyes (US 20090016355).
Regarding claim 1, Filsfils teaches an artificial intelligence (“AI”) method for dynamically rerouting network traffic on a network, the method comprising extracting computer readable instructions stored on a non-transitory medium and executing the computer readable instructions on a processor, wherein execution of the computer readable instructions by the processor:
detects a threshold transmission latency on the network (Filsfils, ¶49 – detects when delay metrics in paths of a network exceed a threshold);
increases network traffic by activating data packet tracing (Filsfils, ¶¶32, 34 – probe packets sent over paths in the network);
based on the data packet tracing, identifies a bottleneck within the network (Filsfils, ¶¶37, 49 – controller determines the delay measurements for all end-to-end paths using probe packets; Filsfils, ¶50 – a candidate path that exceeds the threshold for delay is a bottleneck);
in response to detecting the bottleneck, . . . [executes] a change to the network that alleviates the bottleneck. Filsfils, ¶¶50, 58 (switchover to another candidate path is triggered when delay exceeds a threshold of a path).
Filsfils does not explicitly teach (1) “simulate[]” a change or “based on the simulated change, implements the change on the network” or (2) “wherein the change to the network comprises imposing a hard-coded routing path for network traffic between two nodes.” However, Dechene teaches (1) a digital twin of a real computer network. Dechene, ¶¶73, 80. The routing model generated using the digital twin can then be deployed in the real computer network. Id. at ¶171. At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to use the probe packets, taught by Filsfils, when creating a digital twin of the network, as taught by Dechene, in order to provide the AI reinforcement model with real-world topology information, so that it can optimize its decisions in real-world scenarios. Id. at ¶83.
The combination of Filfils and Dechene does not explicitly teach (2) “wherein the change to the network comprises imposing a hard-coded routing path for network traffic between two nodes.” However, Moyes teaches hardcoded routing tables for network devices. Moyes, ¶7. The routing tables are used when the packet, received by a device, is destined for another device. Id. at ¶5 (forwards packet over link that connects the two nodes). At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to hard-code the routing paths, as taught by Moyes, for communication between the nodes in the network taught by the combination of Filsfils and Dechene in order to prevent deadlock when a network node is initialized. Id. at ¶¶6-7.
Regarding claim 3, the combination of Filsfils, Dechene, and Moyes also detects the threshold transmission latency by:
determining a final destination of a first data packet generated by an edge node on the network (Filsfils, ¶23 – ultimate final destination is second CE node 110);
determining a geodesic network path from the edge node to the final destination (Filsfils, ¶24 – CSPF algorithm identifies shortest path through multiple segments of the network for a path to CE node 110);
determining, based on the data packet tracing, whether the first data packet is being routed along the geodesic network path (Filsfils, ¶49 – controller determines the delay measurements for all end-to-end paths using probe packets; Filsfils, ¶¶50, 58 - switchover to another candidate path is triggered when delay exceeds a threshold of the current path [i.e. it is not the geodesic path]); and
in response to detecting that the first data packet is not being routed along the geodesic network path, imposing a fixed routing pathway for a second data packet generated by the edge node such that the second data packet is transmitted along the geodesic network path. Filsfils, ¶¶50, 58 (switchover to another candidate path is triggered when delay exceeds a threshold of the current path [e.g. if path 1’s delay exceeds a threshold, the network may switch to path 6]).
Regarding claim 5, the combination of Filsfils, Dechene, and Moyes also simulates the change to the network by simulating bypass of a target node in a digital twin environment. Dechene, ¶74 (digital twin can create training scenarios where a switch goes offline, which requires the other switches to no longer forward packets to it [i.e. bypass]).
Regarding claim 7, the combination of Filsfils, Dechene, and Moyes also builds a digital twin of the network based on the data packet tracing. Dechene, ¶77 (builds digital twin by capturing the topology of the operational network); Filsfils, ¶¶32, 34 (probe packets sent over paths to define the network).
Regarding claim 10, Filsfils teaches an artificial intelligence (“AI”) network traffic simulator comprising computer executable instructions, that when executed by a processor on a computer system:
detect a threshold transmission latency on a network (Filsfils, ¶49 – detects when delay metrics in sections of a network exceed a threshold);
increase network traffic by activating data packet tracing on the network (Filsfils, ¶¶32, 34 – probe packets sent over paths in the network);
based on the data packet tracing, detect a circuitous data flow within the network (Filsfils, ¶49 – controller determines the delay measurements for all end-to-end paths using probe packets; Filsfils, ¶50 – a candidate path that exceeds the threshold for delay is a circuitous path);
in response to detecting the circuitous data flow, . . . [execute] a change to the network that alleviates the circuitous data flow . . . [and] reduced the threshold transmission latency. Filsfils, ¶¶50, 58 (switchover to another candidate path is triggered when delay exceeds a threshold of a path).
Filsfils does not explicitly teach (1) “simulate” a change or “based on the simulated change, apply the change on the network” or (2) “wherein the change to the network comprises imposing a hard-coded routing path for network traffic between two nodes.” However, Dechene teaches (1) a digital twin of a real computer network. Dechene, ¶73. In the digital twin, the user can execute both positive and negative scenarios [i.e. simulations] that change how traffic is routed. Dechene, ¶105. Ultimately, the routing model generated using the digital twin can then be deployed in the real computer network. Id. at ¶171. At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to use the probe packets, taught by Filsfils, when creating a digital twin of the network, as taught by Dechene, in order to provide the AI reinforcement model with real-world topology information, so that it can optimize its decisions in real-world scenarios. Id. at ¶83.
The combination of Filfils and Dechene does not explicitly teach (2) “wherein the change to the network comprises imposing a hard-coded routing path for network traffic between two nodes.” However, Moyes teaches hardcoded routing tables for network devices. Moyes, ¶7. The routing tables are used when the packet, received by a device, is destined for another device. Id. at ¶5 (forwards packet over link that connects the two nodes). At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to hard-code the routing paths, as taught by Moyes, for communication between the nodes in the network taught by the combination of Filsfils and Dechene in order to prevent deadlock when a network node is initialized. Id. at ¶¶6-7.
Regarding claim 12, the combination of Filsfils, Dechene, and Moyes also teaches wherein the change to the network forces the second intermediary node to route data packets generated by the target node. Filsfils, ¶¶50, 58 (switchover to another candidate path is triggered when delay exceeds a threshold of a path [e.g. if path 1’s delay exceeds a threshold, the network may switch to path 6]).
Regarding claim 13, the combination of Filsfils, Dechene, and Moyes also teaches the computer executable instructions, when executed by the processor on the computer system:
based on the data packet tracing, builds a digital twin of the network (Dechene, ¶¶77, 80 – builds a digital twin of the operational network based on the capabilities of each node in the network; Dechene, ¶¶91-92 – network traffic is captured and then synthetic flows of the network traffic is used in the digital twin);
on the digital twin, simulates the change to the network that alleviates the circuitous data flow (Dechene, ¶105 – in the digital twin, the user can execute both positive and negative scenarios that change how traffic is routed); and
based on a response of the digital twin to the simulated change, deploys the change on the network. Dechene, ¶171 (trained routing model is deployed the actual physical computer network); Filsfils, ¶¶50, 58 (switchover to another candidate path is triggered when delay exceeds a threshold of a current path [i.e. the current path is circuitous]).
Regarding claim 14, the combination of Filsfils, Dechene, and Moyes also deploys the change on the network by changing a routing configuration setting of at least one edge node on the network. Filsfils, ¶58 and figure 7 (switchover to protection path [e.g. switching from path 1 to path 6, routing by node 2 is changed such that it forwards data to node 4 instead of node 3]); see also id. at ¶48 for updating segment IDs of SR policy when topology changes.
Regarding claim 15, the combination of Filsfils, Dechene, and Moyes also teaches activates data packet tracing by: decrypting a target data packet at each node that transmits the target data packet (Filsfils, ¶32 (incorporates by reference RFC 5357, which on page 12, section 4.2 teaches the reflector node decrypting the test packet received from a sender node); and records a location of each node that routes the target data packet. Dechene, ¶98 (logical and geographic proximity of each node is determined); see also Filsfils, ¶20 for determining segment adjacency.
Claims 2 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Filsfils (US 20200204469) in view of Dechene and Moyes (both of record) and further in view of Cidon (US 20210067468).
Regarding claim 2, the combination of Filsfils, Dechene, and Moyes teaches the AI method of claim 1, but does not explicitly teach “the processor detects the bottleneck by identifying a target node on the network that routes a threshold number of data packets within a threshold time window.” However, Cidon collects statistics, such as the number of data messages or number of bytes that pare process during a particular time period, by each managed forwarding nodes (MFNs) to detect an overutilized MFN. Cidon, ¶355. At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to use the collected statistics, taught by Cidon, to identify the congested node in the network, taught by the combination of Filsfils, Dechene, and Moyes, in order to determine when new resources need to be added to meet minimum bandwidth guarantees. Id. at ¶219.
Regarding claim 4, the combination of Filsfils, Dechene, Moyes, and Cidon also teaches configuring a threshold number of nodes on the network and positioned within a threshold distance of the target node to bypass the target node when transmitting data packets. Cidon, ¶356 (data message flows are diverted from the overutilized MFN to a candidate shared MFN); Cidon, ¶357 (a candidate shared MFN is in the same PCD group [i.e. public cloud datacenters] as the overutilized MFN [see ¶¶6, 15 for PCDs being in different regions, such as a city or state]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Filsfils (US 20200204469) in view of Dechene and Moyes (both of record) and further in view of Vytla (US 20200092211).
Regarding claim 6, the combination of Filsfils, Dechene, and Moyes teaches the AI method of claim 1, but does not explicitly teach “inserting an executable header into a target data packet, the executable header comprising an instruction that when executed at a network node that routes the target data packet, transmits a signal to a receiver associated with the processor.” However, Vytla teaches each node in a path that receives a packet that matches a particular policy, adds a packet telemetry data (PTD) header to the packet and transmits telemetry data back to controller. Vytla, ¶20 and figure 4A (steps 414-418). At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to use the PTD header for centralized collection of telemetry data, as taught by Vytla, within the paths, taught by the combination of Filsfils, Dechene, and Moyes, in order to enable granular tracking of particular flows in a path. Id. at ¶18.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Filsfils (US 20200204469) in view of Dechene and Moyes (both of record) and further in view of Del Regno (US 20140328163).
Regarding claim 8, the combination of Filsfils, Dechene, and Moyes teaches the AI method of claim 7 wherein execution of the computer readable instructions by the processor: based on the data packet tracing, detects a hard-coded circuitous routing path (Filsfils, ¶¶49-50 and figure 7 – using probe packets, a path with a delay over a threshold is detected; Moyes, ¶7 – hardcoded routing table); within the digital twin, simulates . . .the hard-coded circuitous routing path . . . (Dechene, ¶105 – in the digital twin, the user can execute both positive and negative scenarios that change how traffic is routed).
The combination of Filsfils and Dechene does not explicitly teach simulating the deletion a path or “delet[ing] the . . . routing path from at least one node on the network.” However, Del Regno teaches a “dummy” fast re-route event in which a router (LSR) simulates a failure at a port, link, or switch level. Del Regno, ¶31. As a result of the simulated failure, a router can delete or tear down an LSP. Id. at ¶¶36, 38; see also claim 9’s “delete the first LSP from the routing table.” At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to simulate a path failure, as taught by Del Regno, using the simulation twin, taught by the combination of Filsfils, Dechene, and Moyes, in order to simulate moving traffic off of links in preparation for network maintenance of particular LSR. Id. at ¶29.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Filsfils (US 20200204469) in view of Dechene and Moyes (both of record) and further in view of Griffiths (US 20080112328).
Regarding claim 9, the combination of Filsfils and Dechene teaches the method of claim 1 and . . . allocating additional computing resources to a target node. Dechene, ¶79 (“digital twin simulation may support additional functionality depending on the training objective”); Dechene, ¶77 (each node reports its hardware capabilities to the SDN controller [i.e. which during a simulation, can be added to]); see also id. at ¶105 for improvement to throughput being tested by the digital twin. The combination of Filsfils and Dechene does not explicitly teach “the computing resources comprising hardware resources comprising a hub, switch, bridge, or repeater.” However, Griffiths teaches an element management system (EMS) that can simulate a network element. Griffiths, ¶31. Griffiths also teaches installing a new network element and updating the EMS accordingly. Id. at ¶42. The network element of Griffiths may include a DCS, DSLAM, or switch. Id. at ¶¶35, 38. At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to add the network element, taught by Griffiths, to the real network, taught by the combination of Filsfils, Dechene, and Moyes, in order to improve resource allocation and usage through network provisioning and network deployment and planning. Id. at ¶35.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Filsfils (US 20200204469) in view of Dechene and Moyes (both of record) and further in view of Harshavardha (US 20150109934).
Regarding claim 11, the combination of Filsfils, Dechene, and Moyes teaches wherein the circuitous data flow comprises utilizing a first intermediary node to route data packets generated by a target node (Filsfils, figure 7 – all paths between node 2 and node 9 are shown [e.g. in path 1, node 3 is an intermediary node]); , . . . a second intermediary node capable of routing the data packets generated by the target node. Filsfils, figure 7 (e.g. in path 6, node 4 is an intermediary node).
While Filsfils does use a shortest path first routing algorithm, Filsfils does not explicitly disclose which node, either PE 3 or PE 4, is geographically closer to PE 2 (Filfils, figure 1), let alone using the “first intermediary node [that is] geographically further away from the target node than the second intermediary node. However, Harshavardha choses a longer path based on cost, where cost is the distance to the next router. Harshavardha, ¶30 (cost equals distance); Harshavardha, ¶¶ 95 (choses next hop router on a longer alternate path). At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to use a path with greater distance between routers, as taught by Harshavardha, when defining paths through the network, taught by the combination of Filsfils, Dechene, and Moyes, in order to deload congestion from shorter paths. Id. at ¶59.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Filsfils (US 20200204469) in view of Dechene and Moyes (both of record) and further in view of Lapukhov, P. and Chang, R., Data plane probe for in-band telemetry collection, Internet-Draft draft-lapukhov-dataplane-probe-01, June 10, 2016 (hereinafter “Lapukhov”).
Regarding claim 16, the combination of Filsfils, Dechene, and Moyes also teaches configuring each node that processes a target data packet to record an identifier . . . in a header of the target data packet before transmitting the target data packet. Filsfils, ¶64 (segment identifiers of return path carried in the header of the probe message).
Filsfils does not explicitly teach an identifier “of each node,” but rather an identifier of a segment. However, Lapukhov teaches an identifier for each device that reports telemetry data. Lapukhov, pgs. 7-8 (section 3.1). At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to record the device identifier, taught by Lapukhov, within the packet header, taught by the combination of Filsfils, Dechene, and Moyes, in order to provide precise topological identification of a congested location within the network. Lapukhov, pg. 1 (second paragraph of abstract); see also id. at pg. 3 (last paragraph of section 1, which describes recording the transmit device’s name to capture and embed the instanteous state of the device in the telemetry packet).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Filsfils (US 20200204469) in view of Dechene (US 20220245462) and further in view of Lapukhov (of record).
Regarding claim 18, the combination of Dechene and Filsfils also teaches recording, in a header of a target data packet: an identifier . . . on the network that processes the target data packet. Filsfils, ¶64 (segment identifiers of return path carried in the header of the probe message).
The combination of Dechene and Filsfils does not explicitly teach an identifier “of a forwarding node,” “a timestamp when the forwarding node received the target data packet;” or “a destination node for the target data packet.” However, Lapukhov teaches an identifier for each device that reports telemetry data (Lapukhov, pgs. 7-8 (section 3.1)), a timestamp including the receive time (id., pg. 8 (section 3.2)), and a transport header of a probe packet that includes source and destination IP addresses. Id., pg. 10 (section 4, 1st paragraph). At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to record the device identifier, timestamp, and destination, as taught by Lapukhov, within the packet header, taught by the combination of Filsfils and Dechene, in order to provide precise topological identification of a congested location within the network. Lapukhov, pg. 1 (second paragraph of abstract); see also id. at pg. 3 (last paragraph of section 1, which describes recording the transmit device’s name to capture and embed the instanteous state of the device in the telemetry packet).
Claims 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dechene (US 20220245462) in view of Filsfils (US 20200204469).
Regarding claim 17, Dechene teaches an artificial intelligence (“AI”) network traffic simulator comprising computer executable instructions, that when executed by a processor on a computer system:
trace a flow of data within a network (Dechene, ¶113 – traffic flow data is collected by the network control system);
based on the flow of data, build a digital twin of the network (Dechene, ¶¶77, 80 – builds a digital twin of the operational network based on the capabilities of each node in the network; Dechene, ¶¶91-92 – network traffic is captured and then synthetic flows of the network traffic is used in the digital twin);
simulate, within the digital twin, an alternative data routing . . . (Dechene, ¶105 – in the digital twin, the user can execute both positive and negative scenarios that change how traffic is routed); and
configure the network to implement the alternative data routing . . . (Dechene, ¶171 – trained routing model is deployed the actual physical computer network).
Dechene does not explicitly teach an alternate data routing “pathway.” Instead, Dechene’s explicit simulations are of the network as a whole rather than individual paths. However, Filsfils teaches multiple different pathways within a network that provide connectivity between two customer edge devices. Filsfils, figure 1 (connectivity between CE 1 and CE 10) and figure 7 (includes all paths between ingress node PE 2 and egress node PE 9). At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to use the trained routing model, taught by Dechene, to identify one of the alternate paths, taught by Filsfils, to provide connectivity between two customer edge nodes in order to meet individual customer requirements, such as maximum delay, for its traffic. Filsfils, ¶16.
Alternatively, if Dechene is found to not teach “trace a flow of data within a network.” Filsfils teaches sending probe packets to the nodes along a path. Filsfils, ¶¶32, 34. At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to trace individual flows, as taught by Filsfils, when building a digital twin of a network, as taught by Dechene, in order to measure the actual delay experience by the flows (ibid.), thus providing accurate network topology information to the AI model for simulation.
Regarding claim 19, the combination of Dechene and Filsfils also teaches based on the flow of data, determine a first sequence of nodes that process a threshold number of data packets; and the alternative data routing pathway comprises a second sequence of nodes. Filsfils, figure 7 (in path 1, the sequence of nodes is 2-3-6-7-9 and in path 6, the sequence of nodes is 2-4-6-8-9); Filsfils, ¶23 (at least one of the paths in figure 7 routes packets between the two customer edge nodes [i.e. processes a threshold number of packets]).
Regarding claim 20, the combination of Dechene and Filsfils also teaches wherein the alternative data routing pathway forces a target data packet to be processed by a first node within a threshold geographic distance of a second node that generated the target data packet. Filsfils, figure 1 (PE 2 forwards a packet to either PE 3, PE 4, or PE5 as outlined in the paths of figure 7); Dechene, ¶98 (learns geographic proximity of the nodes in the network). At the time of the invention (pre-AIA ) or at the effective filing date of the invention (AIA ), it would have been obvious for one of ordinary skill in the art to select either PE 3, PE 4, or PE 5 to be in the path, taught by Filsfils, using the geographic proximity of each of PE 3, PE 4, and PE 5 in relation to PE 2, as taught by Dechene, in order to route packets between PEs based on clustering. Dechene, ¶99 (ranking clusters).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN S LAMONT whose telephone number is (571)270-7514 and fax number is 571-270-8514 and email address is benjamin.lamont@uspto.gov (see MPEP 502.03 for authorizing unsecure communication). The examiner can normally be reached M-F 7am to 3pm EST.
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/Benjamin Lamont/Primary Examiner, Art Unit 2461