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 .
DETAILED ACTION
This action is in response to application filed 02/17/2026.
Claims 1-20 are pending in this application.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 of this title, 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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 6, 8-10, 14, 17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Choudhury et al (US 10,581,736 B1) in view of Sanchez et al (US 2020/0136957 A1) in further view of Smith (US 2024/0430203 A1 – Priority date 06/23/2023).
Regarding claim 1, Choudhury discloses a method to provide dynamic load balancing and adaptive packet routing (column 5, 16-21: a routing engine may maintain an end-to-end path map identifying different end-to-end paths in the network (e.g., a topology information block (TIB) of a path computation element (PCE)), and for each end-to-end path), the method comprising:
accessing a simulated network fabric(column 5, 7-12: the traffic measurements for an end-to-end path at the selected time lags may then be applied to the nonlinear autoregressive model to predict the end-to-end path traffic at a future time period), the traffic data including data collected for a plurality of paths, each path traversing a different set of network nodes between a source node and a destination node (column 15, 59-64: each of the plurality of nonlinear autoregressive models is associated with a respective one of the plurality of end-to-end paths and is separately trained from at least a portion of the plurality of traffic measurements that is obtained for the one of the plurality of end-to-end paths);
using the model to predict, for each of a plurality of time periods, a traffic pattern for each path based on predicted link and path utilization of each path (column 14, 7-14: , link 391 has a predicted traffic utilization of 50 Gbps (and thus a predicted spare capacity of 50 Gbps), while link 392 has a predicted traffic utilization of 30 Gbps (and thus a predicted spare capacity of 70 Gbps). The minimum spare capacity among these links is 50 Gbps, and therefore the available spare capacity of potential backup path 340 is 50 Gbps during the afternoon time period) and based on seasonality of traffic defining time-of-day traffic patterns for each path (column 5, 44-49: Column 15, 59-64: each of the plurality of nonlinear autoregressive models is associated with a respective one of the plurality of end-to-end paths and is separately trained from at least a portion of the plurality of traffic measurements that is obtained for the one of the plurality of end-to-end paths. Column 13, 47-54: a primary path traffic vector (e.g., a link traffic vector) at different time periods for the portion of the network 300 shown in FIG. 3. For instance, link traffic vector 410 may comprise the link traffic predictions among nodes 301-304 for a time period on a weekday afternoon, while link traffic vector 420 may comprise the link traffic predictions among nodes 301-304 for a time period on a weekday evening);
determine, for each of the plurality of time periods, a network weight for each network node in each path based on the corresponding predicted traffic pattern (column 15, 65 – column 16, 3: each of the plurality of nonlinear autoregressive models generates a traffic estimate for one of the plurality of end-to-end paths for the future time period(s) in accordance with a function of the plurality of traffic measurements from the plurality of end-to-end paths at a plurality of previous time periods. Column 13, 49-58: link traffic vector 410 may comprise the link traffic predictions among nodes 301-304 for a time period on a weekday afternoon, while link traffic vector 420 may comprise the link traffic predictions among nodes 301-304 for a time period on a weekday evening. In the present example, it may be assumed that each link can have a capacity in multiples of 100 Gbps and that the current capacity of each link is 100 Gbps. In addition, the present example may involve calculating a backup path for link 395 from node 310 to node 304);
in the physical network fabric, routing actual traffic through a first path of the plurality of paths during a time period of the plurality of time periods using the network weight for each network node in the first path as determined for the time period (fig. 4, column 13, 47-54: a primary path traffic vector (e.g., a link traffic vector) at different time periods for the portion of the network 300 shown in FIG. 3. For instance, link traffic vector 410 may comprise the link traffic predictions among nodes 301-304 for a time period on a weekday afternoon, while link traffic vector 420 may comprise the link traffic predictions among nodes 301-304 for a time period on a weekday evening. Column 14, 39-48: a more optimal backup path configuration may be calculated for each time interval/time period, and the backup path configuration may be implemented within the network 300 in advance of such time periods. When the backup path configuration changes, the network 300 may switch the backup path to the new configuration).
However, Choudhury does not disclose comparing predicted network costs for routing the actual traffic to actual network costs of routing the actual traffic; and determining whether to trigger reinforcement learning of the model based on the comparison.
In an analogous art, Sanchez discloses comparing predicted network costs for routing the actual traffic to actual network costs of routing the actual traffic; and determining whether to trigger reinforcement learning of the model based on the comparison ([0034]-[0035: The controller updates the model based on observed network performance (314). The controller monitors the network to compare actual network performance to the model. The controller determines whether the actual network performance matches the simulated performance determined by the model. For example, the controller determines whether the global network delay or load of the real network matches the global network delay or load experienced during simulations or as estimated by the model. Performance and traffic data from the network are fed back into the model to improve simulations of the network and continue improving the machine learning model for future routing determinations for network traffic. If the performance of the network with the optimal routing configuration is not meeting expectations, the controller can re-perform the operations of block 304 and determine a different set of pivotal nodes, increasing the number of pivotal nodes, or use different measures of relative importance for the nodes. If the set of pivotal nodes change, the controller retrains the model used at block 308).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury to comprise “comparing predicted network costs for routing the actual traffic to actual network costs of routing the actual traffic; and determining whether to trigger reinforcement learning of the model based on the comparison” taught by Sanchez.
One of ordinary skilled in the art would have been motivated because it would have enabled to determine a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network (Sanchez, [0010]).
However, Choudhury-Sanchez does not disclose determine a network weight for each network node in each path based on the corresponding predicted traffic pattern and based on current queue parameters of each network node.
In an analogous art, Smith discloses determine a network weight for each network node in each path based on the corresponding predicted traffic pattern and based on current queue parameters of each network node ([0109]: A queue 204 (P.sub.i) may be maintained for each node that contains a set of permanently labeled routes (e.g., as discovered by the algorithm), for example, in the order in which they are discovered (e.g., which may be in increasing weight. [0125]: the weight constraint (7,4) of the path of the 4.sup.th row and the weight constraint (10,5) of the path of the 5.sup.th row may be analyzed with any other suitable available data to select one of those two paths for use (e.g., at operation 1206). One of multiple allowable paths may be chosen based on a local load-balancing algorithm or the like.).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury-Sanchez to comprise “determine a network weight for each network node in each path based on the corresponding predicted traffic pattern and based on current queue parameters of each network node” taught by Smith.
One of ordinary skilled in the art would have been motivated because it would have enabled to compute, for each router node of the plurality of router nodes, a best set of routes (Smith, [0006]).
Regarding claim 2, Choudhury-Sanchez-Smith discloses the method of claim 1, wherein the traffic data comprises configurations of each path in the physical network fabric (Choudhury, column 5, 17-21: a routing engine may maintain an end-to-end path map identifying different end-to-end paths in the network (e.g., a topology information block (TIB) of a path computation element (PCE)), and for each end-to-end path: the beginning and ending nodes).
Regarding claim 6, Choudhury-Sanchez-Smith discloses the method of claim 1,wherein the traffic data comprises queue configuration parameters for one or more links in each (Smith, [0109]: A queue 204 (P.sub.i) may be maintained for each node that contains a set of permanently labeled routes (e.g., as discovered by the algorithm), for example, in the order in which they are discovered (e.g., which may be in increasing weight). The same rationale applies as in claim 1.
Regarding claim 8; the claim is interpreted and rejected for the same reason as set forth in claim 1.
Regarding claim 9, Choudhury-Sanchez-Smith discloses the system of claim 8, further comprising: in response to the network costs not matching the actual network costs for the actual traffic, triggering reinforcement learning of the model (Sanchez, [0034]-[0035: The controller updates the model based on observed network performance (314). The controller monitors the network to compare actual network performance to the model. The controller determines whether the actual network performance matches the simulated performance determined by the model. For example, the controller determines whether the global network delay or load of the real network matches the global network delay or load experienced during simulations or as estimated by the model. Performance and traffic data from the network are fed back into the model to improve simulations of the network and continue improving the machine learning model for future routing determinations for network traffic. If the performance of the network with the optimal routing configuration is not meeting expectations, the controller can re-perform the operations of block 304 and determine a different set of pivotal nodes, increasing the number of pivotal nodes, or use different measures of relative importance for the nodes. If the set of pivotal nodes change, the controller retrains the model used at block 308). The same rationale applies as in claim 1.
Regarding claim 10; the claim is interpreted and rejected for the same reason as set forth in claim 2.
Regarding claim 14; the claim is interpreted and rejected for the same reason as set forth in claim 6
Regarding claim 17; Choudhury discloses a device to provide dynamic load balancing and adaptive packet routing, the device comprising:
processing circuitry to: access a model trained with traffic data associated with a plurality of network nodes in a physical network fabric (column 5, 7-12: the traffic measurements for an end-to-end path at the selected time lags may then be applied to the nonlinear autoregressive model to predict the end-to-end path traffic at a future time period), the traffic data including data collected for a plurality of paths, each path traversing a different set of network nodes in the plurality of network nodes between a source node and a destination node (column 15, 59-64: each of the plurality of nonlinear autoregressive models is associated with a respective one of the plurality of end-to-end paths and is separately trained from at least a portion of the plurality of traffic measurements that is obtained for the one of the plurality of end-to-end paths);
use the model to predict for each of a plurality of time periods, a traffic pattern for each path based on predicted link and path utilization of each path (column 14, 7-14: , link 391 has a predicted traffic utilization of 50 Gbps (and thus a predicted spare capacity of 50 Gbps), while link 392 has a predicted traffic utilization of 30 Gbps (and thus a predicted spare capacity of 70 Gbps). The minimum spare capacity among these links is 50 Gbps, and therefore the available spare capacity of potential backup path 340 is 50 Gbps during the afternoon time period) and based on seasonality of traffic defining time-of-day traffic patterns for each path (column 5, 44-49: Column 15, 59-64: each of the plurality of nonlinear autoregressive models is associated with a respective one of the plurality of end-to-end paths and is separately trained from at least a portion of the plurality of traffic measurements that is obtained for the one of the plurality of end-to-end paths. Column 13, 47-54: a primary path traffic vector (e.g., a link traffic vector) at different time periods for the portion of the network 300 shown in FIG. 3. For instance, link traffic vector 410 may comprise the link traffic predictions among nodes 301-304 for a time period on a weekday afternoon, while link traffic vector 420 may comprise the link traffic predictions among nodes 301-304 for a time period on a weekday evening);
determine, for each of the plurality of time periods, a network weight for each network node in each path based on the corresponding predicted traffic pattern (column 15, 65 – column 16, 3: each of the plurality of nonlinear autoregressive models generates a traffic estimate for one of the plurality of end-to-end paths for the future time period(s) in accordance with a function of the plurality of traffic measurements from the plurality of end-to-end paths at a plurality of previous time periods. Column 16, 42-48: computing a primary path traffic vector having rows and columns corresponding to a subset of the nodes in the telecommunication network, where each entry of a plurality of entries in the primary path traffic vector represents a traffic estimate for one of the plurality of primary paths in the telecommunication network);
route actual traffic through a first path of the plurality of paths during a time period of the plurality of time periods using the network weight for each network in the first path as determined for the time period (column 14, 39-48: a more optimal backup path configuration may be calculated for each time interval/time period, and the backup path configuration may be implemented within the network 300 in advance of such time periods. For instance, every 15 minutes, every hour, etc., a backup path configuration may be calculated for a future time period, e.g., 15 minutes later, 30 minutes later, etc. When the backup path configuration changes, the network 300 may switch the backup path to the new configuration).
However, Choudhury discloses compare predicted network costs for routing the actual traffic to actual network costs of routing the actual traffic.
In an analogous art, Sanchez discloses compare predicted network costs for routing the actual traffic to actual network costs of routing the actual traffic ([0034]-[0035: The controller updates the model based on observed network performance (314). The controller monitors the network to compare actual network performance to the model. The controller determines whether the actual network performance matches the simulated performance determined by the model. For example, the controller determines whether the global network delay or load of the real network matches the global network delay or load experienced during simulations or as estimated by the model. Performance and traffic data from the network are fed back into the model to improve simulations of the network and continue improving the machine learning model for future routing determinations for network traffic. If the performance of the network with the optimal routing configuration is not meeting expectations, the controller can re-perform the operations of block 304 and determine a different set of pivotal nodes, increasing the number of pivotal nodes, or use different measures of relative importance for the nodes. If the set of pivotal nodes change, the controller retrains the model used at block 308).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury to comprise “compare predicted network costs for routing the actual traffic to actual network costs of routing the actual traffic” taught by Sanchez.
One of ordinary skilled in the art would have been motivated because it would have enabled to determine a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network (Sanchez, [0010]).
However, Choudhury-Sanchez does not disclose determine a network weight for each network node in each path based on the corresponding predicted traffic pattern and based on current queue parameters of each network node.
In an analogous art, Smith discloses determine a network weight for each network node in each path based on the corresponding predicted traffic pattern and based on current queue parameters of each network node ([0109]: A queue 204 (P.sub.i) may be maintained for each node that contains a set of permanently labeled routes (e.g., as discovered by the algorithm), for example, in the order in which they are discovered (e.g., which may be in increasing weight. [0125]: the weight constraint (7,4) of the path of the 4.sup.th row and the weight constraint (10,5) of the path of the 5.sup.th row may be analyzed with any other suitable available data to select one of those two paths for use (e.g., at operation 1206). One of multiple allowable paths may be chosen based on a local load-balancing algorithm or the like.).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury-Sanchez to comprise “determine a network weight for each network node in each path based on the corresponding predicted traffic pattern and based on current queue parameters of each network node” taught by Smith.
One of ordinary skilled in the art would have been motivated because it would have enabled to compute, for each router node of the plurality of router nodes, a best set of routes (Smith, [0006]).
Regarding claim 20; the claim is interpreted and rejected for the same reason as set forth in claim 9.
Claims 3-5, 7, 11-13, 15-16, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Choudhury in view of Sanchez in view of Smith, as applied to claims 1, 8, and 17, in further view of Levy-Abegnoli et al. (herein after Levy, US 2020/0252300 A1).
Regarding claim 3, Choudhury-Sanchez-Smith discloses the method of claim 1.
However, Choudhury-Sanchez-Smith does not disclose wherein the traffic data comprises path weights for each path in the physical network fabric.
In an analogous art, Levy discloses wherein the traffic data comprises path weights for each path in the physical data center network fabric for the specified time period ([0044]: The supervisory device identifies a period of time associated with the predicted seasonal congestion on the particular link. The supervisory device initiates, in advance of the identified period of time, re-computation of equal-cost multi-path (ECMP) weights associated with the plurality of links that prevent occurrence of the predicted seasonal congestion on the particular link during the identified period of time).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury-Sanchez-Smith to comprise “wherein the traffic data comprises path weights for each path in the physical network fabric” taught by Levy.
One of ordinary skilled in the art would have been motivated because it would have enabled to detecting seasonal congestion in software defined networking (SDN) network fabrics using machine learning (Levy, [0001]).
Regarding claim 4, Choudhury-Sanchez-Smith discloses the method of claim 1.
However, Choudhury-Sanchez does not disclose wherein the traffic data comprises link and path utilization.
In an analogous art, Levy discloses wherein the traffic data comprises link and path utilization ([0048]: the telemetry data may indicate the average load measured on the link, ether globally or per-class, over a period of time P. In another embodiment, the telemetry data may report the median value, or N-Percentile (N=80, 90) of link load or other metrics).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury-Sanchez-Smith to comprise “wherein the traffic data comprises link and path utilization” taught by Levy.
One of ordinary skilled in the art would have been motivated because it would have enabled to detecting seasonal congestion in software defined networking (SDN) network fabrics using machine learning (Levy, [0001]).
Regarding claim 5, Choudhury-Sanchez-Smith discloses the method of claim 1.
However, Choudhury-Sanchez-Smith does not disclose wherein the traffic data comprises queue parameters for one or more links in each path.
In an analogous art, Levy discloses wherein the traffic data comprises queue parameters for one or more links in each path ([0048]: the telemetry data may report the median value, or N-Percentile (N=80, 90) of link load or other metrics, such as the queue length, to the supervisory device. Notably, queue length is a good predictive metric of the quality of service (QoS) experienced by the traffic on the link).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury-Sanchez-Smith to comprise “wherein the traffic data comprises queue parameters for one or more links in each path” taught by Levy.
One of ordinary skilled in the art would have been motivated because it would have enabled to detecting seasonal congestion in software defined networking (SDN) network fabrics using machine learning (Levy, [0001]).
Regarding claim 7, Choudhury-Sanchez-Smith discloses the method of claim 1.
However, Choudhury-Sanchez-Smith does not disclose wherein the traffic data comprises bandwidth utilization parameters.
In an analogous art, Levy disclose wherein the traffic data comprises bandwidth utilization parameters (Levy, [0049]: Such a matrix may report the number of times a link L is in a congested state, where the level of congestion is specified as T (e.g., T could be a queue length in terms of number of packets queued considering the link bandwidth, percentage of load on the link, etc.).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury-Sanchez-Smith to comprise “wherein the traffic data comprises bandwidth utilization parameters” taught by Levy.
One of ordinary skilled in the art would have been motivated because it would have enabled to detecting seasonal congestion in software defined networking (SDN) network fabrics using machine learning (Levy, [0001]).
Regarding claim 11; the claim is interpreted and rejected for the same reason as set forth in claim 3.
Regarding claim 12; the claim is interpreted and rejected for the same reason as set forth in claim 4.
Regarding claim 13; the claim is interpreted and rejected for the same reason as set forth in claim 5.
Regarding claim 15; the claim is interpreted and rejected for the same reason as set forth in claim 7.
Regarding claim 16, Choudhury-Sanchez-Smith discloses the system of claim 8.
However, Choudhury-Sanchez-Smith does not disclose wherein the network weights are determined based on optimal path network costs.
In an analogous art, Levy discloses wherein the network weights are determined based on optimal path network costs ([0044]: re-computation of equal-cost multi-path (ECMP) weights associated with the plurality of links that prevent occurrence of the predicted seasonal congestion on the particular link during the identified period of time).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury-Sanchez-Smith to comprise “wherein the network weights are determined based on optimal path network costs” taught by Levy.
One of ordinary skilled in the art would have been motivated because it would have enabled to detecting seasonal congestion in software defined networking (SDN) network fabrics using machine learning (Levy, [0001]).
Regarding claim 18, Choudhury-Sanchez-Smith discloses the device of claim 17.
However, Choudhury-Sanchez-Smith does not disclose wherein the traffic data comprises at least one of: configurations of each path in the physical network fabric, link and path utilization, queue parameters for each link, queue configuration parameters, and bandwidth utilization parameters.
In an analogous art, Levy discloses wherein the traffic data comprises at least one of: configurations of each path in the physical network fabric, link and path utilization, queue parameters for each link, queue configuration parameters, and bandwidth utilization parameters. ([0048]-[0049]: the telemetry data may indicate the average load measured on the link, ether globally or per-class, over a period of time P. In another embodiment, the telemetry data may report the median value, or N-Percentile (N=80, 90) of link load or other metrics, such as the queue length, to the supervisory device. Notably, queue length is a good predictive metric of the quality of service (QoS) experienced by the traffic on the link.. Where the level of congestion is specified as T (e.g., T could be a queue length in terms of number of packets queued considering the link bandwidth, percentage of load on the link, etc.).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Choudhury-Sanchez-Smith to comprise “wherein the traffic data comprises at least one of: configurations of each path in the physical network fabric, link and path utilization, queue parameters for each link, queue configuration parameters, and bandwidth utilization parameters” taught by Levy.
One of ordinary skilled in the art would have been motivated because it would have enabled to detecting seasonal congestion in software defined networking (SDN) network fabrics using machine learning (Levy, [0001]).
Regarding claim 19; the claim is interpreted and rejected for the same reason as set forth in claim 16.
Additional References
The prior art made of record and not relied upon is considered pertinent to applicants disclosure.
Balasubramonian et al., US 2022/0247652 A1: Link-Quality Estimation and Anomaly Detection in High-Speed Wireline Receivers.
Filsfils et al., US 2019/0215266 A1: Segment-Routing Multiprotocol Label Switching End-to-End Dataplane Continuity.
Conclusion
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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/J.C.T/Examiner, Art Unit 2446
/BRIAN J. GILLIS/Supervisory Patent Examiner, Art Unit 2446
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