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
Response to Arguments
Applicant's arguments filed 11/14/2025 have been fully considered, and when viewed in light of the presently presented claim amendments, are persuasive. However, after further search and consideration, a new grounds of consideration has been made in view of Bergstrom (US-20160192029-A1). Bergstrom discloses a network monitoring system that preemptively adjusts routing paths based on performance thresholds considerations and predicted performance and QoE degradation (Bergstrom, e.g., [53, 56, 70, 113]).
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 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, 2, 4, 11, 12, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Carisimo (Carisimo, Esteban, et al. "Jitterbug: A New Framework for Jitter-Based Congestion Inference." International Conference on Passive and Active Network Measurement. Cham: Springer International Publishing. (Year: 2022)) in view of Mermoud (US-20190356553-A1), further in view of Bergstrom (US-20160192029-A1).
Regarding claim 1, Carisimo shows a method comprising: extracting, by a device, portions of a timeseries (pg. 2 lines 16-18) of a network path metric (pg. 4, Section 3, showing , e.g., the metric represented by jitter) by applying a sliding (pg. 5 lines 34-41) time window to the timeseries (pg. 2 lines 20-28); grouping, by the device, a subset of the portions of the timeseries into a motif based on their similarities (pg. 4 lines 27-37 discussing what is essentially clustering in order to “objectively capture signatures”; the “candidate time intervals” are classified into a group of timeseries data that correspond to “congestion events or other path anomalies”); the motif (such as detection of recurrent period patterns; pg. 1 lines 22-27) indicative of a prevalent network pattern (pg. 2 lines 20-24, see “that characterize a period of congestion”) of the network path metric (e.g., the jitter dispersion time series; pg. 2 lines 20-31) creation of data regarding the motif (Figs. 1, 3, showing where graphs have been created of the identified groups/motifs; the graphs exist and thus have been created). Carisimo does not show: providing the created data for display to a user via a user interface, and receiving, at the device and from a user interface, a label for the motif indicative of whether the motif is associated with degraded application experience for a particular online application, and selecting a network path over which to route the traffic such that the motifs labeled as degrading to application experience for the particular online application are avoided. Mermoud shows: providing data for display to a user via a user interface ([71] discussing presentation of data via a user interface, including where the “interface data may be in the form of display data that shows the time series of the candidate metrics”) receiving, at the device and from the user interface ([81]), a label ([59,76], e.g., showing a “like” or “dislike” label) for the motif ([75]) indicative of whether the motif is associated with degraded application experience ([52,71] discussing a “detected issue anomaly” and consideration of a “video call” application) for a particular online application ([71-75]), and selecting ([55]) a network path ([55] discussing signaling an endpoint to use a particular access point, which implicitly impacts the network path utilized regarding that endpoint) over which to route the traffic ([53] reciting “learn and adapt to network conditions and traffic characteristics” based on predictions - such as those discussed in [52] - and adjusting the path, as discussed in the above citations made to [55]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring and control techniques of Carisimo with user interface displayed by Mermoud, and thus display the created motif data, in order to aid the user in better understanding the determined motifs and the corresponding features of said motifs (i.e., the determined congestion events), and to further modify Carisimo with the traffic classification and classification feedback suggested by Mermoud, in order to provide explicit training feedback and thus better label/detect network anomalies, enabling improved reactions to said anomalies. The above combination does not show: analyzing traffic being communicated by the particular online application with respect to the motif to determine that the traffic is exhibiting the prevalent network pattern; predicting, based on the traffic exhibiting the prevalent network pattern, that the particular online application is to experience the degraded application experience at a future time; prior to the future time, selecting, based on the label, a network path over which to route the traffic, such that the particular online application avoids the degraded application experience. Bergstrom shows: analyzing traffic being communicated by the particular online application (e.g., a video streaming app; [100,103,106]) with respect to the motif ([53,113] discussing monitoring to generate metrics) to determine that the traffic is exhibiting the prevalent network pattern ([64,173-175]); predicting, based on the traffic exhibiting the prevalent network pattern, that the particular online application is to experience the degraded application experience ([29,53-54]) at a future time ([53,70] discussing to forecast/predict congestion); prior to the future time ([175] discussing to “identify and correct problems proactively”, see also [185]), selecting, based on the label, a network path over which to route the traffic ([53], see “dynamically reconfiguring the topology of the overlay network, based on the forecasted congestion levels, discussed further in, e.g., [62], including the dynamic path reconfiguration/selection discussed in [71,113]) such that the particular online application (i.e., the video streaming client app discussed in[100,103,106]) avoids the degraded application experience ([56] discussing maintaining a “consistent QoE” via, as [120] discusses, using metrics to dynamically reconfigure routing paths for upcoming traffic).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination with the QoE awareness and predictive route management of Bergstrom in order to maintain a consistent QoE, improving the service level provided to users even when scaling to accommodate large and variable numbers of users (Bergstrom, Abstract).
Regarding claim 2, the above combination further shows wherein the network path metric comprises at least one of: packet loss, jitter (Carisimo, pg. 4 Section 3), delay, or throughput.
Regarding claim 4, the above combination further shows wherein the timeseries is a multivariate timeseries of a plurality of network path metrics (Carisimo, Section 2 – 2.2, pgs. 2 – 3, discussing busing both jitter and round trip time).
Regarding claim 11, Carisimo shows a method comprising: extracting, by a device, portions of a timeseries (pg. 2 lines 16-18) of a network path metric (pg. 4, Section 3, showing , e.g., the metric represented by jitter) by applying a sliding (pg. 5 lines 34-41) time window to the timeseries (pg. 2 lines 25-28); grouping, by the device, a subset of the portions of the timeseries into a motif based on their similarities (pg. 2 lines 27-37 discussing “clustering” in order to “objectively capture signatures”) the motif (such as detection of recurrent period patterns; pg. 1 lines 22-27) indicative of a prevalent network pattern (pg. 2 lines 20-24, see “that characterize a period of congestion”) of the network path metric (e.g., the jitter dispersion time series; pg. 2 lines 20-31)
creation of data regarding the motif (Figs. 1, 3, showing where graphs have been created of the identified groups/motifs; the graphs exist and thus have been created). Carisimo does not show particular hardware implementation details, such as an apparatus, comprising: one or more network interfaces; a processor coupled to the one or more network interfaces and configured to execute one or more processes; and a memory configured to store a process that is executable by the processor, and further does not show: providing the created data for display to a user via a user interface, and receiving, at the device and from the user interface, a label for the motif indicative of whether the motif is associated with degraded application experience for a particular online application. Mermoud shows hardware implementation details, such as an apparatus, comprising: one or more network interfaces (Fig. 2 item 210); a processor coupled to the one or more network interfaces and configured to execute one or more processes (Fig. 2 item 220); and a memory configured to store a process that is executable by the processor (Fig. 2 item 240), providing data for display to a user via a user interface ([71] discussing presentation of data via a user interface, including where the “interface data may be in the form of display data that shows the time series of the candidate metrics”), and receiving, at the device and from the user interface ([81]), a label ([59,76], e.g., showing a “like” or “dislike” label) for the motif ([75]) indicative of whether the motif is associated with degraded application experience ([52,71] discussing a “detected issue anomaly” and consideration of a “video call” application) for a particular online application ([71-75]),
The above combination does not show to: analyze traffic being communicated by the particular online application with respect to the motif to determine that the traffic is exhibiting the prevalent network pattern; predict, based on the traffic exhibiting the prevalent network pattern, that the particular online application is to experience the degraded application experience at a future time; prior to the future time, select, based on the label, a network path over which to route the traffic, such that the particular online application avoids the degraded application experience. Bergstrom shows: analyze traffic being communicated by the particular online application (e.g., a video streaming app; [100,103,106]) with respect to the motif ([53,113] discussing monitoring to generate metrics) to determine that the traffic is exhibiting the prevalent network pattern ([64,173-175]); predict, based on the traffic exhibiting the prevalent network pattern, that the particular online application is to experience the degraded application experience ([29,53-54]) at a future time ([53,70] discussing to forecast/predict congestion); prior to the future time ([175] discussing to “identify and correct problems proactively”, see also [185]), select, based on the label, a network path over which to route the traffic ([53], see “dynamically reconfiguring the topology of the overlay network, based on the forecasted congestion levels, discussed further in, e.g., [62], including the dynamic path reconfiguration/selection discussed in [71,113]) such that the particular online application (i.e., the video streaming client app discussed in[100,103,106]) avoids the degraded application experience ([56] discussing maintaining a “consistent QoE” via, as [120] discusses, using metrics to dynamically reconfigure routing paths for upcoming traffic).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination with the QoE awareness and predictive route management of Bergstrom in order to maintain a consistent QoE, improving the service level provided to users even when scaling to accommodate large and variable numbers of users (Bergstrom, Abstract).
Regarding claim 12, the limitations of said claim are addressed in the analysis of claim 2.
Regarding claim 14, the limitations of said claim are addressed in the analysis of claim 4.
Regarding claim 20, the limitations of said claim are addressed in the analysis of claim 1.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Carisimo in view of Mermoud and Bergstrom, as applied to claim 1 above, further in view of Palmieri (Palmieri, Francesco, and Ugo Fiore. "A nonlinear, recurrence-based approach to traffic classification." Computer Networks 53.6: 761-773. (Year: 2009)).
Regarding claim 3, Carisimo in view of Mermoud and Bergstrom show claim 1. The above combination does not show determining, by the device, an amount of time between instances of the motif occurring; and providing, by the device, an indication of the amount of time for display to the user via the user interface. Palmieri shows determining, by the device, an amount of time between instances of the motif occurring (Section 3.3, pg. 4 R. col. – pg. 5 L. col., pg. 5 R13 – R17) and providing, by the device, an indication of the amount of time for display to the user via the user interface (pg. 5 R13-R19 and R39 – R54, pg. 7 R39 – pg. 8 L9, pg. 9 R6 – R23).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination with the pattern recurrence awareness and communication of Palmieri in order to improve traffic classification and traffic understanding capabilities (Palmieri, pg. 1 R27 – R32, pg. 3 R17 – R24). Regarding claim 13, the limitations of said claim are addressed in the analysis of claim 3.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Carisimo in view of Mermoud and Bergstrom, as applied to claim 1 above, further in view of Palmieri and Santos (Santos, Adriana C. Ferrari, et al. "Network traffic characterization based on time series analysis and computational intelligence." Journal of Computational Interdisciplinary Sciences 2.3: 197-205. (Year: 2011)).
Regarding claim 5, Carisimo in view of Mermoud and Bergstrom show claim 1. The above combination does not show recursively decreasing the sliding time window into a smaller time interval, based on the label; and forming another motif in part by applying the smaller time interval to the timeseries. Palmieri shows decreasing the sliding time window into a smaller time interval, based on the label; and forming another motif in part by applying the smaller time interval to the timeseries (pg. 7 L13 – L28, suggesting trying smaller intervals to find different patterns in data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination with the pattern detection techniques of Palmieri in order to improve traffic classification and traffic understanding capabilities (pg. 1 R27 – R32, pg. 3 R17 – R24). The above combination does not show recursively decreasing the sliding time window into smaller time intervals. Santos shows recursively decreasing the sliding time window into smaller time intervals (pg. 3 R1-R10 and R44-R54).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination does with the recursive time window adjustment of Santos in order to ensure full coverage of the time window sizes, thus enabling better understanding of the evaluated data and potentially discovering additional patterns within said data.
Regarding claim 15, the limitations of said claim are addressed in the analysis of claim 5.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Carisimo in view of Mermoud and Bergstrom, as applied to claim 1 above, further in view of Palmieri and Khanduja (Khanduja, Shwetabh, et al. "Near real-time service monitoring using high-dimensional time series." 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE. (Year: 2015))
Regarding claim 6, Carisimo in view of Mermoud and Bergstrom show wherein grouping the subset of the portions of the timeseries into the motif based on their similarities comprises: computing portions of the timeseries in the subset (Carisimo, pg. 2 lines 20-23, pg. 4 Section 3). The above combination does do not show consideration of distance between said portions. Palmieri shows consideration of distance between said portions (Section 3.3, pg. 4 R. col. – pg. 5 L. col., pg. 5 R13 – R17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination does with the pattern recurrence awareness and communication of Palmieri in order to improve traffic classification and traffic understanding capabilities (pg. 1 R27 – R32, pg. 3 R17 – R24).
The above combination does not show consideration of one or more other portions outside of the subset. Khanduja shows consideration of one or more other portions outside of the subset (pg. 2 R16 – R20, pg. 2 R53 – pg. 3 L13, pg. 3 R23 – R35, Fig. 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination with the additional data consideration of Khanduja in order to better detect, flag, and respond to unusual network activity.
Regarding claim 16, the limitations of said claim are addressed in the analysis of claim 6.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Carisimo in view of Mermoud and Bergstrom, as applied to claim 1 above, further in view of Tedaldi (US-20210335505-A1).
Regarding claim 8, Carisimo in view of Mermoud and Bergstrom show consideration of motifs (Carisimo, pg. 2 lines 27-37 and Bergstrom, [53,70,119]). The above combination does not show providing, by the device, data regarding a number of users potentially affected by the motif for display by the user interface, in conjunction with the data regarding the motif. Tedaldi shows providing, by the device, data regarding a number of users potentially affected by the motif for display by the user interface, in conjunction with the data regarding the motif ([97-99]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination with the user consideration of Tedaldi in order to better inform the network operator of how widespread particular anomalies are, enabling more informed decisions regarding prioritizing remediation steps.
Regarding claim 18, the limitations of said claim are addressed in the analysis of claim 8.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Carisimo in view of Mermoud and Bergstrom, as applied to claim 1 above, further in view of Feamster (Feamster, Nick, and Jennifer Rexford. "Why (and how) networks should run themselves." arXiv preprint arXiv:1710.11583. (Year: 2017)).
Regarding claim 9, Carisimo in view of Mermoud and Bergstrom show wherein the label indications but that the user does not believe a motif to be important (Mermoud, [82] showing use of an “indifferent” label). The above combination does not show wherein the label indicates that the motif does degrade an application experience for the particular online application. Feamster shows wherein the label indicates that the motif does degrade an application experience for the particular online application (pg. 5 L43 – R48).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination with the application experience feedback of Feamster in order to ensure the network state is updated to reflect the actual performance impacts being experienced by the network’s end users.
Regarding claim 19, the limitations of said claim are addressed in the analysis of claim 9.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Carisimo in view of Mermoud and Bergstrom, as applied to claim 1 above, further in view of Laoutaris (WO-2019086522-A1).
Regarding claim 10, Carisimo in view of Mermoud and Bergstrom show claim 1. The above combination does not show wherein the particular online application is a software-as-a-service (SaaS) application. Laoutaris shows wherein the particular online application is a software-as-a-service (SaaS) application (pg. 3 lines 1-16).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the network monitoring, classification, and control system of the above combination with the SaaS awareness of Laoutaris in order to apply the time series data evaluation shared amongst the disclosures (Laoutaris, pg. 3 lines 30-36) to enable analysis and optimization of the popular SaaS architecture.
Conclusion
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JOHN MACILWINEN
Primary Examiner
Art Unit 2442
/JOHN M MACILWINEN/ Primary Examiner, Art Unit 2454