CTFR 18/735,131 CTFR 88703 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 2. Claims 1-21 are pending. 12-261 AIA Response to argument 3. Applicant amendment/argument filed on 02/26/2026 has been fully considered, but they are not persuasive. 4. Applicant’s argument states Burnette in view of Qian fails to discloses wherein the first granularity defines a resolution at which data packets are collected from the network traffic for further inspection…beginning, by the processing system, to monitor the network traffic at the location according to a second granularity that is more granular than the first granularity. In response Examiner respectfully disagrees with the applicant’s argument. Burnette discloses wherein the first granularity defines a resolution at which data packets are collected from the network traffic for further inspection (para [0028] using IP addresses of data sessions, measured data session performance and IP address to network location (e.g., ECI) information to create a “crowd” of location-specific (e.g., cell or sector specific) information for a given network topology . The combined measured data session performance and IP address to network location (e.g., ECI) information may be referred to as network topology information, and may include per-cell measurements and/or statistics of data session performance. That information is then used for network monitoring purposes as well as for deep dive analysis of problem cells or problem flows in the network at a level and with performance metrics . [0031]-[0032] IP address to ECI mapping information is provided to a network data plane agent. The data plane agent collects detailed statistics on data sessions (e.g., throughput, data size, transmit times and durations, etc.). The data plane agent uses the IP:ECI mapping information to insert ECI into the statistics/metrics that it collects. Statistics can now be aggregated based on network topology (e.g., per ECI) and analyzed to extract patterns and trends from the crowd (e.g., from the plurality of monitored data sessions). This crowd-sourced data is fed back into algorithms which apply the crowd information to improve optimization. It is also fed back to network monitoring system, which allows that system to detect changing network conditions that may justify more detailed monitoring of portions of the network. (see also para. [0044]-[0047]). beginning, by the processing system, to monitor the network traffic at the location according to a second granularity that is more granular than the first granularity (para. [0034] the system may automatically detect problem cells in a network, and may provide more granular (e.g., per second) [second granular] statistics for the problem cells . [0103] Upon detecting a possible anomaly on a cell, the process 700 may change a reporting interval for the cell from a normal interval (such as 15 minutes) to an enhanced interval (such as one second) [0104] For example, the process 700 may monitor for eNodeBs or cells that trigger pre-determined criteria, such as “average user throughput less than 3 Mbps.” For these cells, the system may collect enhanced granularity (e.g., per second) metrics. The enhanced granularity) . Therefore, Burnette in view of Qian discloses the argued limitations. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 5. In the event the determination of the status of the application as subject to AlA 35 U.S.C. 102 and 103 (or as subject to pre-AlA 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. 07-20-aia AIA 6. 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. 7. Claims 1-2, 4-10, 13-15 and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable Burnette et al. (US 20210203710 A1) hereinafter Burnette in view of Qian et al. (US 20220321470 A1) hereinafter Qian. Regarding claims 1, 19 and 20, Burnette discloses a method comprising: monitoring, by a processing system including at least one processor, network traffic at a location in a communications network according to a first granularity (para. [0028] a system that combines, using IP addresses of data sessions, measured data session performance and IP address to network location (e.g., ECI) information to create a “crowd” of location-specific (e.g., cell or sector specific) information for a given network topology. The combined measured data session performance and IP address to network location (e.g., ECI) information may be referred to as network topology information, and may include per-cell measurements and/or statistics of data session performance. That information is then used for network monitoring purposes as well as for deep dive analysis of problem cells or problem flows in the network at a level and with performance metrics [0103] the process 700 monitors the plurality of cells for anomalies on a configurable anomaly detection interval having a low granularity (such as 15 minutes) [i.e. first granularity] so that all the cells may be monitored without having to store too much data) ; wherein the first granularity defines a resolution at which data packets are collected from the network traffic for further inspection (para [0028] using IP addresses of data sessions, measured data session performance and IP address to network location (e.g., ECI) information to create a “crowd” of location-specific (e.g., cell or sector specific) information for a given network topology. The combined measured data session performance and IP address to network location (e.g., ECI) information may be referred to as network topology information, and may include per-cell measurements and/or statistics of data session performance. That information is then used for network monitoring purposes as well as for deep dive analysis of problem cells or problem flows in the network at a level and with performance metrics . [0031]-[0032] IP address to ECI mapping information is provided to a network data plane agent. The data plane agent collects detailed statistics on data sessions (e.g., throughput, data size, transmit times and durations, etc.). The data plane agent uses the IP:ECI mapping information to insert ECI into the statistics/metrics that it collects. Statistics can now be aggregated based on network topology (e.g., per ECI) and analyzed to extract patterns and trends from the crowd (e.g., from the plurality of monitored data sessions). This crowd-sourced data is fed back into algorithms which apply the crowd information to improve optimization. It is also fed back to network monitoring system, which allows that system to detect changing network conditions that may justify more detailed monitoring of portions of the network. (see also para. [0044]-[0047]). determining, by the processing system, that a condition has been detected that indicates a likelihood of degradation of network performance at the location being monitored (para [0028], [0105] the anomaly monitor subprocesses SP702 determines, using measured conditions of a corresponding cell (such as the real-time information that may be produced by the data flow 200 of FIG. 2 and/or the video flow related information collected by the process 600 of FIG. 6) whether data reporting for a cell is performed at the normal interval or the enhanced interval. [0084] While monitoring traffic from the core of the monitored network, the monitoring system 500 may detect video for known domains (e.g., YouTube, Netflix) and use Machine Learning pattern recognition to estimate video player buffer fill levels to detect start time, stall, and resolution change indicators, as shown in FIG. 6 . [0107] At S706, the anomaly monitor subprocesses SP702 determines whether the measured condition of the cell meets the conditions for any one of one or more traps . A trap may be, for example, whether an average user throughput is less than a throughput target , whether an average percentage of delivered video flows that are High Definition (HD) video is less than an HD video delivery target, whether a number of active users in the cell is outside a normal active users range, whether a percentage of video flows in the cell is outside a preferred videos percentage range, whether an average resolution of video flows in the cell is outside a range, whether a buffer stall rate for buffered data flows is outside a range, whether load times for a web service (e.g., a specific social media site, shopping site, streaming video site, etcetera) is slower than a respective load time target, whether any other any measured cell condition is outside a respective normal range, or combinations thereof . The ranges used for trap detection may be predetermined, or may be determined according to a property of the cell (such as a bandwidth of the cell or learned historical norms for the cell). beginning, by the processing system, to monitor the network traffic at the location according to a second granularity that is more granular than the first granularity (para. [0034] the system may automatically detect problem cells in a network, and may provide more granular (e.g., per second) [second granular] statistics for the problem cells . [0103] Upon detecting a possible anomaly on a cell, the process 700 may change a reporting interval for the cell from a normal interval (such as 15 minutes) to an enhanced interval (such as one second) [0104] For example, the process 700 may monitor for eNodeBs or cells that trigger pre-determined criteria, such as “average user throughput less than 3 Mbps.” For these cells, the system may collect enhanced granularity (e.g., per second) metrics. The enhanced granularity) . Burnette may not explicitly disclose determining, by the processing system based on the monitoring according to the second granularity, a determined network performance and adjusting, by the processing system in response to the determined network performance, a manner in which the network traffic at the location is steered through the communications network determining, by the processing system based on the monitoring according to the second granularity, a determined network performance determining, by the processing system based on the monitoring according to the second granularity, a determined network performance (para. [0097] a client 810 may submit a GetVRMetrics request 833 in the depicted embodiment to obtain metrics about the operations performed at a specified VR. Such metrics may include, for example, the number of application data packets that were processed during a time interval, the number of messages pertaining to auxiliary tasks that were processed during a time interval, and so on. Metrics collected for the VR may be indicated via one or more VRMetrics messages 835 [0183] a client of the WAN service may obtain various metrics (e.g., total bytes transferred per unit time, trends in bandwidth use, measured latencies, packet drop rates, etc.) of network traffic flowing between the client's premises in different geographic regions via the private fiber backbone, e.g., via the client-facing WAN management interfaces 2550. The client may select the preferred granularity at which the metrics are to be presented, e.g., from a set of granularities which includes (a) region-level granularity (in which metrics for all the traffic flowing between client premises in a pair of regions is aggregated), (b) client premise-level granularity (in which metrics are presented separately for different pairs of client premises), or (c) isolated network-level granularity [0184] A client of the WAN service may utilize the service for managing various types of exceptional events with respect to their applications, e.g., to fail over the workload of some applications from one region to another in the event of an outage or other network problems . The WAN service may, for example, obtain an indication from the client, via programmatic interfaces, of one or more diversion criteria (e.g., detection of failures, network slowdowns, etc.) for traffic directed to a first set of network endpoints at the client premises in a given geographical region. The WAN service may monitor network performance data associated with traffic to/from the different client premises utilizing the backbone network ); and adjusting, by the processing system in response to the determined network performance, a manner in which the network traffic at the location is steered through the communications network (para. [0184],[0200] a client may provide failover-related settings for their WAN, e.g., using one or more TrafficDiversionConfigInfo messages 2931 to the WAN service. A TrafficDiversionConfigInfo message may indicate one or more diversion criteria for traffic which would normally be directed to some set of network endpoints within one or more of the client premises . A diversion criterion may, for example, comprise a detection that one or more links to the set of network endpoints have failed or that latencies for delivering packets to the set of endpoints have exceeded a threshold . The TrafficDiversionConfigInfo may also indicate substitute endpoints, e.g., at a customer premise in another region, to which the traffic should be diverted if the criteria are satisfied) . Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Burnette and include determining, by the processing system based on the monitoring according to the second granularity, a determined network performance and adjusting, by the processing system in response to the determined network performance, a manner in which the network traffic at the location is steered through the communications network using the teaching of Qian. One would have been motivated to do so in order to effectively manage large-scale computing resources for many customers with diverse needs, thus allowing various computing resources to be efficiently and securely shared by multiple customers. Regarding claim 2 , claim 1 is incorporated. Burnette in view of Qian disclose wherein the location is at least one of: a link within the communications network, a wide area network within the communications network, a site within the communications network, a campus, a city a county a state or a province (para. [0035]-[0036] FIG. 1 illustrates a communication network 100 according to an embodiment. The network 100 includes a Wide-Area Network (WAN) 102, a mobile telecommunication system (MTS) 106, and first and second servers 114A and 114B attached to the WAN 102. The MTS 106 includes an MTS infrastructure 108 that connects first and second Radio Access Networks (RANs) 104A and 104B to each other and to the WAN 102. The MTS infrastructure 108 may include a gateway 112 through which communications between the WAN 102 and the MTS 106 are performed. [0042] First and second servers 114A and 114B may provide services to devices connected to the WAN 102. Examples of services that may be provided include cloud computing, cloud storage, social networking, streaming video, and the like. Qian further discloses [0176]-[0177] as shown, system 2500 includes resources and artifacts of a provider network WAN service 2502 which is used to enable connectivity between premises of an organization A with a headquarters OAHQ 2520 and various other premises distributed among country/region 2510A, country/region 2510B and country/region 2510C. [0188] the potential client has indicated that premises are located in City-A, City-B and City-C, located within State-A of Country-A, Country-B and State-C of Country-C respectively. After the locations of the client premises have been entered in table 2606, the client may use the Submit button 2608 to send the information to the WAN service). Regarding claim 4 , claim 1 is incorporated. Burnette further discloses wherein the first granularity defines at least one of: a frequency with which data packets are collected from the network traffic for further inspection, or a frequency with which data flows are collected from the network traffic for further inspection (para. [0103] the process 700 monitors the plurality of cells for anomalies on a configurable anomaly detection interval having a low granularity (such as 15 minutes) so that all the cells may be monitored without having to store too much data) Regarding claim 5 , claim 1 is incorporated. Burnette further discloses wherein the monitoring at the first granularity further comprises monitoring multimodal signals received from at least one device in the communications network, wherein the multimodal signals contain data other than the network traffic, and wherein the determining that the condition has been detected occurs as a result of the monitoring the multimodal signals (para.[0103] the process 700 monitors the plurality of cells for anomalies on a configurable anomaly detection interval having a low granularity (such as 15 minutes) so that all the cells may be monitored without having to store too much data. [0107] A trap may be, for example, whether an average user throughput is less than a throughput target, whether an average percentage of delivered video flows that are High Definition (HD) video is less than an HD video delivery target, whether a number of active users in the cell is outside a normal active users range, whether a percentage of video flows in the cell is outside a preferred videos percentage range, whether an average resolution of video flows in the cell is outside a range, whether a buffer stall rate for buffered data flows is outside a range, whether load times for a web service (e.g., a specific social media site, shopping site, streaming video site, etcetera) is slower than a respective load time target, whether any other any measured cell condition is outside a respective normal range, or combinations thereof. The ranges used for trap detection may be predetermined, or may be determined according to a property of the cell (such as a bandwidth of the cell or learned historical norms for the cell). Regarding claim 6, claim 5 is incorporated. Burnette further discloses wherein the multimodal signals include at least one of: a resource utilization metric from the at least one device, a log from the at least one device, a change in a number of incidents being reported by users of the at least one device, or a change in a rate of incidents being reported by users of the at least one device (para. [0107] At S706, the anomaly monitor subprocesses SP702 determines whether the measured condition of the cell meets the conditions for any one of one or more traps. A trap may be, for example, whether an average user throughput is less than a throughput target, whether an average percentage of delivered video flows that are High Definition (HD) video is less than an HD video delivery target, whether a number of active users in the cell is outside a normal active users range, whether a percentage of video flows in the cell is outside a preferred videos percentage range, whether an average resolution of video flows in the cell is outside a range, whether a buffer stall rate for buffered data flows is outside a range, whether load times for a web service (e.g., a specific social media site, shopping site, streaming video site, etcetera) is slower than a respective load time target, whether any other any measured cell condition is outside a respective normal range, or combinations thereof. The ranges used for trap detection may be predetermined, or may be determined according to a property of the cell (such as a bandwidth of the cell or learned historical norms for the cell). Regarding claim 7 , claim 5 is incorporated. Burnette may not explicitly disclose wherein the multimodal signals are sent by the at least one device with a granularity that is different from the first granularity. However, Qian discloses wherein the multimodal signals are sent by the at least one device with a granularity that is different from the first granularity (para. [0191] web-based interface which may be used to present status information for traffic flowing between client-specified locations, according to at least some embodiments. In web-based interface 2702, message 2704 indicates how the client may change the granularity at which status information (including health or availability information, as well as measured traffic rates in either direction) for various portions of the client's wide area network is being presented. In some embodiments, the client may choose from, among other granularity options, information aggregated at the region level, at the level of individual premises (as shown in the example of FIG. 27), or even at the level of individual isolated networks within premises and within the provider network. In graph 2710, health stats information (“Status: OK”) and latest traffic rates for both directions of traffic are shown for provider backbone-based connectivity between premise P1 (City-A) and premise P2 (City-B), premise P1 and premise P3 (City-C), and premise P2 and premise P3, with virtual routers VR-1, VR-2 and VR-3 respectively set up at the three premises. Zoom in/out control element 2711 may be used to change the granularity in some embodiments—e.g., if the client zooms out so that several different regions (each including one or more client premises) become visible, the granularity of the information displayed may be changed automatically to the region-level granularity. A client may also change granularities by clicking on the connectors shown between VRs or premises in graph 2710 in the depicted embodiment). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Burnette and include wherein the multimodal signals are sent by the at least one device with a granularity that is different from the first granularity using the teaching of Qian. One would have been motivated to do so in order to effectively manages large-scale computing resources for many customers with diverse needs, thus allowing various computing resources to be efficiently and securely shared by multiple customers. Regarding claim 8 , claim 5 is incorporated Burnette further discloses wherein the condition comprises a software application being used by a user at the location that is not performing as expected (para. [0107] At S706, the anomaly monitor subprocesses SP702 determines whether the measured condition of the cell meets the conditions for any one of one or more traps. A trap may be, for example, whether an average user throughput is less than a throughput target, whether an average percentage of delivered video flows that are High Definition (HD) video is less than an HD video delivery target, whether a number of active users in the cell is outside a normal active users range, whether a percentage of video flows in the cell is outside a preferred videos percentage range, whether an average resolution of video flows in the cell is outside a range, whether a buffer stall rate for buffered data flows is outside a range, whether load times for a web service (e.g., a specific social media site, shopping site, streaming video site, etcetera) is slower than a respective load time target, whether any other any measured cell condition is outside a respective normal range, or combinations thereof. The ranges used for trap detection may be predetermined, or may be determined according to a property of the cell (such as a bandwidth of the cell or learned historical norms for the cell). Regarding claim 9, claim 8 is incorporated. Burnett may not explicitly disclose wherein the condition is inferred from a multimodal signal of the multimodal signals that is provided to the processing system via application integration. However, Qian discloses wherein the condition is inferred from a multimodal signal of the multimodal signals that is provided to the processing system via application integration (para. [0184] a client of the WAN service may utilize the service for managing various types of exceptional events with respect to their applications in some embodiments, e.g., to fail over the workload of some applications from one region to another in the event of an outage or other network problems. [0195] the provider network backbone links may be used by default, and the client's traffic may be switched to the external fiber lines in the event of a failure reported by the WAN service, or in response to performance metrics reaching a specified threshold at the WAN service. In some embodiments, a client serviced may provide configuration information to the WAN service which can be used to access performance metrics for the leased fiber WAN, and a unified tool or interface (similar to the interface depicted in FIG. 27) may be used to provide performance metrics and health status pertaining to both types of WANs to clients). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Burnette and include wherein the condition is inferred from a multimodal signal of the multimodal signals that is provided to the processing system via application integration using the teaching of Qian. One would have been motivated to do so in order to effectively manages large-scale computing resources for many customers with diverse needs, thus allowing various computing resources to be efficiently and securely shared by multiple customers). Regarding claim 10 , claim 5 is incorporated Burnette may not explicitly disclose wherein the condition comprises a malfunction of the at least one device. However, Qian discloses wherein the condition comprises a malfunction of the at least one device (para. [0184] a client of the WAN service may utilize the service for managing various types of exceptional events with respect to their applications in some embodiments, e.g., to fail over the workload of some applications from one region to another in the event of an outage or other network problems. The WAN service may, for example, obtain an indication from the client, via programmatic interfaces, of one or more diversion criteria (e.g., detection of failures, network slowdowns, etc.)). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Burnette and include wherein the condition comprises a malfunction of the at least one device using the teaching of Qian. One would have been motivated to do so in order to effectively manages large-scale computing resources for many customers with diverse needs, thus allowing various computing resources to be efficiently and securely shared by multiple customers). Regarding claim 12 , claim 5 is incorporated. Burnette further discloses wherein the condition comprises congestion in the communications network (para. [0130] identify problem cells and provide fine temporal granularity statistics (e.g., per second statistics) for each cell identified as a problem cell. The fine temporal granularity statistics may include average user throughput, average active users, average connected users, video performance metrics (start time, stall rate, resolution, etcetera), traffic composition, cell congestion level, or combinations thereof). Regarding claim 14 , claim 5 is incorporated. Burnette further discloses wherein the condition is inferred from a multimodal signal of the multimodal signals that indicates a bandwidth utilization that falls outside of a predefined bandwidth utilization range (para. [0107] At S706, the anomaly monitor subprocesses SP702 determines whether the measured condition of the cell meets the conditions for any one of one or more traps. A trap may be, for example, whether an average user throughput is less than a throughput target, whether an average percentage of delivered video flows that are High Definition (HD) video is less than an HD video delivery target, whether a number of active users in the cell is outside a normal active users range, whether a percentage of video flows in the cell is outside a preferred videos percentage range, whether an average resolution of video flows in the cell is outside a range, whether a buffer stall rate for buffered data flows is outside a range, whether load times for a web service (e.g., a specific social media site, shopping site, streaming video site, etcetera) is slower than a respective load time target, whether any other any measured cell condition is outside a respective normal range, or combinations thereof. The ranges used for trap detection may be predetermined, or may be determined according to a property of the cell (such as a bandwidth of the cell or learned historical norms for the cell). Regarding claim 15 , claim 1 is incorporated. Burnette further discloses wherein the condition is inferred from a presence of a pattern in the network traffic that is known to be associated with the degradation in network performance (para [0089] The process 600 may also determine that the flow is a video flow when a pattern of the statistics for the flow matches a pattern for video flows learned through machine learning, which may be determined in an embodiment using a trained neural network. In an embodiment, when the process 600 determines that a flow is a video flow using machine learning, the process 600 may update the database 606 it indicate that an IP address of the source of the flow is an IP address of a video source. [0107] At S706, the anomaly monitor subprocesses SP702 determines whether the measured condition of the cell meets the conditions for any one of one or more traps. A trap may be, for example, whether an average user throughput is less than a throughput target, whether an average percentage of delivered video flows that are High Definition (HD) video is less than an HD video delivery target, whether a number of active users in the cell is outside a normal active users range, whether a percentage of video flows in the cell is outside a preferred videos percentage range, whether an average resolution of video flows in the cell is outside a range, whether a buffer stall rate for buffered data flows is outside a range, whether load times for a web service (e.g., a specific social media site, shopping site, streaming video site, etcetera) is slower than a respective load time target, whether any other any measured cell condition is outside a respective normal range, or combinations thereof. The ranges used for trap detection may be predetermined, or may be determined according to a property of the cell (such as a bandwidth of the cell or learned historical norms for the cell). Regarding claim 17 , claim 1 is incorporated. Burnette may not explicitly disclose wherein the determining calculates a performance metric that is based on a combination of the network traffic that is collected while the monitoring is being performed according to the second granularity. However, Qian discloses wherein the determining calculates a performance metric that is based on a combination of the network traffic that is collected while the monitoring is being performed according to the second granularity (para. [0074] one of the benefits of separating the resources used for auxiliary tasks from the resources used for baseline forwarding tasks is that changes in workload can be handled independently for the two types of tasks. FIG. 4 illustrates an example scenario in which resources used for virtual routers may be automatically scaled independently of resources used for auxiliary tasks associated with traffic routed via the virtual routers, according to at least some embodiments. A set of VR scaling managers 477 may be assigned the responsibility of collecting and analyzing workload and resource utilization levels of VR resources such as FNs and ENs, and initiating the acquisition or release of VR resources as needed, in response to trends or changes in the collected metrics in the depicted embodiment… [0183] client of the WAN service may obtain various metrics (e.g., total bytes transferred per unit time, trends in bandwidth use, measured latencies, packet drop rates, etc.) of network traffic flowing between the client's premises in different geographic regions via the private fiber backbone, e.g., via the client-facing WAN management interfaces 2550. The client may select the preferred granularity at which the metrics are to be presented, e.g., from a set of granularities which includes (a) region-level granularity (in which metrics for all the traffic flowing between client premises in a pair of regions is aggregated), (b) client premise-level granularity (in which metrics are presented separately for different pairs of client premises), or (c) isolated network-level granularity (in which metrics are presented separately for each IVN pair as well as for each combination of IVN and client premise network). In at least some embodiments, a unified interface may be used to present inter-region traffic metrics as well as intra-region traffic metrics. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Burnette and include wherein the determining calculates a performance metric that is based on a combination of the network traffic that is collected while the monitoring is being performed according to the second granularity using the teaching of Qian. One would have been motivated to do so in order to effectively manages large-scale computing resources for many customers with diverse needs, thus allowing various computing resources to be efficiently and securely shared by multiple customers. Regarding claim 18 , claim 1 is incorporated. Burnette may not explicitly disclose resuming, by the processing system, the monitoring the network traffic at the location according to the first granularity, subsequent to the adjusting. However, Qian discloses resuming, by the processing system, the monitoring the network traffic at the location according to the first granularity, subsequent to the adjusting (para. [0200] A client may provide failover-related settings for their WAN, e.g., using one or more TrafficDiversionConfigInfo messages 2931 to the WAN service in the depicted embodiment. A TrafficDiversionConfigInfo message may indicate one or more diversion criteria for traffic which would normally be directed to some set of network endpoints within one or more of the client premises. A diversion criterion may, for example, comprise a detection that one or more links to the set of network endpoints have failed or that latencies for delivering packets to the set of endpoints have exceeded a threshold. The TrafficDiversionConfigInfo may also indicate substitute endpoints, e.g., at a customer premise in another region, to which the traffic should be diverted if the criteria are satisfied… [0203] A client 2910 may request performance metrics, availability metrics and/or health status updates…The metrics may include, for example, data transfer rates, packet latencies, packet drop/loss rates, uptimes, and the like, provided at any of several granularities chosen by the client such as region-to-region granularity, premise-to-premise granularity, per isolated network granularity and so on. [0183],[0191] in web-based interface 2702, message 2704 indicates how the client may change the granularity at which status information (including health or availability information, as well as measured traffic rates in either direction) for various portions of the client's wide area network is being presented. In some embodiments, the client may choose from, among other granularity options, information aggregated at the region level, at the level of individual premises (as shown in the example of FIG. 27)...) Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Burnette and include resuming, by the processing system, the monitoring the network traffic at the location according to the first granularity, subsequent to the adjusting using the teaching of Qian. One would have been motivated to do so in order to effectively manages large-scale computing resources for many customers with diverse needs, thus allowing various computing resources to be efficiently and securely shared by multiple customers. Regarding claim 21 , claim 1 is incorporated. Burnette further discloses wherein the resolution is one of: a per- session, per-link, or a per-application (see par. [0028] [0031]-[0032]). 8. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable Burnette et al. in view of Qian et al. and further in view of Urmanov et al. (US 20200336500 A1) hereinafter Urmanov Regarding claim 11 , claim 10 is incorporated. Burnette in view of Qian may not explicitly discloses wherein the condition is inferred from a multimodal signal of the multimodal signals that comprises a notification generated by the at least one device. However, Urmanov discloses wherein the condition is inferred from a multimodal signal of the multimodal signals that comprises a notification generated by the at least one device (para. [0033] During a subsequent monitoring phase for prognostic-surveillance system 100, model 119 is used by anomaly detection module 120 to classify events in multimodal data 114 to detect various anomalies, such as a hardware failure; a software failure; an intrusion; a malicious activity; and a performance issue. If anomalies are detected, anomaly detection module 120 generates alerts 122. In response to an alert, the system can perform a remedial action…) Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Burnette in view of Qian and include wherein the condition is inferred from a multimodal signal of the multimodal signals that comprises a notification generated by the at least one device using the teaching of Urmanov. One would have been motivated to do so in order to detect operational anomalies in enterprise computer systems based on large datasets of multimodal data. 9. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable Burnette et al. in view of Qian et al. and further in view of Mena et al. (US 20200100143 A1) hereinafter Mena. Regarding claim 13 , claim 12 is incorporated. Burnette in view of Qian may not explicitly discloses wherein the condition is inferred from a subset of the multimodal signals that indicates an existence of a packet queue containing a number of packets that is greater than a threshold number. However, Mena discloses (para. [0065] …identification may be based on identifying that one or more communication-related parameters associated with the multimedia traffic flow (e.g. a channel utilization parameter, a queue depth parameter, and/or a congestion level parameter) exceeds a threshold. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Burnette in view of Qian and include wherein the condition is inferred from a subset of the multimodal signals that indicates an existence of a packet queue containing a number of packets that is greater than a threshold number using the teaching of Mena. One would have been motivated to do so in order to improve quality of experience (QoE) for multimedia content delivered to and/or from wireless mobile devices operating in wireless networks. 10. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable Burnette et al. in view of Qian et al. and further in view of Yates et al. (US 20220182254 A1) hereinafter Yates. Regarding claim 16 , claim 1 is incorporated. Burnette in view of Qian may not explicitly discloses wherein the determining is performed by executing a machine learning model that is trained to take as input at least one characteristic of the network traffic and to generate as an output a likelihood that the degradation in network performance has occurred. However, Yates discloses wherein the determining is performed by executing a machine learning model that is trained to take as input at least one characteristic of the network traffic and to generate as an output a likelihood that the degradation in network performance has occurred (para. [0020] the system may generate a machine learning model. The system may train the machine learning model using historical data (e.g., known patterns, historical performance data) and associated intermittent network issues and sustained node outage. The system may input a detected pattern or measured performance data into the trained machine learning model and the trained machine learning model may output a likelihood indicative of how probable an event is to occur (e.g., how likely the intermittent network issues will occur and/or how likely a sustained node outage will occur). the system may use the historical data to train a machine learning model to classify a node into different classifications (e.g., a classification indicative of nodes having been experiencing intermittent network issues, a classification indicative of nodes having experienced intermittent network issues, a classification indicative of nodes will experience a sustained node outage, a classification indicative of a bad node that is completely off, a classification indicative of a healthy node, or the like). The system may input a detected pattern or measured performance data into the trained machine learning model and the trained machine learning model may output a likelihood indicative of a classification associated with a specific node). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Burnette in view of Qian and include wherein the determining is performed by executing a machine learning model that is trained to take as input at least one characteristic of the network traffic and to generate as an output a likelihood that the degradation in network performance has occurred using the teaching of Yates. One would have been motivated to do so in order to identify network issues in a timely, accurate and efficient manner. Conclusion 07-39 AIA 11. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). 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For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. 06/10/2026 /KIDEST MENDAYE/ Examiner, Art Unit 2457 /ARIO ETIENNE/Supervisory Patent Examiner, Art Unit 2457 Application/Control Number: 18/735,131 Page 2 Art Unit: 2457 Application/Control Number: 18/735,131 Page 3 Art Unit: 2457
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