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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20f U.S. Patent No. 12,143,290. Although the claims at issue are not identical, they are not patentably distinct from each other.
Instant Application
U.S. Patent No. 12,143,290
1. A method comprising: obtaining, by a device, quality of experience metrics for an online application whose traffic traverses a particular interface of a router located at a first site in a network;
identifying, by a device and by using a machine learning model, a correlation between throughput of the particular interface and the quality of experience metrics for the online application;
making, by the device and based on the correlation, a determination that the particular interface is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics;
a priority queue associated with the particular interface for use by traffic.
1.and providing, by the device for presentation by a user interface, an indication of the determination and
2. The method as in claim 1, wherein the quality of experience metrics comprise at least one of: metrics based on feedback supplied by users of the online application or metrics indicative of a probability that a service level agreement will be violated.
3. The method as in claim 1, further comprising: verifying, by the device, the determination by testing whether the degradation can be repeated by sending traffic that mimics the traffic of the online application via the particular interface.
4. The method as in claim 1, further comprising: determining, by the device, whether the quality of experience metrics of the online application are correlated with resource usage metric or sensor measurement of the router.
5. The method as in claim 1, further comprising: configuring, by the device and in response to user feedback, the priority queue associated with the particular interface for use by traffic of the online application.
6. The method as in claim 1, further comprising: identifying, by the device, a threshold throughput of the particular interface at which degradation of the quality of experience metrics is observed.
7. The method as in claim 1, further comprising: determining, by the device, whether degradation of the quality of experience metrics are associated with a particular type of traffic of the online application.
8. The method as in claim 1, further comprising: increasing, by the device, an allocated bandwidth of a priority queue, based on a determination that configuration of the priority queue has not resolved the degradation.
9. The method as in claim 8, further comprising: sending, by the device, a notification to the user interface that a link bandwidth capacity associated with the particular interface should be increased, based on a determination that increasing the allocated bandwidth of the priority queue has not resolved the degradation.
10. The method as in claim 1, wherein network comprises a software-defined network.
11. 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, the process when executed configured to: obtain quality of experience metrics for an online application whose traffic traverses a particular interface of a router located at a first site in a network;
identify, by using a machine learning model, a correlation between throughput of the particular interface and the quality of experience metrics for the online application;
make a determination that the correlation is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics;
11.and provide, for presentation by a user interface, an indication of the determination and a priority queue associated with the particular interface for use by traffic.
12. The apparatus as in claim 11, wherein the quality of experience metrics comprise at least one of: metrics based on feedback supplied by users of the online application or metrics indicative of a probability that a service level agreement will be violated.
13. The apparatus as in claim 11, wherein the process when executed is further configured to: verify the determination by testing whether the degradation can be repeated by sending traffic that mimics the traffic of the online application via the particular interface.
14. The apparatus as in claim 11, wherein the process when executed is further configured to: determine whether the quality of experience metrics of the online application are correlated with resource usage metric or sensor measurement of the router.
15. The apparatus as in claim 11, wherein the process when executed is further configured to: configure, in response to user feedback, the priority queue associated with the particular interface for use by traffic of the online application.
16. The apparatus as in claim 11, wherein the process when executed is further configured to: identify a threshold throughput of the particular interface at which degradation of the quality of experience metrics is observed
.
17. The apparatus as in claim 11, wherein the process when executed is further configured to: determine whether degradation of the quality of experience metrics are associated with a particular type of traffic of the online application.
18. The apparatus as in claim 11, wherein the process when executed is further configured to: increase an allocated bandwidth of a priority queue, based on a determination that configuration of the priority queue has not resolved the degradation.
19. The apparatus as in claim 18, wherein the process when executed is further configured to: send a notification to a user interface that a link bandwidth capacity associated with the particular interface should be increased, based on a determination that increasing the allocated bandwidth of the priority queue has not resolved the degradation.
20. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising: obtaining, by the device, quality of experience metrics for an online application whose traffic traverses a particular interface of a router located at a first site in a network;
identifying, by a device and by using a machine learning model, a correlation between throughput of the particular interface and the quality of experience metrics for the online application;
making, by the device, a determination that the correlation is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics; and
providing, by the device for presentation by a user interface, an indication of the determination and a priority queue associated with the particular interface for use by traffic.
1. A method comprising: obtaining, by a device, quality of experience metrics for an online application whose traffic traverses a particular interface of a router located at a first site in a network,
wherein the quality of experience metrics comprise metrics based on feedback supplied by users of the online application; identifying, by the device, a correlation between throughput of the particular interface and the quality of experience metrics for the online application by using a causal discovery and inference model;
making, by the device and based on the correlation, a determination that the particular interface is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics;
and configuring, by the device and based on the determination,
a priority queue associated with the particular interface for use by traffic of the online application.
5. The method as in claim 1, further comprising: providing, by the device, an indication of the determination for presentation by a user interface.
2. The method as in claim 1, wherein the quality of experience metrics further comprise metrics indicative of a probability that a service level agreement will be violated.
3. The method as in claim 1, further comprising: verifying, by the device, the determination by testing whether the degradation can be repeated by sending traffic that mimics the traffic of the online application via the particular interface.
4. The method as in claim 1, further comprising: determining, by the device, whether the quality of experience metrics of the online application are correlated with resource usage metric or sensor measurement of the router.
1.and configuring, by the device and based on the determination, a priority queue associated with the particular interface for use by traffic of the online application.
6. The method as in claim 1, further comprising: identifying, by the device, a threshold throughput of the particular interface at which degradation of the quality of experience metrics is observed.
7. The method as in claim 1, further comprising: determining, by the device, whether degradation of the quality of experience metrics are associated with a particular type of traffic of the online application.
8. The method as in claim 1, further comprising: increasing, by the device, an allocated bandwidth of the priority queue, based on a determination that configuration of the priority queue has not resolved the degradation.
9. The method as in claim 8, further comprising: sending, by the device, a notification to a user interface that a link bandwidth capacity associated with the particular interface should be increased, based on a determination that increasing the allocated bandwidth of the priority queue has not resolved the degradation.
10. The method as in claim 1, wherein network comprises a software-defined network.
11. 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, the process when executed configured to: obtain quality of experience metrics for an online application whose traffic traverses a particular interface of a router located at a first site in a network,
wherein the quality of experience metrics comprise metrics based on feedback supplied by users of the online application;
identify a correlation between throughput of the particular interface and the quality of experience metrics for the online application by using a causal discovery and inference model;
make, based on the correlation, a determination that the particular interface is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics;
and configure, based on the determination, a priority queue associated with the particular interface for use by traffic of the online application.
15. The apparatus as in claim 11, wherein the process when executed is further configured to: provide an indication of the determination for presentation by a user interface.
12. The apparatus as in claim 11, wherein the quality of experience metrics further comprise metrics indicative of a probability that a service level agreement will be violated.
13. The apparatus as in claim 11, wherein the process when executed is further configured to: verify the determination by testing whether the degradation can be repeated by sending traffic that mimics the traffic of the online application via the particular interface.
14. The apparatus as in claim 11, wherein the process when executed is further configured to: determine whether the quality of experience metrics of the online application are correlated with resource usage metric or sensor measurement of the router.
11. and configure, based on the determination, a priority queue associated with the particular interface for use by traffic of the online application.
16. The apparatus as in claim 11, wherein the process when executed is further configured to: identify a threshold throughput of the particular interface at which degradation of the quality of experience metrics is observed.
17. The apparatus as in claim 11, wherein the process when executed is further configured to: determine whether degradation of the quality of experience metrics are associated with a particular type of traffic of the online application.
18. The apparatus as in claim 11, wherein the process when executed is further configured to: increase an allocated bandwidth of the priority queue, based on a determination that configuration of the priority queue has not resolved the degradation.
19. The apparatus as in claim 18, wherein the process when executed is further configured to: send a notification to a user interface that a link bandwidth capacity associated with the particular interface should be increased, based on a determination that increasing the allocated bandwidth of the priority queue has not resolved the degradation.
20. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising: obtaining, by the device, quality of experience metrics for an online application whose traffic traverses a particular interface of a router located at a first site in a network,
wherein the quality of experience metrics comprise metrics based on feedback supplied by users of the online application; identifying, by the device, a correlation between throughput of the particular interface and the quality of experience metrics for the online application by using a causal discovery and inference model;
making, by the device and based on the correlation, a determination that the particular interface is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics; and
configuring, by the device and based on the determination, a priority queue associated with the particular interface for use by traffic of the online application.
5. The method as in claim 1, further comprising: providing, by the device, an indication of the determination for presentation by a user interface.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: The closes prior art, Mayor et al. – hereinafter Mayor (US 11,356,335) is directed to a machine learning-based network analytics, troubleshoot, and self-healing system to collect various time-series diagnostic parameters to promote autonomous self-healing of network problems without human intervention using machine learning network based analysis and problem resolution.
Saavedra (US 2016/0315808) discloses configure a bonded connection that has increased throughput, and uses a virtual control plane interface to provide a priority queues and quality of service indicator for the data traffic using a unicast patent between the one network server component and each of the plurality of network server components of the MPLS core network. It also uses machine learning techniques to generate network performance insights to train the predictive algorithms in real-time to manage selective access to the multiple networks. Evans (US 2019/0036816) discloses generating a policy, based on the change for at least one device of the set of devices in the SDN and implements QoS monitoring of communication links, the edge network devices 110 may provide for one or more QoS metrics that may be monitored for any communication link, such as jitter, bandwidth, error rate, bit rate and throughput. The control device uses he monitor data for correlation and root cause analysis to define categories of correlated events and then use network domain knowledge to establish a cause and effect relationship between these correlated events. The control device 120 may then narrow the list to a set of potential root causes. This helps reroute traffic from along multiple paths from a first tunnel to a second tunnel and determines next-hop and route path instructions and updates the routing table. However, these references fail to a identify a correlation between the throughput of a particular interface of a remote router and the quality of experience metrics of that interface based on a machine learning algorithm, as claimed.
A thorough review of the prior art fails to anticipate or render obvious,
“making, by the device and based on the correlation, a determination that the particular interface is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics”
Conclusion
The prior art made of record and not relied upon is considered pertinent toapplicant's disclosure. See PTO-892 form.
Any inquiry concerning this communication or earlier communications from theexaminer should be directed to Chirag R Patel whose telephone number is (571)272-7966. The examiner can normally be reached on Monday to Friday from 9:00AM to 6:00PM. If attempts to reach the examiner by telephone are unsuccessful, theexaminer's supervisor, Glenton Burgess, can be reached on 571-272-3949. The fax phone number for the organization where this application or proceedingis assigned is 571-273-8300.
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/Chirag R Patel/
Primary Examiner, Art Unit 2454