DETAILED ACTION
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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4-10, 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watts, US 20210160279 A1 (hereafter referred to as Watts).
Claim 11, Watts teaches an apparatus (p. 28, “The BGW controller may be implemented via one or computing devices 200. The computing devices 200 may include general purpose computers, servers…”), comprising:
one or more network interfaces (p. 29, “The computing devices 200 may include a communication interface 202…”); a processor coupled to the one or more network interfaces and configured to execute one or more processes (p. 29, “one or more processors 204”); and a memory configured to store a process that is executable by the processor (p. 31, “The processors 204 and the memory 208 of the computing devices 200 may implement an operating system 210 and the BGW controller 110.”), the process when executed configured to:
obtain network characteristic data associated with degraded performance in a computer network (p. 21, “The data sources 118 may additionally provide real-time operational statuses of the ASs and/or network links between the ASs. Such status information may include trouble ticket reports, network component error logs and reports, network sensor data, AS status reports, news reports, social media reports, and/or so forth that are collected from multiple sources.”);
obtain configuration change data associated with a Border Gateway Protocol configuration change implemented in the computer network (p. 44, “the isolated BGW router is configured to receive AS route updates from other ASs and send the AS route updates to the BGW controller 110 of the AS 102 for analysis.”);
determine a correlation between the network characteristic data and the configuration change data (p. 45, “At block 304, the AS 102 may analyze the data traffic routing path in the AS route update to determine whether the route update is a problematic route update.” And p. 38, “The AS route updates may be generated based on multiple configuration parameters 236, such as the operational costs of networks or links between ASs, the performance, status, security, and/or capabilities of those networks or links, the topology of the ASs (e.g., geographical locations, distances between ASs, links to other ASs, etc.), the performance guarantees made by the AS 102 to customers, and/or so forth. “); and
provide, based on the correlation (machine, an indication that the Border Gateway Protocol configuration change is a cause of the degraded performance in the computer network (p. 46, “At block 308, the BGW controller 110 of the AS 102 may designate the AS route update from the remote AS is a problematic update that is unsuitable for implementation into one or more operational BGW routers of the AS 102.” And p. 25, “the BGW controller 110 may report the remote AS a problematic AS to online reporting portals of governmental regulatory agencies and/or law enforcement agencies.”). Watts teaches a plurality of machine learning techniques that determine the configuration change is problematic and maps to degraded network performance (p. 16, “a problematic AS route update may include an unintentional misconfiguration of a data traffic routing path that degrades network performance metrics of the AS 102”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that Watts’ teaches for applying machine learning to predict and validate route changes and network performance would have been equivalent to correlation. The motivation would be because both provide correspondence between route updates and current network behavior.
Claim 1 is a method comprising steps similar to claim 11 above. Claim 1 is rejected on a similar rationale.
Claim 20, is a tangible, non-transitory, computer-readable medium storing program instructions for claim 11 above. Claim 20 is rejected on a similar rationale.
Claim 14, Watts teaches the apparatus as in claim 11, wherein the apparatus provides the indication to a user interface configured to allow a user to modify the Border Gateway Protocol configuration change (Watts, p. 40, “the controls of the dashboard 120 may enable the administrator to manually reverse an AS route update that is implemented to one or more operational BGW routers using the deployment log 232.”).
Claim 3 is a method comprising steps similar to claim 14 above. Claim 3 is rejected on a similar rationale.
Claim 15, Watts teaches the apparatus as in claim 11, wherein the process when executed is further configured to: modify the Border Gateway Protocol configuration change to address the degraded performance (Watts, p. 44, “the dynamic implementation of the identified routing table modifications may prevent or mitigate the adverse impact of cyber-attacks, unexpected device failure, abnormal network behavior, and similar events.”).
Claim 5 is a method comprising steps similar to claim 15 above. Claim 5 is rejected on a similar rationale.
Claim 16, Watt teaches the apparatus as in claim 11, wherein the apparatus determines the correlation by: setting a correlation field to indicate the Border Gateway Protocol configuration change is causing the degraded performance (Watts, p. 17, “database may store historical problematic routing path data that tracks data traffic routing paths or routing path segments of the data traffic paths that have been previously identified as problematic by the BGW controller 110.”).
Claim 5 is a method comprising steps similar to claim 16 above. Claim 6 is rejected on a similar rationale.
Claim 17, Watts teaches the apparatus as in claim 11, wherein the network characteristic data comprises at least one of:
network data from a network subsystem monitoring network infrastructure, service data from service subsystem monitoring services, or device data from a device subsystem monitoring individual devices of a network (Watts, p. 34, “Detecting the network traffic may include using one or more sensors and/or monitoring utilities of input processing system 300. Upon receiving the network traffic, data collection component 302 may perform one or more processing steps. The processing steps may include, for example, parsing the network traffic to identify user-/device-information (e.g., user/account name, device name/type), identifying network information (e.g., source IP address, destination IP address, hop limit)”).
Claim 7 is a method comprising steps similar to claim 17 above. Claim 7 is rejected on a similar rationale.
Claim 18, Watts teaches the apparatus as in claim 11, wherein the process when executed is further configured to: a train machine learning model to identify potential degraded performance associated with a potential configuration change using the network characteristic data, , and the correlation (Watts, p. 16, “The analysis techniques that are employed by the BGW controller 110 may include network topology analysis, pattern matching analysis, statistical analysis, and/or so forth. Some of these analysis techniques may include the use of machine learning or artificial intelligence (AI) algorithms.” Applying a model trained for inputs.).
Claim 8 is a method comprising steps similar to claim 18 above. Claim 8 is rejected on a similar rationale.
Claim 19, Watts teaches the apparatus as in claim 11, wherein the process when executed is further configured to: predict potential degraded performance associated with a potential configuration change (Watts, p. 18, “the BGW controller 110 may use the data from one or more internal databases to determine whether a particular AS route update is valid or problematic. For example, the BGW controller 110 may generate a simulation of the data traffic routing path specified by the particular AS route update based on the data to predict whether the AS route update is valid. “) using the network characteristic data (Watts, p. 17), the configuration change data (Watts, p. 17), and the correlation of the Border Gateway Protocol configuration change with the degraded performance (Watts, p. 17).
Claim 9 is a method comprising steps similar to claim 19 above. Claim 9 is rejected on a similar rationale.
Claim(s) 12 and 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watts as applied to claims 1 and 11 respectively, in view of Timmons, US 2022020015 A1 (hereafter referred to as Timmons).
Claim 12, Watts teaches the apparatus as in claim 11, as cited above. Watts does not specifically teaches wherein the network characteristic data comprises a Performance Impact Identifier (PID) assigned to the degraded performance, wherein the PID identifies data indicating details of the degraded performance and a timestamp of when the degraded performance started. However, in the same field of endeavor, Timmons teaches the network characteristic data comprises a Performance Impact Identifier (PID) assigned to the degraded performance, wherein the PID identifies data indicating details of the degraded performance and a timestamp of when the degraded performance started (p. 63, “Router 110D modifies the second packet to further include metadata comprising second performance information and forwards the modified second packet to router 110A.” And p. 64, “In some examples, the metadata comprises a BFD packet.” “In some examples, the metadata comprises a measure of network performance, such as a measure of latency, jitter, packet loss, bandwidth, etc.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Watts to incorporate metadata from Timmons to enhance the network information and thereby provide effective mapping to between configuration changes and network performance.
Claim 2 is a method comprising steps similar to claim 12 above. Claim 2 is rejected on a similar rationale.
Claim(s) 13 and 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watts as applied to claim 11 and 1 respectively, further in view of Fenner et al., US 20160330102 A1 (hereafter referred to as Fenner).
Claim 13, Wats teaches the apparatus as in claim 11, as cited above. Watts does not specifically teach wherein the configuration change data comprises a Commit Identifier (CID) assigned to the Border Gateway Protocol configuration change (p. 28, “In one embodiment of the invention, the timestamp may be a number that is incremented each time it is associated with a route. For example, route 1 may be associated with timestamp (TS)=1, route 2 may have TS=2, etc.” And p. 31, “in Step 400, the FIB agent loads the routes stored in the software FIB, along with the timestamps.” The routes are route updates.), wherein the CID identifies data indicating details of the Border Gateway Protocol configuration change and a timestamp of when the Border Gateway Protocol configuration change was made (p. 33, “In the event that the routes are not individually time stamped, the latest time stamp associated with any of the consecutive routes successfully written to the FIB may be used.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Watts to incorporate timestamps from Timmons to differentiate the routing changes and thereby provide effective mapping to network information and thereby prevent false mappings.
Claim 3 is a method comprising steps similar to claim 13 above. Claim 3 is rejected on a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Woodworth et al., US 20230396509 A1, teaches a router having the ability to process requests for network impact assessments may be implemented without significantly impacting the data plane performance of the router.
Whited, US 20230055046 A1, teaches analyzing the sequence of graphs thus generated the network performance evolution, both to and from devices, network performance may be evaluated and used for machine learning and network routing improvement.
Woodworth et al., US 20220329511 A1, teaches evaluation of the network traffic, the logic components may identify indicators of potential failure or performance degradation events in the network traffic. Upon identifying such indicators, the logic components may cause one or more routes in the routing tables of network devices 206 to be removed, restricted, or otherwise modified.
B. Al-Musawi et al., teaches testing BGP anomalies enables network operators to protect their network from the worst consequence of the anomalous behavior. In this survey, we construct a taxonomy of BGP anomalies with four main categories as follows: 1) Direct intended anomaly. 2) Direct unintended anomaly. 3) Indirect anomaly. 4) Link failure.
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/Patrice L Winder/Primary Examiner, Art Unit 2453