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 .
Allowable Subject Matter
Claims 3-5, 7, 9-12, 15-16 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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) 1-2, 6, 13-14 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chinese Patent Application (No.: CN114221790A) (“CPA” hereinafter, mappings are made to the attached translation) in view of Prakash et al. (Pub. No.: US 20210250228 A1) (1/31/2024 IDS).
As to claim 1, a computer-implemented method comprising: obtaining, from a plurality of data sources, data related to operation or configuration of Border Gateway Protocol (BGP) in an enterprise network (paragraph [0014], “Use an automatic data acquisition program to collect and store BGP update message data from Route Views and RIPE NCC in chronological order by region”);
extracting one or more BGP features based on at least one correlation among the data from the plurality of data sources (Paragraph [0020], “Store the 45 feature values in the order of timestamps. The data under each timestamp is treated as a sample. Add a label to each sample according to the time period of the abnormal event”);
detecting one or more network anomalies by performing a weakly supervised machine learning of the one or more BGP features (paragraph [0052], “The model prediction module uses known event datasets as training samples and untrained datasets as test samples to evaluate the model's detection performance for unknown abnormal events. This module can also be directly used in practical applications.” And paragraph [0066], “abnormal labels are automatically added according to the time period of the abnormal event to construct the experimental dataset”, teaches weakly supervised machine learning).
CPA does not explicitly teach providing information about the anomalies for performing actions.
However, in the same field of endeavor (network anomaly detection) Prakash teaches providing information about the one or more network anomalies for performing one or more actions associated with the enterprise network (paragraph [0009], “the server is configured to cause processing of one or more remedial actions based on the one or more detected anomalies”).
Based on CPA in view of Prakash, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate providing information about the anomalies for performing actions (taught by Prakash) with network anomaly detection (taught by CPA) in order to perform various remedial actions to enable the system to continue operating as motivated by Prakash (paragraph [0032]).
As to claim 2, Prakash further teaches performing the one or more actions to configure one or more network devices in the enterprise network based on the information about the one or more network anomalies (paragraph [0043], “…When these thresholds are reached, the remedial action unit 42 is configured to cause the processing device 12 to perform certain actions that will alleviate or fix the scale issues …”). The limitations of claim 2 are rejected in view of the analysis of claim 1 above, and the rationale to combine, as discussed in claim 1, applies here as well.
As to claim 6, CPA teaches wherein the one or more BGP features include at least one statistical network feature and at least one network topology feature and detecting the one or more network anomalies includes: generating a graph attention network indicative of one or more interrelationships between the at least one statistical network feature and the at least one network topology feature (paragraph [0005], “This invention, based on sliding window and STL decomposition of event datasets, captures feature relationships and temporal dependencies through feature-based graph attention networks and time-series-based graph attention networks, respectively”); and
generating a ranking-based anomaly score by performing a long short-term memory machine learning of the graph attention network (paragraph [0005], “Finally, a basic LSTM model can be used to accurately predict unknown events from training on known events”).
As to claim 13, CPA further teaches an apparatus comprising: a memory; a network interface configured to enable network communications; and a processor, wherein the processor is configured to perform a method (paragraph [0001]). Therefore, the limitations of claim 13 are substantially similar to claim 1. Please refer to claim 1 above.
As to claim 14, the limitations of the claim are substantially similar to claim 2. Please refer to claim 2 above.
As to claim 17, CPA further teaches One or more non-transitory computer readable storage media encoded with software comprising computer executable instructions that, when executed by a processor, cause the processor to perform a method (paragraph [0001]). Therefore, the limitations of claim 17 are substantially similar to claim 1. Please refer to claim 1 above.
As to claim 18, the limitations of the claim are substantially similar to claim 2. Please refer to claim 2 above.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chinese Patent Application (No.: CN114221790A) in view of Prakash et al. (Pub. No.: US 20210250228 A1) and further in view of Pei et al. (Patent No.: US 7889666 B1) (1/31/2024 IDS).
As to claim 8, CPA in view of Prakash does not explicitly teaches determining root cause of network anomaly based on temporal and spatial correlation.
However, in the same field of endeavor (network anomaly detection) Pei teaches determining a root cause of the one or more network anomalies by grouping the data from the plurality of data sources based on a temporal correlation and a spatial topology correlation (fig. 2, 207-213).
Based on CPA in view of Prakash and further in view of Pei, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate determining root cause of network anomaly based on temporal and spatial correlation (taught by Pei) with providing information about the anomalies for performing actions (taught by Prakash) with network anomaly detection (taught by CPA) in order to perform various remedial actions to enable the system to continue operating as motivated by Prakash (paragraph [0032]), and in order to further point out the root cause impact and mitigation analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Givental et al. (Pub. No.: US 20210281592 A1), teaches network anomaly detection based on weakly supervised machine learning model (abstract).
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/Abdulkader M Alriyashi/Primary Examiner, Art Unit 2447 4/1/2026
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