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
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mermoud et al. (U.S. 2022/0191142 A1) in view of Sharma et al. (U.S. 2020/0084087 A1).
Re claim 1, Mermoud et al. discloses a computing apparatus comprising: one or more computer readable storage media, one or more processors operatively coupled with the one or more computer readable storage media, and program instructions stored on the one or more computer readable storage media that, when executed by the one or more processors, direct the computing apparatus (e.g. Figure 2) to at least: process network telemetry data and congestion data using a machine learning model trained to detect anomalous behavior on a wireless communication network (e.g. Abstract; page 3 para. [0033-0034]); in response to detecting the anomalous behavior, identify a source of the anomalous behavior on the wireless communication network (Abstract; Figures 5-6); and initiate an action with respect to a network function associated with the source of the anomalous behavior to mitigate one or more effects (e.g. re-route) of the anomalous behavior (e.g. Abstract; page 5 para. [0051-0053]). Mermoud et al. fail to disclose the limitation of in response to detecting the anomalous behavior, identify at least one of a network function or a hardware component of the wireless communication network as a source of the anomalous behavior on the wireless communication network.
However, Sharma et al. disclose the limitation of in response to detecting the anomalous behavior, identify at least one of a network function or a hardware component of the wireless communication network as a source of the anomalous behavior on the wireless communication network (e.g. page 2 para. [0017]; page 4 para. [0031-0035]; and page 6 para. [0045-0050]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to detecting the anomalous behavior, identify at least one of a network function or a hardware component of the wireless communication network as a source of the anomalous behavior on the wireless communication network as seen in Sharma et al.’s invention into Mermoud et al.’s invention because it will disclosed the root cause in order to enable alerts and mitigation.
Re claim 2, Mermoud et al. further discloses the congestion data comprises data based on Explicit Congestion Notification (ECN) congestion notifications (e.g. page 6 para. [0057]).
Re claim 3, Mermoud et al. further discloses the congestion data is computed based on a quantity of ECN bits which include an ECN congestion notification with respect to ECN-enabled data traffic on the wireless communication network (e.g. page 6 para. [0059]).
Re claim 4, Mermoud et al. further discloses the network telemetry data comprises Quality of Service metrics of the wireless communication network (e.g. page 6 para. [0060-0061]).
Re claim 5, Mermoud et al. further discloses the network telemetry data further comprises signal quality metrics of the wireless communication network (e.g. page 6 para. [0060-0061]).
Re claim 6, Mermoud et al. further discloses to process the network telemetry data and the congestion data, the program instructions direct the computing apparatus to generate an input vector based on segmenting the network telemetry data and the congestion data and submit the input vector to the machine learning model (e.g. page 3-4 para. [0035] and page 6 para. [0064]).
Re claim 7, Mermoud et al. further discloses the machine learning model comprises a recurrent neural network trained for anomaly detection using historical network telemetry data and historical congestion data (e.g. page 6 para. [0063]).
Re claim 8, Mermoud et al. further discloses the computing apparatus comprises a Network Data Analytics Function of the wireless communication network (e.g. analysis; page 6 para. [0056]).
Re claims 9-16, they are method claims having similar limitations cited in claims 1-8 respectively. Thus, claims 9-16 are also rejected under the same rationale as cited in the rejection of claims 1-8 respectively.
Re claims 17-18, they are a method claims having similar limitations cited in claims 1-2 respectively. Thus, claims 1-2 are also rejected under the same rationale as cited in the rejection of claims 1-2 respectively.
Re claims 19, Mermoud et al. further discloses generating a feature vector based on segmenting the input data synchronized in time and submitting the feature vector to the machine learning model (e.g. page 3 para. [0033-0034]).
Re claim 20, Mermoud et al. further discloses identifying the source of the anomalous behavior based on the output comprises identifying a channel of the channels of input data associated with the indication of anomalous behavior (e.g. Abstract; Figures 5-6; and page 3 para. [0033-0034] and page 5 para. [0051-0053]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20200084087-A1
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUOC H NGUYEN whose telephone number is (571)272-3919. The examiner can normally be reached M-F: 7:30 am -3:30 pm.
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/PHUOC H NGUYEN/Primary Examiner, Art Unit 2451
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