DETAILED ACTION
This Office action is in response to the original application filed on 06/30/2025. Claims 1-20 are pending.
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 - 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 claims at issue 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).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1)-706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,395,407 (US 12395407 B2, hereinafter “Patent”). Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are broader in every aspect than the corresponding claims of Patent (US 12395407 B2) and are therefore anticipated by claims 1-20 of Patent (US 12395407 B2).
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhatia et al. (US 2021/0092132 A1, hereinafter “Bhatia”) in view of Salman et al. (US 2020/0177638 A1, hereinafter “Salman”).
Regarding claim 1 (and similarly claims 17 and 20), Bhatia teaches:
A method of anomaly detection, the method comprising:
obtaining, at a network interface component, incoming network data from a network device, the networking data including operating information for the network device and mirrored packet metadata (collecting network traffic including packet headers for analysis, Bhatia: [0057], [0086])
classifying, at the network interface component, the incoming network data using one or more machine learning models, including identifying abnormal network data from the incoming network data (classifying normal and abnormal traffic using machine learning model, Bhatia: [0085]).
Bhatia does not explicitly disclose:
causing a policy rule to be generated based on the abnormal network data and applied at the network device.
However, in the same field of endeavor, Salman teaches:
causing a policy rule to be generated based on the abnormal network data and applied at the network device (generating security rules based on monitored network traffic, Salman: [0030]-[0033], [0044]-[0046]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhatia in view of Salman in order to further modify the method of classifying collected network traffic into abnormal traffic using machine learning model from the teachings of Bhatia with the method of generating security rules based on classified network traffic from the teachings of Salman.
One of ordinary skill in the art would have been motivated because it would have improved resource security (Salman: [0025]).
Regarding claim 2, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the one or more machine learning models comprise a support vector machine (SVM) model (SVM model, Bhatia: [0085]).
Regarding claim 3, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the one or more machine learning models comprise a logistic regression model, a random forest model, or a k-nearest neighbor (KNN) model (ML model, Bhatia: [0102], [0103]).
Regarding claim 4, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the one or more machine learning models include an unsupervised deep learning model (unsupervised ML model, Bhatia: [0102], [0103]).
Regarding claim 5, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the one or more machine learning models include an SVM model and an unsupervised model (SVM model and ML model, Bhatia: [0085], [0102], [0103]).
Regarding claim 6, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 5 above.
Bhatia-Salman further discloses:
wherein the SVM model is trained on pre-labeled data and the unsupervised model is configured to learn autonomously (trained SVM model and untrained ML model, Bhatia: [0085], [0102], [0103]).
Regarding claim 7, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 5 above.
Bhatia-Salman further discloses:
wherein the unsupervised model is configured to learn via a series of rewards and penalties (function based model learning, Bhatia: [0102], [0103]).
Regarding claim 8, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the one or more machine learning models include a model for network traffic analysis, a model for network security analysis, and/or a model for network maintenance analysis (detecting abnormal traffic using machine learning model, Bhatia: [0085]).
Regarding claim 9, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the classifying the incoming network data is performed in real time (classifying normal and abnormal traffic in real-time, Bhatia: [0062]).
Regarding claim 10, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the incoming network data corresponds to a border gateway protocol (BGP) change (collecting raw data of network traffic including source, Bhatia: [0048], [0087]).
Regarding claim 11, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the incoming network data comprises protocol metadata for one or more network packets (classifying network traffic based on network protocols, Bhatia: [0064], [0065]).
Regarding claim 12, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein classifying the incoming network data comprises performing a two-class classification (classifying normal and abnormal traffic, Bhatia: [0085]).
Regarding claim 13, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
further comprising obtaining a classification hyperplane for the one or more machine learning models by training the one or more machine learning models using labeled training data (training model with training data, Bhatia: [0085], [0102], [0103]).
Regarding claim 14, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the network interface component is a component of the network device (network device collecting network traffic, Bhatia: [0037], [0057], [0086]).
Regarding claim 15, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein classifying the incoming network data comprises performing a radial basis function to linearly-separate the incoming network data (classifying network traffic into normal and abnormal traffic, Bhatia: [0085]).
Regarding claim 16, Bhatia-Salman teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Bhatia-Salman further discloses:
wherein the operating information comprises one or more of: information about an operating state of the network device, information about a network state detected by the network device, and information about hardware and/or software of the network device (packet headers for analysis of abnormal traffic detection, Bhatia: [0057], [0086]).
Regarding claims 18 and 19, they do not teach or further define over the limitations in claims 2 and 3. Therefore, claims 18 and 19 are rejected for the same reasons as set forth in the rejection of claims 2 and 3 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Beliveau et al. (US 2014/0233385 A1: Methods and Network Nodes for Traffic Steering Based on Per-Flow Policies), Rahat et al. (US 2019/0045036 A1: Header Modification for Supplement Services), Miriyala et al. (US 2020/0106744 A1: Intent-Based Policy Generation for Virtual Networks), Seddigh et al. (US 2018/0139104 A1: Method and System for Discovery and Mapping of a Network Topology), Kaplan et al. (US 2007/0106605 A1: Apparatus and Method for Determining Billable Resources on a Computer Network), and Ameling et al. (US 2024/0267317 A1: Methods, Systems and Computer Readable Media for Non-Intrusive Queue Analysis).
In the case of amendments, applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and support, for ascertaining the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GIL H. LEE whose telephone number is 571-272-3408. The examiner can normally be reached on Mon-Fri: 9am-6pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Glenton B. Burgess can be reached on 571-272-3949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GIL H. LEE/
Primary Patent Examiner, Art Unit 2454
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