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
This non-final action is responsive to application filed on 08/09/2024. Claims 1-20 are pending, with claims 1, 14 and 19 being independent.
Priority
This application is a continuation of U.S. Application Serial Number 17/512,609, filed 10/27/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/23/2024 and 04/08/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 3-5, 8 and 15 are objected to because of the following informalities:
There are insufficient antecedent basis for
“the alert” in claim 3, and
“the values” in claims 4 and 5.
“a model ID” should read “a model identifier (ID)” in claim 5,
“a central server” should read “the central server” in claim 8, and
“sets of model IDs” should read “sets of model identifiers (IDs)” in claim 15.
Appropriate correction is required.
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).
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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,088,610. Although the claims at issue are not identical, they are not patentably distinct from each other because examined claims are anticipated by patent claims. For instance, claim 1 of the application is anticipated by patent claim 1 as shown below.
Examined Application
US 12,088,610
1. A method comprising:
receiving, at a network device, a set of flow behaviors for data flow traffic;
applying, at the network device, a machine learning (ML) model that comprises labelled network tuples and signatures for flow behaviors to the set of flow behaviors, wherein output from the machine learning model predicts a classified label for a particular data flow of the data flow traffic;
determining, at the network device, an implicit label for a network tuple parameter for the particular data flow, wherein the implicit label comprises a reported value for the network tuple parameter;
comparing the classified label for the network tuple parameter to the implicit label; and
perform an action associated with the comparison.
1. A method comprising:
receiving, at a network device, a set of flow behaviors for data flow traffic;
applying, at the network device, a machine learning (ML) model that comprises labelled network tuples and signatures for flow behaviors to the set of flow behaviors, wherein output from the machine learning model predicts a classified label for a particular data flow of the data flow traffic;
determining, at the network device, an implicit label for a network tuple parameter for the particular data flow, wherein the implicit label comprises a reported value in a packet header of the particular data flow;
comparing the classified label for the network tuple parameter to the implicit label; and
perform an action associated with the comparison.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210135997 A1; Systems And Methods Of Data Flow Classification
Systems and methods of classifying data flows being communicated on a network by one or more network elements. One method includes creating a table including information of packet timestamps and pre-defined packet header fields, grouping packets into data flows based on information in the table, assigning flow identifiers to each data flow, determining a plurality of feature/characteristic sets having one or more features and/or one or more characteristics of the data flows, determining one or more classifiers to predict flow labels using the plurality of feature/characteristic sets, and generating a classification policy that includes the one or more classifiers to classify data flows on the network. The method can also include storing the classification policy in at least one non-transitory computer medium that is accessible by a network element that is classifying data flows on the network, and using the classification policy to classify data flows.
US 20170295196 A1; Network Anomaly Detection
Examples relate to detecting network anomalies. In one example, a computing device may: receive, from each of a plurality of packet capture devices of a private network, domain name system (DNS) query packets that were sent by a particular client computing device operating on the private network, each DNS query packet specifying i) a destination DNS server, ii) a query domain name, and iii) a source address that specifies the particular client computing device; provide at least one of the DNS query packets to a DNS traffic analyzer that is trained to identify DNS anomalies based on characteristics of the DNS query packets; receive anomaly output from the DNS traffic analyzer, the anomaly output indicating a DNS anomaly that was identified for the DNS query packets; and in response to receiving the anomaly output, provide a user device with data specifying the identified DNS anomaly.
US 10757121 B2; Distributed Anomaly Detection Management
In one embodiment, a device in a network performs anomaly detection functions using a machine learning-based anomaly detector to detect anomalous traffic in the network. The device identifies an ability of one or more nodes in the network to perform at least one of the anomaly detection functions. The device selects a particular one of the anomaly detection functions to offload to a particular one of the nodes, based on the ability of the particular node to perform the particular anomaly detection function. The device instructs the particular node to perform the selected anomaly detection function.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANG DO whose telephone number is (571)270-7837. The examiner can normally be reached Monday-Friday 8:00 - 5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RUPAL DHARIA can be reached at (571) 272-3880. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KHANG DO/Primary Examiner, Art Unit 2492