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 communication is responsive to Amendment filed 11/25/2025.
Claims 1-2, 4-6, 8-11, 13-15, 17-22 are pending in this application. Claims 1, 10, and 19 are independent claims. In Amendment, claims 3, 7, 12, and 16 are cancelled and claims 21-22 are added. This Office Action is made final.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-6, 8-11, 13-15, 17-22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Estep et al. (U.S. 11,843,624 B1).
Re claim 1, Estep et al. disclose in Figures 1-11 a computer-implemented method for activity monitoring (e.g. abstract and Figure 1):
accessing a machine learning multiclass classifier (e.g. Figures 1 and 8-10 with network security system classifier), the machine learning multiclass classifier trained to associate HTTP network request features with a plurality of actions with respect to interacting with websites (e.g. col. 9 lines 22-37), wherein the plurality of actions includes at least an upload action, a download action, and a share action (e.g. col. 13 lines 35-42 with all types of request actions);
receiving an HTTP request (e.g. col. 10 lines 10-25);
extracting a feature set from the HTTP request (e.g. col. 9 lines 20-37 and col. 10 lines 30-50);
determining a request action classification for the HTTP request (e.g. col. 25 lines 30-52), determining the request action classification comprising processing the feature set extracted from the HTTP request with the machine learning multiclass classifier to classify the HTTP request (e.g. Figures 3-7 and col. 3 lines 50-65 and col. 4 lines 35-45), wherein the request action classification includes one of the plurality of actions (e.g. col. 13 lines 35-42 with all types of request actions are classified to detect malicious traffic); and
providing the HTTP request and the request action classification to another component via an application programming interface for further processing of the HTTP request (e.g. abstract, Figure 1 and col. 4 lines 35-48 and claims and col. 4 lines 20-45 with further analysis for blocking).
Re claim 2, Estep et al. disclose in Figures 1-11 the HTTP request is received, the feature set extracted, and the request action classification determined at an HTTP proxy server (e.g. Figure 1 with the network security system 110 as proxy).
Re claim 4, Estep et al. disclose in Figures 1-11 providing the HTTP request and the request action classification to a process that automatically initiates a task based on the request action classification (e.g. Figure 2 with the analysis 212 and decision making 222), wherein the task comprises at least one of performing a deep packet inspection, allowing the HTTP request, blocking the HTTP request, or logging the HTTP request in a security log (e.g. col. 15 lines 40-65).
Re claim 5, Estep et al. disclose in Figures 1-11 the request action classification classifies the HTTP request as an upload request (e.g. col. 13 lines 30-42, col. 28 lines 22-30, col. 29 lines 30-38 with upload request for posting information).
Re claim 6, Estep et al. disclose in Figures 1-11 the request action classification classifies the HTTP request as a download request (e.g. col. 13 lines 30-42 with GET request).
Re claim 8, Estep et al. disclose in Figures 1-11 the feature set comprises one or more of an HTTP method feature, a URL feature, a domain feature, a header feature, or a cookie feature (e.g. Figure 1 col. 10 lines 30-50 and col. 29 lines 20-35).
Re claim 9, Estep et al. disclose in Figures 1-11 the feature set comprises an HTTP method feature, a URL feature, a domain feature, a header feature, and a cookie feature (e.g. Figure 1 col. 10 lines 30-50 and col. 29 lines 20-35).
Re claim 10, it is a medium claim having similar limitations cited in claim 1. Thus, claim 10 is also rejected under the same rationale as cited in the rejection of claim 1.
Re claim 11, it is a medium claim having similar limitations cited in claim 2. Thus, claim 11 is also rejected under the same rationale as cited in the rejection of claim 2.
Re claim 13, it is a medium claim having similar limitations cited in claim 4. Thus, claim 13 is also rejected under the same rationale as cited in the rejection of claim 4.
Re claim 14, it is a medium claim having similar limitations cited in claim 5. Thus, claim 14 is also rejected under the same rationale as cited in the rejection of claim 5.
Re claim 15, it is a medium claim having similar limitations cited in claim 6. Thus, claim 15 is also rejected under the same rationale as cited in the rejection of claim 6.
Re claim 17, it is a medium claim having similar limitations cited in claim 8. Thus, claim 17 is also rejected under the same rationale as cited in the rejection of claim 8.
Re claim 18, it is a medium claim having similar limitations cited in claim 9. Thus, claim 18 is also rejected under the same rationale as cited in the rejection of claim 9.
Re claim 19, it is a system claim having similar limitations cited in claim 1. Thus, claim 19 is also rejected under the same rationale as cited in the rejection of claim 1.
Re claim 20, Estep et al. disclose in Figures 1-11 the proxy server code further comprises instructions to allow an HTTP request, deny the HTTP request, or record a security log of the HTTP request to a server based on a respective request action classification assigned to the HTTP request (e.g. Figure 1 with the network security system 110 as proxy and Figures 2 and 10).
Re claim 21, Estep et al. disclose in Figures 1-11 the plurality of actions further includes other actions (e.g. col. 13 lines 35-42 with all types of request actions), and wherein the request action classification classifies the HTTP request according to one of the other actions (e.g. Figures 8-10 and col. 29 lines 5-50 wherein all the requests are intercepted to classify).
Re claim 22, Estep et al. disclose in Figures 1-11 the received HTTP request requests to download a file from a cloud service (e.g. Figure 1 and col. 14 lines 3-18 wherein the client can request to download file from cloud)
Response to Arguments
Applicant's arguments filed 11/25/2025 have been fully considered but they are not persuasive.
The applicant argues in pages 8-11 for claims that the cited reference by Estep fails to specifically disclose the amended limitations, particularly the ML is trained to associate HTTP request with plurality actions and providing the request action classification to another component for further processing. The examiner respectfully submits that the amendment is drafted broadly which clearly covers by the same reference Estep as explained above in the rejection. As mention above in the rejection, Estep discloses all types of traffics would be intercept and analyze for malicious actions/request/data/commands/traffics using the trained ML and once it detects malicious actions/request/data/commands/traffics, it would move the next process/next component for further processing such as more information about the request or block the request completely.
Conclusion
THIS ACTION IS MADE FINAL. 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