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 office cation is responsive to application field on 5/23/2024. Claims 1-20 are pending examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 2 A, Prong One, the independent claim recites: transforming HTTP network requests into feature vectors based on selected features such as URL, headers, domain; and imputing the feature vectors into a machine learning model to train the model to classify new HTTP requests according to actions such as upload and download. The claim describes collecting data, transforming the data into a mathematical representation (feature vectors) and using the mathematical model (machine learning) to classify the data. These steps are considered mathematical concepts and data analysis which fall within the category of abstract ideas identified in judicial precedent (see Electric Power Group v. Alstom). The additional details such as specific types of features (HTTP method, URL, headers), feature vector construction (concatenation), types of requests (upload and download), and model characteristics (multiclass classifier). These additional limitations further define the data being processes and the manner of the processing, but do not change the overall idea of data collection, transformation, and analysis.
Step 2A, Prong Two The claims further recite elements such as HTTP network requests, feature vectors and a machine learning model. These elements are described at a high level and perform their ordinary functions of HTTP requests provide input data; feature vectors represent data I a numerical form, and the machine learning model processes the data to produce classification. The claims do not recite any improvement to the functioning of a computer, a network or a machine learning technique itself. Rather the claims use generic data processing tools to analyze information. Therefore, the additional elements do not integrate the abstract idea into a practical application.
Step 2B, the claims do not include additional elements that amounts to significantly more than the abstract idea. The recited steps of extracting features, converting them into vectors, and using a machine learning model for classification are well understood, routine and conventional activities in data processing and machine learning. When considered individually and as an ordered combination, the additional elements amount to no more than implementing the abstract idea using generic computing techniques.
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 (i.e., changing from AIA to pre-AIA ) 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 Faigon et al. U.S. Patent No. 10,270,788 (referred to hereinafter as Faigon) further in view of Narayanaswamy et al. U.S. Patent No. 10,291,657 (referred to herein after as NARA)
As to claim 1, Faigon teaches an activity monitoring machine learning model method comprising:
transforming HTTP network requests into feature vectors, each feature vector representing selected features from a corresponding HTTP network request and an action selected from a plurality of actions to be monitored (see at least col. 23, and fig. 8, 810, 820 and 830, events include features with certain values, assigning feature value pairs into categorical bins, coding.. feature value pairs with a Boolean value); and
inputting the feature vectors into a machine learning model to train the machine learning model to classify new HTTP requests according to the plurality of actions (see Faigon at least fig.8, step 810, feeding.. events into an online machine learner and constructing activity models .. using an online streaming machine learner);
Faigon implicitly explicitly teach the invention above. Faigon does not explicitly teach wherein the plurality of actions include an upload action and a download action. However, NARA teaches the plurality of actions includes an upload action and a download action (see NARA at least fig. 2B-2C, Figs 17 and 20. File operations and labeled action such as upload /download used for policy classification).
It would have been obvious to one of the to one of the ordinary skilled in the art, at the time of the invention was file to combine the teachings Faigon with those of Nara, to apply the machine learning based feature extraction and event modeling techniques of Faigon to the HTTP request monitoring system of NARA in order to improve the accuracy, scalability and adaptability of classifying user actions of upload and download from HTTP network requests.
As to claim 2, Faigon-Nara teaches, the activity monitoring machine learning model method of Claim 1, wherein the selected features comprise one or more of an HTTP method features, a URL feature, a domain feature, a header feature, or a cookie feature (see Nara at least fig.12 and col. 31, line 55-col 32, line 7, logs http request metadata including URL and session data).
As to claim 3, Faigon-Nara teaches the activity monitoring machine learning model method of Claim 1, wherein the selected features comprise an HTTP method feature, a URL feature, a domain feature, a header feature, and a cookie feature (see Nara at least fig.12 and col. 31, line 55-col 32, line 7, HTTP request components).
As to claim 4, Faigon-Nara teaches the activity monitoring machine learning model method of Claim 1, wherein converting the HTTP network requests into the feature vectors comprises, (see Faigon at least fi. 8, step 810, 820, events.. include features with certain values and are processed by a machine learner assigning feature value pairs into categorical bins; coding feature value pairs), for a selected HTTP request that comprises an HTTP method, a URL, a domain, a header and a cookie (see at least Faigon fig. 5, uses request derived features such as application, device, location and request attribute):
transforming the HTTP method to a first feature vector (see Faigon at least fi8 8, step 820, assigning feature pairs into categorical bins- assigning HTTP method into a feature vector);
transforming the URL to a second feature vector (see Faigon fig. 8, steps, 810, 820 and 830, processes feature pairs derived from even attributes);
transforming the domain to a third feature vector (see Faigon fig.5, includes application/domain level feature);
transforming the header to a fourth feature vector (see Faigon uses metadata features derived from events, headers contain user agent, content type, authentication info, these features are converted into a encoded representations); and
generating an overall feature vector for the selected HTTP request, generating the overall feature vector for the selected HTTP request comprising concatenating a plurality of feature vectors including the first feature vector, the second feature vector, the third feature vector, and the fourth feature vector (see Faigon fig.8 pipeline, processes multiple feature value pairs together as a single impute to the ML model).
As to claim 5, Faigon-Nara teaches the activity monitoring machine learning model method of Claim 4, wherein the URL includes a query string and the second feature vector represents the URL, including the query string (see at least NARA col. 51 lines 39-53, logs full request URLs including parameters (cloud request metadata)).
As to claim 6, Faigon-Nara teaches the activity monitoring machine learning model method of Claim 1, wherein the HTTP requests comprise exemplar upload requests and exemplar download requests to a plurality of cloud applications (see NARA at least figs. 1A. ,2B-2C, monitoring multiple cloud application and user action such as upload and download).
As to claim 7, Faigon-Nara teaches the activity monitoring machine learning model method of Claim 1, wherein the plurality of actions includes at least one additional action (see NARA at least col. 53, line 63- col.54 lines 17 and col. 47 lines 1-14, monitors multiples actions such as upload, download and share).
As to claim 8, Faigon-Nara teaches the activity monitoring machine learning model method of Claim 1, wherein the HTTP network requests comprise HTTP request bodies and wherein the HTTP network requests are transformed into the feature vectors without transforming the HTTP request bodies (see Faigon at least col. 18 line 20, lines 14-58, feature extraction focuses on metadata features such as IP, application, time).
As to claim 9, Faigon-Nara teaches the activity monitoring machine learning model method of Claim 1, wherein the machine learning model is trained to classify the new HTTP requests according to the plurality of actions regardless of any body content of the new HTTP requests by considering only non-body features of the new HTTP requests (see Faigon at least abstract, col. 5 lines 7-16, anomaly detection based on metadata features and behavioral pattern; - no content /body analysis required).
As to claim10, Faigon-Nara teaches the activity monitoring machine learning model method of Claim 1, wherein the machine learning model is a multiclass classifier (see Faigon at least, fig. 8, steps 860-870, probability distribution across event types, multiple possible outcomes is equated to multiclass classification).
Claims 10-20 do not teach anything above and beyond the limitations of claims 1-10 and rejected for similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mudigonda U.S. Patent. Pub. No. 2019/0199587, discloses A Method for stretching a subnet through L3 communications by computing systems in data centers or cloud networks. Uses include but are not limited to desktop computer systems, wired and wireless computing systems, mobile computing systems e.g. mobile telephones, netbooks, tablet or slate-type computers, notebook computers and laptop computers, hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers and mainframe computers.
Vasudevan et al U.S. Patent Pub. No. 2021/0204152, discloses a communication device comprises an anomaly detector, which has a machine learning model trained to predict whether data traffic patterns differ from a set of observed traffic patterns present in a set of training data. A traffic classifier includes a machine learning model trained to predict a quality of service (QoS) class for network connections or data flows. The communication device is provided to evaluate network connections or data flows using the anomaly detector. The traffic classifier is used to predict QoS classes for traffic that the anomaly detector predicts to be similar to the observed traffic patterns.
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/SARGON N NANO/Primary Examiner, Art Unit 2443
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