DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 7/19/24 & 10/10/2025. The submission are in compliance with the provisions of 37 CFR § 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
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.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (Deep Forest with LRRS Feature for Fine-grained Website Fingerprinting with Encrypted SSL/TLS, November 3, 2019, Beijing, China, hereinafter Zhang) in view of Huang et al. (US 20190180375, hereinafter Huang).
Regarding Claim 1, Zhang discloses a computer-implemented method comprising:
obtaining a plurality of machine learning predictions for consecutive sliding windows over a segment of data, wherein each machine learning prediction comprises probabilities for predicted classes in a single sliding window (Section 3.2.1, Fig. 2, use a sliding window of 7×1×3×7 (input channels, width, height, output channels) to scan raw features with a stride of 1×1 (width, height) and get 18×7×1×3 features. Through this sliding process, we get 18 short instances from a long instance. Secondly, these features are put into two classifiers, the Random Forest classifier and the Completely-Random Forest classifier respectively to get predicted class distribution)
removing from the plurality of machine learning predictions one or more machine learning predictions fulfilling a (Section 3.2.1, "2x2 pooling layer is used to compress the redundant features");
adding up probabilities for each predicted class of the filtered machine learning predictions to a sum probability for each predicted class of the filtered machine learning predictions; and selecting the predicted class of the filtered machine learning predictions having a highest sum probability as a dominant class of the segment (Section 3.2.2, Fig. 3, " The sample will firstly be input into the multi-grained process to get different sizes of transformed features. Then these transformed features, along with statistical features, will go through their corresponding layer until the last layer of the cascade forest. After that, the above results will be input into four Random Forests and four Completely-Random Forests, and we get the mean of eight predicted probabilities distribution. Finally, we select the class with the maximum value in predicted class distribution as the predicted class of this sample."
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Zhang does not explicitly disclose the machine learning predictions fulfilling a volatility condition.
Huang teaches the machine learning predictions fulfilling a volatility condition ([0056], FIG. 6, the training set of the AI model is collected from the historical information 900 trading days before November 2017, and the trained AI models are used to predict the 3-day-ahead volatility from a given date and financial risk forecast system with AI models can be used for forecasting market volatility such as volatility graph of volatility prediction of the financial risk forecast system with AI models of this specification versus real market volatility as shown in FIG. 6).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of learning predictions fulfilling a volatility condition as taught by Huang ([0121]) into the machine learning system of Zhang in order to provide system that ensures that invest scale of financial institutions is usually much larger than an invest scale of individual investors, so that financial institutions need more efficiently and more accurately financial risk forecast information to avoid properties lost, thus accurately avoiding financial risk in an efficient manner (Huang, [0009]).
Regarding Claim 2, Zhang in view of Huang discloses the method of claim 1,
Zhang in view of Huang discloses wherein the data comprises network traffic data.
For what concerns the application to a field of technology, it is noted that claims 2-4 further define additional features specifying the nature of the data in the segment of data ("network traffic data" [claim 2]; that"... contains one or more encrypted target websites" [claim 4]), its origin ("intercepted (102) from a data communication of a connected device in a local area network implemented by a customer-premises equipment" [claim 3]), as well as the classes to be predicted ("specific encrypted target website[s]" [claim 4]).
Regarding Claim 3, Zhang in view of Huang discloses the method of claim 2,
Zhang in view of Huang discloses wherein the network traffic data is intercepted from a data communication of a connected device in a local area network implemented by a customer-premises equipment.
For what concerns the application to a field of technology, it is noted that claims 2-4 further define additional features specifying the nature of the data in the segment of data ("network traffic data" [claim 2]; that"... contains one or more encrypted target websites" [claim 4]), its origin ("intercepted (102) from a data communication of a connected device in a local area network implemented by a customer-premises equipment" [claim 3]), as well as the classes to be predicted ("specific encrypted target website[s]" [claim 4]).
Regarding Claim 4, Zhang in view of Huang discloses the method of claim 2,
Zhang in view of Huang discloses wherein the network traffic data contains one or more encrypted target websites, each probability for the predicted class corresponds to a probability of a specific encrypted target website, and the dominant class of the segment predicts an identity of the specific encrypted target website.
For what concerns the application to a field of technology, it is noted that claims 2-4 further define additional features specifying the nature of the data in the segment of data ("network traffic data" [claim 2]; that"... contains one or more encrypted target websites" [claim 4]), its origin ("intercepted (102) from a data communication of a connected device in a local area network implemented by a customer-premises equipment" [claim 3]), as well as the classes to be predicted ("specific encrypted target website[s]" [claim 4]).
Regarding Claim 5, Zhang in view of Huang discloses the method of claim 1,
Zhang in view of Huang discloses wherein removing from the plurality of machine learning predictions the one or more machine learning predictions fulfilling the volatility condition in order to get the filtered machine learning predictions further comprises: in response to one or more probabilities for predicted classes of a single machine learning prediction exceeding a volatility threshold value in comparison with probabilities for predicted classes of other machine learning predictions for the segment, removing the single machine learning prediction.
This distinguishing feature is of a non-technical (mathematical) nature. It must be examined whether the identified difference makes a technical contribution in the context of the claim as a whole. Here, however, the distinguishing features belong to a mathematical method which can neither be seen to be (i) applied to a field of technology, nor (ii) adapted to a specific technical implementation (see section G-I1.3.3 of the guidelines for examination):
while it is recognized that the present method aims at predicting the class of a segment of data, such a generic purpose is not considered sufficient.
in the context of those claims, we are in presence of a straightforward computer implementation.
Regarding Claim 6, Zhang in view of Huang discloses the method of claim 1,
Zhang in view of Huang discloses wherein the sum probability of each predicted class of the filtered machine learning predictions corresponds to an area under a probability curve drawn along the probabilities of each predicted class of the filtered machine learning predictions.
This distinguishing feature is of a non-technical (mathematical) nature. It must be examined whether the identified difference makes a technical contribution in the context of the claim as a whole. Here, however, the distinguishing features belong to a mathematical method which can neither be seen to be (i) applied to a field of technology, nor (ii) adapted to a specific technical implementation (see section G-I1.3.3 of the guidelines for examination):
while it is recognized that the present method aims at predicting the class of a segment of data, such a generic purpose is not considered sufficient.
in the context of those claims, we are in presence of a straightforward computer implementation.
Regarding Claim 7, Zhang in view of Huang discloses the method of claim 1,
Zhang in view of Huang discloses further comprising, after adding up the probabilities for each predicted class of the filtered machine learning predictions to the sum probability for each predicted class of the filtered machine learning predictions, and prior to selecting the predicted class of the filtered machine learning predictions having the highest sum probability as the dominant class of the segment: removing from the predicted classes of the filtered machine learning predictions one or more predicted classes having sum probabilities fulfilling an insignificance condition.
This distinguishing feature is of a non-technical (mathematical) nature. It must be examined whether the identified difference makes a technical contribution in the context of the claim as a whole. Here, however, the distinguishing features belong to a mathematical method which can neither be seen to be (i) applied to a field of technology, nor (ii) adapted to a specific technical implementation (see section G-I1.3.3 of the guidelines for examination):
while it is recognized that the present method aims at predicting the class of a segment of data, such a generic purpose is not considered sufficient.
in the context of those claims, we are in presence of a straightforward computer implementation.
Regarding Claim 8, Zhang in view of Huang discloses the method of claim 7,
Zhang in view of Huang discloses wherein removing from the predicted classes of the filtered machine learning predictions the one or more predicted classes having sum probabilities fulfilling the insignificance condition further comprises: in response to a sum probability for the one or more predicted class being less than an insignificance threshold value, removing the one or more predicted classes.
This distinguishing feature is of a non-technical (mathematical) nature. It must be examined whether the identified difference makes a technical contribution in the context of the claim as a whole. Here, however, the distinguishing features belong to a mathematical method which can neither be seen to be (i) applied to a field of technology, nor (ii) adapted to a specific technical implementation (see section G-I1.3.3 of the guidelines for examination):
while it is recognized that the present method aims at predicting the class of a segment of data, such a generic purpose is not considered sufficient.
in the context of those claims, we are in presence of a straightforward computer implementation.
Regarding Claim 9, Zhang in view of Huang discloses the method of claim 7,
Zhang in view of Huang discloses further comprising, after removing from the predicted classes of the filtered machine learning predictions the one or more predicted classes having sum probabilities fulfilling the insignificance condition: in response to the absence of all predicted classes, selecting a dominant class of a previous segment as the dominant class of the segment; and in response to the presence of at least one predicted class, selecting the predicted class having the highest sum probability as the dominant class of the segment.
This distinguishing feature is of a non-technical (mathematical) nature. It must be examined whether the identified difference makes a technical contribution in the context of the claim as a whole. Here, however, the distinguishing features belong to a mathematical method which can neither be seen to be (i) applied to a field of technology, nor (ii) adapted to a specific technical implementation (see section G-I1.3.3 of the guidelines for examination):
while it is recognized that the present method aims at predicting the class of a segment of data, such a generic purpose is not considered sufficient.
in the context of those claims, we are in presence of a straightforward computer implementation.
Regarding Claim 10, Apparatus claim 10 of using the corresponding method claimed in claim 1, and the rejections of which are incorporated herein for the same reasons as used above.
Regarding Claim 11, Computer-readable storage medium claim 11 of using the corresponding method claimed in claim 1, and the rejections of which are incorporated herein for the same reasons as used above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Samuel D Fereja whose telephone number is (469)295-9243. The examiner can normally be reached 8AM-5PM.
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/SAMUEL D FEREJA/Primary Examiner, Art Unit 2487
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