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 .
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 an abstract idea without significantly more.
Regarding claim 1,
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a method/process.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
extracting a routing prefix from the IP address; (mental observation, a human can look at the IP address and determine the routing prefix)
performing multiclass classification [using a convolutional neural network applied to] the routing prefix to obtain an entity attribute; (mental judgement, using the prefix, a human can make a calculation of where the IP address belongs to, i.e. classification, by looking at the prefix or referencing something else)
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The limitations of:
obtaining an Internet Protocol (IP) address; (data gathering, insignificant extra-solution activity MPEP 2106.05(g))
[…] using a convolutional neural network applied to […] (instructions to apply the abstract idea on generic computer components, MPEP 2106.05(f))
providing the entity attribute for mapping the entity attribute to digital content (applying the abstract idea to a particular field of use, MPEP 2106.05(h))
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The limitations of:
obtaining an Internet Protocol (IP) address; (data gathering, insignificant extra-solution activity MPEP 2106.05(g))
[…] using a convolutional neural network applied to […] (instructions to apply the abstract idea on generic computer components, MPEP 2106.05(f), data gathering is well-known, understood, and routine, MPEP 2106.05(d)(II)(i))
providing the entity attribute for mapping the entity attribute to digital content (applying the abstract idea to a particular field of use, MPEP 2106.05(h))
Dependent claim 2 recites the entity comprising a name or attribute, applying the abstract idea to a particular field of use MPEP 2106.05(h).
Dependent claim 3 recites extracting subnetwork features, mental observation using generic computer components.
Dependent claim 4 recites various subnet features, applying the abstract to a field of use MPEP 2106.05(h).
Dependent claim 5 recites training the convolutional neural network, invoking generic computer components as a tool MPEP 2106.05(f).
Dependent claim 6 recites comparing the entity to determine an error and backpropagation, invoking generic computer components as a tool MPEP 2106.05(f).
Dependent claim 7 recites generating an embedding, invoking generic computer components as a tool MPEP 2106.05(f).
Independent claims 8 and 15 recite the same substantial subject matter as independent claim 1, only differing in embodiment. The differences in embodiments do not meaningfully change the above analysis and therefore the claims are subject to the same rejection.
Dependent claims 9-14 correspond to dependent claims 2-7.
Dependent claims 16-20 correspond to dependent claims 2-6/9-13.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mullooly et al. US 2009/0161668 [herein Mull] in view of Voros et al. US 2022/0353284.
Regarding claims 1, 8, and 15, Mull teaches “a method comprising: obtaining an Internet Protocol (IP) address” ([0038] “As shown in FIG. 7, one or more IP packets are received at 702. The IP source and/or destination address associated with an IP packet is read”);
“extracting a routing prefix from the IP address” ([0038] “the IP packet then is classified to a class of service based on the IP address prefix associated with the IP packet” i.e. extracting a prefix);
“[performing multiclass classification using a convolutional neural network] applied to the routing prefix to obtain an entity attribute” ([0038] “The IP address prefix of the received IP packet is identified in the routing table. Thereafter, the class of service associated with the IP address prefix is located in the routing table. The IP packet received is associated with this class of service located from the routing table”); and
“providing the entity attribute for mapping the entity attribute to digital content” ([0039] “After the received IP packet is associated with a class of service, the IP packet is marked (or tagged) at 706 with a value that is associated with the class of service.”)
Mull does not explicitly teach the use of a convolutional neural network. Voros however teaches “performing multiclass classification using a convolutional neural network” (Voros [0025] “The processor is further configured to provide the matrix as an input to a convolutional neural network (CNN), and receive, from the CNN, a score associated with a maliciousness of the IP address. In some embodiments, the processor can further be configured to identify a set of characteristics associated with the IP address, define a feature vector based on the set of characteristics, and provide the feature vector as an input to the CNN” and [0029] “representing IP addresses in an IP space using machine learning tools, classifying and/or identifying neighborhoods and/or similarities of IP addresses with a reputation that is associated with potentially malicious activities/actors.”)
It would have been obvious to one having ordinary skill in the art at the time that the invention was effectively filed to combine the teachings of Mull with that of Voros since a combination of known methods would yield predictable results. As shown in Voros, CNNs are known to be used as classifiers when dealing with IP addresses since IP addresses lend themselves well to being transformed into matrix form and being input into the model. Therefore with the combination of the two, the systems would operate normally as expected.
Independent claims 8 and 15 recite the same substantial subject matter as independent claim 1, only differing in embodiment. The differences in embodiments, a system and computer-readable medium compared to the method of claim 1 are obvious variations of another and therefore the claims are subject to the same rejection.
Regarding claims 2, 9, and 16, the Mull and Voros references have been addressed above. Mull further teaches “wherein the entity attribute comprises a name of an entity or attribute of the entity” ([0039] “After the received IP packet is associated with a class of service, the IP packet is marked (or tagged) at 706 with a value that is associated with the class of service.”)
Regarding claims 3, 10, and 17, the Mull and Voros references have been addressed above. Voros further teaches “further comprising: extracting, by one or more layers of the convolutional neural network, an intermediate subnetwork feature” (Voros [0043] “The CNN can be configured such that the CNN slides a convolution window across the matric to identify importance of features in the matrix. The ML model can be trained by the model trainer 113 to learn to identify the important features of the matrix representation and generate the feature vector based on the important features of the matrix representation”)
Regarding claims 4, 11, and 18, the Mull and Voros references have been addressed above. Voros further teaches “wherein the intermediate subnetwork feature comprises at least one of a regional subnetwork feature, local subnetwork feature, or Internet Service Provider (ISP) feature” (Voros [0045] “The infrastructure classifier (described in further detail herein) can be a CNN and can be trained to identify the importance of each subnet in the matrix to classify the IP address (e.g., as malicious or benign).”)
Regarding claims 5, 12, and 19, the Mull and Voros references have been addressed above. Voros further teaches “further comprising: iteratively training the convolutional neural network using a training IP address and a corresponding training entity attribute” (Voros [0043] “The ML model can be trained by the model trainer 113 to learn to identify the important features of the matrix representation and generate the feature vector based on the important features of the matrix representation.”)
Regarding claims 6, 13, and 20, the Mull and Voros references have been addressed above. Voros further teaches “wherein iteratively training the convolutional neural network further comprises: comparing a predicted entity attribute to the training entity attribute to determine an error; and backpropagating the error through one or more layers of the convolutional neural network” ([0075] “the ML models described herein can be built and/or trained by the model trainer 113 using the Pytorch framework with an Adam optimizer and 128 sized mini batches, optimizing over a binary classification objective” note this explicitly implies backpropagation)
Regarding claims 7 and 14, the Mull and Voros references have been addressed above. Voros further teaches “further comprising: generating an embedding using the convolutional neural network applied to the routing prefix extracted from the IP address” (Voros [0115] “To learn an embedding space based on an ISP of an IP address, the various models were pre-trained using the triplet loss, CosFace, and softmax functions as described previously. The CNN model configured for IP address information alone was converted to extend the input portion that corresponded to ISP
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zhang, Xinyi, et al. "Fast online packet classification with convolutional neural network." IEEE/ACM Transactions on Networking 29.6 (2021): 2765-2778.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN W FIGUEROA whose telephone number is (571)272-4623. The examiner can normally be reached Monday-Friday, 10AM-6PM EST.
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KEVIN W FIGUEROA
Primary Examiner
Art Unit 2124
/Kevin W Figueroa/Primary Examiner, Art Unit 2124
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