DETAILED ACTION
This action is in response to the communication filed on 10/15/2024. Claims 1-20 are pending examination.
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.
Claim(s) 1,7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Independent claims
Step 1:
Claims 1,7 is drawn to a method, therefore this claim falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A, Prong 1:
Nonetheless, claims 1,7 is directed to a judicially recognized exception of an abstract idea without significantly more. Claim 1,7 recites a method of “generating a random parameter vector”, “generating a random attack dataset”, “inputting a plurality of samples of the random attack dataset into a generator”, “generating, by the generator, a generated attack dataset” that under its broadest reasonable interpretation, enumerates mathematical concepts and mental processes such as probabilistic sampling, statistical modeling, data comparison and parameter optimization. Such operations represent abstract calculations and analytical reasoning that can be performed conceptually by a human (MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
Claims 1,7 recites additional element/steps of “training a discriminator using the random attack dataset and the generated attack dataset”, “training the generator using the trained discriminators and a loss function”, “ receiving a generative model parameter vector”, “generating sample points from a prior distribution of the generative model parameter vector”, “generating attack policy parameters simulating simulated attack policy parameters by a system simulation training a generative model by the simulated attack policy parameters” that fail to integrate the abstract idea into a practical application. The additional elements merely require performing the abstract mathematical and mental processes using generic computing components such as a generator, discriminator and system simulation without reciting any specific improvement to computer functionality. The limitations are necessary for all uses of the judicial exception. The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Step 2B:
The additional elements that are forms of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision have determined that these additional elements to be well-understood, routine, and conventional when claimed in a merely generic manner for training a machine learning model using generative and discriminative modeling techniques (MPEP 2106.05(d)(II)(i). As such claim 1,7 is are not patent eligible.
Dependent claims
Step 1:
Claims 2-6, 8-12 is drawn to a method, therefore this claim falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A-2B:
Claims 2-6, 8-12 recites additional element/steps of “the generated attack dataset comprises an attack policy”, “training the generative model comprises a deep neural network” etc. These limitations recite mathematical concepts and mental processes such as probabilistic sampling, data evaluation and parameter optimization and therefore remain within the judicial exception without integrating the abstract idea into a practical application as they do not improve computer functionality. The additional elements of the dependent claims are well-understood, routine, and conventional and are insignificant extra-solution activity.
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, 5-8 and 13 is/are rejected under 35 U.S.C 103 as being unpatentable over Verma et al. (US 20220180190 A1) hereinafter referred to as Verma, in view of Kallur et al. (US 20190147333 A1), hereinafter referred to as Kallur
As per claim 1, Verma discloses a computer-implemented method of training a machine learning model, the method comprising:
generating a random parameter vector; (Sampling a mini-batch of m noise samples {z (1), z (2) … z(m)} from noisy data generating distribution p data (z), Verma, para [0038]. The noise samples {z(...)} from noisy data generating distribution p data (z)).
generating a random attack dataset; (The generator module generates synthesized data objects to fool the unsupervised discriminator module, Verma, para [0008]. Synthesized data objects correspond to artificially generated datasets. Data generated to fool a discriminator is functionally adversarial. Such data is analogous to an attack dataset as it is used to cause misclassification or error in another model component)
inputting a plurality of samples of the random attack dataset into a generator; (Data input can be fed into each previously trained generator. Sample mini-batch of m noise samples, Verma, para [0036] and [0038]. A mini-batch is a plurality of samples and these samples are provided as input to the generator in GAN training)
training a discriminator using the random attack dataset and the generated attack dataset; and (Iteratively training the discriminator on true data and output of the generator, Verma, para [0030]. True data corresponds to one dataset and the output of the generator corresponds to the generated dataset. Training the discriminator using both real and random data and generated data aligns with training a discriminator)
training the generator using the trained discriminators and a loss function (Update the generator by descending the stochastic gradient, Verma, para [0038]. Gradient descent necessarily optimizes a loss function. The gradient is computed based on discriminator feedback in GAN training. Thus, the generator is trained using the trained discriminator and a loss function)
However, Verma does not explicitly disclose:
generating, by the generator, a generated attack dataset;
Kallur discloses:
generating, by the generator, a generated attack dataset; (Generator 330 can generate data (e.g., G (z, y) =x.sub. G), and device 306 can send generated data 332 via network 302 to device 308. Unsupervised discriminator 340 can take as input data objects such as x(G) from generated data 332 and determine an output, Kallur, para [0080], [0081]. The generated data 332 is expressly provided as input to the unsupervised discriminator 340 and used to determine whether data is real or fake. In GAN systems, generator outputs are designed to fool or attack the discriminator’s decision boundary).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma with Kallur by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur in order to effectively address building conditional models in the semi-supervised setting (See Kallur, para [0081])
As per claim 5, Verma and Kallur disclose the computer-implemented method of claim 2, wherein
Furthermore, Kallur discloses:
the generator comprises a deep neural network (The generator module, the unsupervised discriminator module, and the supervised discriminator module are deep neural networks, Kallur, para [0009])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma with Kallur by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur in order to effectively address building conditional models in the semi-supervised setting (See Kallur, para [0081])
As per claim 6, Verma and Kallur disclose the computer-implemented method of claim 5, wherein
Furthermore, Kallur discloses:
the generator is trained with the loss function:
PNG
media_image1.png
44
416
media_image1.png
Greyscale
(The unsupervised loss functions for the generator and discriminator, respectively, are as follows:
PNG
media_image2.png
205
1597
media_image2.png
Greyscale
, Kallur, para [0044]).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma with Kallur by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur in order to effectively address building conditional models in the semi-supervised setting (See Kallur, para [0081])
As per claim 7, Verma discloses a computer-implemented method for training a machine learning model, the method comprising:
receiving a generative model parameter vector; (G (z; θg) and D (x; θd), Verma, para [0027]. Here, θg is a generative model parameter vector used by the system)
generating sample points from a prior distribution of the generative model parameter vector; (Sample mini-batch from noisy data generating distribution pdata(z), Verma, para [0038]. Pdata(z) functions as a prior distribution)
However, Verma does not explicitly disclose the limitation:
generating attack policy parameters simulating simulated attack policy parameters by a system simulation training a generative model by the simulated attack policy parameters
Kallur discloses:
generating attack policy parameters simulating simulated attack policy parameters by a system simulation training a generative model by the simulated attack policy parameters (Generator 330 can generate data (e.g., G(z,y)= x(G)). Generated data 332 is sent to unsupervised discriminator 340 and it can take input data objects such as x(G) from generated data 332. Unsupervised discriminator 340 can take as input data objects such as x(G) from generated data 332 as well as data objects from training data 322 and determine an output. Supervised discriminator 350 can take as input (x,y) pairs of (data object, attribute) including (h(x(G),y)), Kallur, para [0080]. A conditional generative adversarial network in which a generator produces simulated data outputs according to controllable input parameters, expressed as G (z,y)= x(G), where the conditional variable y governs the behavior of the generated data and therefore functions as a set of policy parameters. The generated outputs x(G) are supplied to discriminator modules during training, meaning the generator operates as a system-level simulation that produces parameter-controlled, simulated behaviors. Because these simulated, parametrized outputs are adversarially generated to fool the discriminator and are used directly in training the generative model. Here, conditional parameters y and the corresponding generated data x(G) to constitute simulated attack policy parameters and the iterative adversarial training process to constitute training a generative model using those simulated attack policy parameters).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma with Kallur by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur in order to effectively address building conditional models in the semi-supervised setting (See Kallur, para [0081])
As per claim 8, Verma, Kallur discloses the computer-implemented method of claim 7, wherein
Furthermore, Kallur discloses:
training the generative model comprises a deep neural network (The generator module are deep neural networks, Kallur, para [0009])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma with Kallur by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur in order to effectively address building conditional models in the semi-supervised setting (See Kallur, para [0081]).
As per claim 13, Verma discloses a cybersecurity system comprising:
a physical plant a network configured for communications and/or control of the physical plant; (The system includes one or more computing devices configured to perform classification and decision-making tasks in an operational environment, Verma, para [0021]. Here, an operational environment encompasses the physical plant or any physical system/ environment whose behavior is monitored or protected)
a cybersecurity controller operably connected to the network, wherein the cybersecurity controller comprises a processor and memory with instructions stored thereon, that, when executed by the processor cause the processor to: receive a trained attack generative model; (The generator G (z; Θg) and discriminator D (x; θd) are trained and then used during operation, Verma, para [0027]. A generator is trained and then used, implies that the system receives or accesses a trained generative model for operational use. Because the generator is trained adversarially to produce outputs that challenge another model, it is analogous to trained attack model)
simulate, by the trained attack generative model, a plurality of attacks on the physical plant; (Sample min-batch of m noise samples {z(1)…z(m)} and generate corresponding outputs using the generator, Verma, para [0038]. The generator produces multiple generated outputs from sampled inputs, each being a synthetic, modeled instance. In adversarial learning, such synthetic outputs simulate attack like behaviors intended to stress or deceive the system. Generating a mini-batch constitutes simulating a plurality of attacks).
However, Verma does not explicitly disclose the limitations:
determine an attack effectiveness of each of the plurality of attacks; and
control access to the network based on the effectiveness of each of the plurality of attacks
Kallur discloses:
determine an attack effectiveness of each of the plurality of attacks; and (Unsupervised discriminator 340 can take as input data objects from generated data 332 and determine an output 342, a probability of whether a given data object x is real or fake, Kallur, para [0045]. The discriminator’s output is a probability value indicating whether each generated sample successfully appears “real”. In adversarial systems, the degree to which a generated sample fools the discriminator is a measure of its effectiveness. Thus, probability for each generated data object determines the effectiveness of each simulated attack because each generated sample represents an adversarial attempt to deceive the system)
control access to the network based on the effectiveness of each of the plurality of attacks (Device 308 can send output 342 as a marginal distribution, a probability of whether the data object is real of fake. Supervised discriminator 350 determines an output 352 of a probability of whether the given (x,y) pair is real or fake, Kallur, para [0046]. The system uses discriminator outputs such as probability distributions to determine whether generated data objects are accepted as valid or rejected as fake. Accepting or rejecting data objects based on discriminator-determined effectiveness constitutes controlling access within the system because only sufficiently effective adversarial samples are permitted to propagate or influence subsequent processing. Since all communications and evaluations occur within a networked architecture 302, selectively permitting or denying data objects based on effectiveness corresponds to controlling access to the network based on attack effectiveness)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma with Kallur by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur in order to effectively address building conditional models in the semi-supervised setting (See Kallur, para [0081]).
Claims 2-4,10-12 and 14-20 is/are rejected under 35 U.S.C 103 as being unpatentable over Verma et al. (US 20220180190 A1) hereinafter referred to as Verma, in view of Kallur et al. (US 20190147333 A1), hereinafter referred to as Kallur in further view of Rivera et al. (US 20210112090 A1), hereinafter referred to as Rivera.
As per claim 2, Verma, Kallur disclose the computer-implemented method of claim 1, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the generated attack dataset comprises an attack policy
Rivera discloses:
the generated attack dataset comprises an attack policy (Attack vectors may be defined as parameterized attack strategies that determine how an attack signal is generated and applied to the system over time, Rivera, para [0071]. Attack strategies are similar to attack policies which are rules governing how attacks are generated)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 3, Verma, Kallur disclose the computer-implemented method of claim 2, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the attack policy comprises a ramp attack
Rivera discloses:
the attack policy comprises a ramp attack (Ramp attack: This attack vector involves adding a time varying ramp signal to the input control signal based on a ramp signal parameter, γ.sub.ramp, Rivera, para [0075]. Ramp attack is listed as an attack vector within the attack strategy framework, it is a part of the attack policy)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 4, Verma, Kallur disclose the computer-implemented method of claim 2, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the attack policy comprises a sensor attack
Rivera discloses:
the attack policy comprises a sensor attack (Single cyber-attacks consist of isolated attacks that can be performed on measurements, Rivera, para [0071]. Sensor attacks are those that corrupt sensor measurements)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 10, Verma, Kallur disclose the computer-implemented method of claim 7, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the attack policy parameters comprise a ramp attack policy.
Rivera discloses:
the attack policy parameters comprise a ramp attack policy (Ramp attack: This attack vector involves adding a time varying ramp signal to the input control signal based on a ramp signal parameter, γ.sub.ramp, Rivera, para [0075]. Ramp attack is listed as an attack vector within the attack strategy framework, it is a part of the attack policy)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 11, Verma, Kallur discloses the computer-implemented method of claim 7, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the attack policy parameters comprise a sine attack policy.
Rivera discloses:
the attack policy parameters comprise a sine attack policy (Malicious tripping attack vector involves malicious tripping of a physical relay. During the attack, false tripping command packets are injected to disconnect the power system components by tripping a circuit breaker, Rivera, para [0073]. This is analogous to a sine attack vector as the malicious attack vector takes a tripping signal input based on a parameter)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 12, Verma, Kallur disclose the computer-implemented method of claim 7, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the attack policy parameters comprise a pulse attack policy
Rivera discloses:
the attack policy parameters comprise a pulse attack policy (Pulse attack: This attack vector involves periodically changing an input control signal by adding the pulse attack parameter, γ.sub. pulse, for a small-time interval, (t1). It retains back the original input for a remaining interval, (T−t1), for the given time period, (T), Rivera, para [0074])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 14, Verma, Kallur disclose the system of claim 13, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the physical plant comprises a networked pipeline system.
Rivera discloses:
the physical plant comprises a networked pipeline system (The main components of the electrical or power grid are generating stations, electrical substations, and transmission lines, Rivera, para [0016])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 15, Verma, Kallur disclose the system of claim 14, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the networked pipeline system comprises a plurality of pressure sensors operably coupled to the network, and controlling access to the network comprises securing at least one of the plurality of pressure sensors
Rivera discloses:
the networked pipeline system comprises a plurality of pressure sensors operably coupled to the network, and controlling access to the network comprises securing at least one of the plurality of pressure sensors (IDS develops comprehensive solutions for monitoring possible intrusions upon a power system network 100. In the exemplary system of FIG. 3, a network-based IDS, a model-based IDS, machine learning IDS, and synchrophasor data are integrated into the cyber-security system 300 to detect unknown, coordinated, and stealthy cyber-attacks targeting the power system network 101, Rivera, para [0050], [0068])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 16, Verma, Kallur discloses the system of claim 13, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the physical plant comprises a power grid
Rivera discloses:
the physical plant comprises a power grid (Cyber physical systems such as power grids, Rivera, para [0016])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 17, Verma, Kallur discloses the system of claim 16, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the power grid comprises a plurality of meters configured to measure the power and frequency of the power grid, and controlling access to the network comprises securing at least one of the plurality of meters
Rivera discloses:
the power grid comprises a plurality of meters configured to measure the power and frequency of the power grid, and controlling access to the network comprises securing at least one of the plurality of meters (Cyber security system receives a first data set from a power system network. The first data set includes frequency data, Rivera, para [0003] and [0004]).
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 18, Verma, Kallur disclose the system of claim 13, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the physical plant comprises a cyber-physical system
Rivera discloses:
the physical plant comprises a cyber-physical system (The supervisory control and data acquisition (SCADA) network is a cyber-security system, Rivera, para [0003])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 19, Verma, Kallur disclose the system of claim 13, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the physical plant comprises an industrial facility
Rivera discloses:
the physical plant comprises an industrial facility (SCADA systems are industry-controlled systems which are monitored, Rivera, para [0054])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
As per claim 20, Verma, Kallur disclose the system of claim 13, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
the controller further contains instructions that cause the processor to simulate a vulnerability of the physical plant and control the physical plant based on the vulnerability
Rivera discloses:
the controller further contains instructions that cause the processor to simulate a vulnerability of the physical plant and control the physical plant based on the vulnerability (An intrusion detection system may use synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatio-temporal behaviors of power system networks. Such a system may include three layers: Layer 1 includes a model-based IDS that uses a set of specific rules that may be developed based on the spatio-temporal behavior of power system networks during cyber-attacks and normal operation, Rivera, para [0048])
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with network visualization, intrusion detection (Rivera). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Rivera in order to effectively identify abnormalities in systems (See Rivera, para [0071])
Claims 9 is/are rejected under 35 U.S.C 103 as being unpatentable over Verma et al. (US 20220180190 A1) hereinafter referred to as Verma, in view of Kallur et al. (US 20190147333 A1), hereinafter referred to as Kallur in further view of Shanbhag et al. (US 20210406681 A1), hereinafter referred to as Shanbhag
As per claim 9, Verma, Kallur disclose the computer-implemented method of claim 7, wherein
However, Verma in view of Kallur does not explicitly disclose the limitation:
training the generative model comprises selecting a best loss value by
PNG
media_image3.png
46
259
media_image3.png
Greyscale
Shanbhag discloses:
training the generative model comprises selecting a best loss value by
PNG
media_image3.png
46
259
media_image3.png
Greyscale
(A loss function metric value of a loss value is computed. The methos employs the first deep learning network to predict the loss function metric value in association with training a second deep learning network to perform a defined deep learning task, Shanbhag, para [0007]. Claim intents to evaluate loss values and adapt training based on minimizing loss over iterations. Here, the system trains a deep learning model using loss evaluations. A first deep network learns a loss function, the output is used to train another network based on the predicted loss metric. This corresponds to tracking loss values and using them to optimize training)
A person of ordinary skill in the art before the effective filing date of the claimed invention would have combined Verma, Kallur with Rivera by incorporating the method of generative adversarial network for classification (Verma) and semi-supervised conditional generative modeling (Kallur) with learning loss function using deep learning network (Shanbhag). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Verma and Kallur with Shanbhag in order to effectively train models using loss functions (See Shanbhag, para [0007])
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAGHAVENDER CHOLLETI whose telephone number is (703) 756-1065. The examiner can normally be reached M-F 9am-5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RUPAL DHARIA can be reached on (571) 272-3880. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Respectfully submitted,
/RAGHAVENDER NMN CHOLLETI/Examiner, Art Unit 2492
/RUPAL DHARIA/Supervisory Patent Examiner, Art Unit 2492