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 action is responsive to the Application filed on 12/31/2025
Claims 1-11 and 14-17 are pending in the case. Claims 1 is the only independent claim. Claim 1 have been currently amended. Claims 12-13 have been canceled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/31/2025
has been entered.
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, 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 1-3, 5, 8-9, 11 and 14-17 are rejected under 35 U.S.C 103 as being unpatentable over Mummadi et al. (Pub No.: 20180308012 A1), hereinafter referred to as Mummadi in view of “On the (Statistical) Detection of Adversarial Example”, https://arxiv.org/abs/1702.06280, Kathrin et al, 02/21/2017 and further in view of “SafetyNet: Detecting and Rejecting Adversarial Examples Robustly”, SafetyNet: Detecting and Rejecting Adversarial Examples Robustly, Jiajun Lu et al, 2017.
With respect to claim 1, Mummadi disclose:
A method of operating a neural network-based sensing system for detecting and correcting for the presence of adversarial perturbations in the sensing system, the neural network-based sensing system including a processor coupled to a neural network module and operatively coupled to at least one sensor, the neural network module configured to implement a neural network, the method comprising at least the following steps: - receiving input signals (x) from the at least one sensor (In Fig. 1 and paragraph [0050], Mummadi discloses the receiving output signal S of the sensor in an optional receiving unit 50, which converts output signal S into a data signal x (alternatively, output signal S may also be directly adopted as data signal x).
generating, from the input signals (x) including at least a given input (xo), respective outputs, the outputs being predictions of the neural network and including a given output yo corresponding to the given input (x0), where yo= fϑ (x0) (In paragraph [0050], Mummadi discloses receiving sensor data, forming input signal x and feeding x into the neural network. In paragraph [0052], further disclose that the neural network generates outputs/segmentation y_cls)
generating, from a plurality of the outputs including the given output yo, a measurement quantity (m), where m is, , (i) a first measurement quantity Mi as a value of a gradient Dxfϑ of the network function fo corresponding to the given input (xo), (ii) a second measurement quantity M2 corresponding to a gradient of a predetermined objective function derived from a training process for the neural network, or (iii) a third measurement quantity M3 derived from a combination of Mi and M2(Examiner selects (ii): (Under the broadest reasonable interpretation, the calculated gradient value constitutes the claimed "second measurement quantity" corresponding to a gradient of a predetermined objective function) In paragraph [0080-0083], Mummadi discloses generating a value corresponding to a gradient of a predetermined objective function derived from training of a neural network. Discusses a weighted cost function (e.g., cross entropy) for a machine learning system and further discloses ascertaining a gradient of the weighted cost function averaged over a training dataset.)
stopping the sensing system and issuing a warning via an output device(In paragraph [0029], Mummadi discloses that the actuator control system indicates when the actuator control system is not robust.)
With respect to claim 1, Mummadi does not explicitly disclose:
detecting the presence of an adversarial perturbation in the sensing system in real time when the measurement quantity (m) is equal to or greater than a threshold
executing, in response to detecting the presence of an adversarial perturbation, at least one remedial action in real time including: rejecting the output prediction of the neural network fϑ (x0) and stopping further action of the sensing system resulting from the rejected output prediction
However, it is known by Kathrin to disclose:
Detecting the presence of an adversarial perturbation in the sensing system in real time when the measurement quantity (m) is equal to or greater than a threshold (On page 1 (Abstract), paragraph 3, Kathrin discloses identifying individual adversarial examples. Table 2 and Table 5 also discuss an adversarial-example, measurement/condition being equal to or greater than a threshold.)
Mummadi and Kathrin are analogous pieces of art because both references concern method and device for adversarial examples. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Mummadi with improving robustness against adversarial examples with detecting adversarial examples. The motivation for doing so would have been to reduce robustness against adversarial examples (See [0004] of Mummadi).
With respect to claim 1, Mummadi in view of Kathrin do not explicitly disclose:
executing, in response to detecting the presence of an adversarial perturbation, at least one remedial action in real time including: rejecting the output prediction of the neural network fϑ (x0) and stopping further action of the sensing system resulting from the rejected output prediction
However, it is known by Jiajun Lu disclose:
Executing, in response to detecting the presence of an adversarial perturbation, at least one remedial action in real time including: rejecting the output prediction of the neural network fϑ (x0) and stopping further action of the sensing system resulting from the rejected output prediction (On page 7 (Table 4), Jungian discloses identifying and rejecting adversarial examples that come from attacking methods not seen in training data. In paragraph [0056], Mummadi discloses the actuator control system being transferred to a safe mode when the assessment indicates that the actuator control system is not robust. (Safe Mode is a protective operating state where a system stop or limits normal operation to avoid any damage, danger or unreliable behavior.))
Mummadi in view of Kathrin and Jiajun Lu are analogous pieces of art because both references concern method and device for adversarial examples. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Jiajun Lu with detecting and rejecting adversarial examples robustly. The motivation for doing so would have been to detect and reject adversarial examples (See Jiajun Lu)
Regarding claim 2, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 1. In addition, Kathrin disclose:
The method according to claim 1, further comprising, if the measurement quantity (m) is determined to be less than the threshold, performing a predetermined usual action resulting from y.( On page 7 and Fig. 1, Kathrin discloses the sample size (i.e., number of adversarial inputs) being less than the threshold.)
Regarding claim 3, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 1. In addition, Mummadi disclose:
The method according to claim 1, wherein generating the first measurement quantity Mi comprises:- computing the gradient Dxfo of the network function fo with respect to the input (x), and - deriving the first measurement quantity Mi as the value of gradient Dxfo corresponding to the given input (xo) (In paragraph [0074], Mummadi discloses corresponding estimated semantic value y.sup.pred.sub.ij is ascertained for each of the image signals x.sup.(k). A substitute value is also ascertained for each point (i, j) that is contained in foreground set l.sub.o.sup.(k).)
Regarding claim 5, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 1. In addition, Mummadi disclose:
The method according to claim 1, wherein generating the second measurement quantity M2 comprises:- computing a gradient Da J(X, Y, fo) of the objective function by J(X, Y, fo) with respect to the network parameters B, whereby J(X, Y, fo) has been previously obtained by calibrating the network function fo in an offline training process based on given training data (In paragraph [0080-0083], Mummadi discloses generating a value corresponding to a gradient of a predetermined objective function derived from training of a neural network. Discusses a weighted cost function (e.g., cross entropy) for a machine learning system and further discloses ascertaining a gradient of the weighted cost function averaged over a training dataset.)
deriving the second measurement quantity M2 as the value of gradient Da J(X, Y, fo) corresponding to the given input (xo) (In paragraph [0083], further disclose ascertaining a gradient of the weighted cost function averaged over a training dataset.)
Regarding claim 8, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 1. In addition, Mummadi disclose:
The method according to claim 1, wherein the first measurement quantity Mi, the second measurement quantity M2 and/or the third measurement quantity M3 is generated based on a predetermined neighborhood of inputs (x) including the given input (xo) (In paragraphs[0052-0053], Mummadi discloses generating adversarial robustness -related quantities using regions, neighborhoods, surrounding pixels, and nearby input values associated with an image/input sample.)
Regarding claim 9, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 8. In addition, Mummadi disclose:
The method according to claim 1, wherein the first measurement quantity Mi, the second measurement quantity M2 and/or the third measurement quantity M3 is generated based on a predetermined neighborhood of inputs (x) including the given input (xo) ()
Regarding claim 11, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 1. In addition, Mummadi disclose:
The method according to claim 1, wherein the at least one remedial action includes saving the value of fo (xo) and wait for a next output fo(x1) in order to verify fa (xo) or to determine that it was a false output (In paragraph [003], Mermaid discloses comparing and verifying to determine whether the first output was false/adversarial.)
Regarding claim 14, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 2. In addition, Mummadi disclose:
A method of classifying outputs of a sensing system employing a neural network, the method comprising the method according to claim 2, wherein the predetermined usual action or the predetermined further actions comprise determining a classification or a regression based on the prediction y (In paragraph [0053], Mummadi discloses characterize that this pixel is classified as belonging to a semantic class (from a plurality of semantic classes). Semantic segmentation y_cls, may be a vectorial-valued variable for each pixel, which indicates, for each of the semantic classes with the aid of an associated number in the value interval [0;1], how high the probability is that this pixel is to be assigned to the respective semantic class.)
Regarding claim 15, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 14. In addition, Mummadi disclose:
The method according to claim 14, wherein the sensing system includes one or more output devices and one or more input devices, and wherein the method further comprises: - outputting via an output device a request for a user to approve or disapprove a determined classification (In paragraph [0056], Mummadi discloses the actuator control system being transferred to a safe mode when the assessment indicates that the actuator control system is not robust. (Safe Mode is a protective operating state where a system stop or limits normal operation to avoid any damage, danger or unreliable behavior.))
receiving a user input via an input device, the user input indicating whether the determined classification is approved or disapproved (In paragraph [0029], Mummadi disclose the actuator control system indicates when the actuator control system is not robust.)
Regarding claim 16, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 1. In addition, Mummadi disclose:
A sensing and/or classifying system, for processing predictions and/or classifications in the presence of adversarial perturbations, the sensing and/or classifying system comprising:- a processor and, coupled thereto (In paragraph [0050], disclose a control system processing of output signal S.)
a memory (In paragraph [0088], Mermaid discloses storing data signals in memory)
wherein the processor is configured to connect to one or more sensors for receiving inputs (x) therefrom (In paragraph [0050], disclose receiving signal S)
wherein the processor is configured to run a module in the memory for implementing a neural network, the neural network having a network function fe, where B are network parameters (In paragraph [0059], Mermaid discloses neural-network functions and parameters.)
wherein the processor, is configured to execute the method of claim 1 (In paragraph [0004], Mermaid discloses adversarial perturbation generation and robustness analysis.)
Regarding claim 17, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 16. In addition, Mummadi disclose:
A vehicle comprising a sensing and/or classifying system according to claim 16 (In paragraph [0050], Mummadi discloses that the actuator control system 40 receives output signal S of the sensor.)
Claims 4 and 6 is rejected under 35 U.S.C 103 as being unpatentable over Mummadi in view of Kathrin, Jiajun Lu and further in view of Ceccaldi et al. (Pub No.: 20190046068 A1), hereinafter referred to as Ceccaldi.
Regarding claim 4, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 3. Mummadi in view of Kathrin and Jiajun Lu does not explicitly disclose:
The method according to claim 3, wherein deriving the first measurement quantity Mi comprises determining the Euclidean norm of Dxfo corresponding to the given input (xo)
However, Ceccaldi disclose the limitation (In paragraph [0067], Ceccaldi disclose a first value comprises calculating a L2-norm (Euclidean norm) corresponding to the given input.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Mummadi in view of Kathrin and Jiajun Lu before them, to include Ceccaldi’s to generate a latent space that is invariant to protocol and the decoder is trained to generate the best output possible for brain and/or tissue extraction. The motivation for doing so would have been to process with adversarial networks to accurately or efficiently process the image data.
Regarding claim 6, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 5. Mummadi in view of Kathrin and Jiajun Lu does not explicitly disclose:
The method according to claim 5, wherein deriving the second measurement quantity M2 comprises determining the Euclidean norm of Da J(X, Y, fo) corresponding to the given input (xo)
However, Ceccaldi disclose the limitation (In paragraph [0067], Ceccaldi disclose a second value comprises calculating a L2-norm (Euclidean norm) corresponding to the given input.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Mummadi in view of Kathrin and Jiajun Lu before them, to include Ceccaldi’s to generate a latent space that is invariant to protocol and the decoder is trained to generate the best output possible for brain and/or tissue extraction. The motivation for doing so would have been to process with adversarial networks to accurately or efficiently process the image data.
Claim 7 is rejected under 35 U.S.C 103 as being unpatentable over Mummadi in view of Kathrin, Jiajun Lu and further in view of Oberbreckling et al. (Pub No.: 20180074786 A1), hereinafter referred to as Oberbreckling.
Regarding claim 7, Mummadi in view of Kathrin and Jiajun Lu disclose the elements of claim 1. Mummadi in view of Kathrin and Jiajun Lu does not explicitly disclose:
The method according to claim 1, wherein the third measurement quantity M3 is computed as a weighted sum of the first measurement quantity Mi and the second measurement quantity M2
However, Oberbreckling disclose the limitation (Oberbreckling [0159], a third measure being the weighted sum of the first measure and the second measure.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Mummadi in view of Kathrin and Jiajun Lu before them, to include Oberbreckling’s determining a weighted sum of the first and second measure. The motivation for doing so would have been to improve the processing of a computer system (See[0036] of Oberbreckling ).
Allowable Subject Matter
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 12/31/2025 have been fully considered, but in part are not persuasive.
Pertaining to rejection under 101
Rejections for claims 1-11 and 14-17 are withdrawn under 35 USC § 101
Pertaining to rejection under 103
Thus, the applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EVEL HONORE whose telephone number is (703)756-1179. The examiner can normally be reached Monday-Friday 8 a.m. -5:30 p.m.
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EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights