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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “module to” in claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.Claim limitation “module to”
has been evaluated under the three-prong test set forth in MPEP § 2181, subsection I, but the result is inconclusive. Thus, it is unclear whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because (a) the term “means” or generic placeholder is modified by a word, which is ambiguous regarding whether it conveys structure or function;. The boundaries of this claim limitation are ambiguous; therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
In response to this rejection, applicant must clarify whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Mere assertion regarding applicant’s intent to invoke or not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is insufficient. Applicant may:
(a) Amend the claim to clearly invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by reciting “means” or a generic placeholder for means, or by reciting “step.” The “means,” generic placeholder, or “step” must be modified by functional language, and must not be modified by sufficient structure, material, or acts for performing the claimed function;
(b) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, should apply because the claim limitation recites a function to be performed and does not recite sufficient structure, material, or acts to perform that function;
(c) Amend the claim to clearly avoid invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by deleting the function or by reciting sufficient structure, material or acts to perform the recited function; or
(d) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply because the limitation does not recite a function or does recite a function along with sufficient structure, material or acts to perform that function.
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.
Claim(s) 1, 8, 15, 16, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Darby US 2023/0145002 in view of Lakshmanan US 2022/0004935.
As per claims 1, 8. Darby teaches A computer-implemented method for counteracting an adversarial attack on an artificial neural network by determining out-of-distribution samples, the method comprising: receiving training data for the artificial neural network comprising a plurality of in- distribution samples in an input space; embedding the training data in the input space into a lower-dimensional embedded space; receiving one or more inputted samples and embedding the one or more inputted samples into the lower-dimensional embedded space; determining a score for each of the one or more inputted samples by determining a distance from each inputted sample to a distribution of the training data in the lower- dimensional embedded space; classifying whether each of the one or more inputted samples is out-of-distribution by determining whether the score is greater than a predetermined distance from the distribution of the training data in the lower-dimensional embedded space; and outputting the classification of each of the one or more inputted samples. [0006][0007][0013][0017][0020][0024][0029]-[0039][0049]-[0052] (teaches an ANN trained with training data, generating an inference based on transforming input to output, comparing the output to a distribution and determine the distance and determining if the output is a likely outlier/attack or falls within the normal classification of data)
Darby does not explicitly teach “out of distribution”.
Lakshmanan teaches classifying whether each of the one or more inputted samples is out-of-distribution by determining whether the score is greater than a predetermined distance the test classifying said point as an anomaly. [0145][0148]
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to use the teaching of Lakshmanan with the prior art because it increases efficiency in detecting anomalous inputs.
As per claim 15. Darby teaches A computer-implemented method for counteracting an adversarial attack on an artificial neural network by determining out-of-distribution samples, the method comprising: receiving an input sample; passing the input sample through the artificial neural network to retrieve outputs from a plurality of layers of the artificial neural network; passing the outputs of the layers of the artificial neural network to one or more first-stage classifiers to predict similarity of the outputs to a learned activity pattern for in-distribution samples from a training dataset, the first-stage classifiers outputting a sequence of labels and a sequence of probabilities; passing the sequence of labels and the sequence of probabilities to one or more second- stage classifiers to determine a class output label and a probability output label; and comparing the prediction of the artificial neural network for the input sample to the class output label and the probability output label to determine whether the sample is out-of- distribution, and where the predictions are the same, outputting a classification of the sample as in-distribution, and otherwise, outputting a classification of the sample as out- of-distribution.
[0006][0007][0013][0017][0020][0024][0036]-[0039][0049]-[0052] (teaches an ANN trained with training data, test data and other input points, teaches detection of potential attacks via detection of inputs that are more than a threshold distance from the trained means, classifying and taking corrective actions)
Darby does not explicitly teach “out of distribution”.
Lakshmanan teaches classifying whether each of the one or more inputted samples is out-of-distribution by determining whether the score is greater than a predetermined distance the test classifying said point as an anomaly. [0145][0148]
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to use the teaching of Lakshmanan with the prior art because it increases efficiency in detecting anomalous inputs.
As per claim 16. Darby teaches The method of claim 15, wherein the one or more second-stage classifiers comprise sequence pattern classifiers. [0034][0049][0051] (classify the pattern and the likelihood that the data is malicious)
As per claim 18. Darby teaches The method of claim 15, wherein the learned activity patterns comprise sequences in the training dataset. [0017]
As per claim 19. Darby teaches The method of claim 15, wherein the sequence of labels comprises a classification of learned labels for each in-distribution sample and associated true class label in the training dataset,and wherein the sequence of probabilities comprises a learned probability for sequences certainty of classification and the associated true class label. [0017][0022]
As per claim 20. Darby teaches The method of claim 15, wherein the artificial neural network comprises a set of local classifiers for each layer in the artificial neural network. [0017] (local topography)
Claim(s) 2, 3, 6, 9, 10, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Darby US 2023/0145002 in view of Lakshmanan US 2022/0004935 in view of Liu US 2024/0032877.
As per claims 2, 9. Liu teaches The method of claim 1, wherein the artificial neural network is Deep Convolutional Neural Network (DCNN). [0064] [0066] (DCNN used is a type of ANN)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to use the teaching of Liu with the prior art because it is processor efficient.
As per claims 3, 10. Liu teaches The method of claim 1, wherein determining the score comprises performing Expectation Maximization (EM). [0068] (EM)
As per claims 6, 13. Liu teaches The method of claim 4, wherein performing the optimization to identify out-of-distribution areas comprises performing particle swarm optimization. [0068] (particle swarm)
Claim(s) 4, 5, 11, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Darby US 2023/0145002 in view of Lakshmanan US 2022/0004935 in view of Jin US 2020/0311557
As per claims 4, 11. The method of claim 1, Jin teaches wherein the score comprises a weighted confidence score. [0045][0046][0048] (teaches detecting out of bounds input and associating a weighted confidence score with said finding)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to use the teaching of Jin with the prior art because it provides a more accurate model.
As per claims 5, 12. The method of claim 3, Jin teaches further comprising optimizing the input space by determining areas of the input space vulnerable to out-of-distribution samples using the weighted confidence score. (teaches detecting out of bounds input and associating a weighted confidence score with said finding)
Claim(s) 7, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Darby US 2023/0145002 in view of Lakshmanan US 2022/0004935 in view of Finn US 2021/0241113
As per claims 7, 14. Finn teaches The method of claim 1, wherein embedding the training data in the input space into the lower- dimensional embedded space comprises embedding in-distribution samples into a lower-dimensional manifold using one or more of Isometric Mapping ("Isomap") and Locally Linear Embedding ("LLE"). [0037][0038] (teaches using LLE or Isomap to train the ANN)
It would have been obvious to one of ordinary skill in the art at the time the application was filed to use the teaching of Finn with the prior art because it reduces dimensionality and increases efficiency. [0002][0004]
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Darby US 2023/0145002 in view of Lakshmanan US 2022/0004935 in view of Kottegoda US 2022/0044543
As per claim 17. Kottegoda teaches The method of claim 15, wherein the artificial neural network comprises a random forest classifier. [0033] (random forest)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to use the teaching of Kottegoda with the prior art because it is a well known ANN model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER BROWN whose telephone number is (571)272-3833. The examiner can normally be reached M-F 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Luu Pham can be reached at (571) 270-5002. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER J BROWN/Primary Examiner, Art Unit 2439