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 .
Response to Amendment and Arguments
Applicant’s amendment filed on March 30, 2026 has been entered and made of record. Claims 1-5 are pending and are being examined in this application.
In light of Applicant’s amendments to the claims, the 101 CRM rejection and 112(b) rejection are withdrawn.
Applicant’s arguments with respect to the 102 rejection have been fully considered, but are moot in view of the new ground(s) of rejection provided below.
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 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Kanai et al. (US Pub. 20220164604) in view of Ding et al. (US Pub. 20200134468).
Referring to claim 1, Kanai discloses A learning apparatus comprising: a memory; and a processor coupled to the memory and programmed to execute a process [figs. 3 and 7; pars. 81 and 82; CPU 1020 reads program module 1093 and program data 1094 into memory 1010 to execute a classification device] comprising:
acquiring first data associated with a correct answer label [pars. 21-24, pars. 30-33, and pars. 37-47; a deep learning model receives image data as input and outputs, for each class, a probability that an object appearing in the image belongs to that class; a probability score is associated with each class label, and the label with the largest score is the image recognition result; the problem here is to perform image recognition on an image x, where the solution (i.e., answer) is to find a (correct) label y from among M labels; to do this, the deep learning model is trained based on N datasets that are prepared beforehand (i.e., training data); per the above discussion and cited paragraphs, it is implied that the training data includes input images, each image associated with a probability score distribution, where the label with the largest score is a correct answer label]; and
training, using the first data, a model representing a probability distribution of labels of the first data [pars. 21-24, 30-33, and 37-47; note the training of the deep learning model based on the training data] by using, as a filter in the training, the correct answer label of the first data [pars. 33-38 and 50-54; training the deep learning model includes using a mask model to preprocess the training data to ensure that only the elements (e.g., features or pixels) of the image that are strongly correlated with the label are provided as input to the training] so as to correctly predict the label for an adversarial example in which noise is added… [pars. 2-4, 33-38, and 50-54; the preprocessing of the training data allows the deep learning model to have robustness against an adversarial attack when there is noise added to an input image].
Kanai does not appear to explicitly disclose an adversarial example in which the noise is added to the first data.
However, Ding discloses an adversarial example in which the noise is added to the first data [pars. 3-8; a neural network generates correct outputs despite adversarial attacks where noise is applied to benign examples].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the training taught by Kanai so that the deep learning model is trained to be robust against adversarial attacks in which noise is added to benign examples as taught by Ding, with a reasonable expectation of success. The motivation for doing so would have been to improve robustness against adversarial attacks [Ding, par. 8].
Referring to claim 2, Kanai discloses The learning apparatus according to claim 1, wherein the training minimizes a loss function for the adversarial example while treating the probability of the labels of the first data as a fixed value [pars. 50-58 and 72; the training minimizes the sum of the loss function and the magnitude of the image by using the masking model to preprocess the training data; note that the preprocessing is performed for all elements with respect to the same label].
Referring to claim 3, Kanai discloses The learning apparatus according to claim 1, further comprising acquiring second data for which a label is to be predicted; and predicting the label of the second data by using the learned model [pars. 2 and 21-24; the deep learning model receives image data as input and outputs, for each class, a probability that an object appearing in the image belongs to that class; a probability score is associated with each class label, and the label with the largest score is the image recognition result].
Referring to claim 4, see the rejection for claim 1, which incorporates the claimed method.
Referring to claim 5, see at least the rejection for claim 1. Ding further discloses A non-transitory computer-readable recording medium having stored a learning program causing a computer to execute a process comprising the claimed steps [figs. 3 and 7; pars. 81 and 82; hard disk drive 1090 stores program module 1093 and program data 1094, which is loaded into memory 1010 and executed by CPU 1020].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
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/Grace Park/Primary Examiner, Art Unit 2144
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