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 .
The following action is in response to the original filing of 01/19/2023.
Claims 1-20 are pending and have been considered below.
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 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claims 11-20, the claims do not fall within at least one of the four categories of patent eligible subject matter because the are drawn to software per se. Each of claims 11-20 recite a machine learning model comprising components that have been interpreted under a broadest reasonable interpretation in light of the specification as software.
Allowable Subject Matter
Claims 1-10 are allowed.
Claim 11 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101, set forth in this Office action.
Claims 12-20 would be allowable if rewritten to overcome the rejections under 35 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
Closest prior art of BARAD (US 2019/0362269 A1) discloses a method for detecting non-problem domain input samples provided to a machine learning (ML), the method comprising: training the ML model using problem domain training data to provide a trained ML model having connected layers for providing first classification predictions (Fig. 1 105, 110, 115, ¶37); adding a fully connected layer to the trained ML model to a connected first layer, and adding a softmax layer connected to an output of the first connected layer to output first and second vectors (Fig. 1 120, ¶40-42); retraining the trained ML model (¶44); providing a plurality of input samples to an input of the ML model for an inference operation (¶45); receiving an output vector from the softmax layer of the ML model (¶45-46); computing a metric using the first and second output vectors from the softmax layer (¶46); and comparing the metric to a threshold metric to determine if the plurality of input samples are problem domain or non-problem domain (¶46-47). BARAD fails to disclose wherein the trained ML model explicitly comprises first fully connected layer and a first softmax layer such that the added fully connected layer is connected in parallel to the first fully connected layer and the added softmax layer is connected to an output of the first fully connected layer and fails to disclose retraining the trained ML model using non-problem domain training data that is not in the problem domain to provide a retrained ML model for providing second classification predictions from the second softmax layer, wherein weights of all layers of the ML model are prevented from being changed during the retraining except for weights of the second fully connected layer.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
dos Santos Silva; Bruno et al.
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Ermans; Brian et al.
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Bos; Joppe Willem et al.
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Bos; Joppe Willem et al.
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Veshchikov; Nikita et al.
US 20200233936 A1
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Abadi; Martin et al.
US 20190171929 A1
ENCODING AND RECONSTRUCTING INPUTS USING NEURAL NETWORKS
Abbaszadeh; Masoud et al.
US 20190058715 A1
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Pal, Soham, et al. "Activethief: Model extraction using active learning and unannotated public data." Proceedings of the AAAI conference on artificial intelligence. Vol. 34. No. 01. 2020.
Juuti, Mika, et al. "PRADA: protecting against DNN model stealing attacks." 2019 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE, 2019.
Meng, Dongyu, and Hao Chen. "Magnet: a two-pronged defense against adversarial examples." Proceedings of the 2017 ACM SIGSAC conference on computer and communications security. 2017.
Grosse, Kathrin, et al. "On the (statistical) detection of adversarial examples." arXiv preprint arXiv:1702.06280 (2017).
Teerapittayanon, Surat, Bradley McDanel, and Hsiang-Tsung Kung. "Branchynet: Fast inference via early exiting from deep neural networks." 2016 23rd international conference on pattern recognition (ICPR). IEEE, 2016.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW L TANK whose telephone number is (571)270-1692. The examiner can normally be reached Monday-Thursday 9a-6p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at 571-270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW L TANK/Primary Examiner, Art Unit 2141