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 1-19, 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the following limitations:
1. (original): A method for implementing inference of a neural network model, the method comprising:
performing, in a rich execution environment (REE), computation for each of a plurality of convolution layers of the neural network model, based on multiplicative perturbation factors of the convolution layer and outputting a computation result of each of the plurality of convolution layers to a trusted execution environment (TEE) (spec [0025]…multiplicative perturbation factors is math; [0054]…computation result is math); and
in the trusted execution environment (TEE):
correcting the computation result of a first convolution layer of the plurality of convolution layers based on the multiplicative perturbation factors corresponding to the first convolution layer, correcting the computation result of each of remaining convolution layers other than the first convolution layer among the plurality of convolution layers based on the multiplicative perturbation factors and intermediate result protection (IRP) noise correction factors corresponding to the remaining convolution layers spec ([0025]…multiplicative perturbation factors is math; [0039]… noise correction factors is math; [0054]…computation result is math),
inputting the corrected computation result of the plurality of convolution layers into corresponding nonlinear layers of the neural network model, and applying IRP noise to an output of the nonlinear layer corresponding to each of the plurality of convolution layers other than a last convolution layer among the plurality of convolution layers, and outputting the nonlinear layer to which the IRP noise has been applied to the REE, wherein the IRP noise correction factors are based on IRP noise applied to the output of the nonlinear layer corresponding to a previous convolution layer of the remaining convolution layers ([0039]… IRP noise correction factors is math), and
The claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites the following additional elements:
wherein an output of a last nonlinear layer of the neural network model is an inference result of the neural network model (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is extra-solution activity of well, understood routine and conventional operation of presentation of offer or statistics under MPEP 2106.05(d)).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claims 2-10: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim with additional math.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites no additional element:
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claims 11-19 are device claims having similar limitation as claims 1-10 and are rejected under the same rationale. The additional elements in claim 11 is A neural network model inference device comprising: at least one first memory that stores first computer code; at least one first processor that accesses the at least one first memory and executes the first computer code to implement at least computing module configured to perform (amounts to performing generic function of execution of stored instructions (MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract into practical application and are not sufficient to amount to significant more than the abstract idea. Therefore, the claims are an abstract idea.
Claim 21 is non-transitory computer readable storage medium claims having similar limitation as claim 1 and is rejected under the same rationale. The additional elements in claim 21 is A non-transitory computer readable storage medium storing a computer program that when executed by at least one processor causes the at least one processor to at least (amounts to performing generic function of execution of stored instructions (MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract into practical application and are not sufficient to amount to significant more than the abstract idea. Therefore, the claims are an abstract idea.
Claims 13-18, 21-23 are non-transitory computer readable storage medium claims having similar limitation as of claim 1-9 and are rejected under the same rationale.
Claims 25-26 are method claims having similar limitation as of claim 1-2 and are rejected under the same rationale.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arora (US 20200082279 A1) disclose neural network inference within Trusted Execution Environment and Rich Execution environment (Fig. 2).
YI et al (US 20220245515 A1) disclose convolution layers of neural network in Trusted Execution Environment and Rich Execution environment (abstract, Fig. 4A, 4B), apply noise to each layer (Fig. 5A, 6B, 7, 11).
Xu et al (“Privacy-Preserving Machine Learning: Methods, Challenges and Directions” 2021) disclose Trusted Execution Environment (section 5.2.5), multiplicative perturbation (section 5.2.1), CNN (pg. 18).
Wei et al (“THE-V: Verifiable Privacy-Preserving Neural Network via Trusted Homomorphic Execution” 2023) disclose DNN inference in Trusted Execution Environment (section 1) and Rich Execution environment (section 1), convolution layers (Fig. 4), nonlinear layers of DNN (Fig. 4).
Hou et al (“Data Protection: Privacy-Preserving Data Collection With Validation” 2023) disclose result correction within TEE (section 1).
LIU et al (“Privacy and Security Issues in Deep Learning: A Survey” 2021) disclose a survey of privacy and security issues in deep learning (abstract), use of perturbation technique (section IIIB).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUT WONG whose telephone number is (571)270-1123. The examiner can normally be reached M-F 10am-6pm EST.
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/LUT WONG/Primary Examiner, Art Unit 2127
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