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 .
This action is in response to amendments and remarks filed on 03/27/2026. In the current amendments, claims 1, 19, and 20 are amended. Claims 1-20 are pending and have been examined.
In response to amendments and remarks filed on 03/27/2026, the 35 U.S.C. 112(b), and the 35 U.S.C. 103 rejections made in the previous office action are withdrawn.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/05/2026 and 03/30/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “improving” in claim 1 is a relative term which renders the claim indefinite. The term “improving” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For examination purposes, “improving computational efficiency and data security in neural network training” has been interpreted as increasing computational efficiency and data security by any amount.
The term “improving” in claim 19 is a relative term which renders the claim indefinite. The term “improving” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For examination purposes, “improving computational efficiency and data security in neural network training” has been interpreted as increasing computational efficiency and data security by any amount.
The term “improving” in claim 20 is a relative term which renders the claim indefinite. The term “improving” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For examination purposes, “improving computational efficiency and data security in neural network training” has been interpreted as increasing computational efficiency and data security by any amount.
Dependent claims 2-18 are rejected based on being directly or indirectly dependent on rejected claim 1.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“sampling a plurality of network inputs from the set of training data”
“determining a clipped gradient for each network input of the plurality of network inputs”
“generating a plurality of augmented versions of the network input, wherein each augmented version of the network input results from applying a respective augmentation transformation to the network input”
“determining, for each of the plurality of augmented versions of the network input, a gradient of the objective function with respect to the neural network parameters of the neural network when the objective function is evaluated on a network output generated by the neural network by processing the augmented version of the network input”
“determining a combined gradient for the network input by combining the gradients determined for the plurality of augmented versions of the network input”
“generating the clipped gradient for the network input by clipping the combined gradient determined by combining gradients for the plurality of augmented versions of the network input”
“updating the neural network parameters using the clipped gradients for the network inputs of the plurality of network inputs”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass sampling network inputs from a set of training data (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can sample inputs from training data set); determining a clipped gradient for each input of the plurality of inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine a clipped gradient for each of the plurality of network inputs); generating augmented versions of the network input, the augmented version resulting from applying a respective augmentation transformation to the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can apply augmentation transformations to a network input to generate respective augmented versions of the network input); determining a gradient of the objective function for each of the augmented versions of the network input with respect to the neural network parameters when the objective function is evaluated based on a neural network output generated from the augmented version of the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine a gradient of the objective function evaluated based on the augmented version of the network input for each of the augmented versions of the input); determining a combined gradient for the network input by combining the gradients determined for the augmented versions of the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can combine the gradients determined for the augmented versions of the network input to determine a combined gradient for the network input); generating the clipped gradient for the network input by clipping the combined gradient determined from combining gradients for the augmented network inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can clip the combined gradient for the gradients of the augmented network inputs to generate the clipped gradient for the network input); and updating the neural network parameters using the clipped gradients for the plurality of network inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the clipped gradients for the plurality of network inputs to update the parameters of the neural network).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“one or more computers”
“a neural network”
“training a set of neural network parameters of a neural network on a set of training data over a plurality of training iterations to optimize an objective function”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Therefore, the additional elements do not integrate the abstracts ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“obtaining a plurality of augmentation transformations, comprising, for each augmentation transformation, randomly sampling parameters defining the augmentation transformation”
“generating each augmented version of the network input by applying a respective augmentation transformation to the network input”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass obtaining a plurality of augmentation transformations by randomly sampling parameters defining the augmentation transformation (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can randomly sample parameters defining augmentation transformations to obtain a plurality of augmentation transformations); and generating augmented versions of the network input by applying a respective augmentation transformation to the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can apply augmentation transformations to the network input to generate augmented versions of the network input).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“processing the augmented version of the network input …, in accordance with current values of the neural network parameters of the neural network, to generate a corresponding network output”
“determining gradients of the objective function with respect to the neural network parameters of the neural network when the objective function is evaluated on the network output”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass processing the augmented version of the network input in accordance with the current values of the neural network parameters to generate a corresponding output (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the current values of the parameters of the neural network to process the augmented version of the network input to generate a network output); and determining gradients of the objective function with respect to the neural network parameters when the objective function is evaluated on the network output (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine gradients of the objective function when the objective function is evaluated on the network output).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“using the neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Additionally, the recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“averaging the gradients determined for the plurality of augmented versions of the network input”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass averaging the determined gradients for the augmented versions of the network input (corresponds to mathematical calculations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“scaling the combined gradient for the network input to cause a norm of the combined gradient for the network input to satisfy a clipping threshold”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass scaling the combined gradient for the network input to cause a norm of the combined gradient to satisfy a clipping threshold (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can cause a norm of the combined gradient for the network input to satisfy a clipping threshold by scaling the combined gradient).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“scaling the combined gradient for the network input by a scaling factor defined as a ratio of: (i) the clipping threshold, and (ii) the norm of the combined gradient for the network input”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass scaling the combined gradient for the network input by a scaling factor defined as a ratio the clipping threshold and the norm of the combined gradient (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use a scaling factor defined as a ratio the clipping threshold and the norm of the combined gradient for the network input to scale the combined gradient).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 5 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating a set of noise parameters, comprising randomly sampling the noise parameters from a noise distribution”
“applying the noise parameters to the clipped gradients for the network inputs of the plurality of network inputs”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass generating a set of noise parameters by randomly sampling the noise parameters from a noise distribution (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can randomly sample noise parameters from a noise distribution to generate a set of noise parameters); and applying the noise parameters to the clipped gradients for the plurality of network inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can apply the noise parameters to the clipped gradients for the plurality of network inputs).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the noise distribution comprises a Gaussian noise distribution”
As drafted, is part of the abstract idea of claim 7 of generating a set of noise parameters by randomly sampling a noise distribution. The limitation of claim 8 further limits the limitation of claim 7 by further defining the noise distribution. The above limitation in the context of this claim encompasses generating a set of noise parameters by randomly sampling the noise parameters from a Gaussian noise distribution (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can randomly sample noise parameters from a Gaussian noise distribution to generate a set of noise parameters).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 7 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 9 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“wherein the neural network does not include any batch normalization layers”
As drafted, is part of the additional element of claim 1 of a neural network. The limitation of claim 9 further limits the limitation of claim 1 by further defining what the neural network does not include. Additionally, the recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 10 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“wherein the neural network includes group normalization layers”
As drafted, is part of the additional element of claim 1 of a neural network. The limitation of claim 10 further limits the limitation of claim 1 by further defining what the neural network comprises. Additionally, the recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“process a network input comprising an image”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass processing a network input comprising an image (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can process an image as a network input).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“the neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Additionally, the recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“process a network input comprising audio data”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass processing a network input comprising audio data (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can process audio data as a network input).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“the neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Additionally, the recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“process a network input comprising electronic medical record data”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass processing a network input comprising medical record data (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can process medical record data as a network input).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“the neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Additionally, the recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“process a network input comprising textual data”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass processing a network input comprising textual data (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can process textual data as a network input).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“the neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Additionally, the recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 15 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“wherein the neural network comprises one or more convolutional neural network layers”
As drafted, is part of the additional element of claim 1 of a neural network. The limitation of claim 15 further limits the limitation of claim 1 by further defining what the neural network comprises. Additionally, the recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 16 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“wherein the objective function comprises a classification loss”
As drafted, is part of the additional element of claim 1 of training neural network parameters of the neural network to optimize an objective function. The limitation of claim 16 further limits the limitation of claim 1 by further defining what the optimization function comprises. Additionally, the recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein at each training iteration, the plurality of network inputs comprises at least 4000 network inputs”
As drafted, is part of the abstract idea of claim 1 of sampling a plurality of network inputs from the set of training data. The limitation of claim 17 further limits the limitation of claim 1 by further defining that the sampled network inputs comprises at least 4000 network inputs. The above limitation in the context of this claim encompasses sampling at least 4000 network inputs from a set of training data (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can sample at least 4000 inputs from training data set).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 18,
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein generating a plurality of augmented versions of the network input comprises generating at least 8 augmented versions of the network input”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass generating at least 8 augmented versions of the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate at least 8 augmented versions of the network input).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic computer, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to non-transitory computer storage media, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“sampling a plurality of network inputs from the set of training data”
“determining a clipped gradient for each network input of the plurality of network inputs”
“generating a plurality of augmented versions of the network input, wherein each augmented version of the network input results from applying a respective augmentation transformation to the network input”
“determining, for each of the plurality of augmented versions of the network input, a gradient of the objective function with respect to the neural network parameters of the neural network when the objective function is evaluated on a network output generated by the neural network by processing the augmented version of the network input”
“determining a combined gradient for the network input by combining the gradients determined for the plurality of augmented versions of the network input”
“generating the clipped gradient for the network input by clipping the combined gradient determined by combining gradients for the plurality of augmented versions of the network input”
“updating the neural network parameters using the clipped gradients for the network inputs of the plurality of network inputs”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass sampling network inputs from a set of training data (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can sample inputs from training data set); determining a clipped gradient for each input of the plurality of inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine a clipped gradient for each of the plurality of network inputs); generating augmented versions of the network input, the augmented version resulting from applying a respective augmentation transformation to the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can apply augmentation transformations to a network input to generate respective augmented versions of the network input); determining a gradient of the objective function for each of the augmented versions of the network input with respect to the neural network parameters when the objective function is evaluated based on a neural network output generated from the augmented version of the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine a gradient of the objective function evaluated based on the augmented version of the network input for each of the augmented versions of the input); determining a combined gradient for the network input by combining the gradients determined for the augmented versions of the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can combine the gradients determined for the augmented versions of the network input to determine a combined gradient for the network input); generating the clipped gradient for the network input by clipping the combined gradient determined from combining gradients for the augmented network inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can clip the combined gradient for the gradients of the augmented network inputs to generate the clipped gradient for the network input); and updating the neural network parameters using the clipped gradients for the plurality of network inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the clipped gradients for the plurality of network inputs to update the parameters of the neural network).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“one or more computers”
“training a set of neural network parameters of a neural network on a set of training data over a plurality of training iterations to optimize an objective function”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Therefore, the additional elements do not integrate the abstracts ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer and generic training of a neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 20,
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“sampling a plurality of network inputs from the set of training data”
“determining a clipped gradient for each network input of the plurality of network inputs”
“generating a plurality of augmented versions of the network input, wherein each augmented version of the network input results from applying a respective augmentation transformation to the network input”
“determining, for each of the plurality of augmented versions of the network input, a gradient of the objective function with respect to the neural network parameters of the neural network when the objective function is evaluated on a network output generated by the neural network by processing the augmented version of the network input”
“determining a combined gradient for the network input by combining the gradients determined for the plurality of augmented versions of the network input”
“generating the clipped gradient for the network input by clipping the combined gradient determined by combining gradients for the plurality of augmented versions of the network input”
“updating the neural network parameters using the clipped gradients for the network inputs of the plurality of network inputs”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass sampling network inputs from a set of training data (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can sample inputs from training data set); determining a clipped gradient for each input of the plurality of inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine a clipped gradient for each of the plurality of network inputs); generating augmented versions of the network input, the augmented version resulting from applying a respective augmentation transformation to the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can apply augmentation transformations to a network input to generate respective augmented versions of the network input); determining a gradient of the objective function for each of the augmented versions of the network input with respect to the neural network parameters when the objective function is evaluated based on a neural network output generated from the augmented version of the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine a gradient of the objective function evaluated based on the augmented version of the network input for each of the augmented versions of the input); determining a combined gradient for the network input by combining the gradients determined for the augmented versions of the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can combine the gradients determined for the augmented versions of the network input to determine a combined gradient for the network input); generating the clipped gradient for the network input by clipping the combined gradient determined from combining gradients for the augmented network inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can clip the combined gradient for the gradients of the augmented network inputs to generate the clipped gradient for the network input); and updating the neural network parameters using the clipped gradients for the plurality of network inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the clipped gradients for the plurality of network inputs to update the parameters of the neural network).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“one or more computers”
“one or more storage devices communicatively coupled to the one or more computers”
“training a set of neural network parameters of a neural network on a set of training data over a plurality of training iterations to optimize an objective function”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Therefore, the additional elements do not integrate the abstracts ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, storage device, and generic training of a neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Response to Arguments
Applicant’s arguments, filed 03/27/2026, with respect to the rejections of claims 19 and 20 under 35 U.S.C. 112(b) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the claim amendments filed 03/27/2026.
Applicant’s arguments, filed 03/27/2026, with respect to the claim rejections under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the 35 U.S.C. 103 prior art rejections have been withdrawn.
Applicant's arguments, filed 03/27/2026, with respect to the 35 U.S.C. 101 abstract idea rejections to claims 1-20 have been fully considered but they are not persuasive. Applicant asserts “Applicant respectfully submits that the claims are patent eligible because they provide a technical solution to a technical problem and are thus integrated into a practical application.
The claimed invention is patent eligible under the reasoning provided in MPEP § 2106.04(d), subsection III. This portion of the MPEP explains that a claimed invention was patent eligible in part because the specification "identified improvements as to how a machine learning model operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks." [ MPEP § 2106.04(d), subsection III ]. The MPEP also explains that the "claims to a method of training a machine learning model were directed to improvements in the machine learning technology itself." [ MPEP § 2106.04(d)(1) ]. Furthermore, the claims included at least a limitation that "reflected the improvement disclosed in the specification" and thus "the claims as a whole integrated what would otherwise be a judicial exception into a practical application." [ MPEP § 2106.04(d), subsection III ].
The present invention provides analogous, technical improvements to neural network training, including improving computational efficiency and data security of neural network training. As described in the Specification:
"[0051] Training a neural network using data augmentation can refer to generating augmented versions of network inputs, i.e., by applying augmentation transformations to network inputs, and then using the augmented versions of the networks inputs for training the neural network. Training a neural network using data augmentation can increase the robustness and prediction accuracy of the neural network, e.g., by reducing the likelihood of overfitting, and by reducing the amount of training data required to train the neural network. Reducing the amount of training data required to train the neural network can enable reduced consumption of computational resources, e.g., memory and computing power, during training. However, conventional approaches for performing data augmentation can result in a significantly increased privacy cost being incurred during training, i.e., resulting in the neural network being more vulnerable to privacy attacks. In particular, the privacy cost incurred by performing conventional data augmentation can scale linearly with the number of augmented versions that are generated for each training example.
[0052] The training system described in this specification addresses this issue by implementing a form of data augmentation that achieves the benefits of data augmentation without incurring any additional privacy loss. In particular, the training system can generate a combined gradient for a network input by combining gradients derived from multiple augmented versions of the network input, clip the combined gradient, and then use the clipped gradient to update the parameter values of the neural network. Combining the gradients derived from the augmented version of the network input enables the training system to generate a richer gradient that encodes more information from the network input. Clipping the combined gradient generated from each network input prior to using the combined gradients to update the neural network parameters limits the impact of any individual network input on the neural network parameters and thus contributes to enhancing the robustness of the neural network to privacy attacks." (emphasis added)
Thus, the claimed invention provides an improvement to computational efficiency and data security in neural network training. For example, the claimed invention provides an improvement to neural network training in a way that increases the robustness and prediction accuracy of a neural network and enables reduced consumption of computational resources, while not increasing the privacy cost incurred during training.
The Specification describes how the claimed invention improves neural network training in a way that achieves the benefits of data augmentation without incurring any additional privacy loss:
" [0052]The training system described in this specification addresses this issue by
implementing a form of data augmentation that achieves the benefits of data augmentation without incurring any additional privacy loss. In particular, the training system can generate a combined gradient for a network input by combining gradients derived from multiple augmented versions of the network input, clip the combined gradient, and then use the clipped gradient to update the parameter values of the neural network. Combining the gradients derived from the augmented version of the network input enables the training system to generate a richer gradient that encodes more information from the network input. Clipping the combined gradient generated from each network input prior to using the combined gradients to update the neural network parameters limits the impact of any individual network input on the neural network parameters and thus contributes to enhancing the robustness of the neural network to privacy attacks." (emphasis added)
For example, the cited portion of the Specification explains how generating a combined gradient for a network input enables the training system to generate a gradient that encodes more information from the ntework input, and how clipping the combined gradient contributes to enhancing the robustness of the neural network to privacy attacks.
Further, the claims "include the components or steps of the invention that provide the improvement described in the specification" (MPEP § 2106.05(a)). For example, claim 1 recites:
"A method performed by one or more computers for improving computational efficiency and data security in neural network training, the method comprising: ...
determining a combined gradient for the network input by combining the gradients determined for the plurality of augmented versions of the network input; and
generating the clipped gradient for the network input by clipping the combined gradient determined by combining gradients for the plurality of augmented versions of the network input."
The claims thus recite a specific combination of steps for improving neural network training.
Accordingly, the claims are not directed to an abstract idea, and the Section 101 rejections to the claims should be withdrawn.” (Remarks Pages 7-10).
Examiner’s Response:
The examiner respectfully disagrees. Applicant has made general assertions that claim 1 recites claim elements that are not directed to an abstract idea and that even if the claim elements are directed to an abstract idea, the judicial exceptions are integrated into a practical application because the claims recite elements that cannot reasonable be characterized as covering mental processes or reflect an improvement to a technology or technical field. Regarding the “determining a combined gradient for the network input by combining the gradients determined for the plurality of augmented versions of the network input” and “generating the clipped gradient for the network input by clipping the combined gradient determined by combining gradients for the plurality of augmented versions of the network input” limitations of claim 1, these limitations, under their broadest reasonable interpretations, are considered abstract ideas that encompass determining a combined gradient for the network input by combining the gradients determined for the augmented versions of the network input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can combine the gradients determined for the augmented versions of the network input to determine a combined gradient for the network input); and generating the clipped gradient for the network input by clipping the combined gradient determined from combining gradients for the augmented network inputs (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can clip the combined gradient for the gradients of the augmented network inputs to generate the clipped gradient for the network input). Furthermore, since the “determining …” and “generating …” limitations are directed to a judicial exception, they cannot provide any alleged solution or improvement. See MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.”
Additionally, claim 1 recites sampling network inputs from a set of training data (corresponds to evaluation and judgement with the assistance of pen and paper); determining a clipped gradient for each input of the plurality of inputs (corresponds to evaluation and judgement with the assistance of pen and paper); generating augmented versions of the network input, the augmented version resulting from applying a respective augmentation transformation to the network input (corresponds to evaluation and judgement with the assistance of pen and paper); determining a gradient of the objective function for each of the augmented versions of the network input with respect to the neural network parameters when the objective function is evaluated based on a neural network output generated from the augmented version of the network input (corresponds to evaluation and judgement with the assistance of pen and paper); and updating the neural network parameters using the clipped gradients for the plurality of network inputs (corresponds to evaluation and judgement with the assistance of pen and paper). Since these limitations are directed to a judicial exception, they cannot provide any alleged solution or improvement. See MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.”
Thus, it is the additional elements that are analyzed to determine whether the judicial exception is integrated into a practical application, not the judicial exception itself. The additional elements of claim 1 of “one or more computers”, “a neural network”, and “training a set of neural network parameters of a neural network on a set of training data over a plurality of training iterations to optimize an objective function”, as drafted, under their broadest reasonable interpretations, are additional elements that are high level recitations of applying a generic computer and generic computer components to implement the abstract ideas such that it amounts to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f): “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. … Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Accordingly, the additional elements do not integrate the abstract ideas into a practical application.
Furthermore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
In other words, the limitations of “determining a combined gradient for the network input by combining the gradients determined for the plurality of augmented versions of the network input” and “generating the clipped gradient for the network input by clipping the combined gradient determined by combining gradients for the plurality of augmented versions of the network input” are abstract ideas that are directed to a judicial exception, so they cannot provide any alleged solution or improvement. Furthermore, the additional elements recited in claim 1 are directed to mere instructions to apply an abstract idea using generic computer components. Therefore, claim 1 does not recite additional element(s) that can provide any alleged solution, improvement, or inventive concept. As such, the judicial exception is not integrated into a practical application, nor do the claims contain significantly more than the judicial exception.
Applicant relies on the arguments above regarding independent claims 19 and 20 and dependent claims 2-18, therefore the response above is applicable to those claims.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN J HALES whose telephone number is (571)272-0878. The examiner can normally be reached M-F 9:00am - 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN J HALES/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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