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. DETAILED ACTION Claims 1-30 are presented for examination in this application, 1 8/157,723 filed 1/20/2023, having an effective filing date of 5/18/2022 via provisional application 63/343,474. The Examiner cites particular sections in the references as applied to the claims below for the convenience of the applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant(s) fully consider the references in their entirety as potentially teaching all or part of the claimed, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. Drawings The drawings submitted on 1/20/2023 have been considered and accepted. Information Disclosure Statement Acknowledgement is made of the information disclosure statement filed 8/3/2023. All patents and non-patent literature have been considered. 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 29 is rejected under 35 U.S.C 101 because the claimed invention is directed to non-statutory subject matter. The claims recite “computer-readable storage medium”. As described in the specification at para [0111]: “In some aspects the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.” Based on this description the computer readable medium does not exclude signals or transmission media which are not statutory embodiments under 35 USC 101. The BRI of computer readable medium can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See In re Nuijten , 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. The following appears to be the closest portions of the specification corresponding to the 35 U.S.C 112(f) invocations: augmenting, via a random style generator having at least one randomly initialized layer, training data to generate augmented training data [0062] The operations can include augmenting, via a random style generator engine 712 (which can also be referred to as a random style generator 712) having at least one randomly initialized layer 714, training data X 0 710 used to generate augmented training data X 1 722 and aggregating data with a plurality of styles from the augmented training data to generate aggregated training data. aggregating data with a plurality of styles from the augmented training data to generate aggregated training data [0062] The operations can include augmenting, via a random style generator engine 712 (which can also be referred to as a random style generator 712) having at least one randomly initialized layer 714, training data X 0 710 used to generate augmented training data X 1 722 and aggregating data with a plurality of styles from the augmented training data to generate aggregated training data applying semantic-aware style fusion to the aggregated training data to generate fused training data [0062] he electronic device can perform further operations including applying semantic-aware style fusion engine 706 to the aggregated training data to generate fused training data and adding the fused training data as fictitious samples to the training data to generate updated training data for training a neural network or machine learning model. adding the fused training data as fictitious samples to the training data to generate updated training data for training a neural network [0062] The electronic device can perform further operations including applying semantic-aware style fusion engine 706 to the aggregated training data to generate fused training data and adding the fused training data as fictitious samples to the training data to generate updated training data for training a neural network or machine learning model. In one aspect, the electronic device may train a single network with only cross-entropy loss. 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 appl icant regards as his invention. Claim 30 is 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 and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C 122, the applicant), regards as the invention. Regarding 35 U.S.C 112(f) invocations: The following limitations invoke 35 U.S.C 112(f) or pre-AIA 35 U.S.C 122, sixth paragraph: “means for augmenting, via a random style generator having at least one randomly initialized layer” as recited in claim 30 However, the written description fails to disclose the corresponding structure, material, or acts to the function. MPEP 2181 II. B. recites “However, if there is no corresponding structure disclosed in the specification (i.e., the limitation is only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm ), the limitation should be deemed indefinite as discussed above, and the claim should be rejected under 35 U.S.C 112(b) or pre-AIA 35 U.S.C 112, second paragraph.”. The portions of the specification identified above do not clearly link the claim language to a “computer or microprocessor programmed with the algorithm ” for the random style generator as recited in the claims. aggregating data with a plurality of styles from the augmented training data to generate aggregated training data However, the written description fails to disclose the corresponding structure, material, or acts to the function. MPEP 2181 II. B. recites “However, if there is no corresponding structure disclosed in the specification (i.e., the limitation is only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm ), the limitation should be deemed indefinite as discussed above, and the claim should be rejected under 35 U.S.C 112(b) or pre-AIA 35 U.S.C 112, second paragraph.”. The portions of the specification identified above do not clearly link the claim language to a “computer or microprocessor programmed with the algorithm” for the aggregating data with a plurality of styles from the augmented training data to generate aggregated training data as recited in the claims. applying semantic-aware style fusion to the aggregated training data to generate fused training data However, the written description fails to disclose the corresponding structure, material, or acts to the function. MPEP 2181 II. B. recites “However, if there is no corresponding structure disclosed in the specification (i.e., the limitation is only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm ), the limitation should be deemed indefinite as discussed above, and the claim should be rejected under 35 U.S.C 112(b) or pre-AIA 35 U.S.C 112, second paragraph.”. The portions of the specification identified above do not clearly link the claim language to a “computer or microprocessor programmed with the algorithm” for the applying semantic-aware style fusion to the aggregated training data to generate fused training data as recited in the claims. adding the fused training data as fictitious samples to the training data to generate updated training data for training a neural network However, the written description fails to disclose the corresponding structure, material, or acts to the function. MPEP 2181 II. B. recites “However, if there is no corresponding structure disclosed in the specification (i.e., the limitation is only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm), the limitation should be deemed indefinite as discussed above, and the claim should be rejected under 35 U.S.C 112(b) or pre-AIA 35 U.S.C 112, second paragraph.”. The portions of the specification identified above do not clearly link the claim language to a “computer or microprocessor programmed with the algorithm” for the adding the fused training data as fictitious samples to the training data to generate updated training data for training a neural network as recited in the claims. Therefore, the claim is indefinite and are rejected under 35 U.S.C 112(b) or pre-AIA 35 U.S.C 112, second paragraph. Applicant may: (a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph; (b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)). If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either: (a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . Claims 1, 2, 11-1 6 , 22-26, and 29-30 are rejected under 35 U.S.C 103 as being unpatentable over Xu et al. (“Robust and Generalizable Visual Representation Learning Via Random Convolutions” hereinafter, Xu ) in view of Lad et al. ( US20220351373A1 hereinafter , Lad ) . Regarding claim 1: Xu teaches augmenting training data, augment, via a random style generator having at least one randomly initialized layer, training data to generate augmented training data (see pg. 1 section Abstract: “In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation. Random convolutions are approximately shape-preserving and may distort local textures. Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”) , aggregate data with a plurality of styles from the augmented training data to generate aggregated training data (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 fig. 2) , apply semantic-aware style fusion to the aggregated training data to generate fused training data (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 fig. 2) , and add the fused training data as fictitious samples to the training data to generate updated training data for training a neural network (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 section 1: “We validate RandConv and its mixing variant in extensive experiments on synthetic and real world benchmarks as well as on the large-scale ImageNet dataset.”.) . Xu does not explicitly teach an apparatus with least one memory and at least one processor coupled to at least one memory. Lad , however, analogously teaches an apparatus with least one memory and at least one processor coupled to at least one memory (see para [0003]: “Disclosed herein is a system using machine learning in detecting geographic atrophy (GA), the system comprising at least one processor; a memory; and a computing platform including the at least one processor and the memory . ” . Also see para [0176]: “ One promising direction is to inject model-dependent perturbations to the input images as strategic augmentations ”. ) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu and Lad before him or her, to modify the apparatus of claim 1 to include attributes of having an apparatus with least one memory and at least one processor coupled to at least one memory in order to perform the steps of the method with a computer (see Lad para [0005]: Disclosed herein is a non-transitory computer readable medium comprising computer executable instructions that when executed by at least one processor of a computer cause the computer to perform steps according to a disclosed method and/or in a disclosed system. ) Regarding claim 2 : Xu in view of Lad teaches the apparatus of claim 1. Xu further teaches wherein the training data includes a plurality of training images (see pg. 6 section 4.1: “To show RandConv is more than a trivial color/contrast adjustment method, we also compare to ColorJitter2 data augmentation (which randomly changes image brightness, contrast, and saturation) and GreyScale (where images are transformed to grey scale for training and testing)” .) Regarding claim 26 : Claim 26 recites analogous limitations to claim 2 and therefore is rejected on the same grounds. Regarding claim 1 1 : Xu in view of Lad teaches the apparatus of claim 1. Xu further teaches wherein, to augment, via the random style generator, training data to generate augmented training data, the at least one processor is configured to randomly initialize at least one weight and at least one offset to achieve texture modification of the training data (see pg. 4 section 3.2: “A simple approach is to use the randomized convolution layer outputs, I ∗ Θ, as new images; where Θ are the randomly sampled weights and I is a training image.”. Also see pg. 4 algorithm 1 line 8.) Regarding claim 1 2 : Xu in view of Lad teaches the apparatus of claim 1. Xu further teaches wherein, to augment, via the random style generator, training data to generate augmented training data, the at least one processor is configured to preserve semantic data in the training data while distorting non-semantic data to increase data diversity (see pg.1 abstract : “Random convolutions are approximately shape-preserving and may distort local textures.”. Also see pg. 13 section A: “For example, in Fig.4, the left occluded triangle shape has texture composed by shapes of cobble stones while cobble stones have their own texture. Random convolution can preserve those large shapes that usually define the image semantics while distorting the small shapes as local texture.”) Regarding claim 1 3 : Xu in view of Lad teaches the apparatus of claim 1. Xu further teaches wherein the augmented training data comprises a randomly generated new style from the training data but maintains data semantics (see pg. 2 section 1: “We provide insights and justification on why RandConv augments images with different local texture but the same semantics with the shape-preserving property of random convolutions.”) . Regarding claim 1 4 : Xu in view of Lad teaches the apparatus of claim 1. Xu further teaches wherein, to aggregate the data with the plurality of styles from the augmented training data to generate the aggregated training data, the at least one processor is configured to use random style aggregation in which the plurality of styles is selected randomly (see pg. 2 fig. 2. See pg. 5 section 3.1: “Inspired by the AugMix ( Hendrycks et al., 2020b) strategy, we propose to blend the original image with the outputs of the RandConv layer via linear convex combinations αI +(1−α)(I ∗ Θ), where α is the mixing weight uniformly sampled from [0,1].In RCmix , the RandConv outputs provide shape-consistent perturbations of the original images. Varying α, we continuously interpolate between the training domain and the randomly sampled domains of RCimg .”) Regarding claim 1 5 : Xu in view of Lad teaches the apparatus of claim 1. Xu further teaches wherein the at least one processor is configured to generate the plurality of styles by passing the augmented training data through the random style generator (see pg. 2 fig. 2. See pg. 5 section 3.1: “Inspired by the AugMix ( Hendrycks et al., 2020b) strategy, we propose to blend the original image with the outputs of the RandConv layer via linear convex combinations αI +(1−α)(I ∗ Θ), where α is the mixing weight uniformly sampled from [0,1].In RCmix , the RandConv outputs provide shape-consistent perturbations of the original images. Varying α, we continuously interpolate between the training domain and the randomly sampled domains of RCimg .”) Regarding claim 16: Xu in view of Lad teaches the apparatus of claim 1. Xu does not explicitly teach wherein, to aggregate data with a plurality of styles from the augmented training data to generate the aggregated training data, the at least one processor is configured to pass a latest set of augmented training data through the random style generator . Lad , however, analogously teaches wherein, to aggregate data with a plurality of styles from the augmented training data to generate the aggregated training data, the at least one processor is configured to pass a latest set of augmented training data through the random style generator (see para [0102]: “ To leverage this dataset to improve the model performance on our GA prediction task, we jointly trained our model with our GA cohort and the additional OCT data ( Kermany D S, et al. (2018) Cell. 172(5):1122-1131) in a multi-task fashion by sharing the CNN image feature extractor but using separate FC layers for different tasks, i.e., one for GA (current and next year) and the other for CNV, DME, drusen and control prediction.”) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu and Lad before him or her, to modify the apparatus of claim 16 to include attributes of wherein, to aggregate data with a plurality of styles from the augmented training data to generate the aggregated training data, the at least one processor is configured to pass a latest set of augmented training data through the random style generator in ord er to achieve a deeper understanding of the performance characteristics of the proposed model (see Lad para [0107]: “ To achieve a deeper understanding of the performance characteristics of the proposed model (multi-scan position-aware model trained with PPI from Table 1), the cross-validated confusion matrices for GA diagnosis (current year) and prognosis (next year) were calculated and shown in Table 2. ”) . Regarding claim 22 : Xu in view of Lad teaches the apparatus of claim 1. Xu further teaches wherein the at least one processor is configured to: combine class-specific semantic information extracted from the aggregated training data in an image space (see pg. 4 section : “The PACS dataset (Li et al., 2018b) considers 7-class classification on 4 domains: photo, art painting, cartoon, and sketch, with very different texture styles. Most recent domain generalization work studies the multi-source domain setting on PACS and uses domain labels of the training data.”. Also see pg. 4 section 4: “We test our RandConv variants RCimg1-7,p=0.5 and RCmix1-7 with and without consistency loss, and ColorJitter / GreyScale / BandPass / MultiAug data augmentation as in the digit datasets.” ) . Regarding claim 23 : Xu in view of Lad teaches the apparatus of claim 1. Xu further teaches wherein the at least one processor is configured to: train the neural network using the updated training data (see pg. 1 abstract: “Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”) Regarding claim 24 : Xu in view of Lad teaches the apparatus of claim 1. Xu does not explicitly teach wherein the at least one processor is configured to: train the neural network using a cross-entropy loss . Lad , however, analogously teaches wherein the at least one processor is configured to: train the neural network using a cross-entropy loss (see para [0075] : “The model is trained to maximize the weighted binary cross-entropy loss, i.e., the likelihood that scans from SD-OCT inputs are correctly assigned (prognosticated) to either the GA or control groups in the assessment of the upcoming year, while adversarially encouraging that regions masked-out by the attention maps are not informative of GA. Additionally, the model concurrently predicts (diagnoses) the probability of GA in the current year.”.) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu and Lad before him or her, to modify the apparatus of claim 24 to include attributes of train ing the neural network using a cross-entropy loss in order to measure the likelihood of correctly assigned data (see Lad para [0075]: “The model is trained to maximize the weighted binary cross-entropy loss, i.e., the likelihood that scans from SD-OCT inputs are correctly assigned (prognosticated)”) . Regarding claim 25 : Xu teaches augmenting training data, augment, via a random style generator having at least one randomly initialized layer, training data to generate augmented training data (see pg. 1 section Abstract: “In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation. Random convolutions are approximately shape-preserving and may distort local textures. Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”) , aggregate data with a plurality of styles from the augmented training data to generate aggregated training data (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 fig. 2) , applying semantic-aware style fusion to the aggregated training data to generate fused training data (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 fig. 2) , and adding the fused training data as fictitious samples to the training data to generate updated training data for training a neural network (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 section 1: “We validate RandConv and its mixing variant in extensive experiments on synthetic and real world benchmarks as well as on the large-scale ImageNet dataset.”.) . Xu does not explicitly teach an apparatus with least one memory and at least one processor coupled to at least one memory. Lad , however, analogously teaches a processor-implemented method of augmenting data (see para [0003]: “Disclosed herein is a system using machine learning in detecting geographic atrophy (GA), the system comprising at least one processor; a memory; and a computing platform including the at least one processor and the memory . ” . Also see para [0176]: “ One promising direction is to inject model-dependent perturbations to the input images as strategic augmentations ”. ) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu and Lad before him or her, to modify the method of claim 25 to include attributes of a processor-implemented method of augmenting data in order to perform the steps of the method with a computer (see Lad para [0005]: Disclosed herein is a non-transitory computer readable medium comprising computer executable instructions that when executed by at least one processor of a computer cause the computer to perform steps according to a disclosed method and/or in a disclosed system. ) Regarding claim 29 : Xu teaches augmenting training data, augment, via a random style generator having at least one randomly initialized layer, training data to generate augmented training data (see pg. 1 section Abstract: “In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation. Random convolutions are approximately shape-preserving and may distort local textures. Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”) , aggregate data with a plurality of styles from the augmented training data to generate aggregated training data (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 fig. 2) , apply semantic-aware style fusion to the aggregated training data to generate fused training data (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 fig. 2) , and add the fused training data as fictitious samples to the training data to generate updated training data for training a neural network (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 section 1: “We validate RandConv and its mixing variant in extensive experiments on synthetic and real world benchmarks as well as on the large-scale ImageNet dataset.”.) . Xu does not explicitly teach an apparatus with least one memory and at least one processor coupled to at least one memory. Lad , however, analogously teaches having a computer-readable storage medium storing instructions executed with one or more processors (see para [0003]: “Disclosed herein is a system using machine learning in detecting geographic atrophy (GA), the system comprising at least one processor; a memory; and a computing platform including the at least one processor and the memory . ” . Also see para [0176]: “ One promising direction is to inject model-dependent perturbations to the input images as strategic augmentations ”. ) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu and Lad before him or her, to modify the apparatus of claim 29 to include attributes of having a computer-readable storage medium storing instructions executed with one or more processors in order to perform the steps of the method with a computer (see Lad para [0005]: Disclosed herein is a non-transitory computer readable medium comprising computer executable instructions that when executed by at least one processor of a computer cause the computer to perform steps according to a disclosed method and/or in a disclosed system. ) . Regarding claim 30 : Xu teaches means for augmenting training data, augment, via a random style generator having at least one randomly initialized layer, training data to generate augmented training data (see pg. 1 section Abstract: “In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation. Random convolutions are approximately shape-preserving and may distort local textures. Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”) , means for aggregat ing data with a plurality of styles from the augmented training data to generate aggregated training data (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 fig. 2) , means for apply ing semantic-aware style fusion to the aggregated training data to generate fused training data (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 fig. 2) , and means for add ing the fused training data as fictitious samples to the training data to generate updated training data for training a neural network (see pg. 1 abstract: “Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training.”. Also see pg. 2 section 1: “We validate RandConv and its mixing variant in extensive experiments on synthetic and real world benchmarks as well as on the large-scale ImageNet dataset.”.) . Xu does not explicitly teach an apparatus . Lad , however, analogously teaches an apparatus (see para [0003]: “Disclosed herein is a system using machine learning in detecting geographic atrophy (GA), the system comprising at least one processor; a memory; and a computing platform including the at least one processor and the memory . ” . ) Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu and Lad before him or her, to modify the apparatus of claim 30 to include attributes of having an apparatus in order to perform the steps of the method with a computer (see Lad para [0005]: Disclosed herein is a non-transitory computer readable medium comprising computer executable instructions that when executed by at least one processor of a computer cause the computer to perform steps according to a disclosed method and/or in a disclosed system. ) Claims 3-10 and 27-28 are rejected under 35 U.S.C 103 as being unpatentable over Xu et al. (“Robust and Generalizable Visual Representation Learning Via Random Convolutions” hereinafter, Xu ) in view of Lad et al. ( US20220351373A1 , hereinafter Lad ) in further view of Dai et al. (“Deformable Convolutional Networks” hereinafter, Dai ). Regarding claim 3: Xu in view of Lad teaches the apparatus of claim 2. Xu does not explicitly teach wherein a size of at least one kernel of the neural network is based on a size of an image of the plurality of training images. Dai , however, analogously teaches wherein a size of at least one kernel of the neural network is based on a size of an image of the plurality of training images (see pg. 765-766 section 2.1: “As illustrated in Figure 2, the offsets are obtained by applying a convolutional layer over the same input feature map. The convolution kernel is of the same spatial resolution and dilation as those of the current convolutional layer (e.g., also 3 × 3 with dilation 1 in Figure 2).” . Also see pg. 767 section 2.3 : “First, a deep fully convolutional network generates feature maps over the whole input image.”) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu , Lad, and Dai before him or her, to modify the apparatus of claim 3 to include attributes of wherein a size of at least one kernel of the neural network is based on a size of an image of the plurality of training images in order to be defined within a receptive field size and dilation (see Dai pg. 765 section 2.1: “The grid R defines the receptive field size and dilation.”) . Regarding claim 27: Claim 27 recites analogous limitations to claim 3 and therefore is rejected on the same grounds. Regarding claim 4: Xu in view of Lad teaches the apparatus of claim 2. Xu further teaches wherein the at least one processor is configured to: augment texture data, contrast data, and brightness data of the plurality of training images (see pg. 6 section 4.1 results: “To show RandConv is more than a trivial color/contrast adjustment method, we also compare to ColorJitter2 data augmentation (which randomly changes image brightness, contrast, and saturation) and GreyScale (where images are transformed to grey scale for training and testing). We also tested data augmentation with a fixed Laplacian of Gaussian filter (Band-Pass) of size=3 and σ = 1 and the data augmentation pipeline (Multi-Aug) that was used in a recently proposed large scale study on domain generalization algorithms and datasets ( Gulrajani &Lopez-Paz, 2020).”. Also see pg. 9 section 5: “Randomized convolution ( RandConv ) is a simple but powerful data augmentation technique for randomizing local image texture” ) Regarding claim 28: Claim 28 recites analogous limitations to claim 3 and therefore is rejected on the same grounds. Regarding claim 5: Xu in view of Lad teaches the apparatus of claim 1. Xu further teaches wherein, to augment the training data, the at least one processor is configured to randomly initialize a brightness parameter and a contrast parameter in an affine transformation layer of the random style generator (see pg. 6 section 4.1: “To show RandConv is more than a trivial color/contrast adjustment method, we also compare to ColorJitter2 data augmentation (which randomly changes image brightness, contrast, and saturation)”) . Xu does not explicitly teach an affine transformation. Dai , however, analogously teaches an affine transformation (see pg. 764 section 1: “This is usually realized by augmenting the existing data samples, e.g., by affine transformation.”) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu , Lad, and Dai before him or her, to modify the apparatus of claim 5 to include attributes of an affine transformation in order to be build training datasets with sufficient desired variations (see Dai pg. 764 section 1: “The first is to build the training datasets with sufficient desired variations. This is usually realized by augmenting the existing data samples,”) . Regarding claim 6: Xu in view of Lad teaches the apparatus of claim 1. Xu does not explicitly teach wherein, to augment the training data, the at least one processor is configured to perform deformable convolution, apply a random convolutional layer, and apply a deformable convolutional layer. Dai , however, analogously teaches wherein, to augment the training data, the at least one processor is configured to perform deformable convolution, apply a random convolutional layer, and apply a deformable convolutional layer (see pg. 764 section 1: “In this work, we introduce two new modules that greatly enhance CNNs’ capability of modeling geometric transformations. The first is deformable convolution.”. Also see pg. 767 section 2.3: “A randomly initialized 1 × 1 convolution is added at last to reduce the channel dimension to 1024”. Also see pg. 770 tables 1 and 2.) Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu , Lad, and Dai before him or her, to modify the apparatus of claim 6 to include attributes of to augment the training data, the at least one processor is configured to perform deformable convolution, apply a random convolutional layer, and apply a deformable convolutional layer in order to greatly enhance CNNs’ capability of modeling geometric transformations (see Dai pg. 764 section 1: “In this work, we introduce two new modules that greatly enhance CNNs’ capability of modeling geometric transformations) . Regarding claim 7: Xu in view of Lad teaches the apparatus of claim 1. Xu does not explicitly teach wherein, to augment the training data, the at least one processor is configured to augment texture data in the training data using a randomly initialized deformable convolution layer. Dai , however, analogously teaches wherein, to augment the training data, the at least one processor is configured to augment texture data in the training data using a randomly initialized deformable convolution layer (see pg. 767 section 2.3 ‘Deformable Convolution for Feature Extraction’: “A randomly initialized 1 × 1 convolution is added at last to reduce the channel dimension to 1024.”. Also see pg. 768 figures 6 and 7.) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu , Lad, and Dai before him or her, to modify the apparatus of claim 7 to include attributes of wherein, to augment the training data, the at least one processor is configured to augment texture data in the training data using a randomly initialized deformable convolution layer in order to greatly enhance CNNs’ capability of modeling geometric transformations (see Dai pg. 764 section 1: “In this work, we introduce two new modules that greatly enhance CNNs’ capability of modeling geometric transformations) . Regarding claim 8 : Xu in view of Lad in further view of Dai teaches the apparatus of claim 7. Xu does not explicitly teach wherein one or more of weights and offsets are randomly initialized using the randomly initialized deformable convolution layer . Dai , however, analogously teaches wherein one or more of weights and offsets are randomly initialized using the randomly initialized deformable convolution layer (see pg. 765 section 2.1 : “The 2D convolution consists of two steps: 1) sampling using a regular grid R over the input feature map x; 2) summation of sampled values weighted by w.”. Also see pg. 767 section 3: “This work is built on the idea of augmenting the spatial sampling locations in convolution and RoI pooling with additional offsets and learning the offsets from target tasks.”. Also see pg. 767 section 2.3: “A randomly initialized 1 × 1 convolution is added at last to reduce the channel dimension to 1024.”. ) Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu , Lad, and Dai before him or her, to modify the apparatus of claim 8 to include attributes of wherein, to augment the training data, the at least one processor is configured to augment texture data in the training data using a randomly initialized deformable convolution layer in order to greatly enhance CNNs’ capability of modeling geometric transformations (see Dai pg. 764 section 1: “In this work, we introduce two new modules that greatly enhance CNNs’ capability of modeling geometric transformations) . Regarding claim 9 : Xu in view of Lad in further view of Dai teaches the apparatus of claim 8 . Xu does not explicitly teach wherein, to augment the training data, the at least one processor is configured to augment contrast data in the training data and brightness data in the training data using instance normalization, affine transformation, and a sigmoid function . Dai , however, analogously teaches wherein, to augment the training data, the at least one processor is configured to augment contrast data in the training data and brightness data in the training data using instance normalization, affine transformation, and a sigmoid function (see pg. 766 section 2.2: “Figure 3 illustrates how to obtain the offsets. Firstly, RoI pooling (Eq. (5)) generates the pooled feature maps. From the maps, a fc layer generates the normalized offsets ∆ bpij , which are then transformed to the offsets ∆ pij in Eq. (6) by element-wise product with the RoI’s width and height, as ∆ pij = γ·∆ bpij ◦( w,h ). Here γ is a pre-defined scalar to modulate the magnitude of the offsets. It is empirically set to γ = 0.1. The offset normalization is necessary to make the offset learning invariant to RoI size.”. Also see pg. 764 section 1: “This is usually realized by augmenting the existing data samples, e.g., by affine transformation.”) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Xu , Lad, and Dai before him or her, to modify the apparatus of claim 9 to include attributes of wherein, to augment the training data, the at least one processor is configured to augment contrast data in the training data and brightness data in the training data using instance normalization, and affine transformation in order to greatly enhance CNNs’ capability of modeling geometric transformations in order to build training datasets with sufficient desired variations ( see Dai pg. 764 section 1: “The first is to build the training datasets with sufficient desired variations. This is usually realized by augmenting the existing data samples,”) . Neither Xu nor Dai teach a sigmoid function to augment the training data. Lad , however, analogously teaches a sigmoid function to augment the training data (see para [0141]: “ To remove causal information from and obtain a negative contrast we apply the following soft-masking transformation … Specifically, we use the thresholded sigmoid as the masking function”.) . Before the effect