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. Priority Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application 63/194,056 filed on May 27, 2021. Information Disclosure Statement The information disclosure statement(s) (IDS) submitted on 3/27/2024, 6/25/2025, and 10/29/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner. Claim Objections Claim 6 is objected to because the claim recites “ wherein the encoder is pre-trained using a dataset different to the dataset that the training data item is belongs to .” The phrase “is belongs to” appears to be a typographical error. 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 1-18 and 28-29 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claims 1-18 are directed to a process. Claims 28-29 are directed to a machine or an article of manufacture. With respect to claim(s) 1, 28, and 29: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: (b) processing the training data item using an encoder to generate an encoding of the training data item; ( Mathematical concepts – This limitation is directed to mathematical concepts because the encoder is represented as z=f x ∈ R d , which outputs the encoding z for an input data item x via mathematical calculations (see specification page 12, lines 10-16) – see MPEP § 2106.04(a)(2)(I)) (c) selecting a subset of neural networks from a plurality of neural networks […] ( Mental process – A person can decide via mind the selection of a neural network – see MPEP § 2106.04(a)(2)(III)) wherein the plurality of neural networks are configured to process the encoding to generate output data indicative of a classification of an aspect of the training data item; ( Mathematical concepts – This limitation is directed to mathematical concepts because the output data (i.e., V M z ) indicative of a classification is generated by processing encoding z via equation in page 13, line 11 of the specification (see specification page 13, lines 6-17) – see MPEP § 2106.04(a)(2)(I)) (d) processing the encoding using the selected subset of neural networks to generate the output data; ( Mathematical concepts – This limitation is directed to mathematical concepts because the output data (i.e., V M z ) indicative of a classification is generated by processing encoding z via equation in page 13, line 11 of the specification (see specification page 13, lines 6-17) – see MPEP § 2106.04(a)(2)(I)) (e) determining an update to the parameters of the selected subset of neural networks based upon a loss function comprising a relationship between the generated output data and the target data associated with the training data item; and ( Mathematical concepts – This limitation is directed to mathematical concepts because determining an update to the parameters of the selected neural networks involves minimizing the function L y, y = - y⋅ y (see specification page 13, lines 18-29) – see MPEP § 2106.04(a)(2)(I)) (f) updating the parameters of the selected subset of neural networks based upon the determined update . ( Mathematical concepts – This limitation is directed to mathematical concepts (see specification page 13, lines 18-29) – see MPEP § 2106.04(a)(2)(I)) If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process, but for the recitation of generic computer components, then the claim limitations fall within the mathematical or mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea. 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: (Claim 1) A computer-implemented method for training a neural network-based system, the method comprising: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 28) A system comprising: one or more computers; and (Mere recitation of a generic computer component – see § MPEP 2106.05(b)(I)) (Claim 28) one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 29) One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (a) receiving a training data item and target data associated with the training data item ; (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).) […] neural networks stored in a memory based upon the encoding; (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: (Claim 1) A computer-implemented method for training a neural network-based system, the method comprising: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 28) A system comprising: one or more computers; and (Mere recitation of a generic computer component – see § MPEP 2106.05(b)(I)) (Claim 28) one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 29) One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (a) receiving a training data item and target data associated with the training data item ; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)( ll )(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) […] neural networks stored in a memory based upon the encoding; (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible . With respect to claim(s) 2: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: further comprising repeating steps (a) to (f) for a plurality of training data items ; ( Mathematical concepts & mental process – Steps (a) to (f) in claim 1 recite mental processes and mathematical concepts – see MPEP § 2106.04(a)(2)) 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the plurality of training data items comprises a first training data item drawn from a first data distribution and a second training data item drawn from a second data distribution, and wherein the first and second data distributions are different . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the plurality of training data items comprises a first training data item drawn from a first data distribution and a second training data item drawn from a second data distribution, and wherein the first and second data distributions are different . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 3: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the plurality of training data items comprise training data items drawn from the first data distribution interspersed with training data items drawn from the second data distribution . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the plurality of training data items comprise training data items drawn from the first data distribution interspersed with training data items drawn from the second data distribution . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 4: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: wherein the relationship between the generated output data and the target data for the training data item is based upon a dot product between the generated output data and the target data . ( Mathematical concepts – This limitation is directed to mathematical concepts because the relationship is based upon a dot product as shown in function L y, y = - y⋅ y (see specification page 13, lines 18-29) – see MPEP § 2106.04(a)(2)(I)) Additionally, the claim(s) do not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 5: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the target data is in the form of a one-hot vector . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the target data is in the form of a one-hot vector . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 6: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the encoder is pre-trained using a dataset different to the dataset that the training data item is belongs to . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the encoder is pre-trained using a dataset different to the dataset that the training data item is belongs to . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 7: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the encoder is pre-trained using a self-supervised learning technique . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the encoder is pre-trained using a self-supervised learning technique . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 8: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the self-supervised learning technique comprises training based upon transformed views of training data items . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the self-supervised learning technique comprises training based upon transformed views of training data items . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 9: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the parameters of the encoder are held fixed . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the parameters of the encoder are held fixed . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 10: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the encoder is based upon a variational autoencoder . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the encoder is based upon a variational autoencoder . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 11: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the encoder is based upon a ResNet architecture . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the encoder is based upon a ResNet architecture . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 12: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: wherein each of the plurality of neural networks are associated with a respective key and wherein selecting a subset of neural networks is further based upon the respective keys . ( Mental process – A person can associate keys with neural networks via mind and select neural networks based on the respective keys via mind – see MPEP § 2106.04(a)(2)(III)) Additionally, the claim(s) do not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 13: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: wherein the method further comprises determining a similarity between the encoding and each respective key ; ( Mathematical concepts – This limitation is directed to mathematical concepts because determining a similarity involves computing a distance metric γ M key J (i,z) ,z such as using a cosine distance – see MPEP § 2106.04(a)(2)(I)) and wherein selecting a subset of neural networks is based upon the determined similarity . ( Mental process – A person can mentally evaluate a determined similarity to select neural networks – see MPEP § 2106.04(a)(2)(III)) Additionally, the claim(s) do not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 14: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: wherein the similarity is based upon a cosine distance between the encoding and the respective key . ( Mathematical concepts – This limitation is directed to mathematical concepts because determining a similarity involves computing a distance metric γ M key J (i,z) ,z such as a cosine distance – see MPEP § 2106.04(a)(2)(I)) Additionally, the claim(s) do not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 15: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the respective keys are generated by sampling a probability distribution based upon the embedding space represented by the encoder . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the respective keys are generated by sampling a probability distribution based upon the embedding space represented by the encoder . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 16: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: wherein the probability distribution is determined based upon a sample of encoded data. (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: wherein the probability distribution is determined based upon a sample of encoded data. (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 17: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: wherein the sample of encoded data comprises encoded data generated by processing data items using the encoder ( Mathematical concepts – This limitation is directed to mathematical concepts because the encoder is represented as z=f x ∈ R d , which outputs the encoding z for an input data item x via mathematical calculations (see specification page 12, lines 10-16) – see MPEP § 2106.04(a)(2)(I)) 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: and wherein the data items are drawn from a dataset different to the dataset that the training data item is belongs to . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: and wherein the data items are drawn from a dataset different to the dataset that the training data item is belongs to . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 18: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: wherein processing the encoding of the training data item using the selected subset of neural networks comprises processing the encoding through each respective neural network of the subset of neural networks to generate intermediate data for each respective neural network; and aggregating the intermediate data for each respective neural network to generate the output data indicative of the classification of an aspect of the training data item . ( Mathematical concepts – This limitation is directed to mathematical concepts because the output data (i.e., V M z ) indicative of a classification is generated by processing encoding z via equation in page 13, line 11 of the specification (see specification page 13, lines 6-17) – see MPEP § 2106.04(a)(2)(I)) Additionally, the claim(s) do not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1, 18, 28, and 29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SHAZEER (US 20190251423 A1), hereafter SHAZEER. Regarding Claim 1: SHAZEER teaches: A computer-implemented method for training a neural network-based system, the method comprising : (SHAZEER [0008] teaches: "Another innovative aspect of the subject matter described in this specification can be embodied in a method including: receiving a network input; and processing the network input using the system described above to generate a network output for the network input.") (a) receiving a training data item and target data associated with the training data item ; (SHAZEER [0054] teaches: "During training of the neural network 102, the processes 200 and 300 can be used as part of generating a network output for a training input (i.e., a training data item ). The gradient of an objective function can be backpropagated to adjust the values of the parameters of various components of the neural network 102 to improve the quality of the network output relative to a known output for the training input (i.e., target data associated with the training data item )." Examiner's note: The network generates outputs for the received training input (i.e., receiving a training data item ).) (b) processing the training data item using an encoder to generate an encoding of the training data item ; (SHAZEER [0017] teaches: “For example, if the inputs to the neural network 102 are images (i.e., training data item ) or features that have been extracted from images, the output generated by the neural network 102 for a given image may be scores for each of a set of object categories, with each score representing an estimated likelihood that the image contains an image of an object belonging to the category.” SHAZEER [0024] teaches: “the neural network 102 includes a MoE subnetwork 130 arranged between a first neural network layer 104 and a second neural network layer 108 in the neural network 102.” SHAZEER [0025] teaches: “Each expert neural network in the MoE subnetwork 130 can be configured to process a first layer output 124 (i.e., encoding ) generated by the first neural network layer 104 (i.e., processing the training data item using an encoder to generate an encoding of the training data item ) in accordance with a respective set of expert parameters of the expert neural network to generate a respective expert output.” SHAZEER [0024] teaches: “the neural network 102 includes a MoE subnetwork 130 arranged between a first neural network layer 104 and a second neural network layer 108 in the neural network 102. The first neural network layer 104 and the second neural network layer 108 can be any kind of neural network layer, for example, a LSTM neural network layer or other recurrent neural network layer, a convolutional neural network layer, or a fully-connected neural network layer." Examiner's note: LSTM and convolutional neural networks are types of encoders, and thus their outputs are encodings.) (c) selecting a subset of neural networks from a plurality of neural networks stored in a memory based upon the encoding ; (SHAZEER [0027] teaches: "Although the MoE subnetwork includes a large number of expert neural networks, only a small number of them are selected during the processing of any given network input by the neural network 102, e.g., only a small number of the expert neural networks are selected (i.e., selecting a subset of neural networks from a plurality of neural networks ) to process the first layer output 124. The neural network 102 includes a gating subsystem 110 that is configured to select, based on the first layer output 124 (i.e., based upon the encoding ), one or more of the expert neural networks, and determine a respective weight for each selected expert neural network." SHAZEER [0060] teaches: "Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data." Examiner's note: The neural networks are part of the data stored in memory used for executing the instructions.) wherein the plurality of neural networks are configured to process the encoding to generate output data indicative of a classification of an aspect of the training data item ; (SHAZEER [0017] teaches: "For example, if the inputs to the neural network 102 are images or features that have been extracted from images (i.e., process the encoding ), the output generated by the neural network 102 for a given image may be scores for each of a set of object categories, with each score representing an estimated likelihood that the image contains an image of an object belonging to the category (i.e., classification of an aspect of the training data item ).” SHAZEER [0027] teaches: “The gating subsystem 110 then provides the first layer output as input to each of the selected expert neural networks (i.e., the plurality of neural networks are configured to process the encoding ). The gating subsystem 110 combines the expert outputs generated by the selected expert neural networks (i.e., to generate output data ) in accordance with the weights for the selected expert neural networks to generate a MoE output 132." SHAZEER [0016] teaches: “Generally, the system 100 includes a neural network 102 that can be configured to receive any kind of digital data input and to generate any kind of score, classification, or regression output based on the input.” Examiner’s note: Under BRI, configured to process the encoding to generate output data indicative of a classification can be interpreted as the mixture of experts ( MoE ) whose outputs are combined to generate a MoE output, which can be a classification score, as described in SHAZEER [0016].) (d) processing the encoding using the selected subset of neural networks to generate the output data ; (SHAZEER [0041] teaches: “The system provides the first layer output as input to each of the selected expert neural networks (step 206). Each of the selected expert neural networks is configured to process the first layer output in accordance with the respective current values of parameters of the selected expert neural network to generate a respective expert output.”) (e) determining an update to the parameters of the selected subset of neural networks based upon a loss function comprising a relationship between the generated output data and the target data associated with the training data item ; (SHAZEER [0054] teaches: "The gradient of an objective function (i.e., determining an update [...] based upon a loss function ) can be backpropagated to adjust the values of the parameters of various components of the neural network 102 (i.e., to the parameters of the selected subset of neural networks ) to improve the quality of the network output relative to a known output for the training input (i.e., comprising a relationship between the generated output data and the target data associated with the training data item ).") (f) updating the parameters of the selected subset of neural networks based upon the determined update . (SHAZEER [0054] teaches: "The gradient of an objective function (i.e., based upon the determined update ) can be backpropagated to adjust the values of the parameters of various components of the neural network 102 (i.e., updating the parameters of the selected subset of neural networks ) to improve the quality of the network output relative to a known output for the training input.") Regarding Claim 18: SHAZEER teaches the elements of claim 1 as outlined above. SHAZEER further teaches: The method of claim 1, wherein processing the encoding of the training data item using the selected subset of neural networks comprises processing the encoding through each respective neural network of the subset of neural networks to generate intermediate data for each respective neural network ; (SHAZEER [0041] teaches: "The system provides the first layer output as input to each of the selected expert neural networks (step 206). Each of the selected expert neural networks is configured to process the first layer output in accordance with the respective current values of parameters of the selected expert neural network to generate a respective expert output." SHAZEER [0042] teaches: "The system then combines the expert outputs generated (i.e., intermediate data ) by the selected expert neural networks in accordance with the weights for the selected expert neural networks to generate the MoE output (step 208).” SHAZEER [0043] teaches: "In particular, the system weights the expert output generated by each of the selected expert neural networks by the weight for the selected expert neural network to generate a weighted expert output. The system then sums the weighted expert outputs to generate the MoE output.") and aggregating the intermediate data for each respective neural network to generate the output data indicative of the classification of an aspect of the training data item . (SHAZEER [0017] teaches: "For example, if the inputs to the neural network 102 are images or features that have been extracted from images, the output generated by the neural network 102 for a given image may be scores for each of a set of object categories, with each score representing an estimated likelihood that the image contains an image of an object belonging to the category (i.e., classification of an aspect of the training data item )." SHAZEER [0042] teaches: "The system then combines (i.e., aggregating ) the expert outputs generated (i.e., intermediate data ) by the selected expert neural networks in accordance with the weights for the selected expert neural networks to generate the MoE output (step 208).” SHAZEER [0043] teaches: "In particular, the system weights the expert output generated by each of the selected expert neural networks by the weight for the selected expert neural network to generate a weighted expert output. The system then sums the weighted expert outputs to generate the MoE output (i.e., output data indicative of the classification of an aspect of the training data item ).") Regarding Claim 28: The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Additionally, SHAZEER teaches: A system comprising: one or more computers; and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising : (SHAZEER [0060] teaches: "Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. The central processing unit and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, e.g., a universal serial bus (USB) flash drive, to name just a few.”) Regarding Claim 29: The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Additionally, SHAZEER teaches: One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising : (SHAZEER [0056] teaches: “Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus.” SHAZEER [0060] teaches: "Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. The central processing unit and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, e.g., a universal serial bus (USB) flash drive, to name just a few.”) Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over SHAZEER in view of MCCALL (GB 2289970 A), hereafter MCCALL. Regarding Claim 2: SHAZEER teaches the elements of claim 1 as outlined above. SHAZEER further teaches: The method of claim 1, further comprising repeating steps (a) to (f) for a plurality of training data items ; (SHAZEER [0047] teaches: "The system adds a noise to the modified first layer output to generate an initial gating output (step 304). The noise helps with load balancing, i.e., to encourage expert neural networks to receive roughly equal numbers of training examples in a training dataset during training, or to receive roughly equal numbers of input examples in input data during testing." SHAZEER [0054] teaches: "During training of the neural network 102, the processes 200 and 300 can be used as part of generating a network output for a training input." Examiner's note: SHAZEER [0047] teaches the neural networks receiving more than one training samples and performing processes 200 and 300 for a training input, and thus repeating the process for each training input.) However, SHAZEER is not relied upon for teaching, but MCCALL teaches: wherein the plurality of training data items comprises a first training data item drawn from a first data distribution and a second training data item drawn from a second data distribution, and wherein the first and second data distributions are different . (MCCALL [page 3, lines 20-24] teaches: "This is achieved by interleaving patterns from different classes rather than presenting them in an arbitrary fashion as in the prior art. In interleaved training, according to the preferred embodiment, each class is treated as a separate sequence, and patterns are taken from each class in rotation.") Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHAZEER and MCCALL before them, to include MCCALL’s data interleaving in SHAZEER’s mixture of experts ( MoE ) method. One would have been motivated to make such a combination in order to solve the problem of feed-forward neural networks converging to local minima in pattern classification tasks the order of pattern presentation is modified during training so that the minority classes get the same number of presentations as the majority class. Patterns from different classes are interleaved. This prevents the training process from finding a deep local minimum which classifies all of the minority class wrongly (MCCALL [page 1, (57)]). Regarding Claim 3: SHAZEER in view of MCCALL teaches the elements of claim 2 as outlined above. MCCALL further teaches: wherein the plurality of training data items comprise training data items drawn from the first data distribution interspersed with training data items drawn from the second data distribution . (MCCALL [page 3, lines 20-24] teaches: "This is achieved by interleaving patterns from different classes rather than presenting them in an arbitrary fashion as in the prior art. In interleaved training, according to the preferred embodiment, each class is treated as a separate sequence, and patterns are taken from each class in rotation.") Claims 4 and 5, are rejected under 35 U.S.C. 103 as being unpatentable over SHAZEER in view of BARZ ("Deep Learning on Small Datasets without Pre-Training using Cosine Loss"), hereafter BARZ. Regarding Claim 4: SHAZEER teaches the elements of claim 1 as outlined above. SHAZEER is not relied upon for teaching, but BARZ teaches: wherein the relationship between the generated output data and the target data for the training data item is based upon a dot product between the generated output data and the target data . (BARZ [page 1373, section 3.1 Cosine Loss] teaches: "We consider the class embeddings φ as fixed and aim at learning the parameters θ of a neural network f θ by maximizing the cosine similarity between the image features and the embeddings of their classes. To this end, we define the cosine loss function to be minimized by the neural network: L cos x,y =1- σ cos f θ x , φ y (3) In practice, this is implemented as a sequence of two operations. First, the features learned by the network (with d=n ) are L 2 -normalized: φ x = x x 2 . This restricts the prediction space to the unit hypersphere, where the cosine similarity is equivalent to the dot product: L cos x,y =1- φ y ,ψ f θ x (4) The class embeddings '(y) need to lie on the unit hypersphere as well for this equation to hold. One-hot vectors, for example, have unit-norm by definition and hence do not need to be L2-normalized explicitly. When working with batches of multiple samples, we compute the average loss over all instances in the batch.”) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of SHAZEER and BARZ before them, to include BARZ’ cosine loss in SHAZEER’s mixture of experts ( MoE ) method. One would have been motivated to make such a combination because the cosine loss function provides substantially better performance than cross-entropy on datasets with only a handful of samples per class (BARZ [Abstract]). Regarding Claim 5: SHAZEER teaches the elements of claim 1 as outlined above. SHAZEER is not relied upon for teaching, but BARZ teaches: wherein the target data is in the form of a one-hot vector . (BARZ [page 1371] section 1. Introduction] teaches: "In this work, however, we propose an extremely simple but surprisingly effective loss function for learning from scratch on small datasets: the cosine loss, which maximizes the cosine similarity between the output of the neural network and one-hot vectors indicating the true class.") Accordingly, it would have been obvious to a person having ordinary skill in the art befo