CTNF 18/458,257 CTNF 96380 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Examiner's Note The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well. Information Disclosure Statement 06-52 The information disclosure statement (IDS) was submitted on 12/05/2023, 11/07/2024, and 03/27/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 101 101 Rejection 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter Step 1 Analysis for all claims : Claims 1-7 are directed to a method , which is directed to a process, one of the statutory categories. Claims 8-14 are directed to a computing system , which is directed to a machine, one of the statutory categories. Claims 15-20 are directed to a computer program product residing on a non-transitory computer readable medium , which is directed to a product, one of the statutory categories. Regarding Claim 1 : Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 2A Prong 1 Analysis : Claim 1 recites in part process steps which, under the broadest reasonable interpretation, are a series of mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process or a mathematical concept but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. The claim recites in part: defining a plurality of weighting features associated with the training data and the prediction data under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator defining features of text or images deemed to be of importance). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. reshaping the plurality of weighting features to generate weights for a trained neural network by processing the plurality of weighting features under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator assigning different weights to the weighting features based on importance). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Step 2A Prong 2 Analysis : The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of : processing training data and prediction data as a plurality of tokens using a classification-based machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). with an attention layer is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B Analysis : Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of: processing training data and prediction data as a plurality of tokens using a classification-based machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). with an attention layer is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception. The additional limitations of the dependent claims contain no additional elements that provide a practical application or amount to significantly more than the abstract idea and are addressed briefly below Dependent claim 2 recites: Step 2A Prong 1 Analysis : The claim is directed to the same abstract idea identified above. Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of: generating a trained neural network using the plurality of weights is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: generating a trained neural network using the plurality of weights is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception. Dependent claim 3 recites: Step 2A Prong 1 Analysis : The claim is directed to the same abstract idea identified above. Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of: generating a prediction for a subsequent portion of tabular data by classifying the tabular data using the trained neural network based upon, at least in part, a plurality of features within the tabular data is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: generating a prediction for a subsequent portion of tabular data by classifying the tabular data using the trained neural network based upon, at least in part, a plurality of features within the tabular data is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception. Dependent claim 4 recites: Step 2A Prong 1 Analysis : The claim is directed to the same abstract idea identified above. Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of: generating the trained neural network includes generating the trained neural network without subsequent finetuning is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: generating the trained neural network includes generating the trained neural network without subsequent finetuning is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception. Dependent claim 5 recites: Step 2A Prong 1 Analysis : The claim is directed to the same abstract idea identified above. Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of: the training data and the prediction data are tabular data This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: the training data and the prediction data are tabular data This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception. Dependent claim 6 recites: Step 2A Prong 1 Analysis : The claim is directed to the same abstract idea identified above. Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of: processing the training data and the prediction data includes processing the plurality of tokens using the classification-based machine learning model in a single forward pass is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: processing the training data and the prediction data includes processing the plurality of tokens using the classification-based machine learning model in a single forward pass is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception. Dependent claim 7 recites: Step 2A Prong 1 Analysis : The claim is directed to the same abstract idea identified above. Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of: the classification-based machine learning model is a foundation machine learning model trained on tabular data is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: the classification-based machine learning model is a foundation machine learning model trained on tabular data is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception. Regarding Claim 8 : Claim 8 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites similar steps to claims 1 and 2 (see above for analysis), with the additional element of A Computing system . Step 2A Prong 2, Step 2B: The additional element of A computing system is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Implementing an abstract idea on generic computer components does not integrate the abstract idea into a practical application, nor does it add significantly more to the exception. Thus, the claim is not patent eligible. Dependent claim 9 recites: Claim 9 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing system with similar steps to claim 2 , and thus is not patent eligible for the same reasons (see above). Dependent claim 10 recites: Claim 10 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing system with similar steps to claim 4 , and thus is not patent eligible for the same reasons (see above). Dependent claim 11 recites: Claim 11 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing system with similar steps to claim 5 , and thus is not patent eligible for the same reasons (see above). Dependent claim 12 recites: Claim 12 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing system with similar steps to claim 6 , and thus is not patent eligible for the same reasons (see above). Dependent claim 13 recites: Claim 13 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing system with similar steps to claim 1 , and thus is not patent eligible for the same reasons (see above). Dependent claim 14 recites: Claim 14 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing system with similar steps to claim 7 , and thus is not patent eligible for the same reasons (see above). Regarding Claim 15 : Claim 15 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites similar steps to claims 1, 2, and 6 (see above for analysis), with the additional element of A computer program product . Step 2A Prong 2, Step 2B: The additional element of A computer program product is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Implementing an abstract idea on generic computer components does not integrate the abstract idea into a practical application, nor does it add significantly more to the exception. Thus, the claim is not patent eligible. Dependent claim 16 recites: Claim 16 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computer program product with similar steps to claim 5 , and thus is not patent eligible for the same reasons (see above). Dependent claim 17 recites: Claim 17 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computer program product with similar steps to claim 1 , and thus is not patent eligible for the same reasons (see above). Dependent claim 18 recites: Claim 18 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computer program product with similar steps to claim 7 , and thus is not patent eligible for the same reasons (see above). Dependent claim 19 recites: Claim 19 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computer program product with similar steps to claim 4 , and thus is not patent eligible for the same reasons (see above). Dependent claim 20 recites: Step 2A Prong 1 Analysis : The claim is directed to the same abstract idea identified above. Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of: training the classification-based machine learning model using a plurality of synthetic tabular datasets and back-propagation This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: training the classification-based machine learning model using a plurality of synthetic tabular datasets and back-propagation This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-4, 8-10, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over BACHRACH (US20240104350A1), in view of ZHMOGINOV (HyperTransformer Model Generation for Supervised and Semi-Supervised) . Regarding claim 1 BACHRACH teaches processing training data and prediction data as a plurality of tokens using a classification-based machine learning model ([0009] There is also provided, in an embodiment, a method that may include providing a neural network, the neural network may include: one or more autoencoder neural networks, and an adversarial neural network that may include a generative model and a discriminative model; at a training stage, training: (i) each of the one or more autoencoder neural networks on a training set that may include a set of CD Es acquired from one of a plurality of Internet-based systems, to generate a common invariant representation of the CDEs in a latent space; and (ii) the adversarial neural network to generate a set of candidate data instances in the latent space using the generative model, and discriminate between the representation and the candidate data instances using the discriminative model, wherein the training is unsupervised, and wherein the trained neural network is configured to generate a final representation of the set of CDEs. The examiner notes that BACHRACH teaches training a neural network on training data that includes common data elements (tokens). defining a plurality of weighting features associated with the training data and the prediction data ([0033] According to some embodiments, the at least one processor may ( e.g., during an autoencoder training stage) be configured to: receive a plurality of training content data elements from a plurality of data sources; and train the one or more autoencoder modules, based on the plurality of training content data elements, to generate said feature vector, while applying a predetermined weight to each source-invariant representations of the feature vector. The predetermined weight may be determined according to the hierarchical level of the respective source-invariant representation). However, BACHRACH is not relied upon to explicitly teach reshaping the plurality of weighting features to generate weights for a trained neural network by processing the plurality of weighting features with an attention layer. On the other hand, ZHMOGINOV teaches reshaping the plurality of weighting features to generate weights for a trained neural network by processing the plurality of weighting features with an attention layer ([Page 5, Para. 3] Along with the input samples, the sequence passed to the Transformer was also populated with special learnable placeholder tokens, each associated with a particular slice of the to-be-generated weight tensor. Each such token was a learnable d-dimensional vector padded with zeros to the size of the input sample token. After the entire input sequence was processed by the Transformer, we read out model outputs associated with the weight slice placeholder tokens and assembled output weight slices into the final weight tensors (see Fig. 2). The examiner notes that ZHMOGINOV teaches generating weights for a trained neural network (Page 5, Section: Training the mode) using a transformer (attention layer) . The examiner further notes that BACHRACH and ZHMOGINOV are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified BACHRACH’s model learning method to incorporate reshaping the plurality of weighting features to generate weights for a trained neural network by processing the plurality of weighting features with an attention layer as taught by ZHMOGINOV [Page 5, Para. 3] in order to process unbalanced batches and batches with a variable number of samples (see Sec. 5.3) [Page 5, Section 4.2]). Regarding claim 2, BACHRACH teaches The computer-implemented method of claim 1. However, BACHRACH is not relied upon to explicitly teach generating a trained neural network using the plurality of weights. On the other hand, ZHMOGINOV teaches generating a trained neural network using the plurality of weights ([Page 5, Section: Training the model] The weight generation model uses the support set to produce the weights of some or all CNN model layers. Then, the cross-entropy loss is computed for the query set samples that are passed through the generated CNN model. The weight generation parameters (including the Transformer model and image/activation feature extractor weights) are learned by optimizing this loss function using stochastic gradient descent. The examiner notes that BACHRACH and ZHMOGINOV are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified BACHRACH’s model learning method to incorporate generating a trained neural network using the plurality of weights as taught by ZHMOGINOV [Page 5, Section: Training the model] in order to process unbalanced batches and batches with a variable number of samples (see Sec. 5.3) [Page 5, Section 4.2]). Regarding claim 3, BACHRACH teaches The computer-implemented method of claim 1. However, BACHRACH is not relied upon to explicitly teach generating a prediction for a subsequent portion of tabular data by classifying the tabular data using the trained neural network based upon, at least in part, a plurality of features within the tabular data. On the other hand, ZHMOGINOV teaches generating a prediction for a subsequent portion of tabular data by classifying the tabular data using the trained neural network based upon, at least in part, a plurality of features within the tabular data ([Page 5, Section: Training the model] The cross-entropy loss is computed for the query set samples that are passed through the generated CNN model. The weight generation parameters (including the Transformer model and image/activation feature extractor weights) are learned by optimizing this loss function using stochastic gradient descent. The examiner notes that BACHRACH and ZHMOGINOV are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified BACHRACH’s model learning method to incorporate generating a prediction for a subsequent portion of tabular data by classifying the tabular data using the trained neural network based upon, at least in part, a plurality of features within the tabular data as taught by ZHMOGINOV [Page 5, Section: Training the model] in order to process unbalanced batches and batches with a variable number of samples (see Sec. 5.3) [Page 5, Section 4.2]). Regarding claim 4, BACHRACH teaches The computer-implemented method of claim 1. However, BACHRACH is not relied upon to explicitly teach wherein generating the trained neural network includes generating the trained neural network without subsequent finetuning. On the other hand, ZHMOGINOV teaches wherein generating the trained neural network includes generating the trained neural network without subsequent finetuning ([Page 7, Sec. 5.2] we can generate an accurate model customized to a particular task in a single pass without having to perform any SGD steps to fine-tune the model. The examiner notes that BACHRACH and ZHMOGINOV are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified BACHRACH’s model learning method to incorporate wherein generating the trained neural network includes generating the trained neural network without subsequent finetuning as taught by ZHMOGINOV [Page 7, Sec. 5.2] to allow the client model needs to be adjusted to a particular set of known classes most widely used by this client [Page 7, Sec. 5.2]). Claim 8 is rejected based upon the same rationale as the rejection of claims 1 and 2 since it’s the computing system claim corresponding to the method claim. Claim 9 is rejected based upon the same rationale as the rejection of claim 2 since it’s the computing system claim corresponding to the method claim. Claim 10 is rejected based upon the same rationale as the rejection of claim 4 since it’s the computing system claim corresponding to the method claim. Claim 13 is rejected based upon the same rationale as the rejection of claim 1 since it’s the computing system claim corresponding to the method claim . 07-21-aia AIA Claim s 5, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over BACHRACH (US20240104350A1), in view of ZHMOGINOV (HyperTransformer Model Generation for Supervised and Semi-Supervised), further in view of WU (US20240144373A1) . Regarding claim 5, BACHRACH teaches The computer-implemented method of claim 1. However, BACHRACH is not relied upon to explicitly teach wherein the training data and the prediction data are tabular data. On the other hand, WU teaches wherein the training data and the prediction data are tabular data ([0122] As shown, the neural network( s) 446 may be trained using user profile data 448, news data 450, financial data 452, and/or prediction data 454 ( e.g., training data) (which may respectively represent, and/or include, the user profile data 110, the news data 112, the financial data 114, and/or the prediction data 116). The examiner notes that WU teaches a neural network that uses data in tabular form [0080]. The examiner also notes that BACHRACH and WU are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified BACHRACH’s model learning method to incorporate wherein the training data and the prediction data are tabular data as taught by WU [0122] to determine a future predicted price(s) associated with an investment [0122]). Claim 11 is rejected based upon the same rationale as the rejection of claim 5 since it’s the computing system claim corresponding to the method claim. Claim 16 is rejected based upon the same rationale as the rejection of claim 5 since it’s the computer program products claim corresponding to the method claim . 07-21-aia AIA Claim s 6, 12, 15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over BACHRACH (US20240104350A1), in view of ZHMOGINOV (HyperTransformer Model Generation for Supervised and Semi-Supervised), further in view of PAVLOVIC (US20230342364A1) . Regarding claim 6, BACHRACH teaches The computer-implemented method of claim 1. However, BACHRACH is not relied upon to explicitly teach wherein processing the training data and the prediction data includes processing the plurality of tokens using the classification-based machine learning model in a single forward pass. On the other hand, PAVLOVIC teaches wherein processing the training data and the prediction data includes processing the plurality of tokens using the classification-based machine learning model in a single forward pass ([0115] After an input is provided into the neural network and passes through a neural network in the forward direction, the results may be compared to the training labels or other values in the training set to determine the neural network's performance. The process of prediction may be repeated for other data in the training sets. The examiner notes that PAVLOVIC teaches a feed-forward neural network that processes data in a single forward pass. The examiner also notes that BACHRACH and PAVLOVIC are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified BACHRACH’s model learning method to incorporate wherein processing the training data and the prediction data includes processing the plurality of tokens using the classification-based machine learning model in a single forward pass as taught by PAVLOVIC [0115] to decide the weight of the output of the node in forward propagation [0115]). Claim 12 is rejected based upon the same rationale as the rejection of claim 6 since it’s the computing system claim corresponding to the method claim. Claim 15 is rejected based upon the same rationale as the rejection of claims 1, 2, and 6 since it’s the computer program product claim corresponding to the method claim. Claim 17 is rejected based upon the same rationale as the rejection of claim 1 since it’s the computer program products claim corresponding to the method claim. Claim 19 is rejected based upon the same rationale as the rejection of claim 4 since it’s the computer program products claim corresponding to the method claim. Regarding claim 20 BACHRACH teaches training the classification-based machine learning model using a plurality of synthetic tabular datasets and back-propagation ([0164] Thereafter, candidates synthesized by the generative network are evaluated by the discriminative network. Independent backpropagation procedures are applied to both networks so that the generative network produces better candidates, while the discriminator becomes more skilled at identifying the data source) . 07-21-aia AIA Claim s 7, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over BACHRACH (US20240104350A1), in view of ZHMOGINOV (HyperTransformer Model Generation for Supervised and Semi-Supervised), further in view of DAVID (US20230259787A1) . Regarding claim 7, BACHRACH teaches The computer-implemented method of claim 1. However, BACHRACH is not relied upon to explicitly teach wherein the classification-based machine learning model is a foundation machine learning model trained on tabular data. On the other hand, DAVID teaches wherein the classification-based machine learning model is a foundation machine learning model trained on tabular data ([0136] FIG. 9C depicts a system for self-supervised obfuscation applied to a tabular-data-based model, which may be a Feature Tokenizer+ Transformer (FT-Transformer 942). The tabular-data based model may be any appropriate tabular-data-based foundation model, such as the tabular-databased FT-Transformer 942, which may be the foundation model. The examiner notes that BACHRACH and DAVID are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified BACHRACH’s model learning method to incorporate wherein the classification-based machine learning model is a foundation machine learning model trained on tabular data as taught by DAVID [0136] to obfuscate sensitive data [0096]). Claim 14 is rejected based upon the same rationale as the rejection of claim 7 since it’s the computing system claim corresponding to the method claim. Claim 18 is rejected based upon the same rationale as the rejection of claim 7 since it’s the computer program product claim corresponding to the method claim . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. MAHESHWARI - US 2021/0224275 Al “MAHESHWARI teaches a method for leveraging recurrent neural networks for query processing” QIU - US 2024/0290082 Al “ QIU teaches a method for highly effective zero-shot anomaly detection approach” Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAMCY ALGHAZZY whose telephone number is (571)272-8824. The examiner can normally be reached on M-F 7:30am-5:00pm EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, OMAR FERNANDEZ RIVAS can be reached on (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SHAMCY ALGHAZZY/Examiner, Art Unit 2128 /OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128 Application/Control Number: 18/458,257 Page 2 Art Unit: 2128 Application/Control Number: 18/458,257 Page 3 Art Unit: 2128 Application/Control Number: 18/458,257 Page 4 Art Unit: 2128 Application/Control Number: 18/458,257 Page 5 Art Unit: 2128 Application/Control Number: 18/458,257 Page 6 Art Unit: 2128 Application/Control Number: 18/458,257 Page 7 Art Unit: 2128 Application/Control Number: 18/458,257 Page 8 Art Unit: 2128 Application/Control Number: 18/458,257 Page 9 Art Unit: 2128 Application/Control Number: 18/458,257 Page 10 Art Unit: 2128 Application/Control Number: 18/458,257 Page 11 Art Unit: 2128 Application/Control Number: 18/458,257 Page 12 Art Unit: 2128 Application/Control Number: 18/458,257 Page 13 Art Unit: 2128 Application/Control Number: 18/458,257 Page 14 Art Unit: 2128 Application/Control Number: 18/458,257 Page 15 Art Unit: 2128 Application/Control Number: 18/458,257 Page 16 Art Unit: 2128 Application/Control Number: 18/458,257 Page 17 Art Unit: 2128 Application/Control Number: 18/458,257 Page 18 Art Unit: 2128 Application/Control Number: 18/458,257 Page 19 Art Unit: 2128 Application/Control Number: 18/458,257 Page 20 Art Unit: 2128 Application/Control Number: 18/458,257 Page 21 Art Unit: 2128 Application/Control Number: 18/458,257 Page 22 Art Unit: 2128 Application/Control Number: 18/458,257 Page 23 Art Unit: 2128 Application/Control Number: 18/458,257 Page 24 Art Unit: 2128 Application/Control Number: 18/458,257 Page 26 Art Unit: 2128 Application/Control Number: 18/458,257 Page 28 Art Unit: 2128 Application/Control Number: 18/458,257 Page 29 Art Unit: 2128
