DETAILED ACTION Claim Status Claims 1-20 are rejected. 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 This application claims neither domestic benefit nor foreign priority to any previous applications. Therefore, the effective filing date of claims 1-20 is 09/14/2022 . Information Disclosure Statement The Information Disclosure Statement filed on 12/22/2022 is in compliance with the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of list of references cited from the IDS is included with this Office Action. Drawings Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO p atent e lectronic f iling s ystem or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO p atent e lectronic f iling s ystem , and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification: The patent or application file contains at least one drawing executed in color Figures 4A and 4B are executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2). Claim Objections Claims 7 and 17 objected to because of the following informalities: the claims state “training data setof [sic] the encoder model” rather than “set of,” which is likely what is meant. Appropriate correction is required. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the appl icant regards as his invention. Claims 1, 11 and 18 recites the limitation "the decoder model" in the second-to-last line of the claims. There is insufficient antecedent basis for this limitation in the claim. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1 : YES ) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon ( Step 2A, Prong 1 ). In the instant application, the claims recite the following limitations that equate to an abstract idea: 1, 11 , 18 . generating a set of projections in the multi-dimensional latent space using representations of a plurality of aptamers 1, 11 , 18 . identifying one or more candidate aptamers for the particular target using the set of projections … wherein the one or more candidate aptamers are a subset of the plurality of aptamers 1, 11, 18. The encoder model 1, 11, 18. The decoder model 5 , 15 . selecting the architecture of the Encoder model based on: the predicted extent to which representations in an embedding space are indicative of specific aptamer sequences; and the predicted extent to which a probability distribution of the embedding space differs from the probability distribution of a source space 6 , 16 . selecting the at least one hyperparameter of the Encoder model based on: the predicted extent to which representations in an embedding space are indicative of specific aptamer sequences; and the predicted extent to which a probability distribution of the embedding space differs from the probability distribution of a source space. 7 , 17 . selecting the at least one characteristic of a training data set of the Encoder model based on: the predicted extent to which representations in an embedding space are indicative of specific aptamer sequences; and the predicted extent to which a probability distribution of the embedding space differs from the probability distribution of a source space The limitations for “generating”, “identifying”, and “selecting” are mathematical relationships because they are verbal equivalents for algorithms and actions taken to manipulate a numerical dataset, namely the multi-dimensional latent space. They are mental processes because a human being could practically perform them using a pen and paper. “Encoder model,” “decoder model,” and “classifier model” are verbal equivalents for a broad group of algorithms, all of which are mathematical relationships, and many of which are mental processes because they could be practically performed by a human being using a pen and paper. Ji et al. (Texas A&M, 2020) states: “ PCA is a special case of autoencoders, where the encoder and d ecoder are both one-layer linear transformations .” (section 5.2) PCA can be performed on pen and paper and the authors show how to do it (section 4.2). De Ryckel (Self Published, 2019) states: “ Logistic Regression is a classification algorithm ,” and shows how to perform it using a pen and paper ( ¶ 1-2) . The remaining limitations merely add details to the judicial exceptions that are not active steps , and are part of the judicial exceptions that they are dependent upon. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 1, 11, and 18 recite performing some aspects of the analysis with a “ computer implemented method ”, “a system,” or “ A computer-program product tangibly embodied in a non-transitory machine- readable storage medium ” there are no additional limitations that indicate that these embodiments require anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-20 recite an abstract idea ( Step 2A, Prong 1 : YES ). Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not ( Step 2A, Prong 2 ). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to effect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements: 1, 11, 18. accessing a multi-dimensional latent space that corresponds to projections of sequences of aptamers, wherein the multi-dimensional latent space was defined by an Encoder model, wherein an architecture of the Encoder model, at least one hyperparameter of the Encoder model, or at least one characteristic of a training data set used to train the Encoder model was selected using an assessment of an encoding-efficiency of the Encoder model that is based on: a predicted extent to which representations in an embedding space are indicative of specific aptamer sequences; and a predicted extent to which a probability distribution of the embedding space differs from a probability distribution of a source space, wherein the source space represents individual base-pairs 2, 12, 19. The classifier model 2, 12, 19. the selection of the architecture of the Encoder network, the at least one hyperparameter of the Encoder network, or the at least one characteristic of the training data set used to train the Encoder network was further based on a classification-performance metric corresponding to predictions … when different architectures, hyperparameters, or training sets were used to configure or train the Encoder network. 3, 13, 20. the extent to which a probability distribution of the embedding space differs from a probability distribution of a source space includes a Kullback-Leibler distance. 4, 14. The computer-implemented method of claim 1, wherein the extent to which representations in an embedding space are indicative of specific aptamer sequences is based on a reconstruction error relative to predictions of the Decoder model when different architectures, hyperparameters, or training sets were used to configure or train the Encoder network. 8. the architecture of the Encoder network was selected using the assessment of the encoding efficiency 9. the at least one hyperparameter of the Encoder network was selected using the assessment of the encoding efficiency 10. the at least one characteristic of the training data set of the Encoder network was selected using the assessment of the encoding efficiency 1. A computer implemented method 1, 11, 18. Outputting an identification of the one or more candidate aptamers 11. A system comprising: One or more data processors, and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of actions 18. A computer-program product tangibly embodied in a non-transitory machine- readable storage medium, including instructions configured to cause one or more data processors to perform a set of actions “Outputting an identification of the one or more candidate aptamers” is an example of a “ necessary data output” limitation because all uses of the judicial exception would require such data output, and it does not add a meaningful limitation to the exception recited above. “Accessing a multidimensional latent space” is an example of a “mere data gathering” limitation because it is directed to retrieving information stored in memory. The additional elements of claims 2-10, 12-14, and 19-20 simply further limit the additional element. There are no limitations that indicate that the claimed “ computer implemented method ”, “system,” or “ computer-program product tangibly embodied in a non-transitory machine- readable storage medium ” or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-20 are directed to an abstract idea ( Step 2A, Prong 2 : NO ). Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself ( Step 2B ). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The additional elements of the instant claims are enumerated above, in the section on Step 2A. “Outputting an identification of the one or more candidate aptamers” and the limitations for “accessing a multi-dimensional latent space” are example s of “mere data gathering” and “necessary data output” limitations that are well-understood, routine and conventional activities, as they amount to s toring and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) . As discussed above, there are no additional limitations to indicate that the claimed “ computer implemented method ”, “system,” or “ computer-program product tangibly embodied in a non-transitory machine- readable storage medium ” require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No ). As such, claims 1-20 are not patent eligible. 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 1-3, 5-13, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Grubisic et al. ( US20210189385A1 , henceforth “ Grubisic ” ) in view of Razavi et al. (arXiv:1901.03416v1, IDS reference, henceforth “ Razavi ”) Claim 1 is directed to accessing the latent space of an encoder model that predicts candidate aptamers from a source space of base pairs, generating projections in the latent space, and obtaining candidate aptamers from a decoder model. The limitations for “Generating a set of projections,” “identifying one or more candidate aptamers” and “outputting an identification” are read on by f ig. 2 of Grubisic , which shows a prediction model that predicts aptamers from sequence data ( Grubisic spec ¶ 59). Grubisic states that the prediction model can be one or more of many machine learning techniques (spec ¶ 60). Claim 1 places limitations on how the parameters, hyperparameters, architectures or characteristics of an accessed multi-dimensional latent space are selected for an encoder model. Outputting predictions from an encoder model, as shown in fig. 2 of Grubisic , is accessing a multi-dimensional latent space. Grubisic goes on to specify that the prediction model may have its parameters, hyperparameters, architectures or characteristics modified to optimize a loss function (spec ¶ 61-63). Both of the limitations for “a predicted extent” can be considered as a loss function being optimized. Grubisic is silent as to the encoder and decoder model in these limitations. Razavi teaches the encoder and decoder model (abstract). Claim 2 states that the architecture or hyperparameters of the encoder are chosen based on the results of a classifier model. Grubisic is silent as to a classifier model. However, s ince there can be multiple machine learning models in the prediction model of Grubisic , a classifier model is a type of machine learning model, and because Grubisic encourages optimizing hyperparameters and architectures based on loss functions, this is suggested by Grubisic . Grubisic is silent as to m inimizing Kullback-Leibler distance, as in claim 3 . It is taught by Razavi ( pg 2 ¶ 1). Regarding claims 5-7, minimizing the difference between the probability distributions of the source and embedding spaces, and optimizing the predicted extent to which representations of an embedding space are indicative of the specific aptamer sequences, could be considered functions to be optimized for. Grubisic states that the prediction model may have its parameters, hyperparameters, architectures or characteristics modified to optimize a loss function, or to incorporate auxiliary information (spec ¶ 61-63). Grubisic is silent as to claims 8-10 . Section 4.2 of Razavi demonstrates choosing a best model, as well as its hyperparameters and characteristics, based on which one has the highest rate of encoding, or “encoding efficiency.” ( pg 6 ¶ 2). Regarding claim s 1-10 , An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use a set of machine learning models for aptamer prediction in the text of Grubisic . There is a suggestion to modify the machine learning models in Grubisic to optimize the generation of aptamers (spec ¶ 61-63) . Razavi teaches the encoder and decoder model architecture, the minimization of Kullback-Leibler distance, and the concept of optimizing for encoding efficiency, all of which helps to “ that the latent variables preserve and encode useful information ” ( Razavi abstract). Alemi teaches the “reconstruction error,” which helps to minimize distortion (Alemi fig. 1 description). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as Grubisic already provides for using different machine learning models and error functions, and there is nothing about the mathematics of the encoder-decoder architecture or the reconstruction error that prevent them from being incorporated into the method of Grubisic . Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Grubisic by incorporating VAEs and reconstruction error , in order to preserve and encode useful information in the latent variables, and to minimize distortion . Claims 11- 13, 15- 17 are identical to claims 1- 3, 5-7 , except that claim 11 is directed towards “a system” having data processors and a non-transitory computer readable medium adapted to perform a method identical to the method described in claim 1. Specification ¶ 24 of Grubisic states: “ In some embodiments, a system is provided that includes one or more data processors and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform part or all of one or more methods or processes disclosed herein. ” This reads on the differing limitation of claim 11. For the rest of the limitations of claims 11- 13, 15-17 , the arguments against claims 1- 3, 5- 7 apply, mutatis mutandis . Claim 18 implements a method identical to that of claim 1 on “ A computer-program product tangibly embodied in a non-transitory machine- readable storage medium, including instructions configured to cause one or more data processors to perform a set of actions ”. Claim 19 of Grubisic is directed to “ A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform processing comprising ” a method identical to that of claim 1 of Grubisic . Claim s 19 and 20 are identical to claim s 2 and 3 except they depend from claim 18. The arguments against claims 1 -3 apply, mutatis mutandis . Claim(s) 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Grubisic and Razavi as applied to claim 1-3, 5-13, 15-20 above, and further in view of Alemi et al. (arXiv:1711.00464v3 [ cs.LG ] 13 Feb 2018, IDS reference, henceforth “Alemi”). Grubisic and Razavi teach the limitations these claims are dependent upon. Grubisic is silent as to a reconstruction error. The concept of a “reconstruction error” that estimates how indicative a representation is of an aptamer is taught by Alemi (Fig. 1 + description ). Regarding claim s 4 and 14 , An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use a “ reconstruction error ” term in the text of Alemi (fig. 1 + description) in order to estimate how indicative a representation is of an aptamer . There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as they are both methods of aptamer design and there is no technical problem precluding the reconstruction error from being added to the algorithm of Grubisic . Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the algorithm of Grubisic by adding reconstruction error , in order to improve prediction accuracy . 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