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. Claim Status Claim s 1-16 are currently pending and under exam herein. Claim s 1-16 are rejected. Priority This application is a national phase filing under 35 U.S.C. § 371 of International PCT Application No. PCT/ US2021 /016916, filed February 5, 2021 and titled "Methods and Apparatus for Efficient and Accurate Assembly of Long-Read Genomic Sequences", which claims the benefit of priority to U.S. Provisional Patent Application Serial No. 62/971,394, filed on February 7, 2020 and titled "Methods and Apparatus for Efficient and Accurate Assembly of Long-Read Genomic Sequences", the contents of each of which are hereby incorporated by reference in their entireties. At this point in the examination, the effective filling date of the claims is 02/07/2020. Information Disclosure Statement The Information Disclosure Statement filed 08/03/2022 has been considered, in part. It is noted that certain references have not been considered and are lined-through, as they do not comply with the requirements set forth in 37 CFR 1.97. The instant citations lack appropriate dates and/or page numbers and/or other public information, for example, entries 023 , 024 and 025 listed websites are inaccessible. Drawings The drawings files on 08/03/2022 are accepted. 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 therefore, subject to the conditions and requirements of this title. Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgment, opinion). Subject matter eligibility evaluation in accordance with MPEP 2106: Eligibility Step 1: Claims Claims 1-10 are directed to a method (process) of identifying biosynthetic gene clusters from long-read sequencing data. Claims 11-13 are directed to a non-transitory processor-readable medium (manufacture). Claims 14-16 are directed to an apparatus. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1. [Step 1: YES] Eligibility Step 2A : First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception. Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Independent claim s 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: partitioning each read from the set of reads into a group of reads from a set of groups, the polynucleotide sequence for each read within each group of reads from the set of groups having a higher alignment length with the polynucleotide sequence for each remaining read in that group of reads than with the polynucleotide sequence for each read within each remaining group of reads from the set of groups ( i.e. “ mental process es”: compartmentalizing and assigning groups and “ mathematical concepts ”: “dividing”) [ claim 1, claim 11 and claim 14] performing a first read error correction for each group of reads from the set of groups by generating a consensus sequence associated with that group of reads ( i.e. “mental processes”: correcting a mistake and drawing a consensus sequence) performing a second read error correction for each group of reads from the set of groups by aligning the consensus sequence for that group of reads with a polynucleotide sequence encoding a polypeptide, wherein the consensus sequence is modified to encode the polypeptide, thereby generating a modified consensus polynucleotide sequence ( i.e. “mental processes”: correcting a mistake and drawing a consensus sequence and “mathematical concepts”: alignment ) Dependent claims 2-3 further recite the following steps which fall within the mental process and/or mathematical concepts groupings of abstract ideas, as noted below. Dependent claim 2 further recites: wherein the polynucleotide sequence of each read within each group of reads has an alignment length of at least 90%, at least 95%, at least 98%, or at least 99% with the polynucleotide sequence of each remaining read in that group of reads (i.e. “mathematical concepts”: alignment length, percentages ) Dependent claim 3 further recites: wherein the long-read sequencing data is obtained from a database (i.e. mental process: can be done with pen and paper, database can be a table) Dependent claim 8 further recites: wherein the features are selected from open-reading-frames, protein-domain content of open reading frames, promoter binding sites, substrate specificity prediction of enzymatic open reading frames, active site prediction of enzymatic open reading frames (i.e. mental process: selecting features can be done mentally or with pen and paper) Independent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: determine an alignment length between the polynucleotide sequence of each read from the set of reads and the polynucleotide sequence of the remaining reads from the set of reads (i.e. “mental processes”: determining length and mathematical concepts: alignment and measuring length) [claims 11 and 14] generate a network graph representation of the set of reads and the alignment length for each read from the set of reads, each node from a set of nodes in the network graph representation represents a single read from the set of reads and each edge from a set of edges between a pair of nodes from the set of nodes represents that the alignment length between the polynucleotide sequence of a first read from a pair of reads and the polynucleotide sequence of a second read from the pair of reads is above an alignment length threshold (i.e. mathematical concepts: generating a graph, alignment length) generate a consensus polynucleotide sequence for each group of reads from the set of groups based on the polynucleotide sequence associated with each read from the set of reads in that group ( i.e. “mental processes”: drawing a consensus sequence) [claim 11 and claim 14] align the consensus polynucleotide sequence for each group of reads from the set of groups with a polynucleotide sequence encoding a polypeptide, wherein the consensus polynucleotide sequence for that group of reads is modified to encode the polypeptide to produce a modified consensus polynucleotide sequence ( i.e. “mental processes”: drawing a consensus sequence and “mathematical concepts”: alignment) [claim 11 and claim 14] Dependent claim s 12 -13, and 16 further recite the following steps which fall within the mental process and/or mathematical concepts groupings of abstract ideas, as noted below. Dependent claim 12 further recites: the alignment length threshold is at least 90%, at least 95%, at least 98%, at least 99%, or 100% alignment length (i.e. mathematical concepts: alignment length , threshold , percentages ). Dependent claims 13 and 16 further recite: wherein the polynucleotide sequence encoding a polypeptide is from a database of biosynthetic gene clusters (i.e. mental processes: selecting from a database) Independent claim 1 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: generate a network graph representation of the set of reads and the polynucleotide alignment lengths for each read from the set of reads, each node from a set of nodes in the network graph representation represents a single read from the set of reads and each edge from a set of edges between a pair of nodes from the set of nodes represents an alignment length between the polynucleotide sequence of a first read from a pair of reads and the polynucleotide sequence of a second read from the pair of reads is above an alignment length threshold of the length of either of the reads (i.e. mathematical concepts: graph, alignment lengths, length threshold). generate a consensus polynucleotide sequence for each group from the set of groups based on the polynucleotide sequence associated with each read from the set of reads in that group; Dependent claim 15 further recite the following steps which fall within the mental process and/or mathematical concepts groupings of abstract ideas, as noted below. Dependent claim 15 further recites: wherein the alignment length threshold is at least 90%, at least 95%, at least 98%, at least 99%, or 100% alignment length (i.e. “mathematical concepts”: alignment, alignment length, percentages) Therefore, claims 1-3, 8, 11-1 3 , and 14-1 6 recite an abstract ide a. [Step 2A Prong One: YES] Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)). The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below. Claims 2-3, 8, 12 -13 , and 15 -16 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception. The additional element in claims 1, 4-6, 11, and 14 include: obtaining long-read sequencing data including a set of reads derived from a sample of genomic deoxyribonucleic acid (claims 1, 4, 11 and 14); wherein the sample of genomic DNA is digested into fragments, and wherein the fragments are cloned into a genomic DNA library prior to sequencing (claim 5); wherein the genomic DNA library includes cosmid vectors (claim 6). The additional element in claims 1, 7- 11 and 14 include: classifying the modified consensus polynucleotide sequence for each group of reads from the set of groups using a machine learning classifier (claims 1, 11, and 14); wherein the machine learning classifier includes at least one of a neural network, a decision tree, a random forest, a support vector machine, a gradient boosting tree, a Bayesian network, or a genetic algorithm (claim 10); wherein the machine learning classifier is trained using a set of training data including features extracted from polynucleotide sequences encoding biosynthetic gene clusters and associated classifications, wherein the set of training data is retrieved from a database (claim 7 ) The additional element in claim 9 include: wherein the classifications are selected from structural, chemical, phenotypic, or biosynthetic higher order categories The additional element in claim 1 include: expressing the modified consensus polynucleotide sequence in a host cell based on identifying the modified consensus sequence as having the features of the biosynthetic gene cluster The additional elements in claim 11 include: A non-transitory processor-readable medium storing code representing instructions to be executed by a processor The additional element s in claim 14 include: An apparatus, comprising: a memory; a communicator; and a processor operatively coupled to the memory and the communicator; and output a report identifying polynucleotide sequences belonging to the biosynthetic gene cluster The additional elements recited above are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g) ). When all limitations in claims 1-16 have been considered as a whole , the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-16 are directed to an abstract idea (MPEP 2106.04(d)). [Step 2A Prong Two: NO] Eligibility Step 2B : Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i -vi). The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because the reasons noted below. Claims 2-3, 8, 12 -13 , and 15 -16 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception. The additional elements in claims 1, 4- 7, 9- 11, and 14 are identified above, and carried over from Step 2A : Prong Two along with their conclusions for analysis at Step 2B . Any additional element or combination of elements that was considered to be insignificant extra-solution activity at step Step 2A : Prong Two was re-evaluated at step 2B , because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d). The additional element of an apparatus, comprising a memory; a communicator; and a processor operatively coupled to the memory and the communicator (claim 14) and a non-transitory processor-readable medium storing code representing instructions to be executed by a processor (claim 11) are conventional. Processors, memory, communicator; non-transitory processor-readable medium storing code are conventional computer components. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016). The additional element of outputting a report identifying polynucleotide sequences belonging to the biosynthetic gene cluster (claim 14) merely invokes a computer tool and does not improve the technology of a generic computer (see MPEP 2106.05(a)). The additional element s of obtaining long-read sequencing data including a set of reads derived from a sample of genomic deoxyribonucleic acid (claims 1, 4, 11 and 14) are conventional. Conventionality is shown in the instant’s application specification “Some known long-read sequencing technologies allow for rapid sequencing of genomes within microbial communities found in the environment” and “Some existing long-read sequencing methods such as single molecule real-time sequencing and nanopore sequencing are currently capable of reading polynucleotide lengths greater than 10kb ” [0002, lns.2 -5] . The additional element of expressing a polynucleotide sequence in a host cell based on identifying the modified consensus sequence as having the features of the biosynthetic gene cluster (claim 1) is conventional. Conventionality is shown in the instant’s application specification “Polynucleotides comprising a sequence encoding the gene clusters can be inserted, or cloned, into expression vectors, such as a plasmid-based or viral vector, according to molecular biological techniques and methods known in the art (see, e.g., Green and Sanbrook . Molecular Cloning: A laboratory Manual (Fourth Edition) (2014)) [0073-0075], pgs.21 -22]. The additional elements of the sample of genomic DNA being digested into fragments, and wherein the fragments are cloned into a genomic DNA library prior to sequencing (claim 5); wherein the genomic DNA library includes cosmid vectors (claim 6) is conventional. Conventionality is shown by Clos and Zander- Dinse . (“ Cosmid Library Construction and Functional Cloning.” Methods in Molecular Biology, 1 Jan. 2019, pp. 123–140). In chapter 6, Clos and Zander- Dinse . state that “ Cosmid libraries can represent an entire genome in a library of circular DNA molecules, allowing for the faithful amplification, cloning and isolation of large genomic DNA fragments. Moreover, using the so-called shuttle cosmid vectors, genomic DNA may be propagated in bacteria and in eukaryotic cells, which is a prerequisite for classic functional cloning and for the newly described Cos-Seq strategies” (abstract , pg. 123), which demonstrate that these are routine steps in the field since before 2019. The additional element of classifying a polynucleotide sequence for each group of reads from the set of groups using a machine learning classifier (claims 1, 11, and 14) ; wherein the machine learning classifier includes at least one of a neural network, a decision tree, a random forest, a support vector machine, a gradient boosting tree, a Bayesian network, or a genetic algorithm (claim 10) , where the set of training data is retrieved from a database (claim 7); wherein the classifications are selected from structural, chemical, phenotypic, or biosynthetic higher order categories (claim 9) are conventional. Conventionality is shown by Soueidan and Nikolski . (“Machine Learning for Metagenomics: Methods and Tools.” ArXiv.org, 2016, arxiv.org/abs/1510.06621 ). In this review from 2016 , Soueidan and Nikolski . show since a decade ago, there were already methods that used machine learning to classify DNA sequences from a group of reads (Table 1, pg.3 ; and para. 6 , lns.6 -11, pg.2 ) . Soueidan and Nikolski . also show that the machine learning classifier can be a linear model, decision trees such as random forest, neural network (Table 1, pg.3 ). Soueidan and Nikolski . also show that training data for the machine learning classifier are derived from labeled reference sequences obtained from a database ( para.4 , lns.1 -4, pg.12 ) . Therefore, when taken alone, all additional elements in claims 1, 4-7, 9-11, and 14 do not amount to significantly more than the above-identified judicial exceptions(s). Even when evaluated as combination, the additional elements fail to transform the exceptions (s) into patent-eligible application of that exception. Thus, claims 1-16 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05 (II)). [Step 2B : NO] 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Blin et al. ( Briefings in Bioinformatics , vol. 20, no. 4, 3 Nov. 2017, pp. 1103–1113) in view of Koren et al. ( Genome Research, vol. 27, no. 5, 15 Mar. 2017, pp. 722–736 ) , and Chung et al. ( BMC Systems Biology , vol. 6, no. 1, 2012, p. 134) and in further view of Zhang et al. ( Methods in Enzymology , 2019, pp. 87–110). The italicized text corresponds to the instant claim limitations. Claim s 1 , 11 and 14 are drawn to a method of finding a groups of genes (BGCs) by analyzing long DNA sequences ; obtaining long DNA sequences from a sample, where each read includes a polynucleotide sequence; dividing the DNA reads into different groups so that reads placed in the same group are more similar to each other than they are to reads in other groups ; correcting errors in the reads within each group by creating a consensus sequence for that group; refine each group’s consensus DNA sequence by aligning it to a protein coding gene and modifying it so it correctly encodes that protein ; using a trained machine learning model to analyze each modified consensus DNA sequence and determine whether it has characteristics of a biosynthetic gene cluster ; inserting the modified DNA sequence into a host cell and having the cell use it, based on determining that the sequence has features of a biosynthetic gene cluster. Claim 11 is further drawn to representing DNA reads as nodes in a graph and connect them when their sequences are similar enough based on the alignment length and comparing each DNA read to other reads and measure how much of their sequences match ; and comparing DNA reads to each other and measure how long the reads align. Claim 14 is further drawn to providing an output indicating sequences classified as belonging to a biosynthetic gene cluster. Blin et al. dis closes identifying BGCs by analyzing genomic (long DNA) sequence. It explains that a “genome of interest” is scanned and “a stretch of observed PFAM domains” is evaluated, such that “a BGC is predicted” when a seque nce region meets the criteria ( col.1 , para .1 . lns -1-11 ; A method of identifying biosynthetic gene clusters from long-read sequencing data ). Blin et al. discloses using a trained computational model to analyze sequence derived features and identify BGC regions ( col.1 , para.1 , lns.5 -11, pg. 1105 ; classifying the modified consensus polynucleotide sequence for each group of reads from the set of groups using a machine learning classifier trained to classify polynucleotide sequences belonging to a biosynthetic gene cluster according to a set of polynucleotide sequence features, the classifying including identifying the modified consensus sequence for each group of reads from the set of groups as having the features of the biosynthetic gene cluste r ) . With respect to claim 14, Blin et al. discloses the antiSMASH workflow, which includes detecting gene clusters followed by output plug ins ( Fig.1 , pg.1106 ; and output a report identifying polynucleotide sequences belonging to the biosynthetic gene cluster ) . Regarding claim 7, Blin et al. discloses using a trained computational model to analyze sequence derived features and identify BGC regions ( col.1 , para.1 , lns.5 -11, pg. 1105), and using labeled examples to identify patterns and classify sequences ( col.2 , para.1 , lns.5 -9, pg.1110 ; the machine learning classifier is trained using a set of training data including features extracted from polynucleotide sequences encoding biosynthetic gene clusters and associated classifications, wherein the set of training data is retrieved from a database ). Regarding claim 8, Blin et al. discloses analyzing sequences “per gene” ( col.2 , para.2 , ln.46 , pg.1109 ) and identifies specific features, meaning that the features as selected from open reading frames and used to predict their features, such as chemical modification ( cols.2 -1, para.2 , lns.49 -53, pgs.1109 -1110 ; the features are selected from open-reading-frames, protein-domain content of open reading frames, promoter binding sites, substrate specificity prediction of enzymatic open reading frames, active site prediction of enzymatic open reading frames ). Regarding claim 9, Blin et al. discloses classifying sequences based on chemical class and biosynthetic class, which are higher level categories used to group and analyze gene clusters ( col.1 , para.1 , lns.13 -17, pg. 1110; the classifications are selected from structural, chemical, phenotypic, or biosynthetic higher order categories ). Regarding claim 10, Blin et al. discloses support vector machines, which are machine learning approaches that use supervising learning and that SMVs can be use for classification ( col.2 , para.1 , lns.4 -6, pg.1105 ; the machine learning classifier includes at least one of a neural network, a decision tree, a random forest, a support vector machine, a gradient boosting tree, a Bayesian network, or a genetic algorithm ). Regarding claims 13 and 16, Blin et al. discloses using the MIBiG database ( col.2 , para.1 , lns.7 -11, pg.1107 ; the polynucleotide sequence encoding a polypeptide is from a database of biosynthetic gene clusters ). Bin et al. is silent to obtaining long-read sequencing data including a set of reads derived from a sample of genomic deoxyribonucleic acid (DNA), each read from the set of reads includes a polynucleotide sequence; partitioning each read from the set of reads into a group of reads from a set of groups, the polynucleotide sequence for each read within each group of reads from the set of groups having a higher alignment length with the polynucleotide sequence for each remaining read in that group of reads than with the polynucleotide sequence for each read within each remaining group of reads from the set of groups; performing a first read error correction for each group of reads from the set of groups by generating a consensus sequence associated with that group of reads; performing a second read error correction for each group of reads from the set of groups by aligning the consensus sequence for that group of reads with a polynucleotide sequence encoding a polypeptide, wherein the consensus sequence is modified to encode the polypeptide, thereby generating a modified consensus polynucleotide sequence and expressing the modified consensus polynucleotide sequence in a host cell based on identifying the modified consensus sequence as having the features of the biosynthetic gene cluster. However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Koren et al ., Chung et al. and Zhang et al. With respect to claim 1, Koren et al . discloses getting long DNA sequencing data made up of multiple reads (title and abstract, pg.1 ) and using multiple sequencing reads to form longer DNA sequences ( col.1 , para.1 , lns.3 -6, pg. 722 ; obtaining long-read sequencing data including a set of reads derived from a sample of genomic deoxyribonucleic acid (DNA), each read from the set of reads includes a polynucleotide sequence ) . Koren et al . discloses DNA reads that are grouped based on similarity by keeping similar reads ( col.2 , para.1 , lns.6 -7, pg.725 ) , removing the ones that are not similar ( col.2 , para.1 , lns.6 -7, pg.72 ) and that reads with higher alignment are placed into the same group ( Fig.1 , pg. 724 and col.1 , para.1 , lns.2 -4, pg.722 ; partitioning each read from the set of reads into a group of reads from a set of groups, the polynucleotide sequence for each read within each group of reads from the set of groups having a higher alignment length with the polynucleotide sequence for each remaining read in that group of reads than with the polynucleotide sequence for each read within each remaining group of reads from the set of groups ). Koren et al . discloses correcting errors in reads within a group ( Fig.1 , pg. 724 and col.1 , para.2 , lns.12 -13, pg. 725) and generating a consensus ( Fig.1 , pg. 724 and col.2 , para.2 , ln.3 , pg.729 ; performing a first read error correction for each group of reads from the set of groups by generating a consensus sequence associated with that group of reads ). With respect to claim 11, Koren et al . discloses an assembly graph, showing sequences connected based on overlaps, corresponding to representing reads as nodes in a graph connected by sequence similarity ( Fig.5 , pg. 729 ; generate a network graph representation of the set of reads and the alignment length for each read from the set of reads, each node from a set of nodes in the network graph representation represents a single read from the set of reads and each edge from a set of edges between a pair of nodes from the set of nodes represents that the alignment length between the polynucleotide sequence of a first read from a pair of reads and the polynucleotide sequence of a second read from the pair of reads is above an alignment length threshold ). Koren et al . discloses comparing DNA reads to each other and measure how long the reads align ( col.1 , para.1 , lns.5 -6, pg.731 ; determine an alignment length between the polynucleotide sequence of each read from the set of reads and the polynucleotide sequence of the remaining reads from the set of reads ). Regarding claim 3, Koren et al . discloses that the long-read sequence data is obtained from MinION data ( col.2 , para.2 , lns.9 -12, pg.729 ; the long-read sequencing data is obtained from a database ). Regarding claim 4, Koren et al . discloses a single molecule sequencing that produces long reads (>10 kbp ) coming from the genome ( col.1 , para.1 , lns . 9-14, pg.722 ; the long-read sequencing data is obtained by sequencing a sample of genomic DNA using a long-read sequencing method ). Regarding claims 2, 12 and 15, Koren et al . discloses that reads are aligned with “>90% of their length”(Figure 5, legend, pg.729 ; the alignment length threshold is at least 90%, at least 95%, at least 98%, at least 99%, or 100% alignment length ). With respect to claim 1, Chung et al. discloses refining DNA sequences based on protein s and that sequences are optimized while ensuring that the codon sequence can be translated into the target protein, meaning the DNA is modified but still encodes the same correct protein ( Fig.2 , pg. 6 and col.2 , para.1 , lns.13 -14, pg.2 ; performing a second read error correction for each group of reads from the set of groups by aligning the consensus sequence for that group of reads with a polynucleotide sequence encoding a polypeptide, wherein the consensus sequence is modified to encode the polypeptide, thereby generating a modified consensus polynucleotide sequence ). With respect to claim 1, Zhang et al. discloses expressing a sequence identified as a biosynthetic gene cluster in a host cell ( Fig.1 , pg.17 and Introduction, lns.14 -18, pg.2 ; and expressing the modified consensus polynucleotide sequence in a host cell based on identifying the modified consensus sequence as having the features of the biosynthetic gene cluster ) . Regarding claim 5, Zhang et al. discloses constructing libraries of genomic DNA fragments, indicating that DNA is fragmented, copied into libraries (collections), and then analyzed or sequenced ( Fig.1 , pg.17 and para.1 , lns.12 -14, pg.2 ; the sample of genomic DNA is digested into fragments, and wherein the fragments are cloned into a genomic DNA library prior to sequencing ). Regarding claim 6, Zhang et al. discloses the use of cosmid libraries ( para.1 , lns.1 -2, pg.3 ; wherein the genomic DNA library includes cosmid vectors ). It would have been obvious to one of the ordinary skill in the art at the time the invention was made to modify the model of Bin et al ., with sequencing methods of Koren et al. , the refining method of Chung et al. and the cell expression techniques of Zhang et al. because all these methods pertain to the same field, and can lead to higher accuracy as stated by Koren et al. “ Canu , FALCON, and SPAdes routinely exceeded 99.99% polished base accuracy” ( col.1 , para.1 , lns.7 -8, pg.729 ). A person of ordinary skill in the art would therefore be motivated to combine these techniques because it shows higher accuracy. One would have had a reasonable expectation of success for making this combination because are in the same field, and putting these approaches together would lead to a more improved method. Conclusion No claims are allowed. 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Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center ( EBC ) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /A.S.E./ Examiner, Art Unit 1687 /Karlheinz R. Skowronek/ Supervisory Patent Examiner, Art Unit 1687