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 .
Applicant's response filed 2/3/2026 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 54-60, 67-68, 76-83, 85-86, and 153 pending and examined on the merits.
Claims 153 newly added.
Claims 1-53, 61-66, 69-75, 84, and 87-15 canceled.
Priority
The instant application filed on 4/8/2022 is a CON of PCT/US2020/070643 filed on 10/9/2020, claims the benefit of priority to U.S. Provisional Patent Application Nos. 62/914,366 (filed 10/11/2019), 62/914,368 (filed 10/11/2019), and 62/944,877 (filed 12/6/2019). Thus, the effective filing date of the claims is 10/11/2019.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Claim Objections
The objection to claim 54 withdrawn in view of Applicant's claim amendments filed on 2/3/2026.
Withdrawn Rejections
35 USC § 112(b)
The rejection of claims 54-57, 67-68, and 76-86 under 35 USC 112(b) withdrawn in view of Applicant's claim amendments filed on 2/3/2026.
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 applicant regards as his invention.
Claims 58-60 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 58 has been amended to recite "the observed signature and expected signature each having a total number of categories, each category containing a corresponding one of the binary values" (Remarks 2/3/2026 page 2). The amendment does not make clear what the definition is for "a total number of categories". Again, Examiner notes that the instant specification para.0134 discusses "categories", and in light of Matrix D, illustrates that "a total number of categories" corresponds to the sum of the binary values of a "signature". To further prosecution, the limitation is interpreted as "then calculating a total number of categories for the observed and expected signatures by summing the binary values of each respective row", and "a category" is interpreted as a hypervariable region present as a column in the count matrix.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 153 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 153 rejected as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. The claim recites "applying the threshold to a ratio of the aggregated counts to a total number of mapped 16S sequence reads", which does not further limit claim 54 because "the aggregated counts" have already been normalized which incorporates a total count and therefore is technically already a kind of ratio. Furthermore, a ratio of normalized aggregated counts (i.e. "the aggregated counts" from claim 54) to total number of mapped reads is not statistically sound, as the numerator has already had the total number of mapped reads incorporated into it via a normalization procedure.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Response to Arguments under 35 USC § 112
Applicant's arguments filed 2/3/2026 for claim 58 is fully considered but it is not persuasive.
Claim 58 has been amended to recite "the observed signature and expected signature each having a total number of categories, each category containing a corresponding one of the binary values" (Remarks 2/3/2026 page 2). The amendment does not make clear what the definition is for "a total number of categories" (see above section “Claim Rejections – 35 USC 112(b)” for interpretation).
Therefore, the rejection of claims 58 under 35 USC 101 is maintained. Claims 59-60 depend from this claim; therefore, their rejection is likewise maintained.
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 54-60, 67-68, 76-83, 85-86, and 153 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon 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:
Claims 54, 78, 68, 80, and 82: “mapping the plurality of 16S sequence reads to a plurality of compressed 16S reference sequences” (claims 54 and 78), “mapping the plurality of 16S sequence reads to the reduced set of full-length 16S reference sequences” (claims 54 and 78), and “mapping the targeted species sequence reads to segmented reference sequences to form targeted species mapped reads” (claims 68 and 80) provides a comparison (mapping sequence reads against a reference involves comparing strings) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“reducing the read count matrix by applying thresholding to the read counts to form a reduced read count matrix” and “applying a threshold to the aggregated counts to detect a presence of a microbe at the given level in a sample” (claims 54, 78, and 82) provides evaluation (application of a threshold and detection involves evaluating data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“compressing a database of full-length 16S reference sequences to form a reduced set of full-length 16S reference sequences based on the reduced read count matrix” provides an evaluation and organizing information (compressing a database based on external information involves comparing, and sorting or structuring data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“normalizing the read counts in the second set of read counts to form normalized counts” provides a mathematical calculation (normalizing count data involves mathematical calculations) that is considered a mathematical concept, which is an abstract idea.
Claim 55: “eliminating rows of the read count matrix when a sum of read counts within the row are less than a row sum threshold to form a first reduced read count matrix” provides evaluation (application of a threshold involves evaluating data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 56: “generating an expected signature for each species and strain indicating on a presence (=1) or an absence (=0) of each of the hypervariable segments in the species and the strain, based on the in silico PCR simulation applied to the species and the strains” provides evaluation (generating an expected signature involves evaluating each hypervariable segment) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“adding the read counts of the rows of the first reduced read count matrix that correspond to identical expected signatures of a corresponding species to form column sums, wherein the expected signatures for multiple strains of the corresponding species are identical; and adding the column sums to form a combined sum” provides a mathematical calculation (adding row and/or columns involves arithmetic) that is considered a mathematical concept, which is an abstract idea.
Claim 57: “eliminating the rows of the first reduced read count matrix when the combined sum is less than a combined sum threshold to form a second reduced read count matrix” provides evaluation (application of a threshold involves evaluating data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 58: “applying a signature threshold to the column sums to assign binary values to form an observed signature for each row of the second reduced read count matrix” provides evaluation (application of a threshold involves evaluating data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“calculating a total number of categories for the observed and expected signatures by summing the binary values of each respective row” (as interpreted above) provides a mathematical calculation (summing totals involves arithmetic) that is considered a mathematical concept, which is an abstract idea.
Claim 59: “determining the ratio of total observed to total expected signature categories” (as interpreted above) provides a mathematical calculation (determining a ratio involves arithmetic and division) that is considered a mathematical concept, which is an abstract idea.
Claim 60: “selecting a corresponding full-length 16S reference sequence from the database of full-length 16S reference sequences stored in memory for a first reduced set of full-length 16S reference sequences when the ratio is greater than a ratio threshold” provides evaluation (application of a threshold involves evaluating data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 153: “applying a threshold further comprises applying the threshold to a ratio of the aggregated counts to a total number of mapped 16S sequence reads” provides evaluation (application of a threshold involves evaluating data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 54-60, 67-68, 76-83, 85-86, and 153 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). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claims 54, 78, and 81: “receiving a plurality of nucleic acid sequence reads, wherein the sequence reads include a plurality of 16S sequence reads” (claims 54 and 78) provides insignificant extra-solution activities (receiving data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“generating a read count matrix containing read counts of 16S sequence reads mapped to each hypervariable segment in the set of hypervariable segments, wherein rows of the read count matrix correspond to strains of species and columns correspond the hypervariable segments” (claims 54 and 78), “counting the 16S sequence reads that mapped to each full-length reference in the reduced set of full-length 16S reference sequences to form a second set of read counts” (claims 54 and 78), and “aggregating the normalized counts for a given level to form aggregated counts, wherein the given level is a species level, a genus level or a family level” (claims 54 and 81) provides insignificant extra-solution activities (generating a data matrix and aggregating counts is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“applying an in silico PCR simulation based on primers of a 16S primer pool” (claims 54 and 78) provides insignificant extra-solution activities (generating sequence data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claims 83: “generating the segmented reference sequences by applying an in silico PCR based on primers of a species primer pool” provides insignificant extra-solution activities (generating sequence data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
The steps for obtaining, generating, counting, and aggregating data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering and manipulation steps (see MPEP 2106.04(d)(2)). Therefore, claims 54-60, 67-68, 76-83, 85-86, and 153 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 are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
The limitations for obtaining, generating, counting, and aggregating data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional.
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 54-60, 67-68, 76-83, 85-86, and 153 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 2/3/2026 are fully considered but they are not persuasive.
Applicant disagrees that claims 54-60, 67-68, and 76-86 are directed to an abstract idea, and subsequently refers to Ex Parte Desjardins as having parallel benefits to those described in the instant specification (Remarks 2/3/2026 pages 2-3). Applicant argues that because "there can be over 100,000 sequences in a database" and multiple hypervariable regions amplified in a sample which would "require extensive processor memory and time and potentially introduce errors and uncertainties into the analysis" that the present invention could reduce (Remarks 2/3/2026 pages 3-4). Applicant further argues that "the second mapping step" results in a smaller memory requirement as the database is reduced to only a few thousand full-length sequences (Remarks 2/3/2026 page 4). Finally, Applicant asserts that "analogous to the analysis for Step2A Prong Two in Ex Parte Desjardins, Applicant's specification [] has identified improvements to the technical field of nucleic acid sequence for identification of microorganisms consistent with an improvement under Step2A Prong Two" (Remarks 2/3/2026 page 5). Examiner notes that even a million sequences in a database is not prohibitive for aligning short or long reads, especially to a database of such short sequences such as rRNA references, as evidenced by Glöckner et al. (Journal of biotechnology 261 (2017): 169-176): Page 2 col 1 paragraph 3 "The current SILVA database release 128 (September 2016) contains 5,616,941 SSU and 735,238 LSU rDNA sequences"; Page 2 section 2.2 "The central component of ARB is a highly compressed hierarchical database. During operation it is loaded into the main memory (RAM) of the computer, ensuring rapid access and operation. The sequences representing genes (DNA) or gene products (rRNA or proteins) are stored in individual database fields"; and Page 6 section 4 "SILVAngs is a web-based service that satisfies the user need for a fast and accurate classification system of rDNA amplicon reads from high throughput next generation sequencing (NGS) technologies. Compared to commonly used stand-alone solutions, like Mothur or QIIME, SILVAngs represents a centralized, online platform targeting (a) com parability of results among studies by centralized and standardized data analysis, (b) ease of use through an intuitive web interface, and (c) to save the users from complex installation procedures and high computational demands. SILVAngs accepts short- and long read sequence data in Multi-FASTA format []. In the framework of the German Network for Bioinformatics Infrastructure project (de.NBI), additional computational resources were made available through the network. This now allows running large scale projects (up to 100 million reads) on user request". Examiner further notes that the idea of reducing the size of a database to achieve reduced computer requirements is not a novel or inventive concept (as evidenced by Wood et al. (page 4 col 1 paragraph 1 "To obtain maximal speed, Kraken needs to avoid page faults (instances where data must be brought from a hard drive into physical memory), so it is important that Kraken runs on a computer with enough RAM to hold the entire database. Although Kraken’s default database requires 70 GB of RAM, we also developed a method to remove k-mers from the database, which dramatically reduces the memory requirements"). Therefore, Applicant's "improvement in computer technology and the technical field" argument is rendered moot.
Therefore, the rejection of claims 54 and 78 under 35 USC 101 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 54-57, 67-68, 76-83, 85-86, and 153 rejected under 35 U.S.C. 103 as being unpatentable over Flygare et al. (US-20180365375) in view of Chaudhary et al. (PloS one 10.2 (2015): e0116106), Wood et al. (Genome biology 15.3 (2014): R46), and More et al. (Journal of Computational Biology 23.8 (2016): 651-661).
Regarding claims 54, 78, 81-83, and 153, Flygare teaches receiving a plurality of nucleic acid sequence reads, wherein the sequence reads include a plurality of 16S sequence reads (Para.0152 "Taxonomer provided fast, and effective means for read and contig classification, [], and achieved accuracies on 16S amplicon data").
Flygare also teaches first mapping the plurality of 16S sequence reads to a plurality of compressed 16S reference sequences, wherein each compressed 16S reference sequence corresponds to a strain of a species (Para.0005 "performing with a computer system a sequence comparison between the sequencing read and a plurality of reference polynucleotide sequences" and para.0008 "The database of reference sequences can comprise marker gene sequences for taxonomic classification of bacterial sequences, such as 16S rRNA sequences" and para.0176 "A Taxonomer database can be constructed containing microbial strain information (e.g. a bacterial database constructed from different strain, including multiple-drug resistant strains)").
Flygare also teaches: generating a read count matrix containing read counts of 16S sequence reads mapped to each hypervariable segment in the set of hypervariable segments, wherein rows of the read count matrix correspond to strains of species and columns correspond the hypervariable segments; (also claim 153) reducing the read count matrix by applying thresholding to the read counts to form a reduced read count matrix; and compressing a database of full-length 16S reference sequences to form a reduced set of full-length 16S reference sequences based on the reduced read count matrix (Para.0005 "the present disclosure provides a method of identifying a plurality of polynucleotides in a sample from a sample source. In some embodiments, the method comprises providing sequencing reads for a plurality of polynucleotides from the sample, and for each sequencing read: (a) performing with a computer system a sequence comparison between the sequencing read and a plurality of reference polynucleotide sequences, wherein the comparison comprises calculating k-mer weights as a measure of how likely it is that k-mers within the sequencing read are derived from a reference sequence within the plurality of reference polynucleotide sequences; (b) identifying the sequencing read as corresponding to a particular reference sequence in a database of reference sequences if the sum of k-mer weights for the reference sequence is above a threshold level; and (c) assembling a record database comprising reference sequences identified in step (b), wherein the record database excludes reference sequences to which no sequencing read corresponds").
Flygare also teaches: normalizing the read counts in the second set of read counts to form normalized counts; (claim 81) aggregating the normalized counts for a given level to form aggregated counts, wherein the given level is a species level, a genus level or a family level; and (claim 82) applying a threshold to the aggregated counts to detect a presence of a microbe at the given level in a sample (Para.0188 "Next one can calculate the proportion of bases for a single organism's reference sequences that have nonzero coverage compared to the total number of bases in the organism's reference sequences, call this term pi. This information can be summarized using a weighted sum into a single number called the rank metric. Given the weights w1 and w2, we can calculate the rank metric as w1*(u1*bc1+u2*bc2+u3*bc3)+w2*pi. The rank metric is a condensed summary of how well an organism's reference sequences are represented by the query sequences. The weight is a number between 0 and 1, and the sum of all weights, in this example w1+w2, is 1. In practice, one can use simulation and machine learning methods, e.g. random forests, to compute optimal weights with training data sets or on extensive simulations, and discover rank metric cutoffs that allow making informed calls about which organisms' DNA and/or RNA is present or absent in a given set of query sequences").
Flygare does not explicitly teach mapping to 16S hypervariable segments, a second mapping of sequence reads to reference sequences, nor applying an in silico PCR simulation based on primers of a 16S primer pool.
However, Chaudhary teaches mapping to 16S hypervariable segments (Page 2 last paragraph "The primer pairs which could extract the sequences for a HVR from more than 50% of the total sequences present in the database were selected" and page 3 first paragraph "The sequences of each HVR were divided into separate groups based on their taxonomic ranks from phylum to genus as per the information available in the taxonomy data retrieved from the Greengenes database").
However, Wood teaches a second mapping of sequence reads to reference sequences (Page 6 col 1 paragraph 2 "Of note is that 68.2% of the reads were not classified by Kraken. To determine why these reads were not classified by Kraken, we aligned a randomly selected subset of 2,500 of these unclassified reads to the RefSeq bacterial genomes using BLASTN. Only 11% (275) of the subset of unclassified reads had a BLASTN alignment with E-value ≤ 10−5 and identity ≥90%"). Although Wood's method does not routinely use a second step of mapping reads to a larger dataset, it was used as an approach to classify reads left unclassified by their classification method.
However, More teaches using primers in silico for extracting variable region segments from reference genomes (page 4 section 2.5 "In order to check the pattern distribution of the signatures in V regions in sequence, previously reported forward and reverse primers for V1 to V9 regions were referred and listed in Supplementary Table S2 (Ercolini et al., 2001; Claesson et al., 2010; Guillou et al., 2013; Zumbrun et al., 2013). All the V1f, V2f, V2r, V3f, V3r, V4f, V4r, V5f, V5r, V6f, V6r, V7f, V8r, and V9f in silico sites were detected in Bacillus subtilis (T) DSM10 AJ276351 (RDP: S000003473; length: 1517 bp) sequence. These locations of V regions were further referred for mapping of patterns of signatures").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the database of Flygare with the 16S hypervariable regions as taught by Chaudhary in order to further improve accuracy at even lower taxonomic ranks (page 10 paragraph 2 "For all HVRs and at all taxonomic ranks [], the results of 16S Classifier were more accurate as compared to RDP classifier []. These results indicate that 16S classifier shows much higher accuracy at lower taxonomic ranks, such as genus, compared to the RDP classifier and attest to the accuracy of 16S classifier on different HVRs at all taxonomic ranks"). One skilled in the art would have a reasonable expectation of success because both approaches are using 16S rRNA databases with hypervariable regions to taxonomically classify sequence reads.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Flygare as taught by Wood in order to produce more accurate classification of taxonomically challenging reads (page 6 col 1-2 "This suggests that the vast majority of the reads not classified by Kraken were significantly different from any known species, and thus simply impossible to identify"). Furthermore, Flygare suggests chaining together multiple classification methods (para.0167 "FIG. 9A shows that Protonomer (94.6±2.7%) and Afterburner (94.5±2.3%) had comparable sensitivity while their combination was slightly more sensitive (95.0±2.4%)"). One skilled in the art would have a reasonable expectation of success because both approaches are mapping sequence reads to reference sequences in order to taxonomically classify the sequence reads.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Flygare as taught by More in order to generate unique signatures in 16S rRNA genes for taxonomic classification (page 1 Abstract "to identify discriminating nucleotide patterns in 16S rRNA gene and then to generate signatures for taxonomic classification"). One skilled in the art would have a reasonable expectation of success because both approaches are using 16S rRNA databases with hypervariable regions to taxonomically classify sequence reads.
Regarding claim 55, Flygare in view of Chaudhary and Wood teach the methods of Claims 54 on which this claim depends/these claims depend. Flygare also teaches the reducing the read count matrix further comprises eliminating rows of the read count matrix when a sum of read counts within the row are less than a row sum threshold to form a first reduced read count matrix (Para.0005 "identifying the sequencing read as corresponding to a particular reference sequence in a database of reference sequences if the sum of k-mer weights for the reference sequence is above a threshold level; and (c) assembling a record database comprising reference sequences identified in step (b), wherein the record database excludes reference sequences to which no sequencing read corresponds").
Regarding claim 56, Flygare in view of Chaudhary and Wood teach the method of Claim 55 on which this claim depends/these claims depend. Wood also teaches adding the read counts of the rows of the first reduced read count matrix that correspond to identical expected signatures for a corresponding species to form column sum (Page 3 figure 1 "To classify a sequence, each k-mer in the sequence is mapped to the lowest common ancestor (LCA) of the genomes that contain that k-mer in a database. The taxa associated with the sequence’s k-mers, as well as the taxa’s ancestors, form a pruned subtree of the general taxonomy tree, which is used for classification. In the classification tree, each node has a weight equal to the number of k-mers in the sequence associated with the node’s taxon. Each root-to-leaf (RTL) path in the classification tree is scored by adding all weights in the path, and the maximal RTL path in the classification tree is the classification path (nodes highlighted in yellow). The leaf of this classification path (the orange, leftmost leaf in the classification tree) is the classification used for the query sequence").
More teaches generating signatures of nucleotides in a binary coding system for discriminant sequence analysis (Page 4 paragraph 4 "The differentiating trinucleotides obtained through composite vector and discriminant analysis were mapped in consensus sequence of each group using binary coding system (1 to presence and 0 for absence). The trails of 1s were considered as distinguishing patterns at higher taxonomic levels. At the family and genus levels, sequences were further grouped into the six different bins based on their lengths with 50 bp intervals, and the same earlier steps were performed to get the patterns").
Regarding claim 57, Flygare in view of Chaudhary, Wood, and More teach the methods of Claim 56 on which this claim depends/these claims depend. Flygare also teaches eliminating the rows of the first reduced read count matrix when the combined sum is less than a combined sum threshold to form a second reduced read count matrix (Para.0188 "Next one can calculate the proportion of bases for a single organism's reference sequences that have nonzero coverage compared to the total number of bases in the organism's reference sequences, call this term pi. This information can be summarized using a weighted sum into a single number called the rank metric. Given the weights w1 and w2, we can calculate the rank metric as w1*(u1*bc1+u2*bc2+u3*bc3)+w2*pi. The rank metric is a condensed summary of how well an organism's reference sequences are represented by the query sequences. The weight is a number between 0 and 1, and the sum of all weights, in this example w1+w2, is 1. In practice, one can use simulation and machine learning methods, e.g. random forests, to compute optimal weights with training data sets or on extensive simulations, and discover rank metric cutoffs that allow making informed calls about which organisms' DNA and/or RNA is present or absent in a given set of query sequences" suggests filtering results based on thresholding weights assigned to reference sequence coverage by query sequences, which is applying a threshold to data based on read or base counts and deriving a new, reduced result of "calls").
Regarding claims 67 and 79, Flygare in view of Chaudhary and Wood teach the methods of Claims 54 and 78 on which this claim depends/these claims depend, respectively. Chaudhary also teaches the plurality of nucleic acid sequence reads further include a plurality of targeted species sequence reads (Page 9 last paragraph "The second dataset consisted of real sequence datasets for all HVRs. The primer regions were removed from the sequences before analysing them using 16S Classifier").
Regarding claims 68 and 80, Flygare in view of Chaudhary and Wood teach the methods of Claims 67 and 79 on which this claim depends/these claims depend, respectively. Flygare also teaches mapping the targeted species sequence reads to segmented reference sequences to form targeted species mapped reads, wherein each segmented reference sequence comprises segments corresponding to expected amplicons for a strain of the targeted species (Para.0176 "The taxonomic methods and systems described herein, such as Taxonomer as described in Example 1, can be used to profile microbial strains. A Taxonomer database can be constructed containing microbial strain information (e.g. a bacterial database constructed from different strain, including multiple-drug resistant strains). For example, whole-genome DNA sequences or sequencing reads from multiple strains of one bacterial species can be used for database construction").
Regarding claims 76 and 85, Flygare in view of Chaudhary and Wood teach the methods of Claims 54 and 78 on which this claim depends/these claims depend, respectively. Chaudhary also teaches the plurality of 16S sequence reads correspond to amplicons produced by amplifying a nucleic acid sample in the presence of one or more primer pairs targeting one or more hypervariable regions of a prokaryotic 16S rRNA gene (Page 9 last paragraph "The second dataset consisted of real sequence datasets for all HVRs. The primer regions were removed from the sequences before analysing them using 16S Classifier").
Regarding claims 77 and 86, Flygare in view of Chaudhary, Wood, and More teach the methods of Claim 67 and 79 on which this claim depends/these claims depend, respectively. More also teaches the plurality of targeted species sequence reads correspond to amplicons produced by amplifying a target nucleic acid sequence contained within a genome of a microorganism that is outside a hypervariable region of a prokaryotic 16S rRNA gene, wherein different primer pairs amplify different target nucleic acid sequences contained within the genome of different microorganisms in the nucleic acid sample (Page 2 section 2.1 "A total of 3149 16S rRNA sequences belonging to phylum Firmicutes were obtained from the Ribosomal Database Project (RDP). We selected only sample species representative for each taxon and the corresponding sequences (nearly full length >1300 bp) were retrieved" suggests using "nearly full length" 16S sequences, indicating that these were produced using primers targeting regions outside of hypervariable regions and the phylum Firmicutes contains many different microorganisms).
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 2/3/2026 are fully considered but they are not persuasive.
Applicant asserts that "Flygare does not teach or suggest 'a plurality of compressed 16S reference sequences comprising a plurality of the hypervariable regions of a 16S rRNA gene derived from a 16S rRNA gene sequence database by applying an in silico PCR simulation based on primers of a 16S primer pool,' as required by [amended] claims 54 and 78" because "Flygare's reference sequence and associated k-mer weights does not represent the plurality of compressed 16S reference sequences recites in claims 54 and 78" (Remarks 2/3/2026 page 6). Furthermore, Applicant contrasts their mapping with Flygare's uses k-mer matching to map reads to references (Remarks 2/3/2026 pages 6-7). Examiner notes that the mapping of 16S sequence reads is not specific to any particular algorithm in the claim, therefore k-mer matching satisfies the mapping requirement of claims 54 and 78. While Flygare does not explicitly teach or suggest the amendment to claims 54 and 78, it is noted above that More does teach or suggest using primers in silico for extracting variable regions from reference genomes (page 4 section 2.5 "In order to check the pattern distribution of the signatures in V regions in sequence, previously reported forward and reverse primers for V1 to V9 regions were referred and listed in Supplementary Table S2 (Ercolini et al., 2001; Claesson et al., 2010; Guillou et al., 2013; Zumbrun et al., 2013). All the V1f, V2f, V2r, V3f, V3r, V4f, V4r, V5f, V5r, V6f, V6r, V7f, V8r, and V9f in silico sites were detected in Bacillus subtilis (T) DSM10 AJ276351 (RDP: S000003473; length: 1517 bp) sequence. These locations of V regions were further referred for mapping of patterns of signatures").
Applicant also asserts that Flygare does not teach or suggest "counts of 16S sequence reads that map to each hypervariable segment in the set of hypervariable segments" (as opposed to Flygare's k-mer weights) (Remarks 2/3/2026 page 7). Examiner notes that Flygare in fact does teach or suggest using counts of reads that map to a reference, and further notes that whether counting whole reads or k-mers is dependent on the mapping approach and therefore using one or the other is rendered obvious (para.0071 "In some embodiments, a method may further comprise quantifying an amount of polynucleotides corresponding to a reference sequence identified in an earlier step. Quantification can be based on a number of corresponding sequencing reads identified. This can include normalizing the count by the total number of reads, the total number of reads associated with sequences, the length of the reference sequence, or a combination thereof. Examples of such normalization include FPKM and RPKM, but may also include other methods that take into account the relative amount of reads in different samples, such as normalizing sequencing reads from samples by the median of ratios of observed counts per sequence. A difference in quantity between samples can indicate a difference between the two samples. The quantitation can be used to identify differences between subjects, such as comparing the taxa present in the microbiota of subjects with different diets, or to observe changes in the same subject over time, such as observing the taxa present in the microbiota of a subject before and after going on a particular diet").
Applicant argues that "Chaudhary does not teach or suggest mapping of 16S sequence reads to 16S hypervariable segments" and instead "teaches clustering the sequences of the reference hypervariable regions (HVR) for use in training a 16S classifier based on a Random Forest algorithm" (Remarks 2/3/2026 pages 7-8). Examiner notes that while Chaudhary does not explicitly state mapping of reads, the use of CD-HIT is in fact a kind of sequence mapping, as it employs sequence similarity for its clustering procedure. Applicant has not claimed any specific mapping algorithm in the independent claims, therefore CD-HIT satisfies the limitation (Fu et al. Bioinformatics. 2012 Dec 1;28(23):3150-2. doi: 10.1093/bioinformatics/bts565. Epub 2012 Oct 11. PMID: 23060610; PMCID: PMC3516142; page 1 col 2 section 2 METHODS "CD-HIT is a greedy incremental algorithm that starts with the longest input sequence as the first cluster representative, and then process the remaining sequences from long to short to classify each sequence as a redundant or representative sequence based on its similarities to the existing representatives. The similarities are estimated by common word counting using word indexing and counting tables to filter out unnecessary sequence alignments, which are used to compute exact similarities").
Finally, Applicant argues that "Wood's teaching of 'a second mapping of sequence reads to reference sequences' does not meet the requirements of [] claims 54 and 78" because "Wood's RefSeq bacterial genomes do not represent a reduced set of 16S reference sequences obtained by compressing a database of full-length 16S reference sequences" (Remarks 2/3/2026 pages 8-9). Examiner notes that it is the combination of Wood with Flygare and Chaudhary that teaches the limitations of claims 54 and 78 because it teaches a second step of mapping reads while the other references teach using the required 16S database. Furthermore, to add to the motivation already outlined above (section "Claim Rejections - 35 USC 103"), Wood teaches building a reduced database for mapping sequence reads, which again could be built from the references of Flygare and Chaudhary (Wood, page 4 col 1 paragraph 1 "To obtain maximal speed, Kraken needs to avoid page faults (instances where data must be brought from a hard drive into physical memory), so it is important that Kraken runs on a computer with enough RAM to hold the entire database. Although Kraken’s default database requires 70 GB of RAM, we also developed a method to remove k-mers from the database, which dramatically reduces the memory requirements").
Therefore, the rejection of claims 54 and 78 under 35 USC 103 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
Examiner’s Note
Claims 58-60 are free from the prior art because the prior art does not fairly suggest or teach: assigning binary values to form an “observed signature” for each row of a reduced read count matrix (post-filtering by some read count threshold) and applying a signature threshold to column sums (claim 58); determining the ratio of total observed to total expected present (=1) signature categories (claim 59); nor selecting a corresponding full-length 16S reference sequence from the database of full-length 16S reference sequences stored in memory for a first reduced set of full-length 16S reference sequences when the ratio is greater than a ratio threshold (claim 60). The closest prior art is Flygare et al. (US-20180365375). Flygare discloses a method for identifying a sequencing read as corresponding to a particular reference sequence in a database of reference sequences if the sum of k-mer weights for the reference sequence is above a threshold level. While the effective result of this method is a reduced database of the reference sequences identified, there are no additional steps suggested or taught by Flygare nor any other prior art that analyze sequence mapped data (to 16S reference sequences or any other reference sequences) in the same manner described by claims 58-60, specifically: applying thresholds to read counts per reference to generate a binary count matrix and summing all binary counts per row, then determining a ratio of these counts to an expected binary count of the reference sequences, and finally identifying corresponding full-length 16S reference sequences where the ratio is greater than a ratio threshold.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the TH REE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this finaI action.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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