DETAILED ACTION
Applicant's response, filed 27 May 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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
Claims 1-16 are pending.
Priority
This application is a 371 of PCT/EP2021/056008, filed 03/10/2021, which claims benefits under U.S.C 119 to application no. 20162646.2, filed 03/12/2020 by the European Patent Office. The instant application has the effective filing date of 12 March 2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/20/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The objection to the drawings is withdrawn, in view of submission of replacement drawings, filed 05/27/2026.
The drawings, submitted 05/27/2026, are accepted by the examiner.
Specification
The objections to the specification are withdrawn, in view of submission of substitute specification on 05/27/2026.
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.
The rejection to claims 1-13 is maintained.
The rejection to claims 15-16 is newly applied, in view of claim amendments.
Claims 1-13 and 15-16 are rejected under U.S.C 101 because the claimed invention is directed to abstract ideas without significantly more, as detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-11 and 14-16 are directed to a statutory category (method).
Claim 12 is directed to a statutory category (product).
Claim 13 is directed to a statutory category (system).
Therefore, in view of claim amendments, all claims have subject matter eligibility.
Claims 1-16 [Eligibility Step 1: YES]
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon). Possible judicial exceptions are explored below.
Recitations of Judicial Exceptions:
Claims 1 and 12: A computer-assisted process for detecting a genome sequence in digital form in a genome of a microorganism in digital form, the process involving:
determining that the genome sequence is present in the genome if the percentage of k-mers detected as being present in the genome is above a predetermined threshold. (mental process)
Amended limitation: predicting the antibiotic susceptibility of the microorganism based on a presence or absence of the genome sequence in the genome of the microorganism in digital form. (mental process, mathematical concept)
Claim 3: The process as claimed in claim 2, in which the digital genome consists of a set of genome sequences produced by a sequencing platform, or "reads",
according to which the determination of the presence or absence of a k-mer in the genome is obtained by detecting Ncov, identical copies of the k-mer in the genome, where the integer Ncov is equal to:
N
c
o
n
v
=
τ
×
N
r
N
g
where: Nr is the total number of bases included in the digital genome, Ng is the total number of bases of a reference genome of the species to which the microorganism, and
τ
is a percentage between 5% and 15%. (mathematical concept, mental process)
Claim 4: The process as claimed in claim 2, in which the genome of the microorganism is included in a set of genomes derived from the direct sequencing of a sample, each digital genome consisting of a set of genome sequences produced by a sequencing platform, or "reads",
and according to which the determination of the presence or absence in the genome is obtained by detecting NCoV identical copies of the k-mer in the genome, where the integer NCoV is equal to:
N
c
o
n
v
=
τ
×
ρ
N
r
N
g
where: Nr is the total number of bases included in the digit al genome, Ng is the mean total number of bases of a genome of the species to which the microorganism belongs, p is the relative proportion of the microorganism in the sample, is the percentage and
τ
is a percentage between 5% and 15. (mathematical concept, mental process)
Claim 5: The process as claimed in claim 1, in which the predetermined threshold is dependent on the length of the genome sequence. (mental process)
Claim 6: The process as claimed in claim 5, in which the predetermined threshold value decreases with the value of the length of the genome sequence. (mental process)
Claim 7: The process as claimed in claim 6, in which the space of the genome sequence lengths is divided into three intervals, and according to which the predetermined threshold takes a single value per interval. (mental process)
Claim 8: The process as claimed in claim 7, according to which k is between 15 and 50, and according to which if L ≤ 61 then
s
u
n
i
= 90%, if 61< L ≤ 100 then
s
u
n
i
= 80% and if 100 < L then
s
u
n
i
= 70%, in which L is the length of the genome sequence and
s
u
n
i
is the predetermined threshold value. (mental process)
Claim 9: determining that the group of genome sequences is present in the genome:
if at least one genome sequence of the group is detected:
if all the genome sequences of the group are detected:
if the percentage of genome sequences of the group that are detected is above a second predetermined threshold:
or with a probability equal to the percentage of genome sequences of the group that are detected as being present. (mental process)
Claim 10: The process as claimed in claim 9, in which the second threshold is greater than or equal to 20%. (mental process)
Step 2A – Prong One Analysis:
Categorizing data based on thresholds equates to making mental observations of data requiring nothing more than the human mind and pen/paper. As such, the noted limitations fall within the mental process grouping of abstract ideas. Limitations that merely recite additional information to mental processes are similarly categorized (claims 5-7 and 10).
Limitations that recite mathematical formulas and calculations require both mental observations of data and mathematical analysis techniques (calculations, relationships, and formulas) that can be fully executed with use of a pen and paper. As such, limitations that involve activities of this manner fall into the mental process and/or mathematical concept grouping of abstract ideas.
Therefore, the claims appear to recite judicial exceptions.
[Step 2A: Prong One: YES]
Eligibility Step 2A – Prong Two: 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. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Additional elements are recited, categorized, and analyzed below.
Data Gathering/Outputting:
Claims 1 and 12: A computer-assisted process for detecting a genome sequence in digital form in a genome of a microorganism in digital form, the process involving:
storing in a computer memory a set of digital genome sequences of constant length k, or "k-mers", the set being obtained by sliding, with a constant step, a window of length k over the genome sequence;
for each k-mer, determining its absence or presence in the genome
Claim 2: The process as claimed in claim 1, in which the determination of the presence or absence of a k-mer in the genome is obtained by detecting at least one identical copy of the k-mer in the genome
Claim 9: The process as claimed in claim 1, comprising the detection of a group of genome sequences, the detection involving: detecting each genome sequence of the group in accordance with the process of claim 1
Claim 15: supplementing a patient’s record with the antibiotic susceptibility of the microorganism. (data output)
Claim 16: displaying the antibiotic susceptibility of the microorganism on a computer screen or storing the antibiotic susceptibility in a computer system. (data output)
Computer + Sequencing Components:
Claim 11: The process as claimed in claim 1, also comprising the total or partial sequencing of the genome of the bacterial strain so as to produce the genome in digital form.
Claim 12: A computer program product storing computer-executable instructions for performing a process as claimed in claim 1.
Claim 13: A system for detecting a genome sequence in a genome of a microorganism, comprising:
a sequencing platform for the partial or total sequencing of the genome of the strain;
a computer unit configured to apply a detection process as claimed in claim 1.
Step 2A – Prong Two Analysis:
The data gathering elements (claims 1-2, and 12) store k-mers in computer memory. Such activities are classified as insignificant extra solution activities per Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).
Detecting identical copies of k-mers present in the genome, can equate to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display and/or limiting a database index to XML tags which are also classified as insignificant extra solution activities per Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); and Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937, respectively.
Outputting the antibiotic susceptibility, at the level of generality recited, equates to data output, also classified as insignificant extra-solution activity via Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) of MPEP 2106.05 (g).
The computer components represent generic computer components and implementations that generate, transmit, and/or receive data necessary to complete the steps of the claimed invention, classified as insignificant extra-solution activity per Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).
The sequencing components merely gather data to complete perform the judicial exceptions per MPEP 2106. 05(g).
As such, the additional elements when viewed separately or in the context of the whole, claimed invention, do not integrate the judicial exceptions into practical application.
[Eligibility Step 2A – Prong Two: No]
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Step 2B Analysis:
The data outputting elements are found well-understood, routine, and conventional per Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
The computer components and storing step are found well-understood, routine, and conventional per Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 for storing and retrieving information in memory.
The sequencing components and limitation that detects identical k-mer copies are found well-understood, routine, and conventional by the courts, in University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 764, 113 USPQ2d 1241, 1247 (Fed. Cir. 2014), which recognized nucleic acid sequencing; Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546, which recognized the analysis of DNA to provide sequence information; and Rowe et al. (Genome Biology; Vol. 20: 199; 2019) which reviews algorithms for processing large sets of genomic data, and teaches counting k-mers, while preventing the storage of singleton k-mers which often arise from sequencing errors (page 7, column 2).
[Eligibility Step 2B: NO]
As such, claims 1-13 and 15-16 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101.
Claim 14 recites a practical application in the form of administering a treatment and is eligible under 35 U.S.C 101 via pathway B.
Response to Arguments:
Applicant argues "storing in computer memory" and "for each k-mer, determining its absence or presence in genome" are cited as abstract ideas, but do not constitute as mental processes because they cannot reasonably be done in the human mind due to the large size of microbial genomes (page 9, para. 2).
Examiner responds "storing in computer memory" was cited as an additional element in the previous office action (page 13, no. 33a) and the action herein.
However, upon consideration of arguments, examiner agrees determining the presence or absence of k-mers within an entire genome increases the complexity to a degree that is unable to be reasonably performed in the human mind; finds arguments persuasive; and therefore, considers the limitation an additional element herein.
Applicant argues the claimed method provides an improvement to technology via step 3 of claim 1, including the amended limitation of "predicting the antibiotic susceptibility of the microorganism based on a presence or absence of the genome sequence in the genome of the microorganism in the digital form" (pages 10-11), because the step allows for the detection of a sequence in a genome even when the genome contains breaks and errors (page 11, para. 1), without a slower genome assembly step included in the process (page 11, para. 2).
Examiner responds the highlighted limitation is cited as a judicial exception in the form of abstract ideas (mental process and mathematical concept), commensurate in scope with collecting and comparing known information per Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); calculating the difference between local and average data values, In re Abele, 684 F.2d 902, 903, 214 USPQ 682, 683-84 (CCPA 1982); and using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979).
Furthermore, MPEP 2106.05(a) states: it is important to note the judicial exception alone cannot provide the improvement. Therefore, the highlighted limitation cannot result in an improvement to technology, and is still drawn to abstract ideas without significantly more.
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.
The rejections to claims 7-9 and 13 are maintained.
Claims 7-9 and 13 are 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 for the following reasons.
Claim 7 recites dividing “the space of the genome sequence lengths” in which “the space” lacks antecedent basis and does not clearly point out what is being divided. Please clarify and provide antecedent basis for the term.
Claim 8 recites “the length of the genome sequence,” wherein it is unclear whether the “genome sequence” is referencing genome sequences of constant length, defined as “k-mers” in claim 1, the entire genome, or a portion of a genomic sequence, which the limitation is instantly interpreted as.
Claim 9 recites “determining that the group of genome sequences is present in the genome”… “with a probability equal to the percentage of genome sequences in the group that are detected as being present”, wherein it is unclear what probability is determined. Clarification is required.
Instantly the limitation is interpreted as determining that a group of genomic sequences is present… if a percentage, equal to if the percentage of each genomic sequence within the group determined to be present according to the technique in claim one, are detected.
Claim 13 recites “…sequencing of the genome of the strain” wherein, “the strain” lacks antecedent basis. To overcome this rejection, please amend to “a strain” or provide antecedent basis for the term in claim 1, which it depends.
Response to Arguments:
In regards to 35 U.S.C 112 (b), no arguments were filed and no amendments appear to be made. As such, the rejections are maintained.
Claim Rejections - 35 USC § 102
Applicant’s arguments, see pages 13-19, with respect to U.S.C 102 have been fully considered and are persuasive.
The rejections of claims 1-2, 9-10, and 12 have been withdrawn.
However, upon further consideration, new grounds of rejection are made in view of Jaillard et al. (IDS ref, filed 12/27/2022; cite no. 5; 2018).
Claims 1, 9-10, and 12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jaillard et al. (IDS ref, filed 12/27/2022; cite no. 5; 2018).
Jaillard et al. describes a k-mer-based GWAS method of producing interpretable genetic variants associated with distinct phenotypes (abstract).
Claims 1 and 12 are directed to computer-implemented methods and products that store sets of genome sequences with a constant length (k-mers) in memory; determine whether a k-mer is present within a microbial genome if each unique k-mer is detected above a threshold percentage; and predict the microorganism’s antibiotic susceptibility based on the presence or absence of the identified sequence in the genome.
Jaillard et al. teaches building a single compacted De Bruijn graphs (cDBG) from all the genomes included in the association study, in which the nodes—called unitigs—represent sequences of variable length (page 3, column 1); and associating each k-mer index to its corresponding unitig index in the cDBG (page 13, column 1).
Jaillard et al. teaches storing the unitig description on all the input genomes into a matrix U, where the index is 1 if the j-th unitig is present in the i-th input genome, and 0 if otherwise (page 15, column 1), where every unitig represents (at least) one k-mer, and conversely every k-mer is represented by one unitig (page 15, column 1).
Jaillard et al. teaches retrieving the index of a given k-mer, and then using the previously computed association between k-mer and unitig indices to know which unitigs the given genome contains (page 13, column 1); where the unitig selection can be either based on the FDR (q-value threshold) or on a number of presence/absence patterns ordered by increasing q-values (page 16, column 1).
Jaillard et al. further teaches individually testing the unitigs for association with phenotype status (page 3, column 1), such as susceptible, intermediary, and resistant (page 19, column 1) to antimicrobial drugs (page 18, column 1), such as antibiotics (page 4, column 1).
Claim 9 is directed to detecting a group of genomic sequences and determining if the group is present if one of the following conditions are satisfied: (i) at least one genomic sequence of a group is detected; (ii) all genomic sequences of a group are detected; (iii) a percentage of genomic sequences of a group are detected; or (iv) a percentage, equal to if the percentage of each genomic sequence within the group determined to be present according to the technique in claim one, are detected.
Jaillard et al. teaches the unitig selection can be either based on the false discovery rate (FDR) in the form of a q-value threshold, or on a number of presence/absence patterns ordered by increasing q-values (page 16, column 1).
Therefore Jaillard et al. teaches detecting a group of genomic sequences and determining the group is present based on the presence or absence of at least one genomic sequence and/or a percentage in the form of a false discovery rate.
Claim 10 is directed to determining whether a group is present if at least 20% of genomic sequences within a group are detected. This claim is contingent on a non-required condition (limitation (iii)) of claim 9 to be met (see MPEP 2111.04 II) and therefore does not require prior art citations to be rejected.
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.
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.
Applicant’s arguments, see page 19, with respect to the rejections of claims 3-8, 11, and 13 under U.S.C 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
However, upon further consideration, new grounds of rejection are made in view of Jaillard et al (IDS ref, filed 12/27/2022; cite no. 5; 2018).
Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Jaillard et al. (IDS ref, filed 12/27/2022; cite no. 5; 2018) as applied to claims 1, 9-10, and 12 above, in view of Melsted et al. (BMC Bioinformatics; Vol. 12: 333; 2011).
Jaillard et al. teaches a method of using a k-mer database to predict antibiotic susceptibility, as described above.
Claim 2 is directed to determining the presence or absence of a k-mer in the genome being by detecting at least one identical copy of the k-mer in the genome.
Jaillard et al. further teaches assembling significant k-mers with ABYSS (page 20, column 1), which uses a Bloom filter (page 28, citation 76).
Jaillard et al. does not explicitly teach detecting identical copies of k-mers.
Melsted et al. describes efficient counting of k-mers in DNA sequences using a Bloom filter.
Melsted et al. teaches a method that identifies all the k-mers that occur more than once in a DNA sequence data set (page 1, column 1) using a Bloom filter, a probabilistic data structure that stores all the observed k-mers implicitly in memory with greatly reduced memory requirements (page 1, column 1).
Melsted et al. teaches although simple in principle, counting k-mers in large modern sequence data sets can easily overwhelm the memory capacity of standard computers (page 1, column 1); in current data sets, a large fraction-often more than 50%-of the storage capacity may be spent on storing k-mers that contain sequencing errors and which are typically observed only a single time in the data; and these singleton k-mers are uninformative for many algorithms without some kind of error correction (page 1, column 1).
Therefore Melsted et al. provides sufficient motivation for one of ordinary skill in the art to determine if k-mers are present or absent in a genome by identifying if at least one identical k-mer copy is present, as it teaches singleton k-mer presence to be uninformative, erroneous, and inefficient for memory storage. It further provides a method of completing the counting of identical k-mer copies applicable to the Bloom filter utilized within the method of Jaillard et al. As such, it would be obvious to one of ordinary skill in the art to apply the method of Melsted et al. to the method of Jaillard et al. with an expectation of predictable results and an improvement to the system.
Claims 3-4, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Jaillard et al. (IDS ref, filed 12/27/2022; cite no. 5; 2018) in view of Melsted et al. (BMC Bioinformatics; Vol. 12: 333; 2011), as applied to claims 1-2, 9-10, and 12 previously, and in further view of Fisher (2017/0364666) and Ranjan et al. (Biochem Biophys Res Commun; 2016).
Jaillard et al., in view of Melsted et al., teach a method of identifying at least one identical copy of a k-mer for a k-mer database to predict antibiotic susceptibility, as described above.
Claim 3 is directed to calculating the sequencing depth of coverage via dividing the number of bases in the reads by the total number of bases in a reference genome of a bacterial strand. The claim also multiplies the calculated coverage by an estimated sequencing error rate (τ), within the range of 5-15% (0.05-0.15), in order to derive a metric of identical k-mers within the genome.
Claim 4 is directed multiplying the result of the previous equation with a relative proportion of microorganism within the sample.
Melsted et al. further teaches storing the k-mers in a hash table, usually with some associated information such as coverage and neighborhood information in the de Bruijn graph (page 1, column 2), in which the exact memory usage depends on the hash table used; for example, the assembly software ABySS uses Google sparsehash library, which has minimal memory overhead (page 1, column 2).
Jaillard et al. in view of Melsted et al. do not teach calculating coverage and the metric of identical copies of k-mers in the genome according to method of claims 3-4.
Fisher describes computer implemented methods and systems of identifying and comparing k-mers present within a microbial genome to enable efficient strain typing.
Regarding claim 3 and 4, Fisher teaches multiplying the estimated coverage by a cutoff multiplier of about 0.2 (e.g., 0.05 to 0.40 or 0.1 to 0.3) provides a suitable cutoff threshold to remove sufficient proportion of error k-mers [0024] and can be adjusted accordingly to account for higher or lower sequencing error rates [0024].
Therefore, Fisher teaches multiplying the sequencing depth of coverage by a percentage (τ) that can fall within the range of .05-.40 or .10-.30, which both overlap within the range of τ (0.05-0.15) for the claimed invention.
Fisher further teaches comparing k-mer profiles with antibiotic resistance k-mer data to generate count values for k-mers present in the particular strain's k-mer profile that are associated with antibiotic resistance [0036].
Fisher does not teach calculating sequencing depth of coverage in the same manner as the claimed invention (claims 3-4); accounting for the proportion of microorganism in the sample (claims 4); or explicitly including a sequencing platform (claim 13) for the total or partial sequencing of the bacterial genome strain within its system (claim 11).
Ranjan et al. describes microbiome analysis via various sequencing techniques.
Regarding claim 3, Ranjan et al. teaches that the percentage of genome coverage was calculated using the formula [total number of bases aligned/genome size (bases) × 100] (page 5, column 1).
Regarding claim 4, Ranjan et al. further teaches taking the percentage of relative abundance of the species into account within the calculation (page 5, column 1).
Regarding claims 11 and 13, Ranjan et al. teaches comparing the 16S rRNA amplicon versus the Whole Genome Sequencing (WGS) method and the Illumina HiSeq versus MiSeq platforms (page 3, column 1) in order to rigorously determine the optimal methods for microbiome analysis (page 6, column 1).
Ranjan et al. further teaches additionally, to identify and understand the bacterial genes in a taxa, it may be necessary to sequence a genome with high coverage (page 3, column 1).
Therefore Jaillard et al. in view of Malsted et al. teach a method of identifying and counting k-mers to quantify microorganism susceptibility to antibiotics, that accounts for coverage; Fisher teaches a framework of microbial data analysis that includes using the product of coverage and sequencing errors to determine a metric of identical k-mers for the identification of antibiotic resistance; and Ranjan et al. provides sufficient motivation to one of ordinary skill in the art to further include sequencing platform analysis and a known coverage calculation technique, applicable to the claimed invention, with an expectation of predictable results and an improvement to the overall system.
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Jaillard et al. (IDS ref, filed 12/27/2022; cite no. 5; 2018), as applied to claims 1, 9-10, and 12 previously, in view of Zhao (2015/0347676).
Jaillard et al. teaches a method of identifying k-mers for a k-mer database used to predict antibiotic susceptibility.
Claim 5 is directed to determining that the genome sequence is present in the genome if the percentage of k-mers detected as being present in the genome is above a predetermined threshold, that is dependent on the length of the genome sequence.
Jaillard et al. does not teach the threshold being dependent on the length of the genome sequence.
Zhao describes a method of diagnostic testing using k-mer sequence data.
Zhao teaches grouping together mapped sequence reads according to various parameters and assigning them to particular portions and that can be used to identify a portion (e.g. the presence, absence) present in a sample [0157].
Zhao further teaches that in some embodiments, a portion, is defined based on partitioning of a particular size, coverage [0157], or length of genomic sequence and can be selected, filtered and/or removed from consideration using any suitable criteria know in the art [0158].
Therefore Zhao et al. teaches a method of thresholding/selecting k-mers to determine if they or present or absent in a sample, based on the length of the genomic sequence. As such, it would be obvious of one of ordinary skill in the art to apply this method to k-mer counting and identification system of Jaillard et al. with an expectation of reasonable success and predictable results.
Claims 6 is rejected under 35 U.S.C. 103 as being unpatentable over Jaillard et al. (IDS ref, filed 12/27/2022; cite no. 5; 2018), in view of Zhao (2015/0347676), as applied to claim 5 above, and in further view of Wang et al. (Genome Biology; Vol. 21:14; 2020).
Jaillard et al. in view of Zhao et al. teach identifying k-mers for a k-mer database, based on a genomic sequence length dependent threshold, used for predicting antibiotic susceptibility status.
Claim 6 is directed to determining that the genome sequence is present in the genome if the percentage of k-mers detected as being present in the genome is above a predetermined threshold value that decreases with the length of the genome sequence.
Jaillard et al. in view of Zhao et al. do not teach that the predetermined threshold value decreases with the length of the genome sequence.
Wang et al. describes performance differences of De Brujin graph-based and alignment-based hybrid error correction methods for error-prone long reads.
Wang et al. teaches overall, the solid k-mer detection rate is close to 1 when long read error rate γ is below certain threshold, such as 15% for k = 21 and L = 1 kb (page 4, column 1); and this threshold increases with L, such as from 15% to 24% for 1 to 10 kb given k = 21 (page 4, column 1).
Therefore Jaillard et al. teaches determining if a k-mer is present or absent based on an error rate-derived threshold. Wang et al. teaches an error rate being proportional to the length of the genome sequence; and therefore, it is prima facie obvious that the value would also decrease as the length of the sequence decreases. As such, it would be obvious to one of ordinary skill in the art to apply the method of Wang et al. to the thresholding method of Jaillard et al., in view of Zhao et al., in order to decrease the number of errors in k-mer identification process, using De Brujin graphs, with a reasonable expectation of success and predictable results.
Claims 7 is rejected under 35 U.S.C. 103 as being unpatentable over Jaillard et al. (IDS ref, filed 12/27/2022; cite no. 5; 2018), in view of Zhao (2015/0347676), and Wang et al. (Genome Biology; Vol. 21:14; 2020) as applied to claim 6 above, and in further view of Kovaka et al. (bioRxiv: 931923; 2020).
Claim 7 is directed to determining different threshold minimums for each of three different genome sequence length intervals.
Wang et al. teaches when L = 1kb, error rate = 15%; when L = 2kb, error rate = 17%; when L=5 kb, error rate = 21%; when L = 10kb, error rate = 24% (page 2, fig. 1e); and in addition to the value of L, the increase of k-mer size has an overall negative effect on solid k-mer detection (page 4, column 1).
Therefore Wang et al. teaches determining different thresholds for genome sequences of different fixed lengths; and teaching that k-mer size also has an impact on k-mer thresholding and detection.
Jaillard et al. in view of Zhao et al. and Wang et al. do not teach assigning thresholds according to three length intervals.
Kovaka et al. describes targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED.
Kovaka et al. teaches UNCALLED uses the BWA library for constructing, storing, and querying the FM index; after the index is constructed, reference-specific probability thresholds must be precomputed to maintain accuracy and speed for references of different sizes and repeat contents; and these thresholds are used to decide which k-mers can be used to extend a path based on the event/k-mer match probabilities (page 20, column 1), with the goal in choosing these thresholds is to limit unnecessary branching in the seed mapping process (page 20, column 1).
Kovaka et al. teaches the correspondence between FM range sizes and probability thresholds must vary depending on the reference, since paths of the same length are likely to have longer FM range sizes as references become larger and more repetitive (page 20, column 1); developing an EM algorithm to assign probability thresholds for intervals of FM index range sizes with the goal of minimizing the total amount of time required to map a read (page 21, column 1); and showing the FM index range lengths assigned to greater than three different probability thresholds for the E. coli reference (page 27, Supplemental Figure S2).
Therefore Kovaka et al. teaches a method of building a k-mer database with reduced computational requirements by only storing k-mers of a predetermined threshold, based on the length of the genomic sequence; and calculating varying thresholds for each of fourteen different sequence length intervals, which includes the claimed requirement of at least three intervals. As such, it would be obvious for one of ordinary skill in the art to apply this interval thresholding technique to the fixed interval threshold technique, dependent on the genomic sequence length, of Jaillard et al. in view of Zhao and Wang et al. with a reasonable expectation of success and improvement to the k-mer database system.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Jaillard et al. (IDS ref, filed 12/27/2022; cite no. 5; 2018), in view of Zhao (2015/0347676), Wang et al. (Genome Biology; Vol. 21:14; 2020), and Kovaka et al. (bioRxiv: 931923; 2020) as applied to claim 7 above, and in further view of Mahé et al. (BMC Bioinformatics; Vol. 19 (383); 2018).
Jaillard et al. in view of Zhao, Wang et al. and Kovaka et al., teach identifying k-mers for a de-brujin graph database according to genome sequence length interval-dependent thresholds.
Claim 8 is directed to using a k-value between 15 and 50, and determining if a k-mer is present within sequencing data based on the minimum presence required being either 90%, 80%, or 70%, depending on if the genome sequence length is less than 61 genomic units, between 61 and 100 genomic units, or over 100 genomic units, respectively.
Jaillard et al. further teaches finding DBGWAS results to be robust to small variations of k between 21 and 41 (page 13, column 1).
Therefore, the prior art teaches using a k-value between 15 and 50.
The references do not teach determining the minimum k-mer presence required to be 70%, 80%, or 90%, based on the specified lengths of the genomic sequence (claim 8).
Mahé et al. describes a method of bacterial resistance prediction using k-mer analysis.
Mahé et al. teaches that for each set of equivalent k-mers which corresponded to a single unitig whose length ranged from 31 (the size of the individual k-mers) to 61 nucleotides (page 6, column 1), the unitigs were highly conserved, with a minimum percent identify equal to 96.7% (page 6, column 1).
Therefore, Mahé et al. teaches that if the length of a genomic sequence is less than or equal to 61 nucleotides, the threshold of 31-mers filtered is above a minimum 90% threshold.
As such, Zhao, Wang et al. and Kovaka et al., support the proportional relationship between the length of a genomic sequence and the threshold value required to filter non-significant k-mers from a microbial sequence database. Jaillard et al. further provides sufficient motivation for one of ordinary skill in the art to utilize k-mers within the claimed range (10-50). Therefore, determining the minimum percent identity for a 31-mer to be greater than 90% is an obvious implement to one of ordinary skill in the art, based on the referenced prior art teachings applied to a known framework with a reasonable expectation of success.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
The rejection to claims 1 and 11- 13 over claim 1 of U.S. Patent Application no. 18/853,496 is withdrawn.
The rejection to claims 1 and 11- 13 over claim 1 of U.S. Patent Application no. 18/853,495 is newly applied and recited herein.
Claims 1 and 11-13 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent Application no. 18/853,495 (reference) in view of Jaillard et al (IDS reference, filed 12/27/2022; Non-Patent Literature; cite no. 5; 2018).
The reference claim is directed to a method of sequencing a microbial organism genome, calculating sequencing depth of coverage, and detecting target genomic sequences as present, based on the calculated amount of identical copies being greater than a predetermined fraction of the sequencing depth.
Therefore, the instant claims and reference claims have the same effect and function. They differ slightly in scope as the reference claim also generates multiple sets of genomic subsequences (k-mers), based the location of the neighboring position of the target sequences. Generating subsequences of this nature is a modification of obvious to one of ordinary skill of the art to apply to the instant claims in view of Jaillard et al. which teaches that exploiting the genetic environment of the significant k-mers through their neighborhood sequences provides a valuable interpretation framework and reduces the amount data and computation time needed to perform other k-mer based approaches (page 12, column 1).
Although the claims at issue are not identical, they are not patentably distinct from each other. As such, claims 1 and 11-13 are provisionally rejected. The rejection is provisional as the co-pending claims have not yet been patented.
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Response to Arguments
Applicant’s arguments, see page 20, para. 1, with respect to the rejections of claims 1 and 11-13 under double patenting have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Jaillard et al (IDS ref, filed 12/27/2022; NPL; cite no. 5; 2018). Examiner apologizes for the typographical error made to the co-pending, reference application number.
Conclusion
No claims are currently allowed.
Correspondence
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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