DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-12 and 21-28 are currently pending and under examination herein.
Claims 1-12 and 21-28 are rejected.
Priority
The instant application claims priority as a 371 to PCT/US2021/031390 filed 07 May 2021 and US Provisional Application 63022296 filed 08 May 2020. In this action, claims 1-12 and 21-28 are examined as though they had an effective filing date of 08 May 2020. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 31 October 2022, 09 June 2025, 25 July 2025, 02 September 2025, 15 January 2026, 22 January 2026, 28 May 2026, and 23 June 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed 31 October 2022 are objected to because Figure 10 contains a nucleic acid (NA) sequence (≥ 10 NA) without providing a sequence listing or a seq ID (see Sequence Disclosure section below).
Nucleotide and/or Amino Acid Sequence Disclosures
REQUIREMENTS FOR PATENT APPLICATIONS CONTAINING NUCLEOTIDE AND/OR AMINO ACID SEQUENCE DISCLOSURES
Items 1) and 2) provide general guidance related to requirements for sequence disclosures.
37 CFR 1.821(c) requires that patent applications which contain disclosures of nucleotide and/or amino acid sequences that fall within the definitions of 37 CFR 1.821(a) must contain a "Sequence Listing," as a separate part of the disclosure, which presents the nucleotide and/or amino acid sequences and associated information using the symbols and format in accordance with the requirements of 37 CFR 1.821 - 1.825. This "Sequence Listing" part of the disclosure may be submitted:
In accordance with 37 CFR 1.821(c)(1) via the USPTO patent electronic filing system (see Section I.1 of the Legal Framework for Patent Electronic System (https://www.uspto.gov/PatentLegalFramework), hereinafter "Legal Framework") as an ASCII text file, together with an incorporation-by-reference of the material in the ASCII text file in a separate paragraph of the specification as required by 37 CFR 1.823(b)(1) identifying:
the name of the ASCII text file;
ii) the date of creation; and
iii) the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(1) on read-only optical disc(s) as permitted by 37 CFR 1.52(e)(1)(ii), labeled according to 37 CFR 1.52(e)(5), with an incorporation-by-reference of the material in the ASCII text file according to 37 CFR 1.52(e)(8) and 37 CFR 1.823(b)(1) in a separate paragraph of the specification identifying:
the name of the ASCII text file;
the date of creation; and
the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(2) via the USPTO patent electronic filing system as a PDF file (not recommended); or
In accordance with 37 CFR 1.821(c)(3) on physical sheets of paper (not recommended).
When a “Sequence Listing” has been submitted as a PDF file as in 1(c) above (37 CFR 1.821(c)(2)) or on physical sheets of paper as in 1(d) above (37 CFR 1.821(c)(3)), 37 CFR 1.821(e)(1) requires a computer readable form (CRF) of the “Sequence Listing” in accordance with the requirements of 37 CFR 1.824.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed via the USPTO patent electronic filing system as a PDF, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the PDF copy and the CRF copy (the ASCII text file copy) are identical.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed on paper or read-only optical disc, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the paper or read-only optical disc copy and the CRF are identical.
Specific deficiencies and the required response to this Office Action are as follows:
Specific deficiency - This application fails to comply with the requirements of 37 CFR 1.821 - 1.825 because it does not contain a "Sequence Listing" as a separate part of the disclosure or a CRF of the “Sequence Listing.”.
Required response - Applicant must provide:
A "Sequence Listing" part of the disclosure; together with
An amendment specifically directing its entry into the application in accordance with 37 CFR 1.825(a)(2);
A statement that the "Sequence Listing" includes no new matter as required by 37 CFR 1.821(a)(4); and
A statement that indicates support for the amendment in the application, as filed, as required by 37 CFR 1.825(a)(3).
If the "Sequence Listing" part of the disclosure is submitted according to item 1) a) or b) above, Applicant must also provide:
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required incorporation-by-reference paragraph, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
If the "Sequence Listing" part of the disclosure is submitted according to item 1) c) or d) above, applicant must also provide:
A CRF in accordance with 37 CFR 1.821(e)(1) or 1.821(e)(2) as required by 1.825(a)(5); and
A statement according to item 2) a) or b) above.
Specific deficiency – Nucleotide and/or amino acid sequences appearing in the drawings are not identified by sequence identifiers in accordance with 37 CFR 1.821(d). Sequence identifiers for nucleotide and/or amino acid sequences must appear either in the drawings or in the Brief Description of the Drawings.
Required response – Applicant must provide:
Replacement and annotated drawings in accordance with 37 CFR 1.121(d) inserting the required sequence identifiers;
AND/OR
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required sequence identifiers into the Brief Description of the Drawings, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
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.
Claim 22 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Claim 22 recites “the expected range for the ratio of the reference allele to the alternative allele is a 1:1 ratio or a 1:0 ratio”. If the ratio is a specific ratio, 1:1 or 1:0 ratio, it cannot be a range. The metes and bounds of the limitation are therefore unclear, rendering the claim indefinite. For the purposes of examination, this limitation is interpreted as saying - the expected ratio of the reference allele to the alternative allele is a 1:1 ratio or a 1:0 ratio.
Claim 22 is also rejected under 112(b) due to “the ratio of the reference allele to the alternative allele” lacking antecedent basis. Claim 22 depends on claim 1, which does not recite a ratio of the reference allele to the alternative allele. Claim 4 recites “a ratio of the reference allele to the alternative allele in the sequence data being within an expected range”. Therefore for the purposes of examination, Claim 22 is considered to depend on claim 4 instead of claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural law 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 or natural law (Step 2A, Prong 1). Claims 1-6 and 21-25 are directed to a method and Claims 7-12 and 26-28 are directed to a system. In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites the limitation - identifying k-mers in the sequence data that have an exact match in a hash table that is initialized with a set of k-mers comprising reference allele k-mers and alternative allele k-mers of the reference allele; determining a distribution of the reference allele and the alternative allele in the sequence data based on a count of the exact matches; and generating a quality metric for the biological sample based on the distribution and during the sequencing run of the biological sample. Based on the broadest reasonable interpretation, identifying, determining, and generating encompass equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 2 recites the limitation - flagging the biological sample as contaminated based on the quality metric. Based on the broadest reasonable interpretation, flagging a sample could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 3 recites the limitation - wherein the alternative allele is present in 5% or less of sequence reads of the sequence data in the contaminated sample. This limitation specifies the distribution of the allele in the judicial exception of claim 1. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 4 recites the limitation - indicating that the biological sample passes the quality metric based on a ratio of the reference allele to the alternative allele in the sequence data being within an expected range. Based on the broadest reasonable interpretation, the indicating based on a ratio encompass equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 5 recites the limitation - wherein the quality metric is generated on the sequencing device. This limitation specifies the location where the judicial exception of claim 1 in being done. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 6 recites the limitation - wherein the alternative allele comprises a previously characterized single nucleotide polymorphism. This limitation specifies the nature of the alternative allele used in the judicial exception of claim 1. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 7 recites the limitation - identify k-mers in the sequence data that have an exact match in a hash table that is initialized with a set of k-mers comprising reference allele k-mers and alternative allele k-mers of the reference allele; determine a distribution of the reference allele and the alternative allele in the sequence data based on a count of the exact matches; and generate a quality metric on the sequencing device for the biological sample based on the distribution during the sequencing run. Based on the broadest reasonable interpretation, identifying, determining, and generating encompass equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 9 recites the limitation – communicate based on the quality metric of the biological sample being associated with passing. Based on the broadest reasonable interpretation, associating the metric with passing could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 10 recites the limitation - wherein the quality metric of the biological sample is associated with a ratio of the reference allele and the alternative allele being within an expected range. Based on the broadest reasonable interpretation, determining a metric based on a ration encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 11 recites the limitation – halt communication based on the quality metric of the biological sample being associated with failing. Based on the broadest reasonable interpretation, associating the metric with failing could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 12 recites the limitation - wherein the quality metric of the biological sample is associated with failing based the alternative allele being present in 5% or less of sequence reads of the sequence data. This limitation specifies the distribution used in the judicial exception of claim 11. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 21 recites the limitation - wherein the k-mers have a fixed length greater than 24 nucleotides. This limitation specifies the k-mers used in the judicial exception of claim 1. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 22 recites the limitation - wherein the expected range for the ratio of the reference allele to the alternative allele is a 1:1 ratio or a 1:0 ratio. This limitation specifies the ratio of the alternative allele used in the judicial exception of claim 4 (see 112(b) rejection above). The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 23 recites the limitation - wherein the distribution comprises a 50% distribution or a 100% distribution of the alternative allele in the sequence reads. This limitation specifies the distribution determined in the judicial exception of claim 1. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 24 recites the limitation - identifying the biological sample as potentially contaminated. Based on the broadest reasonable interpretation, identify the sample as potentially contaminated could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 26 recites the limitation - wherein the memory stores multiple different sets of k-mers or different initialized hash tables that are selected based on a user input. Based on the broadest reasonable interpretation, staring multiple sets of information or tables and selecting could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 27 recites the limitation - identification of a flagged or failing sample based on anomalous allele distribution. Based on the broadest reasonable interpretation, identifying a sample based on a distribution encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 28 recites the limitation - identifying a potentially contaminated sample responsive to the quality metric. Based on the broadest reasonable interpretation, identifying a sample based on a metric encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
These limitations recite concepts of identifying data, comparing information though tables and equations, and generating values that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1-12 and 21-28 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). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (i.e. no improvement to computer/computing; sequencer/sequencing) (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 1 recites generating sequence data from a biological sample using a sequencing device conducting a sequencing run.
Claim 7 recites a substrate having loaded thereon a sequencing library prepared from a sample; a computer programmed to cause the sequencing device to conduct a sequencing run to generate sequence data from sequencing library.
Claim 8 recites a display that displays the quality metric.
Claim 9 recites communication circuitry that communicates the generated sequence data to a cloud computing environment
Claim 11 recites communication circuitry that halts communication of the generated sequence data to a cloud computing environment.
Claim 24 recites wherein the flagging comprises providing a displayed notification on a graphical user interface in real time.
Claim 25 recites wherein the sequence data is streamed to a k-mer aligner in real-time during the sequencing run.
Claim 27 recites the sequencing device communicate passing samples to the cloud computing environment for further analysis.
Claim 28 recites the computer is programmed to provide an error message on a graphical user interface in real time
There are no limitations that indicate that the claimed identifying data, comparing information though tables and equations, and generating values require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-12 and 21-28 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 conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities.
As discussed above, there are no additional limitations to indicate that the claimed identifying data, comparing information though tables and equations, and generating values require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea or natural law eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. MPEP 2106.05(d) indicates Analyzing DNA to provide sequence information or detect allelic variants is a conventional laboratory technique. Additionally, Magi et al. (2018, Briefings in Bioinformatics, Vol. 19, No. 6: 1256–1272) teach sequencing a sample and computer/cloud based sequencing analysis related to quality metrics and pathogen/contamination detection are well-understood, routine, and conventional (Page 1258, Column 2, Paragraph 3: ONT developed a proprietary base-caller, named Metrichor, that works in the cloud; Page 1259, Column 2, Paragraph 4: Two recently published tools allow to evaluate the sequencing process in real time by extracting reads, while the samples are being sequenced on the MinION device, and show streaming plots through a graphical user interface with quality statistics of the run; Page 1266, Column 2, Paragraph 5: Different studies successfully investigated the potential of the MinION device for characterizing bacterial pathogens).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, Claims 1-12 and 21-28 are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4-6, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Hasan et al. (US 20140288844 A1, from IDS 02 September 2025), in view of Illumina (2014, User Guide: 1-33). Italicized text from reference art.
Applicable claims include:
Claim 1. A real-time quality control method, comprising: (Claim 1.i) generating sequence data from a biological sample using a sequencing device conducting a sequencing run; (Claim 1.ii) identifying k-mers in the sequence data that have an exact match in a hash table that is initialized with a set of k-mers comprising reference allele k-mers and alternative allele k-mers of the reference allele; (Claim 1.iii) determining a distribution of the reference allele and the alternative allele in the sequence data based on a count of the exact matches; and (Claim 1.iv) generating a quality metric for the biological sample based on the distribution and during the sequencing run of the biological sample.
Claim 2. The method of claim 1, comprising flagging the biological sample as contaminated based on the quality metric.
Claim 4. The method of claim 1, comprising indicating that the biological sample passes the quality metric based on a ratio of the reference allele to the alternative allele in the sequence data being within an expected range.
Claim 5. The method of claim 1, wherein the quality metric is generated on the sequencing device.
Claim 6. The method of claim 1, wherein the alternative allele comprises a previously characterized single nucleotide polymorphism.
Claim 25. The method of claim 1, wherein the sequence data is streamed to a k-mer aligner in real-time during the sequencing run.
Regarding Claim 1, Hasan et al. teach (Claim 1.i) generating sequence data from a biological sample using a sequencing device (Paragraph 0042: the present invention provides a method of characterizing organisms based on sequence information derived from a sample containing genetic material from the organisms; Paragraph 0092: the sequencing unit may perform sequencing). Hasan et al. also teach (Claim 1.ii) identifying k-mers that have an exact match in a hash table that is initialized with a set of k-mers comprising reference allele k-mers and alternative allele k-mers of the reference allele (Paragraph 0042: compare the unassembled nucleotide fragment reads with trait-specific reference sequence information contained in a trait-specific database catalog and produce probabilistic trait results; Paragraph 0043: compare the unassembled nucleotide fragment reads with reference sequence information contained in a reference database containing genomic identities of organisms and produce probabilistic identity results; Paragraph 0047: the method may include creating a sample sequence library with words or n-mers derived from the unassembled nucleotide fragment reads; Paragraph 0048: The probabilistic methods may compare the unassembled nucleotide fragment reads with trait-specific reference sequence information contained in the trait-specific database catalog by comparing words or n-mers from the sample sequence library with words or n-mers from the trait-specific sequence library. The trait-specific sequence library may be a library of dictionaries of words from the trait-specific reference sequence information, each dictionary containing words for a particular trait. The sample sequence library may be a sample sequence hash table, and the trait-specific sequence library is a trait-specific hash table; Paragraph 0052: performing probabilistic matching that compares the unassembled nucleotide fragment reads with second trait-specific reference sequence information contained in a second trait-specific database catalog; Paragraph 0066: the trait-specific reference sequence information contained in the trait-specific database catalog may consist of sequence information associated with a particular phenotypical characteristic; Paragraph 0102: the probabilistic comparisons performed in the probabilistic methods may include, but are not limited to, perfect matching; Paragraph 0127: various downstream analyses, such as single nucleotide polymorphisms identification). The art teaches comparing a generated sequence to a reference and multiple trait specific sequences. The trait specific methods or second trait are interpreted as equivalent to the alternate allele. An n-mer is interpreted as synonymous with a k-mer. Perfect matching is interpreted as the match being exact. The implementation of a second trait-specific reference sequence, detection of a particular phenotypical characteristic, and SNP identification can be interpreted as the trait specific sequences being equivalent to an alternative allele of the reference allele. Information on the various types of sequences are contained in databases which is interrupted as equivalent to them being initialized for the hash tables as part of the method. Hasan et al. also teach (Claim 1.iii) determining a distribution of the reference allele and the alternative allele in the sequence data based on a count of the exact matches (Paragraph 0125: The matches are then summarized by counting, for each reference sequence, the number of words from the sequence information derived from the sample or isolate that match a word from the reference sequence, which may be, for example, associated with a particular trait or genome identity; Paragraph 0132: the match scoring may include counting, for each organism having reference sequence information in the reference database, the number of words from the sequence information derived from the sample or isolate that match a word from the reference sequence information for the organism). Words are equivalent to k-mers (see regarding claim 1.ii). The distribution of the alleles generated from the counting is interpreted as equivalent to the scores based on count data or the probabilistic results based on the hash table exact matching (see regarding claim 1.ii). The matches include exact matches. Hasan et al. also teach (Claim 1.iv) generating a quality metric for the biological sample based on the distribution (Paragraph 0165: Some particular embodiments of the present invention may diagnose pathogens causing infectious disease or microbial contamination by normalizing results to background populations). The metrics, including the probabilistic results or numerical determination that an organism or trait of an organism identified to be present, indicate infection or contamination of a sample. Numerical estimations of Infection and contamination are related to quality so are themselves quality metrics. No limiting definition for quality or quality metric was found within the specification. Additionally, Hasan et al. teach development of a quality check/score related to the sequencing data (Paragraph 0127: the quality check may be performed using a quality score assigned to the assigned to the received sequence information software integrated into the sequencing platform(s)). No explicit definition for real-time was found within the specification, so the plain meaning of the term is used, which is the actual time during which something takes place.
Regarding Claim 2, Hasan et al. teach flagging the biological sample as contaminated based on the quality metric (Paragraph 0165: Some particular embodiments of the present invention may diagnose pathogens causing infectious disease or microbial contamination by normalizing results to background populations; also see regarding Claim 1.iv). Flagging is interpreted as equivalent to identifying the sample as contaminated. No explicit definition of flagging was found within the specification.
Regarding Claim 4, Hasan et al. teach indicating that the biological sample passes the quality metric based on a ratio of the reference allele to the alternative allele in the sequence data being within an expected range (Paragraph 0133: compare a probability that organisms having reference sequence information in the reference database are in the sample to a threshold. In some embodiments, if the probability is above the threshold, the first comparator engine may accept the organism as contained in the sample or isolate). Being above a minimum threshold is interpreted as being within an expected range. Accepting the organism is interpreted as equivalent to passing the sample. A probability that the organism has a particular sequence is interpreted as equivalent to being based on a ratio of the alternative allele, which is involved in the identification methods, which are related to established the quality metric (see regarding claim 1).
Regarding Claim 5, Hasan et al. teach the quality metric is generated on the sequencing device (Paragraph 0127: the quality check may be performed using a quality score assigned to the assigned to the received sequence information software integrated into the sequencing platform(s) (e.g., sequencing unit)). Additionally, Figure 2 shows, the instrument (100) is composed of a processing unit (102) and sequencing unit (212). The instrument can be interpreted as the sequencing device.
Regarding Claim 6, Hasan et al. teach the alternative allele comprises a previously characterized single nucleotide polymorphism (Paragraph 0127: various downstream analyses, such as single nucleotide polymorphisms identification).
Regarding Claim 25, Hasan et al. teach aligning k-mers with a computer (k-mer aligner) which is in communication (i.e. streamed) with the sequence unit (Paragraph 0095: Instrument may operate under the control of a sequencer that sequences the fragments extracted from a sample or isolate, but no connected processing or even direct communication between instrument and a sequencer is required; see regarding claims 1 and 24). The k-mer aligner is interpreted as equivalent to the matching k-mers using the hash table in claim 1.
Hasan et al. teach does not teach generating a quality metric during the sequencing run of the biological sample (Claim 1.iv and 25)
Regarding Claim 1, Illumina teaches generating a quality metric during the sequencing run of the biological sample (Page 4, Paragraph 1: Sequencing Analysis Viewer Software (SAV) v1.8 is an application that allows you, in real time, to view important quality metrics generated by the Real-Time Analysis (RTA) software on the Illumina sequencing systems; Page 4, Paragraph 2: The SAV Software can be installed on a personal computer to view quality metrics. If the SAV Software is installed on your sequencing instrument, viewing metrics does not interfere with the sequencing run; Page 8, Paragraph 1: Due to variations in RTA processing speeds, the following metrics may not be populated for a number of cycles after the cycle listed in the chart).
Regarding Claim 25, Illumina teaches the sequence data is streamed in real-time during the sequencing run (Page 4, Paragraph 1: Sequencing Analysis Viewer Software (SAV) v1.8 is an application that allows you, in real time, to view important quality metrics generated by the Real-Time Analysis (RTA) software on the Illumina sequencing systems; Page 4, Paragraph 2: The SAV Software can be installed on a personal computer to view quality metrics. If the SAV Software is installed on your sequencing instrument, viewing metrics does not interfere with the sequencing run; Page 8, Paragraph 1: Due to variations in RTA processing speeds, the following metrics may not be populated for a number of cycles after the cycle listed in the chart). The sequencing device is streaming data to a laptop during the run.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Hasan et al. and Illumina. Hasan et al. teach the computing device can be a laptop for generating and evaluating the quality metrics associate with the sequence data (Paragraph 0096: instrument may be a computer (e.g., a laptop computer)). Hasan et al. also teach a multitude of functions related quality score/metrics of the sequencing data but do not describe the nature of the graphical user interface (Paragraph 0141: the first comparator engine may be integrated into a CLC genomics workbench, may manage user accounts with different level of rights, may uploads data files, and/or may allow users to create and update reference databases. the first comparator engine may allow users to submit multiple jobs, may have proprietary algorithms to process data and create matching scores, may show list of processed experiments, may display ranking scores of genomes identified in the uploaded data file, and/or allow user to sort and filter ranking scores). Illumina teach how a user interface software can conveniently run on a generic computer for analyzing quality metrics related to sequencing from numerous sequencing devices (Page 4, Paragraph 2: The SAV Software can be installed on a personal computer to view quality metrics from a remote location; Page 5, Paragraph 2: Sequencing Analysis Viewer Software does not need an advanced personal computer). Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the references indicated above. Furthermore, one of ordinary skill in the art would predict that the methods could be combined with a reasonable expectation of success because both describe computer implemented systems related to a user interfacing with metrics related to the quality of sequence data. Accordingly, Claims 1, 2, 4-6, and 25 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Claims 1, 2, 4-11, and 22-28 are rejected under 35 U.S.C. 103 as being unpatentable over Hasan et al., as applied to claims 1, 2, 4-6, and 25 above, in view of Illumina, as applied to claims 1, 2, 4-6, and 25 above, and in further view of Umbarger and Porreca (US 20140127688 A1, IDS filed 25 July 2025). Italicized text from reference art.
Applicable claims include:
Claims 1, 2, 4-6, and 25 are presented above
Claim 7. A sequencing device, comprising: (Claim 7.i) a substrate having loaded thereon a sequencing library prepared from a sample; (Claim 7.ii) a computer programmed to: cause the sequencing device to (Claim 7.iii) conduct a sequencing run to generate sequence data from sequencing library; (Claim 7.iv) identify k-mers in the sequence data that have an exact match in a hash table that is initialized with a set of k-mers comprising reference allele k-mers and alternative allele k-mers of the reference allele; (Claim 7.v) determine a distribution of the reference allele and the alternative allele in the sequence data based on a count of the exact matches; and (Claim 7.vi) generate a quality metric on the sequencing device for the biological sample based on the distribution during the sequencing run.
Claim 8. The sequencing device of claim 7, comprising a display that displays the quality metric.
Claim 9. The sequencing device of claim 7, comprising communication circuitry that communicates the generated sequence data to a cloud computing environment based on the quality metric of the biological sample being associated with passing.
Claim 10. The sequencing device of claim 9, wherein the quality metric of the biological sample is associated with a ratio of the reference allele and the alternative allele being within an expected range.
Claim 11. The sequencing device of claim 7, comprising communication circuitry that halts communication of the generated sequence data to a cloud computing environment based on the quality metric of the biological sample being associated with failing.
Claim 22. The method of claim 1, wherein the expected range for the ratio of the reference allele to the alternative allele is a 1:1 ratio or a 1:0 ratio.
Claim 23. The method of claim 1, wherein the distribution comprises a 50% distribution or a 100% distribution of the alternative allele in the sequence reads.
Claim 24. The method of claim 2, wherein the flagging comprises providing a displayed notification on a graphical user interface in real time identifying the biological sample as potentially contaminated.
Claim 26. The sequencing device of claim 7, wherein the memory stores multiple different sets of k-mers or different initialized hash tables that are selected based on a user input.
Claim 27. The sequencing device of claim 11, wherein identification of a flagged or failing sample based on anomalous allele distribution causes the sequencing device to communicate only passing samples to the cloud computing environment for further analysis.
Claim 28. The sequencing device of claim 7, wherein the computer is programmed to provide an error message on a graphical user interface in real time identifying a potentially contaminated sample responsive to the quality metric.
Regarding Claims 1, 2, 4-6, and 25, Hassan et al. teach the limitations as indicated above.
Regarding Claim 7, Hasan et al. teach (Claim 7.ii) a computer programmed to cause the sequencing device to function (Paragraph 0085: each of the units may comprise its own processor and memory, or each of the units may share a processor and memory with one or more of the other units; Paragraph 0092: the sequencing unit may be interchangeable and removeably coupled to the instrument). Hasan et al. teach (Claim 7.iii) conduct a sequencing run to generate sequence data from sequencing library (see Regarding Claim 1.i – the recited limitation is interpreted to be equivalent). Hasan et al. also teach (Claim 7.iv) identify k-mers in the sequence data that have an exact match in a hash table that is initialized with a set of k-mers comprising reference allele k-mers and alternative allele k-mers of the reference allele (see Regarding Claim 1.ii – the recited limitation is interpreted to be equivalent). Hasan et al. also teach (Claim 7.v) determine a distribution of the reference allele and the alternative allele in the sequence data based on a count of the exact matches (see Regarding Claim 1.iii – the recited limitation is interpreted to be equivalent). Hasan et al. also teach (Claim 7.vi) generate a quality metric on the sequencing device for the biological sample based on the distribution (see Regarding Claim 1.iv – the recited limitation is interpreted to be equivalent).
Regarding Claim 8, Hasan et al. teach a display that displays the quality metric (Paragraph 0096: instrument may be a computer (e.g., a laptop computer). A laptop computer inherently contains a display.
Regarding Claim 9, Hasan et al. teach communication circuitry that communicates the generated sequence data to a cloud computing environment based on the quality metric of the biological sample being associated with passing (Paragraph 0127: However, if the quality of the received sequence information is determined to be bad, the received sequence information may be corrected before proceeding; Paragraph 0133: if the probability is below the threshold, the first comparator engine may reject the organism. if the probability is above the threshold, the first comparator engine may accept the organism; Paragraph 0141: the first comparator engine may be a web-based application tool). The system utilizing web based processing is interpreted as equivalent to using a cloud computing environment. The probabilistic determination if an organism is present in a sample (i.e. the quality metric) is compared against a threshold which determines if the data passes or is rejected.
Regarding Claim 10, Hasan et al. teach the quality metric of the biological sample is associated with a ratio of the reference allele and the alternative allele being within an expected range (see regarding Claim 4 teachings that the quality metric is based on a ratio of alleles; see regarding Claim 9 for Hasan et al. teachings that that quality is within a range). Comparing the metric to a threshold is interpreted as equivalent to the metric being within an expected range.
Regarding Claim 11, Hasan et al. teach communication circuitry that halts communication of the generated sequence data to a cloud computing environment based on the quality metric of the biological sample being associated with failing (Paragraph 0127: However, if the quality of the received sequence information is determined to be bad, the received sequence information may be corrected before proceeding; Paragraph 0133: if the probability is below the threshold, the first comparator engine may reject the organism. if the probability is above the threshold, the first comparator engine may accept the organism; Paragraph 0141: the first comparator engine may be a web-based application tool). The system utilizing web based processing is interpreted as equivalent to using a cloud computing environment. The probabilistic determination if an organism is present in a sample (i.e. the quality metric) is compared against a threshold which determines if the data passes or is rejected. Rejecting is interpreted as resulting in halting the transmission.
Regarding Claim 24, Hasan et al. teach the flagging comprises providing a displayed notification on a graphical user interface in real time identifying the biological sample as potentially contaminated (Paragraph 0096: instrument 100 may be a computer (e.g., a laptop computer); Page 0165: the present invention may diagnose pathogens causing infectious disease or microbial contamination by normalizing results to background populations). Diagnose is interpreted as equivalent to flag. A laptop computer inherently contains a display and graphical user interface.
Regarding Claim 26, Hasan et al. teach the memory stores multiple different sets of k-mers or different initialized hash tables that are selected based on a user input (Paragraph 0141: the first comparator engine may build word libraries from reference sequence information in reference genome databases and/or trait-specific database catalogs. allow users to create and update reference databases. The first comparator engine may allow users to sort and filter ranking scores; Paragraph 0157: matching begins with the seed and then is extended in both directions until reaching a user-specified threshold value or end of the sequence information; Paragraph 0160: the second comparator engine may compress and store data). The database catalogs in combination with the memory are interpreted as storing the k-mers and hash tables used in the probabilistic hash analysis.
Regarding Claim 27, Hasan et al. teach identification of a flagged or failing sample based on anomalous allele distribution causes the sequencing device to communicate only passing samples to the cloud computing environment for further analysis (Paragraph 0127: The quality check and subsequent correction in step removes these sequence artifacts before downstream analyses to reduce erroneous conclusions; Paragraph 0133: if the probability is below the threshold, the first comparator engine may reject the organism. if the probability is above the threshold, the first comparator engine may accept the organism; Paragraph 0141: the first comparator engine may be a web-based application tool). The system utilizing web based processing is interpreted as equivalent to using a cloud computing environment. The probabilistic determination if an organism is present in a sample (i.e. the quality metric) is compared against a threshold which determines if the data passes or is rejected. Rejecting is interpreted as resulting in halting the transmission. Subsequent correction and downstream analyses are interpreted as further analyses.
Regarding Claim 28, Hasan et al. teach identifying a potentially contaminated sample responsive to the quality metric (see regarding claim 1).
Hassan et al. does not teach a substrate having loaded thereon a sequencing library prepared from a sample (Claim 7.i). Hassan et al. also does not teach generating a quality metric for the biological sample during the sequencing run of the biological sample (7.iv). Hassan et al. does not teach displaying the quality metric (Claim 8). Hassan et al. also does not teach the expected range for the ratio of the reference allele to the alternative allele is a 1:1 ratio or a 1:0 ratio (Claim 22). Hassan et al. also does not teach the distribution comprises a 50% distribution or a 100% distribution of the alternative allele in the sequence reads (Claim 23). Hassan et al. also does not teach the computer is programmed to provide an error message on a graphical user interface (Claim 28).
Regarding Claims 1, 2, 4-6, and 25, Illumina teach the limitations as indicated above.
Regarding Claim 7, Illumina teaches generating a quality metric for the biological sample during the sequencing run of the biological sample (Page 4, Paragraph 1: Sequencing Analysis Viewer Software (SAV) v1.8 is an application that allows you, in real time, to view important quality metrics generated by the Real-Time Analysis (RTA) software on the Illumina sequencing systems; Page 4, Paragraph 2: The SAV Software can be installed on a personal computer to view quality metrics. If the SAV Software is installed on your sequencing instrument, viewing metrics does not interfere with the sequencing run; Page 8, Paragraph 1: Due to variations in RTA processing speeds, the following metrics may not be populated for a number of cycles after the cycle listed in the chart).
Regarding Claim 8, Illumina teaches a display that displays the quality metric (Page 4, Paragraph 1: Sequencing Analysis Viewer Software (SAV) v1.8 is an application that allows you, in real time, to view important quality metrics generated by the Real-Time Analysis (RTA) software on the Illumina sequencing systems; Page 4, Paragraph 2: The SAV Software can be installed on a personal computer to view quality metrics).
Regarding Claim 24, Illumina teaches providing a displayed notification on a graphical user interface in real time identifying the biological sample (Page 4, Paragraph 1: Sequencing Analysis Viewer Software (SAV) v1.8 is an application that allows you, in real time, to view important quality metrics generated by the Real-Time Analysis (RTA) software on the Illumina sequencing systems; Page 4, Paragraph 2: The SAV Software can be installed on a personal computer to view quality metrics).
Regarding Claim 28, Illumina teaches the computer is programmed to provide an error message on a graphical user interface in real time identifying a potentially contaminated sample responsive to the quality metric (Page 4, Paragraph 1: Sequencing Analysis Viewer Software (SAV) v1.8 is an application that allows you, in real time, to view important quality metrics generated by the Real-Time Analysis (RTA) software on the Illumina sequencing systems; Page 4, Paragraph 2: The SAV Software can be installed on a personal computer to view quality metrics).
Illumina does not teach a substrate having loaded thereon a sequencing library prepared from a sample (Claim 7.i). Illumina does not teach the expected range for the ratio of the reference allele to the alternative allele is a 1:1 ratio or a 1:0 ratio (Claim 22). Illumina does not teach the distribution comprises a 50% distribution or a 100% distribution of the alternative allele in the sequence reads (Claim 23).
Regarding Claim 2, Umbarger and Porreca teach flagging the biological sample as contaminated based on the quality metric (Paragraph 0035: Alternatively, a z-score threshold could be set so that any individual z-score above a preset number would result in the sample being flagged for possible contamination).
Regarding Claim 6, Umbarger and Porreca teach the alternative allele comprises a previously characterized single nucleotide polymorphism (0068: a variant (e.g., an SNP or indel) can be identified).
Regarding Claim 7, Umbarger and Porreca teach (Claim 7.i) a substrate having loaded thereon a sequencing library prepared from a sample (Paragraph 0058: a DNA sequencing technique that can be used is SOLiD technology. In SOLiD sequencing, genomic DNA is sheared into fragments, and adaptors are attached to the 5′ and 3′ ends of the fragments to generate a fragment library). Umbarger and Porreca teach (Claim 7.ii) a computer programmed to cause the sequencing device to function (Paragraph 0018: the invention is a system for determining contamination in a genetic sample. The system includes a processor and a computer-readable storage medium. A system of the invention may stand alone, or it may be integrated into a genetic analysis platform, e.g., a next-generation sequencing platform).
Regarding Claim 22, Umbarger and Porreca teach the expected range for the ratio of the reference allele to the alternative allele is a 1:1 ratio or a 1:0 ratio (Paragraph 0029: when measuring genotypes at a locus of a diploid organism, the ratio between minor alleles, or between a minor and a major allele, should theoretically be 2:0, 1:1, or 0:2, corresponding to homozygous (AA), heterozygous (AB), or homozygous (BB). Normalizing those ratios, as is done with genotype calling, a particular allele should have a fraction of 0, ½, or 1). A 1:1 ratio is equivalent to ½ as a fraction and a 1:0 ratio is equivalent to 1 as fraction. Minor allele is interpreted as equivalent to alternative allele.
Regarding Claim 23,Umbarger and Porreca teach the distribution comprises a 50% distribution or a 100% distribution of the alternative allele in the sequence reads (Paragraph 0009: the fraction of alleles in a sample would be expected to be 50% for a heterozygote or 100%/0% for a homozygote).
Regarding Claim 24, Umbarger and Porreca teach the flagging comprises providing a displayed notification on a graphical user interface in real time identifying the biological sample as potentially contaminated (Paragraph 0035: Alternatively, a z-score threshold could be set so that any individual z-score above a preset number would result in the sample being flagged for possible contamination).
Regarding Claim 28, Umbarger and Porreca teach identifying a potentially contaminated sample responsive to the quality metric (see regarding claim 24).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Umbarger and Porreca with Hasan et al. and Illumina. Umbarger and Porreca teach how their methods should be used with Illumina sequencing platforms (Paragraph 0041: the sequence data is from a parallel sequencing platform, e.g., Illumina sequencing), including those suggested by Illumina (Page 4, Paragraph 1: SAV Software is compatible with all HiSeq systems, HiSeq X, NextSeq, MiSeq, GAIIX, and HiScanSQ). Umbarger and Porreca also teach their methods for identifying contamination from sequence data, a major focus of Hasan et al., are useful (Paragraph 0002: The invention is especially useful for quality control in workflows which use massively parallel sequencing). Umbarger and Porreca also teach their methods rely on interfacing with a user (Paragraph 0013: For each locus a score can be produced, and a summary statistic can be prepared from the collected scores to allow a user to quickly and reliably identify samples that are likely contaminated), which is a major focus of Illumina. Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the references indicated above. Furthermore, one of ordinary skill in the art would predict that the methods could be combined with a reasonable expectation of success because all describe computer implemented systems related to a user interfacing with metrics related to the quality of sequencing data. Accordingly, Claims 1, 2, 4-11, and 22-28 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Claims 1-12 and 22-28 are rejected under 35 U.S.C. 103 as being unpatentable over Hasan et al., as applied to claims 1, 2, 4-11, and 22-28 above, in view of Illumina, as applied to claims 1, 2, 4-11, and 22-28 above, and in further view of Umbarger and Porreca, as applied to claims 1, 2, 4-11, and 22-28 above, and Fievet et al. (2019, European Journal of Human Genetics, Vol. 27: 792–800, IDS 15 January 2026). Italicized text from reference art.
Applicable claims include:
Claims 1, 2, 4-11 and 22-28 are presented above.
Claim 3. The method of claim 2, wherein the alternative allele is present in 5% or less of sequence reads of the sequence data in the contaminated sample.
Claim 12. The sequencing device of claim 11, wherein the quality metric of the biological sample is associated with failing based the alternative allele being present in 5% or less of sequence reads of the sequence data.
Regarding Claims 1, 2, 4-11, and 22-28, Hassan et al., Illumina, and Umbarger and Porreca teach the limitations as indicated above.
Hassan et al., Illumina, and Umbarger and Porreca do not teach the alternative allele is present in 5% or less of sequence reads of the sequence data in the contaminated sample (Claim 3). Hassan et al., Illumina, and Umbarger and Porreca also do not teach the quality metric of the biological sample is associated with failing based the alternative allele being present in 5% or less of sequence reads of the sequence data (Claim 12).
Regarding Claim 3, Fievet et al. teach the alternative allele is present in 5% or less of sequence reads of the sequence data in the contaminated sample (Page 793, Column 2, Paragraph 2: This strategy is based on the detection of SNPs presenting unexpected allelic ratio (AR) for constitutional analyses; Page 794, Column 2, Paragraph 1: This screening test is independent of background noise and identifies samples possibly contaminated above a certain cutoff, defined as 1% in the present study). The art teaches 1-5% indicate contamination which encompass the range 5% or less required by the instant claim.
Regarding Claim 4, Fievet et al. teach indicating that the biological sample passes the quality metric based on a ratio of the reference allele to the alternative allele in the sequence data being within an expected range (Page 793, Column 1, Paragraph 1: In constitutional genetic analysis, the variants detected are characterized by their allelic ratio (AR): for a given variation, the AR is defined as the (number of reads supporting the variant) / (number of reads at this position); Page 794, Column 1, Paragraph 4: The first step of identification of contamination consisted of a screening test for each sample of the run, based on estimation of the “worst-case scenario” (WCS) percentage of contamination. This screening test is independent of background noise and identifies samples possibly contaminated above a certain cutoff, defined as 1% in the present study).
Regarding Claim 12, Fievet et al. teach the quality metric of the biological sample is associated with failing based the alternative allele being present in 5% or less of sequence reads of the sequence data (Page 793, Column 2, Paragraph 2: This strategy is based on the detection of SNPs presenting unexpected allelic ratio (AR) for constitutional analyses; Page 794, Column 2, Paragraph 1: This screening test is independent of background noise and identifies samples possibly contaminated above a certain cutoff, defined as 1% in the present study). The art indicates 1-5% indicate contamination and encompass the range 5% or less required by the instant claim. See claim 11 for how contamination, related to the quality metric, indicates failing.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Fievet et al. with Umbarger and Porreca, Hasan et al., and Illumina. Fievet et al. teach accessible methods for identifying contamination for sequencing data (Page 797, Column 2, Paragraph 3: we present an easy method to detect contamination in routine NGS constitutional genetic analysis), which are major focus of Hasan et al. and Umbarger and Porreca. Fievet et al. also teach their methods are flexible for next gen sequencing and interfacing with users (Page 797, Column 2, Paragraph 3: Interestingly, this method can be used for any constitutional NGS workflow and can be customized according to the user’s needs), which are focuses of Hasan et al. and Illumina. Furthermore, one of ordinary skill in the art would predict that the methods could be combined with a reasonable expectation of success because all describe computer implemented methods related to a user interfacing with metrics related to the quality of sequencing data. Accordingly, Claims 1-12 and 22-28 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Claims 1, 2, 4-6, 21 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Hasan et al., as applied to claims 1, 2, 4-6, and 25 above, in view of Illumina, as applied to claims 1, 2, 4-6, and 25 above, and in further view of Zhang et al. (2014, Plos One, Vol. 9, No. 7: 1-13, IDS 09 June 2025). Italicized text from reference art.
Applicable claims include:
Claims 1, 2, 4-6 and 25 are presented above.
Claim 21. The method of claim 1, wherein the k-mers have a fixed length greater than 24 nucleotides.
Regarding Claims 1, 2, 4-6, and 25, the limitations are taught by Hasan et al. and Illumina as indicated above.
Regarding Claim 21, Hasan et al. suggest the k-mers can have a fixed length greater than 24 nucleotides (Paragraph 0145: the second comparator engine may create an n-mer profile and hash the n-mers for each of the available reference genomes, where n is a user-determined parameter).
Hasan et al. and Illumina do not teach he k-mers have a fixed length greater than 24 nucleotides (Claim 21).
Regarding Claim 21, Zhang et al. teach the k-mers have a fixed length greater than 24 nucleotides (Page 12, Column 1, Paragraph 5: The hashing function for each hash table is fixed, and reversibly converts each DNA kmer (for k≤32)). This indicates fixed k-mers of 25-32 were used which are equivalent to the length is greater than 24.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Zhang et al. with Hasan et al. and Illumina. Zhang et al. teach their methods relating to counting k-mers is successful, efficient, and versatile (Page 11, Column 2, Paragraph 3: The khmer software implementation offers good performance, a robust and well-tested Python API, and a number of useful and well-documented scripts. khmer is competitive, and, because it provides a Python API for online counting, is flexible. khmer is particularly useful, because it will not break an imposed memory bound and does not require disk access to store or retrieve k-mer counts). Counting k-mer is a major focus of Hasan et al. (see regarding claim 1). Furthermore, one of ordinary skill in the art would predict that the methods could be combined with a reasonable expectation of success because the k-mer length of Hasan et al. is unrestricted (i.e. user defined, see regarding claim 21) and all function on assessing sequencing data. Accordingly, Claims 1, 2, 4-6, 21 and 25 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Double Patenting
No double patenting is found.
Conclusion
No claims are allowed.
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/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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