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 .
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.
Claims Status
Claims 1-11, 13, and 15-22 are pending. Claims 1, 4, 9-11, 13, and 15 are amended. Claims 12 and 14 are cancelled. Claims 16-22 are new. Claim 1 is independent. Claims 1-11, 13, and 15-22 are examined below.
Priority
As detailed on the 05/29/2024 filing receipt, this application claims domestic priority to as early as 03/31/2022 of application PCT/CN2022/084386.
Information Disclosure Statement
The Information Disclosure Statements filed 08/30/2023 and 02/20/2026 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The drawings filed 03/09/2023 are accepted.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11, 13, and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis of claims in Step 1.
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Independent claim 1 is directed to a 101 process, here a "method for analyzing genome methylation sequencing data," with process steps such as "acquiring…, constructing…"
[Step 1: claims 1-11, 13, and 15-22: YES]
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Mental processes recited include:
Claim 1 recites: "aligning the genome methylation sequencing sequence to the reference genome sequence so as to obtain an alignment result; constructing a window, and moving the window from a first end of the alignment result to a second end of the alignment result successively, and counting a methylation index of a part of the alignment result covered by the window at a different position during each movement, a step size of each movement of the window being smaller than a length of the window; and analyzing a counted methylation index of the part of the alignment result covered by a window at a respective different position" are acts of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 2 recite: "…the step of analyzing the counted methylation index of the part of the alignment result covered by the window at the respective different position and outputting the comprehensive methylation evaluation result of the genome methylation sequencing sequence comprises: calculating a regional window methylation level value corresponding to each window at the different position in the target area, according to a ratio of the total number of the methylated bases at the methylation sites to the total number of bases at the methylation sites included in the alignment result covered by a window at a respective different position in the target area; and analyzing the regional window methylation level value corresponding to each window at the different position in the target region to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence." Analyzing is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 3 recites: "wherein analyzing the regional window methylation level value corresponding to each window at the different position in the target region to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence comprises: averaging the regional window methylation level value corresponding to each window at the different position in the target region, and calculating the regional methylation level value of the target region in the genome methylation sequencing sequence according to an average of the regional window methylation level value." Analyzing is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 4 recites: "…the step of analyzing the counted methylation index of the part of the alignment result covered by a window at a respective different position and outputting a comprehensive methylation evaluation result of the genome methylation sequencing sequence comprises: calculating a site-window methylation level value corresponding to a window covering a respective different position of the target site according to a ratio of the number of the methylated bases at the target site to the total number of the bases at the methylation sites in the alignment result covered by different windows covering the target site; and analyzing the site-window methylation level value corresponding to the window covering the respective different position of the target site to obtain the site methylation level value of the target site" Analyzing is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 6 recites: "...counting the methylation index of the part of the alignment result covered by the window at the different position during each movement comprises: sliding the window from the first end of the alignment result to the second end of the alignment result by a preset length, counting a methylation index of a covered alignment result before a first sliding, and counting a methylation index of the alignment result covered by the window after each sliding…" Counting is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 7 recites: "…aligning each of the reference genomic fragment sequences to the genome methylation sequencing sequence in terms of negative and positive strands so as to obtain the alignment result." Aligning the genome sequences could be practically performed in the human mind and/or with pen and paper because it requires evaluating, analyzing, observing and judging data.
Claim 9 recites: "…the step of aligning each of the reference genomic fragment sequences to the genome methylation sequencing sequence in terms of negative and positive strands so as to obtain the alignment result comprises: performing base conversion on the first amplified methylated genomic sequence to at least obtain a third amplified methylated genomic sequence and obtain a fourth amplified methylated genomic sequence, performing base conversion on the second amplified methylated genomic sequence to at least obtain a fifth amplified methylated genomic sequence and a sixth amplified methylated genomic sequence; aligning the first converted reference genome sequence and the second converted reference genome sequence to the third amplified methylated genomic sequence, the fourth amplified methylated genomic sequence, the fifth amplified methylated genomic sequence and the sixth amplified methylated genomic sequence respectively; and taking a mother sequence of an amplified methylated genomic sequence which is aligned to be identical to the first converted reference genome sequence as the positive strand, and taking a mother sequence of an amplified methylated genomic sequence which is aligned to be identical to the second converted reference genome sequence as the negative strand." Aligning sequences are acts of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 10 recites: "performing base conversion on the first amplified methylated genomic sequence to at least obtain the third amplified methylated genomic sequence and obtain the fourth amplified methylated genomic sequence and performing base conversion on the second amplified methylated genomic sequence to at least obtain the fifth amplified methylated genomic sequence and the sixth amplified methylated genomic sequence comprises: performing base conversion from C to T on the first amplified methylated genomic sequence to obtain the third amplified methylated genomic sequence, performing base conversion from G to A on the first amplified methylated genomic sequence to obtain the fourth amplified methylated genomic sequence; and performing the base conversion from C to T on the second amplified methylated genomic sequence to obtain the fifth amplified methylated genomic sequence, and performing the base conversion from G to A on the second amplified methylated genomic sequence to obtain the sixth amplified methylated genomic sequence. "
Claim 11 recites: "pruning a sequence of a target type in the acquired original gene sequencing sequences, wherein the sequence of the target type comprises at least one of: an adapter sequence, a sequence overlapping with the adapter sequence by more than a preset number of bases, a terminal sequence with a mass value lower than a mass value threshold, after completely pruning, discarding a sequence with a length lower than a length threshold to obtain pruned original gene sequencing sequences, and then filtering the pruned original gene sequencing sequences; and when the pruned original gene sequencing sequences do not meet target requirements, continuously performing filtering on the pruned original gene sequencing sequences until filtered pruned-original gene sequencing sequences meet the target requirements, and taking the filtered pruned-original gene sequencing sequences as the genome methylation sequencing sequence to be detected, wherein the target requirements comprise at least one of a base quality requirement, a base ratio requirement, an average sequence GC distribution requirement, a N content distribution requirement, a sequence length requirement, a repeated sequence requirement and an adapter sequence requirement." The claim limitation includes comparing lengths of sequences and choosing the sequence with the length lower than the threshold to discard, which are acts of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 13 recites: "…analyzing the genome methylation sequencing data…"
Claim 15 recites: "…analyzing the genome methylation sequencing data…"
Claim 16 recites: "… analyzing the counted methylation index of the part of the alignment result covered by the window at the respective different position and outputting the comprehensive methylation evaluation result of the genome methylation sequencing sequence comprises: calculating a regional window methylation level value corresponding to each window at the different position in the target area, according to a ratio of the total number of the methylated bases at the methylation sites to the total number of bases at the methylation sites included in the alignment result covered by a window at a respective different position in the target area; and analyzing the regional window methylation level value corresponding to each window at the different position in the target region to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence. "
Claim 17 recites: "wherein analyzing the regional window methylation level value corresponding to each window at the different position in the target region to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence comprises: averaging the regional window methylation level value corresponding to each window at the different position in the target region, and calculating the regional methylation level value of the target region in the genome methylation sequencing sequence according to an average of the regional window methylation level value."
Claim 18 recites: "wherein when the comprehensive methylation evaluation result is a site methylation level value of a target site, the methylation index comprises a total number of bases at the methylation sites in the alignment result covered by a window covering the target site and a number of methylated bases at the target site; the operation of analyzing the counted methylation index of the part of the alignment result covered by a window at a respective different position and outputting a comprehensive methylation evaluation result of the genome methylation sequencing sequence comprises: calculating a site-window methylation level value corresponding to a window covering a respective different position of the target site according to a ratio of the number of the methylated bases at the target site to the total number of the bases at the methylation sites in the alignment result covered by different windows covering the target site; and analyzing the site-window methylation level value corresponding to the window covering the respective different position of the target site to obtain the site methylation level value of the target site."
Claim 19 recites: "wherein analyzing the site-window methylation level value corresponding to the window covering the respective different position of the target site to obtain the site methylation level value of the target site comprises: averaging the site-window methylation level value corresponding to the window covering the respective different position of the target site, and calculating the site methylation level value of the target site in the genome methylation sequencing sequence according to an average of the site-window methylation level value."
Claim 20 recites: "…counting the methylation index of the part of the alignment result covered by the window at the different position during each movement comprises: sliding the window from the first end of the alignment result to the second end of the alignment result by a preset length, counting a methylation index of a covered alignment result before a first sliding, and counting a methylation index of the alignment result covered by the window after each sliding, wherein a step size of each moving of the window is smaller than the length of the window."
Claim 21 recites: "…aligning each of the reference genomic fragment sequences to the genome methylation sequencing sequence in terms of negative and positive strands so as to obtain the alignment result."
Mathematical concepts recited include:
Claim 2 recites: "…methylation index comprising a total number of methylated bases at methylation sites for the alignment result covered by the window in the target area, and a total number of bases at the methylation sites... calculating a regional window methylation level value corresponding to each window at the different position in the target area, according to a ratio of the total number of the methylated bases at the methylation sites to the total number of bases at the methylation sites included in the alignment result covered by a window at a respective different position in the target area…" Calculating are mathematical concepts and/or formulas.
Claim 3 recites: "…averaging the regional window methylation level value corresponding to each window at the different position in the target region, and calculating the regional methylation level value of the target region in the genome methylation sequencing sequence according to an average of the regional window methylation level value." Averaging is a mathematical concept and/or formula.
Claim 4 recites: "…calculating a site-window methylation level value corresponding to a window covering a respective different position of the target site according to a ratio of the number of the methylated bases at the target site to the total number of the bases at the methylation sites in the alignment result covered by different windows covering the target site."
Claim 5 recites: "… averaging the site-window methylation level value corresponding to the window covering the respective different position of the target site, and calculating the site methylation level value of the target site in the genome methylation sequencing sequence according to an average of the site-window methylation level value."
Claim 16 recites: "… calculating a regional window methylation level value corresponding to each window at the different position in the target area, according to a ratio of the total number of the methylated bases at the methylation sites to the total number of bases at the methylation sites included in the alignment result covered by a window at a respective different position in the target area…"
Claim 17 recites: "…averaging the regional window methylation level value corresponding to each window at the different position in the target region, and calculating the regional methylation level value of the target region in the genome methylation sequencing sequence according to an average of the regional window methylation level value."
Claim 18 recites: "… a total number of bases at the methylation sites in the alignment result covered by a window covering the target site and a number of methylated bases at the target site;… calculating a site-window methylation level value corresponding to a window covering a respective different position of the target site according to a ratio of the number of the methylated bases at the target site to the total number of the bases at the methylation sites in the alignment result covered by different windows covering the target site;…"
Claim 19 recites: "…averaging the site-window methylation level value corresponding to the window covering the respective different position of the target site, and calculating the site methylation level value of the target site in the genome methylation sequencing sequence according to an average of the site-window methylation level value. "
Claim 20 recites: "…counting the methylation index of the part of the alignment result covered by the window at the different position during each movement comprises: sliding the window from the first end of the alignment result to the second end of the alignment result by a preset length, counting a methylation index of a covered alignment result before a first sliding, and counting a methylation index of the alignment result covered by the window after each sliding, wherein a step size of each moving of the window is smaller than the length of the window."
As indicated above, claims 1-4, 6-7, 9-11, 13 and 15-21 recite mental processes. For instance, the indicated claims include aligning sequences, counting, analyzing methylation data and calculating, which are acts of evaluating, analyzing, observing and judging data. Another example is in claim 11. Claim 11 includes comparing lengths of sequences and choosing the sequence with the length lower than the threshold to discard, which are also acts of evaluating, analyzing, observing and judging data. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2) subsection III). Although, claims 13 and 15 recite performing the method as part of a method executed on a computer, there are no additional limitations to indicate that anything other than a generic computer is required. However, merely requiring that the steps are carried out with a generic computer does not negate the mental nature of these steps and equates rather to merely using a computer as a tool to perform the mental process. Therefore, under the broadest reasonable interpretation, the indicated claims above can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "Mental processes" grouping of abstract ideas.
Claims 2-5 and 16-20 recite mathematical concepts and/or formulas as indicated above. For instance, calculating and averaging methylation values requires performing a series of mathematical calculations and using mathematical formulas, which falls under the “mathematical concepts” grouping of abstract ideas.
As such, claims 1-11, 13, and 15-22 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional elements that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claim 1 recites: "acquiring a genome methylation sequencing sequence to be detected and a reference genome sequence… outputting a comprehensive methylation evaluation result of the genome methylation sequencing sequence. "
Claim 2 recites: "…to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence. "
Claim 3 recites: "…to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence. "
Claim 4 recites: "to obtain the site methylation level value of the target site"
Claim 5 recites: "to obtain the site methylation level value of the target site"
Claim 7 recites: "segmenting the reference genome sequence to obtain a plurality of reference genomic fragment sequences"
Claim 8 recites: "wherein segmenting the reference genome sequence to obtain the plurality of reference genomic fragment sequences comprises: segmenting the reference genome sequence by a chromosome unit to obtain a plurality of reference chromosome genomic sequences; and segmenting each of the reference chromosome genomic sequences by a preset length to obtain the plurality of reference genomic fragment sequences"
Claim 9 recites: "…as to obtain the alignment result comprises: performing base conversion on the first amplified methylated genomic sequence to at least obtain a third amplified methylated genomic sequence and obtain a fourth amplified methylated genomic sequence, performing base conversion on the second amplified methylated genomic sequence to at least obtain a fifth amplified methylated genomic sequence and a sixth amplified methylated genomic sequence."
Claim 10 recites: "performing base conversion on the first amplified methylated genomic sequence to at least obtain the third amplified methylated genomic sequence and obtain the fourth amplified methylated genomic sequence, and performing base conversion on the second amplified methylated genomic sequence to at least obtain the fifth amplified methylated genomic sequence and the sixth amplified methylated genomic sequence."
Claim 11 recites: "wherein after methylated genomic sequencing to acquire the genome methylation sequencing sequence to be detected and obtaining the reference genome sequence by downloading from a database…"
Claim 13 recites: "A computing and processing device, comprising: a memory with a computer-readable code stored therein; one or more processors, the computing and processing device."
Claim 15 recites: "A non-transient computer-readable medium with a computer program."
Claim 16 recite: "…to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence."
Claim 17 recite: "…to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence…"
Claim 19 recites: "generating a personalized testing plan for the subject including a frequency of follow-up testing based on the one or more assessments and the different future time."
Claims 21 recites: "…to obtain the alignment result..."
Claim 22 recite: "wherein segmenting the reference genome sequence to obtain the plurality of reference genomic fragment sequences comprises: segmenting the reference genome sequence by a chromosome unit to obtain a plurality of reference chromosome genomic sequences; and segmenting each of the reference chromosome genomic sequences by a preset length to obtain the plurality of reference genomic fragment sequences."
The elements of claims 1-5, 7-11, 13, 15-17, 19, and 21-22 as indicated above equate to insignificant extra solutional activities of data gathering and outputting. Data gathering serves as input to the recited judicial exception in the claims. Claim 13 recites "computing and processing device, comprising: a memory with a computer-readable code stored therein; one or more processors" and claim 15 recites "a non-transient computer-readable medium with a computer program." The elements of claims 13 and 15 equate to generic computer components. Claims 13 and 15 invoke the computer components merely as tools to execute the abstract idea. The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. (see MPEP 2106.05(f)). Additionally, the listed additional elements are mere instructions to apply an exception because they recite no more than an idea of a solution or outcome and does not recite a technological solution to a technological problem. (See MPEP 2106.05(f)(1)). As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1-11, 13, and 15-22 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claim 1 recites: "acquiring a genome methylation sequencing sequence to be detected and a reference genome sequence… outputting a comprehensive methylation evaluation result of the genome methylation sequencing sequence. "
Claim 2 recites: "…to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence. "
Claim 3 recites: "…to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence. "
Claim 4 recites: "to obtain the site methylation level value of the target site"
Claim 5 recites: "to obtain the site methylation level value of the target site"
Claim 7 recites: "segmenting the reference genome sequence to obtain a plurality of reference genomic fragment sequences"
Claim 8 recites: "wherein segmenting the reference genome sequence to obtain the plurality of reference genomic fragment sequences comprises: segmenting the reference genome sequence by a chromosome unit to obtain a plurality of reference chromosome genomic sequences; and segmenting each of the reference chromosome genomic sequences by a preset length to obtain the plurality of reference genomic fragment sequences"
Claim 9 recites: "…as to obtain the alignment result comprises: performing base conversion on the first amplified methylated genomic sequence to at least obtain a third amplified methylated genomic sequence and obtain a fourth amplified methylated genomic sequence, performing base conversion on the second amplified methylated genomic sequence to at least obtain a fifth amplified methylated genomic sequence and a sixth amplified methylated genomic sequence."
Claim 10 recites: "performing base conversion on the first amplified methylated genomic sequence to at least obtain the third amplified methylated genomic sequence and obtain the fourth amplified methylated genomic sequence, and performing base conversion on the second amplified methylated genomic sequence to at least obtain the fifth amplified methylated genomic sequence and the sixth amplified methylated genomic sequence."
Claim 11 recites: "wherein after methylated genomic sequencing to acquire the genome methylation sequencing sequence to be detected and obtaining the reference genome sequence by downloading from a database…"
Claim 13 recites: "A computing and processing device, comprising: a memory with a computer-readable code stored therein; one or more processors, the computing and processing device."
Claim 15 recites: "A non-transient computer-readable medium with a computer program."
Claim 16 recite: "…to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence."
Claim 17 recite: "…to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence…"
Claim 19 recites: "generating a personalized testing plan for the subject including a frequency of follow-up testing based on the one or more assessments and the different future time."
Claims 21 recites: "…to obtain the alignment result..."
Claim 22 recite: "wherein segmenting the reference genome sequence to obtain the plurality of reference genomic fragment sequences comprises: segmenting the reference genome sequence by a chromosome unit to obtain a plurality of reference chromosome genomic sequences; and segmenting each of the reference chromosome genomic sequences by a preset length to obtain the plurality of reference genomic fragment sequences."
The additional elements indicated above 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. The limitations equate to mere data gathering and outputting activities, which are insignificant extra solutional activities. The courts have recognized that techniques for determining the level of a biomarker in blood by any means; analyzing DNA to provide sequence information or detect allelic variants; amplifying and sequencing nucleic acid sequences and detecting DNA or enzymes in a sample as well-understood, routine, conventional activities in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. (See MPEP 2106.05(d)). As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. (see MPEP 2106.05(g)). Also, limitations that equate to mere data gathering and outputting via generic computer components, such as receiving data at a computer or outputting data, amount to insignificant extra-solution activity as set forth by the courts in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and OIP Techs., Inc, v, Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Also, the additional elements include storing and retrieving information in memory. Storing and retrieving information in memory were identified by the courts as well-understood, routine and conventional in 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. Also, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more as identified by the courts in Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-11, 13, and 15-22 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 11, 13 and 15-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by CN112397150A (CN112397150A Google Translated 07/27/2026, published 02/23/2021; as cited on the 08/30/2023 IDS Document and on the attached 892 form).
Regarding independent claim 1, CN112397150A teaches acquiring a genome methylation sequencing sequence to be detected and a reference genome sequence with “…acquiring a FASTQ file for capture and sequencing of a ctDNA sample to be tested, and processing to obtain a filtered FASTQ file.” (Abstract)
CN112397150A teaches aligning the genome methylation sequencing sequence to the reference genome sequence so as to obtain an alignment result with “…align and deduplicate the gene sequences in the obtained FASTQ file with the reference genome to obtain the corresponding Bam file; the reads level filtering module is used to align and deduplicate the gene sequences in the obtained FASTQ file…” (Abstract)
CN112397150A teaches constructing a window, and moving the window from a first end of the alignment result to a second end of the alignment result successively with “…windowing a window of 5 in size from the 5' end of the reads, and if the average base mass in the window is less than 20, the window is cleaved, and the number of bases remaining after the cleavage is required to exceed 75.” (page 5 of 24 of pdf of Google Translated Document, para. 4).
CN112397150A teaches counting a methylation index of a part of the alignment result covered by the window at a different position during each movement, a step size of each movement of the window being smaller than a length of the window and analyzing a counted methylation index of the part of the alignment result covered by a window at a respective different position with “…the methylation number counting unit is used for reading reads in the Bam file generated by the to-be-detected sample comparison module line by line and counting the number of methylated and unmethylated bases under a non-CpG context mode…” (page 3 of 24 of pdf of Google Translated Document, Disclosure of Invention section).
CN112397150A teaches outputting a comprehensive methylation evaluation result of the genome methylation sequencing sequence with “…to obtain the corresponding Bam file; the reads level filtering module is used to align and deduplicate the gene sequences in the obtained FASTQ file, according to the preset C -T conversion rate The reads in the generated Bam file are filtered one by one to obtain the filtered Bam file” (abstract).
Regarding claim 2, CN112397150A teaches wherein when the comprehensive methylation evaluation result is the regional methylation level value of a target area in the alignment result, the methylation index comprising a total number of methylated bases at methylation sites for the alignment result covered by the window in the target area, and a total number of bases at the methylation sites with “Calling FASTQC-0.11.3 to count the base data volume, reads data volume, base distribution and the like of FASTQ files before and after the connection; counting Total C base content, methylated C base content, unmethylated C base content, methylated C base content in CpG context and non-CpG context, unmethylated C base content in CpG context and non-CpG context in human reference genome comparison report generated in comparison process; calling an intersector module of Bedtools-v2.26.0 to count the number of bases in a target region in a finally generated Bam file, and the data volume and proportion captured by the target region; and calling SAMtools-1.3 to count the sequencing depth, the average sequencing depth and the number and proportion of the bases captured by the target region under different sequencing depths of the finally generated Bam file.” (page 18 of 24 of pdf of Google Translated Document, section 2.7 statistics)
CN112397150A teaches the step of analyzing the counted methylation index of the part of the alignment result covered by the window at the respective different position and outputting the comprehensive methylation evaluation result of the genome methylation sequencing sequence comprises: calculating a regional window methylation level value corresponding to each window at the different position in the target area, according to a ratio of the total number of the methylated bases at the methylation sites to the total number of bases at the methylation sites included in the alignment result covered by a window at a respective different position in the target area with “…the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Bam file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement.” (page 5 of 24 of pdf of Google Translated Document, para. 7).
CN112397150A teaches analyzing the regional window methylation level value corresponding to each window at the different position in the target region to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence with “The input of the methylation level prediction module 140 is the minimum requirement for covering CpG sites on the Barn file path, the target region Bed file and each reads obtained after the filtering of the reads level filtering module 130. In the prediction process, firstly, the second filtering unit filters known SNP sites in the dbSNP database and SNP sites generated due to specific variation reasons (such as structural variation, chromosome copy number variation and the like) according to the Bed file of the target region by using BisSNP software (a software for analyzing methylation data and can be used for identifying methylation sites and predicting methylation level) to obtain CpG sites of the ctDNA sample to be detected; then, further filtering the Barn file output by the reads horizontal filtering module 130 according to the CpG sites obtained by filtering and the preset number of covered CpG sites in each of the reads (i.e. the minimum requirement for covering CpG sites on each of the reads), and filtering out the reads which do not meet the minimum requirement for covering CpG sites; and finally, the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Barn file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement. Meanwhile, the Barn file filtered by the reads horizontal filtering module 130 is processed by using Bedtools software (a tool for processing a genome algorithm) in combination with the Bed file, so that the capture efficiency of the ctDNA sample to be detected is obtained; and (3) processing the filtered Barn file by utilizing SAMtools (a tool for processing the Bam/sam file) to obtain the sequencing depth of the ctDNA sample to be detected at each site of the target area, and counting data such as the average sequencing depth of the ctDNA sample to be detected.” (page 5 of 24 of pdf of Google Translated Document, para. 7).
Regarding claim 3, CN112397150A teaches wherein analyzing the regional window methylation level value corresponding to each window at the different position in the target region to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence comprises: averaging the regional window methylation level value corresponding to each window at the different position in the target region, and calculating the regional methylation level value of the target region in the genome methylation sequencing sequence according to an average of the regional window methylation level value with “…the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Bam file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement.” (page 5 of 24 of pdf of Google Translated Document, para. 7)
Regarding claim 4, CN112397150A teaches wherein when the comprehensive methylation evaluation result is a site methylation level value of a target site, the methylation index comprises a total number of bases at the methylation sites in the alignment result covered by a window covering the target site and a number of methylated bases at the target site; the step of analyzing the counted methylation index of the part of the alignment result covered by a window at a respective different position and outputting a comprehensive methylation evaluation result of the genome methylation sequencing sequence comprises: calculating a site-window methylation level value corresponding to a window covering a respective different position of the target site according to a ratio of the number of the methylated bases at the target site to the total number of the bases at the methylation sites in the alignment result covered by different windows covering the target site; and analyzing the site-window methylation level value corresponding to the window covering the respective different position of the target site to obtain the site methylation level value of the target site with “Calling FASTQC-0.11.3 to count the base data volume, reads data volume, base distribution and the like of FASTQ files before and after the connection; counting Total C base content, methylated C base content, unmethylated C base content, methylated C base content in CpG context and non-CpG context, unmethylated C base content in CpG context and non-CpG context in human reference genome comparison report generated in comparison process; calling an intersector module of Bedtools-v2.26.0 to count the number of bases in a target region in a finally generated Bam file, and the data volume and proportion captured by the target region; and calling SAMtools-1.3 to count the sequencing depth, the average sequencing depth and the number and proportion of the bases captured by the target region under different sequencing depths of the finally generated Bam file.” (page 18 of 24 of pdf of Google Translated Document, section 2.7 statistics); “In another aspect, the present invention provides a ctDNA methylation level prediction method based on target region capture sequencing, comprising: acquiring a FASTQ file for capturing and sequencing a ctDNA sample to be detected, and carrying out pretreatment operation on the FASTQ file to obtain a filtered FASTQ file; comparing the gene sequence in the obtained FASTQ file with a reference genome and removing duplication to obtain a corresponding Bam file; filtering reads in the generated Bam file one by one according to a preset C-T conversion rate to obtain a filtered Bam file; and further filtering the filtered Bam file according to the Bed file of the target area and the preset number of covered CpG sites in each read, and predicting the methylation level of the CpG sites according to the residual reads.” (page 3 of 24 of pdf of Google Translated Document, Disclosure of Invention section); “…the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Bam file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement.” (page 5 of 24 of pdf of Google Translated Document, para. 7) and “In an automated methylation data quality detection and methylation level prediction process: inputting at one time: FASTQ file and target region Bed file (containing three columns of information of chromosome, starting point and ending point) for methylation target capture sequencing of ctDNA sample to be detected. The output file includes: a statistical table of ctDNA sample data to be detected (including original base data volume, original reads data volume, filtered base data volume, filtered reads data volume, comparison to reference genome reads data volume and proportion, duplication eliminating data volume, Total C base content, methylated C base content, unmethylated C base content, methylated C base content in CpG context and non-CpG context, unmethylated C base content in CpG context and non-CpG context, C base content before reads horizontal filtering, C-T conversion rate of sample before reads horizontal filtering, lambda C-T conversion rate of internal reference sample, C-T conversion rate of sample after reads horizontal filtering, data volume after reads horizontal filtering, base number of target region, data volume and proportion of target region, base number and proportion of target region capture under different sequencing depths and average sequencing depth), And the methylation level of the CpG sites of the target region of the ctDNA sample to be detected (including five information of chromosomes, starting points, ending points, the methylation level and the sequencing depth).” (page 5 of 24 of pdf of Google Translated Document, para. 8).
Regarding claim 5, CN112397150A teaches wherein analyzing the site-window methylation level value corresponding to the window covering the respective different position of the target site to obtain the site methylation level value of the target site comprises: averaging the site-window methylation level value corresponding to the window covering the respective different position of the target site, and calculating the site methylation level value of the target site in the genome methylation sequencing sequence according to an average of the site-window methylation level value with “In an automated methylation data quality detection and methylation level prediction process: inputting at one time: FASTQ file and target region Bed file (containing three columns of information of chromosome, starting point and ending point) for methylation target capture sequencing of ctDNA sample to be detected. The output file includes: a statistical table of ctDNA sample data to be detected (including original base data volume, original reads data volume, filtered base data volume, filtered reads data volume, comparison to reference genome reads data volume and proportion, duplication eliminating data volume, Total C base content, methylated C base content, unmethylated C base content, methylated C base content in CpG context and non-CpG context, unmethylated C base content in CpG context and non-CpG context, C base content before reads horizontal filtering, C-T conversion rate of sample before reads horizontal filtering, lambda C-T conversion rate of internal reference sample, C-T conversion rate of sample after reads horizontal filtering, data volume after reads horizontal filtering, base number of target region, data volume and proportion of target region, base number and proportion of target region capture under different sequencing depths and average sequencing depth), And the methylation level of the CpG sites of the target region of the ctDNA sample to be detected (including five information of chromosomes, starting points, ending points, the methylation level and the sequencing depth).” (page 5 of 24 of pdf of Google Translated Document, para. 8) and “…the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Bam file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement.” (page 5 of 24 of pdf of Google Translated Document, para. 7)
Regarding claim 6, CN112397150A teaches, wherein sliding the window from the first end of the alignment result to the second end of the alignment result successively and counting the methylation index of the part of the alignment result covered by the window at the different position during each movement comprises: sliding the window from the first end of the alignment result to the second end of the alignment result by a preset length, counting a methylation index of a covered alignment result before a first sliding, and counting a methylation index of the alignment result covered by the window after each sliding, wherein a step size of each moving of the window is smaller than the length of the window with “Specifically, after the adaptor sequence is cleaved, bases having a base mass of less than 20 at the beginning and end of the remaining portion are cleaved, the average mass is calculated by windowing a window of 5 in size from the 5' end of the reads, and if the average base mass in the window is less than 20, the window is cleaved, and the number of bases remaining after the cleavage is required to exceed 75.” (page 5 of 24 of pdf of Google Translated Document, para. 4) and “…the methylation number counting unit is used for reading reads in the Bam file generated by the to-be-detected sample comparison module line by line and counting the number of methylated and unmethylated bases under a non-CpG context mode…” (page 3 of 24 of pdf of Google Translated Document, Disclosure of Invention section).
Regarding claim 11, CN112397150A teaches wherein after methylated genomic sequencing to acquire the genome methylation sequencing sequence to be detected and obtaining the reference genome sequence by downloading from a database, the method further comprises: pruning a sequence of a target type in the acquired original gene sequencing sequences, wherein the sequence of the target type comprises at least one of: an adapter sequence, a sequence overlapping with the adapter sequence by more than a preset number of bases, a terminal sequence with a mass value lower than a mass value threshold, after completely pruning, discarding a sequence with a length lower than a length threshold to obtain pruned original gene sequencing sequences, and then filtering the pruned original gene sequencing sequences; and when the pruned original gene sequencing sequences do not meet target requirements, continuously performing filtering on the pruned original gene sequencing sequences until filtered pruned-original gene sequencing sequences meet the target requirements, and taking the filtered pruned-original gene sequencing sequences as the genome methylation sequencing sequence to be detected, wherein the target requirements comprise at least one of a base quality requirement, a base ratio requirement, an average sequence GC distribution requirement, a N content distribution requirement, a sequence length requirement, a repeated sequence requirement and an adapter sequence requirement with “Calling Trimmomatic-0.36 to take each pair of FASTQ files as pairing sequences (paired reads) to carry out joint removal and low-quality base treatment, and generating FASTQ files after joint removal. Specifically, after the adaptor sequence is cleaved, bases having a base mass of less than 20 at the beginning and end of the remaining portion are cleaved, the average mass is calculated by windowing a window of 5 in size from the 5' end of the reads, and if the average base mass in the window is less than 20, the window is cleaved, and the number of bases remaining after the cleavage is required to exceed 75.” (page 5 of 24 of pdf of Google Translated Document, para. 4, section 2.1 removing the joint); and “Call Bismark-v0.19.0 to align the adaptor-removed FASTQ file as paired reads to hg19 human reference genomic sequence and lambda DNA reference genomic sequence, generating an initial Bam file and alignment report.” (page 5 of 24 of pdf of Google Translated Document, para. 5, section 2.2 alignment)
Regarding claim 13, CN112397150A teaches A computing and processing device, comprising: a memory with a computer-readable code stored therein; one or more processors, the computing and processing device executing the method for analyzing the genome methylation sequencing data according to claims 1 when the computer-readable code is executed by the one or more processors with “In another aspect, the present invention provides a terminal device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above ctDNA methylation level prediction method based on target region capture sequencing.” (page 4 of 24 of pdf of Google Translated Document, Disclosure of Invention section and “In another aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to, when executed by a processor, implement any of the above-mentioned steps of the ctDNA methylation level prediction method based on target region capture sequencing.” (page 4 of 24 of pdf of Google Translated Document, Disclosure of Invention section)
Regarding claim 15, CN112397150A teaches A non-transient computer-readable medium with a computer program of the method for analyzing the genome methylation sequencing data according to claim 1 stored therein with “In another aspect, the present invention provides a terminal device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above ctDNA methylation level prediction method based on target region capture sequencing.” (page 4 of 24 of pdf of Google Translated Document, Disclosure of Invention section and “In another aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to, when executed by a processor, implement any of the above-mentioned steps of the ctDNA methylation level prediction method based on target region capture sequencing.” (page 4 of 24 of pdf of Google Translated Document, Disclosure of Invention section).
Regarding claim 16, CN112397150A teaches wherein when the comprehensive methylation evaluation result is the regional methylation level value of a target area in the alignment result, the methylation index comprising a total number of methylated bases at methylation sites for the alignment result covered by the window in the target area, and a total number of bases at the methylation sites with “Calling FASTQC-0.11.3 to count the base data volume, reads data volume, base distribution and the like of FASTQ files before and after the connection; counting Total C base content, methylated C base content, unmethylated C base content, methylated C base content in CpG context and non-CpG context, unmethylated C base content in CpG context and non-CpG context in human reference genome comparison report generated in comparison process; calling an intersector module of Bedtools-v2.26.0 to count the number of bases in a target region in a finally generated Bam file, and the data volume and proportion captured by the target region; and calling SAMtools-1.3 to count the sequencing depth, the average sequencing depth and the number and proportion of the bases captured by the target region under different sequencing depths of the finally generated Bam file.” (page 18 of 24 of pdf of Google Translated Document, section 2.7 statistics)
CN112397150A teaches the operation of analyzing the counted methylation index of the part of the alignment result covered by the window at the respective different position and outputting the comprehensive methylation evaluation result of the genome methylation sequencing sequence comprises: calculating a regional window methylation level value corresponding to each window at the different position in the target area, according to a ratio of the total number of the methylated bases at the methylation sites to the total number of bases at the methylation sites included in the alignment result covered by a window at a respective different position in the target area with “…the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Bam file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement.” (page 5 of 24 of pdf of Google Translated Document, para. 7).
CN112397150A teaches analyzing the regional window methylation level value corresponding to each window at the different position in the target region to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence with “The input of the methylation level prediction module 140 is the minimum requirement for covering CpG sites on the Barn file path, the target region Bed file and each reads obtained after the filtering of the reads level filtering module 130. In the prediction process, firstly, the second filtering unit filters known SNP sites in the dbSNP database and SNP sites generated due to specific variation reasons (such as structural variation, chromosome copy number variation and the like) according to the Bed file of the target region by using BisSNP software (a software for analyzing methylation data and can be used for identifying methylation sites and predicting methylation level) to obtain CpG sites of the ctDNA sample to be detected; then, further filtering the Barn file output by the reads horizontal filtering module 130 according to the CpG sites obtained by filtering and the preset number of covered CpG sites in each of the reads (i.e. the minimum requirement for covering CpG sites on each of the reads), and filtering out the reads which do not meet the minimum requirement for covering CpG sites; and finally, the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Barn file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement. Meanwhile, the Barn file filtered by the reads horizontal filtering module 130 is processed by using Bedtools software (a tool for processing a genome algorithm) in combination with the Bed file, so that the capture efficiency of the ctDNA sample to be detected is obtained; and (3) processing the filtered Barn file by utilizing SAMtools (a tool for processing the Bam/sam file) to obtain the sequencing depth of the ctDNA sample to be detected at each site of the target area, and counting data such as the average sequencing depth of the ctDNA sample to be detected.” (page 5 of 24 of pdf of Google Translated Document, para. 7).
Regarding claim 17, CN112397150A teaches wherein analyzing the regional window methylation level value corresponding to each window at the different position in the target region to obtain the regional methylation level value of the target region in the genome methylation sequencing sequence comprises: averaging the regional window methylation level value corresponding to each window at the different position in the target region, and calculating the regional methylation level value of the target region in the genome methylation sequencing sequence according to an average of the regional window methylation level value with “…the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Bam file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement.” (page 5 of 24 of pdf of Google Translated Document, para. 7)
Regarding claim 18, CN112397150A teaches wherein when the comprehensive methylation evaluation result is a site methylation level value of a target site, the methylation index comprises a total number of bases at the methylation sites in the alignment result covered by a window covering the target site and a number of methylated bases at the target site; the step of analyzing the counted methylation index of the part of the alignment result covered by a window at a respective different position and outputting a comprehensive methylation evaluation result of the genome methylation sequencing sequence comprises: calculating a site-window methylation level value corresponding to a window covering a respective different position of the target site according to a ratio of the number of the methylated bases at the target site to the total number of the bases at the methylation sites in the alignment result covered by different windows covering the target site; and analyzing the site-window methylation level value corresponding to the window covering the respective different position of the target site to obtain the site methylation level value of the target site with “Calling FASTQC-0.11.3 to count the base data volume, reads data volume, base distribution and the like of FASTQ files before and after the connection; counting Total C base content, methylated C base content, unmethylated C base content, methylated C base content in CpG context and non-CpG context, unmethylated C base content in CpG context and non-CpG context in human reference genome comparison report generated in comparison process; calling an intersector module of Bedtools-v2.26.0 to count the number of bases in a target region in a finally generated Bam file, and the data volume and proportion captured by the target region; and calling SAMtools-1.3 to count the sequencing depth, the average sequencing depth and the number and proportion of the bases captured by the target region under different sequencing depths of the finally generated Bam file.” (page 18 of 24 of pdf of Google Translated Document, section 2.7 statistics); “In another aspect, the present invention provides a ctDNA methylation level prediction method based on target region capture sequencing, comprising: acquiring a FASTQ file for capturing and sequencing a ctDNA sample to be detected, and carrying out pretreatment operation on the FASTQ file to obtain a filtered FASTQ file; comparing the gene sequence in the obtained FASTQ file with a reference genome and removing duplication to obtain a corresponding Bam file; filtering reads in the generated Bam file one by one according to a preset C-T conversion rate to obtain a filtered Bam file; and further filtering the filtered Bam file according to the Bed file of the target area and the preset number of covered CpG sites in each read, and predicting the methylation level of the CpG sites according to the residual reads.” (page 3 of 24 of pdf of Google Translated Document, Disclosure of Invention section); “…the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Bam file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement.” (page 5 of 24 of pdf of Google Translated Document, para. 7) and “In an automated methylation data quality detection and methylation level prediction process: inputting at one time: FASTQ file and target region Bed file (containing three columns of information of chromosome, starting point and ending point) for methylation target capture sequencing of ctDNA sample to be detected. The output file includes: a statistical table of ctDNA sample data to be detected (including original base data volume, original reads data volume, filtered base data volume, filtered reads data volume, comparison to reference genome reads data volume and proportion, duplication eliminating data volume, Total C base content, methylated C base content, unmethylated C base content, methylated C base content in CpG context and non-CpG context, unmethylated C base content in CpG context and non-CpG context, C base content before reads horizontal filtering, C-T conversion rate of sample before reads horizontal filtering, lambda C-T conversion rate of internal reference sample, C-T conversion rate of sample after reads horizontal filtering, data volume after reads horizontal filtering, base number of target region, data volume and proportion of target region, base number and proportion of target region capture under different sequencing depths and average sequencing depth), And the methylation level of the CpG sites of the target region of the ctDNA sample to be detected (including five information of chromosomes, starting points, ending points, the methylation level and the sequencing depth).” (page 5 of 24 of pdf of Google Translated Document, para. 8).
Regarding claim 19, CN112397150A teaches wherein analyzing the site-window methylation level value corresponding to the window covering the respective different position of the target site to obtain the site methylation level value of the target site comprises: averaging the site-window methylation level value corresponding to the window covering the respective different position of the target site, and calculating the site methylation level value of the target site in the genome methylation sequencing sequence according to an average of the site-window methylation level value with “In an automated methylation data quality detection and methylation level prediction process: inputting at one time: FASTQ file and target region Bed file (containing three columns of information of chromosome, starting point and ending point) for methylation target capture sequencing of ctDNA sample to be detected. The output file includes: a statistical table of ctDNA sample data to be detected (including original base data volume, original reads data volume, filtered base data volume, filtered reads data volume, comparison to reference genome reads data volume and proportion, duplication eliminating data volume, Total C base content, methylated C base content, unmethylated C base content, methylated C base content in CpG context and non-CpG context, unmethylated C base content in CpG context and non-CpG context, C base content before reads horizontal filtering, C-T conversion rate of sample before reads horizontal filtering, lambda C-T conversion rate of internal reference sample, C-T conversion rate of sample after reads horizontal filtering, data volume after reads horizontal filtering, base number of target region, data volume and proportion of target region, base number and proportion of target region capture under different sequencing depths and average sequencing depth), And the methylation level of the CpG sites of the target region of the ctDNA sample to be detected (including five information of chromosomes, starting points, ending points, the methylation level and the sequencing depth).” (page 5 of 24 of pdf of Google Translated Document, para. 8) and “…the methylation level calculation unit calculates the methylation level of the CpG sites according to the residual reads of the Bam file after the filtering of the second filtering unit, and the methylation level calculation formula of each CpG site is that the number of the reads covering the CpG sites and subjected to methylation meets the minimum requirement is divided by the number of all the reads covering the sites and the number of the reads meets the minimum requirement.” (page 5 of 24 of pdf of Google Translated Document, para. 7)
Regarding claim 20, CN112397150A teaches, wherein sliding the window from the first end of the alignment result to the second end of the alignment result successively and counting the methylation index of the part of the alignment result covered by the window at the different position during each movement comprises: sliding the window from the first end of the alignment result to the second end of the alignment result by a preset length, counting a methylation index of a covered alignment result before a first sliding, and counting a methylation index of the alignment result covered by the window after each sliding, wherein a step size of each moving of the window is smaller than the length of the window with “Specifically, after the adaptor sequence is cleaved, bases having a base mass of less than 20 at the beginning and end of the remaining portion are cleaved, the average mass is calculated by windowing a window of 5 in size from the 5' end of the reads, and if the average base mass in the window is less than 20, the window is cleaved, and the number of bases remaining after the cleavage is required to exceed 75.” (page 5 of 24 of pdf of Google Translated Document, para. 4) and “…the methylation number counting unit is used for reading reads in the Bam file generated by the to-be-detected sample comparison module line by line and counting the number of methylated and unmethylated bases under a non-CpG context mode…” (page 3 of 24 of pdf of Google Translated Document, Disclosure of Invention section).
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.
Claims 7-10 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over CN112397150A (CN112397150A Google Translated 07/27/2026, published 02/23/2021; as cited on the 08/30/2023 IDS Document) as applied to claims 1-6, 11, 13 and 15-20 as discussed in the 35 U.S.C. 102(a)(2) above, in view of Rauluseviciute (“DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis.” Clinical epigenetics vol. 11,1 193., published 12 Dec. 2019; as cited on the attached 892 form) and Krueger (“Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications.” Bioinformatics (Oxford, England) vol. 27,11 (2011): 1571-2.; as cited on the attached 892 form).
Regarding claims 7 and 21, CN112397150A teaches aligning each of the reference genomic fragment sequences to the genome methylation sequencing sequence in terms of negative and positive strands so as to obtain the alignment result with “Calling a clipOverlap module of the BamHIT-1.0.14 to screen the Bam files after the marks are grouped, and carrying out cigar value conversion processing on reads which overlap bases of the Bam files and pairing sequences and compare the pairing sequences to a negative strand of a reference sequence to generate the Bam files; and calling SAMtools-1.3 view to filter the alignment quality (used for quantifying the possibility of aligning to wrong positions, the higher the value is, the lower the possibility is), of the Bam file with the overlapped sequences removed, wherein the alignment quality is required to exceed 20, and a final Bam file is generated. The Cigar value reports the relative alignment information for each read in the Bam file.” (page 18 of 24 of pdf of Google Translated Document, section 2.5 Screening).
Regarding claim 9, CN112397150A teaches wherein the reference genome sequence comprises a first converted reference genome sequence and a second converted reference genome sequence, and the genome methylation sequencing sequence at least comprises a first amplified methylated genomic sequence and a second amplified methylated genomic sequence; the step of aligning each of the reference genomic fragment sequences to the genome methylation sequencing sequence in terms of negative and positive strands so as to obtain the alignment result comprises: performing base conversion on the first amplified methylated genomic sequence to at least obtain a third amplified methylated genomic sequence and obtain a fourth amplified methylated genomic sequence with “…align and deduplicate the gene sequences in the obtained FASTQ file with the reference genome to obtain the corresponding Bam file; the reads level filtering module is used to align and deduplicate the gene sequences in the obtained FASTQ file…” (Abstract); “Calling a clipOverlap module of the BamHIT-1.0.14 to screen the Bam files after the marks are grouped, and carrying out cigar value conversion processing on reads which overlap bases of the Bam files and pairing sequences and compare the pairing sequences to a negative strand of a reference sequence to generate the Bam files; and calling SAMtools-1.3 view to filter the alignment quality (used for quantifying the possibility of aligning to wrong positions, the higher the value is, the lower the possibility is), of the Bam file with the overlapped sequences removed, wherein the alignment quality is required to exceed 20, and a final Bam file is generated. The Cigar value reports the relative alignment information for each read in the Bam file.” (page 18 of 24 of pdf of Google Translated Document, section 2.5 Screening); “Bisulfite treatment is required during the above-described DNA methylation capture sequencing of the target region to convert all unmethylated cytosines (C) to uracil (U) and uracil to thymine (T) via PCR (polymerase chain reaction), a technique for amplifying a specific DNA fragment, but methylated cytosines are not altered during this process.” (page 3 of 24 of pdf of Google Translated Document, para. 5).
CN112397150A does not explicitly teach aligning the genome methylation sequencing sequence to the reference genome sequence so as to obtain the alignment result comprises: segmenting the reference genome sequence to obtain a plurality of reference genomic fragment sequences in claims 7 and 21 and wherein segmenting the reference genome sequence to obtain the plurality of reference genomic fragment sequences comprises: segmenting the reference genome sequence by a chromosome unit to obtain a plurality of reference chromosome genomic sequences; and segmenting each of the reference chromosome genomic sequences by a preset length to obtain the plurality of reference genomic fragment sequence of claims 8 and 22. However, these limitations are taught by Rauluseviciute.
CN112397150A does not explicitly teach performing base conversion on the second amplified methylated genomic sequence to at least obtain a fifth amplified methylated genomic sequence and a sixth amplified methylated genomic sequence; aligning the first converted reference genome sequence and the second converted reference genome sequence to the third amplified methylated genomic sequence, the fourth amplified methylated genomic sequence, the fifth amplified methylated genomic sequence and the sixth amplified methylated genomic sequence respectively; and taking a mother sequence of an amplified methylated genomic sequence which is aligned to be identical to the first converted reference genome sequence as the positive strand, and taking a mother sequence of an amplified methylated genomic sequence which is aligned to be identical to the second converted reference genome sequence as the negative strand in claim 9 and wherein the step of performing base conversion on the first amplified methylated genomic sequence to at least obtain the third amplified methylated genomic sequence and obtain the fourth amplified methylated genomic sequence performing base conversion on the second amplified methylated genomic sequence to at least obtain the fifth amplified methylated genomic sequence and the sixth amplified methylated genomic sequence comprises: performing base conversion from C to T on the first amplified methylated genomic sequence to obtain the third amplified methylated genomic sequence, performing base conversion from G to A on the first amplified methylated genomic sequence to obtain the fourth amplified methylated genomic sequence; and performing the base conversion from C to T on the second amplified methylated genomic sequence to obtain the fifth amplified methylated genomic sequence, and performing the base conversion from G to A on the second amplified methylated genomic sequence to obtain the sixth amplified methylated genomic sequence of claim 10. However, these limitations are taught by Krueger.
Regarding claims 7 and 21, Rauluseviciute teaches wherein aligning the genome methylation sequencing sequence to the reference genome sequence so as to obtain the alignment result comprises: segmenting the reference genome sequence to obtain a plurality of reference genomic fragment sequences with “A way to reduce the cost of the experiment is to use RRBS, which is a popular choice when certain regions are of interest, rather than the whole genome. In RRBS, DNA is digested into short fragments with CpG dinucleotides at the ends using methylation-insensitive restriction enzyme MspI, which recognizes 5′-CCGG-3′ sequences. Before bisulfite conversion and PCR, fragments that are rich in CpGs are selected—selection of 40–220-bp-long fragments has been shown to cover 85% of CGIs, mostly in promoter regions…” (page 4, col. 1, para. 1) and “In step 1 of the pipeline, the reference genome is prepared by converting all Cs into Ts for both strands and indexing those strands. Reference is cut into target regions, based on the enzyme that was used in the RRBS protocol. In step 2, reads are trimmed and aligned in step 3 (Additional file 1). Two alignment algorithms can be chosen: Bowtie2 or bsmap and their options selected. In step 4, methylation levels are calculated for target regions. DMPs and DMRs are detected in step 5 using Fisher’s exact test when seed number is smaller than 5. Otherwise, t test or chi-square tests are chosen automatically. SNPs and ASM are analyzed in step 6 using Bis-SNP or Bcftools. Heterozygous SNPs are then filtered for ASM event detection. In a final step, results are summarized into a report.” (page 8, col. 1, para. 5).
Regarding claims 8 and 22, Rauluseviciute teaches wherein segmenting the reference genome sequence to obtain the plurality of reference genomic fragment sequences comprises: segmenting the reference genome sequence by a chromosome unit to obtain a plurality of reference chromosome genomic sequences; and segmenting each of the reference chromosome genomic sequences by a preset length to obtain the plurality of reference genomic fragment sequence with “A way to reduce the cost of the experiment is to use RRBS, which is a popular choice when certain regions are of interest, rather than the whole genome. In RRBS, DNA is digested into short fragments with CpG dinucleotides at the ends using methylation-insensitive restriction enzyme MspI, which recognizes 5′-CCGG-3′ sequences. Before bisulfite conversion and PCR, fragments that are rich in CpGs are selected—selection of 40–220-bp-long fragments has been shown to cover 85% of CGIs, mostly in promoter regions…” (page 4, col. 1, para. 1) and “In step 1 of the pipeline, the reference genome is prepared by converting all Cs into Ts for both strands and indexing those strands. Reference is cut into target regions, based on the enzyme that was used in the RRBS protocol. In step 2, reads are trimmed and aligned in step 3 (Additional file 1). Two alignment algorithms can be chosen: Bowtie2 or bsmap and their options selected. In step 4, methylation levels are calculated for target regions. DMPs and DMRs are detected in step 5 using Fisher’s exact test when seed number is smaller than 5. Otherwise, t test or chi-square tests are chosen automatically. SNPs and ASM are analyzed in step 6 using Bis-SNP or Bcftools. Heterozygous SNPs are then filtered for ASM event detection. In a final step, results are summarized into a report.” (page 8, col. 1, para. 5).
Regarding claim 9, Krueger teaches performing base conversion on the second amplified methylated genomic sequence to at least obtain a fifth amplified methylated genomic sequence and a sixth amplified methylated genomic sequence; aligning the first converted reference genome sequence and the second converted reference genome sequence to the third amplified methylated genomic sequence, the fourth amplified methylated genomic sequence, the fifth amplified methylated genomic sequence and the sixth amplified methylated genomic sequence respectively; and taking a mother sequence of an amplified methylated genomic sequence which is aligned to be identical to the first converted reference genome sequence as the positive strand, and taking a mother sequence of an amplified methylated genomic sequence which is aligned to be identical to the second converted reference genome sequence as the negative strand with “Bisulfite libraries are of two distinct types: in the first scenario the sequencing library is generated in a directional manner, i.e. the actual sequencing reads will correspond to a bisulfite converted version of either the original forward or reverse strand.” (Page 1571, col. 2, para. 2); “As the strand identity of a bisulfite read is a priori unknown, our bisulfite mapping tool Bismark aims to find a unique alignment by running four alignment processes simultaneously. First, bisulfite reads are transformed into a C-to-T and G-to-A version (equivalent to a C-to-T conversion on the reverse strand). Then, each of them is aligned to equivalently pre-converted forms of the reference genome using four parallel instances of the short read aligner Bowtie (Fig. 1A). This read mapping enables Bismark to uniquely determine the strand origin of a bisulfite read. Consequently, Bismark can handle BS-Seq data from both directional and non-directional libraries. Since residual cytosines in the sequencing read are converted in silico into a fully bisulfite-converted form before the alignment takes place, mapping performed in this manner handles partial methylation accurately and in an unbiased manner.” (Page 1571, col. 2, para. 3) and Figure 1 (Page 1572).
Regarding claim 10, Krueger teaches, wherein the step of performing base conversion on the first amplified methylated genomic sequence to at least obtain the third amplified methylated genomic sequence and obtain the fourth amplified methylated genomic sequence performing base conversion on the second amplified methylated genomic sequence to at least obtain the fifth amplified methylated genomic sequence and the sixth amplified methylated genomic sequence comprises: performing base conversion from C to T on the first amplified methylated genomic sequence to obtain the third amplified methylated genomic sequence, performing base conversion from G to A on the first amplified methylated genomic sequence to obtain the fourth amplified methylated genomic sequence; and performing the base conversion from C to T on the second amplified methylated genomic sequence to obtain the fifth amplified methylated genomic sequence, and performing the base conversion from G to A on the second amplified methylated genomic sequence to obtain the sixth amplified methylated genomic sequence with “As the strand identity of a bisulfite read is a priori unknown, our bisulfite mapping tool Bismark aims to find a unique alignment by running four alignment processes simultaneously. First, bisulfite reads are transformed into a C-to-T and G-to-A version (equivalent to a C-to-T conversion on the reverse strand). Then, each of them is aligned to equivalently pre-converted forms of the reference genome using four parallel instances of the short read aligner Bowtie (Fig. 1A). This read mapping enables Bismark to uniquely determine the strand origin of a bisulfite read. Consequently, Bismark can handle BS-Seq data from both directional and non-directional libraries. Since residual cytosines in the sequencing read are converted in silico into a fully bisulfite-converted form before the alignment takes place, mapping performed in this manner handles partial methylation accurately and in an unbiased manner.” (Page 1571, col. 2, para. 3) and Figure 1 (Page 1572).
It would have been prima facia obvious to combine the teachings of CN112397150A and Rauluseviciute to arrive at the claimed invention. A person of ordinary skill in the art would have been motivated to modify the method of CN112397150A to include segmenting the reference genome sequence as taught by Rauluseviciute to better analyze target regions. Furthermore, there would have been a reasonable expectation of success, since CN112397150A and Rauluseviciute teach methods that pertain to the analysis of DNA methylation.
It would also have been prima facia obvious to combine the teachings of CN112397150A and Krueger to arrive at the claimed invention. A person of ordinary skill in the art would have been motivated to modify the method of CN112397150A to perform alignment and base conversions as taught by Krueger for the purpose of determining methylation status on both the forward and reverse strands. Furthermore, there would have been a reasonable expectation of success, since CN112397150A and Krueger teach methods that pertain to the analysis of DNA methylation.
Conclusion
No claims are allowed.
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/K.K./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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