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 1-20 are currently pending and under examination herein.
Claims 1-20 are rejected.
Priority
The instant application claims priority to US provisional application 63357537 filed 30 June 2022. In this action, claims 1-20 are examined as though they had an effective filing date of 30 June 2022. 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 27 September 2024 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 29 June 2023 are accepted.
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.
Claims 7-8 and 16-20 are 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 7 recites “a maximum length threshold value”. Claim 3, from which claim 7 depends recites “a maximum length threshold”. The maximum length threshold of claim 3 inherently has a value associated with the threshold. This makes it confusing if “a maximum length threshold value” of claim 7 is the same or different than the “maximum length threshold” of claim 3. If they are the same, “a” should be “the” in claim 7. The metes and bounds of the claim are therefore unclear rendering the claim indefinite. For the purpose of examination, they are considered the same. Claim 8 depends on Claim 7, and thus contain the above issues due to said dependence.
Claim 16 and 19 recite receiving a plurality of nucleic acid sequence reads generated by selectively amplifying nucleic acid sequences at targeted locations in the tumor sample genome by a targeted panel with a low sample input from a tumor sample. It is unclear if this limitation indicates actual sequencing (as recited by claim 1) or just receiving sequencing data. It is unclear how the processor and a data store of the system of claim 16 or the non-transitory computer-readable medium and computer of claim 19 would perform active sequencing steps. The metes and bounds of the claims are therefore unclear rendering the claims indefinite. Claims 17, 18, and 20 depend on Claims 16 and 19, and thus contain the above issues due to said dependence.
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 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 (Step 2A, Prong 1). Claims 1-15 are directed to a method and Claims 16-20 are directed to systems. In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recite the limitation - dividing the genome into segments having homogeneous copy numbers using log odds of heterozygous single nucleotide polymorphisms (SNPs) and copy number variation (CNV) log ratios determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome; applying a threshold count to squared allelic log odds of each segment in autosomes of the genome to identify unbalanced copy number (UCN) segments; adding numbers of bases in the UCN segments to produce a sum of UCN bases; and dividing the sum of UCN bases by a total number of bases in all the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability. Based on the broadest reasonable interpretation, dividing the genome into segments, applying a threshold, adding numbers, and dividing numbers 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 - wherein the ratio is expressed as a percent to give a genomic instability (GI) score. This limitation specifies the ratio producing by judicial exceptions of claim 1. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 3 recites the limitation - applying a maximum length threshold to each of the UCN segments prior to the step of adding. Based on the broadest reasonable interpretation, applying a threshold 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 4 recites the limitation - excluding UCN segments that span the maximum length threshold or more of a whole chromosome. This limitation specifies the application of the threshold judicial exception of claim 3. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 5 recites the limitation - excluding UCN segments that span the maximum length threshold or more of a p-arm of a chromosome. This limitation specifies the application of the threshold judicial exception of claim 3. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 6 recites the limitation - excluding UCN segments that span the maximum length threshold or more of a q-arm of a chromosome. This limitation specifies the application of the threshold judicial exception of claim 3. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 7 recites the limitation - wherein a maximum length threshold value is a percent of a whole chromosome length, a percent of a p-arm length or a percent of a q-arm length. This limitation specifies the application of the threshold judicial exception of claim 3. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 8 recites the limitation - wherein the maximum length threshold value is 90%. This limitation specifies the application of the threshold judicial exception of claim 3. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 9 recites the imitation - applying a minimum length threshold to the UCN segments and excluding the UCN segments that span the minimum length threshold or less prior to the step of adding. Based on the broadest reasonable interpretation, applying a threshold 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 10 recites the limitation - wherein the minimum length threshold is 10 megabases (Mb). This limitation specifies the application of the threshold judicial exception of claim 9. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 11 recites the limitation - dividing a number of bases in the UCN segment by a total number of bases in a chromosome containing the UCN segment to produce a normalized number of UCN bases per UCN segment. Based on the broadest reasonable interpretation, dividing numbers 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 12 recites the limitation - multiplying the normalized number of UCN bases per UCN segment by a weight value to give a weighted number of bases per UCN segment, wherein the step of adding is applied to the weighted number of bases per UCN segment for all the UCN segments to produce the sum of UCN bases. Based on the broadest reasonable interpretation, dividing and adding numbers 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 13 recites the limitation - wherein the weight value is a total number of UCN segments in the chromosome. This limitation specifies the multiplying and adding judicial exception of claim 12. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 14 recites the limitation - excluding the segments having fewer than a minimum number of heterozygous SNPs in the segment. Based on the broadest reasonable interpretation, applying a threshold 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 15 recites the limitation - wherein the ratio indicative of genomic instability is determined based on analyzing the tumor sample only. This limitation specifies the ratio producing by judicial exceptions of claim 1. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 16 recites the limitation - dividing the genome into segments having homogeneous copy numbers using log odds of heterozygous single nucleotide polymorphisms (SNPs) and copy number variation (CNV) log ratios determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome; applying a threshold count to squared allelic log odds of each segment in autosomes of the genome to identify unbalanced copy number (UCN) segments; adding numbers of bases in the UCN segments to produce a sum of UCN bases; and dividing the sum of UCN bases by a total number of bases in all the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability. Based on the broadest reasonable interpretation, dividing the genome into segments, applying a threshold, adding numbers, and dividing numbers 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 17 recites the limitation - wherein the ratio is expressed as a percent to give a genomic instability (GI) score. This limitation specifies the ratio producing by judicial exceptions of claim 16. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 18 recites the limitation - wherein the ratio indicative of genomic instability is determined based on analyzing the tumor sample only. This limitation specifies the ratio producing by judicial exceptions of claim 16. The refined judicial exception indicated by this limitation still represents a judicial expectation.
Claim 19 recites the limitation - dividing the genome into segments having homogeneous copy numbers using log odds of heterozygous single nucleotide polymorphisms (SNPs) and copy number variation (CNV) log ratios determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome; applying a threshold count to squared allelic log odds of each segment in autosomes of the genome to identify unbalanced copy number (UCN) segments; adding numbers of bases in the UCN segments to produce a sum of UCN bases; and dividing the sum of UCN bases by a total number of bases in all the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability. Based on the broadest reasonable interpretation, dividing the genome into segments, applying a threshold, adding numbers, and dividing numbers 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 20 recites the limitation - the ratio is expressed as a percent to give a genomic instability (GI) score. This limitation specifies the ratio producing by judicial exceptions of claim 19. The refined judicial exception indicated by this limitation still represents a judicial expectation.
These limitations recite concepts of dividing, identifying, and calculating information and 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. A mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula (MPEP 2106.04(a)(2)). A claim that requires a computer may still recite a mental process (MPEP 2106.04(a)(2)). Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1-20 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 (MPEP 2106.04(d)(1)) or particular treatment (MPEP 2106.04(d)(2)). 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 selectively amplifying nucleic acid sequences at targeted locations in the tumor sample genome by a targeted panel with a low sample input from a tumor sample to generate a plurality of nucleic acid sequence reads;
Claim 16 recites a processor and a data store; receiving a plurality of nucleic acid sequence reads generated by selectively amplifying nucleic acid sequences at targeted locations in the tumor sample genome by a targeted panel with a low sample input from a tumor sample.
Claim 19 recites a non-transitory computer-readable medium, a computer; receiving a plurality of nucleic acid sequence reads generated by selectively amplifying nucleic acid sequences at targeted locations in the tumor sample genome by a targeted panel with a low sample input from a tumor sample.
There are no limitations that indicate that the claimed dividing, identifying, and calculating information and 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 1-20 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 dividing, identifying, and calculating information and 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 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) indicate that amplifying, sequencing, and analyzing DNA to provide sequence information or detect allelic variants are well understood, routine, and conventional laboratory practices. Additionally, Chang et al. (2013, Cancer Genetics, Vol. 206: 413419) teach selectively amplifying and sequencing DNA from a tumor sample and analyzing the reads data with a computer were well understood, routine, and conventional at the time of the effective filing date (Page 414, Column 2, Section Multiplex PCR-based library preparation and enrichment; Page 418, Table 5).
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 1-20 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.
Claims 1, 2, 9-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (2016, Nucleic Acids Research, Vol. 44, No. 16: 1-9, IDS 27 September 2024), in view of Zhu et al. (US 20210257047 A1). Italicized text from reference art.
Applicable claims include:
Claim 1. A method for analyzing a tumor sample genome for genomic instability, comprising: (Claim 1.i) selectively amplifying nucleic acid sequences at targeted locations in the tumor sample genome by a targeted panel with a low sample input from a tumor sample to generate a plurality of nucleic acid sequence reads; (Claim 1.ii) dividing the genome into segments having homogeneous copy numbers using log odds of heterozygous single nucleotide polymorphisms (SNPs) and copy number variation (CNV) log ratios determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome; (Claim 1.iii) applying a threshold count to squared allelic log odds of each segment in autosomes of the genome to identify unbalanced copy number (UCN) segments; (Claim 1.iv) adding numbers of bases in the UCN segments to produce a sum of UCN bases; and dividing the sum of UCN bases by a total number of bases in all the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability.
Claim 2. The method of claim 1, wherein the ratio is expressed as a percent to give a genomic instability (GI) score.
Claim 9. The method of claim 1, further comprising applying a minimum length threshold to the UCN segments and excluding the UCN segments that span the minimum length threshold or less prior to the step of adding.
Claim 10. The method of claim 9, wherein the minimum length threshold is 10 megabases (Mb).
Claim 11. The method of claim 1, further comprising dividing a number of bases in the UCN segment by a total number of bases in a chromosome containing the UCN segment to produce a normalized number of UCN bases per UCN segment.
Claim 12. The method of claim 11, further comprising multiplying the normalized number of UCN bases per UCN segment by a weight value to give a weighted number of bases per UCN segment, wherein the step of adding is applied to the weighted number of bases per UCN segment for all the UCN segments to produce the sum of UCN bases.
Claim 13. The method of claim 12, wherein the weight value is a total number of UCN segments in the chromosome.
Claim 15. The method of claim 1, wherein the ratio indicative of genomic instability is determined based on analyzing the tumor sample only.
Claim 16. A system for analyzing a tumor sample genome for genomic instability, comprising a processor and a data store communicatively connected with the processor, the processor configured to execute instructions, which, when executed by the processor, cause the system to perform a method, including: (Claim 16.i) receiving a plurality of nucleic acid sequence reads generated by selectively amplifying nucleic acid sequences at targeted locations in the tumor sample genome by a targeted panel with a low sample input from a tumor sample; (Claim 16.ii) dividing the genome into segments having homogeneous copy numbers using log odds of heterozygous single nucleotide polymorphisms (SNPs) and copy number variation (CNV) log ratios determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome; (Claim 16.iii)applying a threshold count to squared allelic log odds of each segment in autosomes of the genome to identify unbalanced copy number (UCN) segments; (Claim 16.iv) adding numbers of bases in the UCN segments to produce a sum of UCN bases; and dividing the sum of UCN bases by a total number of bases in all the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability.
Claim 17. The system of claim 16, wherein the ratio is expressed as a percent to give a genomic instability (GI) score.
Claim 18. The system of claim 16, wherein the ratio indicative of genomic instability is determined based on analyzing the tumor sample only.
Claim 19. A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method for analyzing a tumor sample genome for genomic instability, the method including: (Claim 19.i) receiving a plurality of nucleic acid sequence reads generated by selectively amplifying nucleic acid sequences at targeted locations in the tumor sample genome by a targeted panel with a low sample input from a tumor sample; (Claim 19.ii) dividing the genome into segments having homogeneous copy numbers using log odds of heterozygous single nucleotide polymorphisms (SNPs) and copy number variation (CNV) log ratios determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome; (Claim 19.iii) applying a threshold count to squared allelic log odds of each segment in autosomes of the genome to identify unbalanced copy number (UCN) segments; (Claim 19.iv) adding numbers of bases in the UCN segments to produce a sum of UCN bases; and dividing the sum of UCN bases by a total number of bases in all the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability.
Claim 20. The computer-readable medium of claim 19, wherein the ratio is expressed as a percent to give a genomic instability (GI) score.
Regarding Claims 1, 16, and 19, Shen et al. teach (Claim 1.i) selectively amplifying nucleic acid sequences at targeted locations in the tumor sample genome by a targeted panel with a low sample input from a tumor sample to generate a plurality of nucleic acid sequence reads (Page 8, Column 1, Paragraph 4: Figure 7 shows a FACETS application to the MSK-IMPACT clinical sequencing platform, a hybridization capture-based next-generation sequencing assay for targeted deep sequencing of all exons and selected introns of 410 key cancer genes in FFPE tumor samples). Shen et al. teach (Claim 1.ii) dividing the genome into segments having homogeneous copy numbers using log odds of heterozygous single nucleotide polymorphisms (SNPs) and copy number variation (CNV) log ratios determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome (Page 2, Column 2, Figure 2: The top panel displays total copy number log-ratio (logR), and the second panel displays allele-specific log-odds-ratio data (logOR) with chromosomes alternating in blue and gray; Page 3, Column 2, Figure 3: All SNP sites contribute to total copy log-ratio (logR), and heterozygous sites contribute to allelic logOR; Page 4, Column 1, Paragraph 2: we extended the circular binary segmentation (CBS) algorithm to a joint segmentation of logR and logOR). The log odds corresponding to the SNPs are interpreted as log odds ratios of the art in light of the published specification (Paragraph 0029: The log odds is calculated as the natural logarithm of the ratio). Shen et al. teach (Claim 1.iii) applying a threshold count to squared allelic log odds of each segment in autosomes of the genome to identify unbalanced copy number (UCN) segments (Page 4 Column 2, Paragraph 3: if the maximal statistic is greater than a predetermined critical value, we declare that a change exists. For each segment, the logR data are summarized using the median of the logR values x1 and the logOR data are summarized by x22). Additionally, Shen et al. teach the methods are performed by a computer, which inherently contain memory, computer readable memory, and at least on processor to perform the recited functions (Page 3, Column 1, Paragraph 2: The software is available). Claim 16 recites the limitations of claim 1 directed to a system and claim 19 recites the limitations of claim 1 directed a computer readable medium.
Regarding Claim 9, Shen et al. teach applying a minimum length threshold to the UCN segments and excluding the UCN segments that span the minimum length threshold or less prior to the step of adding (Page 8, Column 1, Paragraph 3: we focused on samples with segments length greater than 10 MB).
Regarding Claim 10, Shen et al. teach the minimum length threshold is 10 megabases (Mb) (Page 8, Column 1, Paragraph 3: we focused on samples with segments length greater than 10 MB).
Regarding Claim 11, Shen et al. teach dividing a number of bases in the UCN segment by a total number of bases in a chromosome containing the UCN segment to produce a normalized number of UCN bases per UCN segment (Page 3, Column 3, Paragraph 5: A normalizing constant is calculated for each tumor/normal pair to correct for total library size). A normalizing constant to correct for total library size is interpreted as equivalent to dividing by a total number of bases in a chromosome.
Regarding Claim 12, Shen et al. teach multiplying the normalized number of UCN bases per UCN segment by a weight value to give a weighted number of bases per UCN segment, wherein the step of adding is applied to the weighted number of bases per UCN segment for all the UCN segments to produce the sum of UCN bases (Page 4, Column 1, Paragraph 2: c is a scaling factor that is inversely proportional to the heterozygous rate; Page 4, Column 1, Paragraph 4: we introduce a weight that is inversely proportional to the heterozygous rate to increase the het-SNP contributions in subsequent segmentation analysis. Specifically, a scaling factor is introduced).
Shen et al. teach does not teach (Claim 1.iv, 16.iv, and 19.iv) adding numbers of bases in the UCN segments to produce a sum of UCN bases; and dividing the sum of UCN bases by a total number of bases in all the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability. Shen et al. does not teach does not teach (Claim 2, 17, and 20) the ratio is expressed as a percent to give a genomic instability (GI) score. Shen et al. does not teach the weight value is a total number of UCN segments in the chromosome (Claim 13). Shen et al. does not teach (Claim 15 and 18) the ratio indicative of genomic instability is determined based on analyzing the tumor sample only.
Regarding Claims 1, 16, and 19, Zhu et al. teach (Claim 1.i) selectively amplifying nucleic acid sequences at targeted locations in the tumor sample genome by a targeted panel with a low sample input from a tumor sample to generate a plurality of nucleic acid sequence reads (Paragraph 0128: the term “targeted panel” or “targeted gene panel” refers to a combination of probes for sequencing nucleic acids present in a biological sample from a subject (e.g., liquid biopsy sample). Zhu et al. teach (Claim 1.iv) adding numbers of bases in the UCN segments to produce a sum of UCN bases; and dividing the sum of UCN bases by a total number of bases in all the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability (Paragraph 0340: Segments having similar copy ratios, e.g., as assigned via circular binary segmentation (CBS) during copy number variations (CNV) analysis, are fit to integer copy states, e.g., via an expectation-maximization algorithm using the sum of squared error of the segment log 2 ratios (normalized to genomic interval size)). CNV are interpreted as equivalent to UCN (i.e. unexpected deviation of copy number). Adding numbers of bases in the UCN segments to produce a sum of UCN bases and dividing the sum of UCN bases by a total number of bases in all the segments is interpreted as equivalent to normalizing. Zhu et al. teach the methods are performed by a computer, which inherently contain memory, computer readable memory, and at least on processor to perform the recited functions (Paragraph 0151: The device in some implementations includes one or more processing units CPU(s) (also referred to as processors), one or more network interfaces, a non-persistent memory, a persistent memory, and one or more communication buses for interconnecting these components). Additionally, Zhu et al. teach performing the analysis on the autosomal chromosomes (Paragraph 0098: As used herein, the term “locus” refers to a position (e.g., a site) within a genome, e.g., on a particular chromosome. Because normal mammalian cells have diploid genomes, a normal mammalian genome (e.g., a human genome) will generally have two copies of every locus in the genome, or at least two copies of every locus located on the autosomal chromosomes). Claim 16 recites the limitations of claim 1 directed to a system and claim 19 recites the limitations of claim 1 directed a computer readable medium.
Regarding Claims 2, 17, and 20, Zhu et al. teach the ratio is expressed as a percent to give a genomic instability (GI) score (Paragraph 0112: a metric representing loss of heterozygosity across the entire genome of a tissue of a subject is represented as a single value, e.g., a percentage or fraction of the genome). This makes it obvious that the value expressed as a ratio can be turned into a percent. Claim 17 recites the limitations of claim 2 directed to a system and claim 20 recites the limitations of claim 2 directed a computer readable medium.
Regarding Claim 13, Zhu et al. teach the weight value is a total number of UCN segments in the chromosome (Paragraph 0445: each bin in the plurality of bins is assigned a weight, and the one or more filtering criteria comprises a threshold weight. In some such embodiments, the weight is determined based on one or more of: a size of the bin (e.g., the number of base pairs in the respective bin)).
Regarding Claims 15 and 18, Zhu et al. teach the ratio indicative of genomic instability is determined based on analyzing the tumor sample only (Paragraph 0039: FIG. 3 provides an example flow chart of processes and features for liquid biopsy sample collection and analysis for use in precision oncology, in accordance with some embodiments of the present disclosure). Biopsying is interpreted as the sample only includes tumor DNA. Therefore, it would be obvious the downstream analysis would only include tumor DNA if that was the only DNA in the sample. Claim 18 recites the limitations of claim 15 directed to a system.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine the methods of Shen et al. and Zhu et al. Shen et al. teach their modeling to analyze genetic variation has enhanced sensitivity, is widely applicable, and highly dependable (Page 3, Column 1, Paragraph 2: We show that FACETS can enhance the sensitivity of identifying aneuploid tumors by joint modeling of total and allele-specific pattern; Page 9, Column 1, Paragraph 2: The fast computation facilitates large-scale application. Accurate, purity- and ploidy-corrected, integer copy number calls provided by FACETS will be essential to more reliably interpret NGSbased cancer gene copy number data in the context of clinical sequencing). Relatedly, Zhu et al. teach their modeling to analyze genetic variation is improved over previous models at assessing copy number variation and vital to treating cancers related to copy number variation (Paragraph 0024: providing improvements in validating copy number variation annotations. the systems and methods described herein reject or validate a focal copy number status annotation for a at a locus that is potentially actionable using precision oncology). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both perform modeling that utilizes sequencing data to analyze genetic variation, including related to SNPs and copy number, applicable to cancer.
Claims 1-7, 9-13, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al., as applied to claims 1, 2, 9-13, and 15-20 above, in view of Zhu et al., as applied to claims 1, 2, 9-13, and 15-20 above, and in further view of Fisch et al. (2021, Journal of Computational and Graphical Statistics, Vol. 31, No. 2: 574-585). Italicized text from reference art.
Applicable claims include:
Claims 1, 2, 9-13, and 15-20 are presented above
Claim 3. The method of claim 1, further comprising applying a maximum length threshold to each of the UCN segments prior to the step of adding.
Claim 4. The method of claim 3, further comprising excluding UCN segments that span the maximum length threshold or more of a whole chromosome.
Claim 5. The method of claim 3, further comprising excluding UCN segments that span the maximum length threshold or more of a p-arm of a chromosome.
Claim 6. The method of claim 3, further comprising excluding UCN segments that span the maximum length threshold or more of a q-arm of a chromosome.
Claim 7. The method of claim 3, wherein a maximum length threshold value is a percent of a whole chromosome length, a percent of a p-arm length or a percent of a q-arm length.
Regarding Claims 1, 2, 9-13, and 15-20, the limitations are taught by Shen et al. and Zhu et al. as above.
Shen et al. and Zhu et al. do not teach the limitations of claims 3-7.
Regarding Claim 3, Fisch et al. teach applying a maximum length threshold to each of the UCN segments prior to the step of adding (Page 580, Column 1, Paragraph 5: Theorem 1 can be extended to allow for both a minimum and Maximum segment length).
Regarding Claim 4, Fisch et al. teach excluding UCN segments that span the maximum length threshold or more of a whole chromosome (Page 580, Column 1, Paragraph 5: Theorem 1 can be extended to allow for both a minimum and Maximum segment length; Page 584, Column 1, Paragraph 2: The results of this analysis on Chromosome 16 can be found in Table 2 while the results for Chromosome 6 can be found in Table 10 in the supplementary material). The analysis was conducted over the whole chromosome. Therefore, the threshold was applied over the whole chromosome.
Regarding Claim 5, Fisch et al. teach further comprising excluding UCN segments that span the maximum length threshold or more of a p-arm of a chromosome (Page 580, Column 1, Paragraph 5: Theorem 1 can be extended to allow for both a minimum and Maximum segment length; Page 584, Column 1, Paragraph 2: The results of this analysis on Chromosome 16 can be found in Table 2 while the results for Chromosome 6 can be found in Table 10 in the supplementary material). The analysis was conducted over the whole chromosome. Therefore, it is obvious the threshold was applied to the p arm of the chromosome.
Regarding Claim 6, Fisch et al. teach excluding UCN segments that span the maximum length threshold or more of a q-arm of a chromosome (Page 580, Column 1, Paragraph 5: Theorem 1 can be extended to allow for both a minimum and Maximum segment length; Page 584, Column 1, Paragraph 2: The results of this analysis on Chromosome 16 can be found in Table 2 while the results for Chromosome 6 can be found in Table 10 in the supplementary material). The analysis was conducted over the whole chromosome. Therefore, it is obvious the threshold was applied to the q arm of the chromosome.
Regarding Claim 7, Fisch et al. suggest a maximum length threshold value is a percent of a whole chromosome length, a percent of a p-arm length or a percent of a q-arm length (Page 580, Column 1, Paragraph 5: Theorem 1 can be extended to allow for both a minimum and Maximum segment length; Page 584, Column 1, Paragraph 2: The results of this analysis on Chromosome 16 can be found in Table 2 while the results for Chromosome 6 can be found in Table 10 in the supplementary material). The size excluded is out of a maximum number representing the size of the chromosome (i.e. the max threshold must be less than the length of the chromosome). This represents a proportion which Zhu et al. teach can obviously be represented as a percentage (see Regarding Claims 2, 17, and 20).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine the methods of Fisch et al. with Shen et al. and Zhu et al. Fisch et al. teaches modeling applied to analyzing copy number variation that is more consistent with increased performance over previous modeling (Page 584, Column 1, Paragraph 2: These tables show that MVCAPA shows much more consistency across folds than PASS. We also see that allowing for lags generally led to a better performance). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all perform modeling that utilizes sequencing data to analyze genetic variation, including related copy number.
Claims 1-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al., as applied to claims 1-7, 9-13, 15-20 above, in view of Zhu et al., as applied to claims 1-7, 9-13, 15-20 above, and in further view of Fisch et al., as applied to claims 1-7, 9-13, 15-20 above, and Bange (US 20070015157 A1). Italicized text from reference art.
Applicable claims include:
Claims 1-7, 9-13, 15-20 are presented above
Claim 8. The method of claim 7, wherein the maximum length threshold value is 90%.
Regarding Claims 1-7, 9-13, 15-20, the limitations are taught by Shen et al., Zhu et al., and Fisch et al. as above.
Shen et al., Zhu et al., and Fisch et al. do not teach the limitations of claim 8.
Regarding Claim 8, Bange teach the maximum length threshold value is 90% (Paragraph 0047: the derived fragments have a sequence length in each case which is preferably maximum 98% of the length the nucleotide sequence, maximum approximately 90%).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine the methods of Bange with Fisch et al., Shen et al., and Zhu et al. Bange teaches methods to accurately identify genetic sequences that are advantageous to optimizing the treatment of disease (Paragraph 0034: infection can be clearly, accurately and in particular simultaneously recognized. This permits particularly advantageously a rapid and controlled therapy of the infected organism). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all perform modeling that utilizes sequencing data to analyze genetic variation.
Claims 1, 2, and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al., as applied to claims 1, 2, 9-13, and 15-20 above, in view of Zhu et al., as applied to claims 1, 2, 9-13, and 15-20 above, and in further view of Sandmann et al. (2020, GigaScience, Vol. 9: 1-10). Italicized text from reference art.
Applicable claims include:
Claims 1, 2, 9-13, and 15-20 are presented above
Claim 14. The method of claim 1, further comprising excluding the segments having fewer than a minimum number of heterozygous SNPs in the segment.
Regarding Claims 1, 2, 9-13, and 15-20, the limitations are taught by Shen et al. and Zhu et al. as above.
Shen et al. and Zhu et al. do not teach the limitations of claim 14.
Regarding Claim 14, Sandmann et al. teaches excluding the segments having fewer than a minimum number of heterozygous SNPs in the segment (Page 4, Figure 2: The optimal detection thresholds are determined. min SNP: minimum number of SNPs).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine the methods of Sandmann et al. with Shen et al. and Zhu et al. Sandmann et al. teach novel and astonishing modeling to analyze a broad range of copy number variation in sequencing data (Page 8, Column 1, Paragraph 2: It may seem astonishing that CNVs reported by CopyDetective match the validated CNVs, spread all over the genome, with respect to coordinates and CFs so well while just analyzing WES data; Page 9, Column 2, Paragraph 2: CopyDetective unites an established idea—evaluating the change in VAF of polymorphisms to detect CNVs—with a completely new aspect—determining individual detection thresholds for every sample. Thereby, CopyDetective shines a new light on CNV calling in WES data). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all perform modeling that utilizes sequencing data to analyze genetic variation, including related to SNPs and copy number, applicable to cancer.
Double Patenting
No double patenting is identified.
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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