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 .
Office Action Overview
Claim Status
Canceled:
2, 6, 8, 15, 21, 22. 26-65
Pending:
1, 3-5, 7, 9-14, 16-20, 23-25, 66, and 67
Withdrawn:
10-13, 16-19
Examined:
1, 3-5, 7, 9, 14, 20, 23-25, 66, and 67
Independent:
1, 67
Amended:
1, 25
New:
67
Allowable:
none
Objected to:
1, 67
Rejections applied
Abbreviations
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
X
102, 103
JE
Judicial Exception
X
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed in the 08/23/2022 filing receipt, this application claims benefit to U.S provisional application 62/673,779, filed 05/18/2018.
Overview of Withdrawal/Revision of Objections/Rejections
In view of the amendment and remarks received 09/19/2025:
• The 101 rejection is maintained with revision.
• The 103 rejection is maintained with revision.
Examiner comment
In independent claims 1 and 67, given the extensive hierarchy among the claim steps and the extensive reliance on indentation, it would be helpful to number the steps, improving readability of the hierarchy. This is only a suggestion and neither an objection nor a rejection.
Claim Interpretation
Claim 67 recites "selecting a new therapeutic treatment or maintaining the therapeutic treatment based on the efficacy of the therapeutic treatment," which is being interpreted as selecting a new therapeutic treatment or selecting maintaining the therapeutic treatment based on the efficacy of the therapeutic treatment. It is noted there is no active step of administering treatment, but only the step for selecting a new treatment or (selecting) the maintaining of treatment, and therefore no step of therapy or prophylaxis. Possibly amending to add steps (after the selecting step) to actively administering, or actively maintaining, therapy which is informed by the JE may be helpful as a step toward reciting a particular therapy and prophylaxis in order to integrate the additional elements into a practical application at Step 2A Prong Two of the 101 analysis. (This is discussed below in answer to 101 arguments section.)
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, 3-5, 7, 9, 14, 20, 23-25, 28, 66, and 67 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 details the following framework to analyze Subject Matter Eligibility:
• Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? (see MPEP § 2106.03)
• Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (see MPEP § 2106.04(a)). Note, the MPEP at 2106.04(a)(2) & 2106.04(b) further explains that abstract ideas and laws of nature are defined as:
• mathematical concepts, (mathematical formulas or equations, mathematical
relationships and mathematical calculations);
• certain methods of organizing human activity (fundamental economic practices
or principles, managing personal behavior or relationships or interactions between
people); and/or
• mental processes (procedures for observing, evaluating, analyzing/ judging and
organizing information).
• laws of nature and natural phenomena are naturally occurring principles/ relations that
are naturally occurring or that do not have markedly different characteristics compared to
what occurs in nature.
• Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application? (see MPEP § 2106.04(d))
• Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05)
Regarding Step 1:
Step 1: Claims 1, 3-5, 7, 9, 14, 20, 23-25, 28, 66, and 67 are directed to related methods, and therefore to a category of statutory subject matter. (See MPEP § 2106.03). Claim 1 is directed to a 101 process, here a "method," with process steps such as "sequencing..." Claim 67 is analyzed similarly.
(Step 1: Yes)
Regarding Step 2A, Prong One:
The claims recite abstract ideas and a law of nature as follows:
Independent claims 1 and 67 recite mental processes and/or mathematical concepts in:
• considering the information of sequence data (reads, allele frequencies, mean counts, expected noise rates, etc.) ;
• detecting somatic mutations in cell-free nucleic acids (cfNA) at loci based on the first sequence reads
• generating candidate mutations
• filtering candidate mutations
• training a machine learning model
• iteratively sampling from a posterior distribution
• iteratively updating the machine learning model parameters during each iteration
• filtering the candidate mutations
• identifying true and false positives in candidate mutations based on expected noise rates at each position at the loci
• detecting somatic mutations in WBC at loci in the second sequence reads;
• comparing the somatic mutations in cfNA and WBC to identify WBC-matched cell free somatic mutations;
• determining a selection coefficient for each locus based on the number of nonsynonymous and synonymous WBC matched mutations at each locus
• comparing the selection coefficient for each locus with a threshold
• identifying positive, neutral, or negative selection when the selection coefficient is respectively greater, the same, or less than a threshold value.
Claim 1 additionally recites mental processes for:
• detecting a disease state by identification of positive, neutral, or negative selection at each locus
• identifying one or more of the loci as one or more targets for therapeutic treatment
Claim 67 additionally recites mental processes for:
• detecting a post-treatment disease state for the subject based on the identification of each locus as positive selection, neutral selection, or negative selection;
• determining efficacy of the therapeutic treatment by comparing the post-treatment disease state to a pre-treatment disease state; and
• selecting a new therapeutic treatment or selecting to maintain the therapeutic treatment based on the efficacy of the therapeutic treatment (see claim interpretation section above for interpretation of this selecting step of claim 67).
Claims 3, 4, 5, 7, 9, 14, 20, 25, and 66 recite mental processes and/or mathematical concepts (or further limit the claims) as follows: Claim 3 further limits the machine learning model. Claim 4 recites applying a read mis-mapping model. Claim 5 recites joint modeling or mixture modeling. Claim 7 further limits the threshold. Claim 9 further limits the somatic mutations. Claim 14 recites identifying the somatic mutations as tumor suppressors when the locus includes a nonsense mutation; and identifying the somatic mutations as oncogenes when the locus includes a nonsense mutation and a missense mutation. Claim 20 further limits the loci to a list of genes. Claim 25 recites assessing risk of, and diagnosing cardiovascular disease or a cancer, based on the somatic mutations. Claims 66 further limits the detected cfNA somatic mutations to having allele frequencies less than 0.01 %.
Additionally, there is a law of nature recited in the naturally occurring correlation between the positive selection, neutral selection, or negative selection at a locus and disease state of an individual (claim 1 and 67); and in the naturally occurring correlation between the somatic mutations at a locus and cardiovascular disease or cancer of an individual (claim 25).
Step 2A Prong One Summary: The claims recite judicial exceptions (JEs) of abstract ideas, characterized as mental processes and mathematical concepts, and a law of nature. When considering the BRI of the claims, the recitations for "detecting somatic mutations," "filtering candidate mutations," "modeling expected noise rates on sequencing data," "filtering by identifying true and false positives based on expected noise; "comparing the somatic mutations," "identifying positive, neutral, or negative selection," etc., may be performed in the human mind, or with pen and paper, as recited. Even regarding 6 million rows/genomic positions, regardless of the volume of sequence data, and although the sequence data analysis could be performed on a computer, the activity still represents an abstract idea. This is because such computations performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea. Additionally, mathematical concepts are inherently recited at least in claim 1 and 67, e.g., "training a machine learning model," "filtering candidate mutations," "training a machine learning model using sequencing data," "modeling the expected noise rates," "determining a selection coefficient," etc., while algorithms used in training the model and calculations (e.g., for determining selection coefficients, etc.) are discussed at least at Specification paras.[0185-0189], [0204], [0263], [0275], [0282-0296], [0298]. Additionally, claims 1, 25, and 67 recite a law of nature in the naturally occurring correlation between positive selection, neutral selection, or negative selection at a locus and disease state of an individual; and in the naturally occurring correlation between the somatic mutations at a locus and cardiovascular disease or cancer of an individual (claim 25). Therefore, the claims recite elements that constitute a judicial exception in the form of an abstract idea and a law of nature.
(Step 2A, Prong One: Yes)
Step 2A, Prong Two:
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). Here at Step 2A, Prong Two, any remaining steps and/or elements not identified as JEs are therefore in addition to the identified JE(s), and are considered additional elements. Because the claims have been interpreted as being directed to judicial exceptions (abstract ideas in this instance) then Step 2A, Prong Two provides that the claims be examined further to determine whether the judicial exception is integrated into a practical application [see MPEP § 2106.04(d)]. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
MPEP § 2106.04(d)(I) lists the following five example considerations for evaluating whether a judicial exception is integrated into a practical application:
(1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
(2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2).
(3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b).
(4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c).
(5) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Additional elements of data gathering and storing: Independent claims 1 and 67 recite additional elements of data gathering by: obtaining data by sequencing, by targeted sequencing to a depth of 1000x, enrichment with a panel of about 10,000 genes; and storing data in a database having a data matrix structure. Claims 23 and 24 respectively recite additional elements for data gathering in cell free DNA and cell free RNA. Data gathering steps and data storing steps are additional elements which perform functions of inputting, collecting, outputting, and storing the data needed to carry out the abstract idea. These steps are considered as adding insignificant extra-solution activity to the judicial exception, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed (see MPEP § 2106.04(d)(I)).
Step 2A Prong Two summary: Referring to the Step 2A, Prong Two considerations listed above, none of (1) an improvement, (2) treatment, (3) a particular machine, or (4) a transformation is clear in the record, such that here in Step 2A, Prong Two, no additional step or element, alone or in combination, clearly demonstrates integration of the JE(s) into a practical application. The additional elements are further discussed below in Step 2B.
(Step 2A, Prong Two: No)
Step 2B analysis:
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05).
Additional elements of data gathering: obtaining data by sequencing, by targeted sequencing to a depth of 1000x, enrichment with a panel of about 10,000 genes; and storing data in a database having a data matrix structure of claims 1 and 67, and cell free DNA/RNA of claims 24 and 25, do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network; storing and retrieving information in memory; analyzing DNA to provide sequence information or detect allelic variants; and amplifying and sequencing nucleic acid sequences [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity 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. Additionally, the following references show the conventionality of the 1000x depth and enrichment using about 10,000 genes limitation, storing of data in a matrix:
Xia, (Scientific reports, vol. 7(1), 7526, pages 1-7 (2017); cited in the 11/18/2022 form PTO-892), shows obtaining sequencing data on cell-free DNA (p.1-6); and enriching a panel of cancer-associated genes before ultradeep target sequencing to an average sequencing depth of 40000X (p.6).
Sorber, (Lung cancer, vol. 107, pages 100-107 (2017); cited in the 11/18/2022 form PTO-892), presents a review which includes NGS analysis of cell free DNA (p.101-103) and cell free RNA (bridging p.104-105).
Ku, (Expert review of molecular diagnostics, vol. 12(3), pages 241-251 (2012); cited in the 12/12/2023 form PTO-892), shows exome enrichment is a prerequisite for whole exome sequencing (WES) (p.243, col.1); shows whole exome sequencing in capture and sequence of almost all the ~200,000 exons in the human genome (p.244, table 1); analysis of RNA (p.244, table 1); and RNA seq analysis of a total of 10,152 genes (p.245, col.1).
Jennings, (The Journal of molecular diagnostics, vol. 19(3), pages 341-365 (2017); cited in the 12/12/2023 form PTO-892), presents a report on validation guidelines for NGS in oncology panels, and shows obtaining sequencing data from targeted sequencing (entire document); custom panels can be developed to interrogate large regions of the genome (typically 50 to several thousand genes) (p.344, col.1); and an average coverage of at least 1000x may be required to identify heterogeneous variants in tissue specimens of low tumor cellularity (p.350, col.1).
Gligorijević, (Proteomics, vol. 16(5), pages 741-758 (2016); cited in the 04/21/2025 form PTO-892), presents a review on methods for analyzing big data in precision medicine, and discusses use of matrices in genomics (p.747, table 2; p.748, col.2 and fig.3; p.749-p.752).
Thus, the additional elements of data gathering/storage using a matrix, and cell free nucleic acids are shown to be routine, well-understood, and conventional in the art, and as a result, do not provide an inventive concept needed to amount to significantly more than the judicial exception.
All the limitations of claims 1, 3-5, 7, 9, 14, 20, 23-25, 66, and 67 have been analyzed with respect to Step 2B, and none of these claims provide a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception, and thus do not transform the judicial exception into a patent eligible application of the exceptions.
(Step 2B: No)
Summary
Therefore, claims 1 and 3-21, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as reciting non-patent eligible subject matter.
Response to Applicant Arguments - 35 USC § 101
Applicant's arguments filed 09/19/2025 regarding the 101 rejection (remarks p.12-15) have been fully considered, but they are not yet persuasive.
Regarding Applicant Arguments for Step 2A Prong One
Applicant asserts (emphasis added):
•…limitations are not to mathematical concepts… first step relates to disease state detection, while the second step relates to identifying one or more targets for therapeutic treatment… these steps cannot be characterized as reciting any mathematical concepts (p.13, ¶ 2).
• … these limitations cannot be characterized as reciting any excluded method of organizing human activity (p.13, ¶ 3).
• classification of somatic mutations as positive selection, neutral selection, or negative selection…a computational process that is rooted in computer technology and data operations... these limitations cannot be characterized as a mental processes... (bridging p.13-14).
The arguments regarding Step 2A Prong One are not yet persuasive for the following reasons:
Mathematical concepts are inherently recited in the claims as concepts and algorithms that are used in e.g., training the model, performing calculations for determining selection coefficients, etc., which are discussed at least at Specification paras.[0185-0189], [0204], [0263], [0275], [0282-0296], [0298] .
Regarding mental processes, the claimed steps do not include details that would prevent analysis in the mind or with pen and paper. Even so, although such analysis performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea (a claim that requires a computer may still recite a mental process; see MPEP 2106.04(a)(2)(III)(c)).
Regarding Applicant Arguments for Step 2A Prong Two
Applicant asserts (emphasis added):
•… the additional elements relate to disease state detection and identification of targets for therapeutic treatment, being two critical components to disease treatment and prophylaxis (p.14, ¶ 4).
• … these additional elements apply the alleged judicial exception by effecting a particular treatment or prophylaxis (p.14, ¶ 4).
• The additional elements of claim 1 effect a treatment or prophylaxis of a medical condition, supporting eligibility under Step 2A, Prong Two (p.15, ¶ 1).
The arguments regarding Step 2A Prong Two are not yet persuasive for the following reasons:
The end of claim 1 recites limitations for detecting a disease state, and identifying loci as targets for therapeutic treatment (and the end of claim 67 recites limitations for detecting a post-treatment disease state, determining treatment efficacy, and selecting a new therapeutic treatment or maintaining the therapeutic treatment). However, there is not yet an additional element of a particular therapy recited in the claims, as these limitations recite abstract ideas for detecting, identifying, and selecting. These steps add to the abstract idea, but do not yet recite an additional element of actually administering therapy, and as such they JEs are not integrated into a practical application at Step 2A Prong Two. See MPEP 2106.04(d)(2).
It is noted the selecting step of claim 67 is interpreted (above, in the Claim Interpretation section) as: either selecting a new treatment or selecting to maintain treatment, and as such recites a mental process. Possibly amending to add steps (after the selecting step) to recite a step for actively administering or actively maintaining therapy informed by the JE may be helpful as a step toward reciting a particular therapy and prophylaxis in order to integrate the additional elements into a practical application at Step 2A Prong Two of the 101 analysis.
Regarding Applicant Arguments for Step 2B
Applicant asserts: "under Step 2B, the additional elements are non-routine and unconventional activity that amount to an inventive concept" (p.12, ¶ 3). No other arguments for step 2B were provided.
The argument regarding Step 2B are not yet persuasive because the additional elements (for obtaining data by sequencing, by targeted sequencing to a depth of 1000x, enrichment with a panel of about 10,000 genes; and storing data in a database having a data matrix structure of claims 1 and 67, and cell free DNA/RNA of claims 24 and 25), do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network; storing and retrieving information in memory; analyzing DNA to provide sequence information or detect allelic variants; and amplifying and sequencing nucleic acid sequences [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity 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. Additionally, several references were used (put forth above in the body of the 101 rejection under Step 2B) which show the conventionality of the additional elements.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-5, 7, 9, 14, 20, 23-25, 66, and 67 are rejected under 35 U.S.C. 103 as being unpatentable over Xia, (Scientific reports, vol. 7(1), 7526, pages 1-7 (2017); cited in the 11/18/2022 form PTO-892), in view of Martincorena, (Cell, vol. 171(5), pages 1029-1041, plus 23 pages supplemental info, total 37 pages (2017); cited in the 04/29/2020 IDS and the 11/18/2022 form PTO-892), in view of Ku, (Expert review of molecular diagnostics, vol. 12(3), pages 241-251 (2012); cited in the 12/12/2023 form PTO-892), in view of Xu, (Computational and structural biotechnology journal, vol. 16, pages 15-24 (Feb 2018); cited in the 12/12/2023 form PTO-892), as evidenced by "Bayesian inference." (Wikipedia, Wikimedia Foundation, version 16 May 2018, https://en.wikipedia.org/w/index.php?title=Bayesian_inference&oldid=841572519. Accessed 07 April 2025; cited in the 04/21/2025 form PTO-892); in view of Sorber, (Lung cancer (Amsterdam, Netherlands), vol. 107, pages 100-107 (2017); cited in the 11/18/2022 form PTO-892), in view of Gligorijević, (Proteomics, vol. 16(5), pages 741-758 (2016); cited in the 04/21/2025 form PTO-892).
Independent claim 1 and 67 recite similar methods for detecting positive, neutral, or negative selection at each of a plurality of genomic loci by: obtaining first and second sequence reads by respectively sequencing cell free nucleic acids and WBC nucleic acids from a subject, using targeted sequencing to a depth of at least 1000X, and an enrichment step prior to sequencing, comprising an enrichment panel of about 10,000 genes; detecting somatic mutations with allele frequencies less than 0.1 % in the cell free nucleic acids at the loci based on the first sequence reads; generating candidate mutations based on the first plurality of sequence reads and a reference genome; filtering the candidate mutations to obtain somatic mutations in the cell free nucleic acids at the loci; training a machine learning model using sequencing data from normal tissue of healthy individuals to model the expected noise rates on the per position per allele basis of the sequencing data, wherein the sequencing data comprises the allele frequencies for genomic positions, and the training comprises iteratively sampling a posterior distribution of allele frequencies for each position; iteratively updating parameters which include expected mean counts of allele frequencies; storing draws of expected mean counts of allele frequencies in a parameter database, the draws stored in a matrix data structure having corresponding to more than 6 million genomic positions; filtering the candidate mutations by identifying true positives and false positives in the candidate mutations based on the parameter database storing expected noise rates at each position at the loci; detecting somatic mutations in the WBC at the loci based on the first sequence reads; comparing the somatic mutations in the cell free nucleic acids and the somatic mutations in the WBC to identify WBC-matched cell free somatic mutations; determining a selection coefficient for each locus based on the number of detected WBC-matched cell free somatic mutations that are nonsynonymous and the number of detected WBC-matched cell free somatic mutations that are synonymous, at the locus; comparing the selection coefficient determined for each locus with a threshold value and identifying the locus as positive selection when the determined selection coefficient is greater than the threshold value, identifying the locus as neutral selection when the determined selection coefficient is at about the threshold value, or identifying the locus as negative selection when the determined selection coefficient is lower than the threshold value.
Independent claim 1 recites further steps for detecting a disease state for the subject based on the identification of each locus as positive selection, neutral selection, or negative selection; and identifying one or more of the loci as one or more targets for therapeutic treatment based on the identification of the one or more loci as positive selection.
Independent claim 67 recites further steps for detecting a post-treatment disease state for the subject based on the identification of each locus as positive selection, neutral selection, or negative selection; determining efficacy of the therapeutic treatment by comparing the post-treatment disease state to a pre-treatment disease state; and selecting a new therapeutic treatment or maintaining the therapeutic treatment based on the efficacy of the therapeutic treatment.
Dependent claim 3 further recites the machine learning model is a noise model. Dependent claim 4 further recites detecting somatic mutations by applying a read mis-mapping model. Dependent claim 5 further recites joint modeling or mixture modeling to detect both the somatic mutations in the cell free nucleic acids and the somatic mutations in the WBC. Dependent claim 7 further recites the threshold is 1. Dependent claim 9 further recites the somatic mutations are single-nucleotide variants. Dependent claim 14 further recites identifying the somatic mutations as one or more tumor suppressors when the locus includes at least one nonsense mutation, or as one or more oncogenes when the locus includes at least one nonsense mutation and at least one missense mutation.
Dependent claim 20 further recites the loci comprise one or more genes selected from the group consisting of DNMT3A, TET2, CHEK2, CBL, TP53, ASXLl, PPMlD, SF3Bl, ARID2, ATM, DNMT3B, SH2B3, RAD21, SRSF2, JAK2, KMT2C, MGA, KDR, KRAS, MSTl, ERRFII, CCND2, EWSRl, MYD88, and CDKN1B. Dependent claim 23 further recites the cell free nucleic acids are cell-free DNA. Dependent claim 24 further recites the cell free nucleic acids are cell-free RNA. Dependent claim 25 further recites assessing a risk of developing a disease state, detecting a disease state, and/or diagnosing a disease state based on the identification of one or more somatic mutations at the locus, wherein the disease state is a cardiovascular disease or a cancer. Dependent claim 66 further recites the somatic mutations detected in the cell free nucleic acids have allele frequencies less than 0.01 %.
Xia shows a method using statistical analysis to examine the allele frequency of background somatic mutations in white blood cells (WBC) and cell-free DNA (cfDNA) in healthy controls based on sequencing data from 821 non-cancer individuals and several cancer samples with the aim of understanding the patterns of mutations detected in cfDNA (p.1). Xia shows extraction of cfDNA from patient and control blood samples (p. 5-6); performing ultra-deep target sequencing of somatic mutations in cancer associated genes for both cfDNA and WBC DNA, analyzing the sequence data by mapping to the human reference genome hg19 by Burrows–Wheeler transformation and filtering the reads (p.6, under 'Data filtering and analysis'). Xia shows analysis of the correlation of the mutant allele frequency in 309 WBC samples and the corresponding cfDNA (p.3 and p.6). Xia shows two technical replicates were performed for both WBC and cfDNA samples from the same individual, and were compared the mutant allele frequency in the two WBC samples and the two cfDNA samples. The reproducibility validation experiment in the two WBC samples was used to estimate the PCR error rate and sequencing errors (i.e., false positives) in the study cohort (p.3, under reproducibility) (showing sequencing of cell free nucleic acids and WBC nucleic acids; detecting candidate mutations in cell free nucleic acids by generating candidate mutations based on the sequence data and a reference genome; filtering to obtain somatic mutations; detecting candidate mutations in WBC nucleic acids; comparing somatic mutations in cell free nucleic acid and WBC nucleic acids; and healthy control sequence data of claim 1, and cfDNA of claim 23).
Xia shows use of a correlation study of mutant allele frequency in both circulating tumor DNA (ctDNA) and white blood cell DNA (fig.2, p. 3). Xia shows a multiplex PCR method to enrich a panel of 50 cancer-associated genes before ultradeep target sequencing to an average sequencing depth of 40000X (p. 6) (showing depth of at least 1000x of claim 1).
Xia shows removal of positions with mutant allele frequency higher than 0.1 (p.6, under "Mutant allele frequency in cfDNA and WBCs in the population and in individuals"). Xia shows several points where mutant allele frequency in cfDNA is at or less than a frequency of 0.0001, which equates to 0.01 % (p.3, fig.2a) (showing the somatic mutations have allele frequencies less than 0.1% of claim 1, and less than 0.01% of claim 66).
Additionally, Xia shows use of a reference genome library which mimics the blood sample derived plasma cfDNA, examining the sensitivity and precision of this cfDNA reference standard using their sample and library preparation method, and testing the sensitivity of the method at variant allele frequencies of 0.0005, 0.001, 0.005 and 0.01, and finally showing their method may be used to detect the variant alleles qualitatively at a frequency of 0.001 and quantitatively at a frequency of 0.01. (p.2, under "Validating the detecting sensitivity using standard reference and tumor sample") (showing the somatic mutations have allele frequencies less than 0.01% of claim 66).
Xia discusses that cfDNA and ctDNA have emerged as the research frontier of non-invasive cancer biomarkers for the detection, monitoring and treatment of cancer (p.4, under Discussion), inherently showing detecting a disease state, identifying… targets for therapeutic treatment of claim 1, and detecting a post-treatment disease state for the subject, determining efficacy of the therapeutic treatment by comparing the post-treatment disease state to a pre-treatment disease state; and selecting a new therapeutic treatment or maintaining the therapeutic treatment based on the efficacy of the therapeutic treatment of claim 67. Note, below, Sorber adds additional teachings regarding these limitations.
Xia does not show the claim 1 limitations: an enrichment panel comprising from about 10,000 targeted genes; using a machine learning model to determine expected noise rates of candidate mutations; iteratively sampling from a posterior distribution of allele frequencies; iteratively updating parameters including expected mean counts of the allele frequencies; storing draws in a parameter databases having a matrix data structure of more than 6 million rows representing more than 6 million genomic positions; determining a selection coefficient and comparing it to a threshold value to detect positive, neutral or negative selection at the locus of claim 1.
Xia does not show a noise model, read mis-mapping model, mixture model, threshold of 1, or single-nucleotide variants respectively of claims 3, 4, 5, 7, and 9.
Xia does not show identifying the somatic mutations as one or more tumor suppressors when the locus includes at least one nonsense mutation, or as one or more oncogenes when the locus includes at least one nonsense mutation and at least one missense mutation of claim 14.
Xia does not show a gene from the group recited in claim 20.
Xia does not show cell-free RNA of claim 24.
Xia does not show assessing a risk, detecting, or diagnosing cardiovascular disease or cancer of claim 25.
Xia does not show a locus detected as having positive selection is identified as a target for a therapeutic treatment of claim 28.
Martincorena shows use of an in-silico identification and removal of sequencing artifacts using a variant calling algorithm that relies on building a base specific error model by using a large collection of unmatched normal samples; these sequencing artifacts can be caused by sequencing errors, PCR errors, DNA damage in a library, misalignment of reads, or other causes (p.e5); these sequencing artifacts are equivalent to mismapped reads, and in this way, a read mismapping model is disclosed (showing the mis-mapping model of claim 4). Martincorena shows using dN/dS to quantify selection in cancer genomes (p. 1030). Note, regarding the instant claims, the dN/dS is equivalent to the selection coefficient (see specification paragraph [00185]). Martincorena shows that identification of genes under positive selection are genes for which dN/dS is higher than 1 (p. 1031, col.1); and that negative (or purifying) selection will lead to the dN/dS < 1 (p. 1031, col.2); and further shows the neutral peak is at dN/dS = 1.0 (p. 1031, col.2); in this way, the dN/dS ratio is compared to "l," "l" is equivalent to the threshold, and therefore Martincorena shows the determining a selection coefficient and comparing it to a threshold value to detect positive, neutral or negative selection at the locus of claim 1, and the threshold value is 1 of claim 7. Martincorena shows use of Poisson noise in the counts of nonsynonymous and synonymous mutations (p.e6, under "dN/dS distributions across genes"), and utilized a simulation model to study how much variation in observed dN/dS values across genes is expected by simple noise under perfect neutral evolution (p.e6, under "Neutral simulations"), (Showing the claim 1 limitation of modeling expected noise rates, and the noise model of claim 3.)
Martincorena shows modeling the variation of the normalized mutation rate per base pair across genes as following a Gamma distribution; in a given dataset, the observed number of synonymous mutations per gene can then be modeled as a Poisson process whose mean is drawn from a Gamma distribution reflecting the variation of the mutation rate across genes (p.e3, under "Modeling variable mutation rates across genes: dNdScv"). Martincorena further shows matrices in performing the regression (p.e4, further under "Modeling variable mutation rates across genes: dNdScv"). Martincorena also shows the probability that a gene has a particular value by calculating the posterior probability of a gene (i.e., allele frequencies), with an equation identical to the empirical Bayes equation (p.e7, 1st complete paragraph). (Showing posterior distribution of allele frequencies and data matrix of claim 1).
Martincorena shows use of a mixture model when inferring distribution of dN/dS values (p. 1032) (showing mixture modeling of claim 5); and analysis of somatic mutations using the global dN/dS which was estimated using a single substitution model (p. e10), which can include C>T substitutions (p. 1037, fig 5D). Martincorena further shows a model of substitutions per site, calculating the relative rate of C > T substitutions per site (p. e2). Looking to the specification, paragraph [00146] states SNV (single nucleotide variants) may be denoted as "C>T," and further states the term "mutation" refers to one or more SNVs or indels [00148]. Thus, Martincorena shows," ... wherein the one or more somatic mutations comprise one or more single-nucleotide variants" of claim 9.
Martincorena discloses that a fifth of the nonsense mutations (observed in a group of 369 cancer genes) occurred in just eight tumor suppressor genes (p. 1038), as well as when considering nonsense or missense mutations, genes generally fall into two classes: oncogenes, with strong selection on missense mutations, or tumor suppressor genes, with stronger selection on truncating mutations (p. 1031 ); (truncating mutation are equivalent to nonsense mutations) (showing tumor suppressor genes and oncogenes of claim 14).
Martincorena shows analysis and identification of cancer genes with a higher frequency of synonymous mutations than expected, and TP53 is discussed in detail (p. e12) (showing TP53 of claim 20). Martincorena shows therapeutic decision support for an individual patient critically depends on accurate identification of which specific mutations drive that person's cancer (p.1029-30); and also shows a method to screen for positive selection at gene level (driver gene discovery) (p.e5)(showing a locus having positive selection is identified as a therapeutic target of claim 28).
Gligorijević shows matrix data structures by evidencing extensive use of matrices in genomics database analysis and storage (p.747, table 2; p.748, col.2 and fig.3; p.749-p.752). (Showing the matrix data structures of claim 1 (along with Martincorena)).
Ku, (Expert review of molecular diagnostics, vol. 12(3), pages 241-251 (2012)), shows exome enrichment is a prerequisite for whole exome sequencing (WES) (p.243, col.1); shows whole exome sequencing in capture and sequence of almost all the ~200,000 exons in the human genome (p.244, table 1); and additionally shows RNA seq analysis of a total of 10,152 genes (p.245, col.1). The Specification at least at paragraphs [0185, 0204, 0222] recites "the whole exome (all genes)". With that in mind, it is understood that one of skill in the art would recognize that at least almost 10,000 genes are shown when Ku shows whole exome sequencing of almost all the ~200,000 exons in the human genome (showing 10,000 target genes of claim 1).
Xu shows several machine learning based tumor-normal somatic variant callers including BAYSIC, MutationSeq, SNooPer, (p.18, table 1), as well as a somatic-germline classifier, ISOWN, that relies on MuTect2 (single-sample mode) to call all the variants in the sample and then uses supervised machine learning algorithms to train a somatic-germline classifier (p.19, col.1, and table 2) (showing training a machine learning model using data from healthy individuals of claim 1). Xu also shows variant calling performance metrics, including true positive (TP) and false positive (FP) (p.21, table 5, and col.2), and SNV caller using noise model, noise level estimation, or Poisson models on read count (p.19, Table 2) (showing identifying true positive and false positives in candidate mutations on expected noise rates from the machine learning model of claim 1).
While Xu does not make explicit statements concerning iterative updating, it would be inherent that a model as in Xu is iteratively updated, as Bayes rule is one of the algorithms used to calculate the posterior probability of the joint genotypes (i.e., allele frequencies), (p.17, col.1), and use of Bayes rule inherently includes updating parameters (as evidenced by "Bayesian inference." Wikipedia, Wikimedia Foundation, version 16 May 2018, https://en.wikipedia.org/w/index.php?title=Bayesian_inference&oldid=841572519. Accessed 07 April 2025).
Sorber shows a method of liquid biopsy by analyzing cell free DNA and platelets, and shows a next generation sequencing method for parallel sequencing of millions of DNA templates (p.102, table 1); as one of skill in the art would understand the template DNA may be, e.g., several hundred bp, then the sequencing of millions of templates would result in sequencing data comprising more than 6 million positions (showing 6 million genomic positions of claim 1). Sorber shows cell-free nucleic acids comprise both cell-free DNA and cell-free RNA (p.100) (showing cell-free RNA of claim 24). Sorber shows analysis of circulating cell-free nucleic acids (cfNA) as a liquid biopsy in lung cancer (p. 100); Sorber further shows applications of liquid biopsy include monitoring of the tumor burden (i.e., which would inherently include detecting a disease state, determining efficacy of treatment, and selecting new treatment or maintaining treatment); both in targeted therapy setting as with surgery and chemotherapy, where detection of certain biomarkers (e.g. KRAS mutations) in NSCLC patients might be indicative of minimal residual disease and in some cases a predictor of prognosis to treatment (i.e., determining treatment efficacy). (fig. 1C, p.103) (showing detecting or diagnosing a cancer based on somatic mutations of claim 25; detecting a disease state and identifying targets for therapeutic treatment of claim 1; and detecting a post-treatment disease state, determining efficacy of the therapeutic treatment; and selecting a new therapeutic treatment or maintaining the therapeutic treatment based on the efficacy of the therapeutic treatment of claim 67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for sequencing, analyzing, detecting, and calculating mutant allele frequencies of somatic mutations in both cfDNA and WBC DNA of Xia with the method for analyzing, detecting, and inferring positive, neutral, or negative selection of somatic mutations from cancer and normal tissue using statistical models to quantify the dN/dS (selection coefficient) of Martincorena; the matrix use of Gligorijević, the trained machine learning using Bayes to calculate posterior distribution and iteratively update of Xu as evidenced by the Bayes inference Wiki; the sequence analysis of all 200,000 exons of the human genome of Ku; and the monitoring of tumor burden, and sequencing millions of DNA templates of Sorber, the to come to a better performing method of detecting, quantifying, and inferring selection of mutations in matched white blood cell DNA and cfDNA samples. This is because Xia states a need to carefully calibrate and measure the background mutations in WBC and cfDNA of healthy individuals to reduce a false positive rate during early cancer diagnosis (p.5); while Martincorena, provides motivation by teaching a critically refined method of quantitative assessment of positive and negative selection in cancer for accurate quantification of selection in cancer mutations (p.1030, and entire document); Gligorijević clearly shows extensive use of matrices in genetic data analysis; Xu states machine learning methods have been very successful in classification, and variant calling is essentially a classification problem (p.18, col.1); Ku shows whole exome sequencing (WES) and RNA-seq are powerful information generation tools (p.247, bottom of col.1); and Sorber clearly lays out the benefits of analyzing cfDNA as a liquid biopsy in lung cancer by teaching that liquid biopsy has been shown to be a high-quality blood based biomarker for use in many aspects, of cancer treatment (p. l 05), and reveals sequencing of mRNA allowed the researchers to scan for general molecular traces of cancer and also provided strong indications on tumor type and molecular subclass, demonstrated by the 71% rate of accuracy achieved when pinpointing the location of the primary tumor across six different tumor types as well as by the accurate identification of MET or HER2- positive tumors, and mutant KRAS, EGFR, and PIK3CA tumors (p.105, col.1). One would have had a reasonable expectation of success, as Xia clearly lays out how to detect and analyze somatic mutations in cfDNA and WBC DNA samples (p.3), showing qualitative detection of allele frequency down to 0.0001 in cfDNA (fig 2a, p. 3); and Martincorena, sets forth a method to investigate and accurately quantify selection in cancer mutations (p.1030, and entire document). Because the combination of Xia, Martincorena, Gligorijević, Ku, Xu as evidenced by the Bayes inference Wiki, and Sorber, appear in the same field of use, one of ordinary skill in the art would have recognized that the combination would have further provided more accurate results for detecting, quantifying, and inferring selection of mutations in matched white blood cell DNA and cfDNA samples, and as such, the combination would have been obvious.
Response to Applicant Arguments - 35 USC § 103
Applicant's arguments filed 09/19/2025 have been fully considered, but they are not yet persuasive.
Applicant asserts the following:
There is no motivation to combine Xia with Martincorena, (pp.15-16) (italicized phrase added by examiner):
• …there is no motivation to combine Xia with Martincorena (p.15, ¶ 5).
• (because Xia and Martincorena present)… disparate technical problems…there is no motivation for applying Martincorena' s analysis of mutations in tumorous samples to Xia' s identification of somatic mutations from healthy samples as background to mutation detection in circulating tumor DNA (remarks, p.16).
In asserting no motivation to combine the references, Applicant asserts, mainly regarding Xia, (p.15):
• …Xia analyzes cfDNA and WBC DNA in healthy samples to show a correlation in the two sample types for somatic mutations.
• Xia' s aim is to examine the background somatic mutations in white blood cells (WBC) and cfDNA in healthy controls ... with the aim of understanding the patterns of mutations detected in cfDNA. Xia, p. 1. (p.15, ¶ 5).
In asserting no motivation to combine the references, Applicant asserts, mainly regarding Martincorena, (p.16):
• Martincorena, on the other hand, uses tumor samples to demonstrate there is an indication in the nonsynonymous versus synonymous mutations that can help to classify selection type of a mutation (remarks, p.16).
• … (Martincorena) can directly enumerate the excess or deficit of mutations in a given gene, a group of genes, or even the whole exome, compared to the expectation for the background mutational processes….This enables us to provide robust estimates of the total number of coding driver mutations across cancers, how many coding point mutations are lost through negative selection, and a detailed dissection of the distribution of driver mutations in individual cancer genes across different tumor types." Martincorena, p. 2, col. 1, (remarks, p.16).
The arguments regarding no motivation to combine Xia with Martincorena are not persuasive for the following reasons:
One would be motivated to combine Xia with Martincorena, as they are drawn to related teachings concerning statistical analysis of somatic mutations in cancer.
While Xia shows using statistical analysis to examine the allele frequency of background somatic mutations in white blood cells (WBC) and cell-free DNA (cfDNA) in healthy controls, their initial study is based on sequencing data from 821 non-cancer individuals and several cancer samples with the aim of understanding the patterns of mutations detected in cfDNA (Xia, p.1). Xia further teaches their study can help define the threshold of mutation detection of ctDNA (circulating tumor DNA) by removing the background mutations in WBC and cfDNA in a healthy population (Xia, p.5, ¶ 3). Further, Xia discusses that a better understanding of the baseline spectrum of somatic mutations in healthy individuals is urgently required before the detection of ctDNA can be useful in early cancer diagnosis (Xia, p.2, ¶ 1). This establishes that while Xia studies mainly healthy samples, their research could help further studies on patterns of somatic mutations in cancer. Because Martincorena focuses on studying patterns of selection in different cancers by statistical analysis of (mainly) somatic mutations, there would be motivation to combine the references as this shows both Xia and Martincorena are concerned with analysis of somatic mutations in cancer, providing motivation to combine.
In addition, while Xia focuses on background somatic mutations, and Martincorena discloses analysis of synonymous and non-synonymous mutations, these are interrelated concepts (background mutations include synonymous and non-synonymous mutations). See instant Specification paragraph [0204], which discloses "the selection coefficient can be an estimate that directly enumerates the excess or deficit of mutations in a given loci, gene, group of genes, or even the whole exome (all genes) compared to the expectation for the background mutational process. For example, dN/dS (also referred to as Ka/Ks), as denoted by ω, can be used to estimate the excess or deficit of mutations in a given loci, gene, group of genes, or even the whole exome (all genes) compared to the expectation for the background mutational process" (i.e., neutral evolution, where dN/dS ≈ 1). Specification paragraph [0204] further discloses, "…used herein, dN/dS… is the ratio between the rate of nonsynonymous per nonsynonymous site and synonymous substitutions per synonymous site. For somatic mutations (or variants), the rate of mutations per site can be analyzed." As such, the background and synonymous/non-synonymous mutations are closely related concepts in analysis of somatic mutations (here, concerning cancer), providing another reason one would be motivated to combine Xia with Martincorena.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.A.V./Examiner, Art Unit 1687
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686