DETAILED ACTION
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Status of the Claims
Claims 1-20 are pending.
Claims 3, 7 and 17 are objected to.
Claims 1-20 are rejected.
Priority
This application US 18/240,445 (08/31/2023) claims benefit of US Application 63/402,512 (08/31/2022) as reflected in the filing receipt mailed on 09/12/2023. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-20 is 08/31/2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/24/2024 and 08/18/2026 were considered.
Claim objections
Claims 3, 7, 10 and 17 are objected to because of the following informalities. Appropriate correction is required.
Claim 3 is objected to under 37 CFR 1.75 as being in improper. The recited “filtering” active step should be listed in a newline and indented. As set forth in 37 CPR 1.75, each element or step of the claim should be separated by a line indentation (608.01(m) Form of Claims). Sub-steps/elements should be indented from their parent step/element. This rule should be applied throughout the claims as needed.
Claim 3 recites “structural variants (SVs),” however, the SV abbreviation should be introduced in claim 1 instead, where the recited “structural variant” term appears for the first time in the claims.
Claim 7 recites “a support vector machine (SVM),” however the term “(SVM)” should be removed since this abbreviation is not used anywhere else in the claims.
Claim 10 should recite “sample handling artifacts” instead of “sample handline artifacts.”
Claim 17 spells out the term FFPE, however, said spelling should be introduced in claim 14 instead, where the term FFPE appears for the first time in the claims.
Claim Rejections - 35 USC § 112(b)
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 2-20 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. following issues cause the respective claims to be rejected under 112(b) as indefinite:
In claim 2, the recited "significant" is a term of relative or vague degree or form of association, neither defined in the specification (pg. 11 para. 2) nor having a well-known and sufficiently particular definition in the art and in the instant context. The disclosure is not interpreted as a definition. MPEP 2173.05(b) pertains.
The following recitations require but lack antecedent basis, rendering their claims indefinite because there is no previous recitations of the followings terms as written:
claims 3 and 19, "the DNA"
claim 14, "the FFPE tissue"
Claim 15 depends on claim 16 and it recites “the assay” which is indefinite because claim 16 does not recite “an assay.” It is unclear if claim 15 should depend on claim 16 or claim 14 which recites “an assay.”
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 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 1-20) which falls within one of the categories of statutory subject matter. [Step 1: claims 1-20: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) as well as a law of nature or a natural phenomenon are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "performing a first mapping of the reads to at least one reference by a first algorithm to identify a structural variant; performing a second mapping of the reads by a second algorithm to identify the structural variant; and merging the first mapping with the second mapping to describe the structural variant" (independent claim 1);
• "wherein the patterns are identified through machine learning analysis of sequence data for known germline SVs or somatic SVs" (claim 6);
• "wherein the designing step comprises machine learning analysis of somatic SV primers with known amplification data" (claim 16);
• "analyzing the sequence reads to identify somatic structural variants (SVs) in the DNA through machine learning analysis of sequence data for known somatic SVs without reference to a matched normal sequence read from the patient" (independent claim 19);
• "identifying and removing germline SVs from a set of putative somatic SVs through machine learning analysis of sequence data for known germline SVs" (claim 20).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed by mathematical operation. The step directed to executing “mapping algorithms to identify structural variants” requires mathematical techniques as the only supported embodiments because it describes a mathematical technique (MPEP 2106.04(a)(2) pertains). Further support for the mathematical techniques used in the claims is provided in the specification at pg. 15 para. 2, which discloses that the described mapping may include decision tree learning such as random forests, support vector machines (SVMs), association rule learning, inductive logic, programming, regression analysis, clustering. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus, mathematical concepts (MPEP 2106.04(a)(2)). 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." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "performing a first mapping of the reads to at least one reference by a first algorithm to identify a structural variant; performing a second mapping of the reads by a second algorithm to identify the structural variant; and merging the first mapping with the second mapping to describe the structural variant" (independent claim 1);
"analyzing the sequence reads to identify putative structural variants (SVs) in the DNA; and filtering the putative SVs to remove germline SVs and/or sample handling artifacts, thereby providing a set of somatic SVs present in the DNA” (claim 3);
• "updating the training set with data from the filtering step" (claim 11); and
• "designing, … at least one primer pair for each somatic SV in the set, wherein the primer pair will successfully amplify a target that includes the somatic SV" (claim 13).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind (i.e. concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) or because the method only requires a user to manually determine action based on an added number. Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of analyzing (i.e. evaluating) data obtained; making judgments to remove data points, updating a list (i.e. training set), designing a sequence read (i.e. primer) and evaluating data to map sequence reads.
Dependent claims 2, 4-7, 10 and 12 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claim 2 recites further details about the first and second mapping algorithms; claims 4-5 and 12 recite further details about the filtering sequence reads step; and claims 6-7 and 10 recite further details about the machine learning analysis.
Furthermore, the instant claims recite a natural correlation by correlating the genetic material naturally found in the body with its structural variant classification. (see MPEP 2106.04(b).I).
[Step 2A Prong One: claims 1-20: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
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);
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);
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);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
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).
Analysis of instant claims
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).
Instant claims 1, 7-9, 13-15, 17 and 19 recite additional elements that are not abstract ideas:
• "obtaining sequence reads from a … sample" (independent claims 1 and 19);
• "wherein the machine learning analysis comprises a neural network" (claims 7-8);
• "wherein the machine learning analysis comprises a convolutional neural network" (claim 9);
• "computer software" (claim 13);
• "using the primer pair to perform an assay on a sample from a subject from whom the FFPE tissue sample was obtained to detect minimal residual disease in the subject" (claim 14);
• "wherein the assay comprises digital PCR on cell-free DNA from blood or plasma" (claim 15);
• "providing amplicons obtained from DNA extracted from the sample" (claim 17); and
• "sequencing the amplicons to obtain the set of sequence reads" (claim 17).
Dependent claims 17-18 recite further details about the sample from which the sequence reads were obtained.
Considerations under Step 2A, Prong Two
The limitations recited in claims 1-20 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
The recited "obtaining sequence reads from a … sample" (independent claims 1 and 19) and "providing amplicons obtained from DNA extracted from the sample sequencing the amplicons …” (claim 17) read on data gathering activities; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means.
The recited "using the primer pair to perform an assay on a sample from a subject from whom the FFPE tissue sample was obtained to detect minimal residual disease in the subject" (claim 14) reads on a generic "apply it" step because the claim recites an idea of a solution or outcome without any indication of how the judicial exception impacts or influences this step. The “using” of said designed “primer pair” to “detect minimal residual disease in the subject” reads on intended use and does not provide evidence of how exactly the recited judicial exceptions are being integrated into a practical application. There are no additional limitations to indicate details of exactly how the judicial exception is being integrated by the additional elements.
With respect to claims 8-9 the computer-related elements or the general-purpose computer and the recited neural network model does not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)). The specification as published also notes that computer processors and systems, as example, are known and widely used examples of neural networks that may be used without limitation (pg. 15 para. 2), which provides evidence that any suitable neural network could be used to model the claimed invention. The additional elements are set forth at such a high level of generality that they can be met by a general-purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Further, the limitation reciting use of a “neural network" provides mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Claim 15 merely limits the intended use of the primer pair from parent claim 13 which does not amount to a practical application as the “assay” is not required to be performed in the claims.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-20: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1-20 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The computer-related elements or the general-purpose computer and the neural network model do not rise to the level of significantly more than the judicial exception. The claims state a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)).
With respect to the instant claims, the prior art review to Richters ("Best practices for bioinformatic characterization of neoantigens for clinical utility." Genome medicine 11.1: 56 (2019); newly cited) discloses that obtaining sequence reads from samples and using sequence-specific PCR amplification and genetic typing to identify genetic variants is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, Table 2.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-20: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless -
(a)(l) the claimed invention was patented, described in a printed publication, or in public use, on sale,
or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen ("BreakDancer: an algorithm for high-resolution mapping of genomic structural variation." Nature methods 6.9 (2009): 677-681 (2009)), as cited on the attached Form PTO-892.
Claim 1 recites:
obtaining sequence reads from a sample; performing a first mapping of the reads to at least one reference by a first algorithm to identify a structural variant; performing a second mapping of the reads by a second algorithm to identify the structural variant; and merging the first mapping with the second mapping to describe the structural variant
• Chen teaches a software package for mapping of genomic structural variation, collectively called BreakDancer consisting of two complementary algorithms - the first, BreakDancerMax, provides genome-wide detection of five types of structural variants: deletions, inser-tions, inversions and intrachromosomal and interchromosomal translocations from one or a pool of DNA samples sequenced by Genome Analyzer II and the second, BreakDancerMini, focuses on detecting small indels (typically 10–100 bp) that are not routinely detected by BreakDancerMax (i.e. performing a first mapping of the reads to at least one reference by a first algorithm to identify a structural variant; performing a second mapping of the reads by a second algorithm to identify the structural variant) (pg. 677 col. 2 para. 3); wherein together, the pooled analysis of these algorithms sensitively and accurately detected many structural variants, as demonstrated in both simulation and real data analysis (pg. 677 col. 2 para. 3) and merging the results of said programs (pg. 678 Fig. 2) (i.e. merging the first mapping with the second mapping to describe the structural variant)
Claim 18 recites:
wherein the sample comprises a tumor biopsy
• Chen teaches detecting variants using data obtained from the tumor
and the normal samples of an individual with cytogenetically normal acute myeloid leukemia (pg. 680 col. 1 para. 3).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claim 2 is rejected under 35 U.S.C. 103(a) as being unpatentable over Chen as applied to claim 1 in the 102 rejection above further in view of Cameron ("GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly." Genome research 27.12:2050-2060 (2017)), as cited on the attached Form PTO-892.
Claim 2 recites:
wherein the first algorithm adds the reads to a genomic graph and finds a path through the graph supported by the reads and wherein the second algorithm aligns read-pairs to a reference and searches for genomic regions in the at least one reference where a significant number of read pairs align to the at least one reference in positions incompatible with an insert size distribution for the read pairs
• Chen teaches " the second algorithm aligns read-pairs to a reference and searches for genomic regions in the at least one reference where a significant number of read pairs align to the at least one reference in positions incompatible with an insert size distribution for the read pairs" as BreakDancerMax maps read pairs to a reference genome and applies a classification process based on (i) the separation distance and alignment orientation between the paired reads, (ii) the user-specified threshold and (iii) the empirical insert size dis-tribution estimated from the alignment of each library contribut-ing genome coverage (pg. 682 col. 1 para. 2); wherein the algorithm then searches for genomic regions that anchor substantially more anomalous read pairs (pg. 682 col. 1 para. 2); wherein anomalously mapped read pairs are MAQ mapping quality > 10 separation distance > 3 s.d. and 43.2% of the known variants contained 2 or more anomalously mapped reads in their flanking regions (i.e. significant number of read pairs align to the at least one reference in positions incompatible with an insert size distribution for the read pairs) (pg. 678 col. 1 para. 3).
• Chen does not teach " the first algorithm adds the reads to a genomic graph and finds a path through the graph supported by the reads." However, Cameron teaches it as Genome Rearrangement IDentification Software Suite (GRIDSS), a high-speed structural variant caller that performs efficient genome-wide break-end assembly prior to variant calling using a positional de Bruijn graph assembler (pg. 2 Abstract) where paths are chosen based on scoring according to the sum of the node weights (pg. 22 para. 4).
Rationale for combining (MPEP §2142-2143)
Regarding claim 2, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Chen in view of Cameron because all references disclose methods for the identification of genetic structural variants. The motivation would have been to identify genomic rearrangements with extreme complexity on cell line and patient tumour sequence data with effectiveness (pg. 5 para. 1 Cameron).
Therefore, it would have been obvious to one of ordinary skill in the art to substitute the identification of genetic structural variants of Chen to the methods by Cameron because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for the identification of genetic structural variants.
B. Claims 3-5 are rejected under 35 U.S.C. 103(a) as being unpatentable over Chen and Cameron as applied to claims 1-2 above further in view of Papp ("Integrated genomic, epigenomic, and expression analyses of ovarian cancer cell lines." Cell reports 25.9:2617-2633 (2018)), as cited on the attached Form PTO-892.
Claim 3 recites:
further comprising analyzing the sequence reads to identify putative structural variants (SVs) in the DNA; and filtering the putative SVs to remove germline SVs and/or sample handling artifacts, thereby providing a set of somatic SVs present in the DNA
Claim 4 recites:
wherein the filtering step is performed without reference to a matched normal sequence
• Neither Chen or Cameron teach the recitation above. However, Papp teaches the development of Trellis algorithm for tumor-only structural variant detection (Title page – highlights); wherein germline filters (pg. 2628 col. 2 para. 5) allowed the removal of common germline variants during data analysis resulted in the discovery of 659 putative driver somatic mutations across 45 ovarian cell lines (i.e. filtering the putative SVs to remove germline SVs and/or sample handling artifacts, thereby providing a set of somatic SVs present in the DNA as in claim 3) (pg. 2618 col. 2 para. 2) and stringent bioinformatic analysis characterized tumor-specific (somatic) changes in tumor samples without matched normal tissue (i.e. wherein the filtering step is performed without reference to a matched normal sequence as in claim 4) (pg. 2618 col. 2 para. 2).
Claim 5 recites:
wherein the filtering step comprises identifying patterns in the sequence reads indicative of germline SVs or somatic SVs
• Neither Chen or Cameron teach the recitation above. However, Papp teaches that, to focus on likely somatic alterations involved in tumorigenesis, the analysis identified the sequence alterations in each cell line correlating changes (i.e. identifying patterns in the sequence reads indicative of germline SVs or somatic SVs) that have been previously detected in the coding genomes of other cancer patients) (pg. 2618 col. 2 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claims 3-5, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Chen and Cameron in view of Papp because all references disclose methods for the identification of genetic structural variants. The motivation would have been to incorporate an integrated genomic and expression data analysis in order to obtain a comprehensive molecular profile of samples (pg. 2618 col. 2 para. 1 Papp) and highlight specific sequence, structural, and epigenetic alterations (pg. 2618 col. 1 para. 1 Papp).
Therefore, it would have been obvious to one of ordinary skill in the art to substitute the identification of genetic structural variants of Chen and Cameron to the methods by Papp because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for the identification of genetic structural variants.
C. Claims 6-13 and 15-16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Chen, Cameron and Papp as applied to claims 1-5 above further in view of Liu ("A deep learning approach for filtering structural variants in short read sequencing data." Briefings in bioinformatics 22.4:bbaa370 (2021)), as cited on the attached Form PTO-892.
Claim 6 recites:
wherein the patterns are identified through machine learning analysis of sequence data for known germline SVs or somatic SVs
Claim 7 recites:
wherein the machine learning analysis comprises one or more of a random forest, a support vector machine (SVM), a boosting algorithm, or a neural network
Claim 8 recites:
wherein the machine learning analysis comprises a neural network
Claim 9 recites:
wherein the machine learning analysis comprises a convolutional neural network
Claim 10 recites:
wherein the machine learning analysis comprises analysis of a training set comprising a database of known germline SVs or sample handline artifacts
• Neither Chen or Cameron or Papp teach the recitation above. However, Liu teaches that DeepSVFilter encodes structural variant signals in the read alignments and adopts the transfer learning with pre-trained convolutional neural networks (i.e. neural network as in claims 7-8 and convolutional neural network as in claim 9) as the classification models for filtering structural variants (pg. 1 Abstract); wherein the HG002 and NA12878 samples (i.e. germline reference samples) were used to construct tandem duplication training set via SV detection approaches, including Delly, Lumpy and Manta (i.e. wherein the machine learning analysis comprises analysis of a training set comprising a database of known germline SVs as in claim 10) where if a tandem duplication was detected by any two approaches (≥90% reciprocal overlap), the tandem duplication was regarded as the positive example (i.e. wherein the patterns are identified through machine learning analysis of sequence data for known germline SVs or somatic SVs as in claim 6) (pg. 5 col. 1 para. 7).
Claim 11 recites:
further comprising updating the training set with data from the filtering step
• Neither Chen or Cameron or Papp teach the recitation above. However, Liu teaches the use of three SV detection approaches to build a training set with tandem duplications, where if a tandem duplication was detected by any two approaches (≥90% reciprocal overlap), the tandem duplication was regarded as the positive example in the training set and, if a tandem duplication was detected by only one approach and did not overlap with any other duplication detected by other approaches, the tandem duplication was regarded as the negative example (i.e. further comprising updating the training set with data from the filtering step) (pg. 5 col. 1 para. 7).
Claim 12 recites:
wherein the filtering step compares the putative SVs to at least one database of known germline SVs and removes matches from the putative SVs
• Neither Chen or Cameron or Papp teach the recitation above. However, Liu teaches the use of common SV detection approaches to generate a candidate unified SV call set (pg. 2 col. 2 para. 1) from HG002 and NA12878 samples (i.e. germline reference samples) (pg. 5 col. 1 para. 7) and employ the trained CNN models to make prediction for each candidate SV via a probability of the classification as the true SV (i.e. "true" hence not putative – therefore "removes it from the putative SVs").
Claim 13 recites:
further comprising designing, by computer software, at least one primer pair for each somatic SV in the set, wherein the primer pair will successfully amplify a target that includes the somatic SV
• Neither Chen or Cameron or Papp teach the recitation above. However, Liu teaches the use of inter-nal software to design and select tailed PCR primers for struc-tural variant validation (pg. 683 col. 2para. 2) where resulting amplified data was classified as either somatic or germline events (pg. 683 col. 2para. 2) and training set included uniquely mapped read pairs with the reverse–forward pair orientation (pg. 2 col. 2 para. 3) (i.e. further comprising designing, by computer software, at least one primer pair for each somatic SV in the set, wherein the primer pair will successfully amplify a target that includes the somatic SV).
Claim 15 recites:
wherein the assay comprises digital PCR on cell-free DNA from blood or plasma
• Neither Chen or Cameron teach the recitation above. However, Papp teaches a droplet digital PCR approach used to validate fusion sequences (pg. 2628 col. 1 para. 4) identified in DNA extracted from blood samples from cell lines (pg. 2628 col. 1 para. 2).
Claim 16 recites:
wherein the designing step comprises machine learning analysis of somatic SV primers with known amplification data
• Neither Chen or Cameron or Papp teach the recitation above. However, Liu teaches a deep learning approach (i.e. machine learning analysis) for filtering structural variants in short read sequencing data (pg. 1 Title); wherein an inter-nal software is used to design and select tailed PCR primers for struc-tural variant validation (pg. 683 col. 2para. 2) where resulting amplified data was classified as either somatic or germline events (pg. 683 col. 2para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claims 6-13 and 15-16, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Chen, Cameron and Papp in view of Liu because all references disclose methods for the identification of genetic structural variants. The motivation would have been to incorporate a filtering effect coupled with commonly used structural variant detection to significantly reduce false positive (pg. 6 col. 2 para. 4 Liu).
Therefore, it would have been obvious to one of ordinary skill in the art to substitute the identification of genetic structural variants of Chen, Cameron and Papp to the methods by Liu because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for the identification of genetic structural variants.
D. Claims 14 and 17 are rejected under 35 U.S.C. 103(a) as being unpatentable over Chen, Cameron, Papp and Liu as applied to claims 1-3 and 13 above further in view of Chaudhary ("A scalable solution for tumor mutational burden from formalin-fixed, paraffin-embedded samples using the Oncomine Tumor Mutation Load Assay." Translational lung cancer research 7.6:616 (2018)), as cited on the attached Form PTO-892.
Claim 14 recites:
further comprising using the primer pair to perform an assay on a sample from a subject from whom the FFPE tissue sample was obtained to detect minimal residual disease in the subject
• Neither Chen or Cameron or Papp or Liu teach the recitation above. However, Chaudhary teaches method for variant calling to estimate tumor mutational burden using the Oncomine Tumor Mutation Load assay in formalin fixed paraffin embedded (FFPE) samples (pg. 616 Abstract); wherein primer pool was used to extract DNA from FFPE samples (pg. 617 col. 2 para. 3); wherein said estimate was done using minimal sample input from FFPE tumor samples (i.e. further comprising using the primer pair to perform an assay on a sample from a subject from whom the FFPE tissue sample was obtained to detect minimal residual disease in the subject) (pg. 617 col. 2 para. 1).
Claim 17 recites:
wherein the sample is a formalin-fixed, paraffin embedded (FFPE) tissue sample, the method further comprising:
providing amplicons obtained from DNA extracted from the sample; and sequencing the amplicons to obtain the set of sequence reads
• Neither Chen or Cameron or Papp or Liu teach "formalin-fixed, paraffin embedded (FFPE) tissue sample." However, Chaudhary teaches method for variant calling to estimate tumor mutational burden using the Oncomine Tumor Mutation Load assay in formalin fixed paraffin embedded (FFPE) samples (pg. 616 Abstract).
• Neither Chen or Cameron or Liu teach "further comprising: providing amplicons obtained from DNA extracted from the sample; and sequencing the amplicons to obtain the set of sequence reads." However, Papp teaches a method for identifying focal amplicons and establish how these amplicons were linked in the tumor genome, via the characterization of genomic structural alterations (pg. 2621 Fig. 3) where analysis of the 45 ovarian cancer samples identified 538 focal amplicons, or an average of 12 amplicons per tumor (pg. 2620 col. 1 para. 3).
Rationale for combining (MPEP §2142-2143)
Regarding claims 14 and 17, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Chen, Cameron, Papp and Liu in view of Chaudhary because all references disclose methods for the identification of genetic structural variants. The motivation would have been to enable scalable, robust research into biomarkers with scarce samples (pg. 616 Abstract Chaudhary).
Therefore, it would have been obvious to one of ordinary skill in the art to substitute the identification of genetic structural variants of Chen, Cameron, Papp and Liu to the methods by Chaudhary because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for the identification of genetic structural variants.
E. Claim 19-20 is rejected under 35 U.S.C. 103(a) as being unpatentable over Papp ("Integrated genomic, epigenomic, and expression analyses of ovarian cancer cell lines." Cell reports 25.9:2617-2633 (2018)) in view of Liu ("A deep learning approach for filtering structural variants in short read sequencing data." Briefings in bioinformatics 22.4:bbaa370 (2021)), as cited on the attached Form PTO-892.
Claim 19 recites:
A method for differentiating structural variants, the method comprising:
obtaining sequence reads from a patient sample; and analyzing the sequence reads to identify somatic structural variants (SVs) in the DNA through machine learning analysis of sequence data for known somatic SVs without reference to a matched normal sequence read from the patient
• Papp teaches the development of Trellis algorithm for tumor-only structural variant detection (Title page – highlights); to identify likely tumor-specific sequence and genome wide structural changes, including amplifications, deletions, and rearrangements (pg. 2618 col. 2 para. 1); wherein stringent bioinformatic analysis characterized tumor-specific (somatic) changes in tumor samples without matched normal tissue (pg. 2618 col. 2 para. 2).
• Papp does not teach "machine learning analysis." However, Liu teaches that DeepSVFilter encodes structural variant signals in the read alignments and adopts the transfer learning with pre-trained convolutional neural networks (i.e. machine learning analysis) as the classification models for filtering structural variants (pg. 1 Abstract).
Claim 20 recites:
wherein the analyzing step comprises identifying and removing germline SVs from a set of putative somatic SVs through machine learning analysis of sequence data for known germline SVs
• Papp teaches the development of Trellis algorithm for tumor-only structural variant detection (Title page – highlights); wherein germline filters (pg. 2628 col. 2 para. 5) allowed the removal of common germline variants during data analysis resulted in the discovery of 659 putative driver somatic mutations across 45 ovarian cell lines (i.e. identifying and removing germline SVs from a set of putative somatic SVs) (pg. 2618 col. 2 para. 2).
• Papp does not teach "machine learning analysis." However, Liu teaches that DeepSVFilter encodes structural variant signals in the read alignments and adopts the transfer learning with pre-trained convolutional neural networks (i.e. machine learning analysis) as the classification models for filtering structural variants (pg. 1 Abstract);
Rationale for combining (MPEP §2142-2143)
Regarding claims 19-20, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Papp in view of Liu because all references disclose methods for the identification of genetic structural variants. The motivation would have been to incorporate a filtering effect coupled with commonly used structural variant detection to significantly reduce false positive (pg. 6 col. 2 para. 4 Liu).
Therefore, it would have been obvious to one of ordinary skill in the art to substitute the identification of genetic structural variants of Papp to the methods by Liu because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for the identification of genetic structural variants.
Conclusion
No claims are allowed.
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/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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