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 .
This Application has been reassigned to AU 1686. Please see the Examiner information at the end of this Action.
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/14/2026 has been entered.
Claims 1, 3-20 are pending in this application. Claims 15-18 stand withdrawn from consideration as being drawn to a non-elected invention. Claims 19-20 are newly added.
Claims 1, 3-14, 19-20 are under examination.
Applicant’s amendments and arguments, filed 1/14/2026, have been entered and carefully considered, but are not completely persuasive.
The Examiner has reviewed the prosecution history, the associated 371 documentation, and the filed PCT related information. The copy of the certified priority document was provided by the IB. The effective filing date for the invention is that of the EP priority document, filed 12 April 2019.
This application has published as US 2022/0199196 A1.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, or the IDS filed 10/11/2021 they have not been considered.
The amendments to the specification, filed 7/14/2025, and 1/14/2026 related to the deletion of active hyperlinks have been entered.
Claim Objections
Claims 6, 12-14 are objected to because of the following informalities:
Claims 6, 12-14 are objected to as they fail to meet MPEP 608.01(m). “Each claim begins with a capital letter and ends with a period. Periods may not be used elsewhere in the claims except for abbreviations. See Fressola v. Manbeck, 36 USPQ2d 1211 (D.D.C. 1995).”
Claim 6 recites “The method of claims 1, further comprising the step of: Identifying…”
This claim should be amended to de-pluralize “claims”, and de-capitalize “Identifying…”
Claim 6 indicates the method of claim 1 detects a structural variation, by the method of claim 1. Claim 12 attempts to create a new independent claim for the identification of a structural variation, and depends from claim 6, which already recites this limitation. These claims appear to possibly be duplicative in scope. Applicant is requested to rewrite claim 12 as a proper independent claim, with the necessary and sufficient limitations to achieve the recited goal, or delete the duplicative claim.
Claim 13 has capitalized words at the beginning of each step. (i.e. “(a) Providing strand specific sequence data” should read “(a) providing strand specific sequence data” and so on for each instance.)
Further in claim 13, at the end of the last clause of step (b)(v) is a period, and then a second period exists at the end of step (d). The period at the end of step (b)(v) should be changed to a semicolon such as: “…a specific haplotype identity;”
Further in claim 13, step b) the repeated use of the same Roman numeral format for different limitations is confusing, and inappropriate as set forth in MPEP 608.01(m). It is suggested that step b) be amended similarly to the following:
“… (b) [[P]]performing a method for analyzing sequencing data of at least one target chromosomal region by single cell tri-channel processing (scTRIP), comprising:
[[(i)]] (I) providing strand specific sequence data …
[[(ii)]] (II) aligning the sequence reads, …
[[(iii)]] (III) analyzing the aligned sequence …
[[(iv)]] (IV) selecting a sequence window; and
[[(v)]] (V) assigning in the selected window the three channels of sequence information: (i) number of total sequence reads, or portions thereof; (ii) number of forward sequence reads, or portions thereof, and number of reverse sequence reads, or portions thereof; (iii) numbers of sequence reads, or portion thereof, assigned with a specific haplotype identity; …”
Claim 14 attempts to set forth an independent method of diagnosing a disease or condition, by reference to claim 11. Claim 11 already adds a step of diagnosis of a disease or condition, to the method of claim 1. These claims appear to possibly be duplicative in scope. Applicant is requested to rewrite claim 14 as a proper independent claim, with the necessary and sufficient limitations to achieve the recited goal, or delete the duplicative claim.
Appropriate correction is required.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art.
In the context of the invention, “SV” is interpreted as the acronym for Structural Variation, and not the broader term “sequence variation” which encompasses SNP/SNV.
In claim 8, and the “optional” step, that may be performed after satisfying a condition, the BRI is analyzed as set forth in MPEP 2111.04: “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.”
Claim 8: “… optionally, wherein haplotype identity is assigned to a sequence read, or a portion thereof, not comprising a SNP, by inferring said haplotype identity in by strand identity and comparison to other sequence reads, or portions thereof, having the same strand identity and which comprise the SNP.”
This limitation is interpreted as: when a haplotype is assigned to a read, which does not comprise an SNP, an optional step of inferring haplotype identity may be performed, but is not required to meet the limitations of claim 8.
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, 3-14, 19-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES, the claims are drawn to statutory categories: processes for analyzing sequencing data, and processes for performing in-silico karyotyping.
With respect to step (2A) (1): YES, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
1. (Currently Amended) A method for analyzing sequencing data of at least one target chromosomal region by single cell tri-channel processing (scTRIP), comprising:
(Preamble, setting forth a method, and the goal of the method.)
(a) providing strand specific sequence data of at least one target chromosomal region of at least one single cell, wherein the strand-specific sequencing data comprise a multitude of strand specific sequence reads obtained by sequencing of the target chromosomal region of at least one single cell;
(EIA- a step of data gathering of a type of sequence read, from “at least one single cell”, of at least one chromosomal region, and a description of the data gathered. No limits on how the data is generated or received. MPEP 2106.05(g).)
(b) aligning the sequence reads, or if the sequence reads are equally fragmented, each fragmented portion of the sequence reads, to a reference assembly;
(Mental Process: a step of observing sequence reads, or sequence read fragments, and matching them by comparison to reference regions. MPEP 2106.04(a)(2)(III).)
(c) analyzing the aligned sequence reads for three channels of sequence information, wherein the three channels comprise read depth, strand orientation and haplotype phase;
(Mental Process and Mathematic Concepts: a step of observing the aligned reads, and analyzing them for information. The analysis of “read depth” is related to the mathematic concept of counting aligned reads. The analysis of “strand orientation” is the mental process of observation of which strand of the reference the aligned read matched. The analysis of “haplotype phase” is related to observation of variants in the sequence read, observing their location in the sequence read and reference, observing the strand where the variant exists, and making a judgement as to the haplotype represented; alternatively, a mathematic process of haplotype imputation. MPEP 2106.04(a)(2) (I, III).)
(d) selecting a sequence window; and
(Mental process of selection of a subset of aligned, analyzed data. MPEP 2106.05(a)(2)(III).)
(e) assigning in the selected window the three channels of sequence information: (i) number of total sequence reads, or portions thereof, (ii) number of forward sequence reads, or portions thereof, and number of reverse sequence reads, or portions thereof; (iii) numbers of sequence reads, or portion thereof, assigned with a specific haplotype identity.
(Mental process of data annotation, wherein a “window” or subset of aligned, analyzed data from step d) is annotated with the data obtained in step c) from mental processes and mathematic concepts. (MPEP 2106.04(a)(2)(III).)
3. (Previously presented) The method according to claim 1, comprising a step of segmenting the at least one target chromosomal region, wherein the segmenting is performed on basis of the channels of sequence information (i) to (iii), each individually, in any combination, or together.
(Mental process of data separation based on data from step e).
4. (Previously presented) The method according to claim 1, wherein the strand-specific sequence data comprises sequence reads mapping to at least two separate strands of the at least one target chromosomal region, wherein one strand is from the paternal and the other strand is from the maternal chromosome.
(Mental process of mapping reads to each strand of the reference assembly. Mapping requires observation, and analysis for matching or similar sequences.)
5. (Previously presented) The method according to claim 1, wherein the sequencing data comprises a multitude of non-overlapping and/or overlapping sequence reads.
(EIA- describing the data gathered in step a).
6. (Previously presented) The method according to claims 1, further comprising the step of: Identifying a structural variation (SV) by assigning said sequence information for a multiplicity of windows within the sequence data, and identifying within the multiplicity of windows a sub- region comprising one or more windows having an unusual/altered/changed distribution of the information of any one, or all of, or any combination of, channels (i) to (iii), compared to a reference state.
(Mental Process of observation of data elements, observing an “unusual/altered/changed” distribution, and making a judgement as to the presence or absence of a structural variation. MPEP 2106.04(a)(2)(III).)
7. (Original) The method according to claim 6, wherein said reference state of said chromosomal region is a state of the information of the channels which is expected for a non- aberrant distribution and/or predetermined state of the information of said chromosomal region.
(EIA- an element of data gathering, describing data required for the reference assembly, and associated limitations)
8. (Currently Amended) The method according to claim 1, wherein a haplotype identity is assigned along the at least one target chromosomal region, while retaining strand orientation information,
and the haplotype is assigned by assigning Single Nucleotide Polymorphisms (SNPs) to the sequence reads, or portions thereof, wherein the SNPs do not have a disease association,
and wherein the haplotype identity is assigned to a sequence read, or a portion thereof, comprising a SNP,
and identifying the allele of the SNP by comparison to a SNP database, or alternatively by comparing the allele to a multiplicity of further sequenced single cells of the same origin; and,
optionally, wherein haplotype identity is assigned to a sequence read, or a portion thereof, not comprising a SNP, by inferring said haplotype identity in by strand identity and comparison to other sequence reads, or portions thereof, having the same strand identity and which comprise the SNP.
(Mental Processes: steps of determining the haplotype in steps c) and/or e) by making an observation of a variant, the location of the variant, information about the variant; by comparison to a database; by comparison to other data; and an optional step of haplotype inference by comparison to “other reads” and making a judgement as to the haplotype represented. MPEP 2106.04(a)(2)(III).)
9. (Previously presented) The method according to claim 1, wherein the target chromosomal region is one or more chromosomes of a diploid organism.
(EIA- describing an element of data gathering for the desired region, as originating from a diploid organism, with one or more chromosomes.)
10. (Previously presented) The method according to claim 1, wherein the strand-specific sequence data of the at least one target chromosomal region of at least one single cell is obtained from a cellular sample of a patient, and wherein said single cell is either a cell associated with a disease, or is a healthy cell of said patient, wherein the method is performed for a multiplicity of single cells associated with the disease and/or healthy cells.
(EIA- an element of data gathering, describing the source of the sample. AND this claim also recites a natural law: the naturally occurring correlations between haplotype and disease, a genotype/phenotype relationship. MPEP 2106.04(b))
11. (Previously presented) The method according to claim 1, wherein the method comprises a further step of diagnosing a disease or condition based on the identity of, location of, or number of detected SV within the target chromosomal region.
(Mental Process of observing a structural variation, and making a judgement as to whether a disease or condition is associated with that SV. MPEP 2106.04(a)(2)(III) AND this claim also recites a natural law: the naturally occurring correlations between haplotype and disease, a genotype/phenotype relationship. MPEP 2106.04(b).)
12. (Previously presented) A method of detecting a structural variation (SV) in a target chromosomal region, the method comprising, performing a method according to claim 6.
(The method of claim 6 incorporates all the limitations of claim 1:
(EIA- a step of data gathering of a type of sequence read, from “at least one single cell”, of at least one chromosomal region, and a description of the data gathered. No limits on how the data is generated or received. MPEP 2106.05(g).)
(Mental Process: a step of observing sequence reads, or sequence read fragments, and matching them by comparison to reference regions. MPEP 2106.04(a)(2)(III).)
(Mental Process and Mathematic Concepts: a step of observing the aligned reads, and analyzing them for information. The analysis of “read depth” is related to the mathematic concept of counting aligned reads. The analysis of “strand orientation” is the mental process of observation of which strand of the reference the aligned read matched. The analysis of “haplotype phase” is related to observation of variants in the sequence read, observing their location in the sequence read and reference, observing the strand where the variant exists, and making a judgement as to the haplotype represented; alternatively, a mathematic process of haplotype imputation. MPEP 2106.04(a)(2) (I, III).)
(Mental process of selection of a subset of aligned, analyzed data. EP 2106.05(a)(2)(III).)(Mental process of data annotation, wherein a “window” or subset of aligned, analyzed data from step d) is annotated with the data obtained in step c from mental processes and mathematic concepts. (MPEP 2106.04(a)(2)(III).) AND the limitation from claim 6:
(Mental Process of observation of data elements, observing an “unusual/altered/changed” distribution, and making a judgement as to the presence or absence of a structural variation. MPEP 2106.04(a)(2)(III).)
13. (Currently Amended) A method of karyotyping a single cell, or a population of multiple single cells, the method comprising,
(Preamble, reciting a method and the goal of the method.)
(a) Providing strand specific sequence data of the at least one target chromosomal region of at least one single cell, or each of the population of single cells,
(EIA- a step of data gathering of a type of sequence read, from “at least one single cell”. No limits on how the data is generated or received. MPEP 2106.05(g).)
(b) Performing a method for analyzing sequencing data of at least one target chromosomal region by single cell tri-channel processing (scTRIP), comprising:
(i) providing strand specific sequence data of at least one target chromosomal region of at least one single cell, wherein the strand-specific sequencing data comprise a multitude of strand specific sequence reads obtained by sequencing of the target chromosomal region of at least one single cell[[,]];
(EIA- a step of data gathering of a type of sequence read, from “at least one single cell”, of at least one chromosomal region, and a description of the data gathered. No limits on how the data is generated or received. MPEP 2106.05(g).)
(ii) aligning the sequence reads, or if the sequence reads are equally fragmented, each fragmented portion of the sequence reads, to a reference assembly;
(Mental Process: a step of observing sequence reads, or sequence read fragments, and matching them by comparison to reference regions. MPEP 2106.04(a)(2)(III).)
(iii) analyzing the aligned sequence reads for three channels of sequence information, wherein the three channels comprise read depth, strand orientation and haplotype phase;
(Mental Process and Mathematic Concepts: a step of observing the aligned reads, and analyzing them for information. The analysis of “read depth” is related to the mathematic concept of counting aligned reads. The analysis of “strand orientation” is the mental process of observation of which strand of the reference the aligned read matched. The analysis of “haplotype phase” is related to observation of variants in the sequence read, observing their location in the sequence read and reference, observing the strand where the variant exists, and making a judgement as to the haplotype represented; alternatively, a mathematic process of haplotype imputation. MPEP 2106.04(a)(2) (I, III).)
(iv) selecting a sequence window; and
(Mental process of selection of a subset of aligned, analyzed data. MPEP 2106.05(a)(2)(III).)
(v) assigning in the selected window the three channels of sequence information: (i) number of total sequence reads, or portions thereof; (ii) number of forward sequence reads, or portions thereof, and number of reverse sequence reads, or portions thereof; (iii) numbers of sequence reads, or portion thereof, assigned with a specific haplotype identity.
(Mental process of data annotation, wherein a “window” or subset of aligned, analyzed data from step iv) is annotated with the data obtained in step iii) from mental processes and mathematic concepts. (MPEP 2106.04(a)(2)(III).)
(c) Detecting SV within the target chromosomal region of said single cell, or the population of single cells, and
(Mental process of observing data from the target region, observing the presence of an anomaly, and making a judgement as to whether it represents an SV. MPEP 2106.04(a)(2)(III).)
(d) Obtaining an in-silico karyotype based on all detected SVs.
(Mental process, in a computing environment, of collating detected SV from the previous step, and generating a karyotype. MPEP 2106.04(a)(2)(III).)
14. (Previously presented) A method of diagnosing a disease or condition in a subject, the method comprising,
(Preamble, reciting a method, and the goal of the method.)
providing strand specific sequence data of one or more cells of the subject,
(EIA- a step of data gathering of a type of sequence read, from cells of a subject. No limits on how the data is generated or received. MPEP 2106.05(g).)
performing a method according to claim 11,
(The limitations of claim 11, and claim 1 from which claim 11 depends, comprise:
(EIA- a step of data gathering of a type of sequence read, from “at least one single cell”, of at least one chromosomal region, and a description of the data gathered. No limits on how the data is generated or received. MPEP 2106.05(g).)
(Mental Process: a step of observing sequence reads, or sequence read fragments, and matching them by comparison to reference regions. MPEP 2106.04(a)(2)(III).)
(Mental Process and Mathematic Concepts: a step of observing the aligned reads, and analyzing them for information. The analysis of “read depth” is related to the mathematic concept of counting aligned reads. The analysis of “strand orientation” is the mental process of observation of which strand of the reference the aligned read matched. The analysis of “haplotype phase” is related to observation of variants in the sequence read, observing their location in the sequence read and reference, observing the strand where the variant exists, and making a judgement as to the haplotype represented; alternatively, a mathematic process of haplotype imputation. MPEP 2106.04(a)(2) (I, III).)
(Mental process of selection of a subset of aligned, analyzed data. MPEP 2106.05(a)(2)(III).)
(Mental process of data annotation, wherein a “window” or subset of aligned, analyzed data from step d) is annotated with the data obtained in step c from mental processes and mathematic concepts. (MPEP 2106.04(a)(2)(III).)
(Mental Process of observing a structural variation, and making a judgement as to whether a disease or condition is associated with that SV. MPEP 2106.04(a)(2)(III) AND this claim also recites a natural law: the naturally occurring correlations between SV and disease, a genotype/phenotype relationship. MPEP 2106.04(b).)
detecting within the one or more cells any SV, and
(Mental Process of observing a structural variation, by any means. MPEP 2106.04(a)(2)(III).)
comparing the detected SV with a reference state, wherein an altered number, type or location of one or SV in the sample of the subject indicated the presence of a cancer.
(Mental process of comparison of SV with reference data, observing an altered characteristic, and making a judgement as to the presence or absence of a cancer. MPEP 2106.04(a)(2)(III) AND this claim also recites a natural law: the naturally occurring correlations between SV and disease, a genotype/phenotype relationship. MPEP 2106.04(b).)
19. (Previously presented) The method according to claim 1, wherein the specific haplotype is H1 or H2.
(Mental Process of observation and recognizing a type of haplotype from the named choices, by any means. MPEP 2106.04(a)(2) III.)
20. (Previously presented) The method according to claim 13, wherein the at least one target chromosomal region is the complete genome of the at least one single cell.
(EIA- an element of data gathering, describing the target region.)
Natural law embraced by claim(s) 10, 11 and 14:
Claims 10, 11 and 14 are directed to identifying the naturally occurring correlation between certain genetic changes in a subject and a phenotype of disease. This is a genotype/phenotype relationship, naturally occurring, whether it is measured or not. MPEP 2106.04(b). (citing Funk Bros., Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, etc.)
With respect to step 2A (2): NO, the claims do not integrate the JE into a practical application (MPEP 2106.04(d)):
“Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).”
Claim(s) 1, 5, 7, 9-10, 12-14 and 20 each recite the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The data gathering steps constitute a general link to a technological environment: the sequencing is to be performed on single cells, or a multiplicity of single cells. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide integration into a practical application. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.).
Dependent claim(s) 3, 4, 6, 8, 11, 12, 14, 19 recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO, the claims do not recite a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05).
“… an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. Alice Corp…”
With respect to claim(s) 1, 5, 7, 9-10, 12-14 and 20: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
The data gathering elements related to providing strand-specific sequence read information, from at least a single cell, and reference sequence read data, or reference assemblies, was provided by the following publications:
Linnarson, S. US 2012/0010091 A1, provides strand-specific sequence read information from at least a single cell, reference sequence read data, and/or reference assemblies.
Zimmerman et al. US 2013/0123120 A1 provides strand-specific sequence read information from at least a single cell, reference sequence read data, and reference assemblies.
Armour, C. US 9,206,418 B2, provides strand-specific sequence read information from at least a single cell, reference sequence read data, and reference assemblies.
Sanders, A. D. et al. (2017) provides “Strand-Seq”, providing strand-specific sequence read information from at least a single cell, reference sequence read data, and reference assemblies.
Porubsky, D. et al. (2017) provides strand-specific sequence read information from at least a single cell, reference sequence read data, and reference assemblies.
Marie, R. et al. (2018) provides strand-specific sequence read information from at least a single cell, reference sequence read data, and reference assemblies.
Falconer (2012; Specification p0023-0024) provides “Strand-seq”, providing strand-specific sequence read information from at least a single cell, reference sequence read data, and reference assemblies.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art. MPEP 2106.05(d): “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”
In the specification at [0023-0024] it is disclosed that the steps identified as data gathering can be met by any next generation or third generation sequencing process, including: parallelized sequencing-by-synthesis, sequencing-by-ligation platforms by Illumina, Life Technologies, and Roche, electronic-detection based methods such as Ion Torrent technology commercialized by ThermoFisher, nanopore sequencing methods, “single molecule real-time (SMRT)" sequencing, "long-read sequencing" and the prior art provided “Strand-seq” method of Falconer et al. (2012).
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide an inventive concept. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.)
The data gathering steps constitute a general link to a technological environment: single cell sequencing. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
Therefore, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
Dependent claim(s) 3, 4, 6, 8, 11-12, 14, 19 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Applicant’s arguments:
Applicant’s arguments have been carefully considered but are not persuasive.
Applicant argues the categorization or identification of abstract ideas, and/or a natural law in the claims. The Examiner has specifically identified each limitation in the claim, and what category of judicial exception is encompassed. The abstract ideas identified in the independent claims are the same as those identified as mathematic correlations, mathematic calculations, and mathematical relationships or as mental processes, concepts performed in the human mind including observations, evaluations, judgements and opinions, in MPEP 2106.04.
Initially, the Examiner points out that the examined claims are not specifically computer-implemented. Therefore, arguments related to improvements in computer-implemented processes are not persuasive. MPEP 2106.05(a):
“It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally).”
With respect to arguments that the claims provide an improvement in technology, these arguments are not persuasive. For example, claim 1 is directed to general sequence data analysis, and not the specific idea of identifying structural variations from single cell strand-specific sequence read data, using the scTRIP method as clearly set forth in the specification. Claim 13 is directed to detecting structural variations, however, the source is not limited to a single cell. Most of the limitations of the claims are recited at a high level of generality, and do not clearly set forth the necessary and sufficient limitations required to achieve the alleged improvement.
For example, the specification, and drawings, strongly suggest the specific “Strand-seq” sequencing limitations are required, using BrdU nucleotides in a round of division or amplification, to obtain the strand-aware sequence reads. These limitations are not a part of the claims.
The use of a hidden Markov model in the analysis of the strand-seq sequence read data is strongly suggested, yet lacking from the claims.
Breakpoint identification, haplotype phasing using a specific algorithm (StrandPhaseR), duplicate removal, use of a multiplicity of windows in SV identification, “haplotagging” and “diagnostic footprinting” all appear to be important in the achievement of the improvement, yet are lacking from the claims.
The Brief Description of Fig 1 indicates the identification of specific types of structural variants, using: a specific Strand-seq protocol, variant identification, haplotype phasing, footprinting, a Baysian framework for SV discovery, and a Baysian framework for haplotype aware SV classification, using particular model parameters, yet these are lacking from the claims.
The nature of the karyotype to be output is not set forth: is it a list of SV? What type of SVs are to be used in the karyotype? What is the actual output? A Table? A picture of chromosomes, marked to identify SV? An overlay of SV data on FISH karyotype images? How any particular SV is linked to any particular diagnosis is not set forth within claims 10, 11, or 14. No disease specific data is specifically provided, beyond those of the subject.
No error analyses, or clear steps of validation for any claimed method are provided by the claims.
MPEP 2106.05(a): “After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification.”
“An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107.”
Further with respect to the alleged improvement, the improvement appears to be within the specific analysis and identification of structural variations, and not the data gathering steps. According to the guidance set forth in MPEP 2106, this is an improvement to the judicial exception itself, and is not reflected back into a specific technological environment or practically applied process.
An improvement in the judicial exception itself is not an improvement in the technology. For example, in In re Board of Trustees of Leland Stanford Junior University, 989 F.3d 1367, 1370, 1373 (Fed. Cir. 2021) (Stanford I), Applicant argued that the claimed process was an improvement over prior processes because it ‘‘yields a greater number of haplotype phase predictions,’’ but the Court found it was not ‘‘an improved technological process’’ and instead was an improved ‘‘mathematical process.’’ The court explained that such claims were directed to an abstract idea because they describe ‘‘mathematically calculating alleles’ haplotype phase,’’ like the ‘‘mathematical algorithms for performing calculations’’ in prior cases. Notably, the Federal Circuit found that the claims did not reflect an improvement to a technological process, which would render the claims eligible (FR89 no.137, p58137, 7/17/2024).
With respect to the identification of certain steps as mental processes, and whether the human mind is equipped to perform the steps classified as mental processes: there is no step in the claims which requires an element which cannot practically be performed in the human mind because there are no limitations requiring an element such as a GPS receiver, specific computer-integrated elements such as network monitors or network packets, nor are there specific limitations to a multistep encryption of data for computer communication. No modified computer structures, such as self-referential tables, neural networks, or artificial intelligence are present in the claims. (See SRI Int’l, Inc. v. Cisco Systems, Inc., (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims"); CyberSource, (as directed to inventions that ‘‘could not, as a practical matter, be performed entirely in a human’s mind’’). MPEP 2106.04(2)(a).)
Applicant’s arguments in this aspect appear to be directed to either the data gathering elements, which is the incorrect application, or refer to an amount of information required to be processed in the steps. The amount of information generated at each step is not a sufficient process which the human mind is not equipped to perform- the process may be long and tedious but there are no limitations requiring a particular amount of data in each step, or a particular manipulation of the data in any step that cannot be performed by mental processes as identified by the Courts.
The limitation to “aligning the sequence reads… to a reference assembly” does not require any process for which the human mind is not equipped. Observing a sequence, and comparing it to the reference assembly is a mental process, performed one sequence read at a time, to the “target chromosomal region”. These steps can all be performed in the human mind, or using a computer as a tool, without any specialized computer elements required.
The newly added limitation of “analyzing the aligned sequence reads for three channels of sequence information…” and “selecting a sequence window” do not require processes for which the human mind is not equipped. Observing data associated with aligned sequence reads, including counting a number of sequence reads, observing strand orientation, and observing haplotype phase, and selecting a sequence window representing a portion of the “target chromosomal region” are each steps which can be done in the human mind or using the computer as a tool, without any specialized computer elements required.
The amount of data to be analyzed (i.e the number of sequence reads provided), in and of itself, is not a limitation which takes a process out of the realm of the human mind. It is the process performed on that data which is the mental step, and mental steps identified in the claims do not have to be fast or efficient. Data observation, comparison, alignment, annotation, and data identification based on certain characteristics of each piece of data can all be performed in the human mind, albeit very slowly, using pen and pencil, and slightly faster using the general-purpose computer as a tool or in a computing environment. Computations on large amounts of data performed mentally, or with paper and pencil, would take considerable time and effort: that is, of course, the singular purpose of computers and computer networks- to perform large numbers of calculations, via algorithms, rapidly, and without error (assuming no error in user input). Although a general-purpose computer can perform calculations at a rate and accuracy that can far outstrip the mental performance of a skilled artisan, the nature of the activity is essentially the same, and constitutes an abstract idea. See Bancorp Serves., L.L. C. v. Sun Life Assur. Co. of Canada (U.S.) (holding that “the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”); see also SiRF Tech., Inc. v. Int’l Trade Comm ’n, (Fed. Cir. 2010) (holding that: In order for the addition of a machine to impose a meaningful limit on the scope of a claim, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly, i.e., through the utilization of a computer for performing calculations)."
The MPEP sets forth that “if the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 CFR 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification.” Applicant’s arguments cannot take the place of evidence.
Applicant is encouraged to closely review the specification, to determine and claim Applicant’s invention. Were the data gathering steps to be limited to performing a particular type of sequencing, that leads to a particular kind of information, followed by particular steps necessary and sufficient to identify SV by Applicant’s analyses, the analysis under this statute would be improved.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-14, 19-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The examiner appreciates the addition of letters/ numbers to individual steps in claims 1 and 13, in an attempt to clarify the claimed invention, however this creates some confusion as to how and where the limitations of the dependent claims are to be integrated into the independent claim. The examiner strongly suggests a detailed review of all the claims to ensure each dependent claim properly further limits the independent claim, in a clear and logical manner.
Further, the claims are largely written in results-based language which fail to particularly point out and distinctly claim how each desired result is to be accomplished.
MPEP 2173.05(g): “For example, when claims merely recite a description of a problem to be solved or a function or result achieved by the invention, the boundaries of the claim scope may be unclear. Halliburton Energy Servs., Inc. v. M-I LLC, 514 F.3d 1244, 1255, 85 USPQ2d 1654, 1663 (Fed. Cir. 2008) (noting that the Supreme Court explained that a vice of functional claiming occurs "when the inventor is painstaking when he recites what has already been seen, and then uses conveniently functional language at the exact point of novelty") (quoting General Elec. Co. v. Wabash Appliance Corp., 304 U.S. 364, 371 (1938));"
MPEP 2173: "The essential inquiry pertaining to this requirement is whether the claims set out and circumscribe a particular subject matter with a reasonable degree of clarity and particularity. "As the statutory language of ‘particular[ity]' and 'distinct[ness]' indicates, claims are required to be cast in clear—as opposed to ambiguous, vague, indefinite—terms. It is the claims that notify the public of what is within the protections of the patent, and what is not." Packard, 751 F.3d at 1313, 110 USPQ2d at 1788.”
The metes and bounds of claim 1 are unclear with respect to what particular steps make up the “scTRP” identified in the preamble. It is unclear if this term only applies to the newly added step of analyzing the aligned data for the three channels now recited in claim 1, or whether it refers to the entire claim. A reading of the specification suggests steps c)-e) make up the scTRP, however this interpretation cannot be read into the claims.
Further, it is unclear what the point of the analysis is, for claim 1 as written. Claim 1 does not specifically identify any type of structural variant, or single nucleotide variant in the single cell that provided the sequence read data. Claim 1 ends with a single annotated “window” of aligned sequence reads, aligned to a target chromosomal region, with 3 types of annotated data. No conclusions are determined as to whether any of the annotated elements are consistent, or inconsistent with reference information. No conclusions are drawn as to the presence or absence of a variant. No particular haplotype is output. No conclusions can be drawn about the initial sample cell, or subject from which the cell was derived.
The term “equally fragmented” in claim 1 is a relative term which renders the claim indefinite. The term “equally fragmented” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear how “equally” is intended to modify “fragmented sequence read” in this context. In the process of sequencing, various lengths of sequence reads are generated, and are ideally present in a normal distribution. It is unclear how to interpret this limitation. It would appear “equally” could be deleted, without affecting the scope, unless this “equally fragmented” term has a specific definition which affects the alignment to the reference assembly.
The examiner notes that the “selection of a sequence window” appears to refer to selection of a subset of the reference assembly, in a computing environment as understood in the prior art and the technology of sequence analysis, however the claim is not computer implemented, no alignments to an assembly are displayed such that a visual “window” could be selected, and it is unclear how the “sequence window” is to be identified or selected, based on the information present in step c). There is no reasoning to pick any particular subset of the assembly. While the specification suggests that an anomaly identified in the alignment and analysis may drive the selection of the sequence window, these limitations cannot be read into the claims.
The metes and bounds of claim 3 are unclear with respect to where in the method of claim 1, the segmentation of the target chromosomal region is to be performed. It is unclear if this is intended to physically segment the DNA of the sample, to modify the sequence read data of at least one target chromosomal region, to modify the reference assembly, to modify the selected sequence window, or whether this is an additional step intended to occur after step e).
The metes and bounds of claim 4 are unclear with respect to the provided sequence read data. Claim 4 appears to further describe the data provided from the strand-specific sequence read data in step a) of claim 1, however if it has already been mapped to two different strands, then the step of alignment is redundant. If it is intending to modify the alignment step b), as identifying a maternal or paternal strand, it is entirely unclear how this is to be performed, and it is further unclear how this identification affects any other step in claim 1. No other element of claim 1 is concerned with the source of the cell, cell lineage, or subject parentage. The identification of a maternal or paternal strand does not appear to affect any of the analysis, or annotation steps of claim 1.
The metes and bounds of claim 5 are unclear with respect to how the presence of overlapping, and non-overlapping sequence reads are intended to modify the method of claim 1. Strand-specific sequencing, next generation sequencing, and third generation sequencing all routinely generate a multiplicity of overlapping and non-overlapping sequence reads, by default. It is unclear if this is intended to reference removal of duplicate reads or other artifacts. It is entirely unclear how this impacts the method of claim 1, and does not appear to be further limiting of claim 1.
The metes and bounds of claim 6 are unclear with respect to where within the method of claim 1 the limitations are to be added. It would appear that this is a step to be performed after step e) of claim 1, however, this interpretation from the specification cannot be read into the claims. Further in claim 6, it is unclear from where, “a multiplicity of windows” are to be selected or identified for use in the assignment, as claim 1 only selects a single sequence window. Claim 6 fails to particularly point out and distinctly claim how the assignment of SV is made, using the data in hand from claim 1, and the “multiplicity of windows within the sequence data” as required. Claim 6 fails to particularly point out and distinctly claim how the identification of a “subregion” is to be carried out.
The term “unusual/altered/changed” in claim 6 is a relative term which renders the claim indefinite. The term “unusual/altered/changed” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The claim fails to point out at what point a deviation from a reference state becomes “unusual, altered and/or changed.” The difference in scope between these words is entirely unclear, nor is it clear how to identify an unusual distribution, as opposed to an altered distribution, or a changed distribution. Claim 1 does not clearly provide any reference state information to which the distribution could be compared, nor does claim 6. The term “reference state” does not provide any particular context: “reference” could be a negative reference (healthy) or a positive reference (disease). No error, or statistical significance values are calculated.
The term “any one of, or all of, or any combination of, channels (i) to (iii)” in claim 6 is a relative term which renders the claim indefinite. The term “any one of, or all of, or any combination of” the data provided from claim 1 is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is entirely unclear how to select or identify the appropriate data to use in the comparison of the sample data to the reference state as required, particularly as this modifies the relative term rejected immediately above. The claim fails to particularly point out and distinctly claim how any SV, multiplicity of windows, a sub-region of the data, or a “unusual/altered/changed distribution.”
Claim 7 fails to particularly point out and distinctly claim what Applicant considers their invention. Claim 7 sets forth that the reference state has an “expected aberrant distribution and/or predetermined state of the information…” which fails to set forth how this is intended to modify claims 6 and 1, and how this information changes, or further describes the invention. Claim 6 already is completely generic as to the reference state. Claims 1 and 6 do not provide any reference state information, which could be used in any comparison. This claim does not appear to further modify claim 6.
Claim 8 fails to particularly point out and distinctly claim Applicant’s invention. Claim 8 is a confusing mix of clauses, and / or statements, alternatives, and conditional limitations. It is entirely unclear how to apply these limitations to the data at hand from claim 1, nor is it clear where in claim 1 these clauses are to be applied. It would appear they are intended to modify step e), however step e) does not identify any SNP, SNP location, Allele, or allele frequency. Step e) of claim 1 does not provide any disease associated data, related to SNP identification, nor does claim 8. The mix of and/ or/ alternatively/ and optionally throughout the claim obscures what the limitation is intended to accomplish, and fails to particularly point out and distinctly claim HOW any SNP are identified, and how those SNP are used to infer or impute haplotype information. Applicant is requested to clearly set forth a list of options, “selected from the group consisting of… and…” which particularly point out what is to be performed or determined.
The metes and bounds of claim 9 are unclear with respect to the recitation of a diploid organism genome. It is unclear how this further modifies the method of claim 1, or how monoploid, diploid and multi-ploid genomes are to be distinguished by claim 1.
The metes and bounds of claim 10 are unclear with respect to the “single cells.” The single cells are noted to be “from a cellular sample of a patient” and “associated with a disease…” it is unclear how, a priori, the cell is known to be associated with a disease, if the patient does not have a disease, or may have a multiplicity of conditions. It is further unclear how, if the cell could be “associated with a disease or is a healthy cell of the patient” one can determine any aspect related to disease, or health. Applicant is requested to clearly point out the source of the sample, the cells sampled, and any a priori information, as well as how this limitation further modifies the method of claim 1. It is unclear how to integrate this information to the steps present in claim 1 to arrive at any relevant conclusion. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
The metes and bounds of claim 11 are entirely unclear. Claim 1 does not provide any data regarding health, or disease, nor does it identify any anomalies present in the data. Claim 1 ends with a single annotated “window” of aligned sequence reads, aligned to a target chromosomal region, with 3 types of annotated data. No conclusions are determined as to whether any of the annotated elements are consistent, or inconsistent with reference information. No conclusions are drawn as to the presence or absence of a variant. No structural variants are identified. No particular haplotype is output. No conclusions can be drawn about the initial sample cell, or subject from which the cell was derived. It is entirely unclear how any and all diseases are to be diagnosed, “based on the identity of, location of, or number of detected SV within the target chromosomal region.”
The metes and bounds of claim 12 are entirely unclear, with respect to how any SV is detected using the “method of claim 6.” As set forth above,
“The metes and bounds of claim 6 are unclear with respect to where within the method of claim 1 the limitations are to be added. It would appear that this is a step to be performed after step e) of claim 1, however, this interpretation from the specification cannot be read into the claims. Further in claim 6, it is unclear from where, “a multiplicity of windows” are to be selected or identified for use in the assignment, as claim 1 only selects a single sequence window. Claim 6 fails to particularly point out and distinctly claim how the assignment of SV is made, using the data in hand from claim 1, and the “multiplicity of windows within the sequence data” as required. Claim 6 fails to particularly point out and distinctly claim how the identification of a “subregion” is to be carried out.
The term “unusual/altered/changed” in claim 6 is a relative term which renders the claim indefinite. The term “unusual/altered/changed” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The claim fails to point out at what point a deviation from a reference state becomes “unusual, altered and/or changed.” The difference in scope between these words is entirely unclear, nor is it clear how to identify an unusual distribution, as opposed to an altered distribution, or a changed distribution. Claim 1 does not clearly provide any reference state information to which the distribution could be compared, nor does claim 6. The term “reference state” does not provide any particular context: “reference” could be a negative reference (healthy) or a positive reference (disease).
The term “any one of, or all of, or any combination of, channels (i) to (iii)” in claim 6 is a relative term which renders the claim indefinite. The term “any one of, or all of, or any combination of” the data provided from claim 1 is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is entirely unclear how to select or identify the appropriate data to use in the comparison of the sample data to the reference state as required, particularly as this modifies the relative term rejected immediately above. The claim fails to particularly point out and distinctly claim how any SV, multiplicity of windows, a sub-region of the data, or a “unusual/altered/changed distribution.””
Claim 12 fails to address any of these deficiencies.
Claim 13 fails to particularly point out and distinctly claim what Applicant considers their invention. The metes and bounds of the term “karyotype” in the context of the invention are unclear. The plain meaning of the term “karyotype” in genetics is an image of an individual’s complete set of chromosomes, which may be annotated (NHGRI, Karyotype, downloaded 2026.). The claims do not provide or analyze image information, nor do they apply any genetic information to a karyotype image. The Examiner notes that the concept of karyotype coding, or chromosomal coding may be what Applicant is interested in claiming, however the claims do not specify, nor recite all the necessary and sufficient steps to provide an in-silico karyotype coded sample. (Ye, C. et al. (2019)).
Ye defines karyotype coding as a part of genome system theory:
“One of the key concepts of the genome system theory is karyotype or chromosomal coding: chromosome sets function as gene organizers, and the genomic topologies provide a context for regulating gene expression and function. In other words, the interaction of individual genes, defined by genomic topology, is part of the full informational system. The genes define the “parts inheritance,” while the karyotype and genomic topology (the physical relationship of genes within a three-dimensional nucleus) plus the gene content defines “system inheritance” …” (abstract.)
The metes and bounds of claim 13, and the data gathered in steps a) and b)(i), are unclear. It is unclear if the data gathered in step a) is intended to provide reference data, healthy cell data, positive control data, or whether it is the genus of data from the sequenced cell, which is then further modified by step b)(i).
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 13 recites the broad recitation “the population of single cells” and “at least one single cell”, and the claim also recites a “method of karyotyping a single cell” which is the narrower statement of the range/ limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
The metes and bounds of claim 13 are unclear with respect to what particular steps make up the “scTRP” identified in step b). It is unclear if this term only applies to the newly added step of analyzing the aligned data for the three channels now recited in claim 1, or whether it refers to the entire claim. A reading of the specification suggests steps b) (iii)-(v), c) and d) make up the scTRP, however this interpretation cannot be read into the claims.
Claim 13 step b)(v) does not specifically identify any type of structural variant, or single nucleotide variant in the single cell that provided the sequence read data. Claim 13, step b)(v) ends with a single annotated “window” of aligned sequence reads, aligned to a target chromosomal region, with 3 types of annotated data. No conclusions are determined as to whether any of the annotated elements are consistent, or inconsistent with reference information. No conclusions are drawn as to the presence or absence of a variant. No particular haplotype is output. No SV are identified. No error, or statistical significance values are calculated. No conclusions can be drawn about the initial sample cell, or subject from which the cell was derived.
The term “equally fragmented” in claim 13 is a relative term which renders the claim indefinite. The term “equally fragmented” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear how “equally” is intended to modify “fragmented sequence read” in this context. In the process of sequencing, various lengths of sequence reads are generated, and are ideally present in a normal distribution. It is unclear how to interpret this limitation. It would appear “equally” could be deleted, without affecting the scope, unless this “equally fragmented” term has a specific definition which affects the alignment to the reference assembly.
The examiner notes that the “selection of a sequence window” appears to refer to selection of a subset of the reference assembly, in a computing environment as understood in the prior art and the technology of sequence analysis, however the claim is not computer implemented, no alignments to an assembly are displayed such that a visual “window” could be selected, and it is unclear how the “sequence window” is to be identified or selected, based on the information present in step c). There is no reasoning to pick any particular subset of the assembly. While the specification suggests that an anomaly identified in the alignment and analysis may drive the selection of the sequence window, these limitations cannot be read into the claims.
Claim 13 is further indefinite as it fails to particularly point out and distinctly claim how the detection of SV within a target chromosomal region is to be carried out in step c) using the data at hand from the previous steps. No anomalies in the data from step b) are clearly detected or analyzed such that any structural variant could be detected.
Claim 13 is further indefinite as it fails to particularly point out and distinctly claim what is intended to be generated as an “in-silico karyotype based on all detected SV’s.” The examiner attempted to identify a specific definition of the term in the prior art. Wikipedia (2026) identifies “virtual karyotypes” as:
“the digital information reflecting a karyotype resulting from the analysis of short sequences of DNA from specific loci all over the genome, which are isolated and enumerated.[1] It detects genomic copy number variations at a higher resolution for level than conventional karyotyping or chromosome-based comparative genomic hybridization (CGH).[2] The main methods used for creating virtual karyotypes are array-comparative genomic hybridization and SNP arrays.”
The definition continues: “The end product does not yet have a consistent name, and has been called virtual karyotyping,[8][10] digital karyotyping,[11] molecular allelokaryotyping,[12] and molecular karyotyping.[13] Other terms used to describe the arrays used for karyotyping include SOMA (SNP oligonucleotide microarrays)[14] and CMA (chromosome microarray).[15][16] Some consider all platforms to be a type of array comparative genomic hybridization (arrayCGH), while others reserve that term for two-dye methods, and still others segregate SNP arrays because they generate more and different information than two-dye arrayCGH methods.[citation needed].”
The steps of claim 13 do not clearly fall within the identified types of digital, in-silico, or virtual karyotyping, nor is it clear what format this output should take, what information is to be included, or how it is to be displayed.
The metes and bounds of claim 14 are unclear, with respect to the steps to be performed and how the steps of claim 11 are to be modified to detect any SV, compare SV with a reference state or determine any kind of alteration, as related to any type of cancer.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 14 recites the broad recitation “a disease or condition”, and the claim also recites “the presence of cancer” which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Further with respect to claim 14, which depends from claim 11, as set forth above, claim 11 was determined to be indefinite, and claim 11 depended from claim 1 which was also determined to be indefinite:
“The metes and bounds of claim 11 are entirely unclear. Claim 1 does not provide any data regarding health, or disease, nor does it identify any anomalies present in the data. Claim 1 ends with a single annotated “window” of aligned sequence reads, aligned to a target chromosomal region, with 3 types of annotated data. No conclusions are determined as to whether any of the annotated elements are consistent, or inconsistent with reference information. No conclusions are drawn as to the presence or absence of a variant. No structural variants are identified. No particular haplotype is output. No conclusions can be drawn about the initial sample cell, or subject from which the cell was derived. It is entirely unclear how any and all diseases are to be diagnosed, (particularly cancer), “based on the identity of, location of, or number of detected SV within the target chromosomal region.”
The metes and bounds of claim 1 are unclear with respect to what particular steps make up the “scTRP” identified in the preamble. It is unclear if this term only applies to the newly added step of analyzing the aligned data for the three channels now recited in claim 1, or whether it refers to the entire claim. A reading of the specification suggests steps c)-e) make up the scTRP, however this interpretation cannot be read into the claims.
Further, it is unclear what the point of the analysis is, for claim 1 as written. Claim 1 does not specifically identify any type of structural variant, or single nucleotide variant in the single cell that provided the sequence read data. Claim 1 ends with a single annotated “window” of aligned sequence reads, aligned to a target chromosomal region, with 3 types of annotated data. No conclusions are determined as to whether any of the annotated elements are consistent, or inconsistent with reference information. No conclusions are drawn as to the presence or absence of a variant. No particular haplotype is output. No conclusions can be drawn about the initial sample cell, or subject from which the cell was derived.
The term “equally fragmented” in claim 1 is a relative term which renders the claim indefinite. The term “equally fragmented” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear how “equally” is intended to modify “fragmented sequence read” in this context. In the process of sequencing, various lengths of sequence reads are generated, and are ideally present in a normal distribution. It is unclear how to interpret this limitation. It would appear “equally” could be deleted, without affecting the scope, unless this “equally fragmented” term has a specific definition which affects the alignment to the reference assembly.
The examiner notes that the “selection of a sequence window” appears to refer to selection of a subset of the reference assembly, in a computing environment as understood in the prior art and the technology of sequence analysis, however the claim is not computer implemented, no alignments to an assembly are displayed such that a visual “window” could be selected, and it is unclear how the “sequence window” is to be identified or selected, based on the information present in step c). There is no reasoning to pick any particular subset of the assembly. While the specification suggests that an anomaly identified in the alignment and analysis may drive the selection of the sequence window, these limitations cannot be read into the claims.”
Claim 14 fails to remedy any of these deficiencies, and fails to particularly point out and distinctly claim how any data from claims 11 or 1 are specifically acted upon to identify SV and then link the SV to any disease, condition, or cancer.
The metes and bounds of claim 19 are unclear, with respect to what the two haplotypes, H1 and H2, actually represent. It is unclear if these are generic categories intended to represent healthy vs disease, or two specific haplotypes. It is unclear how to identify either H1 or H2 from the data present in claim 1 from which claim 19 depends. It is entirely unclear how this is intended to modify the method of claim 1, and what conclusions could be drawn from the addition of this limitation.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 10, 11 and 14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for the diagnosis of specifically recited diseases associated with specifically identified SV, does not reasonably provide enablement for the diagnosis of any disease or condition or cancer. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use the invention commensurate in scope with these claims.
Claims 10, 11 and 14 are directed to the use of the scTRIP method of claim 1, in the diagnosis of any disease, condition, or cancer.
In In re Wands (8 USPQ2d 1400 (CAFC 1988)) the CAFC considered the issue of enablement in molecular biology. The CAFC summarized eight factors to be considered in a determination of "undue experimentation". See MPEP 2164.01(a). These factors include: (a) the quantity of experimentation necessary; (b) the amount of direction or guidance presented; (c) the presence or absence of working examples; (d) the nature of the invention; (e) the state of the prior art; (f) the relative skill of those in the art; (g) the predictability of the art; and (h) the breadth of the claims.
In considering the factors for the instant claims:
a) In order to practice the claimed invention one of skill in the art must analyze strand-specific sequence read data from a single cell of the subject, using the scTRIP method described in claim 1, identify variants, including structural variants, and make a diagnosis of any disease, condition or cancer based on the identification. For the reasons discussed below, there would be an unpredictable amount of experimentation required to practice the claimed invention.
b) The specification provides guidance for detecting structural variations in single cells, for the “identification of deletions, duplications, polyploidies, translocations, inversions, copy number neutral loss of heterozygosity (CNN-LOH) and more” in genetic / genomic data. The specification distinguishes their methods from the identification of SNP or SNV, and their link to disease, in the introduction/description beginning at [0002]. The specification at [0004] notes the prior art already analyzes certain types of copy number alterations (CNA), but the disclosed methods are able to identify additional SV classes. The “detailed description” begins with setting forth “aspects” using the same language as the claims. Definitions are provided beginning at [0021]. Structural variations are defined at [0026], and a possible link to a pathological condition is generically described at [0027-0029]. Generically described “diagnostic footprinting” begins at [0079], and Table 1, however, the disease or condition which is footprinted by Table 1 is not identified. Table 2 indicates certain diagnostic footprints identified for certain aneuploid states, but does not link them to a disease, condition, or cancer.
At [0093] the specification asserts “The methods of the invention are particular useful for diagnosing a disorder, or the probability of a subject to develop a disorder, and finally, in order to stage a disorder or monitor it, or even to estimate disease severity. There are many genetic disorders which are associated 30 with SV s of any kind. Hence, some preferred embodiments of the invention also encompass further step (f) diagnosing a condition based on the identity of, location of, or number of detected SV within the target chromosomal region.”
The section on diagnostics begins at [0101], where a particular characteristic is linked to cancer, an idea of “chromosome instability.” This disclosure does not link any particular instability with any particular cancer, or other disorder or disease, but [0102] indicates that this measure is important in the diagnosis of cancer.
Paragraph [0104] recites a list of known SV, which are linked and diagnostic for specific diseases, disorders, or cancers. However, it does not set forth how to link any newly identified SV not from this list as diagnostic for any disorder, disease or cancer. To be diagnostic for a disease, the tested element must be specific and selective for that disease. [0106] sets forth a long list of cancers. Merely listing types of known cancers, without specifying a type of SV for each is not an identification of how to diagnose those cancers using the claimed methods.
Another listing of desired embodiments follows this listing, however they provide no additional information.
c) The specification provides working examples of certain aspects of the invention. The Examples, beginning at [0130] set forth the laboratory steps for obtaining the single cells, single cell sequencing using a particular “Strand-seq” protocol, and the use of a prior art algorithm (StrandPhaseR) to perform “chromosome-length haplotype phasing of heterozygous SNP.” After the phasing, the discovery of SV in the aligned, phased data is performed as set forth beginning at [0135]. Particular bioinformatic steps are performed on the aligned, phased data, to identify the SV present. Strand-state, and haplotype-aware classification is performed using certain algorithms. The matrix output of these processes is analyzed for SV, and provides probability scores for each. Particular processes for difficult SV’s are described beginning at [0142], including translocations, breakage-fusion-bridges, and CNN-LOH.
Beginning at [0150], the scTRIP method is linked to the idea of the “diagnostic footprint” of SV that can be used to diagnose a condition, disease or cancer. Each type of SV is recognized by a different pattern of information from scTRIP, and are acted upon using particular computer modeling steps [0152]. The diagnostic footprints are further discussed beginning at [0165] however exactly what footprint is specific and selective for which disease, condition or cancer is not clearly provided. The further examples 2-5, beginning at [0176] also fail to link any particular diagnostic footprint to any particular disease, condition or cancer.
d) The claims are broadly drawn to the use of the scTRIP method to diagnose any disease, condition, or cancer. All diseases, conditions, or cancers (known or unknown) do not necessarily exhibit SV. A viral infection (which falls within “diseases” or “conditions”) does not result in somatic structural variations. A broken bone (which falls within “conditions”) does not exhibit genomic structural variations. The claims do not set forth how the identification of any particular SV, or “footprint” is then used to diagnose any disease, condition or cancer. No particular bioinformatic steps are recited, no algorithmic processes, or machine learning elements are applied to the sample data, or reference data, to determine the presence of any anomalies which may or may not be specific or selective for any particular disease, condition or cancer.
e) The state of the prior art, with respect to the identification of structural genomic variants, and disease can be represented by Iourov (2014), Stancu (2017) and Sanders (2017). Every disease has its own etiology in the body, with differing diagnostic characteristics which could range from bodily or physical symptomology, blood test values, body part/ organ imaging, tumor imaging, genetic variations or genomic structural variations of any kind. In genetics, to create a diagnostic test for a given disease, the genetic biomarker, SNP, or SV must be analyzed in multiple contexts to ensure that biomarker, SNP, or SV is both specific to the disease, and not any other, and selective for that disease, with a minimum of false positive/ false negative reports. Significant hypothesis testing, validation in patient populations, and laboratory testing must be carried out.
Iourov et al. (2014) represents an in-silico method of molecular cytogenetics, linking copy number variations in a sample to candidate genes, which may be useful in diagnosis of a disease. However, Iourov does note that:
“Consequently, candidate gene prioritization seems to represent a valuable approach to validate genomic associations in silico and, more importantly, to exacerbate the significance of molecular findings [1,2]. Actually, functional characteristics of genes seem to be the most useful parameters for establishing genetic associations [3-6]. However, there is a strong evidence from molecular cytogenetic studies that copy numbers of genes involved in a variety of critical biological processes can be variable without apparent phenotypic effect [7,8]. Therefore, one can propose bioinformatic classification of genetic variants to be important for distinguishing between benign and pathogenic mutations.” (p2).
This indicates that not all SV that can be detected are necessarily related to a disease, condition or cancer, and that additional steps beyond the identification of the SV are required to achieve a specific and selective diagnosis.
Iourov sets forth: “we have hypothesized that a gene mutation (CNV/chromosome rearrangement) is likely to be associated with specific trait if the gene is expressed more abundantly in the affected tissue. Thus, our model suggests that a genetic variant is more likely to have a phenotypic outcome due to dysfunctions in specific tissues or cell lineages. The latter appears to be achieved through unequal distribution of gene expression patterns in different tissues. Hence, it becomes possible to attribute genes involved in a chromosome rearrangement or CNV to specific cellular processes or tissue pathology.” (p2)
Iourov sets forth a representation of their process in Fig 1, and Fig 4, which uses image data (FISH) or cytogenetic data (array CGH) to identify structural variants present in the cell, or cell sample. But it is the combination of information from the SV, SNV data, epigenetic data, gene expression data, the selection of associated genes, associated protein interactomes, and biologic pathway information that is required to make any attempts at diagnosis.
Iourov notes that “In any case, results of bioinformatic analyses can be confirmed by a “clinical verification” or in other words, genotype-phenotype correlations. However, such verification has a disadvantage inasmuch as phenotypic outcomes can be intricate or can manifest later in life. Genetic variants causing susceptibility to complex diseases represent another problem regardless of the wide spread ignorance in terms of CNV pathogenic value.” (p3).
Stancu et al. (2017) identifies and maps structural variation in patient genomes, using long read sequencing data. Stancu presents NanoSV, a workflow or pipeline for analyzing sequence reads from the long read MinION nanopore sequencer, to identify complex SV. (Abstract).
Stancu sets forth: “Robust methods to detect structural variants (SVs) in human genomes are essential, as SV s represent an important class of genetic variation that accounts for a far greater number of variable bases than single nucleotide variations (SNVs)5. Moreover, SVs have been implicated in a wide range of genetic disorders.” P2
“An important and natural application of the long reads produced by nanopore sequencing is identifying SVs. Long-read sequencing is breaking ground for the discovery of SV s at an unprecedented scale and depth… While short-read next-generation sequencing data rely on multiple (often) indirect sources of information in order to accurately identify SV s, structural changes can be directly reflected in longread data.” (p2).
“We employ a novel computational pipeline to demonstrate the feasibility of using MinION reads to detect de novo complex SV breakpoints, at high sensitivity. The long reads from the MinION allow efficient phasing of genetic variations (SNVs as well as SVs) and enable us to resolve the long-range structure of the chromothripsis in the patients. Moreover, we identify a significant proportion of SVs that are not detected in short-read Illumina sequencing data of the same patient genomes.” (p2)
This indicates that the details of how the computational pipeline acts on the sequence read data, to identify the SV is a critical part of the process. Merely identifying the presence of a possible SV is not sufficient, in and of itself, to make the diagnosis. Figure 3 and its description illustrate the complexity of the resolved structural variants for one patient.
The NanoSV algorithm is disclosed beginning at p9, Methods section. Particular types of alignments, segmentation, alignment and clustering steps are performed, and the algorithm determines read depth, strand orientation and haplotype phasing directly from the sequence read information. The workflow for SV calling using NanoSV is disclosed at p10, using particular parameters, and obtaining particular outputs. Filtering of identified SV is performed using a particular random forest algorithm, with detailed parameters and features, to identify false positive SV. SV validation was performed, as set forth at page 11. Annotation and phasing are performed using the algorithms and parameters as set forth at p11-12. The reconstruction of the complicated chromothipsis related SV is described at p12. The claims lack this level of detail and structure.
Stancu also notes some problems in the field, at page 9. “A drawback of current short-read genome sequencing technology is the need for high capital investment, which often leads to sequencing infrastructure being located in dedicated sequencing centers. This is associated with a complex logistic workflow and relatively long turnaround times.”
With respect to the use of single cell sequence read data, Sanders et al (2017) notes the following:
“The ability to distinguish between genome sequences of homologous chromosomes in single cells is important for studies of copy-neutral genomic rearrangements (such as inversions and translocations), building chromosome-length haplotypes, refining genome assemblies, mapping sister chromatid exchange events and exploring cellular heterogeneity. Strand-seq is a single-cell sequencing technology that resolves the individual homologs within a cell by restricting sequence analysis to the DNA template strands used during DNA replication.” (Abstract).
Sanders points out difficulties present in the art of analyzing single cell sequence read data, and the identification of SV in that data in the introduction:
“Most single-cell sequencing methods begin with a whole-genome amplification step to increase input material, and this can introduce PCR artifacts, amplification biases and allelic dropouts that confound variant calling19. Furthermore, although conventional single cell sequencing allows resolution of subpopulations, results still represent an average of each cell's DNA content, and resolving the individual homologs is near impossible using these types of data. Consequently, the relationship of the variants (both structural and single-nucleotide polymorphisms) to a chromosomal homolog (i.e., the phase) is masked using these approaches.”
To address these issues, Sanders utilizes Strand-seq, a particular laboratory protocol, having specific and unusual steps, to provide:
“… directional genomic libraries that, when aligned to the reference genome, permits a clear distinction between the individual homologs of a chromosome. Homolog resolution allows diverse types of variants to be located in a single cell that would otherwise be very challenging to detect using conventional approaches. This includes inversions, translocations, copy-number changes, aneuploidy events and complex structural variants, with haplotype awareness. By pooling data from several dozen Strand-seq cells, each homologous chromosome can be uniquely characterized and analyzed.” P1151
Sanders provides a detailed laboratory and computer analysis pipeline. Fig 1 is an overview of the particular Strand-seq process. The process is not without drawbacks.
“The Strand-seq technology sacrifices coverage (depth and breadth) for long-range structural information that offers many new applications for genome biology…” p1153.
“The principal limitation of Strand-seq is that the technology centers on hemi-substituted DNA to distinguish template strands. This requirement limits the method to cells that can incorporate BrdU during S-phase. Consequently, the protocol requires viable mitotic cells, and libraries cannot be generated from extracted DNA or fixed tissue samples.” P1154
“A further caveat to this approach is that strand inheritance patterns are confounded in polyploid samples. For example, a trisomic chromosome will have three template strands sequenced that result in complex inheritance patterns (e.g., WWW, WWC, WCW, CWW, CWC, WCC, CCW, CCC), which are marked by differential read doses. As such, chromosomal rearrangement can be more challenging to locate within polyploid loci.” 1154
“Nevertheless, copy-number variation (CNV) analyses can be performed on these data. By measuring shifts in read doses within a chromosome, and between chromosomes, CNV and aneuploidy events can be predicted. In addition, by incorporating the phasing information inherent in Strand-seq, haplotype support for variant predictions can be used to improve sensitivity of conventional single-cell approaches.” P1154.
The bioinformatic details of Sanders are disclosed beginning at p1157, and further details beginning at p 1169, and requires specific algorithms, parameters, and information. Particular anticipated information to use as possible reference state are provided, as well as specific troubleshooting tips for the laboratory protocol and the bioinformatic workflow. Quality assessment, background elimination, the use of a BAIT function to generate certain data, how specific types of SV are to be identified based on the generated data, use of InvertR to identify inversions, phasing using Strand-PhaseR are all required and detailed.
Such details are not present in the claims.
f) The skill of those in the art of bioinformatics is high. Bioinformatics requires the understanding of the underlying biologic processes being studied as well as sophisticated computer-based statistical analyses of the data generated.
g) The prior art predicts that every disease has its own etiology in the body, with differing diagnostic characteristics which could range from bodily or physical symptomology, blood test values, body part/ organ imaging, tumor imaging, genetic variations or genomic structural variations of any kind. In genetics, to create a diagnostic test for a given disease, the genetic biomarker, SNP, or SV must be analyzed in multiple contexts to ensure that biomarker, SNP, or SV is both specific to the disease, and not any other, and selective for that disease, with a minimum of false positive/ false negative reports. Significant hypothesis testing, validation in patient populations, and laboratory testing must be carried out. The prior art indicates that merely identifying the presence of an SV in a sample is insufficient on its own as a diagnostic marker. All diseases, conditions, or cancers (known or unknown) do not necessarily exhibit SV. A viral infection (which falls within “diseases” or “conditions”) does not result in somatic structural variations. A broken bone (which falls within “conditions”) does not exhibit genomic structural variations. Therefore, the prior art predicts that the diagnosis of any disease, condition or cancer, based solely on the identification of an SV, to be highly unpredictable and unlikely. Even within categories of disease known to be related to SV, the analysis of the SV data must be carefully analyzed, and validated, before use as a diagnostic.
h) The claims are broad because they are drawn to methods of diagnosing any disease, condition or cancer, by identification of an SV, without specifying how any particular SV is determined to be diagnostic for any particular disease.
The skilled practitioner would first turn to the instant specification for guidance to practice methods of diagnosing diseases using structural variation data. However, the instant specification does not provide specific guidance to practice these embodiments. As such, the skilled practitioner would turn to the prior art for such guidance, however, the prior art shows that the use of structural variation data for diagnosis depends on the cells profiled, the protocols used to sequence the cells, and the details of the analysis of the generated data. The mere presence of an SV in a sample was not shown to be diagnostic for the scope of the claims. Finally, said practitioner would turn to trial-and-error experimentation to determine how to analyze SV data to develop a diagnostic test for any disease or condition or cancer, that is specific and selective for that disease. Such represents undue experimentation.
Claim Rejections - 35 USC § 102
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 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)(1) 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.
Claim(s) 1, 3-7, 9-14, and 20 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Yuen (2017).
Yuen, M. W-K. (December, 2017) Mapping complex genomic translocation using Strand-Seq. Thesis, University of British Columbia, Vancouver. 80 pages.
Yuen is directed to the analysis of sequence read data, generated from single cell sequencing of cells generated from a subject with a disease, to identify structural variations. The analysis of the Strand-seq data was “able to identify an additional layer of complexity of rearrangements, not previously characterized by other methods.” (Lay summary).
With respect to claim 1 and “A method for analyzing sequencing data of at least one target chromosomal region by single cell tri-channel processing (scTRIP), comprising:
(a) providing strand specific sequence data of at least one target chromosomal region of at least one single cell, wherein the strand-specific sequencing data comprise a multitude of strand specific sequence reads obtained by sequencing of the target chromosomal region of at least one single cell;”
Yuen obtains single cells of an iALL cell line, as set forth at section 2.1, iALL826A cell line. (p12). Yuen identifies single cells during log phase of replication, and separates them, as set forth at section 2.2, p13.
Yuen sequences the single cells using the Strand-seq protocol, which provides strand-specific sequencing data, comprising a multitude of sequence reads, that are each strand specific, and target the desired chromosomal regions. Strand-seq is discussed beginning at section 1.4, p8.
“Template strand sequencing (Strand-seq) is a single cell sequencing approach that selectively sequences the template strands of parental cells inherited by daughter cells [51]. Directionality of reads is maintained using custom oligo-adaptors amenable to lllumina sequencing [52].”
Further details of the sequencing are provided at p15: “Samples were sequenced using an lllumina MiSeq platform, achieving 852k/mm"2 cluster density with 97% passing filter. 3.2GB of sequencing data was produced to obtain an average of 243143.6 reads per cell and an average 0.00578X coverage per cell.”
With respect to claim 1 and: “(b) aligning the sequence reads, or if the sequence reads are equally fragmented, each fragmented portion of the sequence reads, to a reference assembly;”
Yuen aligns the sequence reads from the strand-specific sequencing to the HG19 human reference genome assembly using BWA-MEM, as set forth in section 2.4, p16. After sorting and duplication removal, a subset of the sequence reads having a mapping quality score exceeding a threshold were analyzed by the BreakpointR algorithm. 18 high confidence breakpoints localized to 11 chromosomes were identified by the program.
With respect to claim 1 and “(c) analyzing the aligned sequence reads for three channels of sequence information, wherein the three channels comprise read depth, strand orientation and haplotype phase;”
The aligned reads are analyzed for various types of information, which includes read depth (p15, p30), strand orientation (p22) and haplotype phase (p34, 35).
With respect to claim 1 and “(d) selecting a sequence window; and”
The BAIT analysis segments the aligned reads into bins, which are viewed as a window ideogram, as shown in Fig 4. The window size is 200kb in length (p8). Additional details about the sliding window are set forth in the legend to Figure 9.
“(a) A user defined sliding window moves along an ideogram, adjusting itself to encapsulate an equal number of reads in each frame of the window, calculating the change in the absolute number of Watson reads from Left frame to Right frame. The change is graphed, the top and bottom 10% is trimmed and 3 standard deviations above the remaining mean is annotated as the breakpoint interval (black bar). (b) This process is repeated across all chromosomes, in each single cell. The breakpoints across multiple cells are compiled and disjointed (purple bar) by using the start and end positions of breakpoints to disjoint the breakpoint intervals. The disjointed genomic segments are compiled into a histogram. The threshold is a user defined parameter to isolate the recurrent breakpoint interval, with a minimum of 50%. 1.6 standard deviations above the mean was used in this project.”
This meets the BRI of the limitation. Figure 10 also illustrates selecting a sequence window of a particular chromosome.
With respect to claim 1 and “(e) assigning in the selected window the three channels of sequence information: (i) number of total sequence reads, or portions thereof, (ii) number of forward sequence reads, or portions thereof, and number of reverse sequence reads, or portions thereof; (iii) numbers of sequence reads, or portion thereof, assigned with a specific haplotype identity.”
Yuen annotates the selected windows with the required information.
“Results from BAIT analysis of a single cell are plotted as an ideogram displaying the inheritance pattern of parental DNA template strands for each chromosome (Figure 4). In order to construct these ideograms, BAIT bins every chromosome into discreet variable bins of the reference genome. For my studies these bins were 200kb in size [53]. The number of reads aligned within each bin, and their strand states (Watson and/or Crick) is calculated and this number is represented by the length of each individual horizontal bar, orange for Watson and blue for Crick. Each bin is stacked to produce the final ideogram. The spiky nature of ideograms reflects the variable number of reads per bin, which in turn reflects variability in the quality of single cell Strand-seq libraries. Apart from sub-saturation sequencing [43], areas of the genome that are not nucleosomal, G/C bias [54-56] and the variable BrdU incorporation in nascent DNA all contribute to variability.” P8
“Normal cells exhibit a baseline probability of Sister Chromatid Exchange (SCEs) events which are visualized on the ideograms as switches in strand-state. Stable rearrangements are also visualized as switches in strand-state on an ideogram. The defining feature of stable rearrangements is the recurrent nature of the strand-state switches, across multiple cells, while random SCE events do not have recurrent strand-state switches (Figure 5). When observing stable rearrangements by Strand-seq, they are expected to have higher than normal probability of strand-state switches occurring at the same region of the DNA, compared to random SCE events.” (p9)
“Alignment to the reference will visualize the difference of inheritance patterns between the fragment chromosome and its origin chromosome as a strand-state switch. This difference of inheritance patterns is true for only 50% of independent assortment combinations. Figure 6 shows an example of each scenario, where one combination results in a strand-state switch, while the other combination does not.” (p9)
Whole genome sequencing was used to generate reference data for the bioinformatic process, including SNV analysis and phasing.
“FASTQ files were aligned onto the Hg19 human reference genome using the BWA-MEM alignment algorithm [61] and BAM files were fed into the DELLY pipeline [46] which uses discordant read-pairs and an integrated paired-end, split-read mapping to identify high confidence SVs. An in-house custom pipeline developed by Victor Guryev at the European Research Institute for the Biology of Aging {ERIBA), 1235V [62], was also used to crossvalidate the WGS data. 1235V uses discordant read-pairs and an integrated paired-end, split-read mapping to identify high confidence SVs.” P18.
Figure 11, and its description also meet the BRI of annotating the selected window with particular information:
“Step 1: Identifying the combination of inherited template strands and extrapolating theexpected chromosomal ideogram. This is done by knowing the position of centromeres, and considering the reads surrounding that region. This assumes that the reads surrounding the centromeric region should reflect the original inheritance pattern, unaffected by SVs. Step 2: Annotate the strand-state of the deviate chromosome fragment by comparing the expected ideogram to the observed ideogram. The deviant strand-state on the observed ideogram is the strand-state of the deviate chromosome fragment.”
With respect to claim 3, segmenting of the target chromosomal region is performed based on read depth, coverage, and/or strand orientation, as set forth above. The legend of Fig 9 states: “(a) A user defined sliding window moves along an ideogram, adjusting itself to encapsulate an equal number of reads in each frame of the window, calculating the change in the absolute number of Watson reads from Left frame to Right frame. The change is graphed, the top and bottom 10% is trimmed and 3 standard deviations above the remaining mean is annotated as the breakpoint interval (black bar).”
With respect to claim 4, Yuen maps sequence reads to both strands, where one is maternal and one is paternal, at p 8.
“Reads are aligned to either the Watson (-) or Crick (+) strand of the reference genome and represented as ideograms to reflect the directionality of template strands inherited after independent assortment of sister chromatids [53]. For each chromosome during independent assortment, each cell can inherit one of four possible combinations of parental homologs (Figure 3). A cell can inherit for both parental homologs Watson template strands only, Watson-Watson (WW) strand-state. Both parental homologs can also be present as Crick template strands, Crick-Crick {CC) strand-state. Or the parental homologs can present as strands of alternating directionality, Watson-Crick (WC) strand state. In the case of 23 chromosomes pairs, there are ~7*10"13 possible combinations of independent assortments which can occur.”
With respect to claim 5, the strand-seq protocol provides both overlapping and non-overlapping sequence reads. (section 2.4).
With respect to claim 6, Yuen analyzes a multiplicity of windows within the sequence data, to identify multiple SV having unusual/altered/changed distributions of at least one of the channels of information. The window analysis of Fig 9 is a sliding window analysis, repeated for all chromosomes.
With respect to claim 7, an expected state for the iALL cells is a predetermined state of the information of the chromosomal region. iALL cells have known reference states for the identified SV.
With respect to claim 9, the iALL cells are diploid cells.
With respect to claim 10, the cells are from a cellular sample of an iALL patient, which has a disease, and the method was performed on multiple isolated single cells. Section 2.
With respect to claim 11, the SV identified by Yuen are linked to the diagnosis of iALL of the patient.
Claim 12 is met in all the same places as claims 1, and 6.
With respect to claim 13, claim 13 folds the method of claim 1 into a method of karyotyping a single cell. Only the differing limitations are addressed here: all other limitations are met as set forth above for claim 1.
With respect to claim 13, and steps a) and b)(i), strand-specific sequence data is obtained for reference and sample single cells as set forth in section 2.
The remainder of step b) is met in the same places as set forth above for claim 1.
With respect to step c), Yuen identifies SV present in the data of the single cell or population of single cells. 18 high confidence breakpoints localized to 11 chromosomes were identified.
With respect to step d) in-silico karyotypes are generated, as represented by Figures 4, 5, 6, 7, 10, 11, 13, 14 and 17.
With respect to claim 14, Yuen meets this claim by carrying out the steps of claim 11, which depends from claim 1, detects SV as set forth above by comparison to a reference state. iALL is a type of leukemia, a cancer.
With respect to claim 20, the whole genome can be a target as set forth in Sections 2 and 3.
Claim(s) 1, 3-14, 19, 20 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Sanders (2017).
Sanders, A. D. et al. (2017) Single-cell template strand sequencing by Strand-seq
enables the characterization of individual homologs. Nature Protocols, vol 12, no 6, p1151-1176.
Sanders is directed to the identification of structural variations in single cell genomic data, generated by a strand-specific sequencing protocol.
“The ability to distinguish between genome sequences of homologous chromosomes in single cells is important for studies of copy-neutral genomic rearrangements (such as inversions and translocations), building chromosome-length haplotypes, refining genome assemblies, mapping sister chromatid exchange events and exploring cellular heterogeneity. Strand-seq is a single-cell sequencing technology that resolves the individual homologs within a cell by restricting sequence analysis to the DNA template strands used during DNA replication… Each single-cell library is multiplexed for pooling and sequencing, and the resulting sequence data are aligned, mapping to either the minus or plus strand of the reference genome, to assign template strand states for each chromosome in the cell.” (abstract).
With respect to claim 1 and “A method for analyzing sequencing data of at least one target chromosomal region by single cell tri-channel processing (scTRIP), comprising:
(a) providing strand specific sequence data of at least one target chromosomal region of at least one single cell, wherein the strand-specific sequencing data comprise a multitude of strand specific sequence reads obtained by sequencing of the target chromosomal region of at least one single cell;”
Sanders obtains single cells (Fig 3), that are sequenced using a strand-specific sequencing process. The strand-specific sequencing process targets template strands used during DNA replication, which are parts of the chromosomes. (Fig 1, overview). Page 1156 discusses the isolation of single BrdU marked cells, and refers to Fig 3. Sanders notes that they process between 94 and 188 single cells, including a negative and positive control, per experiment. Figure 2 illustrates the generation of a directional single-cell library (see also Fig 4). The construction of the library, and pooling of samples is set forth at page 1157. The pooled samples are then sequenced using Illumina sequencing protocols.
“This allows the final molarity of the purified product to be calculated for sequencing on a MiSeq or HiSeq Illumina sequencer (Fig. 4b). The type of sequencing depends on the research question: although a 50-bp single-read sequencing protocol is typically sufficient for mapping breakpoints of structural rearrangements, 76-bp paired-end sequencing runs are more suitable for obtaining complete coverage of mononucleosomal fragments for haplotyping purposes. The dinucleosomes can be pooled with the mononucleosomes for sequencing on a single lane (at a 1:1.3 ratio, to reduce sequencing costs), or sequenced on separate lanes (for instance, using a 151-bp paired-end sequencing protocol, to improve overall coverage).”
With respect to claim 1 and: “(b) aligning the sequence reads, or if the sequence reads are equally fragmented, each fragmented portion of the sequence reads, to a reference assembly;”
Sanders aligns the acquired sequence reads to a reference assembly, as set forth at page 1157. Alignment is additionally discussed at p 1169, bioinformatic analysis, and p1170.
“Resulting sequencing reads are aligned to a reference assembly in order to assign template strand states… the primary concerns in analyzing Strand-seq data involve determining (i) where in the reference assembly the sequencing read aligns, and (ii) whether the read is in the Crick (C; aligns to the plus (forward) strand of the reference assembly) or Watson (W; aligns to the minus (reverse) strand) orientation. Using this information, template strand states are assigned and the inheritance pattern is determined for each chromosome of the sequenced cell.” (p1157)
With respect to claim 1 and “(c) analyzing the aligned sequence reads for three channels of sequence information, wherein the three channels comprise read depth, strand orientation and haplotype phase;”
Sanders analyzes the aligned reads for the required information, including read depth (p1153, the legend to Fig 5, p1173, p1175 et al.), strand orientation (p1151-1152, Fig 1, p1157, and 1175) and haplotype phase (p1175).
“For libraries that are of high read depth and low background, BAIT will accurately call these changes in template strand states, and map the breakpoints with high accuracy22.” P1175
“(ii) whether the read is in the Crick (C; aligns to the plus (forward) strand of the reference assembly) or Watson (W; aligns to the minus (reverse) strand) orientation.” P1157,
“When chromosomes are inherited as WC, each template strand represents a single parental homolog. Consequently, all variants (structural variants and single-nucleotide polymorphisms) that map to the Watson strand are present on one homolog (e.g., maternal), and all the variants on the Crick strand are present on the other homolog (e.g., paternal). This means that the variants are phased for the entire length of the chromosome, albeit at very low density for a single cell and only for the WC chromosomes. To build more dense haplotypes that represent the entire genome of an individual, multiple cells can be analyzed. We recently showed how this can be used to build complete and accurate haplotypes without the need for generational (i.e., trio) information23 . The R-based bioinformatic package, called 'Strand-Phase.R' (https:// github.com/ daewoooo/ StrandPhaseR.git), is available for haplotyping Strand-seq data.” P1175.
With respect to claim 1 and “d) selecting a sequence window”
Sanders employs the BAIT program, which displays segmented windows of genetic information. (Fig 1, Fig 5, p1169-1170, 1174, Fig 7 et al.) “BAIT also calculates the template strand state in 200-kb intervals across the entire length of each chromosome.” P1174.
With respect to claim 1 and “(e) assigning in the selected window the three channels of sequence information: (i) number of total sequence reads, or portions thereof, (ii) number of forward sequence reads, or portions thereof, and number of reverse sequence reads, or portions thereof; (iii) numbers of sequence reads, or portion thereof, assigned with a specific haplotype identity.”
Sanders annotates the window with the required information, generated by the previous steps. Read depth is the number of sequence reads aligned to the region. “Reads from a Strand-seq library are aligned to the reference genome and binned, and read counts are displayed on ideograms for
each chromosome by BAIT.” Legend to Fig 1, p1152. Forward or reverse reads are identified and counted. See Fig 1a. “The method takes advantage of the directionality of single-stranded DNA molecules, which are distinguished as either Crick (C; forward, or plus, strand of the reference
assembly) or Watson (W; reverse, or minus, strand) based on their 5’-3’ orientation.” P1151-1152. Haplotype identity is determined using Strand-PhaseR, which identifies SNP and phases them. p1175.
With respect to claim 3, segmenting the target chromosomal region is performed by the BAIT program as set forth above. “BAIT also calculates the template strand state in 200-kb intervals across the entire length of each chromosome.” P1174.
With respect to claim 4, the strand-specific data is mapped to at least two separate strands of the target region, where one strand is maternal, and the other is paternal. See the legend to Fig 1. “(a) Principle of the technology and inheritance of template strands in daughter cells. Maternal (M) and paternal (P) chromosome homologs are composed of a positive (Crick, 'C', blue) and a negative (Watson, 'W', orange) template strand. During DNA replication, BrdU is incorporated exclusively into nascent DNA strands (dashed lines). Upon cell division, each daughter cell inherits CC, WW or WC template strands of each parental chromosome.”
With respect to claim 5, the Strand-seq and Illumina sequencing processes generate overlapping and non-overlapping reads.
With respect to claim 6, the identification of changed, altered, or unusual distributions in any information is used to identify SV. Fig 6, SCE distribution, the protocol steps for fragment size distribution, p1175, et al.
With respect to claim 7, expected normal and non-normal states of inheritance are determined for each sample. Known SV in disease samples is used as a non-normal state, while healthy cell data sets forth a normal state.
With respect to claim 8, the haplotyping using the Strand-PhaseR program performs these steps as set forth above, at page 1175.
With respect to claim 9, humans are diploid organisms, and the cells tested are from human samples.
With respect to claim 10, the cells analyzed can be healthy cells or disease associated cells from a patient.
With respect to claim 11, the presence of an SV can be linked to a congenital disease or disorder, or a tumor process in a patient as set forth in the introduction and overview.
Claim 12 is met in the same places as claims 6 and claim 1.
With respect to claim 13, claim 13 folds the method of claim 1 into a method of karyotyping a single cell. Only the differing limitations are addressed here: all other limitations are met as set forth above for claim 1.
With respect to claim 13, and steps a) and b)(i), strand-specific sequence data is obtained for reference and sample single cells as set forth in Fig 1, the Methods, and the protocol details.
The remainder of step b) is met in the same places as set forth above for claim 1.
With respect to step c), Sanders identifies SV present in the data of the single cell or population of single cells, including SCE, translocations, misorientations, etc. P1153, p1174-1175.
With respect to step d) in-silico karyotypes are generated, as represented by Figures 1, 5, 6 and 7.
With respect to claim 14, Sanders meets this claim by carrying out the steps of claim 11, which depends from claim 1, detects SV as set forth above by comparison to a reference state.
With respect to claim 19, the Strand-PhaseR program provides phased haplotypes for each strand, which appear to meet the BRI of H1 and H2.
With respect to claim 20, the whole genome can be a target as set forth throughout.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chaisson, M et al. (16 April 2019) Multi-platform discovery of haplotype resolved structural variation in human genomes. Nature Communications, vol 10:1784, 16 pages.
Chaisson appears to also anticipate the claims with the caveat that it is not clearly performed on isolated single cells. “The goal of this study was to comprehensively discover, sequence, resolve, and phase all non-single-nucleotide variation in a selected number of human genomes… Since no single technology alone achieved the density, accuracy, and chromosome-spanning haplotyping necessary to comprehensively identify and assemble SV s throughout the entire human genome we systematically evaluated the performance of all possible combinations of technologies. When combining a dense, yet local, technology (such as PB or CHRO) with a chromosome-scale, yet sparse, technology (such as Hi-C or Strand-seq), we obtained dense and global haplotype blocks (Fig. ld, e).” p2.
Porubsky (2017) as previously cited on the PTO-892, discloses the Strand-PhaseR program for phasing Strand-seq sequence reads.
Porubsky is not directed specifically to the identification of structural variants, but single nucleotide variations (SNP or SNV). It receives “Strand-seq” sequence reads, from single cells. However, to achieve the required haplotype density, information from other sequencing processes are used along with the Strand-seq data. “Strand-seq is an effective method to assemble highly accurate chromosome-length haplotypes, albeit with lower density of phased alleles in comparison to read-based phasing. Unlike other haplotyping methods, Strand-seq by design distinguishes parental homologs based on the directionality of single stranded DNA. Therefore, Strand-seq is able to deliver global haplotypes, and its capability to correctly phase two variants with respect to each other does not depend on their distance. To fully exploit this advantage, while at the same time generating dense haplotypes that contain virtually all heterozygous SNV s, we designed a novel unified statistical framework to combine Strand-seq data with short-read, long-read, or linked-read sequencing data. Previously, Strand-seq data were used for phasing on its own, resulting in global yet sparse haplotypes. We demonstrate how the long-range phase information inherent to Strand-seq data can be leveraged to bridge phased segments obtained from Illumina, PacBio, or 10X Genomics sequencing data into contiguous and global haplotypes that span whole chromosomes.” P3.
Marie, R. et al. (30 October, 2018) Single-molecule DNA mapping and whole genome sequencing of individual cells. PNAS, Vol 115, no 44, p11192-11197.
Marie isolates and analyzes single cells for the presence of structural variations, using micro-/ nanofluidic chips for DNA extraction from individual cells. These chips have modules that collect genomic DNA for sequencing or map genomic structure directly, on-chip, with denaturation renaturation (D-R) optical mapping… Processing of single cells from the LS174T colorectal cancer cell line showed that D-R mapping of single molecules can reveal structural variation (SV) in the genome of single cells.” It is unclear if this meets the scTRIP requirement, however the process does seem to produce strand-specific sequence reads. (Fig 1-Fig 2). Fig 4 illustrates the paired end sequencing results. Fig 5 illustrates the sliding window analysis of the sequence reads over a segmented region of the genome, for read depth, length, and variants.
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/MARY K ZEMAN/ Primary Examiner, Art Unit 1686
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