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 .
Claims 1-20 are pending and under examination.
This application claims priority to a US provisional application, filed 10/11/2022, and the effective filing date for the claims is that of the provisional application, 10/11/2022.
This application has been published as US PG-Pub 2024/0127906 A1.
The petition to make special under the Patent Prosecution Highway filed 3/6/2026 has been granted under separate cover. All PPH related paperwork has been reviewed.
The Drawings as filed are suitable for examination.
Four separate IDS statements have been entered and considered.
Claim 6 is objected to because of the following informalities: In claim 6, “based on determining the second quotient by further comparing the false negative rate to the falst positive rate,” the term “falst” appears to be a typographical error, and the correct word appears to be “false”. 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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: computer systems, computer readable storage media, and computer-implemented processes.
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:
Claim 1. (Currently Amended) A system comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:
(Preamble, and EIA identifying a system of a general-purpose computer comprising instructions to carry out a method. The system is an additional element (MPEP 2106.05(b).)
identify, for a methylation sequencing assay, a methylation-level value indicating a level of methylation of a target cytosine base within a sample nucleotide sequence;
(Mental Process of observing data that meets a condition. MPEP 2106.04(a)(2).)
determine a false positive rate at which the methylation sequencing assay converts cytosine bases within nucleotide sequences by comparing (i) a number of converted unmethylated cytosine bases within an unmethylated artificial oligonucleotide subject to the methylation sequencing assay to (ii) a total number of unmethylated cytosine bases within the unmethylated artificial oligonucleotide;
(Mental Process and Mathematic Concepts: elements i) and ii) require the mathematic concept of counting elements meeting a condition. Comparing the numbers is a Mental Process of observation, analysis and judgement. Alternatively, a mathematic calculation of a rate. MPEP 2106.04(a)(2).)
determine a false negative rate at which the methylation sequencing assay converts cytosine bases within nucleotide sequences by comparing (i) a number of unconverted methylated cytosine bases within a methylated artificial oligonucleotide subject to the methylation sequencing assay to (ii) a total number of methylated cytosine bases within the methylated artificial oligonucleotide;
(Mental Process and Mathematic Concepts: elements i) and ii) require the mathematic concept of counting elements meeting a condition. Comparing the numbers is a Mental Process of observation, analysis and judgement. Alternatively, a mathematic calculation of a rate. MPEP 2106.04(a)(2).)
predict, based on a first quotient comparing the false positive rate to the false negative rate,within the sample nucleotide sequence;
(Mental Process: predicting a corrected number is a step of data analysis and making a judgement about data values meeting a condition. MPEP 2106.04(a)(2).)
predict, based on a second quotient comparing the false negative rate to the false positive rate,a second corrected number of nucleotide reads supporting unmethylated cytosine sites within the sample nucleotide sequence; and
(Mental Process: predicting a corrected number is a step of data analysis and making a judgement about data values meeting a condition. MPEP 2106.04(a)(2).)
generate a corrected methylation-level value that corrects for a bias reflected in the methylation-level value for the target cytosine base within the sample nucleotide sequence by determining a third quotient comparing (i) the first corrected number of nucleotide reads to (ii) the first corrected number of nucleotide reads and the second corrected number of nucleotide reads.
(Mathematic concept of calculating a quotient of two numbers; alternatively, a mental process of comparing data values and making a judgement as to a corrected value. MPEP 2106.04(a)(2).)
2. (Original) The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: determine the false positive rate or the false negative rate by estimating the false positive rate or the false negative rate at which the methylation sequencing assay converts cytosine bases flanked by a contextual sequence; and generate the corrected methylation-level value for the target cytosine base specific to the contextual sequence flanking the target cytosine base.
(Mathematic concept of calculating estimates and calculating a value. MPEP 2106.04(a)(2))
3. (Original) The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: determine the false positive rate by estimating a rate at which the methylation sequencing assay incorrectly converts one or more unmethylated cytosine bases within a given nucleotide sequence into one or more uracil bases or thymine bases; and determine the false negative rate by estimating a rate at which the methylation sequencing assay fails to convert one or more methylated cytosine bases within a given nucleotide sequence into one or more uracil bases or thymine bases.
(Mathematic concept of calculating estimates and calculating a value. MPEP 2106.04(a)(2))
4. (Currently Amended) The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to determine the false positive rate at which the methylation sequencing assay converts cytosine bases by: converting, utilizing the methylation sequencing assay, one or more of the unmethylated cytosine bases within the unmethylated artificial oligonucleotide.
(Mathematic concept of calculating rates. MPEP 2106.04(a)(2))
5. (Currently Amended) The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to determine the false negative rate at which the methylation sequencing assay converts cytosine bases by: converting, utilizing the methylation sequencing assay, one or more of the methylated cytosine bases within the methylated artificial oligonucleotide.
(Mathematic concept of calculating rates. MPEP 2106.04(a)(2))
6. (Currently Amended) The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to predict the first corrected number of nucleotide reads or the second corrected number of nucleotide reads by: determining a true positive rate and a true negative rate at which the methylation sequencing assay converts cytosine bases within nucleotide sequences; identifying, from data generated by the methylation sequencing assay, a first counted number of nucleotide reads supporting methylated cytosine sites within the sample nucleotide sequence and a second counted number of nucleotide reads supporting unmethylated cytosine sites within the sample nucleotide sequence; and predicting the first corrected number of nucleotide reads based on determining the first quotient by further comparing the false positive rate to the false negative rate, the true positive rate, the true negative rate, the first counted number of nucleotide reads, and the second counted number of nucleotide reads; or predicting the second corrected number of nucleotide reads based on determining the second quotient by further comparing the false negative rate-to the falst positive rate, the true positive rate, the true negative rate, the first counted number of nucleotide reads, and the second counted number of nucleotide reads.
(Mathematic concepts of counting, calculating rates, and Mental Processes of data comparison, analysis and judgement. MPEP 2106.04(a)(2).)
7. (Currently Amended) The system of claim 6, further comprising instructions that, when executed by the at least one processor, cause the system to predict the first corrected number of nucleotide reads supporting the methylated cytosine sites within the sample nucleotide sequence by: determining a first difference between a first numerator product of the true negative rate and the first counted number of nucleotide reads and a second numerator product of the false positive rate and the second counted number of nucleotide reads; determining a second difference between a first denominator product of the true positive rate and the true negative rate and a second denominator product of the false negative rate and the false positive rate; and determining the first quotient by determining a quotient of the first difference over the second difference.
(Mathematic concepts of subtraction and multiplication. MPEP 2106.04(a)(2).)
8. (Currently Amended) The system of claim 6, further comprising instructions that, when executed by the at least one processor, cause the system to predict the second corrected number of nucleotide reads supporting the unmethylated cytosine sites within the sample nucleotide sequence by: determining a first difference between a first numerator product of the true positive rate and the second counted number of nucleotide reads and a second numerator product of the false negative rate and the first counted number of nucleotide reads; determining a second difference between a first denominator product of the true positive rate and the true negative rate and a second denominator product of the true negative rate and the false positive rate; and determining the second quotient by determining a quotient of the first difference over the second difference.
(Mathematic concepts of subtraction and multiplication. MPEP 2106.04(a)(2).)
9. (Original) The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: predict the first corrected number of nucleotide reads by determining a number of nucleotide reads supporting methylated cytosine sites within at least a first nucleotide sequence of the nucleotide sequences; and predict the second corrected number of nucleotide reads by determining a number of nucleotide reads supporting unmethylated cytosine sites within at least a second nucleotide sequence of the nucleotide sequences.
(Mathematic concepts of counting, and mental processes of observation, analysis and judgement. MPEP 2106.04(a)(2).
10. (Currently Amended) The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to generate the corrected methylation-level value by determining the third quotient by determining a quotient of the first corrected number of nucleotide reads over a sum of the first corrected number of nucleotide reads and the second corrected number of nucleotide reads.
(Mathematic concepts of addition, and multiplication. MPEP 2106.04(a)(2).)
11. (Original) The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: determine that a counted number of nucleotide reads covering the target cytosine base within the sample nucleotide sequence fails to satisfy a coverage threshold; and based on the counted number of nucleotide reads failing to satisfy the coverage threshold, generate the corrected methylation-level value for the target cytosine base.
(Mental process of observation, analysis and judgement, and mathematic concepts of counting. MPEP 2106.04(a)(2).
Claim 12. (Currently Amended) A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause a system to:
(Preamble, and EIA identifying a CRM comprising instructions to carry out a method. The CRM is an additional element (MPEP 2106.05(b).)
identify, for a methylation sequencing assay, a methylation-level value indicating a level of methylation of a target cytosine base within a sample nucleotide sequence;
(Mental Process of observing data that meets a condition. MPEP 2106.04(a)(2).)
determine a false positive rate at which the methylation sequencing assay converts cytosine bases within nucleotide sequences by comparing (i) a number of converted unmethylated cytosine bases within an unmethylated artificial oligonucleotide subject to the methylation sequencing assay to (ii) a total number of unmethylated cytosine bases within the unmethylated artificial oligonucleotide;
(Mental Process and Mathematic Concepts: elements i) and ii) require the mathematic concept of counting elements meeting a condition. Comparing the numbers is a Mental Process of observation, analysis and judgement. Alternatively, a mathematic calculation of a rate. MPEP 2106.04(a)(2).)
determine a false negative rate at which the methylation sequencing assay converts cytosine bases within nucleotide sequences by comparing (i) a number of unconverted methylated cytosine bases within a methylated artificial oligonucleotide subject to the methylation sequencing assay to (ii) a total number of methylated cytosine bases within the methylated artificial oligonucleotide;
(Mental Process and Mathematic Concepts: elements i) and ii) require the mathematic concept of counting elements meeting a condition. Comparing the numbers is a Mental Process of observation, analysis and judgement. Alternatively, a mathematic calculation of a rate. MPEP 2106.04(a)(2).)
predict, based on a first quotient comparing the false positive rate to the false negative rate,within the sample nucleotide sequence;
(Mental Process: predicting a corrected number is a step of data analysis and making a judgement about data values meeting a condition. MPEP 2106.04(a)(2).)
predict, based on a second quotient comparing the false negative rate to the false positive rate,a second corrected number of nucleotide reads supporting unmethylated cytosine sites within the sample nucleotide sequence; and
(Mental Process: predicting a corrected number is a step of data analysis and making a judgement about data values meeting a condition. MPEP 2106.04(a)(2).)
generate a corrected methylation-level value that corrects for a bias reflected in the methylation-level value for the target cytosine base within the sample nucleotide sequence by determining a third quotient comparing (i) the first corrected number of nucleotide reads to (ii) the first corrected number of nucleotide reads and the second corrected number of nucleotide reads.
(Mathematic concept of calculating a quotient of two numbers; alternatively, a mental process of comparing data values and making a judgement as to a corrected value. MPEP 2106.04(a)(2).)
13. (Original) The non-transitory computer-readable medium of claim 12, further comprising instructions that, when executed by the at least one processor, cause the system to change, based on the corrected methylation-level value, a methylation-difference value for a differentially methylated region corresponding to the target cytosine base within the sample nucleotide sequence.
(Mental process of observing a correction and annotating data. MPEP 2106.04(a)(2).)
14. (Original) The non-transitory computer-readable medium of claim 12, further comprising instructions that, when executed by the at least one processor, cause the system to provide, for display within a graphical user interface, the methylation-level value and the corrected methylation-level value.
(EIA- routine display of results. MPEP 2106.05(g).)
15. (Original) The non-transitory computer-readable medium of claim 12, wherein the sample nucleotide sequence is extracted from a non-human organism.
(EIA- a step related to data gathering, or a source of the data gathered. MPEP 2106.05(g).)
16. (Original) The non-transitory computer-readable medium of claim 12, further comprising instructions that, when executed by the at least one processor, cause the system to determine the false positive rate and the false negative rate comprises determining the false positive rate and the false negative rate at which the methylation sequencing assay converts cytosine bases into uracil bases or thymine bases.
(Mathematic concept of calculating a rate. MPEP 2106.04(a)(2).)
Claim 17. (Currently Amended) A computer-implemented method comprising:
(Preamble, and EIA identifying a method, that uses a general purpose computer. (MPEP 2106.05(b).)
identifying, for a methylation sequencing assay, a methylation-level value indicating a level of methylation of a target cytosine base within a sample nucleotide sequence;
(Mental Process of observing data that meets a condition. MPEP 2106.04(a)(2).)
determining a false positive rate at which the methylation sequencing assay converts cytosine bases within nucleotide sequences by comparing (i) a number of converted unmethylated cytosine bases within an unmethylated artificial oligonucleotide subject to the methylation sequencing assay to (ii) a total number of unmethylated cytosine bases within the unmethylated artificial oligonucleotide;
(Mental Process and Mathematic Concepts: elements i) and ii) require the mathematic concept of counting elements meeting a condition. Comparing the numbers is a Mental Process of observation, analysis and judgement. Alternatively, a mathematic calculation of a rate. MPEP 2106.04(a)(2).)
determining a false negative rate at which the methylation sequencing assay converts cytosine bases within nucleotide sequences by comparing (i) a number of unconverted methylated cytosine bases within a methylated artificial oligonucleotide subject to the methylation sequencing assay to (ii) a total number of methylated cytosine bases within the methylated artificial oligonucleotide;
(Mental Process and Mathematic Concepts: elements i) and ii) require the mathematic concept of counting elements meeting a condition. Comparing the numbers is a Mental Process of observation, analysis and judgement. Alternatively, a mathematic calculation of a rate. MPEP 2106.04(a)(2).)
predicting, based on a first quotient comparing the false positive rate toa first corrected number of nucleotide reads supporting methylated cytosine sites within the sample nucleotide sequence;
(Mental Process: predicting a corrected number is a step of data analysis and making a judgement about data values meeting a condition. MPEP 2106.04(a)(2).)
predicting, based on a second quotient comparing the false negative rate to the false positive rate, within the sample nucleotide sequence; and
(Mental Process: predicting a corrected number is a step of data analysis and making a judgement about data values meeting a condition. MPEP 2106.04(a)(2).)
generating a corrected methylation-level value that corrects for a bias reflected in the methylation-level value for the target cytosine base within the sample nucleotide sequence by determining a third quotient comparing (i) the first corrected number of nucleotide reads to (ii) the first corrected number of nucleotide reads and the second corrected number of nucleotide reads.
(Mathematic concept of calculating a quotient of two numbers; alternatively, a mental process of comparing data values and making a judgement as to a corrected value. MPEP 2106.04(a)(2).)
18. (Original) The computer-implemented method of claim 17, wherein: determining the false positive rate or the false negative rate comprises estimating the false positive rate or the false negative rate at which the methylation sequencing assay converts cytosine bases flanked by a contextual sequence; and generating the corrected methylation-level value for the target cytosine base specific to the contextual sequence flanking the target cytosine base.
(Mathematic concept of calculating an estimate rate. MPEP 2106.04(a)(2).)
19. (Original) The computer-implemented method of claim 17, wherein: determining the false positive rate comprises estimating a rate at which the methylation sequencing assay incorrectly converts one or more unmethylated cytosine bases within a given nucleotide sequence into one or more uracil bases or thymine bases; and determining the false negative rate comprises estimating a rate at which the methylation sequencing assay fails to convert one or more methylated cytosine bases within a given nucleotide sequence into one or more uracil bases or thymine bases.
(Mathematic concept of calculating an estimate rate. MPEP 2106.04(a)(2).)
20. (Currently Amended) The computer-implemented method of claim 17, wherein determining the false positive rate at which the methylation sequencing assay converts cytosine bases comprises: converting, utilizing the methylation sequencing assay, one or more of the unmethylated cytosine bases within the unmethylated artificial oligonucleotide.
(Mental process of observing a correction and annotating data. MPEP 2106.04(a)(2).)
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, 12 and 17 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Dependent claim(s) 2-11, 13-16 and 18-20 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, 12 and 17: the limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception.
Lo et al. (WO2021/032060 A1; PTO-1449), Vaisvila et al. (2020; PTO-1449), Feng et al. (2020; PTO-1449) and Yuen et al. (2021) each disclose computer systems or computing elements which meet the BRI of the claimed computer system or computer system elements, comprising input, output/ display, a processor, and memory.
As such, the prior art recognizes that these computing elements are routine, well understood and conventional in the art.
The specification, at [0152+] discloses the use of routine general-purpose computers for carrying out the invention, and/or the use of commercially available computer system elements.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not provide significantly more.
Dependent claim(s) 2-11, 13-16 and 18-20 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, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. 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.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kennedy (2024) in view of Datta (2009) and Gong (Sept 2022).
Applicant’s effective filing date is 10/11/2022.
Kennedy, A. Quality Control Method. US 2024/0093292 A1, published 3/1/2024, having priority to 11/2/2021.
Datta, D. et al. (2009) Effect of false positive and false negative rates on inference of binding target conservation across different conditions and species from ChIP-chip data. BMC Bioinformatics, vol 10:23, pages 1-13.
Gong, W. et al. (Sept 1, 2022) Benchmarking DNA methylation analysis of 14 alignment algorithms for whole genome bisulfite sequencing in mammals. Computational and Structural Biotechnology Journal, vol 20, p4704-4706 (available online 27 Aug 2022).
Kennedy is directed to: “
[0006] Described herein are methods that provide improved quality control for the conversion step in methods for detecting and/or identifying modified nucleosides in a DNA sample that rely on using a base pairing specificity conversion procedure that is sensitive to the modification status of nucleosides.”
Kennedy describes methylation sequencing assays, which use control oligonucleotides including both known modified and known unmodified cytosines, to assess false positive/false negative rates of the methylation process. Kennedy’s data analysis methods are all performed using routine computer systems comprising processors and memory [0408+], and disclose CRM comprising instructions for the analyses [0414].
“[0007] Embodiment 1 is a quality control method for monitoring false negative and/or false positive detection of modified nucleosides in DNA in a sample, the method comprising: (a) ligating the DNA to oligonucleotide adapters, wherein the adapters comprise quality control nucleosides that include modified nucleosides, wherein the quality control nucleosides have the same nucleoside identity and the same or a different modification status to modified nucleosides to be detected in the DNA, and wherein the modification status of the quality control nucleosides is known; (b) subjecting the adapted DNA, or a subsample thereof, to a conversion procedure that changes the base pairing specificity of the quality control nucleosides or does not change the base pairing specificity of the quality control nucleosides, depending on the modification status of the nucleosides, wherein the conversion procedure is selected to (i) change the base pairing specificity of adapted DNA nucleosides having the same nucleoside identity and modification status as quality control nucleosides in the adapters, and not change the base pairing specificity of adapted DNA nucleosides having the same nucleoside identity as quality control nucleosides in the adapters but a different modification status; and/or (ii) not change the base pairing specificity of adapted DNA nucleosides having the same nucleoside identity and modification status as quality control nucleosides in the adapters, and change the base pairing specificity of adapted DNA nucleosides having the same pairing identity as quality control nucleosides in the adapters but a different modification status; (c) sequencing the adapted DNA after conversion step (b); (d) using the sequence data obtained in step (c) to determine base pairing specificity conversion of the quality control nucleosides in the adapters; and (e) using the base pairing specificity conversion of the quality control nucleosides in the adapters as a quality control measure for conversion step (b), wherein sub-optimal conversion of adapter quality control nucleosides following a conversion procedure of step (b)(i) and/or erroneous conversion of adapter quality control nucleosides following a conversion procedure of step (b)(ii) predicts false negative and/or false positive detection of modified nucleosides in the DNA sample.”
Variations 2-50, of the quoted embodiment are also disclosed, for various types of chemistry or enzymes applied. This meets the data required to carry out step 1 of claim 1, identifying a methylation level value of a target cytosine in a sample sequence, wherein the information for the control oligonucleotides is also provided, as the control and target are linked.
The conversion rate for target methylation, and control methylation are both determined:
“[0057] … and (e) using the base pairing specificity conversion of the quality control nucleosides in the adapters as a quality control measure for conversion step (b), wherein sub-optimal conversion of adapter quality control nucleosides following a conversion procedure of step (b)(i) and/or erroneous conversion of adapter quality control nucleosides following a conversion procedure of step (b)(ii) predicts false negative and/or false positive detection of modified nucleosides in the DNA sample.
[0058] In some embodiments, the method further comprises using the sequence data obtained in step (c) to: (i) identify adapted DNA molecules with sub-optimal or erroneous conversion of quality control nucleosides in the adapter sequence; and (ii) infer (additional) sub-optimal or erroneous conversion of nucleosides having the same nucleoside identity and modification status in the full length molecules identified in step (i).
[0059] In some embodiments, the method further comprises determining the conversion rate for quality control nucleosides in the adapted DNA or in individual adapted DNA molecules and (i) applying a weighting to analysis of the modified nucleoside detection in (A) the DNA sample; or (B) individual adapted DNA molecules, wherein the weighting is dependent on the conversion rate;”
“[0073] The methods of the disclosure can be used to infer the conversion rate on either a sample level or molecular level. … The method may further comprise using the sequence data to determine the conversion rate of the known modified nucleosides in the adapter(s) of one or more molecules identified in step (d); and using the determine conversion rate to estimate the conversion rate of the full length adapted DNA molecule.”
“[0075] The conversion rate determined in step (d) provides a quality control measure for the conversion procedure, and can be used to estimate the rate of false negatives, i.e. modified residues in the initial sample that were not effectively converted by the conversion procedure and hence falsely identified.”
Kennedy explains the control oligonucleotides in depth beginning at [0175]. Control oligonucleotides to identify false positive results, and oligonucleotides to identify false negative results are disclosed. A target polynucleotide from the sample may comprise one or more control oligonucleotides after tagging and amplification. [0182]
Determining a false negative rate for control oligonucleotides having known modifications is discussed at [0322-0333]
“[0333] … Therefore, it is possible to determine the conversion rate for a particular molecule equal to the number of correctly converted known modified quality control nucleosides divided by the total number of known modified nucleosides in the quality control nucleosides of the molecule adapters. Likewise, the conversion rate for the whole sample (or partition/subsample thereof) may be determined by dividing the total number of correctly converted known modified quality control nucleosides in the uniquely identified molecules of the sample by the total number of known modified nucleosides in the same set of uniquely identified sample molecules. In other words, the conversion rate for the sample or molecule, including the nucleosides with unknown modification status, is inferred from the conversion rate of the known modified nucleosides in the adapter sequences.”
“[0334] … For example, a score or weighting may be applied to each molecule and/or the sample that is dependent on the determined conversion rate(s). Molecules or samples with higher conversion rates are assigned a higher weighting, i.e., are attributed a greater significance, in downstream analysis or determination of the modified nucleoside profile of the sample DNA. Molecules or samples with lower relative conversion rates are assigned a lower weighting, i.e., are attributed a lesser significance in downstream analysis.”
Detecting false negatives with conversion procedures which convert unmodified nucleosides is disclosed at [0335-0336].
Detecting false negatives with conversion procedure which convert modified nucleosides is disclosed beginning at [0337].
Detecting false negatives with conversion procedure which convert unmodified nucleosides is disclosed beginning at [0339]:
“Likewise, the conversion rate for the whole sample (or partition/subsample thereof) may be determined by dividing the total number of correctly converted known unmodified nucleosides in the uniquely identified molecules of the sample by the total number of known unmodified nucleosides in the same set of uniquely identified sample molecules. In other words, the conversion rate for the whole sample or molecule, including the nucleosides with unknown modification status, is inferred from the conversion rate of the known unmodified nucleosides in the adapter sequences.”
Kennedy discloses example data analysis workflows in an exemplary method beginning at [0424]. See also Fig 3 and its description in the text.
Kennedy does not disclose how TP/FP, TN/FN rates were specifically calculated, or combined/ multiplied however, these calculations were well known in the prior art as shown by Datta et al.
Datta discusses experiments having binary readouts which can include +/- methylation, and the calculation of TP, FP, TN, FN, calculation of overall rates, as well as various quotients of the rates in the model setup section, beginning at p3.
“Consider an experiment with a binary outcome. Let p0 denote the proportion of true negatives, while p1 be the proportion of true positives. We denote p = (p0, p1)t as the vector of true proportions. Due to false positives and false negatives, the observed proportions likely differ from the true proportions. Let denote the vector of the observed proportions. The relationship between p and E( ) can be written as: Eq 1 where s is the false positive rate and t is the false negative rate. Denoting the transformation matrix as M, Equation (1) can be written as: Eq 2. Thus, for different values of false positive and false negative rates, different observed proportions will be obtained based on Equation (2). If the false positive and false negative rates are known, the true proportions may be inferred based on the observed experimental proportions. Multiplying both sides of Equation (2) by M-1 gives us: Eq 3… If we consider Equation (2) to correspond to a 1-dimensional case, for the n-dimensional case, the new transformation matrix would simply be obtained by taking the tensor product of M with itself n times. Here we assume that the false positive and false negative rates to be the same across two conditions. In general that may not be the case.” (See also EQ 4 and 5).
Datta uses these analyses to count, or adjust the count of the genes which are true positives, false positives, false negatives and true negatives as shown in Fig 1-2, and the Simulation Results, p6. Datta explains further at page 7:
“In the section describing the model setup, we stated that multiplication of the vector of the observed proportions with the inverse of the transformation matrix could lead to inferred true proportions with negative components. Here we illustrate the scenario. For the regulator Ste12, in Rich Medium and Mating inducing condition the vector of the observed proportions is p = (0.9761, 0.0144, 0.0040, 0.0056)t. For a false positive rate of 0.001 and a false negative rate of 0.2, multiplying p by the inverse of the transformation matrix results in the vector of the inferred true proportions ˆ p = (0.9743, 0.0150, 0.0020, 0.0087)t. However, for a false positive rate of 0.002 and a false negative rate of 0.3, the vector of the inferred true proportions is ˆ p ˆ p = (0.9748, 0.0144, -0.0005, 0.0113)t. Similarly, for a false positive rate of 0.004 and a false negative rate of 0.4, the vector of the inferred true proportions is = (0.9793, 0.0113, -0.0061, 0.0154)t. Thus, the inferred true proportions obtained by simply multiplying the observed proportions with the inverse of the transformation matrix could contain negative components.”
Neither Kennedy nor Datta specifically link the disclosed calculations of TP/FP/TN/FN rates to read counts of sequence reads.
In the same field of research, Gong analyzes how sequence depth and coverage affect the counting and analysis of methylated cytosine residues from WGBS sequencing data. WGBS sequence read data is mapped, then analyzed for methylation levels at individual sites. WGBS sequencing data from various subjects, having various read depths and replicates were obtained as set forth in section 2.2. After routine data preprocessing, the reads were mapped to the relevant reference genome. Uniquely mapped reads, multiply mapped reads, and unsatisfactory aligned reads were counted. After counting, and the calculation of mapped precision, recall and F1 score, the methylation level was calculated using methyldackel. Differentially methylated sites and regions were identified. (p4706-4707). The accuracy of certain algorithms for calling methylation sites and regions was analyzed and compared. Accurate, concordant calls are TP, inaccurate discordant calls are FP, Accurate concordant non-calls are TN, inaccurate discordant calls are FN. Regions of the target that are high in C-G content often exhibit methylation and are difficult to sequence, and determine an accurate level of sequence reads in WGBS processes. Gong found that regions with CGI led to incorrect mapping of sequence reads, which would affect sequence read and methylation counts of true/false positive/negative methylation sites.
“In humans, cattle, and pigs, the concordant CpGs were likely to over representation at the low (1/3), but the discordant CpGs were likely to under representation at the intermediate methylation (1/3 2/3) (Student’s t-test, P < 0.02, Fig. 5h).” (p4710) The presence of repeated regions can also affect mapping, which then affects sequence read counts and methylation counts. “Moreover, the enrichments of concordant DMCs were 0.92, 0.87, and 0.83 in repetitive sequence of humans, cattle and pigs, respectively (Fig. 6d, Fisher’s exact test, P < 2.2e-16), showing that the concordant DMCs were likely to under representation at repetitive sequence. Nonetheless, the concordant DMCs has no clear preference for CGIs (Fig. 6d).” (p4711)
The indication that one or more category may be over/under represented compared to ground truth data is a suggestion that sequence read counts should be adjusted accordingly in those situations.
“We also found that the unsatisfactory aligned reads were significantly over representation at repetitive sequence (Fig. 4b) and CGI regions (Fig. 4c) in mammals. The enrichment of these five algorithms on repetitive sequence and CGI regions were obviously different in mammalian species
(Fig. 4b and c). Since CGI is rich in CG, the C base on the CGI will be converted to T base in bisulfite conversion, which severely reduces the complexity of the sequence and ultimately affects the reads mapping on CGI. The repetitive sequence also made it difficult to map reads accurately and uniquely to the reference genome. Previous studies found that the mapped precision was lower in the repeat-rich regions [39], and the repetitive sequences led to a significant reduction in the number of uniquely mapped reads [58], which were consistent with our findings. These results suggested that the repetitive sequences and CGIs might make a difference to the unsatisfactory aligned reads as well as mapping performance… We found that the distinct alignment algorithms made a significant influence on the methylomes of mammals, since most of CpG sites (Fig. 5d, Fig. S10a), DMCs (Fig. 6b, Fig. S11a) and DMRs (Fig. 6f, Fig. S11c) called by Bwa-meth, BSMAP, Bismark-bwt2-e2e and Walt were discordant… Furthermore, we observed that repetitive sequences retained more discordant CpG sites, discordant DMCs and discordant DMRs than non-repetitive sequence in humans, cattle and pigs (Fig. 5g, Fig 6d, Fig 6h). This observation may be due to the influence of repetitive sequence on the mapping of the unsatisfactory aligned reads (Fig. 4b). Meanwhile, it was found that more discordant CpG sites were distributed in intermediate methylation than low and high methylation (Fig. 5h). This result was in accord with one recent study that the least concordant calling was exhibited on the CpG sites with intermediate methylation for seven alignment algorithms [38]. The rigorous statistical approaches should be developed based on the information of CpG sites, such as coverage, mapping quality of reads and the number of samples, to correct the CpG sites with intermediate methylation. (p4714-4715).
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard of obviousness to Kennedy, Datta and Gong the examiner concluded that the combination of specific calculations of TP/FP/TN/FN rates, and their additive or multiplicative combinations as disclosed by Datta in combination with Kennedy and Gong represents a combination of known elements which yield the predictable result of data which truly represent the conversion rate of methylation of cytosine bases at a site wide, region wide, and sample wide basis, which can then be used to inform whether the sequence read counts for reads associated with methylated regions should be adjusted up or down. Datta evidences the well-known nature of calculating the types of quotients claimed in experiments with binary readouts, which include methylation. Gong provides the link between methylation levels, and alignment of sequence reads used to determine sequence depth and coverage. Such a combination is merely a "predictable use of prior art elements according to their established functions." KSR Int’l 7, 127 S. Ct. at 1740.
With respect to claim 2, and claim 18 Kennedy considers flanking or contextual sequences, as does Gong.
With respect to claim 3 and claim 20 this is met by Kennedy and Datta.
With respect to claims 4-5, this is addressed by Kennedy with the control oligonucleotide adaptors.
With respect to claim 6, counts and accuracy/precision are discussed by Gong.
With respect to claims 7-9 these are disclosed by Kennedy and Datta.
With respect to claim 9-10, Gong addresses correcting reads and coverage levels.
Independent claims 12 and 17 are met at the same places for claim 1 above.
Claim 13 is met by Kennedy and Gong for correcting values or methylations.
Claim 14 is met by Kennedy [i.e. 0078] and Gong who each use computers which display data.
Claim 15 is met by Kennedy, Datta and Gong who obtain human or non-human sequences.
With respect to claim 16, and claim 19, C-T conversion is discussed by Kennedy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Venn (WO2020/154682 A1) disclosed artificial bait oligonucleotides for use in enriching certain targets or genomic regions prior to methylation sequencing. This is not the disclosure of a control oligonucleotide to assess how a methylation process and assess true/false positive rates.
Balasubramian (WO2023/275268 A1) disclosed control polynucleotides, and oligonucleotides, comprising one or more modified cytosine residues, but these appear to be sequencing control oligonucleotides, and not methylation control oligonucleotides. Balasubramian discloses synthetic ODN used to assess rate of methylation, however Balasubramian does not use this rate to assess true/false positive rates of methylation identification.
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