DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-6, 10-12, 16, 18, 21, 23, 29, 31, 46 and 56-57 are currently pending and under examination herein.
Claims 1-6, 10-12, 16, 18, 21, 23, 29, 31, 46 and 56-57 are rejected.
Priority
The instant application claims the benefit of foreign priority to AU2018904348, filed 11/15/2018. The certified copies of papers required by 37 CFR 1.55 have not been received.
Applicant's claim for the benefit of a prior-filed application, PCT/AU2019/000141 filed 11/15/2019 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Accordingly, the effective filing date of the claimed invention is 11/15/2019.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/29/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the list of cited references was considered in full by the examiner.
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby
withdrawn in view of the amendments filed 12/30/2024.
The 35 U.S.C. 112(b) rejections to claims 1-6, 10-11, 18, 21, 23, 29, and 31 in the office action filed 08/28/2024 has been withdrawn in view of amendments received 12/29/2025 specifically by correcting letter designation of claims 1 and 3.
The following rejections and/or objections are either maintained or newly applied. They constitute the complete set presently being applied to the instant application.
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 1-6, 10-12, 16, 18, 21, 23, 29, 31, 46 and 56-57 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim(s) 1, 12, and 46 being representative) is directed to methods. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claim(s) 1-6, 10-12, 16, 18, 21, 23, 29, 31, 46 and 56-57 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
Claim 1 recites determining the frequency at which the sequence occurs in a reference genome, expressed as a Native Intron Frequency of the first sample splice site sequence (NIFvar-1); wherein a NIFvar-1 of 0 (zero) indicates that the sample splice site is abnormal; the limitation determining a frequency is considered a mathematical calculation, as disclosed in present specification: “One measure of Native Intron Frequency is the number of times a particular nucleotide sequence appears in a splice site in a reference human genome sequence, which may be represented by NIFvar or NIF (count)” (paragraph [0068]). As such, said limitation falls within mathematical concepts groupings of abstract ideas (also mental process of counting and calculating frequency). See MPEP 2106.04(a)(2) I C.
Claim 2 recites the method is repeated with one or more sample splice site sequences comprised in the sample splice site; the limitation repeating is considered a mathematical calculation of repeatedly counting and calculating an event. The said limitation can also be practically performed in human mind (mental process) because human mind is able to repeatedly calculate the frequency of an event.
Claim 3 recites determining a measure of Native Intron Frequency of the first sample splice site sequence (NIFvar-1); the limitation determining frequency The limitation determining a frequency is considered a mathematical calculation. As such, said limitation falls within mathematical concepts groupings of abstract ideas (also mental process of counting and calculating frequency).
Claim 3 further recites determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref-1); the limitation determining frequency The limitation determining a frequency is considered a mathematical calculation. As such, said limitation falls within mathematical concepts groupings of abstract ideas (also mental process of counting and calculating frequency).
Claim 3 further recites determining a risk of abnormal splicing for the sample splice site by comparing NIFvar-1 with NIFref-1 against a Clinical Splice Predictor (CSP) reference database; the limitation determining a risk by comparing is considered a mathematical relationship of comparing values, as such, said limitation falls within mathematical concepts groupings of abstract ideas. See MPEP 2106.04(a)(2) I A. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is able to compare known information. See MPEP 2106.04(a)(2) III A.
Claim 4 recites determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref 1); the limitation determining a frequency is considered a mathematical calculation, and as such, said limitation falls within mathematical concepts groupings of abstract ideas (also mental process of counting and calculating frequency).
Claim 4 further recites determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref 1); the limitation determining a frequency is considered a mathematical calculation, and as such, said limitation falls within mathematical concepts groupings of abstract ideas (also mental process of counting and calculating frequency).
Claim 4 further recites determining a risk of abnormal splicing for the sample splice site by comparing NIFvar1 with NIFref 1 against a Clinical Splice Predictor (CSP) reference database; the limitation determining a risk by comparing is considered a mathematical relationship of comparing values, as such, said limitation falls within mathematical concepts groupings of abstract ideas. See MPEP 2106.04(a)(2) I A. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is able to compare known information. See MPEP 2106.04(a)(2) III A.
Claim 4 further recites that the determining steps are repeated with one or more sample splice site sequences; the limitation repeating is considered a mathematical calculation of repeatedly counting and calculating an event. The said limitation can also be particularly performed in human mind (mental process) because human mind is able to repeatedly calculate the frequency of an event.
Claim 4 further recites comparison of each further NIFvar with each corresponding NIFref against a CSP reference database; the limitation comparison of frequencies is considered a mathematical relationship of comparing values, as such, said limitation falls within mathematical concepts groupings of abstract ideas. See MPEP 2106.04(a)(2) I A. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is able to compare known information. See MPEP 2106.04(a)(2) III A.
Claim 5 recites determining a Percentile (NIFvar-i) of the first sample splice site sequence; the limitation determining a percentile is considered a mathematical calculation, and as such, falls withing mathematical concepts groupings of abstract ideas. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is capable of calculating a percentile.
Claim 5 further recites determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref-1); the limitation determining a frequency is considered a mathematical calculation, and as such, said limitation falls within mathematical concepts groupings of abstract ideas (also mental process of counting and calculating frequency).
Claim 5 further recites determining a Percentile (NIFref-1) of the first reference splice site sequence; the limitation determining a percentile is considered a mathematical calculation, and as such, falls withing mathematical concepts groupings of abstract ideas. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is capable of calculating a percentile.
Claim 5 further recites determining a risk of abnormal splicing for the sample splice site by comparing Percentile (NIFvar-1) with Percentile (NIFref-1) against a CSP reference database; the limitation determining a risk by comparing is considered a mathematical relationship of comparing values, as such, said limitation falls within mathematical concepts groupings of abstract ideas. See MPEP 2106.04(a)(2) I A. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is able to compare known information. See MPEP 2106.04(a)(2) III A.
Claim 6 recites determining a Percentile (NIFVar1) of the first sample splice site sequence; the limitation determining a percentile is considered a mathematical calculation (mathematical concept; also, a mental process).
Claim 6 further discloses determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref 1); the limitation determining a frequency is considered a mathematical calculation (mathematical concept; also, a mental process).
Claim 6 further discloses determining a Percentile (NIFref1) of the first reference splice site sequence; the limitation determining a percentile is considered a mathematical calculation (mathematical concept; also, a mental process).
Claim 6 further discloses determining a risk of abnormal splicing for the sample splice site by comparing Percentile (NIFvar1) with Percentile (NIFref1) against a CSP reference database; the limitation determining a risk by comparing is considered a mathematical relationship of comparing values, as such, said limitation falls within mathematical concepts groupings of abstract ideas. See MPEP 2106.04(a)(2) I A. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is able to compare known information. See MPEP 2106.04(a)(2) III A.
Claim 6 further discloses that the method steps (a) to (e) are repeated with one or more sample splice site sequences comprised in the sample splice site, wherein each sample splice site sequence comprises non-identical, consecutive nucleotides of the sample splice site; the determining steps are repeated with one or more sample splice site sequences; the limitation repeating is considered a mathematical calculation of repeatedly counting and calculating an event. The said limitation can also be particularly performed in human mind (mental process) because human mind is able to repeatedly calculate the frequency of an event.
Claim 6 further discloses comparison of each further Percentile (NIFvar) and each corresponding Percentile (NIFref) against a CSP reference database; the limitation comparison of percentiles is considered a mathematical relationship of comparing values, as such, said limitation falls within mathematical concepts groupings of abstract ideas. See MPEP 2106.04(a)(2) I A. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is able to compare known information. See MPEP 2106.04(a)(2) III A.
Claim 10 recites determining a clinical classification (s) associated with the nucleotide sequence of the first sample splice site sequence; the limitation determining a classification can be particularly performed in human mind (mental process), since human mind is able to categorize information in classes, for example abnormal splice site or benign variant splice site (specification [0007]).
Claim 10 further recites determining a risk of abnormal splicing for the sample splice site by assessing the clinical classification(s) associated with the nucleotide sequence of the first sample splice site sequence determined in step (c) against a CSP reference database. The limitation determining a risk by assessing classification can be particularly performed in human mind (mental process), since human mind is able to determine a risk based on an evaluation.
Claim 11 recites determining a clinical classification(s) associated with the nucleotide sequence of the first sample splice site sequence; the limitation determining a classification can be particularly performed in human mind (mental process), since human mind is able to categorize information in classes, for example “abnormal splice site or benign variant splice site” (specification [0007]).
Claim 11 further recites determining a risk of abnormal splicing for the sample splice site by assessing the clinical classification(s) associated with the nucleotide sequence of the first sample splice site sequence against a CSP reference database; the limitation determining a risk by assessing classification can be particularly performed in human mind (mental process), since human mind is able to determine a risk based on an evaluation.
Claim 11 further recites determining a risk of abnormal splice of the sample splice site by assessing the clinical classifications of each nucleotide sequence of each sample splice site sequence; the limitation determining a risk by assessing classification can be particularly performed in human mind (mental process), since human mind is able to determine a risk based on an evaluation.
Claim 12 recites determining a measure of Native Intron Frequency of the first sample splice site sequence (NIFvar-1) (mathematical concept of measuring a frequency).
Claim 12 further recites determining a Percentile (NIFvar 1) of the first sample splice site sequence and determining a Percentile (NIFref 1) of the first reference splice site sequence (mathematical concept of calculating a percentile).
Claim 12 further recites determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref-1); (mathematical concept of measuring a frequency).
Claim 12 further recites calculating a lower bound and an upper bound for Percentile (NIFvar-1) and calculating a lower bound and an upper bound for Percentile (NIFrefl); the limitation calculating a lower and upper bound is considered a mathematical calculation, as disclosed I n the present specification [0036] “suitable upper and lower bounds of a NIF or Percentile (NIF) may be calculated based on a percentage (eg, 10%, 5%, 2.5%, 2%) of a logarithmic distribution of NIF or Percentile (NIF), median NIF or Percentile median NIF, mean NIF or Percentile mean NIF”. As such, said limitation falls within the mathematical concepts groupings of abstract ideas. Also, the said limitation can be performed in human mind (mental process), since human mind is capable of performing said calculations.
Claim 12 further recites determining a range of NIF-shift by comparing the lower and upper bounds for NIFvar-1 with the lower and upper bounds for NIFref1 calculated; the limitation determining a range by comparison is considered a mathematical relationship of comparing values/numbers and as such falls within mathematical concepts groupings of abstract ideas. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is able to decide a range by comparing values.
Claim 12 further recites identifying unique variant(s) in the CSP database that create the identical nucleotide sequence of one or more sample splice sites from the subject (var-x); the limitation identifying variants can be particularly performed in human mind (mental process), since human mind is able to identify splice sequence appearing in a different splice site in one gene or two different genes.
Claim 12 further recites repeating determining and calculating steps to calculate the NIF-shift for all non-identical, consecutive nucleotide sequences of the sample splice site in the CSP database; the limitation repeating steps to calculate a shift is considered a mathematical relationship, as disclosed in present specification [0007] “the relative shift in frequency of a sample splice site, as determined by a comparison of frequency of a sample splice site with the frequency of the originating splice site”, and as sch, falls within mathematical concepts grouping of abstract ideas.
Claim 12 further recites determining a clinical classification(s) associated with each identical var-x nucleotide sequence in the sample splice site identified in the CSP database (mental process of putting data in different categories).
Claim 12 further recites determining the risk of abnormal splicing or likelihood of maintaining normal splicing for the sample splice site in the subject by assessing the clinical classification; the limitation determining a risk by assessing classification can be particularly performed in human mind (mental process), since human mind is able to determine a risk based on known information.
Claim 16 recites determining a clinical classification (s) associated with the nucleotide sequence of the first sample splice site sequence (mental process of putting data in different categories).
Claim 16 further recites determining a clinical classification(s) associated with the nucleotide sequence of the first reference splice site sequence; (mental process of putting data in different categories).
Claim 16 further recites comparing the NIFvar-I with the NIF ref-1 against a CSP reference database; (mathematical relationship (mathematical concept) of comparing values; also, mental process of comparing values)
Claim 16 further recites assessing the clinical classification(s) associated with the nucleotide sequence of the first sample splice site sequence and the clinical classification(s) associated with the nucleotide sequence of the first reference splice site sequence; the limitation assessing clinical classification can be particularly performed in human mind (mental process) since human mind is able to evaluate known information.
Claim 16 further recites assessing the clinical classification determined in classification determination step for each similar NIF-shift variant; the limitation assessing is a mental process of evaluating information and can be particularly performed in human mind.
Claim 18 recites determining a measure of Native Intron Frequency of the first sample splice site sequence (NIFvar-i) (mathematical calculation/mathematical concepts).
Claim 18 further recites determining a Percentile (NIFvar-1) of the first sample splice site sequence (mathematical calculation/mathematical concepts).
Claim 18 further recites determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref-1) (mathematical calculation/mathematical concepts).
Claim 18 further recites calculating a lower bound and an upper bound for Percentile (NIFvar-1) and calculating a lower bound and an upper bound for Percentile (NIFref-1); ); the limitation calculating a lower and upper bound is considered a mathematical calculation.
Claim 18 further recites determining a range of NIF-shift by comparing the lower and upper bounds for Percentile (NIFvar-1) with the lower and upper bounds for Percentile (NIFref-1) calculated in the calculating step; the limitation determining a range by comparison is considered a mathematical relationship of comparing values/numbers and as such falls within mathematical concepts groupings of abstract ideas. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is able to decide a range by comparing values.
Claim 18 further recites identifying unique variants in the CSP database that affect the same splice site as the sample splice site from the subject; the limitation identifying variants can be particularly performed in human mind (mental process), since human mind is able to identify splice sequence appearing in a different splice site in one gene or two different genes.
Claim 18 further recites repeating the determining and calculating steps to identify unique variants in the CSP database that affect the same splice site as the sample splice site that are calculated to have a similar NIF-Shift as determined in the range determination step; the limitation repeating steps can be particularly performed in human mind (mental process), since human mind is able to identify splice sequence appearing in a different splice site in one gene or two different genes by repeating some steps.
Claim 18 further recites determining the clinical classification(s) associated with each similar unique variant in the CSP database affecting the same splice site and the sample splice site from the test subject; the limitation determining a classification can be particularly performed in human mind (mental process), since human mind is able to categorize information in classes.
Claim 18 further recites determining the risk of abnormal splicing or likelihood of maintaining normal splicing for the sample splice site; the limitation determining a risk by comparing is considered a mathematical relationship of comparing values, as such, said limitation falls within mathematical concepts groupings of abstract ideas. See MPEP 2106.04(a)(2) I A. Also, the said limitation can be particularly performed in human mind (mental process), since human mind is able to compare known information. See MPEP 2106.04(a)(2) III A.
Claim 18 further recites assessing the clinical classification determined in step clinical determination classification step for each unique variant in the CSP database that affect the same splice site and are determined to have a similar NIF-shift variant identified in step the range determination step; the limitation assessing clinical classification can be particularly performed in human mind (mental process) since human mind is able to evolute known information.
Claim 21 provides further information.
Claim 23 provides further information.
Claim 29 provides further information.
Claim 31 recites he sample splice site is obtained by sequencing the splice site of a predetermined gene (further information).
Claim 46 recites generating a first abnormal splicing factor based on a measure of Native Intron Frequency (NIF) of the sample splice site (NIFvar-1) and a measure of NIF of a first reference splice site (NIFref_1); the limitation generating an abnormal splicing factor can be particularly performed in human mind (mental process), since human mind is able to generate/determine splicing factor based on known measurements, as disclosed in FIG. 13A/B.
Claim 46 further recites generating a second abnormal splicing factor by comparing the sample splice site sequence to pre-classified data wherein the pre-classified data includes splice site sequences which have been classified as an abnormal splice site or a benign variant splice site; the limitation generating by comparing is considered a mathematical relationship of comparing values and generating/determining based on the comparison, and as such, falls within mathematical concept groupings of abstract ideas. Also, the said limitation is the mental process of generating/determining based on a companion.
Claim 46 further recites generating a third abnormal splicing factor based on pre-classified splice site sequences having a similar NIFvaf-1 and a similar corresponding NIFrefl; the limitation generating an abnormal splicing factor can be particularly performed in human mind (mental process), since human mind is able to generate/determine splicing factor based on known measurements, as disclosed in FIG. 13A/B.
Claim 46 further recites generating a risk of abnormal splicing of the sample splice site by evaluating the first, second, and third abnormal splice factors; the limitation generating a risk by evaluating can be particularly performed in human mind (mental process), since human mind is able to determine a risk based on the result of an evolution.
Claim 56 provides further information.
Claim 57 recites determining a Percentile (NIFvar-1) of the first sample splice site sequence (mathematical calculation/mathematical concepts); determining a Percentile (NIFref-1) of the first reference splice site sequence (mathematical calculation/mathematical concepts); determining a risk of abnormal splicing for the sample splice site by comparing Percentile (NIFvar-1) with Percentile (NIFref-1) against a CSP reference database; wherein a Percentile (NIFvar-1) of 0 (zero) indicates that the sample splice site is abnormal (mathematical relationship/mathematical concepts; also, mental processes of comparing data to indicate a risk).
The identified claim limitations fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the following reasons. Therefore, claims 1-6, 10-12, 16, 18, 21, 23, 29, 31, 46 and 56-57 recite an abstract idea. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claim(s) 1-6, 10-12, 16, 18, 21, 23, 29, 31, 46 and 56-57 include the following.
Claims 1, 12, 46, and 57 recite obtaining a first sample splice site sequence comprised in the sample splice site from the subject. The additional elements of obtaining a sample from a subject only serves to collect the information for use by the abstract idea. Therefore, these additional elements amount to insignificant extra-solution activity, which is not sufficient to integrate the recited judicial exception into a practical application. See MPEP 2106.05(g).
Therefore, the additionally recited elements amount to insignificant extra-solution activity (See MPEP 2106.05(g)) and, as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1-6, 10-12, 16, 18, 21, 23, 29, 31, 46 and 56 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. An inventive concept cannot be furnished by an abstract idea itself. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
The additional elements of claim(s) 1-6, 10-12, 16, 18, 21, 23, 29, 31, 46 and 56-57 include the following.
Claims 1, 12, 46, and 57 recite obtaining a first sample splice site sequence comprised in the sample splice site from the subject.
The additional elements of obtaining a sample from a subject and obtaining amount to conventional methods for obtaining spice sites. This position is supported by Haque et al. (WO 2016209999 A1). Haque discloses a method of classifying splice site sequences and predict pathogenicity ([0034], [0036], [0045], [0049]) by obtaining test genetic sequence (obtaining a sample) using cost-effective DNA sequencing (abstract).
Therefore, the additional element is not sufficient to amount to significantly more than the judicial exception.
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea (and/or natural correlation) without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Arguments
Applicant's arguments filed 12/30/2024 have been fully considered but they are not persuasive. Applicant states:
First, the claimed invention does not represent merely an abstract idea, law of nature or natural phenomenon. Rather, the claimed invention represents a method to transform input gene sequence data into a powerful, technical measure of whether or not a particular gene splice site variant has high binding strength, and consequently whether it is likely to be abnormal or not. In other words, the invention relates to predicting and outputting physical characteristics of a gene sequence - splice factor binding strength and functionality - based on NIF analysis performed on input gene sequence data.
Although the claimed invention involves evolutionary principles of favoured splice sites
versus less-favoured splice sites that exist within the human genome, the claimed invention does not recite that principle. Instead, the claimed invention relies upon an artificially-generated score and conclusion of where if NIF=O, then a splice variant is likely to result in abnormal splicing. The technical, tangible calculation and application of NIF (and NIF=O) to identifying abnormal splice sites in a sample from a subject, represents more than a mere mathematical equation, abstract concept or natural phenomenon. The Applicants submit that the claimed invention as a whole is not directed to a judicial exception.
It is respectfully submitted that these are not persuasive. The Applicant remarks are directed to Step 2A Prong One of 101 analysis, specifically that whether the claims recite a judicial exception.
The instant claimed invention is directed to identifying abnormal splice sites in a sample of a subject by performing mathematical calculation of determining a frequency in data, which as states above, are considered judicial exceptions. Further, the identified claims recite a correlation between a DNA sequence of a subject and a risk identification. According to MPEP § 2106.04, subsection III., a judicial exception alone is not eligible subject matter, if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application.
Applicant further states:
Second, if the claimed invention is construed as a judicial exception following Step 2A,
which the Applicants do not concede, the claimed invention still qualifies for patentability following Step 2B of the two-prong test. The claimed methods inherently involve a practical application to infer the physical strength of a splice site sequence for splice factor binding (and its ability to functionally undergo correct splicing) based on the sequence's relative frequency among known splice factor binding sites in a reference genome. The claimed invention integrates relative frequency data in the context of known splice sites in a reference genome to transform the frequency data into a strong and accurate measure/predictor of splice site binding strength.
It is respectfully submitted that this is not persuasive. The Applicant remarks are directed to Step 2A Prong Two of 101 analysis, specifically whether the additional elements integrate the recited judicial exception into a practical application of the exception (emphasis added).
With respect to the integration of the judicial exceptions into a practical application, the Examiner evaluated 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 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).” The data gathering steps, and the standard “obtaining a first sample splice site sequence” alone or in combination did not integrate the exceptions into a practical application.
The additional element of “obtaining a first sample splice site sequence comprised in the sample splice site from the subject” amount to necessary data gathering. The courts have found the limitations that amount to necessary data gathering and outputting are insignificant extra-solution activity that do not integrate a recited judicial exception into a practical application in Step 2A Prong Two, nor do they amount to significantly more in Step 2B of subject matter eligibility analysis (see MPEP 2106.05(g)).
Furthermore, with regards to Applicant stating “The claimed invention integrates relative frequency data in the context of known splice sites in a reference genome to transform the frequency data into a strong and accurate measure/predictor of splice site binding strength”, Examiner submits that the courts have found that manipulating information through mathematical correlations are abstract ideas. See Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721.
Furthermore, according to MPEP 2106.05(c) An "article" includes a physical object or substance. The physical object or substance must be particular, meaning it can be specifically identified. "Transformation" of an article means that the "article" has changed to a different state or thing. Purely mental processes in which thoughts or human based actions are "changed" are not considered an eligible transformation. For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)). See MPEP 2106.05(c).
Instantly claimed identification method uses a series of mental and/or mathematical concepts and merely manipulates information that has not been deemed a transformation.
Applicant further states:
Finally, it is important to note that the claimed invention does not relate to a mere frequency measure that can be calculated by the human mind. The claimed invention provides features that distinguish it from the elemental processes of counting, grouping/categorizing, and assessing simple frequency… As examples, those skilled in the art are incapable of calculating that: of 262,144 possible 9- nt sequence combinations for the donor E-3 to D+6 window, only 6,702 sequences actually exist at this position of the exon-intron junction among the 252,939 donors annotated in GRCh38… The human mind cannot perceive or determine that only 12 extremely common E-3 to D+6 donor sequences comprise the highest NIF% decile and range in raw frequency from NIF=1,741 to NIF=2,964. And so on for each decile of a NIF% scale.
It is respectfully submitted that these are not persuasive. The Applicant remarks are directed to Step 2A Prong One of 101 analysis, specifically that whether the claims recite a judicial exception.
In the Office Action dated 08/28/2024, Examiner stated that the step of determining a frequency is a mathematical process. Examiner further stated that the said limitation can also be performed in human mind, for example by using pen and paper or computer. It is important to note the whether the human mind is equipped to perform a task is not linked to the scope of the task. There is not a threshold at which point determining a frequency graduate from what can be performed by the mind to not performable by the human mind. While this may take a long time, the use of a physical aid, such as a pen-and-paper or computer, may accelerate this process, and this does not negate the mental nature of the limitation. Applicant will further note that complexity of operations does not equate to eligibility. The fact remains that the steps are directed to operations that are mental and/or mathematical as above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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, 3, 4, 10, and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Evans et al. (WO2016209999A1) in view of Maston (US20170316149A1).
Regarding claim 1, Evans discloses a method of identifying an abnormal splice site in a sample splice site from a subject (a method and system for predicting the pathogenicity of the subject’s genetic sequence variants [0034]; model for predicting the pathogenicity of a test genetic sequence variant [0036]; the test genetic sequence variant comprises a splice-site genetic sequence variant [0045]; modeling systems rely on a labeled (or “known”) benign genetic sequence variant training data set and a labeled pathogenic genetic sequence variant training data set [0036]).
Further regarding claim 1, Evans discloses (a) obtaining a first sample splice site sequence comprised in the sample splice site from the subject (predicting pathogenicity of a test genetic sequence variant (claim 1); the test genetic sequence variant is a human genetic sequence variant (claim 24); the test genetic sequence variant comprises a splice- site genetic sequence variant (claim 26)).
Further regarding claim 1, Evans discloses (b) determining the frequency at which the sequence occurs in a reference genome, expressed as a Native Intron Frequency of the first sample splice site sequence (NIFvar-1) (dataset comprises a labeled benign genetic sequence variant data set and an unlabeled genetic sequence variant data set,… assigning each genetic sequence variant in the unlabeled genetic sequence variant data set to pathogenic cluster (for example, NIF of zero) or a benign cluster [0054] ) (claim 16). Evans further discloses the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation, a frame-shifting genetic sequence variant (such as an insertion genetic sequence variant or a deletion genetic sequence variant), a splice-site genetic sequence variant (such as a canonical splice-site genetic sequence variant or a non- canonical splice-site genetic sequence variant)), a coding region variant, an intronic region variant, a promoter region variant, an enhancer region variant, a 3’-untranslated region (3’-UTR) variant, a 5’-untranslated region (5’-UTR) variant, an intergenic region variant, a dominant genetic sequence variant, a recessive genetic sequence variant, or a loss-of-function (LoF) genetic sequence variant. [0057]. Evans further discloses a machine learning model trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation is used to predict the pathogenicity of a test genetic sequence variant comprising a splice-site mutation [0062]. Evans further discloses that a feature that is defined on a splice-site genetic sequence variant is generated using a predicted probability that a given genetic sequence variant will alter the splicing of a transcript using bioinformatic pipeline such as MutPred Splice, Human Splicing Finder (HSF), MaxEntScan (models the native frequency of nucleotide sequences within introns to evaluate splice by alignining cDNAs to their respective genomic loci and incorporates native intron frequencies by using maximum entropy modeling to estimate nucleotide dependencies at 5' and 3' splice sites from a database of annotated human intron-exon junctions), NNSplice [0079].
Further regarding claim 1, Evans does not expressly disclose determining the frequency in a reference genome.
However, Maston discloses a method of classifying variants such as splice site variants and assigning pathogenicity score to variants [0010], where a sample of DNA may be obtained from a patient, who may or may not have been diagnosed with a disease or other medical condition. From the sample, the patient's genome may be sequenced in whole or in part. The result of sequencing may then be compared, e.g., to one or more reference genomes to identify variants in the patient's genome [0011]. Maston further discloses a pathogenicity score range of 1 to 7, where score 1 indicate benign and score 7 pathogenic variants [0032 - 0033].
Regarding claim 3, Evans discloses a method of identifying an abnormal splice site in a sample splice site from a subject, said method comprising: (a) obtaining a first sample splice site sequence comprised in the sample splice site from the subject (predicting pathogenicity of a test genetic sequence variant (claim 1); the test genetic sequence variant is a human genetic sequence variant (claim 24); the test genetic sequence variant comprises a splice- site genetic sequence variant (claim 26)).
Further regarding claim 1, Evans discloses (b) determining the frequency at which the sequence occurs in a reference genome, expressed as a Native Intron Frequency of the first sample splice site sequence (NIFvar-1) (dataset comprises a labeled benign genetic sequence variant data set and an unlabeled genetic sequence variant data set,… assigning each genetic sequence variant in the unlabeled genetic sequence variant data set to pathogenic cluster (for example, NIF of zero) or a benign cluster [0054] ) (claim 16). Evans further discloses the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation, a frame-shifting genetic sequence variant (such as an insertion genetic sequence variant or a deletion genetic sequence variant), a splice-site genetic sequence variant (such as a canonical splice-site genetic sequence variant or a non- canonical splice-site genetic sequence variant)), a coding region variant, an intronic region variant, a promoter region variant, an enhancer region variant, a 3’-untranslated region (3’-UTR) variant, a 5’-untranslated region (5’-UTR) variant, an intergenic region variant, a dominant genetic sequence variant, a recessive genetic sequence variant, or a loss-of-function (LoF) genetic sequence variant. [0057]. Evans further discloses a machine learning model trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation is used to predict the pathogenicity of a test genetic sequence variant comprising a splice-site mutation [0062]. Evans further discloses that a feature that is defined on a splice-site genetic sequence variant is generated using a predicted probability that a given genetic sequence variant will alter the splicing of a transcript using bioinformatic pipeline such as MutPred Splice, Human Splicing Finder (HSF), MaxEntScan (models the native frequency of nucleotide sequences within introns to evaluate splice by alignining cDNAs to their respective genomic loci and incorporates native intron frequencies by using maximum entropy modeling to estimate nucleotide dependencies at 5' and 3' splice sites from a database of annotated human intron-exon junctions), NNSplice [0079].
Evans further discloses determining a risk of abnormal splicing for the sample splice site by comparing NIFvar-1 with NIFref-1 against a Clinical Splice Predictor (CSP) reference database (In some embodiments, a feature that is defined on a functional genomic analysis of the genetic sequence variant is generated by comparing the location and sequence of the genetic sequence variant to locations of annotated functional genomic regions. For example, in some embodiments, the functional annotation features evaluate the probability that a given genetic sequence variant will impact an enhancer or promoter region, or other regulatory element, in a genome [00081]; Local mutation rates can be determined, for example, by comparing the species genome to an inferred evolutionary ancestor, for example a human genome [0056]). Evans further discloses that this training method provides a sufficiently large training data set to train a machine learning model useful for predicting pathogenicity, as the unlabeled genetic sequence variants do not require clinical studies to determine pathogenicity [0036]).
Further regarding claim 3, Evans discloses comparing variants to a labeled benign genetic sequence variant training data set (known variants data set) [0036]. Evans does not expressly disclose comparing variants to known variant databases.
However, Maston discloses that one or more of the variants may be compared to databases of known variants. The result of that comparison may be identification of one or more previously unknown variants, one or more variants that are known but unclassified, or both [0011]. Maston further discloses that examples of such databases include the Human Gene Mutation Database (HGMD) and Online Mendelian Inheritance in Man (OMIM) [0052].
Regarding claim 4,. Evans further discloses that the method steps (a) to (c) are repeated with one or more sample splice site sequences comprised in the sample splice site, wherein each sample splice site sequence comprises non-identical nucleotides of the sample splice site (model is trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation. In some embodiments, a machine learning model trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation is used to predict the pathogenicity of a test genetic sequence variant comprising a splice-site mutation [0062]), wherein each sample splice site sequence comprises non-identical (present specification [0068] discloses that a non-identical sample splice site comprises two or more samples splice site sequences obtained from different regions of the same sample), (the unlabeled genetic sequence variants comprising a mixture of benign genetic sequence variants and pathogenic genetic sequence variants (abstract)).
Evans further discloses a comparison of each further NIFvar with each corresponding NIFref against a CSP reference database (In some embodiments, a feature that is defined on a functional genomic analysis of the genetic sequence variant is generated by comparing the location and sequence of the genetic sequence variant to locations of annotated functional genomic regions. For example, in some embodiments, the functional annotation features evaluate the probability that a given genetic sequence variant will impact an enhancer or promoter region, or other regulatory element, in a genome [00081]; Local mutation rates can be determined, for example, by comparing the species genome to an inferred evolutionary ancestor, for example a human genome [0056]). Evans further discloses that this training method provides a sufficiently large training data set to train a machine learning model useful for predicting pathogenicity, as the unlabeled genetic sequence variants do not require clinical studies to determine pathogenicity [0036]).
Further regarding claim 4, Evans discloses comparing variants to a labeled benign genetic sequence variant training data set (known variants data set) [0036]. Evans does not expressly disclose comparing variants to known variant databases.
However, Maston discloses that one or more of the variants may be compared to databases of known variants. The result of that comparison may be identification of one or more previously unknown variants, one or more variants that are known but unclassified, or both [0011]. Maston further discloses that examples of such databases include the Human Gene Mutation Database (HGMD) and Online Mendelian Inheritance in Man (OMIM) [0052].
Regarding claim 10, Evans discloses determining a clinical classification(s) associated with the nucleotide sequence of the first sample splice site sequence (the training comprises assigning each genetic sequence variant in the training data to a benign cluster or a pathogenic cluster (claim 16));
Evans further discloses determining a risk of abnormal splicing for the sample splice site by assessing the clinical classification(s) associated with the nucleotide sequence of the first sample splice site sequence determined in step [[(b)]](c) against a CSP reference database (In some embodiments, a feature that is defined on a functional genomic analysis of the genetic sequence variant is generated by comparing the location and sequence of the genetic sequence variant to locations of annotated functional genomic regions. For example, in some embodiments, the functional annotation features evaluate the probability that a given genetic sequence variant will impact an enhancer or promoter region, or other regulatory element, in a genome [00081]; Local mutation rates can be determined, for example, by comparing the species genome to an inferred evolutionary ancestor, for example a human genome [0056]; Evans further discloses that this training method provides a sufficiently large training data set to train a machine learning model useful for predicting pathogenicity, as the unlabeled genetic sequence variants do not require clinical studies to determine pathogenicity and Evans discloses comparing variants to a labeled benign genetic sequence variant training data set (known variants data set) [0036]).
Further regarding claim 10, Evans does not expressly disclose comparing variants to known variant databases. However, Maston discloses that one or more of the variants may be compared to databases of known variants. The result of that comparison may be identification of one or more previously unknown variants, one or more variants that are known but unclassified, or both [0011]. Maston further discloses that examples of such databases include the Human Gene Mutation Database (HGMD) and Online Mendelian Inheritance in Man (OMIM) [0052].
Regarding claim 31, Evans discloses that the sample splice site is obtained by sequencing the splice site of a predetermined gene (dominant and recessive genes used in the training data set [0070 - 0071] by sequencing (abstract)).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Evans to have used determining the frequency in a reference genome, as shown by Maston (FIG. 1-5, [0186-0187]) to achieve improved predictive accuracy as stated by Jung [0010-0055]. There would be a reasonable expectation of success in combining the technique of Maston to the method of Evans because both are predicting pathogenicity of genetic sequence variants.
Response to Arguments
Applicant's arguments filed 12/30/2024 have been fully considered but they are not persuasive. Applicant states:
The machine learning model describes in Evans et al. utilizes a Training Set comprising labelled benign genetic sequence variants and unlabelled genetic sequence variants comprising a mixture of benign genetic sequence variants and pathogenic sequence variants (e.g., [0006]). Evans et al. does not use "known" benign variants. Variants labelled benign are select variants present in the general population with a high allele frequency (e.g., [0016]). [0017] of Evans et al. defines an embodiment of a 'high' allele frequency as being 'an allele frequency greater than 90% in a selected population'.[0030] of Evans et al. defines another embodiment of a high allele frequency as greater than or equal to 5% and less than 95% in a selected population.
Evans et al. fails to describe a method of identifying abnormal splice sites by assessing and strongly predicting splice-site strength and functionality using NIF. Rather, the ML model described in Evan et al. uses splice sites scores devised by existing algorithms (e.g., [0019]) to merely annotate a "splicing feature" to Training Set variants. [0027] of Evans et al. defines examples of splice-site variant scores sourced as those offered by HSF, MaxEntScan and NNSplice algorithms. None of these splice site variant scores teach nor suggest NIF as required by the claimed invention.
Moreover, the methods of Evans et al. rely upon allele frequency for prediction of
pathogenicity. NIF does not factor in allele frequency, and NIF=0 is a measure that is entirely distinct from an allele frequency of zero. NIF=0, which equates to NIF%=0, uniquely defines an abnormal splice-site sequence window, as one comprising a sequence of consecutive nucleotides spanning the exon-intron junction that does not exist at the analogous position of any human splice- site annotated in a reference human genome. As the Examiner concedes, "Evans does not expressly disclose determining the frequency in a reference genome". NIF is a determination of relative intron frequency that relies upon the context of a reference genome. This provides further evidence that the methods described in Evans et al. do not utilise NIF, nor do they represent methods that are equivalent to assessment of NIF. The skilled person would not routinely and obviously progress from measures of allele frequency to the novel measure of NIF as identified by the inventors.
It is respectfully submitted that the above statements are not persuasive. With regards to Applicant stating that “Evans et al. does not use "known" benign variants”, Examiner Submits that claimed invention does not recite "known" benign variants. Further Evans in one or more embodiments discloses that:
The unlabeled genetic sequence variant data set was simulated using CADD's variant simulation software, which mutates a locus according to local mutation rates in a sliding 1.1 Mb window. The mutation rates were obtained by comparing the human genome to an inferred human ancestor [0100]; The genetic sequence variant testing data set comprised a known pathogenic genetic sequence variant testing data set and a known benign genetic sequence variant testing data set. Solely by way of example, the known pathogenic genetic sequence variant testing data set was obtained from HGMD or the ClinVar database [0103]; In some embodiments of the methods disclosed herein (e.g., SCCM-Pathogenic), pathogenicity of a genetic sequence variant in noncoding regions is successfully predicted. In some embodiments, the methods described herein predicts pathogenicity of a genetic sequence variant in a 3′-UTR, 5′-UTR, intronic region, or intergenic region. These results are illustrated in FIG. 9.[0112]; The labeled benign genetic sequence variant data set can be obtained, for example, by filtering variants from the 1000 Genomes Project (1000G) (described in Abecasis et al., Nature, 491(7422):56-65 (2012)) (for example, determining the frequency of genetic variants in the human genome) [0055]; Further regarding claim 1, Evans discloses (b) determining the frequency at which the sequence occurs in a reference genome, expressed as a Native Intron Frequency of the first sample splice site sequence (NIFvar-1) (dataset comprises a labeled benign genetic sequence variant data set and an unlabeled genetic sequence variant data set,… assigning each genetic sequence variant in the unlabeled genetic sequence variant data set to pathogenic cluster (for example, NIF of zero) or a benign cluster [0054] ) (claim 16). Evans further discloses the genetic sequence variant training data set comprises genetic sequence variants with a missense mutation, a nonsense mutation, a frame-shifting genetic sequence variant (such as an insertion genetic sequence variant or a deletion genetic sequence variant), a splice-site genetic sequence variant (such as a canonical splice-site genetic sequence variant or a non- canonical splice-site genetic sequence variant)), a coding region variant, an intronic region variant, a promoter region variant, an enhancer region variant, a 3’-untranslated region (3’-UTR) variant, a 5’-untranslated region (5’-UTR) variant, an intergenic region variant, a dominant genetic sequence variant, a recessive genetic sequence variant, or a loss-of-function (LoF) genetic sequence variant. [0057]. Evans further discloses a machine learning model trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation is used to predict the pathogenicity of a test genetic sequence variant comprising a splice-site mutation [0062]. Evans further discloses that a feature that is defined on a splice-site genetic sequence variant is generated using a predicted probability that a given genetic sequence variant will alter the splicing of a transcript using bioinformatic pipeline such as MutPred Splice, Human Splicing Finder (HSF), MaxEntScan (models the native frequency of nucleotide sequences within introns to evaluate splice by alignining cDNAs to their respective genomic loci and incorporates native intron frequencies by using maximum entropy modeling to estimate nucleotide dependencies at 5' and 3' splice sites from a database of annotated human intron-exon junctions), NNSplice [0079].
Evans further discloses a machine learning model specified by one or more features [0047] where the features he features are used to characterize properties of the genetic sequence variants, and can include, for example, scores defined on sequence conservation, missense genetic sequence variants, splice-site genetic sequence variants, or regulatory elements [0074]; a feature that is defined on a functional genomic analysis of the genetic sequence variant is generated by comparing the location and sequence of the genetic sequence variant to locations of annotated functional genomic regions…[0081]; the machine learning model is trained using a genetic sequence variant training data set comprising genetic sequence variants with a mutation in an intronic region… a machine learning model trained using a genetic sequence variant training data set comprising genetic sequence variants with a mutation in an intronic region is used to predict the pathogenicity of a test genetic sequence variant comprising a mutation in an intronic region [0064]; clustering of noncoding (intergenic, regulatory, or intronic) region genetic sequence variants along two principal components (using principal component analysis (PCA)) of certain features (verPhyloP, verPhastCons, GerpS, ENCODE H3K27Ac, ENCODE H3K4Me3, ENCODE H3K4Me1) using the methods described herein [0028]; assigning each genetic sequence variant to a benign cluster or a pathogenic cluster [0015-0016].
Therefore, Evans teaches a method of predicting pathogenicity of genetic sequence variants in noncoding regions using one or more features, where one feature is a predicted probability that a given genetic sequence variant will alter the splicing of a transcript using pipelines such as MaxEntScan (incorporates native intron frequencies by using maximum entropy modeling to estimate nucleotide dependencies at 5' and 3' splice sites from a database of annotated human intron-exon junctions), and assigning each genetic sequence variant to a benign cluster or a pathogenic cluster (for example, NIF of zero).
Claim(s) 2, 5, 6, 12, 16, 18, 21, 23, 29, 46, and 56 is/are rejected under 35 U.S.C. 103 as being unpatentable over Evans et al. in view of Maston as applied to claims 1, 3-4,10 and 31 above, and further in view of Jaganathan et al. (KR 20220031940 A).
The limitations of claim 1 has been taught in the above rejection.
Regarding claim 2, Evans discloses that the method is repeated with one or more sample splice site sequences comprised in the sample splice site (model is trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation. In some embodiments, a machine learning model trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation is used to predict the pathogenicity of a test genetic sequence variant comprising a splice-site mutation [0062]), wherein each sample splice site sequence comprises non-identical (present specification [0068] discloses that a non-identical sample splice site comprises two or more samples splice site sequences obtained from different regions of the same sample), (the unlabeled genetic sequence variants comprising a mixture of benign genetic sequence variants and pathogenic genetic sequence variants (abstract)).
Evans further discloses that a NIFvar-1 of 0 (zero) for any sample splice site sequence indicates that the sample splice site is abnormal (High derived allele frequency genetic sequence variants are assumed to be benign due to their evolutionary conservation; pathogenic genetic sequence variants are typically low frequency [0036]; the model assigns he test genetic sequence variant to a benign cluster or a pathogenic cluster (claim 19); the labeled benign genetic sequence variants have an allele frequency greater than 90% in a selected population (claim 22); frequency of >= 0.05 and <0.95 are considered benign [0030] therefore, frequency of zero indicates pathogenic or abnormal).
Further regarding claim 2, Evans and Maston do not expressly disclose that each sample splice site sequence comprises consecutive nucleotides of the sample splice site.
However, Jaganathan discloses a method of splice variant classification where they use benign and pathogenic sequence samples (abstract). Jaganathan further discloses aligning each sample with a reference sequence (pg. 19, para. 4). Jaganathan further discloses that a read represents a short sequence of consecutive base pairs in a sample or reference (pg. 17, para. 2).
Regarding claim 5, Evans discloses the limitations of claim 4. Evans further discloses that the labeled benign genetic sequence variants have an allele frequency greater than 90% in a selected population [0017].
Evans further discloses a feature that is defined on a functional genomic analysis of the genetic sequence variant is generated by comparing the location and sequence of the genetic sequence variant to locations of annotated functional genomic regions. For example, in some embodiments, the functional annotation features evaluate the probability (percentage) that a given genetic sequence variant will impact an enhancer or promoter region, or other regulatory element, in a genome [00081]; Local mutation rates can be determined, for example, by comparing the species genome to an inferred evolutionary ancestor, for example a human genome [0056]).
Evans further disclose determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref-1); wherein the first reference splice site sequence and the first sample splice site sequence each originate from the same corresponding region of a gene (The known benign sequence variant testing data set was obtained by filtering genomic sequence variants from the 1000 Genomes Project (1000G) filtered by derived allele frequency of <0.95 and≥ 0.05 [0101]);
Evans and Maston do not expressly disclose determining a Percentile (NIFvar-i) of the first sample splice site sequence; determining a Percentile (NIFref-1) of the first reference splice site sequence; and (d) determining a risk of abnormal splicing for the sample splice site by comparing Percentile (NIFvar-1) with Percentile (NIFref-1) against a CSP reference database.
However, Jaganathan discloses that the term "variant frequency" refers to the relative frequency of an allele (variant of a gene) at a particular locus in a population expressed as a fraction or percentage. For example, the fraction or percentage may be the fraction of all chromosomes in a population carrying that allele. For example, sample variant frequencies can be calculated as the relative of alleles/variants at a particular locus/position along a genomic sequence of interest to a “population” corresponding to the number of samples and/or reads obtained for the genomic sequence of interest from an individual indicates the frequency (pg. 18, para. 5). Jaganathan further discloses that the results of the variant call analysis identify potential variant calls, sample variant frequencies, reference sequences, and locations within the genomic sequence from which the variants arose (pg. 26, para. 4). Jaganathan further discloses scoring each variant present in 60,706 human exomes from the Exome Aggregation Consortium (ExAC) database and identified variants predicted to alter the exon-intron boundaries. Jaganathan further discloses that the aggregation of whole genome sequences from 15,496 humans from the Genome Aggregation Database (gnomAD) cohort. And that the analysis data was used to calculate observed and expected counts for cryptographic splice mutations at common allele frequencies (pg. 36, paras. 2-5).
Regarding claim 6, Evans and Jaganathan disclose the limitations of claim 5. Evans further discloses that the method is repeated with one or more sample splice site sequences comprised in the sample splice site (model is trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation. In some embodiments, a machine learning model trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation is used to predict the pathogenicity of a test genetic sequence variant comprising a splice-site mutation [0062]), wherein each sample splice site sequence comprises non-identical (present specification [0068] discloses that a non-identical sample splice site comprises two or more samples splice site sequences obtained from different regions of the same sample), (the unlabeled genetic sequence variants comprising a mixture of benign genetic sequence variants and pathogenic genetic sequence variants (abstract)),
Further regarding claim 6, Evan and Maston do not expressly disclose determining a percentile of the sample and reference splice site sequence and comparing them to determine a risk.
However, Jaganathan discloses that the term variant frequency refers to the relative frequency of an allele (variant of a gene) at a particular locus in a population expressed as a fraction or percentage. For example, the fraction or percentage may be the fraction of all chromosomes in a population carrying that allele. For example, sample variant frequencies can be calculated as the relative of alleles/variants at a particular locus/position along a genomic sequence of interest to a “population” corresponding to the number of samples and/or reads obtained for the genomic sequence of interest from an individual indicates the frequency (pg. 18, para. 5). Jaganathan further discloses that the results of the variant call analysis identify potential variant calls, sample variant frequencies, reference sequences, and locations within the genomic sequence from which the variants arose (pg. 26, para. 4). Jaganathan further discloses scoring each variant present in 60,706 human exomes from the Exome Aggregation Consortium (ExAC) database and identified variants predicted to alter the exon-intron boundaries. Jaganathan further discloses that the aggregation of whole genome sequences from 15,496 humans from the Genome Aggregation Database (gnomAD) cohort. And that the analysis data was used to calculate observed and expected counts for cryptographic splice mutations at common allele frequencies (pg. 36, paras. 2-5).
Regarding claim 12, Evans discloses a method of identifying an abnormal splice site in a sample splice site from a subject (a method and system for predicting the pathogenicity of the subject’s genetic sequence variants [0034]; model for predicting the pathogenicity of a test genetic sequence variant [0036]; the test genetic sequence variant comprises a splice-site genetic sequence variant [0045]; modeling systems rely on a labeled (or “known”) benign genetic sequence variant training data set and a labeled pathogenic genetic sequence variant training data set [0036]).
Evans further discloses (a) obtaining a first sample splice site sequence comprised in the sample splice site from the subject (predicting pathogenicity of a test genetic sequence variant (claim 1); the test genetic sequence variant is a human genetic sequence variant (claim 24); the test genetic sequence variant comprises a splice- site genetic sequence variant (claim 26)).
Evans further disclose (b) determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref-1); wherein the first reference splice site sequence and the first sample splice site sequence each originate from the same corresponding region of a gene (The known benign sequence variant testing data set was obtained by filtering genomic sequence variants from the 1000 Genomes Project (1000G) filtered by derived allele frequency of <0.95 and≥ 0.05 [0101]);
Further regarding claim 12, Evans and Maston do not expressly disclose (c) determining a Percentile (NIFvar 1) of the first sample splice site sequence and determining a Percentile (NIFref 1) of the first reference splice site sequence and determining a measure of Native Intron Frequency of sample and reference.
However, Jaganathan discloses that the term "variant frequency" refers to the relative frequency of an allele (variant of a gene) at a particular locus in a population expressed as a fraction or percentage. For example, the fraction or percentage may be the fraction of all chromosomes in a population carrying that allele. For example, sample variant frequencies can be calculated as the relative of alleles/variants at a particular locus/position along a genomic sequence of interest to a “population” corresponding to the number of samples and/or reads obtained for the genomic sequence of interest from an individual indicates the frequency (pg. 18, para. 5). Jaganathan further discloses that the results of the variant call analysis identify potential variant calls, sample variant frequencies, reference sequences, and locations within the genomic sequence from which the variants arose (pg. 26, para. 4). Jaganathan further discloses scoring each variant present in 60,706 human exomes from the Exome Aggregation Consortium (ExAC) database and identified variants predicted to alter the exon-intron boundaries. Jaganathan further discloses that the aggregation of whole genome sequences from 15,496 humans from the Genome Aggregation Database (gnomAD) cohort. And that the analysis data was used to calculate observed and expected counts for cryptographic splice mutations at common allele frequencies (pg. 36, paras. 2-5).
Jaganathan further discloses (d) determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref-1); wherein the first reference splice site sequence and the first sample splice site sequence originate from the same corresponding region of a gene analyze the sequencing data to obtain potential variant call(s) and sample variant frequencies of sample variant call(s). The operation may also be referred to as a variant call application or variant caller (pg. 22, para. 4); sample reads are analyzed to identify potential variant calls. Among other things, the results of the analysis identify potential variant calls, sample variant frequencies, reference sequences, and locations within the genomic sequence from which the variants arose (pg.26, para. 4))
Jaganathan further discloses (e) calculating a lower bound and an upper bound for Percentile (NIFvar-1) and calculating a lower bound and an upper bound for Percentile (NIFrefl); (f) determining a range of NIF-shift by comparing the lower and upper bounds for NIFvar-1 with the lower and upper bounds for NIFref1 calculated in (e) (We found that 35% of cryptic splice variants with weak and intermediate predictive scores (ΔScore 0.35 - 0.8) exhibited significant differences in the fractions of normal and abnormal transcripts generated throughout tissues (in the χ .sup.2 test). Bonferroni-corrected for P < 0.01, Figure 39C). This is in contrast to variants with high predictive scores (ΔScore > 0.8) that are much less likely to produce tissue-specific effects (P = 0.015). Our finding is a previous observation that alternative spliced exons tend to have intermediate predictive scores compared to structurally spliced in or spliced out exons, which have scores close to 1 or 0, respectively (pg. 35, last para.).
Jaganathan further discloses that strong cryptic splice variants have the potential to completely shift splicing from normal to aberrant isoforms regardless of epigenetic context, whereas weak variants bring splice junction selection closer to the decision boundary, leading to different tissue types and It triggers the use of alternative junctions in the cellular context (pg. 36, paras. 1-2).
Jaganathan further discloses identifying unique variant(s) in the CSP database that create the identical nucleotide sequence of one or more sample splice sites from the subject (var-x); wherein the identical sample splice sites identified in other subjects in the CSP database localize to a different splice- site at a different exon-intron junction to the sample splice site in the test subject (validate the model's prediction across different types of cryptic splice variants capable of generating new splice junctions, we extended the variants that generate novel GT or AG dinucleotides. Variants that affect the accepted acceptor or donor motif, as well as variants originating in more distal regions, were evaluated separately (pg. 35, para. 2-3)).
Jaganathan further discloses (g) repeating steps (b-f) to calculate the NIF-shift for all non-identical, consecutive nucleotide sequences of the sample splice site in the CSP database identified in (f); (we scored each variant present in 60,706 human exomes from the Exome Aggregation Consortium (ExAC) database (Lek et al., 2016). and identified variants predicted to alter the exon-intron boundaries. Analysis data were used to calculate observed and expected counts for cryptographic splice mutations at common allele frequencies (pg. 36, para. 3-5); Estimating the likely contribution of cryptic splice mutations to invasive genetic diseases (pg. 37, para. 1)).
Jaganathan further discloses (h) determining a clinical classification(s) associated with each identical var-x nucleotide sequence in the sample splice site identified in the CSP database in (g); (the aberrant splicing detector is further configured to implement an enrichment analysis per gene that determines the pathogenicity of the variant determined to cause aberrant splicing, … determination of pathogenicity of variants (pg. 50, para. 6, Enrichment analysis per gene); the aberrant splicing detector is further configured to implement a genome-wide enrichment analysis to determine the pathogenicity of the variant determined to cause the aberrant splicing (pg. 51, para.1)).
Jaganathan further discloses (i) determining the risk of abnormal splicing or likelihood of maintaining normal splicing for the sample splice site in the subject by assessing the clinical classification determined in step (h) of each identical (variant) var-x nucleotide sequence in a sample splice site in the CSP database (predictions of the deep learning model for both reference and replacement alleles were obtained and ΔScores were calculated. We also obtained where the model predicted abnormal (new or broken) junctions. They then attempted to determine whether there was evidence in the RNA-seq data supporting splicing abnormalities in individuals with variants at predicted positions (pg. 63, last para.)).
Regarding claim 16, Evans and Maston do not expressly disclose assessing classification based on variant frequency shift. However, Jaganathan discloses (d) determining a clinical classification (s) associated with the nucleotide sequence of the first sample splice site sequence; (e) optionally determining a clinical classification(s) associated with the nucleotide sequence of the first reference splice site sequence (the system determines, from the difference in the splice site scores of the target nucleotides in the reference sequence and the variant sequence, whether the variant that generated the variant sequence causes aberrant splicing and is therefore pathogenic; for at least one target nucleotide position, the overall maximum difference in splice site scores exceeds a predetermined threshold, ACNN flags the variant as causing aberrant splicing and thus pathogenic. Classify; processing a first set of reference and variant sequence pairs generated by the positive consensus variant resulting in a first set of aberrant splicing detection, and a second set of reference and variant sequence pairs generated by the pathogenic rare variant; for at least one target nucleotide position, the overall maximum difference in splice site scores is below a predetermined threshold, ACNN classifies the variant as not causing aberrant splicing and thus benign (pg. 43, para.4-7); As noted above, not all system features are repeated herein and should be considered to be repeated by reference (pg. 44, para. 8));
Jaganathan further discloses (j) wherein the determining the risk of abnormal splicing for the sample splice site comprises (1) comparing the NIFvar-I with the NIF ref-1 against a CSP reference database, (2) assessing the clinical classification(s) associated with the nucleotide sequence of the first sample splice site sequence determined in step (a) and, the clinical classification(s) associated with the nucleotide sequence of the first reference splice site sequence optionally determined in step (c); (Experimental validation of de novo cryptic splice mutations in patients with autism; we identified a unique and aberrant splicing event associated with a predicted de novo cryptic splice mutation in 21 of 28 patients; This aberrant splicing event was absent in the other 35 individuals who obtained deep LCL RNA-seq and 149 individuals in the GTEx cohort. Of the 21 identified de novo cryptic splice mutations, 9 new junction creation, 8 exon skipping and 4 intron retention, and more complex splicing abnormalities were observed; Seven cases showed no aberrant splicing in LCL despite adequate expression of the transcript; comparison to control (pgs. 37, last para., pg. 38)).
Regarding claim 18, Evans discloses (a) obtaining a first sample splice site sequence comprised in the sample splice site from the subject (predicting pathogenicity of a test genetic sequence variant (claim 1); the test genetic sequence variant is a human genetic sequence variant (claim 24); the test genetic sequence variant comprises a splice- site genetic sequence variant (claim 26)).
Evans further discloses (b) determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref-1); wherein the first reference splice site sequence and the first sample splice site sequence each originate from the same corresponding region of a gene (The known benign sequence variant testing data set was obtained by filtering genomic sequence variants from the 1000 Genomes Project (1000G) filtered by derived allele frequency of <0.95 and≥ 0.05 [0101]);
Further regarding claim 12, Evans and Maston do not expressly disclose (c) determining a Percentile (NIFvar 1) of the first sample splice site sequence and determining a Percentile (NIFref 1) of the first reference splice site sequence and determining a measure of Native Intron Frequency of sample and reference.
However, Jaganathan discloses that the term "variant frequency" refers to the relative frequency of an allele (variant of a gene) at a particular locus in a population expressed as a fraction or percentage. For example, the fraction or percentage may be the fraction of all chromosomes in a population carrying that allele. For example, sample variant frequencies can be calculated as the relative of alleles/variants at a particular locus/position along a genomic sequence of interest to a “population” corresponding to the number of samples and/or reads obtained for the genomic sequence of interest from an individual indicates the frequency (pg. 18, para. 5). Jaganathan further discloses that the results of the variant call analysis identify potential variant calls, sample variant frequencies, reference sequences, and locations within the genomic sequence from which the variants arose (pg. 26, para. 4). Jaganathan further discloses scoring each variant present in 60,706 human exomes from the Exome Aggregation Consortium (ExAC) database and identified variants predicted to alter the exon-intron boundaries. Jaganathan further discloses that the aggregation of whole genome sequences from 15,496 humans from the Genome Aggregation Database (gnomAD) cohort. And that the analysis data was used to calculate observed and expected counts for cryptographic splice mutations at common allele frequencies (pg. 36, paras. 2-5).
Jaganathan further discloses (d) determining a measure of Native Intron Frequency of a first reference splice site sequence (NIFref-1); wherein the first reference splice site sequence and the first sample splice site sequence originate from the same corresponding region of a gene analyze the sequencing data to obtain potential variant call(s) and sample variant frequencies of sample variant call(s). The operation may also be referred to as a variant call application or variant caller (pg. 22, para. 4); sample reads are analyzed to identify potential variant calls. Among other things, the results of the analysis identify potential variant calls, sample variant frequencies, reference sequences, and locations within the genomic sequence from which the variants arose (pg.26, para. 4))
Jaganathan further discloses (e) calculating a lower bound and an upper bound for Percentile (NIFvar-1) and calculating a lower bound and an upper bound for Percentile (NIFrefl); (f) determining a range of NIF-shift by comparing the lower and upper bounds for NIFvar-1 with the lower and upper bounds for NIFref1 calculated in (e) (We found that 35% of cryptic splice variants with weak and intermediate predictive scores (ΔScore 0.35 - 0.8) exhibited significant differences in the fractions of normal and abnormal transcripts generated throughout tissues (in the χ .sup.2 test). Bonferroni-corrected for P < 0.01, Figure 39C). This is in contrast to variants with high predictive scores (ΔScore > 0.8) that are much less likely to produce tissue-specific effects (P = 0.015). Our finding is a previous observation that alternative spliced exons tend to have intermediate predictive scores compared to structurally spliced in or spliced out exons, which have scores close to 1 or 0, respectively (pg. 35, last para.).
Jaganathan further discloses that strong cryptic splice variants have the potential to completely shift splicing from normal to aberrant isoforms regardless of epigenetic context, whereas weak variants bring splice junction selection closer to the decision boundary, leading to different tissue types and It triggers the use of alternative junctions in the cellular context (pg. 36, paras. 1-2).
Jaganathan further discloses identifying unique variant(s) in the CSP database that create the identical nucleotide sequence of one or more sample splice sites from the subject (var-x); wherein the identical sample splice sites identified in other subjects in the CSP database localize to a different splice- site at a different exon-intron junction to the sample splice site in the test subject (validate the model's prediction across different types of cryptic splice variants capable of generating new splice junctions, we extended the variants that generate novel GT or AG dinucleotides. Variants that affect the accepted acceptor or donor motif, as well as variants originating in more distal regions, were evaluated separately (pg. 35, para. 2-3)).
Jaganathan further discloses (g) repeating steps (b-f) to calculate the NIF-shift for all non-identical, consecutive nucleotide sequences of the sample splice site in the CSP database identified in (f); (we scored each variant present in 60,706 human exomes from the Exome Aggregation Consortium (ExAC) database (Lek et al., 2016). and identified variants predicted to alter the exon-intron boundaries. Analysis data were used to calculate observed and expected counts for cryptographic splice mutations at common allele frequencies (pg. 36, para. 3-5); Estimating the likely contribution of cryptic splice mutations to invasive genetic diseases (pg. 37, para. 1)).
Jaganathan further discloses (h) determining a clinical classification(s) associated with each identical var-x nucleotide sequence in the sample splice site identified in the CSP database in (g); (the aberrant splicing detector is further configured to implement an enrichment analysis per gene that determines the pathogenicity of the variant determined to cause aberrant splicing, … determination of pathogenicity of variants (pg. 50, para. 6, Enrichment analysis per gene); the aberrant splicing detector is further configured to implement a genome-wide enrichment analysis to determine the pathogenicity of the variant determined to cause the aberrant splicing (pg. 51, para.1)).
Jaganathan further discloses (i) determining the risk of abnormal splicing or likelihood of maintaining normal splicing for the sample splice site in the subject by assessing the clinical classification determined in step (h) of each identical (variant) var-x nucleotide sequence in a sample splice site in the CSP database (predictions of the deep learning model for both reference and replacement alleles were obtained and ΔScores were calculated. We also obtained where the model predicted abnormal (new or broken) junctions. They then attempted to determine whether there was evidence in the RNA-seq data supporting splicing abnormalities in individuals with variants at predicted positions (pg. 63, last para.)).
Regrading claim 21, Evans and Maston do not expressly disclose details about that the sample sequence. However, Jaganathan discloses that the sample splice site sequence is a donor splice site sequence, a branch site sequence, or an acceptor splice site sequence (To study the effect of branch point sequence position on receptor strength, we first obtained the receptor scores of 14,289 test set splice receptors (pg.59, para.3); ACNN generates as an output a triplet score for the likelihood that each nucleotide of the target nucleotide sequence is a donor splice site, an acceptor splice site, or a non-splice site (pg. 39, para. 5); the optimal branching sequence TACTAAC was introduced at various distances from each of the 14,289 test set splice acceptors, and acceptor scores were calculated using SpliceNet-10k (pg. 72, para. 5)).
Regrading claim 23, Evans and Maston do not expressly disclose that the sample sequence has consecutive nucleotides. However, Jaganathan discloses each sample splice site sequence comprises at least 4 to 15 consecutive nucleotides of a donor splice site (FIG. 25, the Target nucleotide contains at least 4 to 15 consecutive nucleotides of a donor splice site).
Regarding claim 29, Evans and Maston do not expressly disclose nucleotide positions of the sample sequence. However, Jaganathan discloses that at least one sample splice site sequence corresponds to nucleotide positions E-4 to D+5, E-3 to D+6, E-2 to D+7 and E-1 to D+8 of a donor splice site 25 , 26 and 27 , the input may include a target nucleotide sequence with target nucleotides flanked by 2500 nucleotides on each side. In this embodiment, the target nucleotide sequence is further flanked by 5000 upstream context nucleotides and 5000 downstream context nucleotides. The input may include a target nucleotide sequence having a target nucleotide flanked by 100 nucleotides on each side. In this embodiment, the target nucleotide sequence is further flanked by 200 upstream context nucleotides and 200 downstream context nucleotides. (fig. 25-27, pg. 39, para. 7-8).
Regarding claim 46, Evans and Maston do not disclose generating a risk of abnormal splicing of the sample splice site by evaluating the first, second, and third abnormal splice factors. However, Jaganathan discloses a method of providing a risk of abnormal splicing of a sample splice site from a subject, said method comprising: obtaining a first sample splice site sequence comprised in the sample splice site from the subject; generating a first abnormal splicing factor based on a measure of Native Intron Frequency (NIF) of the sample splice site (NIFvar-1) and a measure of NIF of a first reference splice site (NIFref_1); generating a second abnormal splicing factor by comparing the sample splice site sequence to pre-classified data wherein the pre-classified data includes splice site sequences which have been classified as an abnormal splice site or a benign variant splice site; generating a third abnormal splicing factor based on pre-classified splice site sequences having a similar NIFvaf-1 and a similar corresponding NIFrefl; and generating a risk of abnormal splicing of the sample splice site by evaluating the first, second, and third abnormal splice factors. (For identification of splice alteration variants, they augmented the training set of GENCODE annotations to include novel splice junctions and trained the network on the combined dataset to improve the sensitivity of splice variants (pg. 57, Model training and testing and evaluation, last para.); Splice junction identification and detection (pg. 58)).
Regarding claim 56, Evans and Maston do not disclose details of sample sequence nucleotide positions. However, Jaganathan discloses that at least one sample splice site sequence corresponds to nucleotide positions E-4 to D+5, E-3 to D+6, E-2 to D+7 and E-1 to D+8 of a donor splice site 25 , 26 and 27 , the input may include a target nucleotide sequence with target nucleotides flanked by 2500 nucleotides on each side. In this embodiment, the target nucleotide sequence is further flanked by 5000 upstream context nucleotides and 5000 downstream context nucleotides. The input may include a target nucleotide sequence having a target nucleotide flanked by 100 nucleotides on each side. In this embodiment, the target nucleotide sequence is further flanked by 200 upstream context nucleotides and 200 downstream context nucleotides. (fig. 25-27, pg. 39, para. 7-8).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Evans and Maston to have used consecutive nucleotides of the sample splice site, as shown by Jaganathan (pgs. 17 and 19) for samples to be specifically assigned to a genomic region (pg. 17, para.2). There would be a reasonable expectation of success in combining the technique of Jaganathan to the method of Evans and Maston because all are classifying splice site variants.
Response to Arguments
Applicant's arguments filed 12/30/2024 have been fully considered but they are not persuasive. Applicant states:
Jaganathan describes a splicing prediction tool called SpliceAI. This prediction tool is opposite to prediction based on NIF. SpliceAI uses a 32-layer, convoluted deep neural network that analyses each position in a pre-mRNA transcript and evaluates whether it is likely to be a splice donor, acceptor, or neither. Convoluted neural networks have layers with filters scanning for a particular feature, that slide over gene sequences, divided up into manageable pieces. SpliceAI is not trained on a particular feature. SpliceAI is trained on the raw pre-mRNA sequences of genes in the Training Set (See e.g., Figure 37A). SpliceAI's convoluted layers of deep learning identifies patterns and features, often over large genomic distances. Expert users are able to retrospectively interrogate whether SpliceAI can identify a specific feature (inferred by prediction scores changing when features are moved, created or modified)… SpliceAI and the claimed methods are implicitly different in how they work. NIF relies upon nucleotide "windows" that Jaganathan teaches away from…. nothing in Jaganathan et al. compensates for the deficiencies of Evans et al. and Maston et al. Withdrawal of this rejection is respectfully requested.
It is respectfully submitted that the above statement is not persuasive. Instant claims, as drafted, do not provide details of the model. As stated above, the combination of Evans and Matson teach a method of predicting pathogenicity of genetic sequence variants in noncoding regions using one or more features, where one feature is a predicted probability that a given genetic sequence variant will alter the splicing of a transcript using pipelines such as MaxEntScan (incorporates native intron frequencies by using maximum entropy modeling to estimate nucleotide dependencies at 5' and 3' splice sites from a database of annotated human intron-exon junctions), and assigning each genetic sequence variant to a benign cluster or a pathogenic cluster (for example, NIF of zero).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Evans et al. in view of Maston as applied to claim 1, 3-4, 10, and 31 above, further in view of Rogan et al. (US20140199698A1).
The limitations of claim 1 has been taught in the above rejection.
Regarding claim 11, Evans discloses determining a clinical classification (s) associated with the nucleotide sequence of the first sample splice site sequence (the training comprises assigning each genetic sequence variant in the training data to a benign cluster or a pathogenic cluster (claim 16));
Evans further discloses determining a risk of abnormal splicing for the sample splice site by assessing the clinical classification(s) associated with the nucleotide sequence of the first sample splice site sequence determined in step [[(b)]](c) against a CSP reference database (In some embodiments, a feature that is defined on a functional genomic analysis of the genetic sequence variant is generated by comparing the location and sequence of the genetic sequence variant to locations of annotated functional genomic regions. For example, in some embodiments, the functional annotation features evaluate the probability that a given genetic sequence variant will impact an enhancer or promoter region, or other regulatory element, in a genome [00081]; Local mutation rates can be determined, for example, by comparing the species genome to an inferred evolutionary ancestor, for example a human genome [0056]. Evans further discloses that this training method provides a sufficiently large training data set to train a machine learning model useful for predicting pathogenicity, as the unlabeled genetic sequence variants do not require clinical studies to determine pathogenicity [0036]).
Further regarding claim 11, Evans discloses comparing variants to a labeled benign genetic sequence variant training data set (known variants data set) [0036]. Evans does not expressly disclose comparing variants to known variant databases.
Evans further discloses that the method is repeated with one or more sample splice site sequences comprised in the sample splice site (model is trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation. In some embodiments, a machine learning model trained using a genetic sequence variant training data set comprising genetic sequence variants with a splice-site mutation is used to predict the pathogenicity of a test genetic sequence variant comprising a splice-site mutation [0062]), wherein each sample splice site sequence comprises non-identical (present specification [0068] discloses that a non-identical sample splice site comprises two or more samples splice site sequences obtained from different regions of the same sample), (the unlabeled genetic sequence variants comprising a mixture of benign genetic sequence variants and pathogenic genetic sequence variants (abstract))
Evans further discloses determining a risk of abnormal splicing for the sample splice site by assessing the clinical classification(s) associated with the nucleotide sequence of the first sample splice site sequence determined in step [[(b)]](c) against a CSP reference database (In some embodiments, a feature that is defined on a functional genomic analysis of the genetic sequence variant is generated by comparing the location and sequence of the genetic sequence variant to locations of annotated functional genomic regions. For example, in some embodiments, the functional annotation features evaluate the probability that a given genetic sequence variant will impact an enhancer or promoter region, or other regulatory element, in a genome [00081]; Local mutation rates can be determined, for example, by comparing the species genome to an inferred evolutionary ancestor, for example a human genome [0056]; Evans further discloses that this training method provides a sufficiently large training data set to train a machine learning model useful for predicting pathogenicity, as the unlabeled genetic sequence variants do not require clinical studies to determine pathogenicity [0036]).
Further regarding claim 11, Evans does not expressly disclose comparing variants to known variant databases. However, Maston discloses that one or more of the variants may be compared to databases of known variants. The result of that comparison may be identification of one or more previously unknown variants, one or more variants that are known but unclassified, or both [0011]. Maston further discloses that examples of such databases include the Human Gene Mutation Database (HGMD) and Online Mendelian Inheritance in Man (OMIM) [0052].
Further regarding claim 11, Evans and Maston do not expressly disclose that the classified sample splice sites of other subjects in the CSP reference database have the identical nucleotide sequence as the sample splice site sequence in the test subject but localize to a different exon-intron junction.
However, Rogan discloses a method for assessing changes in expression level of a gene having an mRNA splice-altering mutation, computing and identifying changes in individual information contents of potential donor and acceptor splice sites at each nucleotide position (claim 1). Rogan further discloses that the level of expression of each isoform was measured relative to the level of expression of the same isoform in a reference sample [0148]. Rogan further discloses that the whole genome was scanned and the frequencies of different lengths of exons occurring in the genome and their respective probability of occurrence were calculated [0054]. Rogan further discloses determining the frequency of each interval length between known natural sites and the nearest hnRNP A1 site, separately for exons and introns. Differences between the natural and mutated exon Ri,total values correspond to changes in the abundance of the respective isoforms, and can predict exon skipping [0105].
Rogan further discloses assessing predicted splice isoforms (predicted isoforms/(splice sites of identical nucleotide sequences) have different localization patterns) by ASSEDA and their characterization as pathogenic mutations or benign polymorphisms [0146 - 0151]. ASSEDA server is based on human genome reference sequence hg19 (GRCh37), GenBank and RefSeq cDNA accessions, and SNP (dbSNP 135) tables. Genome-wide information weight matrices for automatically curated acceptor (n=108,079) and donor (n=111,772) splice sites (acceptor genome and donor genome, respectively [0061] (reference database). Based on the result, he concluded that information theory-based exon definition comprehensively detects the experimentally-verified repertoire of mutant isoforms which can determine which of the many potential intronic cryptic splice sites that are predicted by ASSEDA are potentially relevant and which ones can be dismissed as being irrelevant to pathogenicity [0150].
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Evans and Maston to have used have the identical nucleotide sequence as the sample splice site sequence in the test subject but localize to a different exon-intron junction, as shown by Rogan (claim 1; [0146 - 0151]) to identify and characterize unclassified variants for understanding their role in disease development. There would be a reasonable expectation of success in combining the technique of Rogan to the method of Evans and Maston because they are all predicting pathogenicity of genetic sequence variants.
Response to Arguments
Applicant's arguments filed 12/30/2024 have been fully considered but they are not persuasive. Applicant states:
Rogan et al. uses distinctly different input data (i.e., transcriptomic data instead of genomic data), and the methods of Rogan et al. used to produce or analyse the data (position weight matrices of spliceosomal binding motifs; the normal range of exon and intron lengths in human transcripts) fail to provide the claimed methods.
For at least these reasons, Rogan et al. does not compensate for the deficiencies of Evans et al. and Maston et al. Withdrawal of this rejection is respectfully requested.
It is respectfully submitted that the above statement is not persuasive. Instant claims, as drafted, do not provide details of the model. As stated above, the combination of Evans and Matson teach a method of predicting pathogenicity of genetic sequence variants in noncoding regions using one or more features, where one feature is a predicted probability that a given genetic sequence variant will alter the splicing of a transcript using pipelines such as MaxEntScan (incorporates native intron frequencies by using maximum entropy modeling to estimate nucleotide dependencies at 5' and 3' splice sites from a database of annotated human intron-exon junctions), and assigning each genetic sequence variant to a benign cluster or a pathogenic cluster (for example, NIF of zero) by determining a frequency in a reference genome. Further Rogan discloses (a) generate a genomic polynucleotide sequence of the gene, (b) computing and identifying changes in the individual information contents of potential donor and acceptor splice sites at each nucleotide position by computing product of the information theory-based position weight matrices and a unitary position matrix of each sequence (claim 20). Rogan further discloses determining the frequency of each interval length between known natural sites and the nearest hnRNP A1 site, separately for exons and introns. Differences between the natural and mutated exon Ri,total values correspond to changes in the abundance of the respective isoforms, and can predict exon skipping. The calculation is carried out by the Automated Splice Site and Exon Definition Analysis Server (ASSEDA) [0105].
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/G.S./ Examiner, Art Unit 1686
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686