DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims Status
Claims 1-10 are pending.
Claims 1-10 are examined on the merits.
Priority
The instant application claims priority to US provisional Application No. 63/323,287, filed 03/24/2022, and US provisional Application No. 63/355,957, filed 06/27/2022. Therefore, the Effective Filing Date (EFD) assigned to each of the claims 1-10 is the provisional filing date of 63/323,287, filed 03/24/2022.
Information Disclosure Statement
No Information Disclosure Statement has been filed herein.
Drawings
The drawings are objected to because Figures 6-10 are not legible. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "1118" and "1310" have both been used to designate a network, reference characters “1122” and “1330” have both been used to designate data input sources, and reference characters “1122” and “1332” have both been used to designate a database. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
On page 23, “[0089]” should be formatted onto the next line
In paragraph [0043], “e.g,” should read “e.g.,”
On page 32, paragraph [0100], “the non coding RNA genes” should read “the non-coding RNA genes”
In paragraph [0109], “chosen by prossessing one or more similarity scores” should read “chosen by p
In paragraph [0147], “an ORF that encompases the exon, etc” should read “an ORF that encompasses the exon, etc.”
In paragraphs [0156], [0157], and [0158], “parallely” should read “parallelly”
In paragraph [0162], “Eg.,” should read “E.g.,”
On page 59, number 11, “etc)” should read “etc.)”
In paragraph [0167], “throught the gene and the genome” should read “throughout the gene and the genome”
In paragraph [0210], “Shapiro and Senapthy” should read “Shapiro and Senapathy”
Appropriate correction is required.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such a claim limitation is: “a system configured to carry out the method of claim 1” in claim 10.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof, as recited in the Specification paragraphs [0225]-[0231].
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 1, the claim recites the limitation of “training an artificial intelligence program […]; training the artificial intelligence program […]; generating an output dataset of splicing or regulatory elements, wherein the input dataset comprises a new set of genes”. The claim is indefinite because it is unclear if the artificial intelligence program is used to generate the output dataset. The artificial intelligence program is trained, but there is no nexus between the training steps and the generation of output datasets because the artificial intelligence program is not used.
With further respect to claim 1, the claim recites the limitation of “generating an output dataset of splicing or regulatory elements, wherein the input dataset comprises a new set of genes”. The claim is indefinite because it is unclear what “input dataset” is being limited. It is unclear if thus the new set of genes is the input dataset referred to in the first step of claim 1, or if the new set of genes is used as an input in an artificial intelligence program step to generate the output.
With respect to claim 5, the claim recites the limitation of “training an artificial intelligence program […]; generating as an output one or more novel cryptic element mutations”. The claim is indefinite because it is unclear if the artificial intelligence program is used to generate the output. The artificial intelligence program is trained, but there is no nexus between the training steps and the generation of an output because the artificial intelligence program is not used.
With respect to claim 7, the claim recites the limitation of “A computer implemented method of claim 1”. The claim is indefinite because it is unclear if the claim is further limiting the method of claim 1 or is referring to a different method.
With further respect to claim 7, the claim recites the limitation of “the AI model sorting pathogenic or strength altering mutations”. The claim is indefinite because there is no antecedent basis for “the AI model”.
With respect to claim 9, the claim recites the limitation of “A computer implemented method of claim 1”. The claim is indefinite because it is unclear if the claim is further limiting the method of claim 1 or is referring to a different method.
With further respect to claim 9, the claim recites the limitation of “the trained AI model predicting deleterious or strength altering mutations”. The claim is indefinite because there is no antecedent basis for “the trained AI model”.
The remaining claims are rejected due to being dependent upon an indefinite claim without remedying the indefiniteness.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to an abstract idea of mental steps, mathematic concepts, or a natural law without significantly more.
The MPEP at MPEP 2106.03 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of
matter?
(2A)(1) Are the claims directed to a judicially recognized exception, i.e. a law of nature,
a natural phenomenon, or an abstract idea?
(2A)(2) If the claims are directed to a judicial exception under Prong One, then is the
judicial exception integrated into a practical application?
(2B) If the claims are directed to a judicial exception and do not integrate the judicial
exception, do the claims provide an inventive concept?
With respect to step (1): Yes, the claims recite a method and a system.
With respect to step (2A)(1): The claims are directed to abstract ideas of mental processes and mathematical concepts, and laws of nature.
“Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection” (MPEP 2106.04). Abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (procedures for observing, evaluating, analyzing/judging and organizing information (MPEP 2106.04(a)(2)). Laws of nature or natural phenomena include naturally occurring principles/relations that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP 2106(b)).
Mental processes recited in claim 1:
generating one or more similarity scores for the one or more regulatory and/or one or more splicing elements
generating pathogenic or strength altering mutations for the new set of genes
Mathematical concepts recited in claim 1:
generating one or more pathogenic or strength altering mutations, wherein generating one or more pathogenic or strength altering mutations involves calculating pathogenicity of known mutations in the one or more regulatory and/or one or more splicing elements, and the difference between the scores before and after mutation
training an artificial intelligence program with the one or more regulatory and/or one or more splicing elements, wherein the one or more similarity scores are within a present range
training the artificial intelligence program with known pathogenic or strength altering mutations in splicing or regulatory elements in a set of genes with known splicing and regulatory elements, genomic positions, and similarity scores
generating an output dataset of splicing or regulatory elements, wherein the input dataset comprises a new set of genes
Dependent claims 2-9 recite additional steps that either are directed to abstract ideas or further limit the judicial exceptions in independent claim 1, and as such, are further directed to abstract ideas. Hence, the claims explicitly recite numerous elements that individually and in combination constitute abstract ideas. The relevant recitations are:
Claim 2: “identifying pathogenic or strength altering mutations in the plurality of nucleotides from one or more individuals based on the trained artificial intelligence program”
Claim 3: “identifying one or more molecular effects due to pathogenic or strength altering mutations in the plurality of nucleotides from one or more individuals based on the trained artificial intelligence program”
Claim 4: “wherein generating pathogenic or strength altering mutations in genetic elements for the new set of genes identifies phenotypes such as disease and drug response, including therapeutics and harmful side effects”
Claim 5: “training an artificial intelligence program with one or more known cryptic elements from the input dataset, wherein the one or more known cryptic elements include genetic environment of other genetic elements; and their pathogenic or strength altering mutations causing various phenotypes in a set of known genes; generating as an output one or more novel cryptic element mutations causing disease, drug response and harmful side effects”
Claim 6: “identifying true and cryptic genetic elements in the new set of genes using a machine learning model, wherein the machine learning model is trained with one or more known true and cryptic genetic elements from the input dataset, wherein the one or more known true and cryptic genetic elements are categorized based on calculated similarity scores and genomic positions in known genes”
Claim 7: “the AI model sorting pathogenic or strength altering mutations from benign mutations”
Claim 8: “identifying pathogenic or strength altering mutations in the new set of genes using a machine learning model, wherein the machine learning model is trained with known pathogenic and strength altering mutations and non-deleterious or benign mutations, wherein the pathogenic and strength altering mutations and non-deleterious or benign mutations are categorized based on calculated similarity scores, genomic positions in known genes, and their genetic environment of other elements and their parameters within the genes and the genome”
Claim 9: “the trained AI model predicting deleterious or strength altering mutations in different genetic elements of the new set of genes”
The abstract ideas in the claims are evaluated under Broadest Reasonable Interpretation (BRI) and determined herein to each cover mental processes and mathematic concepts because the claims recite no more than using mathematical concepts to analyze genetic elements and using mental processes to alter a sequence. Furthermore, the steps of training the artificial intelligence program are determined to be a mathematical concept because as described in paragraph [0146] of the Specification, the training is performed through an algorithm. Furthermore, as described in the Specification on pages 23 and 24, the generation of mutations is in silico, and not a physical mutation of sequences. Thus, the invention is directed to the use of abstract ideas to predict laws of nature, the effect of regulatory genetic elements on pathogenicity.
With respect to step (2A)(2): The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone or in combination to determine if the judicial exception is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exception, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
Claim 1 recites the following additional elements that are not abstract ideas:
computer-implemented method
receiving an input dataset comprising one or more regulatory and/or one or more splicing elements in a gene set
The step of receiving an input dataset gathers the data on which the judicial exceptions are performed and is thus a data gathering step. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)). The element of a computer-implemented method broadly recites applying the method to a computer environment, such as performing the method on a generic computer. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc. ... are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(f)).
Dependent claims 2 and 3 are directed to further data gathering limitations. Dependent claim 10 is directed to a generic computer system to which the judicial exceptions are applied.
None of these dependent claims recite additional elements, alone or in combination, which would integrate a judicial exception into a practical application.
Lastly, the claims have been evaluated with respect to step (2B): Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims lack a specific inventive concept. Under said analysis, Applicant is reminded that the judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements described above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP at 2106.05(d).I, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to claim 1: The additional elements of a computer implemented method and receiving an input dataset comprising one or more regulatory and/or one or more splicing elements in a gene set do not rise to the level of significantly more than the judicial exception. With respect to the computer-implemented method, as exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the device constitutes no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the abstract idea (see MPEP 2105(b)I-III). With respect to receiving an input dataset comprising regulatory and/or splicing elements in a gene set, the prior art to Beer et al. (WO 2016/183348 A1, published November 2016) discloses numerous uses of functional genomics for analyzing regulatory functions of DNA, such as transcription factors, transcriptional coactivators, chromatin structures and coactivators, etc. (paragraph [90]). Furthermore, the prior art to Beer et al. discloses studying noncoding sequences to analyze functional regulatory elements (paragraph [89]). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 2: The additional element of receiving a plurality of nucleotides from one or more individuals with at least one genetic element, exon, intron or a gene does not rise to the level of significantly more than the judicial exception. As exemplified in the MPEP at 2106.05(d).II, with respect to Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546, analyzing DNA to provide sequence information or detect allelic variants is a well-understood, routine, and conventional activity. As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 3: The additional element of receiving a plurality of nucleotides from one or more individuals with at least one genetic element, exon, intron or a gene does not rise to the level of significantly more than the judicial exception. As exemplified in the MPEP at 2106.05(d).II, with respect to Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546, analyzing DNA to provide sequence information or detect allelic variants is a well-understood, routine, and conventional activity. As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 10: The additional element of a system configured to carry out the method of claim 1 does not rise to the level of significantly more than the judicial exception. as exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the device constitutes no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the abstract idea (see MPEP 2105(b)I-III). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. Individually, the limitations of the claims and the claims as a whole have been found lacking.
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.
Claims 1-4, and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (“A deep learning approach to identify gene targets of a therapeutic for human splicing disorders”, Nature Communications, published June 2021) in view of Beer et al. (WO 2016/183348 A1, published November 2016).
Regarding claim 1, Gao et al. teaches a computer-implemented (Abstract) method for assessing genomic features, comprising:
receiving an input dataset comprising one or more regulatory and/or one or more splicing elements in a gene set (page 2, column 2, Section Evaluation of BPN-1577 on transcriptome splicing);
generating one or more similarity scores for the one or more regulatory and/or one or more splicing elements: Gao et al. teaches generating a score to determine the difference in splicing compared to controls, and thus a similarity score (page 2, column 2, Section Evaluation of BPN-15477 on transcriptome splicing); Gao et al. also teaches determining similarities between identified motifs and known splice site (page 5, column 2);
generating one or more pathogenic or strength altering mutations, wherein generating one or more pathogenic or strength altering mutations involves calculating pathogenicity of known mutations in the one or more regulatory and/or one or more splicing elements, and the difference between the scores before and after mutation: Gao et al. teaches generating strength altering splice variants and performing calculations comparing the splicing to controls (page 2, column 2, Section Evaluation of BPN-15477 on transcriptome splicing);
training an artificial intelligence program with the one or more regulatory and/or one or more splicing elements, wherein the one or more similarity scores are within a preset range: Gao et al. teaches training an artificial intelligence model using the splicing elements and wherein the scores are in preset ranges (page 2, column 2, Section Convolutional neural network identifies sequence signatures responsible for BPN-15477 response – page 3);
generating an output dataset of splicing or regulatory elements, wherein the input dataset comprises a new set of genes: Gao et al. teaches using the artificial intelligence model to predict splicing effects and thus determines potential target genes (page 6, column 1, Section Identification of potential therapeutic targets of BPN-15477); and
generating pathogenic or strength altering mutations for the new set of genes: Gao et al. teaches the artificial intelligence model predicting strength altering mutations in the predicted set of genes (page 6, column 1, Section Identification of potential therapeutic targets of BPN-15477) and teaches validating these predicted mutations through performing treatment on cell lines carrying these mutations (page 6, column 2, Section Experimental validation of therapeutic targets for BPN-15477).
Gao et al. does not teach the claim element of training the artificial intelligence program with known pathogenic or strength altering mutations in splicing or regulatory elements in a set of genes with known splicing and regulatory elements, genomic positions, and similarity scores.
However, Beer et al. teaches using a trained support vector machine identifying variant sequences that can be used to disclose disease and pathologies (Abstract), and is directed to identifying regulatory sequences (paragraphs [4], [8]). Beer et al. teaches collecting a training data set from a database, and training the support vector machine using the dataset (paragraph [9]) and teaches the training dataset comprising positive data sets obtained through experimental assays (paragraph [78]), and the training data set comprising a set of data points having known characteristics (paragraph [92]). Furthermore, Beer et al. teaches training the SVM classifier using kernels that define similarities between data points without explicitly mapping the data into a higher-dimensional feature space vector (paragraph [80]). Beer et al. teaches that SVMs are useful for providing generalization when estimating a multi-dimensional function from a limited collection of data, in solving dependency estimation problems, and for accurately estimation indicator functions and real-valued functions (paragraph [94]).
Therefore, 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 incorporated the training of Beer et al. to the method of Gao et al. because Gao et al. is directed to a machine learning enabled method to predict effects of splicing in order to determine mutations that are a potential target for therapeutics (Abstract), and Beer et al. is directed to using a trained support vector machine classifier to identify nucleic acid regulatory sequences (paragraph [4]) and identifying predictive sequences in DNA that can be used to diagnose disease and pathologies (Abstract). Beer et al. teaches that SVMs are useful for providing generalization when estimating a multi-dimensional function from a limited collection of data, in solving dependency estimation problems, and for accurately estimation indicator functions and real-valued functions (paragraph [94]). Thus, one of ordinary skill in the art would have a reasonable expectation of success of using an SVM to predict splicing effects and genes/mutations affecting splicing and would be motivated to do so in order to estimate multi-dimensional functions using a limited amount of that, to solve dependency estimation problems, and to accurately estimate the real-valued functions.
Regarding claim 2, the claim is directed to receiving a plurality of nucleotides from one or more individuals with at least one genetic element, exon, intron or a gene; and identifying pathogenic or strength altering mutations in the plurality of nucleotides from one or more individuals based on the trained artificial intelligence program. Gao et al. teaches the method of claim 1 in view of Beer et al. Gao et al. teaches generating mutated minigenes and using the CNN to predict strength altering mutations (page 6, column 1, paragraph 2). Although Gao et al. does not specifically teach this being performed on nucleotides obtained from an individual, Gao et al. teaches a precisely targeted treatment approach for patients to increase the amount of normal transcript through modulating RNA splicing (page 10, column 1, Section Discussion). Thus, it would be obvious to apply the method to a patient to determine a target for RNA splice treatment.
Regarding claim 3, the claim is directed to receiving a plurality of nucleotides from one or more individuals with at least one genetic element, exon, intron, or a gene; and identifying one or more molecular effects due to pathogenic or strength altering mutations in the plurality of nucleotides from one or more individuals based on the trained artificial intelligence program. Gao et al. teaches the method of claim 1 in view of Beer et al. Gao et al. teaches generating mutated minigenes and using the CNN to evaluate potential treatment response (page 6, column 1, paragraph 2) and identifying molecular effects due to strength altering mutations in the nucleotides (Figure 3, d and e). Although Gao et al. does not specifically teach this being performed on nucleotides obtained from an individual, Gao et al. teaches a precisely targeted treatment approach for patients to increase the amount of normal transcript through modulating RNA splicing (page 10, column 1, Section Discussion). Thus, it would be obvious to apply the method to a patient to determine a target for RNA splice treatment.
Regarding claim 4, the claim is directed to generating pathogenic or strength altering mutations in genetic elements for the new set of genes identifying phenotypes such as disease and drug response, including therapeutics and harmful side effects. Gao et al. teaches the method of claim 1 in view of Beer et al. Gao et al. also teaches that generating strength altering mutations in genetic elements for the set of generated genes comprises identifying phenotypes such as drug response sensitivity, to determine therapeutic targets (page 6, column 1, Section Identification of potential therapeutic targets of BPN-15477).
Regarding claim 7, the claim is directed to the AI model sorting pathogenic or strength altering mutations from benign mutations. Gao et al. teaches the method of claim 1 in view of Beer et al. Gao et al. also teaches that the convolutional neural network sorts mutations that do not cause a change from mutations that either are predicted to increase drug sensitivity or decrease drug sensitivity (Figure 3).
Regarding claim 8, the claim is directed to identifying pathogenic or strength altering mutations in the new set of genes using a machine learning model, wherein the machine learning model is trained with known pathogenic and strength altering mutations and non-deleterious or benign mutations, wherein the pathogenic and strength altering mutations and non-deleterious or benign mutations are categorized based on calculated similarity scores, genomic positions in known genes, and their genetic environment of other elements and their parameters within the genes and the genome. Gao et al. teaches the method of claim 1 in view of Beer et al. Gao et al. also teaches the trained AI model being applied to a set of genes to predict a plurality of strength altering mutations in different genes (Table 1). Gao et al. teaches training a model with a plurality of regulatory or splicing elements based on the genomic position, calculated similarity to control splice levels and other parameters (page 2, column 2, Section Convolutional neural network identifies sequence signatures responsible for BPN-15477 response).
Gao et al. does not teach the claim element of the model being trained with known pathogenic and strength altering mutations and non-deleterious or benign mutations.
However, Beer et al. teaches identifying regulatory sequences that have pathogenic effects (paragraphs [4] and [8]) using a model trained with a training dataset from a database (paragraph [9]), and teaches the training dataset comprising positive data sets obtained through experimental assays (paragraph [78]), and the training data set comprising a set of data points having known characteristics (paragraph [92]).
Regarding claim 9, the claim is directed to the trained AI model predicting deleterious or strength altering mutations in different genetic elements of the new set of genes. Gao et al. teaches the method of claim 1 in view of Beer et al. Gao et al. also teaches the trained AI model being applied to a set of genes to predict a plurality of strength altering mutations in different genes (Table 1).
Regarding claim 10, the claim is directed to a system for assessing genomic features, comprising a system configured to carry out the method of claim 1. Gao et al. teaches the method of claim 1 in view of Beer et al. Gao et al. also teaches a computer-implemented method as evidenced by a deep learning approach and the use of ClinVar (Abstract). Therefore, Gao et al. teaches a computer system configured to carry out the method.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. in view of Beer et al., as applied to claims 1-4 and 7-10, and further in view of Jaganathan et al. (“Predictive Splicing from Primary Sequence with Deep Learning”, Cell, published January 2019).
Regarding claim 5, the claim is directed to training an artificial intelligence program with one or more known cryptic elements from the input dataset, wherein the one or more known cryptic elements include genetic environment of other genetic elements; and their pathogenic or strength altering mutations causing various phenotypes in a set of known genes; and generating as an output one or more novel cryptic element mutations causing disease, drug response and harmful side effects. Gao et al. teaches the method of claim 1 in view of Beer et al. Gao et al. teaches prediction of a cryptic splice site affecting drug response and a corresponding genomic position (page 8, Figure 4 description).
Neither Gao et al. nor Beer et al. teach the claim elements of training an artificial intelligence program with one or more known cryptic elements from the input dataset.
However, Jaganathan et al. teaches predicting splicing from primary sequence with deep learning. Jaganathan et al. teaches a deep neural network that accuracy predicts splice junctions enabling precise prediction of noncoding genetic variants that cause cryptic splicing (Summary). Jaganathan et al. teaches training the neural network with varying input sequence context (page 537, column 2, paragraph 2), and teaches the neural network being trained on reference splice junction annotations, thus known cryptic splice sites (page 539, column 2, Section Verification of Predicted Cryptic Splice Mutations in RNA-Seq Data; Key Resources Table; page e3, paragraph 3). Jaganathan et al. also teaches using the neural network to generate an output of novel cryptic element mutations that cause disease (page 544, column 1, Section De Novo Cryptic Splice Mutations Are a Major Cause of Rare Genetic Disorders).
Regarding claim 6, the claim is directed to identifying true and cryptic genetic elements in the new set of genes using a machine learning model, wherein the machine learning model is trained with one or more known true and cryptic genetic elements from the input dataset, wherein the one or more known true and cryptic genetic elements are categorized based on calculated similarity scores and genomic positions in known genes. Gao et al. teaches the method of claim 1 in view of Beer et al. Gao et al. teaches prediction of a cryptic splice site (page 8, Figure 4 description).
Neither Gao et al. nor Beer et al. teach the claim elements of identifying true and cryptic genetic elements in the new set of genes using a machine learning model, wherein the machine learning model is trained with one or more known true and cryptic genetic elements from the input dataset.
However, Jaganathan et al. teaches training the neural network with varying input sequence context (page 537, column 2, paragraph 2), and teaches the neural network being trained on reference splice junction annotations, thus known true and cryptic splice sites (page 539, column 2, Section Verification of Predicted Cryptic Splice Mutations in RNA-Seq Data; Key Resources Table; page e3, paragraph 3). Jaganathan et al. also teaches identifying true and cryptic splice sites in a set of genes, based on a similarity to splicing in healthy controls (page 544, column 1, Section De Novo Cryptic Splice Mutations Are a Major Cause of Rare Genetic Disorders).
Therefore, 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 incorporated the cryptic splice sites of Jaganathan et al. to the method of Gao et al. in view of Beer et al. because Gao et al. is directed to a machine learning enabled method to predict effects of splicing in order to determine mutations that are a potential target for therapeutics (Abstract), and Jaganathan et al. is directed to determining splicing consequences from primary mutations. Jaganathan et al. teaches that de novo mutations with cryptic splice sites are significantly enriched in patients with autism and intellectual disabilities and teaches an estimation of 9%-11% of pathogenic mutations in patients with rare genetic disorders being caused by this type of variation (Abstract). Thus, one of ordinary skill in the art would have a reasonable expectation of success of using machine learning models to predict strength altering mutations for cryptic splice sites, and would be motivated to do so in order for treatment of rare diseases.
Conclusion
No claims are allowed.
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/E.A.S./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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