DETAILED ACTION
Applicant's response, filed 8/25/2025, has been fully considered. The following rejections and/or
objections are either reiterated or newly applied. They constitute the complete set presently being
applied to the instant application.
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 11 and 13-19 are pending.
Claims 1-10, 12, and 20 are cancelled.
Claims 11 and 13-19 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/20/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the specification previous rejections under 35 U.S.C. 101 are withdrawn and a new rejection is set forth below.
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 11 and 13-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method for computing a set of variant-induced changes in a condition-specific cell variable for a genetic variant. The judicial exception is not integrated into a practical application, said practical application is a generically recited computer element that does not add meaningful limitation to the abstract idea as it is simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05.
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically methods (claims 11 and 13-19).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, law of nature, or natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 11: Processing a set of variant features, and extracting a set of variant features from the variant sequence are processes of comparing/contrasting and calculating that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. The genetic variant comprising a DNA or RNA variant, and the variant comprising two or more SNVs or combinations of substitutions, insertions and deletions are merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 13: Extracting a set of reference features, and processing the set of reference features are processes of comparing/contrasting and calculating that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 14: Generating a binary matrix, and a set of features are processes of calculating and selecting that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 15: Computing probabilities, summing an absolute difference, summing a Kullback-Leibler divergence, and computing an expected value and subtracting the reference from the calculated are verbal articulations of mathematical concepts and are therefore abstract ideas, specifically mathematical processes.
Claim 17: Summing the variant-induced changes and computing the maximum of the absolute changes are verbal articulations of mathematical concepts and are therefore abstract ideas, specifically mathematical processes.
Claim 18: Applying thresholds to classify the genetic variant are processes of comparing/contrasting and calculating that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 19: Computing a distance between the two genetic variants by summing the output of a nonlinear function is a verbal articulation of a mathematical concept and is therefore an abstract idea, specifically a mathematical process.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claim 11: The cell variable predictor comprising a deep neural network, are merely instructions to implement an abstract idea on a computer (See Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016), Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016), and Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015)) [See MPEP § 2106.05(f)].
Claim 16: The deep neural network comprising a CNN, RNN or LT-ST RNN are merely instructions to implement an abstract idea on a computer (See Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016), Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016), and Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015)) [See MPEP § 2106.05(f)].
Claim 17: Outputting the score for a fixed condition are insignificant extra solution activities, specifically necessary data outputting (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract ideas into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exceptions because the claims recite additional elements that are generic, convention, nonspecific or insignificant extra solution activities.
The additional elements of the cell variable predictor comprising a deep neural network (Conventional: Schmidhuber et al. 2015 in view of Quang et al. 2015 – The first is a review of DNN and the second is an application within the field), the genetic variant comprising a DNA or RNA variant (Conventional: MPEP 2106.05(d) - Analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546), the deep neural network comprising a CNN, RNN or LT-ST RNN (Conventional: Schmidhuber et al. 2015), and outputting the score for a fixed condition (Conventional: MPEP 2106.05(d)(II) Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) ), are insignificant extra solution activities, specifically necessary data gathering (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 11 and 13-19, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 8/25/2025 have been fully considered but they are not persuasive. Applicant asserts that claim 11 has been amended to include subject matter from previous claim 20 and that previous rejections under 35 U.S.C. 101 are moot. Examiner has reviewed amended claim 11 and provided a new grounds of rejection under 35 U.S.C. 101 as set forth above.
Claim Rejections - 35 USC § 112
Response to Amendment
In view of applicant’s amendments to the claims previous rejections under 35 U.S.C. 112 are withdrawn.
Claim Rejections - 35 USC § 103
Response to Amendment
In view of applicant’s amendments to the claims previous rejections under 35 U.S.C 103 have been withdrawn and new grounds of rejection set forth below.
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.
Claims 11 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Barash et al. (Nature (2010) 53-59) in view of Quang et al. (Bioinformatics (2014) 761-763).
Claim 11 is directed to a computer-implemented method for computing variant-induced changes in a condition-specific cell variable for a genetic variant that processes a set of variant features using a predictor to quantify a condition-specific variant cell variable wherein the predictor is a deep neural network that has at least two layers.
Barash et al. teaches on page 53 column 2, paragraph 2 “Code assembly requires a set of relevant features derived from exonic and intronic sequences. We constructed a compendium of 1,014 features of four types: known motifs, new motifs, short motifs and features describing transcript structure… The compendium includes 326 ‘new motifs’ that have weak or no known evidence for roles in tissue-dependent splicing, including 12 clusters of validated or putative exonic and intronic splicing enhancers (ESEs and ISEs) and silencers (ESSs and ISSs), which are 6–8 nucleotides long and were identified without regard to possible tissue-dependent roles, and 314 5–7-nucleotide-long motifs that are conserved in intronic sequences neighbouring alternative exons… In addition to the feature compendium, we constructed a set of ~1,800 ‘unbiased motifs’ by performing a de novo search10 for each tissue type and direction of splicing change”, and in supplemental page 7, paragraph 4 “We use a technique that treats each cellular condition separately and for each one learns simple decision boundaries for selected input features and uses weighted combinations of corresponding indicator variables as input to a softmax (multi-valued logisitic) function. Our model can be viewed as a single-layer logistic Bayesian network or neural network”.
Quang et al. teaches in the abstract “Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge… SVMs cannot capture non-linear relationships among the features, which can limit performance. To address this issue, we have developed DANN. DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among features and are better suited than SVMs for problems with a large number of samples and features”, on page 762, column 1, paragraph 1 “DANN trains a DNN consisting of an input layer, a sigmoid function output layer, and three 1000-node hidden layers…”, and on page 762, column 1, paragraph 2 “There are a total of 949 features defined for each variant… CADD’s training data consist of 16,627,775 ‘observed’ variants and 49,407,057 ‘simulated’ variants”, which in view of the Barash et al. teachings, reads on a computer-implemented method for computing a set of variant- induced changes in a condition-specific cell variable for a genetic variant, comprising processing a set of variant features using a cell variable predictor to quantify a condition- specific variant cell variable, wherein the cell variable predictor comprises a deep neural network comprising at least two connected layers of processing units, wherein the genetic variant comprises a variant in a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) variant sequence relative to a DNA or RNA reference sequence, the genetic variant comprising a) two or more distinct single nucleotide variants (SNVs) or b) a combination of substitutions, insertions, and deletions, wherein the combination is not a single nucleotide variant (SNV), and wherein the method further comprises extracting the set of variant features from the DNA or RNA variant sequence.
It would have been obvious at the time of filing to modify the teachings of Barash et al. for the extraction and processing a set of variant features from a set of variants from DNA using a neural network, with the teachings of Quang et al. for the use of a deep neural network in classification tasks as the latter states in the abstract “DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD’s SVM methodology” and it would be a simple substitution of methods under KSR Int ‘l v. Teleflex. One would have had a reasonable expectation of success given both the former and latter are using neural networks, the question is merely a choice of depth of the network which the latter successfully integrates and shows improved performance with. Therefore, it would have been obvious at the time of filing to modify the teachings of each and to be successful.
Claim 13 is directed to the method of claim 11 but further specifies extracting a set of reference features from the DNA or RNA.
Barash et al. teaches on page 53, column 1, paragraph 2 “Tissue-dependent splicing is regulated by trans-acting factors, cis-acting RNA sequence motifs, and other RNA features, such as exon length and secondary structure. For nearly two decades, researchers have sought to define a regulatory splicing code in the form of a set of RNA features that can account for abundances of spliced isoforms”, in column 2, paragraph 2 “Code assembly requires a set of relevant features derived from exonic and intronic sequences. We constructed a compendium of 1,014 features of four types: known motifs, new motifs, short motifs and features describing transcript structure… The compendium includes 326 ‘new motifs’ that have weak or no known evidence for roles in tissue-dependent splicing, including 12 clusters of validated or putative exonic and intronic splicing enhancers (ESEs and ISEs) and silencers (ESSs and ISSs), which are 6–8 nucleotides long and were identified without regard to possible tissue-dependent roles, and 314 5–7-nucleotide-long motifs that are conserved in intronic sequences neighbouring alternative exons… In addition to the feature compendium, we constructed a set of ~1,800 ‘unbiased motifs’ by performing a de novo search10 for each tissue type and direction of splicing change” and on page 54, column 2, paragraph 3 “our method recursively selects features from the compendium, while optimizing their thresholds and softmax parameters to maximize code quality… To quantify the contributions of its different components, we compared our final assembled code to partial codes whose only inputs were the tissue type, previously described motifs, conservation levels, or the compendium with transcript structure features or conservation levels removed”, reading on extracting a set of reference features from the DNA or RNA reference sequence, and processing the set of reference features using the cell variable predictor to quantify a condition-specific reference cell variable.
Claim 14 is directed to the method of claim 13 and thus claim 11, but further specifies exact method for extracting the variant features from the DNA or RNA.
Quang et al. teaches on page 762, column 1, paragraph 2 “DANN trains a DNN consisting of an input layer, a sigmoid function output layer, and three 1000-node hidden layers with hyperbolic tangent activation function…we preprocess the features to have unit variance before training either model”.
Barash et al. teaches on supplemental page 1 “It takes as input a matrix with rows corresponding to exons (3665 in our experiments) and columns corresponding to tissues (27 in our experiments) and outputs a matrix of splicing patterns with rows corresponding to exons and columns corresponding to cellular conditions (4 in our experiments)”, which in view of Quang et al. reads on wherein the set of variant features is extracted from the DNA or RNA variant sequence by generating: a. a binary matrix with 4 rows and a number of columns equal to a length of the DNA or RNA variant sequence or the DNA or RNA reference sequence, wherein each column contains a bit indicating the nucleotide value at the corresponding position in the DNA or RNA variant sequence or the DNA or RNA reference sequence; b. a set of features computed using one or more layers of an autoencoder other than the input and output layers of the cell variable predictor; or c. a set of features that correspond to one or more of: RNA secondary structures, nucleosome positions, and retroviral repeat elements.
Claim 15 is directed to the method of claim 13 and thus claim 11, but further specifies the method of calculating probabilities for the discrete levels of the condition-specific cell variable.
Barash et al. teaches on supplemental page 7, equation 2 the summing of a Kullback-Leibler divergence, reading on computing, using the cell variable predictor, probabilities for discrete levels of the condition-specific cell variable, wherein each of the set of variant-induced changes is computed by: a. summing an absolute difference between the computed probabilities for the condition-specific reference cell variable and the condition-specific variant cell variable; b. summing a Kullback-Leibler divergence between the computed probabilities of the condition-specific reference cell variable and the condition-specific variant cell variable for each condition; or c. computing an expected value of the condition-specific reference cell variable and the condition-specific variant cell variable, and subtracting the expected value of the condition-specific reference cell variable from the expected value of the condition-specific variant cell variable.
Claim 16 is directed to the method of claim 11 but further specifies the deep neural network comprising one of the specified network architectures.
Quang et al. teaches in the abstract “Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge… SVMs cannot capture non-linear relationships among the features, which can limit performance. To address this issue, we have developed DANN. DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among features and are better suited than SVMs for problems with a large number of samples and features”, which reads on wherein the deep neural network comprises a convolutional neural network, a recurrent neural network, or a long-term short-term memory recurrent neural network.
Claim 17 is directed to the method of claim 11 but further specifies the method of calculating the combination of the set of variant-induced changes.
Barash et al. teaches on supplemental page 13 the scoring and classification under the Feature Information Index section 7, paragraph 1 equation 1, reading on combining the set of variant- induced changes in the condition-specific cell variable to compute a single numerical variant score for the genetic variant, the single numerical variant score computed by :a. outputting the score for a fixed condition; b. summing the variant-induced changes across a plurality of conditions; orc. computing the maximum of the absolute variant-induced changes across a plurality of conditions.
Claim 18 is directed to the method of claim 11 but further specifies the application of fixed thresholds to the classify the variant as one of the specified groups.
Barash et al. teaches on supplemental page 13 “In the results reported we used confidence thresholds W1 = 0.9 and W0 = 0.1, but results remained stable on a wide range of values” and on page 54, column 1, paragraph 3 “The code is combinatorial and accounts for how features cooperate or compete in a given tissue type, by specifying a subset of important features, thresholds on feature values and softmax parameters relating active feature combinations to the prediction… To assemble a code, our method recursively selects features from the compendium, while optimizing their thresholds and softmax parameters to maximize code quality”, reading on applying thresholds that are fixed or selected using labeled data to the single numerical variant score for the genetic variant to classify the genetic variant (i) as one of deleterious or non-deleterious, (ii) as one of pathogenic, likely pathogenic, unknown significance, likely benign, or benign, or (iii) using another discrete set of labels.
Claim 19 is directed to the method of claim 11 but further specifies computing a distance between the two genetic variants in the pair.
Barash et al. teaches on supplemental pages 3-4, section 1.2 “After inference and learning in the above model, for example index i and corresponding exon index ei and condition index ci, gives the splice pattern probabilities…We assembled a compendium of 1014 RNA features consisting of 171 features derived from cis-elements described in literature as affecting alternative splicing or binding a known splice factor…Each feature may be associated with one of the seven unspliced RNA regions indicated in Fig. 1A, or a combination of those regions” and page 13 “In the results reported we used confidence thresholds W1 = 0.9 and W0 = 0.1, but results remained stable on a wide range of values” and on page 54, column 1, paragraph 3 “The code is combinatorial and accounts for how features cooperate or compete in a given tissue type, by specifying a subset of important features, thresholds on feature values and softmax parameters relating active feature combinations to the prediction… To assemble a code, our method recursively selects features from the compendium, while optimizing their thresholds and softmax parameters to maximize code quality”, reading on computing, for a pair of genetic variants, a distance between the two genetic variants in the pair by summing the output of a nonlinear function applied to a difference between the change in the condition-specific cell variable for the first of the two genetic variants and the change in the condition- specific cell variable for the second of the two genetic variants.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 11 and 13-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-8, and 10 of U.S. Patent No. 10185803. Although the claims at issue are not identical, they are not patentably distinct from each other because all of what is taught in the claims of application 17/369,499 is contained within the claims of U.S. patent no. 10185803, albeit in a slightly different order.
Application 17/369,499
US Patent No. 10185803
Claim 1: A computer-implemented method for computing a set of variant- induced changes in a condition-specific cell variable for a genetic variant, comprising processing a set of variant features using a cell variable predictor to quantify a condition- specific variant cell variable, wherein the cell variable predictor comprises a deep neural network comprising at least two connected layers of processing units,wherein the genetic variant comprises a variant in a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) variant sequence relative to a DNA or RNA reference sequence, the genetic variant comprising a) two or more distinct single nucleotide variants (SNVs) or b) a combination of substitutions, insertions, and deletions,wherein the combination is not a single nucleotide variant (SNV), andwherein the method further comprises extracting the set of variant features from the DNA or RNA variant sequence.
Claim 13: The method of claim 11, further comprising extracting a set of reference features from the DNA or RNA reference sequence, and processing the set of reference features using the cell variable predictor to quantify a condition-specific reference cell variable.
Claim 1: A computer-implemented method for computing a set of variant-induced changes in one or more condition-specific cell variables for one or more genetic variants, the method comprising: a. extracting a set of variant features from a DNA or RNA variant sequence, wherein the DNA or RNA variant sequence comprises the one or more genetic variants; b. processing the set of variant features using a cell variable predictor (CVP) to quantify one or more condition-specific variant cell variables, wherein the CVP comprises a deep neural network comprising at least two connected layers of processing units; c. extracting a set of reference features from a DNA or RNA reference sequence; d. processing the set of reference features using the CVP to quantify one or more condition-specific reference cell variables; and e. generating the set of variant-induced changes in the one or more condition-specific cell variables by processing the one or more condition-specific reference cell variables with the one or more condition-specific variant cell variables.
Claim 10: The method of claim 1, wherein the one or more genetic variants contain a) two or more distinct single nucleotide variants (SNVs); or b) a combination of substitutions, insertions, and deletions, wherein the combination is not a single nucleotide variant (SNV).
Claim 14: The method of claim 13, wherein the set of variant features is extracted from the DNA or RNA variant sequence by generating:a. a binary matrix with 4 rows and a number of columns equal to a length of the DNA or RNA variant sequence or the DNA or RNA reference sequence, wherein each column contains a bit indicating the nucleotide value at the correspondingposition in the DNA or RNA variant sequence or the DNA or RNA reference sequence;b. a set of features computed using one or more layers of an autoencoder other than the input and output layers of the cell variable predictor; or c. a set of features that correspond to one or more of: RNA secondary structures, nucleosome positions, and retroviral repeat elements.
Claim 3: The method of claim 1, wherein extracting the set of variant features and the set of reference features comprises processing the DNA or RNA variant sequence or the DNA or RNA reference sequence to generate: a. a binary matrix with 4 rows and a number of columns equal to a length of the DNA or RNA variant sequence or the DNA or RNA reference sequence, wherein each column contains a bit indicating the nucleotide value at the corresponding position in the DNA or RNA variant sequence or the DNA or RNA reference sequence; b. a set of features computed using one or more layers of an autoencoder other than the input and output layers of the CVP; or c. a set of features that correspond to one or more of: RNA secondary structures, nucleosome positions, and retroviral repeat elements.
Claim 15: The method of claim 13, further comprising computing, using the cell variable predictor, probabilities for discrete levels of the condition-specific cell variable, wherein each of the set of variant-induced changes is computed by:a. summing an absolute difference between the computed probabilities for the condition-specific reference cell variable and the condition-specific variant cell variable;b. summing a Kullback-Leibler divergence between the computed probabilities of the condition-specific reference cell variable and the condition-specific variant cell variable for each condition; or c. computing an expected value of the condition-specific reference cell variable and the condition-specific variant cell variable, and subtracting the expected value of the condition-specific reference cell variable from the expected value of the condition-specific variant cell variable.
Claim 5: The method of claim 1, further comprising computing, using the CVP, probabilities for discrete levels of the condition-specific cell variables, wherein each of the set of variant-induced changes is computed by: a. summing an absolute difference between the computed probabilities for the condition-specific reference cell variable and the condition-specific variant cell variable; b. summing a Kullback-Leibler divergence between the computed probabilities of the condition-specific reference cell variable and the condition-specific variant cell variable for each condition; or c. computing an expected value of the condition-specific reference cell variable and the condition-specific variant cell variable, and subtracting the expected value of the condition-specific reference cell variable from the expected value of the condition-specific variant cell variable.
Claim 16: The method of claim 11, wherein the deep neural network comprises a convolutional neural network, a recurrent neural network, or a long-term short-term memory recurrent neural network.
Claim 4: The method of claim 1, wherein the deep neural network comprises: a. a convolutional neural network; b. a recurrent neural network; or c. a long short-term memory recurrent neural network.
Claim 17: The method of claim 11, further comprising combining the set of variant- induced changes in the condition-specific cell variable to compute a single numerical variant score for the genetic variant, the single numerical variant score computed by: a. outputting the score for a fixed condition; b. summing the variant-induced changes across a plurality of conditions; orc. computing the maximum of the absolute variant-induced changes across a plurality of conditions.
Claim 6: The method of claim 1, further comprising combining the set of variant-induced changes in the one or more condition-specific cell variables to compute a single numerical variant score for each of the one or more genetic variants, the single numerical variant score computed by: a. outputting the score for a fixed condition; b. summing the variant-induced changes across a plurality of conditions; or c. computing the maximum of the absolute variant-induced changes across a plurality of conditions.
Claim 18: The method of claim 11, further comprising applying thresholds that are fixed or selected using labeled data to the single numerical variant score for the genetic variant to classify the genetic variant (i) as one of deleterious or non-deleterious, (ii) as one of pathogenic, likely pathogenic, unknown significance, likely benign, or benign, or (iii) using another discrete set of labels.
Claim 7: The method of claim 6, further comprising applying thresholds that are fixed or selected using labeled data to the single numerical variant score for each of the one or more genetic variants to classify each of the one or more genetic variants (i) as one of deleterious or non-deleterious, (ii) as one of pathogenic, likely pathogenic, unknown significance, likely benign, or benign, or (iii) using another discrete set of labels.
Claim 19: The method of claim 11, further comprising computing, for a pair of genetic variants, a distance between the two genetic variants in the pair by summing the output of a nonlinear function applied to a difference between the change in the condition-specific cell variable for the first of the two genetic variants and the change in the condition- specific cell variable for the second of the two genetic variants.
Claim 8: The method of claim 1, further comprising computing for each of one or more pairs of the one or more genetic variants, a distance between the two genetic variants in each of the one or more pairs by summing the output of a nonlinear function applied to the difference between the change in the condition-specific cell variable for the first of the two genetic variants and the change in the condition-specific cell variable for the second of the two genetic variants.
Claims 11, 13-17, and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, and 3-6 of U.S. Patent No. 11887696. Although the claims at issue are not identical, they are not patentably distinct from each other because all of what is taught in the claims of application 17/369,499 is contained within the claims of U.S. patent no. 11887696, albeit in a slightly different order.
Application 17/369,499
US Patent No. 11887696
Claim 1: A computer-implemented method for computing a set of variant- induced changes in a condition-specific cell variable for a genetic variant, comprising processing a set of variant features using a cell variable predictor to quantify a condition- specific variant cell variable, wherein the cell variable predictor comprises a deep neural network comprising at least two connected layers of processing units,wherein the genetic variant comprises a variant in a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) variant sequence relative to a DNA or RNA reference sequence, the genetic variant comprising a) two or more distinct single nucleotide variants (SNVs) or b) a combination of substitutions, insertions, and deletions,wherein the combination is not a single nucleotide variant (SNV), andwherein the method further comprises extracting the set of variant features from the DNA or RNA variant sequence.
Claim 13: The method of claim 11, further comprising extracting a set of reference features from the DNA or RNA reference sequence, and processing the set of reference features using the cell variable predictor to quantify a condition-specific reference cell variable.
Claim 1: A system comprising a deep neural network for computing a set of variant-induced changes in one or more condition-specific cell variables for one or more genetic variants, comprising: a. an input layer configured to receive as input (i) a set of reference features extracted from a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) reference sequence, and (ii) a set of variant features extracted from a DNA or RNA variant sequence, wherein the DNA or RNA variant sequence comprises the one or more genetic variants of the DNA or RNA reference sequence; and b. at least two connected layers configured to: i. process the set of variant features to quantify one or more condition-specific variant cell variables; ii. process the set of reference features to quantify one or more condition-specific reference cell variables; and iii. generate the set of variant-induced changes in the one or more condition-specific cell variables at least in part by processing the one or more condition-specific reference cell variables with the one or more condition-specific variant cell variables.
Claim 14: The method of claim 13, wherein the set of variant features is extracted from the DNA or RNA variant sequence by generating: a. a binary matrix with 4 rows and a number of columns equal to a length of the DNA or RNA variant sequence or the DNA or RNA reference sequence, wherein each column contains a bit indicating the nucleotide value at the correspondingposition in the DNA or RNA variant sequence or the DNA or RNA reference sequence;b. a set of features computed using one or more layers of an autoencoder other than the input and output layers of the cell variable predictor; or c. a set of features that correspond to one or more of: RNA secondary structures, nucleosome positions, and retroviral repeat elements.
Claim 3: The system of claim 1, wherein the set of variant features and the set of reference features comprise: a. a binary matrix with 4 rows and a number of columns equal to a length of the DNA or RNA variant sequence or the DNA or RNA reference sequence, wherein each column contains a bit indicating the nucleotide at the corresponding position in the DNA or RNA variant sequence or the DNA or RNA reference sequence; b. a set of features computed using one or more layers of an autoencoder other than the input and output layers of the deep neural network; or c. a set of features comprising one or more of RNA secondary structures, nucleosome positions, and retroviral repeat elements.
Claim 15: The method of claim 13, further comprising computing, using the cell variable predictor, probabilities for discrete levels of the condition-specific cell variable, wherein each of the set of variant-induced changes is computed by:a. summing an absolute difference between the computed probabilities for the condition-specific reference cell variable and the condition-specific variant cell variable;b. summing a Kullback-Leibler divergence between the computed probabilities of the condition-specific reference cell variable and the condition-specific variant cell variable for each condition; or c. computing an expected value of the condition-specific reference cell variable and the condition-specific variant cell variable, and subtracting the expected value of the condition-specific reference cell variable from the expected value of the condition-specific variant cell variable.
Claim 5: The system of claim 1, wherein generating each of the set of variant-induced changes comprises: a. summing an absolute difference between probabilities for discrete levels of the condition-specific reference cell variables and the condition-specific variant cell variables; b. summing a Kullback-Leibler divergence between probabilities of the condition-specific reference cell variables and the condition-specific variant cell variables for each condition; or c. subtracting expected values of the condition-specific reference cell variables from expected values of the condition-specific variant cell variables.
Claim 16: The method of claim 11, wherein the deep neural network comprises a convolutional neural network, a recurrent neural network, or a long-term short-term memory recurrent neural network.
Claim 4: The system of claim 1, wherein the deep neural network comprises: a. a convolutional neural network; b. a recurrent neural network; or c. a long short-term memory recurrent neural network.
Claim 17: The method of claim 11, further comprising combining the set of variant- induced changes in the condition-specific cell variable to compute a single numerical variant score for the genetic variant, the single numerical variant score computed by:a. outputting the score for a fixed condition;b. summing the variant-induced changes across a plurality of conditions; orc. computing the maximum of the absolute variant-induced changes across a plurality of conditions.
Claim 6: The system of claim 1, further comprising a computer processor programmed to combine the set of variant-induced changes in the one or more condition-specific cell variables to compute a numerical variant score for each of the one or more genetic variants at least in part by: a. outputting a score for a fixed condition; b. summing variant-induced changes across a plurality of conditions; or c. computing a maximum of absolute variant-induced changes across a plurality of conditions.
Claim 19: The method of claim 11, further comprising computing, for a pair of genetic variants, a distance between the two genetic variants in the pair by summing the output of a nonlinear function applied to a difference between the change in the condition-specific cell variable for the first of the two genetic variants and the change in the condition- specific cell variable for the second of the two genetic variants.
Claim 8: The system of claim 1, further comprising a computer processor programmed to compute, for each of one or more pairs of the one or more genetic variants, a distance between two genetic variants in each of the one or more pairs of genetic variants, at least in part by summing an output of a nonlinear function applied to a difference between a change in the condition-specific cell variable for a first of the two genetic variants and a change in the condition-specific cell variable for a second of the two genetic variants.
Conclusion
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/K.N.A./
Examiner, Art Unit 1687
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685