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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. IT102021000006353 filed March 17th, 2021.
Acknowledgment is made of applicant's prior application PCT/IB2022/052386 filed March 16th, 2022.
The effective filing date is March 17th, 2021.
Status of Claims
Claims 1-18 are currently pending and examined on the merits.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (claims 1-18 are drawn to a method) (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea (reasonings in [brackets]):
Claim 1 states:
Defining a set of variants…[which is a mental step, i.e. can be performed with pen and paper]
Determining situations which can occur…[mental step]
calculating a pathogenicity index or score… [which is a mathematical process of a mathematical calculation]
describing phenotypic traits of a patient…[mental step]
calculating or preparing input information for the pathogenicity determination…[mathematical calculation]
processing said input information for the pathogenicity determination by the at least trained algorithm…[mathematical calculation]
Claim 2 states:
….said digenicity or oligogenicity features comprise two digenicity features calculated with reference to the two genes; [mathematical calculation]
said variant-related features comprise two features, one for each of the two genes, calculated, for each gene considered, as a combination of the pathogenicity scores of the variant(s) referring to said gene. [mathematical calculation]
Claim 3 states:
describing phenotypic traits through terms deriving from an ontology…[mental step]
Claim 4 states:
wherein the description of phenotypic traits by HPO is represented by a direct acyclic graph. [mathematical calculation]
Claim 5 states:
providing a list of defined variants…[mental step]
processing said list of possible combinations, generated by the first pre-processing algorithm, as well as said phenotypic traits of the patient, in the terms described, by a second pre-processing algorithm, to calculate…[mathematical calculation]
Claim 6 states:
wherein said gene-phenotype association features comprise, for each of the genes considered:
[[-]] an index or measure of similarity between the set of standardized phenotypic terms describing the patient and the set of standardized phenotypic terms associated with the gene; and [mental step]
[[- ]] a probability of association between the single mutated gene and the set of phenotypes which describes the patient using gene expression data, or starting from a transcriptomics analysis. [mathematical calculation]
Claim 7 states:
wherein said digenicity or oligogenicity features, for each combination of genes, comprises:
[[-]] a measure or index of biological distance, which represents how much the proteins produced by the two genes are involved or not involved in the same biological processes with a certain degree of interaction, or which represents the a degree of functional association between two or more genes, articulated according to a series of levels of evidence; and [mathematical calculation]
[[-]] a measure or index of similarity between the phenotypic sets associated with the genes, regardless of the phenotypic traits describing the patient. [mental step]
Claim 9 states:
a further preliminary training step…[mathematical calculation]
Claim 10 states:
training a plurality of trained algorithms, or classifiers…[mathematical calculation]
and evaluating performance of each classifier…[mathematical calculation]
Claim 11 states:
selecting a subset of trained algorithms, for the processing, based on said preliminary performance evaluation…[mental step]
Claim 12 states:
processing the information by all the trained algorithms or classifiers used in the training, or by the trained algorithms or classifiers selected during the training…[mental step]
Claim 13 states:
said trained algorithms or classifiers comprise one or more of the following:
Random Forest, AdaBoost, Gradient Boosting, Logistic Regression, Multi-layer Perceptron, Decision Tree. [mathematical calculation]
Claim 15 states:
comprising the further step of balancing the data, in the case of data resources unbalanced towards negative cases, to obtain a balanced distribution between the two classes, wherein said data balancing step is performed using the oversampling methodology…[mathematical calculation]
Claim 16 states:
said output information comprises an estimated pathogenicity probability of at least one combination of digenic or oligogenic variants considered, [mathematical calculation]
or of a plurality of combinations of variants among the digenic or oligogenic variants considered, [mental step]
or of all the combinations of digenic or oligogenic variants considered. [mental step]
Claim 17 states:
wherein the output information further comprises, for each combination of digenic or oligogenic variants, a binary result representative of whether the combination of digenic or oligogenic variants is pathogenic or benign, obtained by comparing the pathogenic probability estimated for the digenic or oligogenic variant with a respective threshold, associated with the combination itself of digenic or oligogenic variants. [mental step]
Claim 18 states:
wherein said output information comprises the identification of a most relevant oligogenic combination of variants, [mental step]
or a best digenic pair of variants, from a set of combinations of oligogenic or digenic variants considered, which has a most relevant correlation with a set of phenotypes describing the patient. [mental step]
The claims also recite limitations which amount to data which further limit the claims from which they originate:
Claim 2 states:
and wherein: [[- ]] said gene-phenotype association features comprise four features, two for each of the two genes;
Claim 14 states:
training set is generated from two data resources:
[[-]] a first data resource comprising examples of positive (i.e., pathogenic) inputs, which represent combinations of digenic variants known to cause a digenic disease, in turn characterized by a specific set of phenotypes;
[[- ]] a second data resource comprising examples of negative (i.e., benign) inputs, consisting of combinations of digenic variants of healthy subjects.
The claims recite an abstract idea of analyzing genomic sequencing data (See MPEP 2106.07(a)).
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas.
There are no additional limitations that indicate that these claims require anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claim(s) 1-18 recite(s) an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
Claim 1 states:
a training dataset of known cases
Claim 9 states:
a first subset being used as a training database, or training set and a second subset being used as a validation database or test set.
Claim 14 states:
said first training subset, or training set is generated from two data resources:
a first data resource comprising examples of positive inputs, which represent combinations of digenic variants known to cause a digenic disease, in turn characterized by a specific set of phenotypes;
a second data resource comprising examples of negative inputs, consisting of combinations of digenic variants of healthy subjects.
There are no limitations that indicate that the claimed analysis engine or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. As such, claims 1-18 is/are directed to an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite the following additional elements:
Claim 1 states:
a training dataset of known cases
Claim 9 states:
a first subset being used as a training database, or training set and a second subset being used as a validation database or test set.
Claim 14 states:
said first training subset, or training set is generated from two data resources:
a first data resource comprising examples of positive inputs, which represent combinations of digenic variants known to cause a digenic disease, in turn characterized by a specific set of phenotypes;
a second data resource comprising examples of negative inputs, consisting of combinations of digenic variants of healthy subjects.
Regarding claims 1-18, The steps of obtaining sequencing and/or training data and performing sample collection do not integrate the abstract idea into a practical application and constitutes an insignificant extra-solution activity (i.e., data gathering and presentation), which does not impose a meaningful limit on the abstract idea. As discussed above, there are no additional limitations to indicate that the claimed analysis engine requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. 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. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims.
Furthermore, the additional elements recited in the claims amount to well-understood, routine and conventional activity, as evidenced by Jeroen van den Akker et al. (BMC Genomics. 2018 Apr 17;19:263) who teaches a machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing, and of Patrick Deelen et al. (Nat Commun 10, 2837 (2019) who teaches Improving the diagnostic yield of exomes equencing by predicting gene–phenotype associations using large-scale gene expression analysis.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-18 is/are not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 3, 4, 5, 8-10, 12-14, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sofia Papadimitriou et al. (Proc. Natl. Acad. Sci. U.S.A. 116 (24) 11878-11887) in view of Patrick Deelen et al. (Nat Commun 10, 2837 (2019).
Regarding claim 1, Papadimitriou et al. teaches a machine-learning method able to predict the pathogenicity of variant combinations in gene pairs, based on pathogenic data, termed the Variant
Combinations Pathogenicity Predictor (VarCoPP) (Abstract, pg. 11878). Papadimitriou et al. discloses use of a database (Digenic Diseases Database, DIDA) containing variant combinations, including digenic variants on pg. 11879, Fig. 1 (re: clm. 1, A computer implemented method to determine pathogenicity of combinations of digenic or oligogenic variants, in relation to disease comprising the steps of…)
Papadimitriou et al. further teaches in the caption of Fig. 3 the methodology procedure for the construction of the VarCoPP and the validation process, including a Bilocus variant combination represented always using four alleles (two alleles for gene A and two alleles for gene B), including wild-type alleles, stating:
“(A) Genes and variants were filtered in the same way for both the 1KGP and DIDAv1. Individuals of the 1KGP carrying DIDAv1 combinations, as well as the overlapping combinations, were filtered out. Exonic variants [single-nucleotide polymorphism (SNPs) and indels] were used with a MAF frequency of ≤3%, including intronic and synonymous variants close to the exon edges (±13 nucleotides). The genes involved in the procedure were only confirmed protein-coding genes, following the gene types present in the DIDAv1. (B) Bilocus variant combination is represented always using four alleles (two alleles for gene A and two alleles for gene B), including wild-type alleles.” (pg. 11881).
Papadimitriou et al. states a consideration of variant zygosity (e.g. homozygous variant with both alleles of the gene containing the same variant information) (pg. 11881; re: clm. 1, … defining a set of variants, the pathogenicity of which must be determined, wherein said variants refer to mutations present in one or both alleles of a respective gene of at least two genes, each of the genes being associated with two respective alleles..)
Papadimitriou et al. further teaches a prediction of possible variant combinations, as stated in pg. 11886 (Tool and code availability): “This online tool annotates a list of given variants (single-nucleotide polymorphisms and indels) and scores all possible bilocus variant combinations present in that list, including those with heterozygous compound variants.” (re: clm. 1, … determining situations which can occur, regarding the presence or absence of said variants in the alleles of said at least two genes, wherein each situation is associated with a respective combination in which each variant is present in a respective subset of alleles, among all possible subsets of alleles of all the genes considered, or each variant is present in all the alleles of all the genes considered…)
Papadimitriou et al. further teaches a per-gene pathogenicity score as disclosed on pg. 11881, Fig. 3: “Different variant alleles inside the same gene were ordered based on their CADD pathogenicity score, with the variant present in the first allele of that gene always having the highest CADD score. (C) Initial number of biological features used for classification was 21, but the final selected and more relevant features were filtered to 11. These included information at the variant level [Flex1 and Hydr1 (i.e., flexibility and hydrophobicity amino acid differences of the first variant allele of gene A), as well as CADD1, CADD2, CADD3, and CADD4, (i.e., the CADD scores of the four different alleles of a bilocus combination)], gene level [RecA, RecB, HI_A, HI_B (i.e., recessiveness and haploinsufficiency probabilities for gene A and gene B)], and gene-pair level [BiolDist (i.e., biological distance, a metric of biological relatedness between two genes of a pair based on protein–protein interaction information)].” (re: clm. 1, … for each of said defined situations, or for each combination and for each gene, calculating a pathogenicity index or score, adapted to estimate how much the one or more respective variants modify functioning of the respective gene…)
Papadimitriou et al. further teaches digenicity features that capture gene-gene interaction in Fig. 3, pg. 11881 , stating: “These included information at the variant level [Flex1 and Hydr1 (i.e., flexibility and hydrophobicity amino acid differences of the first variant allele of gene A), as well as CADD1, CADD2, CADD3, and CADD4, (i.e., the CADD scores of the four different alleles of a bilocus combination)], gene level [RecA, RecB, HI_A, HI_B (i.e., recessiveness and haploinsufficiency probabilities for gene A and gene B)], and gene-pair level [BiolDist (i.e., biological distance, a metric of biological relatedness between two genes of a pair based on protein–protein interaction information)].” (re: clm. 1, … digenicity or oligogenicity features, calculated for each of said combinations of genes, adapted to capture interaction between the genes forming each combination…). These features are additionally a priori (re: clm. 1, … a priori property features of the genes, calculated for each of said genes considered…) calculated based on pathogenicity scores (re: clm. 1, … variant-related features, calculated, for each gene considered, based on said pathogenicity indices or scores calculated in relation to all the combinations considered…)
Papadimitriou et al. further teaches providing input for pathogenicity to a machine learning algorithm as disclosed in the materials and methods of pg. 11886: “We used the scikit-learn version 0.18.1 implementation (60) of the Random Forest (RF) algorithm (53) as a classifier…” (re: clm. 1, … providing said input information for the pathogenicity determination to at least one trained algorithm; processing said input information for the pathogenicity determination by the at least trained algorithm,
wherein said trained algorithm is an algorithm trained by artificial intelligence and/or machine learning techniques…). On the same page, Papadimitriou et al. discloses that the algorithm was trained on data from a dataset of known cases: “Stratification of the 1KGP Data and Training. To train the VarCoPP, we created 500 balanced sets (Fig. 3D), each consisting of 200 1KGP bilocus combinations of randomly chosen gene pairs and the 200 disease-causing combinations of the DIDAv1…” (re: clm. 1, … wherein said algorithm is trained in a preliminary training step, based on a training dataset of known cases, providing said input information calculated for each of known cases to algorithm to be trained, and training the algorithm based on the knowledge of the pathogenicity/benignity of the respective known cases…)
Papadimitriou et al. further teaches output of data related to pathogenicity of mutations on pg. 11880, stating the returns of VarCoPP (re: clm. 1, … obtaining output information from the trained algorithm, representing the pathogenicity of each of the combinations of variants or mutations considered.)
Papadimitriou et al. does not explicitly disclose a patient phenotype description in standardized terms (re: clm. 1, … describing phenotypic traits of a patient, by standardized phenotypic terms…), nor superimposable gene-phenotype traits of a patient (re: clm. 1, … gene-phenotype association features, calculated individually for each of the genes considered, and adapted to measure how much said phenotypic traits of the patient are superimposable to phenotypes already known to be associated with the single gene..)
Deelen et al. teaches GeneNetwork Assisted Diagnostic Optimization (GADO), which discloses in Fig. 1, pg. 3 a per-patient encoding of phenotypic features using human phenotype ontology terms (re: clm. 1, …describing phenotypic traits of a patient, by standardized phenotypic terms, comprising standardized information adapted to describe phenotypic anomalies found in the patient…). Deelen et al. further teaches in the results on pg. 3, and the methods on pg. 11 (gene co-regulation and function predictions) prioritization Z-scores of HPO disease phenotypes used to rank genes (pg. 3) (re: clm. 1, …gene-phenotype association features, calculated individually for each of the genes considered, and adapted to measure how much said phenotypic traits of the patient are superimposable to phenotypes already known to be associated with the single gene…)
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Papadimitriou et al. and Deelen et al., the examiner finds that the combination of VarCoPP as taught by Papadimitriou et al. with GADO as taught by Deelen et al. represents some Teaching, suggestion or motivation in the prior art that would have lead one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
One of ordinary skill in genomics would have been motivated to combine the teachings of Papadimitriou et al. and Deelen et al. as the combination would produce a stronger pathogenicity determination method. In support of this motivation, Papadimitriou et al. discloses limitations of their method on pg. 11886, stating that their tool is not phenotypically driven. Therefore, it would benefit from combination with the teachings of Deelen et al.
One of ordinary skill in genomics would have success in combining the teachings of Papadimitriou et al. and Deelen et al. as both arts exist in the same field (computational genomics). Therefore, Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 3, Deelen et al. teaches a description of standardized phenotypic traits using HPO terms (Methods, pg. 11, re: clm. 3, …describing phenotypic traits through terms deriving from an ontology which provides a standardized vocabulary of the phenotypic anomalies found in human diseases, or through the HPO terms, deriving from the resource Human Phenotype Ontology.) Therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitation of claim 3.
Regarding claim 4, Deelen et al. teaches that GADO utilizes hierarchical structure to arrange HPO terms on pg. 7, Fig. 4. Deelen et al. discloses the prioritization using HPO terms such that each term has at least one parent HPO term that describes a more generic phenotype and thus has also more genes assigned to it. Therefore, if an HPO term cannot be used, GADO will make suggestions for suitable parental terms. This hierarchal method reads on and functions as directed acyclic graph as discloed by the applicant’s specification (0145; re: clm. 4, …wherein the description of phenotypic traits by HPO is represented by a direct acyclic graph.) Therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitation of claim 4.
Regarding claim 5, Papadimitriou et al. discloses a method of annotating a submitted variant list , selecting features, disclosure of the possible combinations related to said variants, disease scoring, and validation in Fig. 3, pg. 11881 (with support from Tool and Code availability, Feature Selection and interpretation pg. 11886). The combination of Papadimitriou et al. and Deelen et al. disclose possible combination of patient-related traits at the feature selection/compute/interpretation stage (re: clm. 5, … after the step of calculating, for each variant identified in the patient, a pathogenicity index or score, and after the step of describing a patient's phenotypic traits, the following further steps:
providing a list of defined variants, with the respective genes and respective calculated pathogenicity indices, to a first pre-processing algorithm, configured to generate a list of possible combinations;
processing said list of possible combinations, generated by the first pre-processing algorithm, as well as said phenotypic traits of the patient, in the terms described, by a second pre-processing algorithm, to calculate or prepare input information for the pathogenicity determination.). Therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitations of claim 5.
Regarding claim 8, Papadimitriou et al. discloses in Fig. 3C pg. 11881 haploinsufficiency probabilities per gene amongst selected features (HL_A, HL_B; re: clm. 8, … one or more measures or indices representing how sensitive each gene is or is not to a gene dosage…) Therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitations of claim 8.
Regarding claim 9, Deelen et al. teaches gene pairs divided into training and testing sets on pg. 4 (Table 1) and discloses details of the training/test split on pg. 7 (re: clm. 9, … before using said trained algorithms, a further preliminary training step, carried out based on two subsets of said training dataset containing data referring to known cases,
a first subset being used as a training database, or training set and a second subset being used as a validation database or test set.) Therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitations of claim 9.
Regarding claim 10, Papadimitriou et al. teaches 500 individual predictors trained on a balanced set as disclosed in the methods on pg. 11886 (Stratification of the 1KGP Data and Training, re: clm. 10, … the preliminary training step comprises training a plurality of trained algorithms, or classifiers, and evaluating performance of each classifier.)
Regarding claim 12, Papadimitriou et al. teaches that “VarCoPP is an ensemble predictor (48), meaning that it is composed of a large number (500) of individual predictors that each try to solve the same task.” (pg. 11880, re: clm. 11, … once the preliminary training step has been completed, the step of processing the input information for the pathogenicity determination comprises processing the information by all the trained algorithms or classifiers used in the training, or by the trained algorithms or classifiers selected during the training..) Therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitations of claim 12.
Regarding claim 13, Papadimitriou et al. discloses Random Forest on pg. 118886 (re: clm. 13, … wherein said trained algorithms or classifiers comprise one or more of the following: Random Forest, AdaBoost, Gradient Boosting, Logistic Regression, Multi-layer Perceptron, Decision Tree.) Therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitations of claim 13.
Regarding claim 14, Papadimitriou et al. teaches on pg. 11879 and pg. 11880 use of DIDAv1, and in so, 213 curated disease-related variant combinations from scientific publications. On pg. 11880, to represent unaffected individuals, Papadimitriou et al. removes individuals from the 1KGP neutral set (re: clm. 14, … a first data resource comprising examples of positive inputs, which represent combinations of digenic variants known to cause a digenic disease…). As previously disclosed, Deelen et al. characterizes phenotype-gene relationships; therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitations of claim 14.
Regarding claim 16 Papadimitriou et al. discloses on pg. 11880: “For each variant combination given as input, the VarCoPP generates a final majority class label (“pathogenic” or “neutral”) and two prediction scores: (i) a classification score (CS) (i.e., the median probability that the variant combination is pathogenic)…” (re: clm. 16, … wherein said output information comprises an estimated pathogenicity probability of at least one combination of digenic or oligogenic variants considered, or of a plurality of combinations of variants among the digenic or oligogenic variants considered, or of all the combinations of digenic or oligogenic variants considered.) Therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitations of claim 16.
Regarding claim 17, on pg. 11881 and 11882 (Fig. 4), Papadimitriou et al. discloses a binary result of whether the combination of variants leads to pathogenicity, as disclosed in the support vs. classification score and statistical confidence zones (re: clm. 17, … for each combination of digenic or oligogenic variants, a binary result representative of whether the combination of digenic or oligogenic variants is pathogenic or benign, obtained by comparing pathogenic probability estimated for the digenic or oligogenic variant with a respective threshold, associated with the combination of digenic or oligogenic variants.) Therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitations of claim 17.
Regarding claim 18, on pg. 11881 and 11882 (Fig. 4), Papadimitriou et al. discloses a binary result of whether the combination of variants leads to pathogenicity. Deelen et al. discloses phentoypes describing the patient; therefore, the combination of Papadimitriou et al. and Deelen et al. teach the limitations of claim 18.
Claim(s) 7 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Papadimitriou et al. in view of Deelen et al. as applied to claims 1, 3, 4, 5, 8-10, 12-14, 16-18 above in view of Souhrid Mukherjee et al. (bioRxiv 2020.05.31.125716).
Papadimitriou et al. in view of Deelen et al. is applied to claims 1, 3, 4, 5, 8-10, 12-14, 16-18 above.
Regarding claim 7, Papadimitriou et al. discloses biological distance in Fig. 1, pg. 11881 (“BioDist”; re: clm. 7, … a measure or index of biological distance, which represents how much the proteins produced by the two genes are involved or not involved in same biological processes with a certain degree of interaction, or which represents a degree of functional association between two or more genes, articulated according to a series of levels of evidence; and…)
Papadimitriou et al. in view of Deelen et al. does not disclose similarity between phenotypic sets regardless of traits describing a patient.
Mukherjee et al. discloses independent phenotypic similarity on line 465-469 and in figure 1 (re: clm. 7, … a measure or index of similarity between the phenotypic sets associated with the genes, regardless of the phenotypic traits describing the patient.)
Applying the KSR standard to Papadimitriou et al., Deelen et al., and Mukherjee et al., the examiner finds that the combination of VarCoPP as taught by Papadimitriou et al. with GADO as taught by Deelen et al. and similarity sets as taught by Mukherjee et al. represents some Teaching, suggestion or motivation in the prior art that would have lead one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
One of ordinary skill in genomics would have been motivated to combine the teachings of to Papadimitriou et al., Deelen et al., and Mukherjee et al., as the combination would produce a stronger pathogenicity determination method. In support of this motivation, Papadimitriou et al. discloses limitations of their method on pg. 11886, stating that their tool is not phenotypically driven. Therefore, it would benefit from combination with the teachings of Deelen et al. and Mukherjee et al.
One of ordinary skill in genomics would have success in combining the teachings of to Papadimitriou et al., Deelen et al., and Mukherjee et al., as all arts exist in the same field (computational genomics). Therefore, Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 11, Mukherjee et al. discloses on line 237 the best performing model used for processing variants (re: clm. 11, … further comprising the step of selecting a subset of trained algorithms, for the processing, based on said preliminary performance evaluation.). Therefore, the combination of to Papadimitriou et al., Deelen et al., and Mukherjee et al., teaches the limitation of claim 11.
Claims 2 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Papadimitriou et al. in view of Deelen et al. and Mukherjee et al. as applied to claims 1, 3, 4, 5, 7-11, 12-14, 16-18 above in view of Yue Deng et al. PLoS ONE 10(2): e0115692. (2015)
Papadimitriou et al. in view of Deelen et al. and Mukherjee et al. is applied to claims 1, 3, 4, 5, 7-11, 12-14, 16-18 above.
Regarding claim 2, Papadimitriou et al. discloses a four-allele representation encoding zygosity in Fig. 3 and on pg. 11886 (re: clm. 2, … in said step of determining possible situations, the identifiable situations comprise, for each of the variants considered and for each of the two genes, a situation of simple heterozygosity, in which the variant is present in only one of the two alleles, and a situation of compound heterozygosity, in which two different variants are present and each of the two variants is present on a respective allele, and a further combination of homozygosity, in which a same variant is present in both alleles of the gene…) Papadimitriou et al. also discloses per gene pathogenicity scores as well as haploinsufficiency, gene damage index, and recessiveness in Fig. 3 (pg. 11881, re: clm. 2, …said a priori property features of the genes comprise six features, three for each gene… said variant-related features comprise two features, one for each of the two genes, calculated, for each gene considered, as a combination of the pathogenicity scores of the variant(s) referring to said gene.)
Papadimitriou et al. in view of Deelen et al . and Mukherjee et al. disclose two digenicity features with reference to two genes: Papadimitriou et al. discloses BioDist (Fig. 3) and Deelen et al. disclose HPO (Fig. 1) (re: clm. 2, …said digenicity or oligogenicity features comprise two digenicity features calculated with reference to the two genes…)
Papadimitriou et al. in view of Deelen et al . and Mukherjee et al. do not teach four features (re: clm. 2, … said gene-phenotype association features comprise four features, two for each of the two genes…)
Deng et al. discloses HPOSim, an R package for analyzing phenotypic similarity for genes and diseases based on HPOdata. HPOSim can calculate a phenotype similarity score as the applicant describes in the specification: “…it is possible to calculate a phenotypic similarity score which represents how much each gene of the pair is associated with the phenotypes manifested by the individual…”(re: clm. 2, … said gene-phenotype association features comprise four features, two for each of the two genes…).
Applying the KSR standard to Papadimitriou et al., Deelen et al., Mukherjee et al., and Deng et al., the examiner finds that the combination of VarCoPP as taught by Papadimitriou et al. with GADO as taught by Deelen et al., similarity sets as taught by Mukherjee et al and similarity scores as taught by Deng et al. represents some Teaching, suggestion or motivation in the prior art that would have lead one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
One of ordinary skill in genomics would have been motivated to combine the teachings of Papadimitriou et al., Deelen et al., Mukherjee et al., and Deng et al. as the combination would produce a stronger pathogenicity determination method. In support of this motivation, The applicant states Deng et al.’s methodology is used for pathogenicity exploration in variants in paragraph 0081-0082 of the specification.
One of ordinary skill in genomics would have success in combining the teachings of Papadimitriou et al., Deelen et al., Mukherjee et al., and Deng et al. as all arts exist in the same field (computational genomics). Therefore, Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 6, Deng et al. discloses patient-set and gene-related HPO similarity indexing as disclosed in 0081-0082 of Applicant’s specification (re: clm. 6, … an index or measure of similarity between the set of standardized phenotypic terms describing the patient and the set of standardized phenotypic terms associated with the gene; and…). Deelen et al. discloses gene-phenotype association probabilities also as disclosed in the applicant’s specification, paragraph 0083 (“For example, the Gado (GeneNetwork Assisted Diagnostic Optimization) feature can be used…”, re: clm. 6, … an index or measure of similarity between the set of standardized phenotypic terms describing the patient and the set of standardized phenotypic terms associated with the gene; and
a probability of association between the single mutated gene and the set of phenotypes which describes the patient using gene expression data, or starting from a transcriptomics analysis.). Therefore, the combination of to Papadimitriou et al., Deelen et al., Mukherjee et al., and Deng et al. teach the limitations of claim 6.
Claim 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Papadimitriou et al. in view of Deelen et al., Mukherjee et al. and Deng et al. as applied to claims 1-14, 16-18 above in view of Gijuan Gao et al. (Front. Genet. 11:820. 2020).
Papadimitriou et al. in view of Deelen et al., Mukherjee et al. and Deng et al. is applied to claims 1-14, 16-18 above.
Papadimitriou et al. in view of Deelen et al., Mukherjee et al. and Deng et al. do not teach SMOTE (re: clm. 15, … wherein said data balancing step is performed using the oversampling methodology or by SMOTE methodology.)
Gao et al. teaches a classification model to detect orphan and non-orphan genes in
unbalanced distribution datasets using SMOTE (Abstract, re: clm. 15, … wherein said data balancing step is performed using the oversampling methodology or by SMOTE methodology.)
Applying the KSR standard to Papadimitriou et al., Deelen et al., Mukherjee et al., Deng et al., and Gao et al. the examiner finds that the combination of VarCoPP as taught by Papadimitriou et al. with GADO as taught by Deelen et al., similarity sets as taught by Mukherjee et al, similarity scores as taught by Deng et al. and SMOTE as taught by Gao et al. represents some Teaching, suggestion or motivation in the prior art that would have lead one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
One of ordinary skill in genomics would have been motivated to combine the teachings of Papadimitriou et al., Deelen et al., Mukherjee et al., Deng et al. and Gao et al. as the combination would produce a stronger pathogenicity determination method. In support of this motivation, The applicant states one of ordinary skill in the art would implement SMOTE “…in the case of data resources unbalanced towards negative cases, to obtain a balanced distribution between the two classes..”
One of ordinary skill in genomics would have success in combining the teachings of Papadimitriou et al., Deelen et al., Mukherjee et al., Deng et al. Gao et al. as all arts exist in the same field (computational genomics). Therefore, Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Conclusion
No claims are allowed.
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/J.T.S./Examiner, Art Unit 1686
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