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. AU2020904770 filed December 21st 2020.
Acknowledgment is made of applicant's prior application PCT/AU2021/051511 filed December 17th 2021.
The effective filing date is December 21st 2020.
Status of Claims
Claims 18-36 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 18-36 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 18-36 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]):
18.
b) dividing the data of step (a) into a reference population and a validation population; and [which is a mental step, i.e. can be performed with pen and paper]
c) analyzing the data obtained, wherein the analysis includes: i. calculating the genotype plus genotype x environment (GGE) principle component (PC) for the data of the reference population; [which is a mathematical concept of a mathematical calculation]
ii. identifying polymorphisms in the genetic data of the reference population and calculating the polymorphism effect for each PC; [mathematical calculation]
iii. calculating a genomic estimated breeding value (GEBV) for each genotype of the validation population using the calculated polymorphism effect for each PC; and [mathematical calculation]
iv. converting each GEBV into a phenotypic GEBV (pGEBV) by multiplying the GEBV with an inverse of a rotation matrix, wherein the rotation matrix is (e x e), and wherein e is the number of environments in the validation population. [mathematical calculation]
19. [New] The method according to claim 18, wherein the GEBV is a G matrix (n x e), wherein n is the number of validation individuals and e is the number of environments in the validation population [mathematical calculation]
20. [New] The method according to claim 18, wherein calculating each pGEBV is based on Equation 3 as follows: pGEBV = G x R-1 where G is an (n x e) matrix of GEBVs for the GGE PCs scaled by multiplying each PC with its standard deviation; n is the number of validation individuals; e is the number of environments; and R-1 is an inverse of the rotation matrix (e x e) or an environment coordinate matrix scaled by dividing each column on a standard deviation of the correspondence PC. [mathematical calculation]
22. [New] The method according to claim 18, wherein the polymorphisms include a single nucleotide polymorphism [mathematical calculation]
23. [New] The method according to claim 18, wherein calculating the polymorphism effect for each PC utilises a Bayesian Ridge Regression model [mathematical calculation]
28. [New] The method according to claim 18, wherein the method is used for selecting a genotype for producing an improved organism in a given environment, by one or more of the following:
a. identifying a genotype with the pGEBV which correlates highest with a given environment; [mental step]
b. clustering the reference environments into mega environments and then calculating multiple averages of pGEBVs per genotype for each mega environment, [mathematical calculation]
and identifying a genotype from the average pGEBV that correlates highest with the mega environment which best matches the given environment; and [mental step]
c. identifying a genotype from the following steps:
i. calculating a singular value decomposition for a symmetric pairwise correlation matrix (U matrix) between environments including the reference environments and the selected environment (e+1 x e+1); [mathematical calculation]
ii. calculating a correlation between the U matrix obtained in step (i), and the rotation matrix (e x e); [mathematical calculation]
iii. reordering columns of the U matrix to match an order of the rotation matrix of step (ii), reversing the sign of negative correlations; and [mathematical calculation]
iv. applying Equation 3 as follows: pGEBV = G xR-1 using the reordered U matrix instead of the R matrix, where G is an (n x e) matrix of GEBVs for the GGE PCs scaled by multiplying each PC with its standard deviation; n is the number of validation individuals; and e is the number of environments; and adding a column of zeros to the end of the G matrix to match its dimensions; and d. based thereon, selecting an identified genotype. [mathematical calculation]
29. [New] A method according to claim 28, further comprising locating the selected genotype to said given environment. [mental step]
30 [New] A method for selecting a genotype for producing an improved organism in a given environment, wherein the method comprises the steps of:
e. performing a method for determining phenotypic genomic estimated breeding values (pGEBV) according to claim 18 [mental step]
; and performing one or more of:
i. identifying a genotype with a pGEBV which correlates highest with the given environment; [mental step]
ii. clustering the reference environments into mega environments and then calculating multiple averages of pGEBVs per genotype for each mega environment, and [mathematical calculation]
identifying a genotype from the average pGEBV that correlates highest with the mega environment which best matches the given environment; and [mental step]
iii. identifying a genotype from the following steps:
1. calculating a singular value decomposition for a symmetric pairwise correlation matrix (U matrix) between environments including the reference environments and the given environment (e+1 x e+1); [mathematical calculation]
2. calculating a correlation between the U matrix obtained in step 1, and the rotation matrix (e x e); [mathematical calculation]
3. reordering the columns of the U matrix to match an order of the rotation matrix (of step 2, reversing the sign of negative correlations, and applying Equation 3 as follows:pGEBV= G x R-1 using the reordered U matrix instead of the R matrix, where G is an (n x e) matrix of GEBVs for the GGE PCs scaled by multiplying each PC with its standard deviation; n is the number of validation individuals; and e is the number of environments; and adding a column of zeros to the end of the G matrix to match its dimensions; [mathematical calculation]
and f. based thereon, selecting an identified genotype. [mental step]
31. [New] The method according to claim 30, wherein reordering columns of the U matrix includes ordering the column of the U matrix with the highest absolute correlation coefficient value with a first column of the rotation matrix (e x e). 32. [New] The method according to claim 30, wherein the given environment is a new environment not included in the reference population.
33. [New] The method according to claim 30, further comprising locating the selected genotype to said given environment. [mental step]
35. [New] The method according to claim 30, wherein the step of calculating a singular value decomposition for a symmetric pairwise correlation matrix between environments uses a non- linear iterative partial least squares (NIPALS) algorithm to approximate missing correlation coefficients. [mathematical calculation]
36. [New] A method for producing an improved organism, comprising the steps of:
g. performing a method for determining phenotypic genomic estimated breeding values (pGEBV) according to claim 18; [mathematical calculation]
h. performing a method for selecting a genotype for producing an improved organism according to claim 30; and [mental step]
i. locating the organism comprising said selected genotype in said given environment. [mental step]
The claims also recite the following limitations which further limit the claim from which they derive by limiting the form or type of data:
21. [New] The method according to claim 18, wherein the data obtained for the population of organism genotypes is from a plurality of mega-environments.
24. [New] The method according to claim 18, wherein the organism is a plant.
25. [New] The method according to claim 24, wherein the phenotypic data includes records on yield.
26. [New] The method according to claim 25, wherein the environmental data includes irrigation and/or rain exposure.
27. [New] The method according to claim 18, wherein the environmental data includes irrigation and/or rain exposure.
34. [New] The method according to claim 30, wherein the organism is a plant.
The claims recite an abstract idea of analyzing genomic sequences (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) 18-36 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 an additional element 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 18 states:
a) obtaining genetic, phenotypic, and environmental data for a population of organism genotypes;
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 18-36 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 18 states:
a) obtaining genetic, phenotypic, and environmental data for a population of organism genotypes;
Regarding claims 18-36, The steps of obtaining sequencing 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 Yugandhar Poli et al. ( Sci Rep 8, 15530 (2018).) who teaches Genotype × Environment interactions of Nagina22 rice mutants for yield traits under low phosphorus, water limited and normal irrigated conditions.
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 18-36 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) 18-22, 24 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meuwissen et al. (Genetics 157: 1819–1829 (April 2001)) in view of Yan et al. (Crop Sci. 40:597–605 (2000)).
Regarding claim 18, Meuwissen et al. teaches a method to predict breeding values based upon ~50,000 marker haplotypes derived from phenotypic records (Abstract, Introduction, pg. 1819, re: clm. 18, … A method for determining phenotypic genomic estimated breeding values (pGEBVs), wherein the method comprises the steps of…obtaining genetic, phenotypic, and environmental data for a population of organism genotypes).
Meuwissen et al. teaches a comparison of true and estimated breeding values on pg. 1823 (re: clm. 18, … b) dividing the data of step (a) into a reference population and a validation population… and c) analysing the data obtained, wherein the analysis includes…).
Meuwissen et al. further teaches identification and calculation of the effect of polymorphisms on pg. 1821 (“Estimate the effects of the haplotypes at the QTL…”; re: clm. 18, … ii. identifying polymorphisms in the genetic data of the reference population and calculating the polymorphism effect…).
Meuwissen et al. further teaches calculating a genomic breeding values in consideration of polymorphisms using the equation on pg. 1823 (“Comparison of true and estimated breeding values…”) and 1824 in Table 2 (re: clm. 18, … iii. calculating a genomic estimated breeding value (GEBV) for each genotype of the validation population using the calculated polymorphism effect for each PC; and…)
Meuwissen et al. does not calculate a genotype by environment calculation (re: clm. 18, … and c) analysing the data obtained,wherein the analysis includes: i. calculating the genotype plus genotype x environment (GGE) principle component (PC) for the data of the reference population…) nor does it covert a genomic estimated breeding value into a phenotypic estimated breeding value (re: iv. converting each GEBV into a phenotypic GEBV (pGEBV) by multiplying the GEBV with an inverse of a rotation matrix, wherein the rotation matrix is (e x e), and wherein e is the number of environments in the validation population…)
Yan et al. teaches a genotype plus genotype by environment interaction (GGE) (i.e., G + GE) biplot, which is constructed by the first two symmetrically scaled principal components (PC1 and PC2) derived from singular value decomposition of environment-centered multi-environment trials (MET) data (Abstract, re: clm. 18, … i. calculating the genotype plus genotype x environment (GGE) principle component (PC) for the data of the reference population…) Yan et al. teaches a calculation of genotype plus environment using PCs and therefore teaches an identity of the inverse (using environment by environment to generate a phenotypic GEBV; pg. 598-600, Methods, Model and Biplot Selection, Winning…Genotypes; re: clm. 18, … iv. converting each GEBV into a phenotypic GEBV (pGEBV) by multiplying the GEBV with an inverse of a rotation matrix, wherein the rotation matrix is (e x e), and wherein e is the number of environments in the validation population.)
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 Court et al. as evidenced by Qi et al, Ren et al., and Kim et al., the examiner concludes that some teaching, suggestion, or motivation in the prior art would have led 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 the art of computational chemistry would have been motivated to apply Court et al.’s training in view of Qi et al.’s validation evidence as the combination would lead to a stronger crystal structure generation method.
One of skill in the art before the effective filing date of the claimed invention would have had a reasonable expectation of success to performing the process of training a regression model for the prediction of chemical properties because Court et al. applied their teaching to crystal structure data and provides the necessary code and instructions detailed in their publication. 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 19, Meuwissen et al. teaches prediction of estimated breeding values from individuals on pg. 1823 (Comparison of true and estimated breeding values, re: clm. 19, …wherein n is the number of validation individuals…) but does not teach calculation of an estimated breeding value with an environmental axis (GGE; re: clm. 19, …wherein the GEBV is a G matrix (n x e)… e is the number of environments in the validation population.)
Yan et al. teaches a genotype plus genotype by environment interaction (GGE) as previously disclosed (re: clm. 19, … re: clm. 19, …wherein the GEBV is a G matrix (n x e)… e is the number of environments in the validation population.)
Applying the KSR standard of obviousness to Yan et al. and Meuwissen et al. Examiner concludes that the combination of the genotype by environment calculation of Yan et al. with the number of validation individuals as disclosed by Meuwissen et al. is applying a known technique to a known method with no more than a predictable outcome of a G matrix to calculate the number of environments by number of individuals. One of skill would have had a reasonable expectation of success at applying Yan et al.’s technique to the method of Meuwissen et al. as Yan et al. provides all the necessary instructions (equations) or elements. Therefore, claim 19 would have been prima facie obvious absent evidence to the contrary.
Regarding claim 20, Meuwissen et al. teaches prediction of estimated breeding values from individuals on pg. 1823 (Comparison of true and estimated breeding values, re: clm. 20, …wherein n is the number of validation individuals…) but does not teach calculation of an estimated breeding value with an environmental axis (GGE; re: clm. 20, … wherein calculating each pGEBV is based on Equation 3 as follows: pGEBV = G x R-1 where G is an (n x e) matrix of GEBVs for the GGE PCs scaled by multiplying each PC with its standard deviation; n is the number of validation individuals; e is the number of environments; and R-1 is an inverse of the rotation matrix (e x e) or an environment coordinate matrix scaled by dividing each column on a standard deviation of the correspondence PC.)
Yan et al. teaches a genotype plus genotype by environment interaction (GGE) as previously disclosed and further teaches an environment-standardized version of said equation on pg. 599 which accounts for standard deviation. The equation contains principal components as a variable (Yan et al. 598, Model and Biplot selection) and therefore allows for algebraic manipulation to arrive at varied combinations (“The SREG model…”, re: clm. 20, … wherein calculating each pGEBV is based on Equation 3 as follows:pGEBV = G x R-1 where G is an (n x e) matrix of GEBVs for the GGE PCs scaled by multiplying each PC with its standard deviation; n is the number of validation individuals; e is the number of environments; and R-1 is an inverse of the rotation matrix (e x e) or an environment coordinate matrix scaled by dividing each column on a standard deviation of the correspondence PC..)
Applying the KSR standard of obviousness to Yan et al. and Meuwissen et al. Examiner concludes that the combination of the genotype by environment calculation of Yan et al. with the number of validation individuals as disclosed by Meuwissen et al. is applying a known technique to a known method with no more than a predictable outcome of a phenotypic geneomic estimated breeding value with scaling. One of skill would have had a reasonable expectation of success at applying Yan et al.’s technique to the method of Meuwissen et al. as Yan et al. provides all the necessary instructions (equations) or elements. Therefore, claim 20 would have been prima facie obvious absent evidence to the contrary.
Regarding claim 21, Yan et al. teaches a study using mega environment data as disclosed on pg. 598 (“the question of mega-environment identification…”, re: clm. 21, … The method according to claim 18, wherein the data obtained for the population of organism genotypes is from a plurality of mega-environments.) Yan et al. teaches the limitations of claim 21.
Regarding claim 22, Meuwissen et al. uses single nucleotide polymorphisms on pg. 1819 (re: clm. 22, wherein the polymorphisms include a single nucleotide polymorphism.)
Regarding claim 24, Yan et al. teaches a study of crops (Title, re: clm. 24, … wherein the organism is a plant.).
Regarding claim 25, Yan et al. teaches records on yield on pg. 598 (“Average yield at each location…, re: clm. 25, … the phenotypic data includes records on yield.)
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meuwissen et al. in view of Yan et al. as applied to claims 18-22, 24 and 25 above in view of Pérez et al. (Plant Genome. 2010 ; 3(2): 106–116).
Meuwissen et al. in view of Yan et al. is applied to claims 18-22, 24 and 25 above.
Regarding claim 23, Yan et al. and Meuwissen et al. together teach calculating polymorphism effects per PC (Meuwissen et al. pg. 1819, Yan et al. pg. 598 [Model and Biplot Selection]), however, neither Yan et al. nor Meuwissen et al. teach ridge regression (re: clm. 23, wherein calculating the polymorphism effect for each PC utilises a Bayesian Ridge Regression model.)
Pérez et al. teaches Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R (Title, re: clm. 23, … wherein calculating the polymorphism effect for each PC utilises a Bayesian Ridge Regression model.)
Applying the KSR standard of obviousness to Yan et al. , Meuwissen et al. and Pérez et al., the Examiner concludes that the combination of the genotype by environment calculation of Yan et al. with the total genetic value prediction method as disclosed by Meuwissen et al and the regression model as disclosed by Pérez et al. is applying a known technique to a known method with no more than a predictable outcome of a GGE prediction method using ridge regression with. One of skill would have had a reasonable expectation of success at applying Pérez et al’s technique to the methods of Meuwissen et al. and Yan et al. as Pérez et al. provides all the necessary instructions (scripts) or elements. Therefore, claim 23 would have been prima facie obvious absent evidence to the contrary.
Claim(s) 26-36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meuwissen et al. in view of Yan et al. and Pérez et al. as applied to claims 18-25 above in view of Gillberg et al. (Bioinformatics, 35(20), 2019, 4045–4052)
Meuwissen et al. in view of Yan et al. and Pérez et al. is applied to claims 18-25 above.
Regarding claims 26 and 27, Meuwissen et al. in view of Yan et al. and Pérez et al. do not explicitly disclose rain data in their studies (re: clm. 26, 27, … wherein the environmental data includes irrigation and/or rain exposure.)
Gillberg et al. discloses a model of the interaction between the genotype and the environment (G x E) which states: “We see that the two most influential kernels were the ones representing (i) the non-linear interaction between soil type and daily rainfall, and (ii) the non-linear effect of rain, matching well the biological understanding of the problem.” (re: clm. 26, 27, … wherein the environmental data includes irrigation and/or rain exposure.)
Applying the KSR standard to Meuwissen et al. in view of Yan et al. and Pérez et al. and Gillberg et al., the examiner concludes that some teaching, suggestion, or motivation in the prior art would have led 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 the art of agriculture would have been motivated to apply the teachings of Gillberg et al. to Meuwissen et al. in view of Yan et al. and Pérez et al. as the combination would lead to a stronger genetic breeding yield evaluation method.
One of skill in the art before the effective filing date of the claimed invention would have had a reasonable expectation of success of combining the aforementioned teachings as because Gillberg et al. exists in the same field of invention as Meuwissen et al. in view of Yan et al. and Pérez et al. 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 28, Yan et al. discloses a GGL plot showing tested (read as: selected) genotypes in terms of average yield per location, but does not explicitly disclose a method to select a genotype with a pGEBV as correlated to a particular environment (pg. 603, re: clm. 28, … herein the method is used for selecting a genotype for producing an improved organism in a given environment, by one or more of the following: a. identifying a genotype with the pGEBV which correlates highest with a given environment…)
Meuwissen et al. teaches a method to predict breeding values based upon ~50,000 marker haplotypes derived from phenotypic records, but does not disclose identifying a genotype with the pGEBV which correlates highest with a given environment (Abstract, Introduction, pg. 1819, re: clm. 30, a. identifying a genotype with the pGEBV which correlates highest with a given environment…)
Gillberg et al. discloses traits which are optimal for particular environment in the same genotype in Fig. 1, pg. 4046, and prediction methods with environmental covariates in Fig. 2, pg. 4047 (re: clm. 30, … identifying a genotype with the pGEBV which correlates highest with a given environment.)
Applying the KSR standard to Meuwissen et al. in view of Yan et al., Pérez et al. , and Gillberg et al., the examiner concludes that some teaching, suggestion, or motivation in the prior art would have led 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 the art of agriculture would have been motivated to apply the teachings of Gillberg et al. to Meuwissen et al. in view of Yan et al., Pérez et al., Gillberg et al. as the combination would lead to a stronger genetic breeding yield evaluation method accounting for genomic breeding values as calculated by Meuwissen et al. contextualized per environment with the teaching of Gillberg et al. In support of this motivation, Gillberg et al. mentioned Meuwissen et al.’s genomic selection method on pg. 4046.
One of skill in the art of agriculture before the effective filing date of the claimed invention would have had a reasonable expectation of success of combining the aforementioned teachings as because Gillberg et al. exists in the same field of invention as Meuwissen et al. in view of Yan et al., and Pérez et al. 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 29, Yan et al. teaches locating genotypes to environments via the Fig. 1 Biplot on pg. 601 (re: clm. 29, … locating the selected genotype to said given environment….). Yan et al. teaches the limitations of claim 29.
Regarding claim 30, Meuwissen et al. teaches a method to predict breeding values based upon ~50,000 marker haplotypes derived from phenotypic records (Abstract, Introduction, pg. 1819, re: clm. 30…A method for selecting a genotype for producing an improved organism in a given environment, wherein the method comprises the steps of: e. performing a method for determining phenotypic genomic estimated breeding values (pGEBV) according to claim 18...and performing one or more of:)
Yan et. al further teaches identifying genotypes (Fig. 1; Winning…Genotypes, pg. 600, re: clm. 30, … i. identifying a genotype with a pGEBV which correlates highest with the given environment; ii. clustering the reference environments into mega environments and then calculating multiple averages of pGEBVs per genotype for each mega environment, and identifying a genotype from the average pGEBV that correlates highest with the mega environment which best matches the given environment; and…)
As previously disclosed, the combination of Meuwissen et al. in view of Yan et al., Pérez et al., and Gillberg et al. teaches identifying a genotype with a pGEB V which correlates highest with the given environment (re: clm. 30, … e. performing a method for determining phenotypic genomic estimated breeding values (pGEBV) according to claim 18; and performing one or more of: i. identifying a genotype with a pGEB V which correlates highest with the given environment…) As the instant claim states “performing one or more of”, regarding claim 30, 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 claims 31 and 35, the claims are dependent on claim 30 and is not necessitated by it, as claim 31 discloses “reordering columns of the U matrix”, which is an optional route of claim 30, and claim 35 discloses “calculating a singular value decomposition” which also is optional.
Regarding claims 33, 34, and 36, Yan et al. discloses selected plant genotypes per environmental location (Fig. 1; Winning…Genotypes, pg. 600, re: clm. 33, … locating the selected genotype to said given environment, clm. 34, … wherein the organism is a plant…, clm. 36, … h. performing a method for selecting a genotype… i. locating the organism comprising said selected genotype in said given environment.).
Regarding claim 36, As previously disclosed, the combination of Meuwissen et al. in view of Yan et al., Pérez et al., and Gillberg et al. teaches identifying a genotype with a pGEB V which correlates highest with the given environment (re: clm. 36, …h. performing a method for selecting a genotype for producing an improved organism according to claim 30…). Therefore, regarding claims 33, 34, and 36, 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
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686
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