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 .
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.
Applicant's response, filed on 05/18/2026, is 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.
Status of claims
Claims 1, 5-6, 9-10, 12-21, 23-27 and 36 are pending.
Claims 2-4, 7-8, 11, 22, 28-35, 38-51 and 37-68 are canceled.
Claims 16-21, 23, and 26 are withdrawn.
Claims 1 and 36 are amended.
Claim 1 is an independent claim.
Claims 1, 5-6, 9-10, 12-15, 24-25, 27 and 36 are examined on the merits.
Priority
As detailed on the 02/23/2024 filing receipt, this application claims domestic priority to as early as 08/22/2019.
Withdrawn Rejections/Objections
The objection of the drawings in the Office action mailed 02/18/2026 is withdrawn in view of the amendments filed 05/18/2026.
The rejection of claims 1, 5-6, 9-10, 12-15, 24-25 and 36 under 35 U.S.C. §112(b), Second Paragraph, in the Office action mailed 02/18/2026 is withdrawn in view of the amendments filed 05/18/2026.
The rejection of claims 1, 5-6, 9-10, 12-15, 24-25, 36-37 and 52 under 35 U.S.C. §101, in the Office action mailed 02/18/2026 is withdrawn in view of the amendments filed 05/18/2026.
Regarding 35 USC 101
Claims 1, 5-6, 9-10, 12-15, 24-25, 27 and 36 are patent-eligible under 35 U.S.C. 101 because independent claim 1 recites “h) cultivating or growing the organism having the modified genome obtained from step (g)” which integrates the judicial exceptions into a practical application under Step 2A, 2nd prong of the 101 analysis.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5-6, 9-10, 12, 14-15, 24-25, 27 and 36 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Butruille (U.S. Patent No 2018/285520 A1, published Oct. 04, 2018; 08/22/2022 IDS Document).
Regarding independent claim 1, Butruille teaches the limitation of a) providing a plurality of genetic variants in a genome of the organism with “Further, the genome editing engine 106 may be configured to identify a population of candidate edits for the genome sequence based on one or more of, for example, genome annotation, genome-wide association study (GWAS) analysis, quantitative trait loci (QTL), gene expression data, biochemical pathway models, etc., each retrieved from the data structure 108 (and, potentially, input from one or more breeder persons). The potential edits may be selected by the ordinarily skilled artisan or by an algorithm which has identified potentially useful genetic mutations to achieve the desired phenotype. As used herein, candidate edits may include a single change in the genome or a number of simultaneous changes to a gene, set of genes, or genome. Several approaches singly or in combination will be used to select a population of candidate edits. One may use prior or newly acquired knowledge of genes and pathways known to affect the one or more traits of interest. This knowledge may have been generated through classical mutation screens, complementation tests, and/or comparisons of genomic sequences across a large number of genetically distinct individuals with varied phenotypes for the trait (as in GWAS and other types of QTL studies). Expression studies can aid by providing information about differences in transcript and protein levels among individuals with different phenotypes.” ([0031]).
Butruille teaches the limitation of b) predicting the effects of the genetic variants on the performance of the organism using a statistical model with “Uniquely, the systems and methods herein provide for the selection of multiple candidate edits and prediction of the aggregate effect of the multiple edits, whereby separate and/or individual testing of single edits, may be omitted and/or avoided. In particular, a population of candidate edits for a genome sequence related to one or more traits of interest is identified, from which multiple candidate edits (e.g., edits at multiple genome locations, etc.) are selected, by a genome editing engine, based on a ranking of the candidate edits. An aggregate effect of the multiple selected candidate edits is then predicted, again, by the genome editing engine, for a trait of interest when expressed by an organism having a genomic sequence edited according to the selected candidate edits (as compared to an organism having the same genomic sequence, but unedited).” ([0012]), “The genome editing engine 106 may do so through application of proven models for new genomes (e.g., a particular new inbred line, etc.) and/or inferences through different models (which may be proven out by experiments) and/or learning models, etc.” ([0030]) and “Using a statistical model, which is built based on the training data, the hybrid grain yield impact or effect of each of the 481 premature stop codons may be determined (e.g., at step 304, etc.).” ([0038]).
Butruille teaches the limitation of c) altering one or more of the genetic variants in the genome of the organism with “In particular, a population of candidate edits for a genome sequence related to one or more traits of interest is identified, from which multiple candidate edits (e.g., edits at multiple genome locations, etc.) are selected, by a genome editing engine, based on a ranking of the candidate edits. An aggregate effect of the multiple selected candidate edits is then predicted, again, by the genome editing engine, for a trait of interest when expressed by an organism having a genomic sequence edited according to the selected candidate edits (as compared to an organism having the same genomic sequence, but unedited). Once the aggregate effect is predicted, the selected genome edits may be subject to validation, for example, via a genome editing scheme, whereby the genome of an organism is modified to include the multiple selected candidate edits and tested for purposes of verification of the aggregate effect on the one or more traits of interest (and not the impact of the edits individually).” ([0012])
Butruille teaches the limitation of d) measuring an endophenotype at a sub-organismal level by a biochemical, gene expression, or protein level assay, or visually via microscopy to determine a quantifiable effect of the alteration on the endophenotype with “Once the genome sequence is generated or identified at 302, the genome editing engine 106 (alone, or in combination with one or more persons) identifies, at 304, a population of candidate edits to the genomic sequence. The population of candidate edits, or available edits, is identified generally based on the nature of the traits defined at the outset. That is, in the above illustrative example related to the maize plant, the nature of the trait to be modified is related to an enhancement in the grain yield of inbred maize. Following the dominance hypothesis for hybrid vigor, for example, when editing the genomic sequence of a maize inbred line, the potential edits may be first focused on identifying genes containing stop codons within the genomic sequence that are likely to prevent proper expression of a gene associated with grain yield (i.e., “premature” stop codons). Expression studies which analyze and quantify the presence of such transcripts in hybrid maize varieties could focus the population of edits. For example, two separate maize varieties with enhanced grain yield and differential expression of distinct genes or alleles may provide insight, guidance, and/or instruction into which candidate edits should be identified for the enhanced grain yield for the inbred maize.” (para. [0037]); “Expression studies can aid by providing information about differences in transcript and protein levels among individuals with different phenotypes.” (para. [0031]); and “A non-parametric model could use heuristics to rank candidate edits, for example prioritize editing genes that are not member of multi-gene families or genes that are expressed in certain tissue types.” (para. [0048]). The recited “gene expression, or protein level assay” corresponds to “expression studies” as taught by Butruille and the recited “sub-organismal level” corresponds to “tissue types” as taught by Butruille.
Butruille teaches the limitation of e) updating the statistical model using the determined endophenotypic quantifiable effect with “In one exemplary embodiment, the aggregate effect may be predicted, by the genome editing engine 106, by adding the predicted QTL effects of all QTL regions, in which one of the candidate edit was found. This aggregate effect assumes that, in each QTL region, the identified edit substantially explains the QTL effects. In numerous embodiments, the above aggregate effect determination may not be accurate, as a given candidate edit may only partially explain, or not at all explain, the observed and/or co-located QTL effect. In such embodiments, a correction factor may be used by the genome editing engine 106 to modify the aggregate effect prediction, thus deriving a risk adjusted aggregate effect prediction. This correction factor (e.g., scaled from 0 to 1, etc.) may then depend on the trait(s) of interest and/or the species of the target organism, as well as the experience accumulated at performing this process (e.g., observations from similar experiments to define/redefine the correction factor, etc.). For example, it may have been learned from previous experience that restoring functionality by repairing premature stop codons at all genes within a yield QTL region result in an average increase in yield that is about half or about 80% or some other suitable correction of the estimated effect between least and most favorable allele at the QTL. Thus, a correction factor of about 0.5 or about 0.8, for example, can then applied to models that predict the outcome of performing such tasks across novel yield QTLs and that use the sum of QTL effects as a predictor of the aggregate effect of the edits.” ([0047]). The recited “updating the statistical model” corresponds to applying a correction factor to models as taught by Butruille.
Butruille teaches the limitation of f) determining the genetic variants having a predicted negative effect on the performance of the organism relative to a reference performance level using the updated statistical model with “The genome editing engine 106 is configured to then rate the candidate genome edits based on a predicted ability of the candidate genome edits to affect a trait(s) of interest (e.g., phenotype or multiple phenotypes of target organism). The genome editing engine 106 may be configured to rate the candidate edits based on a probability of causing an effect, a magnitude of a predicted effect, a non-parametric classification parameter, or combinations thereof. As an example, for a user herein, the genome editing engine 106 may provide the probability that a maize plant with a particular edit will have an increased grain yield and/or degree of increased grain yield change compared to an unedited maize plant, and then rate the edits made thereon.” ([0032]) and “It should also be appreciated that if individual edits need to be ascertained with a methodology like above (mean 0.2 Bu/A, variance 0.05 Bu2/A2), there is a greater than 18% probability that any particular edit will have a negative impact (e.g., a reduced yield rather than an enhanced yield or an increased susceptibility to disease rate rather an reduce susceptibility to disease). That is, the actual effect is in the opposite direction of the predicted effect, with an edit resulting in a decrease in grain yield. This can be contrasted to the 0.003% probability that 20 such edits combined will have a negative aggregate effect.” ([0058]).
Butruille teaches the limitation of g) modifying in the genome one or more of the genetic variants having a predicted negative effect on the performance of the organism relative to the reference performance level with “Then, once the candidate genome edits are rated, the genome editing engine 106 is configured to then select multiple of the candidate edits, based on the ratings, such that the selected candidate edits provide a specific and/or desired likelihood of an aggregate effect on the trait(s) of interest (e.g., as defined by the nature of the edits, etc.) (i.e., for a specimen carrying the genome and/or a population of specimen carrying the pan-genome, etc.). The genome editing engine 106 is configured to predict an aggregate effect of the selected candidate edits when expressed in the specimen on at least one of the traits of interest (e.g., yield in maize, etc.) as compared to a specimen with an unedited genomic sequence.” ([0033]); “Thereafter, when the predicted aggregate effect is above a defined threshold, the selected candidate edits may be passed, by the genome editing engine 106 or one or more persons, to the genome editing scheme 102, whereupon specimen(s) (as defined by the genome sequence generated above) is edited consistent with the selected candidate edits. The edited specimens(s) may then be provided to the cultivation space 104, for growing and testing to confirm the predicted aggregate effect.” ([0034]); “It should be appreciated that in other embodiments, the genome editing engine 106 may be omitted from identifying the population of candidate edits, whereby one or more persons skilled in the art rely on information, such as that described above, to identify the candidate edits for modulating the genome sequence as it pertains to the trait of interest.” ([0039]) and “Finally, the systems and methods herein may provide one or more breeder useful information about which gene, set of genes, and/or sequences to manipulate to achieve a desired trait change and/or improvement, by assessing the aggregate effect of various combinations of particular modifications to a gene, set of genes, and/or sequences on a given trait.” ([0062]).
Butruille teaches the limitation of h) cultivating or growing the organism having the modified genome obtained from step (g) with Butruille’s claim 7 “…growing and/or cultivating said modified organism with the selected candidate edit in a cultivation space, along with an unedited organism consistent with the genomic sequence, thereby permitting validation of the predicted aggregate effect.” and “Thereafter, when the predicted aggregate effect is above a defined threshold, the selected candidate edits may be passed, by the genome editing engine 106 or one or more persons, to the genome editing scheme 102, whereupon specimen(s) (as defined by the genome sequence generated above) is edited consistent with the selected candidate edits. The edited specimens(s) may then be provided to the cultivation space 104, for growing and testing to confirm the predicted aggregate effect.” (para. [0034]).
Regarding claim 5, Butruille teaches the limitation of wherein the organism is maize, wheat, barley, oat, rice, soybean, oil palm, safflower, sesame, tobacco, flax, cotton, sunflower, pearl millet, foxtail millet, sorghum, canola, cannabis, a vegetable crop, a forage crop, an industrial crop, a woody crop, cattle, sheep, goat, horse, pig, chicken, duck, goose, rabbit, or fish or a biomass crop with “The genome editing engine 106 is configured to then rate the candidate genome edits based on a predicted ability of the candidate genome edits to affect a trait(s) of interest (e.g., phenotype or multiple phenotypes of target organism). The genome editing engine 106 may be configured to rate the candidate edits based on a probability of causing an effect, a magnitude of a predicted effect, a non-parametric classification parameter, or combinations thereof. As an example, for a user herein, the genome editing engine 106 may provide the probability that a maize plant with a particular edit will have an increased grain yield and/or degree of increased grain yield change compared to an unedited maize plant, and then rate the edits made thereon.” ([0032]).
Regarding claim 6, Butruille teaches the limitation of wherein the performance of the organism is yield, overall fitness, biomass, photosynthetic efficiency, nutrient use efficiency, heat tolerance, drought tolerance, herbicide tolerance, growth rate, feed use efficiency, meat yield, meat quality, milk yield, milk quality, egg yield, egg quality, wool yield, wool quality or disease resistance with “Prior to use of the genome editing engine 106, one or more persons associated with the system 100 may define a nature of edits (e.g., define desired changes in a base pair sequence, insertions, deletions, duplication, etc.), which are a target of the use of the system 100. For example, the one or more persons may define one or more output traits (e.g., a series, etc.) for an organism, such as a maize specimen. Exemplary desired traits specific to maize may include, without limitation, but more typically, traits of economic importance (which may include, for example, generally, traits (of plant, more generally) that if modified, result in an economic benefit that is of value greater than the cost required to achieve the modification, and/or that result in a benefit linked to economics or are based on economics related to developing and/or commercializing the result, etc. Traits of economic importance includes, but are not limited to, traits conferring a preferred phenotype selected from the group consisting of herbicide tolerance, disease resistance, insect or pest resistance, altered fatty acid, protein or carbohydrate metabolism, grain yield, oil content, nutritional content, growth rate, stress tolerance, preferred maturity, organoleptic properties, altered morphological characteristics, other agronomic traits, traits for industrial uses, or traits for improved consumer appeal, etc. In connection therewith, or independent from the nature of the potential edits, the one or more persons may, and/or the genome editing engine 106 is configured to, identify a genome sequence for the genome (e.g., a genome and/or a pan genome, etc.) as a starting or reference point for the processes herein. The genome editing engine 106 may do so through application of proven models for new genomes (e.g., a particular new inbred line, etc.) and/or inferences through different models (which may be proven out by experiments) and/or learning models, etc.” ([0030]).
Regarding claim 9, Butruille teaches the limitation of wherein the performance is a quantitative trait with “…the one or more persons may define one or more output traits (e.g., a series, etc.) for an organism, such as a maize specimen. Exemplary desired traits specific to maize may include, without limitation, but more typically, traits of economic importance (which may include, for example, generally, traits (of plant, more generally) that if modified, result in an economic benefit that is of value greater than the cost required to achieve the modification, and/or that result in a benefit linked to economics or are based on economics related to developing and/or commercializing the result, etc. Traits of economic importance includes, but are not limited to, traits conferring a preferred phenotype selected from the group consisting of herbicide tolerance, disease resistance, insect or pest resistance, altered fatty acid, protein or carbohydrate metabolism, grain yield, oil content, nutritional content, growth rate, stress tolerance, preferred maturity, organoleptic properties, altered morphological characteristics, other agronomic traits, traits for industrial uses, or traits for improved consumer appeal, etc.” ([0030]) and “Further, the genome editing engine 106 may be configured to identify a population of candidate edits for the genome sequence based on one or more of, for example, genome annotation, genome-wide association study (GWAS) analysis, quantitative trait loci (QTL), gene expression data, biochemical pathway models, etc., each retrieved from the data structure 108 (and, potentially, input from one or more breeder persons). The potential edits may be selected by the ordinarily skilled artisan or by an algorithm which has identified potentially useful genetic mutations to achieve the desired phenotype. As used herein, candidate edits may include a single change in the genome or a number of simultaneous changes to a gene, set of genes, or genome. Several approaches singly or in combination will be used to select a population of candidate edits. One may use prior or newly acquired knowledge of genes and pathways known to affect the one or more traits of interest. This knowledge may have been generated through classical mutation screens, complementation tests, and/or comparisons of genomic sequences across a large number of genetically distinct individuals with varied phenotypes for the trait (as in GWAS and other types of QTL studies). Expression studies can aid by providing information about differences in transcript and protein levels among individuals with different phenotypes.” ([0031]).
Regarding claim 10, Butruille teaches the limitation of wherein the genetic variants are identified by a linkage study or an association study with “Further, the genome editing engine 106 may be configured to identify a population of candidate edits for the genome sequence based on one or more of, for example, genome annotation, genome-wide association study (GWAS) analysis, quantitative trait loci (QTL), gene expression data, biochemical pathway models, etc., each retrieved from the data structure 108 (and, potentially, input from one or more breeder persons). The potential edits may be selected by the ordinarily skilled artisan or by an algorithm which has identified potentially useful genetic mutations to achieve the desired phenotype. As used herein, candidate edits may include a single change in the genome or a number of simultaneous changes to a gene, set of genes, or genome. Several approaches singly or in combination will be used to select a population of candidate edits. One may use prior or newly acquired knowledge of genes and pathways known to affect the one or more traits of interest. This knowledge may have been generated through classical mutation screens, complementation tests, and/or comparisons of genomic sequences across a large number of genetically distinct individuals with varied phenotypes for the trait (as in GWAS and other types of QTL studies). Expression studies can aid by providing information about differences in transcript and protein levels among individuals with different phenotypes.” ([0031]).
Regarding claim 12, Butruille teaches the limitation of wherein the association study is a genome-wide association study (GWAS) or a transcriptome-wide association study (TWAS) with “Further, the genome editing engine 106 may be configured to identify a population of candidate edits for the genome sequence based on one or more of, for example, genome annotation, genome-wide association study (GWAS) analysis, quantitative trait loci (QTL), gene expression data, biochemical pathway models, etc., each retrieved from the data structure 108 (and, potentially, input from one or more breeder persons). The potential edits may be selected by the ordinarily skilled artisan or by an algorithm which has identified potentially useful genetic mutations to achieve the desired phenotype. As used herein, candidate edits may include a single change in the genome or a number of simultaneous changes to a gene, set of genes, or genome. Several approaches singly or in combination will be used to select a population of candidate edits. One may use prior or newly acquired knowledge of genes and pathways known to affect the one or more traits of interest. This knowledge may have been generated through classical mutation screens, complementation tests, and/or comparisons of genomic sequences across a large number of genetically distinct individuals with varied phenotypes for the trait (as in GWAS and other types of QTL studies). Expression studies can aid by providing information about differences in transcript and protein levels among individuals with different phenotypes.” ([0031]).
Regarding claim 14, Butruille teaches the limitation of wherein the statistical model comprises a feature based on evolutionary conservation of the genetic variants with “Similarly, ranking candidate edits may be based on the evolutionary conservation of the gene to which the edits belongs, and prioritize editing genes which are modified and/or disrupted in a particular genome, but which have accumulated minimal, or little, genome sequence change within the species of interest or among other more distantly related species.” ([0048]).
Regarding claim 15, Butruille teaches the limitation of wherein the evolutionary conservation is determined by sequence alignment in a genic or an intergenic region with “It should be appreciated that while an additive model is used herein, other models and/or non-additive models could also be built to account for possible dominance or epistatic interactions, and other models and/or non-parametric models could be used when selecting candidate edits with effects of unpredictable magnitude in other embodiments. An epistatic model may be applied when editing multiple genes in a biochemical pathway. If two genes in a pathway are non-functional, editing only one of those will not restore that pathway, but editing both will. Conversely, if an undesired phenotype is reached through multiple pathways, disrupting only one of these pathways may not change the phenotype, while disrupting all will. A non-parametric model could use heuristics to rank candidate edits, for example prioritize editing genes that are not member of multi-gene families or genes that are expressed in certain tissue types. Similarly, ranking candidate edits may be based on the evolutionary conservation of the gene to which the edits belongs, and prioritize editing genes which are modified and/or disrupted in a particular genome, but which have accumulated minimal, or little, genome sequence change within the species of interest or among other more distantly related species.” ([0048]) and “With that said, the method 300 initially includes identifying a genome sequence of the maize plant, at 302, to which the candidate edits may or may not be made. Genome sequence identification may include, for example, de novo generation or imputation from related and/or ancestral organisms. De novo genome sequencing may be accomplished by technologies and algorithms known to those skilled in the art. Sequence information can be identified and/or generated by those skilled in the art through performing conventional methods, by third parties, or be identified from one or more resources available in the public domain.” ([0036]).
Regarding claim 24, Butruille teaches the limitation of wherein the alteration is achieved by genome editing with “The present disclosure generally relates to systems and methods for use in statistical genome editing, and in particular, to systems and methods for use in identifying potential genome edits, rating the potential edits based on one or more parameters, and predicting an aggregate effect of multiple rated genome edits on one or more given traits.” ([0002]).
Regarding claim 25, Butruille teaches the limitation of wherein the genome editing is achieved by a clustered regularly interspersed short palindromic repeats (CRISPR) system, a transcription activator-like effector nuclease (TALEN) system, or a zinc finger nuclease (ZFN) system with “In the genome editing scheme 102, the genome edits are generally made, for example, in gamete cells (however, this is not required in all embodiments). In certain embodiments, for example, the genome edits are made in a zygote, and are effectuated in target cells in a multicellular potential parent organism—for example a sexually mature parent organism—using a vector such as, for example, a viral vector with specific tropism for particular tissues (e.g., gametogenic tissues, etc.). The molecular biologist of ordinary skill is familiar with such techniques, and knows when to use one in preference to another to effectuate a given manipulation in the target organism's genome. Additionally, these manipulations may be achieved with one or more of: CRISPR technology, and particularly with CRISPR/Cas technology and more particularly CRISPR/Cas9 technology; ZFNs; TALENs; homologous recombination; etc. With that said, the above is provided without limitation. The appropriate technique will be identified and executed by the ordinarily skilled artisan in accordance with the type and/or degree of manipulation of the organism selected and/or required.” ([0015]).
Regarding claim 27, Butruille teaches the limitation of wherein the endophenotype is messenger RNA (mRNA) abundance, gene transcript splicing ratio, protein abundance, micro RNA (miRNA) abundance, small RNA (siRNA) abundance, translational efficiency, ribosomal occupancy, protein modification, metabolite abundance, or allele specific expression (ASE) with “Further, the genome editing engine 106 may be configured to identify a population of candidate edits for the genome sequence based on one or more of, for example, genome annotation, genome-wide association study (GWAS) analysis, quantitative trait loci (QTL), gene expression data, biochemical pathway models, etc., each retrieved from the data structure 108 (and, potentially, input from one or more breeder persons).” ([0031]); “Expression studies can aid by providing information about differences in transcript and protein levels among individuals with different phenotypes.” ([0031]) and “Expression studies which analyze and quantify the presence of such transcripts in hybrid maize varieties could focus the population of edits. For example, two separate maize varieties with enhanced grain yield and differential expression of distinct genes or alleles may provide insight, guidance, and/or instruction into which candidate edits should be identified for the enhanced grain yield for the inbred maize.” ([0037]).
Regarding claim 36, Butruille teaches the limitation of An organism with improved performance produced or selected by the method of claim 1 with "The exemplary method further includes selecting one or more of the candidate edits based on the ranking and predicting, by the computing device, an aggregate effect of the selected one or more of the candidate edits for the trait of interest when expressed by a specimen of the organism having a genomic sequence and edited according to the selected one or more of the candidate edits, as compared to an unedited specimen of the organism.” (abstract); “FIG. 1 illustrates an exemplary system 100 in which the one or more aspects of the present disclosure may be implemented. Although the system 100 is presented in one arrangement, other embodiments may include the parts of the system 100 (or additional parts) arranged or otherwise depending on, for example, the manner in which the multiple genome edits are identified, selected, and/or edited into a genome sequence of an organism, etc.” ([0013]); “In the exemplary embodiment of FIG. 1, the system 100 generally includes a genome editing scheme 102 and a cultivation space 104, in which one or more plants, animals, bacteria, fungi, viruses, or other organisms, produced from the genome editing scheme 102, are bred, grown, matured, and/or cultured, etc. The genome editing scheme 102 is provided as an environment in which potential genome edits (as determined herein) are executed in connection with target organisms.” ([0014]).
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.
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.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Butruille (U.S. Patent No 2018/285520 A1, published Oct. 04, 2018; 08/22/2022 IDS Document) as applied to claims 1, 5-6, 9-10, 12, 14-15, 24-25, 27 and 36 above; in view of Serber (U.S. Patent No 2017/159045 A, published Jan. 08, 2017; 08/22/2022 IDS Document).
Butruille is applied to claims 1, 5-6, 9-10, 12, 14-15, 24-25, 27 and 36 as discussed above.
Regarding claim 13, Butruille teaches using machine learning models to predict the performance of candidate edits with “The genome editing engine 106 may do so through application of proven models for new genomes (e.g., a particular new inbred line, etc.) and/or inferences through different models (which may be proven out by experiments) and/or learning models, etc.” ([0030]) and “Expertise and previous experience with the genome editing engine 106, published literature and genome annotation, QTL studies, expressions studies, association tests, and/or use of machine learning may contribute to estimating the value of the probability/magnitude for each of the candidate edit. It should be understood that if those skilled in the art are concerned with altering multiple traits of the target organism (as compared a single trait of interest, for example, as defined at the outset of method 300 (i.e., grain yield)), overall ranking of candidate edits, by the genome editing engine 106, may be included in method 300, at 306, for example, in one or more ways. For example, the genome editing engine 106 may build an index across all or some of the multiple traits, for example, by a linear combination of several traits, and then the index may then become the frame of reference for ranking and evaluating the candidate edits.” ([0041]).
Butruille does not specifically teach the limitation of wherein the statistical model is a linear regression model, a logistic regression model, a ridge regression model, a lasso regression model, an elastic net regression model, a decision tree model, a gradient boosted tree model, a neural network model, or a support vector machine (SVM) model of claim 13. However, this limitation is taught by Serber.
Serber teaches using linear regression with “FIG. 24 illustrates the linear regression coefficient values, which depict the average change (increase or decrease) in relative strain performance associated with each genetic change incorporated into the depicted strains.” ([0074]); The linear regression model described above, which utilized data from constructed strains, can be used to make performance predictions for strains that haven't yet been built.” ([0450]) and “The procedure can be summarized as follows: generate in silico all possible configurations of genetic changes → use the regression model to predict relative strain performance → order the candidate strain designs by performance. Thus, by utilizing the regression model to predict the performance of as-yet-unbuilt strains, the method allows for the production of higher performing strains, while simultaneously conducting fewer experiments.” ([0451]).
It would have been prima facia obvious to combine the teachings of Butruille and Serber to arrive at the claimed invention. A person of ordinary skill in the art would have been motivated to modify the method of Butruille to use a linear regression model to predict the performance of genetic edits as taught by Serber for the advantage of reducing the need to perform experiments to evaluate the effects of genetic edits on the performance of an organism. Furthermore, there would have been a reasonable expectation of success, since Butruille and Serber teach methods that pertain to the analysis of genomic edits on the performance of the organism.
Response to 35 USC § 102/103 Remarks (filed 05/18/2026, pages 18-19)
Applicant amended claims 1 and 36.
It is noted that Applicant’s remarks are based on amended claims.
Applicant argues that the cited reference, Butruille fails to disclose at least "measuring an endophenotype at a sub-organismal level by a biochemical, gene expression, or protein level assay, or visually via microscopy to determine a quantifiable effect of the alteration on the endophenotype," as recited in amended claim 1.
In response, Applicant’s remarks have been fully considered and are not persuasive. As discussed above in the 35 USC 102 rejection section Butruille teaches the claim limitation of measuring an endophenotype at a sub-organismal level by a biochemical, gene expression, or protein level assay, or visually via microscopy to determine a quantifiable effect of the alteration on the endophenotype with “Once the genome sequence is generated or identified at 302, the genome editing engine 106 (alone, or in combination with one or more persons) identifies, at 304, a population of candidate edits to the genomic sequence. The population of candidate edits, or available edits, is identified generally based on the nature of the traits defined at the outset. That is, in the above illustrative example related to the maize plant, the nature of the trait to be modified is related to an enhancement in the grain yield of inbred maize. Following the dominance hypothesis for hybrid vigor, for example, when editing the genomic sequence of a maize inbred line, the potential edits may be first focused on identifying genes containing stop codons within the genomic sequence that are likely to prevent proper expression of a gene associated with grain yield (i.e., “premature” stop codons). Expression studies which analyze and quantify the presence of such transcripts in hybrid maize varieties could focus the population of edits. For example, two separate maize varieties with enhanced grain yield and differential expression of distinct genes or alleles may provide insight, guidance, and/or instruction into which candidate edits should be identified for the enhanced grain yield for the inbred maize.” (para. [0037]); “Expression studies can aid by providing information about differences in transcript and protein levels among individuals with different phenotypes.” (para. [0031]); and “A non-parametric model could use heuristics to rank candidate edits, for example prioritize editing genes that are not member of multi-gene families or genes that are expressed in certain tissue types.” (para. [0048]). The recited “gene expression, or protein level assay” corresponds to “expression studies” as taught by Butruille and the recited “sub-organismal level” corresponds to “tissue types” as taught by Butruille.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.K./Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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