DETAILED ACTION
Examiner Remarks
Examiner agrees with Applicant’s Remarks as submitted on 08/28/2026 that the claimed invention provides a technical improvement to the field of genetic engineering by enabling the rapid assessment and prototyping of genetic edits to strains. Accordingly, the 101 rejection has been withdrawn.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/08/2026, 07/31/2026, and 09/01/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 and 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “the objective of the biological parent” in Claims 5 and 11 recite the limitation “the objective of the biological parent.” There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1 and 6-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kok et al. US 2022/0328128 Al (“Kok”) in view of Chowdhury et al., US 2020/0168291 Al (“Chowdhury”).
Regarding claim 1, Kok teaches a method performed by one or more computers for generating a microbial strain, comprising:
selecting at least two objectives of the microbial strain, wherein the at least two objectives comprise at least two of: (i) a substrate uptake rate, (ii) a substrate-to-product yield coefficient, (iii) a byproduct formation rate, (iv) a tolerance to variations in temperature, pressure, dissolved oxygen concentration, contamination, or osmotic pressure; or (v) genetic stability of the microbial strain(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Referring to the example of FIG. 32A, E coli has a known metabolic path from glucose to product, from which one can determine theoretical maximum product performance (e.g., yield). In the example shown, the performance parameters may include the following: Growth Rate, Viability, Specific Productivity (on a cell level), YPX (Yield Product Per Biomass), Byproduct Output rate[(ii) a substrate-to-product yield coefficient, (iii) a byproduct formation rate(v) genetic stability of the microbial strain]. These performance parameters are known in the industry to influence the KPI … [s]tep 6: FIGS. 32B and 32C illustrate step 6. According to embodiments of the disclosure, the PM engine employs multi-objective optimization ("MOO") techniques to determine optimized condition values that correspond to optimization over multiple objectives that impact the KPI[selecting at least two objectives of the microbial strain, wherein the at least two objectives comprise at least two of:].”);1
determining the microbial strain based on an evaluation of the at least two objectives for a set of microbial strain variants of the biological parent to identify a set of target microbial strain variants(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Referring to the example of FIG. 32A, E coli has a known metabolic path from glucose to product, from which one can determine theoretical maximum product performance (e.g., yield) [of the biological parent] … generates a first-scale statistical model based upon the first-scale physical model, as described in the Transfer Function application. The MOO of Step 6 provides the optimum screening condition values corresponding to the optimum screening parameters. The PM engine uses this data to run experiments using the physical plate model parameters for the strains to determine the statistical plate model[determining the microbial strain based on an evaluation of the at least two objectives for a set of microbial strain variants]. The PM engine may employ the statistical plate model to generate plate-scale performance values as inputs to the transfer function, as described elsewhere herein. The transfer function then predicts performance of the strains at the second (e.g., bench) scale. Step 8: According to embodiments of the disclosure, the PM engine then selects strains having a predicted second-scale performance exceeding a performance threshold[to identify a set of target microbial strain variants].” & Kok, paras., [0185-0187], “Another approach is to apply the percent change in transfer predictions between parent and daughter strain to the actual large-scale performance of the parent… the parent strain at scale (i.e., larger scale), TF _output(strain) is the predicted performance of a strain "strain" due to application of the transfer function, and the daughter strain is a version of the parent strain as modified by one or more genetic modifications.”) comprising:
generating, for each microbial strain variant in the set of microbial strain variants, a predicted performance value for each of the selected at least two objectives using at least one of a machine learning model or a computational simulation(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Step 3A: Using a computer simulation-model of the metabolism of the organism, predict maximum theoretical values of performance parameters of different strains of the organism at first (e.g., plate) scale (112)[ generating, for each microbial strain variant in the set of microbial strain variants, a predicted performance value using at least one of a computational simulation]. This step determines the theoretical maximum conversion rates from the provided substrate to the desired product, alongside determining potential byproducts ( e.g., an undesired organic acid), or limitations (e.g., required presence of certain vitamins or minerals necessary for the organism growth and performance)[ for each of the selected at least two objectives] that could prevent achieving those higher conversion rates.”);2
and ranking the set of microbial strain variants based on a joint evaluation of the generated predicted performance values for each of the selected at least two objectives to identify the set of target microbial strain variants(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Step 4: As noted above, step 2 experimentally determines values of the performance parameters of different strains at second (e.g., bench) scale. In step 4, the PM engine compares experimentally determined performance parameter values with their theoretical maximums. The resulting difference represents the potential performance improvement ("available headroom") that might be achieved in strain performance by adjusting conditions or modifying their genome. Based on these differences and relationships known in the industry between these performance parameters and the KPI, the PM engine ranks the performance parameters, with the highest ranking going to the performance parameter with the greatest available headroom (114)[ and ranking the set of microbial strain variants based on a joint evaluation of the generated predicted performance values for each of the selected at least two objectives to identify the set of target microbial strain variants].”);
and automatically adjusting a screening priority of the set of target microbial strain variants(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “[T]he screening process comprises determining the response (by screening parameter, e.g., yield) at the smaller scale of each candidate strain to a range of condition values of the top-ranked conditions to determine if the candidate strain is viable under those conditions and satisfies a performance threshold. In this example, the PM engine assembles together the initial parameters (103), the candidate screening conditions 110, the environmental conditions 115, and the candidate screening parameters 116 to preliminarily designs experiments to screen strains of E. coli for yield and growth rate while producing low quantities of undesired byproducts under the top-ranked conditions of substrate gradient, maximum oxygen transfer, and maximum sheer, and under the environmental conditions 115[and automatically adjusting a screening priority of the set of target microbial strain variants].”).
While Kok teaches the microbial strain, Kok does not detail: selecting a biological parent.
However, Chowdhury teaches:
selecting a biological parent [of the microbial strain](Chowdhury, paras., [0077- 0102, “In this framework, embodiments may use standard ML models, e.g. Decision Trees, to determine feature importance. Because of the hierarchical nature of ontology classes, features are often correlated or redundant, which can lead to ambiguous model fitting and feature inspection. To address this issue, dimensional reduction may be performed on input features via principal component analysis. Alternatively, feature trimming may be performed based on information gained from child to parent ontology classes[selecting a biological parent].”).3
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kok with the teachings of Chowdhury the motivation to do so would be to develop a system to genetically optimize certain phenotypes of an organism through the use of machine learning(Chowdhury, paras., [0003-0007], “Genetically optimizing an organism to exhibit a desired phenotype is a well-known problem. The two main sub-problems that confront the metabolic engineer are: (1) of all the possible modifications that might be made to the organism, which should be attempted to maximize output of the desired compound; and (2) once a set of modifications has been decided on, in which order should they be performed to maximize the rate of progress… the present disclosure overcome the drawbacks of conventional techniques by prioritizing the genes to be modified and the modifications to be made to those genes”).
Regarding claim 6, Kok in view of Chowdhury teaches the method of claim 1, wherein the evaluation of the at least two objectives for the set of microbial strain variants of the biological parent includes selectively evaluating microbial strain variants that at least maintain at least one of the at least two objectives relative to the biological parent(Kok, paras., [0185-0187] and [0333-0405], see also figs. 32A, 32B, and 32C; “paras. [0185-0187]: Another approach is to apply the percent change in transfer predictions between parent and daughter strain to the actual large-scale performance of the parent where parent_performance_at_scale is the observed performance of the parent strain at scale (i.e., larger scale), TF _output(strain) is the predicted performance of a strain "strain" due to application of the transfer function, and the daughter strain is a version of the parent strain as modified by one or more genetic modifications[at least maintain at least one of the at least two objectives relative to the biological parent]….”; paras. [0333-0405] “Referring to the example of FIG. 32A, E coli has a known metabolic path from glucose to product, from which one can determine theoretical maximum product performance (e.g., yield). In the example shown, the performance parameters may include the following: Growth Rate, Viability, Specific Productivity (on a cell level), YPX (Yield Product Per Biomass), Byproduct Output rate. These performance parameters are known in the industry to influence the KPI…[s]tep 6: FIGS. 32B and 32C illustrate step 6. According to embodiments of the disclosure, the PM engine employs multi-objective optimization ("MOO") techniques to determine optimized condition values that correspond to optimization over multiple objectives that impact the KPI[wherein the evaluation of the at least two objectives for the set of microbial strain variants of the biological parent includes]…[t]he MOO of Step 6 provides the optimum screening condition values corresponding to the optimum screening parameters. The PM engine uses this data to run experiments using the physical plate model parameters for the strains to determine the statistical plate model. The PM engine may employ the statistical plate model to generate plate-scale performance values as inputs to the transfer function, as described elsewhere herein. The transfer function then predicts performance of the strains at the second (e.g., bench) scale[selectively evaluating microbial strain variants that at least maintain at least one of the at least two objectives relative to the biological parent].”).
Regarding claim 7, Kok in view of Chowdhury teaches the method of claim 1, wherein the evaluation of the at least two objectives for the set of microbial strain variants of the biological parent includes, for a respective microbial strain variant of the biological parent, jointly measuring each of the at least two objectives of the respective microbial strain variant(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Referring to the example of FIG. 32A, E coli has a known metabolic path from glucose to product, from which one can determine theoretical maximum product performance (e.g., yield). In the example shown, the performance parameters may include the following: Growth Rate, Viability, Specific Productivity (on a cell level), YPX (Yield Product Per Biomass), Byproduct Output rate. These performance parameters are known in the industry to influence the KPI … [s]tep 6: FIGS. 32B and 32C illustrate step 6. According to embodiments of the disclosure, the PM engine employs multi-objective optimization ("MOO") techniques to determine optimized condition values that correspond to optimization over multiple objectives that impact the KPI.” ).
Regarding claim 8, Kok in view of Chowdhury teaches the method of claim 7, wherein jointly measuring each of the at least two objectives of the respective microbial strain variant includes: determining a first objective of the at least two objectives for the respective microbial strain variant according to a first dimension of an evaluation space, determining a second objective of the at least two objectives for the respective microbial strain variant according to a second dimension of the evaluation space, and evaluating the respective microbial strain variant according to a vector representation in the evaluation space, wherein the vector representation is based on a first objective of the at least two objectives according to the first dimension of the evaluation space and the second objective according to the second dimension of the evaluation space(Kok, paras., [0381-404], see also fig., 34, “FIG. 34 illustrates a surface shape showing how biomass is modeled and values are interpolated for the biomass response for the batch feeding scheme. The example uses the screening conditions in block 154. The figure shows two screening conditions ( dependent variables), inoculation volume and substrate gradients[determining a first objective of the at least two objectives for the respective microbial strain variant according to a first dimension of an evaluation space, determining a second objective of the at least two objectives for the respective microbial strain variant according to a second dimension of the evaluation space]. As seen in the figure, the PM engine can infer optimum values for the screening parameter biomass[and evaluating the respective microbial strain variant according to a vector representation in the evaluation space, wherein the vector representation is based on a first objective of the at least two objectives according to the first dimension of the evaluation space and the second objective according to the second dimension of the evaluation space] for values of the screening conditions that were not necessarily tested in the experiment [as fig. 34 details below]”
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).
Regarding claim 9, Kok in view of Chowdhury teaches the method of claim 8, wherein jointly measuring the first objective of the respective variant and the second objective of the respective microbial strain variant includes,generating a weighted evaluation of the first objective of the at least two objectives for the respective microbial strain variant according to a first weight associated with the first objective,generating a weighted evaluation of the second objective of the at least two objectives for the respective microbial strain variant according to a second weight associated with the second objective, and evaluating the respective microbial strain variant according to a combination of the weighted evaluation of the first objective and the weighted evaluation of the second objective(Kok, paras., [0381-404], see also fig., 34 and table 2, “RSM is an approach to optimizing parameters in complex systems, where the number of parameters and values for those parameters is very large… RSM supports… multi-objective optimization: The approach embodiments of the disclosure to using RSM for multi-objective optimization goes beyond finding multiple Pareto optima by providing a ranking metric… [o]ne approach to RSM supports multi-objective optimization through the use of a desirability metric. The desirability function incorporates response target information, the relative importance of those targets, and response surface models to provide a single metric that ranks the sets of experimental parameter values… the overall desirability is a weighted geometric mean[and evaluating the respective microbial strain variant according to a combination of the weighted evaluation of the first objective and the weighted evaluation of the second objective],
D
=
d
1
w
1
d
2
w
2
…
d
k
w
k
where each d_i is a single desirability for a single screening parameter as defined below, and each
w
i
is the importance[generating a weighted evaluation of the first objective of the at least two objectives for the respective microbial strain variant according to a first weight associated with the first objective,generating a weighted evaluation of the second objective of the at least two objectives for the respective microbial strain variant according to a second weight associated with the second objective,] of the corresponding screening performance parameter as determined by Step 5.”).
Regarding claim 10, Kok in view of Chowdhury teaches the method of claim 9, wherein the evaluation of the respective microbial strain variant of the biological parent includes evaluating a respective microbial strain variant of the biological parent based on an evaluation threshold of at least one objective of the at least two objectives for the respective microbial strain variant(Chowdhury, paras., [0042-0047], see also fig., 2 and table 1, “In one example experiment, each promoter in the ladder was cloned in front of eyfp, a gene encoding yellow fluorescent protein in the shuttle vector pKl8rep. These plasmids were transformed into C. glutamicum NRRL B-11474 and promoter activity was assessed by measuring the accumulation of YFP protein by spectrometry… [t]he metric under consideration in this and other examples is fraction of candidates for improvement, or "hit rate," which is the fraction of modifications whose measured level of improvement is above a noise threshold in one or more phenotypes of interest. The threshold may be set based on the noise (e.g., root mean squared error) in predicting performance at scale (i.e., larger than small scale) relative to performance at a small, high-throughput scale, and also represents a minimum threshold for what can be considered… these cutoffs are 10% above the unmodified parent genome for the productivity model and 3% above parent for the yield model[evaluating a respective microbial strain variant of the biological parent based on an evaluation threshold of at least one objective].”).4
Regarding claim 11, Kok in view of Chowdhury teaches the method of claim 9, wherein the evaluation of the respective microbial strain variant includes at least one of a measurement of an edit distance between a respective microbial strain variant and the biological parent, or a measurement of an objective of the at least two objectives of the respective microbial strain variant and a corresponding measurement of the objective of the biological parent(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Referring to the example of FIG. 32A, E coli has a known metabolic path from glucose to product, from which one can determine theoretical maximum product performance (e.g., yield). In the example shown, the performance parameters may include the following: Growth Rate, Viability, Specific Productivity (on a cell level), YPX (Yield Product Per Biomass), Byproduct Output rate. These performance parameters are known in the industry to influence the KPI … [s]tep 6: FIGS. 32B and 32C illustrate step 6. According to embodiments of the disclosure, the PM engine employs multi-objective optimization ("MOO") techniques to determine optimized condition values that correspond to optimization over multiple objectives that impact the KPI[a measurement of an objective of the at least two objectives of the respective microbial strain variant and a corresponding measurement of the objective of the biological parent].”).5
Regarding claim 12, Kok in view of Chowdhury teaches the method of claim 9, wherein the evaluation of the set of microbial strain variants of the biological parent includes, generating a representation of a portion of a respective microbial strain variant according to a product language, and evaluating the representation of the portion of a respective microbial strain variant according to the product language(Chowdhury, paras., [0067-0098], see also table 2, and fig. 6, “FIG. 6 illustrates an example of a subgraph from the Gene Ontology, with gene classes 602, 604 and 606 enriched for improved yield. In this grouping, gene sets are associated with specific terms in the ontology (and all ancestral terms). All terms (other than the root terms representing each namespace, above) have a sub-class relationship to another term. The following is an example of a GO term taken from the OBO format file… [t]able 2 shows GO Slim terms enriched for a desired amino acid yield and productivity in a given microbial strain based on experimentation[generating a representation of a portion of a respective microbial strain variant according to a product language and evaluating the representation of the portion of a respective microbial strain variant according to the product language].”).6
Regarding claim 13, Kok in view of Chowdhury teaches the method of claim 12, wherein evaluating the representation includes evaluating the representation of the portion of the respective microbial strain variant using a language model(Chowdhury, paras., [0067-0098], see also table 2, and fig. 6, “FIG. 6 illustrates an example of a subgraph from the Gene Ontology, with gene classes 602, 604 and 606 enriched for improved yield. In this grouping, gene sets are associated with specific terms in the ontology (and all ancestral terms). All terms (other than the root terms representing each namespace, above) have a sub-class relationship to another term[wherein evaluating the representation includes evaluating the representation of the portion of the respective microbial strain variant using a language model].”).7
Regarding claim 14, Kok in view of Chowdhury teaches the method of claim 1, wherein the evaluation of the set of microbial strain variants of the biological parent includes evaluating the set of microbial strain variants according to a ranking order of the set of microbial strain variants(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Step 4: As noted above, step 2 experimentally determines values of the performance parameters of different strains at second (e.g., bench) scale. In step 4, the PM engine compares experimentally determined performance parameter values with their theoretical maximums. The resulting difference represents the potential performance improvement ("available headroom") that might be achieved in strain performance by adjusting conditions or modifying their genome. Based on these differences and relationships known in the industry between these performance parameters and the KPI, the PM engine ranks the performance parameters, with the highest ranking going to the performance parameter with the greatest available headroom (114)[ evaluating the set of microbial strain variants according to a ranking order of the set of microbial strain variants].”).
Regarding claim 15, Kok in view of Chowdhury teaches the method of claim 14, wherein the evaluation of the set of microbial strain variants according to the ranking order of the set of microbial strain variants includes, for a respective microbial strain variant of the set of microbial strain variants, determining a score based on a comparison between the respective microbial strain variant and the biological parent, and determining the ranking order based on the score of the respective microbial strain variant(Chowdhury, paras., [0042-0047], see also fig., 2 and table 1, “In one example experiment, each promoter in the ladder was cloned in front of eyfp, a gene encoding yellow fluorescent protein in the shuttle vector pKl8rep. These plasmids were transformed into C. glutamicum NRRL B-11474 and promoter activity was assessed by measuring the accumulation of YFP protein by spectrometry… [t]he metric under consideration in this and other examples is fraction of candidates for improvement, or "hit rate," which is the fraction of modifications whose measured level of improvement is above a noise threshold in one or more phenotypes of interest. The threshold may be set based on the noise (e.g., root mean squared error) in predicting performance at scale (i.e., larger than small scale) relative to performance at a small, high-throughput scale, and also represents a minimum threshold for what can be considered… these cutoffs are 10% above the unmodified parent genome for the productivity model and 3% above parent for the yield model[for a respective microbial strain variant of the set of microbial strain variants, determining a score based on a comparison between the respective microbial strain variant and the biological parent, and determining the ranking order based on the score of the respective microbial strain variant].”).8
Regarding claim 16, Kok in view of Chowdhury teaches the method of claim 14, wherein evaluating the set of microbial strain variants according to the ranking order includes, selecting, from the set of microbial strain variants, a first set of candidate microbial strain variants based on the ranking order; evaluating the first set of candidate microbial strain variants based on each of at least two objectives of respective microbial strain variants of the first set of candidate microbial strain variants; and based on evaluating the first set of candidate microbial strain variants, selecting a second set of candidate microbial strain variants for evaluation(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Step 4: As noted above, step 2 experimentally determines values of the performance parameters of different strains at second (e.g., bench) scale. In step 4, the PM engine compares experimentally determined performance parameter values with their theoretical maximums. The resulting difference represents the potential performance improvement ("available headroom") that might be achieved in strain performance by adjusting conditions or modifying their genome. Based on these differences and relationships known in the industry between these performance parameters and the KPI, the PM engine ranks the performance parameters, with the highest ranking going to the performance parameter with the greatest available headroom (114)[ selecting, from the set of microbial strain variants, a first set of candidate microbial strain variants based on the ranking order;]… the screening process comprises determining the response (by screening parameter, e.g., yield) at the smaller scale of each candidate strain to a range of condition values of the top-ranked conditions to determine if the candidate strain is viable under those conditions and satisfies a performance threshold. In this example, the PM engine assembles together the initial parameters (103), the candidate screening conditions 110, the environmental conditions 115, and the candidate screening parameters 116 to preliminarily designs experiments to screen strains of E. coli for yield and growth rate while producing low quantities of undesired byproducts under the top-ranked conditions of substrate gradient, maximum oxygen transfer, and maximum sheer, and under the environmental conditions 115[evaluating the first set of candidate microbial strain variants based on each of at least two objectives of respective microbial strain variants of the first set of candidate microbial strain variants]… the PM engine employs multi-objective optimization ("MOO") techniques to determine optimized condition values that correspond to optimization over multiple objectives that impact the KPI…the PM engine then selects strains having a predicted second-scale performance exceeding a performance threshold. These strains may serve as base strains for further laboratory experiments in which the base strains' genomes are genetically perturbed[and based on evaluating the first set of candidate microbial strain variants, selecting a second set of candidate microbial strain variants for evaluation].”).
Regarding claim 17, Kok in view of Chowdhury teaches the method of claim 16, wherein evaluating the first set of candidate microbial strain variants includes at least one of: evaluating a simulation of respective microbial strain variants of the first set of candidate microbial strain variants, or evaluating an experimental result of respective microbial strain variants of the first set of candidate microbial strain variants(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Step 3A: Using a computer simulation-model of the metabolism of the organism, predict maximum theoretical values of performance parameters of different strains of the organism at first (e.g., plate) scale (112)[evaluating a simulation of respective microbial strain variants of the first set of candidate microbial strain variants,]. This step determines the theoretical maximum conversion rates from the provided substrate to the desired product, alongside determining potential byproducts ( e.g., an undesired organic acid), or limitations (e.g., required presence of certain vitamins or minerals necessary for the organism growth and performance) that could prevent achieving those higher conversion rates.”).9
Regarding claim 18, Kok in view of Chowdhury teaches the method of claim 16, wherein the second set of candidate microbial strain variants includes at least one of:at least one further microbial strain variant of at least one microbial strain variant of the first set of candidate microbial strain variants, or at least one microbial strain variant of the set of microbial strain variants that is not included in the first set of candidate microbial strain variants(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “[T]he PM engine then selects strains having a predicted second-scale performance exceeding a performance threshold. These strains may serve as base strains for further laboratory experiments in which the base strains' genomes are genetically perturbed. Using these new perturbed strains, the PM engine may repeat steps 2-8 for the perturbed strains until a desired predicted second-scale performance is achieved or an external parameter (e.g., number of iterations) is satisfied[at least one further microbial strain variant of at least one microbial strain variant of the first set of candidate microbial strain variants].”).10
Regarding claim 19, Kok teaches a system comprising: one or more computers; and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions(Kok, para., [0413], “Program code may be stored in non-transitory media such as persistent storage in secondary memory 1110 or main memory 1108 or both. Main memory 1108 may include volatile memory such as random access memory (RAM) or non-volatile memory such as read only memory (ROM), as well as different levels of cache memory for faster access to instructions and data. Secondary memory
may include persistent storage such as solid state drives, hard disk drives or optical disks. One or more processors 1104 reads program code from one or more non-transitory media and executes the code to enable the computer system to accomplish the methods….”)and for all other claim limitations they are rejected on the same basis as independent claim 1 since they are analogous claims.
Regarding claim 20, Kok teaches one or more non-transitory computer storage media storing instructions(Kok, para., [0413], “Program code may be stored in non-transitory media such as persistent storage in secondary memory 1110 or main memory 1108 or both. Main memory 1108 may include volatile memory such as random access memory (RAM) or non-volatile memory such as read only memory (ROM), as well as different levels of cache memory for faster access to instructions and data. Secondary memory may include persistent storage such as solid state drives, hard disk drives or optical disks. One or more processors 1104 reads program code from one or more non-transitory media and executes the code to enable the computer system to accomplish the methods….”) and for all other claim limitations they are rejected on the same basis as independent claim 1 since they are analogous claims.
Claims 3-5 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kok et al. US 2022/0328128 Al (“Kok”) in view of Chowdhury et al., US 2020/0168291 Al (“Chowdhury”) and in view of Serber et al., US 20170159045 A1 (“Serber”).
Regarding claim 3, Kok in view of Chowdhury teaches the method of claim 1, but does not teach: wherein each microbial strain variant is within an edit distance threshold of the biological parent.
However, Serber teaches:
wherein each microbial strain variant is within an edit distance threshold of the biological parent(Serber, paras. [0342-365], see also table 1.1, “The present disclosure teaches methods of improving genetically engineered host strains by providing one or more transcriptional termination sequences at a position 3′ to the end of the RNA encoding element[wherein each microbial strain variant is within an edit distance threshold]… the present disclosure teaches the use of a series of tandem termination sequences…the first transcriptional terminator sequence of a series of 2, 3, 4, 5, 6, 7, or more may be placed…at a distance of at least 1-5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45-50, 50-100, 100-150, 150-200, 200-300, 300-400, 400-500, 500-1,000 or more nucleotides 3′ to the final nucleotide of the dsRNA encoding element…the present disclosure teaches use of annotated Corynebacterium glutamicum[of the biological parent] terminators… [a] non-exhaustive listing of transcriptional terminator sequences of the present disclosure is provided in Table 1.1.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kok in view of Chowdhury with the teachings of Serber the motivation to do so would be to develop a single microbial genomic platform that is capable of implementing all the required tools that are needed for genomic engineering in a cost effective and productive manner(Serber, paras. [0004-0011], “Given the large number of products produced by modern industrial microbes, it comes as no surprise that engineers are under tremendous pressure to improve the speed and efficiency by which a given microorganism is able to produce a target product… identification of improved industrial microbial strains through mutagenesis is time consuming and inefficient. The process, by its very nature, is haphazard and relies upon one stumbling upon a mutation that has a desirable outcome on product output…[t]he present disclosure provides a high-throughput (HTP) microbial genomic engineering platform that does not suffer from the myriad of problems associated with traditional microbial strain improvement programs”).
Regarding claim 4, Kok in view of Chowdhury teaches the method of claim 1, wherein the evaluation of the at least two objectives for the set of microbial strain variants(Kok, paras., [0333-0405], see also figs. 32A, 32B, and 32C, “Referring to the example of FIG. 32A, E coli has a known metabolic path from glucose to product, from which one can determine theoretical maximum product performance (e.g., yield). In the example shown, the performance parameters may include the following: Growth Rate, Viability, Specific Productivity (on a cell level), YPX (Yield Product Per Biomass), Byproduct Output rate[wherein the evaluation of the at least two objectives for the set of microbial strain variants]. These performance parameters are known in the industry to influence the KPI … [s]tep 6: FIGS. 32B and 32C illustrate step 6. According to embodiments of the disclosure, the PM engine employs multi-objective optimization ("MOO") techniques to determine optimized condition values that correspond to optimization over multiple objectives that impact the KPI.”) but do not teach: includes evaluating a distance of the set of microbial strain variants relative to the biological parent.
However, Serber teaches:
includes evaluating a distance of the set of microbial strain variants relative to the biological parent (Serber, paras. [0342-365], see also table 1.1, “The present disclosure teaches methods of improving genetically engineered host strains by providing one or more transcriptional termination sequences at a position 3′ to the end of the RNA encoding element… the present disclosure teaches the use of a series of tandem termination sequences…the first transcriptional terminator sequence of a series of 2, 3, 4, 5, 6, 7, or more may be placed…at a distance of at least 1-5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45-50, 50-100, 100-150, 150-200, 200-300, 300-400, 400-500, 500-1,000 or more nucleotides 3′ to the final nucleotide of the dsRNA encoding element…the present disclosure teaches use of annotated Corynebacterium glutamicum terminators… [a] non-exhaustive listing of transcriptional terminator sequences of the present disclosure is provided in Table 1.1.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kok in view of Chowdhury with the teachings of Serber the motivation to do so would be to develop a single microbial genomic platform that is capable of implementing all the required tools that are needed for genomic engineering in a cost effective and productive manner(Serber, paras. [0004-0011], “Given the large number of products produced by modern industrial microbes, it comes as no surprise that engineers are under tremendous pressure to improve the speed and efficiency by which a given microorganism is able to produce a target product… identification of improved industrial microbial strains through mutagenesis is time consuming and inefficient. The process, by its very nature, is haphazard and relies upon one stumbling upon a mutation that has a desirable outcome on product output…[t]he present disclosure provides a high-throughput (HTP) microbial genomic engineering platform that does not suffer from the myriad of problems associated with traditional microbial strain improvement programs”).
Regarding claim 5, Kok in view of Chowdhury and Serber teaches the method of claim 4, wherein the distance includes at least one of an edit distance between the biological parent and each microbial strain variant, a number of edits between the biological parent and each microbial strain variant, a degree of edits between the biological parent and each microbial strain variant, a difference between a measurement of an objective of the at least two objectives of each microbial strain variant relative to a corresponding measurement of the objective of the biological parent, a structural feature of each microbial strain variant relative to a corresponding structural feature of the biological parent, or a viability score of each microbial strain variant relative to a corresponding viability score of the biological parent(Serber, paras. [0342-365], see also table 1.1, “The present disclosure teaches methods of improving genetically engineered host strains[microbial strain variant] by providing one or more transcriptional termination sequences at a position 3′ to the end of the RNA encoding element[a degree of edits between the biological parent and each microbial strain variant]… the present disclosure teaches the use of a series of tandem termination sequences…the first transcriptional terminator sequence of a series of 2, 3, 4, 5, 6, 7, or more may be placed…at a distance of at least 1-5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45-50, 50-100, 100-150, 150-200, 200-300, 300-400, 400-500, 500-1,000 or more nucleotides 3′ to the final nucleotide of the dsRNA encoding element[wherein the distance includes at least one of an edit distance between the biological parent and each microbial strain variant]…the present disclosure teaches use of annotated Corynebacterium glutamicum [the biological parent] terminators… [a] non-exhaustive listing of transcriptional terminator sequences of the present disclosure is provided in Table 1.1[a number of edits between the biological parent and each microbial strain variant,].”).11 12
Regarding dependent claim 21, it is rejected on the same basis as dependent claims 3 since they are analogous claims.
Conclusion
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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/Adam C Standke/
Primary Examiner
Art Unit 2129
1 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
2 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
3 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are taught by the prior art of Kok.
4 It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kok with the above teachings of Chowdhury for the same rationale stated at Claim 1.
5 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
6 It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kok with the above teachings of Chowdhury for the same rationale stated at Claim 1.
7 It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kok with the above teachings of Chowdhury for the same rationale stated at Claim 1.
8 It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kok with the above teachings of Chowdhury for the same rationale stated at Claim 1.
9 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
10 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
11 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
12 It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kok in view of Chowdhury with the above teachings of Serber for the same rationale stated at Claim 4.
Prosecution Insights