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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/26/2026, 05/14/2026, 07/06/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. While the independent claims 1, 13 and 17 are directed to statutory subject matter under Step 1, the independent claims recite the following judicial exceptions:
integrating, by a set of data integration facilities, content of at least one publication data set relating to the strain and at least one proprietary data set including a set of parameters of a process in which the strain produces the functional output...and generating, the set of recommendations wherein the set of recommendations relate to at least one of a set of modifications to a set of genes of the strain, a set of modifications to a set of environmental parameters for the process in which the strain produces the functional output, a set of modifications to a set of pathways associated with the process in which the strain produces the functional output, or a set of modifications to a set of proteins or enzymes associated with the biological strain; wherein that the set of recommendations enhances the production of the functional output by the strain.
When viewing these claim limitations under the Broadest Reasonable Interpretation, these claim limitations can be performed in the human mind through the use of observations, evaluations, judgements and opinion and thus fall under the mental process grouping under Step 2A, Prong One.
These judicial exceptions are not integrated into a practical application under Step 2A, Prong Two because the additional claim elements of:
wherein an output of data integration facilities is configured as an input to a set of machine learning models amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., output of data as input to a set of machine learning models)
by at least one member of the set of machine learning models recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model(s) and/or configuration(s) used and the training and/or finetuning steps used
one or more computers; and one or more storage devices communicatively coupled to the one or more computers are recited at a high-level of generality using generic computer components (i.e., using a generic computer and generic storage to perform generic computer functions) such that it does not amount to a particular machine
computer storage media storing instructions that when executed by one or more computers are recited at a high-level of generality using generic computer components (i.e., using a generic computer and generic storage to perform generic computer functions) such that it does not amount to a particular machine
And these additional elements are not sufficient to amount to significantly more than the judicial exception under Step 2B because as stated above the additional claim elements of:
wherein an output of data integration facilities is configured as an input to a set of machine learning models are well-understood, routine, conventional activity that court decisions, such as Symantec and buySAFE cited in MPEP 2106.05(d)(II) have indicated that the mere receiving and/or sending of data using a generic computer are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here)
by at least one member of the set of machine learning models only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
one or more computers; and one or more storage devices communicatively coupled to the one or more computers are recited at a high-level of generality using generic computer components (i.e., using a generic computer and generic storage to perform generic computer functions) such that it does not amount to a particular machine
computer storage media storing instructions that when executed by one or more computers are recited at a high-level of generality using generic computer components (i.e., using a generic computer and generic storage to perform generic computer functions) such that it does not amount to a particular machine
Dependent claims 2, 9-12, 14 and 18 are directed to statutory subject matter under Step 1, but when viewed under the Broadest Reasonable Interpretation, do not contain additional claim limitations that transform the judicial exception into a practical application under Step 2A, Prong Two because the additional claim elements of:
wherein the set of machine learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a long short-term memory (LSTM) model, a multi-layer perceptrons, a lin-log model, a large language model, a large protein model, or a protein language model recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model(s) and/or configuration(s) used and the training and/or finetuning steps used
wherein the functional output includes at least one of fuel applications and solutions, industrial applications and solutions, consumer product applications and solutions, pharmaceutical applications and solutions, or medical applications and solutions amount to no more than generally linking the use of a judicial exception to a field of use e.g., fuel applications
wherein processing the inputs by the set of machine learning models includes processing in parallel across multiple AI Processing cores, wherein each processing core handles a subset of the input data amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data
wherein the set of machine learning models use adaptive computation techniques that dynamically adjust a model's computational complexity based on input complexity recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model(s) and/or configuration(s) used and the training and/or finetuning steps used to dynamically adjust a model’s computational complexity based on input complexity
wherein integrating the content includes using dedicated processing cores to perform data transformation or integration operations is recited at a high-level of generality using a generic processor such that it does not amount to a particular machine
and do not include additional elements that amount to significantly more than the judicial exception under Step 2B because the additional claim elements of:
wherein the set of machine learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a long short-term memory (LSTM) model, a multi-layer perceptrons, a lin-log model, a large language model, a large protein model, or a protein language model only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
wherein the functional output includes at least one of fuel applications and solutions, industrial applications and solutions, consumer product applications and solutions, pharmaceutical applications and solutions, or medical applications and solutions amount to no more than generally linking the use of a judicial exception to a field of use e.g., fuel applications
wherein processing the inputs by the set of machine learning models includes processing in parallel across multiple AI Processing cores, wherein each processing core handles a subset of the input data are well-understood, routine, conventional activity that court decisions, such as Versata Dev. Group, Inc. cited in MPEP 2106.05(d)(II) have indicated that mere data gathering and manipulation of data using a generic computer are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here)
wherein the set of machine learning models use adaptive computation techniques that dynamically adjust a model's computational complexity based on input complexity only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
wherein integrating the content includes using dedicated processing cores to perform data transformation or integration operations is recited at a high-level of generality using a generic processor such that it does not amount to a particular machine
Dependent claims 3-8, 15-16, and 19-20 are directed to statutory subject matter under Step 1, but when viewed under the Broadest Reasonable Interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and recite no additional elements beyond the judicial exception of the mental process.
Claim Rejections - 35 USC § 102
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.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1-6, and 9-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Aaron et al., CA 3105722 A1(“Aaron”).
Regarding claim 1, Aaron teaches a method
performed by one or more computers(Aaron, para., [0462], see also figs., 31-34, “[T]he LEVIS system application software 3210 resides in the cloud computing system 3202. The LEVIS system may employ one or more computing systems using one or more processors, of the type illustrated in Figure 34[performed by one or more computers].”)
for using one or more machine learning models to generate a set of recommendations associated with a production of a functional output by a strain(Aaron, para., [0403], “[T]he proposed sequence modifications to a host strain[by a strain] recommended by...predictive model[for using one or more machine learning models to generate a set of recommendations], are carried out by the utilization of one or more of the disclosed molecular tools sets comprising: (1) Promoter swaps, (2) SNP swaps, (3) Start/Stop codon exchanges, (4) Sequence optimization, (5) Stop swaps, and (5) Epistasis mapping[[with a production of a functional output]].”),1 the method comprising:
integrating, by a set of data integration facilities, content of at least one publication data set relating to the strain(Aaron, paras., [0398-0402], “[T]he recommendations of the present system are based on scientific insights. For example, in some embodiments, the recommendations are based on known properties of genes (from sources such as annotated gene databases and the relevant literature)[ integrating, by a set of data integration facilities, content of at least one publication data set relating to the strain].”)
and at least one proprietary data set including a set of parameters of a process in which the strain produces the functional output(Aaron, para., [0404], “[T]he lessons learned from a given HTP genetic engineering process used to create one novel host cell, can be applied to any number of other host cells[in which the strain produces the functional output], as a result of the storage, characterization, and analysis of a
myriad of process parameters[and at least one proprietary data set including a set of parameters of a process] that occurs during the taught methods.”),
wherein an output of data integration facilities is configured as an input to a set of machine learning models(Aaron, para., [0405], “As alluded to in the epistatic mapping section, it is possible to estimate the performance (a.k.a. score) of a hypothetical strain obtained by consolidating a collection of mutations from a HTP genetic design library into a particular background via some preferred predictive model[wherein an output of data integration facilities is configured as an input to a set of machine learning models]. Given such a predictive model, it is possible to score and rank all hypothetical strains accessible to the mutation library via combinatorial consolidation. The below section outlines particular models utilized in the present HTP platform.”);
and generating, by at least one member of the set of machine learning models, the set of recommendations wherein the set of recommendations relate to at least one of a set of modifications to a set of genes of the strain, a set of modifications to a set of environmental parameters for the process in which the strain produces the functional output, a set of modifications to a set of pathways associated with the process in which the strain produces the functional output, or a set of modifications to a set of proteins or enzymes associated with the biological strain(Aaron, paras., [0398-0402],“[T]he present disclosure teaches methods of predicting the effects[and generating, by at least one member of the set of machine learning models,] of particular genetic alterations being incorporated into a given host strain. In further aspects, the disclosure provides methods for generating proposed genetic alterations that should be incorporated into a given host strain, in order for said host to possess a particular phenotypic trait or strain parameter [the set of recommendations wherein the set of recommendations relate to at least one of a set of modifications to a set of genes of the strain]...the recommendations of the present system are based on scientific insights. For example, in some embodiments, the recommendations are based on known properties of genes (from sources such as annotated gene databases and the relevant literature)....”);2
wherein that the set of recommendations enhances the production of the functional output by the strain(Aaron, para., [0406], “Described herein is an approach for predictive strain design, including: methods of describing genetic changes and strain performance, predicting strain performance based on the composition of changes in the strain, recommending candidate designs with high predicted performance[wherein that the set of recommendations enhances the production of the functional output by the strain], and filtering predictions to optimize for second-order considerations, e.g. similarity to existing strains, epistasis, or confidence in predictions.”).
Regarding claim 2, Aaron teaches the method of claim 1, wherein the set of machine learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a long short-term memory (LSTM) model, a multi-layer perceptrons, a lin-log model, a large language model, a large protein model, or a protein language model(Aaron, para., [0435], “In particular, the candidate changes that actually result in sufficiently high measured performance may be added as rows in the database to tables such as Table 4 above. In this manner, the best performing mutations are added to the predictive strain design model in a supervised machine learning fashion[at least one of a supervised model].”).3
Regarding claim 3, Aaron teaches the method of claim 1, wherein the at least one publication data set includes at least one of: gene function description datasets, datasets from metabolic pathway databases, comparative genomics datasets, omics datasets, functional assay datasets, experiment result datasets, bioinformatics analyses datasets, regulatory study datasets, enzyme characterization datasets, case study datasets, or patent literature(Aaron, para., [0402], “[T]he recommendations of the present system are based on scientific insights. For example, in some embodiments, the recommendations are based on
known properties of genes (from sources such as annotated gene databases and the relevant literature)[ at least one of: gene function description datasets]....”).4
Regarding claim 4, Aaron teaches the method of claim 1, wherein the at least one proprietary data set includes at least one of genetic parameters, metabolic parameters, growth and physiological parameters, environmental and culture conditions, process parameters, functional output parameters, regulatory and control parameters, phenotypic parameters, omics parameters, scale-up parameters, or energy consumption parameters(Aaron, para., [0404], “The lessons learned from a given HTP genetic engineering process used to create one novel host cell, can be applied to any number of other host cells, as a result of the storage, characterization, and analysis of a myriad of process parameters[at least one of process parameters] that occurs during the taught methods.”).5
Regarding claim 5, Aaron teaches the method of claim 1, wherein the set of recommendations relates to at least one of knockout mutations, overexpression of target genes, activation of specific genes, insertion of specific genes, gene knockdowns, site-directed mutagenesis, promoter engineering, codon optimization, gene fusion, allele replacement, creation of synthetic gene circuits, introduction of regulatory elements, or application of advanced genome editing technologies(Aaron, para., [0403], “[T]he proposed sequence modifications to a host strain recommended by...predictive model, are carried out by the utilization of one or more of the disclosed molecular tools sets comprising: (1) Promoter swaps[at least one of promoter engineering], (2) SNP swaps, (3) Start/Stop codon exchanges[codon optimization], (4) Sequence optimization[application of advanced genome editing technologies], (5) Stop swaps, and (5) Epistasis mapping.”).6
Regarding claim 6, Aaron teaches the method of claim 1, wherein the set of recommendations relates to modifications of at least one of temperature, pH level, oxygen supply, nutrient composition, fermentation time, stirring and mixing, inoculum size, light conditions, toxicity management, pressure, or salinity(Aaron, para., [0409], “Process parameters that can be adjusted include temperature[modifications of at least one of temperature], pressure[pressure], reactor configuration, and medium composition[fermentation time]. Examples of reactor configuration include the volume of the reactor, whether the process is a batch or continuous, and, if continuous, the volumetric flow rate[stirring and mixing], etc. One can also specify the support structure, if any, on which the cells reside. Examples of medium composition include the concentrations of electrolytes, nutrients, waste products, acids, pH, and the like[pH level, nutrient composition].”).7
Regarding claim 9, Aaron teaches the method of claim 1, wherein the functional output includes at least one of fuel applications and solutions, industrial applications and solutions, consumer product applications and solutions, pharmaceutical applications and solutions, or medical applications and solutions(Aaron, para. [0002], “The advent of genetic engineering technology has enabled scientists to design and program novel biosynthetic pathways into a variety of organisms to produce a broad range of industrial, medical, and
consumer products. Indeed, microbial cellular cultures are now used to produce products ranging
from small molecules, antibiotics, vaccines, insecticides, enzymes, fuels[at least one of fuel applications and solutions], and industrial chemicals.”).
Regarding claim 10, Aaron teaches the method of claim 1, wherein processing the inputs by the set of machine learning models includes processing in parallel across multiple AI Processing cores, wherein each processing core handles a subset of the input data(Aaron, paras., [0481], “The processor(s) 804 may include graphics processing units (GPUs) for handling computationally intensive tasks. Particularly in machine learning, one or more CPUs 804 may offload the processing of large quantities of data to one or more GPUs 804[wherein processing the inputs by the set of machine learning models includes processing in parallel across multiple AI Processing cores, wherein each processing core handles a subset of the input data].”).
Regarding claim 11, Aaron teaches the method of claim 1, wherein the set of machine learning models use adaptive computation techniques that dynamically adjust a model's computational complexity based on input complexity(Aaron, paras., [0284-0287], “[T]he predictive model may be weighted with the similarity matrix. For example, some embodiments may employ a weighted least squares regression using the similarity matrix to characterize the interdependencies of the proposed mutations. As an example, weighting may be performed by applying the kernel trick to the regression model.... [d]uring each iteration, the accuracy can be assessed to determine whether model performance is improving... the dissimilar mutation response profiles may be used by the analysis equipment 214 to augment the score and
rank associated with each hypothetical strain from the predictive model[based on input complexity]. This procedure may be thought of broadly as a re-weighting of scores[use adaptive computation techniques that dynamically adjust a model's computational complexity], so as to favor candidate strains with dissimilar response profiles (e.g., strains drawn from a diversity of clusters).”).
Regarding claim 12, Aaron teaches the method of claim 1, wherein integrating the content includes using dedicated processing cores to perform data transformation or integration operations(Aaron, paras., [0481], “The processor(s) 804 may include graphics processing units (GPUs) for handling computationally intensive tasks[includes using dedicated processing cores to perform data transformation or integration operations]. Particularly in machine learning, one or more CPUs 804 may offload the processing of large quantities of data to one or more GPUs 804.”).8
Regarding claim 13, Aaron 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(Aaron, paras., [0480-0481], “LEVIS system, may be implemented with a computer system like that of computer system 800. Program code may be stored in non-transitory media such as persistent storage in secondary memory 810 or main memory 808 or both. Main memory 808 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 804 reads program code from one or more non-transitory media and executes the code to enable the computer system to accomplish the methods[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]....”) and for all other claim limitations they are rejected on the same basis as independent claim 1 since they are analogous claims.
Referring to dependent claims 14-16, they are rejected on the same basis as
dependent claims 2-4 since they are analogous claims.
Regarding claim 17, Aaron teaches one or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations(Aaron, paras., [0480-0481], “LEVIS system, may be implemented with a computer system like that of computer system 800. Program code may be stored in non-transitory media such as persistent storage in secondary memory 810 or main memory 808 or both. Main memory 808 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[computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations]. Secondary memory may include persistent storage such as solid state drives, hard disk drives or optical disks. One or more processors 804 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.
Referring to dependent claims 18-20, they are rejected on the same basis as
dependent claims 2-4 since they are analogous claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Aaron et al., CA 3105722 A1(“Aaron”) in view of Michael, Drew G., et al. "Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast." Proceedings of the National Academy of Sciences 113.47 (2016)(“Michael”).
Regarding claim 7, Aaron teaches the method of claim 1, but does not teach: wherein the set of recommendations relates to at least one of identification and overexpression of key enzymes, use of stronger or inducible promoters, knockout of competing pathways, pathway engineering, optimization of substrate utilization, feedback regulation modification, cofactor engineering, pathway flux redistribution, integration of pathways, or environmental adaptations.
However, Michael teaches:
wherein the set of recommendations relates to at least one of identification and overexpression of key enzymes, use of stronger or inducible promoters, knockout of competing pathways, pathway engineering, optimization of substrate utilization, feedback regulation modification, cofactor engineering, pathway flux redistribution, integration of pathways, or environmental adaptations(Michael, pg., 5-6, see also fig. 4, “Six of the eight NetSurgeon selected interventions lowered the number of DE[differential expressed] genes. The best intervention
c
a
t
8
∆
reduced the number of DE genes by 22%... [w]e also evaluated the ability of each TF deletion to promote a fermentative state in 11 pathways of central carbon metabolism (Fig. 4D)[ relates to at least one of knockout of competing pathways, pathway engineering]. Seven of the eight NetSurgeon-selected interventions reduced the Euclidean distance between the initial (T24, respiratory) and goal (T4, fermentative) state expression levels of the genes in at least one of the central carbon metabolism pathways evaluated.”).9
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 Aaron with the teachings of Michael the motivation to do so would be to devise a recommendation system and algorithms for transcriptional intervention for genes to complement other recommendations related to strain optimization(Michael, pg., 2, “In this paper, we introduce and systematically evaluate Net-
Surgeon, a broadly applicable algorithm for recommending TF [transcription factor] deletions or overexpressions to move cells from an initial (current) expression state to any desired state associated with any desired phenotype... we apply NetSurgeon to the goal of improving xylose fermentation in S. cerevisiae... [t]his led us to hypothesize that engineering yeast cells to have
a gene expression profile in xylose similar to the profile of wildtype
(WT) cells in glucose would increase ethanol yield.”).
Regarding claim 8, Aaron teaches the method of claim 1, but does not teach: wherein the set of recommendations relates to at least one of enzyme overexpression, use of stronger promoters, site-directed mutagenesis, construction of chimeric proteins, enhancement of cofactor interactions, alleviation of feedback inhibition, application of post-translational modifications, modification of enzyme localization, gene knockouts of competing enzymes, allosteric modulation, or integration of modular enzyme assemblies.
However, Michael teaches:
wherein the set of recommendations relates to at least one of enzyme overexpression, use of stronger promoters, site-directed mutagenesis, construction of chimeric proteins, enhancement of cofactor interactions, alleviation of feedback inhibition, application of post-translational modifications, modification of enzyme localization, gene knockouts of competing enzymes, allosteric modulation, or integration of modular enzyme assemblies(Michael, pg., 5-6, see also fig. 4, “We also evaluated the ability of each TF deletion to promote a fermentative state in 11 pathways of central carbon metabolism (Fig. 4D). Seven of the eight NetSurgeon-selected interventions reduced the Euclidean distance between the initial (T24, respiratory) and goal (T4, fermentative) state expression levels of the genes in at least one of the central carbon metabolism pathways evaluated... [d]eletion of CAT8 promoted a fermentative state in many metabolic pathways essential for xylose fermentation including
genes involved in... the TCA cycle, and acetate/glycerol production. It moved all of the TCA cycle genes toward their expression levels in the goal state[relates to at least one of enhancement of cofactor interactions]...[c]onsidering all genes encoding enzymes in the TCA cycle, deletion of CAT8 reduced the Euclidean distance to the goal state by 60%[gene knockouts of competing enzymes].”).10
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 Aaron with the teachings of Michael the motivation to do so would be to devise a recommendation system and algorithms for transcriptional intervention for genes to complement other recommendations related to strain optimization(Michael, pg., 2, “In this paper, we introduce and systematically evaluate Net-
Surgeon, a broadly applicable algorithm for recommending TF [transcription factor] deletions or overexpressions to move cells from an initial (current) expression state to any desired state associated with any desired phenotype... we apply NetSurgeon to the goal of improving xylose fermentation in S. cerevisiae... [t]his led us to hypothesize that engineering yeast cells to have
a gene expression profile in xylose similar to the profile of wildtype
(WT) cells in glucose would increase ethanol yield.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Neumann, US 11984199-B2(details a system for generating a compatible substance using AI and biomarker data from a database)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM C STANDKE whose telephone number is (571)270-1806. The examiner can normally be reached Gen. M-F 9-9PM EST.
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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 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
4 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
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 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
7 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
8 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
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.
Prosecution Insights