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 .
Applicant’s Response
Applicant’s response, filed 11/25/2025, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Claims Status
Claims 1-24 are pending.
Claims 16-24 are withdrawn from consideration.
Claims 1-15 are examined.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 8-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The rejection is maintained from the previous Office Action.
With respect to claim 8, the Specification as originally filed includes written description of “inducing single gene modifications”, “inducing multiple gene modifications” (paragraph [0007]), and “computer readable program instructions thereon causing a processor to carry out the aspects of the present invention” (paragraph [0037]). However, the Specification does not appear to provide written description for a computer program product comprising program instructions “for inducing single genetic modifications, wherein single genetic modifications refer to manipulation of a genome of a cell through induced mutations”. The specification does not describe how program instructions could lead to induced mutations as this is a physical process and it is not described how code instructions would induce these modifications.
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 1-15 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. This is a new grounds of rejection as necessitated by claim amendments.
With respect to claims 1 and 8, the claims recite the limitation of “improving a classification accuracy of the set of rules for the unboxing algorithm”. The claim is indefinite because there is no antecedent basis for “the set of rules”. Thus, it is unclear what classification accuracy is being improved and for which rules.
Claim Rejections - 35 USC § 101
Claims 8-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to a computer program product comprising one or more computer readable storage media and program instructions and thus, they are directed to “software per se” i.e., software is not limited to a particular structure and given the broadest most reasonable interpretation, the claims encompass a software system containing software components. As such, the claims are non-statutory because they comprise a program comprising no structure reads on carrier waves which read on transitory propagating signals and are not proper patentable subject matter because they do not fit within any of the four statutory categories of invention (In re Nuijten, Federal. Circuit, 2006). Note: Claims 8-15
are included within the steps of identifying eligible subject matter because if the claims
were amended to fit into one of the four statutory categories of invention, they would still be
rejected for being directed to an abstract idea without significantly more. The rejection is maintained from the previous Office Action.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to an abstract idea of mental steps, mathematic concepts, or a natural law without significantly more. Any newly recited portion is necessitated by claim amendment.
The MPEP at MPEP 2106.03 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of
matter?
(2A)(1) Are the claims directed to a judicially recognized exception, i.e. a law of nature,
a natural phenomenon, or an abstract idea?
(2A)(2) If the claims are directed to a judicial exception under Prong One, then is the
judicial exception integrated into a practical application?
(2B) If the claims are directed to a judicial exception and do not integrate the judicial
exception, do the claims provide an inventive concept?
With respect to step (1): Yes, the claims recite a method.
With respect to step (2A)(1): The claims recite an abstract idea of mathematic concepts and mental processes. “Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection” (MPEP 2106.04). Abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (procedures for observing, evaluating, analyzing/judging and organizing information (MPEP 2106.04(a)(2)). Laws of nature or natural phenomena include naturally occurring principles/relations that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP 2106(b)).
Mathematic concepts in claims 1 and 8:
using a reverse engineering automated rule-extraction algorithm to construct an unboxing algorithm, wherein the deep learning neural network uses the unboxing algorithm to learn a non-linear model representing cellular morphology perturbations resulting from single local genetic insults,
constructing the unboxing algorithm by pruning neurons corresponding to input features that do not significantly affect the deep learning neural network’s accuracy
improving a classification accuracy of the set of rules for the unboxing algorithm by updating a data range matrix according to misclassified samples resulting from the constructed set of rules wherein a specific rule condition is updated if the update corresponds to a classification increase
testing the deep learning neural network with cellular morphology features from multiple genetic modifications
the trained and tested deep learning neural network reveals a link, by identifying patters, between cellular morphology features caused by the single genetic modifications and cellular morphology features caused by the multiple genetic modifications and outputs a genotype-phenotype mapping highlighting perturbation subspaces, wherein the perturbation subspaces refers to viable genetic modifications or useful cellular morphology phenotypes, and wherein the genotype-phenotype mapping refers to a set of input ranges that link the cellular morphology features of single and multiple genetic modifications where the input ranges map a genetic space into a morphological space
the unboxing algorithm define a closed loop system capable of facilitating genetic modification by controlled morphology perturbations
Mental processes recited in claims 1 and 8:
wherein the unboxing algorithm provides an efficient starting point for synthetic biology engineering processes by avoiding the implementation of genetic modifications that could lead to non-viable phenotypes
building a computer aided design system to guide genetic engineering for a specific synthetic biology product, based on generation of sufficient data correlating certain genetic insults with viable phenotypes
Dependent claims 2-6, and 9-14 recite additional steps that either are directed to abstract ideas or further limit the judicial exceptions in independent claims 1 and 8 and as such, are further directed to abstract ideas. Hence, the claims explicitly recite numerous elements that individually and in combination constitute abstract ideas. The relevant recitations are:
Claims 2 and 9: “the perturbation subspaces comprise viable genetic modifications that lead to viable phenotypes”
Claims 3 and 10: “the perturbation subspaces exclude unviable genetic modifications that lead to non-viable phenotypes”
Claims 4 and 11: “the perturbation subspaces comprise viable phenotypes for a synthetic biology application and/or product”
Claims 5 and 13: “the genotype-phenotype mapping shows genetic distance between the single genetic modifications and the multiple genetic modifications”
Claims 6 and 14: “the genetic distance and the perturbations subspaces are used to select one or more genetic insults as a defined space to engineer cells in synthetic biology applications”
Claim 12: “measuring genetic distance of the single and multiple genetic insults”
The abstract ideas recited in the claims are evaluated under Broadest Reasonable Interpretation (BRI) and determined herein to each cover mathematic concepts because the claims recite nothing more than performing mathematical calculations, as applied by the neural network, to calculate links between morphological features. The dependent claims further limit the output of the link between the morphological features caused by the genetic modifications and mental processes of determining the usefulness of a phenotype.
Because the claims do recite judicial exceptions, direction under (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim is said to fail to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, claims 1 and 8 recite the following additional elements:
programs instructions for inducing single genetic modifications, wherein single genetic modifications refer to manipulation of a genome of a cell through induced mutations;
inducing single genetic modifications
training a deep learning neural network with cellular morphology features from the single genetic modifications
a computer program product
one or more computer readable storage media and program instructions collectively stored on one or more computer readable storage media
program instructions to perform the method
The elements of a computer program product, a computer readable storage media, and program instructions, when recited so generically, do not integrate the abstract ideas into a practical application. The claims do not describe any specific computation steps by which the computer parts perform or carry out the abstract idea, nor do they provide any details of how structures of the system are used to implement the functions. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc. are recited so generically (i.e. no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exceptions to the technological environment of a computer (see MPEP 2106.05(f)).
Dependent claims 7 and 15 relate to additional limitations of data gathering or describing the data gathered as they recite steps limiting the multiple gene modifications. Data gathering steps are not abstract ideas but represent extra-solution activity, as said steps collect the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)).
None of these dependent claims recite additional elements, alone or in combination, which would integrate a judicial exception into a practical application.
With respect to step (2B): Because the claims recite an abstract idea, and do no integrate that abstract idea into a practical application, the claims lack a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP at 2106.05(d)(I), determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to claims 1 and 8: The additional elements of a computer program product, one or more computer readable storage media and program instructions collectively stored on one or more computer readable storage media, program instructions to perform the method, inducing single genetic modifications, wherein single genetic modifications refer to manipulation of a genome of a cell through induced mutations and training a deep learning neural network with cellular morphology features from single genetic modifications do not rise to the level of significantly more than the judicial exception. With respect to the computer program product, computer readable storage media, and program instructions, these are elements of a generic computer and it is not specified how these components of a generic computer perform the steps. As exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the abstract idea (see MPEP 2105(b)I-III). With respect to the gene modifications, as disclosed in the Specification in paragraph [0003], synthetic biology is a growing billion-dollar market and that cells are genetically engineered to produce a wide range of products. Furthermore, with respect to training of a deep learning neural network, the prior art Claes (WO 2015/173435 A1, published November 2015, IDS reference) discloses that in current genomic prediction methods, that predictions models are trained and tested (page 9, line 5). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claims 7 and 15: The additional element of the multiple genetic modifications comprise a combination of the single genetic modifications does not rise to the level of significantly more than the judicial exception. With respect to the gene modifications, as exemplified in the MPEP at 2106.05(f) with reference to Genetic Techs., 818 F.3d at 1377; 118 USPQ2d at 1546, the analyzing of a DNA sample to provide sequence information is a routine and conventional technique, thus multiple gene modifications are routine and conventional. As such, it is recognized that this additional limitation is routine, well understood, and conventional in the art. The limitation does not improve the functioning of a computer, or comprise an improvement to any other technical field, it does not require or set forth a particular machine, it does not affect a transformation of matter, nor does it provide a non-conventional or unconventional step. As such, this limitation fails to rise to the level of significantly more.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. Individually, the limitations of the claims and the claims as a whole have been found to not meet the eligibility requirements.
Response to Arguments
Applicant states that regarding claim 1 being directed to an abstract idea that “the alleged abstract idea does not fall within any of the subject matter groupings of abstract ideas enumerated in the 2019 Guidance (i.e. “Mathematical concepts,” “Certain methods of organizing human activity,”, or “Mental Processes”)”.
It is respectfully submitted that this is not persuasive. The instant claims are directed to abstract ideas of mathematical concepts and mental processes, and utilize the laws of nature of the correlation between morphological features and genetic modifications. As stated in the MPEP at 2106.04(a)(2).I., mathematical concepts need not be expressed in mathematical symbols, and mathematical relationship and manipulation information through mathematical functions is directed to a mathematical concepts. Furthermore, as stated in the MPEP at 2106.04(a)(2).III., mental processes are defined as concepts performed in the human mind including observations, evaluations, judgements, and opinions, and can be performed using a physical aid, such as a pen and paper, to perform the claim limitation. Therefore the limitations of the instant claims of using an algorithm for genotype-phenotype mapping and building a system to guide genetic engineering are directed to abstract ideas. Therefore, the rejection under 35 USC 101 is maintained.
Applicant states that “claim 1’s features are integrated into the practical application of genotype-phenotype mapping for single and multiple genetic insults.” “By integrating [the claimed] features to the genotype-phenotype mapping for single and multiple genetic insults, claim 1 integrates the features- and any associated abstract ideas- into a practical application”. Applicant states that “claim 1 integrates its features into a practical application of genotype-phenotype mapping for single and multiple genetic insults at least by providing improvements to existing technology” and “The algorithms aims to improve the efficiency of synthetic biology processes by providing accurate insights into cellular responses to genetic changes, reducing the reliance on trial-and-errors methods. The system uses images of cells, extracts features from these images and employs a neural network to classify and predict cellular changes, thereby offering a more precise approach to synthetic biology engineering”.
It is respectfully submitted that this is not persuasive. Firstly, the claims do not recite any limitations regarding using images of cells and extracting features from these images. Furthermore, It is the additional elements of the claims that are analyzed to determine whether the claims are integrated into a practical application (MPEP 2106.04(d).I; MPEP 2106.05(a-h)). Thus, although the claims may recite improvements in genotype-phenotype mapping, an improvement in the abstract idea itself does not provide the practical application, as the improvement must be demonstrated within the additional elements of the claims. Improvement to machine learning is still an abstract idea and therefore does not integrate the judicial exceptions into a practical application. Therefore, the rejection under 35 USC 101 is maintained.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Claes (WO 2015/173435 A1, published November 2015, IDS reference) in view of Guo et al. (“DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing”, IDS reference), further in view of Dusonchet al. (“A Parkinson’s disease gene regulatory network identifies the signaling protein RGS2 as a modulator of LRRK2 activity and neuronal toxicity”, Human Molecular Genetics, 2014, cited in prior Office Action), and further in view of Hu et al. (“Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures”, 2016). This is a new grounds of rejection as necessitated by claim amendment.
Regarding claims 1 and 8, Claes teaches a computer-implemented method comprising:
inducing single genetic modifications, wherein single genetic modifications refer to manipulation of a genome of a cell through induced mutations: Claes teaches genotype estimations being performed through the selection of associated and simplified genotype features (page 10, line 7) and genetic features such as single nucleotide polymorphisms, haplotypes, copy number variations, genomic variations, or any other type of variation that can be measured from DNA has important applications in medicine, forensics, and agriculture (page 1, line 10);
training a deep learning neural network with cellular morphology features from single genetic modifications: Claes teaches training a deep learning neural network (page 29, line 18) using a morphological biomarker for a SNP in the gene SLC35D1 (page 17, line 29); Claes teaches creating classifiers based on previously misclassified training examples, therefore combining previous weak classifiers into a strong classifier or regression model (page 30, line 3);
testing the deep learning neural network with cellular morphology features from multiple genetic modifications: Claes teaches testing the neural network using a dataset of morphological features of multiple genetic modification (page 17, line 29);
Furthermore, Claes teaches imbedding the phenotype in a multidimensional or multivariate phenotype-space that shows the phenotype is context of other phenotypes by measuring similarities between them using a variety of multivariate distances and angles defined within the phenotype-space (page 27, line 9). Claes teaches a closed loop self-learning optimization algorithm (page 8, line 1).
Finally, Claes teaches a computer program product, a computer readable medium, and instructions that may be executed by a processor (page 11, line 32).
Claes does not explicitly teach the claim elements of inducing genetic modifications, using a reverse engineering automated rule-extraction algorithm to construct an unboxing algorithm, pruning neurons correspond to input features, or building a computer aided design system to guide genetic engineering for a specific synthetic biology product, based on generation of sufficient data correlation certain genetic insults with viable phenotypes, or engineering new biological structures on demand by applying features design and segmentation algorithms.
However, Guo et al. teaches a deep learning system to predict phenotype from genome sequencing. Guo et al. teaches an unsupervised training algorithm that uses multiple layers (Figure 1), and teaches the algorithm obtained weights of nonlinear mapping between protein and phenotype layers (page 6, column 2, paragraph 3). The specification of the instant application describes in paragraph [0023] that an “unboxing algorithm” refers to an AI algorithm that uses a “black box” model, which is a model that applies one or more layers of machine and/or deep learning decisions based on a set of rules or parameters without human supervision, thus the unsupervised pre-training algorithm of Guo et al. is considered equivalent to the unboxing algorithm in the instant claims. Furthermore, Guo et al. teaches focusing on extending DeepMetabolism to more complex biological systems for application such as automated design-build-test cycle of industrial microorganisms for product of fuels and pharmaceuticals as well as sequence-based early diagnosis of metabolic diseases (page 5, Section 5), and DeepMetabolism bridging the gap between genotype and phenotype to serve as a springboard for applications in synthetic biology and precision medicine (Abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art to have incorporated the unsupervised pre-training algorithm and application of Guo et al. to the method of Claes because both Claes and Guo et al. are directed to predicting phenotypes from genotypes (see Claes and Guo et al. abstract). Thus, one of ordinary skill would have a reasonable expectation of success of predicting phenotype from genotypes by combining the prior art.
Guo et al. does not teach the claim elements of inducing single genetic modifications, using a reverse engineering automated rule-extraction algorithm to construct an unboxing algorithm, and pruning neurons correspond to input features.
However, Dusonchet et al. teaches the identifying of gene regulatory networks in Parkinson’s. Dusonchet et al. teaches inducing single genetic modifications in C. elegans (page 4891, column 2, Section “Endogenous Irk-1 is required for effects on DA neuron survival in C. elegans”; Figure 3). Dusonchet et al. teaches applying systems biology tools to human transcriptomes to reverse-engineer a LRRK2-centered gene regulatory network (Abstract) and application of an algorithm to perform the reverse-engineering (Figure 1 description), leading to a starting point of a network that can demonstrate pathways to non-viable neurons (page 4903, column 2, paragraph 2; Figure 1; page 4899, column 2, paragraph 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the unboxing algorithm of Dusonchet et al. to the method of Claes in view of Guo et al. because the reverse engineering algorithm determines genes linked by function that the phenotype of a cell (page 4899, column 2, paragraph 2) and Claes is directed to predicting phenotypes from genotypes (Claes Abstract), and thus one of ordinary skill would have a reasonable expectation of understanding the connections between genotypes and the outcome of the phenotype of the cell in order to accurately predict the phenotype.
Dusonchet et al. does not teach the claim element of pruning neurons correspond to input features.
However, Hu et al. teaches a data-driven neuron pruning approach towards efficient deep architectures (Abstract). Hu et al. teaches that network trimming iteratively optimizes the networks by pruning unimportant neurons based on analysis of the output they create (Abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the neuron pruning to the method of Claes in view of Guo et al. further in view of Dusonchet et al. because Claes discloses training of a deep learning neural network using morphological features (page 17, line 29; page 29, line 18), and Hu et al. is directed to improving the efficiency of deep learning models (Abstract). Thus, one of ordinary skill in the art would have had a reasonable expectation of success of pruning unimportant neurons in a deep learning neural network model trained on morphological features and would be motivated to do so in order to make the model more efficient.
Regarding claims 2 and 9, the claims are directed to the perturbation subspaces comprising viable genetic modifications that lead to viable phenotypes. Claes teaches the method of claim 1 and the computer program product of claim 8 in view of Guo et al., further in view of Dusonchet et al., and further in view of Hu et al. Claes also teaches ordinating the phenotype space to follow genetically and/or environmentally meaningful directions (page 6, line 32).
Regarding claims 3 and 10, the claims are directed to the perturbation subspaces exclude unviable genetic modifications that least to non-viable phenotypes. Claes teaches the method of claim 1 and the computer program product of claim 8 in view of Guo et al., further in view of Dusonchet et al., and further in view of Hu et al. Claes also teaches ordinating the phenotype space to follow genetically and/or environmentally meaningful directions (page 6, line 32), which thus excludes modifications that lead to non-viable phenotypes.
Regarding claims 4 and 11, the claims are directed to the perturbation subspaces comprising viable phenotypes for a synthetic biology application and/or product. Claes teaches the method of claim 1 and the computer program product of claim 8 in view of Guo et al., further in view of Dusonchet et al., and further in view of Hu et al. Claes also teaches that the embedding of the phenotype in the phenotype-space allows for the generation of new, synthetic phenotypes (page 6, line 30).
Regarding claims 5 and 13, the claims are directed to the genotype-phenotype mapping showing genetic distance between the single genetic modifications and the multiple genetic modifications. Claes teaches the method of claim 1 and the computer program product of claim 8 in view of Guo et al., further in view of Dusonchet et al., and further in view of Hu et al. Claes also teaches a phenotype-space that shows the phenotype is context of other phenotypes by measuring similarities between them using a variety of multivariate distances and angles defined within the phenotype-space (page 27, line 9). Claes also teaches measuring a genetic distance between the genetic modifications (page 15, line 4).
Regarding claims 6 and 14, the claims are directed to the genetic distance and perturbations subspaces being used to select one or more genetic insults as a defined space to engineer cells in synthetic biology applications. Claes teaches the method of claim 5 and the computer program product of claim 13 in view of Guo et al., further in view of Dusonchet et al., and further in view of Hu et al. Claes also teaches a phenotype-space that shows the phenotype is context of other phenotypes by measuring similarities between them using a variety of multivariate distances and angles defined within the phenotype-space (page 27, line 9). Claes also teaches measuring a genetic distance between the genetic modifications (description of Figure 12). Claes also teaches that the embedding of the phenotype in the phenotype-space allows for the generation of new, synthetic phenotypes (page 6, line 30).
Regarding claims 7 and 15, the claims are directed to the multiple genetic modifications comprising a combination of the single genetic modifications. Claes teaches the method of claim 1 and the computer program product of claim 8 in view of Guo et al., further in view of Dusonchet et al., and further in view of Hu et al. Claes also teaches the data used in the method using genotypes using local SNP information, and multiple SNPs or haplotypes (page 15, line 1).
Regarding claim 12, the claim is directed to measuring genetic distance of the single and multiple genetic insults. Claes teaches the computer program product of claim 8 in view of Guo et al., further in view of Dusonchet et al., and further in view of Hu et al. Claes also teaches measuring a genetic distance between the genetic modifications (page 15, line 4).
Response to Arguments
Applicant states that Dusonchet et al. does “not teach or suggest the amended claim language of ‘constructing the unboxing algorithm by pruning neurons corresponding to input features that do not significantly affect the deep learning neural network’s accuracy… improving a classification accuracy of the set of rules for the unboxing algorithm by updating a data range matrix according to misclassified samples resulting from the constructed set of rules where a specific rule condition is updated if the updated corresponds to a classification accuracy’. Applicant states that “The remaining portions of Dusonchet do not fill this deficiency, nor do Claes and/or Guo”.
It is respectfully submitted that Claes teaches creating classifiers based on previously misclassified training examples, therefore combining previous weak classifiers into a strong classifier or regression model (page 30, line 3). However, Claes, Guo, and Dusonchet do not teach the claim element of pruning neurons. Therefore, a new rejection has been set forth as necessitated by the claim amendments.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emilie A Smith whose telephone number is (571)272-7543. The examiner can normally be reached 9am - 5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571)270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.A.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686