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 .
Claim Status
Amended claims 1, 4-7, 9, 11, 13-14, 16-17, 20-25, 28 are pending and examined here.
Priority
The claim to benefit of U.S. Provisional Application 63/211814, filed on 06/17/2021, is recognized. All examined claims enjoy the filing date of ‘814 filing.
Claim Rejections - 35 USC § 103
Rejection of amended claims 1, 4-7, 9, 11, 13-14, 16-17, 20-25, 28 under 103 in view of Freier, Suzuki, and Aung-Htut et al. and Suzuki have been withdrawn due to amendment of claim 1; however the examined claims are rejected as noted below under 103.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, 4-7, 9, 11, 13-14, 16, 22-25, 28 are rejected under 35 U.S.C. 103 as being unpatentable over Freier (2008, Basic Principles of Antisense Drug Discovery, in Antisense Drug Technology, Principles, Strategies, and Applications, pg. 117-142, of record) and Suzuki et al. (US20210319853, pub. 10/14/2021, but actual filing date of its application 17/269,685 was 02/19/2021, thus applies as a 102(a)(2), referred as Suzuki) and Eggan (US20150301028, pub. 10/22/2015).
Regarding the interpretation of machine learning system (MLS), although not clearly defined, the specification provides “[t]he machine learning systems (e.g., neural networks, random forests, support vector machines, others, or combinations thereof), may read patterns of neuronal activity arising from exposure to one of the newly synthesized ASOs output by the in silico pipeline and given an output rating the ASO for neurotoxicity” (pg. 3, lines 6-9). Thus, MLS comprises neural networks.
Regarding claims 1, 9, 11, 13-14 Freier discloses that the first step in ASO design is to identify the molecular target, i.e. the gene of interest, which is selected because it was known to be important in specific biological pathways or diseases (pg. 118); discloses starting with antisense activity data for about 30,000 ASOs and filtering to create a shortened list of ~10,000 ASOs with reproducible activity and utilized various criteria, such as thermodynamic properties, sequence motifs and gene feature properties, and then using decision tree and neural net models to mine the data and predict its activity (pg. 122, 127), and used the hybrid model to design 39 ASOs to each of 25 target genes (pg. 122); discloses that once leads have been identified in the screening assay, a follow-up assay should be performed (pg. 134); discloses identifying false positive and false negative results and also notes non-antisense activities as undesirable false positives (pg. 128). Further, two goals of optimization is to identify greatest potency and activity without nonspecific activity or toxicity (pg. 134). Freier discloses testing numerous ASOs and ranking them based on their efficiency in vitro in liver hepatocytes due to the target gene being primarily expressed in liver hepatocytes (pg. 137, Fig. 5.10, testing 4 targets in vitro). Freier also discloses that in vitro testing of ASOs can be done in primary cells, such as neurons (pg. 131).
Freier in Fig. 5.9 (pg. 136 ) aptly summarizes the steps: ~100 represents filtered obtained ASOs.
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Freier does not disclose (i) at least one neuron comprises an genetically encoded optical reporter of membrane potential, (ii) using a light detector to measure a neural activity phenotype, and (iii) analyzing the neural activity phenotype with a machine learning system to generate a predictive model of in vivo toxicity, wherein the machine learning system is trained on a data store of neural activity phenotypes correlated with in vivo toxicity data, thereby identifying a candidate therapeutic antisense oligonucleotide predicted to not induce toxicity in vivo.
Suzuki highlights that 34% of agents found to be toxic in clinical trials are toxic to the central nervous system (CNS) and that their invention is an effective method of evaluating toxicity and/or efficacy of compounds on CNS in non-clinical trials (par. 2-3, relevant to iii). Suzuki’s discloses a method for predicting toxicity/efficacy characteristics of a target compound by receiving activity data from nerve cells (i.e. neurons) exposed to target compound, converting the activity data into image, and processing the image through an image recognition model trained by a training data set, which includes data of known compounds, and further providing an output of at least one characteristic of the target compound (abstract, par. 3). One characteristic that is the focus of the invention is toxicity (par. 2, relevant to iii). Suzuki discloses that activity of neurons can be processed as image data obtained by an optical measurement method, i.e. Ca2+ imaging, including, e.g., measuring membrane potential imaging using a membrane potential sensitive dye (par. 115, relevant to optical reporter (i.e. the dye providing data of membrane potential) and light detector (i.e. optical measurement method), relevant to i and ii). Suzuki demonstrates predicting a property of a target compound using artificial intelligence by treating human iPSC-derived neural stem cells with seizure positive agents (Chlorpromazine, 4-AP, Gabazine, pilocarpine) or negative agents (acetaminophen, DMSO) (par. 222, relevant to cl. 9); measuring the neuron’s electrical activity (i.e. micro-electrode array [MEA]), obtaining data that was processed by various software modeling systems trained to be able to predict toxicity (seizure positive or seizure negative, relevant to condition labels of instant cl. 13, 14) (par. 222, Fig. 11). As defined by Suzuki for their test compounds, e.g., that chlorpromazine is “seizure-positive” compound suggests that it has in vivo seizure effects, and thus is relevant to the limitation of “correlated with in vivo toxicity data”. The algorithm(s) leverage neural network system/processing to construct an image recognition model (i.e. what the authors call artificial intelligence) which preferably comprises a feature extraction model that is trained with training data set and at least one property prediction model (par. 146-417, see also, generally, par. 141-152 and Fig. 3, 14). Suzuki discloses using frequency division waveform of a gamma-wave frequency component to identify potential epileptic seizure property during treatment with a compound (par. 132); thus either it would be obvious to label epileptic seizure or that the labeling of seizure comprises epileptic seizures (relevant to instant cl. 14).
Freier and Suzuki do not disclose a genetically encoded optical reporter (cl. 1) nor a light-gated ion channel (cl. 11).
Eggan discloses a method of screening a compound that is exposed to neurons, wherein the neuron expresses an optogenetic reporter of membrane electrical potential and light-gated ion channel, with the neural activity being reported as an optical signal (cl. 1-4, par. 116). Eggan discloses various optogenetic reporters that i) can be introduced into a cell by transformed with a vector (par. 117, 145-155), ii) and have various advantages, including for microbial rhodopsin, such as Archaerhodopsin (Arch) 3 and Arch D95N, that provide high sensitivity and speed, resolves action potential with high signal to noise ratio and low phototoxicity (par. 119, relevant to instant cl. 1). Eggan discloses testing compounds for therapeutic purpose to determine suitability of treatment prior to patient application, including testing epilepsy drugs (par. 237). Eggan demonstrated neurons transduced with light-gated channels and ability to modulate their neural activity (Fig. 10-14, relevant to instant cl. 11).
One of the KSR rationale that may be used to support a conclusion of obviousness is that there is some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. Therefore, it would have been prima facie obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the teachings of Freier in view of Suzuki arrive at the claimed invention with a reasonable expectation of success. Based on Suzuki’s method of predicting the characteristics, including toxicity, of a compound comprising exposing cultured neurons to test compound and processing the neural activity through a neural-network computation layers, and Freier’s method of obtaining a filtered list of ASOs that require testing for various characteristics, including toxicity, a skilled artisan would combine Freier’s methodology of generating a list of ASOs, analyzing the sequence via in silico process, obtaining a filtered list of ASOs, and testing them in vitro for toxicity with Suzuki’s methodology using a chemical reporter to screen compounds, including ASOs, in vitro using neurons to predict compounds’ toxicity using a predictive-trained AI system.
Further, it would have been prima facie obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined Freier, Suzuki in view of Eggan and arrive at the claimed invention with a reasonable expectation of success. Based on Suzuki’s method of predicting the characteristics, including toxicity, of a compound comprising exposing cultured neurons to test compound and processing the neural activity through a neural-network computation layers, and Eggan demonstrating neurons expressing optogenetic reporter with increased sensitivity and light-gated ion channel to screen compounds for pain treatment, a skilled artisan would reasonably expect success by substituting the cultured neurons and chemical optical reporter of Suzuki’s methodology with Eggan’s neurons expressing light-gated ion channel and optogenetic reporter to screen compounds or alternatively introducing the optogenetic reporter of Eggan’s into neurons of Suzuki’s method.
Thus, cl. 1, 9, 11, 13-14 are obvious.
Regarding instant cl. 4, the specification discloses in silico operations (ISO) include a software package that performs a pair-wise a pairwise alignment of each of the oligonucleotide sequence to a human genome or to a primary transcript (pg. 5, line 25-27).
Freier discloses various strategies in avoiding reducing non-target genes, one includes avoiding cross-repeat features and note that they appear frequently in genome and ASOs complementary to them are likely to hit several other genes, and note the use of DUSTMAPPER for simple repeat features, while complex repeats using Repeatmasker (pg. 123).
Regarding claim 5, Freier discloses each ASO compared to each site on a list of cross reactors and notes the use of FASTA to identify as low as one mismatch (pg. 123); cross reactors are members of the family to target gene, and notes that even weak homology to the target gene is classified as a potential cross reactors for ASO design (pg. 119).
Regarding instant cl. 6, Freier discloses a neural net models using a hybrid model that incorporates thermodynamic properties, which include self-structure in the ASO must be removed (Fig. 5.2, pg. 120) and notes a filter of ΔG < -35 kCal/mol (pg. 121, see also Fig. 5.3a-c).
Regarding instant cl. 7, Freier discloses possible variants can be identified by comparison to other species using BLAST to align cDNAs/ESTs to the genome, and notes that by similarity in sequences that it’s possible to speculate similar functioning splicing features across species (pg. 119); discloses that if homology is sufficient, cross-species ASOs can be designed (pg. 124).
Regarding instant cl. 16, for designing ASO, Freier discloses that the design principles using net neural model is for the bulk of their experience for optimal 5-10-5 gapmer, note that gapmers/chimeric ASOs support RNase H and directly cause decay of the RNA target (pg. 118).
Regarding instant cl. 22, Freier discloses that “to identify all potential mismatched sites, one would need to test each candidate ASO versus the entire transcriptome. At Isis, we have developed a rapid string-searching algorithm optimized for this purpose. Even with our optimized algorithm, that process is too slow for testing tens of thousands of candidates. Thus we postpone the transcriptome search until after the initial in vitro screen, when there are only about 10 candidate ASOs” (p. 123).
Regarding instant claim 23, recites the method of claim 22, wherein the predictive module uses a machine learning system to predict expression modulation of off-target genes for each oligonucleotide sequence, the machine learning system trained on results of expression analysis for a plurality of antisense oligonucleotides.
Freier discloses various criteria identified that increases toxicity of ASO, such as pyrimidine-rich motifs were hepatotoxic in mice, or strings of guanosine can result in nonantisense activity, and such methods have been used as a prediction method to exclude ASOs (pg. 123)
Regarding instant claim 24, the specification provides definition of “sequence distance rules,” which specification defines “sequence distance” as number of mismatches (pg. 9, line 5-6).
Freier discloses each ASO compared to each site on a list of cross reactors and notes the use of FASTA to identify as low as one mismatch (pg. 123); cross reactors are members of the family to target gene, and notes that even weak homology to the target gene is classified as a potential cross reactors for ASO design (pg. 119).
Regarding instant cl. 25, Freier discloses ASO is compared to each site on the list of cross reactors, and no ASO with three or fewer mismatches is considered for screening (pg. 123).
Regarding instant cl. 28, Freier discloses correlation of activity with calculated secondary structure in target or ASO (pg. 120) and is in the hybrid neural net model noted above (cl. 1, pg. 122).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Freier (2008, Basic Principles of Antisense Drug Discovery, in Antisense Drug Technology, Principles, Strategies, and Applications, pg. 117-142) and Suzuki et al. (US20210319853, pub. 10/14/2021, with actual filing date of its application 17/269,685 was 02/19/2021, thus applies as a 102(a)(2), referred as Suzuki) and Eggan (US20150301028, pub. 10/22/2015) as applied to claims 1, 4-6, 7, 9, 11, 13-14, 16, 22-25, 28 above, and further in view of Donaldson et al. (2016, Drug Discovery Today, 21, 1787-1798).
Disclosure pertaining to rejection of claims 1, 4-6, 7, 9, 11, 13-14, 16, 22-25, 28 under Freier, Eggan and Suzuki is note above. Freier discloses using net neural model for designing optimal 5-10-5 gapmer ASOs and notes that gapmers/chimeric ASOs support RNase H and directly cause decay of the RNA target (pg. 118).
Freier, Suzuki, and Eggan do not disclose genetic target of a sodium channel.
Donaldson discloses that mutations in certain voltage-gated sodium channels cause pain syndromes (pg. 1792). Donaldson discloses gain-of-function mutations in certain VGSC protein (Nav1.7, Nav1.9), which result in inherited pain syndromes (pg. 1792).
Therefore, it would have been prima facie obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the methodology of generating and identifying ASOs via in silico process of Freier with methodology of screening compounds, including ASOs, with cultured neurons and using neural-network algorithms (i.e. AI system) to predict efficacy/toxicity of compounds tested of Suzuki and of optogenetic reporter of Eggan in view of Donaldson and arrive at the claimed invention with a reasonable expectation of success. Based on Freier’s in silico methodology to screen for ASOs based on a gene of interest for therapeutic purpose, and Suzuki demonstrating using the method of testing characteristics of compounds in cultured neurons by use of neural-network algorithms (i.e. AI system) to predict efficacy/toxicity of compounds, and Eggan demonstrating neurons expressing optogenetic reporter and light-gated ion channel to screen compounds for pain treatment, a skilled artisan would reasonably expect success identifying potential ASOs by screening ASOs based on Freier’s methodology to target Nav1.7’s gain of function mutation causing pain syndrome as taught by Donaldson and use of in vitro screening of compounds in cultured neurons to predict efficacy/toxicity by AI prediction system of Suzuki and substituting chemical optical reporter with optogenetic reporter of Eggan to identify potential potent ASO with decreased toxic effect. Thus, cl. 17 is obvious.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Freier (2008, Basic Principles of Antisense Drug Discovery, in Antisense Drug Technology, Principles, Strategies, and Applications, pg. 117-142) and Suzuki et al. (US20210319853, pub. 10/14/2021, with actual filing date of its application 17/269,685 was 02/19/2021, thus applies as a 102(a)(2), referred as Suzuki) and Eggan (US20150301028, pub. 10/22/2015) as applied to claims 1, 4-6, 7, 9, 11, 13-14, 16, 22-25, 28 above, and further in view of Lima et al. (2018, RNA Biology, 15, pg. 338-352).
Disclosure pertaining to rejection of claims 1, 4-6, 7, 9, 11, 13-14, 16, 22-25, 28 under Freier, Eggan and Suzuki is note above.
However, Freier, Eggan, and Suzuki do not disclose in silico operations predicting the performance of ASOs as steric blocking oligonucleotides that inhibit the function of micro-RNA
Lima discloses design strategies for anti-miR oligonucleotides, which include identifying miRNA of interest, and discloses the use of Basic Local Alignment Search Tool (BLAST) to identify for possible off-targets of the oligonucleotide sequence (pg. 345, relevant to instant cl. 21). Lima discloses that anti-miRNAs cause miRNA silencing mostly by steric blocking the target miRNA (pg. 342, relevant to instant cl. 21). Lima discloses a fully modified antagomir (aka anti-miRs) conjugated with GalNac that targets miR-122 during hepatitis C virus infection in the liver (pg. 343). Lima discloses that ideal anti-miRs should “have no toxicity or other side effects, such as knockdown of non-target molecules” (pg. 342).
One of the KSR rationale that may be used to support a conclusion of obviousness is that there is some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. Therefore, it would have been prima facie obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the in silico operations of Freier and use of in vitro screening of compounds in cultured neurons to predict efficacy/toxicity by AI prediction system of Suzuki and introducing optogenetic reporter of Eggan in view of Lima and arrive at the claimed invention with a reasonable expectation of success. Based on Freier’s in silico methodology to screen for ASOs based on a gene of interest for therapeutic purpose, and Lima’s use of BLAST software to identify off-target binding, and Suzuki’s demonstration of screening compounds with cultured neurons and using neural-network algorithms (i.e. AI system) to predict efficacy/toxicity of compounds tested, and Eggan demonstrating neurons expressing optogenetic reporter and light-gated ion channel to screen compounds for pain treatment, a skilled artisan would expect reasonable success by combining in silico operations of Freier and Lima to identify steric-blocking ASOs targeting miRNA and screening the ASOs in cultured neurons and using neural-network algorithms (i.e. AI system) to predict efficacy/toxicity of compounds tested of Suzuki and substituting chemical optical reporter of Suzuki with optogenetic reporter of Eggan. Thus cl. 21 is obvious.
Claims 1, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Aung-Htut et al. (2019, Int. J. of Molecular Sciences, 20, pg. 1-12, referred as Aung-Htut) and Suzuki et al. (US20210319853, pub. 10/14/2021, but actual filing date of its application 17/269,685 was 02/19/2021, thus applies as a 102(a)(2), referred as Suzuki) and Eggan (US20150301028, pub. 10/22/2015).
Aung-Htut discloses using bioinformatic tools to identify predicted splice motifs and then designing and generating ASO complementary to the potential splice-associated motifs (pg. 3, relevant to instant cl. 1). Aung-Htut discloses removing sequences that are not associated with desired exon to be spliced out (identifying “exon skipping to remove a compromised exon from a disease causing gene” (pg. 3)), and discloses obtaining splice switching ASOs to screen and evaluate in cells (pg. 4, relevant to analyzing sequences and removing sequences of cl. 1), and thus one phenotype to identify here whether the exon is removed. Aung-Htut discloses designing antisense oligonucleotides targeting a pre-mRNA sequence being analyzed by an in silico prediction program, such as SpliceAid 2, to identify potential splice enhancer or silencer motif and to modulate pre-mRNA splicing (pg. 2, see Fig. 1, relevant to instant cl. 1 of in silico operations). Aung-Htut discloses that although the modification chemistry (2-O-methyl and phosphorothioate modifications) is cost-effective, several studies have shown PS backbone to exhibit toxicity, off-target effects (pg. 8).
Aung-Htut does not disclose (i) at least one neuron comprises a genetic encoded optical reporter of membrane potential, (ii) using a light detector to measure a neural activity phenotype, and (iii) analyzing the neural activity phenotype with a machine learning system to generate a predictive model of in vivo toxicity, wherein the machine learning system is trained on a data store of neural activity phenotypes correlated with in vivo toxicity data, thereby identifying a candidate therapeutic antisense oligonucleotide predicted to not induce toxicity in vivo.
Suzuki highlights that 34% of agents found to be toxic in clinical trials is toxic to the central nervous system (CNS) and that their invention is an effective method of evaluating toxicity and/or efficacy of compounds on CNS in non-clinical trials (par. 2-3, relevant to iii). Suzuki’s discloses a method for predicting toxicity/efficacy characteristics of a target compound by receiving activity data from nerve cells (i.e. neurons) exposed to target compound, converting the activity data into image, and processing the image through an image recognition model trained by a training data set, which includes data of known compounds, and further providing an output of at least one characteristic of the target compound (abstract). One characteristic that is the focus of the publication is toxicity (par. 2, relevant to iii). Suzuki discloses that activity of neurons can be processed as image data obtained by an optical measurement method, i.e. Ca2+ imaging, including e.g. measuring membrane potential imaging using a membrane potential sensitive dye (par. 115, relevant to optical reporter (i.e. the dye providing data of membrane potential) and light detector (i.e. optical measurement method), relevant to i and ii). Suzuki discloses predicting a property of a target compound using artificial intelligence and demonstrate by treating seizure positive and negative agents to human iPSC-derived neural stem cells (par. 222), obtaining data that was processed by various software modeling systems for training to be able to predict toxicity (seizure positive or seizure negative) (par. 222, Fig. 11). The algorithms leverage neural network system/processing for image recognition model, which preferably comprises a feature extraction model that is trained with training data set and at least one property prediction model (par. 146-417, see also, generally, par. 141-152 and Fig. 3, 11, 14).
Aung-Htut and Suzuki do not disclose a genetically encoded optical reporter.
Eggan discloses a method of screening a compound that is exposed to neurons, wherein the neuron expresses an optogenetic reporter of membrane electrical potential and light-gated ion channel, with the neural activity being reported as an optical signal (cl. 1-4, par. 116). Eggan discloses various optogenetic reporters that i) can be introduced into a cell by transformed with a vector (pg. 117), ii) and have various advantages, including for microbial rhodopsin, includes Archaerhodopsin 3, Arch D95N, that provide high sensitivity and speed, resolves action potential with high signal to noise and low phototoxicity (par. 119, relevant to instant cl. 1). Eggan discloses testing compounds for therapeutic purpose to determine suitability of treatment prior to patient application, including testing epilepsy drugs (par. 237).
One of the KSR rationale that may be used to support a conclusion of obviousness is that there is some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. Therefore, it would have been prima facie obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the teachings of Aung-Htut in view of Suzuki and Eggan and arrive at the claimed invention with a reasonable expectation of success. Based on Suzuki’s method of predicting the characteristics, including toxicity, of a compound comprising exposing cultured neurons to test compound and processing the neural activity through a neural-network computation layers, and Eggan’s disclosure of introducing optogenetic reporter constructs into neurons, and Aung-Htut’s method of obtaining a filtered-list of AOs that require testing for various characteristics, including toxicity, a skilled artisan would combine the methodology of Aung-Htut to generate a list of ASO, analyze the sequence via in silico process, obtain a filtered list of ASOs, and then test using an in vitro methodology using iPSC-derived neural stem cells to predict toxic ASOs by using a predictive-trained AI system of Suzuki and substituting chemical optical reporter of Suzuki with optogenetic reporter of Eggan. Thus, cl. 1, 20 are obvious.
Response to Arguments
Applicant's arguments filed 02/17/2026 (the “Remarks”) have been fully considered but they are not persuasive.
The Remarks of 02/17/2026 argue the following:
the claim amendment is not taught by the references (pg. 7); and
Suzuki does not teach the limitation of machine learning system (MLS) trained on data store of neural activity phenotypes correlated with in vivo toxicity data as recited in claim 1 (pg. 8). The Remarks indicate that "Suzuki reports a different training paradigm," that it trained on compounds pre-classified as "seizure-positive" or "seizure-negative" based on known pharmacological properties drawn from prior literature (par. 222), pg. 8. "These labels were not derived from Suzuki's own in vivo toxicity experiments. Suzuki did not administer its test compounds to animals, observe in vivo outcomes, and correlate those outcomes with in vitro neural activity measurements" (pg. 8), i.e. Suzuki "borrowed pre-existing drug classification" (pg. 8).
The Remarks are not persuasive.
The claim amendment is addressed by the rejection, using the reference of Eggan, cited in prior action. The use of genetically encoded optical reporters and their advantages are well known in the art.
Regarding argument ii) that is directed to what constitutes a “data store of neural activity phenotypes.” The limitation of MLS trained on a data store of phenotypes does not affect the claim since it is not an active step, which is the limitation “analyzing the neural activity phenotype with a MLS”. Second, the argument is not consistent with the BRI of the claim. Even the specification of the instant application suggests a broad reading of “data store”, see, e.g., pg. 3, 4-5, 23). Pg. 23 notes the following: “Methods of the invention may use a wealth of historical Optopatch data (a data store) characterizing the effects of, e.g., FDA- approved small molecule compounds, allowing ASO-driven Optopatch phenotypes to be put within a wider context of effects that are (or are not) well-tolerated in the clinic.” (Others too, Pg. 3 notes the following: “the analytical system may include machine learning systems trained on the data store with the known effects.” Pg. 4-5, notes: “[t]he neural activity phenotype may be analyzed against a data store (e.g., terabytes or petabytes of historical Optopatch recordings) of phenotypes.”, underline for emphasis) The clearest indication that data store includes phenotype that is ”pre-classified” or “drawn from prior literature” is on pg. 23, indicating that “wealth of historical Optopatch data (a data store)” of FDA-approved compounds can be used. Clearly, the data store cannot be of small molecules, but rather data store is of historical data of effects of known and approved small molecules. Thus, under BRI, the data store of in vivo toxicity data can be compared with data obtained from prior literature results.
Thus the claims are rejected as noted above.
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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/KEYUR A VYAS/Examiner, Art Unit 1637
/Soren Harward/Primary Examiner, TC 1600
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