DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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. Claim Status Claims 1-20 are pending. Claims 1-20 are rejected. Priority The instant Application was filed Jul 28 2022 and does not claim the benefit of an earlier filed application. Information Disclosure Statement The information disclosure statement (IDS) filed on Mar 8 2023 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action. Drawings Applicant’s petition to accept color drawings, filed Nov 29 2022, was accepted Dec 7 2022. The Drawings submitted Jul 28 2022 are accepted. Specification The amendments to the specification filed Sep 19 2022, Oct 4 2022, and Nov 29 2022 are accepted. The disclosure is objected to for the following informalities. It is noted that for purposes of the instant Office Action, any reference to the specification pertains to the clean copy of the substitute specification as filed on Nov 29 2022. Hyperlinks The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01. Non-limiting examples include paragraphs [0190] and [0224]. Applicant will note that this is exemplary and other instances may exist. It is requested that all instances be corrected. Appropriate correction for all objections to the specification is required. Claim Rejections - 35 USC § 112 35 U.S.C. 112(b) 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. Claims 5-6 and 9 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claim 5 recites “wherein… the trained machine learning algorithm is trained by…”. It is unclear whether the wherein clause is intended to require training the machine learning algorithm within the metes and bounds of the claimed invention, or if it is only further limiting the machine learning algorithm such that training is not required within the metes and bounds of the invention. As set forth in MPEP 2111.04.I, “wherein” clauses raise the question as to the limiting effect of the language in a claim. As the claims do not recite an active performance of the training, the metes and bounds of the claims are unclear. For compact examination, it is assumed that the training is not required to be performed. The rejection may be overcome by clarifying what steps are required to be performed. Claim 6 is rejected based on its dependency from claim 5. Claim 9 is similarly rejected and interpreted. 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 one or more judicial exceptions without significantly more. MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials. Framework with which to Evaluate Subject Matter Eligibility : Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter; Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e . a law of nature, a natural phenomenon, or an abstract idea; Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept. Framework Analysis as Pertains to the Instant Claims: Step 1 With respect to Step 1 : yes, the claims are directed to a method, a computer system, and a computing device, i.e. , a process, machine, or manufacture within the above 101 categories [ Step 1: YES ; See MPEP § 2106.03] . Step 2A, Prong One With respect to Step 2A, Prong One , the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as: mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations); certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information). With respect to the instant claims , under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) as well as a law of nature or a natural phenomenon are as follows: Independent claims 1, 10, and 17 : identifying a perturbation that will cause the starting cell state to transition to the target cell state by inputting the starting cell state and the target cell state into a trained machine learning algorithm. Dependent claims 5, 14, and 20 : the trained machine learning algorithm is trained by: training the first network, wherein the first network converts perturbations into real-valued vector representations of the perturbations; training the second network, wherein the second network converts cell states into real-valued vector representations of the cell states; and training the third network to learn relationships between: ( i ) real-valued vector representations of the perturbations, and (ii) the real-valued vector representations of the cell states. Dependent claims 2-4, 9, 11-13, 18-19 recite further steps that limit the judicial exceptions in independent claim 1, 10, and 17 and, as such, also are directed to those abstract ideas. For example, claims 2-3, 11-12, and 18 further limit the perturbation to a chemical or a genetic perturbation; claims 4-5, 13-14, and 19-20 further limit the trained machine learning algorithm; and claim 9 further limits the trained machine learning algorithm to being train on high-throughput, single-cell screening data. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually identify a perturbation that would cause a transition from a starting cell state to a target cell state. Without further detail as to the methodology involved in “identifying”, under the BRI, one may simply, for example, use pen and paper to identify such a perturbation. Additionally, inputting the starting cell state into a trained machine learning algorithm to identify the perturbation and training the machine learning algorithm require mathematical techniques as the only supported embodiments, as the claims explicitly recite steps for converting data into real-valued vector representations, and as is disclosed in the specification as published at: the machine learning models can be k-nearest neighbor or random models, where the models and their training can be represented by mathematical equations (see at least [0138-0145]). Therefore, claims 1, 10, and 17 and those claims dependent therefrom recite an abstract idea [ Step 2A, Prong 1: YES ; See MPEP § 2106.04 ]. Step 2A, Prong Two Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two , provides that the claims must be examined further to determine whether they integrate the judicial exception s 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 judicial exception s are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exception s, the claim is said to fail to integrate the judicial exception s into a practical application (MPEP 2106.04(d).III). Additional elements, Step 2A, Prong Two With respect to the instant recitations, the claims recite the following additional elements : Independent claims 1, 10, and 17 : receiving an indication of a starting cell state; and receiving an indication of a target cell state. Dependent claim 6 : wherein the third network is a conditional invertible neural network ( cINN ). Dependent claims 7-8 and 15-16 recite steps that further limit the recited additional elements in the claims. For example, claims 7-8 and 15-16 further limit the received starting cell state to a diseased or first healthy cell state and the target cell state to a healthy cell state; The claims also include non-abstract computing elements. For example, independent claim 1 includes a computer-implemented method performed via one or more computer processors; claim 10 includes a computer system comprising one or more processors; and claim 17 includes a computing device comprising: one or more processors; and one or more memories coupled to the one or more processors; the one or more memories including computer executable instructions stored therein that, when executed by the one or more processors, cause the one or more processors to perform steps. Considerations under Step 2A, Prong Two With respect to Step 2A, Prong Two , the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “ receiving ” data, perform functions of collecting the data needed to carry out the judicial exception s . Data gathering and outputting do not impose any meaningful limitation on the judicial exception s , or on how the judicial exception s are performed. Data gathering and outputting steps are not sufficient to integrate judicial exception s into a practical application (MPEP 2106.05(g)). Further, the limitation reciting “by inputting the starting cell state and the target cell state into a trained machine learning algorithm” where a third network of the trained machine learning algorithm is a conditional invertible neural network, as in claim 6, provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the limitations merely serve to link the judicial exception of “identifying a perturbation that will cause the starting cell state to transition to the target cell state” to the technological environment of a conditional invertible neural network . Further steps directed to additional non-abstract computing elements do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exception s, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exception s. Hence, these are mere instructions to apply the judicial exception s using a computer, and therefore the claim does not integrate that judicial exception s into a practical application. 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 exception to the technological environment of a computer (MPEP 2106.05(f)). The specification as published discloses that the systems and methods of the invention provide solutions to deep generative models constrained to single-cell data at [0002-0003], but does not provide a clear explanation for how the additional elements provide these improvements. Therefore, the additional elements do not clearly improve the functioning of a computer, or comprise an improvement to any other technical field. Further, the additional elements do not clearly affect a particular treatment; they do not clearly require or set forth a particular machine; they do not clearly effect a transformation of matter; nor do they clearly provide a nonconventional or unconventional step (MPEP2106.04(d)). Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exception s [ Step 2A, Prong 2: NO ; See MPEP § 2106.04(d)]. Step 2B (MPEP 2106.05.A i -vi) According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: 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 the instant claims , the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE , Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)( i )). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)). With respect to claims 1, 10, and 17 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exception s. Hence, these are mere instructions to apply the judicial exception s using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception ( Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)). The specification as published also notes that computer processors and systems and the conditional invertible neural network, as example, are commercially available or widely used at [0112; 0146 ]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. 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 judicial exceptions (see MPEP 2106.05(b)I-III). Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [ Step 2B: NO ; See MPEP § 2106.05]. Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exception s without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(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- 5, 9 -14, and 17-20 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Hetzel et al. ( arXiv:2204.13545v1 , Apr 28 2022; corresponds to Hetzel arXiv:2204.13545v2 cited on the Mar 8 2023 IDS; newly cited) . Claim 1 discloses a computer-implemented method for identifying a perturbation. Claim 10 discloses a computer system for identifying a perturbation, the computer system comprising one or more processors. Claim 17 discloses a computing device for identifying a perturbation, the computing device comprising: one or more processors; and one or more memories coupled to the one or more processors; the one or more memories including computer executable instructions stored therein that, when executed by the one or more processors. The steps performed by the processors of claims 1, 10, and 17 comprise receiving an indication of a starting cell state; receiving an indication of a target cell state; and identifying a perturbation that will cause the starting cell state to transition to the target cell state by inputting the starting cell state and the target cell state into a trained machine learning algorithm. The prior art to Hetzel discloses a computational method (p. 1, par. 2), or model, comprising an encoder-decoder architecture to study the perturbational effects of unseen drugs combined with a transfer learning scheme (abstract). The computational method /model as taught by Hetzel is considered to read on the computer-implemented method of claim 1, the computer system of claim 10, and the computing device of claim 17. Hetzel teaches using trained adversarial classifiers ( i.e. , machine learning program ) to take ( i.e. input ) the latent basal state ( i.e. a starting cell state ) to predict ( i.e. , identify ) the drug ( i.e. , a perturbation ) that has been applied to example i as well as its cell-line c i ( i.e. , a target cell state ) (p. 4, section 3.3) (see also Figure 1; entire document is relevant). Regarding claims 2-3, 11-12, and 18 , Hetzel teaches claims 1, 10, and 17 as described above. Claims 2, 11, and 18 further add that the perturbation is a chemical perturbation. Claims 3 and 12 further add that the perturbation is a genetic perturbation. Hetzel teaches that the perturbations are drugs ( i.e. , chemicals as in claims 2, 11, and 18 ) (abstract; p. 2, section 3; Figure 1a)). Hetzel teaches that different cell lines are examined (p. 2, section 3; Figure 1a), which reads on a genetic perturbation of claims 3 and 12 because each cell line has different genetics. Regarding claims 4 -5, 13 -14, and 19-20 , Hetzel teaches claims 1 , 10, and 1 7 as described above. Claims 4 and 13 further add that the trained machine learning algorithm comprises: a first network that converts perturbations into real-valued vector representations of the perturbations; a second network that converts cell states into real-valued vector representations of the cell states; and a third network that maps relationships between: ( i ) the real-valued vector representations of the perturbations, and (ii) the real-valued vector representations of the cell states. Claim 19 further adds that the trained machine learning algorithm comprises: ( i ) a first network, (ii) a second network, and (iii) a third network; and the third network samples from a distribution of a cell state conditioned on a distribution of a perturbation representation. Claims 5, 14, and 20 further add that the trained machine learning algorithm comprises: ( i ) a first network, (ii) a second network, and (iii) a third network; and that the trained machine learning algorithm is trained by: training, via one or more processors, the first network, wherein the first network converts perturbations into real-valued vector representations of the perturbations; training, via the one or more processors, the second network, wherein the second network converts cell states into real-valued vector representations of the cell states; and training, via the one or more processors, the third network to learn relationships between: ( i ) real-valued vector representations of the perturbations, and (ii) the real-valued vector representations of the cell states. Hetzel teaches an architecture that separately embeds cell-line information ( i.e. , a second network that converts cell states into real-valued vector representations of the cell states ) and the drug perturbations into perturbation networks comprised of vectors ( i.e. , a first network that converts perturbations into real-valued vector representations of the perturbations ) (p. 2-4, section 3.1-3.2), and an encoder-decoder which takes as input the cell line and perturbation network attribute embeddings ( i.e. , a third network ) by mapping ( i.e. , claims 4 and 13 ) a measured gene expression state to its dimensional latent vector (p. 3-4, section 3). The mapping of a measured gene expression state to its dimensional latent vector as taught by Hetzel is also considered to read on the third network sampl ing from a distribution of a cell state conditioned on a distribution of a perturbation representation as recited in instant claim 19 because the decoder inputs the basal cell state and computes component-wise means and variances of normal distributions describing the attribute dependent gene expression state (p. 3, par. 1). Regarding claim 9 , Hetzel teaches claim 1 as described above. Claim 9 further adds that the trained machine learning algorithm is trained on high-throughput, single-cell screening data. Hetzel teaches that using datasets for single-cell experiments from high-throughput screens (abstract) for training and prediction (p. 1-2, section 1; p. 4-5, section 4). Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . 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. A. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Hetzel , as applied to claims 1 and 5 in the above 35 USC 102 rejection, and as evidenced by Lotfollahi et al. ( bioRxiv , May 18 2021, doi.org/10.1101/2021.04.14.439903 ; cited on the Mar 8 2023 IDS ) , and in view of Rombach et al. ( Advances in Neural Information Processing Systems, 2020. 33 : 2784-2797 ; newly cited ). Regarding claim 6 , Hetzel teaches claim 1 as described above. Claim 6 further adds that the third network is a conditional invertible neural network ( cINN ) . Although Hetzel teaches an encoder-decoder model (Figure 1a) following the methods of Lotfollahi , which teach es that the encoder-decoder is a neural network ( p. 3, par. 1; Figure 1 ) . Therefore Hetzel, as evidenced by Lotfollahi , teaches a third network which is a neural network. Hetzel does not teach a conditional invertible neural network ( cINN ) as instantly claimed. However, the prior art to Rombach discloses a conditional invertible neural network (title; entire document is relevant). 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 combine, in the course of routine experimentation and with a reasonable expectation of success, Hetzel as evidenced by Lotfollahi and Rombach because each reference discloses different types of neural networks. The motivation to substitute the neural network autoencoder-decoder taught by Hetzel as evidenced by Lotfollahi for the cINN as taught by Rombach would have been to use a model that can relate between different existing representations, as taught by Rombach (abstract). Therefore, it would have been obvious to one or ordinary skill in the art to use the cINN of Rombach to relate between the different existing representations of the perturbed network and cell line attributes of Hetzel. Such a substitution would have produced the predictable result of determining relations between the different existing embeddings. B. Claims 7-8 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Hetzel , as applied to claims 1 and 10 in the above 35 USC 102 rejection, and as evidenced by Subramanian et al. (Cell, 2017, 171(6):1437-1452; GSE92742 _Broad_LINCS_cell_info.txt.gz accessed from ncbi.nlm.nih.gov/geo/query/ acc.cgi?acc =GSE92742 ; newly cited) and in further view of the features of Hetzel . Regarding claims 7-8 and 15-16 , Hetzel teaches claims 1, 10, and 17 as described above. Claims 7 and 15 further add that the starting cell state is a diseased cell state, and the target cell state is a healthy cell state. Claims 8 and 16 further add that the starting cell state is a first healthy cell state, and the target cell state is a second healthy cell state. Hetzel teaches training their model using data from the L1000 dataset described in Subramanian. As evidenced by Subramanian , the L1000 dataset includes cell lines from multiple cancer cells and immortalized normal cell lines (p. e1, Experimental Models: Cell Lines; GSE92742 , sample_type column indicates every line as being form a tumor or normal sample). Hetzel does not explicitly teach inputting diseased or healthy starting cell states and target cell states which are either healthy as compared to the diseased state, or in a second healthy state as compared to the healthy starting state. However, regarding claims 7-8 and 15-16 , i t would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify , in the course of routine experimentation and with a reasonable expectation of success, the method of Hetzel to predict perturbations that move a diseased cell line to a healthy cell line, as in claims 7 and 15, or from one healthy state to another, as in claims 8 and 16, because the data used to train the model of Hetzel already contains such information. The motivation would have been to examine links between the data already present in the training data of the model to provide drugs to reach desirable outcomes, which one of ordinary skill in the art would have reasonably expected to be successful. Conclusion No claims are allowed. 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