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
Claims 1-20, 30, 33, and 35-37 are pending.
Claims 1-20, 30, 33, and 35-37 were examined.
Claims 1-20, 30, 33, and 35-37 are rejected.
Priority
The instant application filed 17 February 2023 claims priority to PCT/US21/50182 filed 14 September 2021 and Provisional application 63/078,148 filed 14 September 2020. As such, the effective filing date of the instant application is 14 September 2020.
Information Disclosure Statement
The IDSs filed 8 March 2023, 9 May 2023, 11 September 2024, and 21 November 2025 were all considered by the examiner.
Drawings
The drawings filed 17 February 2023 are acceptable.
Nucleotide and/or Amino Acid Sequence Disclosures
REQUIREMENTS FOR PATENT APPLICATIONS CONTAINING NUCLEOTIDE AND/OR AMINO ACID SEQUENCE DISCLOSURES
Items 1) and 2) provide general guidance related to requirements for sequence disclosures.
37 CFR 1.821(c) requires that patent applications which contain disclosures of nucleotide and/or amino acid sequences that fall within the definitions of 37 CFR 1.821(a) must contain a "Sequence Listing," as a separate part of the disclosure, which presents the nucleotide and/or amino acid sequences and associated information using the symbols and format in accordance with the requirements of 37 CFR 1.821 - 1.825. This "Sequence Listing" part of the disclosure may be submitted:
In accordance with 37 CFR 1.821(c)(1) via the USPTO patent electronic filing system (see Section I.1 of the Legal Framework for Patent Electronic System (https://www.uspto.gov/PatentLegalFramework), hereinafter "Legal Framework") as an ASCII text file, together with an incorporation-by-reference of the material in the ASCII text file in a separate paragraph of the specification as required by 37 CFR 1.823(b)(1) identifying:
the name of the ASCII text file;
ii) the date of creation; and
iii) the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(1) on read-only optical disc(s) as permitted by 37 CFR 1.52(e)(1)(ii), labeled according to 37 CFR 1.52(e)(5), with an incorporation-by-reference of the material in the ASCII text file according to 37 CFR 1.52(e)(8) and 37 CFR 1.823(b)(1) in a separate paragraph of the specification identifying:
the name of the ASCII text file;
the date of creation; and
the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(2) via the USPTO patent electronic filing system as a PDF file (not recommended); or
In accordance with 37 CFR 1.821(c)(3) on physical sheets of paper (not recommended).
When a “Sequence Listing” has been submitted as a PDF file as in 1(c) above (37 CFR 1.821(c)(2)) or on physical sheets of paper as in 1(d) above (37 CFR 1.821(c)(3)), 37 CFR 1.821(e)(1) requires a computer readable form (CRF) of the “Sequence Listing” in accordance with the requirements of 37 CFR 1.824.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed via the USPTO patent electronic filing system as a PDF, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the PDF copy and the CRF copy (the ASCII text file copy) are identical.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed on paper or read-only optical disc, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the paper or read-only optical disc copy and the CRF are identical.
Specific deficiencies and the required response to this Office Action are as follows:
Specific deficiency – Nucleotide and/or amino acid sequences appearing in the specification are not identified by sequence identifiers in accordance with 37 CFR 1.821(d).
Required response – Applicant must provide:
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required sequence identifiers, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
Specification
The disclosure is objected to because of the following informalities: Paragraph [0042] includes two enumerated DNA fragment sequences. See above regarding nucleotide sequence disclosures.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Encoding interconnection module in claim 11
Decoding interconnection module in claim 12.
In review of the claims and specification, the functions of these units are disclosed in the specification as:
An encoding interconnection module receives the outputs of the encoders as the encoder hidden layer. In the example shown, encoding interconnection module is a recurrent neural network (RNN). [0144]
A decoding interconnection module (RNN2 module) receives embedding vector and outputs a decoder hidden layer. Decoding interconnection module operates on all of the values of embedding vector, which may be in the latency space, and also receives an input of each trait indicator for the windows, and thus can also operate collectively on values for the different windows. [0142, 0145]
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-8, 30, and 33 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yelmen et al. (bioRxiv, 7 October 2019, pages 1-26) (Herein referred to as Yelmen.)
With respect to claim 1, and dependent claims 2-8, claim 30, and claim 33, Yelmen teaches a GAN model and an RBM model to create Artificial Genomes (AGs) using 2504 individuals (5008 haplotypes) from 1000 Genomes data spanning 805 SNPs from all chromosomes. [lines 90-91 and 103-105] Yelmen further teaches a type of neural network capable of learning probability distributions through input data. [lines 73-74] Yelmen additionally teaches a GAN with an input layer with a latent vector with a normal distribution and two hidden layers proportional to the number of SNPs. [lines 392-98] Yelmen teaches a discriminator that consists of an input layer with the size of the number of SNPs and hidden layers proportional to the number of SNPs. [lines 398-401] Yelmen further teaches generated outputs, an optimization algorithm, and scoring for the artificial genome. [lines 403-415, 422] Yelmen also teaches an RBM model with Gaussian distribution. [lines 425, 433] Yelmen additionally discloses the training of GANs and RBMs to learn the high dimensional distributions of real genomic datasets and create high quality artificial genomes. [lines 25-27]
With respect to claim 2 and dependent claim 3, Yelmen teaches creating artificial genomes utilizing data with SNPs. [lines 102-105, 372-387]
With respect to claim 3, Yelmen teaches the capability to produce artificial datasets combining AGs with multiple phenotypes. [lines 280-81] Yelmen further teaches creating haplotypes for the analyses and that the GAN model can be applied to genotype data by combining two haplotypes if the training data is not phased. [lines 274-76] Additionally, Yelmen discloses that in the data format used, rows are individuals/haplotypes (instances) and
columns are positions/SNPs (features). [lines 383-87]
With respect to claim 4, Yelmen teaches using a dataset with individuals possessing at least one ancestral allele. [lines 253-54]
With respect to claim 5, Yelmen teaches using their model as a starting point for various population genetics analyses such as demographic and selection inference. [lines 363-64] Yelmen further teaches using their GAN and RBM models in combination with other tools for the reconstruction of recombination, demography or selection. [lines 90-98]
With respect to claim 6, Yelmen teaches their generative models can be used to produce artificial datasets combining artificial genomes with multiple phenotypes. [lines 279-281]
With respect to claim 7, Yelmen teaches selecting a probability distribution from more than one probability distribution. [lines 457-462]
With respect to claim 8, Yelmen teaches a GAN trained with ancestral traits and SNPs that has a generator with an input vector and generates a probability distribution. [Materials & Methods section, lines 372-452]
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 1-9, 13-14, 17, 30, 33, 35, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Yelmen in view of Battey et al. (bioRxiv, 13 August 2020, pages 1-38) (Herein referred to as Battey.)
Yelmen teaches the limitations of claims 1-8, 33, and 30 as applied above under 35 U.S.C. 102.
Yelmen does not specifically disclose extracting variant segments from an input genome, determining a probability distribution, obtaining a sample vector, and reconstructing output vectors.
With respect to claims 9 and 35, Battey teaches inputting a set of genotypes and that the dataset consists of N observations (i.e. individual genotypes) [lines 84 and 97] and applying their model to SNPs [line 170]. Battey further teaches a variational autoencoder (VAE) architecture that includes an encoder, latent space, and decoder in the determining of a probability distribution, sampling from the distribution, and generating a genotype vector. [Figure 1, page 4] Battey additionally teaches generating genotypes characteristic of a given population by sampling from the latent space of a trained model. [lines 309-310]
With respect to claims 13 and 37, Battey teaches a multivariate normal distribution. [line 64 and Figure 1] Merriam Webster Dictionary defines a Gaussian distribution as a normal distribution. (Merriam-Webster.com) Battey discloses using ELBO in popvae. [lines 110 and 112] As taught by Kingma and Welling, ELBO is a well-known component of optimizing probability distributions in variational autoencoders [Section 2.2]. (arXiv, 11 December 2019, pages 1-86) Odaibo teaches that ELBO is defined as including mean and variance [page 6, line (36)]. (arXiv, 21 July 2019, pages 1-8) Battey teaches using random seeds when running popvae. [Figures S2 and S9] The limitations of this claim are further described by the well-known reparameterization trick used in variational autoencoders as described by Kingma and Welling. [Section 2.4]
With respect to claim 14, Battey teaches an encoder neural network with input layers, latent space, output layers, and weights. [Methods section, Model subsection, lines 94-116 and Figure 1] Battey further teaches the input is a set of unphased genotypes and outputs sample coordinates in a low-dimensional latent space. [lines 84-85] Battey additionally teaches hidden units per layer and hidden layers. [lines 136-140, Table S1, Figure S3] Battey discloses rescaling coordinates in multidimensional latent space.[lines 195-197 and 220 -230]
With respect to claim 17, Battey teaches that VAEs consist of a pair of neural networks, and the second neural network is a decoder. [lines 11 and 57] Battey further teaches that popvae includes an encoder that is a neural network. [Figure 1, lines 642-643 and 650-651] Battey additionally teaches about the loss function used to update weights and biases of both networks. [Figure 1] Battey also teaches that training samples are used to optimize weights and biases of the neural network, while validation samples are used to measure validation loss after each training epoch. [lines 128-130]
It would have been prima facie obvious to one of ordinary skill in the art at the effective
filing date of the invention to have combined the methods of Yelmen using generative models to create artificial human genomes with the approach of Battey to incorporate a variational autoencoder that used ELBO and the reparameterization trick. Battey discloses that a variational autoencoder (VAE) is a way to create meaningful and interpretable visualizations of population genetic data because it is a method that encodes as much information as possible into just two dimensions while maintaining global structure. [lines 53-56] Battey further explains that the VAE framework also allows the generation of genotypes characteristic of a given population by sampling from the latent space of a trained model and simulated genotypes generated by process-based models are a key tool in population genetics, because they allow the exploration of the impact of various generative processes (such as demography, selection, etc.) on observed genetic variation. [lines 309-313] Kingma and Welling disclose that an important property of the ELBO is that it allows joint optimization with respect to all parameters using stochastic gradient descent. [Page 19, Section 2.3, lines 1-2] Kingma and Welling further explain that one can start out with random initial values and stochastically optimize their values until convergence. [Page 19, Section 2.3, lines 3-4] Kingma and Welling additionally disclose that reparameterization trick can reorganize the gradient computation and reduces variance in the gradients. [Page 4, last 2 lines] Kingma and Welling also disclose that the reparameterization trick ends up backpropagating through the many layers of the deep neural networks embedded inside of it. [Page 5, second paragraph, lines 9-11] Additionally, combining the methods of Yelmen and Battey would allow for the input of both variant sites and traits of the input variant sites into the VAE when generating the simulated genomic sequences. Therefore, one of ordinary skill in the art would have been motivated to combine the GAN of Yelmen and the VAE of Battey to improve the process of creating simulated genomic sequences. The invention is therefore prima facie obvious.
Claim 10, 11, 12, 15-16, 18-20, and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Yelmen and Battey in view of Choi and Chae.(BMC Bioinformatics, 11 May 2020, pages 1-10) (Herein referred to as Choi.)
With respect to claim 10,Choi teaches that with the matrix of DNA methylation beta values and matched cancer type information as input, the methCancer-gen approximates the underlying distribution model of the input data and, after model training, methylation beta value for the specified cancer type can be generated as output. (Page 7, paragraph 3 and Figure 2)
With respect to claims 11 and 36,
Battey teaches VAEs consist of a pair of deep neural networks in which the first network (the encoder) encodes input data as a probability distribution in a latent space and the second (the decoder) seeks to recreate the input given a set of latent coordinates. [lines 57-59] Battey further teaches popvae (for population VAE), a command-line python program that takes as input a set of unphased genotypes and outputs sample coordinates in a low-dimensional latent space. [lines 83-85] Battey additionally discloses the model of their VAE including a dataset with N individual genotypes, the probability distribution of those data, a latent model, and a decoder. [lines 95-107 and Figure 1] Battey further discloses testing a recurrent neural network as one or both of the encoder/decoder pair. [lines 650-651] Battey additionally discloses using a two -dimensional popvae model. [line 321] Battey teaches that popvae's encoder and decoder networks are fully-connected feed-forward networks whose size is controlled by two parameters -- `width`, which sets the number of hidden units per layer, and `depth`, which sets the number of hidden layers. [lines 136-138] Battey experimented with a variety of network sizes, including width, as shown in Table S1 and Figure S3. [Pages 38 and 26]
With respect to claim 12,
Battey teaches testing a recurrent neural network as one or both of the encoder/decoder pair. [lines 650-651] Battey further teaches yielding latent embeddings [line 16] and outputting latent coordinates [line 159]. [See also Figure 1]
With respect to claims 15 and 18, Yelmen teaches values of traits of variant segments and Battey teaches a VAE that comprises multiple weights and scaling. Choi teaches the hidden layer generating values based on combining weighted x (methylation) values and weighted y (cancer type) values. (Page 7, Figure 2)
With respect to claims 16 and 19, Choi teaches multiple weights and multiple hidden layers based on input methylation values. (Page 7, Figure 20
With respect to claim 20,
Choi teaches a neural network-based tool for generating DNA methylome samples for user-specified cancer type and the proposed model employs a class conditional variational autoencoder as a generative model to estimate the distributions that underlie observed methylation values by variational inference while accounting for cancer type (class condition). (Page 7, paragraph 2 and Figure 2) Choi further teaches that for methCancer-gen (a class- conditional VAE), x represents the input data of DNA methylation beta values and y is a cancer type. (Page 8, paragraph after formula 2)
It would have been prima facie obvious to one of ordinary skill in the art at the effective
filing date of the invention to have combined the GAN of Yelmen the VAE of Battey, and the CVAE of Choi. These methods would have been obvious to combine to incorporate collective reconstruction of output vectors, collectively determine the probability distribution for the input variant segments, collectively reconstruct the output vectors using a decoder interconnection module and outputting a hidden vector, and incorporate a class-conditional variational autoencoder. Battey discloses that variational autoencoders have at least two attractive properties for genetic data: they allow users to define the output dimensionality and they preserve global geometry (i.e., relative positions in latent space) better than competing methods.[lines 376-379] Battey further discloses that, as generative models, VAEs allow the creation of genotypes that capture aspects of population genetic variation characteristic of the training set by taking samples from the estimated latent space and passing forward into data space. [lines 383-386] Choi discloses a conditional variational autoencoder (CVAE) is suitable for incorporating a control for the defined condition and allows generating samples similar but not identical to input data from modeling conditional distribution with latent variables and data. (Page 3,paragraph 2, lines 3-6) Choi further states that a CVAE has control on the data generation process, therefore by changing the defined conditional variables, simulation data for specified variables will be generated. (Page 3, paragraph 2, lines 6-7) In reference to class-conditional DENs, Linder et al discloses that class-conditional models can have an embedding layer that transforms the class label into a feature vector, which is concatenated onto every layer of the generator. (Cell Systems, 22 July 2020, pages 49-62 and e1-e16)
(Page 55, right column, lines 1-3) Linder et al further describe that the use of class-conditional features optimizes the generation of sequences based on specific class conditions. (Figure 4(B)) Therefore, one of ordinary skill in the art would have been motivated to combine the GAN of Yelmen, the VAE of Battey, and the class-conditional VAE of Choi to improve the process of creating simulated genomic sequences. The invention is therefore prima facie obvious.
Conclusion
Claims 1-20, 30, 33, and 35-37 are eligible under 35 USC 101 (eligibility). The claims are drawn to the practical application of generating a simulated genome sequence and utilize particular computer steps that do not have reasonable mental analogs.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Killoran et al teaches the use of VAEs for generating and designing DNA. (arXiv, 17 December 2017, pages 1-19) Adrion et al teaches community-maintained collection of empirical genome data and population genetics simulation models. (bioRxiv, 17 May 2020, pages 1-39) Agarwal et al teaches a sequence-to-sequence autoencoder model to learn a latent representation of a fixed dimension for long and variable length DNA sequences. (arXiv, 7 June 2019, pages 1-5) Chan et al teaches the exchangeable variational autoencoder which provides inferential and computational benefits while enabling varying set size data to be robustly handled in the VAE framework with applications to genomic data. (AABI 2019, 16 October 2019, pages 1-6) Churchill et al teaches generating genotypes with neural networks including autoencoders and conditional distributions. (arXiv, 14 April 2016, pages 1-23) Qui et al teaches genomic data imputation with variational auto-encoders. (GigaScience, 6 August 2020, pages 1-13) Huang et al teaches a conditional autoencoder, which uses a neural network to capture the generative process of the data y from latent variable z and observed variable b (bioRxiv, 4 August 2020, pages 29)
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/S.L.G./
Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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