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 are pending. Claims 1-20 rejected.
Priority
No priority claimed. The effective filing date is 08 September 2022.
Information Disclosure Statement
The IDS was considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference signs mentioned in the description:
Page 14, paragraph [0038] (continued): references number “122” in FIG. 1B. This number is missing from the FIG. 1B.
Page 27, paragraph [0065]: references number “1100” in FIG. 10. This number is missing from FIG 10.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Page 1, paragraph [0002], line 6: contains word “is” at the beginning of the line. This word appears to be present in error and should be removed.
Page 14, paragraph [0038] (continued): references number “122” in FIG. 1B. This number is missing from the figure.
Page 27, paragraph [0065]: references number “1100” in FIG. 10. This number is missing from the figure.
Appropriate correction is required.
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 inventions are directed to abstract ideas without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step
1: YES) are then analyzed to determine if the claims recite any concepts that equate to an
abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application,
the claims recite the following limitations that equate to abstract ideas:
Claim 1 and dependent claims 2, 3, 4, and 5 recite a computer-implemented method to encode risk factor variables and adversarially train a generative model to generate synthetic versions of the risk factor variables.
Claim 2 and dependent claim 6 recite extracting correlations among synthetic versions of risk factor variables.
Claim 3 recites using a discriminative model of the processor system to adversarially train the generative model.
Claim 5 recites synthetic versions of risk factor variables fill gaps in sets of non-synthetic risk factor variables.
Claim 6 and dependent claim 7 recite transmitting synthetic versions of risk factor variables and correlations among synthetic versions of the risk factor variables to an omic data analysis system.
Claim 7 and dependent claim 8 recite a generative adversarial network that uses synthetic versions of risk factor variables and the correlations among the synthetic versions of the risk factor variables to generate synthetic portions of diagnostic image data.
Claim 8 recites the operations of the processor performed in a cloud computing system.
Claim 9 and dependent claims 10 - 13 recite a computer-based system with memory and a coupled a processor encoding risk factor variables and adversarially training a generative model to generate synthetic versions of the risk factor variables.
Claim 10 recites extracting correlations among the synthetic versions of the risk factor variables.
Claim 11 recites using a discriminative model of the processor system.
Claim 13 recites synthetic versions of risk factor variables fill gaps in sets of non-synthetic risk factor variables.
Claim 14 and dependent claim 15 recite transmitting synthetic versions of risk factor variables and correlations among synthetic versions of the risk factor variables to an omic data analysis system.
Claim 15 recites a generative adversarial network that uses synthetic versions of risk factor variables and the correlations among the synthetic versions of the risk factor variables to generate synthetic portions of diagnostic image data.
Claim 16 and dependent claims 17, 19, and 20 recite a computer program product executed on a processor system to encode risk factor variables and adversarially train a generative model to generate synthetic versions of risk factor variables.
Claim 17 recites extracting correlations among synthetic versions of risk factor variables.
Claim 18 recites using a discriminative model to adversarially train the generative model.
Claim 19 recite transmitting synthetic versions of risk factor variables and correlations among synthetic versions of the risk factor variables to an omic data analysis system.
Claim 20 recites a generative adversarial network that uses synthetic versions of risk factor variables and the correlations among the synthetic versions of the risk factor variables to generate synthetic portions of diagnostic image data.
The limitations for the listed claims are evaluations or judgements that can be made through mental observations or mathematical calculations which fall under the “mental processes” and “mathematical concepts” groupings of abstract ideas. Under the broadest reasonable interpretation, the abstract ideas recited in the claims are determined to cover performance either in the mind (calculations by hand or pen and paper) or by mathematical operation (calculations/algorithms). See MPEP § 2106.04(a)(2), subsection III. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674: noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. V. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016): holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person's mind" (see Versata Dev. Group V. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016): holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by
humans without a computer").
While claims 9-15 recite performing aspects of the methods with a computer-based system and claims 16-20 recite performing aspects of the methods using a computer program product comprising a computer readable program stored on a computer readable storage medium, there are no additional limitations that indicate that the computer-based system, the computer program product, and the computer readable program stored on a computer readable storage medium require anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation on generic computer components, then it falls into the “mental processes” grouping of abstract ideas. As such, claims 1-20 recite abstract ideas (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further
analyzed to determine if the claims as a whole integrate the recited judicial exception into a
practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a
practical application because the claims do not recite an additional element that reflects an
improvement to technology or applies or uses the recited judicial exception in some other
meaningful way. Rather, the instant claims recite additional elements that amount to mere
instructions to implement the abstract idea or insignificant extra-solution activity. Specifically,
the claims recite the following additional elements:
Claims 4 and 12 recite specific limitations of the terms “synthetic versions of binary risk factor variables”, “synthetic versions of genotypic risk factor variables”, and “synthetic versions of continuous risk factor variables”.
Claims 9-15 recite using a computer-based system.
Claim 16-20 recite using a computer readable storage medium.
The limitations for defining terms describe mental processes with additional elements. This judicial exception is not integrated into a practical application because these additional
elements do not add any meaningful limitations. The claims do not include additional elements
that are sufficient to amount to significantly more than the judicial exception because they only
describe more specificity to the types of variables. As such, these limitations equate to mere
instructions to implement the abstract ideas.
There are no limitations that indicate that the processors or non-transitory computer-readable media require anything other than a generic computing system. As such, these limitations equate to mere instructions to implement the abstract ideas on a generic computer that the courts have stated do not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The above recited additional elements do not provide a practical application of the recited
judicial exception. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2:NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere definitions of terms and to mere instructions to apply the recited exception in a generic computing environment.
As discussed above, there are no additional limitations to indicate that the claimed
method requires more than defining terminology. Additionally, there are no additional limitations to indicate that the claimed computer-based system, computer program product, or computer readable media require anything other than generic computer components in order to carry out the recited abstract ideas in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. In addition, mere display of collected and analyzed information that could be performed by the human mind do not render an abstract idea eligible. See Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
Note: With respect to the recitation of "[a] computer program product comprising a computer readable program stored on a computer readable storage medium…,” the instant claims are not subject to interpretation of both "transitory" and "non-transitory" signals that are typically associated with "computer-readable storage media" language. Rather, the instant Specifications excludes any interpretation of such language as "transitory" at paragraph [0064], reciting, “[a] computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media." As such, claims 16-20 herein are statutory.
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 1-3 and 5 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rodriguez-Almeida et al. (IEEE Journal of Biomedical and Health Informatics, IEEE Xplore, 5 August 2022, pages 1-12) (Herein referred to as Rodriguez-Almeida.)
Regarding claims 1, 9, and 16, Rodriguez-Almeida teaches “to analyze the feasibility of different synthetic data generation algorithms in the medical field to generate trustworthy patient data,” and “eight databases were employed to evaluate the proposed synthetic data generation framework.” (Page 2, right column, lines 30-32 and 46-47) See also Table 1 (Page 3) and reference 33 as an example of the types of risk factor variables included in the databases incorporated in this study. The MNCD database (one of the databases used in the study) includes binary, genetic, and continuous data from patients with Alzheimer’s Disease. (Balea-Fernandez et al, reference 33 in cited work.) Rodriguez-Almeida further teaches “data augmentation was carried out [using] two different algorithms…Gaussian Copulas and Conditional Tabular Generative Adversarial Networks (CTGANs).” (Page 3, left column, lines 38-41) Rodriguez-Almeida also teaches that “[t]he Gaussian Copula is a copula constructed from a multivariate normal distribution, capable to reproduce a large variety of multivariate distributions.’ (Page 3, right column, lines 1-3)
Regarding claims 2, 10, and 17, Rodriguez-Almeida teaches “PCD [Pairwise Correlation Difference] measures if the synthetic data linear correlations correspond with the linear correlations in the real data.” (Page 3, right column, lines 37-38)
Regarding claim 3, 11, and 18, Rodriguez-Almeida teaches that “Generative Adversarial Networks (GANs) are Deep Learning (DL) algorithms based on a discriminative model.” (Page 2, left column, lines 11-12) Rodriguez-Almeida further teaches using “Conditional Tabular Generative Adversarial Networks (CTGANs)…and CTGAN [is] a Deep Learning approach.” (Page 3, left column, lines 40-44)
Regarding claim 5 and 13, Rodriguez-Almeida teaches “[m]issing data of both subsets were imputed using a KNN imputation method…[c]onsidering one instance of the dataset having one missing value in a certain variable, this method will find K instances similar to that one, and will compute the weighted average in such variable to fill the missing one” and “[e]ach instance of the test set was imputed independently using the training set.” (Page 4, right column, lines 8-14)
Regarding claims 9-11 and 13, the instant application claims using a computer-based system to implement the methods of claims 1-3 and 5. Additionally, regarding claims 16-18, the instant application claims using a non-transitory computer-readable medium to implement the methods of claims 1-3. The court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. See MPEP 2144.04 III.
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 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Vinas et al (Bioinformatics, Volume 38, Issue 3, 20 January 2021, pages 730–737), (herein referred to as Vinas), in view of Rodriguez-Almeida. (IEEE Journal of Biomedical and Health Informatics, IEEE Xplore, 5 August 2022, pages 1-12)
Vinas teaches “a method based on a conditional generative adversarial network to generate realistic transcriptomics data for Escherichia coli and humans.” (Page 730, Abstract, lines 4-5). Vinas further teaches a “method to generate human RNA-seq data from a broad range of cancer and normal tissue-types” and “have generated a comprehensive collection of human transcriptome data in a diverse set of tissues and cancer types.” (Page 732, paragraph 4.1.2, lines 2-3 and 6-8) Vinas also references “14 important cancer driver genes with high mutation frequency” and discloses that “for this subset of genes, our model closely matches the correlation and clustering expression patterns.” (Page 734, paragraph 5.2.1, lines 1-2 and 4-5) Vinas further discloses they “generated a gene expression dataset that matches the statistics of the train set” and a “method…able to emulate tissue- and disease-specific traits of gene expression.” (Page 734, paragraph 5.2.2, lines 2-3 and 20-21) Additionally, Vinas teaches their “model affords the opportunity to produce gene expression data for synthetic patients across different tissues and cancer-types.” (Page 734, paragraph 5.2.3, lines 1-2)
Vinas does not teach a multivariate Gaussian (MVG) generative model.
Rodriguez-Almeida teaches “data augmentation was carried out [using] two different algorithms…Gaussian Copulas and Conditional Tabular Generative Adversarial Networks (CTGANs).” (Page 3, left column, lines 38-41) Rodriguez-Almeida also teaches that “[t]he Gaussian Copula is a copula constructed from a multivariate normal distribution, capable to reproduce a large variety of multivariate distributions.’ (Page 3, right column, lines 1-3)
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 applied the method of Rodriguez-Almeida to use a specifically multivariate Gaussian approach with the generative adversarial method of Vinas. Meng et al (Journal of Biomedical Informatics, February 2021, pages 1-11, cited in IDS) teaches “a novel statistical framework based on multivariate Gaussian processes to model time-dependent scale, correlation and smoothness across both time and different clinical variables in EHR [electronic health records] data.” (Page 1, right column, lines 1-2) (Herein referred to as Meng.) Meng discloses that “[e]xisting approaches to modeling EHR data often lack the flexibility to handle time-varying correlations across multiple clinical variables, or they are too complex for clinical interpretation” and as such “propose a novel nonstationary multivariate Gaussian process model for EHR data to address the aforementioned drawbacks of existing methodologies.” (Page 1, Abstract, lines 3-6) Therefore, one of ordinary skill in the art would have been motivated to incorporate a multivariate Gaussian generative model to improve the generation of synthetic gene-based disease risk factor variables and synthetic gene expression risk factor variables to further the study of diseases with genotypic risk factors. The invention is therefore prima facie obvious.
Claims 6, 7, 14, 15, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sui et al (IEEE Access, 16 September 2021, pages 125247-125257),(herein referred to as Sui), in view of Rodriguez-Almeida. (IEEE Journal of Biomedical and Health Informatics, IEEE Xplore, 5 August 2022, pages 1-12)
Regarding claims 6, 14, and 19, Sui teachers “a radiogenomic framework centered on deep learning (DL) to map image features and genomic data, based on our previous work on the correlation between genomics and images.” (Page 125248, left col., lines 10-13) Sui further teaches “[c]onditional autoencoder replace the original model while correlating and more genomic analysis are conducted. (Page 125248, left col., lines 13-15)
Sui does not teach an adversarially-trained multivariate Gaussian (MVG) generative model to generate synthetic versions of the binary risk factor variables, synthetic versions of the genotypic risk factor variables, and synthetic versions of the continuous risk factor variables. (Claim 1) Sui also fails to teach to extract correlations among the synthetic versions of the binary risk factor variables, the synthetic versions of the genotypic risk factor variables, and the synthetic versions of the continuous risk factor variables. (Claim 2)
Rodriguez-Almeida teaches the methods of claims 1 and 2 as rejected above under 35 U.S.C. 102(a)(1). (Page 2, right column, lines 30-32 and 46-47; Page 3, Table 1; Page 3, left column, lines 38-41; Page 3, right column, lines 1-3; Page 3, right column, lines 37-38)
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to enhance the omics analysis method of Sui to with synthetic data from the method of Rodriguez-Almeida. Sui discloses “limitations relevant to the dataset scale and unbalance issues” and therefore propose in the future to “focus on dataset enlargement and combining different dataset from different sources and data categories.” (Page 125256, left column, lines 17-18 and 28-30) Rodriguez-Almeida teaches “the use of synthetic data generation techniques could enhance the development and evaluation of AI-based
algorithms in medical research.” (Page 2, left column, lines 50-53) Rodriguez-Almeida further teaches “[s]ynthetic data have proven to be useful in AI models pre-training phase prior real data are used…[and]…the combination of synthetic and real data in the training phase usually enhances the performance of the models.” (Page 2, right column, lines 15-19) Therefore, one of ordinary skill in the art would have been motivated to combine the synthetic data generation method of Rodriguez-Almeida with the radiomics and radiogenomics methods of Sui to analyze enhanced data sets. The invention is therefore prima facie obvious.
Regarding claims 7, 15, and 20, Sui teaches to “use generative adversarial network to transform genomic data onto tumor images.” (Page 125247, Abstract, line 10) Sui further discloses “a genomic conditional variational autoencoder GAN (GCVAE-GAN).” (Page 125250, right column, lines 34-35) Additionally, Sui teaches a method to “build the correlation between genes expression data and image features of tumor region in CT image series” using a “conditional autoencoder [that] can extract image features which have high correlation with gene data, and simultaneously keep images information “ (Page 125249, right column, lines 6-7; page 125250, left column, lines 35-37) Sui further teaches using the “autoencoder to encode the images under the condition of gene, … [t]he image features are extracted from different levels of encoder…[a] series of analysis experiments…are applied to these features, prognostic data and genes to prove the correlation among these multi-source data…[and]…a modified CVAE [conditional variation autoencoder]-GAN transforms gene to corresponding TR [tumor region] and give an intuitive result.” (Page 125248, right column, lines 51-56; page 125249, left column, lines 1-2) Sui’s method of generating synthetic images is further disclosed in Figure 1 in “Stage 3: Synthetic tumor region generation.” (Page 125249, Figure 1, top of page)
Sui does not teach to use the synthetic versions of the binary risk factor variables, the synthetic versions of the genotypic risk factor variables, the synthetic versions of the continuous risk factor variables, and correlations among the synthetic versions of the binary risk factor variables, the synthetic versions of the genotypic risk factor variables, and the synthetic versions of the continuous risk factor variables.
Rodriguez-Almeida teaches the methods of claims 1 and 2 as rejected above under 35 U.S.C. 102(a)(1). (Page 2, right column, lines 30-32 and 46-47; Page 3, Table 1; Page 3, left column, lines 38-41; Page 3, right column, lines 1-3; Page 3, right column, lines 37-38)
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to enhance the synthetic diagnostic image generation method of Sui with the synthetic data from the method of Rodriguez-Almeida. Sui teaches “radiogenomic methods can give a mathematic demonstration on the correlation between gene and images, but most of these methods merely provide the relationship numerically without any visual demonstration, and the visual results are greatly demanded for prognosis inferences.” (Page 125250, right column, lines 21-25) Sui discloses “limitations relevant to the dataset scale and unbalance issues” and “[a]s for the proposed framework has showed its capability in mapping the genomic data and tumor images, we will focus on dataset enlargement and combining different dataset from different sources and data categories...” (Page 125256, left column, lines 17-18 and 27-30) Rodriguez-Almeida teaches “the use of synthetic data generation techniques could enhance the development and evaluation of AI-based algorithms in medical research. (Page 2, left column, lines 50-53) Rodriguez-Almeida further teaches “[s]ynthetic data have proven to be useful in AI models pre-training phase prior real data are used…[and]…the combination of synthetic and real data in the training phase usually enhances the performance of the models.” (Page 2, right column, lines 15-19) Therefore, one of ordinary skill in the art would have been motivated to combine the synthetic data generation method of Rodriguez-Almeida with the radiomics and radiogenomics methods of Sui to create synthetic tumor imaging. The invention is therefore prima facie obvious.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sui et al (IEEE Access, 16 September 2021, pages 125247-125257) in view of Rodriguez-Almeida (IEEE Journal of Biomedical and Health Informatics, IEEE Xplore, 5 August 2022, pages 1-12) in view of Purandhar et al (Soft Computing, 13 April 2022, pages 5511–5521). (Herein referred to as Purandhar.)
Regarding claim 8, Sui and Rodriguez-Almeida teach the methods of claim 7 as indicated above.
Sui and Rodriguez-Almeida do not teach the methods performed by a cloud computing system.
Purandhar teaches “[m]obile cloud computing in health care data analysis reduces the risks and costs of the public and meets the demand for health care.” (Page 5513, left column, lines 10-12) Purandhar further teaches “cloud-based analysis could be a better choice. The model reported in Forkan et al. (2017) mines the patterns and trends in big data and analyzes the
abnormalities in the cloud through learning models. The detection capability of the reported model is better…” (Page 5513, right column, lines 13-17)
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to implement the methods of Sui and Rodriguez-Almeida in a cloud computing system as suggested by Purandhar. Given the improvements in data privacy and analysis gained from implementing cloud computing, one of ordinary skill in the art would have been motivated to combine the elements of Sui and Rodriguez-Almeida with a cloud computing system as described by Purandhar. One skilled in the art would have had a reasonable expectation of success in using cloud computing to implement generative adversarial networks for the generation and analysis of synthetic data. The invention is therefore prima facie obvious.
Regarding claims 12, 14, and 15, the instant application claims using a computer-based system to implement the methods of claims 4, 6, and 7. Additionally, regarding claims 19 and 20, the instant application claims using a non-transitory computer-readable medium to implement the methods of claims 6 and 7. The court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. See MPEP 2144.04 III.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pezoulas et al (IEEE Open Journal of Engineering in Medicine and Biology, June 2022) teaches a Bayesian-Gaussian mixture model in generating synthetic data for clinical trials. Chaudhari et al (Soft Computing, December 2019) teaches a generative adversarial network for augmentation in gene expression datasets. The generator is fed original data and multivariate noise to generate data with a Gaussian distribution. Yoon et al (IEEE Journal of Biomedical and Health Informatics, August 2020) teaches a generative adversarial network to generate synthetic patient data for binary and continuous risk factors. Torfi and Fox (FLAIRS Conference, 2020) teach a generative adversarial network that can create discrete and synthetic healthcare records and can capture inter-correlation between features. Chen et al (Computers in Biology and Medicine, March 2022) teaches a review of generative adversarial methods used in augmentation of medical images. Ko et al (IEEE Transactions on Medical Imaging, August 2022) teaches using single nucleotide polymorphism data in generating synthetic medical imaging. Bargstein and Schlaefer (International Journal of Computer Assisted Radiology and Surgery, June 2020) teach a generative adversarial method to augment ultrasound image processing. Dikici et al (Journal of Medical Imaging, April 2021) teaches a generative adversarial network to generate synthetic medical images. Jafarkhani et al (U.S. Nonprovisional published application 17/21442, March 2021) teaches a generative adversarial network configured to generate synthetic medical image data and determine probabilities in a Gaussian distribution of the synthetic data corresponding to real medical image data.
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/S.L.G./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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