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 .
Response to Election
Applicant’s election without traverse of claims 1, 2, 6-8, 10, 12, 15, 16, 17, 19, 23, 25, 28, 29, 31, 33-35, 39, 41, 43, 45, 47 and 52 in the reply filed on 04/03/2026 is acknowledged. Claim 51 has been withdrawn.
Claims 4-5, 9, 11, 18, 20-22, 24, 26-27, 30, 32, 36-38, 40, 42, 44, 46 and 48-50 have been canceled. Claims 1, 6-7, 10, 12-17, 25, 33 and 39 have been amended. Claims 1-3, 6-8, 10, 12-17, 19, 23, 25, 28-29, 31, 33-35, 39, 41, 43, 45, 47 and 51-52 are currently pending in the application.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7/18/2024 are being considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
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 1-3, 6-8, 10, 12-17, 19, 23, 25, 28-29, 31, 33-35, 39, 41, 43, 45, 47 and 52 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
In determining whether a claim falls within an excluded category, the Examiner is guided by the Court’s two-part framework, described in Mayo and Alice. Id. at 217-18 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 75-77 (2012)); Bilski v. Kappos, 561 U.S. 593, 611 (2010); 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019); the October 2019 Update of the 2019 Revised Guidance (Oct. 17, 2019); 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (July 17, 2024), and the USPTO’s Paten Subject Matter Eligibility Memorandums of August 4, 2025 and December 5, 2025.
Step 1
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability (i.e., laws of nature, natural phenomena, and abstract ideas). Alice Corp. v. CLS Bank Int'l, 573 U. S. ____ (2014).
The broadest reasonable interpretation of claims 1 and 33 encompasses a computer system (e.g., hardware such as a processor and memory) that implements the recited functions. If assuming that the system comprises a device or set of devices, then the system is directed to a machine, which is a statutory category of invention.
Claim 52 is directed to a statutory category, because a non-transitory computer-readable medium comprising computer-readable instructions satisfies the requirements of a product. (Step 1: Yes).
Next, the claim is analyzed to determine whether it is directed to a judicial exception.
Step 2A – Prong 1
Claims 1 and 33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more of balancing a dataset obtained from a biological sample. The claims recite:
Claim 1. A system configured to balance an imbalanced dataset obtained from a biological sample, the system comprising: one or more computer subsystems; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise a generative adversarial network (GAN) trained with: a first training set comprising data corresponding to an amount of cell-free DNA (cfDNA) biomarker from a subject with an organ injury designated as a first training input; a second training set comprising data corresponding to an amount of cell-free DNA (cfDNA) biomarker from a subject without the organ injury designated as a second training input; wherein the first dataset and the second datasets are imbalanced and the one or more computer subsystems are configured to generate a set of synthetic features for the first dataset and/or the second dataset by inputting a portion of the data from the first training input and the second training input into the generative adversarial network.
Claim 33. A system configured to analyze a dataset obtained from a biological sample, the system comprising: one or more computer subsystems; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise a generative adversarial network trained with a training set corresponding to an amount of cfDNA from a subject; and wherein the one or more computer subsystems are configured to generate a synthetic dataset from the biological sample by inputting a subset of the training set data into the generative adversarial network.
The claims, as currently recited, essentially represent a combination of a mathematical concepts, such as machine-learning model training and synthetic data generation; data analysis; and observation of a natural phenomenon, such as cfDNA biomarkers associated with organ injury. However, courts have found that, without technological improvement, said collecting biological data, analyzing the data with algorithms, and producing diagnostic information constitutes an abstract idea. See, e.g., Mayo Collaborative Services v. Prometheus Laboratories; Electric Power Group v. Alstom; Ariosa Diagnostics v. Sequenom. The recited limitations of receiving biomarker data; training GAN, and generating synthetic data is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or mathematical concepts, which may be practically performed in the human mind using observation, evaluation, judgment, and opinion (MPEP 2106.04(a)(2), subsection III), but for the recitation of generic computer components. (Note: Examiner’s language (e.g. “receiving biomarker data”; “training GAN”; etc.) is an abbreviated reference to the detailed claim steps and is not an oversimplification of the claim language; the Examiner employing such shortcuts (that refer to more specific steps) when attempting to explain the rejection). That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind, and/or performed as mathematical operations. Aside from the general technological environment (addressed below), it covers purely mental and/or mathematical concepts, and the mere nominal recitation of a generic network appliance (e.g. an interface for inputting or outputting data, or generic network-based storage devices and displays) does not take the claim limitation out of the mental processes and/or Mathematical Processes groupings.
Specifically, the utilizing statistical tools to process data and to output the estimated values - said functions could be performed by a human using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas (e.g., mental comparison regarding a sample or test subject to a control or target data in Ambry, Myriad CAFC, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in In re Grams, 888 F.2d 835 (Fed. Cir. 1989) (Grams)). In Grams, the recited functions require obtaining data or patient information (from sensors), and analyze that data to ascertain the existence and identity of an abnormality or estimated responses, and possible causes thereof. While said functions are performed by a computer, they are in essence a mathematical algorithm, in that they represent "[a] procedure for solving a given type of mathematical problem." Gottschalk v. Benson, 409 U.S. 63, 65, 93 S.Ct. 253, 254, 34 L.Ed.2d 273 (1972). Moreover, the Federal Circuit has held, “without additional limitations, a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). Further, “analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, [are] essentially mental processes within the abstract-idea category.” Elec. Power, 830 F.3d at 1354; see also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1146 (Fed. Cir. 2016). “[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.” Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012).
As per the use of artificial intelligence and/or machine learning techniques (AI/ML), said recitation does not make the claim patent eligible, because said tools are utilized merely for data gathering and comparing, and are not utilized in express manipulation and control of functional aspects and/or hardware components/equipment of real-world processes and systems using output of AI models (e.g., manufacturing processes and equipment, medical treatments, communications processes and systems, logistics systems and hardware, interactive smart phone apps, etc.).
It is similar to other abstract ideas held to be non-statutory by the courts. See, also, Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025), wherein the court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. Additionally, applying machine learning to information analysis, an activity predating computers, did not transform the abstract idea into a patent-eligible invention.
As per receiving, storing and outputting data limitations, it has been held that “As many cases make clear, even if a process of collecting and analyzing information is ‘limited to particular content’ or a particular ‘source,’ that limitation does not make the collection and analysis other than abstract.” SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018) (citation omitted); see also In re Jobin, 811 F. App’x 633, 637 (Fed. Cir. 2020) (claims to collecting, organizing, grouping, and storing data using techniques such as conducting a survey or crowdsourcing recited a method of organizing human activity, which is a hallmark of abstract ideas).
All these cases describe the significant aspects of the claimed invention, albeit at another level of abstraction. See Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016) ("An abstract idea can generally be described at different levels of abstraction. As the Board has done, the claimed abstract idea could be described as generating menus on a computer, or generating a second menu from a first menu and sending the second menu to another location. It could be described in other ways, including, as indicated in the specification, taking orders from restaurant customers on a computer.").
Therefore, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” and/or “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A – Prong 1: Yes).
Step 2A – Prong 2
In Prong Two, the Examiner determines whether claims 1 and 33, as a whole, recites additional elements that integrate the judicial exception into a practical application of the exception, i.e., whether the additional elements apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is no more than a drafting effort designed to monopolize the judicial exception. See Guidance, 84 Fed. Reg. at 54-55. If the additional elements do not integrate the judicial exception into a practical application, then the claim is directed to the judicial exception. See id., 84 Fed. Reg. at 54. “An additional element [that] reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field” is indicative of integrating a judicial exception into a practical application. See Guidance, 84 Fed. Reg. at 55.
The Examiner determined that this judicial exception is not integrated into a practical application, because there are no meaningful limitations that transform the exception into a patent eligible application. In particular, the claim recites additional elements – using a processor to perform the steps of receiving biomarker data; training GAN, and generating synthetic data. However, the processor in each step is recited (or implied) at a high level of generality, i.e., as a generic processor performing a generic computer functions of processing data, including receiving, storing, comparing, and outputting data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). The processor that performs the recited steps merely automates these steps which can be done mentally or manually. While the claims recite that GAN is specifically adapted to biological data, Federal Circuit requires more than applying an algorithm to a particular field. Merely limiting the abstract idea to “cfDNA data” or “organ injury data” does not convert the claims into a practical application. The claims do not recite, for instance, a new cfDNA measurement technique, a new sequencing method, an improved biological assay, an improved computer architecture, an new GAN architecture, or a new training mechanism. Instead, the claims apply a known AI/ML technique, such as GAN-based oversampling, to a biological dataset.
Thus, while the additional elements have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." The claims only manipulate abstract data elements into another form, and do not set forth improvements to another technological field or the functioning of the computer itself and, instead, uses computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above.
Further, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually; there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, - their collective functions merely provide conventional computer implementation. None of the additional elements "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
The recited steps do not control or improve operation of a machine (MPEP 2106.05(a)), do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and do not apply the judicial exception with, or by use a particular machine (MPEP 2106.05(b)), but, instead, require receiving, storing, comparing and outputting data.
Regarding the use of AI/ML techniques, said steps are nothing more than an attempt to recycle preexisting AI/ML technologies to apply for a particular computing application. There are no improvements in said AI/ML techniques, such as advances in the field of computer science itself, or designing a new neural network, and there is no controlling of a technological process using the outcome of said AI/ML operations.
Thus, the use of a trained machine learning model does not integrate the abstract idea of limitation into a practical application, because, under its broadest reasonable interpretation when read in light of the specification, the “analyzing received information” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Similar to Recentive Analytics, claims 1 and 33 recite conventional machine learning models without specific improvements to the technology itself. The court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. Claims 1 and 33 do not articulate "how" a technological improvement is achieved.
As per receiving, storing and/or outputting data limitations, these recitations amount to mere data gathering and/or outputting, is insignificant post-solution or extra-solution component and represents nominal recitation of technology. Insignificant "post-solution” or “extra-solution" activity means activity that is not central to the purpose of the method invented by the applicant. However, “(c) Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the execution of the claimed method steps. Use of a machine or apparatus that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would weigh against eligibility”. See Bilski, 138 S. Ct. at 3230 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, ___ (1978)). Thus, claim drafting strategies that attempt to circumvent the basic exceptions to § 101 using, for example, highly stylized language, hollow field-of-use limitations, or the recitation of token post-solution activity should not be credited. See Bilski, 130 S. Ct. at 3230.
Therefore, claims 1 and 33, as a whole, outputs only data structure, - everything remains in the form of a code stored in the computer memory. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea. (Step 2A – Prong 2: No).
Step 2B
If a claim has been determined to be directed to a judicial exception under revised Step 2A, examiners should then evaluate the additional elements individually and in combination under Step 2B to determine whether the provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
The Examiner determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the functions of receiving biomarker data; training GAN, and generating synthetic data amount to no more than mere instructions to apply the exception using a generic computer component. The claims are now re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
The system would require a processor and memory in order to perform basic computer functions of receiving information, storing the information in a database, retrieving information from the database, comparing data, and outputting said information. These components are not explicitly recited and therefore must be
construed at the highest level of generality. Based on the Specification, the invention utilizes conventional computer elements, such as one or more computer subsystems, one or more software components, a GAN, and training datasets. The Examiner notes that GAN itself is well-known ML technique dating to 2014, and widely used for synthetic data generation, oversampling, medical imaging, genomics, biomarker datasets. Thus, the claims appear to merely require “applying a known GAN to a known imbalanced biological dataset”, which is characterized by courts as conventional computer implementations.
Regarding the use of a trained model, and obtaining data via repetitive operation of said model, said steps are nothing more than an attempt to recycle preexisting AI/ML technologies to apply for a biological data analysis application. There are no improvements in said AI/ML techniques, such as advances in the field of computer science itself, or designing a new neural network, and there is no controlling of a technological process using the outcome of said AI/ML operations. Claims 1 and 33 neither specify a specific technical purpose for which the method is used, nor the claims define a specific technical implementation of the method, nor the claims are particularly adapted for that implementation in that its design is motivated by technical considerations of the internal functioning of the computer. Said AI/ML algorithms and computations are done inside of a computer, and do not have a real-world impact and are not tied to the functionality of the computer. Further, there is no evidence that the invention lies in the training phase or execution phase or both; said AI/ML recitation represents merely conventionally applying an existing model to an existing data from accessible databases. Similar to Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025), the ML technology as recited in claims 1 and 33 and described in the Specification is conventional, and “the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output”.
Thus, the background of the current application does not provide any indication that the processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Also, the claim does not involve a non-conventional and non-generic arrangement of known, conventional pieces, as asserted, by receiving information from an external source of data. The receiving of data from an external source over a network, such as via the Internet, can fairly be characterized as insignificant extra-solution activity that does not receive patentable weight. See Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en banc), aff’d sub nom Bilski v. Kappos, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity). Similar to Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014): “And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis.” Here, the claims are clearly focused on the combination of those abstract-idea processes. The advance they purport to make is a process of gathering and analyzing information of a specified content, then displaying the results, and not any particular asserted inventive technology for performing those functions. They are therefore directed to an abstract idea. As such, the additional elements, considered individually and in combination with the other claim elements, do not make the claim as a whole significantly more than the abstract idea itself.
Accordingly, a conclusion that the recited steps are well-understood, routine, conventional activity is supported under Berkheimer Option 2. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Further, similar to Electric Power Group v Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016) (Power Group), claim’ invocation of computers, networks, and displays does not transform the claimed subject matter into patent-eligible applications. Claims 1 and 33 do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of known, conventional pieces,” but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices. Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information. Analogous to Power Group, claims 1 and 33 do not even require a new source or type of information, or new techniques for analyzing it. As a result, the claim does not require an arguably inventive set of components or methods, such as measurement devices or techniques that would generate new data. The claim does not invoke any assertedly inventive programming. Merely requiring the selection and manipulation of information - to provide a “humanly comprehensible” amount of information useful for users - by itself does not transform the otherwise-abstract processes of information collection and analysis into patent eligible subject matter. Merely obtaining and selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas. Therefore, the recited functions represent implementing the abstract idea on a generic computer, or “reciting a commonplace business method aimed at processing business information despite being applied on a general purpose computer” Versata, p. 53; Ultramerical, pp. 11-12.
Furthermore, the recited functions do not improve the functioning of computers itself, including of the processor(s) or the network elements. There are no physical improvements in the claim, like a faster processor or more efficient memory, and there is no operational improvement, like mathematical computation that improve the functioning of the computer. The claim does not recite unconventional data structures, improved data processing, ML innovations, reduced processor usage, or any technical solution to a technical problem. Even when considering the ordered combination as a whole, the recited elements are routine: receiving biomarker data; training GAN, and generating synthetic data. These are standard, conventional features in biodata processing platforms.
Applicant, also, did not invent a new type of computer; Applicant like everyone else programs their computer to perform functions. The Supreme Court in Alice indicated that an abstract claim might be statutory if it improved another technology or the computer processing itself. Using a (programmed) computer to implement a common business practice does neither. The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, storing, comparing and transmitting data—see the Specification as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions). Again, Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually; there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. “However, it is not apparent how appellant’s programmed digital computer can produce any synergistic result. Instead, the computer will simply do the job it is instructed to do. Where is there any surprising or unexpected result? The unlikelihood of any such result is merely one more reason why patents should not be granted in situations where the only novelty is in the programming of general purpose digital computers”. See Sakraida v. Ag. Pro, Inc., 425 U.S. 273 [ 96 S.Ct. 1532, 47 L.Ed.2d 784], 189 USPQ 449 (1976) and A P Tea Co. V. Supermarket Corp., 340 U.S. 147 [ 71 S.Ct. 127, 95 L.Ed. 162], 87 USPQ 303 (1950).
Furthermore, there is no transformation recited in the claims as understood in view of 35 USC 101. The recited steps merely represent abstract ideas which cannot meet the transformation test because they are not physical objects or substances. Bilski, 545 F.3d at 963. Said steps are nothing more than mere manipulation or reorganization of data, which does not satisfy the transformation prong. It is further noted that the underlying idea of the recited steps could be performed via pen and paper or in a person's mind. Moreover, “We agree with the district court that the claimed process manipulates data to organize it in a logical way such that additional fraud tests may be performed. The mere manipulation or reorganization of data, however, does not satisfy the transformation prong.” and “Abele made clear that the basic character of a process claim drawn to an abstract idea is not changed by claiming only its performance by computers, or by claiming the process embodied in program instructions on a computer readable medium. Thus, merely claiming a software implementation of a purely mental process that could otherwise be performed without the use of a computer does not satisfy the machine prong of the machine-or-transformation test”. CyberSource, 659 F.3d 1057, 100 U.S.P.Q.2d 1492 (Fed. Cir. 2011)
Therefore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because, when considered separately and in combination, the claim elements do not add significantly more to the exception. Considered separately and as an ordered combination, the claim elements do not provide an improvement to another technology or technical field; do not provide an improvement to the functioning of the computer itself; do not apply the judicial exception by use of a particular machine; do not effect a transformation or reduce a particular article to a different state or thing; and do not add a specific limitation other than what is well-understood, routine and conventional in the operation of a generic computer. None of the hardware recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Id., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). There is no inventive concept apparent from the claims’ language. As per “the system comprising: one or more computer subsystems; and one or more components executed by the one or more computer subsystems” recitations, these limitations do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment, that is, implementation via computers." Id., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Limiting the claims to the particular technological environment is, without more, insufficient to transform the claim into patent-eligible applications of the abstract idea at their core.
Accordingly, claim 1 and 33 are not directed to significantly more than the exception itself, and is not eligible subject matter under § 101. (Step 2B: No).
Further, although the Examiner takes the steps recited in the independent claims as exemplary, the Examiner points out that limitations recited in dependent claims 2-3, 6-8, 10, 12-17, 19, 23, 25, 28-29, 31, 34-35, 39, 41, 43, 45 and 47 further narrow the abstract idea but do not make the claims any less abstract. Dependent claims 2-3, 6-8, 10, 12-17, 19, 23, 25, 28-29, 31, 34-35, 39, 41, 43, 45 and 47 each merely add further details of the abstract steps recited in claims 1 and 33 without including an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. These claims "add nothing of practical significance to the underlying idea," and thus do not transform the claimed abstract idea into patentable subject matter. Ultramercial, 772 F.3d at 716. Therefore, dependent claims 2-3, 6-8, 10, 12-17, 19, 23, 25, 28-29, 31, 34-35, 39, 41, 43, 45 and 47 are also directed to non-statutory subject matter.
Because Applicant’s computer-readable medium claim 52 adds nothing of substance to the underlying abstract idea, claim 52 too is patent ineligi-ble under §101.
Citations of pertinent art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rastogi et al. - US 2021/0034645 A1 – discloses generating synthetic data for minority classes in a very large and highly imbalanced dataset.
Maroul et al. “Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks” (2020) - discloses a system configured to: balance an imbalanced dataset, augment limited biological datasets, generate synthetic samples to improve downstream analysis
Sungho Suh et al. “CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems” (2020) - discloses data imbalance management and improving classification under imbalanced condition.
Munawara Saiyara Munia et al. “Biosignal Oversampling Using Wasserstein Generative Adversarial Network” (2020) – discloses oversampling for imbalanced biomedical datasets utilizing machine-learning and synthetic minority-class generation.
Giovanni Mariani et al. “BAGAN: Data Augmentation with Balancing GAN” (2018), discloses a balancing GAN architecture.
Distinguishable Subject Matter
The present claims remain distinguishable from the prior art of record. While the prior art of record discloses using biological molecular measurements from biological populations/classes for training GAN, wherein GAN is trained on biological training datasets to learn distributions and generate synthetic samples, and wherein the first dataset and second dataset are imbalanced, the prior art fails to disclose cfDNA or organ injury as specifically recited in the claims. Thus, the Examiner has not made a prior art rejection under 35 USC §102 or §103.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Igor Borissov whose telephone number is 571-272-6801. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor Kambiz Abdi can be reached on 571-272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IGOR N BORISSOV/Primary Examiner, Art Unit 3685 6/09/2026
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