CTFR 18/345,537 CTFR 99946 DETAILED ACTION 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Claims 1-2, 4-9, 11-16, and 18-20 are within the four statutory categories. However, as will be shown below, claims 1-2, 4-9, 11-16, and 18-20 are nonetheless unpatentable under 35 U.S.C. 101. Claim 1 is representative of the inventive concept and recites: A computer system comprising: one or more computer processors; one or more computer readable storage media; and computer readable code stored collectively in the one or more computer readable storage media, with the computer readable code including data and instructions to cause the one or more computer processors to perform at least the following operations: providing, by the one or more computer processors, wherein the computer readable code includes a generative adversarial network (GAN), the GAN included in the computer readable code comprising: an electronic health record (EHR) attribute generator included in the computer readable code, wherein the EHR attribute generator includes a first multi-layer perceptron (MLP); a sensor attribute generator included in the computer readable code, wherein the sensor attribute generator includes a second MLP; a feature generator decoupled from the EHR generator and the sensor attribute generator, the feature generator included in the computer readable code, wherein the feature generator includes one or more recurrent neural networks (RNNs) and one or more MLPs; and a mean and variance generator included in the computer readable code, wherein the mean and variance generator includes a third MLP, wherein the mean and variance generator is decoupled from the feature generator; training, by the one or more processors of the computer system, the GAN to generate time series data using episodic measurement results as metadata for a patient cohort with a specific disease ; receiving, by the trained GAN and the one or more processors of the computer system, an episodic measurement for a patient in the patient cohort with the specific disease to the trained GAN; and generating , by the trained GAN and the one or more processors of the computer system using the episodic measurements, synthetic time series data that simulates the patient in the patient cohort with the specific disease , wherein generating the synthetic time series data comprises: generating , by the EHR generator, one or more first metadata attributes using the episodic measurement ; generating , by the sensor attribute, one or more second metadata attributes using the episodic measurement ; generating , by the mean and variance generator, one or more third metadata attributes comprising statistical characterizations ; and generating, by the feature generator, batched samples of the synthetic time series data using the episodic measurements by connecting a respective output of each RNN of the feature generator with a respective input of each MLP of the feature generator. *Claims 8 and 15 recite similar limitations as claim 1, but for a computer program product and computer system, respectively. Step 2A Prong One The broadest reasonable interpretation of these steps includes mental processes because the highlighted components can practically be performed by the human mind (in this case, the process of generating ) or using pen and paper. Other than reciting generic computer components/functions such as “Sensor”, “generator”, “processor”, “computer system”, “generative adversarial network(GAN)”, “RNN”, “MLP”, and “code”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the system language, the claim encompasses the user collecting and analyzing data. 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/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. The recitation of generic computer components/functions such as generating also covers behavioral or interactions between people (i.e. the computer and user interface), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions — in this case a person is able to physically follow the steps to gather and process), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. Dependent claims 2, 6-9, 9, 11-16, and 19-20 recite additional subject matter which further narrows or defines the abstract idea embodied in the claims. Step 2A Prong Two This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional limitations: Claim 1 recites: “processor”, “computer system”, and “generative adversarial network(GAN)”, “sensor”, “generator”, “RNN”, “MLP”, ”code”, “receiving, by the trained GAN and the one or more processors of the computer system, an episodic measurement for a patient in the patient cohort with the specific disease to the trained GAN”, “and generating, by the trained GAN and the one or more processors of the computer system using the episodic measurement, synthetic time series data that simulates the patient in the patient cohort with the specific disease”, and “and generating, by the feature generator, batched samples of the synthetic time series data using the episodic measurements by connecting a respective output of each RNN of the feature generator with a respective input of each MLP of the feature generator.” In particular, the additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which: Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations are recited as being performed by a “processor”, “computer system”, “generative adversarial network(GAN)”, “sensor”, “generator”, “RNN”, “code” and “MLP”. These limitations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The models (RNN AND MLP) are used to generally apply the abstract idea without limiting how the models function. The models are described at a high level such that it amounts to using a computer with generic models to apply the abstract idea. Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of “receiving, by the trained GAN and the one or more processors of the computer system, an episodic measurement for a patient in the patient cohort with the specific disease to the trained GAN” and “and generating, by the feature generator, batched samples of the synthetic time series data using the episodic measurements by connecting a respective output of each RNN of the feature generator with a respective input of each MLP of the feature generator.” Dependent claims 2, 9, and 16, recite DoppelGANger architecture Dependent claims 7 and 14 recites neural network and RNN In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which: Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations are recited as being performed by a DoppelGANger , neural network, and RNN. These limitations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The neural network, RNN, and DoppelGANger are used to generally apply the abstract idea without limiting how it functions. The neural network and RNN are described at a high level such that it amounts to using a computer with generic models to apply the abstract idea. Dependent claims 4-6, 11-13, and 18-20 do not include any additional elements beyond those already recited in independent claims 1, 8, and 15 and dependent claims 2, 7, 9 14, and 16, hence do not integrate the aforementioned abstract idea into a practical application. 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 machine learning model or improves any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application. Step 2B Claims 1, 8, and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements: A computer in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields as demonstrated by: Recitation of DoppelGANger (claims 2-3, 9-10, and 16-17), which is a known generative adversarial network(GAN) architecture ( Dannels(“Creating…synthetic time series data”, 21 Feb 2023. pp 1-18) discloses: “DoppelGANger was able to learn…”) in a manner that would be well-understood, routine, and conventional. Output, which refers to the processed information produced by a system after it has received and processed input data ( Mayo, 566 U.S. at 79, 101 USPQ2d at 1968) in a manner that would be well-understood, routine, and conventional. Input, which refers to the information or instructions that are fed into a computer or system to initiate a process or generate an output (Para 67, Nielen(US10666702B1) discloses: “This conventional input can include, for example, a push button, touch pad, touch screen, wheel, joystick, keyboard, mouse, trackball, keypad or any other such device or element whereby a user can input a command to the device.”) in a manner that would be well-understood, routine, and conventional. Recitation of receiving, which refers to the process where a computer or device acquires information transmitted from another source (TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional. Recitation of generating data which refers to the process of creating data by use of a computer/processor (Haraguchi(US 20040022110 A1) discloses: “FIG. 28 schematically shows a configuration of a conventional column redundancy data generation unit.”) in a manner that would be well-understood, routine, and conventional. Dependent claims 4-6, 11-13, and 18-20 do not include any additional elements beyond those already recited in independent claims 1, 8, and 15 and dependent claims 2, 7, 9 14, and 16. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 8, and 15 hence do not amount to “significantly more” than the abstract idea. Subject Matter Free of Prior Art Claims 1-2, 4-9, 11-16, and 18-20 distinguish over the prior art for the following reasons. The following is a statement of reasons for the subject matter free of prior art: Claim 1 (in part): “…receiving, by the trained GAN and the one or more processors of the computer system, an episodic measurement for a patient in the patient cohort with the specific disease to the trained GAN; and generating, by the trained GAN and the one or more processors of the computer system using the episodic measurements, synthetic time series data that simulates the patient in the patient cohort with the specific disease, wherein generating the synthetic time series data comprises: generating, by the EHR generator, one or more first metadata attributes using the episodic measurement; generating, by the sensor attribute, one or more second metadata attributes using the episodic measurement; generating, by the mean and variance generator, one or more third metadata attributes comprising statistical characterizations; and generating, by the feature generator, batched samples of the synthetic time series data using the episodic measurements by connecting a respective output of each RNN of the feature generator with a respective input of each MLP of the feature generator.” Claims 8 and 15 recite similar limitations as claim 1 The closest available prior art of record as follows: Soni(US11984201B2) discloses a method for time-series event data generation, but does not fairly disclose or suggest the aforementioned configuration for the claimed invention. Bostic(US20200303047A1) discloses a method for pharmacological tracking of health attributes using a digital twin, but does not fairly disclose or suggest the aforementioned configuration for the claimed invention. Morlot(US20230108874A1) discloses the generation of a digital twin of complex systems, but does not fairly disclose or suggest the aforementioned configuration for the claimed invention. Based on the evidence presented above, none of the closest available prior art of record fairly discloses or suggests the underlined elements of the claimed invention. Claims 8 and 15 would also be found to be subject matter free of prior art for the same rationale as applied to claim 1. Claims 2, 4-7, 9, 11-14, 16, and 19-20 would also be considered to be subject matter free of prior art due to dependency. Response to Arguments 35 U.S.C. 112 Applicant’s amendments have been fully considered. 35 U.S.C. 112(f), 35 U.S.C. 112(b), and 35 U.S.C. 112(a) have been withdrawn. 35 U.S.C. 101 07-37 AIA Applicant's arguments filed have been fully considered but they are not persuasive . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chatzou(US11657898B2) : Chatzou discloses a system that generates biological interaction and disease target predictions for compounds using a cell digital twin. Some disclosures of this invention are similar to this pending instant application. (Specification, pages 2-5) Peterson(US20190087544A1): Peterson discloses an apparatus that provides a digital twin of a healthcare procedure. Some disclosures of this invention are similar to this pending instant application. (Specification, pages 2-5) Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /S.G.P./Examiner, Art Unit 3685 /KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685 Application/Control Number: 18/345,537 Page 2 Art Unit: 3685 Application/Control Number: 18/345,537 Page 3 Art Unit: 3685 Application/Control Number: 18/345,537 Page 4 Art Unit: 3685 Application/Control Number: 18/345,537 Page 5 Art Unit: 3685 Application/Control Number: 18/345,537 Page 6 Art Unit: 3685 Application/Control Number: 18/345,537 Page 7 Art Unit: 3685 Application/Control Number: 18/345,537 Page 8 Art Unit: 3685 Application/Control Number: 18/345,537 Page 9 Art Unit: 3685 Application/Control Number: 18/345,537 Page 10 Art Unit: 3685
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