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 .
Information Disclosure Statement
The information disclosure statement filed 6/18/2025 fails to comply with 37 CFR 1.98(a)(3)(i) because it does not include a concise explanation of the relevance, as it is presently understood by the individual designated in 37 CFR 1.56(c) most knowledgeable about the content of the information, of each reference listed that is not in the English language. It has been placed in the application file, but the information referred to therein has not been considered. The JP office action is not submitted with an English copy or summarizing the content of the information.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process of inferring an occurrence or progress of a disease (from electrocardiograph data) based on a neural network model. This judicial exception is not integrated into a practical application because a processor/memory and network unit is a generically recited computer elements which does not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the processor/memory and network unit is a generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Per Applicant’s specification, Page 13 line 1 states the “processor 110 for performing such data processing may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), or a field programmable gate array (FPGA).” Also, page 16 line20 states “the memory 120 may include at least one type of storage medium of a flash memory type, hard disk type, multimedia card micro type, and card type memory, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, a magnetic disk, and an optical disk”. Also, page 17 line 22 states “network unit 130 may perform data transmission and reception using a wired/wireless communication system such as a local area network (LAN), a wideband code division multiple access (WCDMA) network, a long term evolution (LTE) network, the wireless broadband Internet (WiBro), a 5th generation mobile communication (5G) network, a ultra wide-band wireless communication network, a ZigBee network, a radio frequency (RF) communication network, a wireless LAN, a wireless fidelity network, a near field communication (NFC) network, or a Bluetooth network
Accordingly, in light of Applicant’s specification, the claimed term processor/memory and network unit is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because Claim 10 characterizes the invention as a computer program stored in a computer-readable medium without reciting any physical structure. The claim is directed to a computer program and MPEP2106.03(I) indicates this is not a statutory category as software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8, and 10-11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang (US Publication 2012/0150003).
Referring to Claim 1, 10, and 11, Zhang teaches a method/computer program stored in a computer readable medium/computing device of measuring a continuous body condition based on deep learning, the method being performed by a computing device including at least one processor, the method comprising: acquiring electrocardiogram data (e.g. Figure 4, Element 423 and Paragraph [0022] discloses using ECG signals); inferring a body condition corresponding to an occurrence or progress of a disease in a subject, whose electrocardiogram data was measured, based on the electrocardiogram data by using a pre-trained neural network model (e.g. Paragraphs [0013] and [0024] discloses system detects cardiac disorders, differentiates between cardiac arrhythmias, characterizes pathological severity, predicts life-threatening events, and facilitates evaluation of the effects of drug administration to a patient); wherein the neural network model has been trained based on at least one of a first feature related to biological information representing a body characteristic having a correlation with the disease (e.g. Figure 4, Element 420 discloses using patient weight, gender, and age) and a second feature related to pathological information reflecting a degree of progress of the disease therein (e.g. Figure 4, Element 426 discloses using pathology).
Referring to Claim 2, Zhang teaches the method of claim 1, wherein: the neural network model includes at least one first sub-model trained to output the first feature based on the electrocardiogram data; the at least one first sub-model is configured in accordance with a number of factors to individually output numerical values for one or more factors included in the biological information (e.g. Figure 4, neural network 407).
Referring to Claim 3, Zhang teaches the method of claim 2, wherein: the neural network model further includes at least one second sub-model trained to output the second feature based on the electrocardiogram data; the at least one second sub-model is configured in accordance with a number of factors to individually output numerical values for one or more factors included in the pathological information (e.g. Figure 4, neural network 407).
Referring to Claim 4, Zhang teaches the method of claim 3, wherein the neural network model further includes a third sub-model trained to represent a body condition continuously changing according to the occurrence or progress of the disease in the form of a numerical value based on the first feature, which is an output of the first sub-model, and the second feature, which is an output of the second sub-model (e.g. Figure 4, neural network 407).
Referring to Claim 5, Zhang teaches the method of claim 4, wherein the third sub-model receives a third feature generated by combining the first feature and the second feature based on weights determined according to a type of disease, and outputs the numerical value (e.g. Figure 4, neural network 407).
Referring to Claim 6, Zhang teaches the method of claim 3, wherein each of the first and second sub-models is trained based on self-supervised learning that is performed using training data including unlabeled samples (e.g. Figure 4, neural network 407).
Referring to Claim 7, Zhang teaches the method of claim 1, wherein the disease includes a cardiovascular disease (e.g. Paragraphs [0013] and [0024]).
Referring to Claim 8, Zhang teaches the method of claim 7, wherein the biological information includes at least one of age, gender, height, and weight as a body characteristic factor related to a coronary artery disease included in the cardiovascular disease (e.g. Figure 4, Element 420).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US Publication 2012/0150003) in view of Choi et al (US Publication 2020/0202527).
Referring to Claim 9, Zhang teaches the method of claim 8, but does not disclose wherein the pathological information includes at least one of presence/absence of myocardial infarction, a degree of vascular calcification, stability of blood clots, intravascular velocity of coronary arteries, and a degree of stenosis of coronary arteries as a pathological characteristic factor that reflects a degree of progress of a coronary artery disease included in the cardiovascular disease.
Choi et al teaches that it is known to use a neural network where the pathological information includes degree of vascular calcification as set forth in Paragraph [0502] and/or extent of risk of myocardial infarction as set forth in Paragraph [0505] and/or degree of coronary artery stenosis as set forth in Paragraph [0574] to provide improved heart disease identification by utilizing known precursors in the learning model. It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method as taught by Zhang, with wherein the pathological information includes at least one of presence/absence of myocardial infarction, a degree of vascular calcification, stability of blood clots, intravascular velocity of coronary arteries, and a degree of stenosis of coronary arteries as a pathological characteristic factor that reflects a degree of progress of a coronary artery disease included in the cardiovascular disease as taught by Choi et al, since such a modification would provide the predictable results of improved heart disease identification by utilizing known precursors in the learning model.
Conclusion
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/William J Levicky/Primary Examiner, Art Unit 3796
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