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 Arguments
Applicant's arguments filed 7/15/2026 have been fully considered but they are not persuasive. Regarding 101, the Applicant argues the characterization oversimplifies the claims and does not evaluate the claim language as a whole. The Applicant argues a human mind cannot practically perform neural network inference using model weights where the relevant morphological changes can include visibly and non-visibly observable changes. In addition, Applicant argues that claims directed to a specific improvement in computer-related technology, rather than a generic application of a computer to implement an abstract idea is patent eligible. In support of this argument Applicant argues that claims recite a particular computational process for facilitating identification of a body condition corresponding to progression or occurrence of a disease, which is not a mental process. The examiner respectfully disagrees as illustrated by claim 11, the device comprises one or more processors with one or more memories that cause the one or more processors to perform operations. Applicant argues that a neural network cannot be performed in the human mind, but provides no evidence to support this argument. The examiner notes that a neural network is a mathematical model of a human mind, making the argument that a human mind cannot perform the calculations of a model representing the human mind unpersuasive. MPEP2106.05(a)(II) discloses it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
The claims are directed to applying an abstract idea (e.g., mental process or certain method of organizing human activity) on a general purpose computer without (i) improving the performance of the computer itself (as in McRO, Bascom and Enfish), or (ii) providing a technical solution to a problem in a technical field (as in DDR). In other words, none of the Claims provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in the independent Claims (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, the claims merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR). Applicant argues that models executed on a processor which use inputted electrocardiogram data and correlate to the disease reflecting a degree of progress of the disease are additional structure. The examiner respectfully disagrees as this does not recite an additional element besides the processor and one or more memories, because claims are directed to applying an abstract idea (e.g., mental process or certain method of organizing human activity) on a general purpose computer without improving the performance of the computer itself.
The Applicant argues that the office action has not shown that the claimed ordered combination is well understood, routine and conventional. The examiner notes this is not a requirement as this is not an additional element, the abstract idea implemented on a general purpose computer without improving the performance of the computer itself. Applicant argues that the office action does not provide evidence that processors and network units are well-known. The examiner respectfully disagrees as the 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. The examiner notes how these computer elements are generically described without structure or detailed drawings e.g., schematic drawing. Therefore, Applicant’s specification is admitting that such computer components are well understood, routine and conventional. The arguments regarding the 101 rejection are therefore not persuasive and the rejection is maintained.
Applicant’s arguments with respect to claim(s) 1-9, and 11-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-9, and 11-21 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 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) 1-8, and 11-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US Publication 2012/0150003) in view of Peterson et al (US Publication 2021/0059540).
Referring to Claim 1, 11, and 18, Zhang teaches a method/computing device/ non-transitory readable media storing instructions comprising a processor to: access electrocardiogram data associated with a subject (e.g. Figure 4, Element 423 and Paragraph [0022] discloses using ECG signals); inputting the electrocardiogram data into a pre-trained neural network model to obtain an indication of a body condition corresponding to an occurrence or progress of a disease in the subject, whose electrocardiogram data was measured (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). However, Zhang et al does not disclose a first set of sub-models and a second sub-model configured to use a combination of the one or more features output by the first set of sub-models to obtain a value corresponding to one or more body conditions.
Peterson et al teaches that it is known to use ECG data feed into multiple models including a first set of sub-models (e.g. Figure 5, Element 518 having models 502-508 which estimate features) and a second sub-model (e.g. Figure 5, Element 520, Models 524 or 522 which output body condition (e.g. risk/classification) based on first level outputs) configured to use a combination of the one or more features output by the first set of sub-models to obtain a value corresponding to one or more body conditions forth in Figure 5 and Paragraph [0039] to provide increasing accuracy compared to one model alone (e.g. Paragraph [0037]). 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 system/method as taught by Zhang et al, with a first set of sub-models and a second sub-model configured to use a combination of the one or more features output by the first set of sub-models to obtain a value corresponding to one or more body conditions as taught by Peterson et al, since such a modification would provide the predictable results of increasing accuracy compared to one model alone.
Referring to Claims 2, 12, and 19, Zhang in view of Peterson et al teaches the claimed invention, wherein: the pre-trained neural network model includes at least one first sub-model of the first set of sub-models 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 Claims 3, 13, and 20, Zhang in view of Peterson et al teaches the claimed invention, wherein: the pre-trained neural network model further includes an additional sub-model of the first set of sub-models trained to output the second feature based on the electrocardiogram data; the additional 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. Peterson et al Figure 5, Element 518 having models 502-508).
Referring to Claims 4 and 14, Zhang in view of Peterson et al teaches the claimed invention, wherein the second sub-model trained to represent a body condition continuously changing according to the occurrence or progress of the disease as 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 additional sub-model (e.g. Peterson et al, Paragraph [0039] ).
Referring to Claims 5 and 15, Zhang in view of Peterson et al teaches the claimed invention, wherein the second 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 and Peterson et al Figure 5, model 522 or 524).
Referring to Claim 6, Zhang in view of Peterson et al teaches the method of claim 3, wherein each of the first set of 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 Claims 7, 16, and 21, Zhang in view of Peterson et al teaches the claimed invention, wherein the disease includes a cardiovascular disease (e.g. Paragraphs [0013] and [0024]).
Referring to Claims 8 and 17, Zhang in view of Peterson et al teaches the claimed invention, 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(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US Publication 2012/0150003) in view of Peterson et al (US Publication 2021/0059540), as applied above, and further in view of Choi et al (US Publication 2020/0202527).
Referring to Claim 9, Zhang in view of Peterson et al 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bose et al (US Publication 2025/0201412) discloses machine learning of ECG signal for signs of heart failure and teaches that different models are used to identify different parameters (e.g. Paragraph [0162]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to William J Levicky whose telephone number is (571)270-3983. The examiner can normally be reached Monday-Thursday 8AM-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at (571)270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/William J Levicky/Primary Examiner, Art Unit 3796
Prosecution Insights