Office Action 1: Non-Final Rejection (2024-07-18)
Rejection types: 101 - Patent-Eligible Subject Matter, 102 - Lack of Novelty, 103 - Obviousness, 112 - Written Description/Enablement

CLAIMS:
'What is claimed is:

1\. An apparatus for artificial intelligence-supported diagnostic assessment, wherein the apparatus comprises:at least a processor, wherein the processor is configured to:
receive a plurality of multi-channel sensor readings of physiological data;
generate training data correlating each of the plurality of multi-channel sensor readings with a plurality of diagnostic labels;
train a neural network using the plurality of diagnostic labels;
receive a time series input describing user physiological data from at least a sensor;
input the time series input into the trained neural network;
generate diagnostic data as a function of the time series input and the trained neural network;
determine prognostic data as a function of the diagnostic data; and
output the prognostic data.

2\. The apparatus of claim 1, wherein the time series input is comprised of electrocardiogram (ECG) data.

3\. The apparatus of claim 2, wherein the plurality of multi-channel sensor readings further comprises a plurality of ECG readings, and the plurality of training data further comprises a plurality of ECG readings, and generating the plurality of training data further comprises:receiving echocardiogram data correlated to each ECG reading of the plurality of ECG readings; and
generating the plurality of diagnostic labels using the echocardiogram data.

4\. The apparatus of claim 1, wherein the neural network is a multi-output convolutional neural network.

5\. The apparatus of claim 1, wherein the neural network is trained using general-use lead data.

6\. The apparatus of claim 1, wherein the neural network is trained using reduced general- use lead data.

7\. The apparatus of claim 1, wherein the neural network is trained using data gathered from 12-lead ECGs performed within 14 days of transthoracic echocardiography, wherein the plurality of multi-channel sensor readings of physiological data are comprised of 12-lead ECG data and echocardiography data correlated to each 12-lead ECG datum.

8\. The apparatus of claim 1, wherein the diagnostic data includes an assessment of LVDD.

9\. The apparatus of claim 1, wherein the diagnostic data includes classification under a plurality of groups, wherein the groups are comprised of:normal LV function;
LVSD only;
LVDD only; and
both LVSD and LVDD.

10\. The apparatus of claim 1, wherein the output of prognostic data is displayed at a display device.

11\. A method for training an artificial intelligence-supported diagnostic assessment tool,wherein the method comprises:
receiving a plurality of multi-channel sensor readings of physiological data;
generating training data correlating each of the plurality of multi-channel sensor readings with a plurality of diagnostic labels;
training a neural network using the plurality of diagnostic labels;
receiving a time series input describing user physiological data from at least a sensor;
inputting the time series input into the trained neural network;
generating diagnostic data as a function of the time series input and the trained neural network;
determining prognostic data as a function of the diagnostic data; and
outputting the prognostic data.

12\. The method of claim 11, wherein the time series input is comprised of electrocardiogram (ECG) data.

13\. The method of claim 12, wherein the plurality of multi-channel sensor readings further comprises a plurality of ECG readings, and the plurality of training data further
comprises a plurality of ECG readings, and generating the plurality of training data further comprises:receiving echocardiogram data correlated to each ECG reading of the plurality of ECG readings; and
generating the plurality of diagnostic labels using the echocardiogram data.

14\. The method of claim 11, wherein the neural network is a multi-output convolutional neural network.

15\. The method of claim 11, wherein the neural network is trained using general-use lead data.

16\. The method of claim 11, wherein the neural network is trained using reduced general-use lead data.

17\. The method of claim 11, wherein the neural network is trained using data gathered from 12-lead ECGs performed within 14 days of transthoracic echocardiography, wherein the plurality of multi-channel sensor readings of physiological data are comprised of 12-lead ECG data and echocardiography data correlated to each 12-lead ECG datum.

18\. The method of claim 11, wherein the diagnostic data includes an assessment of both LVSD and LVDD.

19\. The method of claim 11, wherein the diagnostic data includes classification under a plurality of groups, wherein the groups are comprised of:normal LV function;
LVSD only;
LVDD only; and
both LVSD and LVDD.

20\. The method of claim 11, wherein the output of prognostic data is displayed at a display device.

OFFICE ACTION:
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b)  CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 5-6, 10, 13, 15-16 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA  35 U.S.C. 112, the applicant), regards as the invention.
In claim 3, line 3, “a plurality of ECG readings” is vague as line 2 also uses this same phrase.  It is unclear if the plurality in line 3 is the same as the plurality in line 2.  If they are the same, then “the plurality of ECG readings” should be used.  If they are different then a modifier such as “an additional plurality of ECG readings” should be used.  In addition, in line 5, “the plurality of ECG readings” is vague as it is unclear if this is the plurality used in line 2 or the plurality used in line 3.
Similarly, claim 13 has these problems.
In claims 5, 6, 15, and 16, “general-use” lead data is vague and relative as it is unclear what is considered “general-use”.  In addition, “reduced” general use lead data is vague and relative as it is unclear what it is reduced in comparison to.
In claim 7, line 1, “trained using data gathered…” is vague and sounds more like a method step.  In lines 3 and 4, “12 lead ECG data”, “echocardiography data” and “each 12-lead ECG datum” is vague as it is unclear if these terms are referencing the previous 12 lead and echo terms used in line 2 or are different elements.
Similarly, claim 17 has these problems.
In claim 10, “is displayed at a display device” is vague and inferentially including the display device.  It is unclear if the display device is being positively recited or functionally recited.  It is suggested to first state the apparatus “further comprises a display device” to positively recite this element.
In claim 20, the claim is in passive voice and it is unclear if a further method step is being recited.  It is suggested to use active voice, such as “displaying the output of prognostic data on a display device”.
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 invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental concept of performing series of steps to label cardiac data and receive new data to predict a cardiac condition. This judicial exception is not integrated into a practical application because the generically recited computer elements (e.g. processor, display, etc.) do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer, and displaying the results is considered insignificant extra-solution activity. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when the elements are considered separately and in combination, do not add significantly more to the exception.  The additional elements only store or display data and these are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP 2106.05(d).
The claims are directed to an abstract idea and/or the end result of the system/method, the essence of the whole, is a patent-ineligible concept.  The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they amount to a general computer performing a calculation.  The claims are directed to an abstract idea, i.e. implementing the idea of performing a series of steps to label cardiac data and receive new data to predict a cardiac condition based on the labeled data, such as may be done by a mental process, critical thinking, and/or paper and pencil, or done by a mathematical equation, with additional generic computer elements, or additional structure (e.g. processor, display, etc.) recited at a high level of generality that perform generic functions routinely used in the art, and do not add a meaningful limitation to the abstract idea because they would be routine in any computer implementation or in the relevant art.  Thus, the recited generic computer components perform no more than their basic computer functions. These additional elements are well‐understood, routine and conventional limitations (see cited document(s)) that amount to mere instructions or elements to implement the abstract idea.  In addition, the end result of the system/method, the essence of the whole, is a patent-ineligible concept.  See the recent decisions by the U.S. Supreme Court, including Alice Corp., Myriad, and Mayo.  In addition, the current claims are similar to other recent court decisions dealing with analyzing, comparing, and/or displaying data, such as Electric Power Group, Digitech, Grams, and Classen.
Based on the plain meaning of the words in the claim, the broadest reasonable interpretation of the claims (e.g. claim 1 having a processor, display, and corresponding method claim 11) is a system having a display and processor, wherein the processor is programmed with executable instructions to perform the calculations/mental process/critical thinking. The claims do not impose any limits on how the physiological information is received by the processor, and thus this step covers any and all possible ways in which this can be done, for instance by typing the information into the system, or by the system obtaining the information from another device. The claim also does not impose any limits on how the computations are accomplished, and thus it can be performed in any way known to those of ordinary skill in the art.
The calculations are simple enough to be practically performed in the human mind or through critical thinking.  Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. Nor does the recitation of a processor in the claim negate the mental nature of this limitation because the claim here merely uses the processor as a tool to perform the otherwise mental process.
The display and processor are recited so generically (no details whatsoever are provided other than that they are a memory and processor) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
Although the processor or claim limitations may fall under several exceptions (e.g., a mathematical concept-type abstract idea or a mental process-type abstract idea), there are no bright lines between the types of exceptions. See, e.g., MPEP 2106.04(I). Thus, it is sufficient for the examiner to identify that the limitations align with at least one judicial exception, and to conduct further analysis based on that identification.
The limitations of the claims are carried out by the processor and the display.  No element has been set forth to sense the physiological signals, and the only additional element is the display, where the processor performs the necessary software tasks so that the result of the abstract mental process is just data/a prediction of a cardiac condition. The display limitation represents extra-solution activity because it is a mere nominal or tangential addition to the claim. See MPEP 2106.05(g), discussing limitations that the Federal Circuit has considered to be insignificant extra-solution activity. Even when viewed in combination, the additional elements in this claim do no more than automate the mental processes (e.g., the mental computation of  performing a series of steps to label cardiac data and receive new data to predict a cardiac condition, etc.), using the computer components as a tool. While this type of automation may improve the life of a practitioner/physician (by minimizing or eliminating the need for mentally computing metrics), there is no change to the computers and other technology that are recited in the claim as automating the abstract ideas, and thus this claim cannot improve computer functionality or other technology. See, e.g., Trading Technologies Int’l v. IBG, Inc., 921 F.3d 1084, 1093 (Fed. Cir. 2019) (using a computer to provide a trader with more information to facilitate market trades improved the business process of market trading, but not the computer) and the cases discussed in MPEP 2106.05(a)(I), particularly FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095 (Fed. Cir. 2016) (accelerating a process of analyzing audit log data is not an improvement when the increased speed comes solely from the capabilities of a general-purpose computer) and Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055 (Fed. Cir. 2017) (using a generic computer to automate a process of applying to finance a purchase is not an improvement to the computer’s functionality). Accordingly, the claim as a whole does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7, 11-15, and 17 are rejected under 35 U.S.C. 102a1 as being anticipated by Chen et al (“Artificial Intelligence-Enabled…Retrospective Analysis” as supplied by the applicant).  Chen discloses the use of a multi-output convolutional neural network (e.g. page 5 of 15, etc.) using 12 lead ECG and echo data within 14 days to provide labels and then train the neural network (e.g. page 3 of 15, 2.1-2.3, etc.) and then receive further ECG time series data to generate diagnostic data and determine and output predictions (e.g. abstract, figure 1, page 10, etc.) such as left ventricular dysfunction.
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.
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 6, 8, 10, 16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al.  Chen discloses the claimed invention using ECG data to predict left ventricular dysfunction, but does not specifically disclose using reduced general-use lead data, determining LVSD and LVDD, and displaying the results.  It would have been obvious to one having ordinary skill in the art at the time the invention was made/before it was effectively filed to have modified the system and method as taught by Chen, with using reduced general-use lead data, determining LVSD and LVDD, and displaying the results, as is well known and common knowledge in the art (mpep 2144), since it would provide the predictable results of: allowing less leads to be placed on the patient, reducing time and complexity of the measurements, to receive the ECG; determining the specific part of the left ventricular dysfunction during the cardiac cycle to better diagnose the patient’s condition; and allowing the physician to see the results of the test measurements to quickly treat the patient’s cardiac condition.
Allowable Subject Matter
Claims 9 and 19 would be allowed upon correction of the 101 rejection.
Conclusion

APPLICANT REBUTTAL: REMARKS
Claims 1–8, 10–18, and 20–22 are pending, with claims 1 and 11 being independent. Claims 1, 3, 5–7, 10–11, 13, 15–17, and 20 are amended. Claims 9 and 19 are canceled. Claims 21–22 are new.
In taking the foregoing actions, Applicant respectfully submits Applicant is not necessarily conceding in this application that these claims, as originally presented, are not patentable over the references cited by the U.S. Patent and Trademark Office (USPTO), as pertinent ones of the present claim amendments and cancellations are only for facilitating expeditious prosecution.  Applicant respectfully reserves the right to pursue the canceled claims and/or other claims in one or more continuing applications.
Applicant-Initiated Interview Summary
A telephonic interview regarding the instant matter was conducted on July 25, 2025, between Examiner Evanisko and Applicant’s Representative, Oliver Han, Registration No. 72,864. Patentability under 35 U.S.C. § 101, § 102, and § 103 was discussed. Applicant thanks the Examiner for his time and consideration in conducting the interview. An agreement was reached that claim 1, as amended, overcomes the § 102 and § 103 rejections. No agreement was reached regarding patentability under § 101.
Allowable Subject Matter
The Office states “[c]laims 9 and 19 would be allowed upon correction of the 101 rejection.” (Office Action, p. 9). Applicant thanks the Examiner for the identification of the allowable subject matter.
Solely in the interest of advancing prosecution, Applicant has amended independent claims 1 and 11 to include the allowable subject matter of claims 9 and 19, respectively. No new matter has been introduced.
Rejection of the Claims under 35 U.S.C. § 112
Claims 3, 5-7 10, 13, 15-17 and 20 stand rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention.
Regarding claims 3 and 13, the Examiner asserts the recitation “a plurality of ECG readings” in line 3 is vague, as line 2 also uses this same phrase. (Office Action, p. 2). Further, the recitation “the plurality of ECG readings” in line 5 is vague as it is unclear if this is the plurality used in line 2 or the plurality used in line 3. Id.
In the interest of advancing prosecution of the present application and while not acquiescing to the Examiner’s assertions, claims 3 and 13 are amended to indicate that the recitations in lines 3 and 5 are the same as the recitation in line 2.
Regarding claims 5, 6, 15 and 16, the Examiner asserts the terms “general-use” and “reduced” are vague and relative as it is unclear what is considered “general-use” and what is reduced. Id.
In the interest of advancing prosecution of the present application and while not acquiescing to the Examiner’s assertions, claims 5, 6, 15 and 16 are amended to further define the terms “general-use” and “reduced.”
Regarding claims 7 and 17, the Examiner asserts the recitation “trained using data gathered…” is vague and sounds “more like a method step.” Id. Further, the Examiner asserts the terms “12 lead ECG data,” “echocardiography data” and “each 12-lead ECG datum” are vague as it is unclear if these terms are referencing the previous 12 lead and echo terms used in line 2 or are different elements. Id.
In the interest of advancing prosecution of the present application and while not acquiescing to the Examiner’s assertions, claims 7 and 17 are amended to show the terms “12 lead ECG data,” “echocardiography data” and “each 12-lead ECG datum” are referencing the previous 12 lead and echo terms used in line 2. Further, claim 7 is amended to add language acceptable for an apparatus claim. Applicant respectfully traverses the Examiner’s assertion that the recitation “trained using data gathered…” is vague and sounds more like a method claim, because claim 17 is a method claim.
Regarding claim 10, the Examiner asserts the recitation “is displayed at a display device” is vague and inferentially including the display device. Id.
In the interest of advancing prosecution of the present application and while not acquiescing to the Examiner’s assertions, Applicant has amended claim 10 to recite “further comprises a display device,” as suggested by the Examiner.
Regarding claim 20, the Examiner asserts that the claim is in passive voice, and it is unclear if a further method step is being recited. Id.
In the interest of advancing prosecution of the present application and while not acquiescing to the Examiner’s assertions, Applicant has amended claim 20 to recite the claim in active voice.
Applicant respectfully submits that the rejections of the claims are now moot, and that the rejection of claims 3, 5-7, 10, 13, 15-17 and 20 under 35 U.S.C. § 112 be withdrawn.
Rejection of claims under 35 U.S.C. § 101
Claims 1-20 stand rejected under 35 U.S.C. § 101 for allegedly being directed to an abstract idea without significantly more. (Office Action, pp. 3-7). More specifically, the Examiner asserts that the claims fall within the “Mental Processes” grouping.
Under the January 2019 Guidance, now incorporated in the June 2020 revision of Manual of Patent Examining Procedure (“MPEP”), the first step of the Alice Corp. Pty. Ltd/Mayo Collaborative Services test was revised.  MPEP 2106.04 [incorporating 2019 Revised Patent Subject Matter Eligibility Guidance, 84, Fed. Reg. 50, 52 (January 7, 2019)].  Revised Step 2A now focuses on (1) Whether the claim recites a judicial exception and (2) whether a recited judicial exception is integrated into a practical application.  Id.  Only if a claim recites a judicial exception and fails to integrate the exception into a practical application, further analysis pursuant to the second step (2B) in the Alice Corp. Pty. Ltd/Mayo Collaborative Services test is needed.  Id.
Applicant has amended claim 1 to recite:
An apparatus for artificial intelligence-supported diagnostic assessment, wherein the apparatus comprises:
at least a processor, wherein the processor is configured to:
receive a plurality of multi-channel sensor readings of physiological data;
generate training data correlating each of the plurality of multi-channel sensor readings with a plurality of diagnostic labels;
train a neural network using the plurality of diagnostic labels;
update the training data as a function of new training examples;
retrain the neural network as a function of the updated training data, wherein the retraining occurs when a preconfigured threshold of new training examples is exceeded;
receive a time series input describing user physiological data from at least a sensor;
input the time series input into the trained neural network;
generate diagnostic data as a function of the time series input and the trained neural network, wherein the diagnostic data includes a classification under a plurality of groups, wherein the groups are comprised of:
normal LV function;
LVSD only;
LVDD only; and
Both LVSD and LVDD;
determine prognostic data as a function of the diagnostic data; and
output the prognostic data.
Independent claim 11 has been amended in a manner similar to independent claim 1.
Applicant submits that, according to MPEP 2106.04, independent claims 1 and 11, and their dependent claims, are allowable under step 2A and/or 2B of the eligibility analysis, as discussed further below in this paper.
Step 2A, Prong One
Under Step 2A, Prong One, the Examiner alleges that selected limitations of Applicant’s claims fall within the abstract grouping of “Mental Processes.” More specifically, the Examiner asserts the claims recite “a mental concept of performing a series of steps to label cardiac data and receive new data to predict a cardiac condition.” (Office Action, p. 3).
Mental Processes
“The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea.” MPEP § 2106.04(a)(2).III. “Accordingly, the ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Id. For the purposes of this abstract idea, “[t]he 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.” Id. However, “[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations.” Id. MPEP § 2106.04(a)(2).III.A.  The MPEP provides an example cited in dicta of Synopsys, Inc. v. Mentor Graphics Corp., 839 F. 3D 1138, 1148 (Fed. Cir. 2016) as an example of a limitation that cannot be “practically performed in the human mind.”  Id. In that cited portion of its decision, the Synopsis court reasoned that a claim does not recite a mental process where the “invention involves a several-step manipulation of data that,
except in its most simplistic form
, could not conceivably be performed in the human mind or with pencil and paper.”  Synopsys, 839 F. 3D at 1148, citing TQP Development, LLC v. Intuit Inc., 2014 WL 651935 (E.D. Tex. Feb. 19, 2014) (emphasis added). Like the “several-step manipulation” cited in Synopsys, “update the training data as a function of new training examples, retrain the neural network as a function of the updated training data, wherein the retraining occurs when a preconfigured threshold of new training examples is exceeded,” in claim 1 would generally involve acting on a large body of data to modify and create data structures that, unless improperly reduced its “most simplistic form,” could not be performed by a human being with the aid of pen and paper. Synopsys, 839 F.3D at 1148.
Applicant respectfully submits that the machine-learning processes are tied to a machine and cannot be practically performed in the human mind. Amended claim 1 includes Applicant’s neural network that is trained using a plurality of diagnostic labels. The training data comprising the plurality of diagnostic labels is updated as a function of new training examples, with the neural network being retrained as a function of the updated training data. The neural network is retrained when a preconfigured threshold of new training examples is exceeded. Further, MPEP guidance makes clear the necessary existence of boundaries for determination of a judicial exception under a mental process classification.
The MPEP states (emphasis added):
Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims"); CyberSource, 654 F.3d at 1376, 99 USPQ2d at 1699 (distinguishing Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 97 USPQ2d 1274 (Fed. Cir. 2010), and SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 94 USPQ2d 1607 (Fed. Cir. 2010), as directed to inventions that ‘‘could not, as a practical matter, be performed entirely in a human’s mind’’). (MPEP 2106.04(a)(2)(III)(A)).
The Federal Circuit has held that for a “method [that] required the manipulation of computer data structures (e.g., the pixels of a digital image and a two-dimensional array known as a mask) and the output of a modified computer data structure (a halftoned digital image), the method could not, as a practical matter, be performed entirely in a human's mind.” Cybersource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1376 (Fed. Cir. 2011). Here, claim 1 is directed to training a neural network, updating the training data as a function of new training examples, and retraining the neural network as a function of the updated training data, wherein the retraining occurs when a preconfigured threshold of new training examples is exceeded. Therefore, Applicant submits that claim 1, much like the claims at issue in SiRF Tech. does not “’deal[] with ... a method that [could] be performed without a machine’ and that there [is] ‘no evidence... that the calculations [could] be performed entirely in the human mind.’” Cybersource Corp., 654 at 1376 citing SiRF Tech., 601 F.3d at 1331.
Additionally, in Ex Parte Adjaoute, the PTAB explored “claims [that] recite monitoring operation[s] of machines using neural networks, logic decision trees, confidence assessments, fuzzy logic, smart agent profiling, and case-based reasoning (Spec., Claims 1 and 8).” (No.
2018-007443, pg. 10 (PTAB, Oct. 10, 2019)) (Informative). The PTAB held that “the ‘classifying’ steps of claims 1 and ‘modules’ of claim 8 when read in light of the Specification, recite a method and system difficult and challenging for non-experts due to their computational complexity. As such, we conclude that one of ordinary skill in the art would not find it practical to perform the aforementioned ‘classifying’ steps recited in claim 1 and function of the ‘modules’ recited in claim 8 mentally.” (Id. at 11.)
As referenced above, the apparatus recited by claim 1 includes a trained neural network that is “difficult and challenging for non-experts” due to its complexity, as explained in the specification as filed. For instance, a “neural network” also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. (Specification at para. [0047]). Accordingly, much like the claims in In Re Adjaoute, the use of Applicant’s claimed trained neural network could not be practically performed in the human mind.
Furthermore, Applicant submits that the limitations as recited in claim 1 cannot practically be performed in the human mind much like examples 39 listed in the Subject Matter Eligibility Examples: Abstract Ideas issued January 2019 or the claims in SRI Int’l, Inc. v. Cisco, Sys., Inc. 930 F.3d 1295 (Fed. Cir. 2019).
For example, Example 39 presents an allowable claim that recites (Subject Matter Eligibility Examples: Abstract Ideas, pgs. 8–9, Emphasis Added):
collecting a set of digital facial images from a database;
applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images;
creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images;
training the neural network in a first stage using the first training set;
creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training;
and training the neural network in a second stage using the second training set.
The Explanation in the Guidance states:
The claim does not recite any of the judicial exceptions enumerated in the 2019 PEG. For instance, the claim does not recite any mathematical relationships, formulas, or calculations. While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims.
Further, the claim does not recite a mental process because the steps are not
practically performed
in the human mind. Finally, the claim does not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people. Thus, the claim is eligible because it does not recite a judicial exception.
(Id. at pg. 9, Emphasis Added.).
Much like the claim presented in Example 39, Applicant’s claim 1 recites a first training set (training data correlating each of the plurality of multi-channel sensor readings with a plurality of diagnostic labels), training a neural network using the plurality of diagnostic labels, creating a second training set (updated training data as a function of new training examples), and retraining the neural network as a function of the updated training data. In light of this, Applicant submits that the recitations of claim 1 do not recite a mental process much like the recitations of Example 39 because they are “steps [that] are not practically performed in the human mind.”
Accordingly, Applicant submits that the instant disclosure does not disclose in claim 1, or any other claim, any abstract limitation that is directed to a mental process as suggested by the Examiner.
For at least the above reasons, Applicant submits that the Examiner has failed to establish a prima facie case of subject matter ineligibility and respectfully requests withdrawal of the Section 101 rejections.
Step 2A Prong Two
Even though the claims do not recite an abstract idea as alleged by the Examiner, the claims recite additional elements that integrate the judicial exception into a practical application. Applicant respectfully traverses the Examiner’s assertion that the judicial exception is not integrated into a practical application because the generically recited computer elements (e.g. processor, display, etc.) do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer, and displaying the results is considered insignificant extra-solution activity. (Office Action, p. 3).
Applicant respectfully traverses the assertion as the Office fails to evaluate whether claim 1 as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016); See
also MPEP §2106.04 (“examiners should consider the claim as a whole when performing the Step 2A analysis”).
The MPEP states that an Examiner must evaluate whether the claims, if directed to a judicial exception, are integrated into a practical application. Specifically, the MPEP states that “Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).” M.P.E.P., 9e, § 2106.04(d).
The Examiner fails to consider the fact that the technologically complex system trains a neural network, updates training data, and retrains the neural network. Further, a “neural network” also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. (Specification at para. [0047]).
Furthermore, MPEP 2106.04(d) states that a “claim that integrates an alleged judicial exception into a practical application will 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 more than a drafting effort designed to monopolize the judicial exception.”  The Federal Circuit has held that claims are not directed towards an abstract idea if the claim uses limited rules in a process specifically designed to achieve an improved technological result in conventional industry practice.  See
McRo, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1316 (Fed. Cir. 2016).
An apparatus for artificial intelligence-supported diagnostic assessment comprises “generating training data correlating each of the plurality of multi-channel sensor readings with a plurality of diagnostic labels; training a neural network using the plurality of diagnostic labels; updating the training data as a function of new training examples; and retraining the neural network as a function of the updated training data, wherein the retraining occurs when a preconfigured threshold of new training examples is exceeded.” Hence, analogous to McRo, the use of the processor to generate training data, train the neural network, update the training data, and retrain the neural network, and meaningful inputs (correlation of the plurality of multi-channel sensor readings with a plurality of diagnostic labels, and new training examples) imposes limits to the claims beyond any judicial exception, which are not designed to monopolize the judicial exception. Further, in Ex Parte Donovan, the PTAB held that:
The machine learning application, on the other hand, is configured to generate an effective treatment model using population data according to a specific statistical functionality (linear discriminant analysis) and relying on particular inputs (e.g., data for each member of the population taken at two different times, data values corresponding to treatment resistance and health outcomes, and both measured and derived protein expression levels for a plurality of biomarkers ). Hence, as with the incorporation of particular “rules” in the claims of McRO,
we find that the specific operations and inputs employed by the recited machine learning application in generating an effective treatment model impose meaningful limits to the claims beyond any judicial exception
.
(Emphasis Added) (2017-005993 (PTAB 2019), Non-precedential). Similar to the claims in Donovan, amended claim 1 recites a specifically trained neural network, employing machine-learning based determination and generation techniques, with the use of training data and a plurality of diagnostic labels as inputs. Similarly to Donovan, Applicant asserts that these specific means are not merely directed to a result or effect that itself is the abstract idea, because claim 1 as a whole is limited to rules, such as “generating training data correlating each of the plurality of multi-channel sensor readings with a plurality of diagnostic labels; training a neural network using the plurality of diagnostic labels; updating the training data as a function of new training examples; and retraining the neural network as a function of the updated training data, wherein the retraining occurs when a preconfigured threshold of new training examples is exceeded.” As such, the recited trained neural network imposes meaningful limits to the claims beyond any judicial exception. Applicant respectfully requests reconsideration without oversimplification of the claim and consideration of the claim as a whole.
Furthermore, in Ex Parte Baughman, the PTAB held that:
[t]he “function closure”-related software limitations recited in claim 1’s first step are integrated with the limitations that describe the abstract idea for generating answers to a question. Claim 1 uses the queries decomposed from the received function call to obtain “local hypothesis evidence,” and then uses that local hypothesis evidence along with “the set of non-local context evidence” for generating hypotheses. Taken as a whole, claim 1 recites a set of steps for a particular query-and hypothesis-based processing sequence and set of rules, executed by a QA system, that amounts to “us[ing] the limited rules in a process specifically designed to achieve an improved technological result in conventional industry practice,” i.e., to improve the technology of QA systems. See McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 1316 (Fed. Cir. 1016); see
also id. at 1307–08, 1313–16 (explaining “that processes that automate tasks humans are capable of performing are patent eligible if properly claimed” (id. at 1313)). In view of the foregoing, we conclude claim 1 imposes meaningful limits on the application of the recited judicial exception for generating candidate answers to a question. These limitations, in combination, integrate the abstract idea into a practical application for the technology of QA systems. Thus, we agree with Appellant the Examiner erred in rejecting claims 1–20 under 35 U.S.C. § 101.2019-000665, pg. 8–9 (PTAB 2019).
Like the claims in Baughman, amended claim 1 recites meaningful limits on the application of any alleged judicial exception and limitations analogous to the processing sequence in Baughman. The trained neural network is trained with a specific set of rules similar to what is taught in Baughman, such as training the neural network using the plurality of diagnostic labels, updating the training data as a function of new training examples, and retraining the neural network as a function of the updated training data, wherein the retraining occurs when a preconfigured threshold of new training examples is exceeded. Therefore, Applicant submits that, like the claims in Baughman, the recitations of claim 1, in combination, integrate any alleged abstract idea into a practical application for the technology of artificial-intelligence supported diagnostic assessment.
Moreover, Applicant submits that claim 1 as amended is not directed to an abstract idea, because claim 1 as amended is directed to an improvement to a computer process. See
McRo, 837 F.3dat 1310. A claim directed to an improvement to technology, including computer technology, is not directed to an abstract idea. Id. at 1316. Claims are “patent eligible [where] they ‘effect an improvement in [a] technology or technical field.’” Id. at 1310, quoting Alice Corp. v. CLS Bank Int’l, 134 S.Ct. 2347, 2359 (2014). Improvements to computer technology may include improvements in configuration of general purpose computers for specific purposes, such as computer animation. Id. (emphasis added). Here, claim 1 as amended is directed to the training/adjustment/modification of a neural network for artificial intelligence-supported diagnostic assessment.
Claim 1 as amended, recites an inventive concept, at least because the claim contains limitations such as non-conventional and non-generic arrangement of process steps, which amounts to a non-conventional and non-generic arrangement of process steps. See
BASCOM Glob. Internet Servs., Inc. v. AT&T Mobility, LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016). “Examiners should keep in mind that the courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic.” May 4th USPTO Memorandum at p. 4; see also
DDR Holdings, 773 F.3d at 1257. Further, “an inventive concept may be found in the non-conventional and non-generic arrangement” even of generic computer operations on a generic computing device. Bascom, 827 F.3d at 1350.
Considering this, Applicant submits that claim 1, or any other claim, is not directed to “the mental processes and methods of organizing human activity that § 101 is meant to exclude”, if any, and further is not directed to a judicial exception.  Therefore, Applicant respectfully asserts that the activities of claim 1, and other claims, do not recite the abstract idea of mental processes as exemplified by the teachings of the MPEP and the courts.
For at least these additional reasons, Applicant respectfully requests withdrawal of the Section 101 rejections.
Step 2B
Though Applicant contends that amended claim 1 is not directed towards a judicial exception, if claim 1 is found to be directed towards a judicial exception, Applicant submits that amended claim 1 amounts to significantly more than the judicial exception under step 2B. MPEP 2106.05(II).
The 2019 Guidance states that “[i]t is possible that a claim that does not ‘integrate’ a recited judicial exception is nonetheless patent eligible. For example, the claim may recite additional elements that render the claim patent eligible even though a judicial exception is recited in a separate claim element.” 2019 Guidance, p. 56. Further, the 2019 Guidance states that “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 they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself. . . examiners should consider in Step 2B whether an additional element or combination of elements: Adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field.” Id.
The Examiner alleges that claims 1 and 11 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
“they amount to a general computer performing a calculation. The claims are directed to an abstract idea, i.e. implementing the idea of performing a series of steps to label cardiac data and receive new data to predict a cardiac condition based on the labeled data, such as may be done by a mental process, critical thinking, and/or paper and pencil, or done by a mathematical equation, with additional generic computer elements, or additional structure (e.g. processor, display, etc.) recited at a high level of generality that perform generic functions routinely used in the art, and do not add a meaningful limitation to the abstract idea because they would be routine in any computer implementation or in the relevant art. Thus, the recited generic computer components perform no more than their basic computer functions. These additional elements are well-understood, routine and conventional limitations (see cited document(s)) that amount to mere instructions or
elements to implement the abstract idea.”
(Office Action, p. 4).
Applicant respectfully disagrees and submits that the claims recite additional elements that amount to significantly more than the judicial exception (i.e., inventive concept).  This can be evidenced at least by the “practical application” and “improvement” analysis presented above in connection with Prong 2 of Step 2A.
Additionally, Applicant would like to point out that the claims recite details of how a solution to a problem is accomplished, practice a needed use of a computing device (e.g. to train the neural network, update the training date, and retrain the neural network) and are directed to a specific technical field (e.g. machine-learned enhanced improvements in artificial intelligence-supported diagnostic assessment). See MPEP 2106.05(f).
Applicant submits that claims 1 and 11 (and other claims), when analyzed as an ordered combination contain an inventive concept.  An inventive concept can be found in the non-conventional and non-generic arrangement of known conventional pieces. Bascom Global Internet Services v. AT&T Mobility, 827, F.3d, 1341, 1350 (Fed. Cir. 2016). Claim 1 as a whole recites a unique apparatus for artificial intelligence-supported diagnostic assessment. A computing device is used to train a neural network, update training data, and retrain the neural network. The disclosure aims to better optimize cardiac disease assessment.
Additionally, as discussed below, Applicant’s claims amount to an “inventive concept” because they are not taught by the relevant art.
For at least these additional reasons, Applicant respectfully requests withdrawal of the § 101 rejections.
Independent claim 11 recites limitations similar to independent claim 1 and overcomes these rejections for at least the same reasons as discussed above with reference to claim 1.
Claims 2-10 depend directly or indirectly on claim 1 and thus recite all the same elements as claim 1. Claims 12-20 depend directly or indirectly from claim 11 and thus recite all the same elements as claim 11. Applicant therefore submits that claims 2-10 and 12-20 overcome these rejections for at least the same reasons as discussed above with reference to claims 1 and 11.
Therefore, Applicant respectfully requests reconsideration and withdrawal of the 35 U.S.C. § 101 rejections of Applicant’s claims 1-20.
Rejection of the Claims under 35 U.S.C. § 102
Chen
Claims 1-5, 7, 11-15 and 17 stand rejected under 35 U.S.C. § 102 as allegedly being anticipated by “Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis” to Chen et al. (hereinafter “Chen”). Applicant respectfully traverses the rejection.
Claim 1
As agreed to in the above-noted interview, claim 1 has been amended to recite:
An apparatus for artificial intelligence-supported diagnostic assessment, wherein the apparatus comprises:
at least a processor, wherein the processor is configured to:
receive a plurality of multi-channel sensor readings of physiological data;
generate training data correlating each of the plurality of multi-channel sensor readings with a plurality of diagnostic labels;
train a neural network using the plurality of diagnostic labels;
update the training data as a function of new training examples;
retrain the neural network as a function of the updated training data, wherein the retraining occurs when a preconfigured threshold of new training examples is exceeded;
receive a time series input describing user physiological data from at least a sensor;
input the time series input into the trained neural network;
generate diagnostic data as a function of the time series input and the trained neural network, wherein the diagnostic data includes a classification under a plurality of groups, wherein the groups are comprised of:
normal LV function;
LVSD only;
LVDD only; and
both LVSD and LVDD;
determine prognostic data as a function of the diagnostic data; and
output the prognostic data.
The Office does not assert that Chen discloses “wherein the diagnostic data includes a classification under a plurality of groups, wherein the groups are comprised of: normal LV function; LVSD only; LVDD only; and both LVSD and LVDD,” as amended independent claim 1 recites. Accordingly, Applicant respectfully submits that Chen does not disclose “wherein the diagnostic data includes a classification under a plurality of groups, wherein the groups are comprised of: normal LV function; LVSD only; LVDD only; and both LVSD and LVDD.” Therefore, Applicant respectfully submits that claim 1, as amended, is patentably distinguishable over Chen, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of the rejection.
Claim 11
Claim 11 has been amended similarly to claim 1. As noted above, and agreed to in the above-noted interview, amended claim 1 is patentably distinguishable over Chen, for at least the reasons discussed above. As such, Applicant submits that claim 11 is also patentably distinguishable over Chen, for at least the reasons discussed above. Accordingly, Applicant respectfully requests the withdrawal of these rejections.
Claims 2-5, 7, 12-15 and 17
Claims 2-5, 7, 12-15 and 17 depend, directly or indirectly, from amended claims 1 and 11, respectively. As noted above, amended claims 1 and 11 are patentably distinguishable over Chen, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claims 2-5, 7, 12-15 and 17 are also patentably distinguishable over Chen for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of the rejection.
Accordingly, Applicant respectfully requests that the rejections of the claims under 35 U.S.C. § 102 be withdrawn.
Rejection of the Claims under 35 U.S.C. § 103
Chen
Claims 6, 8, 10, 16, 18 and 20 stand rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Chen. Applicant respectfully traverses the rejection.
Claims 6, 8, 10, 16, 18 and 20 depend, directly or indirectly, from amended claims 1 and 11, respectively. As noted above, claims 1 and 11 are patentably distinguishable over Chen, for at least the reasons discussed above. Accordingly, Applicant submits that claims 6, 8, 10, 16, 18 and 20 are also patentably distinguishable over Chen, for at least the reasons discussed above. Therefore, Applicant respectfully submits the withdrawal of this rejection.
Accordingly, Applicant respectfully requests that the rejection of the claims under 35 U.S.C. § 103 be withdrawn.
Conclusion

NEXT OFFICE ACTION: EXAMINER'S AMENDMENT
An examiner’s amendment to the record appears below. Should the changes and/or additions be unacceptable to applicant, an amendment may be filed as provided by 37 CFR 1.312. To ensure consideration of such an amendment, it MUST be submitted no later than the payment of the issue fee.
Authorization for this examiner’s amendment was given in an interview with Charles Dresser on 1/29/25.
The application has been amended as follows:
In claim 1, after line 2, the following has been inserted
--a sensor for sensing a time series input;--.
In claim 1, line 12, the first occurrence of “a” has been deleted and –the—has been inserted.  In line 12, the second occurrence of “a” has been deleted and –the—has been inserted.  In the last line, the “.” has been deleted and --; and—has been inserted.  After the last line of the claim, the following has been inserted
--a display device, wherein the output of the prognostic data is displayed on the display device.--.
In claim 8, line 1, after “of”, --the—has been inserted.
Claims 10 and 20-22 have been canceled.
In claim 11, after line 2, the following has been inserted
--sensing a time series input via a sensor;--.
In claim 11, line 11, the first occurrence of “a” has been deleted and –the—has been inserted.  In line 11, the second occurrence of “a” has been deleted and –the—has been inserted.  In the last line, the “.” has been deleted and --; and—has been inserted.  After the last line of the claim, the following has been inserted
--displaying the output of the prognostic data on a display device.--.
In claim 18, line 2, after “both”, --the— has been inserted.  In line 2, after “and”,
--the—has been inserted.
Conclusion

CITED REFERENCE SUMMARIES: Ref WO2023230345: Claims:
A computer-implemented method of detecting cardiovascular disease in a subject, the method comprising: receiving an electrocardiogram (ECG) image for the subject; applying a machine-learning based algorithm to the ECG image for the subject, the algorithm being trained to distinguish a printed ECG reading of a heart with cardiovascular disease from a printed ECG reading of a healthy heart; comparing outputs of the algorithm to patterns of algorithm outputs for ECG images from healthy subjects and subjects with one or more cardiovascular diseases; and determining if the subject has cardiovascular disease based upon the outputs of the algorithm.
The method of claim 1, wherein the machine-learning based algorithm is a deep neural network, the deep neural network comprising a plurality of nodes trained to distinguish a printed ECG reading of a heart with cardiovascular disease from a printed ECG reading of a healthy heart.
The method of claim 1, wherein the machine-learning based algorithm is a statistical algorithm.
The method of claim 1, wherein the ECG image comprises a printed ECG image of an ECG dataset formed by conversion of ECG waveform data.
The method of claim 1, wherein the method is generalizable to multiple ECG image formats.
The method of claim 1, wherein the algorithm trained on ECG images having incorrectly placed leads.
The method of claim 1, wherein the algorithm is trained on images of ECGs with 

different signal, background, and noise characteristics.
The method of claim 1, further comprising identifying hidden clinical labels.
The method of claim 1, further comprising identifying characteristics of the ECG image that the determination is based on.
The method of claim 1, wherein the method is automated.
The method of claim 1, wherein the cardiovascular disease comprises a disorder selected from the group consisting of structural disorders of the heart, functional disorders of the heart, structural disorders of the structures supporting the heart, functional disorders of the structures supporting the heart, and combinations thereof.
The method of claim 1, wherein the disorder comprises abnormalities of the muscle, valves, blood vessels, or lining of the heart.
The method of claim 1, wherein the disorder is a genetic disorder.
The method of claim 1, wherein the disorder is an acquired disorder.
The method of claim 1, wherein the cardiovascular disease comprises a disease that is not normally discernable by physicians from ECG data.
The method of any one of the preceding claims, wherein, prior to the step of applying the algorithm to the ECG image for the subject, the method further comprises training the algorithm, the training of the algorithm comprising: creating an image-based dataset including a normal subset and a cardiovascular disease subset; optionally pre-training the algorithm on an unrelated clinical or hidden label; and training the algorithm on the image-based dataset.
The method of claim 16, wherein the cardiovascular disease subset includes a low ejection fraction (EF) subset.
The method of claim 17, wherein the low EF subset includes ECG images for individuals with EF of less than 40%.
The method of any one of claims 16-18, wherein the clinical label includes six physician- defined labels and the hidden label includes gender.
The method of claim 16, wherein the normal subset includes ECG images for individuals having hypertrophic cardiomyopathy (HCM).
the method of claim 20, wherein the cardiovascular disease subset includes ECG images for individuals having HCM and left ventricular (LV) systolic dysfunction.
The method of any one of claims 16-21, wherein the image-based dataset includes at least two different plotting schemes for each ECG waveform.
The method of any one of claims 16-21, wherein the image-based dataset includes at least two different ECG image formats.