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


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.