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 .
Priority
This application claims priority to applications KR10-2024-004014 and KR10-2025-0053085 and has an effective filing date of 23 April 2024.
Information Disclosure Statement
The information disclosure statement received 05 December 2025 has been reviewed and considered by the Examiner.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method for providing prognostic information on heart failure implemented by a processor and a device for providing prognostic information on heart failure, which are within a statutory category or are interpreted to be within a statutory category for subject matter eligibility analysis purposes.
Step 2A1
The limitations of …determining data on the prognosis of heart failure based on the received echocardiographic video image, using a prediction model trained to output data on the prognosis of the heart failure by taking the echocardiographic video image as a single input without any other input, as drafted, is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for recitation of generic computer components.
That is, other than reciting processor and a device, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the processor, this claim encompasses a person thinking about determining data on the prognosis of heart failure based on the received echocardiographic video image, using a prediction model trained to output data on the prognosis of the heart failure by taking a specific input in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
The independent claims recite the following additional elements: a processor, receiving an echocardiographic video image of an individual suffering from heart failure, a device comprising a communication unit coupled to a processor.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a processor and device that implements the identified abstract idea. These are not described by the applicant and are recited at a high-level of generality (i.e., a generic server performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites using a prediction model trained to output data on the prognosis of heart failure by taking the echocardiographic video image as a single input without any other input. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained model merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
The claim further recites the additional element: receiving an echocardiographic video image of an individual suffering from heart failure;... This transmitting step is recited at a high level of generality (i.e., as a general means of transmitting data) and amounts to the mere transmission of data, which is a form of extra-solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The claim further recites the additional element of an communication unit. The communication unit merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and device to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, using the trained model to output data on the prognosis of heart failure by taking the echocardiographic video image as a single input without any other input was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of receiving an echocardiographic video image of an individual suffering from heart failure;... This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2016.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of an communication unit was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims and Dependent Additional Elements
Claims 2-8, 10-16 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim 2 merely describes wherein the echocardiographic video image is a cardiac ultrasound video including a plurality of frames, and the prediction model includes a specific encoder configured to extract features for each of the plurality of frames of a received 3D echocardiographic video image, and a transformer configured to derive a temporal relationship for each of the plurality of frames so that an integrated time series feature is generated. Claim 3 merely describes wherein the prediction model further includes a certain type of pooling configured to generate an integrated spatial feature by considering adjacent frame features for one selected frame among the plurality of frames. Claim 4 merely describes wherein the data on the prognosis of the heart failure includes at least one of a survival probability within a predetermined period, a cumulative survival curve for the survival probability, a hazard ration, and a mortality risk score. Claim 5 merely describes wherein the data on the prognosis of the heart failure is provided in a certain form and where a mortality risk at each time point is visually expressed. Claim 6 and 14 merely describe additionally receiving sex and age for the individual, wherein the determining includes determining the data on the prognosis of the heart failure based on the received sex, age, and echocardiographic video image using the prediction model. Claim 7 and 15 merely describe the determining of the data on the prognosis of the heart failure based on the received sex, age, and echocardiographic video image includes extracting a first feature for the received sex and age using the prediction model, extracting a second feature for the received echocardiographic video image, integrating the first feature and the second feature, and determining the data on the prognosis of the heart failure based on the integrated feature. Claim 8 and 16 merely describes wherein the prediction model is a model constructed through binary classifying whether the individual survives for each of a plurality of time intervals, and training to generate a survival probability function by accumulating a binary classification result according to whether the individual survives for each time interval. Claim 10 merely describes wherein the echocardiographic video image is a cardiac ultrasound video including a plurality of frames, and the prediction model includes a 3D encoder configured to extract features for each of the plurality of frames of a received 3D echocardiographic video image, and a transformer configured to derive a temporal relationship for each of the plurality of frames so that an integrated time series feature is generated. Claim 11 merely describe wherein the prediction model further includes a spatial attention pooling configured to generate an integrated spatial feature by considering adjacent frame features for one selected frame among the plurality of frames. Claim 12 merely describes wherein the data on the prognosis of the heart failure includes at least one of a survival probability within a predetermined period, a cumulative survival curve for the survival probability, a hazard ratio, and a mortality risk score. Claim 13 merely describes wherein the data on the prognosis of the heart failure is provided in a certain form and where a mortality risk at each time point is visually expressed.
The dependent claims contain a variety of additional elements including: a graphical user interface (GUI), training (a model) to generate a survival probability function by accumulating a binary classification result according to whether the individual survives for each time interval, a cardiac ultrasound video, 3D encoder, communication unit and a processor.
The dependent claims contain the additional element of a processor which is analyzed the same as the “a computing device” and does not provide a practical application or significantly more for the same reasons.
The “graphical user interface,” cardiac ultrasound video, 3D encoder, communication unit generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more.
Training (a model) to generate a survival probability function by accumulating a binary classification result according to whether the individual survives for each time interval, a cardiac ultrasound video merely represents saying “apply it” or equivalent to the abstract idea. MPEP 2106.04(d)(I) and MPEP2106.05(I)(A) indicate that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application or significantly more.
The use of the prediction model to binary classify whether the individual survives for each of a plurality of time intervals is part of the abstract idea.
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3,9,11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0326604 A1 (hereafter Hare2) in view of US 2021/0052252 A1 (hereafter Hare).
Regarding Claim 1
Hare2 teaches:
[…]
and determining data on the prognosis of heart failure based on the received echocardiographic video image, [Hare2 teaches at the Abstract the cardiac cycle probability score generated for all the cardiac cycles are aggregated for each A4C video, and the aggregated probability scores for all the A4C videos are combined to generate a patient-level conclusion for CA (cardiac amyloidosis) and HCM (hypertrophic cardiomyopathy).]
using a prediction model trained to output data on the prognosis of the heart failure by taking the echocardiographic video image as a single input without any other input. [Hare2 teaches at the Abstract phase detection is performed on the segmented A4C images to determine systole and diastole endpoints per cardiac cycle. Hare2 teaches at the Abstract the cardiac cycle probability score generated for all the cardiac cycles are aggregated for each A4C video, and the aggregated probability scores for all the A4C videos are combined to generate a patient-level conclusion for CA (cardiac amyloidosis) and HCM (hypertrophic cardiomyopathy). This teaches the echocardiographic video images is a single input without any other input. The model generating the probability scores is interpreted as the prediction model. Hare2 teaches at para. [0062] the CNNs will be trained to recognize and segment the various echo image views using thousands of echo images from an online public or private echocardiogram DICOM database. Note that while the training took thousands of echo images, the prediction takes one echo image as the input to generate the probability scores and ultimately, the clinical classification.]
Hare2 may not explicitly teach:
A method for providing prognostic information on heart failure implemented by a processor, the method comprising: receiving an echocardiographic video image of an individual suffering from heart failure;
Hare teaches:
A method for providing prognostic information on heart failure implemented by a processor, the method comprising: receiving an echocardiographic video image of an individual suffering from heart failure; [Hare teaches at Fig. 4A receiving patient studies Item 300. The patient is interpreted to be an individual suffering from heart failure. Hare teaches at Fig. 4A Item 400 downloading echo images and storing in image file archive. Collectively, Hare teaches, receiving an echocardiographic video image of an individual suffering from heart failure.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images for automated cardiac measurements and diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy of Hare2 to the clinical workflow to diagnose heart disease based on cardiac biomarker measurements and AI recognition of 2D and doppler modality echocardiogram images of Hare with the motivation of improving outcomes related to cardiovascular disease including heart failure, which is a major health problem accounting for about 30% of human deaths worldwide (para. [0003]).
Regarding Claim 9
Due to its similarity to Claim 1, Claim 9 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Regarding Claim 2
Hare2/Hare teach the method according to claim 1. Hare2/Hare further teach:
wherein the echocardiographic video image is a cardiac ultrasound video including a plurality of frames, and the prediction model includes a 3D echocardiographic video image, [Hare2 teaches at para. [0013] according to the method and system disclosed herein, the disclosed embodiments use machine learning to recognize and analyze both 2D and Doppler modality Echocardiographic images for automated measurements and the diagnosis, prediction and prognosis of heart disease, and the system will be deployed in workstation or mobile based ultrasound point-of-care systems. The doppler modality Echocardiographic images are interpreted to be 3-D. Hare2 teaches at para. [0084] the 3D CNN model is trained to learn spatiotemporal patterns in A4C videos to identify CA, HCM or neither. Hare2 teaches at para. [0084] specifically, to do so, when given a cardiac cycle video, the model outputs a vector containing the three probability score. Hare2 teaches at para. [0083] referring again to Fig. 3B. CA/HC disease classification is performed by a 3D CNN which receives beat-to-beat images for respective cardiac cycles defined by pairs of systole endpoints and diastole endpoints, an outputs cardiac cycle probability scores per the respective cardiac cycles for at least one of three cardiac cycle outcomes: i) cardiac amyloidosis (CA), ii) hypertrophic cardiomyopathy (HCM) and iii) no CA or HCM (block 324). The beat to beat images are wherein the echocardiographic video image is a cardiac ultrasound video including a plurality of frames, and the prediction model includes a 3D echocardiographic video image.]
and a transformer configured to derive a temporal relationship for each of the plurality of frames so that an integrated time series feature is generated. [Hare2 teaches at para. [0092] patient information from each of the patient studies is extracted and stored in the database. Hare2 teaches at para. [0092] for example, the patient studies will be prioritized in the queue for processing according to the date of the each exam, the time of receipt of the patient study or by estimated severity of the patient’s heart disease. The queue of studies organized by time is interpreted as the integrated time series feature is generated.]
Regarding Claim 3
Hare2/Hare teach the method according to claim 2. Hare2/Hare further teach:
wherein the prediction model further includes a spatial attention pooling configured to generate an integrated spatial feature by considering adjacent frame feature for one selected frame among the plurality of frames. [Hare2 teaches at para. [0108] next the echo workflow engine performs image segmentation to define regions of interest (ROI). Hare2 teaches at para. [0108] in computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels) to locate and boundaries (line, curves, and the like) of objects. Hare2 teaches at para. [0108] typically, annotations are a series of boundary lines overlaying overlaid on the image to highlight segment boundaries/edges. Collectively, Hare2 teaches through the boundary and feature discovery process wherein the prediction model further includes a spatial attention pooling configured to generate an integrated spatial feature by considering adjacent frame feature for one selected frame among the plurality of frames.]
Regarding Claim 11
Due to its similarity to Claim 1, Claim 11 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Claim(s) 4-6,12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0326604 A1 (hereafter Hare2) in view of US 2021/0052252 A1 (hereafter Hare) in view of US 2022/0378379 A1 (hereafter Zimmerman).
Regarding Claim 4
Hare2/Hare teach the method according to claim 1. Hare2/Hare may not explicitly teach:
wherein the data on the prognosis of the heart failure includes at least one of a survival probability within a predetermined period,
a cumulative survival care for the survival probability,
a hazard ratio,
and a mortality risk score.
Zimmerman teaches:
wherein the data on the prognosis of the heart failure includes at least one of a survival probability within a predetermined period, [Zimmerman teaches at Fig. 18 an algorithm for determining 1-uear mortality. This is interpreted as a survival probability within a predetermined period.]
a cumulative survival care for the survival probability, [Zimmerman teaches at Fig. 17 a graph with axes titled survival proportion and time in years. This such a cumulative survival care for the survival probability.]
a hazard ratio, [Zimmerman teaches at para. [0066] Fig. 7G is a plot of hazard ratios (HR) with 95% confidence intervals (CI) for the three models in subpopulations defined by age groups, sex and normal or abnormal ECG label;…]
and a mortality risk score. [Zimmerman teaches at the Abstract the method includes receiving echocardiogram data associated with the patient, providing at least a portion of the echocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated was interpreted to be a mortality risk score, there being nothing beyond broadest reasonable interpretation to define morality risk score.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images for automated cardiac measurements and diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy of Hare2 to the clinical workflow to diagnose heart disease based on cardiac biomarker measurements and AI recognition of 2D and doppler modality echocardiogram images of Hare to the artificial intelligence based cardiac event predictor systems and methods of Zimmerman with the motivation of assisting early detection, which typically means more treatment options that result in either a complete/quicker recovery and/or a less severe clinical outcome (Zimmerman at para. [0007]).
Regarding Claim 12
Due to its similarity to Claim 4, Claim 12 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 4.
Regarding Claim 5
Hare2/Hare teach the method according to claim 1. Hare2/Hare may not explicitly teach:
wherein the data on the prognosis of the heart failure is provided in the form of a graphical user interface (GUI) in which a mortality risk at each time point is visually expressed.
Zimmerman teaches:
wherein the data on the prognosis of the heart failure is provided in the form of a graphical user interface (GUI) in which a mortality risk at each time point is visually expressed. [Zimmerman teaches at Fig. 17 a graph with axes titled survival proportion and time in years. This teaches a graphical user interface (GUI) in which a mortality risk at each time point is visually expressed.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images for automated cardiac measurements and diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy of Hare2 to the clinical workflow to diagnose heart disease based on cardiac biomarker measurements and AI recognition of 2D and doppler modality echocardiogram images of Hare to the artificial intelligence based cardiac event predictor systems and methods of Zimmerman with the motivation of assisting early detection, which typically means more treatment options that result in either a complete/quicker recovery and/or a less severe clinical outcome (Zimmerman at para. [0007]).
Regarding Claim 13
Due to its similarity to Claim 5, Claim 13 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 5.
Regarding Claim 6
Hare2/Hare teach the method according to claim 1. Hare2/Hare may not explicitly teach:
further comprising additionally receiving sex and age for the individual,
wherein the determining includes determining the data on the prognosis of the heart failure based on the received sex, age, and echocardiographic video image using the prediction model.
Zimmerman teaches:
further comprising additionally receiving sex and age for the individual, [Zimmerman teaches at para. [0014] …receiving a sex value associated with the patient, providing the age value, the sex value… Collectively, this teaches receiving sex an age for the individual.]
wherein the determining includes determining the data on the prognosis of the heart failure based on the received sex, age, and echocardiographic video image using the prediction model. [Zimmerman teaches at para. [0014] … a method including receiving electrocardiogram data associated with a patient and an electrocardiogram configuration including a plurality of leads and a time interval, the electrocardiogram data including, for each lead included in the plurality of leads, voltage data associated with at least a portion of the time interval, receiving an age value associated with the patient, receiving a sex value associated with the patient, providing the age value, the sex value, and at least a portion of the electrocardiogram data to a trained model, the trained model being trained to generate a risk score based on input electrocardiogram data associated with the electrocardiogram configuration and supplementary information associated with the patient, receiving a risk score indicative of a likelihood the patient will suffer from a condition within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. Zimmerman teaches at para. [0207] patient status is used as an endpoint to determine predictions for 1-year mortality after an ECG, however, additional clinical outcomes may also be predicted, including, but not limited to, mortality at any interval (1, 2, 3 years, etc.); mortality associated with heart disease, cardiovascular disease, sudden cardiac death; hospitalization for cardiovascular disease; need for intensive care unit admission for cardiovascular disease; emergency department visit for cardiovascular disease; new onset of an abnormal heart rhythm such as atrial fibrillation; need for a heart transplant; need for an implantable cardiac device such as a pacemaker or defibrillator; need for mechanical circulatory support such as a left ventricular/right ventricular/biventricular assist device or a total artificial heart; need for a significant cardiac procedure such as percutaneous coronary intervention or coronary artery bypass graft/surgery; new stroke or transient ischemic attack; new acute coronary syndrome; or new onset of any form of cardiovascular disease such as heart failure; or the likelihood of diagnosis from other diseases which may be informed from an ECG. The condition is interpreted to be heart failure.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images for automated cardiac measurements and diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy of Hare2 to the clinical workflow to diagnose heart disease based on cardiac biomarker measurements and AI recognition of 2D and doppler modality echocardiogram images of Hare to the artificial intelligence based cardiac event predictor systems and methods of Zimmerman with the motivation of assisting early detection, which typically means more treatment options that result in either a complete/quicker recovery and/or a less severe clinical outcome (Zimmerman at para. [0007]).
Regarding Claim 14
Due to its similarity to Claim 6, Claim 14 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 6.
Claim(s) 7,15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0326604 A1 (hereafter Hare2) in view of US 2021/0052252 A1 (hereafter Hare) in view of US 2022/0378379 A1 (hereafter Zimmerman) in view of CN 116152430 A (hereafter Wang).
Regarding Claim 7
Hare2/Hare/Zimmerman teach the method according to claim 6. Hare2/Hare/Zimmerman may not explicitly teach:
wherein the determining of the data on the prognosis of the heart failure based on the received sex, age, and echocardiographic video image includes extracting a first feature for the received sex and age using the prediction model,
extracting a second feature for the received echocardiographic video image,
integrating the first feature and the second feature,
and determining the data on the prognosis of the heart failure based on the integrated feature.
Wang teaches:
wherein the determining of the data on the prognosis of the heart failure based on the received sex, age, and echocardiographic video image includes extracting a first feature for the received sex and age using the prediction model, [Wang teaches at claim 11 the method of claim 10, wherein the demographic parameters include age, sex and smoking status.]
extracting a second feature for the received echocardiographic video image, [Wang teaches at pg. 4 s1.2 extracting three-dimensional ultrasonic data of end diastole and end systole from the three-dimensional ultrasonic cardiac image sequence data, manually drawing left and right ventricular segmentation marks, and constructing a training data set. This teaches extracting a second feature for the received echocardiographic video image. The end diastole or the end systole are interpreted as the second feature.]
integrating the first feature and the second feature, [Wang teaches at pg. 4, s4: and combining the complete cardiac cycle data to generate a left and right ventricular motion state video of the complete cardiac cycle and obtaining cardiac parameters according to the segmentation result.]
and determining the data on the prognosis of the heart failure based on the integrated feature. , [Wang teaches at pg. 4, s4: and combining the complete cardiac cycle data to generate a left and right ventricular motion state video of the complete cardiac cycle and obtaining cardiac parameters according to the segmentation result. Obtaining the cardiac parameters are determining the data on the prognosis of the heart failure based on the integrated features.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images for automated cardiac measurements and diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy of Hare2 to the clinical workflow to diagnose heart disease based on cardiac biomarker measurements and AI recognition of 2D and doppler modality echocardiogram images of Hare to the artificial intelligence based cardiac event predictor systems and methods of Zimmerman to the 3D (three dimensional) echocardiography left and right ventricle three-dimensional modeling method based on edge reinforcement and self-attention mechanism of Wang with the motivation of enabling efficient and fine segmentation of 3-D echocardiography through a self-paying attention dual-branch network (Wang at the Abstract).
Regarding Claim 15
Due to its similarity to Claim 7, Claim 15 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 7.
Claim(s) 8,16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0326604 A1 (hereafter Hare2) in view of US 2021/0052252 A1 (hereafter Hare) in view of US 20210350179 A1 (hereafter Bello).
Regarding Claim 8
Hare2/Hare teach the method according to claim 1. Hare2/Hare may not explicitly teach:
wherein the prediction model is a model constructed through binary classifying whether the individual survives for each of a plurality of time intervals,
and training to generate a survival probability function by accumulating a binary classification result according to whether the individual survives for each time interval.
Bello teaches:
wherein the prediction model is a model constructed through binary classifying whether the individual survives for each of a plurality of time intervals, [Bello teaches at para. [0008] according to a first aspect of the invention there is provided a method of training a machine learning model to receive as input a time-resolved three-dimensional model of a heart or a portion of a heart, and to output a predicted time-to-event or a measure of risk for an adverse cardiac event. Bello teaches at para. [0008] the method of training a machine learning model also includes, using the training set as input, training the machine learning model to recognize latent representations of cardiac motion which are predictive of an adverse cardiac event. Bello teaches at para. [0009] outcome data will indicate the timing and nature of any adverse cardiac event associated with a time-resolved three-dimensional model. Bello teaches at para. [0009] an adverse cardiac event will include death from heart disease. Death is interpreted as the classified binary outcome (e.g. classified as dead or classified as alive).]
and training to generate a survival probability function by accumulating a binary classification result according to whether the individual survives for each time interval. [Bello teaches at the Abstract a method is described from training a machine learning model to receive as input a time-resolved three dimensional model of a heart or a portion of a heart, and to output a predicted time-to-event or a measure of risk for an adverse cardiac event. Bello teaches at the Abstract the method includes receiving a training set. Bello teaches at the Abstract the training set includes a number of time-resolved three-dimensional models of a heart or a portion of a heart. Bello teaches at the abstract, the training set also includes, for each time-resolved three-dimensional model, corresponding outcome data associated with the time-resolved three-dimension model. Bello teaches at the abstract the method of training a machine learning model also includes, using the training set as input, training the machine learning model to recognize latent representations of cardiac motion which are predictive of an adverse cardiac event. Bello teaches at para. [0009] an adverse cardiac event will include death from heart disease. Bello teaches at the Abstract that the method of training a machine learning model also includes storing the trained machine learning model. Storing the trained machine learning model is interpreted to teach accumulating a binary classification result. The classification result is the prediction of the adverse cardiac event, specifically death or no death/alive.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images for automated cardiac measurements and diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy of Hare2 to the clinical workflow to diagnose heart disease based on cardiac biomarker measurements and AI recognition of 2D and doppler modality echocardiogram images of Hare to the method for detecting adverse cardiac events of Bello with the motivation of better understanding motion analysis used in computer vision to understand the behavior of moving object in sequences of images (Bello at para. [0002]).
Regarding Claim 16
Due to its similarity to Claim 8, Claim 16 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 8.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0326604 A1 (hereafter Hare2) in view of US 2021/0052252 A1 (hereafter Hare) in view of Reynaud (Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation).
Regarding Claim 10
Hare2/Hare teach the device according to claim 9. Hare2/Hare may not explicitly teach:
wherein the echocardiographic video image is a cardiac ultrasound video including a plurality of frames, and the prediction model includes a 3D encoder configured to extract feature for each of the plurality of frames of a received 3D echocardiographic video image,
and a transformer configured to derive a temporal relationship for each of the plurality of frames so that an integrated time series feature is generated.
Reynaud teaches:
wherein the echocardiographic video image is a cardiac ultrasound video including a plurality of frames, and the prediction model includes a 3D encoder configured to extract feature for each of the plurality of frames of a received 3D echocardiographic video image, [Reynaud teaches at pg. 4 in order to analyze videos of arbitrary length they used a BER encoder to which they attached a regression network to build a Named Entity Recognition (NER) model for the video. Reynaud teaches at pg. 4 this acts as a spatio-temporal information extractor. Reynaud teaches at the Abstract we achieve an average frame distance of 3.36 frames for the ES and 7.17 frames for the ED on videos of arbitrary length. Reynaud teaches at pg. 5 for all of our experiments, they used the Echonet-Dynamic dataset that consists of a variety of pathologies and healthy heart. Reynaud teaches at pg. 5 it contains 10,030 echocardiogram videos of varied length, frame rate, and image quality, all containing 4-chamber views. This teaches wherein the echocardiographic video image is a cardiac ultrasound video including a plurality of frames.]
and a transformer configured to derive a temporal relationship for each of the plurality of frames so that an integrated time series feature is generated. [Reynaud teaches at pg. 6 this augmentation follows common practices when using a transformer model in order to provide seamless computational implementation. Reynaud teaches at pg. 6 in addition, it ensures that all the ES and ED frames that our transfer sees, are labeled as such, while having no empty frames and retaining spatio-temporal coherence.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images for automated cardiac measurements and diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy of Hare2 to the clinical workflow to diagnose heart disease based on cardiac biomarker measurements and AI recognition of 2D and doppler modality echocardiogram images of Hare to the ultrasound video transformers for cardiac ejection fraction estimation of Reynaud with the motivation of addressing the intra and inter observer variability associated with common analysis pipelines involving manual processing of the video frames by expert clinicians (Reynaud at the Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KR 2022-0102635 A (hereafter Fornwalt) teaches a deep neural network systems and methods for improving prediction of patient endpoints using video of the heart which is tangentially related to the subject matter herein.
Thavendiranathan. Prediction of 30-Day Heart Failure-Specific Readmission Risk by Echocardiographic Parameters, The American Journal of Cardiology, Volume 113, Issue 2, 2014, Pages 335-341, ISSN 0002-9149. Thavendianathan teaches a way of predicting heart failure readmission risk which is tangentially relevant to the specification.
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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