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 .
Notice to Applicant
The examiner has cited new prior art for a more explicit teaching for the argued limitations with respect to the CRT device to correct the previous USC 103 rejection. Hence, this action is a non-final rejection.
Status of Claims
In the reply filed on 21 November 2025 no changes have been made.
Claims 21-40 are currently pending and have been examined.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied 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.
Claims 21-26 and 33-40 are rejected under 35 U.S.C. 103 as being unpatentable over Moturu et al. (US20170000422A1) in view of Wang et al. (Feedback Control of a Rotary Left Ventricular Assist Device Supporting a failing Cardiovascular System), Mansi et al. (US20130197881A1), and further in view of Engels et al. (US20170340887A1).
Regarding claim 21, Moturu discloses receiving, at the modeling and simulation computing device, remote monitoring data from at least one remote monitoring data source ([0021] “The technology can continuously collect and utilize datasets unique to internet-enabled mobile computing devices (e.g., social network usage, text messaging characteristics, application usage, patient response monitoring data facilitated through the mobile computing device, etc.)…”)
the remote monitoring data including at least actual hemodynamic data for the particular patient ([0051] “Additionally or alternatively, the cardiovascular health can include any one or more of a: blood pressure metric (e.g., instantaneous blood pressure, blood pressure variability, etc.), measures indicative of atherosclerosis or other cardiovascular disease, heartbeat metric (e.g., instantaneous heart rate, heart rate variability, average heart rate, resting heart rate, heartbeat signature, etc.), pulse rate metric (e.g., instantaneous pulse rate, pulse rate variability, etc.).”)
updating, using the modeling and simulation computing device, the patient-specific model using the remote monitoring data received from the VAD […] ([0034] “A cardiovascular therapy device can include one or more of a […] ventricular assist devices” [0134] “Block S180 preferably includes updating one or more predictive models with an additional log of use dataset (e.g., a log of used dataset associated with a time period subsequent to generating an initial predictive model).”)
performing, using the modeling and simulation computing device, at least one simulation on the updated patient-specific model to simulate operation of the circulatory system of the particular patient during an event to i) predict an expected outcome of the event in the particular patient ([0050] “As shown in FIGS. 1-4, Block S130 recites: extracting a cardiovascular health metric from at least one of an output of the cardiovascular health predictive model, the log of use dataset, the supplementary dataset, and the survey dataset. […] Block S130 can additionally or alternatively include generating a predictive model S140. Block S130 thus enables assessment of a past or current cardiovascular of the patient and/or predicts risk that the patient will trend toward a different (e.g., worsened, improved, etc.) cardiovascular state at a future time point.” [0051] “Additionally or alternatively, the cardiovascular health can include any one or more of a: blood pressure metric (e.g., instantaneous blood pressure, blood pressure variability, etc.), […] etc.), pulse rate metric (e.g., instantaneous pulse rate, pulse rate variability, etc.), […] measures of blood vessel stiffness, […] and/or any other suitable metric relating to cardiovascular health.” [0031] “The computing system can be implemented in one or more of a processing module of the mobile communication device, a personal computer, a remote server, a cloud-based computing system, a computing module of any other suitable computing device (e.g., mobile computing device, wearable computing device, etc.), and any other suitable computing module.”)
and ii) assist in recognizing a future occurrence of the event
in the particular patient, the event including a dehydration event, an arrhythmia event, an exercise event, or an acute hypertension event ([0048] “passive data (e.g., unobtrusively collected data) and active data (e.g., survey data) that can be taken as inputs in Block S130 to generate analyses pertaining to present, past, and/or future cardiovascular states of a patient.” [0017] “In particular, the method 100 can be used to monitor and/or treat cardiovascular disease patients who are suffering from and/or at-risk for any one or more of: rheumatic heart disease, hypertensive heart disease, coronary artery disease, congestive heart failure, cerebrovascular disease, inflammatory heart disease, cardiomyopathy, heart arrhythmia, congenital heart disease, valvular heart disease, carditis, aortic aneurysms, peripheral artery disease, venous thrombosis, myocardial infarction, angina, aneurysm, hypertension, atherosclerosis, stroke, transient ischemic attacks, pericardial disease, and/or any other suitable cardiovascular condition.”)
analyzing, using the modeling and simulation computing device, the updated patient-specific model to generate an adjusted operating parameter ([0089] “[…] wherein the predictive model component implements an aggregated learning approach based upon multiple individual models (e.g., each assessing different parameters and/or different time periods of patient behavior).” [0090] “The analyses of Block S130 can, however, include generation of any other suitable comparison and/or any other suitable output which serve as parameters of the cardiovascular health state of the individual.”)
Moturu does not explicitly disclose however Wang teaches the at least one remote monitoring data source including a ventricular assist device (VAD) […] implanted in the particular patient ([pg. 1137, Col. 1] “The LVAD is a rotary mechanical pump surgically implanted from the left ventricle to the aorta as a bridge to help maintain the flow of blood from the patient’s heart.”)
controlling operation of the VAD that provided the remote monitoring data by transmitting a control signal including the adjusted operating parameter to the VAD ([pg. 1139, Col. 1] “Since changing SVR could cause obvious change in the mean pump flow if the values of other parameters in the cardiovascular-LVAD model are unchanged, therefore, the system will receive a signal as a response to change in the mean pump flow.”)
the control signal causing the VAD to adjust operation and begin operating at the adjusted operating parameter ([pg. 1141, Col. 1] “Here again, clearly in Fig. 6 the current was properly adjusted up to meet the patient’s blood flow demand while at the same time it did not exceed the suction level for the entire duration of the simulation.” [pg. 1142, Col. 1] “Adaptability means that the control system could automatically adjust PC according to the level of activity of the patient.”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Moturu data from a VAD implanted in a patient, controlling operation of the VAD by transmitting a control signal, and the VAD operation being adjusted to operate at the adjusted parameter as taught by Wang since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Moturu in view of Wang does not explicitly disclose however Mansi teaches
the patient-specific model represents a circulatory system of a particular patient ([0075] “In an embodiment of the present invention, a simplification of a circulatory model is used to model the atrium boundary conditions.”)
wherein updating the patient-specific model comprises varying parameters of the patient-specific model until the patient-specific model replicates the actual hemodynamic data for the particular patient ([0035] “The spatial information is mapped onto the tetrahedral mesh representing the bi-ventricular myocardium. This information is important to simulate the electrical wave around scars, in particular for wave-reentry assessment and correctly capture impaired cardiac mechanics due to ill-functioning or dead cells.” [0072] “The four cardiac phases of filling, isovolumetric contraction, ejection and isovolumetric relaxation are simulated by alternating the boundary conditions of the model according to the following rules. […] A pressure equal to the atrial pressure is applied to the endocardial surface to mimic the active filling.”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Moturu and Wang incorporating a personalized model that replicates the actual hemodynamic data of the patient when the parameters are varied as taught by Mansi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Moturu in view of Wang and Mansi does not explicitly disclose however Engels teaches the at least one remote monitoring data source including […] a cardiac resynchronization therapy (CRT) device implanted in the particular patient ([0111] “All participants underwent routine CRT-defibrillator implantation; all with a quadripolar LV lead.” [0104] “During CRT device implant procedures of 28 patients, haemodynamic measurements and 12-lead ECG recordings were performed during various AV-delays. In addition, unipolar electrograms were recorded from the implanted electrodes. Optimal haemodynamic response was defined as either the largest increase in LV systolic pressure (LVPsyst) or the largest increase in the maximal rate of LV pressure rise (LV dP/dtmax).”)
updating, using the modeling and simulation computing device, a patient-specific model using the remote monitoring data received from […] the CRT device ([0015] “by one or more processors” [0131] “To derive the data presented in FIGS. 12A-12D, the acute haemodynamic response to CRT was assessed by invasive LV pressure measurements in groups of patients receiving CRT according to one of the methods A-RVvis, A-RVVCG, A-RVaCRT, and A-QRSonset.” [0133] “Using this A-RVVCG the adaptive CRT algorithm can be individualized even further, leading to a possible improvement in hemodynamic response.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Moturu, Wang, and Mansi data from a CRT device to update a patient-specific model as taught by Engels since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 22, Moturu discloses displaying a recommendation including the adjusted operating parameter ([0096] “In a variation of Block S150, a mobile computing device of a patient can download and subsequently display the notification for the patient at a display of the mobile computing device.” [0110] “A treatment recommendation can include any one or more of a medication recommendation (e.g., beta blockers, lipid-lowering agents, alpha-2 adrenergic agonists, nitroglycerin, etc.), medical procedure recommendations (e.g., heart surgery, angioplasty, atherectomy, cardiomyoplasty, transplant, ablation, stent, revascularization, etc.), and/or any other suitable medical recommendation. Generating a treatment recommendation can be derived from at least one of a passive dataset, an active dataset, and an output of a cardiovascular health predictive model.”)
Note: the treatment/medication recommendations are adjusted operating parameters in light of [0033] of the applicant’s specification.
Regarding claim 23, Moturu discloses building the patient-specific model using the modeling and simulation computing device ([0025] “As shown in FIGS. 1, 2, and 4, a method 100 for evaluating cardiovascular health of a patient includes receiving a log of use dataset associated with patient digital communication behavior at a mobile computing device,…..generating a cardiovascular health predictive model based upon at least one of the log of use dataset…”)
Regarding claim 24, Moturu discloses generating a database including a plurality of anonymized patient models ([0139] “The processing system 205 and data handling by the modules of the processing system 205 are preferably adherent to health-related privacy laws (e.g., HIPAA), and are preferably configured to privatize and/or or anonymize patient data according to encryption protocols…. Furthermore, data processed or produced by modules of the system 200 can be configured to facilitate storage of data locally (e.g., on the patent's mobile computing device, in a remote database), or in any other suitable manner.” [0068] “As shown in FIGS. 1-2 and 4, extracting a cardiovascular health metric can optionally include generating a cardiovascular health predictive model S140, which functions to leverage one or more predictive models with one or more datasets of the method 100 in generating one or more cardiovascular health metrics.”)
Moturu in view of Wang does not explicitly disclose however Mansi teaches and selecting one of the plurality of anonymized patient models as the patient-specific model ([0037] “Numerous electrophysiological models have been proposed, dealing with different biological scales and theoretical complexity. These models can be organized into three different categories: biophysical, phenomenological and Eikonal. [0041] “The model implemented in an advantageous embodiment of the present invention considers inflow and outflow currents across the cell membranes as two lumped variables. […] This model is referred to herein as the “Mitchell-Schaeffer” model or “M-S” model.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Moturu and Wang selection of one of the plurality of anonymized patient models as the patient-specific model as taught by Mansi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 25, Moturu discloses receiving, at the modeling and simulation computing device, clinical data from at least one clinical data source ([0042] “Additionally or alternatively, Block S125 can include receiving clinical data (e.g., information gathered in a clinic or laboratory setting by a clinician).”)
updating the patient-specific model using the clinical data ([0134] “Additionally or alternatively, a predictive model can be updated with additional supplementary data (e.g., supplemental data collected subsequent to provision of a therapeutic intervention)…”)
Regarding claim 26, Moturu discloses wherein analyzing the updated patient-specific model comprises analyzing the updated patient-specific model using machine learning ([0069] “Regarding Block S140, a cardiovascular health predictive model preferably uses one or more machine learning techniques and training data (e.g., from the patient, from a population of patients) […]”)
Regarding claim 33, Moturu discloses non-transitory computer-readable media having computer-executable instructions thereon, wherein when executed by a processor of a computing device ([0140] “The computer-readable medium can be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device. The computer-executable component can be a processor, though any suitable dedicated hardware device can (alternatively or additionally) execute the instructions.”)
receive remote monitoring data from at least one remote monitoring data source ([0021] “The technology can continuously collect and utilize datasets unique to internet-enabled mobile computing devices (e.g., social network usage, text messaging characteristics, application usage, patient response monitoring data facilitated through the mobile computing device, etc.)…”)
the remote monitoring data including at least actual hemodynamic data for the particular patient ([0051] “Additionally or alternatively, the cardiovascular health can include any one or more of a: blood pressure metric (e.g., instantaneous blood pressure, blood pressure variability, etc.), measures indicative of atherosclerosis or other cardiovascular disease, heartbeat metric (e.g., instantaneous heart rate, heart rate variability, average heart rate, resting heart rate, heartbeat signature, etc.), pulse rate metric (e.g., instantaneous pulse rate, pulse rate variability, etc.).”)
update a patient-specific model using the remote monitoring data received from the VAD […] ([0034] “A cardiovascular therapy device can include one or more of a […] ventricular assist devices” [0134] “Block S180 preferably includes updating one or more predictive models with an additional log of use dataset (e.g., a log of used dataset associated with a time period subsequent to generating an initial predictive model).”)
perform at least one simulation on the updated patient-specific model to simulate operation of the circulatory system of the particular patient during an event to i) predict an expected outcome of the event in the particular patient ([0050] “As shown in FIGS. 1-4, Block S130 recites: extracting a cardiovascular health metric from at least one of an output of the cardiovascular health predictive model, the log of use dataset, the supplementary dataset, and the survey dataset. […] Block S130 can additionally or alternatively include generating a predictive model S140. Block S130 thus enables assessment of a past or current cardiovascular of the patient and/or predicts risk that the patient will trend toward a different (e.g., worsened, improved, etc.) cardiovascular state at a future time point.” [0051] “Additionally or alternatively, the cardiovascular health can include any one or more of a: blood pressure metric (e.g., instantaneous blood pressure, blood pressure variability, etc.), […] etc.), pulse rate metric (e.g., instantaneous pulse rate, pulse rate variability, etc.), […] measures of blood vessel stiffness, […] and/or any other suitable metric relating to cardiovascular health.” [0031] “The computing system can be implemented in one or more of a processing module of the mobile communication device, a personal computer, a remote server, a cloud-based computing system, a computing module of any other suitable computing device (e.g., mobile computing device, wearable computing device, etc.), and any other suitable computing module.”)
and ii) assist in recognizing a future occurrence of the event in the particular patient, the event including a dehydration event, an arrhythmia event, an exercise event, or an acute hypertension event ([0048] “passive data (e.g., unobtrusively collected data) and active data (e.g., survey data) that can be taken as inputs in Block S130 to generate analyses pertaining to present, past, and/or future cardiovascular states of a patient.” [0017] “In particular, the method 100 can be used to monitor and/or treat cardiovascular disease patients who are suffering from and/or at-risk for any one or more of: rheumatic heart disease, hypertensive heart disease, coronary artery disease, congestive heart failure, cerebrovascular disease, inflammatory heart disease, cardiomyopathy, heart arrhythmia, congenital heart disease, valvular heart disease, carditis, aortic aneurysms, peripheral artery disease, venous thrombosis, myocardial infarction, angina, aneurysm, hypertension, atherosclerosis, stroke, transient ischemic attacks, pericardial disease, and/or any other suitable cardiovascular condition.”)
analyze the updated patient-specific model to generate an adjusted operating parameter ([0089] “[…] wherein the predictive model component implements an aggregated learning approach based upon multiple individual models (e.g., each assessing different parameters and/or different time periods of patient behavior).” [0090] “The analyses of Block S130 can, however, include generation of any other suitable comparison and/or any other suitable output which serve as parameters of the cardiovascular health state of the individual.”)
Moturu does not explicitly disclose however Wang teaches the at least one remote monitoring data source including a ventricular assist device (VAD) […] implanted in the particular patient ([pg. 1137, Col. 1] “The LVAD is a rotary mechanical pump surgically implanted from the left ventricle to the aorta as a bridge to help maintain the flow of blood from the patient’s heart.”)
control operation of the VAD that provided the remote monitoring data by transmitting a control signal including the adjusted operating parameter to the VAD ([pg. 1139, Col. 1] “Since changing SVR could cause obvious change in the mean pump flow if the values of other parameters in the cardiovascular-LVAD model are unchanged, therefore, the system will receive a signal as a response to change in the mean pump flow.”)
the control signal causing the VAD source to adjust operation and begin operating at the adjusted operating parameter ([pg. 1141, Col. 1] “Here again, clearly in Fig. 6 the current was properly adjusted up to meet the patient’s blood flow demand while at the same time it did not exceed the suction level for the entire duration of the simulation.” [pg. 1142, Col. 1] “Adaptability means that the control system could automatically adjust PC according to the level of activity of the patient.”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Moturu data from a VAD implanted in a patient, controlling operation of the VAD by transmitting a control signal, and the VAD operation being adjusted to operate at the adjusted parameter as taught by Wang since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Moturu in view of Wang does not explicitly disclose however Mansi teaches
the patient-specific model represents a circulatory system of a particular patient ([0075] “In an embodiment of the present invention, a simplification of a circulatory model is used to model the atrium boundary conditions.”)
the instructions cause the process to vary parameters of the patient-specific model until the patient-specific model replicates the actual hemodynamic data for the particular patient ([0035] “The spatial information is mapped onto the tetrahedral mesh representing the bi-ventricular myocardium. This information is important to simulate the electrical wave around scars, in particular for wave-reentry assessment and correctly capture impaired cardiac mechanics due to ill-functioning or dead cells.” [0072] “The four cardiac phases of filling, isovolumetric contraction, ejection and isovolumetric relaxation are simulated by alternating the boundary conditions of the model according to the following rules. […] A pressure equal to the atrial pressure is applied to the endocardial surface to mimic the active filling.”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Moturu and Wang incorporating a personalized model that replicates the actual hemodynamic data of the patient when the parameters are varied as taught by Mansi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Moturu in view of Wang and Mansi does not explicitly disclose however Engels teaches the at least one remote monitoring data source including […] a cardiac resynchronization therapy (CRT) device implanted in the particular patient ([0111] “All participants underwent routine CRT-defibrillator implantation; all with a quadripolar LV lead.” [0104] “During CRT device implant procedures of 28 patients, haemodynamic measurements and 12-lead ECG recordings were performed during various AV-delays. In addition, unipolar electrograms were recorded from the implanted electrodes. Optimal haemodynamic response was defined as either the largest increase in LV systolic pressure (LVPsyst) or the largest increase in the maximal rate of LV pressure rise (LV dP/dtmax).”)
update a patient-specific model using the remote monitoring data received from […] the CRT device ([0015] “by one or more processors” [0131] “To derive the data presented in FIGS. 12A-12D, the acute haemodynamic response to CRT was assessed by invasive LV pressure measurements in groups of patients receiving CRT according to one of the methods A-RVvis, A-RVVCG, A-RVaCRT, and A-QRSonset.” [0133] “Using this A-RVVCG the adaptive CRT algorithm can be individualized even further, leading to a possible improvement in hemodynamic response.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Moturu, Wang, and Mansi data from a CRT device to update a patient-specific model as taught by Engels since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 34, the limitations are rejected for the same reasons as stated above for clam 22.
Regarding claim 35, the limitations are rejected for the same reasons as stated above for clam 23.
Regarding claim 36, the limitations are rejected for the same reasons as stated above for clam 24.
Regarding claim 37, the limitations are rejected for the same reasons as stated above for clam 25.
Regarding claim 38, the limitations are rejected for the same reasons as stated above for clam 26.
Regarding claim 39, Moturu in view of Wang does not explicitly disclose however Mansi teaches wherein the parameters of the patient-specific model are selected from the group consisting of ventricle elastance curves, valve resistance, aortic compliance, ventricle dimensions, and pulmonary artery pressure ([0073] “The arterial pressure is modeled using a 3-element Windkessel model, which takes as input the arterial flow and returns the pressure within the artery at every time step of the simulation. […] The compliance C 1204 accounts for the elasticity of the arterial walls whereas the characteristic resistance Rc 1206 accounts for the blood mass” [0075] “Minimum and maximum elastance parameters enable the peak systolic and diastolic stiffness to be set, which then controls atrial pressure based on the current volume. A simple model of atrial activation, synchronized with the ventricular electrophysiology model through a time-shift parameter corresponding to the ECG PQ interval, enables controlling of the atrial volume.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Moturu and Wang parameters of the patient-specific model selected from the group consisting of ventricle elastance curves, valve resistance, aortic compliance, ventricle dimensions, and pulmonary artery pressure as taught by Mansi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 40, the limitations are rejected for the same reasons as stated above for clam 39.
No Prior Art Rejection – Allowable
Regarding claims 27-32, no prior art rejection is being presented at this time. The scope of the claim(s) has been clarified to additionally describe “the at least one simulation includes simulating removal of the VAD from the particular patient”, and “the control signal and adjusted operating parameter causing the VAD to periodically reduce pump speed.” These go beyond any teachings or suggestions in the art.
Response to Arguments
The arguments filed on 21 November 2025 have been considered, but are not fully persuasive.
Regarding the USC 103 rejection, applicant’s arguments with respect to the CRT device have been considered but are moot since they do not apply to the newly cited reference of record: Engels et al. Mansi still discloses the aspect of varying the parameters while the rejection for claims 27-32 with respect to Gillberg have been withdrawn.
Prior Art Cited but Not Relied Upon
The following document was found relevant to the disclosure but not applied:
Kilic, A., Macickova, J., Duan, L., Movahedi, F., Seese, L., Zhang, Y., ... & Padman, R. (2021). Machine learning approaches to analyzing adverse events following durable LVAD implantation. The Annals of Thoracic Surgery, 112(3), 770-777.
This reference is relevant since it discloses using machine learning to analyze LVADs.
Conclusion
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/WINSTON R FURTADO/Examiner, Art Unit 3687