DETAILED ACTION
Status of Claims
This action is in reply to the Request for Continued Examination filed on 12/04/2025.
Claims 1, 47, 93-94 and 176 have been amended.
Claims 4, 50, 190-191 and 194 have been cancelled.
Claim 201 has been newly added.
Claims 1-3, 5-49, 51-94, 176-189, 192-193 and 195-201 are currently pending and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/04/2025 has been entered.
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 of this title, 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 1-2, 32, 47-48, 78, 93-94, 195 and 197-201 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1).
Claim 1:
Ong discloses A system comprising:
a cardiac device comprising one or more sensors sensing physiological parameter data of a subject and a controller that controls an operation of the cardiac device, the cardiac device further configured (See Fig. 1, P0085, P0100-P0101 ECG machine and sensors. Also, see Fig. 2 physiological data 110 mentioned in P0093-P0094.) to:
receiving, by a cardiac device, physiological parameter data of a subject sensed by one or more sensors of the cardiac device (See obtain electrical activity readings of the patient's heart, interpreted into an ECG in P0100-P0101 acquired electrical signals from the attached electrodes.), and
extracting, by the cardiac device, a plurality of physiological measurements from the physiological parameter data of the subject (See extraction module to extract ECG parameter from ECG in P0006, P0103-P0104.);
a display device remote from the cardiac device, the display device displaying data associated with the physiological parameter data and the operation of the cardiac device (See display on the ECG 120 device mentioned in P0102, P0106-P0108. Also, see servers in P0092.); and
a non-transitory computer-readable storage medium in communication with one or more processors, the display device, and the cardiac device, the non-transitory computer-readable storage medium having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations (See P0101 an application program on a memory module for operation on a processor on the 12-lead ECG machine, and “module” in P0189.) comprising:
determining, at one or more processors in communication with the cardiac device, a first event estimation risk score for the subject for a first time period by applying a trained machine learning classifier on the extracted plurality of physiological measurements, wherein the machine learning classifier is trained on training data to output an event estimation risk score for the subject (Besides machine learning trained data based on expert classifier to determine an ensemble score or risk score 168 (Fig. 4A, P0154-P0156), see machine learning trained data based on datasets (Fig. 4B, P0159, P0162-P0163).), wherein the first time period spans a current time to a first future time, wherein the training data comprises at least one of cardiac electrophysiologic metrics, demographic metrics and medical history metrics for one or more subjects (See P0056-P0058 input parameters such as medical history, a drug history, a smoking history, a family history of heart disease and electrocardiography procedure of at least 5 minutes, exemplary time domain measures in P0138-P0140 and predicted complications such as all-cause mortality, cardiac arrest, sustained ventricular tachycardia (VT) and hypotension in P0212-P0213 as a dataset of K samples in score prediction process repeated K times so that each sample can be tested individually.);
determining, by the one or more processors, a second event estimation risk score for the subject for a second time period by applying the trained machine learning classifier on the extracted plurality of physiological measurements, wherein the second time period spans a second future time to a third future time (Taught in P0049-P0051 as calculated risk score determined from physiological data, extracted ECG parameter from the ECG and the HRV parameter analysis of a patient. Also, see prediction accuracies when building classification models in P0072-P0075 and [P0147-P0148] the ensemble-based scoring system 160 is provided where an intention is to provide an intelligent scoring system in combining HRV and 12-lead ECG parameters and vital signs to predict acute cardiac complications within 72 hours among critically ill patients presented with chest pain.);
wherein the first event estimation risk score and the second event estimation risk score indicate a probability of a potential adverse cardiac event to occur within the respective time period and a criticality of the potential adverse cardiac event indicative of a severity of the potential adverse cardiac event (See P0056-P0058 input parameters as events in the past 24 hours and 5 minutes, P0130-P01032 Atrial fibrillation (AF) episodes lasting minutes to days Also, See P0147-P0148 as predicting acute cardiac complications within 72 hours.); and
determining, by the one or more processors, a change in the event estimation risk score between the first event estimation risk score and the second event estimation risk score by determining a difference between the event estimation risk of the first time period and the event estimation risk of the second time period (See applying the ensemble-based scoring (P0026, P0028, P0050-P0051) to learning and training what compared parameters contribute to a higher occurrence or severity of cardiac events.).
Although Ong discloses the system and method including a cardio device for extracting displayed physiological parameters mentioned above, Ong does not explicitly teach determining the change in the event estimation risk score between the future time periods, causing a display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods and a change in the controller to adjust operational parameters of the cardiac device based on triggered the event estimation risk score and/or the criticality of the potential adverse cardiac event. An teaches comprising:
in response to determining the change in the event estimation risk score between the plurality of future time periods, causing a display device remote from the cardiac device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods, wherein the risk prediction forecast comprises the first event estimation risk score for the first time period and the second event estimation risk score for the second time period (See exemplary signal trends and systolic timing interval in P0048-P0050, P0070 when sensitive or specific to pulmonary events. See P0036, P0038 and [P0044] a probability value indicative of a likelihood of the patient later developing a precursor physiologic event such as a pulmonary or a cardiac event, a central or a peripheral congestion event, or other events indicative of worsening of HF. Also, see risk indices and indicators in P0027, P0038 as composite risk indicator (CRI) and elevated CRI in P0038-P0040 where the prediction fusion circuit 230 reports, warns and alerts of heart failure risk via interactive user interface.); and
in response to determining the change in the event estimation risk score and/or the criticality of the potential adverse cardiac event triggering a change in a controller to adjust one or more operational parameters of the cardiac device (See Fig. 1, [P0028] The external system 120 can include a remote patient management system that can monitor patient status or send commands to the IMD 110 such as to program diagnostic functions or to adjust one or more therapies such as from a remote location.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting heart failure before the effective filing date of the invention to modify the system and method of Ong to have determining the change in the event estimation risk score between the future time periods, causing a display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods and a change in the controller to adjust operational parameters of the cardiac device based on triggered the event estimation risk score and/or the criticality of the potential adverse cardiac event, as taught by, to help ensure timely treatment, thereby improving the prognosis and patient outcome mentioned in An’s P0005.
Claim 47:
Ong discloses A method comprising:
a cardiac device comprising one or more sensors sensing physiological parameter data of a subject and a controller that controls an operation of the cardiac device, the cardiac device further configured (See Fig. 1, P0085, P0100-P0101 ECG machine and sensors. Also, see Fig. 2 physiological data 110 mentioned in P0093-P0094.) to:
receive, from the one or more sensors, at least one signal comprising the physiological parameter data of the subject (See obtain electrical activity readings of the patient's heart, interpreted into an ECG in P0100-P0101 acquired electrical signals from the attached electrodes.), and
extract a plurality of physiological measurements from the physiological parameter data of the subject (See extraction module to extract ECG parameter from the ECG in P0006, P0103-P0104.);
a display device remote from the cardiac device, the display device displaying data associated with the physiological parameter data and the operation of the cardiac device (See display on the ECG 120 device mentioned in P0102, P0106-P0108. Also, see servers in P0092.); and
a non-transitory computer-readable storage medium in communication with one or more processors, the display device, and the cardiac device, the non-transitory computer-readable storage medium having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations (See P0101 an application program on a memory module for operation on a processor on the 12-lead ECG machine, and “module” in P0189.) comprising:
determining a first event estimation risk score for the subject for a first time period by applying a trained machine learning classifier on the extracted plurality of physiological measurements, wherein the machine learning classifier is trained on training data to output an event estimation risk score for the subject (Besides machine learning trained data based on expert classifier to determine an ensemble score or risk score 168 (Fig. 4A, P0154-P0156), see machine learning trained data based on datasets (Fig. 4B, P0159, P0162-P0163).), wherein the first time period spans a current time to a first future time, wherein the training data comprises at least one of cardiac electrophysiologic metrics, demographic metrics and medical history metrics for one or more subjects (See P0056-P0058 input parameters such as medical history, a drug history, a smoking history, a family history of heart disease and electrocardiography procedure of at least 5 minutes, exemplary time domain measures in P0138-P0140 and predicted complications such as all-cause mortality, cardiac arrest, sustained ventricular tachycardia (VT) and hypotension in P0212-P0213 as a dataset of K samples in score prediction process repeated K times so that each sample can be tested individually.);
determining a second event estimation risk score for the subject for a second time period by applying the trained machine learning classifier on the extracted plurality of physiological measurements, wherein the second time period spans a second future time to a third future time (Taught in P0049-P0051 as calculated risk score determined from physiological data, extracted ECG parameter from the ECG and the HRV parameter analysis of a patient. Also, see prediction accuracies when building classification models in P0072-P0075 and [P0147-P0148] the ensemble-based scoring system 160 is provided where an intention is to provide an intelligent scoring system in combining HRV and 12-lead ECG parameters and vital signs to predict acute cardiac complications within 72 hours among critically ill patients presented with chest pain.);
wherein the first event estimation risk score and the second event estimation risk score indicate a probability of a potential adverse cardiac event to occur within the respective time period and a criticality of the potential adverse cardiac event indicative of a severity of the potential adverse cardiac event (See P0056-P0058 input parameters as events in the past 24 hours and 5 minutes, P0130-P01032 Atrial fibrillation (AF) episodes lasting minutes to days Also, See P0147-P0148 as predicting acute cardiac complications within 72 hours.); and
determining a change in the event estimation risk score between the first event estimation risk score and the second event estimation risk score by determining a difference between the event estimation risk of the first time period and the event estimation risk of the second time period (See applying the ensemble-based scoring (P0026, P0028, P0050-P0051) to learning and training what compared parameters contribute to a higher occurrence or severity of cardiac events.);
Although Ong discloses the system and method including a cardio device for extracting displayed physiological parameters mentioned above, Ong does not explicitly teach determining the change in the event estimation risk score between the future time periods, causing a display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods and a change in the controller to adjust operational parameters of the cardiac device based on triggered the event estimation risk score and/or the criticality of the potential adverse cardiac event. An teaches comprising:
in response to determining the change in the event estimation risk score between the plurality of future time periods, causing the display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods, wherein the risk prediction forecast comprises the first event estimation risk score for the first time period and the second event estimation risk score for the second time period (See exemplary signal trends and systolic timing interval in P0048-P0050, P0070 when sensitive or specific to pulmonary events. See P0036, P0038 and [P0044] a probability value indicative of a likelihood of the patient later developing a precursor physiologic event such as a pulmonary or a cardiac event, a central or a peripheral congestion event, or other events indicative of worsening of HF. Also, see risk indices and indicators in P0027, P0038 as composite risk indicator (CRI) and elevated CRI in P0038-P0040 where the prediction fusion circuit 230 reports, warns and alerts of heart failure risk via interactive user interface.);
in response to determining the change in the event estimation risk score and/or the criticality of the potential adverse cardiac event triggering a change in the controller to adjust one or more operational parameters of the cardiac device (See Fig. 1, [P0028] The external system 120 can include a remote patient management system that can monitor patient status or send commands to the IMD 110 such as to program diagnostic functions or to adjust one or more therapies such as from a remote location.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting heart failure before the effective filing date of the invention to modify the system and method of Ong to have determining the change in the event estimation risk score between the future time periods, causing a display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods and a change in the controller to adjust operational parameters of the cardiac device based on triggered the event estimation risk score and/or the criticality of the potential adverse cardiac event, as taught by, to help ensure timely treatment, thereby improving the prognosis and patient outcome mentioned in An’s P0005.
Claim 93:
Ong discloses A system comprising:
a cardiac device comprising one or more sensors configured to sense physiological parameter data of a subject, the cardiac device further configured (See Fig. 1, P0085, P0100-P0101 ECG machine and sensors. Also, see Fig. 2 physiological data 110 mentioned in P0093-P0094.) to:
receive, from the one or more sensors, at least one signal comprising the physiological parameter data of the subject (See obtain electrical activity readings of the patient's heart, interpreted into an ECG in P0100-P0101 acquired electrical signals from the attached electrodes.), and
extract a plurality of physiological measurements from the physiological parameter data of the subject (See extraction module to extract ECG parameter from ECG in P0006, P0103-P0104.);
a display device remote from the cardiac device (See display on the ECG 120 device mentioned in P0102, P0106-P0108. Also, see servers in P0092.); and
at least one non-transitory computer-readable storage medium in communication with at least one processor, the display device, and the cardiac device, the at least one non-transitory computer- readable storage medium having instructions stored thereon which, when executed by the at least one processor, cause the at least one processor to perform operations (See P0101 an application program on a memory module for operation on a processor on the 12-lead ECG machine, and “module” in P0189.) comprising:
determining a first event estimation risk score for the subject for a first time period by applying a trained machine learning classifier on the extracted plurality of physiological measurements, wherein the machine learning classifier is trained on training data to output an event estimation risk score for the subject, wherein the first time period spans a current time to a first future time (Besides machine learning trained data based on expert classifier to determine an ensemble score or risk score 168 (Fig. 4A, P0154-P0156), see machine learning trained data based on datasets (Fig. 4B, P0159, P0162-P0163).), wherein the training data comprises at least one of cardiac electrophysiologic metrics, demographic metrics and medical history metrics for one or more subjects(See P0056-P0058 input parameters such as medical history, a drug history, a smoking history, a family history of heart disease and electrocardiography procedure of at least 5 minutes, exemplary time domain measures in P0138-P0140 and predicted complications such as all-cause mortality, cardiac arrest, sustained ventricular tachycardia (VT) and hypotension in P0212-P0213 as a dataset of K samples in score prediction process repeated K times so that each sample can be tested individually.), wherein the trained machine learning classifier is configured to output one or more values that are compared to one or more predetermined thresholds to generate the event estimation risk score (See P0151-P0152 where minimum and maximum values or mapped values serve as one or more values that are compared to predetermined thresholds to generate the event estimation risk score. Also, see weightage value of the classifier in P0162-P0163.);
determining a second event estimation risk score for the subject for a second time period by applying the trained machine learning classifier on the extracted plurality of physiological measurements, wherein the second time period spans a second future time to a third future time (Taught in P0049-P0051 as calculated risk score determined from physiological data, extracted ECG parameter from the ECG and the HRV parameter analysis of a patient. Also, see prediction accuracies when building classification models in P0072-P0075 and [P0147-P0148] the ensemble-based scoring system 160 is provided where an intention is to provide an intelligent scoring system in combining HRV and 12-lead ECG parameters and vital signs to predict acute cardiac complications within 72 hours among critically ill patients presented with chest pain.);
wherein the first event estimation risk score and the second event estimation risk score indicate a probability of a potential adverse cardiac event to occur within the respective time period and a criticality of the potential adverse cardiac event indicative of a severity of the potential adverse cardiac event (See P0056-P0058 input parameters as events in the past 24 hours and 5 minutes, P0130-P01032 Atrial fibrillation (AF) episodes lasting minutes to days Also, See P0147-P0148 as predicting acute cardiac complications within 72 hours.); and
determining a change in the event estimation risk score between the first event estimation risk score and the second event estimation risk score by determining a difference between the event estimation risk of the first time period and the event estimation risk of the second time period (See applying the ensemble-based scoring (P0026, P0028, P0050-P0051) to learning and training what compared parameters contribute to a higher occurrence or severity of cardiac events.).
Although Ong discloses the system and method including a cardio device for extracting displayed physiological parameters mentioned above, Ong does not explicitly teach determining the change in the event estimation risk score between the future time periods, causing a display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods and a change in the controller to adjust operational parameters of the cardiac device based on triggered the event estimation risk score and/or the criticality of the potential adverse cardiac event. An teaches comprising:
in response to determining the change in the event estimation risk score between the plurality of future time periods, causing the display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods, wherein the risk prediction forecast comprises the first event estimation risk score for the first time period and the second event estimation risk score for the second time period (See exemplary signal trends and systolic timing interval in P0048-P0050, P0070 when sensitive or specific to pulmonary events. See P0036, P0038 and [P0044] a probability value indicative of a likelihood of the patient later developing a precursor physiologic event such as a pulmonary or a cardiac event, a central or a peripheral congestion event, or other events indicative of worsening of HF. Also, see risk indices and indicators in P0027, P0038 as composite risk indicator (CRI) and elevated CRI in P0038-P0040 where the prediction fusion circuit 230 reports, warns and alerts of heart failure risk via interactive user interface.); and
in response to determining that the change in the event estimation risk score and/or the criticality of the potential adverse cardiac event triggering a change in a controller to adjust one or more operational parameters of the cardiac device (See Fig. 1, [P0028] The external system 120 can include a remote patient management system that can monitor patient status or send commands to the IMD 110 such as to program diagnostic functions or to adjust one or more therapies such as from a remote location.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting heart failure before the effective filing date of the invention to modify the system and method of Ong to have determining the change in the event estimation risk score between the future time periods, causing a display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods and a change in the controller to adjust operational parameters of the cardiac device based on triggered the event estimation risk score and/or the criticality of the potential adverse cardiac event, as taught by, to help ensure timely treatment, thereby improving the prognosis and patient outcome mentioned in An’s P0005.
Claim 94:
Ong discloses a system comprising:
a cardiac device comprising one or more sensors configured to sense physiological parameter data of a subject and extract a plurality of physiological measurements from the physiological parameter data for the subject (See Fig. 1, P0085, P0100-P0101 ECG machine and sensors. Also, see Fig. 2 physiological data 110 mentioned in P0093-P0094. See extraction module to extract ECG parameter from ECG in P0006, P0103-P0104.);
a display device (See display on the ECG 120 device mentioned in P0102, P0106-P0108. Also, see servers in P0092.); and
at least one non-transitory computer-readable storage medium in communication with at least one processor, the display device, and the cardiac device, the at least one non-transitory computer- readable storage medium having instructions stored thereon which, when executed by the at least one processor, cause the at least one processor to perform operations (See P0101 an application program on a memory module for operation on a processor on the 12-lead ECG machine, and “module” in P0189.) comprising:
determining a first event estimation risk score for the subject for a first time period by applying a trained machine learning classifier on the extracted plurality of physiological 18measurements, wherein the machine learning classifier is trained on training data to output an event estimation risk score for the subject, wherein the first time period spans a current time to a first future time (Besides machine learning trained data based on expert classifier to determine an ensemble score or risk score 168 (Fig. 4A, P0154-P0156), see machine learning trained data based on datasets (Fig. 4B, P0159, P0162-P0163).), wherein the training data comprises at least one of cardiac electrophysiologic metrics, demographic metrics and medical history metrics for one or more subjects (See P0056-P0058 input parameters such as medical history, a drug history, a smoking history, a family history of heart disease and electrocardiography procedure of at least 5 minutes, exemplary time domain measures in P0138-P0140 and predicted complications such as all-cause mortality, cardiac arrest, sustained ventricular tachycardia (VT) and hypotension in P0212-P0213 as a dataset of K samples in score prediction process repeated K times so that each sample can be tested individually.), and the trained machine learning classifier is configured to output one or more values that are compared to one or more predetermined thresholds to generate the event estimation risk score (See P0151-P0152 where minimum and maximum values or mapped values serve as one or more values that are compared to predetermined thresholds to generate the event estimation risk score. Also, see weightage value of the classifier in P0162-P0163.);
determining a second event estimation risk score for the subject for a second time period by applying the trained machine learning classifier on the extracted plurality of physiological measurements, wherein the second time period spans a second future time to a third future time (Taught in P0049-P0051 as calculated risk score determined from physiological data, extracted ECG parameter from the ECG and the HRV parameter analysis of a patient. Also, see prediction accuracies when building classification models in P0072-P0075 and [P0147-P0148] the ensemble-based scoring system 160 is provided where an intention is to provide an intelligent scoring system in combining HRV and 12-lead ECG parameters and vital signs to predict acute cardiac complications within 72 hours among critically ill patients presented with chest pain.);
wherein the first event estimation risk score and the second event estimation risk score indicate a probability of a potential adverse cardiac event to occur within the respective time period and a criticality of the potential adverse cardiac event indicative of a severity of the potential adverse cardiac event (See P0056-P0058 input parameters as events in the past 24 hours and 5 minutes, P0130-P01032 Atrial fibrillation (AF) episodes lasting minutes to days Also, See P0147-P0148 as predicting acute cardiac complications within 72 hours.); and
determining a change in the event estimation risk score between the first event estimation risk score and the second event estimation risk score by determining a difference between the event estimation risk of the first time period and the event estimation risk of the second time period (See applying the ensemble-based scoring (P0026, P0028, P0050-P0051) to learning and training what compared parameters contribute to a higher occurrence or severity of cardiac events.).
Although Ong discloses the system and method including a cardio device for extracting displayed physiological parameters mentioned above, Ong does not explicitly teach determining the change in the event estimation risk score between the future time periods, causing a display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods and a change in the controller to adjust operational parameters of the cardiac device based on triggered the event estimation risk score and/or the criticality of the potential adverse cardiac event. An teaches comprising:
in response to determining the change in the event estimation risk score between the plurality of future time periods, causing the display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods, wherein the risk prediction forecast comprises the first event estimation risk score for the first time period and the second event estimation risk score for the second time period (See exemplary signal trends and systolic timing interval in P0048-P0050, P0070 when sensitive or specific to pulmonary events. See P0036, P0038 and [P0044] a probability value indicative of a likelihood of the patient later developing a precursor physiologic event such as a pulmonary or a cardiac event, a central or a peripheral congestion event, or other events indicative of worsening of HF. Also, see risk indices and indicators in P0027, P0038 as composite risk indicator (CRI) and elevated CRI in P0038-P0040 where the prediction fusion circuit 230 reports, warns and alerts of heart failure risk via interactive user interface.); and
in response to determining the change in the event estimation risk score and/or criticality of the potential adverse cardiac event triggering a change in a controller to adjust one or more operational parameters of the cardiac device (See Fig. 1, [P0028] The external system 120 can include a remote patient management system that can monitor patient status or send commands to the IMD 110 such as to program diagnostic functions or to adjust one or more therapies such as from a remote location.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting heart failure before the effective filing date of the invention to modify the system and method of Ong to have determining the change in the event estimation risk score between the future time periods, causing a display device to display within a graphical user interface a risk prediction forecast for the plurality of future time periods and a change in the controller to adjust operational parameters of the cardiac device based on triggered the event estimation risk score and/or the criticality of the potential adverse cardiac event, as taught by, to help ensure timely treatment, thereby improving the prognosis and patient outcome mentioned in An’s P0005.
Claims 2 and 48:
Ong discloses wherein the physiological parameter data comprises ECG data (See Fig. 1, P0085, P0100-P0101 ECG machine and sensors.).
Claim 32:
Ong discloses wherein the medical devices comprises a wearable medical device, wherein the one or more sensors comprise a plurality of ECG sensors, wherein the physiological parameter data of the subject comprises ECG data, and wherein the potential medical event comprises a cardiac event (See Fig. 1, P0085, P0100-P0101 ECG machine and sensors. Also, see Fig. 2 physiological data 110 mentioned in P0093-P0094.).
Claim 78:
Ong discloses wherein the medical devices comprises a wearable medical device, wherein the one or more sensors comprise a plurality of ECG sensors, wherein the physiological parameter data of the subject comprises ECG data (See Fig. 1, P0085, P0100-P0101 ECG machine and sensors.).
Claim 195:
Ong discloses wherein the machine learning classifier model is trained on training metrics comprising at least one of cardiac electrophysiology metrics (Besides machine learning trained data based on expert classifier to determine an ensemble score or risk score 168 (Fig. 4A, P0154-P0156), see machine learning trained data based on datasets (Fig. 4B, P0159, P0162-P0163).), of a plurality of subjects, and/or at least one of demographic metrics and medical history metrics of the plurality of subjects (See P0056-P0058 input parameters such as medical history, a drug history, a smoking history, a family history of heart disease and electrocardiography procedure of at least 5 minutes, exemplary time domain measures in P0138-P0140.).
Regarding Claim 197, Ong discloses the system of claim 1, wherein the event estimation risk score is continuously updated (See P0056, P0089-P0090 where medical status of patient includes risk scoring.).
Regarding Claim 201, Ong discloses the system of claim 1, wherein the trained machine learning classifier is configured to output one or more values that are compared to one or more predetermined thresholds to generate the event estimation risk score (See P0151-P0152 where minimum and maximum values or mapped values serve as one or more values that are compared to predetermined thresholds to generate the event estimation risk score. Also, see weightage value of the classifier in P0162-P0163.).
Regarding claim 198, Ong discloses the system of claim 1, wherein the trained machine learning classifier classifies the potential adverse cardiac event as being at least one of: a heart rate irregularity, QRS morphology and variability irregularity, and ST segment and T-wave morphology and variability irregularity (See P0052, P0109-P0110 , P0130-P0135 ST elevation, tracing QRS and irregular heartbeat.).
Regarding claim 199, Ong discloses the system of claim 1, wherein the trained machine learning classifier is configured to output one or more values that are compared to one or more predetermined thresholds to generate the event estimation risk scores of the risk prediction forecast (See P0151-P0152 where minimum and maximum values or mapped values serve as one or more values that are compared to predetermined thresholds to generate the event estimation risk score. Also, see weightage value of the classifier in P0162-P0163.).
Regarding claim 200, Ong discloses the system of claim 1 the system of claim 1, wherein the training data comprises cardiac electrophysiologic metrics, demographic metrics and medical history metrics for one or more subjects (See P0056-P0058 input parameters such as medical history, a drug history, a smoking history, a family history of heart disease and electrocardiography procedure of at least 5 minutes, exemplary time domain measures in P0138-P0140 and predicted complications such as all-cause mortality, cardiac arrest, sustained ventricular tachycardia (VT) and hypotension in P0212-P0213.).
Claims 192-193 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Luo (WO 2014/201515 A1).
Regarding claim 192 Luo teaches wherein the first future time of the first time period is the same as the second future time of the at least one second time period (See Fig. 2, page 22, line 19-32, where the predictive horizon 210 construes a second time period as a future time period.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical risk analysis before the effective filing date of the invention to modify the system of Ong and An to include future time periods comprises a first time period spanning a current time to a first future time, and at least one second time period spanning a second future time to a third future time, as taught by Luo, to administer preventative care and recognize whether a patient has chance of surviving.
Regarding claim 193 Luo teaches wherein, the second future time of the at least one second time period is subsequent to the first future time of the first time period (See Fig. 2, page 22, line 19-32, where the predictive horizon 210 construes a second time period subsequent to the first future time.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical risk analysis before the effective filing date of the invention to modify the system of Ong and An to include a second time period is subsequent to the first future time of the first time period, as taught by Luo, to administer preventative care and recognize whether a patient has chance of surviving.
Claims 31, 77, 177-178, 182 and 188-189 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of John (US 2008/0188763 A1).
Claims 31 and 77:
John teaches wherein the medical device comprises a defibrillator (Taught in P0004, P0062 and P0093.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac event management before the effective filing date of the invention to modify the system and method of Ong and An to have a defibrillator, as taught by John to better diagnose patients with cardiac disorders.
Claim 177:
Although Ong and An teach the graphical user interface mentioned above, John further teaches wherein further indicates output an external defibrillator to apply treatment to the subject (Taught in P0004, P0062 and P0093.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac event management before the effective filing date of the invention to modify the system and method of Ong and An to have a defibrillator, as taught by John to better diagnose patients with cardiac disorders.
Claim 178:
John further teaches wherein the output comprises an indication of a likelihood of risk associated to high risk or low risk (Taught as heart histogram data in high range in P0081, P0130.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac event management before the effective filing date of the invention to modify the system and method of Ong and An to have an indication of a likelihood of risk associated to high risk or low risk, as taught by John to better prioritize treating patients with more serious cardiac disorders.
Claim 182:
John further teaches wherein the operations comprise: combining at least a portion of the plurality of physiological measurements in a multivariate parameter signal (See physiological state in P0032, P0141. In [P0056] The statistics module 559 and/or multivariate module 562 can allow future values to be projected or forecasted values derived from past data using equations derived from associative or regression analysis.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac event management before the effective filing date of the invention to modify the system and method of Ong and An to have the plurality of physiological measurements in a multivariate parameter signal, as taught by John to better prioritize treating a patient with multiple injuries and medical conditions.
Claim 188:
John further teaches wherein the cardiac device comprises one or more electrodes configured to provide the treatment to the subject (See Fig. 1, P0035, 500A, implantable system (CTI) including electrodes attached to the skin and treatment in [P0084], is accomplished with other types of implanted devices such as neurostimulators, vagal/cranial nerve stimulators, and drug pumps.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac event management before the effective filing date of the invention to modify the system and method of Ong and An to have electrodes configured to provide the treatment to the subject, as taught by John to quickly treat a stroke patient.
Claim 189:
John further teaches wherein triggering the change in the controller of the cardiac therapeutic device comprises adjusting a delivery of the treatment to the subject using the one or more electrodes (See stimulation delivered in P0004 over a duration of time, electrical or drug therapy in P0047 and Fig. 1B 524 intervention and 528 stimulation.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac event management before the effective filing date of the invention to modify the system and method of Ong and An to have triggering the change in the controller of the cardiac therapeutic device comprises adjusting a delivery of the treatment, as taught by John to quickly treat a stroke patient.
Claims 3, 5-17, 20, 22-25, 33-36, 43, 46, 49, 51-63, 66, 68-71, 79-82 and 85-86 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Brockway (US 2009/0275848 A1).
Claims 3 and 49:
Brockway teaches wherein the physiological parameter data comprises at least one of blood pressure data, heart rate data, thoracic impedance data, pulse oxygen level data, respiration rate data, heart sound data, lung sound data, and activity level data (Measured ECG signal is taught in P0044 and heart rate in P042.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have at least one of blood pressure data, heart rate data, thoracic impedance data, pulse oxygen level data, respiration rate data, heart sound data, lung sound data, and activity level data, as taught by Brockway, to quickly treat potential heart attack patients with other pre-existing conditions.
Claims 5 and 51:
Brockway teaches wherein the potential adverse cardiac event comprises at least one of an ectopic beat, a run of ectopic beats, a ventricular tachycardia, a bradycardia, asystole, and a T-wave abnormality (In P0033 noisy data detects ectopic beats (for example, caused by premature ventricular contractions). See Fig. 5A, Fig. B, Fig. 7A, Fig. 7B, T-wave alternans amplitude data versus heart rate.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have a T-wave abnormality, as taught by Brockway, to quickly treat potential heart attack patients with other pre-existing heart conditions.
Claims 6 and 52:
Brockway teaches wherein the potential adverse cardiac event comprises at least one of a plurality of adverse cardiac events, an increase in a rate of adverse cardiac events, and/or an increase in an intensity of adverse cardiac events (Trending in Fig. 5A-5C, P0050-P0051, serves as an increase in a rate of medical events, and/or an increase in an intensity of medical events.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have increase in a rate of medical events, as taught by Brockway, to quickly assess potential heart attack patients in a timely manner.
Claims 7 and 53:
Brockway teaches wherein the potential adverse cardiac event is defined in a multidimensional parameter space comprising the physiological parameter data and at least one other type of physiological parameter data and/or demographic data of the subject (The physiological signal data monitoring effects of cardiovascular, prescription therapy regimen is taught in P0007, P0022.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have at least one other type of physiological parameter data and/or demographic data of the subject, as taught by Brockway, to determine the efficacy of a prescribed medication among a population of patients in a timely manner.
Claims 8 and 54:
Brockway further teaches wherein the one or more processors (Processor shown in Fig. 9, P0097) perform operations comprising: calculating a plurality of different event estimation risk score associated with the potential adverse cardiac event for the subject within the respective future time period based at least partly on the physiological parameter data (Estimating cardiac burden may represent an indicator of cardiac instability or patient vulnerability taught in [P0057], where cardiac signal data may be measured in response to a detected physiologic trigger, or in response to a manual trigger, as might be initiated by the patient or by a health care provider.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have at calculating a plurality of different event estimation of risk score associated with the potential medical event for the subject within the respective future time period based, as taught by Brockway, to have consistent and reliable knowledge of a patient’s treatment in a timely manner.
Claims 9 and 55:
Brockway further teaches:
wherein the one or more processors (Processor shown in Fig. 9, P0097) perform operations comprising:
calculating a plurality of different event estimation risk scores associated with a plurality of different potential medical events for the subject within the respective future time period based at least partly on the physiological parameter data (In [P0045] sensitivity of cardiac function to sympathetic drive can be calculated at various periods of time. In various implementations, sympathetic drive can be estimated from a heart rate variability measurement or parameter determined from the cardiac signal.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating a plurality of different event estimation of risk score associated with the potential medical event for the subject within the respective future time period based at least partly on the physiological parameter data, as taught by Brockway, to have consistent and reliable knowledge of a patient’s treatment in a timely manner.
Claims 10 and 56:
Brockway teaches wherein generating the risk prediction forecast comprises calculating a criticality score indicating a significance of the potential adverse cardiac event with respect to at least one other potential medical event (The risk trend (Item 560) shown in Fig. 5C, mentioned in P0051, where two ventricular episodes (Items 550, 555) serve as indicating a significance of the potential medical event with respect to at least one other potential medical event.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating a criticality score indicating a significance of the potential medical event with respect to at least one other potential medical event, as taught by Brockway, to have consistent and reliable knowledge with the efficacy of a prescribed medication in a timely manner.
Claim 11:
Brockway further teaches wherein the output comprises a confidence score compromising a percentage of a chance that the potential that the potential adverse cardiac event occurs within the respective future time period (See abstract and trending over time in P0006, P0068. (See Fig. 5C mentioned in P0046, as ratio of change and in P0048 the indicator of a degree of cardiac risk construe metric, P0049-P0051.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have a confidence score that the potential medical event occurs over time, as taught by Brockway, to rely upon basic mathematical computations versus time while scoring the medical event.
Claims 12 and 58:
Brockway further teaches:
wherein the one or more processors perform operations comprising:
determining that the event estimation risk score satisfies one or more event estimation risk thresholds for the respective future time period; and
determining a response to the potential adverse cardiac event based at least partly on the one or more event estimation of risk thresholds determined to be satisfied (Taught as exceeded predetermined threshold of a cardiac risk trend mentioned in P0036, P0049, P0051.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have event estimation of risk thresholds for the respective future time period for determining a response to the potential medical event, as taught by Brockway, to rely upon basic mathematical computations while scoring the medical event.
Claims 13 and 59:
Brockway teaches wherein the response to the potential adverse cardiac event comprises at least one of informing the subject of advanced diagnostics, advising the subject against removal of equipment, advising the subject of a behavior modification, alerting a medical professional, and preparing the medical device for treatment (In [P0051] an alert may be provided (e.g., to a health care professional or to the patient) when the cardiac risk trend meets or exceeds 560 the threshold 545, so that therapeutic interventions may be initiated or modified in an attempt to prevent sudden cardiac death.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to include advising the subject of a behavior modification, as taught by Brockway, to consider other avenues of treatment when a path of treatment has been exhausted.
Claim 14:
Brockway teaches wherein each of the one or more event estimation of risk thresholds compromise at least one confidence threshold comprising a required probability that the potential adverse cardiac event occurs within the respective future time period and at least one criticality threshold comprising a required significance of the potential medical event with respect to at least one other potential medical event (In P0045, predicting patient cardiac instability and heart rate history (e.g., acceleration and deceleration) can be tracked, and data corresponding to periods where heart rates are accelerating or decelerating can be analyzed serve as confidence threshold including a required probability. Also see P0066.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have a required probability that the potential medical event occurs within the respective future time period and criticality threshold, as taught by Brockway, to rely upon basic mathematical computations while scoring the medical event.
Claim 60:
Brockway teaches wherein each of the one or more event estimation of risk thresholds compromise at least one confidence threshold comprising a required probability that the potential adverse cardiac event occurs within the respective future time period and at least one criticality threshold comprising a required significance of the potential adverse cardiac event with respect to at least one other potential medical event (In P0045, predicting patient cardiac instability and heart rate history (e.g., acceleration and deceleration) can be tracked, and data corresponding to periods where heart rates are accelerating or decelerating can be analyzed serve as confidence threshold including a required probability. Also see P0066.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have a required probability that the potential medical event occurs within the respective future time period and criticality threshold, as taught by Brockway, to rely upon basic mathematical computations while scoring the medical event.
Claims 15 and 61:
Brockway teaches wherein the one or more event estimation of risk thresholds comprise a plurality of different event estimation of risk thresholds for the respective future time period (In P0026] the cardiac or cardiovascular signal may be measured at multiple periods in time, such as over several seconds, minutes, hours, days, weeks, months, or years, and each recording may include information corresponding to multiple cardiac cycles.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have event estimation of risk thresholds, as taught by Brockway, to rely upon basic mathematical computations while scoring the medical event.
Claim 16 and 62:
Brockway teaches wherein the one or more event estimation of risk thresholds for a first time period are different than the one or more event estimation of risk thresholds for an identified second time period among the at least one second time period (See risk trend verses time increase from March to April in Fig. 5C, P0050-P0052.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have event estimation of risk thresholds, as taught by Brockway, to rely upon basic mathematical computations while scoring the medical event.
Claims 17 and 63:
Brockway teaches wherein the response to the potential adverse cardiac event for the subject occurring within a first respective future time period is different that the response to the potential adverse cardiac event for the subject occurring within a second respective future time period (Fig. 5C and alerting modified therapeutic intervention determine response in [P0051] an alert may be provided (e.g., to a health care professional or to the patient) when the cardiac risk trend meets or exceeds 560 the threshold 545, so that therapeutic interventions may be initiated or modified in an attempt to prevent sudden cardiac death, as may be caused by ventricular fibrillation episodes.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have the subject occurring within a second respective future time period, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 20:
Brockway teaches wherein the one or more processors perform operations comprising:
setting the one or more event estimation of risk thresholds based at least partly on historical patient data collected from a plurality of patients (Taught as studies among patients in P0002, P0045.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have estimation of risk thresholds based on historical patient data, as taught by Brockway, to have consistent and reliable knowledge with the efficacy of a prescribed medication in a timely manner.
Claim 22:
Brockway further teaches wherein the one or more processors perform operations comprising: calculating the event estimation risk score at periodic time intervals (Taught as predetermined interval periods in P0057.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have periodic time intervals, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 23:
Brockway further teaches wherein the one or more processors perform operations comprising: calculating the event estimation risk score is at dynamic time intervals, wherein a duration of the dynamic time intervals is based at least partly on the event estimation risk score (Taught as predetermined interval periods in P0057, where weeks, months and years construe dynamic time intervals.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have periodic time intervals for the risk score, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 24:
Brockway further teaches wherein the one or more processors perform operations comprising: continuously calculating the event estimation risk score (Taught in abstract and as calculating a period between recurring periodic features of the cardiac signal, where the recurring periodic features correspond to a portion of the patient's cardiac cycle in P0061, where weeks, months and years construe continuously calculated.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating the event estimation of risk score, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 25:
Brockway further teaches wherein determining the first or the second event estimation risk score comprises applying a logistic regression model to the physiological parameter data to determine the first or the second event estimation risk score (Taught as autoregressive model in P0051.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to applying a logistic regression model to the physiological parameter data to determine the event estimation risk score, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 70:
Brockway further teaches continuously calculating, by the one or more processors, the event estimation risk score (Taught in abstract and as calculating a period between recurring periodic features of the cardiac signal, where the recurring periodic features correspond to a portion of the patient's cardiac cycle in P0061, where weeks, months and years construe continuously calculated.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating the event estimation of risk score, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 71:
Brockway further teaches wherein generating the risk prediction forecast comprises applying a logistic regression model to the physiological parameter data to determine the event estimation risk score (Taught as autoregressive model in P0051.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to applying a logistic regression model to the physiological parameter data to determine the event estimation risk score, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 33:
Brockway teaches a communications network configured to communicate at least one of the physiological parameter data and the event estimation risk score from the medical device to an another computing device (See Fig. 1, P0036, alerting and network communications with health care professional.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating the event estimation of risk score, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 34:
Brockway teaches a display for displaying a time-based visual indicator of the event estimation risk score for the plurality of time periods (See Fig. 5C, Risk trend over time mentioned in P0051.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An and to have a time-based visual indicator, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 35:
Brockway teaches wherein the one or more processors perform operations comprising: a response to the potential adverse cardiac event based at least partly on the event estimation risk score (Taught in P0024, and cardiac signal data may be measured in response to a detected physiologictrigger, or in response to a manual trigger in P0057. In [P0045] sensitivity of cardiac function to sympathetic drive can be calculated at various periods of time. In various implementations, sympathetic drive can be estimated from a heart rate variability measurement or parameter determined from the cardiac signal, also see P0036.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have a response to the potential medical event based on a estimation of risk score, as taught by Brockway, to have consistent and reliable knowledge with the efficacy of a prescribed medication in a timely manner.
Claims 36 and 86:
Brockway teaches wherein the response to the potential adverse cardiac event comprises providing an instruction to the subject to contact a medical professional (In [P0054] the health care professional can use trending information to determine if a specific therapy is working for a patient, or if an alternate therapy should be considered. In other implementations, the trending information can be used to determine if a patient is adhering to dietary requirements or therapy routines.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to providing an instruction to the subject to contact a medical professional, as taught by Brockway, to have medical knowledge from healthcare providers in a timely manner.
Claims 43 and 82:
Brockway teaches wherein the plurality of future time period comprise at least one time period of less than about ten minutes, at least one time period of less than about one hour, at least one time period of less than about three hours, at least one time period of less than about one day, at least one time period of less than about one week, and at least one time period of less than about one month (In P0026] the cardiac or cardiovascular signal may be measured at multiple periods in time, such as over several seconds, minutes, hours, days, weeks, months, or years, and each recording may include information corresponding to multiple cardiac cycles.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have a plurality of time period, as taught by Brockway, to consider other avenues of treatment when a path of treatment has been exhausted.
46. Claims 46 and 81:
Brockway teaches wherein the plurality of future time periods comprise a plurality of future time periods of less than four hours (In P0026] the cardiac or cardiovascular signal may be measured at multiple periods in time, such as over several seconds, minutes, hours, days, weeks, months, or years, and each recording may include information corresponding to multiple cardiac cycles.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have a plurality of time period, as taught by Brockway, to consider other avenues of treatment when a path of treatment has been exhausted.
Claim 57:
Brockway further teaches wherein generating the risk prediction forecast comprises calculating a confidence score comprising a probability that the potential adverse cardiac event occurs within the respective future time period (See abstract and trending over time in P0006, P0068. (See Fig. 5C mentioned in P0046, as ratio of change and in P0048 the indicator of a degree of cardiac risk construe metric, P0049-P0051.).
Therefore, it would have been obvious to one of ordinary skill in the art of heart monitoring devices before the effective filing date of the invention to modify the method, software and system of Ong and An to have a probability that the potential medical event occurs within the respective future time period, as taught by Brockway, to have consistent and reliable knowledge with the efficacy of a prescribed medication in a timely manner.
Claim 66:
Brockway teaches setting, by the one or more processors, the one or more event estimation of risk thresholds based at least partly on historical patient data collected from a plurality of patients (Taught as studies among patients in P0002, P0045.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have estimation of risk thresholds based on historical patient data, as taught by Brockway, to have consistent and reliable knowledge with the efficacy of a prescribed medication in a timely manner.
Claim 68:
Brockway further teaches calculating, by the one or more processors, the event estimation risk score at periodic time intervals (Taught as predetermined interval periods in P0057.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating the event estimation risk score at periodic time intervals, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 69:
Brockway further teaches calculating, by the one or more processors, the event estimation risk score is at dynamic time intervals, wherein a duration of the dynamic time intervals is based at least partly on the event estimation of risk score (Taught as predetermined interval periods in P0057.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have a duration of the dynamic time intervals is based at least partly on the event estimation of risk score, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 79:
Brockway teaches communicating, by a communications network, at least one of the physiological parameter data and the event estimation risk score from the medical device to an another computing device (See Fig. 1, P0036, alerting and network communications with health care professional.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management at the time of the invention to modify the method, software and system of Ong and An to have another computing device, as taught by Brockway, to consider other avenues of treatment when a path of treatment has been exhausted.
Claim 80:
Although Ong and An teach wherein graphical user interface comprises, as mentioned above, Brockway further teaches a time-based visual indicator of the event estimation of risk score for the plurality of future time periods (See Fig. 5C, Risk trend over time mentioned in P0051. See threshold score in P0036.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to have a time-based visual indicator, as taught by Brockway, to rely upon basic mathematical computations while determining future medical events.
Claim 85:
Although Ong and An teach risk score as mentioned above, Brockway teaches determining a response to the potential adverse cardiac event based at least partly on the event estimation risk score (Taught in P0024, and cardiac signal data may be measured in response to a detected physiologic trigger, or in response to a manual trigger in P0057. In [P0045] sensitivity of cardiac function to sympathetic drive can be calculated at various periods of time. In various implementations, sympathetic drive can be estimated from a heart rate variability measurement or parameter determined from the cardiac signal.).
Therefore, it would have been obvious to one of ordinary skill in the art of heart monitoring devices before the effective filing date of the invention to modify the method, software and system of Ong and An to have a probability that the potential medical event occurs within the respective future time period, as taught by Brockway, to have consistent and reliable knowledge with the efficacy of a prescribed medication in a timely manner.
Claim 21 is rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view Carlson (US 2010/0280335 A1).
Claim 21:
Although Ong and An teach wherein the one or more processors perform operations comprising as mentioned above, Ong and An do not explicitly teach setting the one or more event estimation of risk thresholds based at least partly on input from a user. Carlson teaches, in P0121-P0122, Fig. 3, that it was known in the art of patient sate machine learning before the effective filing date of the invention to have event estimation settings of risk thresholds based at least partly on input from a user to determine risk of bifurcation state to evaluate remotely locate patients experiencing degenerated cognitive abilities.
Therefore, it would have been obvious to one of ordinary skill in the art of patient sate machine learning at the time of the invention to modify the method, software and system of Ong and An and Brockway to have event estimation settings of risk thresholds based at least partly on input from a user, as taught by Carlson, to evaluate remotely locate patients experiencing degenerated cognitive abilities.
Claim 179 is rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further McAleer (US 2009/0054741 A1).
Claim 179:
McAleer further teaches:
wherein the first future time period ends before or when the second future time period begins (By giving warnings of deteriations of tested values over a baseline in P0101-P0104 and rolling average detecting chronic changes (P0114-P0115).)
Therefore, it would have been obvious to one of ordinary skill in the art of heart monitoring devices before the effective filing date of the invention to modify the method, software and system of Ong and An to have a time period ends before or when the second future time period begins, as taught by McAleer, to rely upon basic mathematical computations while determining future medical events.
Claims 18 and 64 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further John (US 2008/0188763 A1).
Claim 18:
Although Ong and An teach the system of claim 1, wherein the one or more processors perform operations as mentioned above, Ong and An do not explicitly teach determining that the event estimation risk score fails to satisfy an event estimation of risk threshold for the associated time period, receiving additional data and calculating an enhanced event estimation of risk score associated with the potential medical event for the subject occurring within the associated time period based at least partly on the physiological parameter data and the additional data. John teaches:
comprising: determining that the event estimation risk score fails to satisfy at least one event estimation of risk threshold for the associated time period; receiving additional data of the subject (Besides analyzing an ischemia score having too few heart beats established in ST segment changes (P0010-P0011, P0064), see P0122-P0124 the use of 2 or more thresholds serve as needing additional data for thresholding within a time period.); and
calculating an enhanced event estimation of risk score respective future with the potential adverse cardiac event for the subject occurring within the respective future time period based at least partly on the physiological parameter data and the additional data (See [P0055-P0056], [P0079] multivariate module 562 can allow future values to be projected or forecasted values derived from past data using equations derived from associative or regression analysis and predicting the size of an ST segment deviation based upon a measurement of the Q wave. Also, see P0103 and P0190 predictive response to particular drugs.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical device alarms before the effective filing date of the invention to modify the method, software and system of Ong and An to have determining that the event estimation risk score fails to satisfy an event estimation of risk threshold for the associated time period, receiving additional data and calculating an enhanced event estimation of risk score associated with the potential medical event for the subject occurring within the associated time period based at least partly on the physiological parameter data and the additional data, as taught by John, to quickly assess potential heart attack patients in a timely manner.
Claim 64:
Although Ong and An teach the method of claim 47 as mentioned above, Ong and An do not explicitly teach determining that the event estimation risk score fails to satisfy an event estimation of risk threshold for the associated time period, receiving additional data and calculating an enhanced event estimation of risk score associated with the potential medical event for the subject occurring within the associated time period based at least partly on the physiological parameter data and the additional data. John teaches:
comprising: determining, by the one or more processors, that the event estimation risk score fails to satisfy at least one event estimation of risk threshold for the respective future time period; receiving, by the one or more processors, additional data of the subject (Besides analyzing an ischemia score having too few heart beats established in ST segment changes (P0010-P0011, P0064), see P0122-P0124 the use of 2 or more thresholds serve as needing additional data for thresholding within a time period.); and
calculating, by the one or more processors, an enhanced event estimation risk score associated with the potential adverse cardiac event for the subject occurring within the respective future time period based at least partly on the physiological parameter data and the additional data (See [P0055-P0056], [P0079] multivariate module 562 can allow future values to be projected or forecasted values derived from past data using equations derived from associative or regression analysis and predicting the size of an ST segment deviation based upon a measurement of the Q wave. Also, see P0103 and P0190 predictive response to particular drugs.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical device alarms before the effective filing date of the invention to modify the method, software and system of Ong and An to have determining that the event estimation risk score fails to satisfy an event estimation of risk threshold for the associated time period, receiving additional data and calculating an enhanced event estimation of risk score associated with the potential medical event for the subject occurring within the associated time period based at least partly on the physiological parameter data and the additional data, as taught by John, to quickly assess potential heart attack patients in a timely manner.
Claims 19 and 65 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further John (US 2008/0188763 A1) and Chung (US 10,055,549 B2).
Claims 19 and 65:
Ong, An and John do not explicitly teach have image data of the subject, audio data including the voice of the subject, and data based on a galvanic skin response of the subject. Chung teaches:
wherein the additional data comprises at least one of image data of the subject, audio data comprising a voice of the subject, and data based on a galvanic skin response of the subject.
Chung teaches that it was known in the art of cardiology sensor management at the time of the invention to have at least one of image data of the subject, audio data including the voice of the subject, and data based on a galvanic skin response of the subject to utilize standard smart phones to assess potential heart attack patients in a timely manner. Acoustic signal taught in column 18, lines 11-67, as electrode pads may have an adhesive contact with the skin and are firmly attached to the sensing device and to connect the skin with an acoustic sensor on the sensing device. See Fig. 13.
Therefore, it would have been obvious to one of ordinary skill in the art of cardiology sensor management before the effective filing date of the invention to modify the method, software and system of Ong, An and John to have image data of the subject, audio data including the voice of the subject, and data based on a galvanic skin response of the subject, as taught by Chung, to utilize standard smart phones to assess potential heart attack patients in a timely manner.
Claims 37 and 87 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Brockway (US 2009/0275848 A1) and Chung (US 10,055,549 B2).
Claim 37:
Ong, An and Brockway do not explicitly teach:
a wearable medical device, wherein the response to the potential adverse cardiac event includes providing an instruction to the subject to check a battery of the wearable medical device.
Chung teaches that it was known in the art of cardiology sensor management at the time of the invention to have determining response to the potential medical event includes providing an instruction to the subject to check a battery of the wearable medical device to utilize standard battery operation of a medical device for safety. Time point recording during battery recharge taught in column 6, lines 26-39, and battery depletion in column 17, lines 21-43. See Fig. 13.
Therefore, it would have been obvious to one of ordinary skill in the art of cardiology sensor management before the effective filing date of the invention to modify the method, software and system of Ong, An and Brockway to have determining response to the potential medical event includes providing an instruction to the subject to check a battery of the wearable medical device, as taught by Chung, to utilize standard battery operation of a medical device for safety.
Claim 87:
Ong, An and Brockway do not explicitly teach:
wherein the response to the potential adverse cardiac event comprise providing an instruction to the subject to check a battery of a wearable medical device.
Chung teaches that it was known in the art of cardiology sensor management at the time of the invention to have determining response to the potential medical event includes providing an instruction to the subject to check a battery of the wearable medical device to utilize standard battery operation of a medical device for safety. Time point recording during battery recharge taught in column 6, lines 26-39, and battery depletion in column 17, lines 21-43. See Fig. 13.
Therefore, it would have been obvious to one of ordinary skill in the art of cardiology sensor management before the effective filing date of the invention to modify the method, software and system of Ong, An and Brockway to have determining response to the potential medical event includes providing an instruction to the subject to check a battery of the wearable medical device, as taught by Chung, to utilize standard battery operation of a medical device for safety.
Claim 67 is rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Brockway (US 2009/0275848 A1) and Carlson (US 2010/0280335 A1).
Claim 67:
Ong, An and Brockway do not explicitly teach:
setting, by the processors, the one or more event estimation of risk thresholds based at least partly on input from a user.
Carlson teaches, in P0121-P0122, Fig. 3, that it was known in the art of patient sate machine learning at the time of the invention to have event estimation settings of risk thresholds based at least partly on input from a user to determine risk of bifurcation state to evaluate remotely locate patients experiencing degenerated cognitive abilities.
Therefore, it would have been obvious to one of ordinary skill in the art of patient sate machine learning before the effective filing date of the invention to modify the method, software and system of Ong, An and Brockway to have event estimation settings of risk thresholds based at least partly on input from a user, as taught by Carlson, to evaluate remotely locate patients experiencing degenerated cognitive abilities.
Claims 26, 27, 30, 72-73, 76 and 176 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Carlson (US 2010/0280335 A1).
Claim 26:
Although Ong and An teach wherein generating the risk prediction forecast comprises as mentioned above, Ong and An do not explicitly teach:
generating at least two generally orthogonal vectors based at least partly on the physiological parameter data;
processing the at least two generally orthogonal vectors to determine a loop trajectory of the physiological parameter data; and
identifying a trajectory bifurcation by:
characterizing a group of control loop trajectories that includes one or more loop trajectories obtained during a first time period;
characterizing a group of test loop trajectories that includes one or more loop trajectories obtained during a second time period that is subsequent to the first time period;
comparing the characterization of the group of control loop trajectories to the characterization of the group of test loop trajectories;
measuring a degree of trajectory bifurcation between the group of control loop trajectories and the group of test loop trajectories; and
calculating the event estimation risk score based at least in part on the measure of the degree of trajectory bifurcation.
Carlson teaches that it was known in the art of patient sate machine learning at the time of the invention to have loop trajectory of the physiological parameter data for a patient, measuring levels of trajectory values in first time period compared to second time period among control groups or characterizations to determine risk viewed in a bifurcation state to assess and a suggest plan of action to treat potential heart attack patients in a timely manner. See evaluation metric in Abstract or severity metric in Fig. 15, P0124, first and second state indications (Items 168, 186) occur at subsequent time periods shown in Fig. 12, 211-215, and exemplary physiological condition taught in [P0108] IMD 16 may be coupled to one lead with eight electrodes on the lead or three or more leads with the aid of bifurcated lead extensions. In [P0023-P0025] the support vector machine algorithm based algorithm defines a classification boundary in a feature space, determining a trajectory of the feature vectors within the feature space relative to the classification boundary, and generating an indication based on the trajectory of the feature vectors within the feature space, having classification boundaries serving as characterization of the group of test. See also P0202, P0227, Fig. 8A-Fig.11.
Therefore, it would have been obvious to one of ordinary skill in the art of patient sate machine learning before the effective filing date of the invention to modify the method, software and system of Ong and An to have loop trajectory of the physiological parameter data for a patient, measuring levels of trajectory values in first time period compared to second time period among control groups or characterizations to determine risk viewed in a bifurcation state, as taught by Carlson, to assess and a suggest plan of action to treat potential heart attack patients in a timely manner.
Claim 27:
Although Ong and An teach wherein generating the risk prediction forecast comprises as mentioned above, Ong and An do not explicitly teach:
wherein the first event estimation risk score comprises a first criticality score for a first potential medical event; and
wherein the second event estimation risk score comprises a second criticality score for a second potential medical event based at least partly on the first event estimation risk score, wherein the first criticality score indicates that a significance of the first potential medical event is different than a significance of the second potential medical event.
Carlson teaches that it was known in the art of patient sate machine learning at the time of the invention to have calculating a second event estimation of risk score including a second criticality score for a second potential medical event based at least partly on the first event estimation of risk score, wherein the first criticality score indicates that a significance of the first potential medical event is different than a significance of the second potential medical event to assess and a suggest plan of action to treat potential heart attack patients in a timely manner. Taught as metric in abstract, separation metric as difference between first and second patient states in P0163, and evaluate the progression of the patient's condition, monitor the severity of the patient condition in P0214. See Fig. 4, Fig. 12.
Therefore, it would have been obvious to one of ordinary skill in the art of patient sate machine learning before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating a second event estimation of risk score including a second criticality score for a second potential medical event based at least partly on the first event estimation of risk score, wherein the first criticality score indicates that a significance of the first potential medical event is different than a significance of the second potential medical event, as taught by Carlson, to assess and a suggest plan of action to treat potential heart attack patients in a timely manner.
Claim 30:
Although Ong and An teach wherein generating the risk prediction forecast comprises as mentioned above, Ong and An do not explicitly teach:
receiving data indicating a viability of a patient; and determining a response to the potential medical event based at least partly on the viability of the patient.
Carlson teaches that it was known in the art of patient sate machine learning at the time of the invention to have receiving data indicating a viability of a patient and determining a response to the potential medical event based at least partly on the viability of the patient to assess and a suggest plan of action to treat patients experiencing multiple medical conditions. In P0060, P0063, P0065, the pulse rate, thermal sensing, seizures and fatigue, attributes to determining the viability of the patient.
Therefore, it would have been obvious to one of ordinary skill in the art of patient sate machine learning before the effective filing date of the invention to modify the method, software and system of Ong and An to have event estimation settings of risk thresholds based at least partly on input from a user, as taught by Carlson, to assess and a suggest plan of action to treat patients experiencing multiple medical conditions.
Claim 72:
Although Ong and An teach wherein generating the risk prediction forecast comprises as mentioned above, Ong and An do not explicitly teach:
generating at least two generally orthogonal vectors based at least partly on the physiological parameter data;
processing the at least two generally orthogonal vectors to determine a loop trajectory of the physiological parameter data; and
identifying a trajectory bifurcation by:
characterizing a group of control loop trajectories that comprised one or more loop trajectories obtained during a first time period;
characterizing a group of test loop trajectories that comprised one or more loop trajectories obtained during a second time period that is subsequent to the first time period;
comparing the characterization of the group of control loop trajectories to the characterization of the group of test loop trajectories;
trajectories and the group of test loop trajectories; and
calculating the event estimation risk score based at least in part on the measure of the degree of trajectory bifurcation.
Carlson teaches that it was known in the art of patient sate machine learning at the time of the invention to have loop trajectory of the physiological parameter data for a patient, measuring levels of trajectory values in first time period compared to second time period among control groups or characterizations to determine risk of bifurcation state to assess and a suggest plan of action to treat potential heart attack patients in a timely manner. See evaluation metric in Abstract or severity metric in Fig. 15, P0124, first and second state indications (Items 168, 186) occur at subsequent time periods shown in Fig. 12, 211-215, and exemplary physiological condition taught in [P0108] IMD 16 may be coupled to one lead with eight electrodes on the lead or three or more leads with the aid of bifurcated lead extensions. In [P0023-P0025] the support vector machine algorithm based algorithm defines a classification boundary in a feature space, determining a trajectory of the feature vectors within the feature space relative to the classification boundary, and generating an indication based on the trajectory of the feature vectors within the feature space, having classification boundaries serving as characterization of the group of test. See also P0202, P0227, Fig. 8A-Fig.11.
Therefore, it would have been obvious to one of ordinary skill in the art of patient sate machine learning before the effective filing date of the invention to modify the method, software and system of Ong and An to have loop trajectory of the physiological parameter data for a patient, measuring levels of trajectory values in first time period compared to second time period among control groups or characterizations to determine risk of bifurcation state, as taught by Carlson, to assess and a suggest plan of action to treat potential heart attack patients in a timely manner.
Claim 73:
Although Ong and An teach wherein generating the risk prediction forecast comprises as mentioned above, Ong and An do not explicitly teach:
calculating a first event estimation risk score comprising a first criticality score for a first potential adverse cardiac event; and
calculating a second event estimation risk score comprising a second criticality score for a second potential adverse cardiac event based at least partly on the first event estimation risk score, wherein the first criticality score indicates that a significance of the first potential adverse cardiac event is different than a significance of the second potential adverse cardiac event.
Carlson teaches that it was known in the art of patient sate machine learning at the time of the invention to have calculating a second event estimation of risk score including a second criticality score for a second potential medical event based at least partly on the first event estimation of risk score, wherein the first criticality score indicates that a significance of the first potential medical event is different than a significance of the second potential medical event to assess and a suggest plan of action to treat potential heart attack patients in a timely manner. Taught as metric in abstract, separation metric as difference between first and second patient states in P0163, and evaluate the progression of the patient's condition, monitor the severity of the patient condition in P0214. See Fig. 4, Fig. 12.
Therefore, it would have been obvious to one of ordinary skill in the art of patient sate machine learning before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating a second event estimation of risk score including a second criticality score for a second potential medical event based at least partly on the first event estimation of risk score, wherein the first criticality score indicates that a significance of the first potential medical event is different than a significance of the second potential medical event, as taught by Carlson, to assess and a suggest plan of action to treat potential heart attack patients in a timely manner.
Claim 76:
Although Ong and An teach wherein generating the risk prediction forecast comprises as mentioned above, Ong and An do not explicitly teach:
receiving data indicating a viability of a patient; and determining a response to the potential adverse cardiac event based at least partly on the viability of the patient.
Carlson teaches that it was known in the art of patient sate machine learning at the time of the invention to have receiving data indicating a viability of a patient and determining a response to the potential medical event based at least partly on the viability of the patient to assess and a suggest plan of action to treat patients experiencing multiple medical conditions. In P0060, P0063, P0065, the pulse rate, thermal sensing, seizures and fatigue, attributes to determining the viability of the patient.
Therefore, it would have been obvious to one of ordinary skill in the art of patient sate machine learning before the effective filing date of the invention to modify the method, software and system of Ong and An to have event estimation settings of risk thresholds based at least partly on input from a user, as taught by Carlson, to assess and a suggest plan of action to treat patients experiencing multiple medical conditions.
Claim 176:
Carlson teaches wherein the risk score comprises a criticality measure indicating a risk of the potential adverse cardiac event and a confidence measure indicating a probability that the potential adverse cardiac event occurs within the respective future time period.
Carlson teaches that it was known in the art of patient sate machine learning at the time of the invention to have calculating a second event estimation of risk score including a second criticality score for a second potential medical event based at least partly on the first event estimation of risk score, wherein the first criticality score indicates that a significance of the first potential medical event is different than a significance of the second potential medical event to assess and a suggest plan of action to treat potential heart attack patients in a timely manner. Taught as metric in abstract, separation metric as difference between first and second patient states in P0163, and evaluate the progression of the patient's condition, monitor the severity of the patient condition in P0214. See Fig. 4, Fig. 12.
Therefore, it would have been obvious to one of ordinary skill in the art of patient sate machine learning before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating a second event estimation of risk score including a second criticality score for a second potential medical event based at least partly on the first event estimation of risk score, wherein the first criticality score indicates that a significance of the first potential medical event is different than a significance of the second potential medical event, as taught by Carlson, to assess and a suggest plan of action to treat potential heart attack patients in a timely manner.
Claims 28, 29, 74 and 75 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of and Linker (US 2008/0221633 A1).
Claim 28:
Although Ong and An teach wherein generating the risk prediction forecast, as mentioned above, Ong and An do not explicitly teach:
calculating the first event estimation risk score associated with the potential medical event for the subject occurring within of the associated time period based on a first shockable rhythm detection algorithm; and
calculating the second event estimation risk score associated with the potential medical event for the subject occurring within of the associated time period based on a second rhythm detection algorithm, wherein the second rhythm detection algorithm is tuned for a higher sensitivity on the physiological data than the first rhythm detection algorithm.
Linker teaches that it was known in the art of arrhythmia detection at the time of the invention to have detecting shock rhythm increased comparing first and second medical event for patient at risk of heart attack or stroke using algorithm to apply standard discrimination of groups of heart rhythm disorders that are life-threatening and need immediate treatment from groups of heart rhythm disorders that do not require immediate treatment. See Fig. 12, taught in P0035, where long-term monitoring construe comparing first and second events detecting arrhythmia and electronic shock, also mentioned in P0059, P0063, P0066.
Therefore, it would have been obvious to one of ordinary skill in the art of arrhythmia detection before the effective filing date of the invention to modify the method, software and system of Ong and An to have detecting shock rhythm increased comparing first and second medical event for patient at risk of heart attack or stroke using algorithm, as taught by Linker, to apply standard discrimination of groups of heart rhythm disorders that are life-threatening and need immediate treatment from groups of heart rhythm disorders that do not require immediate treatment.
Claims 29 and 75:
Although Ong and An teach wherein generating the risk prediction forecast, as mentioned above, Ong and An do not explicitly teach:
wherein the calculating the event estimation of risk score comprises applying at least two different rhythm detection algorithms to different time segments of the physiological data.
Linker teaches that it was known in the art of arrhythmia detection at the time of the invention to have applying at least two different rhythm detection algorithms to different time segments of the physiological data to apply standard discrimination of groups of heart rhythm disorders that are life-threatening and need immediate treatment from groups of heart rhythm disorders that do not require immediate treatment. Taught in abstract, P0036, P0083, [P0102] FIG. 10 is a pictorial diagram showing an illustrative set of three segments of RR intervals 1008. For the arrhythmia detection algorithm the three segments used include segment J-1 1002, segment J 1004, and segment J+1 1006.
Therefore, it would have been obvious to one of ordinary skill in the art of arrhythmia detection before the effective filing date of the invention to modify the method, software and system of Ong and An to have applying at least two different rhythm detection algorithms to different time segments of the physiological data, as taught by Linker, to apply standard discrimination of groups of heart rhythm disorders that are life-threatening and need immediate treatment from groups of heart rhythm disorders that do not require immediate treatment.
Claim 74:
Although Ong and An teach wherein generating the risk prediction forecast, as mentioned above, Ong and An do not explicitly teach:
determining the first event estimation risk score associated with the potential adverse cardiac event for the subject occurring within of the associated time period based on a first shockable rhythm detection algorithm; and
determining the second event estimation risk score associated with the potential adverse cardiac event for the subject occurring within of the associated time period based on a second rhythm detection algorithm, wherein the second rhythm detection algorithm is tuned for a higher sensitivity on the physiological data than the first rhythm detection algorithm.
Linker teaches that it was known in the art of arrhythmia detection at the time of the invention to have detecting shock rhythm increased comparing first and second medical event for patient at risk of heart attack or stroke using algorithm to apply standard discrimination of groups of heart rhythm disorders that are life-threatening and need immediate treatment from groups of heart rhythm disorders that do not require immediate treatment. See Fig. 12, taught in P0035, where long-term monitoring construe comparing first and second events detecting arrhythmia and electronic shock, also mentioned in P0059, P0063, P0066.
Therefore, it would have been obvious to one of ordinary skill in the art of arrhythmia detection before the effective filing date of the invention to modify the method, software and system of Ong and An to have detecting shock rhythm increased comparing first and second medical event for patient at risk of heart attack or stroke using algorithm, as taught by Linker, to apply standard discrimination of groups of heart rhythm disorders that are life-threatening and need immediate treatment from groups of heart rhythm disorders that do not require immediate treatment.
Claims 38-41 and 88-91 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Brockway (US 2009/0275848 A1) and Linker (US 2008/0221633 A1).
Claims 38 and 88:
Ong, An and Brockway do not explicitly teach:
a wearable medical device, wherein the response to the potential adverse cardiac event comprise charging a shocking mechanism of the wearable medical device.
Linker teaches that it was known in the art of arrhythmia detection at the time of the invention to have determining response to the potential medical event includes charging a shocking mechanism of the wearable medical device to apply standard discrimination of groups of heart rhythm disorders that are life-threatening and need immediate treatment from groups of heart rhythm disorders that do not require immediate treatment. See Fig. 1A, Fig. 1B, Fig. 12, taught in P0035, where long-term monitoring events detecting arrhythmia and electronic shock, also mentioned in P0059, P0063, P0066.
Therefore, it would have been obvious to one of ordinary skill in the art of arrhythmia detection before the effective filing date of the invention to modify the method, software and system of Ong, An and Brockway to have determining response to the potential medical event includes charging a shocking mechanism of the wearable medical device, as taught by Linker, to apply standard discrimination of groups of heart rhythm disorders that are life-threatening and need immediate treatment from groups of heart rhythm disorders that do not require immediate treatment.
Claim 39:
Ong, An and Brockway do not explicitly teach:
wherein the one or more processors perform operations comprising: determining the response to the potential adverse cardiac event based at least partly on a sensitivity and a specificity of the event estimation risk score.
Linker teaches that it was known in the art of arrhythmia detection at the time of the invention to have determining the response to the potential medical event based at least partly on a sensitivity and a specificity of the event estimation of risk to determine if immediate medical treatment for atrial fibrillation is the next plan of action. Taught in [P0016-P0019] automatically or manually, based on statistical data, requires the use of thresholds defined with respect to sensitivity and specificity, where increased risk of stroke, P0007, P0101-P0102. See Fig. 8.
Therefore, it would have been obvious to one of ordinary skill in the art of arrhythmia detection before the effective filing date of the invention to modify the method, software and system of Ong, An and Brockway to have determining the response to the potential medical event based at least partly on a sensitivity and a specificity of the event estimation of risk, as taught by Linker, to determine if immediate medical treatment for atrial fibrillation is the next plan of action.
Claims 40 and 90:
Ong, An and Brockway do not explicitly teach:
wherein the response based on a first sensitivity and a first specificity is different than the response based on a second different sensitivity and second different specificity.
Linker teaches that it was known in the art of arrhythmia detection at the time of the invention to have determining response based on a first sensitivity and a first specificity is different than the determined response based on a second different sensitivity and second different specificity to determine if immediate medical treatment for atrial fibrillation is the next plan of action. Taught in P0016-P0019 and [P0097] the sensitivity value 804 versus one minus specificity (1-specificity) value 802, calculated based on different threshold values for the present comprising a median of median values for heart rate deviation. A median of median values for heart rate deviation. Also see Fig. 7, Fig. 8, P0101-P0102.
Therefore, it would have been obvious to one of ordinary skill in the art of arrhythmia detection before the effective filing date of the invention to modify the method, software and system Ong, An and Brockway to have determining response based on a first sensitivity and a first specificity is different than the determined response based on a second different sensitivity and second different specificity, as taught by Linker, to determine if immediate medical treatment for atrial fibrillation is the next plan of action.
Claim 89:
Ong, An and Brockway do not explicitly teach:
determining the response to the potential adverse cardiac event based at least partly on a sensitivity and a specificity of the event estimation risk.
Linker teaches that it was known in the art of arrhythmia detection at the time of the invention to have determining the response to the potential medical event based at least partly on a sensitivity and a specificity of the event estimation of risk to determine if immediate medical treatment for atrial fibrillation is the next plan of action. Taught in [P0016-P0019] automatically or manually, based on statistical data, requires the use of thresholds defined with respect to sensitivity and specificity, where increased risk of stroke, P0007, P0101-P0102. See Fig. 8.
Therefore, it would have been obvious to one of ordinary skill in the art of arrhythmia detection before the effective filing date of the invention to modify the method, software and system of Ong, An and Brockway to have determining the response to the potential medical event based at least partly on a sensitivity and a specificity of the event estimation of risk, as taught by Linker, to determine if immediate medical treatment for atrial fibrillation is the next plan of action.
Claims 41 and 91:
Brockway teaches wherein the response to the potential adverse cardiac event comprise at least one of informing the subject of advanced diagnostics, advising the subject against removal of equipment, advising the subject of a behavior modification, alerting a medical professional, and preparing the medical device for treatment (Taught as alert of disease worsening, P0026 and alerting health care professional (110) in P0036.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac risk management before the effective filing date of the invention to modify the method, software and system of Ong and An to advising the subject of a behavior modification, as taught by Brockway, to consider other avenues of treatment when a path of treatment has been exhausted.
Claims 44, 45, 83 and 84 are rejected under 35 U.S.C. 103(a) as being unpatentable Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view Sims (US 9,687,195 B2).
Claims 44 and 83:
Ong and An do not explicitly teach wherein the calculating the event estimation risk score comprises calculating a confidence band of the event estimation risk.
Sims teaches that it was known in the art of life sign health detection at the time of the invention to have calculating a confidence band of the event estimation of risk score to determine if immediate medical treatment is needed as the next plan of action. Taught as estimated confidence level in abstract, column 5, lines 27-34, regarding heart failure risk in column 7, lines 21-34, column 10, lines 17-32.
Therefore, it would have been obvious to one of ordinary skill in the art of life sign health detection before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating a confidence band of the event estimation of risk score, as taught by Sims, to determine if immediate medical treatment is needed as the next plan of action.
Claims 45 and 84:
Sims further teaches wherein the calculating the event estimation risk score comprises calculating an error band of the event estimation risk score.
Sims teaches that it was known in the art of life sign health detection at the time of the invention to have calculating a confidence band of the event estimation of risk score to determine if immediate medical treatment is needed as the next plan of action. Shown in Fig. 34 as Error Conditions, and band as error heart rate trended in column 31, lines 40-50.
Therefore, it would have been obvious to one of ordinary skill in the art of life sign health detection before the effective filing date of the invention to modify the method, software and system of Ong and An to have calculating a confidence band of the event estimation of risk score, as taught by Sims, to determine if immediate medical treatment is needed as the next plan of action.
Claims 42 and 92 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Brockway (US 2009/0275848 A1) and Adourian (US 2011/0137131 A1).
Claim 42:
Adourian further teaches:
wherein the one or more processors perform operations comprising:
modifying a sensitivity of an algorithm for determining the event estimation risk score based on a risk level of the subject (See Fig. 6-8, Model analysis compared to what’s considered an Established risk according to specificity versus sensitivity, P0100-P0106.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiology sensor management before the effective filing date of the invention to modify the method, software and system of Ong, An and Brockway to have modifying a sensitivity of an algorithm for determining the event estimation, as taught by Adourian, to utilize a next plan of action when treating potential heart attack patients in a timely manner.
Claim 92:
Adourian further teaches:
modifying a sensitivity of an algorithm for determining the event estimation risk score based on a risk level of the subject (See Fig. 6-8, Model analysis compared to what’s considered an Established risk according to specificity versus sensitivity, P0100-P0106.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiology sensor management before the effective filing date of the invention to modify the method, software and system of Ong, An and Brockway to have modifying a sensitivity of an algorithm for determining the event estimation, as taught by Adourian, to utilize a next plan of action when treating potential heart attack patients in a timely manner.
Claims 180 and 181 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Teixeira (US 2012/0022336 A1).
Claim 180:
Although Ong and An disclose the system of claim 1 as mentioned above, Ong and An do not explicitly teach using a Monte Carlo method. Teixeira teaches:
wherein the risk prediction forecast for the plurality of future time periods is generated using a Monte Carlo method (Taught in P0138).
Therefore, it would have been obvious to one of ordinary skill in the art of medical devices before the effective filing date of the invention to modify the method, software and system of Ong and An to using a Monte Carlo method, as taught by Teixeira, to rely upon basic mathematical computations while scoring the medical event.
Claim 181:
Although Ong and An disclose the system of claim 1 as mentioned above, Ong and An do not explicitly teach applying a Kalman filter to the risk prediction forecast. Teixeira teaches:
wherein processing the plurality of physiological measurements comprises: applying a Kalman filter to the risk prediction forecast (Taught in P0034, P0138).
Therefore, it would have been obvious to one of ordinary skill in the art medical device management before the effective filing date of the invention to modify the method, software and system of Ong and An to applying a Kalman filter to the risk prediction forecast, as taught by Teixeira, to rely upon basic mathematical computations while scoring the medical event.
Claim 183 is rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Sier (US 2015/0148621 A1) and Tran (US 9,107,586 B2).
Claim 183:
Sier teaches the system of claim 1, further comprising:
training the machine learning classifier based on training data, wherein the training data (Machine learning algorithm taught in P0131-P0135, where the parametric model, integrated multiple parameters (P0137) and research identifying multi-parametric associations (P0139) allow for classifying trained data.) comprises:
a first plurality of additional physiological measurements regarding a first plurality of additional subjects (Physiological measurements are taught in P0079] as using biomedical sensors to detect, quantify or measure the constant changes of physiological processes by monitoring the human anatomy. The plurality of additional subjects is taught as cross examination between patients in the database (P0082) and in [P0083] using the database in the study phase and moving forward, pattern recognition and algorithms may be applied to identify and document correlations and trends between and among patients, with targeted symptoms and side effects.), and
at least one of first demographic metrics or first medical history metrics regarding each of the first additional subjects (Medical history metrics are taught as exemplary metric recordation of EEG, EKG signals (P0042-P0043) capturing activity of a heart and sensor collect measurements from a patient mentioned in P0060, shown in Fig. 3.); and
validating the machine learning classifier based on validation data, wherein the validation data comprises:
a second plurality of additional physiological measurements (Taught in exemplary as validating step, 520 of algorithm shown as Fig. 5, where sleep and side effects serve as additional physiological measurements mentioned in P0062.), and
at least one of second demographic metrics or second medical history metrics (See medical records from database (P0010, P0054) and trending in [P038-P0139] integrates fixed values of disease, disease characteristics, lifestyle, and therapy with variable values of metrics from biosensors, and mood, emotional, and behavioral aspects.).
Therefore, it would have been obvious to one of ordinary skill in the art fitness monitoring before the effective filing date of the invention to modify the system of Ong and An to have validating machine learning classifiers with additional physiological measurements and demographic metrics or medical history as the training data, as taught by Sier, to utilize data monitoring patients who subscribe to exercise with healthy lifestyles compared to who don’t exercise with unhealthy lifestyles.
Although Sier discloses training data as physiological measurements and medical history metrics from a first population of patients or subjects, Sier does not explicitly teach additional subjects, regarding a second plurality of additional subjects. Tran teaches additional subjects, regarding each of the second additional subjects.
Tran teaches that it is known in the art of fitness monitoring before the effective filing date of the invention to have a second group of additional subjects, to utilize data monitoring patients who subscribe to exercise with healthy lifestyles compared to who don’t exercise with unhealthy lifestyles. Taught in column 19, lines 12-19, modified to form a new population. Also see Fig. 15C, column 57, line 60 to column 58, line 3.
Therefore, it would have been obvious to one of ordinary skill in the art fitness monitoring before the effective filing date of the invention to modify the system of Ong, An and Sier to have a second group of additional subjects, as taught by Tran, to utilize data monitoring patients who subscribe to exercise with healthy lifestyles compared to who don’t exercise with unhealthy lifestyles.
Claims 184-187 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Sweeney (US 6,272,377 B1).
Claims 184 and 186:
Sweeney further teaches wherein the processing includes prioritizing estimated risk scores for at least one of: each future time period and the plurality of future time periods, the prioritizing being based on a weighting of at least one of a criticality measure, a confidence measure, and a risk associated with each event estimation of risk score (See ]column 16, lines 1-22], where the detection value serves as the score and usage weights provide a degree to which the detection value and conditional probability enters into the arrhythmia probability calculation. In such an embodiment, the arrhythmia probability calculation may be normalized, based on the values of the usage weights, such that the arrhythmia probability ranges between 0 and 1.).
Therefore, it would have been obvious to one of ordinary skill in the art cardiac rhythm management before the effective filing date of the invention to modify the system and method of Ong and An to have prioritizing estimated risk scores for the prioritizing being based on a weighting of at least one of a criticality measure and a risk associated with each event estimation of risk score, as taught by Sweeney, to reduce the consequences of an arrhythmia from occurring.
Claims 185 and 187:
Sweeney further teaches wherein the operations further comprise triggering (See Abstract, column 2, line 39-45, where a cardiac arrhythmia trigger/marker is detected.), using the one or more processors (See device with microprocessor/controller and modules in column 8, line 59-45, column 9, lines 18-32.), based on the risk prediction forecast (See predictions of future arrhythmias in column 13, lines 41-46, column 14, lines 22-50.), a change of at least one operational setting of the cardiac device for configuring a time to apply a treatment to the subject by the cardiac device after a detection of occurrence of a medical event (See column 11, line 32 to column 12, line 4, Fig. 4A, where detection value can be set for the activity of a stroke and other sequence type triggers. In [column 8, line 56 to column 9, line 17] A therapy module 315 provides therapy for treatment of present arrhythmias and prevention of future arrhythmias. In one embodiment, such therapy is provided at electrodes associated with heart 115 or portions of the nervous system.).
Therefore, it would have been obvious to one of ordinary skill in the art of cardiac rhythm management before the effective filing date of the invention to modify the system and method of Ong and An to have triggering based on the risk prediction forecast, a change of at least one operational setting of the medical device for configuring a time to apply a treatment to the subject by the medical device after a detection of occurrence of a medical event, as taught by Sweeney, to reduce the consequences of an arrhythmia from occurring.
Claim 196 is rejected under 35 U.S.C. 103(a) as being unpatentable over Ong (US 2014/0257122 A1) in view of An (US 2015/0157273 A1) further in view of Najarian (US 2015/0374300 A1).
Claim 196:
Although Ong and An teach the system of claim 1 as mentioned above, Ong and An do not explicitly teach administering a treatment. Najarian teaches wherein adjusting one or more operational parameters of the cardiac device further comprises administering a treatment including a defibrillation, a shock, or a compression (See Fig. 9 Therapeutic Delivery Vehicle mentioned in P0078 and P0020, where a care provider acting more quickly could compress the patient’s chest by performing CPR.).
Therefore, it would have been obvious to one of ordinary skill in the art of detecting hemodynamic conditions before the effective filing date of the invention to modify the system and method of Ong and An to have administering a treatment, as taught by Najarian, to fully address the subject's condition, in the event of a life-threatening emergency.
Response to Arguments
Regarding the prior art rejections, Applicant’s arguments have been fully considered, but are now moot in view of the new grounds of rejection. The Examiner has entered a new rejection under 35 USC § 103(a) and applied art already of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. (See Schneider (US 2009/0177102 A1) & Edelberg (US 12,402,839 B2).).
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/T.S.W./Examiner, Art Unit 3687 04/29/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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