DETAILED ACTION
Applicant’s arguments, filed on 09/29/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 09/29/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-22 are the current claims hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-10, and 12-22 are rejected under 35 U.S.C. 103 as being unpatentable over Al Hatib (US 20180008205) in further view of Stapelfeldt (US 20140107504).
Regarding independent claim 1, Al Hatib teaches a method for monitoring of arterial pressure of a patient and providing a warning to medical personnel of a predicted future hypotension event of the patient ([0006]: “There are provided systems and methods for performing predictive weighting of hypotension profiling parameters, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.”), the method comprising:
receiving, by a hemodynamic monitor, sensed hemodynamic data representative of an arterial pressure waveform of the patient ([0019]: “Signal 142 received by health monitoring system 100 from hemodynamic sensor 140 may include signals corresponding to the arterial pressure of living subject 130, such as an arterial pressure waveform of living subject”);
performing, by the hemodynamic monitor, waveform analysis of the hemodynamic data to determine a plurality of hypotension profiling parameters predictive of the future hypotension event for the patient ([0020]: “System processor 104 is further configured to execute hypotension prediction software code 110 to transform digital hemodynamic data 144 to multiple hypotension profiling parameters”).
Al Hatib teaches generating a set of transformed profiling parameters ([0014]: “The present application discloses systems and methods for performing predictive weighting of hypotension profiling parameters. By converting data received from a hemodynamic sensor to digital hemodynamic data of a living subject, and by transforming the digital hemodynamic data to multiple hypotension profiling parameters, the present solution employs a powerful multivariate model for predicting future hypotension”) and analyzing the MAP results based on a threshold ([0052]: “It is noted that in implementations in which one or more vital sign parameters characterizing vital sign data includes the MAP of living subject 130/230, the weighting applied to the MAP may depend on the value of the MAP itself. Where the MAP is very high, the MAP may be a relatively unreliable predictor of hypotension and may consequently be very lightly weighted. That is to say, where the MAP exceeds a predetermined upper limit threshold, for example, the weighting applied to the MAP may be such that the weighted combination of hypotension profiling parameters 112/212 results in the MAP being substantially disregarded in determination of the risk score. By contrast, in other cases, the MAP may dominate the determination of the risk score”), however Al Hatib does not disclose generating, by the hemodynamic monitor in real time, the set of transformed profiling parameters in response to receiving an adjusted mean arterial pressure (MAP) threshold.
Stapelfeldt discloses systems and method for monitoring a patient, including their blood pressure. Specifically, Stapelfeldt teaches monitoring in real-time ([0060]: “The guidance engine 152 can analyze such information and compute decision support data that can be presented to a user in the form of real-time assistance”), and utilizing an adjusted mean arterial pressure (MAP) threshold for analysis ([0026]: “The threshold values as well as the maximum cumulative times permissible exceeding these threshold values can be defined via any appropriate means, including individually for each patient based on a patient history as, for example, extracted from an electronic health records (EHR) system; applicable to certain patient populations (such as adult patients undergoing non-cardiac surgery); selected as a list of standard threshold values; derived from a determined minimum or maximum threshold for the patient and a standardized inter-threshold interval; or provided by a user at an associated input device “; [0035]: “The relationship between the severity of hypotension (the hypotensive MAP threshold exceeded), the cumulative amount of time spent below that threshold and adjusted odds of 30-day mortality is depicted in FIG. 7. As demonstrated in the figure, the cumulative amount of time spent below each of the various MAP thresholds was independently associated with an increased odds ratio for 30-day mortality. Furthermore, dropping below progressively lower MAP thresholds had a progressively greater adverse association with 30-day mortality per unit of time accumulated below that threshold. For any given MAP threshold, patients carrying a preoperative diagnosis of hypertension (prevalence of 40% according to the definition above) required less cumulative time to be accrued below that threshold to incur the same increase in 30-day mortality odds ratio as patients without a history of hypertension. The resulting cumulative exposure times below each of the MAP thresholds (in minutes) that corresponded to the same respective increases in the odds ratio of 30-day mortality ranging from 5% to 50% (risk-based "smart sets") are listed in Tables 1 and 2”). Al Hatib and Stapelfeldt are analogous arts as they are both related to monitoring user’s physiological parameters, including blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the real-time monitoring from Stapelfeldt into the method from Al Hatib as it allows the method to determine the profiles in real time, which can provide the user with their results as soon as they are determined, ensuring that they are aware of their health status in real time. Additionally, utilizing the adjusted threshold allows for the analysis to use more personalized thresholds, ensuring that the results are more accurate to the specific user.
The Al Hatib/Stapelfeldt combination teaches the steps of generating, by the hemodynamic monitor in real-time (Stapelfeldt, [0060]: “The guidance engine 152 can analyze such information and compute decision support data that can be presented to a user in the form of real-time assistance”), a set of transformed hypotension profiling parameters (Al Hatib, [0014]: “The present application discloses systems and methods for performing predictive weighting of hypotension profiling parameters. By converting data received from a hemodynamic sensor to digital hemodynamic data of a living subject, and by transforming the digital hemodynamic data to multiple hypotension profiling parameters, the present solution employs a powerful multivariate model for predicting future hypotension”) in response to receiving an adjusted mean arterial pressure (MAP) threshold (Stapelfeldt, [0026]: “The threshold values as well as the maximum cumulative times permissible exceeding these threshold values can be defined via any appropriate means, including individually for each patient based on a patient history as, for example, extracted from an electronic health records (EHR) system; applicable to certain patient populations (such as adult patients undergoing non-cardiac surgery); selected as a list of standard threshold values; derived from a determined minimum or maximum threshold for the patient and a standardized inter-threshold interval; or provided by a user at an associated input device “; [0035]: “The relationship between the severity of hypotension (the hypotensive MAP threshold exceeded), the cumulative amount of time spent below that threshold and adjusted odds of 30-day mortality is depicted in FIG. 7. As demonstrated in the figure, the cumulative amount of time spent below each of the various MAP thresholds was independently associated with an increased odds ratio for 30-day mortality. Furthermore, dropping below progressively lower MAP thresholds had a progressively greater adverse association with 30-day mortality per unit of time accumulated below that threshold. For any given MAP threshold, patients carrying a preoperative diagnosis of hypertension (prevalence of 40% according to the definition above) required less cumulative time to be accrued below that threshold to incur the same increase in 30-day mortality odds ratio as patients without a history of hypertension. The resulting cumulative exposure times below each of the MAP thresholds (in minutes) that corresponded to the same respective increases in the odds ratio of 30-day mortality ranging from 5% to 50% (risk-based "smart sets") are listed in Tables 1 and 2”), each transformed hypotension profiling parameter being a function of a corresponding one of the plurality of hypotension profiling parameters at a standard MAP threshold for hypotension, a mean of the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold for hypotension, a standard deviation of the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold, a mean of the corresponding one of the plurality of hypotension profiling parameters at the adjusted MAP threshold for hypotension, and a standard deviation of the corresponding one of the plurality of hypotension profiling parameters at the adjusted MAP threshold for hypotension (Al Hatib, [0045]: “changes in mean arterial pressure (ΔMAP) can be derived as a differential parameter with respect to time and/or sampling frequency, and so forth. As a further example, changes in mean arterial pressure with respect to time can be derived by subtracting the average of the mean arterial pressure over the past 5 minutes, over the past 10 minutes, and so on from the current value of the mean arterial pressure.”; [0039]: “Also of potential diagnostic interest is the behavior of arterial pressure waveform 580 in the interval from the maximum systolic pressure at indicia 584 to the diastole at indicia 588 (hereinafter “interval 584-588”), as well as the behavior of arterial pressure waveform 580 from the start of the heartbeat at indicia 582 to the diastole at indicia 588 (hereinafter “heartbeat interval 582-588”). The behavior of arterial pressure waveform 580 during intervals: 1) systolic rise 582-584, 2) systolic decay 584-586, 3) systolic phase 582-586, 4) diastolic phase 586-588, 5) interval 584-588, and 6) heartbeat interval 582-588 may be determined by measuring the area under the curve of arterial pressure waveform 580 and the standard deviation of arterial pressure waveform 580 in each of those intervals, for example. The respective areas and standard deviations measured for intervals 1, 2, 3, 4, 5, and 6 above (hereinafter “intervals 1-6”) may serve as additional indicia predictive of future hypotension for living subject 130/240.”);
determining, by the hemodynamic monitor based on the set of transformed hypotension profiling parameters, a risk score representing a probability of the future hypotension event for the patient (Al Hatib, [0021]: “System processor 104 is further configured to execute hypotension prediction software code 110 to use predictive weighting module 116 to determine a risk score corresponding to the probability of a future hypotension event for living subject 130 based on a weighted combination of hypotension profiling parameters 112”); and
invoking, by the hemodynamic monitor, a sensory alarm to produce a sensory signal in response to the risk score satisfying a predetermined risk criterion (Al Hatib, [0021]: “system processor 104 is configured to execute hypotension prediction software code 110 to invoke sensory alarm 128 if the risk score satisfies a predetermined risk criterion”).
Regarding claim 3, the Al Hatib/Stapelfeldt combination teaches the method of claim 1, wherein determining the risk score representing the probability of the future hypotension event for the patient comprises applying a plurality of risk coefficients to the set of transformed hypotension profiling parameters to determine the risk score (Al Hatib, [0053]-0074]).
Regarding claim 4, the Al Hatib/Stapelfeldt combination teaches the method of claim 3, wherein the plurality of risk coefficients are determined based on the standard MAP threshold (Stapelfeldt, [0026]: “The threshold values as well as the maximum cumulative times permissible exceeding these threshold values can be defined via any appropriate means, including individually for each patient based on a patient history as, for example, extracted from an electronic health records (EHR) system; applicable to certain patient populations (such as adult patients undergoing non-cardiac surgery); selected as a list of standard threshold values; derived from a determined minimum or maximum threshold for the patient and a standardized inter-threshold interval; or provided by a user at an associated input device “; [0035]: “The relationship between the severity of hypotension (the hypotensive MAP threshold exceeded), the cumulative amount of time spent below that threshold and adjusted odds of 30-day mortality is depicted in FIG. 7. As demonstrated in the figure, the cumulative amount of time spent below each of the various MAP thresholds was independently associated with an increased odds ratio for 30-day mortality. Furthermore, dropping below progressively lower MAP thresholds had a progressively greater adverse association with 30-day mortality per unit of time accumulated below that threshold. For any given MAP threshold, patients carrying a preoperative diagnosis of hypertension (prevalence of 40% according to the definition above) required less cumulative time to be accrued below that threshold to incur the same increase in 30-day mortality odds ratio as patients without a history of hypertension. The resulting cumulative exposure times below each of the MAP thresholds (in minutes) that corresponded to the same respective increases in the odds ratio of 30-day mortality ranging from 5% to 50% (risk-based "smart sets") are listed in Tables 1 and 2”; Al Hatib, [0021]: “System processor 104 is further configured to execute hypotension prediction software code 110 to use predictive weighting module 116 to determine a risk score corresponding to the probability of a future hypotension event for living subject 130 based on a weighted combination of hypotension profiling parameters 112”).
Regarding claim 5, the Al Hatib/Stapelfeldt combination teaches the method of claim 1, wherein performing the waveform analysis of the hemodynamic data to determine the plurality of hypotension profiling parameters predictive of the future hypotension event for the patient comprises: performing the waveform analysis of the hemodynamic data to obtain vital sign parameters from the hemodynamic data (Al Hatib, [0016]: “System memory 106 is further shown to store hypotension profiling parameters 112 including one or more vital sign parameters characterizing vital sign data”); deriving differential parameters based on one or more of the vital sign parameters; and generating combinatorial parameters using one or more of the vital sign parameters and/or one or more of the differential parameters; wherein the plurality of hypotension profiling parameters include one or more of the vital sign parameters, the differential parameters, and the combinatorial parameters (Al Hatib, [0020]: “It is noted that hypotension profiling parameters 112 include one or more vital sign parameters characterizing vital sign data, as well as differential and combinatorial parameters derived from the one or more vital sign parameters.”).
Regarding claim 6, the Al Hatib/Stapelfeldt combination teaches the method of claim 5, wherein the vital sign parameters include one or more of stroke volume, heart rate, respiration, and cardiac contractibility (Al Hatib, [0041]: “The one or more vital sign parameters characterizing vital sign data may include stroke volume, heart rate, respiration, and cardiac contractility”).
Regarding claim 7, the Al Hatib/Stapelfeldt combination teaches the method of claim 5, wherein deriving the differential parameters based on one or more of the vital sign parameters comprises deriving the differential parameters to represent variations in the one or more of the vital sign parameters with respect to time or with respect to frequency, or with respect to other of the vital sign parameters (Al Hatib, [0044]: “The differential parameters may be derived by determining the variations of one or more vital sign parameters with respect to time, with respect to frequency, or with respect to other parameters from among one or more vital sign parameters”).
Regarding claim 8, the Al Hatib/Stapelfeldt combination teaches the method of claim 5, wherein generating the combinatorial parameters comprises generating the combinatorial parameters as a combination of the vital sign parameters, a combination of the differential parameters, or a combination of at least one of the vital sign parameter and at least one differential parameter (Al Hatib, [0046]: “the combinatorial parameters may be generated using the one or more vital sign parameters and the differential parameters by generating a power combination of a subset of the one or more vital sign parameters and the differential parameters. It is noted that, as used in the present application, the characterization “a subset of the one or more vital sign parameters and the differential parameters” refers to a subset that includes at least one of the one or more vital sign parameters and/or at least one of the differential parameters.”).
Regarding claim 9, the Al Hatib/Stapelfeldt combination teaches the method of claim 1, further comprising: receiving, by the hemodynamic monitor, the adjusted MAP threshold for hypotension via a user interface of the hemodynamic monitor (Al Hatib, [0018]: “user interface 120 is configured to receive inputs”; Stapelfeldt, [0026]: “The threshold values as well as the maximum cumulative times permissible exceeding these threshold values can be defined via any appropriate means, including individually for each patient based on a patient history as, for example, extracted from an electronic health records (EHR) system; applicable to certain patient populations (such as adult patients undergoing non-cardiac surgery); selected as a list of standard threshold values; derived from a determined minimum or maximum threshold for the patient and a standardized inter-threshold interval; or provided by a user at an associated input device “; [0035]: “The relationship between the severity of hypotension (the hypotensive MAP threshold exceeded), the cumulative amount of time spent below that threshold and adjusted odds of 30-day mortality is depicted in FIG. 7. As demonstrated in the figure, the cumulative amount of time spent below each of the various MAP thresholds was independently associated with an increased odds ratio for 30-day mortality. Furthermore, dropping below progressively lower MAP thresholds had a progressively greater adverse association with 30-day mortality per unit of time accumulated below that threshold. For any given MAP threshold, patients carrying a preoperative diagnosis of hypertension (prevalence of 40% according to the definition above) required less cumulative time to be accrued below that threshold to incur the same increase in 30-day mortality odds ratio as patients without a history of hypertension. The resulting cumulative exposure times below each of the MAP thresholds (in minutes) that corresponded to the same respective increases in the odds ratio of 30-day mortality ranging from 5% to 50% (risk-based "smart sets") are listed in Tables 1 and 2”; Al Hatib, [0021]: “System processor 104 is further configured to execute hypotension prediction software code 110 to use predictive weighting module 116 to determine a risk score corresponding to the probability of a future hypotension event for living subject 130 based on a weighted combination of hypotension profiling parameters 112”).
Regarding independent claim 10, Al Hatib teaches a system for monitoring of arterial pressure of a patient and providing a warning to medical personnel of a predicted future hypotension event of the patient ([0006]: “There are provided systems and methods for performing predictive weighting of hypotension profiling parameters, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.”), the system comprising:
a hemodynamic sensor that produces hemodynamic data representative of an arterial pressure waveform of the patient ([0019]: “Signal 142 received by health monitoring system 100 from hemodynamic sensor 140 may include signals corresponding to the arterial pressure of living subject 130, such as an arterial pressure waveform of living subject”);
a system memory that stores hypotension prediction software code including a predictive weighting module ([0016]: “Health monitoring system 100 includes hardware unit 102, which may be an integrated hardware unit, for example, including system processor 104, implemented as a hardware processor, analog-to-digital converter (ADC) 122, and system memory 106. As shown in FIG. 1, health monitoring system 100 also includes hypotension prediction software code 110 including predictive weighting module 116, stored in system memory 106”);
a user interface that includes a sensory alarm that provides a sensory signal to warn the medical personnel of the predicted future hypotension event prior to the patient entering a hypotensive state ([0018]: “user interface 120 is configured to receive inputs 152 from user 150, and to provide sensory alarm 128 if a risk score predictive of a future hypotension event for living subject 130 satisfies a predetermined risk criterion.”); and
a hardware processor that is configured to execute the hypotension prediction software code ([0016]: “Health monitoring system 100 includes hardware unit 102, which may be an integrated hardware unit, for example, including system processor 104, implemented as a hardware processor … health monitoring system 100 also includes hypotension prediction software code 110 including predictive weighting module 116, stored in system memory 106”) to:
perform waveform analysis of the hemodynamic data to determine a plurality of hypotension profiling parameters predictive of the future hypotension event for the patient ([0020]: “System processor 104 is further configured to execute hypotension prediction software code 110 to transform digital hemodynamic data 144 to multiple hypotension profiling parameters”).
Al Hatib teaches generating a set of transformed profiling parameters ([0014]: “The present application discloses systems and methods for performing predictive weighting of hypotension profiling parameters. By converting data received from a hemodynamic sensor to digital hemodynamic data of a living subject, and by transforming the digital hemodynamic data to multiple hypotension profiling parameters, the present solution employs a powerful multivariate model for predicting future hypotension”) and analyzing the MAP results based on a threshold ([0052]: “It is noted that in implementations in which one or more vital sign parameters characterizing vital sign data includes the MAP of living subject 130/230, the weighting applied to the MAP may depend on the value of the MAP itself. Where the MAP is very high, the MAP may be a relatively unreliable predictor of hypotension and may consequently be very lightly weighted. That is to say, where the MAP exceeds a predetermined upper limit threshold, for example, the weighting applied to the MAP may be such that the weighted combination of hypotension profiling parameters 112/212 results in the MAP being substantially disregarded in determination of the risk score. By contrast, in other cases, the MAP may dominate the determination of the risk score”), however Al Hatib does not disclose generating, by the hemodynamic monitor in real time, the set of transformed profiling parameters in response to receiving an adjusted mean arterial pressure (MAP) threshold.
Stapelfeldt discloses systems and method for monitoring a patient, including their blood pressure. Specifically, Stapelfeldt teaches monitoring in real-time ([0060]: “The guidance engine 152 can analyze such information and compute decision support data that can be presented to a user in the form of real-time assistance”), and utilizing an adjusted mean arterial pressure (MAP) threshold for analysis ([0026]: “The threshold values as well as the maximum cumulative times permissible exceeding these threshold values can be defined via any appropriate means, including individually for each patient based on a patient history as, for example, extracted from an electronic health records (EHR) system; applicable to certain patient populations (such as adult patients undergoing non-cardiac surgery); selected as a list of standard threshold values; derived from a determined minimum or maximum threshold for the patient and a standardized inter-threshold interval; or provided by a user at an associated input device “; [0035]: “The relationship between the severity of hypotension (the hypotensive MAP threshold exceeded), the cumulative amount of time spent below that threshold and adjusted odds of 30-day mortality is depicted in FIG. 7. As demonstrated in the figure, the cumulative amount of time spent below each of the various MAP thresholds was independently associated with an increased odds ratio for 30-day mortality. Furthermore, dropping below progressively lower MAP thresholds had a progressively greater adverse association with 30-day mortality per unit of time accumulated below that threshold. For any given MAP threshold, patients carrying a preoperative diagnosis of hypertension (prevalence of 40% according to the definition above) required less cumulative time to be accrued below that threshold to incur the same increase in 30-day mortality odds ratio as patients without a history of hypertension. The resulting cumulative exposure times below each of the MAP thresholds (in minutes) that corresponded to the same respective increases in the odds ratio of 30-day mortality ranging from 5% to 50% (risk-based "smart sets") are listed in Tables 1 and 2”). Al Hatib and Stapelfeldt are analogous arts as they are both related to monitoring user’s physiological parameters, including blood pressure.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the real-time monitoring from Stapelfeldt into the method from Al Hatib as it allows the system to determine the profiles in real time, which can provide the user with their results as soon as they are determined, ensuring that they are aware of their health status in real time. Additionally, utilizing the adjusted threshold allows for the analysis to use more personalized thresholds, ensuring that the results are more accurate to the specific user.
The Al Hatib/Stapelfeldt combination teaches the steps of generating, by the hemodynamic monitor in real-time (Stapelfeldt, [0060]: “The guidance engine 152 can analyze such information and compute decision support data that can be presented to a user in the form of real-time assistance”), a set of transformed hypotension profiling parameters (Al Hatib, [0014]: “The present application discloses systems and methods for performing predictive weighting of hypotension profiling parameters. By converting data received from a hemodynamic sensor to digital hemodynamic data of a living subject, and by transforming the digital hemodynamic data to multiple hypotension profiling parameters, the present solution employs a powerful multivariate model for predicting future hypotension”) in response to receiving an adjusted mean arterial pressure (MAP) threshold (Stapelfeldt, [0026]: “The threshold values as well as the maximum cumulative times permissible exceeding these threshold values can be defined via any appropriate means, including individually for each patient based on a patient history as, for example, extracted from an electronic health records (EHR) system; applicable to certain patient populations (such as adult patients undergoing non-cardiac surgery); selected as a list of standard threshold values; derived from a determined minimum or maximum threshold for the patient and a standardized inter-threshold interval; or provided by a user at an associated input device “; [0035]: “The relationship between the severity of hypotension (the hypotensive MAP threshold exceeded), the cumulative amount of time spent below that threshold and adjusted odds of 30-day mortality is depicted in FIG. 7. As demonstrated in the figure, the cumulative amount of time spent below each of the various MAP thresholds was independently associated with an increased odds ratio for 30-day mortality. Furthermore, dropping below progressively lower MAP thresholds had a progressively greater adverse association with 30-day mortality per unit of time accumulated below that threshold. For any given MAP threshold, patients carrying a preoperative diagnosis of hypertension (prevalence of 40% according to the definition above) required less cumulative time to be accrued below that threshold to incur the same increase in 30-day mortality odds ratio as patients without a history of hypertension. The resulting cumulative exposure times below each of the MAP thresholds (in minutes) that corresponded to the same respective increases in the odds ratio of 30-day mortality ranging from 5% to 50% (risk-based "smart sets") are listed in Tables 1 and 2”), each transformed hypotension profiling parameter being a function of a corresponding one of the plurality of hypotension profiling parameters at a standard MAP threshold for hypotension, a mean of the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold for hypotension, a standard deviation of the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold, a mean of the corresponding one of the plurality of hypotension profiling parameters at the adjusted MAP threshold for hypotension, and a standard deviation of the corresponding one of the plurality of hypotension profiling parameters at the adjusted MAP threshold for hypotension (Al Hatib, [0045]: “changes in mean arterial pressure (ΔMAP) can be derived as a differential parameter with respect to time and/or sampling frequency, and so forth. As a further example, changes in mean arterial pressure with respect to time can be derived by subtracting the average of the mean arterial pressure over the past 5 minutes, over the past 10 minutes, and so on from the current value of the mean arterial pressure.”; [0039]: “Also of potential diagnostic interest is the behavior of arterial pressure waveform 580 in the interval from the maximum systolic pressure at indicia 584 to the diastole at indicia 588 (hereinafter “interval 584-588”), as well as the behavior of arterial pressure waveform 580 from the start of the heartbeat at indicia 582 to the diastole at indicia 588 (hereinafter “heartbeat interval 582-588”). The behavior of arterial pressure waveform 580 during intervals: 1) systolic rise 582-584, 2) systolic decay 584-586, 3) systolic phase 582-586, 4) diastolic phase 586-588, 5) interval 584-588, and 6) heartbeat interval 582-588 may be determined by measuring the area under the curve of arterial pressure waveform 580 and the standard deviation of arterial pressure waveform 580 in each of those intervals, for example. The respective areas and standard deviations measured for intervals 1, 2, 3, 4, 5, and 6 above (hereinafter “intervals 1-6”) may serve as additional indicia predictive of future hypotension for living subject 130/240.”);
determine, based on the set of transformed hypotension profiling parameters, a risk score representing a probability of the future hypotension event for the patient (Al Hatib, [0021]: “System processor 104 is further configured to execute hypotension prediction software code 110 to use predictive weighting module 116 to determine a risk score corresponding to the probability of a future hypotension event for living subject 130 based on a weighted combination of hypotension profiling parameters 112”); and
invoke the sensory alarm of the user interface in response to the risk score satisfying a predetermined risk criterion (Al Hatib, [0021]: “system processor 104 is configured to execute hypotension prediction software code 110 to invoke sensory alarm 128 if the risk score satisfies a predetermined risk criterion”).
Regarding claim 12, the Al Hatib/Stapelfeldt combination teaches the system of claim 10, wherein the hardware processor is configured to execute the hypotension prediction software code to determine the risk score representing the probability of the future hypotension event for the patient by applying, using the predictive weighting module, a plurality of risk coefficients to the plurality of hypotension profiling parameters to determine the risk score (Al Hatib, [0053]-0074]; [0021]: “System processor 104 is further configured to execute hypotension prediction software code 110 to use predictive weighting module 116 to determine a risk score corresponding to the probability of a future hypotension event for living subject 130 based on a weighted combination of hypotension profiling parameters 112”).
Regarding claim 13, the Al Hatib/Stapelfeldt combination teaches the system of claim 12, wherein the plurality of risk coefficients are determined based on the standard MAP threshold (Al Hatib, [0053]-0074]; [0021]: “System processor 104 is further configured to execute hypotension prediction software code 110 to use predictive weighting module 116 to determine a risk score corresponding to the probability of a future hypotension event for living subject 130 based on a weighted combination of hypotension profiling parameters 112”; Stapelfeldt, [0026]: “The threshold values as well as the maximum cumulative times permissible exceeding these threshold values can be defined via any appropriate means, including individually for each patient based on a patient history as, for example, extracted from an electronic health records (EwaHR) system; applicable to certain patient populations (such as adult patients undergoing non-cardiac surgery); selected as a list of standard threshold values; derived from a determined minimum or maximum threshold for the patient and a standardized inter-threshold interval; or provided by a user at an associated input device “; [0035]: “The relationship between the severity of hypotension (the hypotensive MAP threshold exceeded), the cumulative amount of time spent below that threshold and adjusted odds of 30-day mortality is depicted in FIG. 7. As demonstrated in the figure, the cumulative amount of time spent below each of the various MAP thresholds was independently associated with an increased odds ratio for 30-day mortality. Furthermore, dropping below progressively lower MAP thresholds had a progressively greater adverse association with 30-day mortality per unit of time accumulated below that threshold. For any given MAP threshold, patients carrying a preoperative diagnosis of hypertension (prevalence of 40% according to the definition above) required less cumulative time to be accrued below that threshold to incur the same increase in 30-day mortality odds ratio as patients without a history of hypertension. The resulting cumulative exposure times below each of the MAP thresholds (in minutes) that corresponded to the same respective increases in the odds ratio of 30-day mortality ranging from 5% to 50% (risk-based "smart sets") are listed in Tables 1 and 2”; Al Hatib, [0021]: “System processor 104 is further configured to execute hypotension prediction software code 110 to use predictive weighting module 116 to determine a risk score corresponding to the probability of a future hypotension event for living subject 130 based on a weighted combination of hypotension profiling parameters 112”).
Regarding claim 14, the Al Hatib/Stapelfeldt combination teaches the system of claim 10, wherein the hardware processor is configured to execute the hypotension prediction software code to determine the plurality of hypotension profiling parameters predictive of the future hypotension event for the patient by executing the hypotension prediction software code to: perform the waveform analysis of the hemodynamic data to obtain vital sign parameters from the adjusted hemodynamic data (Al Hatib, [0016]: “System memory 106 is further shown to store hypotension profiling parameters 112 including one or more vital sign parameters characterizing vital sign data”); derive differential parameters based on one or more of the vital sign parameters; wherein the plurality of hypotension profiling parameters include one or more of the vital sign parameters, the differential parameters, and the combinatorial parameters (Al Hatib, [0020]: “It is noted that hypotension profiling parameters 112 include one or more vital sign parameters characterizing vital sign data, as well as differential and combinatorial parameters derived from the one or more vital sign parameters.”).
Regarding claim 15, the Al Hatib/Stapelfeldt combination teaches the system of claim 14, wherein the vital sign parameters include one or more of stroke volume, heart rate, respiration, and cardiac contractibility (Al Hatib, [0041]: “The one or more vital sign parameters characterizing vital sign data may include stroke volume, heart rate, respiration, and cardiac contractility”).
Regarding claim 16, the Al Hatib/Stapelfeldt combination teaches the system of claim 15, wherein the hardware processor is configured to execute the hypotension prediction software code to derive the differential parameters based on one or more of the vital sign parameters by deriving the differential parameters to represent variations in the one or more of the vital sign parameters with respect to time or with respect to frequency, or with respect to other of the vital sign parameters (Al Hatib, [0044]: “The differential parameters may be derived by determining the variations of one or more vital sign parameters with respect to time, with respect to frequency, or with respect to other parameters from among one or more vital sign parameters”).
Regarding claim 17, the Al Hatib/Stapelfeldt combination teaches the system of claim 15, wherein the hardware processor is configured to execute the hypotension prediction software code to generate the combinatorial parameters by generating the combinatorial parameters as a combination of the vital sign parameters, a combination of the differential parameters, or a combination of at least one of the vital sign parameter and at least one of the differential parameter (Al Hatib, [0046]: “the combinatorial parameters may be generated using the one or more vital sign parameters and the differential parameters by generating a power combination of a subset of the one or more vital sign parameters and the differential parameters. It is noted that, as used in the present application, the characterization “a subset of the one or more vital sign parameters and the differential parameters” refers to a subset that includes at least one of the one or more vital sign parameters and/or at least one of the differential parameters.”).
Regarding claim 18, the Al Hatib/Stapelfeldt combination teaches the system of claim 10, wherein the hemodynamic sensor is a noninvasive hemodynamic sensor that is attachable to an extremity of the patient (Al Hatib, [0019]: “it is noted that hemodynamic sensor 140 may be a non-invasive or minimally invasive sensor attached to living subject 130. In one implementation, as represented in FIG. 1, hemodynamic sensor 140 may be attached non-invasively at an extremity of living subject 130, such as a wrist or finger of living subject 130”).
Regarding claim 19, the Al Hatib/Stapelfeldt combination teaches the system of claim 10, wherein the hemodynamic sensor is a minimally invasive arterial catheter based hemodynamic sensor (Al Hatib, [0030]: “hemodynamic sensor 240b is designed to sense an arterial pressure of living subject 230 in a minimally invasive manner. For example, hemodynamic sensor 240b may be attached to living subject 230 via a radial arterial catheter inserted into an arm of living subject 230”).
Regarding claim 20, the Al Hatib/Stapelfeldt combination teaches the system of claim 10, wherein the hemodynamic sensor produces the hemodynamic data as an analog hemodynamic sensor signal representative of the arterial pressure waveform of the patient (Al Hatib, [0036]: “Hardware unit 102/202/302 of system 100/200A/200B/300 may be configured to receive the determined central arterial pressure of living subject 130/230 as signal 142/242, which may be received as analog signals”).
Regarding claim 21, the Al Hatib/Stapelfeldt combination teaches the system of claim 20, further comprising: an analog-to-digital converter that converts the analog hemodynamic sensor signal to digital hemodynamic data representative of the arterial pressure waveform of the patient (Al Hatib, [0017]: “health monitoring system 100 includes digital-to-analog converter 124 (hereinafter “DAC 124”)”; [0036]: “Hardware unit 102/202/302 of system 100/200A/200B/300 may be configured to receive the determined central arterial pressure of living subject 130/230 as signal 142/242, which may be received as analog signals. In such an implementation, ADC 122/222 is used to convert signal 142/242 into digital hemodynamic data 144/244”).
Regarding claim 22, the Al Hatib/Stapelfeldt combination teaches the system of claim 10, wherein the user interface further includes control elements that enable user input of the adjusted MAP threshold for hypotension (Al Hatib, [0018]: “user interface 120 is configured to receive inputs”; Stapelfeldt, [0026]: “The threshold values as well as the maximum cumulative times permissible exceeding these threshold values can be defined via any appropriate means, including individually for each patient based on a patient history as, for example, extracted from an electronic health records (EHR) system; applicable to certain patient populations (such as adult patients undergoing non-cardiac surgery); selected as a list of standard threshold values; derived from a determined minimum or maximum threshold for the patient and a standardized inter-threshold interval; or provided by a user at an associated input device “; [0035]: “The relationship between the severity of hypotension (the hypotensive MAP threshold exceeded), the cumulative amount of time spent below that threshold and adjusted odds of 30-day mortality is depicted in FIG. 7. As demonstrated in the figure, the cumulative amount of time spent below each of the various MAP thresholds was independently associated with an increased odds ratio for 30-day mortality. Furthermore, dropping below progressively lower MAP thresholds had a progressively greater adverse association with 30-day mortality per unit of time accumulated below that threshold. For any given MAP threshold, patients carrying a preoperative diagnosis of hypertension (prevalence of 40% according to the definition above) required less cumulative time to be accrued below that threshold to incur the same increase in 30-day mortality odds ratio as patients without a history of hypertension. The resulting cumulative exposure times below each of the MAP thresholds (in minutes) that corresponded to the same respective increases in the odds ratio of 30-day mortality ranging from 5% to 50% (risk-based "smart sets") are listed in Tables 1 and 2”; Al Hatib, [0021]: “System processor 104 is further configured to execute hypotension prediction software code 110 to use predictive weighting module 116 to determine a risk score corresponding to the probability of a future hypotension event for living subject 130 based on a weighted combination of hypotension profiling parameters 112”).
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over the Al Hatib/Stapelfeldt combination as applied to claims 1 and 10 above, and further in view of Stockburger (Introductory Statistics: Concepts, Models, and Applications, “Chapter 13: Linear Transformations”).
Regarding claim 2, the Al Hatib/Stapelfeldt combination teaches the method of claim 1.
However, the Al Hatib/Stapelfeldt combination does not teach wherein generating the set of transformed hypotension profiling parameters comprises transforming each of the plurality of hypotension profiling parameters according to the following equation:
PNG
media_image1.png
40
247
media_image1.png
Greyscale
wherein vkθ is one of the set of transformed set of hypotension profiling parameters associated with the corresponding one of the plurality of hypotension profiling parameters; wherein σkθ is the standard deviation of the corresponding one of the plurality of hypotension profiling parameters at the adjusted MAP threshold for hypotension; wherein σk is the standard deviation of the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold for hypotension; wherein vk is the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold for hypotension; wherein μkθ is the mean of the corresponding one of the plurality of hypotension profiling parameters at the adjusted MAP threshold for hypotension; and wherein μk is the mean of the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold for hypotension.
Stockburger discloses linear transformation calculations. Specifically, Stockburger teaches the calculations and equations involved in linear transformations (Full page). Stockburger and the Al Hatib/Stapelfeldt combination are analogous arts as they are both related to calculations done on processing data.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the calculations from Stockburger into the Al Hatib/Stapelfeldt combination, as the Al Hatib/Stapelfeldt combination is silent on the types of calculations performed, and Stockburger provides suitable calculations that can be used in the method.
Regarding claim 11, the Al Hatib/Stapelfeldt combination teaches the system of claim 10.
However, the Al Hatib/Stapelfeldt combination does not teach wherein the hardware processor is configured to generate the set of transformed hypotension profiling parameters according to the following equation:
PNG
media_image1.png
40
247
media_image1.png
Greyscale
wherein vkθ is one of the set of transformed set of hypotension profiling parameters associated with the corresponding one of the plurality of hypotension profiling parameters; wherein σkθ is the standard deviation of the corresponding one of the plurality of hypotension profiling parameters at the adjusted MAP threshold for hypotension; wherein σk is the standard deviation of the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold for hypotension; wherein vk is the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold for hypotension; wherein μkθ is the mean of the corresponding one of the plurality of hypotension profiling parameters at the adjusted MAP threshold for hypotension; and wherein μk is the mean of the corresponding one of the plurality of hypotension profiling parameters at the standard MAP threshold for hypotension.
Stockburger teaches the calculations and equations involved in linear transformations (Full page).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the calculations from Stockburger into the Al Hatib/Stapelfeldt combination, as the Al Hatib/Stapelfeldt combination is silent on the types of calculations performed, and Stockburger provides suitable calculations that can be used in the system.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant’s arguments with respect to the adjusted MAP threshold in claims 1-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments with respect to the five-part function of claims 1-22 have been fully considered but they are not persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a five-part function by which the hypotension profiling parameters are transformed) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Independent claims 1 and 10 do not claim a specific five-part function, all that is required in the claims is a set of transformed profiling parameters being a function of any of the listed parameters, not a five part function or any specific function utilized in the hypotension profiling parameters.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIN K MCCORMACK whose telephone number is (703)756-1886. The examiner can normally be reached Mon-Fri 7:30-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Sims can be reached at 5712727540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
Prosecution Insights