DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The Information Disclosure Statements (IDS) filed 06/01/2024, 04/01/2025, 06/12/2025, and 10/29/2025 have been considered by the Examiner.
Response to Arguments
Rejections under 35 USC 101
Applicant's arguments filed 04/27/2026 have been fully considered but they are not persuasive.
On Pages 9-11 of the Remarks filed 04/27/2026, Applicant argues that claims 1-20 are not directed to and do not recite an abstract idea – including a mathematical concept or a mental process. Applicant further argues that the claim feature of measuring chest impedance cannot practically be performed by a human.
Examiner respectfully disagrees and offers the following elaboration upon the rejection of the claims under 35 USC 101:
Examiner maintains that the claims recite an abstract idea including observations, evaluations, and judgements. The claims set forth a method which involves the observation of physiological signals by gathering data through clinical tests which is used as the input for an equation, in this case gathering chest impedance and cardiac signals as an input to predict an apnea event. See MPEP 2106.05(g), In re Grams, 888 F.2d 835. Following the observation of the physiological signals, the method evaluates the signals by filtering out a cardiac artifact and then using an isolated respiration signal to calculate the likelihood of an apnea event. Based on the evaluation, a judgement is made based on the isolated signal meeting a set of criteria which would generate an alarm indication. See MPEP 2106.05(g), Parker v. Flook, 437 U.S. 584 which held that post-solution activity, including that of adjusting an alarm limit, was not enough to integrate an abstract idea into practical application.
Therefore, even when the pre and post solution activity is considered, the invention as claimed still does not integrate the abstract idea into practical application because it does not amount to significantly more than the abstract idea of making a judgement (probability of an apnea event), based on an evaluation (filtering respiratory signal and comparing the filtered signal to a threshold), of an observed signal (collected chest impedance signals and cardiac physiological signals).
With this in appreciation, the rejection of claims 1-20 under 35 USC 101 is maintained. Examiner also notes that new claims 21-24 are also rejected under 35 USC 101 and are detailed below.
Rejections under 35 USC 103
Applicant's arguments filed 04/27/2026 have been fully considered but they are not persuasive.
On pages 11-13 of the Remarks dated 04/27/2026, Applicant argues that the combination of references cited in the Office Action fails to disclose the amended features of “modeling, through one or more periods of a cardiac cycle and using the cardiac physiological data, a progression of a cardiac phase and one or more harmonics of the cardiac phase, the cardiac phase and the one or more harmonica comprising a respective phase angle.”
Specifically, Applicant points to Von Behren as failing to disclose the claimed “modeling approach.” Examiner respectfully disagrees and points to acts 22: phase analysis, 24: display, and 26: highlight isolated phase, wherein is it explained in Von Behren paragraphs [0024-0036] that after a phase analysis is performed with respect to a phase and corresponding phase angle of the cardiac cycle, that the cardiac cycle is modeled by displaying a plurality of images in act 24 where each of the images is associated with a specific time interval within the physiological cycle and corresponds to the plurality of image frames used for performing the phase analysis, and wherein the displayed images include phase information. Applicant argues that at best Von Behren discloses ‘isolated phase information,’ but this ignores the fact that act 26, which isolates phase information, occurs throughout the sequence of displayed images which model the cardiac cycle at different phase angles throughout one or more cycles.
Applicant additionally argues that the disclosure of harmonics in Von Behren is limited to adding harmonics to a signal and does not disclose the limitation of modeling a progression of a cardiac phase and one or more harmonics of the cardiac phase. Examiner respectfully disagrees and points to paragraphs [0025-0031], which describes a method of modeling one or more harmonics of the cardiac phase by identifying a fundamental (i.e. first harmonic) in the frequency domain, and modeling the progression of the cardiac phase with respect to the fundamental frequency comprising a phase angle.
Rejections of the amended claims are further detailed below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 4, 6-11, 13-19, and 21-24 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claim 1 recites a process and claims 8 and 15 recite a machine.
Step 2A, Prong 1
Claims 1, 8, and 15 recite the limitations of calculating a probability of an apnea event based on chest impedance data which has been filtered to remove interfering cardiac artifacts from the respiratory signal. These steps, given their Broadest Reasonable Interpretation, can be practically performed in the human mind and are thereby considered to be directed to an abstract idea/mental process. A person of ordinary skill in the art could model a cardiac cycle and identify and remove a cardiac artifact based on the cardiac cycle from a chest impedance signal, and based on the filtered signal make an evaluation of the probability of an apnea event, and generate an alert if a threshold was crossed.
Step 2A, Prong 2
Claims 1, 8, and 15 do not include any additional elements that integrate the abstract idea into a practical application.
Claim 1 includes the additional elements of receiving chest impedance physiological data and cardiac physiological data from bedside monitoring equipment, and generating an alarm indication.
The limitation of receiving physiological data from bedside monitoring equipment is pre-solution activity of data collection in the form of performing clinical tests to obtain input for an equation, in this case gathering chest impedance and cardiac signals as an input to predict an apnea event. See MPEP 2106.05(g), In re Grams, 888 F.2d 835.
The limitation of generating an alarm indication is post-solution activity which does not amount to an inventive concept as it is merely an outputting of the conclusion of the abstract idea as performed. See MPEP 2106.05(g), Parker v. Flook, 437 U.S. 584 which held that post-solution activity, including that of adjusting an alarm limit, was not enough to integrate an abstract idea into practical application.
Therefore, the additional elements do not amount to integrating the abstract idea into practical application.
Claim 8 includes the additional elements of receiving chest impedance physiological data and cardiac physiological data from bedside monitoring equipment, generating an alarm indication, and a computer system. The limitation of receiving physiological data from bedside monitoring equipment is pre-solution activity of data collection, and the limitation of generating an alarm indication is post-solution activity. The computer system is generally claimed such that it amounts to generic computer implementation of the abstract idea. Therefore, the additional elements do not amount to integrating the abstract idea into practical application.
Claim 15 includes the additional elements of receiving chest impedance physiological data and cardiac physiological data from bedside monitoring equipment, generating an alarm indication, and a stored software. The limitation of receiving physiological data from bedside monitoring equipment is pre-solution activity of data collection, and the limitation of generating an alarm indication is post-solution activity. The stored software is generally claimed such that it amounts to generic computer implementation of the abstract idea. Therefore, the additional elements do not amount to integrating the abstract idea into practical application.
Step 2B
Claims 1, 8, and 15 do not include any additional elements that amount to significantly more than the abstract idea.
Claim 1 includes the additional elements of receiving chest impedance physiological data and cardiac physiological data from bedside monitoring equipment, and generating an alarm indication. The limitation of receiving physiological data from bedside monitoring equipment is pre-solution activity of data collection, and the limitation of generating an alarm indication is post-solution activity (as discussed above in detail, under section Step 2A, Prong 2). Additionally, the additional elements of the patient monitoring device which collects the patient’s chest impedance and cardiac physiological signals in claim 1 can be held to be well-understood, routine, and conventional in the art, and they are recited with a high level of generality which does not amount to significantly more than the abstract idea itself.
Therefore, the additional elements do not amount to significantly more than the abstract idea itself.
Claim 8 includes the additional elements of receiving chest impedance physiological data and cardiac physiological data from bedside monitoring equipment, generating an alarm indication, and a computer system. The limitation of receiving physiological data from bedside monitoring equipment is pre-solution activity of data collection, and the limitation of generating an alarm indication is post-solution activity. The computer system is generally claimed such that it amounts to generic computer implementation of the abstract idea. Therefore, the additional elements do not amount to significantly more than the abstract idea itself.
Claim 15 includes the additional elements of receiving chest impedance physiological data and cardiac physiological data from bedside monitoring equipment, generating an alarm indication, and a stored software. The limitation of receiving physiological data from bedside monitoring equipment is pre-solution activity of data collection, and the limitation of generating an alarm indication is post-solution activity. The stored software is generally claimed such that it amounts to generic computer implementation of the abstract idea. Therefore, the additional elements do not amount to significantly more than the abstract idea itself.
Claims 2, 9, 16, and 24 further limit the extra-solution activity of generating an alarm.
Claims 4-6, 10, 11, 13, and 17-19 further limit the extra-solution activity of data gathering via the patient monitoring device and the recording of apnea events.
Claims 7 and 14 merely specify details of the patient population for which the method and device are intended, and therefore any additional elements of those claims do not amount to integrate the judicial exception into a practical application under Step 2A Prong 2, or amount to significantly more under Step 2B.
Claims 21-23 further limit the abstract idea/mathematical operation itself of calculating a probability of an apnea event.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4, 6-11, 13-19, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708 A1) in view of Mallas (US 20160278711 A1) and Von Behren et al (US 20050107704 A1).
Regarding claim 1, Boute teaches a computer-implemented method of detecting apnea in a patient (see Fig. 3 for flowchart for method of detecting sleep apnea), comprising:
receiving chest impedance physiological data (see Fig. 2, respiration signal source (210), [0027]; respiration signal source 210 may be an impedance signal obtained from cardiac or auxiliary electrodes) and cardiac physiological data (see Fig. 2 ECG/EGM (208), [0021]; the IMD 100 collects cardiac electrogram signals for use in deriving heart rate related parameters) from bedside monitoring equipment ([0016]) associated with the patient (see Fig. 2, [0027]; data sources 207 comprising ECG or EGM for cardiac electrical signals and respiration signal source 210 for providing chest impedance signals to obtain physiological data about the patient, [0032]; processing module 202 receives chest impedance data from respiration source 210, and cardiac data from EGM/ECG source 208);
modeling, through one or more periods of a cardiac cycle and using the cardiac physiological data, a cardiac phase (see [0027]; ECG/EGM source 208 provides cardiac electrical signals such as P-waves, R-waves or T-waves used to monitor the patient's heart rhythm or conduction times);
calculating a probability of an apnea event based on the chest impedance data (see Fig. 3; compute probability 340, [0023]; impedance signals are used in computing a sleep apnea probability); and
generating an alarm indication responsive to a set of criteria being met based on the probability of the apnea event over a period of time (see Fig. 4, [0055]; if the probability exceeds a response threshold, an alert is generated at step 455).
Boute is silent regarding wherein the progression of a cardiac phase and one or more harmonics of the cardiac phase is modeled comprising a respective phase angle, determining, from the cardiac phase and the chest impedance physiological data, an approximation of a cardiac artifact; and
removing the cardiac artifact from the chest impedance physiological data, producing a filtered chest impedance data thereby isolating a respiration signal.
Mallas teaches a method for detecting apneas (Mallas [0006]) using chest impedance data and cardiac physiological data (see Mallas Fig 2, [0034]; receive stream of respiration samples of sensed respiration signal, receive heart rate data measured concurrently with sensed respiration signal) comprising:
determining, from the cardiac phase and the chest impedance physiological data, an approximation of a cardiac artifact (see Mallas [0007]; cardiac artifacts are removed after signals establishing cardiac physiological data measured concurrently with chest impedance data, [0037-0052]; where the periodic cardiac artifact is approximated and filtered using the ECG signal and respiration signals); and
removing the cardiac artifact from the chest impedance physiological data, producing a filtered chest impedance data thereby isolating a respiration signal (see Mallas Fig. 2, [0034]; the cardiac artifact is removed and the filtered respiration signals are promoted).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Boute’s method for detecting apnea with the cardiac artifact filtering as taught by Mallas. One of ordinary skill in the art would have been motivated to make this modification in order to minimize false positives from high respiration rates caused by cardiac artifact signal interference (Mallas [0006]).
Mallas teaches where the cardiac artifact is a periodic signal (Mallas [0040-0041]) but is silent regarding modeling the cardiac phase and one or more harmonics of the cardiac phase, the cardiac phase and the one or more harmonics comprising a respective phase angle.
Von Behren teaches a method for cardiac cycle analysis via cardiac phase modeling where a heart cycle is determined using cardiac physiological data (see Von Behren [0026]; the heart cycle is determined using ECG or analysis of ultrasound data), wherein a progression of a cardiac phase and one or more harmonics of the cardiac phase is modeled (see Von Behren Fig. 4, [0032-0036]; in act 24 a plurality of images is displayed wherein each of the images is associated with a specific time interval within the physiological cycle and corresponds to the plurality of image frames used for performing phase analysis, and wherein the displayed images include phase information), the cardiac phase and the one or more harmonics comprising a respective phase angle (see Von Behren Figs. 4-6, [0024-0031]; each phase is determined based on its relative position in the heart cycle, for example the phase represented in Fig. 5 at the beginning of the heart cycle or at the R wave of the heart cycle is about 270 degrees, a sinusoid or sine wave is matched to variation in the B-mode values or other data during the physiological cycle, wherein the B-mode variation is represented as a function of time with respect to an amplitude and phase angle of the selected harmonic, where the selected harmonic may be the fundamental, i.e. first harmonic, calculated in the frequency domain).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Mallas’ method for removing cardiac artifacts to produce a filtered signal with cardiac phase modeling comprising a phase angle as taught by Von Behren. One of ordinary skill in the art would have been motivated to make this modification in order to perform phase analysis of a cardiac cycle by isolating the phase information (Von Behren [0016]) and match up various signals by identifying waveforms within the cycle. It can be appreciated that modeling the cardiac cycle using phase angles to represent the relative location of each phase within a cardiac cycle allows for a consistent time scale by which multiple signals can be overlaid, which in the case of the present invention would allow for the identification of a cardiac artifact of an impedance signal.
Regarding claim 2, Boute in view of Mallas and Von Behren teaches the computer-implemented method of claim 1. Boute teaches the method further comprising: triggering an automatic physical stimulation of the patient responsive to the apnea event (see Boute [0033]; processing module 202 may trigger an appropriate response including therapy delivery in response to a detected apnea, Fig. 4, [0055]; if the probability exceeds a threshold, therapy is delivered at step 450).
Regarding claim 4, Boute in view of Mallas and Von Behren teaches the computer-implemented method of claim 1. Boute teaches the method further comprising: recording apnea events for a patient associated with the apnea event (see Boute [0025]; data is obtained, processed, and stored in memory 104);
wherein recording apnea events for the patient comprises recording oxygen saturation and heart rate data associated with the apnea events (see Boute [0034]; reports may include information about sleep apnea episode detections such as the time, date and duration and the severity of the episode, the physiological data collected, and any other appropriate data, [0024]; physiological signals recorded may include blood oxygen saturation and heart rate variability);
sending information about historical apnea events to a graphical user interface (see Boute [0036]; information stored in memory 204 may be provided to an external device to aid in diagnosis or treatment of the patient);
calculating aggregate information about the recorded apnea events for the patient (see Boute [0045-0049]; historical collected sensor data may be used to derive weighted coefficient values used in probability calculations); and
sending the aggregate information about the recorded apnea events to the graphical user interface (see Boute [0049]; clinician can review the sensor data and determine correlation between monitored parameter values and periods of sleep apnea, [0036]; information stored in memory 204 may be provided to an external device).
Regarding claim 6, Boute in view of Mallas and Von Behren teaches the computer-implemented method of claim 1. Boute teaches the method further comprising receiving heart rate and oxygen saturation measurements from the bedside monitoring equipment, wherein the set of criteria includes heart rate and oxygen saturation criteria (see Boute Fig. 3, [0039-0044]; apnea monitoring and prediction begins by sensing an EMG/ECG signal at step 302 and a blood oxygen saturation signal at step 306 simultaneously, threshold comparisons and probability computations are performed based on measurements from the collected physiological signals).
Regarding claim 7, Boute in view of Mallas and Von Behren teaches the computer-implemented method of claim 1. Boute is silent regarding wherein the patient is a neonatal infant.
However, Mallas teaches wherein the patient is a neonatal infant (see Mallas [0006]; respiration monitoring is important especially in neonates to detect apnea episodes).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Boute’s method for detecting apnea in a patient for use with a neonatal infant as taught by Mallas. One of ordinary skill in the art would have been motivated to make this modification because respiration monitoring is especially important in neonatal infants due to sudden infant death syndrome (SIDS) where an infant experiences a lethal apnea event (Mallas [0003]).
Regarding claim 8, Boute teaches a patient monitoring system for detecting apnea events in a patient, comprising:
bedside monitoring equipment associated with the patient that produces a chest impedance signal and a cardiac signal (see Boute [0027]; data sources 207 are generally embodied as sensors that can monitor physiological data of the patient including an ECG or EMG to produce a cardiac signal and an array of cardiac or auxiliary electrodes to produce an impedance signal);
a computer system programmed to:
receive chest impedance physiological data and cardiac physiological data from bedside monitoring equipment associated with the patient (see Boute Fig. 2, [0032]; processing module 202 receives data from respiration source 210 and EGM/ECG source 208);
model, through one or more periods of a cardiac cycle and using the cardiac physiological data, a progression of a cardiac phase (see Boute [0027]; ECG/EGM source 208 provides cardiac electrical signals such as P-waves, R-waves or T-waves used to monitor the patient's heart rhythm or conduction times);
calculate a probability of an apnea event based on the chest impedance data (see Boute Fig. 3; compute probability 340, [0023]; impedance signals are used in computing a sleep apnea probability); and
responsive to a set of criteria being met based on the probability of the apnea event over a period of time, generating an alarm indication (see Boute Fig. 4, [0055]; if the probability exceeds a response threshold, an alert is generated at step 455).
Boute is silent regarding wherein the computer system is programmed to:
model a progression of a cardiac phase and one or more harmonics of the cardiac phase, the cardiac phase and the one or more harmonics comprising a respective phase angle;
determine, from the cardiac phase and the chest impedance physiological data, an approximation of a cardiac artifact; and
remove the cardiac artifact from the chest impedance physiological data, producing a filtered chest impedance data thereby isolating a respiration signal.
Mallas teaches a system for detecting apneas (Mallas [0006]) using chest impedance data and cardiac physiological data (see Mallas Fig 2, [0034]; receive stream of respiration samples of sensed respiration signal, receive heart rate data measured concurrently with sensed respiration signal) comprising:
determining, from the cardiac phase and the chest impedance physiological data, an approximation of a cardiac artifact (see Mallas [0007]; cardiac artifacts are removed after signals establishing cardiac physiological data measured concurrently with chest impedance data, [0037-0052]; where the periodic cardiac artifact is approximated and filtered using the ECG signal and respiration signals); and
removing the cardiac artifact from the chest impedance physiological data, producing a filtered chest impedance data thereby isolating a respiration signal (see Mallas Fig. 2, [0034]; the cardiac artifact is removed and the filtered respiration signals are promoted).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Boute’s system for detecting apnea with the cardiac artifact filtering as taught by Mallas. One of ordinary skill in the art would have been motivated to make this modification in order to minimize false positives from high respiration rates caused by cardiac artifact signal interference (Mallas [0006]).
Mallas teaches where the cardiac artifact is a periodic signal (Mallas [0040-0041]) but is silent regarding modeling the cardiac phase and one or more harmonics of the cardiac phase, the cardiac phase and the one or more harmonics comprising a respective phase angle.
Von Behren teaches a method for cardiac cycle analysis via cardiac phase modeling where a heart cycle is determined using cardiac physiological data (see Von Behren [0026]; the heart cycle is determined using ECG or analysis of ultrasound data), wherein a progression of a cardiac phase and one or more harmonics of the cardiac phase is modeled (see Von Behren Fig. 4, [0032-0036]; in act 24 a plurality of images is displayed wherein each of the images is associated with a specific time interval within the physiological cycle and corresponds to the plurality of image frames used for performing phase analysis, and wherein the displayed images include phase information), the cardiac phase and the one or more harmonics comprising a respective phase angle (see Von Behren Figs. 4-6, [0024-0031]; each phase is determined based on its relative position in the heart cycle, for example the phase represented in Fig. 5 at the beginning of the heart cycle or at the R wave of the heart cycle is about 270 degrees, a sinusoid or sine wave is matched to variation in the B-mode values or other data during the physiological cycle, wherein the B-mode variation is represented as a function of time with respect to an amplitude and phase angle of the selected harmonic, where the selected harmonic may be the fundamental, i.e. first harmonic, calculated in the frequency domain).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Mallas’ method for removing cardiac artifacts to produce a filtered signal with cardiac phase modeling comprising a phase angle as taught by Von Behren. One of ordinary skill in the art would have been motivated to make this modification in order to perform phase analysis of a cardiac cycle by isolating the phase information (Von Behren [0016]) and match up various signals by identifying waveforms within the cycle. It can be appreciated that modeling the cardiac cycle using phase angles to represent the relative location of each phase within a cardiac cycle allows for a consistent time scale by which multiple signals can be overlaid, which in the case of the present invention would allow for the identification of a cardiac artifact of an impedance signal.
Regarding claim 9, Boute in view of Mallas and Von Behren teaches the patient monitoring system of claim 8. Boute further teaches wherein the computer system is further programmed to: trigger an automatic physical stimulation of the patient responsive to the apnea event (see Boute [0033]; processing module 202 may trigger an appropriate response including therapy delivery in response to a detected apnea, Fig. 4, [0055]; if the probability exceeds a threshold, therapy is delivered at step 450).
Regarding claim 10, Boute in view of Mallas and Von Behren teaches the patient monitoring system of claim 8. Boute further teaches wherein the computer system is further programmed to: calculate a length of the apnea event (see Boute [0034]; reporting an apnea event can include the date and duration of the episode).
Regarding claim 11, Boute in view of Mallas and Von Behren teaches the patient monitoring system of claim 8. Boute further teaches wherein the computer system is further programmed to:
record apnea events for a patient (see Boute [0025]; data is obtained, processed, and stored in memory 104);
wherein recording apnea events for the patient comprises recording oxygen saturation and heart rate data associated with the apnea events (see Boute [0034]; reports may include information about sleep apnea episode detections such as the time, date and duration and the severity of the episode, the physiological data collected, and any other appropriate data, [0024]; physiological signals recorded may include blood oxygen saturation and heart rate variability);
send information about historical apnea events to a graphical user interface (see Boute [0036]; information stored in memory 204 may be provided to an external device to aid in diagnosis or treatment of the patient);
calculate aggregate information about the recorded apnea events for the patient (see Boute [0045-0049]; historical collected sensor data may be used to derive weighted coefficient values used in probability calculations); and
send the aggregate information about the recorded apnea events to the graphical user interface (see Boute [0049]; clinician can review the sensor data and determine correlation between monitored parameter values and periods of sleep apnea, [0036]; information stored in memory 204 may be provided to an external device).
Regarding claim 13, Boute in view of Mallas and Von Behren teaches the patient monitoring system of claim 8. Boute further teaches wherein the computer system is further programmed to receive heart rate and oxygen saturation measurements from the bedside monitoring equipment, wherein the set of criteria includes heart rate and oxygen saturation criteria (see Boute Fig. 3, [0039-0044]; apnea monitoring and prediction begins by sensing an EMG/ECG signal at step 302 and a blood oxygen saturation signal at step 306 simultaneously, threshold comparisons and probability computations are performed based on measurements from the collected physiological signals).
Regarding claim 14, Boute in view of Mallas and Von Behren teaches the computer-implemented method of claim 8. Boute is silent regarding wherein the patient is a neonatal infant.
However, Mallas teaches wherein the patient is a neonatal infant (see Mallas [0006]; respiration monitoring is important especially in neonates to detect apnea episodes).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Boute’s system for detecting apnea in a patient for use with a neonatal infant as taught by Mallas. One of ordinary skill in the art would have been motivated to make this modification because respiration monitoring is especially important in neonatal infants due to sudden infant death syndrome (SIDS) where an infant experiences a lethal apnea event (Mallas [0003]).
Regarding claim 15, Boute teaches a non-transitory computer-readable medium on which is stored software for detecting apnea events in a patient (see Boute [0009]; instructions stored on a computer-readable medium which when implemented by the medical device causes the medical device to perform a method for computing the probability of an apnea event), comprising software that when executed causes a computer system to:
receive the chest impedance signal and the cardiac signal, wherein the chest impedance signal comprises chest impedance physiological data and the cardiac signal comprises cardiac physiological data (see Boute Fig. 2, [0032]; processing module 202 receives data from respiration source 210 and EGM/ECG source 208, [0027]; data sources 207 are generally embodied as sensors that can monitor physiological data of the patient including an ECG or EMG to produce a cardiac signal and an array of cardiac or auxiliary electrodes to produce an impedance signal);
model, through one or more periods of a cardiac cycle and using the cardiac physiological data, a progression of a cardiac phase (see Boute [0027]; ECG/EGM source 208 provides cardiac electrical signals such as P-waves, R-waves or T-waves used to monitor the patient's heart rhythm or conduction times);
calculate a probability of an apnea event based on the chest impedance data (see Boute Fig. 3; compute probability 340, [0023]; impedance signals are used in computing a sleep apnea probability); and
responsive to a set of criteria being met based on the probability of the apnea event over a period of time, generating an alarm indication (see Boute Fig. 4, [0055]; if the probability exceeds a response threshold, an alert is generated at step 455).
Boute is silent regarding wherein the computer system is programmed to:
model a progression of a cardiac phase and one or more harmonics of the cardiac phase, the cardiac phase and the one or more harmonics comprising a respective phase angle;
determine, from the cardiac phase and the chest impedance physiological data, an approximation of a cardiac artifact; and
remove the cardiac artifact from the chest impedance physiological data, producing a filtered chest impedance data thereby isolating a respiration signal.
Mallas teaches a method for detecting apneas (Mallas [0006]) using chest impedance data and cardiac physiological data (see Mallas Fig 2, [0034]; receive stream of respiration samples of sensed respiration signal, receive heart rate data measured concurrently with sensed respiration signal) comprising:
determining, from the cardiac phase and the chest impedance physiological data, an approximation of a cardiac artifact (see Mallas [0007]; cardiac artifacts are removed after signals establishing cardiac physiological data measured concurrently with chest impedance data, [0037-0052]; where the periodic cardiac artifact is approximated and filtered using the ECG signal and respiration signals); and
removing the cardiac artifact from the chest impedance physiological data, producing a filtered chest impedance data thereby isolating a respiration signal (see Mallas Fig. 2, [0034]; the cardiac artifact is removed and the filtered respiration signals are promoted).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Boute’s method for detecting apnea with the cardiac artifact filtering as taught by Mallas. One of ordinary skill in the art would have been motivated to make this modification in order to minimize false positives from high respiration rates caused by cardiac artifact signal interference (Mallas [0006]).
Mallas teaches where the cardiac artifact is a periodic signal (Mallas [0040-0041]) but is silent regarding modeling the cardiac phase and one or more harmonics of the cardiac phase, the cardiac phase and the one or more harmonics comprising a respective phase angle.
Von Behren teaches a method for cardiac cycle analysis via cardiac phase modeling where a heart cycle is determined using cardiac physiological data (see Von Behren [0026]; the heart cycle is determined using ECG or analysis of ultrasound data), wherein a progression of a cardiac phase and one or more harmonics of the cardiac phase is modeled (see Von Behren Fig. 4, [0032-0036]; in act 24 a plurality of images is displayed wherein each of the images is associated with a specific time interval within the physiological cycle and corresponds to the plurality of image frames used for performing phase analysis, and wherein the displayed images include phase information), the cardiac phase and the one or more harmonics comprising a respective phase angle (see Von Behren Figs. 4-6, [0024-0031]; each phase is determined based on its relative position in the heart cycle, for example the phase represented in Fig. 5 at the beginning of the heart cycle or at the R wave of the heart cycle is about 270 degrees, a sinusoid or sine wave is matched to variation in the B-mode values or other data during the physiological cycle, wherein the B-mode variation is represented as a function of time with respect to an amplitude and phase angle of the selected harmonic, where the selected harmonic may be the fundamental, i.e. first harmonic, calculated in the frequency domain).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Mallas’ method for removing cardiac artifacts to produce a filtered signal with cardiac phase modeling comprising a phase angle as taught by Von Behren. One of ordinary skill in the art would have been motivated to make this modification in order to perform phase analysis of a cardiac cycle by isolating the phase information (Von Behren [0016]) and match up various signals by identifying waveforms within the cycle. It can be appreciated that modeling the cardiac cycle using phase angles to represent the relative location of each phase within a cardiac cycle allows for a consistent time scale by which multiple signals can be overlaid, which in the case of the present invention would allow for the identification of a cardiac artifact of an impedance signal.
Regarding claim 16, Boute in view of Mallas and Von Behren teaches the non-transitory computer-readable medium of claim 15. Boute further teaches wherein the software when executed further causes the computer system to: trigger an automatic physical stimulation of the patient (see Boute [0033]; processing module 202 may trigger an appropriate response including therapy delivery in response to a detected apnea, Fig. 4, [0055]; if the probability exceeds a threshold, therapy is delivered at step 450).
Regarding claim 17, Boute in view of Mallas and Von Behren teaches the non-transient medium of claim 15. Boute further teaches wherein the software when executed further causes the computer system to: calculate a length of the apnea event (see Boute [0034]; reporting an apnea event can include the date and duration of the episode).
Regarding claim 18, Boute in view of Mallas and Von Behren teaches the non-transient medium of claim 15. Boute further teaches wherein the software when executed further causes the computer system to:
record apnea events for the patient (see Boute [0025]; data is obtained, processed, and stored in memory 104);
wherein recording apnea events for the patient comprises recording oxygen saturation and heart rate data associated with the apnea events (see Boute [0034]; reports may include information about sleep apnea episode detections such as the time, date and duration and the severity of the episode, the physiological data collected, and any other appropriate data, [0024]; physiological signals recorded may include blood oxygen saturation and heart rate variability);
send information about historical apnea events to a graphical user interface (see Boute [0036]; information stored in memory 204 may be provided to an external device to aid in diagnosis or treatment of the patient);
calculate aggregate information about the recorded apnea events for the patient (see Boute [0045-0049]; historical collected sensor data may be used to derive weighted coefficient values used in probability calculations); and
send the aggregate information about the recorded apnea events to the graphical user interface (see Boute [0049]; clinician can review the sensor data and determine correlation between monitored parameter values and periods of sleep apnea, [0036]; information stored in memory 204 may be provided to an external device).
Regarding claim 19, Boute in view of Mallas and Von Behren teaches the non-transitory computer-readable medium of claim 18. Boute further teaches wherein the software when executed further causes the computer system to record oxygen saturation and heart rate data associated with the apnea events (see Boute [0034]; reports may include information about sleep apnea episode detections such as the time, date and duration and the severity of the episode, the physiological data collected, and any other appropriate data, [0024]; physiological signals recorded may include blood oxygen saturation and heart rate variability).
Regarding claim 24, Boute in view of Mallas and Von Behren teaches the patient monitoring system of claim 8. Boute further teaches wherein the computer system is further programmed to: responsive to the set of criteria being met, trigger an automatic response to stimulate the patient (see Boute [0033]; processing module 202 may trigger an appropriate response including therapy delivery in response to a detected apnea, Fig. 4, [0055]; if the probability exceeds a threshold, therapy is delivered at step 450).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708 A1) in view of Mallas (US 20160278711 A1), Von Behren et al (US 20050107704 A1), and Flower (US 20170181647 A1).
Regarding claim 22, Boute in view of Mallas and Von Behren teaches the patient monitoring system of claim 8. Boute is silent regarding wherein modeling the progression of the cardiac phase comprises generating a Fourier series approximation of the chest impedance signal and analyzing the cardiac phase and a predetermined number of the one or more harmonics of the cardiac phase.
Von Behren teaches wherein modeling the progression of the cardiac phase (see Von Behren [0026]; the heart cycle is determined using ECG or analysis of ultrasound data) comprises generating a Fourier series approximation of the cardiac signal (see Von Behren [0025-0030]; the B-mode variation varying as a function over time of about two heart cycles can be represented as a time intensity curve, where the time intensity curve is mathematically represented by a Fourier series, Fourier transforms may be used to identify the desired or principal phase and amplitude components of a cardiac cycle) and analyzing the cardiac phase and a predetermined number of the one or more harmonics of the cardiac phase (see Von Behren Figs. 4-6, [0024-0031]; each phase is determined based on its relative position in the heart cycle, for example the phase represented in Fig. 5 at the beginning of the heart cycle or at the R wave of the heart cycle is about 270 degrees, a sinusoid or sine wave is matched to variation in the B-mode values or other data during the physiological cycle, wherein the B-mode variation is represented as a function of time with respect to an amplitude and phase angle of the selected harmonic, where the selected harmonic may be the fundamental, i.e. first harmonic, calculated in the frequency domain).
Von Behren is silent regarding wherein a Fourier series approximation of the chest impedance signal is generated.
Flower teaches a method for monitoring a patient wherein an impedance-based respiratory signal is filtered to isolate a cardiac artifact (Fig. 2), wherein the impedance is measured with respect to the cardiac cycle (see Flower Fig. 2; 240 resample respiratory waveform over a time domain in which heart is clock) and a Fourier series approximation of the chest impedance signal is generated (see Flower Fig. 2; 250 take a Fourier transform of the resample respiratory rate waveform) in order to isolate cardiac artifacts from the impedance signal (see Flower Fig. 2; 260).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Von Behren’s method of modeling the progression of a cardiac phase with Flower’s method of modeling a chest impedance signal with respect to a cardiac phase. One of ordinary skill in the art would have been motivated to make this modification in order to identify a cardiac artifact in the chest impedance signal by modeling the chest impedance signal with respect to the cardiac phase.
Claims 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Boute (US 20060241708 A1) in view of Mallas (US 20160278711 A1), Von Behren et al (US 20050107704 A1), and Kim et al (S. Kim, A.S. Arun, M.E. Ahsen, R. Vogel, & G. Stolovitzky, The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers, Proc. Natl. Acad. Sci. U.S.A. 118 (34) e2100761118, https://doi.org/10.1073/pnas.2100761118 (2021)).
Regarding claims 21 and 23, Boute, Mallas, and Von Behren teach the computer implemented method of claim 1 and the non-transitory computer readable medium of claim 15. They are silent regarding wherein calculating the probability of an apnea event based on the filtered chest impedance data comprises estimating the probability by applying a Fermi function to a standard deviation of the filtered chest impedance data. Boute teaches calculating the probability of an apnea event using parameters which may be derived from the chest impedance signals, and assigning each of the parameters a weighing coefficient which may be determined through optimization and trained machine learning models (Boute [0047-0049]).
Kim teaches a method for binary classification using a machine learning model which uses a Fermi-Dirac distribution to compute a calibrated probabilistic output for binary classifiers (Kim, Abstract).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Boute’s classification model with Kim’s calibrated probabilistic binary classifier using Fermi-Dirac distributions. One of ordinary skill in the art would have been motivated to make this modification in order to utilize an ensemble learning algorithm which allows the model to calculate the probability of correctness of classification model, which can lead to higher accuracy (Kim, Significance).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Aggarwal et al (US 20060224062 A1) which teaches adaptive acquisition and reconstruction of dynamic MR images.
Coyle et al (US 20050119586 A1) which teaches systems and methods for respiratory event detection.
Deno et al (US 5999854 A) which teaches an implantable cardiac stimulator with at least one physiologic sensor based on mechanical-electric phase relation.
Wright et al (US 4781201 A) which teaches a cardiovascular artifact filter for suppressing cardiovascular artifacts from a respiration signal derived from a transthoracic impedance signal.
Prince et al (US 6892089 B1) which teaches cardiac motion tracking using cine harmonic phase magnetic resonance imaging.
Blomqvist (US 20100280394 A1) which teaches a medical device for atrial fibrillation prediction using cardiovascular signals and chest impedance signals.
THIS ACTION IS MADE FINAL. 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.
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/A.J.S./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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