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 10/29/2025 have been fully considered but they are not persuasive.
On Pages 8-10 of the Remarks filed 10/29/2025, 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.
Applicant states that the claims are eligible at Step 2A, prong 2 as directed to a technological improvement to apnea detection technologies by isolating a respiration signal, and that pre and post solution activity must be considered when evaluating whether the claim as a whole integrated a judicial exception into a practical application.
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.
Rejections under 35 USC 103
Applicant’s arguments with respect to claim(s) 1-20 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. New grounds of rejection elaborated upon 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 1-20 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, and 16 further limits the extra-solution activity of generating an alarm.
Claims 3-6, 10-13, and 16-20 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.
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.
Claim(s) 1-20 is/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 cardiac phase is modeled comprising a 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 comprising a 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 the cardiac phase is modeled comprising a phase angle (see Von Behren Figs. 4-6, [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).
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 3, Boute in view of Mallas and Von Behren teaches the computer-implemented method of claim 1. Boute teaches the method further comprising: calculating a length of the apnea event (see Boute [0034]; reporting an apnea event can include the date and duration of the episode).
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 the patient (see Boute [0025]; data is obtained, processed, and stored in memory 104);
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 5, Boute in view of Mallas and Von Behren teaches the computer-implemented method of claim 4. Boute further teaches 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).
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 [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 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 the cardiac phase comprising a 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 comprising a phase angle.
Von Behren teaches a system 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 the cardiac phase is modeled comprising a phase angle (see Von Behren Figs. 4-6, [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).
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’ system 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 the patient (see Boute [0025]; data is obtained, processed, and stored in memory 104);
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 12, Boute in view of Mallas and Von Behren teaches the patient monitoring system of claim 11. Boute further teaches wherein the computer system is further programmed 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 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 [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-transient 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 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 the cardiac phase comprising a 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 comprising a 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 the cardiac phase is modeled comprising a phase angle (see Von Behren Figs. 4-6, [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).
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-transient 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 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 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);
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-transient 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 20, 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 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).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALISHA J SIRCAR whose telephone number is (571)272-0450. The examiner can normally be reached Monday - Thursday 9-6:30, Friday 9-5:30 CT.
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/A.J.S./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792