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 statement(s) filed on 12/11/2023 and 03/25/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived 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.
Claim(s) 1-2, 4 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahadevan (US 20150320955 A1) in view of Wariar (US 20150148699 A1) and Lee (US 20050074741 A1).
Regarding claim 1, Mahadevan teaches a computer implemented method of controlling a positive airway pressure device (pressure generator 14, see Fig. 1) for providing ventilation support therapy (Mahadevan teaches pressure generator 14 to generate the flow of gas with a positive pressure support therapy regime and is controlled by processor 20 which executes one or more computer program modules as seen in Fig. 3 and [0029] and [0031]-[0033]), wherein the positive airway pressure device is configured to deliver pressurized air to a subject, during subject inspiration and expiration, according to a therapeutic pressure protocol (pressure generator 14 generates the flow in gas in accordance to a positive pressure support therapy regime which dictates an inspiratory pressure level and an expiratory pressure level as seen in [0033]-[0034]), the method comprising:
receiving physiological data from a physiological sensor arrangement (sensors 18, see Fig. 1) (sensors 18 generate output signals conveying information related to one or more parameters of the gas within system 10, including tidal volume, respiration rate and peak inspiratory and expiratory pressures as seen in Fig. 1 and [0026]);
running a detection algorithm configured to:
determine an expiration pressure burden based on the physiological data,
perform a comparison of the physiological data to at least one threshold,
determine whether the expiration pressure burden is indicative of hyperinflation based on the comparison (Mahadevan teaches a hyperinflation module 56 to identify hyperinflation during exhalation using the output signals 18 from the sensors as seen in Figs. 1 and 3 and [0036]. The hyperinflation module can identify hyperinflation based on tidal volume and may perform a comparison of the first pressure in the lungs at the end of a first exhalation (taken as a threshold) to a second pressure in the lungs at the end of a second exhalation to identify air trapping in the lungs of subject 12 as seen in [0036]. The air trapped in the lungs is indicative of hyperinflation as seen in [0037]).
running a pressure adjustment algorithm, in response to determining the expiration pressure burden is indicative of hyperinflation, wherein the pressure adjustment algorithm is configured to generate a pressure adjustment instruction for adjusting the therapeutic pressure protocol based on the determined expiration pressure burden (Mahadevan teaches method 300 with identifying hyperinflation during exhalation in operation 310 using the hyperinflation module and further teaches an expiratory pressure module which will adjust the expiratory pressure level due to the indication of hyperinflation as seen in Figs. 1 and 3 and [0062]-[0063]. The expiratory pressure module 58 is to increase/decrease the PEEP level to reduce and/or eliminate hyperinflation as seen in [0037]-[0042]); and
outputting the pressure adjustment instruction to adjust the therapeutic pressure protocol based on the determined expiration pressure burden (Mahadevan teaches method 300 with controlling the pressure generator to adjust the expiratory pressure level to relieve hyperinflation during exhalation in operation 312 as seen in Fig. 3 and [0063]-[0064])
but does not teach wherein the physiological data comprises chest movement data and abdominal movement data;
running a detection algorithm configured to:
determine an expiration pressure burden based on the chest movement data and the abdominal movement data,
perform a comparison of the chest movement data and the abdominal movement data to at least one threshold,
determine whether the expiration pressure burden is indicative of hyperinflation based on the comparison;
However, Wariar teaches wherein the physiological data comprises chest movement data (Wariar teaches using an accelerometer to detect chest motion for an acceleration sensing circuit as seen in [0019] and [0029]);
running a detection algorithm configured to:
determine an expiration pressure burden based on the chest movement data,
perform a comparison of the chest movement data to at least one threshold,
determine whether the expiration pressure burden is indicative of hyperinflation based on the comparison (Wariar teaches lung hyperinflation can be detected when the value of end expiratory volume exceeds a detection threshold value of lung hyperinflation and the relative end expiratory volume can be monitored using lung impedance, motion or the change in dimensions of the chest, or respiration airflow of the subject as seen in Fig. 2 and [0015]-[0020]. Wariar further teaches using an accelerometer to sense motion of the subject’s chest cavity to estimate a value of EEV as (see in [0019] and [0029]) and a processor 310 with both an EEV module 315 and hyperinflation detection module 320 as seen in Fig. 3 and [0023], [0025]-[0027] and [0029]. As such, an accelerometer is used to detect the motion of a user’s chest which is used to estimate a value of EEV to be compared to a detection threshold value that may indicate lung hyperinflation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by Mahadevan to include the sensing circuit with accelerometer and processor as taught by Wariar since having multiple physiological signals to indicate hyperinflation can help eliminate noise that may occur from talking or coughing (see [0032]).
However, Lee teaches using accelerometer 440 to sense patient motion associated with respiratory effort, in both the motion of the chest wall and abdomen diaphragm, and generating a signal corresponding to patient movement as seen in Fig. 4 and [0066]. The accelerometer 440 is used with a disordered breathing classification processor 430 to classify the disordered breathing events such as Cheyne-Stokes respiration as seen in Fig. 4 and [0034] and [0066]. Lee further points out dyspnea to be a form of disordered breathing as seen in [0006].
Mahadevan teaches system 10 may be used to treat symptoms and/or conditions related to dyspnea and Cheyne-Stokes respiration as seen in [0016]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by Mahadevan in view of Wariar to have the accelerometer sense patient motion with both the chest and abdomen as taught by Lee as both parts of the body are used with respiration and will aid in providing more details regarding a user’s breathing (see [0043] and [0066]).
Regarding claim 2, modified Mahadevan teaches the method of claim 1, and Mahadevan further teaches wherein the pressure adjustment algorithm is configured to, in response to determining that the expiration pressure burden is indicative of hyperinflation, generate a pressure adjustment instruction for reducing the air pressure of the pressurized air delivered to a subject during an expiration and/or inspiration cycle (Mahadevan teaches method 300 with identifying hyperinflation during exhalation in operation 310 using the hyperinflation module and further teaches an expiratory pressure module which will adjust the expiratory pressure level due to the indication of hyperinflation as seen in Figs. 1 and 3 and [0062]-[0063]. The expiratory pressure module 58 is to increase/decrease the PEEP level to reduce and/or eliminate hyperinflation as seen in [0037]-[0042]).
Regarding claim 4, modified Mahadevan teaches the method of claim 1, and Mahadevan further teaches wherein the physiological data comprises respiration data (sensors 18 generate output signals conveying information related to one or more parameters of the gas within system 10, including tidal volume, respiration rate and peak inspiratory and expiratory pressures as seen in Fig. 1 and [0026]) and the pressure adjustment algorithm is configured to, in response to determining that the expiration pressure burden is indicative of active expiration, generate a pressure adjustment instruction for reducing the air pressure of the pressurized air delivered to a subject during an expiration and/or inspiration cycle (Mahadevan teaches method 300 with identifying hyperinflation during exhalation in operation 310 using the hyperinflation module and further teaches an expiratory pressure module which will adjust the expiratory pressure level due to the indication of hyperinflation as seen in Figs. 1 and 3 and [0062]-[0063]. The expiratory pressure module 58 is to increase/decrease the PEEP level to reduce and/or eliminate hyperinflation as seen in [0037]-[0042]).
Regarding claim 14, modified Mahadevan teaches a ventilation support system (portable handheld pressure support system 10, see Fig. 1 and [0033]-[0034] of Mahadevan) comprising:
a positive airway pressure device (pressure generator 14, see Fig. 1 of Mahadevan) configured to deliver pressurized air to a subject, during subject inspiration and expiration, according to a therapeutic pressure protocol (pressure generator 14 generates the flow in gas in accordance to a positive pressure support therapy regime which dictates an inspiratory pressure level and an expiratory pressure level as seen in [0033]-[0034] of Mahadevan);
a physiological sensor arrangement for obtaining physiological data (Modified Mahadeven teaches sensors 18 generate output signals conveying information related to one or more parameters of the gas within system 10, including tidal volume, respiration rate and peak inspiratory and expiratory pressures as seen in Fig. 1 and [0026] of Mahadevan. Modified Mahadeven further teaches an accelerometer to detect chest motion as taught by Wariar); and
a computer having a processor configured to carry out the steps of claim 1 (see claim 1 above; Modified Mahadevan teaches processor 20 executing one or more computer program modules as seen in Fig. 1 and [0028]-[0029] of Mahadevan and as such there is a computer. Modified Mahadevan further teaches processor 310 with an EEV module 315 and hyperinflation detection module 320 as seen in Fig. 3 and [0023] and [0025] of Wariar).
Regarding claim 15, modified Mahadevan teaches a computer program product comprising computer program code means which, when executed on a ventilation support system having a computer, cause the ventilation support system to perform all of the steps of the method according to claim 1 (see claim 1 above; Modified Mahadevan teaches processor 20 executing one or more computer program modules as seen in Fig. 1 and [0028]-[0029] of Mahadevan. Modified Mahadevan further teaches processor 310 with an EEV module 315 and hyperinflation detection module 320 as seen in Fig. 3 and [0023], [0025] and [0056] of Wariar. As such modified Mahadevan teaches processor 20 and processor 310 comprising of computer program codes to execute system 10 with pressure generator 14 as seen in Fig. 1 and [0029] and [0033]-[0034] of Mahadevan).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahadevan (US 20150320955 A1) in view of Wariar (US 20150148699 A1) and Lee (US 20050074741 A1), as applied to claim 1 above, and further in view of Jafari (US 20140230818 A1).
Regarding claim 3, modified Mahadevan teaches the method of claim 1, and further teaches lung hyperinflation can be detected when the value of end expiratory volume exceeds a detection threshold value of lung hyperinflation, wherein the relative end expiratory volume can be monitored using lung impedance, motion or the change in dimensions of the chest, or respiration airflow of the subject as seen in Fig. 2 and [0015]-[0020] of Wariar. Lee further teaches measuring both the motions of the chest wall and abdomen diaphragm using an accelerometer.
but does not teach wherein the detection algorithm is configured to determine a ratio of chest to abdominal breathing based on the chest movement data and the abdomen movement data,
determine the expiration pressure burden based on the ratio of chest to abdominal breathing, and
perform the comparison of the chest movement data and the abdominal movement data to at least one threshold based on the ratio of chest to abdominal breathing compared to a threshold.
However, Jafari teaches external sensors 232, such as accelerometers, to monitor a patient’s chest and lungs to see the magnitude/direction of chest movement and can indicate the displacement of a patient’s lungs in conjunction with tidal volume as seen in [0027]. In particular, a user’s chest wall movement and abdomen can be monitored and compared to be correlated with a tidal volumetric displacement as seen in [0027]. Fig. 4 and [0030] teaches a method of monitoring both the chest and the abdomen in step S400 and in step S402, the tidal volume displacement into and out of the lungs is determined. S410 compares the monitored chest wall movement to a previous wall chest displacement and S430 calculates the local tide volume displacement and is compared to a previous local tide displacement. The comparisons are then checked to see if they meet a specified criterion and/or predetermined threshold as seen in [0030].
Modified Mahadevan teaches a threshold to detect lung hyperinflation due to expiration pressure burden based on the change in dimensions in the chest. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan to determine a ratio of chest to abdominal breathing based on the chest movement data and the abdomen movement data, determine the tidal volume based on the ratio of chest to abdominal breathing, perform the comparison of the chest movement data and the abdominal movement data to at least one threshold based on the ratio of chest to abdominal breathing as taught by Jafari for a more accurate assessment of a patient’s condition for use by a clinician (see [0006]). Modified Mahadevan in view of Jafari teaches determine the expiration pressure burden based on the ratio of chest to abdominal breathing as Wariar teaches lung hyperinflation can be detected when the value of end expiratory volume exceeds a detection threshold value of lung hyperinflation, wherein the relative end expiratory volume can be monitored using motion or the change in dimensions of the chest and Jafari teaches a user’s chest wall movement and abdomen can be monitored and compared to be correlated with a tidal volumetric displacement. As such, modified Mahadevan in view of Jafari teaches an expiration pressure burden (from the end expiratory volume and tidal volumetric displacement) based on the ratio of chest to abdominal breathing.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahadevan (US 20150320955 A1) in view of Wariar (US 20150148699 A1) and Lee (US 20050074741 A1), as applied to claim 4 above, and further in view of Burg (US 20150313484 A1).
Regarding claim 5, modified Mahadevan teaches the method of claim 4, but does not teach further comprising:
receiving validation physiological data for validation the expiration pressure burden;
running a validation algorithm for validating the expiration pressure burden; and
in response to determining that the expiration pressure burden is valid, running the pressure adjustment algorithm.
However, Burg teaches one or more signals of sensor data may be used to validate other data or vital sign measurements in a measurement validation process to remove noise and using a data fusion algorithm to validate or enhance the quality of the measurements as seen in [0158]. Burg further teaches if the respiration rate from one sensor data (e.g. PPG-ECG sensor data) is comparable to the respiration rate determined from the accelerometer, it would give higher confidence/weight in the weighted sums of the outputs as seen in [0158].
Modified Mahadevan teaches receiving sensor data from sensor 18 (of Mahadevan) and the accelerometer (of Wariar), wherein Wariar further teaches having multiple physiological signals can help eliminate noise (see [0032]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan to have a measurement validation process as taught by Burg to remove possible noise and enhance the quality of measurements (see [0158]), by having one source of data (from either sensor 18 or the accelerometer) verify the other.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahadevan (US 20150320955 A1) in view of Wariar (US 20150148699 A1) and Lee (US 20050074741 A1), as applied to claim 1 above, and further in view of Truschel (US 20020014240 A1).
Regarding claim 7, modified Mahadevan teaches the method of claim 1, and Mahadevan further teaches wherein the physiological data comprises expiration flow data and inspiration flow data (sensors 18 generate output signals conveying information related to one or more parameters of the gas within system 10, including flow rate, peak inhaled and exhaled flow and an expiratory flow limitation as seen in Fig. 1 and [0026]) and Mahadevan teaches an exhaust port 62 to direct gas to the ambient atmosphere as seen in Fig. 1 and [0052]
But does not teach wherein the detection algorithm is configured to determine based on the expiration flow data and the inspiration flow data, whether the expiration pressure burden is indicative of an insufficient leakage at an exhaust port at an interface of the positive airway pressure device.
However, Truschel teaches wherein the detection algorithm is configured to determine based on the expiration flow data and the inspiration flow data, whether the expiration pressure burden is indicative of an insufficient leakage at an exhaust port (exhaust vent 40, see Fig. 2) at an interface (patient interface 36, see Fig. 2 and [0008]) of the positive airway pressure device (pressure generating device 33, see Fig. 2) (Truschel teaches flow sensor 42 that measures a rate at which breathing gas flows within conduit 34 (see [0026]) and a pressure sensor 44 that detects pressure of the gas at the patient (see [0027]). Control unit 38 determines whether the system leak rate falls below an acceptable leak rate level based on the output of flow and pressure sensors 42 and 44 as seen in [0029] and [0047]-[0057], wherein the patient flow is taken over a complete breathing cycle. Furthermore, the drop in the leakage system indicates that the exhaust port is at least partially occluded as seen in [0029] and [0041]).
Mahadevan teaches an exhaust port 62 to direct gas to the ambient atmosphere as seen in [0052]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan to include whether the expiration pressure burden is indicative of an insufficient leakage at an exhaust port at an interface of the positive airway pressure device as taught by Truschel to determine whether the rate of exhaust is sufficient and that the exhaust ports are not occluded in a hazardous manner (see [0030]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahadevan (US 20150320955 A1) in view of Wariar (US 20150148699 A1) and Lee (US 20050074741 A1), as applied to claim 1 above, and further in view of Mulcahy (US 20090038616 A1) and Sanders (US 5148802 A).
Regarding claim 8, modified Mahadevan teaches the method of claim 1, and but does not teach wherein running the detection algorithm further comprises determining a therapeutic pressure recommendation, and the method further comprises:
analyzing the physiological data to detect initiation of an expiration cycle; and
in response to detecting initiation of an expiration cycle, generating a pressure adjustment instruction for adjusting the therapeutic pressure setting of the positive airway pressure device according to the therapeutic pressure recommendation.
However, Mulcahy teaches wherein running the detection algorithm further comprises determining a therapeutic pressure recommendation (Mulcahy teaches acclimatization therapy for new users of CPAP therapy which includes determining a therapeutic pressure a user in step 201 and setting pressure for the next therapy session in step 208 of Fig 2 and [0056]), and the method further comprises:
during an expiration cycle, generating a pressure adjustment instruction for adjusting the therapeutic pressure setting of the positive airway pressure device (see Fig. 1 and [0035] and [0050]) according to the therapeutic pressure recommendation (Mulcahy teaches processor 15 and a sensor for determining a sleep stage of a patient as seen in Fig. 1 and [0031] and [0053]. Mulcahy further teaches providing a breathable gas at a second pressure during patient expiration as seen in [0020]. Fig. 7 shows a flowchart wherein s702 determines a sleep stage and s704 provides a pressure based on the sleep stage as seen in [0101]. As such, Mulcahy teaches providing breathing gas during an expiration cycle wherein there is a pressure adjustment based on settings for the sleep stage).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan to include the controller and sensor to adjust the therapeutic pressure setting of the positive airway pressure device based on sleep stage as taught by Mulcahy to aid a patient in acclimatizing to therapy treatment of sleep-disordered breathing (see [0035]).
However, Sanders teaches analyzing the physiological data to detect initiation of an expiration cycle (“At the start of exhalation, air flow into the patient's lungs is nil and as a result the instantaneous flow rate signal will be less than the average flow rate signal which, as noted is a relatively constant positive flow value. The decision circuitry 34 senses this condition as the start of exhalation and provides a drive signal to pressure controller 26 which, in response, provides gas flow within conduit 20 at a lower pressure which is the lower magnitude pressure value of the bi-level CPAP system, referred to hereinbelow as EPAP (exhalation positive airway pressure)” see Col. 6, lines 49-59).
Mulcahy teaches providing a breathable gas at a second pressure during patient expiration as seen in [0020]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan in view of Mulcahy to include the circuit/processor and sensor to sense the start of exhalation as taught by Sanders to provide pressure at an optimal moment.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahadevan (US 20150320955 A1) in view of Wariar (US 20150148699 A1), Lee (US 20050074741 A1), Mulcahy (US 20090038616 A1) and Sanders (US 5148802 A), as applied to claim 8 above, and further in view of Eklund (US 20030000528 A1).
Regarding claim 9, modified Mahadevan teaches the method of claim 8, and further teaches further comprising:
receiving sleep stage data indicative of a subject's sleep stage (Mulcahy teaches a sensor for determining a sleep stage of a patient as seen in Fig. 1 and [0031]);
determining a therapeutic pressure recommendation, based on the sleep stage data (Mulcahy teaches Fig. 7 showing a flowchart wherein s702 determines a sleep stage and s704 provides a pressure based on the sleep stage as seen in [0101]);
analyzing the physiological data to detect initiation of an expiration cycle (Sanders teaches decision circuitry 34 to sense the start of exhalation due to the instantaneous flow rate signal being less than the average flow rate signal, and provide a signal to provide gas as seen in Col. 6, lines 49-59); and
in response to detecting initiation of an expiration cycle, generating a pressure adjustment instruction for adjusting the therapeutic pressure setting of the positive airway pressure device according to the therapeutic pressure recommendation (Modified Mahadevan teaches starting exhalation due to the instantaneous flow rate signal being less than the average flow rate signal, and providing a signal to provide gas as seen in Col. 6, lines 49-59 of Sanders. The processor 15 and sensor of Mulcahy will determine a sleep stage and provide the pressure based on the sleep stage as seen in Fig. 7 and [0101])
But does not teach receiving body position data indicative of a subject's body position;
determining a therapeutic pressure recommendation, based on the body position data.
However, Eklund teaches receiving body position data indicative of a subject's body position (Eklund teaches the patient is set up with sensors for body position, which will also aid in determining sleep stage and more as seen in [0110]);
determining a therapeutic pressure recommendation, based on the body position data (Eklund teaches the required CPAP pressure varies depending on sleep stage and body position as seen in [0112]).
Mulcahy teaches Fig. 7 showing a flowchart wherein s702 determines a sleep stage and s704 provides a pressure based on the sleep stage as seen in [0101]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan to receive body position data indicative of a subject's body position and determine a therapeutic pressure recommendation based on the body position data as taught by Eklund as it will aid in determining sleep stages and other sleep related events (see [0110]).
Claim(s) 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahadevan (US 20150320955 A1) in view of Wariar (US 20150148699 A1) and Lee (US 20050074741 A1), as applied to claim 1 above, and further in view of Christopherson (US 20110264164 A1) and Lynn (US 20060161071 A1).
Regarding claim 10, modified Mahadevan teaches the method of claim 1, but does not teach further comprising:
running a pressure tolerance analysis algorithm for analyzing a subject's pressure tolerance during expiration, wherein the pressure tolerance algorithm is configured to:
receive titration respiration data, wherein the titration respiration data indicates respiration as a function of therapeutic pressure;
receive cortical arousal data; and
determine a therapeutic pressure recommendation, based on the titration respiration data and the cortical arousal data.
However, Christopherson teaches running a pressure tolerance analysis algorithm for analyzing a subject's pressure tolerance during expiration, wherein the pressure tolerance algorithm is configured to:
receive titration respiration data, wherein the titration respiration data indicates respiration as a function of therapeutic pressure (Christopherson teaches a sensing module 102 including body parameter 130, ECG parameter 140 and pressure parameter 150 as seen in Fig. 3A and [0041]-[0044] (similar to applicant’s titration respiration data as seen on page 14, lines 18-21. Christopherson further teaches Fig. 4F showing additional parameters such as a sleep stage parameter 421 as seen in [0110]-[0111]);
receive arousal data (Christopherson teaches the awake or sleep state of the patient is further indicated in sensing module 102 including the heart rate parameter 158 or respiratory rate parameter 159 as seen in Fig. 1 and [0068], [0071] and [0078]. Christopherson further teaches a high amplitude signal followed by a very high amplitude signal for respiratory pressure measurement is due to arousal from sleep as seen in [0123]); and
determine a therapeutic pressure recommendation, based on the titration respiration data and the arousal data (Christopherson teaches when obtaining data from one of more of physiologic sensing parameters of sensing module 102 reveals an ongoing inconsistent respiratory pattern that is used to indicate a potential waking state in which therapy should not be applied (see [0048]). Christopherson further teaches using a multi-tier system 200 in which the auto-titrate module 170 is to automatically increase or decrease the level of therapy as implemented by various treatment parameters 168 as seen in Figs. 3A, 4A, and 8A and [0049]. Christopherson also teaches the controller pre-determining maximum stimulation settings that establishes boundaries for the auto-tirate method such that the parameters of therapy generally remain within the limits of comfort of most patients as seen in [0144]).
Mahadevan teaches pressure support system 10 may be used to treat other symptoms and conditions such as dyspnea and Cheyne-Stokes respiration and/or other disordered breathing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan to receive titration respiration data, wherein the titration respiration data indicates respiration as a function of therapeutic pressure, receive arousal data; and determine a therapeutic pressure recommendation, based on the titration respiration data and the arousal data as taught by Christopherson to comfortably treat disordered breathing behavior while one is asleep (see [0028] and [0144]).
However, Lynn teaches adjusting its titration algorithm with the received data from EEG sets that is monitored for clustered EEG arousals as seen in [0198].
Modified Mahadevan in view of Christopherson teaches when obtaining data from sensors including arousal data to increase or decrease the level of therapy based on user comfort/arousal (taught by Christopherson). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan in view of Christopherson to receive cortical arousal data to be used in titration for adjusting pressures as taught by Lynn as alternative known data used to measure if a user is awoken by treatment (see [0198]).
Regarding claim 11, modified Mahadevan teaches the method of claim 10, and further teaches wherein running the pressure tolerance analysis algorithm further comprises:
receiving body position data indicative of a subject's body position (Modified Mahadevan teaches accelerometers on a user’s body (as taught by Wariar and Lee), which is further taught by Christopherson);
receiving sleep stage data, indicative of a subject's sleep stage (Christopherson teaches using a sleep stage parameter 421 to track the which sleep stage and how many within a sleep period as seen in Fig. 4F and [0110]); and
for a respective combination of body position and sleep stage, determine a respective maximum tolerated therapeutic pressure (Christopherson teaches a first state 202 of system 200 which includes body motion/activity sensor 310 sensing body posture (see [0068]) and further discusses tracking sleep stages to see whether therapy should be suspended (see [0095]. Christopherson further teaches the controller pre-determining maximum stimulation settings that establishes boundaries for the auto-tirate method such that the parameters of therapy generally remain within the limits of comfort of most patients as seen in [0144]).
Regarding claim 12, modified Mahadevan teaches the method of claim 1, but does not teach further comprising running a titration monitoring algorithm, wherein the titration monitoring algorithm is configured to:
receive subject data, the subject data comprising data corresponding to at least one parameter indicative of a change in expiration pressure intolerance; and
determine, based on the subject data, a change in expiration pressure intolerance; and
output a re-titration instruction.
However, Christopherson teaches further comprising running a titration monitoring algorithm (see [0049]-[0050]), wherein the titration monitoring algorithm is configured to:
receive subject data, the subject data comprising data corresponding to at least one parameter indicative of a change in expiration pressure intolerance (Christopherson teaches the awake or sleep state of the patient is further indicated in sensing module 102 including the heart rate parameter 158 or respiratory rate parameter 159 as seen in Fig. 1 and [0068], [0071] and [0078]. Christopherson further teaches a high amplitude signal followed by a very high amplitude signal for respiratory pressure measurement is due to arousal from sleep as seen in [0123]. As such, the data corresponds to at least one parameter indicative of a change in expiration pressure intolerance due to arousal of the patient); and
determine, based on the subject data, a change in expiration pressure intolerance; and
output a re-titration instruction (Christopherson teaches when obtaining data from one of more of physiologic sensing parameters of sensing module 102 reveals an ongoing inconsistent respiratory pattern that is used to indicate a potential waking state in which therapy should not be applied (see [0048]). Christopherson further teaches using a multi-tier system 200 in which the auto-titrate module 170 is to automatically increase or decrease the level of therapy as implemented by various treatment parameters 168 as seen in Figs. 3A, 4A, and 8A and [0049]-[0050]).
Mahadevan teaches pressure support system 10 may be used to treat other symptoms and conditions such as dyspnea and Cheyne-Stokes respiration and/or other disordered breathing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan to receive subject data comprising data corresponding to at least one parameter indicative of a change in expiration pressure intolerance; and determining, based on the subject data, a change in expiration pressure intolerance and outputting a re-titration instruction as taught by Christopherson to comfortably treat disordered breathing behavior while one is asleep (see [0028] and [0144]).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahadevan (US 20150320955 A1) in view of Wariar (US 20150148699 A1) and Lee (US 20050074741 A1), as applied to claim 1 above, and further in view of Shouldice (WO 2021220247 A1).
Regarding claim 13, modified Mahadevan teaches the method of claim 1, but does not teach further comprising running a prediction algorithm for predicting an expiration intolerance event wherein the prediction algorithm is configured to:
receive, as an input variable, physiological data;
determine, as an output, based on the input variable, a predicted expiration intolerance event; and
the method further comprises determining, based on the predicted expiration intolerance event, a therapeutic pressure recommendation.
However, Shouldice teaches comprising running a prediction algorithm for predicting an expiration intolerance event wherein the prediction algorithm is configured to:
receive, as an input variable, physiological data;
determine, as an output, based on the input variable, a predicted expiration intolerance event; and
the method further comprises determining, based on the predicted expiration intolerance event, a therapeutic pressure recommendation (Applicant teaches the predicted expiration intolerance event to involve active expiration (see page 17, lines 1-4) and the need for active expiration can cause arousals (see page 1, lines 14-23). Shouldice teaches the system detecting when a person is about to wake up (taken as expiration intolerance event) due to EEG or changes in a flow signal or heart signal, the respiratory therapy system can set pressures so that comfort is maximized to promote a sleeping state by using prediction as seen in [0147]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the method taught by modified Mahadevan to include the prediction algorithm for predicting an expiration intolerance event as taught by Shouldice to manage settings prior to arousal for user comfort (see [0147]).
Allowable Subject Matter
Claim(s) 6 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Claim 6 recites “wherein the detection algorithm is configured to: receive heart rate variability data; process the heart rate variability data to identify an altered venous return flow; in response to identifying the altered venous return flow, determine a change in heart rate and/or change in heart rate variability; and determine whether the change in heart rate and/or change in heart rate variability is indicative of a respiration and circulation mismatch; in response to determining that the change in heart rate and/or the change in heart rate variability is indicative of the respiration and circulation mismatch, generate a pressure adjustment instruction for reducing the air pressure of the pressurized air delivered to a subject during a respiration cycle.” Claim 6 depends on claim 1, wherein the prior arts for rejecting claim 1 are Mahadevan (US 20150320955 A1) in view of Wariar (US 20150148699 A1) and Lee (US 20050074741 A1). However, none of the above prior arts teaches receiving heart rate variability data, identifying an altered venous return flow and in response to determining that the change in heart rate and/or the change in heart rate variability is indicative of the respiration and circulation mismatch, generate a pressure adjustment instruction for reducing the air pressure of the pressurized air delivered to a subject during a respiration cycle.
Burton (US 20070032733 A1) teaches detecting a heart rate variability from ECG signals, extracting parameters from said ECG signals and utilizing said parameters to detect SDB, cardiac events or HRV as seen in [0026]. Burton further teaches linking changes in HRV with an associated SDB event (see [0136]) and using CPAP to assist in control of therapeutic treatment as seen in [0088]-[0089]. However, Burton does not teach identifying an altered venous return flow and in response to determining that the change in heart rate and/or the change in heart rate variability is indicative of the respiration and circulation mismatch.
As a result, because no references of record or reasonable conclusion thereof, could be found which disclose or suggest all features of claim 6, claim 6 is allowable subject matter over prior arts.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Shimizu (US 20150351694 A1) teaches looking at both chest and abdominal measurements.
Kayyali (US 9615773 B1) teaches using sensors to monitor both sleep stage and body position.
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/TINA ZHANG/Examiner, Art Unit 3785
/BRANDY S LEE/Supervisory Patent Examiner, Art Unit 3785
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