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 .
Claim Objections
Claims 1, 9, and 17-25 are objected to because of the following informalities:
Claim 1 line 8 should include a colon “:” after “configured to”;
Claim 9 line 1 should recite “wherein the controller”.
The examiner notes that claims 17-25 are dependent claims drawn to a method. However, claims 17-25 are dependent upon an apparatus claim 15. The examiner believes this is a typographical error and the claims should depend from independent claim 16 that is also drawn to a method. The examiner recommends the following amendment to the claims:
Claim 17 should be amended to recite “The method of claim 16…”;
Claim 18 should be amended to recite “The method of claim 16…”;
Claim 19 should be amended to recite “The method of claim 16…”;
Claim 21 should be amended to recite “The method of claim 16…”;
Claim 22 should be amended to recite “The method of claim 16…”;
Claim 23 should be amended to recite “The method of claim 16…”;
Claim 24 should be amended to recite “The method of claim 16…”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the limitation "the filter" in line 1. There is insufficient antecedent basis for this limitation in the claim.
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.
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 1-7, 10-22, and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Greenberg (US 20210290961 A1) in view of Bandyopadhyay (US 20170055898 A1).
Regarding claim 1, Greenberg teaches a computer-implemented system for treating obstructive sleep apnea (“OSA”) in a human subject, comprising:
one or more sensors [0013 “internal sensor” and “external sensor”], wherein each sensor is configured to collect sensor data indicative of respiratory activity and/or a physical state of the human subject [0013 “…internal sensor configured to generate sensory data corresponding to movement of the thoracic or abdominal cavity of a patient during respiration”] when placed on, in proximity to, or implanted in [“0013 “…an implanted stimulator with an internal sensor…”], the human subject, and wherein the one or more sensors includes at least one implanted sensor [“0013 “…an implanted stimulator with an internal sensor…”]; and
a controller comprising a processor [0025 “By way of example, an element, or any portion of an element, or any combination of elements may be implemented as a “processing system” that includes one or more processors. Examples of processors include microprocessors, microcontrollers…”] and memory [0026], communicatively linked to the one or more sensors [0013 “The stimulator has a controller coupled to one in more internal and/or external (e.g., external) sensors”] and configured to
receive the sensor data from the one or more sensors [0041 “The controller 104 receives the sensory data from one or more internal sensors 102 and/or sensory data from one or more external sensor 120…”],
detect a respiratory event experienced by the subject, using the sensor data [0040 “These features can be used by the controller to aid in the detection of the respiration pattern of the patient”], and
classify the detected respiratory event, using a trained classifier comprising an electronic representation of a classification system [0040 “…the controller 104 may apply a system of machine learning algorithms to the features to aid in detection of the respiration pattern”, see also 0046 “One or more machine learning algorithms may classify the EEG data and generate probabilistic outputs for the various sleep stages”, see also 0050 “…the stimulator 110 may transmit data related to therapy applied (e.g., duration of applied stimulation or intensity of applied stimulation) or related to efficacy of treatment (e.g., apnea-hypopnea index (AHI)) to the remote control 130 or to the clinician programmer 140, and the remote control 130 or the clinician programmer 140 may transmit the data to the Internet and/or cloud service 150 to be compiled”]; and
a stimulation system [Fig. 4 Item 106], communicatively linked to the controller [0047 “…the controller 104 may monitor the wireless link 116 between the external sensor 120 and the stimulator 110…”] and configured to deliver stimulation to a nerve which innervates an upper airway muscle of the human subject [0041 “The system 100 may be configured to deliver stimulation to a nerve innervating the upper airway…”] based on the classification by the controller [0043 “…the controller 104 may use machine learning methods to make the sleep stage decisions. The controller 104 may control the stimulation applied by the stimulation system 106 based on the sleep stage decisions”].
Greenberg teaches using machine learning techniques to classify the detected respiratory event, but fails to teach the classifier is a trained classifier.
Bandyopadhyay teaches a trained classifier [0083 “…data can be provided to a trained classifier that provides outputs indicative of likelihoods that the input signals correspond to different sleep stages”].
It would have been obvious too one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Greenberg and incorporate the teachings of Bandyopadhyay to include a trained classifier. Doing so configures the classifier to intake data with the purpose of improving the accuracy and efficiency of said classifier.
Regarding claim 2, Greenberg and Bandyopadhyay teach the system of claim 1, where the controller is configured to classify the detected respiratory event as normal breathing, an apnea event, or a hypopnea event [Greenberg 0046 “One or more machine learning algorithms may classify the EEG data and generate probabilistic outputs for the various sleep stages. Such a system can be used to: quantify metrics of improvement of sleep quality, such as for example being able to measure OSA patients getting more REM sleep, with therapy on versus therapy off”, the examiner interprets “OSA” as “obstructive sleep apnea”].
Regarding claim 3, Greenberg and Bandyopadhyay teach the system of claim 1, wherein the one or more sensors each comprise: a pressure sensor [Greenberg 0036], an accelerometer [Greenberg 0040 “inertial measurement unit”, the examiner notes the three primary components of an IMU are an accelerometer, a gyroscope, and a magnetometer], a sound sensor [Greenberg 0036 “audio sensor”], a gyroscope [Greenberg 0036 “inertial sensor”, see also 0040], a heart rate monitor [Greenberg 0032], an electrocardiogram (“ECG”) sensor [Greenberg 0028], and a blood oxygen level sensor [Greenberg 0028 “peripheral oxygen saturation”].
Regarding claim 4, Greenberg and Bandyopadhyay teach the system of claim 1, wherein the controller is further configured to generate a sleep quality metric for the human subject [Greenberg 0046 “Such a system can be used to: quantify metrics of improvement of sleep quality, such as for example being able to measure OSA patients getting more REM sleep, with therapy on versus therapy off”], wherein the sleep quality metric is based on the number of detected apnea or hypopnea events experienced by the subject [Greenberg 0042 “The external sensor 120 may have at least one physiological sensor configured to generate sensory data based on monitoring a physiological parameter of the patient, such as SpO.sub.2, heart rate, or respiration, or based on sense ECG, EEG, or EOG data. SpO.sub.2 data can be used to measure oxygen desaturation events, which could be used to provide additional data for apneic events. Apneic events can be detected by determining that the regular respiratory pattern, as detected by the implanted IMU or pressure sensor, has become irregular for a number of cycles (e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 cycles)”].
Regarding claim 5, Greenberg and Bandyopadhyay teach the system of claim 4, wherein the sleep quality metric is an Apnea-Hypopnea Index (“AHI”) [Greenberg 0045].
Regarding claim 6, Greenberg and Bandyopadhyay teach the system of claim 1, wherein the one or more sensors comprises a sub-clavically implanted inertial measurement unit (“IMU”) [Greenberg Fig. 1 Item 110, 0028 “The stimulator 110 may comprise or be in communication with at least one internal sensor implanted into a patient, such as a pressure sensor or inertial measurement unit (IMU)…”].
Regarding claim 7, Greenberg and Bandyopadhyay teach the system of claim 1, wherein the controller [Greenberg Fig. 4 Item 104] is located within a housing [Greenberg Fig. 4 Item 110] implanted in the human subject [Greenberg Fig. 4 Item 117], and configured to predict an airflow reduction amount [Greenberg 0042 “respiration”, the examiner notes that recording respiration will inherently record increases and/or decreases in respiration rate] and an oxygen desaturation level for the human subject using sensor data obtained from the one or more sensors [Greenberg 0042 “…data can be used to measure oxygen desaturation events, which could be used to provide additional data for apneic events”].
Regarding claim 10, Greenberg and Bandyopadhyay teach the system of claim 7, wherein the one or more sensors comprises an IMU configured to detect motion by the human subject [Greenberg 0028 “The stimulator 110 may comprise or be in communication with at least one internal sensor implanted into a patient, such as a pressure sensor or inertial measurement unit (IMU)…”], and wherein the controller is configured to identify one or more regions of the respiratory activity signal as a motion artifact [Greenberg 0040 “The controller 104 may process the SCG data received from the IMU sensor to extract various features, such as heart rate and heart rate variability”] based on detected motion by the human subject [Greenberg 0028 “…inertial measurement unit (IMU) configured to generate sensory data corresponding to the movement of the thoracic or abdominal cavity of a patient during respiration”].
Regarding claim 11, Greenberg and Bandyopadhyay teach system of claim 1, wherein the trained classifier was trained using a baseline dataset [Bandyopadhyay 0083 “…data can be provided to a trained classifier that provides outputs indicative of likelihoods that the input signals correspond to different sleep stages”], wherein the baseline dataset comprises: a) data generated during a prior single or multi-night polysomnography (PSG) study of the human subject; and/or b) data generated from a prior single or multi-night PSG study of a population of human subjects [Bandyopadhyay 0083 “This process may train the system using any of various signals used in polysomnograms (PSGs) such as EEGs (electroencephalograms), EMGs (electromyograms), EOGs (electrooculograms), and pulse-oximetry”].
Regarding claim 12, Greenberg and Bandyopadhyay teach the system of claim 4, wherein the system is configured to transmit the sleep quality metric to a local, remote, or cloud-based server [Bandyopadhyay 0161 “…the device is equipped with Wi-Fi to communicate with routers, mobile phones to transfer data. Wi-Fi can be used in this embodiment to upload sensor or processed data to servers for storage or to execute further processing to achieve goals such as stage classification or apnea event detection”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Greenberg with the teachings of Bandyopadhyay to include the system is configured to transmit the sleep quality metric to a local, remote, or cloud-based server. Doing so configures the system to streamline data manipulation tasks improve overall data processing speed.
Regarding claim 13, Greenberg and Bandyopadhyay teach the system of claim 1, wherein the controller is configured to detect the respiratory event experienced by the subject using sensor data received from at least or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 sensors [Greenberg 0052 “…one more internal sensors 102 and external sensors 120 described herein…”].
Regarding claim 14, Greenberg and Bandyopadhyay teach the system of claim 1, wherein the controller is configured to cause the stimulation system to apply, increase, decrease, temporarily pause, or terminate the stimulation based on the classification by the controller [Greenberg 0038 “The controller 104 may be configured to control when and/or how the stimulation system 106 applies stimulation to the electrode 112”, see also 0038 “For example, the controller 104 may control the stimulation system 106 to apply stimulation during the inspiratory period of the respiration waveform, to apply stimulation during the expiratory period of the respiratory waveform, or to apply stimulation during particular parts of the inspiratory and/or expiratory portions of the respiratory waveform”].
Regarding claim 15, Greenberg and Bandyopadhyay teach the system of claim 13, wherein the controller is configured to cause the stimulation system to change an amplitude, pulse width, or frequency of the stimulation based on the classification by the controller [Greenberg 0041 “…the control 104 controls the amplitude, the pulse width, or the frequency of the stimulation applied by the stimulation system 106”].
Regarding claim 16, Greenberg teaches a method for treating obstructive sleep apnea (“OSA”) in a human subject comprising:
collecting sensor data indicative of respiratory activity and/or a physical state of the human subject [0013 “…internal sensor configured to generate sensory data corresponding to movement of the thoracic or abdominal cavity of a patient during respiration”], using one or more sensors [0013 “internal sensor” and “external sensor”] configured to collect data when placed on, in proximity to, or implanted within [“0013 “…an implanted stimulator with an internal sensor…”], the human subject, wherein the one or more sensors includes at least one implanted sensor [“0013 “…an implanted stimulator with an internal sensor…”];
receiving, by a controller comprising a processor [0025 “By way of example, an element, or any portion of an element, or any combination of elements may be implemented as a “processing system” that includes one or more processors. Examples of processors include microprocessors, microcontrollers…”] and memory [0026], the sensor data from the one or more sensors [0041 “The controller 104 receives the sensory data from one or more internal sensors 102 and/or sensory data from one or more external sensor 120…”];
detecting a respiratory event experienced by the subject, using the received sensor data [0040 “These features can be used by the controller to aid in the detection of the respiration pattern of the patient”];
classifying the detected respiratory event, by the controller;
wherein the controller is configured to perform the classification using a classifier comprising an electronic representation of a classification system [0040 “…the controller 104 may apply a system of machine learning algorithms to the features to aid in detection of the respiration pattern”, see also 0046 “One or more machine learning algorithms may classify the EEG data and generate probabilistic outputs for the various sleep stages”], and
delivering stimulation to a nerve which innervates an upper airway muscle of the human subject [0041 “The system 100 may be configured to deliver stimulation to a nerve innervating the upper airway…”] based on the classification by the controller [0043 “…the controller 104 may use machine learning methods to make the sleep stage decisions. The controller 104 may control the stimulation applied by the stimulation system 106 based on the sleep stage decisions”].
Greenberg teaches using machine learning techniques to classify the detected respiratory event, but fails to teach the classifier is a trained classifier.
Bandyopadhyay teaches a trained classifier [0083 “…data can be provided to a trained classifier that provides outputs indicative of likelihoods that the input signals correspond to different sleep stages”].
It would have been obvious too one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Greenberg and incorporate the teachings of Bandyopadhyay to include a trained classifier. Doing so configures the classifier to intake data with the purpose of improving the accuracy and efficiency of said classifier.
A combination of Greenberg and Bandyopadhyay teaches transmitting the data and performing classification using a classifier and an electronic representation of a classification system [Greenberg 0050 “…the stimulator 110 may transmit data related to therapy applied (e.g., duration of applied stimulation or intensity of applied stimulation) or related to efficacy of treatment (e.g., apnea-hypopnea index (AHI)) to the remote control 130 or to the clinician programmer 140, and the remote control 130 or the clinician programmer 140 may transmit the data to the Internet and/or cloud service 150 to be compiled”], but fail to teach the system is configured to explicitly transmit the received sensor data to a server.
Bandyopadhyay teaches the system is configured to transmit the received sensor data to a server [Bandyopadhyay 0161 “…the device is equipped with Wi-Fi to communicate with routers, mobile phones to transfer data. Wi-Fi can be used in this embodiment to upload sensor or processed data to servers for storage or to execute further processing to achieve goals such as stage classification or apnea event detection”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Greenberg with the teachings of Bandyopadhyay to include the system is configured to transmit the sleep quality metric to a local, remote, or cloud-based server. Doing so configures the system to streamline data manipulation tasks improve overall data processing speed.
Regarding claim 17, Greenberg and Bandyopadhyay teach the method of claim 15, where the controller is configured to classify the detected respiratory event as normal breathing, an apnea event, or a hypopnea event [Greenberg 0046 “One or more machine learning algorithms may classify the EEG data and generate probabilistic outputs for the various sleep stages. Such a system can be used to: quantify metrics of improvement of sleep quality, such as for example being able to measure OSA patients getting more REM sleep, with therapy on versus therapy off”, the examiner interprets “OSA” as “obstructive sleep apnea”].
Regarding claim 18, Greenberg and Bandyopadhyay teach the method of claim 15, wherein the one or more sensors each comprise: a pressure sensor [Greenberg 0036], an accelerometer [Greenberg 0040 “inertial measurement unit”, the examiner notes the three primary components of an IMU are an accelerometer, a gyroscope, and a magnetometer], a sound sensor [Greenberg 0036 “audio sensor”], a gyroscope [Greenberg 0036 “inertial sensor”, see also 0040], a heart rate monitor [Greenberg 0032], an electrocardiogram (“ECG”) sensor [Greenberg 0028], and a blood oxygen level sensor [Greenberg 0028 “peripheral oxygen saturation”].
Regarding claim 19, Greenberg and Bandyopadhyay teach the method of claim 15, wherein the controller is further configured to generate a sleep quality metric for the human subject [Greenberg 0046 “Such a system can be used to: quantify metrics of improvement of sleep quality, such as for example being able to measure OSA patients getting more REM sleep, with therapy on versus therapy off”], wherein the sleep quality metric is based on the number of detected apnea or hypopnea events experienced by the subject [Greenberg 0042 “The external sensor 120 may have at least one physiological sensor configured to generate sensory data based on monitoring a physiological parameter of the patient, such as SpO.sub.2, heart rate, or respiration, or based on sense ECG, EEG, or EOG data. SpO.sub.2 data can be used to measure oxygen desaturation events, which could be used to provide additional data for apneic events. Apneic events can be detected by determining that the regular respiratory pattern, as detected by the implanted IMU or pressure sensor, has become irregular for a number of cycles (e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 cycles)”].
Regarding claim 20, Greenberg and Bandyopadhyay teach the method of claim 18, wherein the sleep quality metric is an Apnea-Hypopnea Index (“AHI”) [Greenberg 0045].
Regarding claim 21, Greenberg and Bandyopadhyay teach the method of claim 15, wherein the one or more sensors comprises a sub-clavically implanted inertial measurement unit (“IMU”) [Greenberg Fig. 1 Item 110, 0028 “The stimulator 110 may comprise or be in communication with at least one internal sensor implanted into a patient, such as a pressure sensor or inertial measurement unit (IMU)…”].
Regarding claim 22, Greenberg and Bandyopadhyay teach the method of claim 15, wherein the controller [Greenberg Fig. 4 Item 104] is located within a housing [Greenberg Fig. 4 Item 110] implanted in the human subject [Greenberg Fig. 4 Item 117], and configured to predict an airflow reduction amount [Greenberg 0042 “respiration”, the examiner notes that recording respiration will inherently record increases and/or decreases in respiration rate] and an oxygen desaturation level for the human subject using sensor data obtained from the one or more sensors [Greenberg 0042 “…data can be used to measure oxygen desaturation events, which could be used to provide additional data for apneic events”].
Regarding claim 23, Greenberg and Bandyopadhyay teach the method of claim 15, wherein the one or more sensors comprises a sound sensor configured to detect a respiratory activity signal when positioned on, within, or in proximity to the chest, bronchi, or trachea of the human subject, and wherein the controller is further configured to apply a filter to the respiratory activity signal, wherein the filter is configured to reduce or eliminate a component of the respiratory activity signal caused by the human subject’s heartbeat and/or snoring activity.
Regarding claim 24, Greenberg and Bandyopadhyay teach the method of claim 15, wherein the controller is configured to cause the stimulation system to apply, increase, decrease, temporarily pause, or terminate the stimulation based on the classification by the controller [Greenberg 0038 “…the controller 104 may control the stimulation system 106 to apply stimulation during the inspiratory period of the respiration waveform, to apply stimulation during the expiratory period of the respiratory waveform, or to apply stimulation during particular parts of the inspiratory and/or expiratory portions of the respiratory waveform”].
Regarding claim 25, Greenberg and Bandyopadhyay teach the method of claim 23, wherein the controller is configured to cause the stimulation system to change an amplitude, pulse width, or frequency of the stimulation based on the classification by the controller [Greenberg 0041 “…the control 104 controls the amplitude, the pulse width, or the frequency of the stimulation applied by the stimulation system 106”]
Claims 8 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Greenberg and Bandyopadhyay as applied to claim 1 and 15, and further in view of Kawabe (US 20180014792 A1).
Regarding claim 8, Greenberg and Bandyopadhyay teach the system of claim 1, wherein the one or more sensors comprises a sound sensor [Greenberg 0014 “audio sensors”] configured to detect a respiratory activity signal [Greenberg 0014 “The use of external sensors decoupled from the implanted system allows for a wide range of sensors to be used to detect respiration artifacts…”] when positioned on, within, or in proximity to the chest, bronchi, or trachea of the human subject [Greenberg 0014 “…the external sensors can now be placed in various anatomical locations…”, see also 0032 “An external sensor 120C may be configured to be worn around a chest of a patient”].
Greenberg and Bandyopadhyay teach acquiring heartbeat signals from the patient to aid in the detection of respiration patterns, but fails to explicitly teach filtering the respiration signal by reducing or eliminating a component of the respiratory activity signal caused by the human subject’s heartbeat and/or snoring activity.
Kawabe teaches applying a filter to the respiratory activity signal, wherein the filter is configured to reduce or eliminate a component of the respiratory activity signal caused by the human subject’s heartbeat [0080 “…the respiration signal SR is caused to pass through, for example, a low pass filter or band pass filter that allows passage of frequencies lower than frequency components constituting the heartbeat signal SH, so as to remove the heartbeat signal SH from the respiration signal SR…”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Greenberg and Bandyopadhyay and incorporate the teachings of Kawabe to include filtering the respiration signal by reducing or eliminating a component of the respiratory activity signal caused by the human subject’s heartbeat and/or snoring activity. Doing so configures the system to remove interference caused by the heartbeat and provide for a more accurate and isolated respiration signal that provides for an accurate respiratory condition assessment.
Regarding claim 23, Greenberg and Bandyopadhyay teach the method of claim 15, wherein the one or more sensors comprises a sound sensor [Greenberg 0014 “audio sensors”] configured to detect a respiratory activity signal [Greenberg 0014 “The use of external sensors decoupled from the implanted system allows for a wide range of sensors to be used to detect respiration artifacts…”] when positioned on, within, or in proximity to the chest, bronchi, or trachea of the human subject [Greenberg 0014 “…the external sensors can now be placed in various anatomical locations…”, see also 0032 “An external sensor 120C may be configured to be worn around a chest of a patient”].
Greenberg and Bandyopadhyay teach acquiring heartbeat signals from the patient to aid in the detection of respiration patterns, but fails to explicitly teach filtering the respiration signal by reducing or eliminating a component of the respiratory activity signal caused by the human subject’s heartbeat and/or snoring activity.
Kawabe teaches applying a filter to the respiratory activity signal, wherein the filter is configured to reduce or eliminate a component of the respiratory activity signal caused by the human subject’s heartbeat [0080 “…the respiration signal SR is caused to pass through, for example, a low pass filter or band pass filter that allows passage of frequencies lower than frequency components constituting the heartbeat signal SH, so as to remove the heartbeat signal SH from the respiration signal SR…”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Greenberg and Bandyopadhyay and incorporate the teachings of Kawabe to include filtering the respiration signal by reducing or eliminating a component of the respiratory activity signal caused by the human subject’s heartbeat and/or snoring activity. Doing so configures the system to remove interference caused by the heartbeat and provide for a more accurate and isolated respiration signal that provides for an accurate respiratory condition assessment.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Greenberg and Bandyopadhyay as applied to claim 7 above, and further in view of Li (US 20170273635 A1) and Balji (US 20100268093 A1).
Regarding claim 9, Greenberg and Bandyopadhyay teach the system of claim 7, wherein controller is configured to apply the trained classifier to identify regions of the respiratory signal corresponding to apnea and/or hypopnea events [Greenberg 0040 “…the controller 104 may apply a system of machine learning algorithms to the features to aid in detection of the respiration pattern”].
Greenberg and Bandyopadhyay fails to teach a filter, wherein the filter comprises a Hilbert transform.
Li teaches a filter comprises a Hilbert transform [0078 “…BCG signal decomposition based on one or more of a plurality of signal decomposition techniques, including, but not limited to Hilbert transform, one or more finite impulse response (FIR)/infinite impulse response (IIR) filters with different cut-off and stop bands, a time-domain based moving average method and multi-order derivatives”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Greenberg and Bandyopadhyay and incorporate the teachings of Li to include a filter comprises a Hilbert transform. Doing so configures the system to provide phase selectivity, allowing for the manipulation of signals based on their phase content.
Greenberg teaches a controller, but fails to teach wherein controller is configured to apply an adaptive threshold.
Balji teaches applying an adaptive threshold [0042 “…the ECG respiration signal quality check component 533 and the pressure respiration signal quality check component 535 may use one or more of the following methods: (1) the adaptive moving average filtering, variable length peak detection window, and comparison of detected peaks to an adaptive threshold to distinguish noise peaks from respiration signal peaks…”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Greenberg and Bandyopadhyay and incorporate the teachings of Balji to include applying an adaptive threshold. Doing so configures the system to allow for dynamic adjustments to the threshold based on the varying characteristics and states of the patient, leading to improved accuracy and robustness in respiration detection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN M HANEY whose telephone number is (571)272-0985. The examiner can normally be reached Monday through Friday, 0730-1630 ET.
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/JONATHAN M HANEY/ Examiner, Art Unit 3791
/JUSTIN XU/ Primary Examiner, Art Unit 3791