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 .
Response to Amendment
The amendment filed on August 07, 2025 was considered by the examiner. Claims 1-6, 8-10, and 12-13 are pending in the application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6, 8, 10, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Giovangrandi et al. (US Patent Publication 2011/0021928 – cited in prior action), hereinafter Giovangrandi, in view of Wahlström et al. (“A Hidden Markov Model for Seismocardiography”, IEEE Transactions on Biomedical Engineering, Vol. 64, NO. 10, 2361-2372, 09 January 2017), hereinafter Wahlström, in view of Peters et al. (WIPO Publication WO 2019/166359 A1), hereinafter Peters, and in view of Kwok et al. (US Patent Publication 2007/0129643 – cited in prior action), hereinafter Kwok.
Regarding Claim 1, Giovangrandi teaches a noninvasive systems/methods of monitoring a subject’s cardiorespiratory parameters (see abstract). Giovangrandi teaches a sleep monitoring system, for monitoring a subject (see abstract), comprising:
a movement sensing arrangement (¶[0009], ¶[0040], ¶[0083]-[0086], and ¶[0094] the chest-worn sensor, the accelerometer; Fig. 1);
wherein the movement sensing arrangement comprises an acceleration or gyroscope sensor arrangement (¶[0009], ¶[0040], ¶[0083]-[0086], and ¶[0094] the chest-worn sensor, the accelerometer; Fig. 1) for generating and recording seismocardiography signals from the subject (¶[0084], ¶[0119], and ¶[0129]-[0131] the system is for measuring seismocardiogram (SCG) signals utilizing the accelerometer),
and a controller (¶[0094]-[0098] the processor and/or the computer; Fig. 1), adapted to:
during a sleep monitoring period, determine and identify from the seismocardiography signals sleep-disordered breathing events of the subject (¶[0094]-[0098] the processor is configured to receive and to process the recorded sensor data, ¶[0040]-[0048] a plethora of respiratory disorders may be monitored with the current system, including sleep apnea, which would indicate that the subject would be asleep, ¶[0057], ¶[0073], ¶[0086], and ¶[0098] the system may be utilized for detection of sleep apnea, which may utilize the subject’s sleep posture/position, ¶[0008], ¶[0039], ¶[0098] the output of the system is used to determine the respiratory disorder, which may be sleep apnea),
based on a time series a time series analysis of the seismocardiography signals in a time domain (¶[0009], ¶[0039]-[0041], ¶[0048], and ¶[0055]-[0056] the system utilizes the recorded signal to determine the respiratory disorders, ¶[0019]-[0022] and ¶[0024] the S1-S1 interval is in time series from time series acceleration data), including
determining a plurality of repetitive patterns in the seismocardiography signals, determining an inter-beat interval time series based solely on temporal distances between each respective instance of the plurality of detected repetitive patterns (¶[0100] and ¶[0138]-[0141] the system determines inter-beat interval, also known as S1-S1 interval or respiratory sinus arrhythmia (RSA), ¶[0019]-[0022] and ¶[0024] the S1-S1 interval is in time series from time series acceleration data, ¶[0133] and ¶[0138]-[0141] the interval calculation based off of the locations of fiducial peaks; Figs. 6-9 and 11), and determining the sleep-disordered breath events from the inter-beat interval time series (¶[0009], ¶[0040], ¶[0054], ¶[0072]-[0074] the system utilizes the determined S1-S1 interval to determine respiratory disorders),
determine from output signals of the movement sensing arrangement a number of sleep positions of the subject out of a set of possible sleep positions (¶[0057], ¶[0080] ¶[0086], ¶[0106], ¶[0108], and ¶[0126]-[0127] the acceleration may also be used to determine sleep posture/position, ¶[0086] the three-axis accelerometer may be used for both the SCG data and the subject posture/position data).
Giovangrandi is silent regarding determining an expected duration range based on a first heartbeat duration and a second heartbeat duration, wherein the first heartbeat duration is shorter than the second heartbeat duration, and that the plurality of repetitive patterns are determined with the expected range in the seismocardiography signal.
Wahlström teaches a hidden Markov model (HMM) approach for processing seismocardiograms (see abstract), in which the HMM is used to describe the heart beat (in the time domain) via parameters learned utilizing the Baum-Welch algorithm, and finally the Viterbi algorithm is utilized to find a maximum a posteriori (MAP) estimate of the complete sequence of states, from which the beat-to-beat interval is estimated, as well as other cardiac time intervals (see pg. 2362-2365 § D. Contributions and § II. Model and Estimation Framework; Fig. 3), which utilizes specific intervals of length “N” over which the signal energy is locally maximized (see § C. Initialization of the Baum–Welch Algorithm and Fig. 2).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the HMM IBI determination of Wahlström for the IBI determination in of Giovangrandi because it is the application of a known technique to a known device ready for improvement to yield predictable results; and/or (2) Giovangrandi requires a IBI determination, and Wahlström teaches one such determination; and/or (3) the HMM-based calculation demonstrated a superior performance in all respects compared to previously proposed envelope-based and spectral-based methods (see pg. 2369 § IV. Summary).
The modified Giovangrandi teaches to identify the IBI via intervals of specific length (expected duration), but not specifically that the expected duration is determined based on a first heartbeat duration and a second heartbeat duration, wherein the first heartbeat duration is shorter than the second heartbeat duration.
Peters teaches a wearable health device that acquires SCG data from the subject and events are determined based off of an identified cardiac cycle segment (see abstract), in which the cardiac cycle segments are interpolated to a unit length and the arithmetic average is aggregated to provide an average cardiac cycle segment (see ¶[0076]). In this case, the segments would not have the same length, of which, two with differing lengths may be taken as the first and second heart beat duration.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the average cardiac cycle segment of Peters for the interval length as used in the modified Giovangrandi because it is the simple substitution of one known element for another to yield predictable results and/or (2) the modified Giovangrandi teaches to use one interval length and Peters teaches one such alternative interval length.
Giovangrandi contemplates the importance of measuring posture/position at the time of the apneic event, but the modified Giovangrandi does not specifically teach the step of providing an indication of a level of sleep-disordered breathing for each determined sleep position based on the determined sleep-disordered breathing events.
Kwok teaches a system that includes monitoring a subject’s respiration and generating a disordered breathing index (see abstract), such that the disordered breathing may be apnea (see ¶[0025] and ¶[0033]-[0038]), and may also monitor the subject’s posture utilizing a multiaxial accelerometer (see ¶[0075]). Kwok teaches to monitor the patient’s posture during disordered breathing (DB) episodes (see ¶[0043]), and may develop a modified apnea/hypopnea index (AHI) based on the different postures/tilt of the subject (see ¶[0052]-[0054]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the posture/tilt to AHI correlation of Kwok with the modified device of Giovangrandi because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) the posture correlation to AHI would provide a better indicator of when apnea is occurring for the subject as it would take their position/posture into account, as well as the cardiorespiratory parameters.
Regarding Claim 2, Giovangrandi in view of Wahlström, Peters, and Kwok teaches the device of claim 1 as stated above. Giovangrandi further teaches the sleep-disordered breath events comprise sleep apnea events (¶[0040]-[0048] a plethora of respiratory disorders may be monitored with the current system, including sleep apnea, ¶[0057], ¶[0073], ¶[0086], and ¶[0098] the system may be utilized for detection of sleep apnea, which may utilize the subject’s sleep posture/position, ¶[0008], ¶[0039], ¶[0098] the output of the system is used to determine the respiratory disorder, which may be sleep apnea).
Regarding Claim 3, Giovangrandi in view of Wahlström, Peters, and Kwok teaches the device of claim 1 as stated above. Giovangrandi further teaches one or more of: a sensor arrangement for detecting respiratory effort (¶[0078] the system determines respiration effort from the respiration parameters, ¶[0084], ¶[0119], ¶[0129]-[0131] the accelerometer for measuring the SCG signal, which is used for determining the respiration parameters, would be the sensor arrangement); a microphone for detecting breathing sounds.
Regarding Claim 6, Giovangrandi in view of Wahlström, Peters, and Kwok teaches the device of claim 1 as stated above. Giovangrandi further teaches the controller is adapted to extract from the movement sensing arrangement output signals separate signal components (¶[0086] the three-axis accelerometer may be used for both the SCG data and the subject posture/position data, the acceleration signal would thus be separable into its components for the appropriate signal analysis),
for detection of the sleep positions (¶[0057], ¶[0080] ¶[0086], ¶[0106], ¶[0108], and ¶[0126]-[0127] the acceleration may also be used to determine sleep posture/position),
respiratory movements (¶[0009], ¶[0040], ¶[0064]-[0065], and ¶[0076] the accelerometer is used for chest wall motion measurements; Figs. 6-9 and 11),
and the seismocardiography signals (¶[0084], ¶[0119], ¶[0129]-[0131] the accelerometer for measuring the SCG signal, which is used for determining the respiration parameters).
Regarding Claim 8, Giovangrandi in view of Wahlström, Peters, and Kwok teaches the device of claim 1 as stated above. The modified Giovangrandi further teaches the level of sleep-disordered breathing for each determined sleep position comprises an apnea-hypopnea index value (see Giovangrandi ¶[0057], ¶[0080] ¶[0086], ¶[0106], ¶[0108], and ¶[0126]-[0127] the acceleration may also be used to determine sleep posture/position; see Kwok ¶[0052]-[0054] the modified AHI based on the different postures/tilt of the subject).
Regarding Claim 10, Giovangrandi teaches a noninvasive systems/methods of monitoring a subject’s cardiorespiratory parameters (see abstract). Giovangrandi teaches a sleep monitoring method for monitoring a subject (see abstract), comprising:
monitoring movements of the subject using a movement sensing arrangement (¶[0009], ¶[0040], ¶[0083]-[0086], and ¶[0094] the chest-worn sensor, the accelerometer; Fig. 1) which collects seismocardiography signals from the subject (¶[0084], ¶[0119], ¶[0129]-[0131] the system is for measuring seismocardiogram (SCG) signals utilizing the accelerometer);
identifying from the seismocardiography signals sleep-disordered breathing events of the subject (¶[0009], ¶[0039]-[0041], ¶[0048], and ¶[0055]-[0056] the system utilizes the recorded signal to determine the respiratory disorders, ¶[0094]-[0098] the processor is configured to receive and to process the recorded sensor data, ¶[0040]-[0048] a plethora of respiratory disorders may be monitored with the current system, including sleep apnea, which would indicate that the subject would be asleep, ¶[0057], ¶[0073], ¶[0086], and ¶[0098] the system may be utilized for detection of sleep apnea, which may utilize the subject’s sleep posture/position, ¶[0008], ¶[0039], ¶[0098] the output of the system is used to determine the respiratory disorder, which may be sleep apnea)
based on a time series analysis of the seismocardiography signals in a time domain (¶[0009], ¶[0039]-[0041], ¶[0048], and ¶[0055]-[0056] the system utilizes the recorded signal to determine the respiratory disorders, ¶[0019]-[0022] and ¶[0024] the S1-S1 interval is in time series from time series acceleration data) including
determining a plurality of repetitive patterns in the seismocardiography signals, determining an inter-beat interval time series based solely on temporal distances between each respective instance of the plurality of detected repetitive patterns (¶[0100] and ¶[0138]-[0141] the system determines inter-beat interval, also known as S1-S1 interval or respiratory sinus arrhythmia (RSA), ¶[0019]-[0022] and ¶[0024] the S1-S1 interval is in time series from time series acceleration data, ¶[0133] and ¶[0138]-[0141] the interval calculation based off of the locations of fiducial peaks; Figs. 6-9 and 11), and determining the sleep-disordered breath events from the inter-beat interval time series (¶[0009], ¶[0040], ¶[0054], ¶[0072]-[0074] the system utilizes the determined S1-S1 interval to determine respiratory disorders), and
determining the sleep-disordered breath events from the inter-beat interval time series (¶[0009], ¶[0040], ¶[0054], ¶[0072]-[0074] the system utilizes the determined S1-S1 interval to determine respiratory disorders; Figs. 6-9 and 11),
determining from output signals of the movement sensing arrangement a number of sleep positions of the subject out of a set of possible sleep positions (¶[0057], ¶[0080] ¶[0086], ¶[0106], ¶[0108], and ¶[0126]-[0127] the acceleration may also be used to determine sleep posture/position, ¶[0086] the three-axis accelerometer may be used for both the SCG data and the subject posture/position data).
Giovangrandi is silent regarding determining an expected duration range based on a first heartbeat duration and a second heartbeat duration, wherein the first heartbeat duration is shorter than the second heartbeat duration, and that the plurality of repetitive patterns are determined with the expected range in the seismocardiography signal.
Wahlström teaches a hidden Markov model (HMM) approach for processing seismocardiograms (see abstract), in which the HMM is used to describe the heart beat (in the time domain) via parameters learned utilizing the Baum-Welch algorithm, and finally the Viterbi algorithm is utilized to find a maximum a posteriori (MAP) estimate of the complete sequence of states, from which the beat-to-beat interval is estimated, as well as other cardiac time intervals (see pg. 2362-2365 § D. Contributions and § II. Model and Estimation Framework; Fig. 3), which utilizes specific intervals of length “N” over which the signal energy is locally maximized (see § C. Initialization of the Baum–Welch Algorithm and Fig. 2).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the HMM IBI determination of Wahlström for the IBI determination in of Giovangrandi because it is the application of a known technique to a known method ready for improvement to yield predictable results; and/or (2) Giovangrandi requires a IBI determination, and Wahlström teaches one such determination; and/or (3) the HMM-based calculation demonstrated a superior performance in all respects compared to previously proposed envelope-based and spectral-based methods (see pg. 2369 § IV. Summary).
The modified Giovangrandi teaches to identify the IBI via intervals of specific length (expected duration), but not specifically that the expected duration is determined based on a first heartbeat duration and a second heartbeat duration, wherein the first heartbeat duration is shorter than the second heartbeat duration.
Peters teaches a wearable health device that acquires SCG data from the subject and events are determined based off of an identified cardiac cycle segment (see abstract), in which the cardiac cycle segments are interpolated to a unit length and the arithmetic average is aggregated to provide an average cardiac cycle segment (see ¶[0076]). In this case, the segments would not have the same length, of which, two with differing lengths may be taken as the first and second heart beat duration.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the average cardiac cycle segment of Peters for the interval length as used in the modified Giovangrandi because it is the simple substitution of one known element for another to yield predictable results and/or (2) the modified Giovangrandi teaches to use one interval length and Peters teaches one such alternative interval length.
Giovangrandi contemplates the importance of measuring posture/position at the time of the apneic event, but the modified Giovangrandi does not specifically teach the step of providing an indication of a level of sleep-disordered breathing for each of the determined sleep positions based on the determined sleep-disordered breathing events.
Kwok teaches a system that includes monitoring a subject’s respiration and generating a disordered breathing index (see abstract), such that the disordered breathing may be apnea (see ¶[0025] and ¶[0033]-[0038]), and may also monitor the subject’s posture utilizing a multiaxial accelerometer (see ¶[0075]). Kwok teaches to monitor the patient’s posture during disordered breathing (DB) episodes (see ¶[0043]), and may develop a modified apnea/hypopnea index (AHI) based on the different postures/tilt of the subject (see ¶[0052]-[0054]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the posture/tilt to AHI correlation of Kwok with the modified method of Giovangrandi because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) the posture correlation to AHI would provide a better indicator of when apnea is occurring for the subject as it would take their position/posture into account, as well as the cardiorespiratory parameters.
Regarding Claim 13, Giovangrandi teaches a noninvasive systems/methods of monitoring a subject’s cardiorespiratory parameters (see abstract). Giovangrandi teaches a non-transitory computer readable medium having a computer readable program code embodied therein that when executed by a processor implements (¶[0094]-[0098] the processor and/or the computer is adapted for performing the method utilizing the system, the processor/computer would have program code configured to perform the method; Fig. 1), a sleep monitoring method for monitoring a subject (see abstract), comprising:
monitoring movements of the subject using a movement sensing arrangement (¶[0009], ¶[0040], ¶[0083]-[0086], and ¶[0094] the chest-worn sensor, the accelerometer; Fig. 1) which collects seismocardiography signals from the subject (¶[0084], ¶[0119], ¶[0129]-[0131] the system is for measuring seismocardiogram (SCG) signals utilizing the accelerometer);
identifying from the seismocardiography signals sleep-disordered breathing events of the subject (¶[0009], ¶[0039]-[0041], ¶[0048], and ¶[0055]-[0056] the system utilizes the recorded signal to determine the respiratory disorders, ¶[0094]-[0098] the processor is configured to receive and to process the recorded sensor data, ¶[0040]-[0048] a plethora of respiratory disorders may be monitored with the current system, including sleep apnea, which would indicate that the subject would be asleep, ¶[0057], ¶[0073], ¶[0086], and ¶[0098] the system may be utilized for detection of sleep apnea, which may utilize the subject’s sleep posture/position, ¶[0008], ¶[0039], ¶[0098] the output of the system is used to determine the respiratory disorder, which may be sleep apnea)
based on a time series analysis of the seismocardiography signals in a time domain (¶[0009], ¶[0039]-[0041], ¶[0048], and ¶[0055]-[0056] the system utilizes the recorded signal to determine the respiratory disorders, ¶[0019]-[0022] and ¶[0024] the S1-S1 interval is in time series from time series acceleration data) including
determining a plurality of repetitive patterns in the seismocardiography signals, determining an inter-beat interval time series based solely on temporal distances between each respective instance of the plurality of detected repetitive patterns (¶[0100] and ¶[0138]-[0141] the system determines inter-beat interval, also known as S1-S1 interval or respiratory sinus arrhythmia (RSA), ¶[0019]-[0022] and ¶[0024] the S1-S1 interval is in time series from time series acceleration data, ¶[0133] and ¶[0138]-[0141] the interval calculation based off of the locations of fiducial peaks; Figs. 6-9 and 11), and determining the sleep-disordered breath events from the inter-beat interval time series (¶[0009], ¶[0040], ¶[0054], ¶[0072]-[0074] the system utilizes the determined S1-S1 interval to determine respiratory disorders), and
determining the sleep-disordered breath events from the inter-beat interval time series (¶[0009], ¶[0040], ¶[0054], ¶[0072]-[0074] the system utilizes the determined S1-S1 interval to determine respiratory disorders; Figs. 6-9 and 11),
determining from output signals of the movement sensing arrangement a number of sleep positions of the subject out of a set of possible sleep positions (¶[0057], ¶[0080] ¶[0086], ¶[0106], ¶[0108], and ¶[0126]-[0127] the acceleration may also be used to determine sleep posture/position, ¶[0086] the three-axis accelerometer may be used for both the SCG data and the subject posture/position data).
Giovangrandi is silent regarding determining an expected duration range based on a first heartbeat duration and a second heartbeat duration, wherein the first heartbeat duration is shorter than the second heartbeat duration, and that the plurality of repetitive patterns are determined with the expected range in the seismocardiography signal.
Wahlström teaches a hidden Markov model (HMM) approach for processing seismocardiograms (see abstract), in which the HMM is used to describe the heart beat (in the time domain) via parameters learned utilizing the Baum-Welch algorithm, and finally the Viterbi algorithm is utilized to find a maximum a posteriori (MAP) estimate of the complete sequence of states, from which the beat-to-beat interval is estimated, as well as other cardiac time intervals (see pg. 2362-2365 § D. Contributions and § II. Model and Estimation Framework; Fig. 3), which utilizes specific intervals of length “N” over which the signal energy is locally maximized (see § C. Initialization of the Baum–Welch Algorithm and Fig. 2).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the HMM IBI determination of Wahlström for the IBI determination in of Giovangrandi because it is the application of a known technique to a known device ready for improvement to yield predictable results; and/or (2) Giovangrandi requires a IBI determination, and Wahlström teaches one such determination; and/or (3) the HMM-based calculation demonstrated a superior performance in all respects compared to previously proposed envelope-based and spectral-based methods (see pg. 2369 § IV. Summary).
The modified Giovangrandi teaches to identify the IBI via intervals of specific length (expected duration), but not specifically that the expected duration is determined based on a first heartbeat duration and a second heartbeat duration, wherein the first heartbeat duration is shorter than the second heartbeat duration.
Peters teaches a wearable health device that acquires SCG data from the subject and events are determined based off of an identified cardiac cycle segment (see abstract), in which the cardiac cycle segments are interpolated to a unit length and the arithmetic average is aggregated to provide an average cardiac cycle segment (see ¶[0076]). In this case, the segments would not have the same length, of which, two with differing lengths may be taken as the first and second heart beat duration.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the average cardiac cycle segment of Peters for the interval length as used in the modified Giovangrandi because it is the simple substitution of one known element for another to yield predictable results and/or (2) the modified Giovangrandi teaches to use one interval length and Peters teaches one such alternative interval length.
Giovangrandi contemplates the importance of measuring posture/position at the time of the apneic event, but the modified Giovangrandi does not specifically teach the step of providing an indication of a level of sleep-disordered breathing for each of the determined sleep positions based on the determined sleep-disordered breathing events.
Kwok teaches a system that includes monitoring a subject’s respiration and generating a disordered breathing index (see abstract), such that the disordered breathing may be apnea (see ¶[0025] and ¶[0033]-[0038]), and may also monitor the subject’s posture utilizing a multiaxial accelerometer (see ¶[0075]). Kwok teaches to monitor the patient’s posture during disordered breathing (DB) episodes (see ¶[0043]), and may develop a modified apnea/hypopnea index (AHI) based on the different postures/tilt of the subject (see ¶[0052]-[0054]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the posture/tilt to AHI correlation of Kwok with the modified method of Giovangrandi because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) the posture correlation to AHI would provide a better indicator of when apnea is occurring for the subject as it would take their position/posture into account, as well as the cardiorespiratory parameters.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Giovangrandi in view of Wahlström, Peters, and Kwok as applied to claim 3 above, and in view of Woo et al. (US Patent Publication 2019/0328277 – cited in prior action), hereinafter Woo.
Regarding Claim 4, Giovangrandi in view of Wahlström, Peters, and Kwok teaches the device of claim 3 as stated above. The modified Giovangrandi is silent regarding the controller is adapted to detect snoring from the breathing sounds.
Woo teaches an apparatus and method for measuring sleep apnea based off of images and impedance data (see abstract), and the system may include an accelerometer or gyro sensor for monitoring posture (see ¶[0110]-[0111]). Woo teaches that a sound sensor may also be utilized to monitor for sensing sound while the subject is sleeping, including monitoring for snoring (see ¶[0106]-[0109] and ¶[0168]-[0174]; Figs. 7A-7B).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the sound sensor and snoring monitoring of Woo with the modified device of Giovangrandi because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) the snoring monitoring would further help to improve the respiration disorder and AHI determination of Giovangrandi.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Giovangrandi in view of Wahlström, Peters, and Kwok as applied to claim 1 above, and in view of Mouchantaf et al. (US Patent Publication 2020/0260962 – cited in prior action), hereinafter Mouchantaf.
Regarding Claim 5, Giovangrandi in view of Wahlström, Peters, and Kwok teaches the device of claim 1 as stated above. The modified Giovangrandi is silent regarding the controller is adapted to determine sleep stages, and thereby determine a sleep time period and an awake time period during the sleep monitoring period.
Mouchantaf teaches a device for measuring cardiopulmonary data of a subject (see abstract), including the use of an accelerometer for measuring bodily movements of the subject (see ¶[0008]-[0009], ¶[0073]-[0074], and ¶[0083]-[0090]). Mouchantaf teaches determining monitoring the sleep of the subject and determining sleep stages as a sleep score, whether there was an apnea or hypopnea interval; and may also determine quantity of sleep, the number of wakenings, and the sleep onset time (see ¶[0077], ¶[0217], ¶[0221], and ¶[0223]-¶[0225]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the sleep monitoring of Mouchantaf with the modified device of Giovangrandi because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) the sleep stage determination would further help to improve the respiration disorder and AHI determination of Giovangrandi.
Claims 9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Giovangrandi in view of Wahlström, Peters, and Kwok as applied to claims 1 and 10 above, respectively, and in view of Gurievsky et al. (US Patent Publication 2019/0008450 – cited in prior action), hereinafter Gurievsky.
Regarding Claim 9, Giovangrandi in view of Wahlström, Peters, and Kwok teaches the device of claim 1 as stated above. The modified Giovangrandi is silent regarding a position therapy device for providing a stimulus to induce the subject to change sleep position.
Gurievsky teaches systems and methods for monitoring a subject relating to optical and volumetric measurements (see abstract), which may also include monitoring the position of the subject with an accelerometer and/or displacement sensor (see ¶[0023]). Gurievsky teaches that when a sleep disorder, such as apnea, is detected, to deliver a stimulus via a stimulation arrangement to the subject so that the condition is treated while the subject remains asleep (see ¶[0012], ¶[0016], ¶[0028], ¶[0030]-[0033], and ¶[0043]-[0047]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the sleep position stimulation arrangement of Gurievsky with the modified device of Giovangrandi because (1) it is the application of a known technique to a known device ready for improvement to yield predictable results and/or (2) the stimulation will help to alleviate the respiratory condition (apnea) impacting the subject by changing the subject’s position, without disturbing the subject’s sleep (see Gurievsky ¶[0012] and ¶[0046]).
Regarding Claim 12, Giovangrandi in view of Wahlström, Peters, and Kwok teaches the method of claim 10 as stated above. The modified Giovangrandi is silent regarding providing a stimulus to induce the subject to change the sleep position.
Gurievsky teaches systems and methods for monitoring a subject relating to optical and volumetric measurements (see abstract), which may also include monitoring the position of the subject with an accelerometer and/or displacement sensor (see ¶[0023]). Gurievsky teaches that when a sleep disorder, such as apnea, is detected, to deliver a stimulus via a stimulation arrangement to the subject so that the condition is treated while the subject remains asleep (see ¶[0012], ¶[0016], ¶[0028], ¶[0030]-[0033], and ¶[0043]-[0047]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the sleep position stimulation arrangement of Gurievsky with the modified method of Giovangrandi because (1) it is the application of a known technique to a known method ready for improvement to yield predictable results and/or (2) the stimulation will help to alleviate the respiratory condition (apnea) impacting the subject by changing the subject’s position, without disturbing the subject’s sleep (see Gurievsky ¶[0012] and ¶[0046]).
Response to Arguments
Applicant’s arguments, 35 U.S.C. § 112(b)
Applicant’s arguments, see pg. 6, filed August 07, 2025, with respect to the rejections of claims 1-6, 8-10, and 12-13 under 35 U.S.C. § 112(b) have been fully considered and are persuasive. Therefore, the rejections have been withdrawn.
Applicant’s arguments, 35 U.S.C. § 103
Applicant’s arguments, see pg. 6-7, filed August 07, 2025, with respect to the rejections of claims 1-6, 8-10, and 12-13 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new grounds of rejection are made in view of Peters et al. (WIPO Publication WO 2019/166359 A1).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.D.M./Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
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