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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/11/2026 has been entered.
Response to Amendment
This office action is responsive to the amendment filed on 5/11/2026. As directed by the amendment, the status of the claim(s) are:
Claim(s) 21-22, 24, 26, 41 has/have been amended;
Claim(s) 1-20, 25, 36-40 is/are cancelled;
Claim(s) 46 is/are new;
Claim(s) 21-24, 26-35, 41-46 is/are presently pending.
The amendment(s) to the claim(s) is sufficient to overcome the 35 U.S.C. 112 rejection(s) from the previous office action.
Response to Arguments
With regard to claim rejections under 35 USC 103, Applicant’s arguments have been fully considered but are moot in light of new grounds of rejection due to claim amendment(s).
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.
Claim(s) 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen (US 20210007658 A1; 1/14/2021; cited in previous office action) in view of Luo (US 20200289321 A1; 9/17/2020; cited in previous office action), and further in view of Arnold (US 20170352287 A1; 12/7/2017).
Regarding claim 21, Kinnunen teaches a computer program product for suggesting adjustments to a sleep regimen, the computer program product comprising non-transitory computer executable code embodied in a computer readable medium that, when executing on one or more computing devices, performs the steps of (Fig. 1-2; [0121]):
acquiring heart rate data for a user from a wearable physiological monitoring device ([0011]; [0120]).
Kinnunen does not teach determining a prior sleep history for the user based on the heart rate data. Note that Kinnunen does teach identifying a current sleep cycle from heart rate data ([0206]) and comparing to prior sleep history (Fig. 5; [0016]; [0097]-[0101]; [0146]-[0147]; [0214]-[0229]) with deep data analysis of the measured physiological data ([0121]). However, to avoid doubt, Luo teaches in the same field of endeavor (Abstract) determining a prior sleep history for the user based on the heart rate data ([0063] “Physiological parameters can be captured by sensors…to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…Heart rate, which increases when the user enters REM sleep…may be detected by photoplethysmogram (PPG) or pulse oximetry…One or several of these sensors and parameters may be used independently or in combination, to detect different states and stages of sleep…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen to include this feature as taught by Luo because this enables better analysis of the physiological data ([0063]).
The combination of Kinnunen and Luo teaches identifying a circadian cycle for the user based on the prior sleep history (Kinnunen [0015]; [0167] “comparing shift of circadian rhythm over days…use heart rate data…to fill in the excluded times”; [0182]; [0214]; Luo [0063] “to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”);
identifying a current sleep cycle for the user based on the heart rate data (Kinnunen [0015]; [0167] “comparing shift of circadian rhythm over days…use heart rate data…to fill in the excluded times”; [0182]; [0214]; Luo [0063] “to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”);
identifying a sleep efficiency for the user based on a ratio of time asleep to a time in bed from the prior sleep history (Kinnunen [0167] “comparing shift of circadian rhythm over days”; [0138] “Sleep Efficiency”; Luo [0063] “history”);
calculating a sleep debt for the user based on the prior sleep history (Kinnunen [0146]; [0152]); and
generating a sleep recommendation for the user (Kinnunen Fig. 5; Luo Fig. 13; Fig. 21B), the sleep recommendation including:
an amount of sleep for the user, the amount of sleep adjusted based on the sleep debt (Kinnunen [0274]-[0275]; [0282] “sleep debt”; [0283]-[0286]; claim 1), and
a timing of sleep for the user, the timing of sleep including a recommended time into bed based on the current sleep cycle, and the circadian cycle for the user (Kinnunen Fig. 5; [0182]-[0184]; [0214]-[0229]; Luo Fig. 13; Fig. 21B; [0102]).
The combination of Kinnunen and Luo does not teach the timing of sleep including a recommended time into bed based on the sleep efficiency, wherein the sleep efficiency accounts for awake intervals during a night. Note that Kinnunen teaches measure of sleep efficiency which is total sleep divided by time spent in bed as part of measuring sleep score ([0138]) and sleep score is used to find optimum zone for bedtimes ([0274] “sleep scores could be correlated with determined bedtimes in order to find an optimum zone”). However, Arnold teaches in the same field of endeavor (Abstract; Fig. 37; [0173] “sleep debt”) the timing of sleep including a recommended time into bed based on the sleep efficiency, wherein the sleep efficiency accounts for awake intervals during a night (Fig. 4; Fig. 29-31; Fig. 37-38; [0144]-[0147]; [0151]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen and Luo to include this feature as taught by Arnold because this enables user to obtain desired sleep goal (Fig. 37).
Regarding claim 22, Kinnunen teaches a system comprising:
a wearable physiological monitor including one or more sensors ([0011]; [0120]), a first processor configured to substantially continuously acquire heart rate data for a user based on a signal from the one or more sensors (Fig. 1-2; [0121]), and a communications interface for coupling with a remote resource (Fig. 1-2; [0121]-[0122]; [0125]);
a server coupled in a communicating relationship with the wearable physiological monitor ([0125]), the server including a second processor ([0125]) configured by computer executable code to:
receive heart rate data form the wearable physiological monitor ([0123]-[0126]).
Kinnunen does not teach determine a prior sleep history for the user based on the heart rate data. Note that Kinnunen does teach identifying a current sleep cycle from heart rate data ([0206]) and comparing to prior sleep history (Fig. 5; [0016]; [0097]-[0101]; [0146]-[0147]; [0214]-[0229]) with deep data analysis of the measured physiological data ([0121]). However, to avoid doubt, Luo teaches in the same field of endeavor (Abstract) determine a prior sleep history for the user based on the heart rate data([0063] “Physiological parameters can be captured by sensors…to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…Heart rate, which increases when the user enters REM sleep…may be detected by photoplethysmogram (PPG) or pulse oximetry…One or several of these sensors and parameters may be used independently or in combination, to detect different states and stages of sleep…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen to include this feature as taught by Luo because this enables better analysis of the physiological data ([0063]).
The combination of Kinnunen and Luo teaches identify a circadian cycle for the user based on the prior sleep history (Kinnunen [0015]; [0167] “comparing shift of circadian rhythm over days…use heart rate data…to fill in the excluded times”; [0182]; [0214]; Luo [0063] “to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”);
identify a current sleep cycle for the user based on the heart rate data ((Kinnunen [0015]; [0167] “comparing shift of circadian rhythm over days…use heart rate data…to fill in the excluded times”; [0182]; [0214]; Luo [0063] “to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”);
identify a sleep efficiency for the user based on a ratio of a time asleep to a time in bed from the prior sleep history (Kinnunen [0167] “comparing shift of circadian rhythm over days”; [0138] “Sleep Efficiency”; Luo [0063] “history”);
calculate a sleep debt for the user based on the prior sleep history (Kinnunen [0146]; [0152]); and
generate a sleep recommendation for the user (Kinnunen Fig. 5; Luo Fig. 13; Fig. 21B), the sleep recommendation including:
an amount of sleep for the user, the amount of sleep adjusted based on the sleep debt (Kinnunen [0274]-[0275]; [0282] “sleep debt”; [0283]-[0286]; claim 1), and
a timing of sleep for the user, the timing of sleep including a recommended time into bed based on the current sleep cycle, and the circadian cycle for the user (Kinnunen Fig. 5; [0182]-[0184]; [0214]-[0229]; Luo Fig. 13; Fig. 21B; [0102]).
The combination of Kinnunen and Luo does not teach the timing of sleep including a recommended time into bed based on the sleep efficiency, wherein the sleep efficiency accounts for awake intervals during a night. Note that Kinnunen teaches measure of sleep efficiency which is total sleep divided by time spent in bed as part of measuring sleep score ([0138]) and sleep score is used to find optimum zone for bedtimes ([0274] “sleep scores could be correlated with determined bedtimes in order to find an optimum zone”). However, Arnold teaches in the same field of endeavor (Abstract; Fig. 37; [0173] “sleep debt”) the timing of sleep including a recommended time into bed based on the sleep efficiency, wherein the sleep efficiency accounts for awake intervals during a night (Fig. 4; Fig. 29-31; Fig. 37-38; [0144]-[0147]; [0151]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen and Luo to include this feature as taught by Arnold because this enables user to obtain desired sleep goal (Fig. 37).
The combination of Kinnunen, Luo, and Arnold teaches a user interface configured to present the sleep recommendation to the user (Kinnunen Fig. 5; [0182]-[0184]; [0214]-[0229]; Luo Fig. 13; Fig. 21B; [0102]; Arnold Fig. 37).
Regarding claim 23, in the combination of Kinnunen, Luo, and Arnold, Luo teaches wherein the sleep recommendation is based in part on a phase within the circadian cycle for the user based on a machine learning model trained to detect the phase of the user based on a respiratory rate and resting heart rate ([0063] “Physiological parameters can be captured by sensors…to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…Respiration rate…may be detected by sensors…Heart rate, which increases when the user enters REM sleep…may be detected by photoplethysmogram (PPG) or pulse oximetry…One or several of these sensors and parameters may be used independently or in combination, to detect different states and stages of sleep…The system may store a history of heart rate data, breathing data…to establish a base line heart rate…breathing patterns…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”).
Claim(s) 24, 26, 28, 30-35, 41-43, 45-46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen (US 20210007658 A1; 1/14/2021; cited in previous office action) in view of Luo (US 20200289321 A1; 9/17/2020; cited in previous office action), in view of Breslow (US 20160374567 A1; 12/29/2016; cited in IDS; cited in previous office action), and further in view of Arnold (US 20170352287 A1; 12/7/2017).
Regarding claim 24, Kinnunen teaches a method comprising:
acquiring heart rate data for a user from a wearable physiological monitoring device ([0011]; [0120]).
Kinnunen does not teach determining a prior sleep history for the user based on the heart rate data. Note that Kinnunen does teach identifying a current sleep cycle from heart rate data ([0206]) and comparing to prior sleep history (Fig. 5; [0016]; [0097]-[0101]; [0146]-[0147]; [0214]-[0229]) with deep data analysis of the measured physiological data ([0121]). However, to avoid doubt, Luo teaches in the same field of endeavor (Abstract) determining a prior sleep history for the user based on the heart rate data ([0063] “Physiological parameters can be captured by sensors…to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…Heart rate, which increases when the user enters REM sleep…may be detected by photoplethysmogram (PPG) or pulse oximetry…One or several of these sensors and parameters may be used independently or in combination, to detect different states and stages of sleep…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen to include this feature as taught by Luo because this enables better analysis of the physiological data ([0063]).
The combination of Kinnunen and Luo teaches identifying a circadian cycle for the user based on the prior sleep history (Kinnunen [0015]; [0167] “comparing shift of circadian rhythm over days…use heart rate data…to fill in the excluded times”; [0182]; [0214]; Luo [0063] “to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”);
identifying a current sleep cycle for the user based on the heart rate data (Kinnunen [0015]; [0167] “comparing shift of circadian rhythm over days…use heart rate data…to fill in the excluded times”; [0182]; [0214]; Luo [0063] “to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”).
The combination of Kinnunen and Luo does not teach determining a recent physiological strain experienced by the user based on the heart rate data. Note that the combination of Kunnunen and Luo does teach that stress and physical activity/provocation affect sleep (Kinnunen [0003]; [0124]; [0161]; Luo Fig. 4A; [0120]). However, Breslow teaches in the same field of endeavor (Abstract; [0002]) determining a recent physiological strain experienced by the user based on the heart rate data (Fig. 10; Fig. 27; [0262] “a measure of strain or exercise intensity over some predetermined prior interval”; [0264] “The strain component, ƒ.sub.1(strain), may be assessed based on a previous day's physical intensity, and will typically increase the sleep need”; [0139]-[0140]; [0158] reference is teaching intensity score measures recent physiological strain and is measured from heart rate data). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen and Luo to include this feature as taught by Breslow because this enables more accurate assessment of sleep need by including physical strain or exercise ([0262]).
The combination of Kinnunen, Luo, and Breslow teaches calculating a sleep debt for the user based on the prior sleep history and the recent physiological strain (Kinnunen [0146]; [0152]; Breslow [0262]; [0264]); and
generating a sleep recommendation for the user (Kinnunen Fig. 5; Luo Fig. 13; Fig. 21B), the sleep recommendation including:
an amount of sleep for the user, the amount of sleep adjusted based on the sleep debt (Kinnunen [0274]-[0275]; [0282] “sleep debt”; [0283]-[0286]; claim 1), and the sleep recommendation including a timing of sleep for the user, the timing of sleep including a recommended time into bed based on the current sleep cycle, and the circadian cycle for the user (Kinnunen Fig. 5; [0182]-[0184]; [0214]-[0229]; Luo Fig. 13; Fig. 21B; [0102]).
The combination of Kinnunen, Luo, and Breslow does not teach the timing of sleep including a recommended time into bed based on the sleep efficiency for the user, wherein the sleep efficiency accounts for awake intervals during a night. Note that Kinnunen teaches measure of sleep efficiency which is total sleep divided by time spent in bed as part of measuring sleep score ([0138]) and sleep score is used to find optimum zone for bedtimes ([0274] “sleep scores could be correlated with determined bedtimes in order to find an optimum zone”), Breslow also teaches sleep efficiency as part of sleep score ([0163] “sleep efficiency”). However, Arnold teaches in the same field of endeavor (Abstract; Fig. 37; [0173] “sleep debt”) the timing of sleep including a recommended time into bed based on the sleep efficiency for the user, wherein the sleep efficiency accounts for awake intervals during a night. (Fig. 4; Fig. 29-31; Fig. 37-38; [0144]-[0147]; [0151]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen, Luo, and Breslow to include this feature as taught by Arnold because this enables user to obtain desired sleep goal (Fig. 37).
Regarding claim 26, in the combination of Kinnunen, Luo, Breslow, and Arnold, Kinnunen teaches wherein identifying the circadian cycle includes identifying the circadian cycle based on a pattern of change in a heart rate variability for the user over a period of the circadian cycle ([0214]).
Regarding claim 28, the combination of Kinnunen, Luo, Breslow, and Arnold teaches wherein identifying the circadian cycle includes training a machine learning model to detect a phase within the circadian cycle based on one or more of a respiratory rate and a resting heart rate for the user (Luo ([0063] “Physiological parameters can be captured by sensors…to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…Respiration rate…may be detected by sensors…Heart rate, which increases when the user enters REM sleep…may be detected by photoplethysmogram (PPG) or pulse oximetry…One or several of these sensors and parameters may be used independently or in combination, to detect different states and stages of sleep…The system may store a history of heart rate data, breathing data…to establish a base line heart rate…breathing patterns…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”; base line heart rate reads on “resting heart rate”, alternatively, since heart rate is obtained throughout sleep, resting heart rate is inherently included in the heart rate data that is analyzed; [0132] “stable heart rate”).
Regarding claim 30, in the combination of Kinnunen, Luo, Breslow, and Arnold, Kinnunen teaches wherein identifying the circadian cycle includes identifying the circadian cycle based on duration a skin temperature measured for the user with the wearable physiological monitoring device ([0167] “he body temperature of the user is measured by the wearable device (for example a ring) and the time point when the lowest body temperature during the night time, or certain part of the night time, is measured is used as a marking and reference point for a circadian rhythm.”; [0169]).
Regarding claim 31, in the combination of Kinnunen, Luo, Breslow, and Arnold, Kinnunen teaches wherein the sleep cycle for the user includes information related to a duration of sleep for a prior sleep event ([0015]-[0016]; [0043]-[0044]; [0134]-[0139]; [0154]).
Regarding claim 32, in the combination of Kinnunen, Luo, Breslow, and Arnold, Kinnunen teaches wherein the heart rate data spans at least 24 hours for the user ([0041] “over a plurality of days”; [0153]; [0198] “collect data over many days”).
Regarding claim 33, in the combination of Kinnunen, Luo, Breslow, and Arnold, Kinnunen teaches wherein the heart rate data is captured substantially continuously by the wearable physiological monitoring device ([0198] “collect data over many days”).
Regarding claim 34, the combination of Kinnunen, Luo, Breslow, and Arnold teaches further comprising presenting the sleep recommendation to the user in a user interface (Kinnunen Fig. 5; [0182]-[0184]; [0214]-[0229]; Luo Fig. 13; Fig. 21B; [0102]; Arnold Fig. 37).
Regarding claim 35, in the combination of Kinnunen, Luo, Breslow, and Arnold, Kinnunen teaches further comprising transmitting the heart rate data to a server for remote processing ([0123]-[0126]) and transmitting the sleep recommendation from the server to a local device for viewing by the user ([0123]-[0127]; Fig. 1-2; Fig. 5; [0182]-[0184]; [0214]-[0229]).
Regarding claim 41, Kinnunen teaches a computer program product for suggesting adjustments to a sleep regimen, the computer program product comprising non-transitory computer executable code embodied in a computer readable medium that, when executing on one or more computing devices, performs the steps of (Fig. 1-2; [0121]):
acquiring heart rate data for a user from a wearable physiological monitoring device ([0011]; [0120]).
Kinnunen does not teach determining a prior sleep history for the user based on the heart rate data. Note that Kinnunen does teach identifying a current sleep cycle from heart rate data ([0206]) and comparing to prior sleep history (Fig. 5; [0016]; [0097]-[0101]; [0146]-[0147]; [0214]-[0229]) with deep data analysis of the measured physiological data ([0121]). However, to avoid doubt, Luo teaches in the same field of endeavor (Abstract) determining a prior sleep history for the user based on the heart rate data ([0063] “Physiological parameters can be captured by sensors…to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…Heart rate, which increases when the user enters REM sleep…may be detected by photoplethysmogram (PPG) or pulse oximetry…One or several of these sensors and parameters may be used independently or in combination, to detect different states and stages of sleep…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen to include this feature as taught by Luo because this enables better analysis of the physiological data ([0063]).
The combination of Kinnunen and Luo teaches identifying a circadian cycle for the user (Kinnunen [0015]; [0167] “comparing shift of circadian rhythm over days…use heart rate data…to fill in the excluded times”; [0182]; [0214]; Luo [0063] “to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”);
identifying a current sleep cycle for the user based on the heart rate data (Kinnunen [0015]; [0167] “comparing shift of circadian rhythm over days…use heart rate data…to fill in the excluded times”; [0182]; [0214]; Luo [0063] “to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…The system may store a history of heart rate data…to establish a base line heart rate…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”).
The combination of Kinnunen and Luo does not teach determining a recent physiological strain experienced by the user based on the heart rate data. Note that the combination of Kunnunen and Luo does teach that stress and physical activity/provocation affect sleep (Kinnunen [0003]; [0124]; [0161]; Luo Fig. 4A; [0120]). However, Breslow teaches in the same field of endeavor (Abstract; [0002]) determining a recent physiological strain experienced by the user based on the heart rate data (Fig. 10; Fig. 27; [0262] “a measure of strain or exercise intensity over some predetermined prior interval”; [0264] “The strain component, ƒ.sub.1(strain), may be assessed based on a previous day's physical intensity, and will typically increase the sleep need”; [0139]-[0140]; [0158] reference is teaching intensity score measures recent physiological strain and is measured from heart rate data). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen and Luo to include this feature as taught by Breslow because this enables more accurate assessment of sleep need by including physical strain or exercise ([0262]).
The combination of Kinnunen, Luo, and Breslow teaches calculating a sleep debt for the user based on the prior sleep history and the recent physiological strain (Kinnunen [0146]; [0152]; Breslow [0262]; [0264]); and
generating a sleep recommendation for the user (Kinnunen Fig. 5; Luo Fig. 13; Fig. 21B), the sleep recommendation including
an amount of sleep for the user, the amount of sleep adjusted based on the sleep debt (Kinnunen [0274]-[0275]; [0282] “sleep debt”; [0283]-[0286]; claim 1), and the sleep recommendation including a timing of sleep for the user, the timing of sleep including a recommended time into bed based on one or more of the current sleep cycle, and the circadian cycle for the user (Kinnunen Fig. 5; [0182]-[0184]; [0214]-[0229]; Luo Fig. 13; Fig. 21B; [0102]).
The combination of Kinnunen, Luo, and Breslow does not teach the timing of sleep including a recommended time into bed based on a sleep efficiency for the user, wherein the sleep efficiency accounts for awake intervals during a night. Note that Kinnunen teaches measure of sleep efficiency which is total sleep divided by time spent in bed as part of measuring sleep score ([0138]) and sleep score is used to find optimum zone for bedtimes ([0274] “sleep scores could be correlated with determined bedtimes in order to find an optimum zone”), Breslow also teaches sleep efficiency as part of sleep score ([0163] “sleep efficiency”). However, Arnold teaches in the same field of endeavor (Abstract; Fig. 37; [0173] “sleep debt”) the timing of sleep including a recommended time into bed based on a sleep efficiency for the user, wherein the sleep efficiency accounts for awake intervals during a night (Fig. 4; Fig. 29-31; Fig. 37-38; [0144]-[0147]; [0151]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen, Luo, and Breslow to include this feature as taught by Arnold because this enables user to obtain desired sleep goal (Fig. 37).
Regarding claim 42, the combination of Kinnunen, Luo, Breslow, and Arnold teaches wherein identifying the circadian cycle includes identifying the circadian cycle based on the heart rate data (Kinnunen [0015]; [0167] “comparing shift of circadian rhythm over days…use heart rate data…to fill in the excluded times”; [0182]; [0214]).
Regarding claim 43, in the combination of Kinnunen, Luo, Breslow, and Arnold, Kinnunen teaches wherein identifying the circadian cycle includes identifying the circadian cycle based on a pattern of change in a heart rate variability for the user over a period of the circadian cycle ([0214]).
Regarding claim 45, the combination of Kinnunen, Luo, Breslow, and Arnold teaches wherein identifying the circadian cycle includes training a machine learning model to detect a phase within the circadian cycle based on one or more of a respiratory rate and a resting heart rate for the user (Luo ([0063] “Physiological parameters can be captured by sensors…to detect where the body is in the sleep cycle and/or the circadian rhythm cycle…Respiration rate…may be detected by sensors…Heart rate, which increases when the user enters REM sleep…may be detected by photoplethysmogram (PPG) or pulse oximetry…One or several of these sensors and parameters may be used independently or in combination, to detect different states and stages of sleep…The system may store a history of heart rate data, breathing data…to establish a base line heart rate…breathing patterns…and learn from the data collected by the sensors through, for example, machine learning. By collecting these physiological parameters throughout sleep, the system may…determine which state of sleep the user is in”; base line heart rate reads on “resting heart rate”, alternatively, since heart rate is obtained throughout sleep, resting heart rate is inherently included in the heart rate data that is analyzed; [0132] “stable heart rate”).
Regarding claim 46, the combination of Kinnunen, Luo, Breslow, and Arnold teaches wherein the wearable physiological monitoring device includes a photoplethysmography device (Kinnunen [0120] “heart rate may be measured using a photon”; Luo [0063] “photoplethysmogram (PPG) or pulse oximetry”; Breslow [0119]; Arnold [0094] “photoplethysmographic sensor”).
Claim(s) 27, 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen, Luo, Breslow, and Arnold as applied to claims 24, 41 above, further in view of Verrier (US 5902250 A; 5/11/1999; cited in previous office action), and further in view of Pastore (US 20100049270 A1; 2/25/2010; cited in previous office action).
Regarding claim 27, the combination of Kinnunen, Luo, Breslow, and Arnold does not teach wherein identifying the circadian cycle includes determining a respiratory rate for the user based on a heart rate variability for the user. However, Verrier teaches in the same field of endeavor (Abstract; Fig. 3-4A) wherein identifying the circadian cycle includes determining a respiratory rate for the user based on a heart rate variability for the user (Col. 4 lines 35-40). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen, Luo, Breslow, and Arnold to include this feature as taught by Verrier because this enables monitoring with minimal equipment (Col. 3 lines 25-30).
The combination of Kinnunen, Luo, Breslow, Arnold, and Verrier does not teach identifying the circadian cycle based on a pattern of change in the respiratory rate. However, Pastore teaches in the same field of endeavor (Fig. 3; [0031] “circadian”) identifying the circadian cycle based on a pattern of change in the respiratory rate ([0031] “detect such circadian patterns based upon…changes in heart rate or breathing patterns, and changes in autonomic balance as detected from heart rate variability”; claim 14). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen, Luo, Breslow, Arnold, and Verrier to include this feature as taught by Pastore because this enables detecting circadian pattern from a variety of sources including respiratory rate ([0031]; claim 14).
Regarding claim 44, the combination of Kinnunen, Luo, Breslow, and Arnold does not teach wherein identifying the circadian cycle includes determining a respiratory rate for the user based on a heart rate variability for the user. However, Verrier teaches in the same field of endeavor (Abstract; Fig. 3-4A) wherein identifying the circadian cycle includes determining a respiratory rate for the user based on a heart rate variability for the user (Col. 4 lines 35-40). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen, Luo, Breslow, and Arnold to include this feature as taught by Verrier because this enables monitoring with minimal equipment (Col. 3 lines 25-30).
The combination of Kinnunen, Luo, Breslow, Arnold, and Verrier does not teach identifying the circadian cycle based on a pattern of change in the respiratory rate. However, Pastore teaches in the same field of endeavor (Fig. 3; [0031] “circadian”) identifying the circadian cycle based on a pattern of change in the respiratory rate ([0031] “detect such circadian patterns based upon…changes in heart rate or breathing patterns, and changes in autonomic balance as detected from heart rate variability”; claim 14). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen, Luo, Breslow, Arnold, and Verrier to include this feature as taught by Pastore because this enables detecting circadian pattern from a variety of sources including respiratory rate ([0031]; claim 14).
Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen, Luo, Breslow, and Arnold as applied to claim(s) 24 above, further in view of Burton (US 20210169417 A1; Filed 10/22/2020; cited in previous office action).
Regarding claim 29, in the combination of Kinnunen, Luo, Breslow, and Arnold, Kinnunen teaches wherein identifying the circadian cycle includes identifying the circadian cycle based on user input ([0175]; [0199]; [0208]; [0249]-[0251]; [0254]; [0261]; the reference is teaching that user input is used to calibrate or re-calibrate the system for physiological data analysis in which circadian cycle is obtained as explained above).
However, to avoid doubt and demonstrate prior art that is closer to the invention as disclosed, Burton teaches in the same field of endeavor (Abstract) wherein identifying the circadian cycle includes identifying the circadian cycle based on user input ([0650] “questionnaires and/or subject/patient/user sleep journals (i.e. start sleep period time, lights off for sleep time, awakening sleep time) in order to determine an approximation of internal circadian cycle clock time”). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Kinnunen, Luo, Breslow, and Arnold to include this feature as taught by Burton because this enables obtaining circadian cycle via user input in addition to other methods (Fig. 1A; [0650]).
Conclusion
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/JONATHAN T KUO/ Primary Examiner, Art Unit 3792
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