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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/18/2025 has been entered.
Claim Rejections - 35 USC § 103
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) 1, 7, 11-12, 14, and 16-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Odio (US 2010/0331630) in view of Westbrook et al (US 2010/0240982) (“Westbrook”) as noted in Applicant IDS dated 2/09/2024 and further in view of Tsutsumi et al (US 2013/0310662) (“Tsutsumi”) as noted in Applicant IDS dated 2/09/2024 and further in view of Tehrani et al (US 2008/0177347) (“Tehrani”).
Regarding Claim 1, while Odio teaches a method for a medical screening device for estimating a total sleep time of a patient during a monitoring session (Abstract, [0032]-[0039], [0069]), the medical screening device comprising a plurality of sensors including an actigraph ([0032]-[0039]), the medical screening device being configured to be mounted, when in use, on the patient's body during sleep ([0107] actigraph is worn, [0032] respiratory efforts worn as piezoelectric belt), the method performed by one or more processors of the medical screening device and/or in communication with the medical screening device ([0020]-[0036], [0038]) and comprising:
receiving an actigraphy signal generated by the actigraph during the monitoring session ([0032]-[0039] a polysomnography device comprising breathing effort sensing and respiratory airflow sensing and actigraphy module may be used together as the sleep initiation monitoring module, the sleep initiation monitoring module also acts as a sleep evaluation module);
receiving one of a respiratory flow rate signal or a respiratory effort signal during the monitoring session ([0032]-[0039] the polysomnography device comprising breathing effort sensing and/or respiratory airflow sensing);
determining an activity count from the actigraphy signal ([0036]);
determining a sleep/wake state of the patient based on (a) the activity counts ([0069]); and
estimating the total sleep time from the determined sleep/wake states ([0069] gathering the duration and number of sleep episodes and duration and number of wake episodes is estimating the total sleep time and the total wake time).
Odio fails to teach
partitioning the actigraphy signal into a plurality of epochs;
determining an activity count for each epoch from the actigraphy signal; and
determining a sleep/wake state of the patient for each epoch of the plurality of epochs
estimate the total sleep from the determined sleep/wake states of the plurality of epochs;
wherein the determining the sleep/wake state of the patient for each epoch of the plurality of epochs comprises:
determining the sleep/wake state of the patient for the epoch to be “wake” based at least in part on determining that the activity count for the epoch satisfies one or more threshold comparisons, and
when it is determined that the activity count for the epoch does not satisfy the one or more threshold comparisons, determining the sleep/wake state to be “wake” based at least I part on determining that the one of the respiratory flow rate signal or the respiratory effort signal (a) is unstable during the epoch, and (b) does not contain an indication that a sleep-disordered breathing (SBD) event took place during the epoch.
However Westbrook teaches a sleep monitoring system (Abstract) comprising
Receiving an actigraphy signal generated during monitoring ([0033]-[0034] data acquisition unit generating signals for monitoring [0090] a signal for monitoring may be actigraphy)
Receiving one of a respiratory flow or a respiratory effort signal during the monitoring ([0048] airflow signal is monitored [0049], [0090]-[0093] respiratory effort may be additionally monitored);
Partitioning the data into a plurality of epochs ([0096] generated signals as partitioned into one hour intervals to judge sleep architecture and continuity);
Determining a sleep/wake state of the patient for the epoch ([0042], [0076], [0079] broad description of how sleep state is considered [0090]-[0093] how actigraphy and breath data relate to this determination, [0096] overall sleep continuity judged on hourly basis based on the various parameters);
estimate the total sleep time from the sleep/wake states ([0079] determined parameter from the sleep analysis is total sleep time);
wherein the processor ([0052]) is configured to determine the sleep/wake state of the patient during each epoch by:
determining the sleep/wake state of the patient for the epoch to be “wake” based at least in part on determining that the activity count for the epoch satisfies one or more threshold comparisons ([0090]-[0093] actigraphy signals reviewed for presence of substantial head movement indicating wake, where [0090] nots “high intensity” movements and the “duration and frequency of head or body movements” as differentiating parameters, indicating the presence of a threshold as a basis for the differentiation), and
when the sleep/wake state for the epoch is determined not to be “wake” based on the actigraphy signal for the epoch, determining whether the sleep/wake state is “wake” based on the respiratory flow rate signal or the respiratory effort signal (Fig. 9, [0025]-[0026], [0090]-[0093] actigraphy signals of substantial head movement considered, and then are noted as being confirmed by respiratory effort/snoring signals. In confirming the actigraphy determination, the determination of sleep/wake state by respiratory effort only occurs when a determination is made by actigraphy first).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to partition the physiological data of Odio and perform monitoring and analysis steps for each epoch as taught by Westbrook to provide results more representative of the change in time for the patient, rather than utilizing single values for metric for the entire sleep monitoring duration. Furthermore, it would be obvious to use respiratory effort data for the sleep/wake classification as taught by Westbrook along with the actigraphy data as Westbrook teaches that using two datasets can provide improved identification of sleep state over a single dataset. Also, it would be obvious to identify in Westbrook that the order of sleep/wake identification indicates that actigraphy data is evaluated first and only when a determination of sleep/wake is made off of actigraphy, does an evaluation of sleep/wake occur in view of respiratory effort. Next, it would be obvious that the activity count of Odio may used to judge the head movement of Westbrook as a specific metric to differentiate when the amount of head movement can be characterized as substantial.
Yet their combined efforts fail to teach wherein the determining the sleep/wake state of the patient for each epoch of the plurality of epochs comprises:
when it is determined that the activity count for the epoch does not satisfy the one or more threshold comparisons, determining the sleep/wake state to be “wake” based at least I part on determining that the one of the respiratory flow rate signal or the respiratory effort signal (a) is unstable during the epoch, and (b) does not contain an indication that a sleep-disordered breathing (SBD) event took place during the epoch.
However Tsutsumi teaches a sleep evaluation (Abstract) comprising the wake/sleep state of a patient ([0020]) wherein the determining the sleep/wake state of the patient for each epoch of a plurality of epochs comprises:
when it is determined that the body movement for the epoch does not satisfy a threshold comparison, determining the sleep/wake state to be “wake” based at least in part on determining that the respiratory signal is unstable during the epoch ([0089], [0093]-[0094] discrimination of sleeping state of the person is based on a body motion waveform and a respiration waveform, where the classification of waking state can be characterized by body motion greater than a threshold and periodicity, i.e. stability, in the respiration waveform, and where the classification of waking state can occur if only one of these signs are present. Thus when it is determined that the body movement for the epoch does not satisfy a threshold comparison, a patient can still be determined to be in the “wake” state based at least in part on determining that the respiratory signal is unstable during the epoch), and
Tehrani teaches that sleep disordered breathing may be characterized by unstable breathing ([0014]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to include Tsutsumi’s discrimination rule for identifying wake based on motion and respiration information to the motion and respiration-based identification of Westbrook as Tsutsumi further identifies what decision should be made when the datasets disagree with one another on whether the patient should be classified as sleep or wake, a detail missing from Westbrook. Together, they make a more cohesive analysis. Furthermore, it would be obvious that the determination of wake should be further characterized by whether an epoch (b) does not contain an indication that a sleep-disordered breathing (SBD) event took place during the epoch in view of Tehrani. Specifically, Tehrani teaches that a sleep based condition may present with unstable breathing. If one only adds the analysis of Tsutsumi to Westbrook, epochs of low movement and unstable breathing that reflect sleep with sleep disordered breathing and epoch of low movement and unstable breathing that reflect an awake state will all be classified as wake. With Tehrani’s context, using a third dataset will increase accuracy by clarifying the subject state for these epochs. Finally, Westbrook already provides an indication that sleep-disordered breathing may have occurred during an epoch ([0086] “These snoring pattern changes are used as behavioral arousal indicators to independently confirm that changes in airflow are a result of sleep disordered breathing (see FIG. 8).”) so Tsutsumi and Tehrani teach the how and why modifying the evaluation in Westbrook can be improved when determining the sleep/wake state of the patient.
Regarding Claim 7, Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 1, wherein the determining the sleep/wake state is based on the respiratory effort signal (See Claim 1 Rejection).
Regarding Claim 11, Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 1, and Westbrook teaches the method comprising computing an index of severity of sleep- disordered breathing of the patient from the estimated total sleep time ([0081]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to further determine the total sleep time of Odio and consider the amount of sleep-disordered breathing experienced by the subject in relation to the total sleep time as taught by Westbrook as this contributes “to improved differential diagnoses or estimated risk for chronic diseases.” (Westbrook [0081]).
Regarding Claim 12, Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 11, wherein the computing the index comprises:
detecting apneas and hypopneas during the monitoring session, and
dividing a number of detected apneas and hypopneas during the monitoring session by the estimated total sleep time (See Claim 11 Rejection, Westbrook [0081]).
Regarding Claim 21, Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 1, wherein the determining that the one of the respiratory flow rate signal or the respiratory effort signal is unstable during the epoch comprises:
determining that a variability over the epoch is at or above a threshold (See Claim 1 Rejection, the respiration is judged by respiration effort and/or airflow, Tsutsumi [0093] variation of respiratory waveform of an epoch being above a threshold indicates it doesn’t have periodicity and is thus unstable) and Tehrani teaches that respiratory instability may be identified a respiratory-related variable ([0062] instability judged by variability, Table 1, where breathing rate and tidal volume are respiratory-related variable used to identify the respiratory instability).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to judge the respiration waveforms of respiratory effort or respiratory airflow Odio and Westbrook for instability as taught by Tsutsumi against a threshold of variability as this enables a standardized rule to be applied across applications of the inventions and increase consistency in results. Furthermore, it would be obvious to judge that variability by the parameter of respiratory rate or tidal volume, as taught by Tehrani, as these singular metrics that can be derived from the waveform provide simpler comparison over the plurality of values that constitute a waveform.
Regarding Claim 22, Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 21, wherein the one or more respiratory-related variables comprises at least one of: tidal volume, inspiratory time, respiratory rate, inspiratory peak flow, expiratory peak flow location, and time since last breath (See Claim 21 Rejection, Tehrani: tidal volume).
Regarding Claim 14, while Odio teaches a system using a medical screening device for estimating a total sleep time of a patient during a monitoring session comprising a plurality of epochs (Abstract, [0032]-[0039], [0069]), the system comprising:
an actigraph configured to generate an actigraphy signal representing acceleration of the actigraph ([0032]-[0039] a polysomnography device comprising breathing effort sensing and respiratory airflow sensing and actigraphy module may be used together as the sleep initiation monitoring module, the sleep initiation monitoring module also acts as a sleep evaluation module);
a sensor configured to generate one of a respiratory flow rate signal or a respiratory effort signal associated with the patient's breathing ([0032]-[0039] a polysomnography device comprising breathing effort sensing and respiratory airflow sensing and actigraphy module may be used together as the sleep initiation monitoring module, the sleep initiation monitoring module also acts as a sleep evaluation module); and
one or more processors ([0020]-[0036], [0038]) configured to:
determine an activity count from the actigraphy signal ([0036]);
determine a sleep/wake state of the patient based on (a) the activity counts ([0069]); and
estimate the total sleep time from the determined sleep/wake states ([0069] gathering the duration and number of sleep episodes and duration and number of wake episodes is estimating the total sleep time and the total wake time).
Odio fails to teach one or more processors configured to
determine an activity count for each epoch from the actigraphy signal; and
determine a sleep/wake state of the patient for each epoch of the plurality of epochs,
estimate the total sleep time from the determined sleep/wake states of the plurality of epochs,
wherein the one or more processors are configured to determine the sleep/wake state of the patient for each epoch by:
determining the sleep/wake state of the patient for the epoch to be “wake” based at least in part on determining that the activity count for the epoch satisfies one or more threshold comparisons.
when it is determined that the activity count for the epoch does not satisfy the one or more threshold comparisons, determining the sleep/wake state to be “wake” based at least in part on determining that the one of the respiratory flow rate signal or the respiratory effort signal (a) is unstable during the epoch, and (b) does not contain an indication that a sleep-disordered breathing (SBD) event took place during the epoch.
However Westbrook teaches a sleep monitoring system (Abstract) comprising
Receiving an actigraphy signal generated during monitoring ([0033]-[0034] data acquisition unit generating signals for monitoring [0090] a signal for monitoring may be actigraphy)
Receiving one of a respiratory flow or a respiratory effort signal during the monitoring ([0048] airflow signal is monitored [0049], [0090]-[0093] respiratory effort may be additionally monitored);
Partitioning the data into a plurality of epochs ([0096] generated signals as partitioned into one hour intervals to judge sleep architecture and continuity);
Determining a sleep/wake state of the patient for the epoch ([0042], [0076], [0079] broad description of how sleep state is considered [0090]-[0093] how actigraphy and breath data relate to this determination, [0096] overall sleep continuity judged on hourly basis based on the various parameters);
estimate the total sleep time from the sleep/wake states ([0079] determined parameter from the sleep analysis is total sleep time);
wherein the processor ([0052]) is configured to determine the sleep/wake state of the patient during each epoch by:
determining whether the sleep/wake state of the patient for the epoch is “wake” based on the actigraphy signal for the epoch ([0090]-[0093] actigraphy signals reviewed for presence of substantial head movement indicating wake), and
when the sleep/wake state for the epoch is determined not to be “wake” based on the actigraphy signal for the epoch, determining whether the sleep/wake state is “wake” based on the respiratory flow rate signal or the respiratory effort signal (Fig. 9, [0025]-[0026], [0090]-[0093] actigraphy signals of substantial head movement considered, and then are noted as being confirmed by respiratory effort/snoring signals. In confirming the actigraphy determination, the determination of sleep/wake state by respiratory effort only occurs when a determination is made by actigraphy first).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to partition the physiological data of Odio and perform monitoring and analysis steps for each epoch as taught by Westbrook to provide results more representative of the change in time for the patient, rather than utilizing single values for metric for the entire sleep monitoring duration. Furthermore, it would be obvious to use respiratory effort data for the sleep/wake classification as taught by Westbrook along with the actigraphy data as Westbrook teaches that using two datasets can provide improved identification of sleep state over a single dataset. Also, it would be obvious to identify in Westbrook that the order of sleep/wake identification indicates that actigraphy data is evaluated first and only when a determination of sleep/wake is made off of actigraphy, does an evaluation of sleep/wake occur in view of respiratory effort. Next, it would be obvious that the activity count of Odio may used to judge the head movement of Westbrook as a specific metric to differentiate when the amount of head movement can be characterized as substantial.
Yet their combined efforts fail to teach wherein the determining the sleep/wake state of the patient for each epoch of the plurality of epochs comprises:
when it is determined that the activity count for the epoch does not satisfy the one or more threshold comparisons, determining the sleep/wake state to be “wake” based at least I part on determining that the one of the respiratory flow rate signal or the respiratory effort signal (a) is unstable during the epoch, and (b) does not contain an indication that a sleep-disordered breathing (SBD) event took place during the epoch.
However Tsutsumi teaches a sleep evaluation (Abstract) comprising the wake/sleep state of a patient ([0020]) wherein the determining the sleep/wake state of the patient for each epoch of a plurality of epochs comprises:
when it is determined that the body movement for the epoch does not satisfy a threshold comparison, determining the sleep/wake state to be “wake” based at least in part on determining that the respiratory signal is unstable during the epoch ([0089], [0093]-[0094] discrimination of sleeping state of the person is based on a body motion waveform and a respiration waveform, where the classification of waking state can be characterized by body motion greater than a threshold and periodicity, i.e. stability, in the respiration waveform, and where the classification of waking state can occur if only one of these signs are present. Thus when it is determined that the body movement for the epoch does not satisfy a threshold comparison, a patient can still be determined to be in the “wake” state based at least in part on determining that the respiratory signal is unstable during the epoch), and
Tehrani teaches that sleep disordered breathing may be characterized by unstable breathing ([0014]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to include Tsutsumi’s discrimination rule for identifying wake based on motion and respiration information to the motion and respiration-based identification of Westbrook as Tsutsumi further identifies what decision should be made when the datasets disagree with one another on whether the patient should be classified as sleep or wake, a detail missing from Westbrook. Together, they make a more cohesive analysis. Furthermore, it would be obvious that the determination of wake should be further characterized by whether an epoch (b) does not contain an indication that a sleep-disordered breathing (SBD) event took place during the epoch in view of Tehrani. Specifically, Tehrani teaches that a sleep based condition may present with unstable breathing. If one only adds the analysis of Tsutsumi to Westbrook, epochs of low movement and unstable breathing that reflect sleep with sleep disordered breathing and epoch of low movement and unstable breathing that reflect an awake state will all be classified as wake. With Tehrani’s context, using a third dataset will increase accuracy by clarifying the subject state for these epochs. Finally, Westbrook already provides an indication that sleep-disordered breathing may have occurred during an epoch ([0086] “These snoring pattern changes are used as behavioral arousal indicators to independently confirm that changes in airflow are a result of sleep disordered breathing (see FIG. 8).”) so Tsutsumi and Tehrani teach the how and why modifying the evaluation in Westbrook can be improved when determining the sleep/wake state of the patient.
Regarding Claim 16, Odio, Westbrook, Tsutsumi, and Tehrani teach the system of claim 14, wherein the sensor is a respiratory effort sensor configured to generate the respiratory effort signal, wherein determination of the sleep/wake state is based on the generated respiratory effort signal (See Claim 14 Rejection).
Regarding Claim 17, Odio, Westbrook, Tsutsumi, and Tehrani teach the system of claim 14, and Odio teaches wherein a processor of the one or more processors forms part of a local computer device and the system further comprises:
a communication interface (Fig. 1, communication interface from 206, 208, 210 to computer system 100),
wherein the local processor of the one or more processors is configured to relay the actigraphy signal and the respiratory flow signal or the respiratory effort signal to the processor of a remote computing device ([0022]), and Westbrook teaches wherein a processor of the one or more processors forms part of a remote computing device (Fig. 3, [0057] external computer system) and the system further comprises:
a communication interface ([0057] wireless transmitter/receiver 377),
wherein a local processor of the one or more processors is configured to relay the actigraphy signal and the respiratory flow rate signal or the respiratory effort signal to the processor of the remote computing device via the communication interface (Fig. 3, [0049], [0057] local processor of DAU 210 communicates data through wireless transmitter/receiver 377 for analysis at external computer system 390).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to substitute the analyzing processor in the network of Odio as taught by Westbrook as a simple substitution of one processor for analysis for another to obtain the predictable results of accurately assessed sleep length.
Regarding Claim 18, Odio, Westbrook, Tsutsumi, and Tehrani teach the system of claim 14, and Odio further teaches a removable memory configured to store the actigraphy signal (See Claim 14 Rejection, [0035], Fig. 1, removable non-volatile memory interface 90 connected to memory 148).
Regarding Claim 19, while Odio teaches a medical screening device for estimating a total sleep time of a patient during a monitoring session (Abstract, [0032]-[0039], [0069]), the medical screening device comprising:
an actigraph configured to generate an actigraphy signal representing acceleration of the actigraph ([0032]-[0039] a polysomnography device comprising breathing effort sensing and respiratory airflow sensing and actigraphy module may be used together as the sleep initiation monitoring module, the sleep initiation monitoring module also acts as a sleep evaluation module);
a sensor configured to generate one of a respiratory flow rate signal or a respiratory effort signal associated with the patient's breathing ([0032]-[0039] a polysomnography device comprising breathing effort sensing and respiratory airflow sensing and actigraphy module may be used together as the sleep initiation monitoring module, the sleep initiation monitoring module also acts as a sleep evaluation module); and
a processor coupled with the actigraph and the sensor ([0020], [0036], [0038]), and configured to:
receive the one of the actigraphy signal during the monitoring session ([0032]-[0039] a polysomnography device comprising breathing effort sensing and respiratory airflow sensing and actigraphy module may be used together as the sleep initiation monitoring module, the sleep initiation monitoring module also acts as a sleep evaluation module);
receive the respiratory flow rate signal or the respiratory effort signal during the monitoring session ([0032]-[0039] a polysomnography device comprising breathing effort sensing and respiratory airflow sensing and actigraphy module may be used together as the sleep initiation monitoring module, the sleep initiation monitoring module also acts as a sleep evaluation module);
determining an activity count from the actigraphy signal ([0036]);
determining a sleep/wake state of the patient ([0069]); and
estimating the total sleep time from the determined sleep/wake states ([0069] gathering the duration and number of sleep episodes and duration and number of wake episodes is estimating the total sleep time and the total wake time).
Odio fails to teach the processor configured to
partition the actigraphy signal into a plurality of epochs;
determine an activity count for each epoch from the actigraphy signal; and
determine a sleep/wake state of the patient for each epoch of the plurality of epochs;
estimate the total sleep time from the determined sleep/wake states of the plurality of epochs,
wherein the processor is configured to determine the sleep/wake state of the patient during each epoch by:
determining whether the sleep/wake state of the patient for the epoch is “wake” based on the activity count for the epoch, and
when the sleep/wake state for the epoch is determined not to be “wake” based on the activity count for the epoch, determining whether the sleep/wake state is “wake” based on the respiratory flow rate signal or the respiratory effort signal.
However Westbrook teaches a sleep monitoring system (Abstract) comprising
Receiving an actigraphy signal generated during monitoring ([0033]-[0034] data acquisition unit generating signals for monitoring [0090] a signal for monitoring may be actigraphy)
Receiving one of a respiratory flow or a respiratory effort signal during the monitoring ([0048] airflow signal is monitored [0049], [0090]-[0093] respiratory effort may be additionally monitored);
Partitioning the data into a plurality of epochs ([0096] generated signals as partitioned into one hour intervals to judge sleep architecture and continuity);
Determining a sleep/wake state of the patient for the epoch ([0042], [0076], [0079] broad description of how sleep state is considered [0090]-[0093] how actigraphy and breath data relate to this determination, [0096] overall sleep continuity judged on hourly basis based on the various parameters);
estimate the total sleep time from the sleep/wake states ([0079] determined parameter from the sleep analysis is total sleep time);
wherein the processor ([0052]) is configured to determine the sleep/wake state of the patient during each epoch by:
determining whether the sleep/wake state of the patient for the epoch is “wake” based on the actigraphy signal for the epoch ([0090]-[0093] actigraphy signals reviewed for presence of substantial head movement indicating wake), and
when the sleep/wake state for the epoch is determined not to be “wake” based on the actigraphy signal for the epoch, determining whether the sleep/wake state is “wake” based on the respiratory flow rate signal or the respiratory effort signal (Fig. 9, [0025]-[0026], [0090]-[0093] actigraphy signals of substantial head movement considered, and then are noted as being confirmed by respiratory effort/snoring signals. In confirming the actigraphy determination, the determination of sleep/wake state by respiratory effort only occurs when a determination is made by actigraphy first).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to partition the physiological data of Odio and perform monitoring and analysis steps for each epoch as taught by Westbrook to provide results more representative of the change in time for the patient, rather than utilizing single values for metric for the entire sleep monitoring duration. Furthermore, it would be obvious to use respiratory effort data for the sleep/wake classification as taught by Westbrook along with the actigraphy data as Westbrook teaches that using two datasets can provide improved identification of sleep state over a single dataset. Also, it would be obvious to identify in Westbrook that the order of sleep/wake identification indicates that actigraphy data is evaluated first and only when a determination of sleep/wake is made off of actigraphy, does an evaluation of sleep/wake occur in view of respiratory effort. Next, it would be obvious that the activity count of Odio may be used to judge the head movement of Westbrook as a specific metric to differentiate when the amount of head movement can be characterized as substantial.
Yet their combined efforts fail to teach wherein the processor is configured to determine the sleep/wake state of the patient for each epoch by:
when it is determined that the activity count for the epoch does not satisfy the one or more threshold comparisons, determining the sleep/wake state to be “wake” based at least I part on determining that the one of the respiratory flow rate signal or the respiratory effort signal (a) is unstable during the epoch, and (b) does not contain an indication that a sleep-disordered breathing (SBD) event took place during the epoch.
However Tsutsumi teaches a sleep evaluation (Abstract) comprising the wake/sleep state of a patient ([0020]) wherein the determining the sleep/wake state of the patient for each epoch of a plurality of epochs comprises:
when it is determined that the body movement for the epoch does not satisfy a threshold comparison, determining the sleep/wake state to be “wake” based at least in part on determining that the respiratory signal is unstable during the epoch ([0089], [0093]-[0094] discrimination of sleeping state of the person is based on a body motion waveform and a respiration waveform, where the classification of waking state can be characterized by body motion greater than a threshold and periodicity, i.e. stability, in the respiration waveform, and where the classification of waking state can occur if only one of these signs are present. Thus when it is determined that the body movement for the epoch does not satisfy a threshold comparison, a patient can still be determined to be in the “wake” state based at least in part on determining that the respiratory signal is unstable during the epoch), and
Tehrani teaches that sleep disordered breathing may be characterized by unstable breathing ([0014]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to include Tsutsumi’s discrimination rule for identifying wake based on motion and respiration information to the motion and respiration-based identification of Westbrook as Tsutsumi further identifies what decision should be made when the datasets disagree with one another on whether the patient should be classified as sleep or wake, a detail missing from Westbrook. Together, they make a more cohesive analysis. Furthermore, it would be obvious that the determination of wake should be further characterized by whether an epoch (b) does not contain an indication that a sleep-disordered breathing (SBD) event took place during the epoch in view of Tehrani. Specifically, Tehrani teaches that a sleep based condition may present with unstable breathing. If one only adds the analysis of Tsutsumi to Westbrook, epochs of low movement and unstable breathing that reflect sleep with sleep disordered breathing and epoch of low movement and unstable breathing that reflect an awake state will all be classified as wake. With Tehrani’s context, using a third dataset will increase accuracy by clarifying the subject state for these epochs. Finally, Westbrook already provides an indication that sleep-disordered breathing may have occurred during an epoch ([0086] “These snoring pattern changes are used as behavioral arousal indicators to independently confirm that changes in airflow are a result of sleep disordered breathing (see FIG. 8).”) so Tsutsumi and Tehrani teach the how and why modifying the evaluation in Westbrook can be improved when determining the sleep/wake state of the patient.
Regarding Claim 20, Odio, Westbrook, Tsutsumi, and Tehrani teach the medical screening device of claim 19, wherein the medical screening device is configured to be mounted, when in use, on the patient's body during sleep (Odio: [0107] actigraph is worn, [0032] respiratory efforts worn as piezoelectric belt and Westbrook: [0034] worn sensor strip, [0040], [0042], [0062], [0090]-[0091] sensor strip can house airflow sensors and accelerometer for actigraphy, airflow, and respiratory effort, and further may utilize respiratory effort belts for respiratory effort).
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Odio in view of Westbrook and further in view of Tsutsumi and further in view of Tehrani and further in view of Hwang et al (“Sleep Period Time Estimation Based on Electrodermal Activity”) as noted in Applicant IDS dated 2/09/2024.
Regarding Claim 2, while Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 1, their combined efforts fail to teach wherein the determining that the activity count for the epoch satisfies one ore more threshold comparisons comprises determining that a ratio of the activity count of the epoch to the activity count for a previous epoch is greater than a first activity threshold.
However Hwang teaches a sleep monitoring technique (Abstract) wherein sleep/wake state transition of a patient may be determined if a ratio of the magnitude of a physiological parameter of the epoch to the physiological parameter for a preceding epoch is greater than a first threshold (p118, 3) Waking Epoch Estimation, where condition 4 may be rewritten as SEF[n] / mean( SEF n-5:n-1] ) > 1.2, signifying that if a ratio of smoothed electrodermal activity SEF of epoch n to SEF data of epochs n-5:n-1 is greater than a first threshold 1.2, the patient is considered to be awake).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to improve the sleep/wake determination of Odio, Westbrook, Tsutsumi, and Tehrani by utilizing the teachings of Hwang as the application of a known technique for distinguishing between transitions in a data set seen during waking, to the known waking state determiner of Odio and Westbrook ready for improvement to yield predictable results of accurate wake state determination.
Regarding Claim 3, Odio, Westbrook, Tsutsumi, Tehrani, and Hwang teach the method of claim 2, and Odio teaches wherein the determining that the activity count for the epoch satisfies one or more threshold comparisons comprises determining that the activity count of the epoch is greater than a second activity threshold (See Claim 2 Rejection, [0036]).
Claim(s) 4-5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Odio in view of Westbrook and further in view of Tsutsumi and further in view of Tehrani and further in view of Tatkov et al (US 2015/0128942) (“Tatkov”).
Regarding Claim 4, while Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 1, wherein the determining the sleep/wake state is based on the respiratory flow signal (See Claim 1 Rejection, respiratory flow may be obtained by a pulse oximeter), their combined efforts fail to teach metric utilized being respiratory flow rate.
However Tatkov teaches a physiological monitoring system (Abstract) and teaches that respiratory flow monitoring of sleep/wake state may utilize the metric of respiratory flow rate ([0042]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have the respiratory airflow metric judging sleep/wake of Westbrook to be flow rate as taught by Tatkov as Westbrook does not identify how airflow will be utilized to achieve this judgement. Tatkov’s teaching identifies how this result may be achieved and will ensure standardization of monitoring, increasing consistency of results across the invention’s applications.
Regarding Claim 5, Odio, Westbrook, Tsutsumi, Tehrani, and Tatkov teach the method of claim 4, and Tsutsumi teaches wherein the determining the sleep/wake state further comprises determining the sleep/wake state to be "sleep" if the respiratory flow rate signal is stable during the epoch (See Claim 1 and 4 Rejection, [0089], [0093]-[0094] determining the sleep/wake state to be "sleep" if the respiratory flow rate signal is stable during the epoch and the movement is below a threshold).
Regarding Claim 15, while Odio, Westbrook, Tsutsumi, and Tehrani teach the system of claim 14, wherein the sensor is a breathing sensor configured to generate the respiratory flow signal, wherein determination of the sleep/wake state is based on the generated respiratory flow signal from the breathing sensor (See Claim 14 Rejection, Westbrook [0093] teaches that air flow may equivalently be used to breathing effort related snoring in evaluating wake state), their combined efforts fail to teach the respiratory flow metric being judged to identify sleep/wake being respiratory flow rate.
However Tatkov teaches a physiological monitoring system (Abstract) and teaches that respiratory flow monitoring of sleep/wake state may be utilize the metric of respiratory flow rate ([0042]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have the respiratory airflow metric judging sleep/wake of Westbrook to be flow rate as taught by Tatkov as Westbrook does not identify how airflow will be utilized to achieve this judgement. Tatkov’s teaching identifies how this result may be achieved and will ensure standardization of monitoring, increasing consistency of results across the invention’s applications.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Odio in view of Westbrook and further in view of Tsutsumi and further in view of Tehrani and further in view of Tatkov and further in view of Thompson et al (US 2014/0116440) (“Thompson”) as noted in Applicant IDS dated 2/09/2024.
Regarding Claim 6, while Odio, Westbrook, Tsutsumi, Tehrani, and Tatkov teach the method of claim 4, their combined efforts fail to teach wherein the determining the sleep/wake state further comprises determining the sleep/wake state to be "sleep" if the respiratory flow rate signal contains an indication that the SDB event took place during the epoch.
However Thompson teaches a breathing apparatus (Abstract) wherein the breath monitoring may utilize the occurrence of sleep disordered breathing as an indication that the subject is sleeping ([0070] “The controller 109 processes the data to determine sleep in a patient based on the occurrence of SDB events detected from the received data. In particular, it is programmed to detect SDB events from the received data, and from one or more of those detected SDB events, determine patient sleep.”, [0066] from flow signal sensing 112).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to further determine the sleep/wake state of Odio based on sleep disordered breathing as taught by Thompson as using multiple sets of data to confirm the patient state will increase confidence in the final determination of sleep/wake state
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Odio in view of Westbrook and further in view of Tsutsumi and further in view of Tehrani and further in view of Heneghan et al (US 2010/0152543) (“Heneghan”) as noted in Applicant IDS dated 2/09/2024.
Regarding Claim 8, while Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 7, their combined efforts fail to teach wherein the determining the sleep/wake state further comprises determining the sleep/wake state to be "sleep" if the respiratory effort signal is stable during the epoch.
However Heneghan teaches a physiological monitoring system (Abstract) comprising determining the sleep/wake state depending on a steady respiratory effort signal of the patient ([0061] steady breathing effort characterizes stage 4 sleep).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to further determine the sleep/wake state of Odio based on steady respiratory effort as taught by Heneghan as this provides a teaching for how the respiratory effort monitoring of Odio will output a sleep/wake conclusion.
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Odio in view of Westbrook and further in view of Tsutsumi and further in view of Tehrani and further in view of Thompson.
Regarding Claim 9, while Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 7, and Westbrook teaches wherein respiratory effort can be used to identify sleep disordered breathing ([0091]), their combined efforts fail to teach wherein the determining the sleep/wake state further comprises determining the sleep/wake state to be "sleep" if the respiratory effort signal contains an indication that the SDB event took place during the epoch.
However Thompson teaches a breathing apparatus (Abstract) wherein the breath monitoring may utilize the occurrence of sleep disordered breathing as an indication that the subject is sleeping ([0070] “The controller 109 processes the data to determine sleep in a patient based on the occurrence of SDB events detected from the received data. In particular, it is programmed to detect SDB events from the received data, and from one or more of those detected SDB events, determine patient sleep.”, [0066] from a breath dataset).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to further determine the sleep/wake state of Odio, Westbrook, Tsutsumi, and Tehrani based on sleep disordered breathing as taught by Thompson as using multiple sets of data to confirm the patient state will increase confidence in the final determination of sleep/wake state.
Regarding Claim 10, Odio, Westbrook, Tsutsumi, Tehrani, and Thompson teach the method of claim 9, wherein the SDB event comprises one of snore, flow limitation, respiratory-effort-related arousal, obstructive hypopnea, and obstructive apnea (Tehrani: [0014] list includes SDB events that create unstable breathing and Thompson: [0005] flow limitation, obstructive sleep apnea, hypopnea).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Odio in view of Westbrook and further in view of Tsutsumi and further in view of Tehrani and further in view of LeBoeuf et al (WO 2015/131065) (“LeBoeuf”) as noted in Applicant IDS dated 2/09/2024.
Regarding Claim 13, while Odio, Westbrook, Tsutsumi, and Tehrani teach the method of claim 1, their combined efforts fail to teach wherein the determining an activity count for an epoch comprises:
rectifying each channel of the actigraphy signal,
summing the rectified channels to obtain a single actigraphy signal, and
computing a root mean squared value of the single actigraphy signal over the epoch.
However LeBoeuf teaches a physiological monitor (Abstract) and teaches that determining a physical activity parameter may comprise rectifying each channel of an accelerometer signal, summing the rectified channels to obtain a single acclerometer signal, and computing a root mean squared value of the single accelerometer signal over the epoch (p5, L. 18-31, “A physical activity parameter represents a parameter relating to a physical activity of an organism. Exemplary physical activity parameters include, but are not limited to, a motion parameter e.g., a walking cadence, running cadence, sprinting cadence, cycling cadence, limb cadence, walking speed, running speed, cycling speed, limb motion speed, head motion, a parameterization of sampled data from the at least one motion sensor, or the like. Examples of a parameterization of digitally sampled data from a motion sensor (e.g., an accelerometer) include, but are not limited to, filtering (low-pass, high-pass, bandwidth, notch, etc.) of each accelerometer axis, processing the sampled data to generate root-mean-squared (RMS) sampled data (e.g., squaring the sampled data from each accelerometer axis, summing the result, and taking the square root of the sum),…”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to further determine activity count of while Odio, Westbrook, Tsutsumi, and Tehrani teach with the signal processing steps of LeBoeuf as LeBoeuf teaches that these steps are known steps for parameterize activity data from a multiple axis activity monitor. Thus it is the application of a known technique to a known device ready for improvement to yield predictable results of accurately characterized activity data.
Response to Arguments
Applicant’s amendments and arguments filed 12/18/2025 with respect to the claim objections have been fully considered, and are persuasive. The objection(s) is/are withdrawn.
Applicant’s amendments and arguments filed 12/18/2025 with respect to the 35 USC 101 rejections have been fully considered, and are persuasive. Applicant’s newly added language on how to judge wake when actigraphy suggests sleep, but respiratory effort/flow suggest wake provides details that distinguish the claim as an improvement over the prior art. Specifically, Applicant provides clear details on how one should evaluate actigraphy and respiratory data together to identify sleep wake. When actigraphy is above the one or more threshold comparisons, the patient is classified as awake. When the actigraphy is below the threshold for wake, a wake state may still be determined if respiratory flow or respiratory effort identifies the patient to be in the wake state and the epoch does not contain an indication that sleep disordered breathing occurred. When the actigraphy is below the threshold for wake, and the respiratory flow or respiratory effort identifies the patient to be in the sleep state, the patient would be classified as asleep. These rules clarify how respiratory flow/effort and actigraphy can be used together to classify, even in view of epochs where the datasets disagree.
The most relevant art for identifying an improvement and/or whether the claims amount to more than is well understood, routine, and conventional are:
Westbrook et al (US 2010/0240982) (“Westbrook”) as noted in Applicant IDS dated 2/09/2024
Tsutsumi et al (US 2013/0310662) (“Tsutsumi”) as noted in Applicant IDS dated 2/09/2024 and
Izumi (US 2008/0306351).
Izumi teaches a special bed-based system where movement and respiration are gathered from pressure data gathered by the bed. While it is making judgement of sleep and wake in view of movement and respiration stability, it does not contextualize epochs by sleep disordered breathing and may misclassify epochs for this reason.
Tsutsumi teaches a non-contact motion sensor and teaches clear rules for how to classify wake/sleep for a patient in view of stable breathing and body motion and teaches identifying snoring simultaneously, but does not account for sleep disordered breathing in the wake/sleep classification.
Westbrook teaches an actigraph-based system that measures airflow and a sign of sleep disordered breathing, teaches comparing data motion, airflow, and snoring when identifying wake/sleep state, and teaches optimizing for accuracy, but does not give specific rules for how to handle conflict between datasets.
In view of the art, the methodology provides more than what is well-understood, routine, and conventional as it is applied upon a patient-mounted actigraph of a medical screening device, enabling the simplest integration of this technique to a patient’s daily life as it doesn’t require specialized equipment of a special mattress (Izumi) and doesn’t require the space of a room to focus on the device’s field of view to enable an accurate classification. And Westbrook, as the single reference with a similar methodology and similar hardware fails to support the idea that the invention was “well-understood, routine, and conventional.”
The rejection is withdrawn.
Applicant’s amendments and arguments filed 12/18/2025 with respect to the 35 USC 103 rejections have been fully considered, and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Odio, Westbrook, Tsutsumi, and Tehrani.
Conclusion
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/JAIRO H. PORTILLO/
Examiner
Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights