DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-8, 11, 12, 14-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Heruth(US 7717848 B2) (cited previously) in view of Katra(US 20160256060 A1), and further in view of Dinesh(JP 2015161906 A).
Regarding claim 1, Heruth discloses a system for informing a therapeutic procedure, comprising:
a pulse generator configured to generate a therapeutic electrical signal(Therapy delivery module 44 may, for example, include an output pulse generator coupled to a power source such as a battery(Detailed Description, paragraph 27));
one or more leads in communication with the pulse generator and configured to transmit the electrical signal to a plurality of electrodes(Electrodes 42 are electrically coupled to a therapy delivery module 44 via leads 16A and 16B(Detailed Description, paragraph 27));
the plurality of electrodes in communication with the one or more leads, the plurality of electrodes configured to apply the electrical signal to an anatomical element of a patient and configured to measure a physiological response; (Therapy delivery module 44 may deliver electrical pulses to patient 12 via at least some of electrodes 42 under the control of a processor 46, which controls therapy delivery module 44 to deliver neurostimulation therapy according to a current therapy parameter set(Detailed Description, paragraph 27));
a processor(a processor (Summary, paragraph 20)); and
a memory storing data for processing by the processor(Memory 48 may also include parameter information 62 recorded by processor 46(Detailed Description, paragraph 51)) the data, when processed, causes the processor to:
determine specific states associated with a sleep-wake cycle of the patient(Sleep efficiency, sleep latency, and time spent in deeper sleep states are example sleep quality metrics for which values may be determined(abstract);
adjust, in response to determining that the current cardiac activity falls outside of the longitudinal trend of ECG metrics, one or more parameters for applying the therapeutic electrical signal to the anatomical element(Clinician programmer 20 may receive sleep quality metric values from IMD 14, and present a variety of types of sleep information to a clinician, e.g., via display 22, based on the sleep quality metric values. For example, clinician programmer 20 may present a graphical representation of the sleep quality metric values, such as a trend diagram of values of one or more sleep quality metrics over time, or a histogram or pie chart illustrating percentages of time that a sleep quality metric was within various value ranges(Detailed Description, paragraph 23). Processor 46 may also determine when patient 12 is asleep, e.g., identify the times that patient 12 falls asleep and wakes up, in order to determine one or more sleep quality metric values. The detected values of physiological parameters of patient 12, such as activity level, heart rate, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity, core temperature, arterial blood flow, and galvanic skin response may discernibly change when patient 12 falls asleep or wakes up(Detailed Description, paragraph 38For example, processor 150 may periodically receive sleep quality metric values 66 from IMD 14 when placed in telecommunicative communication with IMD 14 by patient 12, e.g., for therapy selection or adjustment(Detailed Description, paragraph 71)).
Heruth fails to explicitly state “establish a longitudinal trend of electrocardiogram (ECG) metrics indicative of heart rate variability (HRV) for the patient based at least in part on a plurality of signals with cardiac activity for the patient measured at same times of day and night over a first period of time that spans days, weeks, or months;
in response to detecting that the sleep-wake cycle of the patient has entered a first specific state, trigger a measurement, via one or more of the plurality of electrodes, of one or more signals indicative of HRV of the patient during a second period of time after the first period of time, the measurement of the one or more signals being triggered during the same times of day and night during the second period of time as the first period of time;
compare the measurements obtained during the second period of time to the longitudinal trend of ECG metrics for the patient, wherein the measurements obtained during the second period of time represent current cardiac activity of the patient; and
determine, based on the comparison, that the current cardiac activity of the patient falls outside of the longitudinal trend of ECG metrics”.
However, Katra discloses “ The method shown in FIG. 3 involves detecting 302 a cardiac electrical signal from the patient, computing 304 a first measure of HRV, and computing, receiving, inputting or storing 306 a second measure of HRV differing from the first HRV measure. In some embodiments, the second HRV measure is computed and stored in a memory of a medical device or an external system, and subsequently retrieved from memory when calculating an HRV index. In other embodiments, the second HRV measure is input by a healthcare professional and used when calculating an HRV index. The method also involves producing 308 and index of patient status derived from a ratio of the first and second measures of HRV. The method further involves trending 310 the index over relatively long period of time and generating chronic assessment data. A chronic change in the patient's status can be detected 312 using the chronic assessment data, such as by detecting a change that exceeds a predetermined threshold. The threshold can vary depending on the physiologic condition or parameter being assessed in the particular index being used to detect the chronic change[0052]. In other embodiments, the apparatus, system, method, and/or algorithm can use an average HRV collected at night when the patient is believed to be asleep or minimally active and compare this measure of HRV.sub.night/lo activity to that of the daily HRV, HRV.sub.day. In some embodiments, HRV.sub.peak or HRV.sub.mean during the day can be used. In other embodiments, RR, delta RR, SDNN, or the like can be used instead of or in addition to basic HRV. In some embodiments, nocturnal detection around a fixed time/schedule, such as, for instance, 3:00 AM, can be used[0047]. FIG. 6 is a graph showing a change in an index 602 of patient status given by a ratio of HRV measures (HRV.sub.1/HRV.sub.2) in accordance with various embodiments. The plot of index 602 shown in FIG. 6 can be illustrative of acute changes or chronic changes in the index 602 as a function of time[0062]. A patient status monitoring methodology according to various embodiments involves detecting if and when the index 602 exceeds or falls below a zone threshold 610 or 612 over time. When a given zone threshold 610 or 612 is crossed, an appropriate alert can be generated and reported to the patient and/or the patient's physician[0063]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the sleep quality medical device of Heruth with the HRV measurements of the HRV medical device of Katra. Doing so would specify multiple HRV measurements at different times and comparing these measurements to an established ECG trend in order to accurately examine the condition of the patients heart.
Dinesh further discloses “Detection of a particular sleep state (eg, non-REM sleep state) can trigger or enable detection of cardiac flow(See attached translation, page 33, paragraph 3)). In such an embodiment, this state detection may be performed using conventional sleep stage sensor identification techniques (i.e., electroencephalograms) from data measured by the respiratory flow sensor(See attached translation, page 9, paragraph 10))”. It would be obvious to one of ordinary skill in the art before the effective filing date to configure the sleep quality medical device of Heruth with the sleep stage detection of the sleep condition detector of Dinesh. Doing so would specify patient metrics to be measured when the patient reaches particular sleep stages.
Regarding claim 3, Heruth in view of Katra and Dinesh teaches discloses the system of claim 1, wherein the memory stores further data for processing by the processor that, when processed, causes the processor to: provide, via a user interface, an alert indicating that the current cardiac activity falls outside the longitudinal trend of ECG metrics(One or both of programmers 20, 26 may receive sleep quality metric values from IMD 14, and may provide sleep quality information to a user based on the sleep quality metric values. For example, patient programmer 26 may provide a message to patient 12, e.g., via display 28, related to sleep quality based on received sleep quality metric values. Patient programmer 26 may, for example, suggest that patient 12 visit a clinician for prescription of sleep medication or for an adjustment to the therapy delivered by IMD 14(Detailed Description, paragraph 23). In embodiments where IMD 14 delivers neurostimulation therapy in the form of electrical pulses, the parameters in each parameter set may include voltage or current pulse amplitudes, pulse widths, pulse rates, and the like(Detailed Description, paragraph 4)).
Regarding claim 4, Heruth in view of Katra and Dinesh teaches the system of claim 1, wherein the one or more adjusted therapy parameters comprise an amplitude adjustment, adjustment of duty cycling, adjustment of cycling of high frequency or low frequency components independently, adjustment of frequency, adjustment of pulse width, adjustment of charge balancing strategy, adjustment of selection of the plurality of electrodes, or a combination thereof(One or both of programmers 20, 26 may receive sleep quality metric values from IMD 14, and may provide sleep quality information to a user based on the sleep quality metric values. For example, patient programmer 26 may provide a message to patient 12, e.g., via display 28, related to sleep quality based on received sleep quality metric values. Patient programmer 26 may, for example, suggest that patient 12 visit a clinician for prescription of sleep medication or for an adjustment to the therapy delivered by IMD 14(Detailed Description, paragraph 23). In embodiments where IMD 14 delivers neurostimulation therapy in the form of electrical pulses, the parameters in each parameter set may include voltage or current pulse amplitudes, pulse widths, pulse rates, and the like(Detailed Description, paragraph 4)).
Regarding claim 5, Heruth in view of Katra and Dinesh teaches the system of claim 1, wherein the memory stores further data for processing by the processor that, when processed, causes the processor to: output, via the user interface, the longitudinal trend of ECG metrics for the patient, wherein the one or more parameters for applying the therapeutic electrical signal to the anatomical element are adjusted based at least in part on outputting the longitudinal trend of ECG metrics for the patient(Consequently, in order to detect when patient 12 falls asleep and wakes up, processor 46 may monitor one or more of these physiological parameters, or the variability of these physiological parameters, and detect the discernable changes in their values associated with a transition between a sleeping state and an awake state. In some embodiments, processor 46 may determine a mean or median value for a parameter based on values of a signal over time, and determine whether patient 12 is asleep or awake based on the mean or median value. Processor 46 may compare one or more parameter or parameter variability values to thresholds stored in memory 48 to detect when patient 12 falls asleep or awakes. The thresholds may be absolute values of a physiological parameter, or time rate of change values for the physiological parameter, e.g., to detect sudden changes in the value of a parameter or parameter variability.(Detailed Description, paragraph 40)).
Regarding claim 6, Heruth in view of Katra and Dinesh teaches the system of claim 1, wherein the data stored in the memory that, when processed causes the processor to measure the one or more signals with cardiac activity further causes the system to: measure, via one or more of the plurality of electrodes, a first signal with cardiac activity during a first specific state of the sleep-wake cycle; and measure, via one or more of the plurality of electrodes, a second signal with cardiac activity during a second specific state of the sleep-wake cycle, wherein the one or more parameters for applying the therapeutic electrical signal to the anatomical element are adjusted based at least in part on a comparison of the first signal and the second signal(In some embodiments, which sleep state the patient is in, e.g., rapid eye movement (REM), or one of the nonrapid eye movement (NREM) states (S1, S2, S3, S4) may be determined based on physiological parameters monitored by the medical device, and the amount of time per day spent in these various sleep states may be a sleep quality metric.(Summary, paragraph 8). In particular, memory 48 may store one or more thresholds for each of sleep states, and processor 46 may compare physiological parameter or sleep probability metric values to the thresholds to determine which sleep state patient 12 is currently in(Detailed Description, paragraph 49)).
Regarding claim 7, Heruth in view of Katra and Dinesh teaches the system of claim 6, wherein the first specific state comprises a first sleep state for the patient, and the second specific state comprises a second sleep state for the patient(In some embodiments, which sleep state the patient is in, e.g., rapid eye movement (REM), or one of the nonrapid eye movement (NREM) states (S1, S2, S3, S4) may be determined based on physiological parameters monitored by the medical device, and the amount of time per day spent in these various sleep states may be a sleep quality metric.(Summary, paragraph 8)).
Regarding claim 8, Heruth in view of Katra and Dinesh teaches the system of claim 1, wherein the specific states of the sleep-wake cycle comprise states within a threshold amount of time after the patient falls asleep, when the patient is resting before sleep, when the patient is initially waking, sleep stages before waking occurs, throughout a total duration of sleep, or a combination thereof(Consequently, in order to detect when patient 12 falls asleep and wakes up, processor 46 may monitor one or more of these physiological parameters, or the variability of these physiological parameters, and detect the discernable changes in their values associated with a transition between a sleeping state and an awake state. In some embodiments, processor 46 may determine a mean or median value for a parameter based on values of a signal over time, and determine whether patient 12 is asleep or awake based on the mean or median value. Processor 46 may compare one or more parameter or parameter variability values to thresholds stored in memory 48 to detect when patient 12 falls asleep or awakes. The thresholds may be absolute values of a physiological parameter, or time rate of change values for the physiological parameter, e.g., to detect sudden changes in the value of a parameter or parameter variability. In some embodiments, a threshold used by processor 46 to determine whether patient 12 is asleep may include a time component. For example, a threshold may require that a physiological parameter be above or below a threshold value for a period of time before processor 46 determines that patient is awake or asleep(Detailed Description, paragraph 40)).
Regarding claim 9, Heruth in view of Katra and Dinesh teaches the system of claim 1, wherein the memory stores further data for processing by the processor that, when processed, causes the processor to: configure a duration for measuring the one or more signals with cardiac activity, wherein the one or more signals with cardiac activity are measured according to the configured duration(The medical device may provide the recorded physiological parameter values to the programming device in real time, or may provide physiological parameter values recorded over a period of time to the programming device when interrogated by the programming device(Summary, paragraph 3).
Regarding claim 11, Heruth in view of Katra and Dinesh teaches the system of claim 1, wherein the signals with cardiac activity comprise cardiac electrogram measurements, and wherein one or more cardiac metrics are derived from the cardiac electrogram measurements, the one or more ECG metrics comprising a heart rate, respiration, or a combination thereof, for the patient(As another example, sensors 40 may include electrodes located on leads or integrated as part of the housing of IMD 14 that generate an electrogram signal as a function of electrical activity of the heart of patient 12, and processor 46 may monitor the heart rate of patient 12 based on the electrogram signal(Detailed Description, Paragraph 43)).
Regarding claim 12, Heruth discloses a system for informing a therapeutic procedure, comprising:
a processor(a processor (Summary, paragraph 20));
and a memory storing data for processing by the processor(Memory 48 may also include parameter information 62 recorded by processor 46(Detailed Description, paragraph 51)), the data, when processed, causes the processor to:
determine specific states associated with a sleep-wake cycle of a patient(Sleep efficiency, sleep latency, and time spent in deeper sleep states are example sleep quality metrics for which values may be determined(abstract)); and
adjust, based on determining that the current cardiac activity falls outside of the longitudinal trend of ECG metrics one or more parameters for applying a therapeutic electrical signal to an anatomical element of the patient(Clinician programmer 20 may receive sleep quality metric values from IMD 14, and present a variety of types of sleep information to a clinician, e.g., via display 22, based on the sleep quality metric values. For example, clinician programmer 20 may present a graphical representation of the sleep quality metric values, such as a trend diagram of values of one or more sleep quality metrics over time, or a histogram or pie chart illustrating percentages of time that a sleep quality metric was within various value ranges(Detailed Description, paragraph 23). Processor 46 may also determine when patient 12 is asleep, e.g., identify the times that patient 12 falls asleep and wakes up, in order to determine one or more sleep quality metric values. The detected values of physiological parameters of patient 12, such as activity level, heart rate, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity, core temperature, arterial blood flow, and galvanic skin response may discernibly change when patient 12 falls asleep or wakes up(Detailed Description, paragraph 38). For example, processor 150 may periodically receive sleep quality metric values 66 from IMD 14 when placed in telecommunicative communication with IMD 14 by patient 12, e.g., for therapy selection or adjustment(Detailed Description, paragraph 71))
Heruth fails to explicitly state “establish a longitudinal trend of electrocardiogram (ECG) metrics indicative of heart rate variability (HRV) for the patient based at least in part on a plurality of signals with cardiac activity for the patient measured at the same times of day and night over a first period of time that spans days, weeks, or months;
in response to detecting that the sleep wake cycle of the patient has entered a first specific state, trigger a measurement of one or more signals with cardiac activity of the patient during a second period of time after the first period of time, the measurement of the one or more signals being triggered during the same times of day and night during the second period of time as the first period of time ;
compare the measurements obtained during the second period of time to the longitudinal trend of ECG metrics for the patient, wherein the measurements obtained during the second period of time represent current cardiac activity of the patient; determine, based on the comparison, that the current cardiac activity falls outside of the longitudinal trend of ECG metrics” .
However, Katra discloses “ The method shown in FIG. 3 involves detecting 302 a cardiac electrical signal from the patient, computing 304 a first measure of HRV, and computing, receiving, inputting or storing 306 a second measure of HRV differing from the first HRV measure. In some embodiments, the second HRV measure is computed and stored in a memory of a medical device or an external system, and subsequently retrieved from memory when calculating an HRV index. In other embodiments, the second HRV measure is input by a healthcare professional and used when calculating an HRV index. The method also involves producing 308 and index of patient status derived from a ratio of the first and second measures of HRV. The method further involves trending 310 the index over relatively long period of time and generating chronic assessment data. A chronic change in the patient's status can be detected 312 using the chronic assessment data, such as by detecting a change that exceeds a predetermined threshold. The threshold can vary depending on the physiologic condition or parameter being assessed in the particular index being used to detect the chronic change[0052]. In other embodiments, the apparatus, system, method, and/or algorithm can use an average HRV collected at night when the patient is believed to be asleep or minimally active and compare this measure of HRV.sub.night/lo activity to that of the daily HRV, HRV.sub.day. In some embodiments, HRV.sub.peak or HRV.sub.mean during the day can be used. In other embodiments, RR, delta RR, SDNN, or the like can be used instead of or in addition to basic HRV. In some embodiments, nocturnal detection around a fixed time/schedule, such as, for instance, 3:00 AM, can be used[0047]. FIG. 6 is a graph showing a change in an index 602 of patient status given by a ratio of HRV measures (HRV.sub.1/HRV.sub.2) in accordance with various embodiments. The plot of index 602 shown in FIG. 6 can be illustrative of acute changes or chronic changes in the index 602 as a function of time[0062]. A patient status monitoring methodology according to various embodiments involves detecting if and when the index 602 exceeds or falls below a zone threshold 610 or 612 over time. When a given zone threshold 610 or 612 is crossed, an appropriate alert can be generated and reported to the patient and/or the patient's physician[0063]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the sleep quality medical device of Heruth with the HRV measurements of the HRV medical device of Katra. Doing so would specify multiple HRV measurements at different times and comparing these measurements to an established ECG trend in order to accurately examine the condition of the patients heart.
Dinesh further discloses “Detection of a particular sleep state (eg, non-REM sleep state) can trigger or enable detection of cardiac flow(See attached translation, page 33, paragraph 3)). In such an embodiment, this state detection may be performed using conventional sleep stage sensor identification techniques (i.e., electroencephalograms) from data measured by the respiratory flow sensor(See attached translation, page 9, paragraph 10))”. It would be obvious to one of ordinary skill in the art before the effective filing date to configure the sleep quality medical device of Heruth with the sleep stage detection of the sleep condition detector of Dinesh. Doing so would specify patient metrics to be measured when the patient reaches particular sleep stages.
Regarding claim 14, Heruth in view of Katra and Dinesh teaches the system of claim 13, wherein the memory stores further data for processing by the processor that, when processed, causes the processor to: provide, via a user interface, an alert indicating that the one or more current cardiac activity falls outside the longitudinal trend of ECG metrics(One or both of programmers 20, 26 may receive sleep quality metric values from IMD 14, and may provide sleep quality information to a user based on the sleep quality metric values. For example, patient programmer 26 may provide a message to patient 12, e.g., via display 28, related to sleep quality based on received sleep quality metric values. Patient programmer 26 may, for example, suggest that patient 12 visit a clinician for prescription of sleep medication or for an adjustment to the therapy delivered by IMD 14(Detailed Description, paragraph 23). In embodiments where IMD 14 delivers neurostimulation therapy in the form of electrical pulses, the parameters in each parameter set may include voltage or current pulse amplitudes, pulse widths, pulse rates, and the like(Detailed Description, paragraph 4)).
Regarding claim 15, Heruth in view of Katra and Dinesh teaches the system of claim 12, wherein the one or more adjusted therapy parameters comprise an amplitude adjustment, adjustment of duty cycling, adjustment of frequency, adjustment of pulse width, adjustment of cycling of high frequency or low frequency components independently, adjustment of charge balancing strategy, adjustment of selection of the plurality of electrodes, or a combination thereof(One or both of programmers 20, 26 may receive sleep quality metric values from IMD 14, and may provide sleep quality information to a user based on the sleep quality metric values. For example, patient programmer 26 may provide a message to patient 12, e.g., via display 28, related to sleep quality based on received sleep quality metric values. Patient programmer 26 may, for example, suggest that patient 12 visit a clinician for prescription of sleep medication or for an adjustment to the therapy delivered by IMD 14(Detailed Description, paragraph 23). In embodiments where IMD 14 delivers neurostimulation therapy in the form of electrical pulses, the parameters in each parameter set may include voltage or current pulse amplitudes, pulse widths, pulse rates, and the like(Detailed Description, paragraph 4)).
Regarding claim 16, Heruth in view of Katra and Dinesh teaches the system of claim 12, wherein the data stored in the memory that, when processed causes the processor to measure the one or more signals with cardiac activity further causes the system to: measure a first signal with cardiac activity during a first specific state of the sleep-wake cycle; and measure a second signal with cardiac activity during a second specific state of the sleep-wake cycle, wherein the one or more parameters for applying the therapeutic electrical signal to the anatomical element are adjusted based at least in part on a comparison of the first signal and the second signal(In some embodiments, which sleep state the patient is in, e.g., rapid eye movement (REM), or one of the nonrapid eye movement (NREM) states (S1, S2, S3, S4) may be determined based on physiological parameters monitored by the medical device, and the amount of time per day spent in these various sleep states may be a sleep quality metric.(Summary, paragraph 8) . In particular, memory 48 may store one or more thresholds for each of sleep states, and processor 46 may compare physiological parameter or sleep probability metric values to the thresholds to determine which sleep state patient 12 is currently in(Detailed Description, paragraph 49)).
Regarding claim 17, Heruth in view of Katra and Dinesh teaches the system of claim 12, wherein the specific states of the sleep-wake cycle comprise states within a threshold amount of time after the patient falls asleep, when the patient is resting before sleep, when the patient is initially waking, sleep stages before waking occurs, throughout a total duration of sleep, or a combination thereof(Consequently, in order to detect when patient 12 falls asleep and wakes up, processor 46 may monitor one or more of these physiological parameters, or the variability of these physiological parameters, and detect the discernable changes in their values associated with a transition between a sleeping state and an awake state. In some embodiments, processor 46 may determine a mean or median value for a parameter based on values of a signal over time, and determine whether patient 12 is asleep or awake based on the mean or median value. Processor 46 may compare one or more parameter or parameter variability values to thresholds stored in memory 48 to detect when patient 12 falls asleep or awakes. The thresholds may be absolute values of a physiological parameter, or time rate of change values for the physiological parameter, e.g., to detect sudden changes in the value of a parameter or parameter variability. In some embodiments, a threshold used by processor 46 to determine whether patient 12 is asleep may include a time component. For example, a threshold may require that a physiological parameter be above or below a threshold value for a period of time before processor 46 determines that patient is awake or asleep(Detailed Description, paragraph 40)).
Regarding claim 19, Heruth discloses a system for informing a therapeutic procedure, comprising:
a pulse generator configured to generate a therapeutic electrical signal((Therapy delivery module 44 may, for example, include an output pulse generator coupled to a power source such as a battery(Detailed Description, paragraph 27));
one or more leads in communication with the pulse generator and configured to transmit the therapeutic electrical signal to a plurality of electrodes(Electrodes 42 are electrically coupled to a therapy delivery module 44 via leads 16A and 16B(Detailed Description, paragraph 27)); and
the plurality of electrodes in communication with the one or more leads, the plurality of electrodes configured to apply the therapeutic electrical signal to an anatomical element of a patient and configured to measure a physiological response((Therapy delivery module 44 may deliver electrical pulses to patient 12 via at least some of electrodes 42 under the control of a processor 46, which controls therapy delivery module 44 to deliver neurostimulation therapy according to a current therapy parameter set(Detailed Description, paragraph 27)),
wherein the therapeutic electrical signal is adjusted and applied to the anatomical element(For example, processor 150 may periodically receive sleep quality metric values 66 from IMD 14 when placed in telecommunicative communication with IMD 14 by patient 12, e.g., for therapy selection or adjustment(Detailed Description, paragraph 71). and IMD 14 may deliver deep brain stimulation (DBS) therapy to patient 12 (Detailed Description, paragraph 3)).
Heruth fails to disclose “based at least in part on an established trend of electrocardiogram (ECG) indicative of heart rate variability (HRV) for the patient based at least in part on a plurality of signals with cardiac activity for the patient measured at same times of day and night over a first period of time that spans days, weeks, or months, during a specific state associated with a sleep-wake cycle of the patient, and
one or more measurements, of one or more signals indicative of heart rate variability (HRV) output by the plurality of electrodes during a second period of time after the first period of time, wherein the one or more measurements are triggered in response to detecting that the patient enters the specific state during the second period and represent current cardiac activity of the patient, the one or more measurements obtained during the second period of time are compared to the longitudinal trend of ECG metrics for the patient and after determining, based on the comparison, that the current cardiac activity falls outside of the longitudinal trend of ECG metrics”.
However, Katra discloses “ The method shown in FIG. 3 involves detecting 302 a cardiac electrical signal from the patient, computing 304 a first measure of HRV, and computing, receiving, inputting or storing 306 a second measure of HRV differing from the first HRV measure. In some embodiments, the second HRV measure is computed and stored in a memory of a medical device or an external system, and subsequently retrieved from memory when calculating an HRV index. In other embodiments, the second HRV measure is input by a healthcare professional and used when calculating an HRV index. The method also involves producing 308 and index of patient status derived from a ratio of the first and second measures of HRV. The method further involves trending 310 the index over relatively long period of time and generating chronic assessment data. A chronic change in the patient's status can be detected 312 using the chronic assessment data, such as by detecting a change that exceeds a predetermined threshold. The threshold can vary depending on the physiologic condition or parameter being assessed in the particular index being used to detect the chronic change[0052]. In other embodiments, the apparatus, system, method, and/or algorithm can use an average HRV collected at night when the patient is believed to be asleep or minimally active and compare this measure of HRV.sub.night/lo activity to that of the daily HRV, HRV.sub.day. In some embodiments, HRV.sub.peak or HRV.sub.mean during the day can be used. In other embodiments, RR, delta RR, SDNN, or the like can be used instead of or in addition to basic HRV. In some embodiments, nocturnal detection around a fixed time/schedule, such as, for instance, 3:00 AM, can be used[0047]. FIG. 6 is a graph showing a change in an index 602 of patient status given by a ratio of HRV measures (HRV.sub.1/HRV.sub.2) in accordance with various embodiments. The plot of index 602 shown in FIG. 6 can be illustrative of acute changes or chronic changes in the index 602 as a function of time[0062]. A patient status monitoring methodology according to various embodiments involves detecting if and when the index 602 exceeds or falls below a zone threshold 610 or 612 over time. When a given zone threshold 610 or 612 is crossed, an appropriate alert can be generated and reported to the patient and/or the patient's physician[0063]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the sleep quality medical device of Heruth with the HRV measurements of the HRV medical device of Katra. Doing so would specify multiple HRV measurements at different times and comparing these measurements to an established ECG trend in order to accurately examine the condition of the patients heart.
Dinesh further discloses “Detection of a particular sleep state (eg, non-REM sleep state) can trigger or enable detection of cardiac flow(See attached translation, page 33, paragraph 3)). In such an embodiment, this state detection may be performed using conventional sleep stage sensor identification techniques (i.e., electroencephalograms) from data measured by the respiratory flow sensor(see attached translation, page 9, paragraph 10))”. It would be obvious to one of ordinary skill in the art before the effective filing date to configure the sleep quality medical device of Heruth with the sleep stage detection of the sleep condition detector of Dinesh. Doing so would specify patient metrics to be measured when the patient reaches particular sleep stages.
Regarding claim 20, Heruth in view of Katra and Dinesh teaches the system of claim 19, wherein the specific states of the sleep-wake cycle comprise states within a threshold amount of time after the patient falls asleep, when the patient is resting before sleep, when the patient is initially waking, sleep stages before waking occurs, or a combination thereof(Consequently, in order to detect when patient 12 falls asleep and wakes up, processor 46 may monitor one or more of these physiological parameters, or the variability of these physiological parameters, and detect the discernable changes in their values associated with a transition between a sleeping state and an awake state. In some embodiments, processor 46 may determine a mean or median value for a parameter based on values of a signal over time, and determine whether patient 12 is asleep or awake based on the mean or median value. Processor 46 may compare one or more parameter or parameter variability values to thresholds stored in memory 48 to detect when patient 12 falls asleep or awakes. The thresholds may be absolute values of a physiological parameter, or time rate of change values for the physiological parameter, e.g., to detect sudden changes in the value of a parameter or parameter variability. In some embodiments, a threshold used by processor 46 to determine whether patient 12 is asleep may include a time component. For example, a threshold may require that a physiological parameter be above or below a threshold value for a period of time before processor 46 determines that patient is awake or asleep(Detailed Description, paragraph 40)).
Claim(s) 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Heruth in view of Katra, Dinesh, and further in view of Srivastava(10960210) (cited previously).
Regarding claim 10, Heruth in view of Katra and Dinesh teaches the system of claim 1, but fails to specify wherein the specific states of the sleep-wake cycle are detected based at least in part on an algorithm that uses accelerometry measurements, ECG metrics, a time of day, respiration measurements, additional information from external devices, or a combination thereof, as input parameters.
However, Srivastava teaches “In various embodiments, control circuit 106 can optimize the therapy parameters for the determined sleep state. In various embodiments, control circuit 104 can execute a closed-loop therapy algorithm for treating the one or more pathological conditions using the determined sleep state as an input.(Detailed Description, paragraph 8)”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the sleep quality detection device of Heruth with the method for pain management and sleep detection of Srivastava. Doing so would configure the processor to include a processing algorithm to help detect specific sleep stages.
Regarding claim 18, Heruth in view of Katra and Dinesh teaches the system of claim 12, but fails to disclose wherein the specific states of the sleep-wake cycle are detected based at least in part on an algorithm that uses accelerometry measurements, ECG metrics, a time of day, respiration measurements, additional information from external devices, or a combination thereof, as input parameters.
However, Srivastava teaches “In various embodiments, control circuit 106 can optimize the therapy parameters for the determined sleep state. In various embodiments, control circuit 104 can execute a closed-loop therapy algorithm for treating the one or more pathological conditions using the determined sleep state as an input.(Detailed Description, paragraph 8)”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the sleep quality detection device of Heruth with the method for pain management and sleep detection of Srivastava. Doing so would configure the processor to include a processing algorithm to help detect specific sleep stages.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 3-12, and 14-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant argues that Heruth in view of Katra fails to disclose the amendments to the independent claims, specifically “in response to detecting that the sleep-wake cycle of the patient entered a first specific state". However, new art Dinesh teaches detecting when the patient enters specific sleep cycles, such as the REM cycle, and triggering detecting of patient cardiac flow. Dinesh further teaches how the heart rate is measured from the cardiac flow and these measurements can be take multiple times during different sleep stages(See attached translation, page 33, paragraphs 1-3). Dinesh can be obviously combined with Katra and Heruth, and further with Srivastava to disclose all claimed limitations. Therefore 103 rejections for all claims stand.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure; the sleep scoring system of Russell(US 10111615 B2).
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/MARIA CATHERINE ANTHONY/Examiner, Art Unit 3796
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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