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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8, 11-17, and 19-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being unpatentable over Heruth(US 7717848 B2) (cited previously).
Regarding claim 1, Heruth discloses a system for informing a therapeutic procedure, comprising: a pulse generator configured to generate a therapeutic electrical signal; one or more leads in communication with the pulse generator and configured to transmit the electrical signal to a plurality of electrodes; the plurality of electrodes in communication with the one or more leads(Electrodes 42 are electrically coupled to a therapy delivery module 44 via leads 16A and 16B. Therapy delivery module 44 may, for example, include an output pulse generator coupled to a power source such as a battery. 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)), the plurality of electrodes configured to apply the electrical signal to an anatomical element of a patient and configured to measure a physiological response; a processor; and a memory storing data for processing by the processor, the data, when processed, causes the processor to: determine specific states associated with a sleep-wake cycle of the patient; measure, via one or more of the plurality of electrodes, one or more signals with cardiac activity of the patient upon detecting the specific states of the sleep-wake cycle of the patient; establish a longitudinal trend of cardiac metrics for the patient based at least in part on a plurality of signals with cardiac activity for the patient measured during the specific states of the sleep-wake cycle over a period of time: compare the one or more signals with cardiac activity of the patient to the longitudinal trend of cardiac metrics for the patient; and determine one or more parameters for applying the therapeutic electrical signal to the anatomical element based at least in part on the comparison of the one or more signals with cardiac activity of the patient to the longitudinal trend of cardiac metrics for the patient(At least one of a medical device, such as an implantable medical device, and a programming device determines values for one or more metrics that indicate the quality of a patient's sleep. Sleep efficiency, sleep latency, and time spent in deeper sleep states are example sleep quality metrics for which values may be determined(abstract). Example physiological parameters that IMD 14 may monitor include activity level, posture, heart rate, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid (CSF), muscular activity, core temperature, arterial blood flow, and the level of melatonin within one or more bodily fluids(Detailed Description, paragraph 12). In some embodiments, IMD 14 may identify the current set of therapy parameters when a value of one or more sleep quality metrics is collected, and may associate that value with the current therapy parameter sets. For example, for each of a plurality therapy parameter sets used over time by IMD 14 to deliver therapy to patient 12, IMD 14 may store a representative value of each of one or more sleep quality metrics in a memory with an indication of the therapy parameter set with which that representative value is associated(Detailed Description, paragraph 21). 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). 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 39)).
Regarding claim 2, Heruth discloses the system of claim 1, wherein the period of time comprises a plurality of at least one of days, weeks, and months(In other embodiments, the programming device is a clinician programmer that presents information relating to the quality of the patient's sleep to a clinician. The clinician programmer may present, for example, a trend diagram of values of one or more sleep quality metrics over time(Summary, paragraph 11). Processor 46 may store each sleep quality metric value determined within memory 48 as a sleep quality metric value 66, or may store mean or median sleep quality metric values over periods of time such as weeks or months as sleep quality metric values 66(Detailed Description, paragraph 51)).
Regarding claim 3, Heruth discloses the system of claim 2, wherein the memory stores further data for processing by the processor that, when processed, causes the processor to: determine one or more cardiac metrics fall outside the longitudinal trend of cardiac metrics; and adjust one or more therapy parameters for applying the therapeutic electrical signal to the anatomical element based at least in part on determining the one or more cardiac metrics fall outside the longitudinal trend of cardiac metrics, or provide, via a user interface, an alert indicating that the one or more cardiac metrics fall outside the longitudinal trend of cardiac 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 discloses the system of claim 3, 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 discloses the system of claim 2, wherein the memory stores further data for processing by the processor that, when processed, causes the processor to: output, via a user interface, the longitudinal trend of cardiac metrics for the patient, wherein the one or more parameters for applying the therapeutic electrical signal to the anatomical element are determined based at least in part on outputting the longitudinal trend of cardiac 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 discloses 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 determined 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 discloses 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 discloses 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 discloses 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 discloses 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 cardiac metrics comprising a heart rate, a heart rate variability, 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; and a memory storing data for processing by the processor, the data, when processed, causes the processor to: determine specific states associated with a sleep-wake cycle of a patient; measure one or more signals with cardiac activity of the patient upon detecting the specific states of the sleep-wake cycle of the patient; establish a longitudinal trend of cardiac metrics for the patient based at least in part on a plurality of signals with cardiac activity for the patient measured during the specific states of the sleep-wake cycle over a period of time; compare the one or more signals with cardiac activity of the patient to the longitudinal trend of cardiac metrics for the patient; and determine one or more parameters for applying a therapeutic electrical signal to an anatomical element of the patient based at least in part on the comparison of the one or more signals with cardiac activity to the longitudinal trend of cardiac metrics for the patient(Electrodes 42 are electrically coupled to a therapy delivery module 44 via leads 16A and 16B. Therapy delivery module 44 may, for example, include an output pulse generator coupled to a power source such as a battery. 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). At least one of a medical device, such as an implantable medical device, and a programming device determines values for one or more metrics that indicate the quality of a patient's sleep. Sleep efficiency, sleep latency, and time spent in deeper sleep states are example sleep quality metrics for which values may be determined(abstract). Example physiological parameters that IMD 14 may monitor include activity level, posture, heart rate, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid (CSF), muscular activity, core temperature, arterial blood flow, and the level of melatonin within one or more bodily fluids(Detailed Description, paragraph 12). In some embodiments, IMD 14 may identify the current set of therapy parameters when a value of one or more sleep quality metrics is collected, and may associate that value with the current therapy parameter sets. For example, for each of a plurality therapy parameter sets used over time by IMD 14 to deliver therapy to patient 12, IMD 14 may store a representative value of each of one or more sleep quality metrics in a memory with an indication of the therapy parameter set with which that representative value is associated(Detailed Description, paragraph 21). ). 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). 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 39)).
Regarding claim 13, Heruth discloses the system of claim 12, wherein the period of time comprises a plurality of at least one of days, weeks, and months(In other embodiments, the programming device is a clinician programmer that presents information relating to the quality of the patient's sleep to a clinician. The clinician programmer may present, for example, a trend diagram of values of one or more sleep quality metrics over time(Summary, paragraph 11). Processor 46 may store each sleep quality metric value determined within memory 48 as a sleep quality metric value 66, or may store mean or median sleep quality metric values over periods of time such as weeks or months as sleep quality metric values 66(Detailed Description, paragraph 51)).
Regarding claim 14, Heruth discloses the system of claim 13, wherein the memory stores further data for processing by the processor that, when processed, causes the processor to: determine one or more cardiac metrics fall outside the longitudinal trend of cardiac metrics; and adjust one or more therapy parameters for applying the therapeutic electrical signal to the anatomical element based at least in part on determining the one or more cardiac metrics fall outside the longitudinal trend of cardiac metrics, or provide, via a user interface, an alert indicating that the one or more cardiac metrics fall outside the longitudinal trend of cardiac 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 discloses the system of claim 14, 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 discloses 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 determined 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 discloses 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; one or more leads in communication with the pulse generator and configured to transmit the therapeutic electrical signal to a plurality of electrodes; 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, wherein the therapeutic electrical signal is applied to the anatomical element based at least in part on one or more cardiac metrics derived from one or more signals with cardiac activity measured via one or more of the plurality of electrodes at specific states of a sleep-wake cycle of the patient compared to a longitudinal trend of cardiac metrics for the patient based on a plurality of signals with cardiac activity measured during the specific states of the sleep-wake cycle of the patient across a period of time (Electrodes 42 are electrically coupled to a therapy delivery module 44 via leads 16A and 16B. Therapy delivery module 44 may, for example, include an output pulse generator coupled to a power source such as a battery. 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). At least one of a medical device, such as an implantable medical device, and a programming device determines values for one or more metrics that indicate the quality of a patient's sleep. Sleep efficiency, sleep latency, and time spent in deeper sleep states are example sleep quality metrics for which values may be determined(abstract). Example physiological parameters that IMD 14 may monitor include activity level, posture, heart rate, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid (CSF), muscular activity, core temperature, arterial blood flow, and the level of melatonin within one or more bodily fluids(Detailed Description, paragraph 12). In some embodiments, IMD 14 may identify the current set of therapy parameters when a value of one or more sleep quality metrics is collected, and may associate that value with the current therapy parameter sets. For example, for each of a plurality therapy parameter sets used over time by IMD 14 to deliver therapy to patient 12, IMD 14 may store a representative value of each of one or more sleep quality metrics in a memory with an indication of the therapy parameter set with which that representative value is associated(Detailed Description, paragraph 21). 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 39)).
Regarding claim 20, Heruth discloses 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 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) 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Heruth in view of Srivastava(10960210).
Regarding claim 10, Heruth discloses 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, cardiac 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 discloses 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, cardiac 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 filed 1/27/2026 have been fully considered but they are not persuasive. Applicant amends claim 1 to incorporate previous subject matter from claim 2 and argues current art fails to disclose "...establish a longitudinal trend of cardiac metrics for the patient based at least in part on a plurality of signals with cardiac activity for the patient measured during the specific states of the sleep-wake cycle over a period of time; and determine one or more parameters for applying the therapeutic electrical signal to the anatomical element based at least in part on the comparison of the one or more signals with cardiac activity of the patient to the longitudinal trend of cardiac metrics for the patient”. However, prior art Heruth teaches the heart rate or cardiac signal as a parameter that is measured during patient sleep and the obtained parameters are compared to previous values for a period of time. These values can then be used to create a trend diagram to show the patient metrics over time(Detailed Description, paragraphs 23, 37-39)). Therefore the prior art discloses all claimed material and the previous rejections to the claims stand.
Conclusion
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).
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA CATHERINE ANTHONY whose telephone number is (703)756-4514. The examiner can normally be reached 7:30 am - 4:30 pm, EST, M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CARL LAYNO can be reached at (571) 272-4949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARIA CATHERINE ANTHONY/Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796