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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 18 is objected to because of the following informalities: in line 1, "at least one" should be --at least one of--. Appropriate correction is required.
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) 1-2, 7, 14-15, and 18 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Thakur (US 20180085584 A1).
Regarding claim 1, Thakur discloses a system for monitoring mobility of a patient treated with a neuromodulation therapy (Paragraph 0040-0041, 0049-0050-- The present system may be applied in any neurostimulation (neuromodulation) therapies, including but not limited to SCS, DBS, PNS, FES, and Vagus Nerve Stimulation (VNS) therapies… various examples of a pain management system, which may be an embodiment of the neuromodulation system 100; neuromodulation system 100), the system comprising:
a sensor circuit (sensor circuit 211) configured to sense a physiological or functional signal indicative of or correlated to patient mobility (Paragraph 0050-- The implantable neuromodulator 210A may include one or more of a sensor circuit 211… The sensor circuit 211 may be coupled to electrodes or various types of ambulatory sensors associated with the patient, and sense two or more signals from the patient. The signals may include physiological or functional signals);
an electrostimulator (electrostimulator 213) configured to generate and deliver the neuromodulation therapy to the patient (Paragraph 0050); and
a controller circuit (System 200A including controller 214, signal metrics generator 212, pain analyzer 231, and programmer circuit 235 as elements) configured to:
generate a mobility metric using the sensed physiological or functional signal, the mobility metric representing mobility times spent in respective different activity intensities associated with one or more types of activities during a specific time period (Paragraph 0052-- The sensor circuit 211 may additionally or alternatively sense functional signals including, but not limited to, patient posture, gait, balance, or physical activity signals, among others. In an example, the sensor circuit 211 may be coupled to a wearable or implantable accelerometer to detect an activity intensity or activity duration; paragraph 0054-- The signal metrics generator 212 may generate a plurality of signal metrics from the sensed physiological or functional signals. The signal metrics may include statistical parameters extracted from the sensed physiological signal, such as signal mean, median, or other central tendency measures or a histogram of the signal intensity, among others… In another example, physical activity metrics may include physical activity intensity, or a time duration when the activity intensity is within a specified range or above a specified threshold);
trend the mobility metric over time (Paragraph 0070-0073--The output unit may also display information including the physiological and functional signals, trends of the signal metric… The pain analyzer 233 may determine the signal metrics' sensitivity to pain by trending the signal metric over time, such as over approximately six months. The signal sensitivity to pain may be represented by a rate of change of the signal metrics over time during a pain episode. The signal sensitivity to pain may be evaluated under a controlled condition such as when the patient posture or activity is at a specified level or during specified time of the day… the pain analyzer 231 may trend the signal metric over time to compute an indication of abruptness of change of the signal metrics, such as a rate of change over a specified time period.) and determine a progress toward a mobility goal of the patient (Paragraph 0074-0078-- The pain analyzer 231 includes the pain score generator 233 that determine a pain score using weight factors stored in the memory 215 and the signal metrics from the signal metrics generator 212 which may also be included in the pain analyzer 231… in response to the multi-sensor indicated pain score exceeding a threshold which indicates elevated pain symptom, an alert may be generated and presented at the user interface 234… For example, if an inappropriate or generally undesired change in a signal metric (e.g., decrease in HRV) is associated with less pain and thus used to drive the closed-loop neurostimulation, a red-flag alert may be issued to warn the user that the signal metric (e.g., HRV) for use in programming the closed-loop pain therapy is outside of a generally recognized desirable range…NOTE: a mobility goal may thus be seen to be a pain score below a threshold with a signal metric in a desired range); and
generate a control signal to the electrostimulator to initiate or adjust the neuromodulation therapy in accordance with the trended mobility metric or the determined progress toward the mobility goal (Paragraph 0057-0058-- The controller 214, coupled to the electrostimulator 213, may control the generation and delivery of the neurostimulation energy. The controller 214 may control the generation of electrostimulation pulses according to specified stimulation parameters. The stimulation parameters may be provided by a system user. Alternatively, the stimulation parameters may be automatically determined based on the intensity, severity, duration, or pattern of pain, which may be subjectively described by the patient or automatically quantified based on the physiological or functional signals sensed by the sensor circuit 211; paragraph 0073-0078-- The pain therapy may be delivered, withheld, or otherwise modified in accordance with the pain type…The pain characterization and quantification may be provided to a system user such as the patient or a clinician, or to a process such as automatic generation of recommendations or an alert to the system user regarding pain medication (e.g., medication dosage and time for taking a dose), electrostimulation therapy, or other pain management regimes).
Regarding claim 2, Thakur teaches the system of claim 1. Thakur additionally discloses wherein the controller circuit is configured to: based on the trended mobility metric or the determined progress toward the mobility goal, generate a recommendation for future activities or a modification of the mobility goal; and present the recommendation and the trended mobility metric or the determined progress on a user interface (Paragraph 0073-0078-- The pain characterization and quantification may be provided to a system user such as the patient or a clinician, or to a process such as automatic generation of recommendations or an alert to the system user regarding pain medication (e.g., medication dosage and time for taking a dose), electrostimulation therapy, or other pain management regimes… In an example, in response to the multi-sensor indicated pain score exceeding a threshold which indicates elevated pain symptom, an alert may be generated and presented at the user interface 234, such as via a mobile App… In some examples, if the programmer circuit 235 produces neurostimulation parameter values for closed-loop pain therapy but the neurostimulation parameter is determined based on signal metrics that change in a undesirable or inappropriate direction, then a red-flag alert may be generated and presented to the system user such as via the user interface 234 to warn of such an effect).
Regarding claim 7, Thakur teaches the system of claim 1. Thakur additionally discloses wherein the controller circuit is configured to categorize the different activity intensities into a plurality of intensity bins (Paragraph 0052-0054, 0096--Examples of the signal metrics for assessing pain may include heart rate, heart rate variability, intensity of one or more heart sounds components such as S1, S2, S3 or S4 heart sounds or relative intensity such as a ratio between two heart sound components, a respiratory rate, a tidal volume, or a rapid-shallow breathing index (RSBI) computed as a ratio of a respiratory rate measurement to a tidal volume measurement, physical activity intensity, or a time duration when the activity intensity is within a specified range or above a specified threshold, or gait or locomotion pattern, etc.), and to generate the mobility metric including an entropy of the mobility times across the plurality of intensity bins (Paragraph 0073--In an example, the pain analyzer 231 may trend the signal metric over time to compute an indication of abruptness of change of the signal metrics, such as a rate of change over a specified time period. The pain episode may be characterized as chronic pain if the signal metric changes abruptly (e.g., the rate of change of the signal metric exceeding a threshold), or as acute pain if the signal metric changes gradually (e.g., the rate of change of the signal metric falling below a threshold).).
Regarding claim 14, Thakur discloses method of monitoring mobility of a patient treated with a neuromodulation therapy (Paragraph 0040-0041, 0049-0050-- The present system may be applied in any neurostimulation (neuromodulation) therapies, including but not limited to SCS, DBS, PNS, FES, and Vagus Nerve Stimulation (VNS) therapies… various examples of a pain management system, which may be an embodiment of the neuromodulation system 100; neuromodulation system 100), the method comprising:
Sensing, via a sensor circuit (sensor circuit 211), a physiological or functional signal indicative of or correlated to patient mobility (Paragraph 0050-- The implantable neuromodulator 210A may include one or more of a sensor circuit 211… The sensor circuit 211 may be coupled to electrodes or various types of ambulatory sensors associated with the patient, and sense two or more signals from the patient. The signals may include physiological or functional signals);
generating, via a controller circuit (System 200A including controller 214, signal metrics generator 212, pain analyzer 231, and programmer circuit 235 as elements), a mobility metric using the sensed physiological or functional signal, the mobility metric representing mobility times spent in respective different activity intensities associated with one or more types of activities during a specific time period (Paragraph 0052-- The sensor circuit 211 may additionally or alternatively sense functional signals including, but not limited to, patient posture, gait, balance, or physical activity signals, among others. In an example, the sensor circuit 211 may be coupled to a wearable or implantable accelerometer to detect an activity intensity or activity duration; paragraph 0054-- The signal metrics generator 212 may generate a plurality of signal metrics from the sensed physiological or functional signals. The signal metrics may include statistical parameters extracted from the sensed physiological signal, such as signal mean, median, or other central tendency measures or a histogram of the signal intensity, among others… In another example, physical activity metrics may include physical activity intensity, or a time duration when the activity intensity is within a specified range or above a specified threshold);
via the controller circuit, trending the mobility metric over time and determining a progress toward a mobility goal of the patient (Paragraph 0070-0073--The output unit may also display information including the physiological and functional signals, trends of the signal metric… The pain analyzer 233 may determine the signal metrics' sensitivity to pain by trending the signal metric over time, such as over approximately six months. The signal sensitivity to pain may be represented by a rate of change of the signal metrics over time during a pain episode. The signal sensitivity to pain may be evaluated under a controlled condition such as when the patient posture or activity is at a specified level or during specified time of the day… the pain analyzer 231 may trend the signal metric over time to compute an indication of abruptness of change of the signal metrics, such as a rate of change over a specified time period.) and determine a progress toward a mobility goal of the patient (Paragraph 0074-0078-- The pain analyzer 231 includes the pain score generator 233 that determine a pain score using weight factors stored in the memory 215 and the signal metrics from the signal metrics generator 212 which may also be included in the pain analyzer 231… in response to the multi-sensor indicated pain score exceeding a threshold which indicates elevated pain symptom, an alert may be generated and presented at the user interface 234… For example, if an inappropriate or generally undesired change in a signal metric (e.g., decrease in HRV) is associated with less pain and thus used to drive the closed-loop neurostimulation, a red-flag alert may be issued to warn the user that the signal metric (e.g., HRV) for use in programming the closed-loop pain therapy is outside of a generally recognized desirable range…NOTE: a mobility goal may thus be seen to be a pain score below a threshold with a signal metric in a desired range); and
initiating or adjusting the neuromodulation therapy via a neuromodulator (electrostimulator 213; paragraph 0050) in accordance with the trended mobility metric or the determined progress toward the mobility goal (Paragraph 0057-0058-- The controller 214, coupled to the electrostimulator 213, may control the generation and delivery of the neurostimulation energy. The controller 214 may control the generation of electrostimulation pulses according to specified stimulation parameters. The stimulation parameters may be provided by a system user. Alternatively, the stimulation parameters may be automatically determined based on the intensity, severity, duration, or pattern of pain, which may be subjectively described by the patient or automatically quantified based on the physiological or functional signals sensed by the sensor circuit 211; paragraph 0073-0078-- The pain therapy may be delivered, withheld, or otherwise modified in accordance with the pain type…The pain characterization and quantification may be provided to a system user such as the patient or a clinician, or to a process such as automatic generation of recommendations or an alert to the system user regarding pain medication (e.g., medication dosage and time for taking a dose), electrostimulation therapy, or other pain management regimes).
Regarding claim 15, Thakur teaches the method of claim 14. Thakur additionally discloses further comprising: based on the trended mobility metric or the determined progress toward the mobility goal, generating a recommendation for future activities or a modification of the mobility goal; and presenting the recommendation and the trended mobility metric or the determined progress on a user interface (Paragraph 0073-0078-- The pain characterization and quantification may be provided to a system user such as the patient or a clinician, or to a process such as automatic generation of recommendations or an alert to the system user regarding pain medication (e.g., medication dosage and time for taking a dose), electrostimulation therapy, or other pain management regimes… In an example, in response to the multi-sensor indicated pain score exceeding a threshold which indicates elevated pain symptom, an alert may be generated and presented at the user interface 234, such as via a mobile App… In some examples, if the programmer circuit 235 produces neurostimulation parameter values for closed-loop pain therapy but the neurostimulation parameter is determined based on signal metrics that change in a undesirable or inappropriate direction, then a red-flag alert may be generated and presented to the system user such as via the user interface 234 to warn of such an effect).
Regarding claim 18, Thakur teaches the method of claim 14. Thakur additionally discloses wherein the mobility metric includes at least one: a mobility score computed using a weighted combination of the mobility times spent in different activity intensities, the mobility times each scaled by respective weight factors proportional to the respective different activity intensities (Paragraph 0052-0054, 0096--Examples of the signal metrics for assessing pain may include heart rate, heart rate variability, intensity of one or more heart sounds components such as S1, S2, S3 or S4 heart sounds or relative intensity such as a ratio between two heart sound components, a respiratory rate, a tidal volume, or a rapid-shallow breathing index (RSBI) computed as a ratio of a respiratory rate measurement to a tidal volume measurement, physical activity intensity, or a time duration when the activity intensity is within a specified range or above a specified threshold, or gait or locomotion pattern, etc.; Paragraph 0074-0078-- The pain analyzer 231 includes the pain score generator 233 that determine a pain score using weight factors stored in the memory 215 and the signal metrics from the signal metrics generator 212 which may also be included in the pain analyzer 231…), or an entropy of the mobility times across a plurality of intensity bins representing categorized activity intensities (Paragraph 0073--In an example, the pain analyzer 231 may trend the signal metric over time to compute an indication of abruptness of change of the signal metrics, such as a rate of change over a specified time period. The pain episode may be characterized as chronic pain if the signal metric changes abruptly (e.g., the rate of change of the signal metric exceeding a threshold), or as acute pain if the signal metric changes gradually (e.g., the rate of change of the signal metric falling below a threshold).).
Claim(s) 1, 7-8, 11, 13-14, 18, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Qu (US 20250176858 A1 ).
Regarding claim 1, Qu teaches a system for monitoring mobility of a patient treated with a neuromodulation therapy (Paragraph 0028—a medical system), the system comprising:
a sensor circuit configured to sense a physiological or functional signal indicative of or correlated to patient mobility (Paragraph 0015-0018, 0105-0108—the sensor may be part of a mobile device carried by the patient…may be an accelerometer…);
an electrostimulator configured to generate and deliver the neuromodulation therapy to the patient (Paragraph 0028—the medical system may, for example, be a neurostimulator such as a spinal cord stimulator…); and
a controller circuit (Paragraph 0027-0028-- a data processing device comprising a processor configured for carrying out the method as described above and below) configured to: generate a mobility metric using the sensed physiological or functional signal, the mobility metric representing mobility times spent in respective different activity intensities associated with one or more types of activities during a specific time period (Paragraph 0029, 0033-0037, 0044-0049—the behavior assessment score may be determined from the sensor signal by the mobile device and/or by the server… the sensor signal may be further analyzed to detect, with respect to the determined physical activity classes, specific types of body movements which may indicate a change in the patient's quality of life… classifying the signal intensity values may comprise: comparing each signal intensity value to different value ranges, each value range corresponding to one of the physical activity classes; when a value range which includes the signal intensity value is found: assigning the physical activity class of the found value range to the signal intensity value (and, thus, to the respective measurement period of the signal intensity value); paragraph 0085-0096, 0108-0109-- the physical activity classes 13a, 13b, 13c, 13d may comprise at least a first physical activity class 13a corresponding to sedentary activities, a second physical activity class 13b corresponding to light activities, a third physical activity class 13c corresponding to moderate activities and a fourth physical activity class 13d corresponding to vigorous activities… the classification in step S03 may be done in such a way that the classification result 15 of each epoch 10 indicates a physical activity duration 19);
trend the mobility metric over time and determine a progress toward a mobility goal of the patient (Paragraph 0052-0055, 0096-0098-- determining a physical activity trend for each physical activity class from the physical activity durations of the physical activity class in at least two consecutive epochs; determining the behavior assessment score by comparing the physical activity trends. The physical activity trends may be compared with each other and/or with one or more baselines to determine the behavior assessment score and/or a behavior assessment score trend…a physical activity trend 21 for each physical activity class 13a, 13b, 13c, 13d is determined by the third module; paragraph 0106-0111, 0115-- Monitor the trend of the activity intensity distribution over multiple epochs 10, and interpret the change of the activity levels of interest as being indicative of the change of the patient's ability to move around, work or exercise, and provide information on the life quality improvement. For instance, increased “high-intensity” motion signals 8 may indicate that the patient 3 suffers less from the pain and is able to integrate more movement in their daily life); and
generate a control signal to the electrostimulator to initiate or adjust the neuromodulation therapy in accordance with the trended mobility metric or the determined progress toward the mobility goal (Paragraph 0028-- The medical system may be used not only to assess pain automatically or to complement conventional pain assessment methods (which are essentially based on the patient's subjective feedback), but also to automatically treat the patient in dependence of the (automatically obtained) pain assessment results in such a way that pain is alleviated accordingly; paragraph 0104-- The behavior assessment score 16 may be used by the mobile device 5 and/or the server 6 to control the implant 4, e.g., a pulse generator of the implant 4).
Regarding claim 7, Qu teaches the system of claim 1. Qu additionally teaches wherein the controller circuit is configured to categorize the different activity intensities into a plurality of intensity bins (Paragraph 0085-0086, 0092-0098-- the signal intensity values 12 of each epoch 10 are classified with different physical activity classes 13a, 13b, 13c, 13d by a second module 14… the physical activity classes 13a, 13b, 13c, 13d may comprise at least a first physical activity class 13a corresponding to sedentary activities, a second physical activity class 13b corresponding to light activities, a third physical activity class 13c corresponding to moderate activities and a fourth physical activity class 13d corresponding to vigorous activities; Fig. 3), and to generate the mobility metric including an entropy of the mobility times across the plurality of intensity bins (Paragraph 0096-0098-- a physical activity trend 21 for each physical activity class 13a, 13b, 13c, 13d is determined by the third module 17 from the physical activity durations 19 of the respective physical activity class in at least two consecutive epochs 10… the third module 17 may determine the behavior assessment score 16 by comparing, in step S08, the physical activity trends 21 of the different physical activity classes 13a, 13b, 13c, 13d with respect to the same sequence of epochs 10; paragraph 0106-0110-- Monitor the trend of the activity intensity distribution over multiple epochs 10, and interpret the change of the activity levels of interest as being indicative of the change of the patient's ability to move around, work or exercise, and provide information on the life quality improvement).
Regarding claim 8, Qu teaches the system of claim 1. Qu additionally teaches wherein: the sensor circuit includes a physiological sensor configured to sense a physiological signal correlated to patient mobility (Paragraph 0015-0016, 0064, 0077—the sensor signal may, for example, indicate at least one of an acceleration, angular rate, position, orientation, velocity, breathing rate, electrocardiogram, blood oxygen saturation, or blood glucose level…); and
the controller circuit is configured to apply the sensed physiological signal to a trained estimation model to generate the mobility metric (Paragraph 0044-0049-- This score can be estimated by classifying activity signals analyzed over time into levels of pain or a continuous scale of pain. This can be done by, for example, creating a machine learning model and training it on data containing both reported pain scores and activity signals, so that the model learns how different levels/scores of pain correlate with different activity signals, and can then predict levels of pain based on new activity signals… a behavior score is e.g. using machine learning methods similar to those described for the pain and disability scores, but with a behavior score as an outcome, which can correspond to different activity signal signatures over days/weeks/months… an exercise score is e.g. average time spent exercising each day/week/month, activity intensity during exercise periods, or activity intensity patterns during exercise periods, or overall activity intensity over days/weeks/months, or a combination of exercise frequency and intensity over days/weeks/months, or any combination of exercise-related activity signal features. This can be estimated using machine learning methods similar to those described for the pain and disability scores…).
Regarding claim 11, Qu teaches the system of claim 1. Qu additionally teaches wherein the controller circuit is configured to determine the mobility goal based on at least one of:
a baseline mobility metric of the patient during a baseline time period; or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient (Paragraph 0025-0026--The behavior assessment score may be determined from the classification results of at least two different (e.g., consecutive) epochs, i.e., a current epoch and at least one previous epoch. The classification results may relate to the same patient or different patients... the classification results of a first epoch in the sequence of epochs, possibly in conjunction with a corresponding behavior assessment score or corresponding behavior assessment score range, may be used as a baseline or reference. These results may be derived with respect to the same patient, a different patient or a group of patients; paragraph 0052-0055--The physical activity trends may be compared with each other and/or with one or more baselines to determine the behavior assessment score and/or a behavior assessment score trend…The baseline may have been determined with respect to the same patient, a different patient or a group of patients). Note that the mobility goal of Qu is explained as being a change relative to a baseline, such as a personal baseline or a baseline based on a different patient or group of patients, where the goal is improvement relative to that baseline (paragraph 0013, 0110, 0115-- For example, if patients stand up or walk more often during the day, or do more activities, this may indicate that they are experiencing an improvement in their quality of life. Thus, using the method as described above and below, changes in the patient's quality of life can be evaluated in a technically efficient and reliable manner.).
Regarding claim 13, Qu teaches the system of claim 1. Qu additionally teaches wherein the controller circuit is configured to: establish a correlation between the mobility metric and a patient-reported functional state of the patient (Paragraph 0044-0045--a pain score is e.g. similar to the Numeric Rate Scale (NRS), the Visual Analogue Scale (VAS), or any numeric or visual scale to rate pain. This score can be estimated by classifying activity signals analyzed over time into levels of pain or a continuous scale of pain. This can be done by, for example, creating a machine learning model and training it on data containing both reported pain scores and activity signals, so that the model learns how different levels/scores of pain correlate with different activity signals); and predict a future functional state of the patient using the established correlation (Paragraph 0044-0045--…can then predict levels of pain based on new activity signals).
Regarding claim 14, Qu teaches a method for monitoring mobility of a patient treated with a neuromodulation therapy (Paragraph 0009, 0013-0028—a computer-implemented method for assessing pain…), the system comprising:
Sensing, via a sensor circuit, a physiological or functional signal indicative of or correlated to patient mobility (Paragraph 0015-0018, 0105-0108—the sensor may be part of a mobile device carried by the patient…may be an accelerometer…);
generating, via a controller circuit (Paragraph 0027-0028-- a data processing device comprising a processor configured for carrying out the method as described above and below), a mobility metric using the sensed physiological or functional signal, the mobility metric representing mobility times spent in respective different activity intensities associated with one or more types of activities during a specific time period (Paragraph 0029, 0033-0037, 0044-0049—the behavior assessment score may be determined from the sensor signal by the mobile device and/or by the server… the sensor signal may be further analyzed to detect, with respect to the determined physical activity classes, specific types of body movements which may indicate a change in the patient's quality of life… classifying the signal intensity values may comprise: comparing each signal intensity value to different value ranges, each value range corresponding to one of the physical activity classes; when a value range which includes the signal intensity value is found: assigning the physical activity class of the found value range to the signal intensity value (and, thus, to the respective measurement period of the signal intensity value); paragraph 0085-0096, 0108-0109-- the physical activity classes 13a, 13b, 13c, 13d may comprise at least a first physical activity class 13a corresponding to sedentary activities, a second physical activity class 13b corresponding to light activities, a third physical activity class 13c corresponding to moderate activities and a fourth physical activity class 13d corresponding to vigorous activities… the classification in step S03 may be done in such a way that the classification result 15 of each epoch 10 indicates a physical activity duration 19);
via the controller circuit, trending the mobility metric over time and determining a progress toward a mobility goal of the patient (Paragraph 0052-0055, 0096-0098-- determining a physical activity trend for each physical activity class from the physical activity durations of the physical activity class in at least two consecutive epochs; determining the behavior assessment score by comparing the physical activity trends. The physical activity trends may be compared with each other and/or with one or more baselines to determine the behavior assessment score and/or a behavior assessment score trend…a physical activity trend 21 for each physical activity class 13a, 13b, 13c, 13d is determined by the third module; paragraph 0106-0111, 0115-- Monitor the trend of the activity intensity distribution over multiple epochs 10, and interpret the change of the activity levels of interest as being indicative of the change of the patient's ability to move around, work or exercise, and provide information on the life quality improvement. For instance, increased “high-intensity” motion signals 8 may indicate that the patient 3 suffers less from the pain and is able to integrate more movement in their daily life); and
initiating or adjusting the neuromodulation therapy via a neuromodulator (Paragraph 0028—the medical system may, for example, be a neurostimulator such as a spinal cord stimulator…) in accordance with the trended mobility metric or the determined progress toward the mobility goal (Paragraph 0028-- The medical system may be used not only to assess pain automatically or to complement conventional pain assessment methods (which are essentially based on the patient's subjective feedback), but also to automatically treat the patient in dependence of the (automatically obtained) pain assessment results in such a way that pain is alleviated accordingly; paragraph 0104-- The behavior assessment score 16 may be used by the mobile device 5 and/or the server 6 to control the implant 4, e.g., a pulse generator of the implant 4).
Regarding claim 18, Qu teaches the method of claim 14. Qu additionally teaches a mobility score computed using a weighted combination of the mobility times spent in different activity intensities, the mobility times each scaled by respective weight factors proportional to the respective different activity intensities; or an entropy of the mobility times across a plurality of intensity bins representing categorized activity intensities (Paragraph 0023--The physical activity classes may, for example, indicate different levels of physical activity, different types of physical activity or different types of body movements. Different physical activity classes may have different or identical weights; paragraph 0044-0049--the behavior assessment score can be a metric of a disability score, health score, behavior score, exercise score, or pain score…an exercise score is e.g. average time spent exercising each day/week/month, activity intensity during exercise periods, or activity intensity patterns during exercise periods, or overall activity intensity over days/weeks/months, or a combination of exercise frequency and intensity over days/weeks/months, or any combination of exercise-related activity signal features.).
Regarding claim 20, Qu teaches the method of claim 14. Qu additionally teaches comprising determining the mobility goal based on at least one of: a baseline mobility metric of the patient during a baseline time period; or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient (Paragraph 0025-0026--The behavior assessment score may be determined from the classification results of at least two different (e.g., consecutive) epochs, i.e., a current epoch and at least one previous epoch. The classification results may relate to the same patient or different patients... the classification results of a first epoch in the sequence of epochs, possibly in conjunction with a corresponding behavior assessment score or corresponding behavior assessment score range, may be used as a baseline or reference. These results may be derived with respect to the same patient, a different patient or a group of patients; paragraph 0052-0055--The physical activity trends may be compared with each other and/or with one or more baselines to determine the behavior assessment score and/or a behavior assessment score trend…The baseline may have been determined with respect to the same patient, a different patient or a group of patients). Note that the mobility goal of Qu is explained as being a change relative to a baseline, such as a personal baseline or a baseline based on a different patient or group of patients, where the goal is improvement relative to that baseline (paragraph 0013, 0110, 0115-- For example, if patients stand up or walk more often during the day, or do more activities, this may indicate that they are experiencing an improvement in their quality of life. Thus, using the method as described above and below, changes in the patient's quality of life can be evaluated in a technically efficient and reliable manner.).
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) 3-4, 11, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thakur in view of Goodall (US 10390755 B2).
Regarding claim 3, Thakur teaches the system of claim 1. As noted above in this action, Thakur discloses wherein the mobility metric includes a mobility metric representing mobility times spent in the respective different activity intensities, wherein the controller circuit is further configured to: trend the mobility metric over time and determine a progress toward a mobility goal of the patient; and generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy in accordance with the trended mobility metric or the determined progress toward the mobility goal.
However, Thakur does not explicitly disclose that the metric is an activity-specific mobility metric associated with a specific activity type or a specific activity context, or initiating or adjusting the neuromodulation therapy in accordance with the trended mobility metric or the determined progress toward the mobility goal when the patient engages in an activity of the specific activity type or under the specific activity context
Goodall, in the same field of endeavor of a system for providing and modifying neuromodulation (Col. 28, line 18-26-- the systems and methods described herein employ one or more effectors to affect a body portion responsive to processing of sense signals generated by the sensor assembly. The effectors include…nerve stimulators (e.g., a nerve stimulator configured to provide therapeutic stimulation or electrical blockage of nerve conduction), discloses wherein the mobility metric includes an activity- specific mobility metric representing mobility times spent in the respective different activity intensities associated with a specific activity type or a specific activity context (Col. 70, line 15-Col. 71, line 40; col. 77, line 38-Col. 78, line 44--The individual subject can be monitored for one or more of a movement, such as a movement of a particular body portion, or a physiological parameter, such as a muscle activity or one or more indicators of pain… methods described herein can include an effector to effect a predetermined motion (e.g., choreographed motion) of a body portion of the individual subject responsive to monitoring one or more of the motion of the body portion or a physiological parameter of the individual subject. For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen, whereby the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities... In an embodiment, the sensor assembly 1004 measures a number of repetitions of a movement of a body portion…Measuring the number of repetitions can include, but is not limited to, measuring that zero repetitions have occurred, measuring a finite number of repetitions, measuring the number of repetitions taken over a specified time period, and determining that the number of repetitions exceeds a threshold number (e.g., a threshold associated with the motion regimen)… In an embodiment, the sensor assembly 1004 measures a duration of a movement of a body portion. The duration can include one or more of a total duration of movement within a period of time (e.g., duration encompassing multiple repetitions of movement) and a total duration of movement for a single repetition of movement….Measurement by the sensor assembly 1004 of one or more of a repeated motion of a body portion, a number of repetitions of the movement of the body portion, a speed of the movement of the body portion, a duration of the movement of the body portion, a disposition of the body portion relative to a second body portion, and an angle of movement of the body portion provides information that can aid in the determination by the system 1000 of whether the subject is adhering to the motion regimen)
wherein the controller circuit is further configured to:
trend the activity-specific mobility metric over time and determine a progress toward an activity-specific mobility goal of the patient (Col. 70, line 15-Col. 71, line 40-- For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen… As another example, a pain state of the individual subject (e.g., determined via at least one of a movement of the body portion or at least one physiological parameter of the body portion, as described herein) can be compared against a target pain state associated with the motion regimen); and
generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy in accordance with the trended activity-specific mobility metric or the determined progress toward the activity-specific mobility goal when the patient engages in an activity of the specific activity type or under the specific activity context (Col. 70, line 15-Col. 71, line 40-- the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities to effect an increase or decrease in the muscle activity of the individual… the individual subject can be informed to alter their effort level to increase or decrease the pain state to within the target pain state, or the effector can induce movement to increase or decrease the pain state to fall within the target pain state).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Thakur to include the activity-specific mobility metric of Goodall in order to predictably improve the system by allowing a user or provider to monitor not only general mobility and activity of a user but also to monitor specific activity goals which may be important for activities of daily living, for monitoring therapy or recovery (e.g., monitoring performance of a particular exercise or regimen), or for monitoring athletic performance toward reaching a goal.
Regarding claim 4, the combination of Thakur and Goodall teaches the system of claim 3. Goodall additionally teaches wherein the controller circuit is further configured to: generate respective activity-specific mobility metrics for one or more activity types or activity contexts (Col. 70, line 15-Col. 71, line 40; col. 77, line 38-Col. 78, line 44--The individual subject can be monitored for one or more of a movement, such as a movement of a particular body portion, or a physiological parameter, such as a muscle activity or one or more indicators of pain…methods described herein can include an effector to effect a predetermined motion (e.g., choreographed motion) of a body portion of the individual subject responsive to monitoring one or more of the motion of the body portion or a physiological parameter of the individual subject. For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen, whereby the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities to effect an increase or decrease in the muscle activity of the individual)
present on a user interface an association between (i) the one or more activity types or activity contexts and (ii) the respective activity-specific mobility metrics (Col. 70, line 15-Col. 71, line 40; col. 72, line 33-41-- the systems, devices, and methods described herein employ a communicator configured to generate one or more communication signals responsive to instruction by the processor. The one or more communication signals from the communicator are associated with comparison between at least one of the pain state of the individual subject, the movement of the body portion, or the at least one physiological parameter to one or more threshold target values; Col. 68, line 27-30, Col. 90, line 4-Col. 91, line 27--the processor 1006 is operably coupled to the user interface 3600 and is configured to generate one or more communication signals for display by the user interface 3600…the system 1000 can send one or more communication signals to the external device 3406 or external object 3800 indicating that the individual subject is in compliance with the motion regimen (e.g., performing the choreographed motions within the motion thresholds or target values, performing the choreographed motions according to a predetermined schedule of performance dates or frequencies, etc.), whereby the external device 3406 or external object 3800 can provide at least one of haptic feedback (e.g., a vibration-based message), audio feedback (e.g., an audio message or signal), or visual feedback (e.g., a visually-displayed message, an image of a virtual reality or augmented reality display device, etc.) to the individual subject or other user of the system 1000 (e.g., a trainer, a coach, a healthcare professional, etc.)).
Regarding claim 11, Thakur teaches the system of claim 1. However, Thakur does not explicitly disclose wherein the controller circuit is configured to determine the mobility goal based on at least one of: a baseline mobility metric of the patient during a baseline time period; or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient.
Goodall, in the same field of endeavor of a system for providing and modifying neuromodulation (Col. 28, line 18-26-- the systems and methods described herein employ one or more effectors to affect a body portion responsive to processing of sense signals generated by the sensor assembly. The effectors include…nerve stimulators (e.g., a nerve stimulator configured to provide therapeutic stimulation or electrical blockage of nerve conduction), discloses wherein a controller circuit is configured to determine the mobility goal based on at least one of: a baseline mobility metric of the patient during a baseline time period (Col. 43, line 39-45--Assessment tools to evaluate a pain state, for example, can include subjective tools such as the Pain Quality Assessment Scale and the McGill Pain Questionnaire. In an embodiment, the subjective tools can provide the system 1000 with data associated with a baseline or comparative pain level, which in turn can serve as a threshold pain level or other comparative pain indicator; Col. 94, line 36-39-- For example, the one or more threshold target values can be associated with a prior execution of the motion regimen by the individual subject, such as to provide a baseline or comparative performance guideline.); or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient.
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Thakur with the baseline mobility metric determination of Goodall in order to predictably improve the ability of the device to be used to monitor improvements in performance or experience of the patient over time by comparing a current level of pain, mobility, or ability, to a previous baseline level which may enable further modifications of stimulation or goals as needed to improve the patient experience.
Regarding claim 16, Thakur teaches the method of claim 14. As noted above in this action, Thakur discloses wherein the mobility metric includes a mobility metric representing mobility times spent in the respective different activity intensities, trending the mobility metric over time and determining a progress toward a mobility goal of the patient; and initiating or adjusting the neuromodulation therapy in accordance with the trended mobility metric or the determined progress toward the mobility goal.
However, Thakur does not explicitly disclose that the metric is an activity-specific mobility metric associated with a specific activity type or a specific activity context, or initiating or adjusting the neuromodulation therapy in accordance with the trended mobility metric or the determined progress toward the mobility goal when the patient engages in an activity of the specific activity type or under the specific activity context
Goodall, in the same field of endeavor of a system for providing and modifying neuromodulation (Col. 28, line 18-26-- the systems and methods described herein employ one or more effectors to affect a body portion responsive to processing of sense signals generated by the sensor assembly. The effectors include…nerve stimulators (e.g., a nerve stimulator configured to provide therapeutic stimulation or electrical blockage of nerve conduction)).
Goodall additionally discloses wherein the mobility metric includes an activity- specific mobility metric representing mobility times spent in the respective different activity intensities associated with a specific activity type or a specific activity context (Col. 70, line 15-Col. 71, line 40; col. 77, line 38-Col. 78, line 44--The individual subject can be monitored for one or more of a movement, such as a movement of a particular body portion, or a physiological parameter, such as a muscle activity or one or more indicators of pain… methods described herein can include an effector to effect a predetermined motion (e.g., choreographed motion) of a body portion of the individual subject responsive to monitoring one or more of the motion of the body portion or a physiological parameter of the individual subject. For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen, whereby the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities... In an embodiment, the sensor assembly 1004 measures a number of repetitions of a movement of a body portion…Measuring the number of repetitions can include, but is not limited to, measuring that zero repetitions have occurred, measuring a finite number of repetitions, measuring the number of repetitions taken over a specified time period, and determining that the number of repetitions exceeds a threshold number (e.g., a threshold associated with the motion regimen)… In an embodiment, the sensor assembly 1004 measures a duration of a movement of a body portion. The duration can include one or more of a total duration of movement within a period of time (e.g., duration encompassing multiple repetitions of movement) and a total duration of movement for a single repetition of movement….Measurement by the sensor assembly 1004 of one or more of a repeated motion of a body portion, a number of repetitions of the movement of the body portion, a speed of the movement of the body portion, a duration of the movement of the body portion, a disposition of the body portion relative to a second body portion, and an angle of movement of the body portion provides information that can aid in the determination by the system 1000 of whether the subject is adhering to the motion regimen)
wherein the controller circuit is further configured to:
trend the activity-specific mobility metric over time and determine a progress toward an activity-specific mobility goal of the patient (Col. 70, line 15-Col. 71, line 40-- For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen… As another example, a pain state of the individual subject (e.g., determined via at least one of a movement of the body portion or at least one physiological parameter of the body portion, as described herein) can be compared against a target pain state associated with the motion regimen); and
generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy in accordance with the trended activity-specific mobility metric or the determined progress toward the activity-specific mobility goal when the patient engages in an activity of the specific activity type or under the specific activity context (Col. 70, line 15-Col. 71, line 40-- the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities to effect an increase or decrease in the muscle activity of the individual… the individual subject can be informed to alter their effort level to increase or decrease the pain state to within the target pain state, or the effector can induce movement to increase or decrease the pain state to fall within the target pain state).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Thakur to include the activity-specific mobility metric of Goodall in order to predictably improve the system by allowing a user or provider to monitor not only general mobility and activity of a user but also to monitor specific activity goals which may be important for activities of daily living, for monitoring therapy or recovery (e.g., monitoring performance of a particular exercise or regimen), or for monitoring athletic performance toward reaching a goal.
Regarding claim 20, Thakur teaches the method of claim 14. However, Thakur does not explicitly disclose determining the mobility goal based on at least one of a baseline mobility metric of the patient during a baseline time period, or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient.
Goodall, in the same field of endeavor of a system for providing and modifying neuromodulation (Col. 28, line 18-26-- the systems and methods described herein employ one or more effectors to affect a body portion responsive to processing of sense signals generated by the sensor assembly. The effectors include…nerve stimulators (e.g., a nerve stimulator configured to provide therapeutic stimulation or electrical blockage of nerve conduction), discloses determining the mobility goal based on at least one of: a baseline mobility metric of the patient during a baseline time period (Col. 43, line 39-45--Assessment tools to evaluate a pain state, for example, can include subjective tools such as the Pain Quality Assessment Scale and the McGill Pain Questionnaire. In an embodiment, the subjective tools can provide the system 1000 with data associated with a baseline or comparative pain level, which in turn can serve as a threshold pain level or other comparative pain indicator; Col. 94, line 36-39-- For example, the one or more threshold target values can be associated with a prior execution of the motion regimen by the individual subject, such as to provide a baseline or comparative performance guideline.); or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient.
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Thakur with the baseline mobility metric determination of Goodall in order to predictably improve the ability of the device to be used to monitor improvements in performance or experience of the patient over time by comparing a current level of pain, mobility, or ability, to a previous baseline level which may enable further modifications of stimulation or goals as needed to improve the patient experience.
Claim(s) 10-11, 13, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thakur in view of Qu.
Regarding claim 10, Thakur teaches the system of claim 1. Thakur additionally discloses a controller circuit is configured to present information to a user via a user interface (Paragraph 0073-0078). However, Thakur does not disclose generate a population-based mobility metric using physiological or functional signals sensed from a number of patients having similar medical conditions or similar demographics to the patient; and determine a relative position of the mobility metric of the patient with respect to the population-based mobility metric.
Qu, in the same field of endeavor of a system for monitoring mobility of a user and modifying neuromodulation of that user (Paragraph 0028, 0104), discloses wherein the controller circuit is configured to:
generate a population-based mobility metric using physiological or functional signals sensed from a number of patients having similar medical conditions or similar demographics to the patient; and
determine, and present on a user interface, a relative position of the mobility metric of the patient with respect to the population-based mobility metric (Paragraph 0025-0026--The behavior assessment score may be determined from the classification results of at least two different (e.g., consecutive) epochs, i.e., a current epoch and at least one previous epoch. The classification results may relate to the same patient or different patients... the classification results of a first epoch in the sequence of epochs, possibly in conjunction with a corresponding behavior assessment score or corresponding behavior assessment score range, may be used as a baseline or reference. These results may be derived with respect to the same patient, a different patient or a group of patients; paragraph 0052-0055--The physical activity trends may be compared with each other and/or with one or more baselines to determine the behavior assessment score and/or a behavior assessment score trend…The baseline may have been determined with respect to the same patient, a different patient or a group of patients).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Thakur with the population-based metric and comparison of Qu in order to predictably improve the system by allowing a user to be compared to a population-wide average rather than only monitoring a personal level of improvement relative to past performance which may be useful for obtaining a broader picture of user health and performance.
Regarding claim 11, Thakur teaches the system of claim 1. However, Thakur does not explicitly disclose wherein the controller circuit is configured to determine the mobility goal based on at least one of: a baseline mobility metric of the patient during a baseline time period; or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient.
Qu, in the same field of endeavor of a system for monitoring mobility of a user and modifying neuromodulation of that user (Paragraph 0028, 0104), discloses wherein the controller circuit is configured to determine the mobility goal based on at least one of:
a baseline mobility metric of the patient during a baseline time period; or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient (Paragraph 0025-0026--The behavior assessment score may be determined from the classification results of at least two different (e.g., consecutive) epochs, i.e., a current epoch and at least one previous epoch. The classification results may relate to the same patient or different patients... the classification results of a first epoch in the sequence of epochs, possibly in conjunction with a corresponding behavior assessment score or corresponding behavior assessment score range, may be used as a baseline or reference. These results may be derived with respect to the same patient, a different patient or a group of patients; paragraph 0052-0055--The physical activity trends may be compared with each other and/or with one or more baselines to determine the behavior assessment score and/or a behavior assessment score trend…The baseline may have been determined with respect to the same patient, a different patient or a group of patients). Note that the mobility goal of Qu is explained as being a change relative to a baseline, such as a personal baseline or a baseline based on a different patient or group of patients, where the goal is improvement relative to that baseline (paragraph 0013, 0110, 0115-- For example, if patients stand up or walk more often during the day, or do more activities, this may indicate that they are experiencing an improvement in their quality of life. Thus, using the method as described above and below, changes in the patient's quality of life can be evaluated in a technically efficient and reliable manner.).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Thakur with the baseline and population-based metric of Qu in order to predictably improve the system by allowing a user to be compared to a population-wide average rather as well as a personal level of improvement relative to past performance which may be useful for obtaining a complete picture of user health and performance.
Regarding claim 13, Thakur teaches the system of claim 1. Thakur does not explicitly disclose wherein the controller circuit is configured to: establish a correlation between the mobility metric and a patient-reported functional state of the patient; and predict a future functional state of the patient using the established correlation.
Qu, in the same field of endeavor of a system for monitoring mobility of a user and modifying neuromodulation of that user (Paragraph 0028, 0104), discloses wherein the controller circuit is configured to: establish a correlation between the mobility metric and a patient-reported functional state of the patient (Paragraph 0044-0045--a pain score is e.g. similar to the Numeric Rate Scale (NRS), the Visual Analogue Scale (VAS), or any numeric or visual scale to rate pain. This score can be estimated by classifying activity signals analyzed over time into levels of pain or a continuous scale of pain. This can be done by, for example, creating a machine learning model and training it on data containing both reported pain scores and activity signals, so that the model learns how different levels/scores of pain correlate with different activity signals); and predict a future functional state of the patient using the established correlation (Paragraph 0044-0045--…can then predict levels of pain based on new activity signals).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Thakur to include the correlation and prediction of Qu in order to predictably improve the system by allowing a system to modify neuromodulation according to both a mobility metric-based measure of pain as well as a patient-reported state of pain so that such a modification may be based on both the objective (e.g., mobility) and subjective (e.g., perceived pain) state of the user.
Regarding claim 19, Thakur teaches the method of claim 14. Thakur additionally discloses a controller circuit is configured to present information to a user via a user interface (Paragraph 0073-0078). However, Thakur does not disclose generating a population-based mobility metric using physiological or functional signals sensed from a number of patients having similar medical conditions or similar demographics to the patient, the population-based mobility metric representing respective population-based mobility times spent in different activity intensities; and determining a relative position of the mobility metric of the patient with respect to the population-based mobility metric.
Qu, in the same field of endeavor of a method for monitoring mobility of a user and modifying neuromodulation of that user (Paragraph 0028, 0104), discloses generating a population-based mobility metric using physiological or functional signals sensed from a number of patients having similar medical conditions or similar demographics to the patient, the population-based mobility metric representing respective population-based mobility times spent in different activity intensities and determining relative position of the mobility metric of the patient with respect to the population-based mobility metric (Paragraph 0025-0026--The behavior assessment score may be determined from the classification results of at least two different (e.g., consecutive) epochs, i.e., a current epoch and at least one previous epoch. The classification results may relate to the same patient or different patients... the classification results of a first epoch in the sequence of epochs, possibly in conjunction with a corresponding behavior assessment score or corresponding behavior assessment score range, may be used as a baseline or reference. These results may be derived with respect to the same patient, a different patient or a group of patients; paragraph 0052-0055--The physical activity trends may be compared with each other and/or with one or more baselines to determine the behavior assessment score and/or a behavior assessment score trend…The baseline may have been determined with respect to the same patient, a different patient or a group of patients).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Thakur with the population-based metric and comparison of Qu in order to predictably improve the method by allowing a user to be compared to a population-wide average rather than only monitoring a personal level of improvement relative to past performance which may be useful for obtaining a broader picture of user health and performance.
Regarding claim 20, Thakur teaches the method of claim 14. However, Thakur fails to explicitly disclose determining the mobility goal based on at least one of a baseline mobility metric of the patient during a baseline time period, or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient.
Qu, in the same field of endeavor of a method for monitoring mobility of a user and modifying neuromodulation of that user (Paragraph 0028, 0104), discloses determining the mobility goal based on at least one of: a baseline mobility metric of the patient during a baseline time period; or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient (Paragraph 0025-0026--The behavior assessment score may be determined from the classification results of at least two different (e.g., consecutive) epochs, i.e., a current epoch and at least one previous epoch. The classification results may relate to the same patient or different patients... the classification results of a first epoch in the sequence of epochs, possibly in conjunction with a corresponding behavior assessment score or corresponding behavior assessment score range, may be used as a baseline or reference. These results may be derived with respect to the same patient, a different patient or a group of patients; paragraph 0052-0055--The physical activity trends may be compared with each other and/or with one or more baselines to determine the behavior assessment score and/or a behavior assessment score trend…The baseline may have been determined with respect to the same patient, a different patient or a group of patients). Note that the mobility goal of Qu is explained as being a change relative to a baseline, such as a personal baseline or a baseline based on a different patient or group of patients, where the goal is improvement relative to that baseline (paragraph 0013, 0110, 0115-- For example, if patients stand up or walk more often during the day, or do more activities, this may indicate that they are experiencing an improvement in their quality of life. Thus, using the method as described above and below, changes in the patient's quality of life can be evaluated in a technically efficient and reliable manner.).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Thakur with the baseline and population-based metric of Qu in order to predictably improve the method by allowing a user to be compared to a population-wide average rather as well as a personal level of improvement relative to past performance which may be useful for obtaining a complete picture of user health and performance.
Claim(s) 2-4, 10, 12, and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qu in view of Goodall.
Regarding claim 2, Qu teaches the system of claim 1. Qu additionally teaches the system includes a user mobile device having a display or other interface (Paragraph 0015, 0027-0028—may be part of a mobile device carried by the patient, e.g., a mobile medical device, smartphone, smartwatch, wearable (such as a fitness or sleep tracker), tablet, or laptop…; paragraph 0078—the medical system 1 may further comprise a mobile device 5). However, Qu does not explicitly disclose presenting on a user interface.
However, Qu does not explicitly disclose wherein the controller circuit is configured to: based on the trended mobility metric or the determined progress toward the mobility goal, generate a recommendation for future activities or a modification of the mobility goal; and present the recommendation, and the trended mobility metric or the determined progress, on a user interface.
Goodall, in the same field of endeavor of a system for providing and modifying neuromodulation (Col. 28, line 18-26-- the systems and methods described herein employ one or more effectors to affect a body portion responsive to processing of sense signals generated by the sensor assembly. The effectors include…nerve stimulators (e.g., a nerve stimulator configured to provide therapeutic stimulation or electrical blockage of nerve conduction)).
Goodall discloses based on the trended mobility metric or the determined progress toward the mobility goal (Col. 70, lines 17-21; col. 77, line 38-Col. 78, line 44--The individual subject can be monitored for one or more of a movement, such as a movement of a particular body portion, or a physiological parameter, such as a muscle activity or one or more indicators of pain), generate a recommendation for future activities or a modification of the mobility goal; and
present the recommendation, and the trended mobility metric or the determined progress, on a user interface (Col. 70, line 21-33 --Such monitoring can provide a basis for effecting a predetermined motion of a motion regimen, providing instructions to the individual subject or external device regarding compliance or non-compliance with the motion regimen (e.g., with target thresholds thereof), providing recommendations to the individual subject or external device regarding the motion regimen or the individual's performance thereof (e.g., the individual subject is exceeding a recommended threshold of muscle activity and should reduce effort of the motions, the individual subject has incorrectly performed one or more motions of the motion regimen and should review a particular section of the motion regimen, etc.), or the like.; Col. 71, lines 18-27--As another example, a pain state of the individual subject (e.g., determined via at least one of a movement of the body portion or at least one physiological parameter of the body portion, as described herein) can be compared against a target pain state associated with the motion regimen, whereby the individual subject can be informed to alter their effort level to increase or decrease the pain state to within the target pain state, or the effector can induce movement to increase or decrease the pain state to fall within the target pain state.; Col. 95, lines 15-45--The processor 1006 then provides control signals to the communicator 4000 to generate one or more communication signals to direct the at least one of the pain state of the individual subject, the movement of the body portion, or the at least one physiological parameter between the maximum activity level and the minimum activity level, according to one or more of displaying information about the activity level, transmitting information about the activity level, or controlling activation of an effector (e.g., effector 1008). For example, the communicator 4000 can include circuitry configured to generate the one or more communication signals to provide control instructions to one or more components of the system 1000 or external device (e.g., external device 3406, external object 3800, etc.), to provide information (e.g., via reporting) to one or more components of the system 1000 or external device (e.g., external device 3406, external object 3800, etc.), to provide other functionalities, or combinations thereof. For example, referring to the embodiments shown in FIGS. 41A-41C, the communicator 4000 is configured to direct the one or more communication signals to one or more of a display device (e.g., display 1502), shown in FIG. 41A, an external device (e.g., external device 3406, external object 3800, etc.), shown in FIG. 41B, or an effector (e.g., effector 1008), shown in FIG. 41C, to manage a pain state of the individual subject, such as to bring the activity level of the individual subject during execution of the motion regimen to between the maximum activity level and the minimum activity level.).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Qu to include a recommendation as disclosed by Goodall in order to predictably improve the system by empowering a user to take actions to affect the user’s progress rather than only modifying the neuromodulation.
Regarding claim 3, Qu teaches the system of claim 1. As noted above in this action, Qu discloses wherein the mobility metric includes a mobility metric representing mobility times spent in the respective different activity intensities, wherein the controller circuit is further configured to: trend the mobility metric over time and determine a progress toward a mobility goal of the patient; and generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy in accordance with the trended mobility metric or the determined progress toward the mobility goal.
However, Qu does not explicitly disclose wherein the mobility metric includes an activity- specific mobility metric representing mobility times spent in the respective different activity intensities associated with a specific activity type or a specific activity context, wherein the controller circuit is further configured to: trend the activity-specific mobility metric over time and determine a progress toward an activity-specific mobility goal of the patient; and generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy in accordance with the trended activity-specific mobility metric or the determined progress toward the activity-specific mobility goal when the patient engages in an activity of the specific activity type or under the specific activity context.
Goodall, in the same field of endeavor of a system for providing and modifying neuromodulation (Col. 28, line 18-26-- the systems and methods described herein employ one or more effectors to affect a body portion responsive to processing of sense signals generated by the sensor assembly. The effectors include…nerve stimulators (e.g., a nerve stimulator configured to provide therapeutic stimulation or electrical blockage of nerve conduction), discloses wherein the mobility metric includes an activity- specific mobility metric representing mobility times spent in the respective different activity intensities associated with a specific activity type or a specific activity context (Col. 70, line 15-Col. 71, line 40; col. 77, line 38-Col. 78, line 44--The individual subject can be monitored for one or more of a movement, such as a movement of a particular body portion, or a physiological parameter, such as a muscle activity or one or more indicators of pain… methods described herein can include an effector to effect a predetermined motion (e.g., choreographed motion) of a body portion of the individual subject responsive to monitoring one or more of the motion of the body portion or a physiological parameter of the individual subject. For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen, whereby the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities... In an embodiment, the sensor assembly 1004 measures a number of repetitions of a movement of a body portion…Measuring the number of repetitions can include, but is not limited to, measuring that zero repetitions have occurred, measuring a finite number of repetitions, measuring the number of repetitions taken over a specified time period, and determining that the number of repetitions exceeds a threshold number (e.g., a threshold associated with the motion regimen)… In an embodiment, the sensor assembly 1004 measures a duration of a movement of a body portion. The duration can include one or more of a total duration of movement within a period of time (e.g., duration encompassing multiple repetitions of movement) and a total duration of movement for a single repetition of movement….Measurement by the sensor assembly 1004 of one or more of a repeated motion of a body portion, a number of repetitions of the movement of the body portion, a speed of the movement of the body portion, a duration of the movement of the body portion, a disposition of the body portion relative to a second body portion, and an angle of movement of the body portion provides information that can aid in the determination by the system 1000 of whether the subject is adhering to the motion regimen)
wherein the controller circuit is further configured to:
trend the activity-specific mobility metric over time and determine a progress toward an activity-specific mobility goal of the patient (Col. 70, line 15-Col. 71, line 40-- For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen… As another example, a pain state of the individual subject (e.g., determined via at least one of a movement of the body portion or at least one physiological parameter of the body portion, as described herein) can be compared against a target pain state associated with the motion regimen); and
generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy in accordance with the trended activity-specific mobility metric or the determined progress toward the activity-specific mobility goal when the patient engages in an activity of the specific activity type or under the specific activity context (Col. 70, line 15-Col. 71, line 40-- the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities to effect an increase or decrease in the muscle activity of the individual… the individual subject can be informed to alter their effort level to increase or decrease the pain state to within the target pain state, or the effector can induce movement to increase or decrease the pain state to fall within the target pain state).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Qu to include the activity-specific mobility metric of Goodall in order to predictably improve the system by allowing a user or provider to monitor not only general mobility and activity of a user but also to monitor specific activity goals which may be important for activities of daily living, for monitoring therapy or recovery (e.g., monitoring performance of a particular exercise or regimen), or for monitoring athletic performance toward reaching a goal.
Regarding claim 4, the combination of Qu and Goodall teaches the system of claim 3. However, Qu does not explicitly disclose wherein the controller circuit is further configured to: generate respective activity-specific mobility metrics for one or more activity types or activity contexts; and present on a user interface an association between (i) the one or more activity types or activity contexts and (ii) the respective activity-specific mobility metrics.
Goodall additionally discloses the controller circuit is further configured to: generate respective activity-specific mobility metrics for one or more activity types or activity contexts (Col. 70, line 15-Col. 71, line 40; col. 77, line 38-Col. 78, line 44--The individual subject can be monitored for one or more of a movement, such as a movement of a particular body portion, or a physiological parameter, such as a muscle activity or one or more indicators of pain…methods described herein can include an effector to effect a predetermined motion (e.g., choreographed motion) of a body portion of the individual subject responsive to monitoring one or more of the motion of the body portion or a physiological parameter of the individual subject. For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen, whereby the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities to effect an increase or decrease in the muscle activity of the individual)
present on a user interface an association between (i) the one or more activity types or activity contexts and (ii) the respective activity-specific mobility metrics (Col. 70, line 15-Col. 71, line 40; col. 72, line 33-41-- the systems, devices, and methods described herein employ a communicator configured to generate one or more communication signals responsive to instruction by the processor. The one or more communication signals from the communicator are associated with comparison between at least one of the pain state of the individual subject, the movement of the body portion, or the at least one physiological parameter to one or more threshold target values; Col. 68, line 27-30, Col. 90, line 4-Col. 91, line 27--the processor 1006 is operably coupled to the user interface 3600 and is configured to generate one or more communication signals for display by the user interface 3600…the system 1000 can send one or more communication signals to the external device 3406 or external object 3800 indicating that the individual subject is in compliance with the motion regimen (e.g., performing the choreographed motions within the motion thresholds or target values, performing the choreographed motions according to a predetermined schedule of performance dates or frequencies, etc.), whereby the external device 3406 or external object 3800 can provide at least one of haptic feedback (e.g., a vibration-based message), audio feedback (e.g., an audio message or signal), or visual feedback (e.g., a visually-displayed message, an image of a virtual reality or augmented reality display device, etc.) to the individual subject or other user of the system 1000 (e.g., a trainer, a coach, a healthcare professional, etc.)).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Qu to include the activity-specific mobility metric of Goodall in order to predictably improve the system by allowing a user or provider to monitor not only general mobility and activity of a user but also to monitor specific activity goals which may be important for activities of daily living, for monitoring therapy or recovery (e.g., monitoring performance of a particular exercise or regimen), or for monitoring athletic performance toward reaching a goal.
Regarding claim 10, Qu teaches the system of claim 1. Qu additionally discloses wherein the controller circuit is configured to:
generate a population-based mobility metric using physiological or functional signals sensed from a number of patients having similar medical conditions or similar demographics to the patient; and
determine a relative position of the mobility metric of the patient with respect to the population-based mobility metric (Paragraph 0025-0026--The behavior assessment score may be determined from the classification results of at least two different (e.g., consecutive) epochs, i.e., a current epoch and at least one previous epoch. The classification results may relate to the same patient or different patients... the classification results of a first epoch in the sequence of epochs, possibly in conjunction with a corresponding behavior assessment score or corresponding behavior assessment score range, may be used as a baseline or reference. These results may be derived with respect to the same patient, a different patient or a group of patients; paragraph 0052-0055--The physical activity trends may be compared with each other and/or with one or more baselines to determine the behavior assessment score and/or a behavior assessment score trend…The baseline may have been determined with respect to the same patient, a different patient or a group of patients).
Qu additionally teaches the system includes a user mobile device having a display or other interface (Paragraph 0015, 0027-0028—may be part of a mobile device carried by the patient, e.g., a mobile medical device, smartphone, smartwatch, wearable (such as a fitness or sleep tracker), tablet, or laptop…; paragraph 0078—the medical system 1 may further comprise a mobile device 5). However, Qu does not explicitly disclose presenting on a user interface.
Goodall discloses presenting recommendations and metrics on a user interface (Col. 70, line 15-Col. 71, line 40; col. 72, line 33-41-- the systems, devices, and methods described herein employ a communicator configured to generate one or more communication signals responsive to instruction by the processor. The one or more communication signals from the communicator are associated with comparison between at least one of the pain state of the individual subject, the movement of the body portion, or the at least one physiological parameter to one or more threshold target values; Col. 68, line 27-30, Col. 90, line 4-Col. 91, line 27--the processor 1006 is operably coupled to the user interface 3600 and is configured to generate one or more communication signals for display by the user interface 3600…the system 1000 can send one or more communication signals to the external device 3406 or external object 3800 indicating that the individual subject is in compliance with the motion regimen (e.g., performing the choreographed motions within the motion thresholds or target values, performing the choreographed motions according to a predetermined schedule of performance dates or frequencies, etc.), whereby the external device 3406 or external object 3800 can provide at least one of haptic feedback (e.g., a vibration-based message), audio feedback (e.g., an audio message or signal), or visual feedback (e.g., a visually-displayed message, an image of a virtual reality or augmented reality display device, etc.) to the individual subject or other user of the system 1000 (e.g., a trainer, a coach, a healthcare professional, etc.)).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Qu to include presenting on an interface as described by Goodall in order to predictably improve the user-friendliness of the system by allowing the user to observe changes in the monitored metrics and/or recommendations.
Regarding claim 12, Qu teaches the system of claim 1. Qu additionally discloses wherein the controller circuit is configured to: categorize the different activity intensities into a plurality of intensity bins; and compare (i) a first distribution of mobility times across the plurality of intensity bins during a first time period and (ii) a second distribution of mobility times across the plurality of intensity bins during a second time period prior to the first time period (Fig. 3—distributions of mobility times across a plurality of intensity bins during a first and second time period or epoch; paragraph 0095-0098).
Qu additionally teaches the system includes a user mobile device having a display or other interface (Paragraph 0015, 0027-0028—may be part of a mobile device carried by the patient, e.g., a mobile medical device, smartphone, smartwatch, wearable (such as a fitness or sleep tracker), tablet, or laptop…; paragraph 0078—the medical system 1 may further comprise a mobile device 5). However, Qu does not explicitly disclose presenting on a user interface.
However, Qu does not explicitly disclose present, on a user interface: a graphical comparison between (i) the mobility metric represented by a distribution of the mobility times across the plurality of intensity bins and (ii) the mobility goal represented by a target distribution of target mobility times across the plurality of intensity bins; or a graphical comparison between (i) a first distribution of mobility times across the plurality of intensity bins during a first time period and (ii) a second distribution of mobility times across the plurality of intensity bins during a second time period prior to the first time period.
Goodall discloses presenting graphical representations on a user interface (Col. 70, line 15-Col. 71, line 40; col. 72, line 33-41-- the systems, devices, and methods described herein employ a communicator configured to generate one or more communication signals responsive to instruction by the processor. The one or more communication signals from the communicator are associated with comparison between at least one of the pain state of the individual subject, the movement of the body portion, or the at least one physiological parameter to one or more threshold target values; Col. 68, line 27-30, Col. 90, line 4-Col. 91, line 27--the processor 1006 is operably coupled to the user interface 3600 and is configured to generate one or more communication signals for display by the user interface 3600…the system 1000 can send one or more communication signals to the external device 3406 or external object 3800 indicating that the individual subject is in compliance with the motion regimen (e.g., performing the choreographed motions within the motion thresholds or target values, performing the choreographed motions according to a predetermined schedule of performance dates or frequencies, etc.), whereby the external device 3406 or external object 3800 can provide at least one of haptic feedback (e.g., a vibration-based message), audio feedback (e.g., an audio message or signal), or visual feedback (e.g., a visually-displayed message, an image of a virtual reality or augmented reality display device, etc.) to the individual subject or other user of the system 1000 (e.g., a trainer, a coach, a healthcare professional, etc.)).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Qu to include presenting on an interface as described by Goodall in order to predictably improve the user-friendliness of the system by allowing the user to observe changes in the monitored metrics and/or recommendations.
Regarding claim 15, Qu teaches the method of claim 14. Qu additionally teaches the system includes a user mobile device having a display or other interface (Paragraph 0015, 0027-0028—may be part of a mobile device carried by the patient, e.g., a mobile medical device, smartphone, smartwatch, wearable (such as a fitness or sleep tracker), tablet, or laptop…; paragraph 0078—the medical system 1 may further comprise a mobile device 5). However, Qu does not explicitly disclose presenting on a user interface.
However, Qu does not explicitly disclose based on the trended mobility metric or the determined progress toward the mobility goal, generating a recommendation for future activities or a modification of the mobility goal; and presenting the recommendation, and the trended mobility metric or the determined progress, on a user interface.
Goodall, in the same field of endeavor of a system for providing and modifying neuromodulation (Col. 28, line 18-26-- the systems and methods described herein employ one or more effectors to affect a body portion responsive to processing of sense signals generated by the sensor assembly. The effectors include…nerve stimulators (e.g., a nerve stimulator configured to provide therapeutic stimulation or electrical blockage of nerve conduction)).
Goodall discloses based on the trended mobility metric or the determined progress toward the mobility goal (Col. 70, lines 17-21; col. 77, line 38-Col. 78, line 44--The individual subject can be monitored for one or more of a movement, such as a movement of a particular body portion, or a physiological parameter, such as a muscle activity or one or more indicators of pain), generating a recommendation for future activities or a modification of the mobility goal; and
presenting the recommendation, and the trended mobility metric or the determined progress, on a user interface (Col. 70, line 21-33 --Such monitoring can provide a basis for effecting a predetermined motion of a motion regimen, providing instructions to the individual subject or external device regarding compliance or non-compliance with the motion regimen (e.g., with target thresholds thereof), providing recommendations to the individual subject or external device regarding the motion regimen or the individual's performance thereof (e.g., the individual subject is exceeding a recommended threshold of muscle activity and should reduce effort of the motions, the individual subject has incorrectly performed one or more motions of the motion regimen and should review a particular section of the motion regimen, etc.), or the like.; Col. 71, lines 18-27--As another example, a pain state of the individual subject (e.g., determined via at least one of a movement of the body portion or at least one physiological parameter of the body portion, as described herein) can be compared against a target pain state associated with the motion regimen, whereby the individual subject can be informed to alter their effort level to increase or decrease the pain state to within the target pain state, or the effector can induce movement to increase or decrease the pain state to fall within the target pain state.; Col. 95, lines 15-45--The processor 1006 then provides control signals to the communicator 4000 to generate one or more communication signals to direct the at least one of the pain state of the individual subject, the movement of the body portion, or the at least one physiological parameter between the maximum activity level and the minimum activity level, according to one or more of displaying information about the activity level, transmitting information about the activity level, or controlling activation of an effector (e.g., effector 1008). For example, the communicator 4000 can include circuitry configured to generate the one or more communication signals to provide control instructions to one or more components of the system 1000 or external device (e.g., external device 3406, external object 3800, etc.), to provide information (e.g., via reporting) to one or more components of the system 1000 or external device (e.g., external device 3406, external object 3800, etc.), to provide other functionalities, or combinations thereof. For example, referring to the embodiments shown in FIGS. 41A-41C, the communicator 4000 is configured to direct the one or more communication signals to one or more of a display device (e.g., display 1502), shown in FIG. 41A, an external device (e.g., external device 3406, external object 3800, etc.), shown in FIG. 41B, or an effector (e.g., effector 1008), shown in FIG. 41C, to manage a pain state of the individual subject, such as to bring the activity level of the individual subject during execution of the motion regimen to between the maximum activity level and the minimum activity level.).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Qu to include a recommendation as disclosed by Goodall in order to predictably improve the system by empowering a user to take actions to affect the user’s progress rather than only modifying the neuromodulation.
Regarding claim 16, Qu teaches the method of claim 14. As noted above in this action, Qu discloses wherein the mobility metric includes a mobility metric representing mobility times spent in the respective different activity intensities, trending the mobility metric over time and determining a progress toward a mobility goal of the patient; and initiating or adjusting the neuromodulation therapy in accordance with the trended mobility metric or the determined progress toward the mobility goal.
However, Qu does not explicitly disclose wherein the mobility metric includes an activity- specific mobility metric representing mobility times spent in the respective different activity intensities associated with a specific activity type or a specific activity context, trending the activity-specific mobility metric over time and determining a progress toward an activity-specific mobility goal of the patient; and initiating or adjusting the neuromodulation therapy in accordance with the trended activity-specific mobility metric or the determined progress toward the activity-specific mobility goal when the patient engages in an activity of the specific activity type or under the specific activity context.
Goodall, in the same field of endeavor of a system for providing and modifying neuromodulation (Col. 28, line 18-26-- the systems and methods described herein employ one or more effectors to affect a body portion responsive to processing of sense signals generated by the sensor assembly. The effectors include…nerve stimulators (e.g., a nerve stimulator configured to provide therapeutic stimulation or electrical blockage of nerve conduction), discloses wherein the mobility metric includes an activity- specific mobility metric representing mobility times spent in the respective different activity intensities associated with a specific activity type or a specific activity context (Col. 70, line 15-Col. 71, line 40; col. 77, line 38-Col. 78, line 44--The individual subject can be monitored for one or more of a movement, such as a movement of a particular body portion, or a physiological parameter, such as a muscle activity or one or more indicators of pain… methods described herein can include an effector to effect a predetermined motion (e.g., choreographed motion) of a body portion of the individual subject responsive to monitoring one or more of the motion of the body portion or a physiological parameter of the individual subject. For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen, whereby the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities... In an embodiment, the sensor assembly 1004 measures a number of repetitions of a movement of a body portion…Measuring the number of repetitions can include, but is not limited to, measuring that zero repetitions have occurred, measuring a finite number of repetitions, measuring the number of repetitions taken over a specified time period, and determining that the number of repetitions exceeds a threshold number (e.g., a threshold associated with the motion regimen)… In an embodiment, the sensor assembly 1004 measures a duration of a movement of a body portion. The duration can include one or more of a total duration of movement within a period of time (e.g., duration encompassing multiple repetitions of movement) and a total duration of movement for a single repetition of movement….Measurement by the sensor assembly 1004 of one or more of a repeated motion of a body portion, a number of repetitions of the movement of the body portion, a speed of the movement of the body portion, a duration of the movement of the body portion, a disposition of the body portion relative to a second body portion, and an angle of movement of the body portion provides information that can aid in the determination by the system 1000 of whether the subject is adhering to the motion regimen)
trending the activity-specific mobility metric over time and determining a progress toward an activity-specific mobility goal of the patient (Col. 70, line 15-Col. 71, line 40-- For example, a muscle activity of the individual can be compared against a target activity associated with the motion regimen… As another example, a pain state of the individual subject (e.g., determined via at least one of a movement of the body portion or at least one physiological parameter of the body portion, as described herein) can be compared against a target pain state associated with the motion regimen); and
initiating or adjusting the neuromodulation therapy in accordance with the trended activity-specific mobility metric or the determined progress toward the activity-specific mobility goal when the patient engages in an activity of the specific activity type or under the specific activity context (Col. 70, line 15-Col. 71, line 40-- the effector can induce movement in the body portion to cause a subsequent motion to bring muscle activity within the target activity. For instance, when engaged in a strengthening workout as the motion regimen, one or more muscle activities of the individual's legs can be monitored and compared against target activities to effect an increase or decrease in the muscle activity of the individual… the individual subject can be informed to alter their effort level to increase or decrease the pain state to within the target pain state, or the effector can induce movement to increase or decrease the pain state to fall within the target pain state).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Qu to include the activity-specific mobility metric of Goodall in order to predictably improve the system by allowing a user or provider to monitor not only general mobility and activity of a user but also to monitor specific activity goals which may be important for activities of daily living, for monitoring therapy or recovery (e.g., monitoring performance of a particular exercise or regimen), or for monitoring athletic performance toward reaching a goal.
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thakur in view of Bink (US 20220266028 A1).
Regarding claim 5, Thakur teaches the system of claim 1. Thakur additionally discloses generating a plurality of mobility metrics in response to neurostimulation (See Fig. 6, steps 631 and 633—induce pain episodes according to a pain assessment protocol and generate a plurality of signal metrics) and discloses generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy using one of a first or a second stimulation program selected based on a comparison between the respective progresses toward the mobility goal (see Fig. 7, steps 751, 75, 753—deliver first or second electrostimulation depending on if an indicated pain score exceeds a threshold).
However, Thakur does not disclose generate (i) a first mobility metric from a first physiological or functional signal sensed in response to neurostimulation according to a first stimulation program, and (ii) a second mobility metric from a second physiological or functional signal sensed in response to neurostimulation according to a second stimulation program different than the first stimulation program; trend respectively the first mobility metric and the second mobility metric over time, and determine respective progresses toward the mobility goal of the patient; and generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy using one of the first or the second stimulation program selected based on a comparison of the trended first mobility metric to the trended second mobility metric, or a comparison between the respective progresses toward the mobility goal.
Bink, in the same field of endeavor of monitoring a user and modifying neurostimulation (Paragraph 0002-0006), teaches a controller circuit (external programmer 150) configured to:
generate (i) a first mobility metric from a first physiological or functional signal sensed in response to neurostimulation according to a first stimulation program, and (ii) a second mobility metric from a second physiological or functional signal sensed in response to neurostimulation according to a second stimulation program different than the first stimulation program (Paragraph 0077-0087-- IMD 200A may monitor a biomarker (410), e.g., during or after delivering of the electric stimulation in accordance with the second therapy program. In some examples, the biomarker may be one or more second biomarkers that are different from the one or more first biomarkers, e.g., one or more first biomarkers monitored during or after delivery of electric stimulation according to the first therapy program at (404). In some examples, the second biomarker(s) may be the same as the first biomarkers(s)…);
trend respectively the first mobility metric and the second mobility metric over time, and determine respective progresses toward the mobility goal of the patient (Paragraph 0078-0087—the biomarker…the second biomarker may be a patient posture and/or patient behavior data such as patient position, patient movement, patient movement history over a predetermined amount of time, a history of patent-selected stimulation parameters over a predetermined amount of time, and the like); and
generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy using one of the first or the second stimulation program selected based on a comparison of the trended first mobility metric to the trended second mobility metric, or a comparison between the respective progresses toward the mobility goal (Paragraph 0046-0048, 0077-0082-- system 100 and/or IMD 110 and/or external programmer 150 may be configured to control the delivery and/or parameters of electric stimulation based on one or more biomarkers… IMD 110 and/or external programmer 150 may be configured to toggle back and forth between therapy programs. For example, IMD 110 and/or external programmer 150 may be configured to determine which of the first or second therapy programs to deliver based on one or more biomarkers and switch the delivery of electric stimulation between the first and second programs accordingly).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Thakur, which discloses detecting multiple metrics and adjusting neuromodulation between different programs based on a comparison, in order to predictably improve the ability of the device to modify a neuromodulation signal not only in response to an immediate metric determination but also to a trended metric so that stimulation may be more appropriate for the particular state of the user rather than adjusting stimulation to possible outlier metric values.
Regarding claim 17, Thakur teaches the method of claim 14. Thakur additionally discloses generating a plurality of mobility metrics in response to neurostimulation (See Fig. 6, steps 631 and 633—induce pain episodes according to a pain assessment protocol and generate a plurality of signal metrics) and discloses generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy using one of a first or a second stimulation program selected based on a comparison between the respective progresses toward the mobility goal (see Fig. 7, steps 751, 75, 753—deliver first or second electrostimulation depending on if an indicated pain score exceeds a threshold).
However, Thakur does not disclose generating the mobility metric includes generating (i) a first mobility metric from a first physiological or functional signal sensed in response to neurostimulation according to a first stimulation program, and (ii) a second mobility metric from a second physiological or functional signal sensed in response to neurostimulation according to a second stimulation program different than the first stimulation program; trending the mobility metric include trending respectively the first mobility metric and the second mobility metric over time; determining the progress toward the mobility goal includes respective progresses toward the mobility goal of the patient; and initiating or adjusting the neuromodulation therapy is in accordance with one of the first or the second stimulation program selected based on a comparison of the trended first mobility metric to the trended second mobility metric trend, or a comparison between the respective progresses toward the mobility goal.
Bink, in the same field of endeavor of monitoring a user and modifying neurostimulation (Paragraph 0002-0006), teaches a controller circuit (external programmer 150) configured to:
generating (i) a first mobility metric from a first physiological or functional signal sensed in response to neurostimulation according to a first stimulation program, and (ii) a second mobility metric from a second physiological or functional signal sensed in response to neurostimulation according to a second stimulation program different than the first stimulation program (Paragraph 0077-0087-- IMD 200A may monitor a biomarker (410), e.g., during or after delivering of the electric stimulation in accordance with the second therapy program. In some examples, the biomarker may be one or more second biomarkers that are different from the one or more first biomarkers, e.g., one or more first biomarkers monitored during or after delivery of electric stimulation according to the first therapy program at (404). In some examples, the second biomarker(s) may be the same as the first biomarkers(s)…);
trending respectively the first mobility metric and the second mobility metric over time, and determine respective progresses toward the mobility goal of the patient (Paragraph 0078-0087—the biomarker…the second biomarker may be a patient posture and/or patient behavior data such as patient position, patient movement, patient movement history over a predetermined amount of time, a history of patent-selected stimulation parameters over a predetermined amount of time, and the like);
determining the progress toward the mobility goal includes respective progresses toward the mobility goal of the patient (Paragraph 0078-0087-- In another example, the second biomarker may be an ECAP signal including a signal feature (e.g., a frequency, a peak, a valley, a duration, an integrated value over time, and the like) which may satisfy a second threshold pertaining to the feature by its presence and/or value, indicating that more therapy is needed and/or would be beneficial…For example, there may be a set of biomarker values between the first and second thresholds for which either the first or second therapy programs may be delivered depending on which therapy program is currently being delivered, or method 200 may include hysteresis. For example, the first threshold may be a first pain score and the second threshold may be a second pain score that is greater than the first pain score. As an illustrative example, the first threshold may be a pain score of 4 on a scale from 1 to 10, and the second threshold may be a pain score of 6. IMD 200A may deliver electric stimulation according to the first therapy program (402), monitor the biomarker (404), and switch to delivering the electric stimulation in accordance with the second therapy program (408) if the pain score satisfies the first threshold, e.g., is equal to or less than 4 (406); NOTE: in this instance, the mobility goal is a low pain level of a user which is reflected by the metric satisfying (or not satisfying) a given threshold); and
initiating or adjusting the neuromodulation therapy is in accordance with one of the first or the second stimulation program selected based on a comparison of the trended first mobility metric to the trended second mobility metric trend, or a comparison between the respective progresses toward the mobility goal (Paragraph 0046-0048, 0077-0082-- system 100 and/or IMD 110 and/or external programmer 150 may be configured to control the delivery and/or parameters of electric stimulation based on one or more biomarkers… IMD 110 and/or external programmer 150 may be configured to toggle back and forth between therapy programs. For example, IMD 110 and/or external programmer 150 may be configured to determine which of the first or second therapy programs to deliver based on one or more biomarkers and switch the delivery of electric stimulation between the first and second programs accordingly).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Thakur, which discloses detecting multiple metrics and adjusting neuromodulation between different programs based on a comparison, in order to predictably improve the ability of the device to modify a neuromodulation signal not only in response to an immediate metric determination but also to a trended metric so that stimulation may be more appropriate for the particular state of the user rather than adjusting stimulation to possible outlier metric values.
Claim(s) 5-6 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qu in view of Bink.
Regarding claim 5, Qu teaches the system of claim 1. However, Qu does not disclose generate (i) a first mobility metric from a first physiological or functional signal sensed in response to neurostimulation according to a first stimulation program, and (ii) a second mobility metric from a second physiological or functional signal sensed in response to neurostimulation according to a second stimulation program different than the first stimulation program; trend respectively the first mobility metric and the second mobility metric over time, and determine respective progresses toward the mobility goal of the patient; and generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy using one of the first or the second stimulation program selected based on a comparison of the trended first mobility metric to the trended second mobility metric, or a comparison between the respective progresses toward the mobility goal.
Bink, in the same field of endeavor of monitoring a user and modifying neurostimulation (Paragraph 0002-0006), teaches a controller circuit (external programmer 150) configured to:
generate (i) a first mobility metric from a first physiological or functional signal sensed in response to neurostimulation according to a first stimulation program, and (ii) a second mobility metric from a second physiological or functional signal sensed in response to neurostimulation according to a second stimulation program different than the first stimulation program (Paragraph 0077-0087-- IMD 200A may monitor a biomarker (410), e.g., during or after delivering of the electric stimulation in accordance with the second therapy program. In some examples, the biomarker may be one or more second biomarkers that are different from the one or more first biomarkers, e.g., one or more first biomarkers monitored during or after delivery of electric stimulation according to the first therapy program at (404). In some examples, the second biomarker(s) may be the same as the first biomarkers(s)…);
trend respectively the first mobility metric and the second mobility metric over time, and determine respective progresses toward the mobility goal of the patient (Paragraph 0078-0087—the biomarker…the second biomarker may be a patient posture and/or patient behavior data such as patient position, patient movement, patient movement history over a predetermined amount of time, a history of patent-selected stimulation parameters over a predetermined amount of time, and the like); and
generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy using one of the first or the second stimulation program selected based on a comparison of the trended first mobility metric to the trended second mobility metric, or a comparison between the respective progresses toward the mobility goal (Paragraph 0046-0048, 0077-0082-- system 100 and/or IMD 110 and/or external programmer 150 may be configured to control the delivery and/or parameters of electric stimulation based on one or more biomarkers… IMD 110 and/or external programmer 150 may be configured to toggle back and forth between therapy programs. For example, IMD 110 and/or external programmer 150 may be configured to determine which of the first or second therapy programs to deliver based on one or more biomarkers and switch the delivery of electric stimulation between the first and second programs accordingly).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Qu, in order to predictably improve the ability of the device to modify a neuromodulation signal not only in response to an immediate metric determination but also to a trended metric so that stimulation may be more appropriate for the particular state of the user rather than adjusting stimulation to possible outlier metric values.
Regarding claim 6, the combination of Qu and Bink discloses the system of claim 5. Qu additionally discloses wherein the first and the second physiological or functional signals are sensed when the patient engages in a same type of activity or under a same activity context (Paragraph 0085-0097-- the physical activity classes 13a, 13b, 13c, 13d may comprise at least a first physical activity class 13a corresponding to sedentary activities, a second physical activity class 13b corresponding to light activities, a third physical activity class 13c corresponding to moderate activities and a fourth physical activity class 13d corresponding to vigorous activities NOTE: each physical class may be considered a same type or context of activity) wherein the controller circuit is configured to generate the control signal to the electrostimulator to initiate or adjust the neuromodulation therapy when the patient engages in the same type of activity or under the same activity context (Paragraph 0093-0104-- a behavior assessment score 16 indicative of the pain experienced by the patient 3 is determined from the classification results 15 of different epochs 10 by a third module 17…The behavior assessment score 16 may be used by the mobile device 5 and/or the server 6 to control the implant 4, e.g., a pulse generator of the implant 4).
Qu does not explicitly disclose initiate or adjust the neuromodulation therapy using the selected stimulation program when the patient engages in the same type of activity or under the same activity context.
Bink, in the same field of endeavor, discloses initiate or adjust the neuromodulation therapy using the selected stimulation program according to metrics of the user (Paragraph 0046-0048, 0077-0082-- system 100 and/or IMD 110 and/or external programmer 150 may be configured to control the delivery and/or parameters of electric stimulation based on one or more biomarkers… IMD 110 and/or external programmer 150 may be configured to toggle back and forth between therapy programs. For example, IMD 110 and/or external programmer 150 may be configured to determine which of the first or second therapy programs to deliver based on one or more biomarkers and switch the delivery of electric stimulation between the first and second programs accordingly).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify Qu to include the selected stimulation program of Bink in order to predictably improve the ability of the device to modify a neuromodulation signal not only in response to an immediate metric determination but also to a trended metric so that stimulation may be more appropriate for the particular state of the user rather than adjusting stimulation to possible outlier metric values.
Regarding claim 17, Qu teaches the method of claim 14. However, Qu does not disclose generating the mobility metric includes generating (i) a first mobility metric from a first physiological or functional signal sensed in response to neurostimulation according to a first stimulation program, and (ii) a second mobility metric from a second physiological or functional signal sensed in response to neurostimulation according to a second stimulation program different than the first stimulation program; trending the mobility metric include trending respectively the first mobility metric and the second mobility metric over time; determining the progress toward the mobility goal includes respective progresses toward the mobility goal of the patient; and initiating or adjusting the neuromodulation therapy is in accordance with one of the first or the second stimulation program selected based on a comparison of the trended first mobility metric to the trended second mobility metric trend, or a comparison between the respective progresses toward the mobility goal.
Bink, in the same field of endeavor of monitoring a user and modifying neurostimulation (Paragraph 0002-0006), teaches a controller circuit (external programmer 150) configured to:
generating (i) a first mobility metric from a first physiological or functional signal sensed in response to neurostimulation according to a first stimulation program, and (ii) a second mobility metric from a second physiological or functional signal sensed in response to neurostimulation according to a second stimulation program different than the first stimulation program (Paragraph 0077-0087-- IMD 200A may monitor a biomarker (410), e.g., during or after delivering of the electric stimulation in accordance with the second therapy program. In some examples, the biomarker may be one or more second biomarkers that are different from the one or more first biomarkers, e.g., one or more first biomarkers monitored during or after delivery of electric stimulation according to the first therapy program at (404). In some examples, the second biomarker(s) may be the same as the first biomarkers(s)…);
trending respectively the first mobility metric and the second mobility metric over time, and determine respective progresses toward the mobility goal of the patient (Paragraph 0078-0087—the biomarker…the second biomarker may be a patient posture and/or patient behavior data such as patient position, patient movement, patient movement history over a predetermined amount of time, a history of patent-selected stimulation parameters over a predetermined amount of time, and the like);
determining the progress toward the mobility goal includes respective progresses toward the mobility goal of the patient (Paragraph 0078-0087-- In another example, the second biomarker may be an ECAP signal including a signal feature (e.g., a frequency, a peak, a valley, a duration, an integrated value over time, and the like) which may satisfy a second threshold pertaining to the feature by its presence and/or value, indicating that more therapy is needed and/or would be beneficial…For example, there may be a set of biomarker values between the first and second thresholds for which either the first or second therapy programs may be delivered depending on which therapy program is currently being delivered, or method 200 may include hysteresis. For example, the first threshold may be a first pain score and the second threshold may be a second pain score that is greater than the first pain score. As an illustrative example, the first threshold may be a pain score of 4 on a scale from 1 to 10, and the second threshold may be a pain score of 6. IMD 200A may deliver electric stimulation according to the first therapy program (402), monitor the biomarker (404), and switch to delivering the electric stimulation in accordance with the second therapy program (408) if the pain score satisfies the first threshold, e.g., is equal to or less than 4 (406); NOTE: in this instance, the mobility goal is a low pain level of a user which is reflected by the metric satisfying (or not satisfying) a given threshold); and
initiating or adjusting the neuromodulation therapy is in accordance with one of the first or the second stimulation program selected based on a comparison of the trended first mobility metric to the trended second mobility metric trend, or a comparison between the respective progresses toward the mobility goal (Paragraph 0046-0048, 0077-0082-- system 100 and/or IMD 110 and/or external programmer 150 may be configured to control the delivery and/or parameters of electric stimulation based on one or more biomarkers… IMD 110 and/or external programmer 150 may be configured to toggle back and forth between therapy programs. For example, IMD 110 and/or external programmer 150 may be configured to determine which of the first or second therapy programs to deliver based on one or more biomarkers and switch the delivery of electric stimulation between the first and second programs accordingly).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Qu in order to predictably improve the ability of the device to modify a neuromodulation signal not only in response to an immediate metric determination but also to a trended metric so that stimulation may be more appropriate for the particular state of the user rather than adjusting stimulation to possible outlier metric values.
Allowable Subject Matter
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art of the record fails to teach and/or fairly suggest, in combination with all other recited limitations, “determine accelerometer-based mobility metric values using the set of activity signals; and generate the trained estimation model using the training dataset and the accelerometer-based mobility metric values, the trained estimation model mapping the set of physiological signals to the accelerometer-based mobility metric values”.
The most pertinent prior art of the record, Qu (cited above), generally discloses creating a machine learning model and training it on data measured by the system (Paragraph 0044-0049) which may include acceleration data or physiological data (Paragraph 0015-0016)/ However, Qu is silent as to receive a training dataset comprising a set of activity signals sensed by accelerometers and a set of physiological signals sensed by physiological sensors different from the accelerometers, the set of activity signals and the set of physiological signals sensed substantially concurrently from the patient, determine accelerometer-based mobility metric values using the set of activity signals; and generate the trained estimation model using the training dataset and the accelerometer-based mobility metric values, the trained estimation model mapping the set of physiological signals to the accelerometer-based mobility metric values.
Koh (US 20120215274 A1), in analogous art of an implantable device including monitoring physiological and activity signals of a user, discloses receive a training dataset comprising a set of activity signals sensed by accelerometers and a set of physiological signals sensed by physiological sensors different from the accelerometers, the set of activity signals and the set of physiological signals sensed substantially concurrently from the patient (Paragraph 0053--The measured patient heart sounds correspond to specific respective measured patient accelerometer readings. In one embodiment, the pre-recorded heart sounds are recorded simultaneously with the accelerometer data of block 300 for approximately 10 seconds. At block 320 accelerometer readings are correlated with respective heart sounds). However, the system of Koh utilizes this data to determine physiological signals from sensed activity signals such that the estimation model maps the set of accelerometer-based mobility metric values to the physiological signals rather than mapping the set of physiological signals to the accelerometer-based mobility metric values as claimed in the instant application.
Conclusion
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/ANNA ROBERTS/Examiner, Art Unit 3791