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 .
Response to Amendment
The amendment filed 28 April 2026 has been entered. Claim(s) 1-20 remain pending in the application. Applicant’s amendments to the claims have overcome each and every objection to the claims, and each and every rejection under 35 U.S.C. 102/103 in view of Thakur previously set forth in the Office Action mailed 28 January 2026.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the mobility goal is repressed” in line 17 of the claim. It is not clear whether this limitation was intended to refer to the goal being represented, or if some other function constituting repressing of the mobility goal is performed. The limitation is interpreted as referring to the mobility goal being represented by a target distribution.
Claims 2-13 are additionally rejected under 35 U.S.C. 112(b) as indefinite due to their dependence on claim 1, which has been rejected as indefinite.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 7-8, 10-15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qu (US 20250176858 A1) in view of Blahnik (US 20210110908 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),
wherein the mobility metric is represented by a distribution of mobility times across a plurality of intensity bins (Fig. 3—distributions of mobility times across a plurality of intensity bins during a 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 the mobility goal represented by a target distribution of target mobility times across the plurality of intensity bins.
Blahnik, in the same field of endeavor of a system for monitoring activity of a user, teaches the system monitors a mobility metric represented by a distribution of mobility times across different intensities (Paragraph 00333-0334-- line 1902 represents an attribute of a first type of activity, such as the total amount of all activity performed by a user of the device, and line 1904 represents an attribute of a second type of activity, such as the total amount of activity above a threshold intensity performed by the same user; Fig. 19-20), wherein the system additionally provides a mobility goal represented by a target distribution of target mobility times across the plurality of intensity bins (Paragraph 0333-0334-- Line 1902 can be scaled such that the entire length represents the goal amount of all activity (the first goal value)… line 1904 can be scaled such that the entire length represents the goal amount for activity above a threshold intensity (the second goal value); Fig. 19-20) and where the system may determine a progress toward a mobility goal of the patient (Paragraph 0333-0334-- solid part 1902a represents the actual amount of activity performed by the user. Empty part 1902b represents the amount of activity remaining to be performed by the user to achieve the goal. As more activity is detected, solid part 1902a can be increased in size and empty part 1902b can be decreased in size… solid part 1904a represents the actual amount of activity above a threshold intensity performed by the user. Empty part 1904b represents the amount of activity above a threshold intensity remaining to be performed by the user to achieve the goal. As more activity above a threshold intensity is detected, solid part 1904a can be increased in size and empty part 1904b can be decreased in size. Thus, lines 1902 and 1904 can represent progressive measures of the total amount of all activity and the total amount of activity above a threshold intensity performed by the user; Fig. 19-20).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Qu, having a mobility metric represented by a distribution of mobility times across a plurality of intensity bins and a general mobility goal of improvement relative to a baseline, to include the particular mobility goal teachings of Blahnik in order to predictably improve the system by allowing a user to monitor not only their past and present performance but also to observe progress or lack thereof toward a specific goal distribution of mobility times across a plurality of intensity bins which may prompt changes in behavior or therapy, such as performing additional exercise of a particular intensity when a daily goal is not yet met.
Regarding claim 2, the combination of Qu and Blahnik 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.
Blahnik additionally discloses 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 0321-0322-- The goal values can be determined based on the user's progress over a certain period of time and/or the training level selected by the user. Moreover, the goal can be recalculated periodically based on the user's performance over each previous period of time; Paragraph 0333-0334-- lines 1902 and 1904 can represent progressive measures of the total amount of all activity and the total amount of activity above a threshold intensity performed by the user. In certain examples, lines 1902 and 1904 can be scaled such that the ratio of the entire length of the line to the solid part matches the ratio of the goal value to the actual amount performed by the user. Optionally, texts 1906 and 1908 and/or carets 1918 and 1928, which have similar configuration, functionalities, and possible variations as texts 1706 and 1708 and/or carets 1718 and 1728; Figs. 19-20).
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 Blahnik 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 7, the combination of Qu and Blahnik teaches the system of claim 1. Qu additionally teaches wherein the controller circuit is configured to categorize the different activity intensities into the 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, the combination of Qu and Blahnik 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 10, the combination of Qu and Blahnik 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.
Blahnik additionally discloses the system 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 (Paragraph 0322-- additional goal values…activity data of other users different from the user of the device (e.g., a highest/lowest or daily average amount of activity of certain category performed by other users different from the user of the device, average amount of activity of a certain category performed by other users at a given time during the day, etc.)), a relative position of the mobility metric of the patient with respect to the population-based mobility metric (Paragraph 0333-0334-- solid part 1902a represents the actual amount of activity performed by the user. Empty part 1902b represents the amount of activity remaining to be performed by the user to achieve the goal. As more activity is detected, solid part 1902a can be increased in size and empty part 1902b can be decreased in size… solid part 1904a represents the actual amount of activity above a threshold intensity performed by the user. Empty part 1904b represents the amount of activity above a threshold intensity remaining to be performed by the user to achieve the goal. As more activity above a threshold intensity is detected, solid part 1904a can be increased in size and empty part 1904b can be decreased in size. Thus, lines 1902 and 1904 can represent progressive measures of the total amount of all activity and the total amount of activity above a threshold intensity performed by the user; Fig. 19-20).
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 Blahnik 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 11, the combination of Qu and Blahnik 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.).
Blahnik additionally teaches that the system can determine the mobility goal based on at least one of: a baseline mobility metric of the patient during a baseline time period (Paragraph 0321-0322-- The device and/or the external device can perform predetermined computing instructions (e.g., algorithms) on any portion of the user's health data to automatically determine the goal values. The goal values can be determined based on the user's progress over a certain period of time and/or the training level selected by the user. Moreover, the goal can be recalculated periodically based on the user's performance over each previous period of time.) or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient (Paragraph 0322-- additional goal values…activity data of other users different from the user of the device (e.g., a highest/lowest or daily average amount of activity of certain category performed by other users different from the user of the device, average amount of activity of a certain category performed by other users at a given time during the day, etc.)).
It may be seen that it would have been obvious to one having ordinary skill in the art at the time of filing to modify the mobility goal of Qu to include the particular goal determinations of a distribution of mobility times across intensity bins of Blahnik as options which would predictably improve the device by allowing a user to monitor progress toward various goals relating to the times spent in each of the individual intensities and/or overall amount of time spent in all of the mobility activities which are being monitored.
Regarding claim 12, the combination of Qu and Blahnik 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 distribution of the mobility times across the plurality of intensity bins and (ii) the 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.
Blahnik, in the same field of endeavor of a system for monitoring activity of a user, teaches the present, on a user interface: a graphical comparison between (i) the distribution of the mobility times across the plurality of intensity bins (Paragraph 00333-0334-- line 1902 represents an attribute of a first type of activity, such as the total amount of all activity performed by a user of the device, and line 1904 represents an attribute of a second type of activity, such as the total amount of activity above a threshold intensity performed by the same user; Fig. 19-20) and (ii) the target distribution of target mobility times across the plurality of intensity bins (Paragraph 0333-0334-- solid part 1902a represents the actual amount of activity performed by the user. Empty part 1902b represents the amount of activity remaining to be performed by the user to achieve the goal. As more activity is detected, solid part 1902a can be increased in size and empty part 1902b can be decreased in size… solid part 1904a represents the actual amount of activity above a threshold intensity performed by the user. Empty part 1904b represents the amount of activity above a threshold intensity remaining to be performed by the user to achieve the goal. As more activity above a threshold intensity is detected, solid part 1904a can be increased in size and empty part 1904b can be decreased in size. Thus, lines 1902 and 1904 can represent progressive measures of the total amount of all activity and the total amount of activity above a threshold intensity performed by the user; Fig. 19-20); or a graphical comparison between (i) a first distribution of mobility times across the plurality of intensity bins during a first time period (Paragraph 00333-0334-- line 1902 represents an attribute of a first type of activity, such as the total amount of all activity performed by a user of the device, and line 1904 represents an attribute of a second type of activity, such as the total amount of activity above a threshold intensity performed by the same user; Fig. 19-20) 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 (Paragraph 0321-0322, 0475-- The goal values can be determined based on the user's progress over a certain period of time and/or the training level selected by the user. Moreover, the goal can be recalculated periodically based on the user's performance over each previous period of time… calculate the new goal value based on the historical performance of the user).
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 Blahnik 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. Furthermore, it would have been obvious to modify Qu to provide the additional comparison disclosed by Blahnik in order to allow a user to observe the progress or lack thereof beyond a previous performance.
Regarding claim 13, the combination of Qu and Blahnik 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),
wherein the mobility metric is represented by a distribution of mobility times across a plurality of intensity bins (Fig. 3—distributions of mobility times across a plurality of intensity bins during a 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 the mobility goal represented by a target distribution of target mobility times across the plurality of intensity bins.
Blahnik, in the same field of endeavor of a method of monitoring activity of a user, teaches monitoring a mobility metric represented by a distribution of mobility times across different intensities (Paragraph 00333-0334-- line 1902 represents an attribute of a first type of activity, such as the total amount of all activity performed by a user of the device, and line 1904 represents an attribute of a second type of activity, such as the total amount of activity above a threshold intensity performed by the same user; Fig. 19-20), wherein the method additionally provides a mobility goal represented by a target distribution of target mobility times across the plurality of intensity bins (Paragraph 0333-0334-- Line 1902 can be scaled such that the entire length represents the goal amount of all activity (the first goal value)… line 1904 can be scaled such that the entire length represents the goal amount for activity above a threshold intensity (the second goal value); Fig. 19-20) and where the method may determine a progress toward a mobility goal of the patient (Paragraph 0333-0334-- solid part 1902a represents the actual amount of activity performed by the user. Empty part 1902b represents the amount of activity remaining to be performed by the user to achieve the goal. As more activity is detected, solid part 1902a can be increased in size and empty part 1902b can be decreased in size… solid part 1904a represents the actual amount of activity above a threshold intensity performed by the user. Empty part 1904b represents the amount of activity above a threshold intensity remaining to be performed by the user to achieve the goal. As more activity above a threshold intensity is detected, solid part 1904a can be increased in size and empty part 1904b can be decreased in size. Thus, lines 1902 and 1904 can represent progressive measures of the total amount of all activity and the total amount of activity above a threshold intensity performed by the user; Fig. 19-20).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Qu, having a mobility metric represented by a distribution of mobility times across a plurality of intensity bins and a general mobility goal of improvement relative to a baseline, to include the particular mobility goal teachings of Blahnik in order to predictably improve the method by allowing a user to monitor not only their past and present performance but also to observe progress or lack thereof toward a specific goal distribution of mobility times across a plurality of intensity bins which may prompt changes in behavior or therapy, such as performing additional exercise of a particular intensity when a daily goal is not yet met.
Regarding claim 15, the combination of Qu and Blahnik 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, 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.
Blahnik additionally discloses 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 0321-0322-- The goal values can be determined based on the user's progress over a certain period of time and/or the training level selected by the user. Moreover, the goal can be recalculated periodically based on the user's performance over each previous period of time; Paragraph 0333-0334-- lines 1902 and 1904 can represent progressive measures of the total amount of all activity and the total amount of activity above a threshold intensity performed by the user. In certain examples, lines 1902 and 1904 can be scaled such that the ratio of the entire length of the line to the solid part matches the ratio of the goal value to the actual amount performed by the user. Optionally, texts 1906 and 1908 and/or carets 1918 and 1928, which have similar configuration, functionalities, and possible variations as texts 1706 and 1708 and/or carets 1718 and 1728; Figs. 19-20).
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 Blahnik in order to predictably improve the method by empowering a user to take actions to affect the user’s progress rather than only modifying the neuromodulation.
Regarding claim 18, the combination of Qu and Blahnik 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 19, the combination of Qu and Blahnik teaches the method of claim 14. Qu additionally 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; and
determining 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.
Blahnik additionally discloses the system 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 (Paragraph 0322-- additional goal values…activity data of other users different from the user of the device (e.g., a highest/lowest or daily average amount of activity of certain category performed by other users different from the user of the device, average amount of activity of a certain category performed by other users at a given time during the day, etc.)), a relative position of the mobility metric of the patient with respect to the population-based mobility metric (Paragraph 0333-0334-- solid part 1902a represents the actual amount of activity performed by the user. Empty part 1902b represents the amount of activity remaining to be performed by the user to achieve the goal. As more activity is detected, solid part 1902a can be increased in size and empty part 1902b can be decreased in size… solid part 1904a represents the actual amount of activity above a threshold intensity performed by the user. Empty part 1904b represents the amount of activity above a threshold intensity remaining to be performed by the user to achieve the goal. As more activity above a threshold intensity is detected, solid part 1904a can be increased in size and empty part 1904b can be decreased in size. Thus, lines 1902 and 1904 can represent progressive measures of the total amount of all activity and the total amount of activity above a threshold intensity performed by the user; Fig. 19-20).
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 Blahnik 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 20, the combination of Qu and Blahnik 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.).
Blahnik additionally teaches that the method can determine the mobility goal based on at least one of: a baseline mobility metric of the patient during a baseline time period (Paragraph 0321-0322-- The device and/or the external device can perform predetermined computing instructions (e.g., algorithms) on any portion of the user's health data to automatically determine the goal values. The goal values can be determined based on the user's progress over a certain period of time and/or the training level selected by the user. Moreover, the goal can be recalculated periodically based on the user's performance over each previous period of time.) or a population-based mobility metric generated from patients having similar medical conditions or similar demographics to the patient (Paragraph 0322-- additional goal values…activity data of other users different from the user of the device (e.g., a highest/lowest or daily average amount of activity of certain category performed by other users different from the user of the device, average amount of activity of a certain category performed by other users at a given time during the day, etc.)).
It may be seen that it would have been obvious to one having ordinary skill in the art at the time of filing to modify the mobility goal of Qu to include the particular goal determinations of a distribution of mobility times across intensity bins of Blahnik as options which would predictably improve the device by allowing a user to monitor progress toward various goals relating to the times spent in each of the individual intensities and/or overall amount of time spent in all of the mobility activities which are being monitored.
Claim(s) 3-4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qu in view of Blahnik, further in view of Goodall (US 10390755 B2).
Regarding claim 3, the combination of Qu and Blahnik 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, Blahnik, 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 16, the combination of Qu and Blahnik 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) 5-6 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qu in view of Blahnik, further in view of Bink (US 20220266028 A1).
Regarding claim 5, the combination of Qu and Blahnik 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, Blahnik, 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, the combination of Qu and Blahnik 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 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, of claim 1 as set forth in this Office action and to include 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.
Response to Arguments
Applicant’s arguments, see pages 1-2 of applicant's remarks, filed 28 April 2026, with respect to the rejection of the claims under 35 U.S.C. 102/103 in view of Thakur have been fully considered and are persuasive. The rejection of 28 January 2026 has been withdrawn.
Applicant’s arguments with respect to claim(s) 1, 7-8, 11, 13-14, 18, and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Specifically, while Qu is still cited in the rejection of the claims, a new reference, Blahnik, has been cited to teach the newly amended limitations of claim 1 which were the basis of the applicant’s arguments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNA ROBERTS whose telephone number is (571)272-7912. The examiner can normally be reached M-F 8:30-4:30 EST.
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/ANNA ROBERTS/Examiner, Art Unit 3791
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