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
In response to the amendment filed 1/16/2025; claims 1 – 16 and 31 – 34 are pending; claims 17 - 30 have been cancelled.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 16 and 31 – 34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Is the claimed invention a statutory category of invention?
Claims 1, 16 and 31 are directed to a method / system / wearable device for processing motion data (Step 1, Yes).
Step 2A, Prong 1: Does the claim recite an abstract idea?
The limitation of steps: … obtaining motion data of a user at different stride frequencies, the motion data at least including electromyographic (EMG) signals; determining a physiological state of the user based on the motion data; and determining a recommended stride frequency for the user based on the physiological state as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (claims 16 and 31). Claim 1 does not require the claimed processing steps to be performed by any statutory machine or product. The claimed method akin to mental process of observations, evaluations, and judgements for a therapist to recommend running pace based on the physiological states. The mere nominal recitation of at least one processor performing these steps does not take the claim limitation outside of the mental processes grouping. Thus, the claim recites a mental process (Step 2A, Prong 1: yes).
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Per the 2019 Revised Patent Subject Matter Eligibility Guidance, if a claim as a whole integrates the recited judicial exception into a practical application of that exception, a claim is not "directed to" a judicial exception. Alternatively, a claim that does not integrate a recited judicial exception into a practical application is directed to the exception. Evaluating whether a claim integrates an abstract idea into a practical application is performed by a) identifying whether there are any additional elements recited in the claim beyond the abstract idea, and b) evaluating those additional elements individual and in combination to determine whether they integrate the abstract idea into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. Exemplary considerations indicative that an additional element (or combination of elements) may have or has not been integrated into a practical application are set forth in the 2019 PEG.
With respect to the instant claims, Claim 1 does not require any statutory product; nor tied to any statutory product. Claims 16 and 31 recite the additional elements of: at least one computer-readable storage medium for storing a set of instructions; and at least one processor in communication with the computer-readable storage medium and [A] wearable device comprising: a wearing body provided with at least one sensor, the at least one sensor being configured to obtain motion data of a user; and a processor. It is particularly noted that the use of at least processor "as a tool" to perform an abstract method and steps for at least one sensor that only amount to extra solution activity are indicated in the 2019 PEG as examples that an additional element has not been integrated into a practical application. Even in combination, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits, such as an improvement to a computing system, on practicing the abstract idea (STEP 2A, Prong 2: NO).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 1 does not require any statutory product; nor tied to any statutory product. Claims 16 and 31 recite the additional elements of: at least one computer-readable storage medium for storing a set of instructions; and at least one processor in communication with the computer-readable storage medium and [A] wearable device comprising: a wearing body provided with at least one sensor, the at least one sensor being configured to obtain motion data of a user; and a processor set forth above for Step 2A, Prong 2. Regarding these limitations: Applicant's specification describes these features in generic manner "… the EMG data collection device includes a plurality of electrodes disposed at different positions of the wearable device 160 for fitting to different portions of the user (e.g., a chest, a back, elbows, legs, abdomen, wrists, etc.) to collect the EMG signals from the different portions of the user. In some embodiments, the posture data collection device includes a velocity sensor, an inertial sensor ( e.g., an acceleration sensor, an angular velocity sensor (e.g., a gyroscope), etc.), an optical sensor (e.g., an optical distance sensor, a video/image grabber), an acoustic distance sensor, a tension sensor, etc., or any combination thereof” in the Applicant’s specification, para. [0046]) and “a system for processing motion data 100 includes a processing device 110, a network 120, a storage device 130, a terminal device 140, and a data collection device 150” in para. [0035]. There is no indication in the Specification that Applicants have achieved an advancement or improvement in computer for processing motion and generating running pace. Dependent claims 2 – 15 and 32 – 34 inherit the deficiencies of their respective parent claims through their dependencies and do not recite additional limitations sufficient to direct the claims to more than the claimed abstract idea, and are thus rejected for the same reasons.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 2, 5 – 16 and 31 – 34 are rejected under 35 U.S.C. 103 as being unpatentable over Moreau et al. (US 2026/0198804 A1) in view of Phillips et al. (US 2014/0357960 A1).
Re claims 1, 16, 31:
Moreau teaches 1. A method for processing motion data (Moreau, Abstract), comprising:
obtaining motion data of a user at different stride (Moreau, Abstract, “a gait cycle”; [0014], “in the gait cycle”; [0209], “the stride length … the speed of gait, the cadence”), the motion data at least including electromyographic (EMG) signals (Moreau, [0062]);
determining a physiological state of the user based on the motion data (Moreau, [0208], “electromyography sensors to access muscle fatigue”); and
determining a recommended stride for the user based on the physiological state (Moreau, [0044]; [0047], “feed said patient motion data and patient foot plantar pressure data to said personalized model of a gait cycle and output a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0113], “training the personalized model of a gait cycle 20”; [0001], “The present invention relates to the field of neuro-rehabilitation systems for stimulating the nerves and/or the muscles of a patient suffering from a pathological gait. More specifically, the invention related to a device for training a personalized model of a gait cycle configured to generate a gait phase information representative of a state of advancement of a patient in the gait cycle”).
16. A system for processing motion data (Moreau, Abstract), comprising:
at least one computer-readable storage medium for storing a set of instructions; and at least one processor in communication with the computer-readable storage medium, wherein when executing the set of instructions (Moreau, Abstract; [0026]; [0084]), the at least one processor is directed to:
obtain motion data of a user at different stride (Moreau, Abstract, “a gait cycle”; [0014], “in the gait cycle”; [0209], “the stride length … the speed of gait, the cadence”), the motion data at least including electromyographic (EMG) signals (Moreau, [0062]);
determine a physiological state of the user based on the motion data (Moreau, [0208], “electromyography sensors to access muscle fatigue”); and
determine a recommended stride for the user based on the physiological state (Moreau, [0044]; [0047], “feed said patient motion data and patient foot plantar pressure data to said personalized model of a gait cycle and output a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0113], “training the personalized model of a gait cycle 20”; [0001], “The present invention relates to the field of neuro-rehabilitation systems for stimulating the nerves and/or the muscles of a patient suffering from a pathological gait. More specifically, the invention related to a device for training a personalized model of a gait cycle configured to generate a gait phase information representative of a state of advancement of a patient in the gait cycle”).
31. A wearable device (Moreau, Abstract; [0026]; [0084]) comprising:
a wearing body provided with at least one sensor, the at least one sensor being configured to obtain motion data of a user; and a processor (Moreau, Abstract; [0026]; [0084]) configured to perform a method for processing motion data, the method comprising:
obtaining motion data of a user at different stride (Moreau, Abstract, “a gait cycle”; [0014], “in the gait cycle”; [0209], “the stride length … the speed of gait, the cadence”), the motion data at least including electromyographic (EMG) signals (Moreau, [0062]; [0208]);
determining a physiological state of the user based on the motion data (Moreau, [0208], “electromyography sensors to access muscle fatigue”); and
determining a recommended stride for the user based on the physiological state (Moreau, [0044]; [0047], “feed said patient motion data and patient foot plantar pressure data to said personalized model of a gait cycle and output a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0113], “training the personalized model of a gait cycle 20”; [0001], “The present invention relates to the field of neuro-rehabilitation systems for stimulating the nerves and/or the muscles of a patient suffering from a pathological gait. More specifically, the invention related to a device for training a personalized model of a gait cycle configured to generate a gait phase information representative of a state of advancement of a patient in the gait cycle”).
Moreau does not explicitly disclose different stride frequencies.
Phillips et al. (US 2014/0357960 A1) teaches a method and device which measures and records one or more repetitive biological signals, such as heartbeat, breathing rate, and/or intrinsic brainwave frequency, and uses these tempos and timing information as a feedback mechanism to an individual doing one or more repetitive motion activities (Phillips, Abstract). Phillips teaches different stride frequencies (Phillips, [0023], “stride rate can be recorded using a pedometer”; [0034], “average pace frequency, accuracy of pace to the specified pace”; [0037], “if the user is running, the NP may be approximately 180 spm”). Therefore, in view of Phillips, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method / system / wearable device described in Moreau, by providing the stride rate instead of gait cycle as taught by Phillips, so that the stimulus could be adjusted to be in phase with runner’s stride (Phillips, [0041]).
Re claim 2:
2. The method of claim 1, wherein the obtaining the motion data of a user at different stride frequencies includes: determining a target speed interval; and obtaining the motion data at different stride frequencies within the target speed interval based on the target speed interval (Moreau, [0044]; [0047], “feed said patient motion data and patient foot plantar pressure data to said personalized model of a gait cycle and output a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0113], “training the personalized model of a gait cycle 20”; [0001], “The present invention relates to the field of neuro-rehabilitation systems for stimulating the nerves and/or the muscles of a patient suffering from a pathological gait. More specifically, the invention related to a device for training a personalized model of a gait cycle configured to generate a gait phase information representative of a state of advancement of a patient in the gait cycle”; Phillips, [0097], “the acceptable range for pacing frequency, can be adjusted automatically by the device”; [0056], “This may result in improved performance of the activity itself and promote wellness, focus, and concentration, and help to reduce injuries”).
Re claim 5:
5. The method of claim 1, wherein the motion data includes electrocardio (ECG) signals and/or posture signals (Moreau, [0209], “the patient motion data 33 coming from the motion sensors and the patient foot plantar pressure data 34 coming from the pressure sensors may be used to determine the foot strike pattern, the foot inclination angle, the tibia angle, the hip flexion and extension, the trunk lean, the ankle inversion and eversion, the foot progression angle, the pelvic drop, the knee flexion and extension, the stride length, or the displacement of the center of mass, the speed of gait, the cadence, etc”), and the determining a recommended stride frequency for the user based on the physiological state (Moreau, [0044]; [0047], “feed said patient motion data and patient foot plantar pressure data to said personalized model of a gait cycle and output a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0113], “training the personalized model of a gait cycle 20”; [0001], “The present invention relates to the field of neuro-rehabilitation systems for stimulating the nerves and/or the muscles of a patient suffering from a pathological gait. More specifically, the invention related to a device for training a personalized model of a gait cycle configured to generate a gait phase information representative of a state of advancement of a patient in the gait cycle”; Phillips, [0019], “the device could use a set of electrocardiogram (ECG) electrodes”) includes:
determining a correspondence between physiological states and stride frequencies based on the ECG signals and/or the posture signals; and determining the recommended stride frequency for the user based on the correspondence (Phillips, [0097], “the acceptable range for pacing frequency, can be adjusted automatically by the device”; [0056], “This may result in improved performance of the activity itself and promote wellness, focus, and concentration, and help to reduce injuries”).
Re claim 6:
6. The method of claim 5, wherein the determining the physiological state of the user based on the motion data includes:
determining the physiological state of the user based on the ECG signals and/or the posture signals, the physiological state including an injury risk for the motion of the user (Moreau, [0004], “the gait of some patients may be altered, which leads to an increased risk of injuries, for instance due to a fall or a repeated incorrect movement”; Phillips, [0057], “which allows for improved performance of the individual, and may help to prevent injuries, and reduce stress on body organs, such as the heart”).
Re claim 7:
7. The method of claim 1, wherein the obtaining motion data of a user at different stride frequencies includes:
obtaining a real-time stride frequency and real-time motion data of the user (Moreau, [0041], “facilitates real-time adjustments in the stimulation process”; [0054]; [0169]);
dividing the real-time stride frequency into a plurality of stride frequency intervals (Moreau, Abstract, “generate a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0010], “swing phase”; [0040], “Gait involves complex temporal dynamics, with distinct phases such as heel strike, midstance, toe-off, and swing”; [0108], “personalized model of a gait cycle 30 predicts the different phases of the patient's gait”); and
determining, based on the plurality of stride frequency intervals, motion data within each different stride frequency interval from the real-time motion data (Moreau, Abstract, “generate a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0010], “swing phase”; [0040], “Gait involves complex temporal dynamics, with distinct phases such as heel strike, midstance, toe-off, and swing”; [0108], “personalized model of a gait cycle 30 predicts the different phases of the patient's gait”; Phillips, [0124], “FIG. 12 shows a representative example of a subject's heart rate during sham (1201) versus active pacing (1202) during a clinical trial using pacing adapted to the subject's heart beat during running”).
Re claim 8:
8. The method of claim 7, wherein the real-time stride frequency is obtained by an inertial sensor, or the real-time stride frequency is obtained from the EMG signals (Moreau, Abstract, “a gait cycle”; [0014], “in the gait cycle”; [0209], “the stride length … the speed of gait, the cadence”; Phillips, [0023], “stride rate can be recorded using a pedometer”; [0034], “average pace frequency, accuracy of pace to the specified pace”; [0037], “if the user is running, the NP may be approximately 180 spm”).
Re claim 9:
9. The method of claim 1, further including: feeding back the recommended stride frequency to the user (Moreau, [0025], “prediction of the personalized model of a gait cycle, it is possible to generate electrical stimulation that are very precise and adapted to correct the pathological gait of the patient”; [0041], “real-time adjustments in the stimulation process, ensuring optimal muscle activation corresponding to the current phase of the gait cycle”; [0170], “device 2 may be used to stimulate both legs of said patient at a motor level (e.g. by stimulating motor nerves) to correct a pathological gait”; Phillips, [0013], “The feedback given to the user can be an audio or visual stimulus, or a tactile stimulus (e.g., vibration, tapping), which has a noticeable beat, allowing the user to time the rhythmic activity to match the beat”; [0016], “pacing stimulus may be delivered visually”; [0036], “the stimulation frequency based on the measured biological beat frequency, the algorithm may take into account the following: Biological Signal Beat Frequency (BF); Normal Pace Frequency (NP); Maximum Acceptable Pace Frequency (PH); and/or Minimum Acceptable Pace Frequency (PL)”).
Re claim 10:
10. The method of claim 9, wherein the feeding back the recommended stride frequency to the user includes:
obtaining a real-time stride frequency of the user; determining a stride frequency adjustment trend for the user based on a frequency difference between the real-time stride frequency and the recommended stride frequency; and guiding the user based on the stride frequency adjustment trend until a stride frequency of the user is the same as the recommended stride frequency (Moreau, [0025], “prediction of the personalized model of a gait cycle, it is possible to generate electrical stimulation that are very precise and adapted to correct the pathological gait of the patient”; [0041], “real-time adjustments in the stimulation process, ensuring optimal muscle activation corresponding to the current phase of the gait cycle”; [0170], “device 2 may be used to stimulate both legs of said patient at a motor level (e.g. by stimulating motor nerves) to correct a pathological gait”; Phillips, [0013], “The feedback given to the user can be an audio or visual stimulus, or a tactile stimulus (e.g., vibration, tapping), which has a noticeable beat, allowing the user to time the rhythmic activity to match the beat”; [0016], “pacing stimulus may be delivered visually”; [0036], “the stimulation frequency based on the measured biological beat frequency, the algorithm may take into account the following: Biological Signal Beat Frequency (BF); Normal Pace Frequency (NP); Maximum Acceptable Pace Frequency (PH); and/or Minimum Acceptable Pace Frequency (PL)”).
Re claim 11:
11. The method of claim 9, wherein the feeding back the recommended stride frequency to the user includes: feeding back a beat that is the same as the recommended stride frequency to the user based on a preset feedback manner (Moreau, [0025], “prediction of the personalized model of a gait cycle, it is possible to generate electrical stimulation that are very precise and adapted to correct the pathological gait of the patient”; [0041], “real-time adjustments in the stimulation process, ensuring optimal muscle activation corresponding to the current phase of the gait cycle”; [0170], “device 2 may be used to stimulate both legs of said patient at a motor level (e.g. by stimulating motor nerves) to correct a pathological gait”; Phillips, [0013], “The feedback given to the user can be an audio or visual stimulus, or a tactile stimulus (e.g., vibration, tapping), which has a noticeable beat, allowing the user to time the rhythmic activity to match the beat”; [0016], “pacing stimulus may be delivered visually”; [0036], “the stimulation frequency based on the measured biological beat frequency, the algorithm may take into account the following: Biological Signal Beat Frequency (BF); Normal Pace Frequency (NP); Maximum Acceptable Pace Frequency (PH); and/or Minimum Acceptable Pace Frequency (PL)”).
Re claim 12:
12. The method of claim 11, wherein the preset feedback manner includes at least one of a voice feedback, a vibration feedback, a light feedback, and a display feedback (Moreau, [0172], “The user interface 19 includes any means appropriate for entering or retrieving data, information or instructions, notably visual, tactile and/or audio capacities that can encompass any or several of the following means as well known by a person skilled in the art: a screen, a keyboard, a trackball, a touchpad, a touchscreen, a loudspeaker, a voice recognition system”; [0188]; Phillips, [0069]).
Re claim 13:
13. The method of claim 9, wherein the feeding back the recommended stride frequency to the user includes: stimulating a body portion of the user based on a biofeedback manner to feed back the recommended stride frequency to the user, wherein the stimulation has a stimulation frequency same as the recommended stride frequency (Moreau, [0025], “generate electrical stimulation that are very precise and adapted to correct the pathological gait of the patient”; [0170], “The device 2 may be used to stimulate both legs of said patient at a motor level”; [0211], “the settings of the stimulation parameters for each muscle, intensities and timings”; Phillips, [0007]).
Re claim 14:
14. The method of claim 1, wherein the obtaining motion data of a user at different stride frequencies includes: obtaining a plurality of sets of motion data of the user at a plurality of preset stride frequencies, each of the plurality of sets of motion data corresponding to one of the plurality of preset stride frequencies (Moreau, [0044]; [0047], “feed said patient motion data and patient foot plantar pressure data to said personalized model of a gait cycle and output a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0113], “training the personalized model of a gait cycle 20”; [0001], “The present invention relates to the field of neuro-rehabilitation systems for stimulating the nerves and/or the muscles of a patient suffering from a pathological gait. More specifically, the invention related to a device for training a personalized model of a gait cycle configured to generate a gait phase information representative of a state of advancement of a patient in the gait cycle”; Phillips, [0097], “the acceptable range for pacing frequency, can be adjusted automatically by the device”; [0036], “the stimulation frequency based on the measured biological beat frequency, the algorithm may take into account the following: Biological Signal Beat Frequency (BF); Normal Pace Frequency (NP); Maximum Acceptable Pace Frequency (PH); and/or Minimum Acceptable Pace Frequency (PL)”; [0067], “pre-set comfortable range”; [0097], “the acceptable range for pacing frequency”).
Re claim 15:
15. The method of claim 14, wherein the obtaining a plurality of sets of motion data of the user at a plurality of preset stride frequencies includes: obtaining physiological information of the user, the physiological information including body information and motion information; and determining the plurality of sets of motion data of the user at the plurality of preset stride frequencies based on the physiological information (Moreau, [0044]; [0047], “feed said patient motion data and patient foot plantar pressure data to said personalized model of a gait cycle and output a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0113], “training the personalized model of a gait cycle 20”; [0001], “The present invention relates to the field of neuro-rehabilitation systems for stimulating the nerves and/or the muscles of a patient suffering from a pathological gait. More specifically, the invention related to a device for training a personalized model of a gait cycle configured to generate a gait phase information representative of a state of advancement of a patient in the gait cycle”; Phillips, [0097], “the acceptable range for pacing frequency, can be adjusted automatically by the device”; [0036], “the stimulation frequency based on the measured biological beat frequency, the algorithm may take into account the following: Biological Signal Beat Frequency (BF); Normal Pace Frequency (NP); Maximum Acceptable Pace Frequency (PH); and/or Minimum Acceptable Pace Frequency (PL)”; [0067], “pre-set comfortable range”; [0097], “the acceptable range for pacing frequency”).
Re claim 32:
32. The wearable device of claim 31, wherein the wearing body is further provided with electrodes contacting a skin of the user, and the electrodes are configured to provide stimulation to a body portion of the user (Moreau, [0198], “The generator 81 is in electrical connection 85 with a set of electrodes 82 configured to be positioned on the skin of the patient”).
Re claim 33:
33. The method of claim 10, wherein the guiding the user based on the stride frequency adjustment trend until a stride frequency of the user is the same as the recommended stride frequency includes: feeding back a dynamic beat with gradually changing frequency to the user based on a preset feedback manner, the dynamic beat having the same frequency change trend as the stride frequency adjustment trend, and a target frequency of the dynamic beat is the recommended stride frequency (Moreau, [0044]; [0047], “feed said patient motion data and patient foot plantar pressure data to said personalized model of a gait cycle and output a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0113], “training the personalized model of a gait cycle 20”; [0001], “The present invention relates to the field of neuro-rehabilitation systems for stimulating the nerves and/or the muscles of a patient suffering from a pathological gait. More specifically, the invention related to a device for training a personalized model of a gait cycle configured to generate a gait phase information representative of a state of advancement of a patient in the gait cycle”; Phillips, [0097], “the acceptable range for pacing frequency, can be adjusted automatically by the device”; [0036], “the stimulation frequency based on the measured biological beat frequency, the algorithm may take into account the following: Biological Signal Beat Frequency (BF); Normal Pace Frequency (NP); Maximum Acceptable Pace Frequency (PH); and/or Minimum Acceptable Pace Frequency (PL)”; [0067], “pre-set comfortable range”; [0097], “the acceptable range for pacing frequency”).
Re claim 34:
34. The method of claim 33, wherein a frequency adjustment process of the dynamic beat changes uniformly (Moreau, [0114], “subtle changes in gait dynamics and aid in gradually correcting the gait abnormalities as the patient progresses in his rehabilitation”; Phillips, [0057], “If the runner's heart rate increased to 130 bpm, then the optimal running rate is no longer 180 spm. It has changed to 195 spm to maintain 3 running steps for every 2 beats”; [0124], “Active pacing resulted in faster run times”).
Claims 3 – 4 are rejected under 35 U.S.C. 103 as being unpatentable over Moreau et al. (US 2026/0198804 A1) in view of Phillips et al. (US 2014/0357960 A1) as applied to claim 1 above, and further in view of Fahey (US 2013/0030277 A1).
Re claim 3:
Moreau teaches 3. The method of claim 1, wherein
the physiological state includes a muscle fatigue, and
the determining a recommended stride frequency for the user based on the physiological state includes:
determining a correspondence between physiological states and stride frequencies based on at least one of the muscle efficiency, the muscle tension, and the muscle fatigue; and
determining the recommended stride frequency for the user based on the correspondence (Moreau, [0044]; [0047], “feed said patient motion data and patient foot plantar pressure data to said personalized model of a gait cycle and output a gait phase information representative of a state of advancement of a patient in the gait cycle”; [0113], “training the personalized model of a gait cycle 20”; [0001], “The present invention relates to the field of neuro-rehabilitation systems for stimulating the nerves and/or the muscles of a patient suffering from a pathological gait. More specifically, the invention related to a device for training a personalized model of a gait cycle configured to generate a gait phase information representative of a state of advancement of a patient in the gait cycle”; Phillips, [0097], “the acceptable range for pacing frequency, can be adjusted automatically by the device”; [0036], “the stimulation frequency based on the measured biological beat frequency, the algorithm may take into account the following: Biological Signal Beat Frequency (BF); Normal Pace Frequency (NP); Maximum Acceptable Pace Frequency (PH); and/or Minimum Acceptable Pace Frequency (PL)”; [0067], “pre-set comfortable range”; [0097], “the acceptable range for pacing frequency”).
Moreau teaches the physiological state includes a muscle fatigue (Moreau, [0208], “electromyography sensors to access muscle fatigue”). Moreau does not explicitly disclose muscle efficiency and tension.
Fahey teaches Devices, systems, and methods for automated optimization of muscle stimulation energy (Fahey, Abstract). Fahey teaches muscle efficiency and tension (Fahey , [0034]; [0041]). Therefore, in view of Fahey, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in muscle efficiency and tension as taught by Fahey, algorithms executed by the control unit will use the sensor data to identify the most efficient pair or group of stimulation electrodes to utilize to deliver energy to the user (as defined as the pair or group of stimulation electrodes leading to the strongest muscle contraction, as measured by the sensor) (Fahey, [0118]) and assess tissue stiffness in the anatomic region of the tendon (Fahey, [0014]).
Re claim 4:
4. The method of claim 3, wherein the determining a physiological state of the user based on the motion data includes: determining at least one of the muscle efficiency, the muscle tension, and the muscle fatigue of the user based on the EMG signals (Moreau, [0208], “electromyography sensors to access muscle fatigue”).
Conclusion
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/JACK YIP/ Primary Examiner, Art Unit 3715
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