Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-11, 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Robison (US 20220175555 A1; 6/9/2022) in view of Nazari (US 20160106344 A1; 4/21/2016).
Regarding claim 1, Robison teaches a device for overcoming freezing of gait (FoG) ([0123]), the device comprising:
sized and shaped to be worn on a leg of a human subject (Fig. 7A-8B; [0106] “leggings”; [0107] “stretch material”).
Robison does not teach a sleeve sized and shaped to be worn on a leg of a human subject. Note that Robison teaches stretch material over legs (Fig. 7A-8B; [0106] “leggings”; [0107] “stretch material”). However, for the sake of clarity and to avoid doubt, Nazari teaches in the same field of endeavor (Abstract; [0054] “Parkinson’s disease”) a sleeve sized and shaped to be worn on a leg of a human subject (Fig. 14; [0065] “stretchable fabric”; [0066]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Robison to include this feature as taught by Nazari because this is a suitable shape for treating movement disorders (Fig. 11; Fig. 14; [0054]; [0065]-[0066]), furthermore, this would be an obvious change in shape; MPEP 2144.04.
In the combination of Robison and Nazari, Robison teaches an array of electrodes carried by the sleeve and positioned to make surface contact with skin of the leg when the sleeve is worn on by the human subject (Fig. 7A-7B; [0106]); and
a controller in communication with the array of electrodes (Fig. 1; [0034]), said controller being operative to:
receive muscle activity data from the array of electrodes indicative of muscle activity in the leg (Fig. 1; Fig. 3; [0062]-[0063]; [0106]; [0123]);
identify an onset of a FoG episode based on the received muscle activity data ([0123]); and
trigger energization of one or more targeted electrodes in response to identification of the onset of the FoG episode to mitigate against the FoG episode ([0091]; [0123]).
Regarding claim 2, in the combination of Robison and Nazari, Robison teaches wherein the muscle activity data comprises electromyography (EMG) measurements obtained via the array of electrodes (Fig. 1; Fig. 3; [0062]-[0063]; [0106]; [0123]).
Regarding claim 3, in the combination of Robison and Nazari, Robison teaches wherein the sleeve further includes an inertial measurement unit (IMU) that is responsive to movement of the leg (Fig. 3; [0004]; [0062]; [0091]-[0092]).
Regarding claim 4, in the combination of Robison and Nazari, Robison teaches wherein the controller is further operative to:
obtain motion data from the IMU indicative of movement of the leg (Fig. 3; [0004]; [0062]; [0091]-[0092]);
determine a phase of a gait cycle the subject is in based on the obtained motion data (Fig. 4-5; Fig. 14; [0063]-[0062]; [0123]); and
select one or more of the array of electrodes as the one or more target electrodes that are energized based on the determined phase of the gait cycle the subject is in ([0091]-[0092]; [0123]).
Regarding claim 5, in the combination of Robison and Nazari, Robison teaches wherein the controller comprises:
a decoder that processes the muscle activity data received from the electrodes to identify the onset of the FoG episode (Fig. 2-3; Fig. 10-11; Fig. 12-13; [0062]-[0063]; [0123]); and
a stimulator that provides an electrical signal to the one or more target electrodes to thereby energize the one or more target electrodes (Fig. 2-3; Fig. 10-11; Fig. 12-13; [0062]-[0063]; [0091]-[0092]; [0123]).
Regarding claim 6, in the combination of Robison and Nazari, Robison teaches wherein the FoG episode is identified by detecting a deviation in the muscle activity data received from the electrodes from muscle activity data recognized as corresponding to a normal gait (Fig. 3; Fig. 10-11; [0063]-[0064]; [0123]).
Regarding claim 7, in the combination of Robison and Nazari, Robison teaches wherein the onset of the FoG episode is identified within less than or equal to 1 second from a time that the FoG episode begins to be onset (Fig. 3; Fig. 10; Fig. 12-13; [0123]; [0127]-[0128]; [0129]; the reference teaches especially in Fig. 12-13 that the system responds within the claimed time frame).
Regarding claim 8, in the combination of Robison and Nazari, Robison teaches wherein the energization of the one or more target electrodes occurs within less than or equal to one second from a time that the onset of the FoG episode is identified (Fig. 3; Fig. 10; Fig. 12-13; [0123]; [0127]-[0128]; [0129]; the reference teaches especially in Fig. 12-13 that the system responds within the claimed time frame).
Regarding claim 9, in the combination of Robison and Nazari, Robison teaches wherein the energization of the one or more target electrodes provides one of functional electrical stimulation (FES) (Fig. 3) and neuromuscular electrical stimulation (NMES) to one or more muscles of the leg corresponding to positions of the one or more target electrodes (Fig. 7A-8B), such that the one or more muscles of the leg are induced to contract accordingly (Fig. 10; [0091]; [0106]; [0123]).
Regarding claim 10, the combination of Robison and Nazari teaches wherein the sleeve is stretchable and constricts about the leg when worn by the subject so that the array of electrodes are pressed against the skin of the leg (Robison Fig. 7A-8B; [0106] “leggings”; [0107] “stretch material”; Nazari Fig. 14; [0065] “stretchable fabric”; [0066]).
Regarding claim 11, Robison teaches an apparatus providing walking assistance for a subject with a neurological impairment ([0123]), the apparatus comprising:
device arranged to be fitted on a leg of the subject (Fig. 7A-8B; [0106] “leggings”; [0107] “stretch material”).
Robison does not teach a sleeve arranged to be fitted on a leg of the subject. Note that Robison teaches stretch material over legs (Fig. 7A-8B; [0106] “leggings”; [0107] “stretch material”). However, for the sake of clarity and to avoid doubt, Nazari teaches in the same field of endeavor (Abstract; [0054] “Parkinson’s disease”) a sleeve arranged to be fitted on a leg of the subject (Fig. 14; [0065] “stretchable fabric”; [0066]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Robison to include this feature as taught by Nazari because this is a suitable shape for treating movement disorders (Fig. 11; Fig. 14; [0054]; [0065]-[0066]), furthermore, this would be an obvious change in shape; MPEP 2144.04.
In the combination of Robison and Nazari, Robison teaches an array of electrodes carried by the sleeve and positioned to make contact with a skin of the leg when the sleeve is fitted to the subject (Fig. 7A-7B; [0106]); and
at least one processor which executes computer program code from at least one memory, wherein the at least one memory and the computer program code are configured, with the at least one processor (Fig. 1; [0034]), to cause the apparatus at least to:
collect electromyography (EMG) data from the array of electrodes, the EMG data being indicative of muscle activity in the leg (Fig. 1; Fig. 3; [0062]-[0063]; [0106]; [0123]);
model the EMG data to determine assistive neuromuscular electrical stimulation (NMES) for improving a gait of the subject (Fig. 2-3; Fig. 10-11; [0007]; [0062]-[0064]; [0091]; [0123]); and
trigger energization of one or more targeted electrodes in accordance with the determined assistive NMES (Fig. 2-3; Fig. 10-11; [0007]; [0062]-[0064]; [0091]; [0123]).
Regarding claim 13, in the combination of Robison and Nazari, Robison teaches wherein the sleeve further includes an inertial measurement unit (IMU) that is responsive to movement of the leg (Fig. 3; [0004]; [0062]; [0091]-[0092]) and wherein the at least one memory and the computer program code are configured, with the at least one processor (Fig. 1; Fig. 3; [0034]), to further cause the apparatus at least to:
obtain motion data from the IMU indicative of movement of the leg (Fig. 3; [0004]; [0062]; [0091]-[0092]);
wherein the assistive NMES is further determined based on the motion data from the IMU (Fig. 3; Fig. 10; [0091]-[0092]; [0123]).
Regarding claim 14, in the combination of Robison and Nazari, Robison teaches wherein the assistive NMES is further determined by estimating a phase of a gait cycle of the subject based on the EMG data and/or motion data from an inertial measurement unit (IMU) that is indicative of movement of the leg (Fig. 4-5; Fig. 14; [0063]-[0062]; [0123]).
Regarding claim 15, the combination of Robison and Nazari teaches wherein the at least one processor executing the computer program code from the at least one memory further provides a user interface via which a clinician can adjust the modeling of the EMG data to determine the assistive NMES (Robison Fig. 1; [0034] “input interface”; [0038]; [0048]; [0074]; Nazari [0088]).
Claim(s) 12, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Robison and Nazari as applied to claim 11 above, and further in view of Sharma (US 20200406035 A1; 12/31/2020).
Regarding claim 12, the combination of Robison and Nazari does not teach wherein the EMG data is modeled to determine the assistive NMES by operations including performing muscle unit (MU) decomposition of the EMG data. However, Sharma teaches in the same field of endeavor (Abstract; Fig. 1) wherein the EMG data is modeled to determine the assistive NMES by operations including performing muscle unit (MU) decomposition of the EMG data (Fig. 2; [0017]; [0030]; [0041]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Robison and Nazari to include this feature as taught by Sharma because this enables increased accuracy in EMG signal processing (Fig. 2; [0017]; [0030]).
Regarding claim 18, Robison teaches a method for detecting and overcoming freezing of gait (FoG) ([0123]), the method comprising:
monitoring muscle activity of a leg of a subject with an array of electrodes positioned in contact with a skin of the leg (Fig. 7A-8B; [0106] “leggings”; [0107] “stretch material”).
Robison does not teach the array of electrodes being carried by a sleeve worn on the leg. Note that Robison teaches stretch material over legs (Fig. 7A-8B; [0106] “leggings”; [0107] “stretch material”). However, for the sake of clarity and to avoid doubt, Nazari teaches in the same field of endeavor (Abstract; [0054] “Parkinson’s disease”) the array of electrodes being carried by a sleeve worn on the leg (Fig. 14; [0065] “stretchable fabric”; [0066]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Robison to include this feature as taught by Nazari because this is a suitable shape for treating movement disorders (Fig. 11; Fig. 14; [0054]; [0065]-[0066]), furthermore, this would be an obvious change in shape; MPEP 2144.04.
In the combination of Robison and Nazari, Robison teaches identifying an onset of a FoG episode based on the monitored muscle activity ([0123]);
and selectively energizing of one or more targeted electrodes of the array of electrodes in response to identification of the onset of the FoG episode to remediate the FoG episode ([0091]; [0123]).
Regarding claim 19, in the combination of Robison and Nazari, Robison teaches monitoring motion of the leg (Fig. 3; [0004]; [0062]; [0091]-[0092]); and
determining a phase of the subject's gait within a gait cycle base at least in part on the monitored motion (Fig. 4-5; Fig. 14; [0063]-[0062]; [0123]).
Regarding claim 20, in the combination of Robison and Nazari, Robison teaches selecting one or more of the array of electrodes as the one or more target electrodes that are selectively energized based on the determined phase of the subject's gait ([0091]-[0092]; [0123]).
Claim(s) 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Robison and Nazari as applied to claim 11 above, and further in view of Herr (US 20130310979 A1; 11/21/2013).
Regarding claim 16, the combination of Robison and Nazari does not teach wherein the modeling of the EMG data to determine the assistive NMES for improving the gait of the subject includes modeling the EMG data to determine a joint torque for improving the gait. However, Herr teaches in the same field of endeavor (Abstract; Fig. 1) wherein the modeling of the EMG data to determine the assistive NMES for improving the gait of the subject includes modeling the EMG data to determine a joint torque for improving the gait ([0084] “model…joint torques”; [0339]). Thus it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Robison and Nazari to include this feature as taught by Herr because this can improve the intended movement derived from EMG via biomimetic approach ([0084]; [0339]).
Regarding claim 17, the combination of Robison, Nazari, and Herr teaches wherein the modeling of the EMG data to determine the joint torque for improving the gait comprises modeling using a reinforcement learning (RL) model (Robison [0060] “EMG…reinforcement learning”; [0077]; Nazari [0084]; [0339]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan T Kuo whose telephone number is (408)918-7534. The examiner can normally be reached M-F 10 a.m. - 6 p.m. PT.
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/JONATHAN T KUO/ Primary Examiner, Art Unit 3792