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 amendments, filed August 1, 2025, claims 1 and 2 have been amended. Claims 1-3, 5, and 7-8 remain pending.
Response to Arguments
Applicant's arguments, see Remarks, filed August 1, 2025, with respect to means plus function interpretation in light of the amendments have been fully considered but they are not persuasive. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Applicant’s arguments with respect to prior art rejections have been considered but are moot because the new ground of rejection does not rely on the same reference combination applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new ground(s) of rejection is made in view of the combinations of Gao (CN 108836347 A)/Einav (WO 2005086574 A2)/Shideler (US 20170360333 A1)/D’Arcy (WO 2019222833 A1)/Lee (US 20120116258 A1)/Lee (KR 20140063362 A). Any arguments still relevant based on the new grounds of rejection are addressed below.
In response to applicant’s argument that Shideler does not evaluate specific joint angles or determine whether such angles meet predefined thresholds, Examiner respectfully disagrees. Shideler discloses in [0033 and 0034] that a foot icon changes to represent the phase change of the subject’s gait, based on specific thresholds regarding the subject’s angle of knee flexion.
Further, in response to applicant’s argument that the combination of Gao, Einav, Shideler, D’Arcy, and Lee’258, and Lee’362 is improper due to impermissible hindsight reasoning, Examiner respectfully disagrees. The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have:
modified the lower limb rehabilitation system of Gao to include music rhythm as disclosed in Einav to enhance rehabilitation efficiency by translating complex trajectories into music and music parameters that can be readily perceived (Einav, Pg. 2, lines 8-9);
modified the combination of Gao/Einav to include projecting and superimposing onto the real word as disclosed in Shideler to enable effective gait rehabilitation training to take place in a variety of different environments, rather than being limited to implementation with a treadmill (Shideler [0057]);
modified the combination of Gao/Einav/Shideler to include the neural network model for processing acquired EEG signals as disclosed in D’Arcy to quantify a subject’s motor control in a fast, repeatable and automated manner (D’Arcy [0001, 0005]);
modified the rehabilitation system of the Gao/Einav/Shideler/D’Arcy to include the locations of the plurality of motion sensors as disclosed in Lee’258 in order to directly measure the angle of at least one of the knee joint, the ankle joint and the hip joint (i.e., coax) of the lower limb, or indirectly measure the joint angle of the lower limb to detect the usage of the paralyzed muscles thereby enabling rehabilitation training for the paralyzed muscles based upon measurement data, and a rehabilitation apparatus using game device (Lee’258 [0013]); and
modified the rehabilitation system of the Gao/Einav/Shideler/D’Arcy to include a functional electrical stimulator configured to stimulate a tibialis anterior muscle as disclosed in Lee’362 to improve coordination ability in the walking cycle and improve the walking quality such as walking speed and angle of the ankle joint (Lee’362, Pg. 2, [4]).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are recited below and refer to the analysis platform as a Raspberry Pi 3/4 computer per [0028] of the amended Specification:
“an analysis platform coupled to the display, …, wherein the analysis platform is configured” … “the analysis platform receives the gait data” and “the analysis platform controls” in claim 1
“the analysis platform has a display screen” in claim 2;
“the analysis platform is configured to select one of the virtual scene videos” in claim 3;
“electrical stimulator coupled to the analysis platform” in claim 7;
“alarm coupled to the analysis platform, where the analysis platform is configured to evaluate the index value… the analysis platform controls” claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Gao (CN 108836347 A), in view of Einav (WO 2005086574 A2), Shideler (US 20170360333 A1), and D’Arcy (WO 2019222833 A1).
Regarding claim 1, Gao teaches a lower limb rehabilitation system (Pg. 4, [10], “rehabilitation training system”; Figure 1) based on augmented reality and a brain computer interface, comprising:
a display for a user to wear and configured to receive and play a virtual scene video for the user to watch, to guide the user to perform gait rehabilitation training (Pg. 5, [3], “the virtual scenario module 130 is a head mounted virtual display device”);
a plurality of motion sensors configured to be respectively disposed at a plurality of parts of a lower limb of the user and configured to sense gait data, the gait data including a position, an angle, a speed and an acceleration of joints of the lower limb of the user (Pg. 5, [6], “motion collecting unit 112 comprises a gyroscope, an acceleration sensor, an angle sensor or other sensor device”; Pg. 5, [5], “gait collecting unit 111 for collecting patient gait information in the process of rehabilitation training. Specifically, the gait information comprises a step length, stride length, speed, pace, cadence, single time, step time or other gait information… said gait information collecting unit 111 collecting the sent to the control module 120.”);
a brain wave monitor configured to record an electroencephalogram signal by detecting an electric current change in a brain wave of the user, wherein the electroencephalogram signal is a brain wave signal in a brain motor area of the user (Pg. 6, [2], “brain wave collecting unit 113 for collecting patient EEG information in the process of rehabilitation training”);
and an analysis platform coupled to the display, the plurality of motion sensors, and the brain wave monitor (Pg. 5, [4], “control module 120 connected with … the information collecting module 110, including gait collecting unit 111, motion collecting unit 112 and a brain wave collecting unit 113”).
However, Gao fails to disclose music rhythm, accuracy of footsteps, superimposing onto the real word, machine learning, and angle thresholds.
Einav teaches a rehabilitation apparatus correlating movement during rehabilitation exercises with music rhythm in a virtual world. The combination of Gao/Einav discloses wherein the analysis platform (controller 170) is configured to:
store a plurality of virtual scene videos having a respective music rhythm by using a database unit for the user to select one of the virtual scene videos stored in the database unit (Einav: Pg. 26, lines 22-24, “Music can be used with gait training… music is used to set the pace of walking or to synchronize the multiple body motions”; Pg. 5, lines 12-13, “controller comprises a memory that links musical elements with motion elements”; Pg. 13, lines 18-20, At 430, “the user selects a rehabilitation program that is required for the user, for example the user may select between different stress levels or programs for dealing with different limbs”; Fig. 4);
transmit the selected virtual scene video to the display and control the display to synchronously play the selected virtual scene video and the music rhythm thereof (Einav: Pg. 26, lines 22-24, “Music can be used with gait training… music is used to set the pace of walking or to synchronize the multiple body motions”; Pg. 5, lines 13-19, “Optionally, said controller generates said audio from musical elements corresponding to different body parts. Alternatively or additionally, said controller generates said audio from musical elements corresponding to different motions. In an exemplary embodiment of the invention, said controller generates said audio according to a difference between a desired motion and an actual motion. In an exemplary embodiment of the invention, the apparatus comprises a sensor which generates an indication of said motion. Optionally, said controller generates a music stream according to said motion”; Pg. 12, lines 1-3, “a user is instructed visually, for example by the required motion being displayed on a display device, for example display 120 or a TV screen”).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the lower limb rehabilitation system of Gao to include music rhythm as disclosed in Einav to enhance rehabilitation efficiency by translating complex trajectories into music and music parameters that can be readily perceived (Einav, Pg. 2, lines 8-9).
However, the combination of Gao/Einav fails to disclose superimposing onto the real word, machine learning, and angle thresholds.
Shideler teaches a rehabilitation display for gait training. Shideler discloses wherein, the display projects and superimposes, onto the real world, two virtual channels and virtual signs which are generated from the selected virtual scene video and are moving toward the user along the two virtual channels (Shideler: display 10; [0057] “a projection on a wall large enough for the subject to see throughout the walking path… the display 10 could be presented to the user on a heads up type goggle display worn by the subject;” [0045] “To train gait, subjects are instructed to step on virtual targets 12 [and avoid virtual obstacles, if used]”; Fig. 2; [0025] “The display 10 according to the invention includes a plurality of foot placement targets 12 visible to the subject on the display 10 and moving along the display 10 at a speed proportional to the desired gait speed of the subject and in a direction along the display 10 representative of the direction of locomotion of the subject. The motion of the targets 12 on the display 10 is shown by arrows in FIG. 2 and generally they will flow down the screen or field of the display 10.”).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Gao/Einav to include projecting and superimposing onto the real word as disclosed in Shideler to enable effective gait rehabilitation training to take place in a variety of different environments, rather than being limited to implementation with a treadmill (Shideler [0057]).
The combination of Gao/Einav/Shideler discloses:
virtual signs moving with the music rhythm for the user to perform the gait rehabilitation training (Shideler: Fig. 2; Einav: Pg. 26, lines 22-24, “Music can be used with gait training… music is used to set the pace of walking or to synchronize the multiple body motions”);
receive the gait data sensed by the plurality of motion sensors and calculate a footstep information from the gait data, and the footstep information including a step speed, a step frequency and a step distance (Gao: Pg. 5, [6], “motion collecting unit 112 comprises a gyroscope, an acceleration sensor, an angle sensor or other sensor device; Pg. 5, [5], gait collecting unit 111 for collecting patient gait information in the process of rehabilitation training. Specifically, the gait information comprises a step length, stride length, speed, pace, cadence, single time, step time or other gait information… said gait information collecting unit 111 collecting the sent to the control module 120.”);
compare the gait data with the virtual signs correspondingly generated by the selected virtual scene video, to determine an accuracy that the footstep information matches movements of the virtual signs and provide the user with feedback (Shideler: [0045] “Subjects receive biofeedback in real-time on the accuracy of their foot placement on each step [including accuracy of the gait information as described in Gao] with respect to the targets 12 [and obstacles, if used] by a visible real-time proximity measurement 18 all visible to the subject on the display 10”; Gao: Pg. 5, [5], “the gait information comprises a step length, stride length, speed, pace, cadence, single time, step time or other gait information”).
However, the combination of Gao/Einav/Shideler fails to disclose a machine learning model.
D’Arcy teaches a system and method for quantifying motor function using electroencephalography (EEG) measurements. D’Arcy discloses:
and input the electroencephalogram signal to a machine learning model, so that the machine learning model quantifies the electroencephalogram signal into an index value (EEG acquisition hardware 200; [0051] “Data processing module 160 is operable to process the acquired EEG and/or EOG signals to automatically identify ERD/ERS waveform features of interest and for calculating the corresponding motor control score”; [0097] “EEG data to quantify motor function can be implemented in the context of one or more artificial neural network models”).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Gao/Einav/Shideler to include the neural network model for processing acquired EEG signals as disclosed in D’Arcy to quantify a subject’s motor control in a fast, repeatable and automated manner (D’Arcy [0001, 0005]).
The combination of Gao/Einav/Shideler/D’Arcy discloses:
wherein the index value is used for representing a lower limb motor function of the user (D’Arcy: [0009] motor control score; Gao: Pg. 6 [2] “the EEG information can predict gait freeze state of the patient to a certain extent, and the electroencephalogram information also can be used to diagnose whether a patient is at gait freezing state and the gait information and the motion information.”); and output the index value (D’Arcy: [0099] “output measures may be arranged geometrically such that the motor control score is represented as a shape [e.g., a hexagon for normal motor function and a triangle for impairment, as seen in FIG. 10]”);
wherein, when the user performs gait rehabilitation training, the analysis platform receives the gait data to determine an analyzed result by analyzing whether an angle of bending the knee joint of the user reaches a predetermined threshold before a corresponding one of the virtual signs move to the rear of the user, the predetermined threshold being 30 degrees (Shideler: Fig. 1; [0033] “Heel off or terminal stance phase begins when the heel leaves the floor. In this stance phase, the body weight is divided over the metatarsal heads. Typically about 10-13° of hip hyperextension is present, which then goes into flexion. The knee becomes flexed (up to about 5°) and the ankle supinates and plantar flexes. The foot icon 14 will be displayed throughout the Heel off or terminal stance phase. [0034] In the toe-off or pre-swing phase, the hip becomes less extended. The knee is flexed about 35-40° and plantar flexion of the ankle increases to about 20°.” [0036] “In the mid swing phase, which is a swing phase, the hip flexes to about 30° via contraction of the adductors, and the ankle becomes dorsiflexed due to a contraction of the tibialis anterior muscle. The knee flexes about 60° but then extends approximately 30° due to contraction of the sartorius muscle. This extension is caused by the quadriceps muscles. The foot icon 16 will be displayed throughout the mid swing phase.”);
wherein in a condition that the analyzed result is positive, the analysis platform controls the selected virtual scene video to generate a virtual object (Shideler: [0034] “In toe-off, like the name says, the toes leave the ground and this phase marks the transition between stance phase and swing phase with the foot icon 14, 16 changing from the visible icon 14 for that foot (left or right) representation to the icon 16 for that foot (left or right) representation based upon the phase change.”), and causes the virtual object and the virtual sign on the corresponding virtual channel to offset each other, thereby obtaining a rehabilitation score (Shideler: [0047] “In FIG. 2 there are two distinct types of measurements 18. The first type of measurement 18 displayed in FIG. 2 is in a bar chart on the right side of the display 10 for the current left and right stance phase 14 of the subject in the form of a left and right bar graph corresponding to the measurement relative to the target 12, represented by a line across the bar graph. This bar graph format is helpful if the step length in the direction of locomotion is important, and particularly where the additional information of too short or too long of a step is important. The total height of the bar graph will be the distance from the base of the graph to the line representing the target 12 position and + the measurement 18 for steps beyond the target (too long) or − the measurement 18 for steps prior to the target 12 (too short). The bar itself can include the color coding (Green, Yellow, Red) discussed above in connection with the measurement 18 shown in FIG. 1, namely a green bar is acceptable error range, red is unacceptable error range and yellow is in between.”) and in a condition that the analyzed result is negative, no extra operation is performed (Shideler: [0034] “In toe-off, like the name says, the toes leave the ground and this phase marks the transition between stance phase and swing phase with the foot icon 14, 16 changing from the visible icon 14 for that foot (left or right) representation to the icon 16 for that foot (left or right) representation based upon the phase change.” [if the knee fails to reach 30 degrees of flexion, then a change of phase is not detected and the foot icon 14 will not transition to icon 16]).
Regarding claim 2, the combination of Gao/Einav/Shideler/D’Arcy discloses the lower limb rehabilitation system as claimed in claim 1, wherein the analysis platform has a display screen, and the display screen is configured to display an index value determined by the machine learning model, for a rehabilitation therapist to observe a brain electrophysiological activity during the gait rehabilitation training of the user (D’Arcy: Data processing module 160; user interface 110; [0097] “EEG data to quantify motor function can be implemented in the context of one or more artificial neural network models; [0099] output measures may be arranged geometrically such that the motor control score is represented as a shape; [0040] The EEG acquisition and processing system disclosed herein can be used by clinicians, physiotherapists and other healthcare professionals as a tool for assessing motor function at a particular point during rehabilitation after brain injury”).
Regarding claim 3, the combination of Gao/Einav/Shideler/D’Arcy discloses the lower limb rehabilitation system based on augmented reality and the brain computer interface as claimed in claim 1, wherein the plurality of virtual scene videos has different rehabilitation difficulty levels, and the analysis platform is configured to select one of the virtual scene videos having the corresponding difficulty level according to the index value of the user previously obtained, for the user to perform gait rehabilitation training in conformity with a current status of the user (Shideler: [0052] “The display 10 may be used to train a variety of subjects within a wide range of gait abilities from young, healthy subjects to subjects with limited mobility [or limited motor function via EEG data per D’Arcy]. An easier setting or training episode could display targets 12 spaced evenly in both the mediolateral and forward directions to train a consistent gait pattern, useful for elderly subjects in rehabilitation. A more difficult setting or training regime could display targets 12 that appear at varying or random mediolateral positions and at varying step-length separations, useful for training gait adaptability for a changing environment.”).
Claims 5 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Gao/Einav/Shideler/D’Arcy as applied to claim 1 above, and further in view of Lee (US 20120116258 A1).
Regarding claim 5, the combination of Gao/Einav/Shideler/D’Arcy discloses the lower limb rehabilitation system based on augmented reality and the brain computer interface as claimed in claim 1. However, the combination of Gao/Einav/Shideler/D’Arcy fails to disclose specific locations of the motion sensors.
Lee’258 teaches an apparatus for lower-limb rehabilitation training of a patient with lower limb paralysis and a rehabilitation apparatus using game device. Lee discloses wherein the plurality of motion sensors is configured to be respectively disposed on a waist, two thighs, two calves, and at least one instep of the user, and a plurality of reference planes is defined by positions of the plurality of motion sensors (Lee’258: Fig. 6; [0066] “FIG. 6 illustrates the variable resistance goniometer 220 used as the joint angle-measuring part 200. As shown in FIG. 6, the variable resistance goniometer 220 includes a variable resistor 221, which is variable in resistance according to the angle of the joint, and an attachment support 222 for fixing the variable resistor 221 to the lower limb. Like the attachment support 212 of the electric goniometer 210, the attachment support 222 is placed on the thigh and the calf in case of the knee joint goniometer 201, on the thigh and the foot in case of the ankle joint goniometer 203, and on the waist and the thigh in case of the hip joint goniometer 202. The variable resistor 221 of the variable resistance goniometer 220 has a resistance adjuster that is provided to move along with the joint so that the resistance of the variable resistor is varied according to changes in the angle of the joint. In this way, it is possible to measure the angle of the joint based upon the resistance varying in response to the angle of the joint.”).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the rehabilitation system of the Gao/Einav/Shideler/D’Arcy to include the locations of the plurality of motion sensors as disclosed in Lee’258 in order to directly measure the angle of at least one of the knee joint, the ankle joint and the hip joint (i.e., coax) of the lower limb, or indirectly measure the joint angle of the lower limb to detect the usage of the paralyzed muscles thereby enabling rehabilitation training for the paralyzed muscles based upon measurement data, and a rehabilitation apparatus using game device (Lee’258 [0013]).
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Gao/Einav/Shideler/D’Arcy as applied to claim 1 above, and further in view of Lee (KR 20140063362 A).
Regarding claim 7, the combination of Gao/Einav/Shideler/D’Arcy discloses the lower limb rehabilitation system as claimed in claim 1. However, the Gao/Einav/Shideler/D’Arcy combination fails to disclose a functional electrical stimulator configured to stimulate a tibialis anterior muscle.
Lee‘362 teaches functional brain-computer interface-based electrical stimulation therapy device for training lower body function and walking. Lee’362 discloses:
the lower limb rehabilitation system further comprising a functional electrical stimulator coupled to the analysis platform (FES electrode unit 410; Pg. 5, [4], “control unit 300 obtains the concentration index from the received EEG, generates an FES driving signal according to the concentration index, and transmits the FES driving signal to the FES driving unit 450”),
wherein the functional electrical stimulator is configured to be disposed on the lower limb of the user, and is configured to electrically stimulate a tibialis anterior muscle of the user, to cause the tibialis anterior muscle of the user to contract (Pg. 8, [5], “FES electrodes can be placed at the proximal part of the tibialis anterior [5 cm below the fibular head]”; Pg. 5, [4], “to excite the muscles of the patient's legs, thereby moving the legs of the patient”).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the rehabilitation system of the Gao/Einav/Shideler/D’Arcy to include a functional electrical stimulator configured to stimulate a tibialis anterior muscle as disclosed in Lee’362 to improve coordination ability in the walking cycle and improve the walking quality such as walking speed and angle of the ankle joint (Lee’362, Pg. 2, [4]).
Regarding claim 8, the Gao/Einav/Shideler/D’Arcy/Lee’362 combination discloses the lower limb rehabilitation system as claimed in claim 7, the lower limb rehabilitation system further comprising an alarm coupled to the analysis platform wherein the analysis platform is configured to evaluate whether the index value is greater than an index threshold and if an evaluation result is no, the analysis platform controls the alarm to transmit a warning signal to remind a rehabilitation therapist to adjust a parameter of the functional electric stimulator (D’Arcy: [0055] system controller 170 may query acquisition hardware interface 120 of its status. If the status indicates that there is no active connection, or the attempts to connect to the EEG and/or EOG hardware fail, then an error indicator or a message can be generated for display on user interface 110 as described previously. Alternatively, a sound may be generated to alert the user of a connection problem. These alerts would prompt the user to address the connection problem.” Lee’362: FES electrode unit 410; Pg. 5, [4], “control unit 300 obtains the concentration index from the received EEG, generates an FES driving signal according to the concentration index, and transmits the FES driving signal to the FES driving unit 450.”).
Conclusion
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/M.H./Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791