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 .
Claim Objections
Claim 4 is objected to because of the following informalities:
Claim 4 reads “performing synthesized control of voluntary control of… and autonomous control of… and causing the drive unit to generate power…”, Claim 4 should change to “performing synthesized control, the synthesized control comprising: voluntary control of… , and autonomous control of…, and causing the drive unit to generate power…” to clearly indicate that synthesized control is applied to the voluntary control and autonomous control for generating power.
Appropriate correction is required.
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: drive unit, voluntary control unit, periarticular detection unit, autonomous control unit, drive current generation unit, gait synchronization calculation unit, signal normalization unit, similarity calculation unit, gait function evaluation unit in claim 1-3.
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. The corresponding structures are: actuators for the drive unit (par. 0060); the voluntary control unit, autonomous control unit, drive current generation unit, gait synchronization calculation unit, signal normalization unit, similarity calculation unit, gait function evaluation unit are interpreted as a central processing unit (CPU) chip having a memory with corresponding functions (par. 0048, “The control apparatus 30 is constituted with, for example, a central processing unit (CPU) chip having a memory and includes a voluntary control unit 50, an autonomous control unit 51, a phase determination unit 52, and a gain change unit 53”; par. 0068, “The gait function evaluation apparatus 70 is a control-system component provided inside the control apparatus 30 in the wearable motion support apparatus 2 described above, and as illustrated in Figure 4, includes a gait synchronization calculation unit 71, a signal normalization unit 72, a similarity calculation unit 73, a gait function evaluation unit 74, and a gait speed calculation unit 75”), and potentiometer or angle sensor for periarticular detection unit (par. 0061).
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
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, 2, 4, 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US20110071442), hereafter Park, in view of Lee et al. (US10434027), hereafter Lee.
Regarding Claim 1, Park discloses a gait function evaluation apparatus (Abstract, Fig. 1) that evaluates a gait function of a subject (par. 0009, “analyzing and determining whether the gait training is correctly performed”) using a wearable motion support apparatus (Fig. 1, walking assist robot 100) that provides to the subject, power in accordance with each of gait phases constituting gait motion of the subject (par. 0011, “a robot for gait training including… about a speed, angle and rotational force of each joint required for training of the walking trainee”), the wearable motion support apparatus comprising: a drive unit (Fig. 1, joint actuators of the robot 100) that actively or passively performs driving in coordination with lower extremity motion of the subject (par. 0058, “a robot including joints corresponding to legs of a human body and driven in specific patterns);
Park is silent on a biological signal detection unit disposed in a region of a body surface of the subject based on joints associated with the lower extremity motion of the subject and including electrodes for detecting a biological potential signal of the subject.
However, Lee teaches a gait function evaluation apparatus (Abstract, Fig. 1) that evaluates a gait function of a subject (col. 7 line 4-5, “The present disclosure is applied to gait rehabilitation robots and medical equipment for lower limb rehabilitation”) using a wearable motion support apparatus (Fig. 1, the walk assist robot 10) that provides to the subject, power in accordance with each of gait phases constituting gait motion of the subject (Fig. 2, providing power in accordance with each gait is the intended use of the walk assist robot), the wearable motion support apparatus comprising: a drive unit that actively or passively performs driving in coordination with lower extremity motion of the subject (col. 5 line 63-66, “Accordingly, a speed intention of the walking trainee may be recognized by suitably selecting a gain K, an assisting force of the walk assist robot may be increased or decreased by adjusting the dimension of gain”); a biological signal detection unit (Fig. 1, electromyogram (EMG) signal measurement unit 12) disposed in a region of a body surface of the subject based on joints associated with the lower extremity motion of the subject (col. 2, line 48-50, “an EMG signal measurement unit disposed on a muscle related to ankle joint extension of the walking trainee” ) and including electrodes for detecting a biological potential signal of the subject (col. 4 line 34-36, “an electromyogram (EMG) signal measurement unit 12 is arranged at the position of muscles related to extension of the ankle joints of the walking trainee”; an EMG measurement unit includes electrodes for detecting biological potential signal). Lee further teaches a signal normalization unit for the biological signal detection unit (Lee, col. 2 line 59, “a signal processing unit”) that normalizes the biological potential signal (Lee, col. 2 line 59-63, “the control unit of the present disclosure may further include a signal processing unit to process the joint angle signal and the plantar pressure signal, a waveform length calculation unit to calculate a waveform length from the EMG signal”). Therefore, it would have been obvious for one of ordinary skilled in the art to modify the known apparatus of Park, and further include the biological signal detection unit and the signal normalization unit of Lee in the wearable motion support apparatus of Park, to record EMG signals of the user’s muscles for determining the walking speed of the user as taught by Lee (Lee, col. 2 line 45-58).
The modified Park further discloses a voluntary control unit (Park, controller 400, par. 0011, “a controller (400) including an input unit (410) for receiving or inputting information or commands about the size of the body of the walking trainee, and about a speed, angle and rotational force of each joint required for training of the walking trainee”) that causes the drive unit to generate power in accordance with a will of the subject based on the biological potential signal acquired by the biological signal detection unit (Lee, col. 2 line 52-68, “a control unit to recognize signals measured from the joint angle signal measurement unit, the EMG signal measurement unit and the plantar pressure signal measurement unit and process the signals to recognize a walking speed intention of the walking trainee, wherein the control unit controls a walking speed of the walk assist robot from the walking speed intention of the walking trainee”); a periarticular detection unit (Park par. 0012, a position sensor) that detects a physical amount around the joints associated with the lower extremity motion of the subject based on an output signal from the drive unit (Park, par. 0012, “a position sensor for transmitting a position of each joint of the walking-assist robot (100) to the control unit of the controller (400)”); an autonomous control unit (Park, controller 400) that determines each of the gait phases in accordance with a gait task of the subject (Park, par. 0017, “a gait training or a walking test”) based on the physical amount detected by the periarticular detection unit (Park, Fig. 10 shows an operating method of apparatus, par. 0017, “an information input step of acquiring the size of the body of the walking trainee, information about a walking pattern obtained through a gait training or a walking test”; the apparatus obtains physical amounts through a gait training task, therefore the controller 400 includes an autonomous control unit) and causes the drive unit to generate power in accordance with each of the gait phases (Park, par. 0017, “a controller (400) for receiving and selectively storing information about the size of the body of the walking trainee, and speed, angle and rotational force of each joint… controlling driving states of the walking-assist robot (100), the treadmill (200) and the load hoist (300)”; see Fig. 5); a drive current generation unit (Park, controller 400) that synthesizes a control signal from the voluntary control unit and a control signal from the autonomous control unit (Park, par. 0012, “a signal from the control unit of the controller (400)”) and supplies a drive current in accordance with the synthesized control signal to the drive unit (Park, par. 0012, “a gear member for receiving a signal from the control unit of the controller (400) and adjusting the position of each joint and lengths of segments of the walking-assist robot (100).”; adjusting the position of each joint includes supplying a current to the drive unit); a gait synchronization calculation unit (Park, par. 0019, “the walking-assist robot (100) may include… a both leg speed synchronization step of driving the treadmill at the same speed as the walking speed obtained in the walking speed calculating step”) that calculates a gait cycle of the subject (Par. par. 0078 discloses calculating the gait for gait synchronization) based on a detection result of a floor reaction force sensor (par. 0013, “a pressure sensor for transmitting the signal of a contact between a sole of the walking trainee and the treadmill (200) to the control unit of the controller (400)”) that detects a pressure distribution to sole surfaces of right and left feet of the subject (Park, Fig. 9 shows pressure distribution by displaying the contact of user’s sole, par. 0030 discloses applying the pressure sensor to both the left and right feet); a signal normalization unit (Lee, col. 2 line 59, a signal processing unit) that normalizes the biological potential signal detected by the biological signal detection unit to a first signal pattern expressed in a planar coordinate system of time and an amplitude for each gait cycle (See Lee Fig. 3, the biological potential signal are expressed in a planar coordinate system of time and amplitude, col. 2 line 59-63, “the control unit of the present disclosure may further include a signal processing unit to process the joint angle signal and the plantar pressure signal, a waveform length calculation unit to calculate a waveform length from the EMG signal”) based on the physical amount detected by the periarticular detection unit and the gait cycle calculated from the gait synchronization calculation unit (Park par. 0017, “…a training data output step of outputting the information input or stored in the training data generating step on a screen of the a monitor (420) of the controller in real time”; Park teaches outputting signal based on physical amount detected by the periarticular detection unit and the gait cycle calculated from the gait synchronization calculation unit ); a similarity calculation unit (Park, controller 400) that compares the first signal pattern obtained from the signal normalization unit (Park, par. 0026, “information about the angle, speed, rotational force, and hoist level of each joint inputted in real time in the training data generating step”) and a second signal pattern corresponding to a healthy subject who serves as a reference (Park, par. 0026, “information about the angle, speed, rotational force and hoist level of each joint in a standard type appropriate for the walking trainee”) and quantitatively calculates similarity between the first signal pattern and the second signal pattern (Park, par. 0027, “checking and comparing a difference between the standard walking pattern and the currently performed walking and displaying both the standardized walking patterns appropriate for the training for the walking trainee and the current walking on one screen.”; par. 0018, “a length comparison and calculation step of comparing the lengths of the segments obtained in the segment length calculation step and the size data of the body of the walking trainee input in the information input step and calculating differences therebetween”); and a gait function evaluation unit (Park, controller 400) that evaluates the gait function of the subject based on the similarity calculated by the similarity calculation unit (Park, Fig. 3, par. 0052, “an output displayed on a screen, showing a speed and angle of a hip joint and a speed and angle of a knee joint, which are inputted during training in real time, and pre-input information about a standardized walking pattern appropriate for the training of the walking trainee”; the gait function is evaluated by determining the similarity between the gait of the user and the standardized gait using the graphs).
Regarding Claim 2, the modified Park discloses the gait function evaluation apparatus according to claim 1, comprising: a gait speed calculation unit (Park, controller 400) that obtains a step length in the gait motion of the subject based on lengths of legs of the subject input in advance (Park, par. 0074, “In the segment length calculation step, a relative distance between position data of the respective joints inputted in the joint position input step is calculated to obtain the lengths of the segments of the walking-assist robot 100”) and transition of the physical amount detected by the periarticular detection unit (Park, par. 0073, “In the joint position input step, the position of each joint of the walking-assist robot 100 is inputted from the position sensor of the walking-assist robot 100. In the segment length calculation step, a relative distance between position data of the respective joints inputted in the joint position input step is calculated to obtain the lengths of the segments of the walking-assist robot 100.”) and calculates a gait speed of the subject based on the step length and the gait cycle calculated from the gait synchronization calculation unit (Park, par. 0019, “a walking speed calculation step of calculating a walking speed of the walking trainee by dividing a stride between two legs by a walking cycle defined by a time difference of the contacts of the two legs with the treadmill (200) during one stride of the walking trainee in real time or periodically”), wherein the gait function evaluation unit analyzes a correlation between the similarity calculated by the similarity calculation unit and a gait distance per predetermined time period based on the gait speed calculated by the gait speed calculation unit (Park, par. 0026, “in the training data output step, both the information about the angle, speed, rotational force and hoist level of each joint in a standard type appropriate for the walking trainee previously inputted in the information input step and the information about the angle, speed, rotational force, and hoist level of each joint inputted in real time in the training data generating step may be displayed together on one screen”; the angle is calculated from the similarity calculation unit and speed is from the gait speed calculation unit).
Regarding Claim 4, Park discloses a gait function evaluation method for evaluating a gait function of a subject (par. 0003, “a robot for gait training and an operating method thereof for the purpose of rehabilitation of patients with walking disability”) using a wearable motion support apparatus that provides to the subject (Fig. 1, walking assist robot 100), power in accordance with each of gait phases constituting gait motion of the subject (par. 0011, “a robot for gait training including… about a speed, angle and rotational force of each joint required for training of the walking trainee”), the wearable motion support apparatus comprising a drive unit (Fig. 1, joint actuators of the robot 100) that actively or passively performs driving in coordination with lower extremity motion of the subject (par. 0058, “a robot including joints corresponding to legs of a human body and driven in specific patterns).
Park is silent on performing voluntary control of causing the drive unit to generate power in accordance with a will of the subject based on a biological potential signal acquired from a region of a body surface of the subject based on joints associated with the lower extremity motion of the subject.
However, Lee teaches a gait function evaluation method (col. 1 line 31-35, “a method which selects a linear or non-linear function in proportion to a plantar flexor EMG waveform length maximum value in the stance phase during the gait cycle of a walking trainee, sets coefficients of the function…”), comprising of using a wearable motion support apparatus (Fig. 1, the walk assist robot 10) that provides to the subject, power in accordance with each of gait phases constituting gait motion of the subject (Fig. 2, providing power in accordance with each gait is the intended use of the walk assist robot), the wearable motion support apparatus comprising: a drive unit that actively or passively performs driving in coordination with lower extremity motion of the subject (col. 5 line 63-66, “Accordingly, a speed intention of the walking trainee may be recognized by suitably selecting a gain K, an assisting force of the walk assist robot may be increased or decreased by adjusting the dimension of gain”), performing voluntary control of causing the drive unit to generate power in accordance with a will of the subject (col. 5 line 63-66, “Accordingly, a speed intention of the walking trainee may be recognized by suitably selecting a gain K, an assisting force of the walk assist robot may be increased or decreased by adjusting the dimension of gain”) based on a biological potential signal acquired from a region of a body surface of the subject (Fig. 1, electromyogram (EMG) signal measurement unit 12; an EMG measurement unit includes electrodes for detecting biological potential signa) based on joints associated with the lower extremity motion of the subject (col. 4 line 34-36, “an electromyogram (EMG) signal measurement unit 12 is arranged at the position of muscles related to extension of the ankle joints of the walking trainee”). Therefore, it would have been obvious for one of ordinary skilled in the art to modify the known method of Park ,with the method of Lee, to record EMG signals of the user’s muscles for determining the walking speed of the user as taught by Lee (Lee, col. 2 line 45-58).
The modified Park further discloses performing synthesized control of voluntary control of causing the drive unit to generate power in accordance with a will of the subject based on a biological potential signal acquired from a region of a body surface of the subject based on joints associated with the lower extremity motion of the subject (Lee, col. 5 line 63-66) and autonomous control of determining each of the gait phases (Park, controller 400, par. 0017, “a gait training or a walking test”) in accordance with a gait task of the subject based on a physical amount around the joints associated with the lower extremity motion of the subject detected based on an output signal of the drive unit and causing the drive unit to generate power corresponding to each of the gait phases (Park, Fig. 10 shows an operating method of apparatus par. 0017, “selectively storing information about the size of the body of the walking trainee, and speed, angle and rotational force of each joint… controlling driving states of the walking-assist robot (100), the treadmill (200) and the load hoist (300)”; see Fig. 5) and supplying a drive current in accordance with the synthesized control to the drive unit (Park, par. 0012, “a gear member for receiving a signal from the control unit of the controller (400) and adjusting the position of each joint and lengths of segments of the walking-assist robot (100).”), the gait function evaluation method comprising: a first step of normalizing the biological potential signal to a first signal pattern expressed in a planar coordinate system of time and an amplitude for each gait cycle based on the physical amount around the joints (Lee, col. 2 line 59-63, “the control unit of the present disclosure may further include a signal processing unit to process the joint angle signal and the plantar pressure signal, a waveform length calculation unit to calculate a waveform length from the EMG signal”; Park par. 0017, “a controller (400)… and numerically or graphically displaying the information”) and a gait cycle calculated based on a detection result of a pressure distribution to sole surfaces of right and left feet of the subject (Park, Fig. 7 and 9, par. 0019, “a pressure sensor for transmitting a signal for contact between a sole of the walking trainee and the treadmill (200) to the control unit of the controller (400)”); a second step of comparing the first signal pattern obtained from the first step and a second signal pattern corresponding to a healthy subject who serves as a reference and quantitatively calculating similarity between the first signal pattern and the second signal pattern (Park, par. 0027, “checking and comparing a difference between the standard walking pattern and the currently performed walking and displaying both the standardized walking patterns appropriate for the training for the walking trainee and the current walking on one screen.”; par. 0018, “a length comparison and calculation step of comparing the lengths of the segments obtained in the segment length calculation step and the size data of the body of the walking trainee input in the information input step and calculating differences therebetween”); and a third step of evaluating the gait function of the subject based on the similarity calculated in the second step (Park, Fig. 3, par. 0052, “an output displayed on a screen, showing a speed and angle of a hip joint and a speed and angle of a knee joint, which are inputted during training in real time, and pre-input information about a standardized walking pattern appropriate for the training of the walking trainee”; the gait function is evaluated by determining the similarity between the gait of the user and the standardized gait using the graphs).
Regarding Claim 5, the modified Park discloses the gait function evaluation method according to claim 4, wherein a step length in the gait motion of the subject is obtained based on lengths of legs of the subject input in advance and transition of the physical amount around the joints (Park, par. 0011, “an input unit (410) for receiving or inputting information or commands about the size of the body of the walking trainee, and about a speed, angle and rotational force of each joint required for training of the walking trainee”), and a gait speed of the subject is calculated based on the step length and the gait cycle (Park, par. 0019, “a walking speed calculation step of calculating a walking speed of the walking trainee by dividing a stride between two legs by a walking cycle defined by a time difference of the contacts of the two legs with the treadmill (200) during one stride of the walking trainee in real time or periodically”), and in the third step, a correlation between the similarity calculated in the second step and a gait distance per predetermined time period based on the calculated gait speed is analyzed (Park, par. 0026, “in the training data output step, both the information about the angle, speed, rotational force and hoist level of each joint in a standard type appropriate for the walking trainee previously inputted in the information input step and the information about the angle, speed, rotational force, and hoist level of each joint inputted in real time in the training data generating step may be displayed together on one screen”).
Claim(s) 3 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park, in view of Lee, further in view of Hu et al. (US20170087416), hereafter Hu.
Regarding Claim 3, the modified Park discloses the gait function evaluation apparatus according to claim 1, but is silent on wherein the similarity calculation unit compares shapes of the first signal pattern and the second signal pattern on a time-series basis using differential dynamic time warping (DDTW) and calculates pattern similarity as the similarity from correspondence relationships of an ascent trend and a descent trend.
However, Hu teaches a gait function evaluation apparatus (Fig. 1B), comprising of measuring the gait pattern of a user (par. 0006-0009, “The system can include a thermal imaging device for imaging a subject or a plurality of sensors for sensing force or pressure exerted by a portion of the subject's body.”) and a similarity calculation unit (par. 0063, “the computing device 200 can be configured to calculate motion cycle similarity scores (e.g., a quantitative measure of the subject's limb motion)”; the computing device 200 calculates a similarity score and therefore is a similarity calculation unit), wherein the similarity calculation unit compares shapes of the first signal pattern and the second signal pattern on a time-series basis using differential dynamic time warping (DDTW) (par. 0076-0077, “Another method for recognition or matching the time series is Dynamic Time Warping (DTW). It can calculate the similarity between two time series with different lengths… Keogh et al. have proposed a Derivative Dynamic Time Warping (DDTW), which alleviates the above-mentioned mismatch problem of DTW by introducing the first order derivative of the time series”; Fig. 5A and 5B) and calculates pattern similarity as the similarity from correspondence relationships of an ascent trend and a descent trend (Fig. 5A and 5B shows DTW analysis in an ascent and descent trend). Therefore, it would have been obvious for one of ordinary skilled in the art to further modify the known apparatus of Park, with the similarity calculation unit of Hu, for calculating the gait similarity between gait signal patterns with different time lengths and prevent mismatch data points as taught by Hu (Hu, par. 0076-0077).
Regarding Claim 6, the modified Park discloses the gait function evaluation method according to claim 4, but is silent on wherein in the third step, shapes of the first signal pattern and the second signal pattern are compared on a time-series basis using differential dynamic time warping (DDTW), and pattern similarity is calculated as the similarity from correspondence relationships of an ascent trend and a descent trend.
However, Hu teaches a gait function evaluation method (Abstract), comprising of measuring the gait pattern of a user (par. 0006-0009, “The system can include a thermal imaging device for imaging a subject or a plurality of sensors for sensing force or pressure exerted by a portion of the subject's body.”) and calculating a similarity between two signal patterns (par. 0063, “the computing device 200 can be configured to calculate motion cycle similarity scores (e.g., a quantitative measure of the subject's limb motion)”, ), wherein shapes of the first signal pattern and the second signal pattern are compared on a time-series basis using differential dynamic time warping (DDTW) (par. 0076-0077, “Another method for recognition or matching the time series is Dynamic Time Warping (DTW). It can calculate the similarity between two time series with different lengths… Keogh et al. have proposed a Derivative Dynamic Time Warping (DDTW), which alleviates the above-mentioned mismatch problem of DTW by introducing the first order derivative of the time series”; Fig. 5A and 5B), and pattern similarity is calculated as the similarity from correspondence relationships of an ascent trend and a descent trend (Fig. 5A and 5B shows DTW analysis in an ascent and descent trend). Therefore, it would have been obvious for one of ordinary skilled in the art to further modify the known method of Park, with the method of Hu, for calculating the gait similarity between gait signal patterns with different time lengths and prevent mismatch data points as taught by Hu (Hu, par. 0076-0077).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US8613691 discloses a robot rehabilitation apparatus for subjects with lower limb gait impairment and measures biopotential in the user’s leg muscles.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRIS HANYU GONG whose telephone number is (703)756-5898. The examiner can normally be reached M-F 8:30-4:30.
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/KRIS HANYU GONG/Examiner, Art Unit 3785
/BRANDY S LEE/Supervisory Patent Examiner, Art Unit 3785
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