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 .
Information Disclosure Statement
The Information Disclosure Statement filed 12/02/2024 has been considered by the Examiner.
Election/Restrictions
Claims 16-22 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected inventions, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 06/05/2026.
Applicant’s election without traverse of Group I (Claims 1-12 and 15) in the reply filed on 06/05/2026 is acknowledged.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 600, 700, S705. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
*Reference character 600 is mentioned in Par. [0056], but not shown in Fig. 6. Reference character 700 is mentioned in Par. [0057], but not shown in Fig. 7. Reference character S705 is mentioned in Par. [0057], but not shown in Fig. 7.
The drawings are objected to because Fig. 1 includes an arrow pointing to an element of the figure, without a corresponding reference character. It is believed that reference character 12 (IMU) was intended to correspond to this arrow. The drawings are also objected to because the box in the middle of Fig. 2 should be labeled with a reference character. It is believed that this box should be labeled with reference character 14 (control unit). The drawings are also objected to because “FEES” in box S604 should be “FES”. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Par. [0057]: “to define a phase vector” should be changed to “to define a phase vector (Step S703)”. Other variations are possible.
Par. [0057]: “step S703” should be changed to “step S704”.
Par. [0057]: “step S704” should be changed to “step S705”.
Appropriate correction is required.
Claim Objections
Claim 5 objected to because of the following informalities:
Line 1: “of gait a subject” should be changed to “of gait of a subject”.
Claim 9 objected to because of the following informalities:
Line 3: “a plurality of sub-gait phase” should be changed to “a plurality of sub-gait phases”.
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 limitations are:
“estimating a gait phase of the subject using the control unit” in claim 1.
“wherein the step of estimating the gait phase of the subject using the control unit comprises: … determining the probability of the phase vector lying within each of a number of pre-defined gait phases” in claim 3.
Because these claim limitations 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 Examiner notes that for a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function (MPEP 2181(II)(B)). Evidence of such an algorithm for covering the corresponding structure, material, or acts are found in these locations of the specification:
Claim 6; Fig. 4, # 30, 32, and 34; Par. [0048] – In some embodiments, the phase vector (30) may be compared to a plurality of baseline gait phase vectors stored in the neural network to assign a similarity score for the phase vector (30) against each baseline gait phase vector. Through further processing, a probability distribution is assigned over gait phase labels 1 to 13 for the phase vector (30) … A probability of the phase vector (30) lying within a gait phase identified by each gait phase label may be determined by reference to the similarity score between the subject's phase vector (30) and each of the baseline phase vectors. In some embodiments, the gait phase label having the highest probability of the phase vector lying within the represented gait phase may be selected.
Claim 6; Fig. 4, # 30, 32, and 34; Par. [0048] – In some embodiments, the phase vector (30) may be compared to a plurality of baseline gait phase vectors stored in the neural network to assign a similarity score for the phase vector (30) against each baseline gait phase vector. Through further processing, a probability distribution is assigned over gait phase labels 1 to 13 for the phase vector (30) … A probability of the phase vector (30) lying within a gait phase identified by each gait phase label may be determined by reference to the similarity score between the subject's phase vector (30) and each of the baseline phase vectors. In some embodiments, the gait phase label having the highest probability of the phase vector lying within the represented gait phase may be selected.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-12 and 15 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "the probability of the phase vector lying within each of a number of pre-defined gait phases" in lines 6-7. There is insufficient antecedent basis for this limitation in the claim.
Claim 5 recites the limitation "the raw data" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the likelihood of the phase vector lying within a gait phase represented by each of the gait phase labels" in lines 5-6. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the step of selecting the gait phase label having the maximum probability of the phase vector lying therein" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claims 10-11 recites the limitation "the subject’s gait cycle" in line 2. There is insufficient antecedent basis for this limitation in the claim. The Examiner believes it would be best if this term was introduced in claim 8.
Claim 12 recites the limitation "the step of applying FES" in line 2. There is insufficient antecedent basis for this limitation in the claim. The Examiner ponders whether this claim 12 was intended to depend from claim 2?
Claim 12 recites the limitation "the FES controller" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation “FES” in line 3, whereas FES was already introduced in claim 12 (line 2). It is unclear whether the Applicant intended to claim the same or a different FES. Consider changing to “the FES”.
Claim 15 recites the limitation "the method for applying FES to a subject" in line 1. There is insufficient antecedent basis for this limitation in the claim.
*All other claims are rejected due to their dependency on a rejected claim.
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, 3-11, and 15 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of estimating a gait phase of a subject and executing a pre-determined instruction and an action) without significantly more.
Step 1
Independent claim 1 is directed to a method, and thus meets the requirements of step 1.
Step 2A, Prong 1
“estimating a gait phase of the subject” is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(III), the mental process grouping includes observations, evaluation, judgements, and opinions. In this case, a human could mentally estimate a gait phase of the subject by observing and evaluating a subject while they are walking.
“executing at least any one of a pre-determined instruction and an action” is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(III), the mental process grouping includes observations, evaluation, judgements, and opinions. In this case, a pre-determined instruction or action can be interpreted broadly, and observations, evaluation, judgements, and opinions can all be considered instructions or actions.
Step 2A, Prong 2
Regarding claim 1, the claim does not include any additional elements that integrate the abstract idea into a practical application. The following elements do not add any meaningful limitation to the abstract idea:
transmitting from at least one motion sensor, data corresponding to motion of a part of a lower limb of the subject to a control unit – insignificant pre-solution activity, i.e. mere data gathering [MPEP 2106.05(g)]
… using the control unit – The control unit is described at a high level of generality in the Applicant’s specification, with “control unit” being synonymous with many examples of processing circuitry including microcontroller units, digital signal control units, programmable logic devices, etc. (Par. [0052]). The involvement of the “control unit” is insignificant extra-solution activity in that it amounts to generic computer implementation of the abstract idea [MPEP 2106.04(a)(2)(III)(C)].
sending an input signal based on the estimated gait phase to a controller – The involvement of an input signal and a controller, which is described generally in the specification (see Par. [0052]), is insignificant extra-solution activity in that it amounts to generic computer implementation of the abstract idea [MPEP 2106.04(a)(2)(III)(C)].
Therefore, the claim is directed to an abstract idea without a practical application.
Step 2B
The additional elements of claim 1, when considered either individually or in an ordered combination, are not enough to qualify as significantly more than the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the “control unit”, “input signal”, and “controller”, along with their associated functions and components, are recited with a high level of generality and simply amount to implementing the abstract idea on a computer. The additional elements that were considered insignificant extra-solution activity have been re-analyzed and do not amount to anything more than what is well-understood, routine, and conventional. Also, simply appending well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept [MPEP 2106.05(d)].
transmitting from at least one motion sensor, data corresponding to motion of a part of a lower limb of the subject to a control unit – MPEP 2106.05(d)(II)(“i. Receiving or transmitting data over a network, e.g., using the internet to gather data”); Doheny, et al. (US 2012/0144916) – Conventional approaches to conducting gait analysis may involve mounting multiple gyroscopes to each leg of the subject (Par. [0004]).
… using the control unit – MPEP 2106.05(d)(II)(“i. Receiving or transmitting data over a network, e.g., using the internet to gather data”); Lee, et al. (US 2014/0188280) discussing KR 10-1179159 – This conventional apparatus includes a first sensor provided on an upper surface of each foot of the wearable robot where the toes of the foot of the wearer are positioned, a second sensor provided on the upper surface of each foot of the robot where the ball of the foot of the wearer is positioned, a third sensor provided on the upper surface of each foot of the robot where the heel of the foot of the wearer is positioned, and a controller which determines the intended gait of the wearer based on signals from the first, second and third sensors (Par. [0008]).
Sending an input signal based on the estimated gait phase to a controller – MPEP 2106.05(d)(II)(“i. Receiving or transmitting data over a network, e.g., using the internet to gather data”)
Therefore, the claim is directed to an abstract idea without a practical application and without significantly more.
Dependent claims
Regarding dependent claims 3, 6-11, and 15, the limitations only further define the abstract idea.
Regarding dependent claims 4-5, the limitations only further define insignificant extra-solution activity of gathering data.
*Regarding dependent claims 2 and 12, it is noted that these claims are not rejected under 35 U.S.C. 101. The 35 U.S.C. 101 rejections would be obviated by amending independent claim 1 to incorporate the limitations of claims 2 and/or 12. This is because the limitations of claims 2 and 12 provide a practical application by effecting a particular treatment (i.e., functional electrical stimulation to the lower limb of a subject) based on the estimating a gait phase step. The Examiner notes that it is believed that claim 12 was intended to depend from claim 2.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Mantovani, et al. (US 2020/0215324 – cited on IDS).
Regarding claim 1, Mantovani teaches (Figs. 3 and 5) a method for determining the phase of gait of a subject (Title; Abstract; Par. [0002]; Par. [0083] – the client device 100 and one or more servers 334 can determine at least one of a (1) foot strike pattern, (2) a foot inclination angle at initial contact, (3) a tibia angle at loading response, (4) a hip extension during late stance, (5) a trunk lean, (6) a heel eversion, (7) a foot progression angle, (8) a pelvic drop, (9) a knee flexion during stance, (10) a stride length, (11) a knee window, (12) a vertical displacement of the center mass, and (13) a heel whip of the user based in part on the mapped three-dimensional angles, the accelerometer readings, the gyroscope readings, and the gait cycle percentages 500 (see FIG. 5) calculated), the method comprising:
(Fig. 1, # 104, 106 – control unit; Fig. 3, # 106, 306 – IMU, i.e. motion sensor) transmitting from at least one motion sensor, data corresponding to motion of a part of a lower limb of the subject to a control unit (Par. [0036] – The second elastic sleeve 104 can be configured to be worn on or cover at least part of a lower leg of the user; Par. [0039]; Par. [0072] – The IMU 306 can comprise a gyroscope 314, an accelerometer 316, and a magnetometer 318; Par. [0083] – one or more processors 302 of the device 100 can be programmed to execute instructions stored in the one or more memory units 304 to retrieve accelerometer readings from the accelerometer 316 of the IMU 306. Once such readings are retrieved, the device 100 can transmit the readings to the client device 100 or the one or more servers 334 to map these accelerometer readings (along with gyroscope readings) to three-dimensional angles of at least one of a hip, a knee, and a foot of the user through a gait cycle of the user);
(Figs. 3, 5, and 7) estimating a gait phase of the subject using the control unit (Par. [0083]; Pars. [0090-0091] – The one or more processors 302 of the FES device 100 can be programmed to execute instructions stored in the one or more memory units 304 of the device 100 to retrieve real-time gyroscope readings and accelerometer readings from the IMU 306 and calculate the gait cycle percentage by inputting the gyroscope readings and accelerometer readings into a machine learning algorithm 700 (see FIG. 7)); and
(Figs. 1, 3, and 5) sending an input signal based on the estimated gait phase to a controller (Par. [0083] - … based in part on the mapped three-dimensional angles, the accelerometer readings, the gyroscope readings, and the gait cycle percentages 500 (see FIG. 5) calculated; Par. [0104] – FIG. 5 illustrates example operations for calculating a gait cycle percentage 500 and generating an asymmetrical biphasic square pulse 502 used to stimulate a neuromuscular system of a limb of a user; Par. [0111] – Once the device 100 has calculated/estimated the gait cycle percentage 500, the one or more processors 302 can instruct the EMS generator 310 to provide electrical stimulation to the nerves and muscles of the limb in physical contact with the electrodes 112 of the one or more electrode arrays 108);
(Figs. 1, 3, and 5) based on the input signal, executing at least any one of a pre-determined instruction and an action (Par. [0111] – Once the device 100 has calculated/estimated the gait cycle percentage 500, the one or more processors 302 can instruct the EMS generator 310 to provide electrical stimulation to the nerves and muscles of the limb in physical contact with the electrodes 112 of the one or more electrode arrays 108. In this manner, the electrical stimulation can be timed and set by the gait cycle percentages 500 calculated.).
Therefore, claim 1 is unpatentable over Mantovani, et al.
Regarding claim 2, Mantovani teaches the method for determining the phase of gait of a subject according to claim 1, wherein (Figs. 1, 3, and 5) said at least any one of a pre-determined instruction and an action comprises applying functional electrical stimulation (FES) to the lower limb of the subject (Par. [0111] – Once the device 100 has calculated/estimated the gait cycle percentage 500, the one or more processors 302 can instruct the EMS generator 310 to provide electrical stimulation to the nerves and muscles of the limb in physical contact with the electrodes 112 of the one or more electrode arrays 108. In this manner, the electrical stimulation can be timed and set by the gait cycle percentages 500 calculated.).
Therefore, claim 2 is unpatentable over Mantovani, et al.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3-12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mantovani, et al. (US 2020/0215324 – cited on IDS) in view of Su, et al. (“Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units”).
Regarding claim 3, Mantovani teaches the method for determining the phase of gait of a subject according to claim 1, as indicted hereinabove. Mantovani does not explicitly teach the limitations of instant claim 1, that is wherein the step of estimating the gait phase of the subject using the control unit comprises:
assigning a phase vector to data received by the control unit from the at least one motion sensor;
determining the probability of the phase vector lying within each of a number of pre-defined gait phases; and
estimating the gait phase of the subject by selecting the gait phase having the highest probability of the phase vector lying therein.
However, it is noted that Mantovani does teach that a machine learning algorithm (Fig. 7, # 700) can be optimized to fit the IMU data (e.g., the gyroscope readings) obtained from the IUM 306 coupled to the leg of the model subject to the two periodic functions (i.e., cosine and sine functions – see Fig. 5) (Par. [0097]). Mantovani further teaches that the machine learning algorithm 700 can be a multilayer perception neural network that can be optimized to fit the IMU data (more specifically, the gyroscope readings from the gyroscope 314) to the two periodic functions (Par. [0098]). Mantovani further teaches (Fig. 7, # 700) that once the machine learning algorithm 700 is optimized and able to fit the IMU data to the two periodic functions derived from the motion tracking data, the optimized machine learning algorithm 700 can be used by the device 100 to calculate the gait cycle percentage directly from IMU data obtained from any user wearing the IMU 306 in roughly the same body location as the model subject (e.g., on the anterior side of the lower leg below the knee) (Par. [0100]). Mantovani further teaches (Fig. 7) that as a more specific example, once the weights of the two multilayer perceptron neural networks are optimized and able to fit the IMU data (e.g., the gyroscope readings and/or the accelerometer readings) to the two periodic functions derived from the motion tracking data, the two multilayer perceptron neural networks can be used by the device 100 to calculate the gait cycle percentage directly from IMU data (more specifically, gyroscope readings) obtained from any user wearing the IMU 306 in roughly the same body location as the model subject (e.g., on the anterior side of the lower leg below the knee) (Par. [0101]).
While Mantovani does not explicitly teach the steps required by claim 3, it is found to be implicit that Mantovani’s described neural network would function by performing these steps. However, in order to not solely rely on implicitness, Su, et al. (“Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units”) is further relied on to teach and suggest these limitations.
Su teaches gait phase recognition using deep convolutional neural network with inertial measurement units (Title; Abstract). Su teaches the limitations of instant claim 3, that is wherein the step of estimating the gait phase of the subject using the control unit comprises:
assigning a phase vector to data received by the control unit from the at least one motion sensor (Abstract – User kinematics, measured from inertial measurement unit (IMU) output, can be considered as an ‘image’ since it exhibits some local ‘spatial’ pattern when the sensor data is arranged in sequence.; Introduction – When the IMU sensor data are put together to form a grid-shape ‘image’ as the input matrix, the DCNN should be able to map the input to an output vector of gait phases);
determining the probability of the phase vector lying within each of a number of pre-defined gait phases (3. DCNN Architecture – 3.1 Deep Convolutional Neural Networks (DCNN) – We then introduced a fully connected layer that gives the final probabilities for each gait phase. The number of nodes in the fully connected layer is 5, which matches the number of the predicted gait phases, therefore the output s is an array of 5. To transform the values in s to probability p, we used a Softmatrix activation (Equation (2)) which maps non-normalized output to the probability distribution over predicted classes.); and
estimating the gait phase of the subject by selecting the gait phase having the highest probability of the phase vector lying therein (3. DCNN Architecture – 3.1 Deep Convolutional Neural Networks (DCNN) – Finally the predicted class is assigned to the label with the highest probability).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have implemented Su’s steps for estimating the gait phase of the subject into Mantovani’s method because doing so would be an example of simple substitution of one known element for another to obtain predictable results. Since both Mantovani and Su teach using a neural network to make an estimation of gait phase, it would be predictable to one of ordinary skill in the art to implement Su’s neural network in place of Mantovani’s neural network to obtain predictable results. One of ordinary skill in the art would have also desired implementing Su’s neural network because of its 97% accuracy (Abstract) as opposed to Mantovani’s disclosed 90% accuracy (Par. [0102] of Mantovani).
Therefore, claim 3 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 4, Mantovani, in view of Su, renders obvious the method for determining the phase of gait of a subject according to claim 3, as indicated hereinabove. Su also teaches the limitation of instant claim 4, that is wherein the phase vector is assigned based on a combination of data captured by the said at least one motion sensor that is representative of linear and rotational motion in x, y and z axes (Conclusion – The presented DCNN model can recognize five gait phases from raw data of seven IMUs attached to the pelvic, thighs, shanks and feet. The proposed model can capture the inner kinematic representation of linear acceleration, rotational velocity and IMU magnetic field between different gait phases, and achieve an overall recognition accuracy of 97.5% on a well-trained model, with up to 99.6% accuracy in detecting the swing phase.).
Therefore, claim 4 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 5, Mantovani, in view of Su, renders obvious the method for determining the phase of gait a subject according to claim 4, as indicated hereinabove. Mantovani teaches the limitation of instant claim 5, that is wherein (Fig. 3, # 302, 304; Fig. 5; Fig. 7, # 700) the data received by the control unit from said at least one motion sensor is transformed through a neural network stored in non-volatile memory in communication with the control unit to transform the raw data from said at least one motion sensor into the phase vector (Par. [0042] – One unexpected discovery made by the applicant is that gyroscope readings obtained from a gyroscope 314 positioned on the anterior side of the lower leg of the user below the knee of the user results in more robust input data that can be introduced to a machine learning algorithm 700 (see FIG. 7) to map to periodic functions used to calculate a more accurate gait cycle percentage 500 (see FIG. 5).; Par. [0092]; Pars. [0098-0100]).
Therefore, claim 5 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 6, Mantovani, in view of Su, renders obvious the method for determining the phase of gait of a subject according to claim 5, as indicated hereinabove. Su also teaches the limitation of instant claim 6, that is wherein the neural network comprises a plurality of baseline gait phase vectors against which the phase vector is compared and assigned a similarity score (3. DCNN Architecture – 3.1 Deep Convolutional Neural Networks (DCNN) – We then introduced a fully connected layer that gives the final probabilities for each gait phase. The number of nodes in the fully connected layer is 5, which matches the number of the predicted gait phases, therefore the output s is an array of 5. To transform the values in s to probability p, we used a Softmatrix activation (Equation (2)) which maps non-normalized output to the probability distribution over predicted classes.; 3.2. DCNN Performance Evaluation – training on 70% of data (i.e., baseline) and then tested with 30% data; 5. Discussion – The DCNN has been widely used for image classification which means it can also capture the features of the IMU ‘images’ by recognizing the similarity of the same phases and distinguishing the discrepancy of different gait phases.), and wherein the similarity score is used to define a probability distribution over a plurality of gait phase labels to determine the likelihood of the phase vector lying within a gait phase represented by each of the gait phase labels (3. DCNN Architecture – 3.1 Deep Convolutional Neural Networks (DCNN) – We then introduced a fully connected layer that gives the final probabilities for each gait phase. The number of nodes in the fully connected layer is 5, which matches the number of the predicted gait phases, therefore the output s is an array of 5. To transform the values in s to probability p, we used a Softmatrix activation (Equation (2)) which maps non-normalized output to the probability distribution over predicted classes.).
Therefore, claim 6 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 7, Mantovani, in view of Su, renders obvious the method for determining the phase of gait of a subject according to claim 6, as indicated hereinabove. Su also teaches the limitation of instant claim 7, that is wherein the method further comprises the step of selecting the gait phase label having the maximum probability of the phase vector lying therein (3. DCNN Architecture – 3.1 Deep Convolutional Neural Networks (DCNN) – Finally the predicted class is assigned to the label with the highest probability).
Therefore, claim 7 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 8, Mantovani, in view of Su, renders obvious the method for determining the phase of gait of a subject according to claim 7, as indicated hereinabove. Su also teaches the limitation of instant claim 8, that is wherein (Fig. 3) the selected gait phase identifies whether the gait phase of the subject is any one of stationary, in a stance phase, and in a swing phase (2. Experimental Setup and Methods – The labeling procedure of each gait phase was implemented by defining the phases detected by foot switches as the “ground truth” for 5 gait phases: loading response (LR), midstance (MS), terminal stance (TS), pre-swing (PSw) and swing (SW)).
Therefore, claim 8 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 9, Mantovani, in view of Su, renders obvious the method for determining the phase of gait of a subject according to claim 8, as indicated hereinabove. Su also teaches the limitation of instant claim 9, that is wherein (Fig. 3) at least one of the stance phase and swing phase is sub-divided into a plurality of sub-gait phase represented by the gait phase labels (2. Experimental Setup and Methods – The labeling procedure of each gait phase was implemented by defining the phases detected by foot switches as the “ground truth” for 5 gait phases: loading response (LR), midstance (MS), terminal stance (TS), pre-swing (PSw) and swing (SW)).
Therefore, claim 9 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 10, Mantovani, in view of Su, renders obvious the method for determining the phase of gait of a subject according to claim 9, as indicated hereinabove. Su does not explicitly teach the limitation of instant claim 10, that is wherein the stance phase of the subject's gait cycle is sub-divided into sub-phases 1- 6. Su teaches that the stance phase has three sub-phases (i.e., loading response, mid-stance, and terminal stance) (Fig. 3; 2. Experimental Setup and Methods). Su also teaches that several investigators have focused on a two-phase (stance and swing) classification during one gait cycle. Su further teaches that to provide a subtle and continuous control during walking, classifying more specific gait phases is required (1. Introduction). Su also explains that Attal et al. used six gait phases (1. Introduction). The Examiner further notes that Mantovani teaches 7 sub-phases, with the stance phase also being sub-divided into 3 sub-phases (Fig. 4). It would have been obvious to one having ordinary skill in the art at the time the invention was made to optimize and arrive at six sub-phases for the stance phase of the subject’s gait cycle, recognizing that the number of sub-phases is directly correlated to more specific and precise determination of the current point in the gait phase, which is a desirable characteristic, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 105 USPQ 233. Please note that in the instant application, the Applicant has not disclosed any criticality for the claimed limitation.
Therefore, claim 10 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 11, Mantovani, in view of Su, renders obvious the method for determining the phase of gait of a subject according to claim 10, as indicated hereinabove. Su does not explicitly teach the limitation of instant claim 11, that is wherein the swing phase of the subject's gait cycle is sub-divided into sub-phases 7- 12. Su teaches that the swing phase has two phases (Pre-swing and Swing) (Fig. 3 and 2. Experimental Setup and Methods). Su also teaches that several investigators have focused on a two-phase (stance and swing) classification during one gait cycle. Su further teaches that to provide a subtle and continuous control during walking, classifying more specific gait phases is required (1. Introduction). Su also explains that Attal et al. used six gait phases (1. Introduction). The Examiner further notes that Mantovani teaches 7 sub-phases, with the swing phase being sub-divided into 3 sub-phases (Fig. 4). It would have been obvious to one having ordinary skill in the art at the time the invention was made to optimize and arrive at six sub-phases for the swing phase of the subject’s gait cycle, recognizing that the number of sub-phases is directly correlated to more specific and precise determination of the current point in the gait phase, which is a desirable characteristic, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 105 USPQ 233. Please note that in the instant application, the Applicant has not disclosed any criticality for the claimed limitation.
Therefore, claim 11 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 12, Mantovani, in view of Su, renders obvious the method for determining the phase of gait of a subject according to claim 10, as indicated hereinabove. Mantovani also teaches the limitation of instant claim 12, that is wherein (Figs. 1, 3, and 5) the step of applying FES to a lower limb of the subject comprises the FES controller applying FES at at least one gait phase of the subject (Par. [0111] – Once the device 100 has calculated/estimated the gait cycle percentage 500, the one or more processors 302 can instruct the EMS generator 310 to provide electrical stimulation to the nerves and muscles of the limb in physical contact with the electrodes 112 of the one or more electrode arrays 108. In this manner, the electrical stimulation can be timed and set by the gait cycle percentages 500 calculated.).
Therefore, claim 12 is unpatentable over Mantovani, et al. and Su, et al.
Regarding claim 15, Mantovani, in view of Su, renders obvious the method for applying FES to a subject according to claim 11, as indicated hereinabove. Mantovani also teaches the limitation of instant claim 15, that is wherein the plurality of gait phase labels further comprises gait phase label 13 that is representative of the subject having any one of both feet on the floor and undertaking an activity that does not involve any one of walking and running (Par. [0114] – The one or more processors 302 can also use machine learning to classify raw inertial measurements to human activities that may require altering stimulation control. These human activities can include locomotor patterns such as sitting, standing, walking, jogging, running, stair ascent, stair descent, cycling, rowing, jumping, tripping, scuffing, etc.).
Therefore, claim 15 is unpatentable over Mantovani, et al. and Su, et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Agrawal, et al. (US 2020/0000373)
Hu, et al. (US 2023/0040650)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL TAYLOR HOLTZCLAW whose telephone number is (571)272-6626. The examiner can normally be reached Monday-Friday (7:30 a.m.-5:00 p.m. EST).
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/MICHAEL T. HOLTZCLAW/Primary Examiner, Art Unit 3796
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