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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 1, 3, 9, and 15 are objected to because of the following informalities:
Claim 1, line 2 “the specific muscular activity” should be “a specific muscular activity”.
Claim 3, line 2 “step. A” should be “step a)” for consistency.
Claim 9, line 3 “the multilayer perceptron” should be “a multilayer perceptron”.
Claim 15, line 1 “computer program product” should be “non-transitory computer program product” in order to clarify that the claim is not directed toward transitory signals and thus ineligible under 35 U.S.C. 101.
Appropriate correction is required.
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 2-3, 5-6, and 11-14 are 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.
Regarding claim 2, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 3 recites the limitations “the number of sensors” and “the number of deep muscles and superficial muscles”. There is insufficient antecedent basis for each of these limitations of the claim.
Regarding claim 5, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Regarding claim 6, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Regarding claim 11, the phrases "such as" and “in particular” render the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 12 recites the limitation “the external oblique” in line 2 of the claim. There is insufficient antecedent basis for this limitation of the claim. As a human being has two external oblique muscles, it is unclear which external oblique muscle is being referred to in this limitation.
Claim 13 recites the limitations “at least one sensor” and “at least one time variable, at least one frequency variable, at least one time-frequency variable, at least one fractal variable, at least one cepstral variable, and at least one statistical variable”. It is unclear if these limitations are meant to refer to the elements having the same names of claim 1, or if these refer to separate sensors and variables. The limitation is currently interpreted as referring to the same elements since the claim depends from claim 12, which depends from claim 1.
Claim 13 additionally recites the limitation “the variables”. There is insufficient antecedent basis for this limitation of the claim. It is not clear if “the variables” encompasses all of the listed “at least one” assorted variables, or if it may refer to some group of variables. The limitation is currently interpreted as referring to all of the extracted variables.
Claim 14 is rejected under 35 U.S.C. 112(b) as indefinite as it recites a use but fails to recite steps.
Claim 14 is additionally rejected under 35 U.S.C. 112(b) as indefinite due to its dependence on claim 13, which has been rejected as indefinite.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1-3 and 4-15 is/are rejected under 35 U.S.C. 102(2) as being anticipated by Zariffa (US 20240197238 A1).
Regarding claim 1, Zariffa teaches a computer-implemented method (Paragraph 0030) for classifying physiological signals arising from the specific muscular activity of a muscle group (Paragraph 0056—muscles could be classified as FES-T responders…) comprising at least one deep muscle and at least one superficial muscle of a subject (Paragraph 0033-0034, 0055— the subject performs standardized movements to isolate the activity of the target muscle. For upper limb evaluations, the target muscle may be the deltoid muscles, elbow flexors, elbow extensors, wrist extensors, extensor digitorum communis, opponens pollicis, flexor pollicis longus, flexor pollicis brevis, finger flexors, finger abductors, and/or dorsal interossei. For lower limb evaluations, the target muscle may be the bilateral quadriceps, hamstrings, dorsiflexors, and/or plantar flexors. For trunk evaluations, the target muscle may be the rectus abdominis and/or erector spinae. For facial evaluations, the target muscle may be the zygomaticus major and/or the orbicularis oculi. These lists are not exhaustive, and other target muscles are within the scope of the present disclosure…set of muscles may be individualized for the subject), comprising the following steps:
a) acquiring electrical signals from at least one sensor placed on the skin of a subject (Paragraph 0007, 0023, 0029, 0031—sensor configured to record surface electromyography (sEMG)…for a muscle of muscle group; sensor 125),
b) pre-processing the electrical signals acquired during step a), to eliminate spurious noise and subject-motion artifacts (Paragraph 0029-- The sensor 125 may further include components such as filters (e.g., bandpass filters, low-pass filters, high-pass filters, etc.), amplifiers, converters (e.g., analog-to-digital converters (ADCs), digital-to-analog converters (DACs), etc.), samplers, and the like),
c) extracting at least one time variable, at least one frequency variable, at least one time-frequency variable, at least one fractal variable, at least one cepstral variable and at least one statistical variable from the pre-processed signals arising from step b) (Paragraph 0037-- the subject undergoes an EMG evaluation to characterize electrophysiological profiles of individual muscles; for example, to produce a set of feature values… features, which may be mean absolute values, zero crossings, slope sign changes, waveform length, Willison amplitude, variance, v-order, log-detection, EMG histogram, peak amplitude, autoregression coefficients, median frequency, Cepstrum coefficients, wavelet transform coefficients, maximum fractal length, cardinality, sample entropy, and/or the estimated number of active motor units),
d) selecting variables by analyzing the variance to classify subsequent data according to selected classes, the selected variables forming a classifier (Paragraph 0037-0040, 0043-0044-- FIG. 2 shows a 2-feature correlation, in practice additional features may be simultaneously correlated or analyzed. In one example, six features may be correlated. Features may additionally be combined to create a smaller or larger set of new features. The clustering algorithms may categorize muscles based on the sEMG data into three groups; for example: unimpaired muscles, muscles with impaired upper motor neuron function but intact lower motor neuron function, and muscles with impaired lower motor neuron function),
e) implementing the classifier from the variables selected in step d) (Paragraph 0037-0040, 0043-0047--Stage two may result in a relatively small number of clusters, corresponding to the different possible patterns of injury (e.g., unimpaired, denervated muscle, partial upper motor neuron damage with intact lower motor neuron, and so on…)… if successful clusters were identified in the EMG evaluation, regression models are used to predict recovery profile metrics (e.g., the absolute improvement in function, speed of improvement in function, and/or course of improvement in function as described above)),
f) evaluating classifier performance by cross-validation and performance indicators (Paragraph 0045, 0047-0050— A prediction may be evaluated from the LOSOCV process on its accuracy in distinguishing responder from non-responder muscles and on the Pearson correlation between the correlation between the predicted and actual recovery profiles (e.g., by determining whether the accuracy and/or Pearson correlation exceeds predetermined thresholds). In one example, the prediction may be defined as successful if the LOSOCV evaluation results in both greater than 90% accuracy in distinguishing responder from non-responder muscles, and greater than 0.5 average Pearson correlation between the predicted and actual recovery profiles…),
g) using the classifier to characterize the muscular activity of the muscle groups (Paragraph 0046, 0051-- may use the systems, methods, and devices described herein to identify electrophysiological biomarkers that can predict muscle recovery profiles from baseline sEMG data… linking the sEMG features or clusters derived from feature correlation plot 220 to the corresponding recovery profile 230).
Regarding claim 2, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein the at least one sensor is selected from non-invasive sensors such as electromyographic (EMG) or medical imaging sensors, patches or temporary electronic tattoos (Paragraph 0029—surface or skin electrode…) and invasive sensors, such as patches with a needle.
Regarding claim 3, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein the number of sensors used in step a. is strictly less than the sum of the number of deep muscles and superficial muscles, while still being strictly greater than 0 (Paragraph 0029-- The sensor 125 may be configured to measure and record the muscle response (e.g., from individual muscles or muscle groups) of the subject (e.g., sEMG data) such that one sensor may measure from a muscle group having more than one muscle).
Regarding claim 5, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein the at least one cepstral variable is chosen from cepstral coefficients (Cci) (Paragraph 0037—Cepstrum coefficients), calculations of temporal indicators such as mean, median, standard deviation (std) or root mean square (rms) on the cepstral coefficients (Cci), and calculations of mathematical indicators such as minimum (min) and maximum (max) on the cepstral coefficients (Cci).
Regarding claim 6, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein the at least one statistical variable is chosen from sample entropy (SampEn) (Paragraph 0037—sample entropy), fuzzy entropy (FuzzyEn) and Shannon entropy (ShanEn).
Regarding claim 7, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein: - the at least one temporal variable is selected from the length of the waveform (WL) (Paragraph 0037—waveform length), the average signal amplitude (MAV) (Paragraph 0037-0038, 0042—amplitude…), the sum of the temporal signal amplitudes divided by the maximum signal value (sum(amp)/max), the temporal correlation between two measurement channels (corr), the Hjorth parameters (Hj) such as activity (A), mobility or complexity and skewness (Sk) (Paragraph 0042—complexity characteristics), - the at least one frequency variable is selected from the mean frequency (mf), the median frequency (mdf) (Paragraph 0037—median frequency) and the cut-off frequency (fp) (Paragraph 0042--frequency), and - the at least one time-frequency variable is chosen from Empirical Mode Decomposition (EMD) and Immediate Average Frequency DWT (IAFdwt) (Paragraph 0037—wavelet transform coefficients).
Regarding claim 8, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein the variance analysis comprises intra-group variance analysis and/or inter-group variance analysis (Paragraph 0037-0040, 0043-0044).
Regarding claim 9, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein the implementation is carried out by at least one method chosen from support vector machines and the multilayer perceptron (Paragraph 0047-- non-linear regression models may be used by applying machine learning algorithms (e.g., support-vector machine (SVM)…).
Regarding claim 10, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein said at least one deep muscle and said at least one superficial muscle is a muscle of the abdominal wall, a dorsal muscle, a gluteal muscle, an arm muscle, a forearm muscle, a leg muscle, a thigh muscle, a face muscle, a neck muscle, a thorax muscle, a shoulder muscle, a foot muscle (Paragraph 0024, 0033-0034, 0055).
Regarding claim 11, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein said at least one deep muscle is selected from the transverse abdominis, psoas, quadratus lumborum, oblique internus, ilio dorsalis, long dorsalis, intervertebral, supraspinatus, perineal muscles, multifidi, spinal muscles, in particular those connecting the spinous process and transverse processes of each vertebra, and the native musculature of the back (Paragraph 0033-0034, 0055).
Regarding claim 12, Zariffa teaches the method according to claim 1. Zariffa additionally teaches wherein said at least one superficial muscle is selected from the external oblique, rectus abdominis, scalene, sternocleidomastoid, trapezius, pectoralis major, deltoids, dorsalis major, intercostal muscles, biceps, triceps, forearm flexors or extensors, gluteals, abductors, adductors, hamstrings, quadriceps and gastrocnemius (Paragraph 0033-0034, 0055).
Regarding claim 13, Zariffa teaches the method according to claim 12, including the classifier. Zariffa additionally teaches a method for measuring the specific muscular activity of a muscle group comprising at least one deep muscle and at least one superficial muscle of a subject (Paragraph 0033-0034, 0055— the subject performs standardized movements to isolate the activity of the target muscle. For upper limb evaluations, the target muscle may be the deltoid muscles, elbow flexors, elbow extensors, wrist extensors, extensor digitorum communis, opponens pollicis, flexor pollicis longus, flexor pollicis brevis, finger flexors, finger abductors, and/or dorsal interossei. For lower limb evaluations, the target muscle may be the bilateral quadriceps, hamstrings, dorsiflexors, and/or plantar flexors. For trunk evaluations, the target muscle may be the rectus abdominis and/or erector spinae. For facial evaluations, the target muscle may be the zygomaticus major and/or the orbicularis oculi. These lists are not exhaustive, and other target muscles are within the scope of the present disclosure…set of muscles may be individualized for the subject), comprising the following steps:
1) acquiring electrical signals from at least one sensor placed on the skin of a subject (Paragraph 0007, 0023, 0029, 0031—sensor configured to record surface electromyography (sEMG)…for a muscle of muscle group; sensor 125),
2) pre-processing the electrical signals acquired during step (a), to eliminate spurious noise and subject-motion artifacts (Paragraph 0029-- The sensor 125 may further include components such as filters (e.g., bandpass filters, low-pass filters, high-pass filters, etc.), amplifiers, converters (e.g., analog-to-digital converters (ADCs), digital-to-analog converters (DACs), etc.), samplers, and the like),
3) extracting at least one time variable, at least one frequency variable, at least one time-frequency variable, at least one fractal variable, at least one cepstral variable and at least one statistical variable from the pre-processed signals arising from step (b) (Paragraph 0037-- the subject undergoes an EMG evaluation to characterize electrophysiological profiles of individual muscles; for example, to produce a set of feature values… features, which may be mean absolute values, zero crossings, slope sign changes, waveform length, Willison amplitude, variance, v-order, log-detection, EMG histogram, peak amplitude, autoregression coefficients, median frequency, Cepstrum coefficients, wavelet transform coefficients, maximum fractal length, cardinality, sample entropy, and/or the estimated number of active motor units),
4) classifying the variables extracted in step 3) using the classifier as defined in claim 12 (Paragraph 0037-0040, 0043-0047, 0051--Stage two may result in a relatively small number of clusters, corresponding to the different possible patterns of injury (e.g., unimpaired, denervated muscle, partial upper motor neuron damage with intact lower motor neuron, and so on…)… wrapper methods of features selection (e.g., sequential forward selection, in which features are added progressively based on which feature most improves the result)… machine learning approaches may be used to predict recovery profiles directly based on one or more of the sEMG features described above).
Regarding claim 14, Zariffa teaches the method according to claim 13. Zariffa additionally teaches a use of a measuring method according to claim 13, for an application selected from functional rehabilitation, muscle strengthening for well-being and/or aesthetic purposes, prevention of lumbar, spinal, sports or pelvic pathologies, and functional diagnosis (Paragraph 0002, 0006, 0018, 0022-0024, 0028).
Regarding claim 15, Zariffa teaches the method according to claim 1. Zariffa additionally teaches a computer program product downloadable from a communication network and/or stored on a computer-readable medium and/or executable by a microprocessor, characterized in that it comprises program code instructions for executing the method according to claim 1, when it is executed on a computer (Paragraph 0030, 0058—computer-readable recording medium… computer-readable recording medium may also be distributed over network-coupled computer systems so that the computer-readable code is stored and executed in a distributed fashion…).
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.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marri (Classification of muscle fatigue using surface electromyography signals and multifractals) in view of Zariffa.
Regarding claim 4, Zariffa teaches the method according to claim 1. Zariffa additionally teaches at least one fractal variable (Paragraph 0037). However, Zariffa does not explicitly disclose the at least one fractal variable is the Hurst exponent.
Marri, in the same field of endeavor of a system for classifying muscle activity based on variables of sEMG signals, teaches the system extracts at least one fractal variable, wherein the at least one fractal variable is the Hurst exponent (Page 2—Multifractal detrended moving average algorithm—Hurst exponent…; table 1).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Zariffa to include a Hurst exponent as described by Marri in order to predictably improve the method by extracting additional variables which may correspond to different classifications of the muscle activity and thus may help to improve the accuracy of classification or enable additional classifications, as Marri discloses that a Hurst exponent may differ between subjects whose muscles fall into different classes (such as fatigued and not-fatigued).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNA ROBERTS whose telephone number is (571)272-7912. The examiner can normally be reached M-F 8:30-4:30 EST.
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/ANNA ROBERTS/Examiner, Art Unit 3791
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