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 .
Election/Restrictions
Claims 6-10 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected system, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 24 March 2025.
Applicant’s election without traverse of Group I: Claims 1-5 in the reply filed on 24 March 2025 is acknowledged.
Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Response to Amendments
This Office Action is responsive to the amendment filed on 03/24/2025. As directed by the amendment: Claims 6-10 are withdrawn and claims 1-5 remain as previously presented, and no claims have been added or cancelled. Thus, claims 1-5 are currently under consideration.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 09/28/2022 and 04/26/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-5 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 1 recites the limitation "the multi-frequency impact response signal m-FIRS" in line 16. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this is the same m-FIRS recited in lines 5-6 of claim 1. For examination purposes, the multi-frequency impact response signal will be read as it is the same signal recited in lines 5-6 of claim 1 (i.e. the multi-frequency impulse response signal m-FIRS). Claims 2-5 are also rejected by virtue of its dependency on claim 1.
Claim 3 recites the limitation “specific muscle diagnostic equipment”, however it is unclear what is this “specific” equipment. For examination purposes, this limitation will be interpreted as any system or apparatus that is able to sense, detect, measure, or diagnose a muscle condition or function. Claim 4 is also rejected by virtue of its dependency on claim 3.
Claim 4 recites the limitation "the elasticity of the muscle" in line 18. There is insufficient antecedent basis for this limitation in the claim.
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 and 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rutkove et al. (US Patent 11,589,766 B2), hereinafter Rutkove in view of McLeod et al. (US Patent 9,402,579 B2), hereinafter McLeod further in view of Alim, Onsy Abdul, Mohamed Moselhy, and Fatima Mroueh. "EMG signal processing and diagnostic of muscle diseases." 2012 2nd international conference on advances in computational tools for engineering applications (ACTEA). IEEE, 2012., hereinafter Alim.
Regarding claim 1, Rutkove discloses a sarcopenia diagnostic system (Abstract: “a device for determining muscle condition of a region of tissue”), comprising: an electrical stimulation and measurement unit (an apparatus 100) configured to apply multi-frequency electrical stimulation to a body and measure a multi-frequency impulse response signal m-FIRS to the multi-frequency electrical stimulation (Column 13, lines 16-19: “Apparatus 100 may include any components in any arrangement capable of delivering electrical signals and measuring electrical signals resulting from the electrical signals delivers”, Abstract: “apply multi-frequency electrical signals to the region of tissue and pickup electrodes that are used to collect electrical signals resulting from the application of the multi-frequency electrical signals”);
Rutkova fails to disclose a response signal analysis unit configured to remove noise and distortion from the multi-frequency impulse response signal m-FIRS to obtain an involuntary muscle contraction signal and wherein the multi-frequency impact response signal m-FIRS is provided in units of a plurality of segments divided by frequency.
However, McLeod teaches an invention that provides a means by which clinicians and trainers can perform real-time muscle assessment wherein a response signal analysis unit configured to remove noise and distortion from the multi-frequency impulse response signal m-FIRS to obtain an involuntary muscle contraction signal (Column 30, lines 46-49: “involuntary muscle (Type I and IIa) contractions are generally below 40 Hz. For such muscles, a cutoff as low as 60 Hz or lower may be preferred.”, Figure 22, Column 16, lines 29-30: “low-pass filters tend to create “reduced-noise” signals.”) and wherein the multi-frequency impact response signal m-FIRS is provided in units of a plurality of segments divided by frequency (Column 12, lines 20-25: “The term “band” (or “waveband”) when used herein in connection with waves, refers to a set of waves having wave frequencies that range over a “frequency domain,” wherein the extent of the range is referred to as the “bandwidth.” Subsets of the waves in a band are referred to herein as “sub-bands””, Column 15, lines 13-16: “The objective of wavelet analysis is to resolve a waveform into a set of components (sub-bands) that differ with respect to the range of wave frequencies in each sub-band”, Column 19, lines 7-9: “Each of the 8 sub-bands comprises a plurality of waves having a range of frequencies, with a “center frequency” mid-range.”)
It would have been prima facia obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rutkova to incorporate the teachings of McLeod to have a response signal analysis unit configured to remove noise and distortion from the multi-frequency impulse response signal m-FIRS to obtain an involuntary muscle contraction signal and where the multi-frequency impact response signal m-FIRS is provided in units of a plurality of segments divided by frequency, as these prior art references are directed to muscle assessments. One would be motivated to do this as involuntary muscle contraction signals can be a primary result of sarcopenia and to have the signal divided by frequency as it allows for focusing on or treating only on the signal of interest (Column 3, lines 9-11), as recognized by McLeod.
Rutkova and McLeod, alone or in combination, fails to teach extract a feature vector in each of time domain and frequency domain from the involuntary muscle contraction signal.
However, Alim teaches a method of recording signals which are processed to be further classified for the diagnosis of neuromuscular pathologies (Abstract) wherein the process includes to extract a feature vector in each of time domain and frequency domain from the involuntary muscle contraction signal (pg. 1, II. EMG Signal Processing: “Signal processing techniques are mathematical procedures that can be usefully applied to extract information or features from the signals. After the features are extracted, the data is transformed into a reduces representation set of features also named feature vectors…the signal may be studied in the time domain and in the frequency domain”), and an artificial intelligence model learning unit receiving the extracted feature vector as input, and generated a classification for muscle strength and muscular endurance from the feature vector through artificial intelligence-based model learning to diagnose sarcopenia (IV. Artificial Neural Network, pg. 4, III. Classification Using Neural Network: “Neural Networks…were constructed to classify individual motor unit action potentials (MUAPs) into MYO, NEU, or NOR category, each with different input features vector: time domain input features vector, frequency domain input feature vectors, time and frequency domain input features vector”)
Although Alim doesn’t explicitly state that the classification is for muscle strength and muscular endurance or that the artificial intelligence-based model is learning to diagnose sarcopenia, it would have been obvious to one of ordinary skill in the art that these would be known outputs of the model as sarcopenia is an “intrinsic myopathy” linked to loss of muscle strength and muscular endurance, as known to those skilled in the art.
It would have been prima facia obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rutkova and McLeod to incorporate the teachings of Alim to extract a feature in each of the time domain and frequency domain from the involuntary muscle contraction signal and to have an artificial intelligence model learning unit receiving the extracted feature vector as input, and generates a classification for muscle strength and muscular endurance from the feature vector through artificial intelligence-based model learning to diagnose sarcopenia, as these prior art references are directed toward assessing the condition of muscles using EMGs. One would be motivated to do this since muscle activity is complicated and is dependent on anatomical and physiological properties of muscles and analyzing the bioelectrical activity to diagnose pathology for the increasing neuromuscular patients is not feasible, a computer aided expert system that can analyze and interprets the signal is needed (pg. 1, I. Introduction) and the use of feature extraction allows for dimensionality reduction and data compaction for efficient processing by machine learning and artificial intelligence models.
Regarding claim 3, Rutkove/McLeod/Alim teaches the system of claim 1 (as shown above).
Rutkove and McLeod, alone or in combination, fail to teach wherein the feature vector in the time domain includes at least one of a feature used in a specific muscle diagnostic equipment, an envelope characteristics, a waveform pattern and shape, and a level crossing rate, and wherein the feature vector in the frequency domain includes at least one of a Percentile of Spectral Cumulative Sum (PoSCS), a Log Power Spectrum, a Percentile Pattern of Spectral Cumulative Sum (PPoSCS), and a log power spectrum shift.
Alim further teaches wherein the feature vector in the time domain includes at least one of a feature used in a specific muscle diagnostic equipment, an envelope characteristics, a waveform pattern and shape, and a level crossing rate (II. EMG signal, A. Temporal Analysis: amplitude, duration, number of phases, Figure 1: Temporal characteristics of an EMG signal), and wherein the feature vector in the frequency domain includes at least one of a Percentile of Spectral Cumulative Sum (PoSCS), a Log Power Spectrum, a Percentile Pattern of Spectral Cumulative Sum (PPoSCS), and a log power spectrum shift (II. EMG Signal, B. Frequency Domain Analysis: average power spectral density, spectral moment, mean power frequency, peak frequency, median frequency, skewness or dissymmetry coefficient, kurtosis, relative energy by frequency band, spectral entropy).
It would have been prima facia obvious for one of ordinary skill in before the effective filing date of the claimed invention to have modified Rutkove and McLeod to incorporate the teachings of Alim to have the feature vector in the time domain includes at least one of a feature used in a specific muscle diagnostic equipment, an envelope characteristics, a waveform pattern and shape, and a level crossing rate and the feature vector in the frequency domain includes at least one of a Percentile of Spectral Cumulative Sum (PoSCS), a Log Power Spectrum, a Percentile Pattern of Spectral Cumulative Sum (PPoSCS), and a log power spectrum shift, as these prior art references are directed to assessing the condition of muscles using EMGs. One would be motivated to do this as these particular parameters help with disease and muscle function classifications, as recognized by Alim (II. EMG Signal Processing).
Claim(s) 2 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rutkove in view of McLeod further in view of Alim as applied to claim 1 above, and further in view of Hosen, Md Rubel, et al. "Age classification based on EMG signal using Artificial Neural Network." 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT). IEEE, 2015, hereinafter Hosen.
Regarding claim 2, Rutkove/McLeod/Alim teaches the system of claim 1 (as shown above). McLeod further teaches an electrical stimulation filter for extracting the involuntary muscle contraction signal by performing a pre-processing operation to remove a noise signal or a distortion included in the multi-frequency impact response signal m-FIRs (Column 30, lines 46-49: “involuntary muscle (Type I and IIa) contractions are generally below 40 Hz. For such muscles, a cutoff as low as 60 Hz or lower may be preferred.”, Figure 22, Column 16, lines 29-30: “low-pass filters tend to create “reduced-noise” signals.”). Rutkove, McLeod, and Alim, alone or in combination, fail to teach a feature extraction unit for extracting the feature vector related to muscle strength or muscular endurance based on the involuntary muscle contraction signal provided from the electrical stimulation filter.
However, Hosen teaches an approach for classifying different aged people based on their forearm electromyography (EMG) wherein a feature extraction unit for extracting the feature vector related to muscle strength or muscular endurance based on the involuntary muscle contraction signal provided from the electrical stimulation filter (Figure 1: Features extraction & Dimensionality reduction, pg. 2, I. Introduction: “The raw EMG signal has been collected from the body and then it is filtered for the purpose of reducing noise and artifacts. Wavelet transform is used for de-noising and compressing the signal and then proper features are extracted.”, pg. 3, III. Methodology, B. Features Extraction: time and frequency domain features are extracted, pg. 1, I. Introduction: “we have analyzed EMG signal for maximum isometric contraction for a particular movement and then by extracting time and time-frequency domain based feature, different age group is classified using Artificial Neural Network (ANN)”).
Although Hosen doesn’t explicitly state that the feature vector is related to muscle strength or muscular endurance it would have be known to one skilled in the art that Hosen’s feature vectors would in fact teach the muscle strength and muscular endurance as Hosen states the features are used to classify different age groups and states the link between muscle strength and age as a classification factor (pg. 1-2. I. Introduction).
It would have been prima facia obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rutkove, McLeod, and Alim to incorporate the teachings Hosen to have a feature extraction unit for extracting the feature vector related to muscle strength or muscular endurance based on the involuntary muscle contraction signal provided from the electrical stimulation filter, as these prior art references are directed to assessing muscle function. One would be motivated to do this as muscle strength decreases and results in impaired motor performance, as recognized by Hosen (pg. 1, I. Introduction )
Regarding claim 5, Rutkove/McLeod/Alim teach the system of claim 1 (as shown above).
Rutkove, McLeod, and Alim, alone or in combination, fail to teach wherein the artificial intelligence model learning unit includes a deep learning model using at least one of an initialization method of a random initialization method, a fine tuning of a backpropagation method, and an optimization algorithm of an adaptive moment estimation Adam, a cost function of Minimum Mean Square Error MMSE, and an active function of an exponential linear unit ELU.
However, Hosen teaches an approach for classifying different aged people based on their forearm electromyography (EMG) (Introduction) as age-related changes are so much more significant in elderly subjects due to impaired motor performance which is characterized by a decrease in muscle strength wherein the artificial intelligence model learning unit (pg. 4, C. Artificial Neural Network) includes a deep learning model (Figure 3: Neural Network containing hidden layers) using at least one of a fine tuning of a backpropagation method and a cost function of Minimum Mean Square Error MMSE (pg. 4, C. Artificial Neural Network (ANN) Architecture: “The ANN used in this work is based on backpropagation (BP) algorithm with three layer feed forward network with 10 hidden nodes…the backpropagation learning algorithm can be divided into two phases: propagation of errors and weight update…Back propagation is an iterative descent algorithm to minimize the mean square error between the desired output and the actual network output”).
It would have been prima facia obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rutkove, McLeod, and Alim to incorporate the teachings of Hosen to have the artificial intelligence model learning unit includes a deep learning model using at least one of an initialization method of a random initialization method, a fine tuning of a backpropagation method, and an optimization algorithm of an adaptive moment estimation Adam, a cost function of Minimum Mean Square Error MMSE, and an active function of an exponential linear unit ELU, as these prior art references are directed to assessing muscle function. One would be motivated to do this as this method is dynamic and powerful in obtaining a desired classification performance, as recognized by Hosen.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rutkove in view of McLeod further in view of Alim as applied to claim 3 above, and further in view of Han et al. (KR 2020/0115376 A, citations below are from NPL Translation), hereinafter Han.
Regarding claim 4, Rutkove/McLeod/Alim teaches the system of claim 3 (as shown above). Rutkove, McLeod, and Alim, alone or in combination, fail to teach wherein the feature used in a specific muscle diagnostic equipment includes at least one of a muscle tone state, a stiffness of a muscle, a decrement indicating the elasticity of the muscle, a relaxation time of the muscle, and a creep of the muscle .
However, Han teaches a personal exercise management system fusing artificial intelligence and wireless EMG signal processing to calculate muscle activity ([0008]) wherein the feature used in a specific muscle diagnostic equipment includes at least one of a muscle tone state, a stiffness of a muscle, a decrement indicating the elasticity of the muscle, a relaxation time of the muscle, and a creep of the muscle ([0049] “the feature extraction unit can extract muscle contraction, muscle tension, fatigue, and muscle contraction timing and transmit them as muscle activity”).
It would have been prima facia obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rutkove, McLeod, and Alim to incorporate the teachings of Han to have the feature used in a specific muscle diagnostic equipment include at least one of a muscle tone state, a stiffness of a muscle, a decrement indicating the elasticity of the muscle, a relaxation time of the muscle, and a creep of the muscle, as these prior are references are directed to assessing the muscle activity and health. One would be motivated to do this as these features are a good indication of muscle activity, as recognized by Han ([0049]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Soe et al. (US 2020/0383635 A1) teaches a method and system for monitoring and determining progress of a patient’s rehabilitative treatment (Abstract) through an objective assessment of motor function and quantification of functional abilities which reflects the individual’s functional abilities ([0005]) with a feature extraction unit for extracting in the time and frequency domain ([0040]).
Kim, Jeong-Kyun, et al. "Identification of patients with sarcopenia using gait parameters based on inertial sensors." Sensors 21.5 (2021): 1786. teaches machine learning processes for identifying sarcopenia.
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/ATTIYA SAYYADA HUSSAINI/Examiner, Art Unit 3792
/GARY JACKSON/Supervisory Patent Examiner
Art Unit 3792