DETAILED ACTION
Note: The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s arguments filed in the reply on August 10, 2026 were received and fully considered. Claims 1, 3, 4, 6, 9, 12, 48, and 49 were amended. Claim 50 is new. Please see corresponding rejection headings and response to arguments section below for more detail.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on August 10, 2026 has been entered.
Claim Rejections - 35 USC § 112B
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 1-15, 18, 20, 21, and 48-50 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.
Claim 1, and all dependent claims thereof, recite “the muscle” in line 12, which is unclear. Does this refer to the same “at least one muscle” previously recited in line 3; and subsequently recited in lines 12-14?
Claim 5 recites “the plurality of sessions” in line 1, which lacks antecedent basis.
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.
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 1-15, 18, 20, 21, and 48-50 are rejected under 35 U.S.C. 103 as being unpatentable over Charlesworth et al. (US PG Pub. No. 2022/0143393 A1) (hereinafter “Charlesworth”).
With respect to claim 1, Charlesworth teaches therapy (FES-T) to be administered to the at least one muscle (par.0118 “EMG signal at baseline… before applying the electrical nerve stimulation”; par.0121 “pre-stimulation baseline value”); a memory (par.0127 “memory circuitry”; see also par.0132, 0159, 0259); a user interface configured to present information to a user (par.0138 “external computing or display device…provide signal processing or user interface capability”); and a processor configured to: apply signal processing and/or machine learning to the sEMG data and determine relationships or correlations between the sEMG data and reference data stored in the memory (par.0099 “the system may monitor or calculate one or more system or body parameters that correlate with therapeutic efficacy such as… sEMG activity”; par.0100 “one or more system processors and/or algorithms”); based on the relationships or correlations, generate a predicted responsiveness of the at least one muscle to therapy and recovery profile for the muscle (par.0010 “predictor feedback signal… combination of parameters can be established or adjusted in a manner to serve… a particular patient”; par.0157 “the NPNS-based differences in this physiological signal may be used to predict the strength of therapeutic responses to various NPNS parameter combinations (personalization/optimization) and/or the response of a various patient to NPNS (patient selection)”), the predicted responsiveness at least generated prior to the at least one session of FES-T to be administered to the at least one muscle (par.0010 “predictor feedback signal… even before the subject has reported feeling the presence of any stimulation, that is, even while the stimulation is sub-sensory”; Note: sub-sensory stimulation equates to prior to at least one session of FES-T when considering broadest reasonable interpretation); and output, via the user interface, the predicted responsiveness of the at least one muscle to therapy and recovery profile to the user, the recovery profile comprising at least one of a prediction of success of recovery, an expected amount of recovery, and an expected time for muscle response (par.0010 “predictor feedback signal”; par.0012 “resulting surface EMG signal can be observed and used to select an electrostimulation waveform such as for use at a particular time of day”; par.0142 “displaying the results of parameter optimization or information associated with electrical nerve stimulation”).
Although Charlesworth does not explicitly teach a portable, hand-held device, further modification to incorporate this feature would have been prima facie obvious to a person having ordinary skill in the art (“PHOSITA”) when the invention was filed for the following reasons. First, Charlesworth teaches a wearable patch device 120 that is small enough to be placed on the subject’s leg (see Fig. 1); and it is understood that device 120 would need to be placed on the subject via the subject’s hand, thereby suggesting portability. Therefore, PHOSITA would have had predictable success modifying Charlesworth when the invention was filed to allow for hand-held portability of the device in order to aid in placement in the device with respect to a target site, limb, leg, etc.
With respect to claim 2, Charlesworth suggests wherein the processor is configured to detect sEMG biomarkers for the at least one muscle in the sEMG data for the at least one muscle, and identify correlations between the sEMG biomarkers and responsiveness to the therapy (par.0088, 0099, 0136).
With respect to claim 3, Charlesworth teaches wherein the machine learning algorithm is trained by: applying a clustering algorithm to the sEMG data, thereby to assign the at least one muscle to a category, and based on the category or directly from the sEMG data, determining at least one electrophysiological biomarker, and generate the predicted responsiveness of the at least one muscle to therapy using the electrophysiological biomarker (Note: the machine learning algorithm is not required when signal processing is applied; see claim 1 “apply signal processing and/or machine learning”). Nonetheless, further modification to incorporate this feature would have been prime facie obvious to PHOSITA when the invention was filed for the following reasons. First, Charlesworth expressly teaches refining algorithms as part of the signal processing system (par.0100, 0120, 0158, 0166, 0187, 0257). Examiner further notes that machine learning algorithms, cluster algorithms, etc. are widely known (See for example, prior art cited in previous office actions). As such, Examiner argues that PHOSITA would have had predictable success when the invention was filed to incorporate a clustering algorithm, a series of calculations/algorithms, in the manner recited in place of Charlesworth’s algorithm as doing so would be a simple substitution.
With respect to claim 4, Charlesworth teaches wherein the sensor is configured to record at least an additional portion of the sEMG data for the at least one muscle over the at least one session of FES-T (par.0057, 0079).
With respect to claim 5, Charlesworth does not explicitly teach wherein the plurality of sessions is 20-40 sessions. However, Charlesworth expressly teaches a 30-minute electrostimulation therapy session (par.0079). Although Charlesworth does not explicitly teach 20-40 therapy sessions, further modification to rely on data from additional therapy sessions (at least 19 more sessions) would only involve routine skill in the art. Moreover, it is generally known in diagnostics that the accuracy of calculations increase with more input data, which in this case would come as a result of more therapy sessions.
With respect to claim 6, Charlesworth does not expressly teach wherein the processor is configured to apply the machine learning to categorize the at least one muscle into one of a predetermined number of groups. However, further modification to incorporate this feature would have been prima facie obvious to PHOSITA when the invention was filed for the following reasons. First, Charlesworth expressly teaches refining algorithms as part of the signal processing system (par.0100, 0120, 0158, 0166, 0187, 0257). Examiner further notes that machine learning is widely known (See for example, prior art cited in previous office actions). As such, Examiner argues that PHOSITA would have had predictable success when the invention was filed to incorporate machine learning, a series of calculations/algorithms, in the manner recited in place of Charlesworth’s algorithm as doing so would be a simple substitution.
With respect to claim 7, Charlesworth teaches wherein the processor is configured to extract a plurality of sEMG features from the sEMG data (par.0149-150, 0159 “peak… peak values”).
With respect to claim 8, Charlesworth teaches wherein respective ones of the plurality of sEMG features are selected from the group consisting of mean absolute value, 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 an estimated number of active motor units (par.0149-150, 0159).
With respect to claim 9, Charlesworth does not expressly teach wherein the processor is configured to apply the machine learning to analyze the sEMG data in a feature space using at least two of the plurality of sEMG features. However, further modification to incorporate this feature would have been prima facie obvious to PHOSITA when the invention was filed for the following reasons. First, Charlesworth expressly teaches refining algorithms as part of the signal processing system (par.0100, 0120, 0158, 0166, 0187, 0257). Examiner further notes that machine learning is widely known (See for example, prior art cited in previous office actions). As such, Examiner argues that PHOSITA would have had predictable success when the invention was filed to incorporate machine learning, a series of calculations/algorithms, in the manner recited in place of Charlesworth’s algorithm as doing so would be a simple substitution.
With respect to claim 10, Charlesworth teaches herein the sEMG data includes first data corresponding to a maximal voluntary contraction (MVC) and second data corresponding to a predetermined percentage of MVC (par.0066-67, 87, 0146, 0244).
With respect to claim 11, Charlesworth teaches further including a filter configured to apply a bandpass filter to the sEMG data, an amplifier configured to amplify the filtered sEMG data, and sampling circuitry configured to sample the filtered and amplified sEMG data (par.0085, 0115, 0141, 0174-0175, 0180-181).
With respect to claim 12, Charlesworth does not expressly teach wherein the processor is configured to apply the machine learning algorithm to generate the predicted recovery profile using a regression model. However, further modification to incorporate this feature would have been prima facie obvious to PHOSITA when the invention was filed for the following reasons. First, Charlesworth expressly teaches refining algorithms as part of the signal processing system (par.0100, 0120, 0158, 0166, 0187, 0257). Examiner further notes that machine learning and regression models are widely known (See for example, prior art cited in previous office actions). As such, Examiner argues that PHOSITA would have had predictable success when the invention was filed to incorporate machine learning and regression model, in the manner recited in place of Charlesworth’s algorithm as doing so would be a simple substitution.
With respect to claim 13, Charlesworth teaches wherein the reference data includes information relating to a relationship between at least one electrophysiological biomarker and a likelihood of muscle recovery, wherein the likelihood of muscle recovery is a percent chance of recovery (par.0010, 51-53, 157).
With respect to claim 14, Charlesworth teaches further comprising a housing configured to contain the sensor, the memory, and the processor (Figs. 1-3).
With respect to claim 15, Charlesworth teaches wherein the housing comprises a base portion containing the memory and the processor, and a probe portion containing the sensor, wherein the probe portion is configured to removably attach to the base portion, wherein the probe portion is configured to be covered by a sterile drape (Figs. 1-3; par.0068, 74; Examiner notes that sensor is covered by an attachment component, which is presumably sterile as it is covering a patient’s body part).
With respect to claim 18, Charlesworth teaches wherein the user interface includes at least one of a display, a touch screen, a speaker, a microphone, a camera, a haptic feedback device, a physical device, or a soft button (par.0138).
With respect to claim 20, Charlesworth teaches further comprising communication circuitry configured to provide wired or wireless communication with an external device (par.0138).
With respect to claim 21, Charlesworth teaches wherein the at least one muscle is selecting from the group consisting of upper limb muscles, lower limb muscles, trunk muscles, and face muscles (Figs. 1-3).
With respect to claim 48, Charlesworth teaches wherein the therapy is FES-T, and wherein the reference data comprises correlations between electrophysiological biomarkers detected in the sEMG data and responsiveness to FES-T (par.0064, 0088, 0099, 0118, 0136).
With respect to claim 49, Charlesworth teaches wherein the sensor is further configured to apply a stimulus to the at least one muscle, wherein the sensor is configured to apply an electrical pulse to the at least one muscle and measure a resulting change in voltage in the at least one muscle to obtain the sEMG data (par.0118, 0122, 0171, 0177, 0179-0181).
With respect to claim 50, Charlesworth teaches wherein the at least a portion of the sEMG data is recorded in the absence of the FES-T being administered to the at least one muscle (par.0118, 0121-122).
Response to Arguments
Applicant’s arguments filed with respect to the 35 USC 101 rejections raised in the previous office action were fully considered, but they were not persuasive. First, Applicant argues that the claims recite a specific combination of hardware elements (a portable, hand-held device with a sensor, memory, user interface and processor) configured to perform a specific function for predicting muscle responsiveness to therapy using correlations between sEMG biomarkers and responsiveness and produce a specific output (remarks, pg. 8). Examiner respectfully disagrees. While the claims include hardware, they are recited at a high level of generality and pertain to mere extra-solution activity (data gathering) and accordingly, fail to integrate the claims into a practical application. MPEP 2106.05(g). Applicant goes on to argue that the claimed invention recites an improvement to the relevant technology (remarks, pgs. 8-9). Examiner respectfully disagrees. There is no improvement to the additional/structural limitations as they are recited at a high level of generality. As such, any purported improvement lies within the judicial itself1. However, an alleged better calculation is still a calculation nonetheless and is not patent eligible. Applicant goes on to argue that a person cannot mentally or with pen and paper perform the limitations corresponding to the abstract idea. Examiner respectfully disagrees. First, Examiner maintains that the judicial exception also amounts to mathematical calculations. Accordingly, whether a skilled artisan could practically perform these limitations mentally is immaterial (as implementing complex algorithm on a generic processor is not patent eligible). Notwithstanding, Examiner maintains that nothing from the claims suggest that the skilled artisan would not be able to practically perform the identified judicial exception mentally, using simple pen and paper (having first obtained sEMG data via conventional sensor). Applicant also appears to argue that the claims require a specific transformation of a physiological measurement in that a stimulus is utilized to obtain sEMG data. However, this is still akin to extra-solution activity. Examiner argues that applying FES-T, a conventional therapy technique, to a patient is mere data gathering and does not integrate the claims into a practical application. For at lease these reasons, the 35 USC 101 rejections are maintained.
Applicant’s arguments filed with respect to the prior art rejections raised in the previous office action have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see prior art rejection above for more detail, updated citations (new Charlesworth reference), and updated obviousness rationale.
Conclusion
No claim is allowed.
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/PUYA AGAHI/Primary Examiner, Art Unit 3791
1 “the judicial exception alone cannot provide the improvement.” See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981).
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