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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-15, 18, 20, and 21 in the reply filed on January 14, 2026 is acknowledged.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under U.S.C. 120, 121, or 365 is acknowledged. The prior-filed applications (PCT/CA2022/050574 filed on 13 April 2022; and PRO 63/174328 filed on 13 April 2021) are acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 13, 2023 has been considered by the examiner.
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.
Claim 13 is 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 13 recites “a likelihood of muscle recovery” in line 2, which is indefinite for reciting relative terminology. What is “a likelihood”? As everyone is understood to have different muscle responses, it is unclear what “a likelihood” of muscle recovery constitutes for the population at large. While the instant specification discloses that likelihood equates to “as percent chance” ([0022], [0028]), there is no disclosure as to the particular percent range that equates to the percent chance. Therefore, the scope of what constitutes “a likelihood” is indefinite.
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 1-15, 18, 20, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding claim 1, the claim recites a portable, hand-held device. Thus, the claim is directed to a product/apparatus, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The following limitations set forth a judicial exception:
“apply predetermined relationships between the sEMG data and reference data stored in the memory, and based on the relationships, generate a predicted recovery profile for the muscle.”
These limitations describe a mathematical calculation. Furthermore, the limitations also describe a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human, or using simple pen/paper.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application.
For this part of the 101 analysis, the following additional limitations are considered:
“A portable, hand-held device, comprising: a sensor configured to record surface electromyography (sEMG) data for at least one muscle; a memory; and a processor…”
These additional limitations do not integrate the judicial exception into a practical application. Rather, the additional limitations are each recited at a high level of generality such that it amounts to insignificant extra-solution activity, e.g., mere data gathering steps necessary to perform the identified judicial exception. Furthermore, the claims recite a general-purpose processor that merely executes the judicial exception and is not a particular machine. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 706-17 (Fed. Cir. 2014), cited in MPEP § 2106.05(b)(I).
The additional limitations also do not add significantly more to the identified judicial exception because they relate to widely-understood, routine, and conventional components in the prior art. Moreover, the additional (structural) limitations are recited at a high level of generality such that they do not amount to significantly more.
Dependent claims 2-15, 18, 20, and 21 also fail to add something more to the abstract independent claims as they merely further limit the abstract idea, recite limitations that do not integrate the claims into a practical application for substantially similar reasons as set forth above, and/or do not recite significantly more than the identified abstract idea for substantially similar reasons as set forth above. Moreover, the “machine learning algorithm”, “clustering algorithm”, etc. (see claims 2, 3, 6, 8, 9, 12) describe a mathematical calculation. When given their broadest reasonable interpretation in light of the specification, machine learning algorithm is a mathematical calculations. Moreover, the plain meaning a machine learning algorithm is a series of mathematical calculations. See also 2024 AI SME Update, which held a similar claim construction was not patent eligible (see claim 2 of example 47, using a trained artificial neural network to analyze anomalies on input data was not patent eligible). The 2024 AI SME Update also sets forth that a trained machine learning model/engine amounts to a mental process (claim 2 of example 47). As such, the limitations also describe a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human, or using simple pen/paper.
Therefore, claims 1-15, 18, 20, and 21 are not patent eligible under 35 USC 101.
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 1-9, 12-15, 18, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over WEFFERS-ALBU et al. (US PG Pub. No. 2017/0143229 A1) (hereinafter “Weffers”).
With respect to claim 1, Weffers teaches a portable, hand-held device, comprising: a sensor configured to record “database 42 may be used for storing the measurement results of…the muscle sensor 18.”), and based on the relationships, generate a predicted recovery profile for the muscle (par.0077 “Storing these measurement results allows evaluating the recovery process of the patient 24 over time… compare the measurement results gathered over time with each other in order to determine a recovery progress of the patient 24…may be an indicator of a positive effect of the therapy”).
Although Weffers does not explicitly teach recording surface electromyography (sEMG) data, such a modification would have been prima facie obvious to person having ordinary skill in the art (“PHOSITA”) when the invention was filed for the following reasons. First, Weffers expressly teaches attaching EMG electrodes 26 to a patient’s limb (see Figs. 2A-2B). Accordingly, PHOSITA would have had predictable success modifying Weffers to obtain EMG data directly from the patient’s skin surface (electromyography (sEMG) data) as this would be obvious as a simple substitution. Moreover, Examiner argues that obtaining sEMG data for the purposes of assessing muscle responses is widely known. Please see prior art of record section at the end of the current office action for further example teachings.
With respect to claim 2, Weffers does not explicitly teach wherein the processor is configured to identify correlations by applying a machine learning algorithm to the sEMG data. 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, Weffers expressly teaches performing calculations (par.0027), calculate statistics to compare the difference of signals (par.0041, 0077), etc. Moreover, machine learning algorithms are widely known and the instant claims provide no specificity with respect to identify correlations by applying ML such that would provide for a nonobvious distinction over prior ML techniques. As such, Examiner argues that PHOSITA would have had predictable success when the invention was filed to incorporate ML, a series of calculations, in place of Weffer’s calculations as doing so would be a simple substitution.
With respect to claim 3, Weffers does not explicitly teach 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 associating the electrophysiological biomarker with a likelihood of muscle recovery. 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, Weffers expressly teaches performing calculations (par.0027), calculate statistics to compare the difference of signals (par.0041, 0077), etc. Moreover, machine learning algorithms, clustering algorithms, etc. are widely known. 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, in the manner recited in place of Weffer’s calculations as doing so would be a simple substitution.
With respect to claim 4, Weffers teaches wherein the sensor is configured to record the sEMG data for the at least one muscle over for at least one session of functional electrical stimulation therapy (FES-T) (par.0041 “during the therapy”; see also par.0044+).
With respect to claim 5, Weffers does not explicitly teach wherein the plurality of sessions is 20-40 sessions. However, Weffers expressly teaches utilizing a “therapy unit” (par.0044+), which suggests at least more than one therapy session. Although Weffers does not explicitly teach 20-40 therapy sessions, further modification to rely on data from 20-40 therapy 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 use from Weffer’s therapy unit.
With respect to claim 6, Weffers does not expressly teach wherein the machine learning algorithm is configured 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, Weffers expressly teaches performing calculations (par.0027), calculate statistics to compare the difference of signals (par.0041, 0077), etc. Moreover, machine learning algorithms are widely known. As such, Examiner argues that PHOSITA would have had predictable success when the invention was filed to incorporate a machine learning algorithm, a series of calculations, in the manner recited in place of Weffer’s calculations as doing so would be a simple substitution.
With respect to claim 7, Weffers teaches wherein the processor is configured to extract a plurality of sEMG features from the sEMG data (par.0040-41, 0077).
With respect to claim 8, Weffers 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.0027, 0070).
With respect to claim 9, Weffers does not explicitly teach wherein the machine learning algorithm is configured 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, Weffers expressly teaches performing calculations (par.0027), calculate statistics to compare the difference of signals (par.0041, 0077), etc. Moreover, machine learning algorithms are widely known. As such, Examiner argues that PHOSITA would have had predictable success when the invention was filed to incorporate a machine learning algorithm, a series of calculations, in the manner recited in place of Weffer’s calculations as doing so would be a simple substitution.
With respect to claim 12, Weffers does not explicitly teach wherein the machine learning algorithm is configured 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, Weffers expressly teaches performing calculations (par.0027), calculate statistics to compare the difference of signals (par.0041, 0077), etc. Moreover, machine learning algorithms, regression models, etc. are widely known. As such, Examiner argues that PHOSITA would have had predictable success when the invention was filed to incorporate a machine learning algorithm, a series of calculations, and using a regression model in the manner recited in place of Weffer’s calculations as doing so would be a simple substitution.
With respect to claim 13, Weffers teaches wherein the reference data includes information relating to a relationship between at least one electrophysiological biomarker and a likelihood of muscle recovery (par.0077).
With respect to claim 14, Weffers teaches further comprising a housing configured to contain the sensor, the memory, and the processor (see Fig. 2A).
With respect to claim 15, Weffers suggests 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 (Fig. 2A; Examiner notes that muscle sensor 18 is covered by attachment component 16, which is presumably sterile as it is covering a patient’s body part; see also par.0060).
With respect to claim 18, Weffers teaches a user interface configured to present information to a user and/or receive information from the user, 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.0049).
With respect to claim 20, Weffers teaches further comprising communication circuitry configured to provide wired or wireless communication with an external device (par.0063).
With respect to claim 21, Weffers 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 (Fig. 2A).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Weffers, as applied to claim 2 above, in further view of Gong et al. (US PG Pub. No. 2020/0029882 A1) (hereinafter “Gong”).
With respect to claim 10, Weffers teaches portable, hand-held device, as established above.
However, Weffers does not teach the limitations further recited in claim 10.
Gong teaches wherein the sEMG data includes first data corresponding to a maximal voluntary contraction (MVC) and second data corresponding to a predetermined percentage of MVC (par.0056, 0058, 0067).
Therefore, it would have been prima facie obvious to PHOSITA when the invention was filed to modify Weffers to obtain MVC and a predetermined percentage of MVC to allow for analysis of body posture symmetry, to identify a source of fatigue or potential injury, and so on, as suggested by Gong (par.0057).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Weffers, as applied to claim 2 above, in further view of Linderman (US PG Pub. No. 2010/0106044 A1).
With respect to claim 11, Weffers teaches portable, hand-held device, as established above.
However, Weffers does not teach the limitations further recited in claim 11.
Linderman 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.0143 -0146).
Therefore, it would have been prima facie obvious to PHOSITA when the invention was filed to modify Weffers to further incorporate a bandpass filter, amplifier, and sampling circuity in the manner recited in order to improve signal to noise ratio of the obtained sEMG signal, as evidence by Linderman (par.0143-0146).
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG Pub. No. 2018/0177447, see par.0001
US PG Pub. No. 2021/0267537, see par.0026
Conclusion
No claim is allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PUYA AGAHI whose telephone number is (571)270-1906. The examiner can normally be reached M-F 8 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Valvis can be reached at 5712724233. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PUYA AGAHI/Primary Examiner, Art Unit 3791