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 .
Response to Amendment
The amendment filed 01/23/2026 has been entered. Amendments to claims 1,3, and 17, and cancellation of claim 2 are acknowledged. Claims 1, 3-9, and 17-27 remain pending in the application.
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.
Claim(s) 1-9 and 17-27 is/are rejected under 35 U.S.C. 101 because the claimed invention, considering all claim elements both individually and in combination as a whole, do not amount to significantly more than a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
Claim 1 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 1 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “acquiring multichannel electromyography (EMG) data for an anatomical region”, “generating a pairwise EMG channel-EMG channel similarity matrix from the acquired multichannel EMG data;”, “performing network analysis on the pairwise EMG channel-EMG channel similarity matrix to generate a network representing the pairwise EMG channel-EMG channel similarity matrix;”, and “computing one or more metrics of the network representing the pairwise EMG channel-EMG channel; and determining one or more biomarkers for the anatomical region based on the one or more metrics of the network representing the pairwise EMG channel-EMG channel”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). Calculation of a similarity matrix, network analysis, generation of a network, and determination of a biomarker constitute mathematical tasks that may be performed by a human with a pen and paper. The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. Additional elements are “electrodes disposed in a garment” in claim 1. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0005]: “development is going more and more towards EMS garments” of Horter et al. (US 20170173324 A1), hereinafter Horter. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 1 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Dependent claim(s) 3-9 and 17-27 fail to cure the deficiencies of independent claim 1 by merely reciting additional abstract ideas, further limitations on abstract ideas already recited, and/or additional elements that are not significantly more. Thus, claim(s) 1-9 and 17-27 is/are rejected under 35 U.S.C. 101.
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) 1, 3, 6-9, 18-19, and 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xi et al. (CN 112541415 A), hereinafter Xi in further view Lee et. al (US 20230136666 A1), hereinafter Lee.
Regarding claim 1, Xi discloses a method of neurological assessment comprising (page 7 para 1: “a neuromuscular network”): acquiring multichannel electromyography (EMG) data for an anatomical region (page 12 para 8: “EMG signals tested in 2 experimental paradigms were collected separately. Each subject was experimented with the paradigm E1, E2, with ten sets of data collected for 6s each subject in each paradigm”, Fig 1(A)); generating a pairwise EMG channel-EMG channel similarity matrix from the acquired multichannel EMG data (page 9 para 5: “calculating the coupling strength value between each pair of signals by using the symbol transfer entropy, and establishing a symbol transfer entropy adjacency matrix.”, page 4 para 6: “each pair of electromyogram signals”); performing network analysis on the EMG channel-EMG channel similarity matrix to generate a network representing the EMG channel-EMG channel similarity matrix (Page 12 para 10:“ a 13 × 13 weighted adjacency matrix to construct a weighted brain muscle function network,”); computing one or more metrics of the network representing the EMG channel-EMG channel similarity matrix (Page 12 para 11: “network feature extraction is the most critical step in all the steps, and the average degree, the global efficiency and the global clustering coefficient are calc”); and determining one or more biomarkers for the anatomical region based on the one or more metrics of the network representing the EMG channel-EMG channel similarity matrix (page 12 para 11: “the global efficiency and the global clustering coefficient are calculated and calculated by the binary brain muscle function network topological graph obtained in the third step. Whether the tested person enters the muscle fatigue state is detected by the parameter.”).
Xi fails to disclose acquiring the multichannel EMG data using electrodes disposed in a garment worn on the anatomical region.
Lee discloses acquiring multichannel EMG data using electrodes disposed in a garment worn on the anatomical region ([0009]: “an electrode that is configured to contact and provide an electrical connection with a subject while the subject is wearing the fabric material.”, [0043]: “allowing for (1) multichannel data acquisition (up to 8 single-ended channels)”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Xi to include the acquiring of multichannel EMG data using electrodes disposed in a garment worn on the anatomical region as disclosed by Lee in order to enable the ambulatory monitoring of health conditions (Lee [0004]).
Regarding claim 3, Xi further discloses wherein the generating of the pairwise EMG channel-EMG channel similarity matrix includes binarizing the elements of the pairwise EMG channel-EMG channel similarity matrix (page 5 para 1: “and binarizing the weighted brain muscle function network”).
Regarding claim 6, Xi further discloses wherein the one or more metrics of the network include one or more network metrics (Page 5 para 3: “D represents the network density and is a closed value of the actual edge number and the maximum edge number of the network”).
Regarding claim 7, Xi further discloses wherein the one or more network metrics include one or more of a density metric measuring a fraction of present connections to possible connections (Page 5 para 3: “D represents the network density and is a closed value of the actual edge number and the maximum edge number of the network”, equation 8).
Regarding claim 8, Xi discloses wherein the one or more metrics of the network include one or more nodal metrics (page 5 para 5: “N is the number of nodes in the network.”).
Regarding claim 9, Xi further discloses wherein the one or more nodal metrics include degree metric measuring a number of links connected to a node of the network (page 5 para 5: “N is the number of nodes in the network.”).
Regarding claim 18, Xi further discloses wherein the one or more network metrics include a global efficiency metric measuring an average inverse shortest path length in the network ( Page 5 para 11 “global efficiency is a scalar measure of the information flow, defined as the inverse of all shortest path lengths in a given network”).
Regarding claim 19, Xi further discloses wherein the one or more network metrics include a characteristic path length metric measuring an average shortest path length in the network (Page 5 para 5: “L isi,jRepresents the direct shortest path of two nodes,”).
Regarding claim 21, Xi further discloses wherein the one or more nodal metrics include a clustering coefficient metric measuring a fraction of neighbors of a node of the network that are neighbors of each other (Page 5 para 12: “the clustering coefficient represents the probability that the neighbors of the nodes become the neighbors of each other, if the clustering coefficient is low, the network connection is not tight, and the calculation formula is as follows”).
Regarding claim 22, Xi further discloses wherein the one or more nodal metrics include a local efficiency metric measuring a global efficiency computed on a neighborhood of a node of the network (Page 5 para 3: “Eg represents the global efficiency”, equation 9 wherein the calculation is computed on each node).
Regarding claim 23, Xi further discloses wherein the one or more nodal metrics include a betweenness centrality metric measuring a fraction of all shortest paths in the network that contain a node of the network (page 6 para 3: “3) calculating the distance between the sample to be classified and each central point; yielding weight coefficients in determining the class of a sample to be classified”).
Claim(s) 4 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xi in view of Lee in view of Kerkman et al. (“Functional connectivity analysis of multiplex muscle network across frequencies”), hereinafter Kerkman .
Regarding claim 4, Xi as modified by Lee discloses method of claim 1 but fails to disclose wherein the network analysis comprises a coherence network analysis.
Kerman discloses acquiring EMG data and performing network analysis (abstract) wherein the network analysis comprises a coherence network analysis (abstract, pg. 1 col 2 para 3: “The squared modulus of the coherency spectra (magnitude-squared coherences) were decomposed in four components using NNMF”.)
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to substitute the known method of network analysis disclosed by Xi with the known method of coherence network analysis disclosed by Kerkman for the predictable result of determining network properties.
Regarding claim 20, Xi as modified by Lee discloses the method of claim 1 but fails to disclose wherein the one or more network metrics include a core periphery q-stat metric.
Kerman discloses acquiring EMG data and performing network analysis (abstract) wherein one or more network metrics include a core periphery q-stat metric (pg. 2 col 1 para 3: “To quantify the multiplex networks, we computed the network metrics ‘mean degree’ [9] and ‘core/periphery structure’ (‘maximized coreness’) [10] for each network layer using the Brain Connectivity Toolbox [9]”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the network metrics disclosed by Xi to include core-periphery q-stat metrics as disclosed by Kerkman in order to obtain a more robust data set.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xi in view of Lee in view of Cranmer et al. (US 20210209761 A1), hereinafter Cranmer.
Xi as modified by Lee discloses the method of claim 1 but fails to disclose wherein the network analysis comprises a correlation network analysis.
Cranmer discloses an EMG network modeling (abstract, [0026]: “electromyogram (EMG) data, “) wherein the network analysis comprises a correlation network analysis ([0046]: “model correlation networks”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to substitute the known method of network analysis disclosed by Xi with the known method of correlation network analysis disclosed by Cranmer for the predictable result of determining network properties.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xi in view Lee in view of of Pale et al. (“Variability of Muscle Synergies in Hand Grasps: Analysis of Intra- and Inter-Session Data”), hereinafter Pale.
Regarding claim 17, Xi as modified by Lee discloses the method of claim 1 but fails to disclose wherein the performing of network analysis on the similarity matrix to generate the network representing the similarity matrix includes: performing non-negative matrix factorization (NMF) on the similarity matrix.
Pale discloses a method of neurological assessment comprising acquiring multichannel electromyography (EMG) data (abstract) including performing non-negative matrix factorization (NMF) on the similarity matrix (section 2.2.2.-2.2.3. "For each condition (e.g., session or sub-selection of movement repetitions) and subject, muscle synergies were extracted from concatenated EMG data of all exercises using nonnegative matrix factorization (NNMF) [ 42,43]…. Cosine similarity was used as a measure of synergy similarity”.).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to substitute the known method of network analysis disclosed by Xi with the known method of correlation network analysis disclosed by Pale for the predictable result of determining network properties.
Claim(s) 24-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xi view of Lee in view of Liu et al. (“Correlation Evaluation of Functional Corticomuscular Coupling With Abnormal Muscle Synergy After Stroke”), hereinafter Liu.
Regarding claim 24, Xi as modified by Lee discloses the method of claim 1 but fails to disclose wherein the one or more biomarkers for the anatomical region includes a level of neurological impairment from a prior stroke.
Liu discloses a method of neurological assessment comprising acquiring multichannel electromyography (EMG) data (abstract, page 2 para 2: “EMG”) wherein the one or more biomarkers for the anatomical region includes a level of neurological impairment from a prior stroke (page 5 col 1 para 5: “Then, the synergy similarity within groups is first assessed using the scalar product (SP) [39], i.e., quantifying the similarity of the weight coefficients in terms of the maximum normalized scalar product. After that, for comparison of MSM between stroke patients and healthy controls.”).
As Xi discloses their method may be used in evaluation of stroke patients (Xi page 6 para 11), it would have been obvious to a person of ordinary skill in the art to modify the method disclosed by Xi to include the biomarkers for the anatomical region that includes a level of neurological impairment from a prior stroke in order to directly apply the method to evaluate stroke patients.
Regarding claim 25, Xi as modified by Lee discloses the method of claim 1 but fails to disclose determining a muscle synergy based on the one or more metrics.
Liu discloses a method of neurological assessment comprising acquiring multichannel electromyography (EMG) data (abstract, page 2 para 2: “EMG”) including determining a muscle synergy based on the one or more metrics (page 5 col 1 para 1: “For each subject, in order to explore the underlying correlation between synergy movement and fCMC, the Non-negative Matrix Factorization (NMF) algorithm [35] is applied to obtain muscle synergy model (MSM) from a matrix that includes the normalized EMG”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the network metrics disclosed by Xi to include muscle synergy as disclosed by Liu in order to obtain a more robust data set.
Regarding claim 26, Liu further discloses tracking changes in the muscle synergy over time to assess corticospinal plasticity (page 10 col 1 para 1: “significant coupling values were detected and comparable in both healthy controls and stroke patients, demonstrating the superiority and reliability of the optimized fCMC method in evaluating corticomuscular interaction.”)
Regarding claim 27, Liu further discloses assessing motor recovery from a prior stroke based on the muscle synergy (page 7 col 1 para 2: “high difference of synergistic pattern among stroke patients, which are associated with abnormal changes in MSM during the recovery of motor function after stroke, characterized by high variability”).
Response to Arguments
Applicant’s arguments, see pages 5 and 6 of Remarks, filed 01/23/2026, with respect to the rejection(s) of claim(s) 1-9 and 17-27 under 35 U.S.C. § 102/103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. § 103 (see rejection above).
Applicant’s arguments on page 6 of remarks are not persuasive. An adjacency matrix may be used interchangeably with a similarity matrix in the context of representing the EMG channel-EMG channel matrix (see Xi page 4 para 7: “where P() represents the probability distribution… in this case, the value of STE represents the strength of the coupling relationship between X and Y;”).
Applicant's arguments with respect to 35 U.S.C. § 101 have been fully considered but they are not persuasive. Incorporation of the claim limitation “using electrodes disposed in a garment worn on the anatomical region” does not preclude the claim from an abstract idea as electrodes disposed in a garment are well-known, routine, and conventional. Additionally, observation and interpretation of sensor data is a mental process. Referring to the improvement in the field of technology, while the MPEP does not require the claim itself to explicitly recite an improvement, the claim language as written does not show a clear improvement in the field. Per MPEP 2106.04, “the "improvements" analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology without reference to what is well-understood, routine, conventional activity.” . Use of garments to collect EMG data is a well-understood, routine, conventional activity. Additionally, ensuring an accurate diagnosis is made falls under “gathering and analyzing information using conventional techniques and displaying the result”, which does not show a sufficient improvement to a field of technology.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
He et al. (CN-110720911-A) – discloses a similarity matrix of EMG-EMG channels
He et al. (CN 110507324 A) – discloses a similarity matrix of EMG-EMG channels
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KAVYA SHOBANA BALAJI/Examiner, Art Unit 3791
/DANIEL L CERIONI/Primary Examiner, Art Unit 3791
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