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
This action is in response to the remarks filed on 12/05/2025.
The amendments filed on 12/05/2025 have been entered. Accordingly claims 1-27 remain pending. Claims 26-27 are newly added.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1, 9 and 17 recite “decomposing”, “correlating” and “determining”.
The limitation of “decomposing”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “decomposing” in the context of this claim encompasses the user manually calculating the amount of use of each icon. Similarly, the limitation of “correlating” and “determining”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “by a processor” language, “correlating” and “determining” in the context of this claim encompasses the user thinking that the correlating numbers and making determinations, or by simply using pen and paper to make simple calculations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform the limitation of “decomposing”, “correlating” and “determining”. The processor in all the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “decomposing”, “correlating” and “determining”. such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform “decomposing”, “correlating” and “determining” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The depending claims also recite similar abstract ideas (e.g., “decomposing”, “correlating” and “determining”. etc.) without additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application.
Therefore, the claims are not patent eligible.
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.
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, 3, 6, 9, 11, 14, 17, 19 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Giuffrida et al (US20130123666A1) in view of Hug et al (Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi-joint isometric task, J Physiol 601.15 (2023) pp 3201–3219, published online 30 June 2022) and Crawford et al (Tremor: Sorting Through the Differential Diagnosis, American Family Physician, February 1, 2018, Volume 97, Number 3).
Regarding claims 1, 9 and 17, Giuffrida teaches method, system and computer medium for operating a neuromuscular assessment system (“a movement disorder monitor, and a method of measuring the severity of a subject's movement disorder.” Abst; “device and method disclosed herein further relate to monitoring symptoms of movement disorders, such as those associated with Parkinson's disease (PD), essential tremor, dystonia, and Tourette's syndrome, including tremor, bradykinesia, rigidity, gait/balance disturbances, and dyskinesia.” [0004]) comprising:
affixing a first electrode array to an agonist muscle, the first electrode array configured to detect a first Electromyography (EMG) signal (“normal movement, an agonist muscle contracts while the antagonist muscles” [0009]; “the movement disorder device with an external sensor module for the hand and EMG electrodes.” [0036]; also see figs. 5, 8, 14-16 and the associated pars.);
affixing a second electrode array to an antagonist muscle, the second electrode array configured to detect a second EMG signal, the agonist muscle and the antagonist muscle forming an agonist/antagonist muscle pair (see figs. 5, 8, 14-16 and the associated pars.);
decomposing the first EMG signal into a first motor unit spike train (“EMG signal is calculated over discrete time windows. The amplitude and frequency 156 of the processed EMG signal is calculated. Specific variables are then computed for each Parkinson's symptom based on the processed kinetic and EMG data. Tremor symptom variables may include but are not limited to the peak frequency of the kinetic sensors, the average amplitude of the kinetic sensors, the average power of the kinetic sensors, and the frequency of the EMG signals. Bradykinesia symptom variables may include but are not limited to the peak frequency of EMG or kinetic data, the average amplitude of the kinetic sensors” [0098]);
decomposing the second EMG signal into a second motor unit spike train (“EMG signal is calculated over discrete time windows. The amplitude and frequency 156 of the processed EMG signal is calculated. Specific variables are then computed for each Parkinson's symptom based on the processed kinetic and EMG data. Tremor symptom variables may include but are not limited to the peak frequency of the kinetic sensors, the average amplitude of the kinetic sensors, the average power of the kinetic sensors, and the frequency of the EMG signals. Bradykinesia symptom variables may include but are not limited to the peak frequency of EMG or kinetic data, the average amplitude of the kinetic sensors” [0098]);
correlating the first motor unit spike train and the second motor unit spike train to generate correlated signals (“the movement data from the movement measuring apparatus to a processor; training an algorithm to distinguish at least in part between voluntary and involuntary movement, and between postural and kinetic tremor; processing, with the processor, the movement data using the trained algorithm to calculate a score that correlates to a clinician's standardized rating score; and outputting the score” [0028]) representing a cross-correlation between the agonist and antagonist muscles of the muscle pair (“quantifying tremor during activities of daily living in the home, comprising: a movement measuring apparatus for continuously acquiring movement data corresponding to continuous movement of a subject and comprising at least one sensor; a processor in communication with the movement measuring apparatus capable of using a trained algorithm to calculate a score correlated at least in part to a clinician's standardized rating score” [0024]; “a trained algorithm for distinguishing at least in part between voluntary and involuntary movement, and between postural and kinetic tremor; a processor in communication with the movement measuring apparatus capable of using the trained algorithm to calculate a score correlated at least in part to a clinician's standardized rating score” [0026]; also see [0029]);
determining synchronicity and periodicity within the correlated signals (“Resting tremors usually occur at frequencies of approximately 4-7 Hz while the frequency of action of postural tremors is higher, usually between 9-11 Hz.” [0007]; “trained algorithm preferably correlates with a central database 220 and outputs a patient customized treatment which may then be displayed on a monitor 544 or as input to control a treatment device such as an electric stimulator, automated medicine delivery” [0064]); and generating a tremor fraction determined to have both the synchronicity and the periodicity (“ systems and methods of the present invention is the ability to distinguish the subject's symptoms from activities of daily living during analysis of the recorded movement data. Such symptoms may include tremor, …gait/balance” [0054]).
Giuffrida does not seem to point out the specifics of the tremor fraction being a percentage of the correlated signals determined to have both the synchronicity and the periodicity and the periodicity, and the tremor fraction indicative of a resting tremor before the resting tremor is physically observable.
However, in the same field of endeavor, Hug teaches High-density surface electromyographic (HDsEMG) signals were recorded from six lower limb-muscles of the right leg: biceps femoris (long head, BF), semitendinosus (ST), gastrocnemius medialis (GM),gastrocnemius lateralis (GL), vastus lateralis (VL) andvastus medialis (VM). The high-density electromyography signals were decomposed into motor unit spike trains. For each pair of motor neurons, we assessed the correlation between their smoothed discharge rates to determine whether they shared a common input. Then, we used a purely data-driven method grounded on graph theory to extract networks of common inputs and we applied a clustering procedure to group the motor neurons according to their positions in the graph (i.e. their correlated activity) (abst). we used a purely data-driven method to identify the groups of motor neurons based on their level of common low-frequency modulation in discharge rate (intro). The length of the Hanning window was chosen such that the correlation was calculated based on the low-frequency oscillations of the signal thereby limiting the effect of the non-linear relationship between the synaptic input and the output signal (data analysis). Correlation networks of motor neurons. To account for the fact that the strength of common synaptic inputs between two motor neurons does not necessarily trans-late into a proportional degree of correlation between their outputs (common drive) the networks were based on the significance of the correlations between motor neuron spiking activities rather than on the strength of the correlations. The significance threshold was defined as the 99th percentile of the cross-correlation coefficient distribution generated with resampled versions of the motor unit spike trains. Specifically, for each motor unit, we generated a surrogate spike train by bootstrapping (random sampling with replacement) the inter spike intervals. This random spike train had the same number of spikes, and the same discharge rate (mean ± SD) as the original motor unit spike train (Correlation networks of motor neurons).
We applied a clustering procedure to group the motor neurons according to their positions in the graph, and therefore based on their correlated activity. We defined a cluster as a group of motor neurons densely connected to each other and loosely connected to the rest of the network. We used a multiresolution consensus clustering method to identify significant clusters at different resolutions, i.e. levels (A). Because the clusters were decoupled from the muscle innervation, we also reported the occurrence of each muscle pair within the same cluster (B). Cells with the same muscles in the x- and y-axes (e.g. BF-BF) represent the percentage of participants with a cluster that only groups motor neurons from this muscle. BF, biceps femoris, ST, semitendinosus; GM, gastrocnemius medialis; GL, gastrocnemius lateralis; VL, vastus lateralis; VM, vastus medialis (fig. 7).
Further, Hug also teaches representing a cross-correlation between the agonist and antagonist muscles of the muscle pair (“cross-correlation between smoothed discharge rates for each pair of motor units (Fig. 2B) and we considered that a significant correlation indicated the presence of common inputs. We first assessed the repeatability of the correlation matrices (Fig. 2C) between the two contractions interspaced by 10 s of rest.” see Correlation between motor neurons section);
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with tremor fraction being a percentage of the correlated signals as taught by Hug because it helps to get a deeper understanding of the structure of common inputs to motor neurons. (Abst of Hug).
The above noted combination does not teach the tremor fraction indicative of a resting tremor before the resting tremor is physically observable.
However, in the same field of endeavor, Crawford teaches Tremor: Sorting Through the Differential Diagnosis (title). The first step in evaluating a patient with tremor is to categorize the tremor based on its activation condition, topographic distribution, and frequency. Resting tremors occur in a body part that is relaxed and completely supported against gravity. Action tremors occur with voluntary contraction of a muscle and can be further subdivided into postural, isometric, and kinetic tremors. The most common pathologic tremor is essential tremor, which affects 0.4% to 6% of the population (abst). Approximately 70% of patients with Parkinson disease have resting tremor as the presenting feature. The classic parkinsonian tremor begins as a low-frequency, pill-rolling motion of the fingers, progressing to forearm pronation/supination and elbow flexion/extension. It is typically unilateral, occurs at rest, and fades with voluntary movement. It can reemerge after latency with maintenance of posture, which can create diagnostic confusion. Tremor can involve the leg and jaw as well (see Parkinsonism section).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with tremor fraction indicative of a resting tremor before the resting tremor is physically observable as taught by Crawford because it helps with patients presenting to primary care physicians are enhanced physiologic tremor, essential tremor, and parkinsonian tremor (abst of Crawford).
Regarding claims 3, 11 and 19, Giuffrida teaches low pass filtering the first motor unit spike train and the second motor unit spike train (“This filtering improves measurement resolution and helps prevent aliasing. After being low-pass filtered, the analog signal is converted to a duty cycle modulated signal by the DCM stage 37” [0059]).
Regarding claims 6, 14 and 22, Giuffrida teaches determining the synchronicity includes determining one of the correlated signals include peaks separated by a time, the time being below a synchronicity threshold (“Tremor symptom variables may include but are not limited to the peak frequency of the kinetic sensors, the average amplitude of the kinetic sensors, the average power of the kinetic sensors, and the frequency of the EMG signals. Bradykinesia symptom variables may include but are not limited to the peak frequency of EMG or kinetic data” [0098]; also see [0110]-[0111]).
Claims 2, 8, 10, 16, 18 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Giuffrida et al in view of Hug and Crawford as applied to claims above and further in view of Bloch (WO 2023209150 A1).
Regarding claims 2, 10 and 18 , Giuffrida teaches determining asynchrony within the correlated signals (“the external processing unit 539 feeds the data into a trained algorithm preferably loaded into the processor. The trained algorithm preferably correlates with a central database 220 and outputs a patient customized treatment which may then be displayed on a monitor 544” [0064]); and
wherein:
affixing the first electrode array and affixing the second electrode array include affixing the first electrode array and the second electrode array to the agonist/antagonist muscle pair within a leg (“measuring the severity of a subject's movement disorder can be worn in any way likely to provide good data on a subject's movement disorder. Examples would include but are not limited to the use of the device on the subject's finger, hand, wrist, arm; legs, thighs, feet, ankles, heels, toes, torso, and/or head” [0093]).
The combination noted above does not point out the specifics of generating a freeze of gait fraction, the freeze of gait fraction being a second percentage of the correlated signals determined to have the asynchrony.
However, in the same field of endeavor, Bloch teaches study quantified these deficits using a validated Parkinsonian Disability (PD) score (8) assessing tremor, general level of activity, body posture, vocalization, freezing and frequency/rigidity of arm movements (Preparation and assessment of the NHP model of PD section). Microelectrode arrays inserted into the leg region of left and right Ml, and EMG activity from leg muscles. A control computer detects gait events from the received neural signals. Fig. 11 b. Leg muscle activity underlying locomotion projected onto the location of motor neurons to generate spatiotemporal maps of motor neuron activity. Bar plots compare the correlation between two maps (see fig. 11 and the associated sections).
Figs. 16a-c show diagrams of spatiotemporal maps of motor neuron activity in the NHP experiment of Fig. 9. The spatial maps of motor neuron activity corresponding to the time at which each hotspot reached a maximum (center) are laid over the schematics of the spinal cord. Fig. 16b. Bar plots report the correlation between each spatial map of motor neuron activity and the spatial map corresponding to the targeted hotspot (see fig. 16 and the associated sections). Parkinson's disease (PD) is one of the most prevalent neurodegenerative disorders that PD suffer from motor, in particular locomotor, disturbances that significantly affect their quality of life and increase comorbid conditions (1), including gait and balance locomotor deficits as well as freezing-of-gait (FOG).
Then, calculated the mean firing rate during these periods and the modulation depth were calculated. The modulation depth was defined non- parametrically as the difference between the upper and lower 95th percentile of all firing rates observed within each period. Then, the activity of each neuron was mapped during locomotion to the respective gait phase. The gait phase was defined as a percentage (0 - 100%), with 0% and 100% occurring at the time of foot strike from the right hindlimb. The average activity over all the gait cycles was calculated using PC analysis. To ensure that correlation values could not be explained trivially by the cyclical motor output, this procedure was repeated using the envelopes of the electromyography signals from the eight recorded leg muscles. To estimate the lower bound of the correlation values (Analysis of Ml activity before and after MPTP administration section).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with of generating a freeze of gait fraction, the freeze of gait fraction being a second percentage of the correlated signals determined to have the asynchrony as taught by Bloch because it helps to provide for a synergistic effect that allows to obtain the most encouraging results in terms of locomotor improvements (e.g., fastest walking) in subjects with PD symptoms. (of Bloch).
Regarding claims 8, 16 and 24, the combination noted above teaches all the claimed limitations except for inertial measurement unit (IMU).
However, in the same field of endeavor, Bloch teaches study quantified these deficits using a validated Parkinsonian Disability (PD) score (8) assessing tremor, general level of activity, body posture, vocalization, freezing and frequency/rigidity of arm movements (Preparation and assessment of the NHP model of PD section). Microelectrode arrays inserted into the leg region of left and right Ml, and EMG activity from leg muscles. A control computer detects gait events from the received neural signals. Fig. 11 b. Leg muscle activity underlying locomotion projected onto the location of motor neurons to generate spatiotemporal maps of motor neuron activity. Bar plots compare the correlation between two maps (see fig. 11 and the associated sections).
A custom G-Drive Plus software application was developed to configure and control the spinal cord neuroprosthesis. G-Drive Plus runs on a desktop computer, laptop or tablet and interfaces with the stimulation system (through NRPA) and collects the signals from different sensors that can be used for closed-loop stimulation. The sensors include the wireless Next Generation Inertial Measurement Units (G-Drive Plus software for configuration and control of the spinal cord neuroprosthesis in P1 section).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with inertial measurement unit as taught by Bloch because it helps to provide for a synergistic effect that allows to obtain the most encouraging results in terms of locomotor improvements (e.g., fastest walking) in subjects with PD symptoms (of Bloch).
Claims 4, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Giuffrida et al in view of Hug as applied to claims above and further in view of Dai et al (Independent component analysis-based algorithms for high-density electromyogram decomposition: Systematic evaluation through simulation, Computers in Biology and Medicine, Volume 109, June 2019, Pages 171-181).
Regarding claims 4, 12 and 20, the combination noted above teaches all the claimed limitations except for decomposing the first EMG signal into the first motor unit spike train includes decomposing the first EMG signal into multiple motor unit spike trains; and further comprising: summing the multiple motor unit spike trains into a cumulative spike train.
However, in the same field of endeavor, Dai teaches EMG signal can be regarded as a convoluted mixing process of hundreds of MU spike trains with their MUAPs. To decompose the raw EMG signals in the time domain into constituent MUAP trains. Identify the common MUs. RoA was used as a metric to predict the decomposition performance, and the common MUs detected by two algorithms were identified (Materials and methods section). The RoA between algorithms was also calculated as the number of matched spikes divided by the sum of matched and unmatched spikes from either algorithm (Evaluation of decomposition performance section).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with decomposing the first EMG signal into the first motor unit spike train includes decomposing the first EMG signal into multiple motor unit spike trains; and further comprising: summing the multiple motor unit spike trains into a cumulative spike train as taught by Dai because it helps with high decomposition accuracy among the three algorithms under a variety of signal conditions, especially with a low signal quality and varying contraction levels (abst of Dai).
Claims 5, 13 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Giuffrida et al in view of Hug and Crawford as applied to claims above and further in view of Germer et al (Surface EMG cross talk quantified at the motor unit population level for muscles of the hand, thigh, and calf. J Appl Physiol 131: 808–820, 2021).
Regarding claims 5, 13 and 21, the combination noted above teaches all the claimed limitations except for determining the periodicity includes calculating a power spectral density of the correlated signal.
However, in the same field of endeavor, Germer teaches EMG signals are decomposed into motor unit dis-charge times, the cross talk from individual motor units can be estimated by spike-triggered averaging (STA) the EMG signals recorded from multiple muscles (intro). The influence of cross talk on the frequency content of the EMG signal was assessed by comparing the EMG and Clean EMG power spectrum. The power spectrum was estimated by Welch’s averaged periodogram with nonoverlapping Hanning window of 1-s duration. First, the EMG signals were full wave rectified and detrended. The median frequency was estimated for each participant, and we computed the average power spectrum for all participants for visualization (pg. 811, right col, 3rd par.).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determining the periodicity includes calculating a power spectral density of the correlated signal as taught by Germer because it allows exact quantification of cross talk and its effects on the interpretation of the EMG signals (intro of Germer).
Claims 7, 15 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Giuffrida et al in view of Hug, Crawford and Bloch further in view of Tran (US 20210106281).
Regarding claim 7, 15 and 23, the combination noted above teaches all the claimed limitations except for affixing a Photoplethysmography sensor configured to detect a cardiovascular parameter.
However, in the same field of endeavor, Tran teaches detecting breathing rate by photoplethysmography (PPG) which detects changes in the volume of blood flowing through blood vessels due to the rhythmic activity of the heart. This volume change is measured by illuminating the capillary bed with a small light source and measuring the amount of light that reflects or passes through the tissue with a photodiode [0062]. EMG signals are the superposition of activities of multiple motor units. The EMG signal can be decomposed to reveal the mechanisms pertaining to muscle and nerve control. Decomposition of EMG signal can be done by wavelet spectrum matching and principle component analysis of wavelet coefficients where the signal is de-noised and then EMG spikes are detected, classified and separated [0105].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with Photoplethysmography sensor configured to detect a cardiovascular parameter as taught by Tran because it helps with better tracking and management of disease conditions, earlier detection of changes in the animal condition, and reduction of overall health care expenses associated with long term disease management ([0016] of Tran).
Claims 25 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Giuffrida et al in view of Hug, and Crawford further in view of Rosenbluth et al (US 20190001129).
Regarding claim 25, the combination noted above teaches all the claimed limitations except for wherein the tremor fraction is generated via a neural network of the neuromuscular assessment system.
However, in the same field of endeavor, Rosenbluth teaches the method further includes sensing motion of the patient's extremity using a measurement unit to generate motion data; and determining tremor information from the motion data. In some embodiments, delivery comprises delivering the first stimulus based on the tremor (or other) information. In some embodiments, the information (e.g., tremor information) comprises a maximum deviation from a resting position for the patient's extremity. In some embodiments, the information (e.g., tremor information) comprises a resting position for the patient's extremity [0018]. The method further includes determining a period of the patient's tremor or other dysfunction, wherein delivering the second stimulus comprises offsetting delivery of the second stimulus from the delivery of the first stimulus by a predetermined fraction or multiple of a period of the tremor or other dysfunction. In some embodiments, the method further includes dephasing the synchronicity of a neural network in the patient's brain [0021].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with the tremor fraction is generated via a neural network as taught by Rosenbluth because treatment options for some of these conditions are also limited and alternative treatments are needed ([0005] of Rosenbluth).
Regarding claim 26, the combination noted above teaches all the claimed limitations except for outputting a Parkinson's propensity score based on the tremor fraction, including at least one of outputting on a display or print out, through an audio device or speaker, or through a tactile or haptic device.
However, in the same field of endeavor, Rosenbluth teaches the method further includes sensing motion of the patient's extremity using a measurement unit to generate motion data; and determining tremor information from the motion data. In some embodiments, delivery comprises delivering the first stimulus based on the tremor (or other) information. In some embodiments, the information (e.g., tremor information) comprises a maximum deviation from a resting position for the patient's extremity. In some embodiments, the information (e.g., tremor information) comprises a resting position for the patient's extremity [0018]. The method further includes determining a period of the patient's tremor or other dysfunction, wherein delivering the second stimulus comprises offsetting delivery of the second stimulus from the delivery of the first stimulus by a predetermined fraction or multiple of a period of the tremor or other dysfunction. In some embodiments, the method further includes dephasing the synchronicity of a neural network in the patient's brain [0021]. Detecting postural and kinetic tremors is more challenging than detecting resting tremors. Resting tremors are present in other movement disorders including Parkinson's disease and can be easily identified by analyzing tremors present only while the limb is at rest. Extracting kinetic tremors from motion data is challenging because it is necessary to separate the motion due to tremor from the motion due to the task [0200]. Customized treatment based on feedback and/or algorithms are provided in several embodiments [0202].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with for outputting a Parkinson's propensity score based on the tremor fraction, including at least one of outputting on a display or print out, through an audio device or speaker, or through a tactile or haptic device as taught by Rosenbluth because treatment options for some of these conditions are also limited and alternative treatments are needed ([0005] of Rosenbluth).
Response to Arguments
Applicant’s arguments 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.
Regarding the rejection of claims under 35 USC 101, the applicant argues the following;
Applicant respectfully submits that the claimed features do not recite matter which falls within the enumerated groupings of abstract ideas, i.e., mathematical concepts, certain methods of organizing human activity, and mental processes.
Even assuming arguendo, that amended independent claims 1, 9, and 17 should nonetheless be treated as reciting an abstract idea, Applicant respectfully submits that these claims recite a practical application of a judicial exception, and thus are not directed to a judicial exception. Specifically, amended independent claims 1, 9, and 17 as currently presented include one or more additional elements that reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field.
For example, the features of "generate correlated signals representing a cross-correlation between the agonist and antagonist muscles of the muscle pair... determining synchronicity and periodicity within the correlated signals... generating a tremor fraction, the tremor fraction being a percentage of the correlated signals determined to have both the synchronicity and the periodicity, and the tremor fraction indicative of a resting tremor before the resting tremor is physically observable" (emphasis added) as is recited, inter alia, in amended independent claim 1 and similarly in amended independent claims 9 and 17 are technological improvements and have a practical application that allows detection of pre-Parkinson's disease and/or early diagnosis of Parkinson's disease in a patient before the patient exhibits symptoms that only become conspicuous/visible in later stages of Parkinson's disease.
Contrary to the applicant’s assertion, claims still recite abstract idea as "decomposing", “generating” and "determining" and "correlating" which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. Other than the recitation of generic computer components (“a processor” except for claim which does not even call for processor) nothing in the claim element precludes the step from practically being performed in the mind.
Therefore, under its broadest reasonable interpretation, claims cover performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Judicial exception is not integrated into a practical application since the claim only recites additional element generic computer components (“a processor”).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims are not patent eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/SERKAN AKAR/ Primary Examiner, Art Unit 3797