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 .
Specification
The disclosure is objected to because of the following informalities: the cross reference data needs to be updated to reflect the status of the copending applications.
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities: line 18 recites “GSRsignal”, this should likely recite “GSR signal”. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1 and 7-9 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3 and 12-13 of U.S. Patent No. 10,231,666. Although the claims at issue are not identical, they are not patentably distinct from each other.
18/946,039
10,231,666
Claim 1: A system for calibrating spinal cord stimulation (SCS) treatment in a subject, the system comprising: a processing module, a sensor module; and a classifier module, wherein the processing module is configured to: receive a first indication from a SCS device that a first SCS treatment is being provided, wherein the first SCS treatment is characterized by a first value of at least one SCS parameter, wherein the first value of the at least one SCS parameter is different from that of a second value of the at least one SCS parameter; initiate the sensor module to conduct a first measurement of at least one physiological signal in response to the first indication from the SCS device, wherein the at least one physiological signal comprises at least one of a GSR signal or movement (accelerometer) signal; receive a second indication from the SCS device that a second SCS treatment is being provided, wherein the second SCS treatment is characterized by the second value of the at least one SCS parameter; initiate the sensor module to conduct a second measurement of the at least one physiological signal in response to the second indication from the SCS device; initiate the classifier module to extract, by a non-transitory computer program, at least two features from each of the at least one physiological signal obtained from the first and second measurements, wherein the at least two features comprise galvanic skin response (GSR) amplitude, GSR mean amplitude, GSR amplitude standard deviation (std), GSR Peak (P) amplitude, mean Peak (P) amplitude and Peak (P) amplitude std, GSR peak to peak time intervals, mean GSR peak to peak time interval, GSR peak to peak time intervals std, 3-axis accelerometer data (X, Y, Z, Theta) value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) average value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) variability, or any combination thereof; classify the at least two features by applying a classification algorithm thereon; and output, based on the classification, whether the first SCS treatment or the second SCS treatment correlates with a higher efficacy.
Claim 1: A system for SCS treatment efficacy assessment, the system comprising a processing module, a sensor module and a classifier module; wherein said processing module is configured to: receive a first indication from a stimulus evoking device that a first set of at least one stimulus is being provided; initiate said sensor module to conduct a first measurement of at least one physiological signal in response to the received first indication, wherein said sensor module comprises at least one sensor configured to non-invasively sense the at least one physiological signal of a subject; receive an indication from a SCS device that a SCS treatment is being provided; receive a second indication from the stimulus evoking device that a second set of the at least one stimulus is being provided; initiate said sensor module to conduct a second measurement of the at least one physiological signal in response to the second indication from the stimulus evoking device and the indication from the SCS device; and initiate said classifier module to, by a non-transitory computer program, extract at least two features from each of said at least one physiological signals obtained from the first and second measurements; and classify the at least two features by applying a classification algorithm thereon, wherein the at least one physiological signal comprises a photoplethysmograph (PPG) signal and/or a galvanic skin response (GSR) signal and wherein the at least two features comprise PPG amplitude, PPG amplitude variation, pulse rate (PR) interval, PR variability, GSR Amplitude, GSR fluctuations or any combination thereof.
Claim 7: The system of claim 1, further comprising a SCS device.
Claim 3: The system of claim 1, further comprising a SCS device.
Claim 8: The system of claim 1, wherein the at least two features further comprise photoplethysmograph (PPG) Peak (P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude, std of Trough (T) amplitude, PPG dicrotic notch (N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPG peak to peak time intervals, PPG peak to peak interval mean, PPG peak to peak interval std, power spectrum of the PPGpeak to peak intervals: VLF Power, LF Power and HF Power, ECG Q/R/S/T/P) amplitude, ECG (Q/R/S/T/P) amplitude variation, heart rate (HR) interval (RR), HR variability, intra-pulse (R-R) average, intra-pulse (R-R) variance or standard deviation, variance or standard deviation of PQ segment or PR segment or QT segment or RS segment, P-wave width average, ST segment amplitude average, ST segment amplitude variance, QRS width variance or standard deviation, energy of ECG residues, Phasic EDA: amplitude, mean amplitude and std of amplitude, Temperature amplitude, mean amplitude and std of amplitude, Temp Peak (P) amplitude, mean amplitude and std of amplitude, Temperature peak to peak time intervals, mean and std (variability) of interval, ECG to PPG Pulse Transition time, PPG to PPG Pulse Transition time, ECGPower of VLF, LF and HF frequency bands of power spectrum of the ECG R to R intervals (heart rate variability), Respiratory rate, mean rate and std rate, Power of the frequency bands of power spectrum of EMG signal, EMG Power Spectrum Mean frequency, EMG Power Spectrum Highest Peak Frequency, Power of the alpha, beta, gamma, delta, theta frequency bands of power spectrum of EEG/FEMG signal, EMG Power Spectrum Mean frequency, EMG Power Spectral edge frequency, Coherence between 2 or more EEG/FEMG channels, frequency of movement, axis of movement, or any combination thereof.
Claim 13: The system of claim 12, wherein the at least two features further comprise PPG Peak (P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude, std of Trough (T) amplitude; PPG dicrotic notch (N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPG peak to peak time intervals, PPG peak to peak interval mean, PPG peak to peak interval std; power spectrum of the PPG peak to peak intervals: VLF Power, LF Power and HF Power; GSR amplitude, GSR mean amplitude, GSR amplitude std; GSR Peak (P) amplitude, mean Peak (P) amplitude and Peak (P) amplitude std; GSR peak to peak time intervals, mean GSR peak to peak time interval; GSR peak to peak time intervals std; Phasic EDA: amplitude, mean amplitude and std of amplitude, Temperature amplitude, mean amplitude and std of amplitude; Temp Peak (P) amplitude, mean amplitude and std of amplitude; Temperature peak to peak time intervals, mean and std (variability) of interval; ECG to PPG Pulse Transition time; PPG to PPG Pulse Transition time; ECG R to R time intervals, mean and std (variability) of intervals; Power of VLF, LF and HF frequency bands of power spectrum of the ECG R to R intervals (heart rate variability); Upper peak amplitude, mean amplitude and STD of amplitude; Respiratory rate, mean rate and std rate; Power of the frequency bands of power spectrum of EMG signal; EMG Power Spectrum Mean frequency; EMG Power Spectrum Highest Peak Frequency; Power of the alpha, beta, gamma, delta, theta frequency bands of power spectrum of EEG/FEMG signal; EMG Power Spectrum Mean frequency; EMG Power Spectral edge frequency; Coherence between 2 or more EEG/FEMG channels; frequency of movement; axis of movement; or any combination thereof.
Claim 9: The system of claim 1, wherein the at least one physiological signal further comprises a plethysmogram (PPG) signal, a blood pressure signal, a respiration signal, an internal body temperature signal, a skin temperature signal, a electrooculography (EOG) signal, a pupil diameter signal, a electroencephalogram (EEG) signal, a frontalis electromyogram (FEMG) signal, a electromyography (EMG) signal, an electro-gastro-gram (EGG) signal, a laser Doppler velocimetry (LDV) signal, a dynamic light scattering (DLS) signal, a near-infrared spectroscopy (NIRS) signal, a partial pressure of carbon dioxide signal, or an accelerometer reading.
Claim 12: The system of claim 1, wherein the at least one physiological signal further comprises an electrocardiogram (ECG) signal, a blood pressure signal, a respiration signal, an internal body temperature signal, a skin temperature signal, a electrooculography (EOG) signal, a pupil diameter signal, a electroencephalogram (EEG) signal, a frontalis electromyogram (FEMG) signal, a electromyography (EMG) signal, an electro-gastro-gram (EGG) signal, a laser Doppler velocimetry (LDV) signal, a dynamic light scattering (DLS) signal, a near-infrared spectroscopy (NIRS) signal, a partial pressure of carbon dioxide signal, or an accelerometer reading.
Claims 1 and 7-9 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3 and 12-13 of U.S. Patent No. 11,020,052. Although the claims at issue are not identical, they are not patentably distinct from each other.
18/946,039
11,020,052
Claim 1: A system for calibrating spinal cord stimulation (SCS) treatment in a subject, the system comprising: a processing module, a sensor module; and a classifier module, wherein the processing module is configured to: receive a first indication from a SCS device that a first SCS treatment is being provided, wherein the first SCS treatment is characterized by a first value of at least one SCS parameter, wherein the first value of the at least one SCS parameter is different from that of a second value of the at least one SCS parameter; initiate the sensor module to conduct a first measurement of at least one physiological signal in response to the first indication from the SCS device, wherein the at least one physiological signal comprises at least one of a GSR signal or movement (accelerometer) signal; receive a second indication from the SCS device that a second SCS treatment is being provided, wherein the second SCS treatment is characterized by the second value of the at least one SCS parameter; initiate the sensor module to conduct a second measurement of the at least one physiological signal in response to the second indication from the SCS device; initiate the classifier module to extract, by a non-transitory computer program, at least two features from each of the at least one physiological signal obtained from the first and second measurements, wherein the at least two features comprise galvanic skin response (GSR) amplitude, GSR mean amplitude, GSR amplitude standard deviation (std), GSR Peak (P) amplitude, mean Peak (P) amplitude and Peak (P) amplitude std, GSR peak to peak time intervals, mean GSR peak to peak time interval, GSR peak to peak time intervals std, 3-axis accelerometer data (X, Y, Z, Theta) value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) average value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) variability, or any combination thereof; classify the at least two features by applying a classification algorithm thereon; and output, based on the classification, whether the first SCS treatment or the second SCS treatment correlates with a higher efficacy.
Claim 1: A system for SCS treatment efficacy assessment, the system comprising a processing module, a sensor module and a classifier module; wherein said processing module is configured to: receive a first indication from a stimulus evoking device that a first set of at least one stimulus is being provided; initiate said sensor module to conduct a first measurement of at least one physiological signal in response to the received first indication, wherein said sensor module comprises at least one sensor configured to non-invasively sense the at least one physiological signal of a subject; receive an indication from a SCS device that a SCS treatment is being provided; receive a second indication from the stimulus evoking device that a second set of the at least one stimulus is being provided; initiate said sensor module to conduct a second measurement of the at least one physiological signal in response to the second indication from the stimulus evoking device and the indication from the SCS device; and initiate said classifier module to, by a non-transitory computer program, extract at least two features from each of said at least one physiological signals obtained from the first and second measurements; and classify the at least two features by applying a classification algorithm thereon, wherein the at least one physiological signal comprises a photoplethysmograph (PPG) signal and/or a galvanic skin response (GSR) signal and wherein the at least two features comprise PPG amplitude, PPG amplitude variation, pulse rate (PR) interval, PR variability, GSR Amplitude, GSR fluctuations or any combination thereof.
Claim 7: The system of claim 1, further comprising a SCS device.
Claim 3: The system of claim 1, further comprising a SCS device.
Claim 8: The system of claim 1, wherein the at least two features further comprise photoplethysmograph (PPG) Peak (P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude, std of Trough (T) amplitude, PPG dicrotic notch (N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPG peak to peak time intervals, PPG peak to peak interval mean, PPG peak to peak interval std, power spectrum of the PPGpeak to peak intervals: VLF Power, LF Power and HF Power, ECG Q/R/S/T/P) amplitude, ECG (Q/R/S/T/P) amplitude variation, heart rate (HR) interval (RR), HR variability, intra-pulse (R-R) average, intra-pulse (R-R) variance or standard deviation, variance or standard deviation of PQ segment or PR segment or QT segment or RS segment, P-wave width average, ST segment amplitude average, ST segment amplitude variance, QRS width variance or standard deviation, energy of ECG residues, Phasic EDA: amplitude, mean amplitude and std of amplitude, Temperature amplitude, mean amplitude and std of amplitude, Temp Peak (P) amplitude, mean amplitude and std of amplitude, Temperature peak to peak time intervals, mean and std (variability) of interval, ECG to PPG Pulse Transition time, PPG to PPG Pulse Transition time, ECGPower of VLF, LF and HF frequency bands of power spectrum of the ECG R to R intervals (heart rate variability), Respiratory rate, mean rate and std rate, Power of the frequency bands of power spectrum of EMG signal, EMG Power Spectrum Mean frequency, EMG Power Spectrum Highest Peak Frequency, Power of the alpha, beta, gamma, delta, theta frequency bands of power spectrum of EEG/FEMG signal, EMG Power Spectrum Mean frequency, EMG Power Spectral edge frequency, Coherence between 2 or more EEG/FEMG channels, frequency of movement, axis of movement, or any combination thereof.
Claim 13: The system of claim 12, wherein the at least two features further comprise PPG Peak (P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude, std of Trough (T) amplitude; PPG dicrotic notch (N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPG peak to peak time intervals, PPG peak to peak interval mean, PPG peak to peak interval std; power spectrum of the PPG peak to peak intervals: VLF Power, LF Power and HF Power; GSR amplitude, GSR mean amplitude, GSR amplitude std; GSR Peak (P) amplitude, mean Peak (P) amplitude and Peak (P) amplitude std; GSR peak to peak time intervals, mean GSR peak to peak time interval; GSR peak to peak time intervals std; Phasic EDA: amplitude, mean amplitude and std of amplitude, Temperature amplitude, mean amplitude and std of amplitude; Temp Peak (P) amplitude, mean amplitude and std of amplitude; Temperature peak to peak time intervals, mean and std (variability) of interval; ECG to PPG Pulse Transition time; PPG to PPG Pulse Transition time; ECG R to R time intervals, mean and std (variability) of intervals; Power of VLF, LF and HF frequency bands of power spectrum of the ECG R to R intervals (heart rate variability); Upper peak amplitude, mean amplitude and STD of amplitude; Respiratory rate, mean rate and std rate; Power of the frequency bands of power spectrum of EMG signal; EMG Power Spectrum Mean frequency; EMG Power Spectrum Highest Peak Frequency; Power of the alpha, beta, gamma, delta, theta frequency bands of power spectrum of EEG/FEMG signal; EMG Power Spectrum Mean frequency; EMG Power Spectral edge frequency; Coherence between 2 or more EEG/FEMG channels; frequency of movement; axis of movement; or any combination thereof.
Claim 9: The system of claim 1, wherein the at least one physiological signal further comprises a plethysmogram (PPG) signal, a blood pressure signal, a respiration signal, an internal body temperature signal, a skin temperature signal, a electrooculography (EOG) signal, a pupil diameter signal, a electroencephalogram (EEG) signal, a frontalis electromyogram (FEMG) signal, a electromyography (EMG) signal, an electro-gastro-gram (EGG) signal, a laser Doppler velocimetry (LDV) signal, a dynamic light scattering (DLS) signal, a near-infrared spectroscopy (NIRS) signal, a partial pressure of carbon dioxide signal, or an accelerometer reading.
Claim 12: The system of claim 1, wherein the at least one physiological signal further comprises an electrocardiogram (ECG) signal, a blood pressure signal, a respiration signal, an internal body temperature signal, a skin temperature signal, a electrooculography (EOG) signal, a pupil diameter signal, a electroencephalogram (EEG) signal, a frontalis electromyogram (FEMG) signal, a electromyography (EMG) signal, an electro-gastro-gram (EGG) signal, a laser Doppler velocimetry (LDV) signal, a dynamic light scattering (DLS) signal, a near-infrared spectroscopy (NIRS) signal, a partial pressure of carbon dioxide signal, or an accelerometer reading.
Claims 1-10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of U.S. Patent No. 11,771,369. Although the claims at issue are not identical, they are not patentably distinct from each other.
18/946,039
11,771,369
Claim 1: A system for calibrating spinal cord stimulation (SCS) treatment in a subject, the system comprising: a processing module, a sensor module; and a classifier module, wherein the processing module is configured to: receive a first indication from a SCS device that a first SCS treatment is being provided, wherein the first SCS treatment is characterized by a first value of at least one SCS parameter, wherein the first value of the at least one SCS parameter is different from that of a second value of the at least one SCS parameter; initiate the sensor module to conduct a first measurement of at least one physiological signal in response to the first indication from the SCS device, wherein the at least one physiological signal comprises at least one of a GSR signal or movement (accelerometer) signal; receive a second indication from the SCS device that a second SCS treatment is being provided, wherein the second SCS treatment is characterized by the second value of the at least one SCS parameter; initiate the sensor module to conduct a second measurement of the at least one physiological signal in response to the second indication from the SCS device; initiate the classifier module to extract, by a non-transitory computer program, at least two features from each of the at least one physiological signal obtained from the first and second measurements, wherein the at least two features comprise galvanic skin response (GSR) amplitude, GSR mean amplitude, GSR amplitude standard deviation (std), GSR Peak (P) amplitude, mean Peak (P) amplitude and Peak (P) amplitude std, GSR peak to peak time intervals, mean GSR peak to peak time interval, GSR peak to peak time intervals std, 3-axis accelerometer data (X, Y, Z, Theta) value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) average value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) variability, or any combination thereof; classify the at least two features by applying a classification algorithm thereon; and output, based on the classification, whether the first SCS treatment or the second SCS treatment correlates with a higher efficacy.
Claim 1: A system for calibrating spinal cord stimulation (SCS) treatment in a subject, the system comprising a processing module, a sensor module and a classifier module; wherein the processing module is configured to: receive a first indication from a SCS device that a first SCS treatment is being provided; wherein the first SCS treatment is characterized by a first value of at least one SCS parameter, wherein the first value of the at least one SCS parameter is different from that of a second value of the at least one SCS parameter; initiate the sensor module to conduct a first measurement of at least one physiological signal in response to the first indication from the SCS device; wherein the at least one physiological signal comprises at least one of a PPG signal or movement (accelerometer) signal; receive a second indication from the SCS device that a second SCS treatment is being provided; wherein the second SCS treatment is characterized by the second value of the at least one SCS parameter; initiate the sensor module to conduct a second measurement of the at least one physiological signal in response to the second indication from the SCS device; initiate the classifier module to, by a non-transitory computer program, extract at least two features from each of the at least one physiological signal obtained from the first and second measurements; wherein the at least two features comprise PPG amplitude, PPG amplitude variation, pulse rate (PR) interval, PR variability, 3-axis accelerometer data (X, Y, Z, Theta) value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) average value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) variability or any combination thereof; classify the at least two features by applying a classification algorithm thereon; and output, based on the classification, whether the first SCS treatment or the second SCS treatment correlates with a higher efficacy.
Claim 2: The system according to claim 1, wherein the processing module is further configured to select a value of the one SCS parameter yielding a highest efficacy based on the classification.
Claim 2: The system according to claim 1, wherein the processing module is further configured to select a value of the one SCS parameter yielding a highest efficacy based on the classification.
Claim 3: The system according to claim 1, wherein varying the SCS parameters comprises changing the SCS parameter over its dynamic range and making at least one of continuous, incremental or step wise changes in the value of the SCS parameter.
Claim 3: The system according to claim 1, wherein varying the SCS parameters comprises changing the SCS parameter over its dynamic range and making at least one of continuous, incremental or step wise changes in the value of the SCS parameter.
Claim 4: The system of claim 1, wherein the remaining of the at least one SCS parameters are fixed while varying the one SCS parameter.
Claim 4: The system of claim 1, wherein the remaining of the at least one SCS parameters are fixed while varying the one SCS parameter.
Claim 5: The system of claim 1, wherein the at least one SCS parameter comprises a type of stimulation, stimulation frequency, duration, pulse width, intensity, waveform, wave pattern, signal, amplitude, onset timing, delay, treatment length, treatment period, onset delay, or any combination thereof.
Claim 5: The system of claim 1, wherein the at least one SCS parameter comprises a type of stimulation, stimulation frequency, duration, pulse width, intensity, waveform, wave pattern, signal, amplitude, onset timing, delay, treatment length, treatment period, onset delay, or any combination thereof.
Claim 6: The system of claim 1, wherein applying the classification algorithm further comprises directly or indirectly comparing the first and second measurements to pre- stored data sets of measurements obtained from subjects with known SCS treatment efficacies and taking into consideration demographic data of the patient.
Claim 6: The system of claim 1, wherein applying the classification algorithm further comprises directly or indirectly comparing the first and second measurements to pre-stored data sets of measurements obtained from subjects with known SCS treatment efficacies and taking into consideration demographic data of the patient.
Claim 7: The system of claim 1, further comprising a SCS device.
Claim 7: The system of claim 1, further comprising a SCS device.
Claim 8: The system of claim 1, wherein the at least two features further comprise photoplethysmograph (PPG) Peak (P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude, std of Trough (T) amplitude, PPG dicrotic notch (N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPG peak to peak time intervals, PPG peak to peak interval mean, PPG peak to peak interval std, power spectrum of the PPGpeak to peak intervals: VLF Power, LF Power and HF Power, ECG Q/R/S/T/P) amplitude, ECG (Q/R/S/T/P) amplitude variation, heart rate (HR) interval (RR), HR variability, intra-pulse (R-R) average, intra-pulse (R-R) variance or standard deviation, variance or standard deviation of PQ segment or PR segment or QT segment or RS segment, P-wave width average, ST segment amplitude average, ST segment amplitude variance, QRS width variance or standard deviation, energy of ECG residues, Phasic EDA: amplitude, mean amplitude and std of amplitude, Temperature amplitude, mean amplitude and std of amplitude, Temp Peak (P) amplitude, mean amplitude and std of amplitude, Temperature peak to peak time intervals, mean and std (variability) of interval, ECG to PPG Pulse Transition time, PPG to PPG Pulse Transition time, ECGPower of VLF, LF and HF frequency bands of power spectrum of the ECG R to R intervals (heart rate variability), Respiratory rate, mean rate and std rate, Power of the frequency bands of power spectrum of EMG signal, EMG Power Spectrum Mean frequency, EMG Power Spectrum Highest Peak Frequency, Power of the alpha, beta, gamma, delta, theta frequency bands of power spectrum of EEG/FEMG signal, EMG Power Spectrum Mean frequency, EMG Power Spectral edge frequency, Coherence between 2 or more EEG/FEMG channels, frequency of movement, axis of movement, or any combination thereof.
Claim 8: The system of claim 1, wherein the at least two features further comprise photoplethysmograph (PPG) Peak (P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude, std of Trough (T) amplitude; PPG dicrotic notch (N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPG peak to peak time intervals, PPG peak to peak interval mean, PPG peak to peak interval std; power spectrum of the PPG peak to peak intervals: VLF Power, LF Power and HF Power; galvanic skin response (GSR) amplitude, GSR mean amplitude, GSR amplitude std; GSR Peak (P) amplitude, mean Peak (P) amplitude and Peak (P) amplitude std; GSR peak to peak time intervals, mean GSR peak to peak time interval; GSR peak to peak time intervals std; Phasic EDA: amplitude, mean amplitude and std of amplitude, Temperature amplitude, mean amplitude and std of amplitude; Temp Peak (P) amplitude, mean amplitude and std of amplitude; Temperature peak to peak time intervals, mean and std (variability) of interval; ECG to PPG Pulse Transition time; PPG to PPG Pulse Transition time; ECG R to R time intervals, mean and std (variability) of intervals; Power of VLF, LF and HF frequency bands of power spectrum of the ECG R to R intervals (heart rate variability); Upper peak amplitude, mean amplitude and STD of amplitude; Respiratory rate, mean rate and std rate; Power of the frequency bands of power spectrum of EMG signal; EMG Power Spectrum Mean frequency; EMG Power Spectrum Highest Peak Frequency; Power of the alpha, beta, gamma, delta, theta frequency bands of power spectrum of EEG/FEMG signal; EMG Power Spectrum Mean frequency; EMG Power Spectral edge frequency; Coherence between 2 or more EEG/FEMG channels; frequency of movement; axis of movement; or any combination thereof.
Claim 9: The system of claim 1, wherein the at least one physiological signal further comprises a plethysmogram (PPG) signal, a blood pressure signal, a respiration signal, an internal body temperature signal, a skin temperature signal, a electrooculography (EOG) signal, a pupil diameter signal, a electroencephalogram (EEG) signal, a frontalis electromyogram (FEMG) signal, a electromyography (EMG) signal, an electro-gastro-gram (EGG) signal, a laser Doppler velocimetry (LDV) signal, a dynamic light scattering (DLS) signal, a near-infrared spectroscopy (NIRS) signal, a partial pressure of carbon dioxide signal, or an accelerometer reading.
Claim 9: The system of claim 1, wherein the at least one physiological signal further comprises an electrocardiogram (ECG) signal, a blood pressure signal, a respiration signal, an internal body temperature signal, a skin temperature signal, a electrooculography (EOG) signal, a pupil diameter signal, a electroencephalogram (EEG) signal, a frontalis electromyogram (FEMG) signal, a electromyography (EMG) signal, an electro-gastro-gram (EGG) signal, a laser Doppler velocimetry (LDV) signal, a dynamic light scattering (DLS) signal, a near-infrared spectroscopy (NIRS) signal, a partial pressure of carbon dioxide signal, or an accelerometer reading.
Claim 10: The system of claim 1, wherein the processing module is further configured to display a first efficacy of the first SCS treatment and a second efficacy of the second SCS treatment.
Claim 10: The system of claim 1, wherein the processing module is further configured to display a first efficacy of the first SCS treatment and a second efficacy of the second SCS treatment.
Claims 1-10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of U.S. Patent No. 12,171,571. Although the claims at issue are not identical, they are not patentably distinct from each other.
18/946,039
12,171,571
Claim 1: A system for calibrating spinal cord stimulation (SCS) treatment in a subject, the system comprising: a processing module, a sensor module; and a classifier module, wherein the processing module is configured to: receive a first indication from a SCS device that a first SCS treatment is being provided, wherein the first SCS treatment is characterized by a first value of at least one SCS parameter, wherein the first value of the at least one SCS parameter is different from that of a second value of the at least one SCS parameter; initiate the sensor module to conduct a first measurement of at least one physiological signal in response to the first indication from the SCS device, wherein the at least one physiological signal comprises at least one of a GSR signal or movement (accelerometer) signal; receive a second indication from the SCS device that a second SCS treatment is being provided, wherein the second SCS treatment is characterized by the second value of the at least one SCS parameter; initiate the sensor module to conduct a second measurement of the at least one physiological signal in response to the second indication from the SCS device; initiate the classifier module to extract, by a non-transitory computer program, at least two features from each of the at least one physiological signal obtained from the first and second measurements, wherein the at least two features comprise galvanic skin response (GSR) amplitude, GSR mean amplitude, GSR amplitude standard deviation (std), GSR Peak (P) amplitude, mean Peak (P) amplitude and Peak (P) amplitude std, GSR peak to peak time intervals, mean GSR peak to peak time interval, GSR peak to peak time intervals std, 3-axis accelerometer data (X, Y, Z, Theta) value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) average value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) variability, or any combination thereof; classify the at least two features by applying a classification algorithm thereon; and output, based on the classification, whether the first SCS treatment or the second SCS treatment correlates with a higher efficacy.
Claim 1: A system for calibrating spinal cord stimulation (SCS) treatment in a subject, the system comprising a processing module, a sensor module and a classifier module; wherein the processing module is configured to: receive a first indication from a SCS device that a first SCS treatment is being provided; wherein the first SCS treatment is characterized by a first value of at least one SCS parameter, wherein the first value of the at least one SCS parameter is different from that of a second value of the at least one SCS parameter; initiate the sensor module to conduct a first measurement of at least one physiological signal in response to the first indication from the SCS device; wherein the at least one physiological signal comprises at least one of an ECG signal or movement (accelerometer) signal; receive a second indication from the SCS device that a second SCS treatment is being provided; wherein the second SCS treatment is characterized by the second value of the at least one SCS parameter; initiate the sensor module to conduct a second measurement of the at least one physiological signal in response to the second indication from the SCS device; initiate the classifier module to, by a non-transitory computer program, extract at least two features from each of the at least one physiological signal obtained from the first and second measurements; wherein the at least two features comprise ECG (Q/R/S/T/P) amplitude, ECG (Q/R/S/T/P) amplitude variation, heart rate (HR) interval (RR), HR variability, intra-pulse (R-R) average, intra-pulse (R-R) variance or standard deviation, variance or standard deviation of PQ segment or PR segment or QT segment or RS segment, P-wave width average, ST segment amplitude average, ST segment amplitude variance, QRS width variance or standard deviation, energy of ECG residues, 3-axis accelerometer data (X, Y, Z, Theta) value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) average value, 3-axis accelerometer data (X, Y, Z, Theta (θ)) variability or any combination thereof; classify the at least two features by applying a classification algorithm thereon; and output, based on the classification, whether the first SCS treatment or the second SCS treatment correlates with a higher efficacy.
Claim 2: The system according to claim 1, wherein the processing module is further configured to select a value of the one SCS parameter yielding a highest efficacy based on the classification.
Claim 2: The system according to claim 1, wherein the processing module is further configured to select a value of the one SCS parameter yielding a highest efficacy based on the classification.
Claim 3: The system according to claim 1, wherein varying the SCS parameters comprises changing the SCS parameter over its dynamic range and making at least one of continuous, incremental or step wise changes in the value of the SCS parameter.
Claim 3: The system according to claim 1, wherein varying the SCS parameters comprises changing the SCS parameter over its dynamic range and making at least one of continuous, incremental or step wise changes in the value of the SCS parameter.
Claim 4: The system of claim 1, wherein the remaining of the at least one SCS parameters are fixed while varying the one SCS parameter.
Claim 4: The system of claim 1, wherein the remaining of the at least one SCS parameters are fixed while varying the one SCS parameter.
Claim 5: The system of claim 1, wherein the at least one SCS parameter comprises a type of stimulation, stimulation frequency, duration, pulse width, intensity, waveform, wave pattern, signal, amplitude, onset timing, delay, treatment length, treatment period, onset delay, or any combination thereof.
Claim 5: The system of claim 1, wherein the at least one SCS parameter comprises a type of stimulation, stimulation frequency, duration, pulse width, intensity, waveform, wave pattern, signal, amplitude, onset timing, delay, treatment length, treatment period, onset delay, or any combination thereof.
Claim 6: The system of claim 1, wherein applying the classification algorithm further comprises directly or indirectly comparing the first and second measurements to pre- stored data sets of measurements obtained from subjects with known SCS treatment efficacies and taking into consideration demographic data of the patient.
Claim 6: The system of claim 1, wherein applying the classification algorithm further comprises directly or indirectly comparing the first and second measurements to pre-stored data sets of measurements obtained from subjects with known SCS treatment efficacies and taking into consideration demographic data of the patient.
Claim 7: The system of claim 1, further comprising a SCS device.
Claim 7: The system of claim 1, further comprising a SCS device.
Claim 8: The system of claim 1, wherein the at least two features further comprise photoplethysmograph (PPG) Peak (P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude, std of Trough (T) amplitude, PPG dicrotic notch (N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPG peak to peak time intervals, PPG peak to peak interval mean, PPG peak to peak interval std, power spectrum of the PPGpeak to peak intervals: VLF Power, LF Power and HF Power, ECG Q/R/S/T/P) amplitude, ECG (Q/R/S/T/P) amplitude variation, heart rate (HR) interval (RR), HR variability, intra-pulse (R-R) average, intra-pulse (R-R) variance or standard deviation, variance or standard deviation of PQ segment or PR segment or QT segment or RS segment, P-wave width average, ST segment amplitude average, ST segment amplitude variance, QRS width variance or standard deviation, energy of ECG residues, Phasic EDA: amplitude, mean amplitude and std of amplitude, Temperature amplitude, mean amplitude and std of amplitude, Temp Peak (P) amplitude, mean amplitude and std of amplitude, Temperature peak to peak time intervals, mean and std (variability) of interval, ECG to PPG Pulse Transition time, PPG to PPG Pulse Transition time, ECGPower of VLF, LF and HF frequency bands of power spectrum of the ECG R to R intervals (heart rate variability), Respiratory rate, mean rate and std rate, Power of the frequency bands of power spectrum of EMG signal, EMG Power Spectrum Mean frequency, EMG Power Spectrum Highest Peak Frequency, Power of the alpha, beta, gamma, delta, theta frequency bands of power spectrum of EEG/FEMG signal, EMG Power Spectrum Mean frequency, EMG Power Spectral edge frequency, Coherence between 2 or more EEG/FEMG channels, frequency of movement, axis of movement, or any combination thereof.
Claim 8: The system of claim 1, wherein the at least two features further comprise photoplethysmograph (PPG) Peak (P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude, std of Trough (T) amplitude; PPG dicrotic notch (N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPG peak to peak time intervals, PPG peak to peak interval mean, PPG peak to peak interval std; power spectrum of the PPG peak to peak intervals: VLF Power, LF Power and HF Power; galvanic skin response (GSR) amplitude, GSR mean amplitude, GSR amplitude std; GSR Peak (P) amplitude, mean Peak (P) amplitude and Peak (P) amplitude std; GSR peak to peak time intervals, mean GSR peak to peak time interval; GSR peak to peak time intervals std; Phasic EDA: amplitude, mean amplitude and std of amplitude, Temperature amplitude, mean amplitude and std of amplitude; Temp Peak (P) amplitude, mean amplitude and std of amplitude; Temperature peak to peak time intervals, mean and std (variability) of interval; ECG to PPG Pulse Transition time; PPG to PPG Pulse Transition time; ECGPower of VLF, LF and HF frequency bands of power spectrum of the ECG R to R intervals (heart rate variability); Respiratory rate, mean rate and std rate; Power of the frequency bands of power spectrum of EMG signal; EMG Power Spectrum Mean frequency; EMG Power Spectrum Highest Peak Frequency; Power of the alpha, beta, gamma, delta, theta frequency bands of power spectrum of EEG/FEMG signal; EMG Power Spectrum Mean frequency; EMG Power Spectral edge frequency; Coherence between 2 or more EEG/FEMG channels; frequency of movement; axis of movement; or any combination thereof.
Claim 9: The system of claim 1, wherein the at least one physiological signal further comprises a plethysmogram (PPG) signal, a blood pressure signal, a respiration signal, an internal body temperature signal, a skin temperature signal, a electrooculography (EOG) signal, a pupil diameter signal, a electroencephalogram (EEG) signal, a frontalis electromyogram (FEMG) signal, a electromyography (EMG) signal, an electro-gastro-gram (EGG) signal, a laser Doppler velocimetry (LDV) signal, a dynamic light scattering (DLS) signal, a near-infrared spectroscopy (NIRS) signal, a partial pressure of carbon dioxide signal, or an accelerometer reading.
Claim 9: The system of claim 1, wherein the at least one physiological signal further comprises a plethysmogram (PPG) signal, a blood pressure signal, a respiration signal, an internal body temperature signal, a skin temperature signal, a electrooculography (EOG) signal, a pupil diameter signal, a electroencephalogram (EEG) signal, a frontalis electromyogram (FEMG) signal, a electromyography (EMG) signal, an electro-gastro-gram (EGG) signal, a laser Doppler velocimetry (LDV) signal, a dynamic light scattering (DLS) signal, a near-infrared spectroscopy (NIRS) signal, a partial pressure of carbon dioxide signal, or an accelerometer reading.
Claim 10: The system of claim 1, wherein the processing module is further configured to display a first efficacy of the first SCS treatment and a second efficacy of the second SCS treatment.
Claim 10: The system of claim 1, wherein the processing module is further configured to display a first efficacy of the first SCS treatment and a second efficacy of the second SCS treatment.
Allowable Subject Matter
Claims 1-10 will be allowable upon the receipt of proper terminal disclosures regarding the following patents;
10,231,666 (Serial No. 15/025,284)
11,020,052 (Serial No. 16/255,221)
11,771,369 (Serial No. 17/328,617)
12,171,571 (Serial No. 18/237,056).
The following is a statement of reasons for the indication of allowable subject matter: the invention is directed towards a system for calibrating spinal cord stimulation treatment. In this system individuals who may benefit from SCS are identified (paragraph 0128 of US 2025/0064393). The system includes a processing module, sensor module and classifier module. The prior art fails to disclose or render obvious all of the limitations of claim 1 in combination with using a GSR signal or a movement signal to measure signals and classifying specific GSR or movement signals using a classification algorithm in order to determine efficacy.
Conclusion
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PAULA J. STICE
Primary Examiner
Art Unit 3796
/PAULA J STICE/Primary Examiner, Art Unit 3796
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