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 .
Election/Restrictions
Claims 25-49 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected inventions, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 15 June 2026.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Esteller et al (WO 2021/080727). Esteller discloses (Fig. 6A; par. 0038) a method of evaluating spinal cord stimulation (SCS) (“An increasingly interesting development in pulse generator systems, and in Spinal Cord Stimulator (SCS) pulse generator systems specifically, is the addition of sensing capability to complement the stimulation that such systems provide. Thus, IPGs such as IPG 100 as shown in Figure 6A can include the ability to sense ElectroSpinoGram (ESG) signals in a patient's tissue.”), comprising: obtaining neural response data generated by a spinal cord stimulator (IPG 100), where the neural response data describes one or more neural signals evoked in the spinal cord of a patient by applying diagnostic stimuli (Fig. 6B; par. 0039; “it can be beneficial to sense in an ESG signal a neural response in neural tissue that has received stimulation from an SCS pulse generator. One such neural response is an Evoked Compound Action Potential (ECAP). An ECAP comprises a cumulative response provided by neural fibers that are recruited by the stimulation, and essentially comprises the sum of the action potentials of recruited neural elements (ganglia or fibers) when they "fire." An ECAP is shown in Figure 6B”); detecting one or more evoked compound action potentials (ECAPs) associated with the one or more neural signals respectively (Fig. 6A-B; par. 0038-0039; “Note that not all ECAPs will have the exact shape and number of peaks as illustrated in Figure 6B, because an ECAP's shape is a function of the number and types of neural elements that are recruited and that are involved in its conduction. An ECAP is generally a small signal, and may have a peak-to-peak amplitude on the order of units to hundreds of microVolts depending on the amplification gain and location within the nervous system where these are sensed (brain, spinal cord, peripheral nervous system, somatic nervous system, motor elements, or other).”); processing the one or more detected ECAPs to generate one or more evaluation metrics (Figs. 8 and 10; par. 0025; “determining a score for the applied waveform using the at least one measurement, and determining the effectiveness of the time-varying pulses for the patient using the score. In one example, the effectiveness of the time-varying pulses for the patient is determined by comparing the score to at least one threshold”; par. 0034; “Figure 8 shows an example of a time-varying pulse (TVP) algorithm useful to determine one or more best TVPs for a patient, where the algorithm uses one or more objective or subjective measurements to determine a TVP score for the patient.”; par. 0051; “Assessing the success of various stimulation parameters can also involve the use of objective measurements taken form the patient, such as by assessing one or more ECAP features or other objective measurables”; par. 0061; “The TVPs can be defined using the GUI 160 described earlier (Figs. 7A and 7B), or otherwise. One or more measurements are taken during the application of a given TVP to the patient, and such measurements may be objective or subjective in nature. A number of objective measurements which can be taken for each TVP are shown in Figure 8 (OMa, OMb, etc.), as are a number of different subjective measurements (SMa, SMb, etc.). The algorithm 170 may use one or more objective measurements, and one or more subjective measurement”; par. 0070; “Once relevant objective and/or subjective measurements have been determined for each TVP tested, the TVP algorithm 170 may compute a score for each, and Figure 10 shows a simple example. In this example, one objective measurement is used by the algorithm, specifically the normalized spread of ECAP area which was explained earlier with reference to Figure 9. Further, two subjective measurements are used, specifically a pain score, and a perception threshold Pth”); and using the one or more evaluation metrics to generate a confidence score indicating a degree of effectiveness of SCS on the patient (Fig. 11; par. 0025 and 0077; “In step 186, the algorithm 180 calculates a score for the TVP using the measurement(s) from step 184, similarly to what was described earlier in conjunction with TVP algorithm 170. As noted previously, the score can be determined by, or comprise, a single subjective or objective measurement, such as the ECAP area metrics described earlier. The resulting score can then be assessed by the algorithm at step 186 to see if the TVP is suitably effective or needs adjustment”).
Regarding claim 2, Esteller discloses confidence score indicates a likelihood that the SCS is effective to provide a therapeutic benefit to the patient (Fig. 11; par. 0004; “the goal of SCS therapy is to provide electrical stimulation from the electrodes 16 to alleviate a patient's symptoms, such as chronic back pain.”; par. 0025 and 0061; “The algorithm 170 may use one or more objective measurements, and one or more subjective measurements. Alternatively, the algorithm 170 may only use one or more objective measurements, or only use one or more subjective measurements. Ultimately, the algorithm 170 computes a score (Sx) for each TVP using the one or more objective and/or subjective measurements, which are used to determine a best one or more TVPs to use for the patient as therapy going forward”; par. 0063; “during the application of each TVP to the patient, one or more objective measurements can be made. Such measurements can include for example one or more features of an ESG signal sensed by the patient's IPG 100 or ETS. Such ESG features can include ECAP features (as explained with particularity further below with reference to Figure 9), ECAP threshold, stimulation artifact features, or background signal features”; par. 0077).
Regarding claim 3, Esteller discloses in response to the confidence score failing to exceed a predetermined threshold value, providing an indication of at least one or more additional activities to further assess whether the SCS is likely to provide a long-term therapeutic effect to the patient (par. 0077 and 0079; “If the score is worse (e.g., beyond a threshold) at step 186, the algorithm 180 can proceed to step 188 where adjustments to the TVP can be made. In a preferred example, the algorithm 180 at step 188 will adjust an aspect of the modulation function 150, which could comprise changing the shape of modulation, changing one or more modulation parameters, and/or changing the tonic stimulation parameter to which the modulation function 150 is applied (e.g., by applying the modulation function to pulse width instead of amplitude).”; par. 0081; “the threshold(s) used in step 186 may be automatically updated based on long term values of the measurement(s) used to determine the score. For example, if the ECAP area is the only feature used in determining the score, it can be averaged over a short time window spanning between 1 and 5 consecutive modulation periods (e.g., 1 to 5 T = 1/FM), while the threshold for the score can be averaged over a longer time window spanning minutes, or hours, or days (e.g., > 5 T). This enables the algorithm 180 to adapt to changes due to disease progression in the patient, the development of scar tissue, and lead migration.”).
Regarding claim 4, Esteller discloses the method of claim 1, further comprising, in response to the confidence score failing to exceed a predetermined threshold value: determining one or more SCS parameters of an SCS program for adjustment; and adjusting the determined one or more SCS parameters by an amount determined, at least in part, by processing the one or more evaluation metrics (par. 0068; “This may of course be due to the particular modulation parameters used for the three different types of modulation illustrated in Figure 9. In short, an objective measurement can comprise any metric that quantifies a degree of the variance in the measured response, such as the variance in an ECAP feature.”; par. 0069; “another objective measurable which can be gleaned from Figure 9 and used in TVP algorithm 170 is the extent to which a particular modulation scheme results in ECAP areas (or other features) within such preferred regions. The TVP algorithm 170 may compute or determine a fit metric (another objective measurement) that quantifies how well the resulting ECAP area matches such preferred regions or other desired ECAP area thresholds, or fit metrics for any other feature extracted from the ECAP response within the ESG.”; par. 0077, 0079 and 0081).
Regarding claim 5, Esteller discloses the one or more evaluation metrics comprises one or more functionality metrics, including at least a likelihood metric indicating the confidence with which the given ECAP is detected, wherein the given ECAP is one of the detected ECAPs (par. 0040; “it can be useful to sense in an ESG signal a stimulation artifact, i.e., the voltage that is formed in the tissue as a result of the stimulation”; par. 0063 and 0069).
Regarding claim 6, Esteller discloses (par. 0076) the one or more evaluation metrics comprises a postural robustness metric.
Regarding claim 7, Esteller discloses (par. 0039 and 0076) determining the postural robustness metric comprises:(i) providing SCS to the patient via the spinal cord stimulator, wherein the patient is positioned in a candidate posture;(ii) recording, in response to the SCS, a set of neural recruitment magnitudes (NRMs) of the detected ECAPs associated with the neural signals in the spinal cord of the patient in the candidate posture;(iii) determining a first measure of variation to measure variation in values of the set of NRMs; and(iv) computing a second measure of variation of a plurality of first measures of variation, wherein the plurality of first measures of variation are obtained by iteratively performing steps (i)-(iii) for different candidate postures.
Regarding claim 8, Esteller discloses (par. 0078) the one or more evaluation metrics comprises one or more therapy quality metrics, the one or more therapy quality metrics including at least a therapy target level value.
Regarding claim 9, Esteller discloses (par. 0039 and 0076) determining the NAS comprises:(i) providing SCS to the patient via the spinal cord stimulator, wherein the patient is positioned in a candidate posture;(ii) recording, in response to the SCS, a set of neural recruitment magnitudes (NRMs) of the detected ECAPs associated with the neural signals in the spinal cord of the patient in the candidate posture; and(iii) processing the recorded set of NRMs to compute values of the NAS.
Regarding claim 10, Esteller discloses (par. 0039 and 0076) the steps (i)-(ii) are iteratively repeated for different candidate postures such that step (iii) includes processing respective sets of NRMs recorded for the different candidate postures.
Regarding claim 11, Esteller discloses (par. 0062) computing values of the NAS comprises: calculating a representative error value of each recorded set of NRMs relative to corresponding NRMs of a baseline target therapy level; determining a neural activation error (NAE) value by processing one or more of the calculated representative error values; and transforming the NAE value to generate a value of the NAS between zero and a predetermined maximum value.
Regarding claim 12, Esteller discloses (par. 0084) the representative error value of the set of NRMs is a root mean square error (RMSE) value.
Regarding claim 13, Esteller discloses (par. 0084) the NAE value is normalized or scaled relative to a predetermined feedback variable.
Regarding claim 14, Esteller discloses (par. 0084) the predetermined maximum value is 100.
Regarding claim 15, Esteller discloses (par. 0077) a confidence sub-score is generated for each of the one or more therapy quality metrics of the evaluation metrics.
Regarding claim 16, Esteller discloses (par. 0084) generating the confidence score comprises calculating a weighted sum of the confidence sub-scores.
Regarding claim 17, Esteller discloses (par. 0025) comparing one or more therapy quality metric values to one or more corresponding predetermined metric thresholds; classifying each of the one or more therapy quality metric values as one of a plurality of predetermined efficacy categories based on the comparing; and determining the confidence score based on the classifications of the one or more therapy quality metric values.
Regarding claim 18, Esteller discloses (par. 0025) the confidence score is determined as one of a set of predetermined confidence score values based on a number of classifications of the one or more therapy quality metric values in each efficacy category.
Regarding claim 19, Esteller discloses (par. 0077) performing one or more evaluation tests against the one or more therapy quality metric values, each evaluation test involving performing a series of one or more comparisons between a therapy quality metric value and one or more corresponding threshold values; and setting or adjusting the confidence score in response to the outcome of the one or more comparisons of each evaluation test.
Regarding claim 20, Esteller discloses (par. 0084) the steps of performing evaluation tests and setting or adjusting the confidence score are organized according to a decision tree to prioritize a relative degree of importance of the therapy quality metrics.
Regarding claim 21, Esteller discloses (par. 0076) generating the confidence score further comprises, in response to a therapy quality metric value failing to exceed the corresponding threshold value, setting the confidence score to a minimum value.
Regarding claim 22, Esteller discloses (par. 0023) the confidence score is generated by further using a patient feedback metric.
Regarding claim 23, Esteller discloses (par. 0074) the confidence score is generated using a machine learning mode.
Regarding claim 24, Esteller discloses (par. 0046) the confidence score is generated by calculating a sum of values of the one or more evaluation metrics.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEBORAH L MALAMUD whose telephone number is (571)272-2106. The examiner can normally be reached Mon - Fri 1:00-9:30 Eastern.
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/DEBORAH L MALAMUD/Primary Examiner, Art Unit 3792
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