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 .
Claim Objections
Claim 2 recites “wherein processing”, this language lacks proper antecedent basis and should read “wherein the processing”.
Claim 3 recites “wherein determining” and “pain”. Both recitations lack proper antecedent basis, these should read “wherein the determining” and “the chronic pain”.
Claim 9 line 6 recites “indicate pain”, this language lacks proper antecedent basis and should read “indicate the pain”.
Claim 11 recites “neural activity” this language lacks proper antecedent basis and should read “the neural activity”.
Claim 19 recites “in neural activity” this language lacks proper antecedent basis and should read “in the neural activity”.
Claim 19, the last line recites “pain” this language lacks proper antecedent basis and should read “the pain”.
Claim Rejections - 35 USC § 112
Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, line 1 recites “treating chronic pain”, line 7 recites “whether pain is indicated” and line 8 recites “when pain is indicated”. Initially the use of chronic pain in the preamble is a more narrow recitation of “pain” in lines 7 and 8. It is unclear if the “pain” in lines 7 and 8 are chronic pain or the broad class of “pain”. It is suggested that the language is amended to recite that the pain is chronic pain. This would result in an amendment to line 7 reading “whether the chronic pain is indicated” and line 8 would read “when the chronic pain is indicated”.
Claims 2-8 are also rejected in that they depend from claim 1.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by the journal article within the IDS Shirvalkar, Prasad, et al. “Closed-loop deep brain stimulation for refractory chronic pain.” Frontiers in Computational Neuroscience, vol. 12, 26 Mar. 2018.
Regarding claim 1: Shirvalkar discloses a computer-implemented method for detecting and treating chronic pain (page 2, column 2 “DBS for Somatosensory Pain Symptoms”; page 2, column 2 through page 3 column 1 “pain syndromes”, “pelvic pain”, “trigeminal neuralgia”), comprising: receiving neural signals from multiple brain regions of a patient brain via probes implanted in the multiple brain regions (S1, ACC, page 5 column 1, last line through column 2 first line), the neural signals including local field potentials (LFP, “Local Fiend Potentials Are the Most Tractable Signals for Identifying Biomarkers or Closed-Loop DBS”, page 4, column 2) of the multiple brain regions (“somatosensory cortex (S1 and S2), insula, ACC, PFC and thalamus , page 6, column 2); processing the neural signals and inputting the processed neural signals to a machine learning pain decoder model (“This is distinguished from sensor-triggered stimulation in that in a closed-loop protocol, the same coordinate location in a state space may trigger different stimulation patterns or update stimulation parameters (pulse width, frequency, amplitude) dependent on the history and context of the neural trajectory. For this to be possible, we must create a predictive model of the multidimensional pain state such that the future path of each trajectory can be determined based on history and the current state (Churchland et al., 2006; Mante et al., 2013; Figure 4A). There are several methods for producing such a model, including (1) modeling the state as a three dimensional flow field (Rabinovich et al., 2012; Ashwin et al., 2016), or (2) creating a map outlining the probability of transitioning from any point in the field to every other point (i.e., Hidden Markov Models, Radons et al., 1994). Based on the assumption that stimulation can control or influence neural trajectories related to pain, the model must additionally contain predictions about the effect of stimulation on the pain trajectory (Figure 4B); page 9 column 2 paragraphs 2-3); determining, based on the processed neural signals, whether pain is indicated (“For this to be possible, we must create a predictive model of the multidimensional pain state such that the future path of each trajectory can be determined based on history and the current state (Churchland et al., 2006; Mante et al., 2013; Figure 4A). There are several methods for producing such a model, including (1) modeling the state as a three dimensional flow field (Rabinovich et al., 2012; Ashwin et al., 2016), or (2) creating a map outlining the probability of transitioning from any point in the field to every other point (i.e., Hidden Markov Models, Radons et al., 1994). Based on the assumption that stimulation can control or influence neural trajectories related to pain, the model must additionally contain predictions about the effect of stimulation on the pain trajectory” (figure 4B); page 9, column 2 paragraph 2); and triggering, when pain is indicated via the pain decoder model, a stimulation of a target region of the patient brain based on an indication of the pain (“Based on the assumption that stimulation can control or influence neural trajectories related to pain, the model must additionally contain predictions about the effect of stimulation on the pain trajectory (Figure 4B)”; page 9 column 2, paragraph).
Regarding claim 2: Shirvalkar discloses that the processing the neural signals includes computing frequency dependent power features of the local field potentials of the multiple brain regions (“Assuming that recorded action potentials from human patients would experience similar instability, chronic biomarkers based on these signals are not tractable. A potential work-around would be to calculate biomarkers based on dynamics from population neural firing combined with high frequency local field potential, a promising strategy used in human brain-machine interfaces (Pandarinath et al., 2017).”; page 5 column 1, paragraph 2).
Regarding claim 3: Shirvalkar discloses that the determining whether pain is indicated includes identifying relative changes in neural activity in the multiple brain regions (“While the term LFP usually refers to signals captured by implanted depth electrodes or cortical electrodes, LFP is thought to reflect brain oscillations similar to those captured by intracranial electroencephalography (iEEG) and magnetoencephalography (MEG). Previous attempts at decoding subjective pain intensity with resting state EEG (Schulz et al., 2012) or MEG (Kuo et al., 2017) have used time-frequency representations of brain oscillations with high accuracy, supporting the feasibility of using LFP to define a pain state”; page 5 column 1 paragraph 2).
Regarding claim 4: Shirvalkar discloses that the multiple brain regions include an anterior cingulate cortex and a primary somatosensory cortex (“Human functional imaging data point to a widely distributed neural network that is activated by acute experimental pain perception including the primary and secondary somatosensory cortex (S1 and S2), insula, ACC, PFC and thalamus (Coghill et al., 2003; Apkarian et al., 2005; Wager et al., 2013). However, not all signals can strictly be interpreted to represent somatosensory perception”; page 6 column 1 paragraph 4).
Regarding claim 5: Shirvalkar discloses that the stimulation of the target region of the patient brain includes an optical stimulation and an electrical stimulation (“Eventually, direct electrical stimulation of the dorsal column (Shealy, 1969), internal capsule (Adams et al., 1974) and sensory thalamus (Hosobuchi et al., 1973) provided a reversible alternative to ablation.”; page 2 column 2 paragraph 2).
Regarding claim 6: Shirvalkar discloses that the target region of the patient brain includes a prefrontal cortex (“Modulating the affective component of pain reflects a paradigm shift for DBS in the twenty-first century. Recent studies measuring cerebral blood flow with positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) have specifically identified the dorsal anterior cingulate (dACC), insula, and dorsolateral prefrontal cortex (DLPFC) as key substrates underlying subjective pain experience (Coghill et al., 2003; Wager et al., 2013) of which the ACC may be specific to the affective component of pain Rainville et al., 1997)”; page 3, column 1, paragraph 4).
Regarding claim 7: Shirvalkar discloses that the target region of the patient brain includes one of a primary motor cortex, an anterior cingulate cortex, and or periaqueductal gray and thalamus (“Based on animal studies implicating limbic system structures in emotional experience and expression (Papez, 1937; Nauta, 1958), early brain surgery for chronic pain involved anterior cingulotomy to alleviate pain. Case studies of these patients described individuals with intact somatosensation, but who seemed to lack "emotional tension" (Whitty et al., 1952; Ballantine et al., 1967) and lacked "emotional reactivity" to pain stimuli (Foltz and White, 1962) without being emotionally blunted.” page 3, column 1 paragraph 2).
Regarding claim 8: Shirvalkar discloses, further comprising training the pain decoder model using a state space model based on spectral features from low gamma (30-50Hz), high gamma (50- 100Hz), and ultra-high frequency (300-500 Hz) bands (“We argue that using LFP signals from three brain regions-S1, dACC, and OFC-could be used to calculate multidimensional, patient-specific pain states (Figure 3). (While we believe these brain regions are critical sites for detecting pain signals there are other valuable regions that have been omitted for clarity in Figure 3). Each patient's biomarkers will need to be determined empirically, but prior literature (elaborated below), suggests high gamma power in S1, high gamma and low alpha power in dACC, and low alpha power in OFC as reasonable starting points.” page 6, column 1 paragraph 3).
Regarding claim 9: Shirvalkar discloses a system for treating pain, comprising: a plurality probes implantable in multiple brain regions of a patient to detect neural signals including local field potentials of the multiple brain regions (“To extend these case series, Medtronic conducted two large, multicenter, randomized controlled trials in the early 1990s for a heterogeneous group of chronic pain conditions). All patients were implanted with bilateral electrodes targeted the VT and PAG. These trials established the primary endpoint still used by most modern chronic pain trials: >50% reduction of the pain visual analog score (VAS) at 1 year” page 2, column 2 paragraph 3 and “Neural state-space representations can consist of a number of time dependent input variables, such as firing rates from neurons or local field potentials (LFP) power time series from multiple recording channels. If the number of variables (i.e., neurons or electrode contacts) is very large, it is useful to first reduce the dimensionality of the data to a set of orthogonal dimensions that describes the phenomena of interest with fewer variables (Cunningham and Yu, 2014)” page 4, column 1 paragraph 1); a processing device receiving the neural signals from the multiple brain regions of a patient brain to process the neural signals and input the processed neural signals to a machine learning pain decoder model that is configured to indicate pain (“State-space representations are used in control engineering to model systems with multiple inputs, multiple outputs and latent state variables which can be used to represent dynamic sequences of brain states (Smith and Brown, 2003; Hsieh and Shanechi, 2016). Neural state-space representations can consist of a number of time dependent input variables, such as firing rates from neurons or local field potentials (LFP) power time series from multiple recording channels: page 4 column 2 paragraph 1 and “This is distinguished from sensor-triggered stimulation in that in a closed-loop protocol, the same coordinate location in a state space may trigger different stimulation patterns or update stimulation parameters (pulse width, frequency, amplitude) dependent on the history and context of the neural trajectory. For this to be possible, we must create a predictive model of the multidimensional pain state such that the future path of each trajectory can be determined based on history and the current state (Churchland et al., 2006; Mante et al., 2013”; figure 4A and page 9 column 2 paragraphs 2-3); and a stimulation device implantable in a target region of the patient brain to provide stimulation of the target region based upon an indication of pain (“Based on the assumption that stimulation can control or influence neural trajectories related to pain, the model must additionally contain predictions about the effect of stimulation on the pain trajectory.” Figure 4B, page 9 column 2 paragraph 3).
Regarding claim 10: Shirvalkar discloses the processing device is configured to process the neural signals by computing frequency dependent power features of the local field potentials of the multiple brain regions (“Assuming that recorded action potentials from human patients would experience similar instability, chronic biomarkers based on these signals are not tractable. A potential work-around would be to calculate biomarkers based on dynamics from population neural firing combined with high frequency local field potential, a promising strategy used in human brain-machine interfaces (Pandarinath et al., 2017).”page 5 column 1 paragraph 2).
Regarding claim 11: Shirvalkar discloses the pain decoder model is trained to identify relative changes in neural activity in the multiple brain regions (“While dynamical systems analysis of movement has so far mostly relied on single-neuron signals, there are also ample reports of using LFP from motor cortex to decode movements and screen cursor location (Flint et al., 2012; Orsborn et al., 2013; So et al., 2014; Stavisky et al., 2015). Because shifts in pain state are slow, multiregional neural phenomena, we predict that LFP changes across multiple brain regions will provide a temporally appropriate neural report of pain state fluctuations. Multivariate data such as LFPs from multiple brain regions can be represented in a "state-space" for pain (Figure 4). These are particularly appropriate for analyzing multidimensional phenomena like dimensions of pain. In the next section, we will outline the specific nature of the neural signals which can be interpreted as biomarkers of internal pain states.” page 4 column 2 paragraph 2).
Regarding claim 12: Shirvalkar discloses the pain decoder model is trained using a state space model based on spectral features from low gamma (30-50Hz), high gamma (50-100Hz), and ultra-high frequency (300-500 Hz) bands (“We argue that using LFP signals from three brain regions--S1, dACC, and OFC-could be used to calculate multidimensional, patient-specific pain states (figure 3). (While we believe these brain regions are critical sites for detecting pain signals there are other valuable regions that have been omitted for clarity in figure 3). Each patient's biomarkers will need to be determined empirically, but prior literature (elaborated below), suggests high gamma power In S1, high gamma and low alpha power in dACC, and low alpha power in OFC as reasonable starting points”. page 6 column 1 paragraph 3).
Regarding claim 13: Shirvalkar discloses the processing device is configured to trigger activation of the stimulation device upon an indication of pain (“This is distinguished from sensor-triggered stimulation in that in a closed-loop protocol, the same coordinate location in a state space may trigger different stimulation patterns or update stimulation parameters (pulse width, frequency, amplitude) dependent on the history and context of the neural trajectory. For this to be possible, we must create a predictive model of the multidimensional pain state such that the future path of each trajectory can be determined based on history and the current state (Churchland et al., 2006; Mante et al., 2013; Figure 4A). There are several methods for producing such a model, including (1) modeling the state as a three dimensional flow field (Rabinovich et al., 2012; Ashwin et al., 2016), or (2) creating a map outlining the probability of transitioning from any point in the field to every other point (i.e., Hidden Markov Models, Radons et al., 1994). Based on the assumption that stimulation can control or influence neural trajectories related to pain, the model must additionally contain predictions about the effect of stimulation on the pain trajectory (figure 4B); page 9 column 2 paragraphs 2-3).
Regarding claim 14: Shirvalkar discloses the stimulation device is configured to provide one of optical and electrical stimulation of the target region (“Eventually, direct electrical stimulation of the dorsal column (Shealy, 1969), internal capsule (Adams et al., 1974) and sensory thalamus (Hosobuchi et al., 1973) provided a reversible alternative to ablation”.; Pg 2 Col 2 Para 2).
Regarding claim 15: Shirvalkar discloses the stimulation device is configured to be implanted in one of a prefrontal cortex, a primary motor cortex, an anterior cingulate cortex, a periaqueductal gray, and thalamus (“Modulating the affective component of pain reflects a paradigm shift for DBS in the twenty-first century. Recent studies measuring cerebral blood flow with positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) have specifically identified the dorsal anterior cingulate (dACC), insula, and dorsolateral prefrontal cortex (DLPFC) as key substrates underlying subjective pain experience (Coghill et al., 2003; Wager et al., 2013) of which the ACC may be specific to the affective component of pain Rainville et al., 1997)” page 3 column 1 paragraph 4).
Regarding Claim 16: Shirvalkar discloses the plurality of probes is configured to be implanted in the multiple brain regions include an anterior cingulate cortex and a primary somatosensory cortex (“Human functional imaging data point to a widely distributed neural network that is activated by acute experimental pain perception including the primary and secondary somatosensory cortex (S1 and S2), insula, ACC, PFC and thalamus (Coghill et al., 2003; Apkarian et al., 2005; Wager et al., 2013). However, not all signals can strictly be interpreted to represent somatosensory perception” page 6 column 1 paragraph 4).
Regarding Claim 17: Shirvalkar discloses that each of the plurality of probes include a silicon probe array (“However, action potentials collected from chronically implanted tungsten or silicone probes are unstable due to probe drift and sensitivity to behavioral context (i.e. sensory stimulation, arousal state, etc.)” page 4 column 2 paragraph 4).
Regarding Claim 18: Shirvalkar discloses further comprising a graphical user interface displaying LFP signals in real-time and providing options to change threshold criterion (“In depth methods for developing multivariate classifiers based on logistic regression have been presented previously (Hastie et al., 2009), as has their personalized application to closed-loop DBS systems based on brain-state (Ezzyat et al., 2017). Using receiver operating characteristic (ROC) curves, one could then calculate optimal threshold values for each biomarker such that real-time crossing of ACC gamma or OFC theta power above/below this threshold would activate stimulation. This scheme represents a sensor triggered protocol which is a good first-step approximation to building a fully closed-loop system that would adjust stimulation amplitude or other parameters based on ongoing neural activity” page 10 column 2 paragraph 2).
Regarding claim 19: Shirvalkar discloses a non-transitory computer-readable storage medium including a set of instructions executable by a processor, the set of instructions, when executed by the processor causing the processor to perform operations, comprising (“The possible utility of separately defining such a reference state has been suggested by a computational model for closed-loop control to treat essential tremor in non-human primates (Santaniello et al., 2011); page 8 column 2 paragraph 3, further the paper describes the computational model which has to be executed using a processor): receiving neural signals from multiple brain regions of a patient, the neural signals including local field potentials (LFP) of the multiple brain regions (“State-space representations are used in control engineering to model systems with multiple inputs, multiple outputs and latent state variables which can be used to represent dynamic sequences of brain states (Smith and Brown, 2003; Hsieh and Shanechi, 2016). Neural state-space representations can consist of a number of time dependent input variables, such as firing rates from neurons or local field potentials (LFP) power time series from multiple recording channels” page 4 column 2 paragraph 1); computing frequency dependent power features of the local field potentials of the multiple brain regions (“Assuming that recorded action potentials from human patients would experience similar instability, chronic biomarkers based on these signals are not tractable. A potential work-around would be to calculate biomarkers based on dynamics from population neural firing combined with high frequency local field potential, a promising strategy used in human brain-machine interfaces (Pandarinath et al., 2017)”. page 5 column 1 paragraph 2); inputting the power features to a machine learning pain decoder model to identify relative changes in neural activity in the multiple brain regions to indicate pain (“While the term LFP usually refers to signals captured by implanted depth electrodes or cortical electrodes, LFP is thought to reflect brain oscillations similar to those captured by intracranial electroencephalography (iEEG) and magnetoencephalography (MEG). Previous attempts at decoding subjective pain intensity with resting state EEG (Schulz et al., 2012) or MEG (Kuo et al., 2017) have used time-frequency representations of brain oscillations with high accuracy, supporting the feasibility of using LFP to define a pain state; Pg 5 Col 1 Para 2).; and triggering stimulation of a target region of a brain based on an indication of pain (Based on the assumption that stimulation can control or influence neural trajectories related to pain, the model must additionally contain predictions about the effect of stimulation on the pain trajectory” figure 4B, page 9 column 2 paragraph 3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Annoni et al. US 11,751,804 discloses a method and apparatus to manage pain.
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PAULA J. STICE
Primary Examiner
Art Unit 3796
/PAULA J STICE/Primary Examiner, Art Unit 3796