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 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 reli1ed 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)(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.
(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 1-20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C 102(a)(2) as being anticipated by Shanechi (U.S. Patent No. 11399773).
In regards to claim 1, Shanechi discloses a method for determining stimulation settings for a neurostimulation device, the method comprising (a) measuring neural activity in a subject using a recording electrode, the neural activity being in response to a neurostimulation delivered to a neural target in the subject by a neurostimulation device (Col. 4, lns. 3-5: "For example, the brain-response system may include an electrode implanted in a living brain with the outputs measured by an electrode array."), (b) constructing an autoregressive model based on the measured neural activity (Col. 23, lns. 55-63: "Linear autoregressive models have been built to study EEG dynamics, and linear models used to characterize MEG dynamics for estimation of brain source current. Linear models have also been used to quantify rotational spontaneous neural dynamics during reaching behaviors. While a goal includes modeling the input-output neural dynamics in response to DBS rather than spontaneous neural dynamics, these prior studies provide evidence that linear models could also be sufficient for IO identification."), (c) estimating a neural state of the subject based on the autoregressive model, wherein the neural state comprises a real-time state of the subject’s nervous system (Col. 24, lns. 58-61: "The proposed LSSM modeling and BN identification methods have been verified by developing a real-time closed-loop BMI simulation testbed using the clinical Neuro Omega™ microelectrode recording and stimulation system."), (d) determining stimulation settings based on the estimated neural state, and (e) delivering neurostimulation to the neural target in the subject with the neurostimulation device using the stimulation settings (Col. 2, lns. 58-62: "Closed-loop deep brain stimulation (DBS) systems can improve traditional open-loop DBS treatment of neurological disorders such as Parkinson's disease and depression by adjusting DBS parameters in real time based on neural feedback.").
In regards to claim 2, Shanechi discloses that the autoregressive model is constructed based on the measured neural activity to minimize prediction error of a response to the neurostimulation delivered to the neural target ((139) Col. 23, lns. 55-63: "Linear autoregressive models have been built to study EEG dynamics, and linear models used to characterize MEG dynamics for estimation of brain source current. Linear models have also been used to quantify rotational spontaneous neural dynamics during reaching behaviors. While a goal includes modeling the input-output neural dynamics in response to DBS rather than spontaneous neural dynamics, these prior studies provide evidence that linear models could also be sufficient for IO identification.").
In regards to claim 3, Shanechi discloses that the neural state is estimated using a Kalman filter constructed based on the autoregressive model (Col. 14, lns. 1-5: "The LQG controller 308 includes a recursive Kalman state estimator and a linear-quadratic-regulator (LQR) feedback controller. Derivation of Kalman filter is straightforward and more detail presented below under the heading Kalman Filter.").
In regards to claims 4 and 5, Shanechi discloses extracting model coefficients from the autoregressive model and constructing the Kalman filter using the model coefficients and that the Kalman filter is constructed using the model coefficients to generate Kalman filter coefficient matrices and Kalman gain for the Kalman filter (Col. 26, lns. 10-11: "...where the gain matrix L is the solution of the algebraic Riccati equation [53] …", see equation (11)).
In regards to claim 6, Shanechi discloses that the stimulation settings are generated using a linear quadratic regulator (Col. 13, lns. 66-67 - Col. 14, ln. 1: "Once the LSSM 324 is fully identified, a standard linear-quadratic-Gaussian (LQG) closed-loop controller 308 is designed to control the network activity.").
In regards to claims 7 and 8, Shanechi discloses that the linear quadratic regulator is constructed using the estimated neural state to define a cost function of the linear quadratic regulator and tat the linear quadratic regulator is further constructed based on model coefficients extracted from the autoregressive model (Col. 14, lns. 6-7: "More briefly, the LQR controller design uses an LQR cost function defined as…", see equation on line 10).
In regards to claims 9 and 10, Shanechi discloses that the neural activity is measured using one or more recording electrodes and that the one or more recording electrodes are part of the neurostimulation device (Col. 3, lns. 66-67 - Col. 4, lns. 1-5: “In an alternative, or in addition, the brain-response system may be an electronic system that provides and optionally records an electrical response of an actual brain using microelectrodes or equivalent monitoring tools. For example, the brain-response system may include an electrode implanted in a living brain with the outputs measured by an electrode array.").
In regards to claim 11, Shanechi discloses that the stimulation settings comprise a neurostimulation waveform (Col. 6, lns. 59-63: "With the model developed, LSSM parameters are identified by using open-loop system-identification experiments applying an appropriate input DBS waveform to stimulate the brain, and then collecting the input-output data to estimate LSSM parameters.").
In regards to claim 12, Shanechi discloses a controller for controlling a neurostimulation device, comprising an input that receives neural activity signals measured from a subject (Col. 4, lns. 3-5: "For example, the brain-response system may include an electrode implanted in a living brain with the outputs measured by an electrode array."), a processor to: receive the neural activity signals from the input (Col. 4, lns. 35-40: "In related aspects, an apparatus for any of the aforementioned purposes may include a hardware processor coupled to a memory and to a waveform generator, the memory holding instructions that when executed by the processor cause the apparatus to perform the operations of the methods as summarized above.", see paragraphs 9-14), construct an autoregressive model based on the neural activity signals (Col. 23, lns. 55-63: "Linear autoregressive models have been built to study EEG dynamics, and linear models used to characterize MEG dynamics for estimation of brain source current. Linear models have also been used to quantify rotational spontaneous neural dynamics during reaching behaviors. While a goal includes modeling the input-output neural dynamics in response to DBS rather than spontaneous neural dynamics, these prior studies provide evidence that linear models could also be sufficient for IO identification."), extract coefficients from the autoregressive model (Col. 26, lns. 10-11: "...where the gain matrix L is the solution of the algebraic Riccati equation [53] …", see equation (11)), estimate a neural state based on the extracted autoregressive model coefficients, wherein the neural state comprises a real-time state of the subject’s nervous system (Col. 24, lns. 58-61: "The proposed LSSM modeling and BN identification methods have been verified by developing a real-time closed-loop BMI simulation testbed using the clinical Neuro Omega™ microelectrode recording and stimulation system."), determine stimulation settings based on the estimated neural state, and an output that receives the stimulation settings from the processor and communicates the stimulation settings to a neurostimulation device (Col. 2, lns. 58-62: "Closed-loop deep brain stimulation (DBS) systems can improve traditional open-loop DBS treatment of neurological disorders such as Parkinson's disease and depression by adjusting DBS parameters in real time based on neural feedback.").
In regards to claim 13, Shanechi discloses that the autoregressive model is constructed by the processor to minimize prediction error of a response to neurostimulation delivered to a neural target in a subject (Col. 23, lns. 15-19: "In addition, to estimate LSSMs, projection and subspace identification algorithms can be exploited, which are numerically more stable and have lower computational burdens than recursive identification algorithms for nonlinear systems, e.g., prediction-error-methods.").
In regards to claims 14 and 15, Shanechi discloses that the neural state is estimated using a Kalman filter constructed based on the extracted autoregressive model coefficients and that the processor constructs the Kalman filter by using the model coefficients to generate Kalman filter coefficient matrices and Kalman gain for the Kalman filter (Col. 14, lns. 1-5: "The LQG controller 308 includes a recursive Kalman state estimator and a linear-quadratic-regulator (LQR) feedback controller. Derivation of Kalman filter is straightforward and more detail presented below under the heading Kalman Filter.").
In regards to claim 16, Shanechi discloses that the processor generates the stimulation settings using a linear quadratic regulator (Col. 13, lns. 66-67 - Col. 14, ln. 1: "Once the LSSM 324 is fully identified, a standard linear-quadratic-Gaussian (LQG) closed-loop controller 308 is designed to control the network activity.").
In regards to claims 17 and 18, Shanechi discloses that the linear quadratic regulator is constructed by the processor using the estimated neural state to define a cost function of the linear quadratic regulator and that the linear quadratic regulator is further constructed by the processor based on the extracted autoregressive model coefficients (Col. 14, lns. 6-7: "More briefly, the LQR controller design uses an LQR cost function defined as…", see equation on line 10).
In regards to claim 19, Shanechi discloses that the neural activity signals are received by the input from one or more recording electrodes (Col. 3, lns. 66-67 - Col. 4, lns. 1-5: "In an alternative, or in addition, the brain-response system may be an electronic system that provides and optionally records an electrical response of an actual brain using microelectrodes or equivalent monitoring tools. For example, the brain-response system may include an electrode implanted in a living brain with the outputs measured by an electrode array.").
In regards to claim 20, Shanechi discloses that the stimulation settings comprise a neurostimulation waveform (Col. 6, lns. 59-63: "With the model developed, LSSM parameters are identified by using open-loop system-identification experiments applying an appropriate input DBS waveform to stimulate the brain, and then collecting the input-output data to estimate LSSM parameters.").
Conclusion
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/B.M.L./Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796