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 . 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 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.
Response to Amendment
This Office Action is responsive to the amendment filed 10/16/2025 (“Amendment”). Claims 1-22 are currently under consideration. The Office acknowledges the amendments to claims 1, 5, 12, 13, and 15-17, as well as the addition of new claim 22.
The objection(s) to the drawings, specification, and/or claims, the interpretation(s) under 35 USC 112(f), and/or the rejection(s) under 35 USC 101 and/or 35 USC 112 not reproduced below has/have been withdrawn in view of the corresponding amendments.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 13 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 4, there is no support for the desired brain response being configured to heighten cognitive state.
Regarding claims 13 and 15, there is no support for the electrodes being local field potential electrodes, since these are not mentioned in the disclosure.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-5, 7, 12-18, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 2007/0142873 (“Esteller”) in view of US Patent 5,347,446 (“Iino”).
Regarding claim 1, Esteller teaches [a] closed-loop control method for causing a desired brain activity response to an input brain stimulation signal (¶¶s 0024 and 0059), comprising: stimulating a true brain system by providing the input brain stimulation signal to the true brain system (Fig. 8 shows the outputs of stimulation unit 340 (e.g. an electrical output), which is part of the low-level controller 300 in Fig. 1. ¶ 0101 describes element 330, which incorporates adjustments to intensity, duration, and frequency as needed; Fig. 1 shows the stimulation being inputted to data generation block 100, which includes electrodes, cables, and sensors used to capture physiological variables (see ¶ 0059). The system is a true brain system as described in the Abstract), according to a closed-loop control algorithm (¶ 0024, closed-loop control) based on a brain network input-output (“IO”) dynamics model (¶¶s 0101 and 0102 describe modulating a stimulation signal based on received feedback and a model until a desired response is achieved. The model is the intelligent prediction block of Fig. 1 and e.g. the low level controller 300, also shown in Fig. 4 as block 260 and in Fig. 8 in more detail, including stimulation blocks 330 and 340 which are part of the low level controller (¶ 0101), each of which contemplate determining stimulation adjustments based on a model that correlates a probability of a seizure (and other metrics as shown in Fig. 8) with associated stimulation parameters and recorded data - also see ¶ 0099) …; detecting a current brain activity response of the true brain system to the input brain stimulation signal (Fig. 1, intelligent data processing unit 200, which is shown in detail in Fig. 4 (including a processor 310 and a memory 270), records physiological data and extracts features therefrom – also note that ¶ 0024 describes the low-level control as operating in a continuous fashion and monitoring its performance over time); comparing the current brain activity response to the desired brain activity response (the desired response is e.g. seizure mitigation as described in ¶ 0090, and the comparison occurs based on the receipt of feedback signals and seizure probability which are used to adjust stimulation parameters dynamically and/or continuously, as described in ¶¶s 0090, 0092, 0101, etc. - also see ¶ 0102, describing fine-tuning of control laws for the purpose of better matching the response to the desired response), …; and modulating the input brain stimulation signal, according to the closed-loop control algorithm based on one or more predicted brain activity responses that are output from the brain network input-output (“IO”) dynamics model (as above, ¶¶s 0101 and 0102 describe modulating the signal based on received feedback, the modulation predicted to prevent a seizure from occurring – also see ¶ 0092, describing using a probability of having a seizure (i.e., a predicted brain activity response) to determine the stimulations to provide) in response to inputting one or more model brain stimulation inputs into the brain network input-output (“IO”) dynamics model (the current brain activity responses, when filtered and feature-extracted, are considered model brain stimulation inputs because they are inputted into the intelligent prediction block) and determining a subsequent brain stimulation input to the true brain system based on the one or more predicted brain activity responses and the current brain activity response, until the current brain activity response is matched with the desired brain activity response (¶¶s 0092, 0101, 0102, etc., as above – note that the subsequent stimulation is based on the current brain activity response at least because the current brain activity response leads to the predicted brain activity response outputted from the model), … .
Esteller does not appear to explicitly teach the brain network IO dynamics model including a model brain stimulation input to predict an effect of the model brain stimulation input on a brain activity response that is output of the true brain system when the true brain system is stimulated with the model brain stimulation input, wherein: the comparing comprises computing a difference between the desired brain activity response and the current brain activity response over a time interval, the current brain activity response is a current neurophysiological signal that is output from the true brain system prior to the comparing, and the desired brain activity response is a desired neurophysiological signal that is a desired theoretical output from the true brain system, and wherein each of the one or more predicted brain activity responses of the true brain system is a predicted neurophysiological signal that is predicted to be output from the true brain system in response to the true brain system receiving the one or more model brain stimulation inputs as the subsequent brain stimulation input.
Iino teaches a model predictive control apparatus (title) which, using a model approximating a dynamic characteristic of a controlled system, computes a difference between a future reference value and a manipulated value to minimize a cost function, thereby determining an optimal manipulated variable based on a variety of inputs, one of which satisfies the cost function optimally (col. 2, lines 30-39; col. 6, lines 35-51; col. 8, lines 28-49). The model takes as input model inputs, to predict an effect of the model inputs on control of an apparatus or system (col. 1, lines 5-14, col. 6, lines 36-51 as above, etc. – approximating a dynamic characteristic of a controlled system).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate model predictive control into Esteller as in Iino, by e.g. testing a variety of different inputs in the model (i.e., model brain stimulation inputs) and seeing which one would have a minimum cost (i.e., an effect of the model brain stimulation input on a brain activity response of the true brain system), and then issuing the stimulation based thereon, for the purpose of optimizing control (Iino: col. 4, lines 59-68), including closed loop control (Iino: col. 25, lines 12-17, Fig. 19, etc.). The incorporation of the model predictive control of Iino into Esteller would also have been obvious as the application of a known technique (Iino’s MPC) to a known device (that of Esteller) ready for improvement to yield predictable results (optimized control of a controlled system), and as the use of a known technique to improve similar devices in the same way (Iino: Abstract, suitable for a multi-input/output system; col. 1, lines 5-14, suitable for a controlled system; to calculate manipulated variables. Esteller teaches a controlled system to which MPC could have been applied to improve it in the same way).
Regarding claim 5, Esteller teaches [a]n apparatus for causing a desired brain activity response to an input brain stimulation signal (¶¶s 0024 and 0059), comprising one or more processors coupled to one or more memories and to a waveform generator (Fig. 4, processor 310 and memory 270 - also see ¶¶s 0271 and 0272; Figs. 1 and 3, processing unit 200 is connected to a stimulation generator of controller 300), the one or more memories holding instructions that when executed by the one or more processors cause the apparatus to perform (¶¶s 0271 and 0272): stimulating a true brain system by providing the input brain stimulation signal to the true brain system (Fig. 8 shows the outputs of stimulation unit 340 (e.g. an electrical output), which is part of the low-level controller 300 in Fig. 1. ¶ 0101 describes element 330, which incorporates adjustments to intensity, duration, and frequency as needed; Fig. 1 shows the stimulation being inputted to data generation block 100, which includes electrodes, cables, and sensors used to capture physiological variables (see ¶ 0059). The system is a true brain system as described in the Abstract), according to a closed-loop control algorithm (¶ 0024, closed-loop control) based on a brain network input-output (“IO”) dynamics model (¶¶s 0101 and 0102 describe modulating a stimulation signal based on received feedback and a model until a desired response is achieved. The model is the intelligent prediction block of Fig. 1 and e.g. the low level controller 300, also shown in Fig. 4 as block 260 and in Fig. 8 in more detail, including stimulation blocks 330 and 340 which are part of the low level controller (¶ 0101), each of which contemplate determining stimulation adjustments based on a model that correlates a probability of a seizure (and other metrics as shown in Fig. 8) with associated stimulation parameters and recorded data - also see ¶ 0099) …; comparing a current brain activity response to the desired brain activity response (the desired response is e.g. seizure mitigation as described in ¶ 0090, and the comparison occurs based on the receipt of feedback signals and seizure probability which are used to adjust stimulation parameters dynamically and/or continuously, as described in ¶¶s 0090, 0092, 0101, etc. - also see ¶ 0102, describing fine-tuning of control laws for the purpose of better matching the response to the desired response), …; and modulating the input brain stimulation signal, according to the closed-loop control algorithm based on one or more predicted brain activity responses that are output from the brain network input-output (“IO”) dynamics model (as above, ¶¶s 0101 and 0102 describe modulating the signal based on received feedback, the modulation predicted to prevent a seizure from occurring – also see ¶ 0092, describing using a probability of having a seizure (i.e., a predicted brain activity response) to determine the stimulations to provide) in response to inputting one or more model brain stimulation inputs into the brain network input-output (“IO”) dynamics model (the current brain activity responses, when filtered and feature-extracted, are considered model stimulation inputs because they are inputted into the intelligent prediction block) and determining a subsequent brain stimulation input to the true brain system based on the one or more predicted brain activity responses and the current brain activity response until the current brain activity response is matched with the desired brain activity response (¶¶s 0092, 0101, 0102, etc., as above – note that the subsequent stimulation is based on the current brain activity response at least because the current brain activity response leads to the predicted brain activity response outputted from the model), … .
Esteller does not appear to explicitly teach the brain network IO dynamics model including a model brain stimulation input to predict an effect of the model brain stimulation input on a brain activity response that is output of the true brain system when the true brain system is stimulated with the model brain stimulation input, wherein: the comparing comprises computing a difference between the desired brain activity response and the current brain activity response over a time interval, the current brain activity response is a current neurophysiological signal that is output from the true brain system prior to the comparing, and the desired brain activity response is a desired neurophysiological signal that is a desired theoretical output from the true brain system, and wherein each of the one or more predicted brain activity responses of the true brain system is a predicted neurophysiological signal that is predicted to be output from the true brain system in response to the true brain system receiving the one or more model brain stimulation inputs as the subsequent brain stimulation input.
Iino teaches a model predictive control apparatus (title) which, using a model approximating a dynamic characteristic of a controlled system, computes a difference between a future reference value and a manipulated value to minimize a cost function, thereby determining an optimal manipulated variable based on a variety of inputs, one of which satisfies the cost function optimally (col. 2, lines 30-39; col. 6, lines 35-51; col. 8, lines 28-49). The model takes as input model inputs, to predict an effect of the model inputs on control of an apparatus or system (col. 1, lines 5-14, col. 6, lines 36-51 as above, etc. – approximating a dynamic characteristic of a controlled system).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate model predictive control into Esteller as in Iino, by e.g. testing a variety of different inputs in the model (i.e., model brain stimulation inputs) and seeing which one would have a minimum cost (i.e., an effect of the model brain stimulation input on a brain activity response of the true brain system), and then issuing the stimulation based thereon, for the purpose of optimizing control (Iino: col. 4, lines 59-68), including closed loop control (Iino: col. 25, lines 12-17, Fig. 19, etc.). The incorporation of the model predictive control of Iino into Esteller would also have been obvious as the application of a known technique (Iino’s MPC) to a known device (that of Esteller) ready for improvement to yield predictable results (optimized control of a controlled system), and as the use of a known technique to improve similar devices in the same way (Iino: Abstract, suitable for a multi-input/output system; col. 1, lines 5-14, suitable for a controlled system; to calculate manipulated variables. Esteller teaches a controlled system to which MPC could have been applied to improve it in the same way).
Regarding claim 16, Esteller teaches [a] closed-loop control method for causing a desired brain activity response to an input brain stimulation signal (¶¶s 0024 and 0059), comprising: supplying, by a closed loop controller (¶ 0024, closed-loop control), the input brain stimulation signal to a true brain system (Fig. 8 shows the outputs of stimulation unit 340 (e.g. an electrical output), which is part of the low-level controller 300 in Fig. 1. ¶ 0101 describes element 330, which incorporates adjustments to intensity, duration, and frequency as needed; Fig. 1 shows the stimulation being inputted to data generation block 100, which includes electrodes, cables, and sensors used to capture physiological variables (see ¶ 0059). The system is a true brain system as described in the Abstract), wherein the true brain system is configured to output a current brain activity response to the closed-loop controller in response to the supplying the input brain stimulation signal (Fig. 1, intelligent data processing unit 200, which is shown in detail in Fig. 4 (including a processor 310 and a memory 270), records physiological data and extracts features therefrom – also note that ¶ 0024 describes the low-level control as operating in a continuous fashion and monitoring its performance over time); receiving, by the closed-loop controller, the current brain activity response of the true brain system (as above, the current brain activity response is received); comparing, by the closed-loop controller, the current brain activity response to the desired brain activity response (the desired response is e.g. seizure mitigation as described in ¶ 0090, and the comparison occurs based on the receipt of feedback signals and seizure probability which are used to adjust stimulation parameters dynamically and/or continuously, as described in ¶¶s 0090, 0092, 0101, etc. - also see ¶ 0102, describing fine-tuning of control laws for the purpose of better matching the response to the desired response), … ; and modulating, by the closed loop controller and based on a brain network input-output (“IO”) dynamics model (¶¶s 0101 and 0102 describe modulating a stimulation signal based on received feedback and a model until a desired response is achieved. The model is the intelligent prediction block of Fig. 1 and e.g. the low level controller 300, also shown in Fig. 4 as block 260 and in Fig. 8 in more detail, including stimulation blocks 330 and 340 which are part of the low level controller (¶ 0101), each of which contemplate determining stimulation adjustments based on a model that correlates a probability of a seizure (and other metrics as shown in Fig. 8) with associated stimulation parameters and recorded data - also see ¶ 0099), the input brain stimulation signal to the true brain system, wherein the brain network input-output (“IO”) dynamics model … is configured to generate one or more predicted brain activity responses of the true brain system (as above, ¶¶s 0101 and 0102 describe modulating the signal based on received feedback, the modulation predicted to prevent a seizure from occurring – also see ¶ 0092, describing using a probability of having a seizure (i.e., a predicted brain activity response) to determine the stimulations to provide) from one or more model brain stimulation inputs (the current brain activity responses, when filtered and feature-extracted, are considered model stimulation inputs because they are inputted into the intelligent prediction block), and the closed-loop controller determines a subsequent brain stimulation input to the true brain system based on the one or more predicted brain activity responses and the current brain activity response until the current brain activity response is matched with the desired brain activity response (¶¶s 0092, 0101, 0102, etc., as above – note that the subsequent stimulation is based on the current brain activity response at least because the current brain activity response leads to the predicted brain activity response outputted from the model), … .
Esteller does not appear to explicitly teach the brain network IO dynamics model including a model brain stimulation input to predict an effect of the model brain stimulation input on a brain activity response that is output of the true brain system when the true brain system is stimulated with the model brain stimulation input, wherein: the comparing comprises computing a difference between the desired brain activity response and the current brain activity response over a time interval, the current brain activity response is a current neurophysiological signal that is output from the true brain system immediately prior to the comparing, and the desired brain activity response is a desired neurophysiological signal that is a desired theoretical output from the true brain system, and wherein each of the one or more predicted brain activity responses of the true brain system is a predicted neurophysiological signal that is predicted to be output from the true brain system in response to the true brain system receiving the one or more model brain stimulation inputs as the subsequent brain stimulation input.
Iino teaches a model predictive control apparatus (title) which, using a model approximating a dynamic characteristic of a controlled system, computes a difference between a future reference value and a manipulated value to minimize a cost function, thereby determining an optimal manipulated variable based on a variety of inputs, one of which satisfies the cost function optimally (col. 2, lines 30-39; col. 6, lines 35-51; col. 8, lines 28-49). The model takes as input model inputs, to predict an effect of the model inputs on control of an apparatus or system (col. 1, lines 5-14, col. 6, lines 36-51 as above, etc. – approximating a dynamic characteristic of a controlled system).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate model predictive control into Esteller as in Iino, by e.g. testing a variety of different inputs in the model (i.e., model brain stimulation inputs) and seeing which one would have a minimum cost (i.e., an effect of the model brain stimulation input on a brain activity response of the true brain system), and then issuing the stimulation based thereon, for the purpose of optimizing control (Iino: col. 4, lines 59-68), including closed loop control (Iino: col. 25, lines 12-17, Fig. 19, etc.). The incorporation of the model predictive control of Iino into Esteller would also have been obvious as the application of a known technique (Iino’s MPC) to a known device (that of Esteller) ready for improvement to yield predictable results (optimized control of a controlled system), and as the use of a known technique to improve similar devices in the same way (Iino: Abstract, suitable for a multi-input/output system; col. 1, lines 5-14, suitable for a controlled system; to calculate manipulated variables. Esteller teaches a controlled system to which MPC could have been applied to improve it in the same way).
Regarding claim 3, Esteller-Iino teaches all the features with respect to claim 1, as outlined above. Esteller-Iino further teaches wherein the desired brain activity response is configured to mitigate at least one of Parkinson’s disease or a neuropsychiatric disorder (Esteller: ¶ 0090, seizure mitigation; ¶¶s 0020, 0100, 0102, etc., auras including anxiety).
Regarding claim 4, Esteller-Iino teaches all the features with respect to claim 1, as outlined above. Esteller-Iino further teaches wherein the desired brain activity response is configured to heighten at least one of wakefulness, focus, memory or cognitive state (Esteller: ¶¶s 0090, 0020, 0100, 0102, etc., seizure or aura mitigation is considered heightening a cognitive state).
Regarding claims 7, 12, 14, and 21, Esteller-Iino teaches all the features with respect to claim 1, as outlined above. Esteller-Iino further teaches wherein the true brain system comprises a living brain which receives the input brain stimulation signal, wherein the input brain stimulation signal is an electrical signal applied to the true brain system via at least one electrode implanted in a living brain, wherein a brain activity response of the true brain system is a neurophysiological signal measured via at least one electrode implanted in a living brain, wherein the electrical signal is a deep brain stimulation (DBS) signal (Esteller: ¶¶s 0022, 0024, etc., contemplating use in a patient; Abstract, the feedback information coming from an implantable device; ¶¶s 0059, 0067, etc., electrodes that detect brain (neurophysiological) activity; ¶¶s 0093, 0095, etc., deep brain).
Regarding claims 13 and 15, Esteller-Iino teaches all the features with respect to the corresponding claims 12 and 14, as outlined above. Regarding claim 13, Esteller-Iino further teaches wherein the at least one electrode is selected from a group consisting of microelectrodes, local field potential (LFP) electrodes, and ECoG electrodes (Esteller: ¶¶s 0066, 0067, implanted brain electrodes, intracranial electrodes, foramen ovale electrodes, etc.).
Claim 15 is rejected in like manner.
Regarding claim 17, Esteller-Iino teaches all the features with respect to claim 16, as outlined above. Esteller-Iino further teaches wherein the subsequent brain stimulation input is selected, by the closed-loop controller, from a plurality of model brain stimulation inputs, the plurality of model stimulation inputs including the one or more model brain stimulation inputs (as above, the stimulation which minimizes the cost function is selected from a plurality of stimulations, most of which do not minimize it).
Regarding claim 18, Esteller-Iino teaches all the features with respect to claim 17, as outlined above. Esteller-Iino further teaches wherein the subsequent brain stimulation input is selected such that relative to the plurality of model brain stimulation inputs, the subsequent brain stimulation input is predicted to optimize a cost function, wherein the cost function is a function of the one or more model brain stimulation inputs and the one or more predicted brain activity responses that are output from the brain network input-output ("IO") dynamics model in response to the inputting the one or more model brain stimulation inputs into the brain network input-output ("IO") dynamics model (as above, minimizing a cost function).
Regarding claim 20, Esteller-Iino teaches all the features with respect to claim 16, as outlined above. Esteller-Iino further teaches wherein responsive to the receiving the current brain activity response of the true brain system to the input brain stimulation signal, the modulating by the closed-loop controller of the input brain stimulation signal to the true brain system is performed automatically based on the brain network input-output ("IO") dynamics model (¶ 0022, automatic system).
Claims 2, 6, 8-11, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Esteller-Iino in view of US Patent Application Publication 2007/0213786 (“Sackellares”) and International Application Publication WO 2016/168485 (“Robinson”).
Regarding claims 2 and 6, Esteller-Iino teaches all the features with respect to the corresponding claims 1 and 5, as outlined above. Regarding claim 2, Esteller-Iino does not appear to explicitly teach wherein the brain network input-output ("IO") dynamics model is developed and incorporated into the closed-loop control algorithm prior to the stimulating the true brain system in the closed-loop control method by a model generation step that includes: applying an input stimulation waveform to the true brain system, the input stimulation waveform comprising an input stochastic-switched noise-modulated stimulation waveform, the input stochastic switched noise-modulated stimulation waveform comprising a pulse train, detecting a brain activity response output of the true brain system to the input stimulation waveform, correlating the brain activity response output from the true brain system with the input stimulation waveform to develop the brain network input-output ("IO") dynamics model, and incorporating the brain network input-output ("IO") dynamics model that is developed into the closed-loop control algorithm (although Esteller does teach, in ¶¶s 0025, 0060, 0102, 0103, etc., using and updating control laws based on which interventions cause which results. Note that initial control laws are also provided).
Sackellares teaches model-based control, where the model represents a relationship between a dynamical descriptor (i.e., a feature of an EEG signal) and a stimulator output (¶ 0254, Fig. 16C). To obtain this kind of model, the relationship must be obtained beforehand.
Robinson teaches an implanted neuro-stimulation device that stimulates sensory neurons with a pulse train from a pseudo random binary noise (PRBN) generator (page 23, lines 29-31). The PRBN includes a “white noise” sequence (page 23, line 21 to page 24, line 5. A white spectrum input is considered an optimality constraint as described in Applicant’s ¶ 055 of the specification as filed, and ¶ 064 describes a binary noise sequence as stochastically switched).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to develop the model of Esteller-Iino as in Sackellares, by building a relationship/correlation between inputs and outputs based on testing, as already contemplated by Esteller (the pre-existing control laws), as a known alternative for implementing a control system, that enables timing, amount, and duration of stimulation to be determined (Sackellares: ¶ 0254). It would have been obvious to use the stimulation described in Robinson to develop the model of the combination and to perform active stimulation, as the simple substitution of one known type of stimulation (that of Esteller) for another (that of Robinson) with predictable results (stimulating the brain according to the model, as part of the closed-loop control of Esteller), and since the stimulation enables generation of an ideal input function (Robinson: page 23, lines 28-29) for monitoring the relationship between inputs and outputs (Robinson: page 23, lines 16-17).
Claim 6 is rejected in like manner.
Regarding claims 8-10, Esteller-Iino-Sackellares-Robinson teaches all the features with respect to claim 2, as outlined above. Esteller-Iino-Sackellares-Robinson further teaches wherein at least one of a frequency, an amplitude, and a pulse width of the pulse train in the input stochastic-switched noise-modulated stimulation waveform is switched between two or more values at randomly determined intervals during the model generation step, wherein random switches of the pulse train of the input stochastic-switched noise-modulated stimulation waveform are performed on at least one of a frequency, an amplitude, and a pulse width of the pulse train, wherein at least one parameter of the pulse train in the input stochastic-switched noise-modulated waveform is switched between two or more values at randomly determined intervals during the model generation step (Robinson: the PRBN described above, which e.g. requires no sequence repetition as further described on page 24, lines 6-12, meets this criteria, as further shown in Figs. 30, 32, etc.).
Regarding claim 11, Esteller-Iino teaches all the features with respect to claim 1, as outlined above. Esteller-Iino does not appear to explicitly teach wherein the brain network input-output ("IO") dynamics model is developed by: generating an input stochastic-switched noise-modulated stimulation waveform characterized by at least one parameter of a pulse train modulated according to a stochastic-switched noise sequence; inputting the input stochastic-switched noise-modulated stimulation waveform to the true brain system through a clinical brain-response system; recording a true brain response through one or more time-correlated outputs of the clinical brain-response system responsive to the input stochastic-switched noise-modulated stimulation waveform; and identifying, as the brain network input-output ("IO") dynamics model for brain stimulation, a mathematical model that correlates the input stochastic-switched noise-modulated stimulation waveform to the one or more time-correlated outputs of the clinical brain-response system (although Esteller does teach, in ¶¶s 0025, 0060, 0102, 0103, etc., using and updating control laws based on which interventions cause which results. Note that initial control laws are also provided).
Sackellares teaches model-based control, where the model represents a mathematical relationship/correlation between a dynamical descriptor (i.e., a feature of an EEG signal) and a stimulator output (¶ 0254, Fig. 16C). To obtain this kind of model, the relationship must be obtained beforehand.
Robinson teaches an implanted neuro-stimulation device that stimulates sensory neurons with a pulse train from a pseudo random binary noise (PRBN) generator (page 23, lines 29-31). The PRBN includes a “white noise” sequence (page 23, line 21 to page 24, line 5. A white spectrum input is considered an optimality constraint as described in Applicant’s ¶ 055 of the specification as filed, and ¶ 064 describes a binary noise sequence as stochastically switched).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to develop the model of Esteller-Iino as in Sackellares, by building a relationship/correlation between inputs and outputs based on testing, as already contemplated by Esteller (the pre-existing control laws), as a known alternative for implementing a control system, that enables timing, amount, and duration of stimulation to be determined (Sackellares: ¶ 0254). It would have been obvious to use the stimulation described in Robinson to develop the model and to perform active stimulation, as the simple substitution of one known type of stimulation (that of Esteller) for another (that of Robinson) with predictable results (stimulating the brain according to the model, as part of the closed-loop control of Esteller), and since the stimulation enables generation of an ideal input function (Robinson: page 23, lines 28-29) for monitoring the relationship between inputs and outputs (Robinson: page 23, lines 16-17).
Regarding claim 22, Esteller-Iino-Sackellares-Robinson teaches all the features with respect to claim 2, as outlined above. Esteller-Iino-Sackellares-Robinson further teaches wherein an amplitude, a frequency and/or a pulse width (Esteller: ¶ 0101 describes element 330, which incorporate adjustments to intensity, duration, and frequency as needed) of the pulse train of the input stimulation waveform applied to the true brain system are stochastically changed between two values at randomly determined intervals according to a binary noise (BN) sequence (Robinson: page 23, lines 29-31, stimulation from a pseudo random binary noise (PRBN) generator).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Esteller-Iino in view of US Patent Application Publication 2007/0213786 (“Sackellares”).
Regarding claim 19, Esteller-Iino teaches all the features with respect to claim 16, as outlined above. Esteller-Iino does not appear to explicitly teach wherein the subsequent brain stimulation input is selected in part by applying a gain to at least one predicted brain activity response, wherein the gain is computed based on the brain network input-output ("IO") dynamics model.
Sackellares teaches model-based control, where the model represents a relationship between a dynamical descriptor (i.e., a feature of an EEG signal) and a stimulator output (¶ 0254, Fig. 16C). Sackellares also teaches applying a gain to find an optimal relationship that gives the best control performance (¶ 0259, Fig. 17 – also see G(z) in Fig. 16C).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to develop the model of Esteller-Iino as in Sackellares, by building a relationship/correlation between inputs and outputs based on testing, as already contemplated by Esteller (the pre-existing control laws), as a known alternative for implementing a control system, that enables timing, amount, and duration of stimulation to be determined (Sackellares: ¶ 0254). It would have been obvious to incorporate a gain determination into the model, as in Sackellares, for the purpose of performing an additional optimization to arrive at a stimulation input that gives the best control performance (Sackellares: ¶¶s 0091, 0094, 0095, 0259, etc.).
Response to Arguments
Applicant’s arguments filed 10/16/2025 have been fully considered.
In response to the arguments and amendments regarding the rejections under 35 USC 112, they are not persuasive. Although ¶¶s 004, 008, 056, 061, and 0112 of the specification as filed mention “LFP” or “local field potential,” they do not mention an LFP electrode, or that these electrodes are used in the claimed method/apparatus. It is unclear how ¶¶s 082 and 0140 provide support for the desired brain response being configured to heighten cognitive state. This is at least because heightened cognitive state is broader than what is described.
The amendments and arguments with respect to the rejections under 35 USC 103 are not persuasive. The amendments are similar to the language already found e.g. at the end of claim 1, for which the teachings of Iino were used. The inputs of Iino are considered the model inputs of the combination. These inputs are tested in the model and then an optimized input is selected to be applied to control the apparatus/system.
Iino is analogous art. It is both in the same field as the claimed invention (system control) and solves the same problem (how to improve or optimize this control). Applicant’s own specification at ¶ 0112 describes model predictive control as usable for closed-loop controller design. Iino is not limited to chemical or industrial processes (or designed or engineered processes), which are only tangentially mentioned as possible fields of application. The teachings of Iino apply to control of any controlled system. Applicant argues that Iino must be analogous to Esteller, but this is not so. Their own citation explains that analogous art is analyzed with respect to the claimed invention. Even so, Iino is analogous to Esteller because they both deal with closed-loop control.
Applicant’s arguments on the incompatibility of the combination are not persuasive. There is no need to modify the probabilistic prediction of Esteller, since the control of Iino is being used. This control is readily applicable to computation of differences between desired and current neurophysiological signals, or to any other signals, because the signals are simply data. There is nothing unique about data of neurophysiological signals that is incompatible with MPC. The manipulated variables that may be input into the model include different stimulations, one of which will be selected as an actual stimulation. Applying a known technique to another device (even e.g. a neurophysiological device) is not inventive.
All claims remain rejected in light of the prior art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANDREY SHOSTAK/Primary Examiner, Art Unit 3791