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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/30/2024 is being considered by the examiner.
Status of Claims
Claims 54-73 are currently pending and under examination. Claims 1-53 are canceled.
Priority
The instant application (filed on 12/30/2024) is filed under 35 USC 371 as a national stage of PCT/US2023/026790 (filed on 06/30/2023). Acknowledgment is made of applicant's claim for domestic priority based on provisional applications 63/500191 (filed 05/04/2023) and 63/357238 (filed 06/30/2022). Instant claims 54-73 are sufficiently supported in provisional application 63/357238 to receive an effective filing date of 06/30/2022. Therefore, all prior art will be evaluated with respect to this date.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claims 54-63 are rejected under 35 U.S.C. 101 because the claimed invention is directed to nonstatutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claims 54-63 are directed to a signal per se (“computer program product”).
Step 1
The invention in claims 54-63 is not to a statutory subject matter as the claims recite a “computerized planning device” (which is interpreted as a computer program being a “signal per se”). The claims can be amended to recite “non-transitory” forms of signal transmission (see MPEP 2106.03 II). Because the claims could be amended to fall within a statutory category, the following eligibility analysis is performed.
Step 2A, Prong One
Claim 54 recites abstract ideas in the form of mental processes that "can be performed in the human mind, or by a human using a pen and paper" (see MPEP 2106.04(a)(2) subsection (III)).
Regarding claim 54, the limitations “(c) predicting a control cardiac response of the circulatory loop with at least a first control criterion over a control time period”, “(d) calculating a desired adjustment value of the stimulation parameters for MAP and/or HR to approach the control cardiac response”) could be performed by the human mind.
Step 2A, Prong Two
For the claim 54 limitations from Step 2A Prong One, the claim recites additional elements that integrate the judicial exceptions into a practical application. The mental processes are integrated into a practical application by generating a vagal nerve stimulation signal which applies a therapy to particular body locations (claim 54 – “(e) executing a signal command to stimulate the vagal nerve with an electrical pulse sufficient to adjust the MAP and/or HR in real-time with a magnitude corresponding to the desired adjustment value at a first, second and third location within a circulatory loop”).
Claims 55-63 further describe the mental processes with MPC formulas and additional calculations (which could be mental processes) used to create and apply the stimulation signal.
Therefore, claims 54-63 are directed to nonstatutory subject matter.
Claim Objections
The following claims are objected to because of the identified informalities:
Claims 57 and 68: The “measured values of (a) and (b)” would best be referred to as steps for consistency with other claims.
Claims 63 and 73: The formula image is somewhat blurry (some of the variable subscripts in particular are indistinct) and the clarity of the claim would benefit from the image being more readable at a better resolution. Additionally, the use of “k + i|k” would be preferably over using the letter l in “k + ilk.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 54-73 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.
Numerous terms lack an antecedent basis:
Claim 54:
“the mean arterial pressure (MAP)”
“the heart rate (HR)”
“the circulatory loop”
“the stimulation parameters”
“the control cardiac response”
“the vagal nerve”
“the desired adjustment value at a first, second and third location”
Claim 56
“the probability of accomplishing the control cardiac response”
Claim 57:
“the weight of the step”
Claim 58:
“the total frequency of action potentials”
“the real-time measured values”
Claim 62:
“the piece-wise linear function”
“the model number”
“the cardiac cycle number”
“the operating region in cycle k”
“the operating region in cycle k+1”
Claim 63:
“the number of cardiac cycles”
“the estimated output number”
“the set point”
“the baseline input of MAP”
“the output weight matrix”
“the input weight matrix”
“the integral action”
Claim 65:
“the subject”
“the stimulus parameters”
“the vagal nerve”
“the desired adjustment value at a first, second and third location”
Claim 67
“the probability of accomplishing the control cardiac response”
Claim 68
“the weight”
Claim 69
“the total frequency of action potentials”
“the real-time measured values”
Claim 72
“the controller”
Claim 73
1) “the number of cardiac cycles”
“the estimated output number”
“the set point”
“the baseline input of MAP”
“the output weight matrix”
“the input weight matrix”
“the integral action”
Claims 56 and 67: The reference to a (ii) sub-step does not make sense because a (i) sub-step is not defined in claims 54 or 65, respectively.
Claims 57-58 and 68-69: The references to (iii) and (iv) sub-steps does not make sense because (i) or (ii) sub-steps are not defined in claims 54 or 65, respectively.
Claims 63 and 73: It is unclear to what “yA” is referring in the equation.
Claims 55, 59-61, 64, 66, and 70-71 are rejected for being dependent on rejected claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C.
103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 54, 58-61, 64-65, and 69-72 are rejected under U.S.C 103 as being unpatentable over Yao (NPL, “Model Predictive Control of Selective Vagal Nerve Stimulation for Regulating Cardiovascular System”) in view of Ziegler (US 2012/0185007 A1) and Rawlings (NPL, Model Predictive Control: Theory, Computation, and Design, 2nd Edition).
Regarding Claim 54, Yao discloses a computer program product (p. 567-568 – “Controller Performance and Evaluation” section makes it clear a program in MATLAB is used) for:
measuring the mean arterial pressure (MAP) in a given cardiac cycle (p. 563 – “In this study, we present a NMPC framework for the multi-location VNS system which controls HR and MAP simultaneously with each cardiac beat by independently manipulating pulse width and frequency in three stimulation locations”);
measuring the heart rate (HR) in a given cardiac cycle (p. 563 – same as the evaluation of (a) above)
predicting a control cardiac response of the circulatory loop with at least a first control criterion over a control time period (p. 563 - “The pulsatile model is simulated by the lumped-parameter approach and predicts the periodic equilibria representing the intra-beat dynamics of blood flow, while the non-pulsatile model is developed by averaging the pulsatile model over each cardiac cycle and is coupled with a mean pressure based baroreflex model”);
calculating a desired adjustment value of the stimulation parameters for MAP and/or HR to approach the control cardiac response (p. 563 – “Both pulsatile and non-pulsatile models of the integrated cardiovascular system and baroreflex regulation are developed to predict the dynamic response of MAP and HR during the VNS”; Table 3 – predicted outputs in MAP and HR for a theoretical stimulation);
executing a signal command to stimulate the vagal nerve with an electrical pulse sufficient to adjust the MAP and/or HR in with a magnitude corresponding to the desired adjustment value at a first, second and third location within a circulatory loop (p. 567 – “Controller Performance and Evaluation” section discusses vagal stimulation at three locations to adjust MAP and HR).
Note Yao states “the computational time is about 1.5 seconds for most steps, which is much longer than the heart period of the rat. Implementation of the nonlinear MPC in embedded hardware
for real-time simulation remains an open challenge” (p. 568), which suggests nonlinear MPC involves more complex formulations which exact a higher computational load. However, Yao does not explicitly disclose encoded on a computer-readable storage medium comprising instructions. Yao does not explicitly state a real-time adjustment occurs.
Ziegler, in the same field of endeavor of a vagal nerve stimulator ([0005]), teaches a processor and memory for determining and providing a therapy signal (see Ziegler Claim 32 and [0052-0053]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Yao’s MPC model to determine vagal stimulation parameters by incorporating the implantable vagal stimulator in Ziegler. This would have been obvious because both Yao and Ziegler discuss providing vagal stimulation and Ziegler provides a solution/improvement by providing a means to implement model-predicted stimulation of the vagal nerve with an implantable device containing a processor and memory. Therefore, a person of ordinary skill in the art would be motivated to improve the system of Yao by incorporating the implantable vagal stimulator in Ziegler.
Rawlings, in the same field of endeavor of a model predictive control algorithms, teaches real-time analysis can be achieved by either increasing computational resources to reduce analysis time or using numerical techniques to trade accuracy for speed (“8.92 – Continuation Methods and Real-Time Iterations,” p. 577-578). Rawlings also teaches the use of linear MPC (see page 2) and emphasizes the difficulties of solving nonlinear MPC equations due to increasing complexity (p. 38).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of non-linear MPC using heart cycle and MAP variables in Yao to increase computational speed by upgrading hardware or decreasing model complexity (such as with linear models) as seen in Rawlings. This would have been obvious because both Yao and Rawlings discuss MPC algorithms and Rawlings provides a solution/improvement to improve computational speed by using higher capacity hardware or reducing computational complexity by using linear models or numerical estimation techniques. Therefore, a person of ordinary skill in the art would be motivated to improve Yao by incorporating upgraded hardware or decreasing model complexity as seen in Rawlings.
Regarding Claim 64, Yao discloses a system comprising:
the computer program product according to claim 54 (see rejection of claim 54).
Note MATLAB is mentioned as the tool to run the program on a computer on pages 566 and 567 where MATLAB would be assumed to make use of the processor and memory of the computer system. However, Yao does not explicitly disclose (ii) a processor operable to execute programs; and/or a memory associated with the processor.
Ziegler, in the same field of endeavor of a vagal nerve stimulator ([0005]), teaches a processor and memory for determining and providing a therapy signal (see Ziegler Claim 32 and [0052-0053]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Yao’s MPC model to determine vagal stimulation parameters by incorporating the implantable vagal stimulator in Ziegler. This would have been obvious because both Yao and Ziegler discuss providing vagal stimulation and Ziegler provides a solution/improvement by providing a means to implement model-predicted stimulation of the vagal nerve with an implantable device containing a processor and memory. Therefore, a person of ordinary skill in the art would be motivated to improve the system of Yao by incorporating the implantable vagal stimulator in Ziegler.
Regarding Claim 65, Yao discloses a system for identifying modulating HR and/or MAP (Abstract) comprising:
(i) a processing element operable to execute programs (MATLAB is mentioned in pages 566 and 567 – “Open Loop Performance” - as running the program, where MATLAB would be assumed to make use of the processor of the computer system);
(ii) a storage element associated with the processor (MATLAB is mentioned in pages 566 and 567 – “Open Loop Performance” - as running the program, where MATLAB would be assumed to make use of the memory of the computer system);
(iii) a database associated with and operably connected to said computing elements (model parameters are able to loaded into the models from literature data, as discussed on p.566: “The open-loop pulsatile model was solved in MATLAB using dde23, while the open-loop non-pulsatile model is solved by ode15s. The parameters in the pulsatile model are adapted from those used for the human in [17], [19], [20] to a rat”). Additionally, values need to be stored during computations in MATLAB in order for the data to be used in subsequent cycles (p.566 – “Open Loop Performance” – multiple cycles of the cycle-by-cycle analysis are performed over a specified time or number of cycles);
(iv) a computer program product (p. 567-568 – “Controller Performance and Evaluation” section makes it clear a program in MATLAB is used), the program being operable for:
(a) measuring a mean arterial pressure (MAP) in a given cardiac cycle within a circulatory loop of the subject (p. 563 – “In this study, we present a NMPC framework for the multi-location VNS system which controls HR and MAP simultaneously with each cardiac beat by independently manipulating pulse width and frequency in three stimulation locations”);
(b) measuring a heart rate (HR) in a given cardiac cycle within a circulatory loop (p. 563 – same as the evaluation of (a) above);
(c) predicting a control cardiac response of the circulatory loop with at least a first control criterion over a control time period (p. 563 - “The pulsatile model is simulated by the lumped-parameter approach and predicts the periodic equilibria representing the intra-beat dynamics of blood flow, while the non-pulsatile model is developed by averaging the pulsatile model over each cardiac cycle and is coupled with a mean pressure based baroreflex model”);
(d) calculating a desired adjustment value of the stimulus parameters for MAP and/or HR to approach the control cardiac response (p. 563 – “Both pulsatile and non-pulsatile models of the integrated cardiovascular system and baroreflex regulation are developed to predict the dynamic response of MAP and HR during the VNS”; Table 3 – predicted outputs in MAP and HR for a theoretical stimulation);
(e) executing a signal command to stimulate the vagal nerve with an electrical pulse sufficient to adjust the MAP and/or HR with a magnitude corresponding to the desired adjustment value at a first, second and third location within the vagal nerve (p. 567 – “Controller Performance and Evaluation” section discusses vagal stimulation at three locations to adjust MAP and HR).
Note Yao states “the computational time is about 1.5 seconds for most steps, which is much longer than the heart period of the rat. Implementation of the nonlinear MPC in embedded hardware
for real-time simulation remains an open challenge” (p. 568), which suggests nonlinear MPC involves more complex formulations which exact a higher computational load. However, Yao does not disclose (v) an implantable device comprising the computer program product and at least a first, second, and third electrode in operable electrical communication with the processor. Yao also does not explicitly disclose a processor, a memory, or a computer program product stored in the memory. Yao does not explicitly state a real-time adjustment occurs.
Ziegler, in the same field of endeavor of a vagal nerve stimulator ([0005]), teaches sensors are used to determine parameters for vagal nerve stimulation ([0052] – “Monitored signals may be used for sensing the need for delivering, adjusting, terminating, and/or initiating therapy under control of the operating system, [0058] – heart rate measured, [0061] – mean arterial pressure measured). Note the device is implantable ([0021]) and contains a processor and memory for determining and providing therapy (see Ziegler Claim 32 and [0052-0053]). Note two or more electrodes can be positioned for stimulation to the vagus nerve ([0048-0049]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Yao’s MPC model to determine vagal stimulation parameters by incorporating the implantable vagal stimulator in Ziegler. This would have been obvious because both Yao and Ziegler discuss providing vagal stimulation and Ziegler provides a solution/improvement by providing a means to implement model-predicted stimulation of the vagal nerve with an implantable device containing a processor and memory. Therefore, a person of ordinary skill in the art would be motivated to improve the system of Yao by incorporating the implantable vagal stimulator in Ziegler.
Rawlings, in the same field of endeavor of a model predictive control algorithms, teaches real-time analysis can be achieved by either increasing computational resources to reduce analysis time or using numerical techniques to trade accuracy for speed (“8.92 – Continuation Methods and Real-Time Iterations,” p. 577-578). Rawlings also teaches the use of linear MPC (see page 2) and emphasizes the difficulties of solving nonlinear MPC equations due to increasing complexity (p. 38).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of non-linear MPC using heart cycle and MAP variables in Yao to increase computational speed by upgrading hardware or decreasing model complexity (such as with linear models) as seen in Rawlings. This would have been obvious because both Yao and Rawlings discuss MPC algorithms and Rawlings provides a solution/improvement to improve computational speed by using higher capacity hardware or reducing computational complexity by using linear models or numerical estimation techniques. Therefore, a person of ordinary skill in the art would be motivated to improve Yao by incorporating upgraded hardware or decreasing model complexity as seen in Rawlings.
Regarding Claims 58 and 69, the computer program product and system according to Claims 54 and 65 are obvious over Yao in view of Ziegler and Rawlings, as indicated hereinabove. Yao further discloses wherein step (d) comprises: (iv) calculating the total frequency of action potentials sufficient to adjust the MAP and/or HR with a magnitude corresponding to the desired adjustment value, wherein the total frequency of action potentials is based upon a modeled output value of step (c) and the real-time measured values of steps (a) and (b) (p. 563 – “Both pulsatile and non-pulsatile models of the integrated cardiovascular system and baroreflex regulation are developed to predict the dynamic response of MAP and HR during the VNS”; p. 567-568 – “Controller Performance and Evaluation” section and Table 3 – predicted outputs in MAP and HR for a theoretical stimulation where the stimulation waveform properties, such as pulse width and frequency, are modulated to produce action potentials in the vagus nerve).
Note Yao states “the computational time is about 1.5 seconds for most steps, which is much longer than the heart period of the rat. Implementation of the nonlinear MPC in embedded hardware
for real-time simulation remains an open challenge” (p. 568), which suggests nonlinear MPC involves more complex formulations which exact a higher computational load. However, Yao does not explicitly state a real-time adjustment occurs.
As stated in claims 54 and 65, the proposed combination with Rawlings yields real-time analysis can be achieved by either increasing computational resources to reduce analysis time or using numerical techniques to trade accuracy for speed (“8.92 – Continuation Methods and Real-Time Iterations,” p. 577-578). Rawlings also teaches the use of linear MPC (see page 2) and emphasizes the difficulties of solving nonlinear MPC equations due to increasing complexity (p. 38).
Regarding Claims 59 and 70, the computer program product and system according to Claims 54 and 65 are obvious over Yao in view of Ziegler and Rawlings, as indicated hereinabove. Yao further discloses comprising: step (f) repeating steps (a) through (e) over a set time period for continuous monitoring of HR and MAP (p.566 – “Open Loop Performance” – multiple cycles of the cycle-by-cycle analysis are performed over a specified time or number of cycles).
Regarding Claims 60 and 71, the computer program product and system according to Claims 54 and 65 are obvious over Yao in view of Ziegler and Rawlings, as indicated hereinabove. Yao further discloses wherein step (e) comprises: adjusting pulse amplitude and pulse frequency across the first, second and third locations of the circulatory loop to alter HR and MAP (p. 567-568 – “Controller Performance and Evaluation” section and Table 3 – predicted outputs in MAP and HR for a theoretical stimulation where the stimulation waveform properties, such as pulse width and frequency, are modulated to produce action potential in three locations). While amplitude is not explicitly mentioned in the waveform properties, Yao mentions amplitude as a variable in “most of them use different forms of PI and PID controllers with a single input representing either frequency or amplitude of continuous stimulus and a single output representing the HR” (p.563).
Regarding Claim 61, the computer program product according to Claim 60 is obvious over Yao in view of Ziegler and Rawlings, as indicated hereinabove. Yao further discloses wherein at least one of the first, second or third locations is a nerve fiber on the vagal nerve (p. 567 - “Controller Performance and Evaluation” section discusses vagal stimulation at one of the three stimulation locations to adjust MAP and HR).
Regarding Claim 72, the system according to Claim 65 is obvious over Yao in view of Ziegler and Rawlings, as indicated hereinabove. Yao further discloses wherein the device comprises the computer program product. However, Yao does not explicitly describe the controller.
As stated in claim 65, the proposed combination with Ziegler yields an implantable device ([0021]) containing a processor and memory for determining and providing therapy (see Ziegler Claim 13 and [0052-0053]). Note a controller is also discussed as part of the system in Ziegler ([0057]).
Claims 55-57, 62, and 66-68 are rejected under U.S.C 103 as being unpatentable over Yao (NPL, “Model Predictive Control of Selective Vagal Nerve Stimulation for Regulating Cardiovascular System”) in view of Ziegler (US 2012/0185007 A1), Rawlings (NPL, Model Predictive Control: Theory, Computation, and Design, 2nd Edition), and Rao (NPL, “Experimental Studies on Multiple-Model Predictive Control for Automated Regulation of Hemodynamic Variables”).
Regarding Claims 55 and 66, the computer program product and system according to Claims 54 and 65 are obvious over Yao in view of Ziegler and Rawlings, as indicated hereinabove. Yao discloses a non-linear model predictive controller (NMPC) to predict MAP and HR (Abstract). Yao further discloses “the developed NMPC was able to control multiple physiological parameters at the same time and track set points in a wide range which cannot be obtained by a standard MPC with a single linear model or single-loop PID control” (p.568).
However, Yao does not disclose wherein step (c) comprises: (i) applying a piece-wise linear or multiple local linear functions corresponding to interaction of MAP and HR within the circulatory loop.
Rao, in the same field of endeavor of an MPC model to predict hemodynamic variables (p. 278 – “This paper presents experimental results on controlling MAP and CO using MMPC in canines. Section II provides an overview of the circulatory system and the motivation for developing the control system. The model predictive control (MPC) algorithm is presented in Section III and the derivation of the multimodel approach to MPC is discussed in Section IV”), teaches a series of linear MPC models to predict the effects of a stimulation therapy are known in the art (p. 278 – “Yu et al. occluded the coronary arteries with micro-beads to simulate congestive heart failure, and used 36 first-order-plus-time-delay models, each model having a linear MPC that could be solved analytically). Rao specifically uses a group of first order MIMO models for prediction (p. 280).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Yao’s non-linear MPC model by incorporating the set of linear MPC models in Rao. This would have been obvious because both Yao and Rao discuss MPC models and Rao provides a solution/improvement by using multiple linear models in an MPC as a known solution to increase signal biofidelity. Therefore, a person of ordinary skill in the art would be motivated to improve the program of Yao by incorporating the set of linear MPC models in Rao.
Regarding Claims 56 and 67, the computer program product and system according to Claims 54 and 65 are obvious over Yao in view of Ziegler and Rawlings, as indicated hereinabove. Yao further discloses a non-linear model predictive controller to predict MAP and HR (Abstract). Yao further discloses “the developed NMPC was able to control multiple physiological parameters at the same time and track set points in a wide range which cannot be obtained by a standard MPC with a single linear model or single-loop PID control” (p.568). However, Yao does not disclose wherein step (c) comprises: (ii) determining the probability of accomplishing the control cardiac response using a switch function.
Rao, in the same field of endeavor of an MPC model to predict hemodynamic variables (p. 278), teaches a series of linear MPC models to predict the effects of a stimulation therapy are known in the art (p. 278 – “Yu et al. occluded the coronary arteries with micro-beads to simulate congestive heart failure, and used 36 first-order-plus-time-delay models, each model having a linear MPC that could be solved analytically”). Rao specifically uses a group of first order MIMO models for prediction (p. 280– “MMPC” section). The probabilities of the different models are updated and compared using a recursive Bayesian scheme to determine which models should be given higher priority (i.e. switch) (p.281 – “MMPC” section)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Yao’s non-linear MPC model by incorporating the set of linear MPC models in Rao. This would have been obvious because both Yao and Rao discuss MPC models and Rao provides a solution/improvement by using multiple linear models in an MPC as a known solution to increase signal biofidelity. Therefore, a person of ordinary skill in the art would be motivated to improve the program of Yao by incorporating the set of linear MPC models in Rao.
Regarding Claims 57 and 68, the computer program product and system according to Claims 54 and 65 are obvious over Yao in view of Ziegler and Rawlings, as indicated hereinabove. Yao discloses a non-linear model predictive controller to predict MAP and HR (Abstract). Yao further discloses “the developed NMPC was able to control multiple physiological parameters at the same time and track set points in a wide range which cannot be obtained by a standard MPC with a single linear model or single-loop PID control” (p.568). However, Yao does not disclose wherein step (d) comprises: (iii) calculating the weight of the step of predicting using the measured values of (a) and (b).
Rao, in the same field of endeavor of an MPC model to predict hemodynamic variables (p. 278), teaches a series of linear MPC models to predict the effects of a stimulation therapy are known in the art (p. 278 – “Yu et al. occluded the coronary arteries with micro-beads to simulate congestive heart failure, and used 36 first-order-plus-time-delay models, each model having a linear MPC that could be solved analytically). Rao specifically uses a group of first order MIMO models for prediction (p. 280– “MMPC” section). The weighted probabilities of the different models are updated and compared using a recursive Bayesian scheme (p.281 – “The Bayesian weights for each MIMO model are (re)computed at every sample interval and the resulting weighted prediction model constituted a super-positioned, linear combination of MIMO models”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Yao’s non-linear MPC model by incorporating the set of linear MPC models in Rao. This would have been obvious because both Yao and Rao discuss MPC models and Rao provides a solution/improvement by using multiple linear models in an MPC as a known solution to increase signal biofidelity. Therefore, a person of ordinary skill in the art would be motivated to improve the program of Yao by incorporating the set of linear MPC models in Rao.
Regarding Claim 62, the computer program product according to Claim 55 is obvious over Yao in view of Ziegler, Rawlings, and Rao, as indicated hereinabove. Yao further discloses an NMPC formulation used to model HR and MAP (p. 563 – “In this study, we present a NMPC framework for the multi-location VNS system which controls HR and MAP simultaneously with each cardiac beat by independently manipulating pulse width and frequency in three stimulation locations”), with the optimization problem using variables related to cardiac cycle and MAP (p.566 – “Here, k is current cycle index. x is the state variables. u is the manipulated variables. N is the prediction horizon. Xs and us are steady state value of states and inputs, which are calculated by target problem whenever a new set point is given”).
However, Yao does not disclose: wherein the piece-wise linear function comprises
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wherein the superscript i represents the model number; di(k) is assumed Gaussian noise with zero mean imposed on the outputs, Ai, Bi, Ci, Di are operating ranges of MAP in a cardiac cycle, k is the cardiac cycle number in which the numbers are being calculated, x is the operating region in cycle k, and y is the operating region in cycle k+1, u is an input value of MAP.
Rawlings, in the same field of endeavor of a model predictive control algorithms, teaches the most general representation of the linear state space model as:
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Where A is the state transition matrix, B is the input matrix, C is the output matrix, and D allows a direct coupling between u and y (where in many applications D = 0). Variables include x (as the state), u (as the input), y (as the output), and t (as time) (see page 2).
Rawlings further teaches “to provide some insight into essential differences, as well as similarities, between estimation and regulation, consider again the estimation problem in the simplest possible setting with a linear time invariant model and Gaussian noise”:
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Where Gaussian noise terms Gw and v are added to the linear state space model equation (see p. 278).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of non-linear MPC using heart cycle and MAP variables in Yao and Rao’s multiple linear MPC to include the linear state space equations in Rawlings. Rawlings presents a variety of formulations for different applications, including the basic linear state space equations and additional Gaussian noise terms to describe estimate error, which would be obvious to try in the combination of Yao and Rao. A person of ordinary skill in the art would have a reasonable expectation of successfully implementing the equations in Rawlings.
Claim 63 is rejected under U.S.C 103 as being unpatentable over Yao (NPL, “Model Predictive Control of Selective Vagal Nerve Stimulation for Regulating Cardiovascular System”) in view of Ziegler (US 2012/0185007 A1), Rawlings (NPL, Model Predictive Control: Theory, Computation, and Design, 2nd Edition), Rao (NPL, “Experimental Studies on Multiple-Model Predictive Control for Automated Regulation of Hemodynamic Variables”), and Verheijen (NPL, “Data–Driven Predictive Control with Estimated Prediction Matrices and Integral Action”).
Regarding Claim 63, the computer program product according to Claim 57 is obvious over Yao in view of Ziegler, Rawlings, and Rao and the system according to Claim 65 is obvious over Yao in view of Ziegler and Rawlings, as indicated hereinabove. Yao further discloses an NMPC formulation used to model HR and MAP (p. 563 – “In this study, we present a NMPC framework for the multi-location VNS system which controls HR and MAP simultaneously with each cardiac beat by independently manipulating pulse width and frequency in three stimulation locations”), with the optimization problem using the following variables related to cardiac cycle and MAP (p.566 – “Here, k is current cycle index. x is the state variables. u is the manipulated variables. N is the prediction horizon. Xs and us are steady state value of states and inputs, which are calculated by target problem whenever a new set point is given”). The cost function of this analysis is provided (see page 566) with weighting terms Q, R, and P:
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However, Yao does not disclose: wherein the desired adjustment value of the stimulation parameters is calculated by formula:
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wherein N c is the number of cardiac cycles in a control horizon; wherein k + ilk is prediction into future cardiac cycle number time k + i based on the measurement at current sampling instance k; yA is the estimated output number, r is the set point, Ub is the baseline input of MAP; and wherein Q is the output weight matrix; R is the input weight matrix; and P is the integral action.
Rao, in the same field of endeavor of an MPC model to predict hemodynamic variables (p. 278), teaches a series of linear MPC models to predict the effects of a stimulation therapy are known in the art (p. 278 – “Yu et al. occluded the coronary arteries with micro-beads to simulate congestive heart failure, and used 36 first-order-plus-time-delay models, each model having a linear MPC that could be solved analytically). Rao specifically uses a group of first order MIMO models for prediction (p. 280). The quadratic objective function is provided by equation [1] on page 279:
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Where the V1 and V2 terms are described as weighting matrices for error penalties due to deviations from a set constraint: “The last two terms in the objective function denote the penalty, where V1 and V2 are weighting matrices for the upper and lower soft constraints on the output variables. V1 and V2 penalize the error function when the output variables are outside the desired min–max range and Q penalizes deviations from the middle of the min–max range. The values of V1 and V2 are chosen to be larger than Q to emphasize tighter control when outside the min–max band” (page 279, col 2). This constraint could be applied to determine the deviation of from any reference value, such as baseline.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Yao’s non-linear MPC model by incorporating the set of linear MPC models in Rao. This would have been obvious because both Yao and Rao discuss MPC models and Rao provides a solution/improvement by using multiple linear models in an MPC as a known solution to increase signal biofidelity. Therefore, a person of ordinary skill in the art would be motivated to improve the program of Yao by incorporating the set of linear MPC models in Rao.
Verheijen, in the same field of endeavor of model predictive control algorithms (Abstract), teaches integral action where the differences between adjacent samples (e.g. Δx(k) = x(k) – x(k-1)) are evaluated (“B. Integral Action”, page 4). The cost function adds integral action into the following equation on col 2 of page 4:
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Verheijen describes integral action as beneficial: “Embedding integral action within a model–based predictive controller is known to be beneficial both with respect to tracking time–varying references and removing off–sets” (“B. Integral Action”, page 4, col 1).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of non-linear MPC using heart cycle and MAP variables in Yao and Rao’s multiple linear MPC to include an integral action modification as presented in Verheijen. This would have been obvious because both Yao and Verheijen discuss MPC algorithms and Verheijen provides a solution/improvement modify MPC formulas to include an integral action to improve tracking of time-varying references. Therefore, a person of ordinary skill in the art would be motivated to improve Yao by incorporating the integral action modification as presented in Verheijen.
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/Benjamin A. Schmitt/
Examiner
Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796
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