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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/20/2025 has been entered.
Response to Arguments
Applicant’s arguments filed 08/20/2025 have been fully considered but are moot in view of a new grounds of rejection.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
In re claim 1:
“a controller for controlling a controllable medical device”,
In re claim 3, see in re claim 1 above.
In re claim 13, see in re claim 1 above.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 9, 17-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Grado (WO 2019/152858) in view of Burdick et al. (US 2019/0374777).
In re claim 1, Grado discloses a controller (fig. 1: controller; [0028-0029]) for controlling a controllable medical device [0028], comprising:
an input that receives feedback data representative of a treatment response or effect in a subject [0009];
a processor [0009] in communication with the input [0009] and programmed to:
receive the feedback data from the input [0009] and generate a Bayesian preference model therefrom ([0009]: posterior distribution is generated and is a Bayesian preference model; [0032]);
generate control parameter settings by sampling the Bayesian preference model ([0032]: posterior distribution used to generate acquisition function which models utility and determines next sample points; [0009]: acquisition function used to generate updated control parameter settings; [0029, 0042];
arrange the control parameter settings for testing the control parameter settings ([0062]: control parameter settings are updated for the controllable medical device; [0059-0060]: optimized settings are used to deliver electrical stimulations to a subject),
a memory (fig. 10: 1014; [0057]) in communication with the input (1016; [0058-0059]) and the processor (fig. 10; [0059]),
wherein the memory stores
instructions for generating control parameter settings [0059],
the feedback data received from the input [0058-0059].
Grado fails to disclose
a processor… programmed to: … arrange the control parameter settings in an ordered sequence for testing the control parameter settings,
wherein the processor is programmed to arrange the control parameter settings in the ordered sequence such that
exploitation of known control parameter settings is maximized by prioritizing settings with highest upper confidence bound values and
regret in exploration of unknown control parameter settings is minimized by selecting exploratory settings that maximize information gain from pairwise comparisons;
wherein the memory stores… the ordered sequence of control parameter settings generated by the processor;
an output that communicates the ordered sequence of control parameter settings to a controllable medical device; and
wherein the controllable medical device delivers electrical stimulation to a subject in accordance with the ordered sequence of control parameter setting.
Burdick teaches a neurostimulator device [0006] for stimulation [0006],
comprising a processor [0285, 0295] programmed to: arrange control parameter settings ([0256]: complex stimulation waveform is ranked) in an ordered sequence for testing the control parameter settings ([0264]: tests are arranged which compare two arms; [0064]: multiple pairs of arms are tested in a sequence; [0251]: feedback from results are used to determine next set of stimuli to be tested; [0208-0209]),
wherein the processor is programmed to arrange the control parameter settings in the ordered sequence such that
exploitation of known control parameter settings is maximized by prioritizing settings with highest upper confidence bound values
([0264]: when comparting arms to determine optimal arms, the arm with upper confidence value less than the other arm is eliminated until the most confident arm is left i.e. an optimal complex stimulation pattern is found, which maximizes exploitation since the arms are tested until an optimal complex stimulation pattern is found; [0207-0208]: determines highest upper confidence bound acquisition function values is determined i.e. a performance function that uses an upper confidence bound update rule and converges with high probability; [0065-0066]) and
regret in exploration of unknown control parameter settings is minimized ([0064]: goal is to minimize cumulative regret compared to the best arm by using a feedback of comparison between a pair of arms in each test) by selecting exploratory settings ([0066]: there’s a need to reduce exploration while exploiting correlations with known parameters; [0207]) that maximize information gain from pairwise comparisons ([0270]: each pairwise preferences may be used to maximize information gain i.e. choosing between two arms is interpreted as maximizing information gain since it gathers information; [0264]);
wherein a memory [0294] stores the ordered sequence of control parameter settings generated by the processor [0294];
an output [0214-0215, 0252] that communicates the ordered sequence of control parameter settings to a controllable medical device (fig. 1: 120; [0091, 0213, 0252]); and
wherein the controllable medical device delivers electrical stimulation to a subject in accordance with the ordered sequence of control parameter setting ([0251]: neurostimulator devices 120 operate according to the dueling bandits algorithm and feedback is used to determine next set of stimuli to be tested).
Burdick further teaches that all dueling bandit algorithms [0065] must trade off exploration of unknown parameters and exploitation of high performing stimuli [0065], and that there is a need to limit an amount of time in the exploration process [0065] and increase an amount of time spent in useful exploitation [0065]. Burdick additionally teaches that a goal is to explore possible stimuli to improve patient performance [0215], while exploiting optimal stimuli [0215].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the controller taught by the Grado, to provide wherein a processor is programmed to arrange the control parameter settings in an ordered sequence for testing the control parameter settings, wherein the processor is programmed to arrange the control parameter settings in the ordered sequence such that exploitation of known control parameter settings is maximized by prioritizing settings with highest upper confidence bound values and regret in exploration of unknown control parameter settings is minimized by selecting exploratory settings that maximize information gain from pairwise comparisons; wherein the memory stores the ordered sequence of control parameter settings generated by the processor; an output that communicates the ordered sequence of control parameter settings to a controllable medical device; and wherein the controllable medical device delivers electrical stimulation to a subject in accordance with the ordered sequence of control parameter setting, as taught by Burdick, because there is a tradeoff between exploration of unknown parameters and exploitation of high performing stimuli that is achieved by limiting an amount of time in the exploration process while increasing an amount of time spent in useful exploitation, and also because possible stimuli should be explored to improve patient performance, while exploiting optimal stimuli.
In re claim 2, the proposed combination yields wherein the processor is further programmed to validate the Bayesian preference model according to a validation protocol (Grado uses the Bayesian preference models to generate the control parameter settings as discussed in re claim 1 above) by at least one of
predicting subject preference outcomes of a sequence of comparisons using the Bayesian preference model or
programming the controller with the ordered sequence of control parameter settings and comparing a predicted outcome to a subject preference outcome (Burdick [0206-0207]: new untested stimuli receives inference from previously explored stimulus patterns; [0208]: patient’s response to stimulus is used to update testing and gaussian process system which means that a comparison must be done between the predicted outcome and the subject’s response so that the system is updated for the next stimuli pattern based on upper confidence procedure)
In re claim 3, regarding the limitations, “a controller for controlling a controllable medical device, comprising:
an input that receives feedback data representative of a treatment response or effect in a subject;
a processor in communication with the input and programmed to:
receive the feedback data from the input and generate a Bayesian preference model therefrom;
generate control parameter settings by sampling the Bayesian preference model;
arrange the control parameter settings in an ordered sequence for testing the control parameter settings such that
exploitation of known control parameter settings is maximized by prioritizing settings with highest upper confidence bound acquisition function values and
regret in exploration of unknown control parameter settings is minimized by selecting settings that maximize expected information gain;
a memory in communication with the input and the processor,
wherein the memory stores
instructions for generating control parameter settings,
the feedback data received from the input, and
the ordered sequence of control parameter settings generated by the processor;
an output that communicates the ordered sequence of control parameter settings to a controllable medical device; and
wherein the controllable medical device delivers electrical stimulation to a subject in accordance with the ordered sequence of control parameter setting”,
see in re claim 1 above.
Regarding the limitation, “validate the Bayesian preference model according to a validation protocol by at least one of:
predicting subject preference outcomes of a sequence of comparisons using the Bayesian preference model; or
programming the controller with the ordered sequence of control parameter settings and comparing the predicted outcome to a subject preference outcome,”
see in re claim 2 above.
In re claim 4, regarding the limitation, “wherein the processor is configured to arrange the control parameter settings in the ordered sequence such that exploitation of known control parameter settings is maximized and regret in exploration of unknown control parameter settings is minimized”, see in re claim 1 above.
In re claim 5, Grado teaches wherein the feedback data received from the input comprise at least one of behavior metrics [0044] or user preferences [0044].
In re claim 6, Grado teaches wherein the processor is programmed to generate a probit function based on the feedback data [0044].
In re claim 7, Grado teaches wherein the feedback data comprise user preferences between two different control parameter settings [0044].
In re claim 9, the proposed combination yields wherein the processor is configured to arrange the control parameter settings in the ordered sequence such that information about subject preference to different control parameter settings is maximized (Burdick: 0214]: patient feedback is used to determine next optimal complex stimulation pattern so that good stimuli parameter choices are exploited; [0207-0209]: patient preference regarding different control parameter settings is maximized by testing new stimuli and using feedback to update the next tested stimuli).
In re claim 17, regarding the limitation, “wherein the feedback data received from the input comprise at least one of behavior metrics or user preferences”, see in re claim 5 above.
In re claim 18, regarding the limitation, “wherein the processor is programmed to generate a probit function based on the feedback data”, see in re claim 6 above.
In re claim 19, regarding the limitation, “wherein the feedback data comprise user preferences between two different control parameter settings”, see in re claim 7 above.
In re claim 21, regarding the limitation, “wherein the processor is configured to arrange the control parameter settings in the ordered sequence such that information about subject preference to different control parameter settings is maximized”, see in re claim 9 above.
Claims 8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Grado (WO 2019/152858) in view of Burdick et al. (US 2019/0374777) in view of McDermott (US 2019/0388693).
In re claim 8, the proposed combination yields wherein the processor is configured to arrange the control parameter settings in the ordered sequence such that information obtained from comparison of the control parameter settings is maximized (Col. 16, lines 47-65: the patient scores the programs and the programs are sorted based on the score meeting a minimum score i.e. comparison of the control parameter settings).
The proposed combination fails to yield wherein the processor is configured to arrange the control parameter settings in the ordered sequence such that information obtained from pairwise comparison of the control parameter settings is maximized.
McDermott teaches an analogous controller (fig. 1: 120; [0059]) for adjusting stimulation [0007], wherein a processor (fig. 2: 125; [0059]) is configured to arrange control parameter settings such that the information obtained from pairwise comparison of the control parameters settings is maximized ([0019]: two therapeutic stimulations 152 based on different sets of stimulation parameters are compared and when enough positive feedback is accumulated, the set of parameters can be retained as reference parameters 153, where it is then compared with alternative parameters 154; [0099]).
McDermott further teaches that that this piecewise comparison allows the therapeutic stimulation to be adapted in a way where the “fittest” therapeutic stimulation parameters survive [0119], and that parameters with higher rating i.e. good feedback are used as the basis for generating the adjusted therapeutic stimulation [0119].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the arrangement of the control parameter settings in the ordered sequence yielded by the proposed combination, to provide wherein the information obtained from pairwise comparison of the control parameter settings is maximized, as taught McDermott, because doing so will allow for the stimulation parameters associated with higher rating to be used as the basis for generating adjusted therapeutic stimulation, and also ensures that the “fittest” therapeutic stimulation parameters survive.
In re claim 20, regarding the limitation, “wherein the processor is configured to arrange the control parameter settings in the ordered sequence such that information obtained from pairwise comparison of the control parameter settings is maximized”, see in re claim 8 above.
Claims 10-12 and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Grado (WO 2019/152858) in view of Burdick et al. (US 2019/0374777) in view of Zanos et al. (US 2020/0179600).
In re claim 10, the proposed combination fails to yield wherein the processor is programmed to generate the control parameter settings by sampling the Bayesian preference model using batch sampling.
Zanos teaches an analogous controller for a medical device [0053] wherein a processor [0027-0028] is programmed to generate event rates [0116, 0123] by sampling a Bayesian preference model ([0123]: a Gaussian Naïve Bayes classifier is considered a Bayesian preference model since it’s based on Bayes’ theorem) using batch sampling ([0123]: K-fold cross-validation is considered batch sampling since each fold is considered a batch, and the k-fold cross-validation is used to train the Gaussian Naïve Bayes classifier).
Zanos further teaches that the k-fold cross-validation is used to minimize overfitting [0123].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the processor programmed to generate the control parameter settings yielded by the proposed combination, to provide sampling the Bayesian preference model using batch sampling, as taught Zanos, because doing so will minimize overfitting.
In re claim 11, the proposed combination fails to yield wherein the validation protocol is an internal validation protocol comprising a k-fold validation.
Zanos teaches wherein a validation protocol is an internal validation protocol comprising a k-fold validation [0123].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the controller yielded by the proposed combination, to provide wherein the validation protocol is an internal validation protocol comprising a k-fold validation, as taught Zanos, for substantially the same reason as discussed in re claim 10 above.
In re claim 12, the proposed combination fails to yield wherein the validation protocol is a prospective validation protocol comprising an out-of- sample validation.
Zanos teaches wherein the validation protocol is a prospective validation protocol comprising an out-of- sample validation [0017].
Zanos further teaches that the out-of-sample validation validates an algorithm and results in it being accurate [0017].
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the controller yielded by the proposed combination, to provide wherein the validation protocol is a prospective validation protocol comprising an out-of- sample validation, as taught Zanos, because doing so will validate an algorithm and allow it to be accurate.
In re claim 22, regarding the limitation, “wherein the processor is programmed to generate the control parameter settings by sampling the Bayesian preference model using batch sampling”, see in re claim 10 above.
In re claim 23, regarding the limitation, “wherein the validation protocol is an internal validation protocol comprising a k-fold validation”, see in re claim 11 above.
In re claim 24, regarding the limitation, “wherein the validation protocol is an prospective validation protocol comprising an out-of-sample validation”, see in re claim 12 above.
Claims 13 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Grado (WO 2019/152858) in view of Burdick et al. (US 2019/0374777) in view of Erickson (US 7,295,876).
In re claim 13, regarding the limitations, “controller for controlling a controllable medical device, comprising:
an input configured to receive feedback data representative of a treatment response or effect in a subject;
a memory,
wherein the memory stores
the feedback data received from the input and
control parameter settings for controlling a controllable medical device;
a processor in communication with the input and the memory,
the processor being programmed to:
receive the feedback data from the input;
receive control parameter settings from the memory; and
arrange the control parameter settings in an ordered sequence for testing the control parameter settings by the subject based at least in part on the feedback data such that
exploitation of known control parameter settings is maximized by selecting settings with highest demonstrated preference values and
regret in exploration of unknown control parameter settings is minimized by arranging exploratory settings to maximize information gain from each pairwise comparison;
an output that communicates the ordered sequence of control parameter settings to a controllable medical device; and
wherein the controllable medical device delivers electrical stimulation to a subject in accordance with the ordered sequence of control parameter setting,”
see the proposed combination yielded re claim 1 above.
The proposed combination fails to yield regret in exploration of unknown control parameter settings is minimized by arranging exploratory settings to maximize information gain from each pairwise comparison while preventing direct comparisons between unknown settings.
Erickson teaches an analogous stimulation system (Col. 1, lines 25-37), wherein control parameter settings (Col. 7, lines 8-16) are arranged in an ordered sequence for testing (Col. 7, lines 17-45: treatment programs are tested by having the patient choose between a current best program and a new program) such that regret in exploration of unknown control parameter settings is minimized (Col. 7, lines 17-35: minimizes regret in exploration by comparing one new program with one previously tested program) by arranging exploratory settings (Col. 7, lines 17-30: exploratory settings are programs not yet tested; Col. 7, lines 8-14) to maximize information gain from each pairwise comparison (Col. 7, lines 17-30: comparing between two programs is a pairwise comparison) while preventing direct comparisons between unknown settings (Col. 7, lines 16-30: a previously tested “best-so-far” program is compared against a new test program, and a patient chooses which of the two programs is better and then the better of the two is stored to be tested in a new ordered sequence for testing).
Erickson further teaches that comparing between a previously tested program and a new program is quickly developed similar to a process of determining an eyeglasses prescription (col 7, lines 17-24), and ends when the patient indicates that full-coverage relief has been achieved (Col. 7, lines 31-35), or when best practical coverage has been achieved (Col. 7, lines 31-35).
It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the controller yielded by the proposed combination, to provide wherein regret in exploration of unknown control parameter settings is minimized by arranging exploratory settings to maximize information gain from each pairwise comparison while preventing direct comparisons between unknown settings, as taught Erickson, because comparing between a previously tested program and a new program is quickly developed similar to a process of determining an eyeglasses prescription, and ends when the patient indicates that full-coverage relief has been achieved, or when best practical coverage has been achieved.
In re claim 15, regarding the limitation, “wherein the processor is configured to arrange the control parameter settings in the ordered sequence such that information from the feedback data about subject preference to different control parameter settings is maximized,” see in re claim 9 above.
In re claim 16, regarding the limitation, “wherein the processor is configured to arrange the control parameter settings in the ordered sequence such that exploitation of known control parameter settings is maximized and regret in exploration of unknown control parameter settings is minimized,” see in re claim 1 above.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Grado (WO 2019/152858) in view of Burdick et al. (US 2019/0374777) in view of Erickson (US 7,295,876) in view of McDermott (US 2019/0388693).
In re claim 14, regarding the limitation, “wherein the processor is configured to arrange the control parameter settings in the ordered sequence such that information in the feedback data obtained from pairwise comparison of the control parameter settings is maximized,” see in re claim 8 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Miocinovic et al. (US 2021/0346699) discloses programming a neurostimulation device (abstract) for deep brain stimulation (abstract) and teaches Bayesian optimization followed by an upper confidence bound acquisition function [0095, 0097].
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUMAISA R BAIG whose telephone number is (571)270-0175. The examiner can normally be reached Mon-Fri: 8am- 5pm.
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/RUMAISA RASHID BAIG/Examiner, Art Unit 3796
/WILLIAM J LEVICKY/Primary Examiner, Art Unit 3796