DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1-20 are objected to because of the following informalities: 1) In Claims 1, 19-20, “the desirability” should be “a desirability”, 2) In Claim 2, “the effectiveness” should be “an effectiveness”, 3) In Claim 20, line 4, “configured provide” should be corrected. 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 1-20 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.
1) Regarding Claims 1, 19 and 20, “data set indicative of the desirability of providing stimulation at possible of the combinations” makes the metes and bounds of the claims unclear, as: a) “desirability” and “indicative of …desirability” are relative and subjective (What is and what is not indicative of the desirability? Whose desirability? By what standard is the desirability measurable?), and b) “at possible of the combinations” is incomplete and unclear (At possible what? Something is missing here, and it is not clear what is missing).
2) In Claim 20, last line, “the patient” lacks antecedent basis.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by US 20220257950 by Moore and incorporated therein US 20200001091 by Marnfeldt.
Examiner’s note regarding prior art dates:
Moore was published on 8/18/22, which before the effective filing date of the instant application, 2/7/23, and qualifies under 102 (a)(1) and (a)(2) (ie. with publication or filing dates under these statutes, and available for both 102 and 103 rejections). Moore appears to be commonly owned by Boston Scientific Neuromodulation Corporation, and names one common inventor (Paz), but also names two additional inventors (Mustakos and Moore). As such, Applicant has the option to invoke exception 102(b)(2)(C) [common ownership], which would overcome the rejections based on the prior art date under 102(a)(2). See MPEP 717. However, this will not overcome the rejections based on the prior art date under 102(a)(1), as exceptions 102(b)(2) apply only to 102(a)(2). If indeed all claimed subject matter is shown to be supported by Moore publication, and applicant corrects said common ownership statement: To overcome the rejection based on the 102(a)(1) date, applicant can submit an effective 130(a) declaration to invoke the 102(b)(1)(A) exception. This declaration needs to attribute the disclosure that the Office Action is relying on in the Moore reference (entire document in this case) to the inventor of the instant application (Paz), and clearly explain the involvement of the “additional” inventors, Moore and Mustakos, who are not part of the inventive entity of the instant application. It is also the examiner’s understanding that under limited circumstances it may be possible to use a 130(b) declaration to invoke the 102(b)(1)(B) exception for a limited window after the grace period and as it relates only to the inventor of the instant application. Further information and limitations to the above can be found in MPEP 2155, and 717 and subsections thereof.
Regarding Claim 1, Moore discloses a method for optimizing stimulation for a patient having a stimulator device (e.g. ¶ 23: optimizing stimulation for a patient having an IPG stimulator), wherein the stimulator device comprises a plurality of electrodes in an electrode array for providing stimulation (e.g. ¶ 23, 48: IPG has a plurality of electrodes in an electrode array), the method providing test stimulation at a plurality of different combinations of a position in the electrode array and a value of one stimulation parameter [e.g. ¶ 26-27: finding the optimal combinations of position along an axis of the lead and amplitude via iterative testing; Fig. 13 shows determination of the optimal parameters for position and amplitude involve activating a specific combination of directional electrodes, which are arranged at specific positions along and around the lead, at specific percentages of the total amplitude, see ¶ 47], the method comprising:
(a) receiving information about tissue structures surrounding the electrode array [e.g. ¶¶ 50-51, Fig. 5A: GUI 82 displays tissue images of different tissue structures 114a, 114b, and 114 c surrounding the electrode; In addition, the stimulation field image 112 which is formed by field modeling, is also “information about tissue structures” as it is based on electrical imaging determination of tissue structural properties, directional resistance or impedance, to model the tissue surrounding the electrode and derive the stimulation field therefrom, see Marnfeldt, e.g. abstract, ¶¶ 72-73, 89; Marnfeldt is incorporated by reference for the stimulation field image in ¶¶ 3, 45, 49-50];
(b) processing the information to determine a first data set indicative of the desirability of providing stimulation at possible of the combinations [e.g. ¶ 51, 53-54, 55, 71-72, 84: automating the process by which a physician determines which tissue zones to exclude from stimulation based on side effect scores. As also noted above in the discussion of Marnfeldt, the iterative algorithm includes the stimulation field imaging via Stimulation Field Modeling (which comprises tissue structure information, ie. resistance/impedance properties) and the relevant Volume of Activation (VOA), which are used to determine whether stimulation will overlap with an exclusion zone and thus be undesirable or not. ¶ 72, 80-85 and Fig. 9F: “SFM modelling may also be beneficial to determining whether stimulation will overlap with an exclusion zone”, wherein the algorithm, excludes zones and combinations of position L and amplitude I, based on SFM (which includes information about tissue structure). These excluded combinations of position and amplitude, which are always taken into account as boundaries, indicate the desirability to not stimulate the respective areas.]
(c) executing an algorithm to provide test stimulation at a plurality of the combinations, wherein the algorithm iteratively determines a next combination to test using a score for each previously tested combination and the first data set (e.g ¶¶ 24, 27, 68: the scores of the previous combinations are used to determine the score of the next combination; ¶ 72, 80-85 and Fig. 9F: exclusion zones and combinations based on SFM tissue information, ie. the “first” data set, are always part of the algorithm as boundaries to the stimulation); and
(d) using the scores at the tested combinations to determine an optimal therapeutic stimulation for the patient (e.g. Fig. 13 shows that the algorithm arrives at the determination of the optimal parameters for position and amplitude involve activating a specific combination of directional electrodes, which are arranged at specific positions along and around the lead, at specific percentages of the total amplitude, see ¶ 47; Claim 20 and Fig. 8A).
Regarding Claim 2, Moore discloses the method of claim 1, wherein each at least one score is indicative of the effectiveness of the test stimulation at the tested combinations (e.g. ¶ 82: the score are indicative of stimulation effectiveness).
Regarding Claim 3, Moore discloses the method of claim 1, wherein the electrode array is implanted in a brain of the patient (e.g. ¶ 4: Deep Brain Stimulation system).
Regarding Claim 4, Moore discloses the method of claim 1, wherein at least one of the plurality of electrodes comprises a ring electrode which is circumferential around a longitudinal position in the electrode array (e.g. ¶ 6: ring electrodes E1 and E8).
Regarding Claim 5, Moore discloses the method of claim 1, wherein at least two of the plurality of electrodes comprise split-ring electrodes at a common longitudinal position in the electrode array (e.g. ¶ 6, Fig. 1C: split-electrodes E6 and E7 are at the same L position).
Regarding Claim 6, Moore discloses the method of claim 1, wherein the at least one stimulation parameter comprises an amplitude (e.g. abstract: amplitude I).
Regarding Claim 7, Moore discloses the method of claim 1, wherein the positions vary longitudinally in the electrode array (e.g. Fig. 7: longitudinal position L varies along the electrode array).
Regarding Claim 8, Moore discloses the method of claim 1, wherein the positions vary rotationally in the electrode array (e.g. ¶58: the rotational angle of directional electrodes is also optimized).
Regarding Claim 9, Moore discloses the method of claim 1, wherein the at least one score is indicative of a patient symptom, a patient response, or a side effect in response to the test stimulation (e.g. ¶ 25: “the at least one second score at each of the second combinations is indicative of a patient symptom, a patient response, or a side effect to the provided stimulation”).
Regarding Claim 10, Moore discloses the method of claim 1, wherein the optimal therapeutic stimulation comprises an optimal combination of a position and a value of the at least one stimulation parameter (e.g. abstract: optimizing position L and amplitude I).
Regarding Claim 11, Moore discloses the method of claim 1, wherein the optimal therapeutic stimulation indicates which of the electrodes should be active to provide the optimal therapeutic stimulation, the polarity of the active electrodes, and an amplitude at the active electrodes (e.g. ¶47: the optimization includes electrode position L, amplitude I, and the amplitude I includes polarity).
Regarding Claim 12, Moore discloses the method of claim 1, wherein the first data set comprises a value indicative of the desirability of providing stimulation at the possible combinations (¶ 72, 80-85 and Fig. 9F: exclusion zones and combinations based on SFM tissue information, are always part of the algorithm as boundaries to the stimulation).
Regarding Claim 13, Moore discloses the method of claim 1, wherein the algorithm iteratively determines a next combination to test using at least one score for each previously tested combination, the first data set, and at least one other data set (e.g. ¶ 70: factors R are “other data sets”).
Regarding Claim 14, Moore discloses the method of claim 13, wherein the at least one other data set is determined at the possible combinations (e.g. ¶70: factors R are determined at all possible L, I combinations).
Regarding Claim 15, Moore discloses the method of claim 14, wherein the at least one other data set at the possible combinations is determined using a distance between the possible combinations and each of the previously tested combinations (e.g. ¶¶ 69-74: distance metric).
Regarding Claim 16, Moore discloses the method of claim 14, wherein the at least one other data set at the possible combinations is determined using the at least one score for each previously tested combination (e.g. ¶ 30: the factors are determined using the scores)
Regarding Claim 17, Moore discloses the method of claim 14, wherein the first data set and the at least one other data set are weighted to determine a weighted data set at the possible combinations, wherein the next combination is determined as the possible combination having a best value in the weighted data set (e.g. ¶24: the factors are weighted).
Regarding Claim 18, Moore discloses the method of claim 1, wherein the information about tissue structures surrounding the electrode array comprises tissue imaging information [e.g. ¶¶ 50-51, Fig. 5A: the stimulation field image 112 which is formed by field modeling, is also “information about tissue structures” as it is based on electrical imaging determination of tissue structural properties, directional resistance or impedance, to model the tissue surrounding the electrode and derive the stimulation field therefrom, see Marnfeldt, e.g. abstract, ¶¶ 72-73, 89; Marnfeldt is incorporated by reference for the stimulation field image in ¶¶ 3, 45, 49-50];
Regarding Claim 19, Moore discloses a system, comprising:
Moore discloses a method for optimizing stimulation for a patient having a stimulator device (e.g. ¶ 23: optimizing stimulation for a patient having an IPG stimulator), wherein the stimulator device comprises a plurality of electrodes in an electrode array for providing stimulation (e.g. ¶ 23, 48: IPG has a plurality of electrodes in an electrode array), the method providing test stimulation at a plurality of different combinations of a position in the electrode array and a value of one stimulation parameter [e.g. ¶ 26-27: finding the optimal combinations of position along an axis of the lead and amplitude via iterative testing; Fig. 13 shows determination of the optimal parameters for position and amplitude involve activating a specific combination of directional electrodes, which are arranged at specific positions along and around the lead, at specific percentages of the total amplitude, see ¶ 47],
a stimulator device comprising a plurality of electrodes in an electrode array for providing stimulation (e.g. ¶ 23, 48: IPG has a plurality of electrodes in an electrode array); and
an external device for optimizing stimulation for a patient having the stimulator device, the external device configured to communicate with the stimulator device to provide test stimulation at a plurality of different combinations of a position in the electrode array and a value of at least one stimulation parameter (e.g. ¶¶ 10,18-19, 59: external controllers 60/70 includes the algorithm disclosed), wherein the external device is configured to:
(a) receive information about tissue structures surrounding the electrode array [e.g. ¶¶ 50-51, Fig. 5A: GUI 82 displays tissue images of different tissue structures 114a, 114b, and 114 c surrounding the electrode; It is also noted here, that the stimulation field image 112 which is formed by field modeling, is also “information about tissue structures” as it is based on electrical imaging determination of tissue structural properties, directional resistance or impedance, to model the tissue surrounding the electrode and derive the stimulation field therefrom, see Marnfeldt, e.g. abstract, ¶¶ 72-73, 89; Marnfeldt is incorporated by reference for the stimulation field image in ¶¶ 3, 45, 49-50];
(b) process the information to determine a first data set indicative of the desirability of providing stimulation at possible of the combinations [e.g. ¶ 51, 53-54, 55, 71-72, 84: automating the process by which a physician determines which tissue zones to exclude from stimulation based on side effect scores. As also noted above in the discussion of Marnfeldt, the iterative algorithm includes the stimulation field imaging via Stimulation Field Modeling (which comprises tissue structure information, ie. resistance/impedance properties) and the relevant Volume of Activation (VOA), which are used to determine whether stimulation will overlap with an exclusion zone and thus be undesirable or not. ¶ 72, 80-85 and Fig. 9F: “SFM modelling may also be beneficial to determining whether stimulation will overlap with an exclusion zone”, wherein the algorithm, excludes zones and combinations of position L and amplitude I, based on SFM (which includes information about tissue structure). These excluded combinations of position and amplitude, which are always taken into account as boundaries, indicate the desirability to not stimulate the respective areas];
(c) execute an algorithm to provide test stimulation at a plurality of the combinations, wherein the algorithm iteratively determines a next combination to test using at least one score for each previously tested combination and the first data set (e.g ¶¶ 24, 27, 68: the scores of the previous combinations are used to determine the score of the next combination; ¶ 72, 80-85 and Fig. 9F: exclusion zones and combinations based on SFM tissue information, ie. the “first” data set, are always part of the algorithm as boundaries to the stimulation); and
(d) use the scores at the tested combinations to determine an optimal therapeutic stimulation for the patient (e.g. Fig. 13 shows that the algorithm arrives at the determination of the optimal parameters for position and amplitude involve activating a specific combination of directional electrodes, which are arranged at specific positions along and around the lead, at specific percentages of the total amplitude, see ¶ 47; Claim 20 and Fig. 8A).
Regarding Claim 20, Moore discloses a non-transitory computer readable medium comprising instructions (e.g. ¶ 59: computer readable medium stores the algorithm) operable in conjunction with a stimulator device, wherein the stimulator device comprises a plurality of electrodes in an electrode array for providing stimulation, wherein the instructions when executed are configured provide test stimulation at a plurality of different combinations of a position in the electrode array and a value of at least one stimulation parameter, wherein the instructions are further configured to: (a) receive information about tissue structures surrounding the electrode array [e.g. ¶¶ 50-51, Fig. 5A: GUI 82 displays tissue images of different tissue structures 114a, 114b, and 114 c surrounding the electrode; It is also noted here, that the stimulation field image 112 which is formed by field modeling, is also “information about tissue structures” as it is based on electrical imaging determination of tissue structural properties, directional resistance or impedance, to model the tissue surrounding the electrode and derive the stimulation field therefrom, see Marnfeldt, e.g. abstract, ¶¶ 72-73, 89; Marnfeldt is incorporated by reference for the stimulation field image in ¶¶ 3, 45, 49-50];
(b) process the information to determine a first data set indicative of the desirability of providing stimulation at possible of the combinations [e.g. ¶ 51, 53-54, 55, 71-72, 84: automating the process by which a physician determines which tissue zones to exclude from stimulation based on side effect scores. As also noted above in the discussion of Marnfeldt, the iterative algorithm includes the stimulation field imaging via Stimulation Field Modeling (which comprises tissue structure information, ie. resistance/impedance properties) and the relevant Volume of Activation (VOA), which are used to determine whether stimulation will overlap with an exclusion zone and thus be undesirable or not. ¶ 72, 80-85 and Fig. 9F: “SFM modelling may also be beneficial to determining whether stimulation will overlap with an exclusion zone”, wherein the algorithm, excludes zones and combinations of position L and amplitude I, based on SFM (which includes information about tissue structure). These excluded combinations of position and amplitude, which are always taken into account as boundaries, indicate the desirability to not stimulate the respective areas];
(c) execute an algorithm to provide test stimulation at a plurality of the combinations, wherein the algorithm iteratively determines a next combination to test using at least one score for each previously tested combination and the first data set (e.g ¶¶ 24, 27, 68: the scores of the previous combinations are used to determine the score of the next combination; ¶ 72, 80-85 and Fig. 9F: exclusion zones and combinations based on SFM tissue information, ie. the “first” data set, are always part of the algorithm as boundaries to the stimulation); and
(d) use the scores at the tested combinations to determine an optimal therapeutic stimulation for the patient (e.g. Fig. 13 shows that the algorithm arrives at the determination of the optimal parameters for position and amplitude involve activating a specific combination of directional electrodes, which are arranged at specific positions along and around the lead, at specific percentages of the total amplitude, see ¶ 47; Claim 20 and Fig. 8A).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANOLIS Y PAHAKIS whose telephone number is (571)272-7179. The examiner can normally be reached M-F 9-5, EST.
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/MANOLIS PAHAKIS/Examiner, Art Unit 3796