DETAILED ACTION
After further review, claims 1-20 should have been rejected under 35 USC 103 in view of the references Molnar et al. (US 20110144521 A1, “Molnar”), Jackson et al. (US 20220096841 A1, "Jackson"), Massoumi et al. (US 20160136429 A1, “Massoumi”), Park et al. (US 20230218900 A1, “Park”), Mogul (US 20190321638 A1, “Mogul”), and Giftakis et al. (US 20130218232 A1, “Giftakis”). Please see rejections below. Accordingly, the actions dated 05/05/2026 and 02/11/2026 have been withdrawn.
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 .
Response to Arguments
Applicant’s arguments, see pg. 7-10, filed 07/29/2026, with respect to the rejections of claims 1-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of the cited references below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 9, 11-13, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1, “Molnar”) in view of Jackson et al. (US 20220096841 A1, "Jackson").
Regarding claim 1, Molnar teaches a method for identifying electrodes for stimulation (para. [0037]; “a stimulation electrode combination can be selected based on the one or more electrodes with which the bioelectrical brain signal with the highest relative band power (or energy) level in a selected frequency band was sensed.” Molnar discloses that the sensed power band identifies which selection of electrode combinations are stimulated based on the power level sensed in each frequency band from the bioelectrical signals; para. [0039]; “That is, some algorithms described herein help identify which electrode 24, 26 along the respective lead 20 is closest to the target tissue site.” Molnar teaches identifying the electrodes closest to a target tissue site, and selecting those electrode combinations for stimulation.) of a patient using a stimulation system (Fig. 1; patient (12); therapy system (10)) the stimulation system comprising at least one stimulation lead (Fig. 1; 20A and 20B; para. [0039]; “both leads 20.”) implanted in a patient (Fig. 1; leads shown implanted in the brain of patient 12), the at least one stimulation lead comprising a plurality of electrodes (para. [0039]; “electrode 24,26.”), the method comprising: obtaining a plurality of bioelectrical signals (para. [0041]; “sensing a plurality of bioelectrical brain signals and determining the relative beta band power levels”), wherein each of the bioelectrical signals is obtained using a different one, or a different combination, of the electrodes (para. [0046]; "For example, processor 40 may compare the power levels of a frequency band other than the beta band in bioelectrical signals sensed by different electrodes to determine relative values of the power levels for combinations of electrodes.”); analyzing a dataset comprising the bioelectrical signals to identify at least one fundamental component (para. [0098]; “a frequency domain characteristic”) of the dataset (para. [0098]; “Processor 40 may evaluate different stimulation electrode combinations by, at least in part, sensing bioelectrical brain signals with one or more of the sense electrode combinations associated with a respective one of the stimulation electrode combinations and analyzing a frequency domain characteristic of the sensed bioelectrical brain signals.”; The signal domain characteristics from the signal are considered to be a dataset.) identifying a contribution of one or more of the electrodes to the fundamental component (para. [0099]; " a ratio of the power level in two or more frequency bands, a correlation in change of power between two or more frequency bands, a pattern in the power level of one or more frequency bands over time, and the like." In the case of Molnar, the power level is the contribution to the frequency band, as shown by the correlation between the components of the signal); and using at least one of the at least one fundamental component (para. [0099]; “frequency band”) to identify one or more of the electrodes for stimulation according to the contribution of each of the one or more of the electrodes to the at least one of the at least one fundamental component (para. [0099]; "processor 40 may select a stimulation electrode combination that is associated with the sense electrode combination that is closest to a target tissue site, as indicated by a bioelectrical brain signal comprising a power level in a particular frequency band above a threshold value."); and stimulating the patient using the identified one or more of the electrodes (para. [0034];” delivers electrical stimulation therapy to patient 12 via a subset of electrodes 24, 26 of leads 20A and 20B, respectively”) to provide therapeutic benefit to the patient (Fig. 1; patient (12); therapy system (10)). However, Molnar does not expressly teach wherein the analyzing comprises decomposing the dataset.
Jackson, in the same field of endeavor of deep brain stimulation, discloses a system and technique for stimulation with electrodes. Jackson discloses wherein the analyzing comprises decomposing the dataset (para. [0122]: “In other examples, IMD 106 may rank each electrode combination to the magnitude or other characteristic of the sensed electrical signals. Instead of an amplitude of the signal, other characteristics such as spectral power may be used in the matrix of other examples.”; para. [0051]: “In some examples, IMD 106 may generate a matrix representing the characteristics of the sensed electrical signals”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include decomposition of the signal dataset, as disclosed in Jackson, in the method of Molnar. As disclosed by Jackson, spectral power used in a matrix is a known technique for decomposing a signal, and Jackson discloses using decomposition to rank the electrodes based on their signal strength. One of ordinary skill would recognize that this technique could be used to rank the electrodes in Molnar since Molnar’s objective is to determining electrodes for stimulation. Therefore, it would have been obvious to one of ordinary skill in the art to implement Jackson’s decomposition technique for analyzing a dataset from an obtained signal in the method of Molnar.
Regarding claims 2 and 3, Molnar, in combination with Jackson, discloses the method of claim 1 (see above). Jackson further discloses wherein the obtaining comprises obtaining the plurality of bioelectrical signals, wherein each of the bioelectrical signals is obtained using a different one of the electrodes. (para. [0098]: "The one or more characteristics may include some aspect of the electrical signals that can be used to compare the electrodes to each other over time. For example, the characteristic may be an amplitude of the sensed signal (e.g., absolute amplitude, a normalized amplitude, a categorized amplitude (e.g., amplitude values fall within separate predetermined ranges), or a ranked amplitude). This amplitude may be the maximum amplitude over a period of time, for example. In other examples, the characteristic may be a differential signal between electrodes or a spatial derivative (e.g., first or second spatial derivative) in the axial and/or angular directions to estimate the proximity of each electrode to a signal source."; para. [0099]: "For example, IMD 106 may calculate the power of the beta frequency band for each sensed electrical signal, which may indicate the proximity of each electrode combination to a target neural location expected to generate signals in the beta frequency band."; para. [0098] and [0099] show that characteristics of each electrode signal are compared over time and ranked, which demonstrates each signal is obtained from a separate electrode.) (claim 2); and wherein the obtaining comprises obtaining the plurality of bioelectrical signals, wherein a one of the bioelectrical signals is obtained for each of the electrodes of the at least one stimulation lead (Fig. 4A and 4B show electrodes on a lead; para. [0098]: "The one or more characteristics may include some aspect of the electrical signals that can be used to compare the electrodes to each other over time. For example, the characteristic may be an amplitude of the sensed signal (e.g., absolute amplitude, a normalized amplitude, a categorized amplitude (e.g., amplitude values fall within separate predetermined ranges), or a ranked amplitude). This amplitude may be the maximum amplitude over a period of time, for example. In other examples, the characteristic may be a differential signal between electrodes or a spatial derivative (e.g., first or second spatial derivative) in the axial and/or angular directions to estimate the proximity of each electrode to a signal source."; para. [0099]: "For example, IMD 106 may calculate the power of the beta frequency band for each sensed electrical signal, which may indicate the proximity of each electrode combination to a target neural location expected to generate signals in the beta frequency band."; para. [0098] and [0099] show that characteristics of each electrode signal are compared over time and ranked, which demonstrates each signal is obtained from a separate electrode.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include the steps of obtaining a separate signal for each separate electrode, as disclosed by Jackson, in the method of Molnar. Jackson’s technique of sensing electrode signals is used for determining an optimal electrode orientation during implantation, which helps the clinician achieve the most effective therapeutic stimulation for the patient. Therefore, it would have been obvious to modify the technique for measuring separate signals for each lead, as disclosed by Jackson, in the method of Molnar.
Regarding claim 4, Molnar, in combination with Jackson, teaches the method of claim 1 (see above). Jackson further discloses applying an electrical field to the patient using the stimulation system, wherein the obtaining comprises obtaining the plurality of bioelectrical signals, wherein each of the bioelectrical signals is a response to the application of the electrical field to the patient using the stimulation system. (para. [0033]: "In some examples, IMD 106 may also, or alternatively, deliver electrical stimulation intended to be sensed by other electrode and/or elicit a physiological response, such as an evoked compound action potential (ECAP), that can be sensed by electrodes."; para. [0107]: "As shown in FIG. 8C, IMD 106 may determine lead movement based on changes to sensed evoked responses (e.g., one or more characteristics of a physiologically generated electrical signal, such as an evoked compound action potential (ECAP)) sensed by one or more electrode combinations."; Evoked compound action potentials are responses measured (sensed) by electrodes in response to delivered stimulation."; Additionally, the application of an electric field is mentioned in para. [0023] and [0038]).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include the steps of applying an electric field and recording the bioelectrical signal as a response to the application of the electric field. One of ordinary skill in the art would recognize that evoked response signals, such as ECAPs, are effective in determining if the stimulation parameters and orientation are delivering effective therapy. One of ordinary skill in the art would recognize that recording the response of an applied electric field would improve the method of Molnar, and that doing so would be effective in optimizing stimulation therapy.
Regarding claim 9, Molnar in combination with Jackson, discloses the method of claim 1 (see rejection above). Further, Jackson discloses wherein the decomposing comprises computing a cross-spectral matrix of the dataset. (para. [0122]: “In other examples, IMD 106 may rank each electrode combination to the magnitude or other characteristic of the sensed electrical signals. Instead of an amplitude of the signal, other characteristics such as spectral power may be used in the matrix of other examples.”; para. [0051]: “In some examples, IMD 106 may generate a matrix representing the characteristics of the sensed electrical signals”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include decomposition of the signal dataset in the form of computing a spectral power matrix, as disclosed in Jackson, in the method of Molnar. As disclosed by Jackson, spectral power used in a matrix is a known technique for decomposing a signal, and Jackson discloses using decomposition to rank the electrodes based on their signal strength. One of ordinary skill would recognize that this technique could be used to rank the electrodes in Molnar since Molnar’s objective is to determining electrodes for stimulation. Therefore, it would have been obvious to one of ordinary skill in the art to implement Jackson’s cross-spectral power matrix calculation and signal decomposition technique for analyzing a dataset in the method of Molnar.
Regarding claim 11, Molnar, in combination with Jackson, discloses the method of claim 1 (see above). Molnar further discloses wherein the analyzing comprises analyzing the dataset to identify a plurality of the fundamental components of the dataset (para. [0004]; “…based on one or more frequency domain characteristics of the sensed signals. For example, the stimulation electrode combination may be selected by at least determining a frequency domain characteristic (e.g., an energy level within a particular frequency band) for each bioelectrical brain signal of a plurality of bioelectrical brain signals.”), wherein the using comprises using a plurality of the fundamental components to identify one or more of the electrodes for stimulation according to the contribution of each of the one or more of the electrodes to the plurality of the fundamental components (para. [0004]; “In some cases, a stimulation electrode combination is selected based on the one or more electrodes used to sense the bioelectrical brain signal that has the relatively highest energy level within a particular frequency band. However, other relative frequency domain characteristics can be used to select the stimulation electrode combination, such as the relatively lowest energy level within a particular frequency band.”; This shows that multiple frequency domain characteristics from the bioelectrical signal determine which electrodes are selected.)
Regarding claim 12, Molnar, in combination with Jackson, discloses the method of claim 1 (see above). Molnar further discloses wherein the using comprises using at least one of the at least one fundamental component (para. [004]; frequency domain characteristics) to identify a plurality of the electrodes for stimulation according to the contribution of each of the electrodes of the plurality of electrodes to the at least one of the at least one fundamental component. (para. [0005]; "each bioelectrical brain signal of a plurality of bioelectrical signals sensed in a brain of a patient with a respective electrode, determining a plurality of relative values”; "of the frequency domain characteristic, wherein each of the plurality of relative values is based on at least two of the frequency domain characteristics, and selecting at least one of the electrodes for delivering stimulation to the patient based on the plurality of relative values.")
Regarding claim 13, Molnar, in combination with Jackson, discloses the method of claim 12 (see above). Molnar further discloses where the method further comprises determining a fractionalization of the identified electrodes according to the contribution of each of the identified electrodes to the at least one of the at least one fundamental component (para. [0043]; “ In one example, a processor of IMD 16 (or another device, such as programmer 14) may determine an overall power level of a sensed bioelectrical brain signal based on the total power level of a swept spectrum of the brain signal. To generate the swept spectrum, the processor may control a sensing module to tune to consecutive frequency bands over time, and the processor may assemble a pseudo-spectrogram of the sensed bioelectrical brain signal based on the power level in each of the extracted frequency bands. The pseudo-spectrogram may be indicative of the energy of the frequency content of the bioelectrical brain signal within a particular window of time; para. [0044]; “The algorithm further includes determining a plurality of relative values of the relative beta band power level, where each relative value is based on the relative beta band power levels of two bioelectrical signals sensed by two different electrodes, and selecting the sense electrode or electrodes that are closest to the target tissue site based on the plurality of relative values. The selected electrode or electrodes may be associated with one or more stimulation electrode combinations, which may be programmed into IMD 16 for the delivery of stimulation therapy to brain 28. In this way, the stimulation electrode combination may be selected based on a frequency domain characteristic of a bioelectrical brain signal” ).
Regarding claim 17, Molnar, in combination with Jackson, discloses the method of claim 1 (see above). Molnar further discloses where the method further comprises programming a pulse generator (para. [0034]; “IMD 16 includes a therapy module that includes a stimulation generator that generates and delivers electrical stimulation therapy to patient 12 via a subset of electrodes 24, 26 of leads 20A and 20B, respectively.”; para. [0064]; “the stimulation generator of IMD 16 is configured to generate and deliver electrical pulses to patient 12 via electrodes of a selected stimulation electrode combination.” ) to deliver stimulation using the identified one or more of the electrodes (para. [0034];” delivers electrical stimulation therapy to patient 12 via a subset of electrodes 24, 26 of leads 20A and 20B, respectively”; and delivering stimulation to the patient using the pulse generator and the identified one or more of the electrodes (para. [0064]; “In examples in which IMD 16 delivers electrical stimulation in the form of stimulation pulses, a therapy program may include a set of therapy parameter values, such as a stimulation electrode combination for delivering stimulation to patient 12, pulse frequency, pulse width, and a current or voltage amplitude of the pulses. As previously indicated, the stimulation electrode combination may indicate the specific electrodes 24, 26 that are selected to deliver stimulation signals to tissue of patient 12 and the respective polarity of the selected electrodes.”)
Regarding independent claim 18, Molnar discloses a stimulation system (Fig. 1; therapy system 10), comprising at least one lead (Fig. 1; 20A and 20B) comprising a plurality of electrodes (Fig. 1; electrodes 24 and 26); a pulse generator (para. [0034]; “IMD 16 includes a therapy module that includes a stimulation generator that generates and delivers electrical stimulation therapy to patient 12 via a subset of electrodes 24, 26 of leads 20A and 20B, respectively.”) coupled to the at least one lead and configured to deliver electrical energy through at least one of the electrodes of the at least one lead (para. [0034]; “IMD 16 includes a therapy module that includes a stimulation generator that generates and delivers electrical stimulation therapy to patient 12 via a subset of electrodes 24, 26 of leads 20A and 20B, respectively.”); a programmer for programming the pulse generator (Fig. 1; 14), the programmer comprising a memory having instructions stored thereon and a processor coupled to the memory and configured to execute the instructions to perform actions (para. [0013]; “The instructions cause a programmable processor to perform any part of the techniques described herein. The instructions may be, for example, software instructions, such as those used to define a software or computer program. The computer-readable medium may be a computer-readable storage medium such as a storage device (e.g., a disk drive, or an optical drive), memory (e.g., a Flash memory, random access memory or RAM) or any other type of volatile or non-volatile memory that stores instructions (e.g., in the form of a computer program or other executable) to cause a programmable processor to perform the techniques described herein.”), the actions comprising: obtaining a plurality of bioelectrical signals, wherein each of the bioelectrical signals is obtained using a different one, or a different combination, of the electrodes (para. [0041]; “sensing a plurality of bioelectrical brain signals and determining the relative beta band power levels”); analyzing a dataset comprising the bioelectrical signals to identify at least one fundamental component (para. [0098]; “a frequency domain characteristic”) of the dataset (para. [0098]; “Processor 40 may evaluate different stimulation electrode combinations by, at least in part, sensing bioelectrical brain signals with one or more of the sense electrode combinations associated with a respective one of the stimulation electrode combinations and analyzing a frequency domain characteristic of the sensed bioelectrical brain signals.), each of the at least one fundamental component identifying a contribution of one or more of the electrodes to the fundamental component (para. [0099]; " a ratio of the power level in two or more frequency bands, a correlation in change of power between two or more frequency bands, a pattern in the power level of one or more frequency bands over time, and the like." In the case of Molnar, the power level is the contribution to the frequency band, as shown by the correlation between the components of the signal”); and using at least one of the at least one fundamental component to identify one or more of the electrodes for stimulation according to the contribution of each of the one or more of the electrodes to the at least one of the at least one fundamental component (para. [0099]; "processor 40 may select a stimulation electrode combination that is associated with the sense electrode combination that is closest to a target tissue site, as indicated by a bioelectrical brain signal comprising a power level in a particular frequency band above a threshold value." As disclosed by Molnar, each power level must reach a threshold of contribution to a particular frequency band. Those that meet the threshold contribution are identified); and programming the pulse generator (para. [0034]; “IMD 16 includes a therapy module that includes a stimulation generator that generates and delivers electrical stimulation therapy to patient 12 via a subset of electrodes 24, 26 of leads 20A and 20B, respectively.”; para. [0064]; “the stimulation generator of IMD 16 is configured to generate and deliver electrical pulses to patient 12 via electrodes of a selected stimulation electrode combination.”) to deliver stimulation using the one or more identified electrodes, wherein the pulse generator is configured to deliver the stimulation using the identified one or more of the electrodes (para. [0064]; “In examples in which IMD 16 delivers electrical stimulation in the form of stimulation pulses, a therapy program may include a set of therapy parameter values, such as a stimulation electrode combination for delivering stimulation to patient 12, pulse frequency, pulse width, and a current or voltage amplitude of the pulses. As previously indicated, the stimulation electrode combination may indicate the specific electrodes 24, 26 that are selected to deliver stimulation signals to tissue of patient 12 and the respective polarity of the selected electrodes.”). However, Molnar does not expressly teach wherein the analyzing comprises decomposing the dataset.
Jackson discloses wherein the analyzing comprises decomposing the dataset (para. [0122]: “In other examples, IMD 106 may rank each electrode combination to the magnitude or other characteristic of the sensed electrical signals. Instead of an amplitude of the signal, other characteristics such as spectral power may be used in the matrix of other examples.”; para. [0051]: “In some examples, IMD 106 may generate a matrix representing the characteristics of the sensed electrical signals”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include decomposition of the signal dataset, as disclosed in Jackson, in the system of Molnar. As disclosed by Jackson, spectral power used in a matrix is a known technique for decomposing a signal, and Jackson discloses using decomposition to rank the electrodes based on their signal strength. One of ordinary skill would recognize that this technique could be used to rank the electrodes in Molnar since Molnar’s objective is to determining electrodes for stimulation. Therefore, it would have been obvious to one of ordinary skill in the art to implement Jackson’s decomposition technique for analyzing a dataset from an obtained signal in the system of Molnar.
Regarding claim 19, Molnar, in combination with Jackson, discloses the method of claim 18 (see above). Molnar further discloses wherein the using comprises using at least one of the at least one fundamental component (para. [004]; frequency domain characteristics) to identify a plurality of the electrodes for stimulation according to the contribution of each of the electrodes of the plurality of electrodes to the at least one of the at least one fundamental component (para. [0005]; "each bioelectrical brain signal of a plurality of bioelectrical signals sensed in a brain of a patient with a respective electrode, determining a plurality of relative values”; "of the frequency domain characteristic, wherein each of the plurality of relative values is based on at least two of the frequency domain characteristics, and selecting at least one of the electrodes for delivering stimulation to the patient based on the plurality of relative values."), wherein the actions further comprise determining a fractionalization of the identified electrodes according to the contribution of each of the identified one or more of the electrodes to the at least one of the at least one fundamental component (para. [0043]; “ In one example, a processor of IMD 16 (or another device, such as programmer 14) may determine an overall power level of a sensed bioelectrical brain signal based on the total power level of a swept spectrum of the brain signal. To generate the swept spectrum, the processor may control a sensing module to tune to consecutive frequency bands over time, and the processor may assemble a pseudo-spectrogram of the sensed bioelectrical brain signal based on the power level in each of the extracted frequency bands. The pseudo-spectrogram may be indicative of the energy of the frequency content of the bioelectrical brain signal within a particular window of time; para. [0044]; “The algorithm further includes determining a plurality of relative values of the relative beta band power level, where each relative value is based on the relative beta band power levels of two bioelectrical signals sensed by two different electrodes, and selecting the sense electrode or electrodes that are closest to the target tissue site based on the plurality of relative values. The selected electrode or electrodes may be associated with one or more stimulation electrode combinations, which may be programmed into IMD 16 for the delivery of stimulation therapy to brain 28. In this way, the stimulation electrode combination may be selected based on a frequency domain characteristic of a bioelectrical brain signal”).
Regarding independent claim 20, Molnar discloses a non-transitory computer readable memory (para. [0013]; “The computer-readable medium may be a computer-readable storage medium such as a storage device (e.g., a disk drive, or an optical drive), memory (e.g., a Flash memory, random access memory or RAM) or any other type of volatile or non-volatile memory that stores instructions (e.g., in the form of a computer program or other executable) to cause a programmable processor to perform the techniques described herein.”) having instructions stored thereon for identifying electrodes for stimulation of a patient using a stimulation system (para. [0013]; “instructions”) the stimulation system comprising at least one stimulation lead (Fig. 1; 20A and 20B) implanted in a patient (Fig. 1; leads shown implanted in patient 12), the at least one stimulation lead comprising a plurality of electrodes (Fig. 1; electrodes 24 and 26), wherein the instructions (para. [0013]; “instructions”) when executed by a processor (para. [0013]; “The instructions cause a programmable processor to perform any part of the techniques described herein.”), perform actions, the actions comprising: obtaining a plurality of bioelectrical signals (para. [0041]; “sensing a plurality of bioelectrical brain signals and determining the relative beta band power levels”), wherein each of the bioelectrical signals is obtained using a different one, or a different combination, of the electrodes (para. [0046]; "For example, processor 40 may compare the power levels of a frequency band other than the beta band in bioelectrical signals sensed by different electrodes to determine relative values of the power levels for combinations of electrodes.”); analyzing a dataset comprising the bioelectrical signals to identify at least one fundamental component of the dataset para. [0098]; “Processor 40 may evaluate different stimulation electrode combinations by, at least in part, sensing bioelectrical brain signals with one or more of the sense electrode combinations associated with a respective one of the stimulation electrode combinations and analyzing a frequency domain characteristic of the sensed bioelectrical brain signals.”), each of the at least one fundamental component identifying a contribution of one or more of the electrodes to the fundamental component (para. [0099]; " a ratio of the power level in two or more frequency bands, a correlation in change of power between two or more frequency bands, a pattern in the power level of one or more frequency bands over time, and the like." In the case of Molnar, the power level is the contribution to the frequency band, as shown by the correlation between the components of the signal); using at least one of the at least one fundamental component to identify one or more of the electrodes for stimulation according to the contribution of each of the one or more of the electrodes to the at least one of the at least one fundamental component (para. [0099]; "processor 40 may select a stimulation electrode combination that is associated with the sense electrode combination that is closest to a target tissue site, as indicated by a bioelectrical brain signal comprising a power level in a particular frequency band above a threshold value."); and programming a pulse generator to deliver stimulation using the one or more identified electrodes (para. [0034]; “IMD 16 includes a therapy module that includes a stimulation generator that generates and delivers electrical stimulation therapy to patient 12 via a subset of electrodes 24, 26 of leads 20A and 20B, respectively.”; para. [0064]; “the stimulation generator of IMD 16 is configured to generate and deliver electrical pulses to patient 12 via electrodes of a selected stimulation electrode combination.”; The pulse (or, in Molnar’s case, stimulation in the form of pulses) generator is located within the IMD), wherein the pulse generator is configured to deliver the stimulation using the identified one or more of the electrodes (para. [0064]; “In examples in which IMD 16 delivers electrical stimulation in the form of stimulation pulses, a therapy program may include a set of therapy parameter values, such as a stimulation electrode combination for delivering stimulation to patient 12, pulse frequency, pulse width, and a current or voltage amplitude of the pulses. As previously indicated, the stimulation electrode combination may indicate the specific electrodes 24, 26 that are selected to deliver stimulation signals to tissue of patient 12 and the respective polarity of the selected electrodes.”). However, Molnar does not expressly teach wherein the analyzing comprises decomposing the dataset.
Jackson discloses wherein the analyzing comprises decomposing the dataset (para. [0122]: “In other examples, IMD 106 may rank each electrode combination to the magnitude or other characteristic of the sensed electrical signals. Instead of an amplitude of the signal, other characteristics such as spectral power may be used in the matrix of other examples.”; para. [0051]: “In some examples, IMD 106 may generate a matrix representing the characteristics of the sensed electrical signals”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include decomposition of the signal dataset, as disclosed in Jackson, in the system of Molnar. As disclosed by Jackson, spectral power used in a matrix is a known technique for decomposing a signal, and Jackson discloses using decomposition to rank the electrodes based on their signal strength. One of ordinary skill would recognize that this technique could be used to rank the electrodes in Molnar since Molnar’s objective is to determining electrodes for stimulation. Therefore, it would have been obvious to one of ordinary skill in the art to implement Jackson’s decomposition technique for analyzing a dataset from an obtained signal in the system of Molnar.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1, “Molnar”), Jackson et al. (US 20220096841 A1, "Jackson"), and Carcieri (US 20140094823 A1).
Regarding claim 5, Molnar, in combination with Jackson, teaches the method of claim 1 (see above). However, neither reference expressly discloses wherein the obtaining comprises obtaining the plurality of bioelectrical signals, wherein each of the bioelectrical signals is recorded without the application of an electrical field to the patient to evoke the bioelectrical signal.
Carcieri, in the same field of endeavor of deep brain stimulation, discloses a method for implanting leads in brain tissue. Carcieri discloses wherein obtaining comprises obtaining the plurality of bioelectrical signals, wherein each of the bioelectrical signals is recorded without the application of an electrical field to the patient to evoke the bioelectrical signal. (In para. [0044], Carcieri discloses using micro-electrode recording to guide the implantation of leads for DBS, which records the neural activity prior to implantation of and stimulation of electrode leads; para. [0044]: "Prior to implanting the neurostimulation leads 12 into the patient's brain tissue, microelectrode recording (MER) is performed to determine optimal placement of the leads 12. Thus, a method for implanting a lead within brain tissue of a patient includes a step of performing a plurality of microelectrode recordings through a respective plurality of recording tracts in the brain tissue. An electrode guide tool, such as a Ben-Gun, having multiple (e.g., five) parallel channels may be used for guiding a microelectrode into the brain tissue in multiple different locations. While the microelectrode is being advanced through each recording tract, patterns of neuronal activity recorded by the microelectrode indicate which brain structures the microelectrode is passing through. Each brain structure has a unique pattern of neuronal activity. In this manner, MER may be used to determine the borders of a target brain structure. For example, if the target brain structure is the subthalamic nucleus (STN), the borders of the STN may be determined based on the section of the recording tract during which the pattern of neuronal activity unique to the STN is encountered.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Molnar to include the technique of recording a baseline neural signal, as disclosed by Carcieri. One of ordinary skill would recognize the benefit to recording a baseline signal of neural activity to determine optimal electrode placement prior to stimulation. This would ensure correct placement of the leads prior to stimulation. Therefore, since Carcieri discloses implementing the technique of recording brain signals without the application of an electric field, it would have been obvious to include this step for mapping the neural activity in the method of Molnar.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1, “Molnar”), Jackson et al. (US 20220096841 A1, "Jackson"), and Massoumi et al. (US 20160136429 A1, “Massoumi”).
Regarding claim 6, Molnar, in combination with Jackson, discloses the method of claim 1 (see rejection above). However, neither Molnar nor Jackson disclose wherein the obtaining comprises directing the patient to perform a particular activity and recording the plurality of bioelectrical signals during performance of the particular activity.
Massoumi discloses wherein the obtaining comprises directing the patient to perform a particular activity and recording the plurality of bioelectrical signals during performance of the particular activity. (para. [0083]; "In at least some embodiments, the clinician may direct the patient to perform a particular activity (for example, finger tapping, drawing a spiral or other shape, walking, or the like) and the system uses the sensor measurements during this activity to evaluate and determine adjustments to the stimulation parameters.")
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the method of Molnar with Massoumi’s method of measuring a patient’s bioelectric signal after they are instructed to perform an activity. One of ordinary skill would recognize that asking a patient to perform a task is a known technique for recording brain activity. As disclosed by Massoumi, adjusting patient stimulation parameters based on recordings during a task could be used for optimizing the therapeutic benefit of the stimulation. One of ordinary skill would recognize that including the steps, as disclosed by Massoumi, would improve the method of Molnar as parameters could be adjusted based on signals recorded activities, which would improve the efficacy of the stimulation therapy.
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1,” Molnar”), Jackson et al. (US 20220096841 A1, "Jackson"), and Park et al. (US 20230218900 A1, “Park”).
Regarding claim 7, Molnar, in combination with Jackson, discloses the method of claim 1 (see 102 above). However, neither Molnar nor Jackson discloses wherein the obtaining comprises sequentially obtaining groups of the bioelectrical signals, wherein each of the groups comprises a plurality of the bioelectrical signals obtained simultaneously.
Park, in the same field of endeavor of stimulating nerve tissue, discloses a closed-loop stimulation device and methods. Park discloses wherein the obtaining comprises sequentially obtaining groups of the bioelectrical signals (para. [0035]; “…sensed by electrodes of the first plurality of electrodes…subsequent to stimulation of patient tissue by the second plurality of electrodes.), wherein each of the groups comprises a plurality of the bioelectrical signals obtained simultaneously (para. [0035]; “sensed by electrodes of the first plurality of electrodes when the sensing is performed simultaneously with or subsequent to stimulation of patient tissue by the second plurality of electrodes").
It would have been obvious for one of ordinary skill in the art to implement the technique of sequentially obtaining bioelectrical signals and dividing the electrodes into groups that obtain the signals simultaneously, as disclosed by Park, into the method of Molnar. This technique improves the method of obtaining bioelectrical signals by dividing the electrodes into groups that obtain distinct signals from other groups, allowing a distinct signal to be obtained. Further, by obtaining the signals simultaneously, the groups can be compared to determine signal strength and quality at each electrode group location. One of ordinary skill would recognize that the techniques of Park would improve the reliability of signal sensory data, and that improved reliability would lead to improved adjustment of stimulation parameters. Therefore, it would have been obvious to combine the technique with the methods of Molnar.
Regarding claim 16, Molnar, in combination with Jackson, discloses the method of claim 1 (see 102 rejection above). However, neither Molnar nor Jackson expressly disclose wherein the using comprises identifying the one or more of the electrodes for stimulation with a requirement of a threshold amount of contribution to the at least one of the at least one fundamental component.
Park discloses wherein the using comprises identifying the one or more of the electrodes for stimulation with a requirement of a threshold amount of contribution to the at least one of the at least one fundamental component. (para. [0052]; “For example, a clinician may use a clinician programmer device to configure the boundary values (e.g., an upper and lower threshold for different stimulation parameters, such as frequency, amplitude, pulse width, and the like)…When modifying the stimulation parameters to lower the blocking effect, controller 110 may ramp down the stimulation parameters gradually until a desired blocking effect is achieved. In this manner, the discomfort caused by overstimulation may be mitigated without unintentionally dropping the effectiveness of the blocking effect to a level that is too low and causes the patient's perceived pain level to increase sharply.) In the case of Park, different boundary values are selected (a threshold) and stimulation is adjusted based on the contribution to the stimulation parameters, which can be considered a fundamental component of the signal such as amplitude or frequency).
It would have been obvious to one of ordinary skill in the art to modify the method of Molnar, with the threshold of Park. Using threshold contribution of fundamental components is a known technique, as disclosed by Park. One of ordinary skill would recognize that the threshold contribution requirement of Park could be used in the method of Molnar with a reasonable expectation of success for identifying electrodes for stimulation. Therefore, it would have been obvious to use this technique in the method of Molnar to achieve the same result of selecting optimal electrodes for effective stimulation.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1, “Molnar”), Jackson et al. (US 20220096841 A1, "Jackson"), and Mogul (US 20190321638 A1, “Mogul”).
Regarding claim 10, Molnar and Jackson, in combination, disclose the method of claim 9 (see rejection above). However, neither Molnar nor Jackson disclose wherein their method further comprises determining a plurality of eigenvalues and eigenvectors of a matrix comprising the dataset, wherein the at least one fundamental component comprises the eigenvectors.
Mogul, in the same field of endeavor of brain stimulation devices and methods, discloses an implantable brain stimulation apparatus and corresponding methods. Mogul discloses wherein the methods further comprises determining a plurality of eigenvalues and eigenvectors of a matrix comprising the dataset (para. [0080]; "The eigenvalue decomposition was performed by solving R.sub.N×Nv.sub.i=λ.sub.iv.sub.i, where λ.sub.i and v.sub.i are the obtained eigenvalues and their corresponding eigenvectors, respectively."), wherein the fundamental components comprise the eigenvectors. (para. [0079]; “Eigenvalue decomposition of the square, bivariate mean-phase coherence matrix was carried out in order to achieve a multi-variate measure for capturing phase-synchrony among all the extracted neuronal oscillators. All the eigenvalues were sorted in ascending order to construct an eigenvalue spectrum. Each eigenvalue indicates how strongly oscillators are phase-correlated in the direction of its associated eigenvector.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method of Molnar with the signal decomposition method that includes computing eigenvectors and eigenvalues, as disclosed by Mogul. One of ordinary skill would recognize that decomposing a signal to compute eigenvalues and eigenvectors is a known technique for decomposing a signal that can provide more information about signal strength and quality. Therefore, since Mogul discloses using this technique for biological signal decomposition and analysis, it would have been obvious to include the same technique of Mogul in the method of Molnar since doing so would improve the electrode selection technique of Molnar to provide optimal stimulation.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1, “Molnar”), Jackson et al. (US 20220096841 A1, "Jackson"), and Giftakis et al. (US 20130218232 A1, “Giftakis”).
Regarding claim 14, Molnar, in combination with Jackson, discloses the method of claim 1 (see above). However, neither reference expressly discloses wherein the using comprises selecting a frequency based on concentration of energy in the one of the at least one fundamental component.
Giftakis, in the same field of endeavor of brain stimulation, discloses a system and method of sensing and delivering different levels of stimulation. Giftakis discloses wherein the using comprises selecting a frequency based on concentration of energy in the one of the at least one fundamental component (para. [0075] mentions using frequency domain characteristics in a sensed brain signal to select a frequency band, which correlates to the power or energy level within the selected frequency band: " In various embodiments, the frequency domain characteristic may comprise a relative power level in a particular frequency band or a plurality of frequency bands. While "power levels" or "energy levels" within a selected frequency band of a sensed bioelectrical brain signal are generally referred to herein, the power or energy level may be a relative power or energy level. A relative power or energy level may include a ratio of a power level in a selected frequency band of a sensed brain signal to the overall power of the sensed brain signal. The power or energy level in the selected frequency band may be determined using any suitable technique. In some examples, control circuitry may average the power or energy level of the selected frequency band of a sensed brain signal over a predetermined time period, such as about ten seconds to about two minutes, although other time ranges are also contemplated. In other examples, the selected frequency band power or energy level may be a median level over a predetermined range of time, such as about ten seconds to about two minutes. The activity within the selected frequency band of a bioelectrical signal sensed from a brain area, as well as other frequency bands of interest, may fluctuate over time. Thus, the power or energy level in the selected frequency band at one instant in time may not provide an accurate and precise indication of the energy of the bioelectrical signal in the selected frequency band. Averaging or otherwise monitoring the power or energy level in the selected frequency band over time may help capture a range of levels, and, therefore, a better indication of the state of the brain area.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Molnar to include a frequency selection based on the concentration of energy of a frequency domain characteristic, as disclosed by Giftakis. One of ordinary skill would recognize that concentration of energy of a frequency component of a signal is useful in optimizing the stimulation parameters for providing brain stimulation. One of ordinary skill would have also recognized that this same technique could have been implemented in the method of Molnar to optimize the stimulation parameters in the delivery of brain stimulation. Therefore, it would have been obvious to modify the method of Molnar with the techniques of Giftakis.
Regarding claim 15, Molnar, in combination with Jackson, discloses the method of claim 1 (see above). However, neither reference expressly discloses wherein the using comprises identifying one or more of the at least one fundamental component meeting a requirement of a threshold amount of a concentration of energy, wherein the identified one or more of the at least one fundamental component is used for the identification of the one or more of the electrodes for stimulation.
Giftakis discloses wherein using comprises identifying one or more of the at least one fundamental component meeting a requirement of a threshold amount of a concentration of energy, wherein the identified one or more of the at least one fundamental component are used for the identification of the one or more of the electrodes for stimulation. (para. [0069]: "A preferred electrode or electrode combination can then be selected, and an energy level above the suppression threshold yet below the after-discharge threshold, can be selected for therapy delivery 490 using the selected electrode or electrode combination.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Molnar to include a threshold concentration of energy for electrode selection, as disclosed by Giftakis. One of ordinary skill would recognize that a concentration of energy threshold requirement of a frequency component of a signal is useful in optimizing the stimulation parameters for providing brain stimulation. One of ordinary skill would have also recognized that this same technique could have been implemented in the method of Molnar to optimize the stimulation parameters in the delivery of brain stimulation. Therefore, it would have been obvious to modify the method of Molnar with the techniques of Giftakis.
Conclusion
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/O.L.M./Examiner, Art Unit 3796
/Jennifer Pitrak McDonald/Supervisory Patent Examiner, Art Unit 3796
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