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 .
Response to Arguments
Applicant’s arguments on pg. 8 of the Remarks, filed 04/08/2026, with respect to the specification, have been fully considered and are persuasive. The objection to the specification has been withdrawn.
Applicant’s arguments on pgs. 8-9 of the Remarks, filed 04/08/2026, with respect to the rejection of claim 1 under §101, have been fully considered and are persuasive in light of the amendment to the claim. The rejection of claim 1 under §101 has been withdrawn.
Applicant’s arguments on pg. 9 of the Remarks, filed 04/08/2026, with respect to the rejection of claim 4 under § 112(B), have been fully considered and are persuasive in light of the amendment to the claim. The rejection of claim 4 under §112(b) has been withdrawn.
Applicant's arguments made in the Remarks filed 4/08/2026 have been fully considered but they are not persuasive. On pg. 10 of the Remarks, the Applicant argues that “Hald is nonanalagous art to Molnar in the present invention.” In response to applicant's argument that Hald is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor' s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). The Examiner respectfully disagrees that Hald is non-analagous art. Original claim 8 from the claims filed on 07/03/2024, now amended to be incorporated into claim 1 of the amended claims filed on 04/08/2026, recites “wherein the analyzing comprises decomposing the data set”. The recited limitation is generally directed towards analyzing a data set from a signal. As argued by the Examiner on pg. 21 of the Non-Final rejection filed on 02/11/2026, both Hald and Molnar are in the field of endeavor of signal processing, and both references are concerned with techniques for analyzing a signal. Although Hald is concerned with using signal decomposition for use in solving a different problem, Hald is still in the same field of endeavor of processing signals. The question of analogous art is not whether one of ordinary skill would turn to Hald’s teachings in order to modify the electrical stimulation device of Molnar, but rather is based on what the reference discloses, and what the combination of references would suggest to those of ordinary skill in the art. Although Hald and Molnar are processing different signal types, both references are concerned with methods of processing signals. Hald suggests decomposing the signal as a method of analyzing the signal. Thus, one of ordinary skill would recognize that, since Molnar is also concerned with analyzing and processing signals, Hald’s technique of decomposing could be used with the signals and methods of processing signals in Molnar. For the reasons above, the Examiner maintains that Molnar and Hald are analogous art.
In addition, as detailed by the Applicant on pg. 10 of the Remarks filed on 04/08/2026, claims 1, 18, and 20 have been amended to include the contents of claim 8. The Examiner has not examined claims 1, 18, and 20 with the amendments to the claim, and the scope of claim 1 has now changed. The Examiner notes that new rejections below are necessitated by the amendments to independent claims 1, 18, and 20 in the claims filed on 04/08/2026 since the scope of the claim has changed. Therefore, new rejections are detailed in the 35 USC § 103 sections below.
Applicant's arguments made in the Remarks filed 4/08/2026 have been fully considered but they are not persuasive. On pg. 10 of the Remarks, the Applicant argues that “at least Ibrahim and Huang are also non-analogous art.” In response to applicant's argument that Ibrahim is non-analogous art, it has been held that a prior art reference must either be in the field of the inventor' s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). The Examiner respectfully disagrees that Ibrahim is non-analogous art. As argued by the Examiner on pg. 17 of the Non-Final rejection filed on 02/11/2026, Ibrahim and Molnar are both in the same field of endeavor of signal processing. Although Molnar and Ibrahim are concerned with solving a different problem, both references are in the same field of endeavor of processing biological signals. For art to be analogous, the problem does not need to be the same for each reference (See MPEP 2141.05(a)). Further, claim 2 is generally recited as “obtaining a plurality of bioelectrical signals.” Both Molnar and Ibrahim are also in the field of processing signals that fall into the category of “biological signals.” This further bolsters the argument that both Molnar and Ibrahim are in the same field of endeavor since, not only are they both in the field of signal processing, but they are both in the field of processing biological signals. For the reasons above, the Examiner maintains that Molnar and Ibrahim are analogous art.
Applicant's arguments made in the Remarks filed 4/08/2026 have been fully considered but they are not persuasive. On pg. 11 of the Remarks, the Applicant argues that “at least Ibrahim and Huang are also non-analagous art.” The Examiner respectfully disagrees. As argued by the Examiner on pg. 23 of the Non-Final rejection filed on 02/11/2026, Huang and Molnar are both in the same field of endeavor of signal processing for implantable devices. Although Molnar and Huang are concerned with solving a different problem, both references are in the same field of endeavor of processing biological signals. For art to be analogous, the problem does not need to be the same for each reference (See MPEP 2141.05(a)). Further claim 15 is to a method of signal processing for a biological signal. Both Huang and Molnar are in the field of processing biological signals, even though the data obtained from processing the biological signals is used to solve different problems, the methods disclosed are still in the field of endeavor of signal processing, as both references disclose techniques for processing obtained biological data. For the reasons above, the Examiner maintains that Molnar and Huang are analogous art.
In summary, the Examiner maintains the stance that the prior art used in the Non-Final filed on 02/11/2026 for the claim rejections under 35 USC § § 103 are analogous. Further, the Examiner notes that new rejections below are necessitated by the amendments to independent claims 1, 18, and 20 in the claims filed on 04/08/2026 since the scope of the claim has changed.
Claim Objections
Claim 17 is objected to because of the following informalities: Line 5 recites, “using the pulse generator and the identified one or more of the electrodes electrodes”. There is a repetition of the word “electrodes” that is unnecessary. Appropriate correction is required.
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.
Claim 1, 9, 11, 12, 13, 17, 18, 19, and 20 is rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1, “Molnar”) in view of Hald et al. (US 20160161325 A1, “Hald”).
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.
Hald, in the same field of endeavor as Molnar of signal processing to determine components of the signals, discloses a method of determining properties in a sound field. Hald discloses wherein the analyzing comprises decomposing the dataset. (para. [0021]; “A principal component decomposition of the cross-spectral matrix between the reference transducers is performed.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the methods of claim 1 as disclosed by Molnar with the method of decomposing a dataset obtained by a signal as disclosed by Hald. Decomposing a signal is known technique in the art of signal processing, as disclosed by Hald. By decomposing a signal, the components of a signal can be obtained for further analysis. It would have been obvious to include this in combination with the methods of claim 1 since Molnar is attempting to select electrodes based on signal frequency components, and decomposing the data set would provide one with more information about the signal. It would have been an obvious improvement to include the decomposition technique in the method of claim 1.
Regarding claim 9, Molnar in combination with Hald, discloses the method of claim 1 (see rejection above). Further, Hald discloses wherein the decomposing comprises computing a cross-spectral matrix of the dataset. (para. [0021]; "If multiple reference transducers are used, the cross-spectral matrix between all reference transducers and the cross-spectral matrix between the reference transducers and the measurement microphones are determined. A principal component decomposition of the cross-spectral matrix between the reference transducers is performed.")
It would have been obvious for one of ordinary skill in the art before the effective filing date to apply the known technique of signal decomposition as disclosed in Hald. Computing a cross-spectral matrix is a known method of signal processing that provides information about components of a signal. It would have been obvious to apply this technique on a signal obtained from a stimulating electrode to gain a better understanding of the power distribution and frequency of the obtained signal. Using this technique would yield predictable results such as the components that make up the signal after computing the cross-spectral matrix (i.e. eigenvalues and eigenvectors).
Regarding claim 11, Molnar, in combination with Hald, 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 Hald, 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 Hald, discloses the method of claim 12 (see above). Molnar further discloses where the method further comprising 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 Hald, discloses the method of claim 1 (see above). Molnar further discloses where the method is further comprising 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 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.
Hald, in the same field of endeavor as Molnar of signal processing to determine components of the signals, discloses a method of determining properties in a sound field. Hald discloses wherein the analyzing comprises decomposing the dataset. (para. [0021]; “A principal component decomposition of the cross-spectral matrix between the reference transducers is performed.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the methods of claim 1 as disclosed by Molnar with the method of decomposing a dataset obtained by a signal as disclosed by Hald. Decomposing a signal is known technique in the art of signal processing, as disclosed by Hald. By decomposing a signal, the components of a signal can be obtained for further analysis. It would have been obvious to include this in combination with the system of claim 18 since Molnar is attempting to select electrodes based on signal frequency components, and decomposing the data set would provide one with more information about the signal. It would have been an obvious improvement to include the decomposition technique in the system of claim 18.
Regarding claim 19, Molnar, in combination with Hald, 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.
Hald, in the same field of endeavor as Molnar of signal processing to determine components of the signals, discloses a method of determining properties in a sound field. Hald discloses wherein the analyzing comprises decomposing the dataset. (para. [0021]; “A principal component decomposition of the cross-spectral matrix between the reference transducers is performed.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the methods of claim 1 as disclosed by Molnar with the method of decomposing a dataset obtained by a signal as disclosed by Hald. Decomposing a signal is known technique in the art of signal processing, as disclosed by Hald. By decomposing a signal, the components of a signal can be obtained for further analysis. It would have been obvious to include this in combination with the system of claim 18 since Molnar is attempting to select electrodes based on signal frequency components, and decomposing the data set would provide one with more information about the signal. It would have been an obvious improvement to include the decomposition technique in the system of claim 18.
Claim(s) 2, 3, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20190321638 A1,” Molnar”), Hald et al. (US 20160161325 A1, “Hald”), and Ibrahim et al. (US 20050008177 A1, “Ibrahim”).
Regarding claim 2, Molnar, in combination with Hald, discloses the method of claim 1 (see rejection above). Molnar further discloses wherein the obtaining comprises obtaining the plurality of bioelectrical signals (para. [0041]; “sensing a plurality of bioelectrical brain signals and determining the relative beta band power levels.”) However, neither Molnar nor Hald disclose wherein each of the bioelectrical signals is obtained using a different one of the electrodes.
Ibrahim, in the same field of endeavor as Molnar of signal processing, discloses a hearing prosthesis and method of for detecting change in the performance of an audio signal processing path. Ibrahim discloses wherein each of the bioelectrical signals is obtained using a different one of the electrodes (para. [0030]; The individual response pattern data, includes threshold and comfort levels for each electrode in electrode array 134.” Ibrahim discloses obtaining responses from each electrode, which is considered to be “a different one of the electrodes.”
It would have been obvious to one of ordinary skill in the art to combine the method of claim 1, as disclosed by Molnar and Hald, with the method of obtaining a signal from each individual electrode as disclosed by Ibrahim. Doing so would provide relevant information and data for each electrode, such as data regarding comfort and effectiveness. This would allow the user to better control the stimulation parameters of each individual electrode.
Regarding claim 3, Molnar, in combination with Hald, discloses the method of claim 1 (see rejection above). Molnar further discloses wherein the obtaining comprises obtaining the plurality of bioelectrical signals (para. [0041]; “sensing a plurality of bioelectrical brain signals and determining the relative beta band power levels.”). Molnar also discloses that the electrodes are positioned on the stimulation lead (Fig. 1; electrodes 24 and 26 positioned on leads 20A and 20B). However, neither Molnar nor Hald disclose wherein a one of the bioelectrical signals is obtained for each of the electrodes of the at least one stimulation lead.
Ibrahim discloses wherein a one of the bioelectrical signals is obtained for each of the electrodes (para. [0030]; “The individual response pattern data, includes threshold and comfort levels for each electrode in electrode array 134. This individual response data is stored in memory. The output signals from each channel 318 are digitized and modified by a microprocessor of loudness and growth function block 308 to reflect normal variations of hearing sensitivity with frequency.” Ibrahim discloses output channels from the signals obtained from each electrode in array 134.)
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the methods of claim 1, as disclosed by Molnar and Hald, with the method of obtaining individual electrode signals as disclosed by Ibrahim. Further, it would have been obvious to implement this method on the stimulation lead of Molnar. Doing so would enable individual, distinct signal processing for each electrode, which is an improvement on the previously disclosed method since data can be obtained for each electrode. This enables the user to tune the stimulation based on the data from individual signals for each electrode. Further, it would have been obvious to include this on a stimulation lead since a lead is disclosed by Molnar as a known component of stimulation systems that delivers stimulation to the patient.
Regarding claim 14, Molnar, in combination with Hald, discloses the method of claim 1 (see rejection above). However, neither Molnar nor Hald disclose wherein the using comprises selecting a frequency based on concentration of energy in the one of the at least one fundamental component.
Ibrahim discloses wherein the using comprises selecting a frequency based on concentration of energy in the one of the at least one fundamental component (para. [0011]; “a characteristic of a received audio signal”). (para. [0011]; "selecting a characteristic of a received audio signal indicative of its energy content”; “determining first and second predetermined values of the selected energy characteristic at respective first and second audio signal frequency bands.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the methods of Molnar with Ibrahim’s method of selecting frequency bands based on a characteristic indicative of an audio signals energy content. Ibrahim uses the energy content as a means of comparing signal performance, and including this technique would be an obvious improvement of the methods of claim 1 to compare the performance of stimulation signals. Further, including this method would have been obvious since the user would be able to use energy content as a metric for indicating which electrodes to select at a specific frequency to measure which electrodes perform best.
Claim(s) 4 and 6 is rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1,” Molnar”), Hald et al. (US 20160161325 A1, “Hald”), and Massoumi et al. (US 20160136429 A1, “Massoumi”).
Regarding claim 4, Molnar, in combination with Hald, discloses the method of claim 1 (see rejection above). Molnar further discloses wherein the obtaining comprises obtaining the plurality of bioelectrical signals (para. [0041]; “sensing a plurality of bioelectrical brain signals and determining the relative beta band power levels.”) However, Neither Molnar nor Hald disclose where the method further comprises applying an electric field to the patient using the stimulation system, wherein each of the bioelectrical signals is a response to application of an electrical field to the patient using the stimulation system.
Massoumi, in the same field of endeavor as Molnar of methods and systems for delivering and measuring electrical stimulation, discloses a wearable device that uses biosensors to measure electrical stimulation and adjust stimulation parameters. Massoumi discloses where the method further comprises applying an electric field to the patient using the stimulation system, wherein each of the bioelectrical signals is a response to application of an electrical field to the patient using the stimulation system. (para. [0098]; ] In step 804, the biosignal is analyzed and an adjustment to one or more stimulation parameters is generated. Examples of stimulation parameters that can be adjusted include, but are not limited to, pulse frequency, pulse width, electrode field selection.”) Massoumi demonstrates that an electric field is applied to the patient (as one of the parameters that can be adjusted), and that the electric field response is further analyzed.
It would have been obvious to one of ordinary skill in the art before the effective filing date to use the known technique of applying an electric field to a patient and measuring the response. Further, it would have been obvious to combine this technique with the methods of claim 1, as disclosed by Molnar. Doing so would allow the user to steer the electric field and provide stimulation to specific regions (see Massoumi para. [0040] for specific details on the benefits of steering electric fields). This would be an obvious improvement to Massoumi’s technique of providing stimulation.
Regarding claim 6, Molnar, in combination with Hald, discloses the method of claim 1 (see rejection above). However, neither Molnar nor Hald 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. As disclosed, Massoumi’s device is implantable, meaning it will be measuring signals throughout the day of the patient, which includes when the patient is active. This differs from the time of initial implantation while a patient is at rest. It is obvious that the signal would need to be recorded during a patient’s active state, in addition to their resting state at implantation, to determine the device’s efficacy. Doing so would be an obvious step to further include in the methods of claim 1, as disclosed by Molnar.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1,” Molnar”), Hald et al. (US 20160161325 A1, “Hald”), and Gross et al. (US 20170215950 A1, “Gross”).
Regarding claim 5, Molnar, in combination with Hald, discloses the method of claim 1 (see rejection above). Molnar further discloses wherein the obtaining comprises obtaining the plurality of bioelectrical signals ((para. [0041]; “sensing a plurality of bioelectrical brain signals and determining the relative beta band power levels.”) However, neither Molnar nor Hald disclose wherein each of the bioelectrical signals is recorded without the application of an electrical field to the patient to evoke the bioelectrical signal.
Gross, in the same field of endeavor as Molnar of recording and applying bioelectric signals with stimulating electrodes, discloses a method of using a stimulation device on a renal nerve. Gross discloses wherein each of the bioelectrical signals is recorded without the application of an electrical field to the patient to evoke the bioelectrical signal (para. [0766]; "For some applications, between steps 872 and 874, another “preliminary” value is detected (e.g., while the subject is at rest, and without application of excitatory current”).
It would have been obvious for one of ordinary skill in the art to include the step of obtaining and recording bioelectrical signals without the application of an electric field with the methods of Molnar since doing so would yield predictable results. It is well known in the art that recording the signal without an applied electric field would allow a physician to record a baseline bioelectrical signal for the patient. In doing so, the physician could compare the results of the recording with and without an electric field to determine the efficacy of the electric field in providing stimulation.
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1,” Molnar”), Hald et al. (US 20160161325 A1, “Hald”), and Park et al. (US 20230218900 A1, “Park”).
Regarding claim 7, Molnar, in combination with Hald, discloses the method of claim 1 (see 102 above). However, neither Molnar nor Hald disclose 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 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 apply the known technique of sequentially obtaining bioelectrical signals, and dividing the electrodes into groups that obtain the signals simultaneously. 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. Lastly, it would have been obvious to combine the technique with the method of claim 1, as disclosed by Molnar, since it would improve the method of obtaining distinct signals for stimulation.
Regarding claim 16, Molnar, in combination with Hald, discloses the method of claim 1 (see 102 rejection above). However, neither Molnar nor Hald 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 combine the methods of claim 1, as disclosed by Molnar, with the threshold of Park. Doing so would improve the stimulation device by controlling the stimulation based on filtered values. Further, using the threshold to filter unwanted signal components could be used to isolate the signal into only the desired components, which are used in a method of control by Park. It would have been obvious to include a threshold in the stimulation method.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US US 20110144521 A1, “Molnar”), Hald et al. (US 20160161325 A1, “Hald”), and Mogul (US 20190321638 A1, “Mogul”).
Regarding claim 10, Molnar and Hald, in combination, disclose the method of claim 9 (see rejection above). However, neither Molnar nor Hald 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. (Hald discloses an eigenvector/eigenvalue factorization may be performed after obtaining the positive semi-definite matrix (para. [0061]), but does not specifically say these values derive from the cross-spectral matrix computation).
Mogul, in the same field of endeavor as Molnar and Hald of signal processing, discloses a method and apparatus for stimulating the brain with electrodes to emulate neural synchrony. 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 include Mogul’s method of determining eigenvectors with the methods of Molnar and Hald in signal decomposition and computing a cross-spectral matrix. In doing so, the computation of a cross-spectral matrix provides eigenvectors, which are indicators of specific correlations between signal components. In analyzing the signals to determine their eigenvalues, more information about the frequency and power of the signal can be determined.
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Molnar et al. (US 20110144521 A1, “Molnar”), Hald et al. (US 20160161325 A1, “Hald”), and Huang et al. (US 20220068289 A1, “Huang”).
Regarding claim 15, Molnar, in combination with Hald, discloses the method of claim 1 (see rejection above). However, neither Molnar nor Hald disclose wherein the using comprises identifying one or more of the fundamental components meeting a requirement of a threshold amount of a concentration of energy, wherein the identified one or more of the fundamental components are used for the identification of the one or more of the electrodes for stimulation.
Huang, in the same field of endeavor as Molnar of signal processing and implantable stimulation devices, discloses a speech processing method and cochlear implant. Huang discloses wherein the using comprises identifying one or more of the at least one fundamental component (para. [0041]; “energy values of these frequency components”) meeting a requirement of a threshold amount of a concentration of energy (para. [0041]; “the energy values of these electrode frequency components are higher than the preset threshold. Here, the energy values of the electrode frequency bands are limited, mainly to prevent unnecessary noise from being generated at the speech pause"), 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. [0063]; “the most energetic components are selected from the corresponding electrode frequency bands, the number of selected electrodes is not more than 6 at the present time, and the number could increase when technology warrants, and the energy values of these components are higher than preset threshold”; This demonstrates that the energy is filtered by the threshold, and the electrodes can be selected from the energy components of the signal. Additionally, para. [0013] describes this process as the “principle of electrode selection,” further demonstrating that the process is used to determine which electrodes are stimulated.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the methods of claim 1 as disclosed by Molnar with the threshold method of Huang. Including the threshold would provide a means of filtering out unwanted signal noise. It would have been obvious to remove signal noise during the method of electrode processing since it would improve the method of selecting the electrode by increasing accuracy. Further, including a threshold would improve the selection of electrodes by only including values within a certain range. This is a well-known technique that could be applied so that electrodes are only selected with specific energy content, further improving the method by selecting only the effective electrodes that correlate with the threshold.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/O.L.M./Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796
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