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, see Remarks, filed 07/27/2026, with respect to Double Patenting have been fully considered and are persuasive. The Rejection of Claims 1-8 and 11-18 under Double Patenting have been withdrawn.
Applicant’s arguments, see Remarks, filed 07/27/2026, with respect to the rejections of claims 1-20 under 35 USC 101 have been fully considered and are persuasive. The rejections of claims 1-20 under 35 USC 101 have been withdrawn.
Applicant’s arguments, see Remarks, filed 07/27/2026, with respect to the rejections of claims 1 and 11 under 35 USC 112(b) have been fully considered and are persuasive. The rejections of claims 1-20 under 35 USC 101 have been withdrawn.
Applicant’s arguments, see Remarks, filed 07/27/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 102 and 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made as necessitated by amendments to independent claims 1 and 11 (see Response to Amendments section below).
Response to Amendment
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, 6, 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Moore et al. (US 20230141183 A1, “Moore”), Bokil (US 20170061627 A1), Bergman (US 20190321106 A1), and Achatz et al. (US 20200237326 A1).
Regarding claim 1, Moore teaches a method for programming electrical stimulation parameters (abstract: "A system comprises an implantable stimulator, and a programming device including a controller to identify first and second sets of base stimulation settings each comprising an electrode configuration and stimulation parameter values selected from a configuration and parameter search space.") for providing deep brain stimulation (DBS) to a subject patient (para. [0033]: "an electrical stimulation system that may be used to deliver deep brain stimulation (DBS).") for treatment of a movement disorder (para. [0056]: “In a DBS application, as is useful in the treatment of tremor in Parkinson's disease…”), wherein the subject patient is implanted with an implantable medical device comprising an implantable pulse generator (IPG) (Fig. 1; para. [0045]; IPG 127) connected to one or more electrode leads (Fig. 1; para. [0045]; neuromodulation leads 126) configured to be implanted in the subject patient’s brain (system 100 is used in DBS), wherein each electrode lead comprises a plurality of electrodes (Fig. 1; para. [0045]; "plurality of electrodes 132"), the method comprising: receiving imaging data for the subject patient (para. [0087]: "the search space 512 can be determined based on spatial information of the lead, such as lead positions with respect to neural targets, which can be obtained from imaging data of the lead and patient anatomy.), using the imaging data to determine a position of at least one of the electrode leads with respect to at least one anatomical feature of a brain structure of the subject patient’s brain (para. [0087]: "the search space 512 can be determined based on spatial information of the lead, such as lead positions with respect to neural targets, which can be obtained from imaging data of the lead and patient anatomy."), receiving accumulated data from a database, wherein the accumulated data comprises data from previous patients (para. [0091]: "For example, data from other programming sessions for the same patient as well as from other patients may be used to train the machine learning engine 502."; para. [0091]: “In some examples all patient data utilizing lead location information (knowledge of lead location in space relative to anatomy) may be used. Different other combinations are also possible.”) relating electrode lead position with respect to at least one anatomical feature of a brain structure of the previous patients’ brains (para. [0091]: " In some examples all patient data utilizing lead location information (knowledge of lead location in space relative to anatomy) may be used.") to stimulation parameters providing therapeutic benefits in the previous patients (para. [0124]: "the directly tested electrode configurations can be differentiated by their respective clinical effects (e.g., therapeutic benefits versus side effects). For example, as illustrated in FIG. 7, the directly tested electrode configuration 772 is associated with clinical benefits without side effects, and located in the positive effect region 750."), and using the accumulated data and the imaging data (para. [0087]: "In an example, the search space 512 can be determined based on spatial information of the lead, such as lead positions with respect to neural targets, which can be obtained from imaging data of the lead and patient anatomy.") to determine stimulation parameters for the subject patient (para [0005]: " The tested and predicted clinical response data can be used to determine, for one or more monopolar electrode configurations, characteristic stimulation amplitudes with respective clinical response data satisfying respective conditions."); and providing stimulation to the subject patient according to the determined stimulation parameters for the treatment of the subject patient's movement disorder (para. [0087] describes using the accumulated and imaging data to determine stimulation parameters for a subject, and controlling the delivery of stimulation based on the determined parameters: “In an example, the search space 512 can be determined based on spatial information of the lead, such as lead positions with respect to neural targets, which can be obtained from imaging data of the lead and patient anatomy. Additionally or alternatively, the search space 512 can be determined based on physiological information such as physiological signals sensed by the electrodes at their respective tissue contact locations. The physiological information may include patient clinical responses to stimulation. In some examples, prior knowledge about patient medical condition, health status, DBS treatment history may also be utilized to determine the search space 512. In an example, the search space identifier 510 may exclude those electrodes on the lead that are out of a region of interest, such that the search space includes only those electrodes within the target of interest. One or more stimulation parameters may be restricted to take certain values or within value ranges. For example, the restricted search space may include certain electrode positions and value ranges for stimulation current amplitude, frequency, or pulse width. The feedback control logic 501 can determine one or more optimal base stimulation settings (e.g., BSS.sub.1-BSS.sub.4) by searching through the identified search space 512. The identified search space 512 can be stored in the memory 404.”; para. [0094]: “The feedback control logic 501 may be used to search and configure different types of stimulation parameters of the various leads potentially causing different clinical effects upon the patient 506. Examples of the stimulation parameters may include electrode configurations (electrode selection, polarities, monopolar or bipolar modes of stimulation), current fractionalization, current amplitude, pulse width, frequency, among others. Given these possible stimulation parameters, the stimulation parameter control system 500 can move about the parameter space in different orders, by different increments, and limited to specific ranges. In some examples, the stimulation parameter control system 500 may allow the user to provide search range limitations to one or more of the stimulation parameters to limit the range for that stimulation parameter over which the system will search for parameters.”). However, Moore does not explicitly teach where the lead position is a distance and orientation of the at least one electrode lead with respect to a plurality of anatomical features of a brain structure of a subject patient’s brain, wherein the plurality of anatomical features comprise at least one axis and at least one border of the brain structure, the previous patient accumulated data is an electrode lead distance and orientation with respect to corresponding anatomical features of corresponding brain structure, or wherein using the accumulated data comprises comparing the determined distances and orientations for the subject patient with corresponding distances and orientations stored in the accumulated data.
Bokil, in the same field of endeavor of deep brain stimulation, discloses a method and system for identifying a rotational orientation of an implanted electrical stimulation lead. Bokil discloses using imaging data to determine a distance and orientation of the at least one electrode lead with respect to a plurality of anatomical features of a brain structure of a subject patient’s brain (para. [0065]: “In some embodiments, the user interface can utilize the images and the identification of the position of the longitudinal band of the marker to then depict the location and orientation of the lead (as, for example, a model of the lead that optionally includes models of the lead electrodes). In at least some embodiments, the user interface may also depict the location and orientation of the lead with respect to anatomical or physiological structures. In some embodiments, the user interface may include controls to provide calculated distances between the different electrodes of the leads and anatomical or physiological structures (for example, brain structures) in the image once the lead marker orientation is determined.”).
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 Moore with Bokil’s technique of using image data to determine distance and orientation of electrode leads with respect to anatomical features. One of ordinary skill would recognize that Moore is using imaging data to determine positions of a lead, and that distance and orientation relative to a target would provide more specific information about the location of the lead relative to a target brain structure. This would improve the method of Moore as it would improve the electrode location determination accuracy during implantation, which would improve patient safety and efficacy during surgical procedures. One would also recognize that electrode distance and orientation are descriptive of an electrode position. It would have been obvious to use those specific elements to describe the electrode position since Bokil discloses doing so for determining an electrode position.
However, Bokil does not expressly disclose wherein the plurality of anatomical features comprise at least one axis and at least one border of the brain structure.
Bergman, in the same field of endeavor of deep brain stimulation, discloses processing circuitry for detection of an anatomical position during deep brain stimulation. Bergman discloses wherein the plurality of anatomical features of the brain comprise at least one axis (describing electrode navigation, para. [0380] describes calculating a location along the dorsolateral-ventromedial axis and that the depth is the location on the dorsolateral-ventromedial axis) and at least one border of the brain structure (para. [0013]: “The system according to example 1, wherein said processing circuitry calculation of said anatomical position comprises calculation of whether said distal end of said electrical lead has crossed a border between two anatomical regions.”; para. [0085]: “determining a border location of the target area relative to the at least two macro-electrodes, based on the difference and the predefined axial separation.”).
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 Bokil with the specific anatomical features disclosed by Bergman. One would recognize that an axis and border are known brain features used for determining electrode position. One would also recognize that these features are used to define a target site. It would have been obvious to incorporate an axis and a border as brain structures in the method of Bokil since one would have recognized these anatomical features as features of the brain. Similarly, one would have a reasonable expectation of success in using these features as location markers for an electrode during DBS since Bergman discloses using these features for identifying an electrode spatial orientation and distance.
Further, since Moore discloses receiving accumulated data from a database of previous patient data of electrode lead locations (para. [0091]: "In some examples all patient data utilizing lead location information (knowledge of lead location in space relative to anatomy) may be used."), it would have been obvious, based on the teachings of Bokil and Bergman, for the accumulated data to comprise electrode lead distance and orientation corresponding to anatomical features of a corresponding brain structure. One of ordinary skill would recognize that distance and orientation would provide specific information about the lead in relation to patient anatomy that would improve implantation and stimulation accuracy. Further, one would recognize that the distance and orientation could be relative to an axis and border of a brain structure since Bergman discloses that these are known anatomical features of a patient’s brain. One would reasonably expect that the method of Moore could be modified to use this specific imaging data and accumulated data based on distance and orientation of an electrode lead, and patient brain axis and border anatomical features.
However, none of the references disclose wherein using accumulated data comprises comparing the determined distances and orientation for the subject patient with corresponding distances and orientations stored in the accumulated data.
Achatz, in the same field of endeavor of deep brain stimulation, discloses a method for determining a rotational orientation of a DBS electrode. Achatz discloses comparing the determined distances (para. [0019]: “constructional data of the electrode (such as at least one of its geometry and the spatial relationship—at least one of position and orientation—between at least one directional contact and the orientation marker).”) and orientations (para. [0016]: “The rotational orientation data for the subject patient with corresponding distances and orientations stored in the accumulated data”) for the subject patient with corresponding distances and orientations stored in the accumulated data (para. [0019]: “…comparing the image appearance of the electrode in the at least one or each of the two-dimensional medical images to previously acquired and predetermined electrode template data describing constructional data of the electrode…” Previously acquired data is accumulated data.) (para. [0016]: “The rotational orientation data is determined for example based on the rotational image data, for example from the image depiction of the electrode in the respective two-dimensional image. To this end, at least one of the two-dimensional medical images is analysed concerning the image appearance of the orientation marker in the two-dimensional image. In one example, all of the two-dimensional medical images are accordingly analysed. This can be done for example by determining, based on the rotational image data, an image appearance of the orientation marker, for example by at least one of: [0017] segmenting an image appearance of the electrode in the at least one or each of the two-dimensional medical images; [0018] edge detection of constituents of the at least one or each of the two-dimensional medical images; [0019] comparing the image appearance of the electrode in the at least one or each of the two-dimensional medical images to previously acquired and predetermined electrode template data describing constructional data of the electrode (such as at least one of its geometry and the spatial relationship—at least one of position and orientation—between at least one directional contact and the orientation marker).”).
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 Moore to include a comparison of previously acquired patient data with that of a subject patient, based on the teachings of Achatz. One would recognize that comparing previous patient data to current subject patient data would be useful in optimizing the location of implantation of an electrode. Further, one would recognize that the reference data would be useful in calculating the spatial relationships between electrodes. Including the comparison between previously accumulated data, such as a template, to a subject patient’s lead location data would have been an obvious improvement to the accuracy of measuring the orientation of an electrode lead implanted in the brain.
Regarding claim 11, Moore teaches a system for programming electrical stimulation parameters (abstract: "A system comprises an implantable stimulator, and a programming device including a controller to identify first and second sets of base stimulation settings each comprising an electrode configuration and stimulation parameter values selected from a configuration and parameter search space.") for providing deep brain stimulation (DBS) to a subject patient (para. [0033]: "an electrical stimulation system that may be used to deliver deep brain stimulation (DBS).") for treatment of a movement disorder (para. [0056]: “In a DBS application, as is useful in the treatment of tremor in Parkinson's disease…”), wherein the subject patient is implanted with an implantable medical device comprising an implantable pulse generator (IPG) (Fig. 1; para. [0045]; IPG 127) connected to one or more electrode leads (Fig. 1; para. [0045]; neuromodulation leads 126) configured to be implanted in the subject patient’s brain (system 100 is used in DBS), wherein each electrode lead comprises a plurality of electrodes (Fig. 1; para. [0045]; "plurality of electrodes 132"), the system comprising: an external computing device (Fig. 1; CP 129; para. [0048]) comprising control circuitry (para. [0048]: "the CP 129 may actively control the characteristics of the electrical modulation generated by the IPG…") configured to perform a method, the method comprising the method comprising: receiving imaging data for the subject patient (para. [0087]: "the search space 512 can be determined based on spatial information of the lead, such as lead positions with respect to neural targets, which can be obtained from imaging data of the lead and patient anatomy.), using the imaging data to determine a position of at least one of the electrode leads with respect to at least one anatomical feature of a brain structure of the subject patient’s brain (para. [0087]: "the search space 512 can be determined based on spatial information of the lead, such as lead positions with respect to neural targets, which can be obtained from imaging data of the lead and patient anatomy."), receiving accumulated data from a database, wherein the accumulated data comprises data from previous patients (para. [0091]: "For example, data from other programming sessions for the same patient as well as from other patients may be used to train the machine learning engine 502."; para. [0091]: “In some examples all patient data utilizing lead location information (knowledge of lead location in space relative to anatomy) may be used. Different other combinations are also possible.”) relating electrode lead position with respect to at least one anatomical feature of a brain structure of the previous patients’ brains (para. [0091]: " In some examples all patient data utilizing lead location information (knowledge of lead location in space relative to anatomy) may be used.") to stimulation parameters providing therapeutic benefits in the previous patients (para. [0124]: "the directly tested electrode configurations can be differentiated by their respective clinical effects (e.g., therapeutic benefits versus side effects). For example, as illustrated in FIG. 7, the directly tested electrode configuration 772 is associated with clinical benefits without side effects, and located in the positive effect region 750."), and using the accumulated data and the imaging data (para. [0087]: "In an example, the search space 512 can be determined based on spatial information of the lead, such as lead positions with respect to neural targets, which can be obtained from imaging data of the lead and patient anatomy.") to determine stimulation parameters for the subject patient (para [0005]: " The tested and predicted clinical response data can be used to determine, for one or more monopolar electrode configurations, characteristic stimulation amplitudes with respective clinical response data satisfying respective conditions."); and providing stimulation to the subject patient according to the determined stimulation parameters for the treatment of the subject patient's movement disorder (para. [0087] describes using the accumulated and imaging data to determine stimulation parameters for a subject, and controlling the delivery of stimulation based on the determined parameters: “In an example, the search space 512 can be determined based on spatial information of the lead, such as lead positions with respect to neural targets, which can be obtained from imaging data of the lead and patient anatomy. Additionally or alternatively, the search space 512 can be determined based on physiological information such as physiological signals sensed by the electrodes at their respective tissue contact locations. The physiological information may include patient clinical responses to stimulation. In some examples, prior knowledge about patient medical condition, health status, DBS treatment history may also be utilized to determine the search space 512. In an example, the search space identifier 510 may exclude those electrodes on the lead that are out of a region of interest, such that the search space includes only those electrodes within the target of interest. One or more stimulation parameters may be restricted to take certain values or within value ranges. For example, the restricted search space may include certain electrode positions and value ranges for stimulation current amplitude, frequency, or pulse width. The feedback control logic 501 can determine one or more optimal base stimulation settings (e.g., BSS.sub.1-BSS.sub.4) by searching through the identified search space 512. The identified search space 512 can be stored in the memory 404.”; para. [0094]: “The feedback control logic 501 may be used to search and configure different types of stimulation parameters of the various leads potentially causing different clinical effects upon the patient 506. Examples of the stimulation parameters may include electrode configurations (electrode selection, polarities, monopolar or bipolar modes of stimulation), current fractionalization, current amplitude, pulse width, frequency, among others. Given these possible stimulation parameters, the stimulation parameter control system 500 can move about the parameter space in different orders, by different increments, and limited to specific ranges. In some examples, the stimulation parameter control system 500 may allow the user to provide search range limitations to one or more of the stimulation parameters to limit the range for that stimulation parameter over which the system will search for parameters.”). However, Moore does not explicitly teach where the lead position is a distance and orientation of the at least one electrode lead with respect to a plurality of anatomical features of a brain structure of a subject patient’s brain, wherein the plurality of anatomical features comprise at least one axis and at least one border of the brain structure, the previous patient accumulated data is an electrode lead distance and orientation with respect to corresponding anatomical features of corresponding brain structure, or wherein using the accumulated data comprises comparing the determined distances and orientations for the subject patient with corresponding distances and orientations stored in the accumulated data.
Bokil, in the same field of endeavor of deep brain stimulation, discloses a method and system for identifying a rotational orientation of an implanted electrical stimulation lead. Bokil discloses using imaging data to determine a distance and orientation of the at least one electrode lead with respect to a plurality of anatomical features of a brain structure of a subject patient’s brain (para. [0065]: “In some embodiments, the user interface can utilize the images and the identification of the position of the longitudinal band of the marker to then depict the location and orientation of the lead (as, for example, a model of the lead that optionally includes models of the lead electrodes). In at least some embodiments, the user interface may also depict the location and orientation of the lead with respect to anatomical or physiological structures. In some embodiments, the user interface may include controls to provide calculated distances between the different electrodes of the leads and anatomical or physiological structures (for example, brain structures) in the image once the lead marker orientation is determined.”).
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 Moore with Bokil’s technique of using image data to determine distance and orientation of electrode leads with respect to anatomical features. One of ordinary skill would recognize that Moore is using imaging data to determine positions of a lead, and that distance and orientation relative to a target would provide more specific information about the location of the lead relative to a target brain structure. This would improve the method of Moore as it would improve the electrode location determination accuracy during implantation, which would improve patient safety and efficacy during surgical procedures. One would also recognize that electrode distance and orientation are descriptive of an electrode position. It would have been obvious to use those specific elements to describe the electrode position since Bokil discloses doing so for determining an electrode position.
However, Bokil does not expressly disclose wherein the plurality of anatomical features comprise at least one axis and at least one border of the brain structure.
Bergman, in the same field of endeavor of deep brain stimulation, discloses processing circuitry for detection of an anatomical position during deep brain stimulation. Bergman discloses wherein the plurality of anatomical features of the brain comprise at least one axis (describing electrode navigation, para. [0380] describes calculating a location along the dorsolateral-ventromedial axis and that the depth is the location on the dorsolateral-ventromedial axis) and at least one border of the brain structure (para. [0013]: “The system according to example 1, wherein said processing circuitry calculation of said anatomical position comprises calculation of whether said distal end of said electrical lead has crossed a border between two anatomical regions.”; para. [0085]: “determining a border location of the target area relative to the at least two macro-electrodes, based on the difference and the predefined axial separation.”).
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 Bokil with the specific anatomical features disclosed by Bergman. One would recognize that an axis and border are known brain features used for determining electrode position. One would also recognize that these features are used to define a target site. It would have been obvious to incorporate an axis and a border as brain structures in the method of Bokil since one would have recognized these anatomical features as features of the brain. Similarly, one would have a reasonable expectation of success in using these features as location markers for an electrode during DBS since Bergman discloses using these features for identifying an electrode spatial orientation and distance.
Further, since Moore discloses receiving accumulated data from a database of previous patient data of electrode lead locations (para. [0091]: "In some examples all patient data utilizing lead location information (knowledge of lead location in space relative to anatomy) may be used."), it would have been obvious, based on the teachings of Bokil and Bergman, for the accumulated data to comprise electrode lead distance and orientation corresponding to anatomical features of a corresponding brain structure. One of ordinary skill would recognize that distance and orientation would provide specific information about the lead in relation to patient anatomy that would improve implantation and stimulation accuracy. Further, one would recognize that the distance and orientation could be relative to an axis and border of a brain structure since Bergman discloses that these are known anatomical features of a patient’s brain. One would reasonably expect that the method of Moore could be modified to use this specific imaging data and accumulated data based on distance and orientation of an electrode lead, and patient brain axis and border anatomical features.
However, none of the references disclose wherein using accumulated data comprises comparing the determined distances and orientation for the subject patient with corresponding distances and orientations stored in the accumulated data.
Achatz, in the same field of endeavor of deep brain stimulation, discloses a method for determining a rotational orientation of a DBS electrode. Achatz discloses comparing the determined distances (para. [0019]: “constructional data of the electrode (such as at least one of its geometry and the spatial relationship—at least one of position and orientation—between at least one directional contact and the orientation marker).”) and orientations (para. [0016]: “The rotational orientation data for the subject patient with corresponding distances and orientations stored in the accumulated data”) for the subject patient with corresponding distances and orientations stored in the accumulated data (para. [0019]: “…comparing the image appearance of the electrode in the at least one or each of the two-dimensional medical images to previously acquired and predetermined electrode template data describing constructional data of the electrode…” Previously acquired data is accumulated data.) (para. [0016]: “The rotational orientation data is determined for example based on the rotational image data, for example from the image depiction of the electrode in the respective two-dimensional image. To this end, at least one of the two-dimensional medical images is analysed concerning the image appearance of the orientation marker in the two-dimensional image. In one example, all of the two-dimensional medical images are accordingly analysed. This can be done for example by determining, based on the rotational image data, an image appearance of the orientation marker, for example by at least one of: [0017] segmenting an image appearance of the electrode in the at least one or each of the two-dimensional medical images; [0018] edge detection of constituents of the at least one or each of the two-dimensional medical images; [0019] comparing the image appearance of the electrode in the at least one or each of the two-dimensional medical images to previously acquired and predetermined electrode template data describing constructional data of the electrode (such as at least one of its geometry and the spatial relationship—at least one of position and orientation—between at least one directional contact and the orientation marker).”).
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 Moore to include a comparison of previously acquired patient data with that of a subject patient, based on the teachings of Achatz. One would recognize that comparing previous patient data to current subject patient data would be useful in optimizing the location of implantation of an electrode. Further, one would recognize that the reference data would be useful in calculating the spatial relationships between electrodes. Including the comparison between previously accumulated data, such as a template, to a subject patient’s lead location data would have been an obvious improvement to the accuracy of measuring the orientation of an electrode lead implanted in the brain.
Regarding claim 6, Moore, in combination with Bolik, Bergman, and Achatz, discloses the method of claim 1 (see above). Moore further discloses wherein the accumulated data comprises indications of stimulation field model (SFM) (para. [0108]: "The GUI 600 can further include a visualization interface 606 that allows a user to view a stimulation field image 622 formed on a lead given the selected stimulation parameters and electrode configuration. The stimulation field image 622 is formed by field modelling in the CP 129.") radii (para. [0092]: " In order to use this data for machine learning purposes, the data may first be cleansed, optionally transformed, and then modeled. In some examples, new variables are derived, such as for use with directional leads, including central point of stimulation, maximum radius, spread of stimulation field, or the like.") corresponding to stimulation parameters providing therapeutic benefits in at least some of the previous patients (para. [0125]: "the characteristic stimulation amplitudes can be searched or determined within the amplitude limit (I.sub.max), represented by the radius of the circular shape, that can be set by a user. The characteristic stimulation amplitudes can include a first amplitude I.sub.1 (any one of the markers 831A, 831B, or 831E) corresponding to a clinical response indicative of an initial improvement in patient symptom from a baseline").
Regarding claim 16 Moore, in combination with Bolik, Bergman, and Achatz, discloses the system of claim 11 (see above). Moore further discloses wherein the accumulated data comprises indications of stimulation field model (SFM) (para. [0108]: "The GUI 600 can further include a visualization interface 606 that allows a user to view a stimulation field image 622 formed on a lead given the selected stimulation parameters and electrode configuration. The stimulation field image 622 is formed by field modelling in the CP 129.") radii (para. [0092]: " In order to use this data for machine learning purposes, the data may first be cleansed, optionally transformed, and then modeled. In some examples, new variables are derived, such as for use with directional leads, including central point of stimulation, maximum radius, spread of stimulation field, or the like.") corresponding to stimulation parameters providing therapeutic benefits in at least some of the previous patients (para. [0125]: "the characteristic stimulation amplitudes can be searched or determined within the amplitude limit (I.sub.max), represented by the radius of the circular shape, that can be set by a user. The characteristic stimulation amplitudes can include a first amplitude I.sub.1 (any one of the markers 831A, 831B, or 831E) corresponding to a clinical response indicative of an initial improvement in patient symptom from a baseline").
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Moore et al. (US 20230141183 A1, “Moore”), Bokil (US 20170061627 A1), Bergman (US 20190321106 A1), Achatz et al. (US 20200237326 A1), and (Pathak et al. (US 20220175458 A1, “Pathak”).
Regarding claim 2, Moore, in combination with Bolik, Bergman, and Achatz, discloses the method of claim 1 (see above). Moore further discloses wherein the imaging data for the subject patient comprises magnetic resonance imaging (MRI) data and computed tomography (para. [0038]: “Such tissue imaging information may come from a Magnetic Resonance Image (MRI) or Computed Tomography (CT) image of the patient”). However, Moore does not expressly disclose that the MRI data is preoperative and that the CT data is postoperative.
Pathak, in the same field of endeavor of using images to optimize deep brain stimulation lead placement, discloses a method and systems for determining brain regions for stimulation. Pathak discloses wherein the imaging data for the subject patient comprises preoperative magnetic resonance imaging (MRI) data (para. [0038]: "The initial image dataset may further include pre-operative MRI images identifying anatomical features of a brain in which the DBS lead is to be implanted.") and postoperative computed tomography (CT) (para. [0038]: "post-operative CT images.").
It would have been obvious for one of ordinary skill to in the art before the effective filing date of the claimed invention to further include the limitation that the MRI data is preoperative and the CT data is postoperative, as disclosed by Pathak, with the modified method and modified system of claims 1 and 11. Using the MRI preoperatively and CT postoperatively is known to be effective for obtaining imaging data to use for determining stimulation parameters in a patient, as disclosed by Pathak. Therefore, it would have been obvious to incorporate the same images into the method and systems of claim 1 and 11.
Regarding claim 12, Moore in combination with Bolik, Bergman, and Achatz discloses the system of claim 11 (see above). Moore further discloses wherein the imaging data for the subject patient comprises magnetic resonance imaging (MRI) data and computed tomography (para. [0038]: “Such tissue imaging information may come from a Magnetic Resonance Image (MRI) or Computed Tomography (CT) image of the patient”). However, Moore does not expressly disclose that the MRI data is preoperative and that the CT data is postoperative.
Pathak, in the same field of endeavor of using images to optimize deep brain stimulation lead placement, discloses a method and systems for determining brain regions for stimulation. Pathak discloses wherein the imaging data for the subject patient comprises preoperative magnetic resonance imaging (MRI) data (para. [0038]: "The initial image dataset may further include pre-operative MRI images identifying anatomical features of a brain in which the DBS lead is to be implanted.") and postoperative computed tomography (CT) (para. [0038]: "post-operative CT images.").
It would have been obvious for one of ordinary skill to in the art before the effective filing date of the claimed invention to further include the limitation that the MRI data is preoperative and the CT data is postoperative, as disclosed by Pathak, with the method and system of claims 1 and 11, as disclosed by Moore. Using the MRI preoperatively and CT postoperatively is known to be effective for obtaining imaging data to use for determining stimulation parameters in a patient, as disclosed by Pathak. Therefore, it would have been obvious to incorporate the same images into the method and systems of claim 1 and 11.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Moore et al. (US 20230141183 A1, “Moore”), Bokil (US 20170061627 A1), Bergman (US 20190321106 A1), Achatz et al. (US 20200237326 A1), and Charles et al. (US 20200188672 A1, “Charles”).
Regarding claim 3, Moore, in combination with Bolik, Bergman, and Achatz, discloses the method of claim 1 (see above). However, Moore does not expressly disclose wherein the at least one anatomical feature of the subject patient’s brain structure comprises one or more of a medial/lateral axis, an anterior/posterior axis, a medial border, a lateral border, an anterior border, and a posterior border of the brain structure.
Charles, in the same field endeavor of providing therapeutic treatment to a patient using deep brain stimulation, discloses a method and system for targeting a specific region of the brain. Charles discloses wherein the at least one anatomical feature of the subject patient’s brain structure comprises one or more of a medial/lateral axis, an anterior/posterior axis, a medial border, a lateral border, an anterior border, and a posterior border of the brain structure (para. [0007]: "The two groups were similar in electrode tip location (p>0.05) with regard to 3D distance (p=0.759), lateral-medial (x) axis distance (p=0.983), anterior-posterior (y) axis distance (p=0.949) or superior-inferior (z) axis distance (p=0.894) from the intended anatomical target.).
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 device and system of claims 1 and 11, as disclosed by Moore, to further include specific axes as regions of the patient’s brain, as disclosed by Charles. Defining the axes of the brain of regions of interest for stimulation would have been obvious to use since it would improve accuracy in targeting the correct brain region. Charles discloses using this technique, and therefore, it would have been an obvious improvement to include this in the device and system of claim 11.
Regarding claim 13, Moore, in combination with Bolik, Bergman, and Achatz discloses the system of claim 11 (see above). However, Moore does not expressly disclose wherein the at least one anatomical feature of the subject patient’s brain structure comprises one or more of a medial/lateral axis, an anterior/posterior axis, a medial border, a lateral border, an anterior border, and a posterior border of the brain structure.
Charles, in the same field endeavor of providing therapeutic treatment to a patient using deep brain stimulation, discloses a method and system for targeting a specific region of the brain. Charles discloses wherein the at least one anatomical feature of the subject patient’s brain structure comprises one or more of a medial/lateral axis, an anterior/posterior axis, a medial border, a lateral border, an anterior border, and a posterior border of the brain structure (para. [0007]: "The two groups were similar in electrode tip location (p>0.05) with regard to 3D distance (p=0.759), lateral-medial (x) axis distance (p=0.983), anterior-posterior (y) axis distance (p=0.949) or superior-inferior (z) axis distance (p=0.894) from the intended anatomical target.).
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 device and system of claims 1 and 11, as disclosed by Moore, to further include specific axes as regions of the patient’s brain, as disclosed by Charles. Defining the axes of the brain of regions of interest for stimulation would have been obvious to use since it would improve accuracy in targeting the correct brain region. Charles discloses using this technique, and therefore, it would have been an obvious improvement to include this in the device and system of claim 11.
Claims 4, 9, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Moore et al. (US 20230141183 A1, “Moore”), Bokil (US 20170061627 A1), Bergman (US 20190321106 A1), Achatz et al. (US 20200237326 A1), and Mustakos et al. (US 20190329040 A1, “Mustakos”).
Regarding claim 4, Moore, in combination with Bolik, Bergman, and Achatz, discloses the method of claim 1 (see above). Moore further discloses wherein using the imaging data to determine a position of at least one of the electrode leads with respect to at least one anatomical feature of the subject patient’s brain structure comprises using the imaging data to determine a 3-D model of the patient’s brain structure (para. [0107]: " The CP 129 can include and execute an electrode configuration algorithm to determine a position of the cathode pole 619 in the three-dimensional space from a given electrode configuration, or determine an electrode configuration from a given position of the cathode pole 619."). However, Moore does not expressly teach voxelizing the 3-D model.
Mustakos, in the same field of endeavor of deep brain stimulation, discloses systems and methods for adjusting neurostimulation parameters. Mustakos discloses voxelizing a 3D model for electric stimulation (para. [0099]: "The regions of space for which I.sub.th values are determined may be represented using “voxels”….The 3D voxelized model may include a computer-generated graphic model representing volumetric tissue elements and their responses to the electrostimulation.").
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 and system of claim 1 and 11, as disclosed by Moore, to include the technique of voxelizing the 3D images, as disclosed by Mustakos. Voxelizing is advantageous in that it quantifies a 3D image, which can then be used to specify a region of the image. One of ordinary skill in the art would recognize that including this technique in the method and systems of Moore would be an obvious improvement since voxelizing is disclosed as a technique used for quantifying brain images for deep brain stimulation that can be used to determine regions of interest, as Mustakos discloses.
Regarding claim 14, Moore, in combination with Bolik, Bergman, and Achatz, discloses the system of claim 11 (see above). Moore further discloses wherein using the imaging data to determine a position of at least one of the electrode leads with respect to at least one anatomical feature of the subject patient’s brain structure comprises using the imaging data to determine a 3-D model of the patient’s brain structure (para. [0107]: " The CP 129 can include and execute an electrode configuration algorithm to determine a position of the cathode pole 619 in the three-dimensional space from a given electrode configuration, or determine an electrode configuration from a given position of the cathode pole 619."). However, Moore does not expressly teach voxelizing the 3-D model.
Mustakos, in the same field of endeavor of deep brain stimulation, discloses systems and methods for adjusting neurostimulation parameters. Mustakos discloses voxelizing a 3D model for electric stimulation (para. [0099]: "The regions of space for which I.sub.th values are determined may be represented using “voxels”….The 3D voxelized model may include a computer-generated graphic model representing volumetric tissue elements and their responses to the electrostimulation.").
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 and system of claim 1 and 11, as disclosed by Moore, to include the technique of voxelizing the 3D images, as disclosed by Mustakos. Voxelizing is advantageous in that it quantifies a 3D image, which can then be used to specify a region of the image. One of ordinary skill in the art would recognize that including this technique in the method and systems of Moore would be an obvious improvement since voxelizing is disclosed as a technique used for quantifying brain images for deep brain stimulation that can be used to determine regions of interest, as Mustakos discloses.
Regarding claim 9, Moore, in combination with Bolik, Bergman, and Achatz, discloses the method of claim 1 (see above). However, Moore does not expressly teach wherein using the accumulated data and the imaging data to determine stimulation parameters for the subject patient comprises: determining a target volume within the subject patient’s brain for stimulation based on the accumulated data and the imaging data, and determining stimulation parameters that provide stimulation within the target volume.
Mustakos discloses wherein using the accumulated data (para. [0092]: “The target volume can be defined and refined by one or more iterations using the one or more clinical effects resulting from the test volume used in each iteration.”; The test volume data is considered to be accumulated data.) and the imaging data (para. [0086]: “The patient information be stored in the implant storage device 746 may include, for example, various types of neuromodulation settings. Examples may include positions of lead(s) 708 and electrodes 706 relative to the patient's anatomy (transformation for fusing computerized tomogram (CT) of post-operative lead placement to magnetic resonance imaging (MRI) of the brain.”) to determine stimulation parameters for the subject patient comprises: determining a target volume within the subject patient’s brain for stimulation based on the accumulated data and the imaging data, and determining stimulation parameters that provide stimulation within the target volume. (para. [0093]: “After the target volume is determined, stimulation configuration circuitry 962 can automatically generate the stimulation configuration for activating a volume of tissue substantially matching the target volume.”).
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 and system of claims 1 and 11, as disclosed by Moore, with the technique for determining target volume from image and accumulated data, and using that data to provide stimulation parameters within that target, as disclosed by Mustakos. The technique of determining a target volume for stimulation parameters is an obvious improvement to a stimulation device since Mustakos discloses using optimizing the stimulation effects with a determined target volume. One of ordinary skill would recognize that this would be an obvious improvement to determine a target volume using accumulated and image data, and setting the stimulation device parameters to match the target volume since this would optimize the therapeutic effects in the method and system of claims 1 and 11.
Regarding claim 19, Moore, in combination with Bolik, Bergman, and Achatz, discloses the system of claim 11 (see above). However, Moore does not expressly teach wherein using the accumulated data and the imaging data to determine stimulation parameters for the subject patient comprises: determining a target volume within the subject patient’s brain for stimulation based on the accumulated data and the imaging data, and determining stimulation parameters that provide stimulation within the target volume.
Mustakos discloses wherein using the accumulated data (para. [0092]: “The target volume can be defined and refined by one or more iterations using the one or more clinical effects resulting from the test volume used in each iteration.”; The test volume data is considered to be accumulated data.) and the imaging data (para. [0086]: “The patient information be stored in the implant storage device 746 may include, for example, various types of neuromodulation settings. Examples may include positions of lead(s) 708 and electrodes 706 relative to the patient's anatomy (transformation for fusing computerized tomogram (CT) of post-operative lead placement to magnetic resonance imaging (MRI) of the brain.”) to determine stimulation parameters for the subject patient comprises: determining a target volume within the subject patient’s brain for stimulation based on the accumulated data and the imaging data, and determining stimulation parameters that provide stimulation within the target volume. (para. [0093]: “After the target volume is determined, stimulation configuration circuitry 962 can automatically generate the stimulation configuration for activating a volume of tissue substantially matching the target volume.”).
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 and system of claims 1 and 11, as disclosed by Moore, with the technique for determining target volume from image and accumulated data, and using that data to provide stimulation parameters within that target, as disclosed by Mustakos. The technique of determining a target volume for stimulation parameters is an obvious improvement to a stimulation device since Mustakos discloses using optimizing the stimulation effects with a determined target volume. One of ordinary skill would recognize that this would be an obvious improvement to determine a target volume using accumulated and image data, and setting the stimulation device parameters to match the target volume since this would optimize the therapeutic effects in the method and system of claims 1 and 11.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Moore et al. (US 20230141183 A1, “Moore”), Bokil (US 20170061627 A1), Bergman (US 20190321106 A1), Achatz et al. (US 20200237326 A1), Mustakos et al. (US 20190329040 A1, “Mustakos”), and Zou (US 20230205820 A1, “Zou”).
Regarding claim 5, Moore, in combination with Bolik, Bergman, Achatz, and Mustakos, disclose the method of claim 4 (see above). However, neither reference discloses using principal component analysis to determine one or more axes of the subject patient’s brain structure using the 3-D model.
Zou, in the same field of endeavor of processing image data, discloses a method and system for searching a drawing of computer generated images. Zou discloses using principal component analysis to determine one or more axes of an image using a 3-D model. (para. [0051]: " The axis of the feature coordinate is determined by using the PCA (Principal Component Analysis) method. More specifically, the eigenvector of a covariance matrix of the vertex collection is determined and defines two directions for the feature coordinate. In this regard, the PCA method can provide the axis of the feature coordinates."; para. [0094]: "In one or more embodiments the axis of the feature coordinate system may be determined using principal component analysis (PCA)."). The Examiner notes that Zou does not disclose using the images on brain structures specifically. However, regardless of what the 3D model is, Zou’s PCA technique could be used on any 3D model. Absent evidence of the contrary, the PCA technique of Zou could be used on 3D model images of the brain.
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 PCA technique, as disclosed by Zou, with the method and systems of claims 4 and 14, as disclosed by the combination of Moore and Mustakos. The technique of using PCA is a known method for determining axes in a 3D model, as Zou discloses. One of ordinary skill in the art would recognize that using this technique would yield a reasonable expectation of accomplishing the goal of establishing axes. Therefore, it would be obvious to use PCA in method and system of claims 4 and 14 since they are concerned with 3D modeling of the brain.
Regarding claim 15, Moore, in combination with Bolik, Bergman, Achatz, and Mustakos, disclose the system of claim 14 (see above). However, neither reference discloses using principal component analysis to determine one or more axes of the subject patient’s brain structure using the 3-D model.
Zou, in the same field of endeavor of processing image data, discloses a method and system for searching a drawing of computer generated images. Zou discloses using principal component analysis to determine one or more axes of an image using a 3-D model. (para. [0051]: " The axis of the feature coordinate is determined by using the PCA (Principal Component Analysis) method. More specifically, the eigenvector of a covariance matrix of the vertex collection is determined and defines two directions for the feature coordinate. In this regard, the PCA method can provide the axis of the feature coordinates."; para. [0094]: "In one or more embodiments the axis of the feature coordinate system may be determined using principal component analysis (PCA)."). The Examiner notes that Zou does not disclose using the images on brain structures specifically. However, regardless of what the 3D model is, Zou’s PCA technique could be used on any 3D model. Absent evidence of the contrary, the PCA technique of Zou could be used on 3D model images of the brain.
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 PCA technique, as disclosed by Zou, with the method and systems of claims 4 and 14, as disclosed by the combination of Moore and Mustakos. The technique of using PCA is a known method for determining axes in a 3D model, as Zou discloses. One of ordinary skill in the art would recognize that using this technique would yield a reasonable expectation of accomplishing the goal of establishing axes. Therefore, it would be obvious to use PCA in method and system of claims 4 and 14 since they are concerned with 3D modeling of the brain.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Moore et al. (US 20230141183 A1, “Moore”), Bokil (US 20170061627 A1), Bergman (US 20190321106 A1), Achatz et al. (US 20200237326 A1), Mustakos et al. (US 20190329040 A1, “Mustakos”), and Zhang (US 20180193655 A1, “Zhang”).
Regarding claim 7, Moore, in combination with Bolik, Bergman, and Achatz, discloses the method of claim 1 (see above). However, Moore does not expressly teach wherein determining stimulation parameters for the subject patient comprises: receiving information indicative of a trial stimulation parameter set for the subject patient, determining an SFM for the trial stimulation parameter set, determining a SFM radius for the trial stimulation parameter set, and comparing the SFM radius for the trial stimulation parameter set to SFM radii from the accumulated data.
Mustakos, in the same field of endeavor, discloses wherein determining stimulation parameters (para. [0093]: “VOA designates an estimated region of tissue that will be stimulated for a particular set of stimulation parameters.”) for the subject patient comprises: receiving information indicative of a trial (para. [0093]; The “test” scenario can be considered to be a trial) stimulation parameter set for the subject patient (para. [0093]: “The target volume can be defined and refined by one or more iterations using the one or more clinical effects resulting from the test volume used in each iteration. For example, a test volume may be generated, where the test volume would from delivery of neuromodulation using the stimulation configuration. Alternatively, a test volume can be specified, and the inverse modeling algorithm can be used to automatically generate the stimulation configuration for activating that test volume.”), determining an SFM for the trial stimulation parameter set (para. [0093]: “This information may be used to create a SFM.” (This is mentioned in reference to the test volume)), determining a SFM radius for the trial stimulation parameter set (para. [0110]: “determining the maximum radius r of the SFM at the original amplitude.”).
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 method of receiving test (trial) stimulation parameters, determining an SFM, and and determining a radius from that SFM as disclosed by Mustakos, with the method and system of claims 1 and 11, as disclosed by Moore. One of ordinary skill in the art would recognize that performing a test stimulation is a known technique that determines a baseline stimulation that can later be adjusted, as disclosed by Mustakos. Further, one of ordinary skill in the art would also recognize that determining an SFM and radius are metrics that can be used to model stimulation in a patient since Mustakos discloses using an SFM for a similar purpose of modeling stimulation parameters. Incorporating the SFM and radius would be an obvious improvement to the methods and system of Moore.
However, Mustakos is silent regarding comparison between the SFM radius of a trial (test) and accumulated data (However, Mustakos does mention comparison between patient data (but not specifically the radius) in para. [0039]: "For example, in some embodiments, stimulation data (e.g., parameters, therapeutic effects, side effects, or the like) for multiple patients can be used. In some embodiments, the data or a corresponding SFM can be registered to an anatomic atlas for comparison between different patients.").
Zhang, in the same field of endeavor of programming electrical stimulation for brain stimulation devices, discloses a system and method for programming controls for electrical stimulation. Zhang discloses comparing the SFM for the trial stimulation parameter set to SFM radii from the accumulated data. (para. [0102]: “In at least some embodiments, the stimulation program is determined by selecting stimulation parameters that produce a SFM/VOA that matches the desired stimulation region within a predetermined degree or tolerance or that best matches the desired stimulation region. This may include, for example, selecting an initial set of stimulation parameters, calculating a SFM/VOA using those stimulation parameters, comparing that SFM/VOA to the desired stimulation region, and then refining the set of stimulation parameters in view of the comparison.”; Although it is not explicitly disclosed that the radii are compared between SFM, it is disclosed that any metric can be used for comparison, and the radii being used is implied in para. [0115]: “ Any suitable metric may be used for determining the degree of matching between the desired stimulation region and an SFM/VOA… The points may be uniformly or nonuniformly distributed or may be (or at least include) one or more critical points such as inflection/local maximum/local minimum points on the surface or boundary, surface or boundary points associated with lines radiating from a center or center of mass of the region or volume, or special points associated with particular shapes (for example, elliptical foci).”).
It would have been obvious for one of ordinary skill in the art to modify the method and system of using SFM, as disclosed by the combination of Moore and Mustakos, with the SFM comparison technique of Zhang. By including this in the method and system of claims 1 and 11, one would be able to measure the baseline, test (or trial) parameters to those of other patients. This would be an obvious improvement that would allow the user to adjust stimulation settings based on those parameters that are most effective in other patients. Therefore, it would have been obvious to include this improvement in the method and system of claims 1 and 11.
Regarding claim 17, Moore, in combination with Bolik, Bergman, and Achatz, discloses the system of claim 11 (see above). However, Moore does not expressly teach wherein determining stimulation parameters for the subject patient comprises: receiving information indicative of a trial stimulation parameter set for the subject patient, determining an SFM for the trial stimulation parameter set, determining a SFM radius for the trial stimulation parameter set, and comparing the SFM radius for the trial stimulation parameter set to SFM radii from the accumulated data.
Mustakos, in the same field of endeavor, discloses wherein determining stimulation parameters (para. [0093]: “VOA designates an estimated region of tissue that will be stimulated for a particular set of stimulation parameters.”) for the subject patient comprises: receiving information indicative of a trial (para. [0093]; The “test” scenario can be considered to be a trial) stimulation parameter set for the subject patient (para. [0093]: “The target volume can be defined and refined by one or more iterations using the one or more clinical effects resulting from the test volume used in each iteration. For example, a test volume may be generated, where the test volume would from delivery of neuromodulation using the stimulation configuration. Alternatively, a test volume can be specified, and the inverse modeling algorithm can be used to automatically generate the stimulation configuration for activating that test volume.”), determining an SFM for the trial stimulation parameter set (para. [0093]: “This information may be used to create a SFM.” (This is mentioned in reference to the test volume)), determining a SFM radius for the trial stimulation parameter set (para. [0110]: “determining the maximum radius r of the SFM at the original amplitude.”).
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 method of receiving test (trial) stimulation parameters, determining an SFM, and and determining a radius from that SFM as disclosed by Mustakos, with the method and system of claims 1 and 11, as disclosed by Moore. One of ordinary skill in the art would recognize that performing a test stimulation is a known technique that determines a baseline stimulation that can later be adjusted, as disclosed by Mustakos. Further, one of ordinary skill in the art would also recognize that determining an SFM and radius are metrics that can be used to model stimulation in a patient since Mustakos discloses using an SFM for a similar purpose of modeling stimulation parameters. Incorporating the SFM and radius would be an obvious improvement to the methods and system of Moore. However, Mustakos is silent regarding comparison between the SFM radius of a trial (test) and accumulated data (However, Mustakos does mention comparison between patient data (but not specifically the radius) in para. [0039]: "For example, in some embodiments, stimulation data (e.g., parameters, therapeutic effects, side effects, or the like) for multiple patients can be used. In some embodiments, the data or a corresponding SFM can be registered to an anatomic atlas for comparison between different patients.").
Zhang, in the same field of endeavor of programming electrical stimulation for brain stimulation devices, discloses a system and method for programming controls for electrical stimulation. Zhang discloses comparing the SFM for the trial stimulation parameter set to SFM radii from the accumulated data. (para. [0102]: “In at least some embodiments, the stimulation program is determined by selecting stimulation parameters that produce a SFM/VOA that matches the desired stimulation region within a predetermined degree or tolerance or that best matches the desired stimulation region. This may include, for example, selecting an initial set of stimulation parameters, calculating a SFM/VOA using those stimulation parameters, comparing that SFM/VOA to the desired stimulation region, and then refining the set of stimulation parameters in view of the comparison.”; Although it is not explicitly disclosed that the radii are compared between SFM, it is disclosed that any metric can be used for comparison, and the radii being used is implied in para. [0115]: “ Any suitable metric may be used for determining the degree of matching between the desired stimulation region and an SFM/VOA… The points may be uniformly or nonuniformly distributed or may be (or at least include) one or more critical points such as inflection/local maximum/local minimum points on the surface or boundary, surface or boundary points associated with lines radiating from a center or center of mass of the region or volume, or special points associated with particular shapes (for example, elliptical foci).”).
It would have been obvious for one of ordinary skill in the art to modify the method and system of using SFM, as disclosed by the combination of Moore and Mustakos, with the SFM comparison technique of Zhang. By including this in the method and system of claims 1 and 11, one would be able to measure the baseline, test (or trial) parameters to those of other patients. This would be an obvious improvement that would allow the user to adjust stimulation settings based on those parameters that are most effective in other patients. Therefore, it would have been obvious to include this improvement in the method and system of claims 1 and 11.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Moore et al. (US 20230141183 A1, “Moore”), Bokil (US 20170061627 A1), Bergman (US 20190321106 A1), Achatz et al. (US 20200237326 A1), and Butson et al. (US 20110040351 A1, “Butson”).
Regarding claim 8, Moore, in combination with Bolik, Bergman, and Achatz, discloses the device of claim 8 (see above). However, Moore does not disclose wherein using an accumulated data and an imaging data to determine stimulation parameters for the subject patient comprises: displaying on a graphical user interface (GUI): a representation of a search space indicative of potential trial stimulation parameter sets, and one or more likelihood maps derived from the accumulated data, wherein a one or more likelihood maps indicate trial stimulation parameter sets within a search space that are likely to be beneficial for a patient.
Butson, in the same field of endeavor of optimizing stimulation parameters, discloses a system and method for determining a target volume of tissue activation in a patient. Butson discloses wherein using the accumulated data and the imaging data (para. [0055]: " For instance, the target VTA can be provided to define a volume of tissue in a generic atlas brain or spinal cord or other neural tissue, which can be mapped to the given patient based on corresponding anatomical data acquired for the given patient via a suitable imaging modality, such as MRI, CT and the like.") to determine stimulation parameters for the subject patient comprises: displaying on a graphical user interface (GUI)(Fig. 1; para. [0061]; the display 32): a representation of a search space indicative of potential trial stimulation parameter sets (para. [0061]: " As one example, a graphical interface can provide data to the display 32 for overlaying an expected VTA for one or more given designs over the target VTA. Such a representation provides a visual demonstration of expected performance that can help determine which design parameters should be utilized to construct an electrode for given situation."), and one or more likelihood maps derived from the accumulated data (para. [0008]: " For example, the VTA data structure can be embodied as a statistical atlas of the brain, spinal cord or other neural target site that is constructed from anatomical and electrical data acquired for a patient population."), wherein the one or more likelihood maps indicate trial stimulation parameter sets within the search space that are likely to be beneficial for the patient (para. [0049]: " The 3D probabilistic maps defined by the VTA data structure 24 can be used to ascertain a target VTA for achieving a desired therapeutic results for a given patient according to the tissue volume determined to provide a statistically maximal clinical benefit.").
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 system of claims 1 and 11, as disclosed by Moore, with the GUI including a representation of a search space and one or more likelihood maps that indicate which parameter sets are likely to be beneficial for a patient, as disclosed by Butson. The technique of mapping stimulation parameters to an associated likelihood is a known technique, as disclosed by Butson. It would have been obvious to use this technique since it would enhance the device by outputting giving a prediction of which parameter is most effective before changing the settings. One of ordinary skill in the art would recognize that including this feature in a deep brain stimulation device would be an obvious improvement to the method and system of claims 1 and 11. Further, a including this on a user interface is a generic means of displaying parameters, and one of ordinary skill would recognize that it is obvious to include parameters on a GUI, as demonstrated by Butson.
Regarding claim 18, Moore, in combination with Bolik, Bergman, and Achatz, discloses the system of claim 11 (see above). However, Moore does not expressly teach wherein using the accumulated data and the imaging data to determine stimulation parameters for the subject patient comprises: displaying on a graphical user interface (GUI): a representation of a search space indicative of potential trial stimulation parameter sets, and one or more likelihood maps derived from the accumulated data, wherein the one or more likelihood maps indicate trial stimulation parameter sets within the search space that are likely to be beneficial for the patient.
Butson, in the same field of endeavor of optimizing stimulation parameters, discloses a system and method for determining a target volume of tissue activation in a patient. Butson discloses wherein using the accumulated data and the imaging data (para. [0055]: " For instance, the target VTA can be provided to define a volume of tissue in a generic atlas brain or spinal cord or other neural tissue, which can be mapped to the given patient based on corresponding anatomical data acquired for the given patient via a suitable imaging modality, such as MRI, CT and the like.") to determine stimulation parameters for the subject patient comprises: displaying on a graphical user interface (GUI)(Fig. 1; para. [0061]; the display 32): a representation of a search space indicative of potential trial stimulation parameter sets (para. [0061]: " As one example, a graphical interface can provide data to the display 32 for overlaying an expected VTA for one or more given designs over the target VTA. Such a representation provides a visual demonstration of expected performance that can help determine which design parameters should be utilized to construct an electrode for given situation."), and one or more likelihood maps derived from the accumulated data (para. [0008]: " For example, the VTA data structure can be embodied as a statistical atlas of the brain, spinal cord or other neural target site that is constructed from anatomical and electrical data acquired for a patient population."), wherein the one or more likelihood maps indicate trial stimulation parameter sets within the search space that are likely to be beneficial for the patient (para. [0049]: " The 3D probabilistic maps defined by the VTA data structure 24 can be used to ascertain a target VTA for achieving a desired therapeutic results for a given patient according to the tissue volume determined to provide a statistically maximal clinical benefit.").
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 system of claims 1 and 11, as disclosed by Moore, with the GUI including a representation of a search space and one or more likelihood maps that indicate which parameter sets are likely to be beneficial for a patient, as disclosed by Butson. The technique of mapping stimulation parameters to an associated likelihood is a known technique, as disclosed by Butson. It would have been obvious to use this technique since it would enhance the device by outputting giving a prediction of which parameter is most effective before changing the settings. One of ordinary skill in the art would recognize that including this feature in a deep brain stimulation device would be an obvious improvement to the method and system of claims 1 and 11. Further, a including this on a user interface is a generic means of displaying parameters, and one of ordinary skill would recognize that it is obvious to include parameters on a GUI, as demonstrated by Butson.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Moore et al. (US 20230141183 A1, “Moore”), Bokil (US 20170061627 A1), Bergman (US 20190321106 A1), Achatz et al. (US 20200237326 A1), Mustakos et al. (US 20190329040 A1, “Mustakos”), and Zhang (US 20180193655 A1, “Zhang”).
Regarding claim 10, Moore, in combination with Bolik, Bergman, Achatz, and Mustakos, discloses the method of claim 9 (see above). However, neither reference discloses wherein the accumulated data comprises indications of stimulation field models (SFMs) corresponding to stimulation parameters providing therapeutic benefits in at least some of the previous patients and wherein the target volume is based on a volume of overlap of the SFMs.
Zhang, in the same field of endeavor of programming electrical stimulation for brain stimulation devices, discloses a system and method for programming controls for electrical stimulation. Zhang discloses wherein the accumulated data comprises indications of stimulation field models (SFMs) corresponding to stimulation parameters providing therapeutic benefits in at least some of the previous patients and wherein the target volume is based on a volume of overlap of the SFMs (para. [0018]: "[0018] In at least some embodiments, determining the SFM includes determining when the second determined volume overlaps at least one portion of first determined volume; and generating the stimulation program based, at least in part, on the SFM includes, responsive to the second determined volume overlapping the at least one portion of the first determined volume.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to further include that the target volume is based on the volume of overlap of the SFM, as disclosed by Zhang, with the method and system of claim 9 and 19, as disclosed by the combination of Moore and Mustakos. One of ordinary skill in the art would recognize that the volume of overlap between SFMs could be used to determine a target volume, as disclosed by Zhang. One of ordinary skill would also recognize that including the overlap as the target volume would yield a reasonable expectation of success in determining a target volume for similar stimulation methods and systems as it does in Zhang. Therefore, modifying the device to further include the target volume as an overlap is an obvious improvement to the method and system of claims 9 and 19.
Regarding claim 20, Moore, in combination with Bolik, Bergman, Achatz, and Mustakos, discloses the system of claim 19 (see above). However, neither reference discloses wherein the accumulated data comprises indications of stimulation field models (SFMs) corresponding to stimulation parameters providing therapeutic benefits in at least some of the previous patients and wherein the target volume is based on a volume of overlap of the SFMs.
Zhang, in the same field of endeavor of programming electrical stimulation for brain stimulation devices, discloses a system and method for programming controls for electrical stimulation. Zhang discloses wherein the accumulated data comprises indications of stimulation field models (SFMs) corresponding to stimulation parameters providing therapeutic benefits in at least some of the previous patients and wherein the target volume is based on a volume of overlap of the SFMs (para. [0018]: "[0018] In at least some embodiments, determining the SFM includes determining when the second determined volume overlaps at least one portion of first determined volume; and generating the stimulation program based, at least in part, on the SFM includes, responsive to the second determined volume overlapping the at least one portion of the first determined volume.")
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to further include that the target volume is based on the volume of overlap of the SFM, as disclosed by Zhang, with the method and system of claim 9 and 19, as disclosed by the combination of Moore and Mustakos. One of ordinary skill in the art would recognize that the volume of overlap between SFMs could be used to determine a target volume, as disclosed by Zhang. One of ordinary skill would also recognize that including the overlap as the target volume would yield a reasonable expectation of success in determining a target volume for similar stimulation methods and systems as it does in Zhang. Therefore, modifying the device to further include the target volume as an overlap is an obvious improvement to the method and system of claims 9 and 19.
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 /ALLEN PORTER/Primary Examiner, Art Unit 3796
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