DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 5 recite the limitation "the first and second feature state" in lines 5 and 2 respectively. There is insufficient antecedent basis for this limitation in the claim. There is no mention of a first and second feature state in any prior claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, and 9-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being unpatentable by Krummen(US 20190304183 A1).
Regarding claim 1, Krummen discloses one or more non-transitory computer-readable media having data and machine readable instructions executable by a processor, the data comprising electrophysiological data captured from a patient, the machine readable instructions comprising: a feature state quantifier to compute feature states based on feature signals, the feature signals being generated based on the electrophysiological data; and a state change detector to detect a feature state change indicative of a change in electrical activity on a surface of interest within a patient's body(These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and/or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium[0111]. In some implementations, endocardial and EKG data can be measured when a patient's heart is excited and/or when a patient's heart is in a relaxed state. In an example implementation, the heart's electrical activity can be recorded from routine pacing within the heart at one or more locations to establish the relationship between cardiac activation and surface EKG recordings and/or computed VCGs. In another implementation, VF/AF can be induced within a patient, and the electrical data during this time can be measured/recorded. As the data demonstrates how a patient's heart acts during VF/AF, it can be compared against baseline data and data from other patients to identify VF/AF sources[0062]).
Regarding claim 2, Krummen discloses the one or more non-transitory computer-readable media of claim 1, further comprising a target generator to output target map data based on the detected feature state change, the target map data identifying a location on the surface of interest within the patient's body(In some aspects, a source of VF can be identified based on the fibrillatory source mapping 320. For example, based upon the fibrillatory mapping 320, the computing system 110 can determine that electrical voltage indicative of VF rotates around a particular point/area of pro-arrhythmic substrate, which can be identified as a rotor in this case (e.g., alternatively as focal activation in others), as indicated by the highlighted source 330, illustrated through the white sites in FIG. 3B. In some aspects, the source 330 can be a location of diseased cardiac substrate.[0070]).
Regarding claim 3, Krummen discloses the one or more non-transitory computer-readable media of claim 2, wherein the state change detector is further to: provide data characterizing a set of feature states computed over a period of time; and predict a likelihood of procedure success based on the data(The computational model 700 can be based upon EKG data and/or can be used to generate EKG data. For example, FIG. 7B depicts a graph 730 of EKG readings generated from the computational model, in accordance with some example implementations. The process to create EKG readings can be through first computing VCGs from the electrical dipole of a computational model for each time period. EKG tracings can then be computed. For example, FIG. 7C depicts VCG models 740, 750, generated from a cardiac model, in accordance with some example implementations. One or more VCG models, such as the VCG model 740 from a lateral viewpoint and/or the VCG model 750 from a vertical viewpoint are computed from the computational model. EKG tracings may be derived from this data thereafter. Importantly, the VCG models 740, 750, allow comparison and matching to VCG data from a patient, as described herein. As illustrated, the VCGs models 740, 750 are color-coded based on timing information.[0085]. In some aspects, method 1100 can include determining a change in fibrillation between the new computational model(s) and the original computational model(s). In some aspects, the displaying the side-by-side comparison can enables a targeted ventricular fibrillation or ventricular fibrillation ablation therapy by providing predictive ablation outcome data and/or selection of an optimized ablation strategy[0107]).
Regarding claim 9, Krummen discloses the one or more non-transitory computer-readable media of claim 1, wherein the state change detector evaluates the feature state change relative to a threshold and provides a treatment suggestion based on the evaluation, the treatment suggestion indicating whether a clinician is to continue applying therapy to one or more target sites during a treatment(Using the composite model, one VF/AF source can be removed (e.g., removal is simulated) to generate a new model, which can be compared against the composite model to determine whether the removal of each VF/AF source would be beneficial to the patient. If the change in VF/AF exhibited between the composite model and the new model is below a threshold value/percentage, then it can be determined that the removal of the VF/AF source may not be beneficial to the patient. The threshold value/percentage can vary depending upon the patient, medical professional, and/or other factors[0083]. Performance of the method 1100 and/or a portion thereof can allow for determinations of whether the elimination of each individual VF/AF source may or may not benefit a patient. For example, if the change in VF/AF in a model where a particular source is removed is not decreased by a threshold amount (e.g., percentage), then invasive procedures removing (or reducing the strength of) the particular source can be avoided[0108]).
Regarding claim 10, Krummen discloses the one or more non-transitory computer-readable media of claim 9, wherein the state change detector causes the treatment suggestion to be rendered on a display(In some aspects, the displaying the side-by-side comparison can enables a targeted ventricular fibrillation or ventricular fibrillation ablation therapy by providing predictive ablation outcome data and/or selection of an optimized ablation strategy[0107]).
Regarding claim 11, Krummen discloses the one or more non-transitory computer-readable media of claim 1, wherein the feature state quantifier computes the feature states based on a feature signal segment from one of the feature signals(Measurements of a patient's cardiac electrical properties may be generated and/or received. For example, the computing system 100 may receive and/or record a patient's EKG data. In some implementations, the EKG data may be obtained from an EKG sensor device, such as 12-lead EKG, that records the continuous, dynamic signals of cardiac electrical function from multiple body locations (e.g., on the surface of the chest, arms, legs, head, etc.) of the patient. Additionally, the computing system 100 may receive and/or record a patient's endocardial data[0061]).
Regarding claim 12, Krummen discloses the one or more non-transitory computer-readable media of claim 11, wherein the feature state quantifier is to compute a number of feature values for each feature based on respective portions of electrophysiological signals of the electrophysiological data(As a patient may have more than one VF/AF source, multiple comparisons can be made. For example, a VCG can be obtained from a patient over a series of time intervals. In some aspects, a VCG can be generated for each time interval, which can be 1 ms in duration, for each VF cycle (which can be approximately 200 ms in duration). Each of the VF cycle VCG can be compared against stored VF cycle VCGs to identify which VCG correlates most with the patient's. A corresponding computational model from the model library 192 can be identified based on whichever VCG has the highest correlation. The data associated with the identified computational model, including but not limited to VF source type, location, direction of rotation (if any), and/or the like as described herein, can be regarded as representative of the patient's heart during the specific time interval[0080]).
Regarding claim 13, Krummen discloses the one or more non-transitory computer-readable media of claim 1, wherein the state change detector is to: evaluate a state ratio representing a time occurrence of states over a period of time relative to a threshold; and detect the feature state change in response to the state ratio being equal to or greater than the threshold(In some implementations, additional computational models can be generated based upon the comparison of patient-derived VCGs to stored VCGs. For example, one or more models can be identified from the model library 192 that is most similar to the VF/AF source(s) identified within the patient. These one or more models can be combined to generate a composite model, which can include data representative of all VF/AF sources in the patient. Using the composite model, one VF/AF source can be removed (e.g., removal is simulated) to generate a new model, which can be compared against the composite model to determine whether the removal of each VF/AF source would be beneficial to the patient. If the change in VF/AF exhibited between the composite model and the new model is below a threshold value/percentage, then it can be determined that the removal of the VF/AF source may not be beneficial to the patient. The threshold value/percentage can vary depending upon the patient, medical professional, and/or other factors.[0083]. Performance of the method 1100 and/or a portion thereof can allow for determinations of whether the elimination of each individual VF/AF source may or may not benefit a patient. For example, if the change in VF/AF in a model where a particular source is removed is not decreased by a threshold amount (e.g., percentage), then invasive procedures removing (or reducing the strength of) the particular source can be avoided.[0108]. . In some aspects, a VCG can be generated for each time interval, which can be 1 ms in duration, for each VF cycle (which can be approximately 200 ms in duration)[0080]).
Regarding claim 14, Krummen discloses the one or more non-transitory computer-readable media of claim 1, wherein the state change detector is to: detect feature states corresponding to first feature states; detect a given feature state; and evaluate a respective value of one of the first feature states and the given feature state relative to a threshold to detect the feature state change, wherein a value of the feature state change is a difference between the given feature state and one of the first feature states that is nearest in value to the given feature state(Identification of the location of the source(s) 220, 270, 285, and 295[0057]. Using the composite model, one VF/AF source can be removed (e.g., removal is simulated) to generate a new model, which can be compared against the composite model to determine whether the removal of each VF/AF source would be beneficial to the patient. If the change in VF/AF exhibited between the composite model and the new model is below a threshold value/percentage, then it can be determined that the removal of the VF/AF source may not be beneficial to the patient. The threshold value/percentage can vary depending upon the patient, medical professional, and/or other factors.[0083]. . For example, VCG models can be simulated based on the computational models in the model library 192 (e.g., based upon EKG data associated with each model). In some implementations, VCG models can include three-dimensional tracings of electrical activity in a heart or some portion thereof. In some aspects, the VCG data can include temporospatial VCG data. The VCG data in the VCG library 196 may serve as diagnostic templates against which VCGs constructed from patient data can be matched[0043]).
Regarding claim 15, Krummen discloses the system comprising: memory configured to store machine readable instructions and data comprising electrophysiological data representing electrophysiological signals captured from a patient during a treatment, at least one processor configured to access the memory and configured to execute the machine readable instructions, the machine readable instructions comprising: a feature state quantifier comprising: a feature signal generator to compute a number of feature values for features based on respective electrophysiological signals, and combine the feature values for each feature to generate feature signals; a feature state calculator to compute feature states based on a feature signal segment from a respective feature signal of the feature signals, a state change detector to detect a feature state change indicative of a change in electrical activity on a surface of interest within a patient's body; and a target generator to output target map data based on the detected feature state change, the target map data identifying a location on the surface of interest within the patient's body(These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and/or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium[0111]. In some aspects, a source of VF can be identified based on the fibrillatory source mapping 320. For example, based upon the fibrillatory mapping 320, the computing system 110 can determine that electrical voltage indicative of VF rotates around a particular point/area of pro-arrhythmic substrate, which can be identified as a rotor in this case (e.g., alternatively as focal activation in others), as indicated by the highlighted source 330, illustrated through the white sites in FIG. 3B. In some aspects, the source 330 can be a location of diseased cardiac substrate.[0070]. A four-dimensional (4D) patient-specific computational model with data on a patient's electrical activity may be generated. For example, the computing system 110 may generate a 4D model based on 3D data received with the added dynamics from the electrical activity data. The 4D computational model may provide a 3D representation of the morphology and anatomy of the heart (or portions thereof) over time, and can provide time-varying electrical dynamics of the heart (or portions thereof), such as time-varying EKG and/or endocardial data[0064].a computational model may include EKG data overlaid and/or registered on 3D biventricular geometry of the patient's heart, the human fiber architecture of the heart, region(s) of heterogeneous conductivities caused by the presence of myocardial ischemia, infarction(s), anatomic (and/or functional) electrical conduction defects, such as partial and/or complete bundle branch block, and/or the like[0065]).
Regarding claim 16, Krummen discloses the system of claim 15, wherein the target generator is to modify a graphical map for the patient to include a graphical element identifying the location on the surface of interest within the patient's body(In some aspects, a source of VF can be identified based on the fibrillatory source mapping 320. For example, based upon the fibrillatory mapping 320, the computing system 110 can determine that electrical voltage indicative of VF rotates around a particular point/area of pro-arrhythmic substrate, which can be identified as a rotor in this case (e.g., alternatively as focal activation in others), as indicated by the highlighted source 330, illustrated through the white sites in FIG. 3B. In some aspects, the source 330 can be a location of diseased cardiac substrate[0070]).
Regarding claim 17, Krummen discloses the system of claim 16, wherein the machine readable instructions comprise a state dynamic calculator to: create a feature state matrix based on at least the feature states; compute state dynamics based on the feature state matrix; and predict a likelihood of procedure success based on the computed state dynamics(The patient-specific models 194 can include computational models similar to the models in the model library 192, but the models in the patient-specific models 194 can be generated based upon data from actual patients. For example, as illustrated, an endocardial sensor device 150A can be applied to record data from the interior of a heart in a patient 130A. At the same (or approximately the same) time, an EKG sensor 150B can be applied to the exterior of the patient 130A to record EKG readings. Based upon the combination of these readings, one or more patient-specific models 194 can be generated and stored. In some implementations, patient-specific models 194 can additionally or alternatively be based upon CT scan data, MRI scan data, sestamibi scan data, thallium scan data, multi-gated acquisition scan data, fluoroscopy data, x-ray data, echocardiography data, and/or other cardiac imaging data, which can be used to identify the shape, scarring, etc. of the heart of the patient 130N.[0042]. As a patient may have more than one VF/AF source, multiple comparisons can be made. For example, a VCG can be obtained from a patient over a series of time intervals. In some aspects, a VCG can be generated for each time interval, which can be 1 ms in duration, for each VF cycle (which can be approximately 200 ms in duration)[0080]. Method 1100 can proceed to operational block 1150 where the apparatus 900, for example, can display at least a portion of the heart models through a user interface (e.g., user interface 950). The heart models can include one or more of the left atrium, the right atrium, the ventricle, or the right ventricle. The models can be two-dimensional, three-dimensional, four-dimensional, and/or may be animated. In some aspects, a side-by-side comparison of models with and without VF/AF sources can be provided. In some aspects, method 1100 can include determining a change in fibrillation between the new computational model(s) and the original computational model(s)[0107]).
Regarding claim 18, Krummen discloses a computer-implemented method comprising: receiving, by a processor, electrophysiological data captured from a patient during a therapy treatment of a target site identified prior to a treatment, the target site corresponding to a potential ablation site on a surface of interest within a patient's body; generating, by the processor, feature signals based on the electrophysiological data captured from the patient; computing, by the processor, feature states based on the feature signals, detecting, by the processor, a respective feature state change based on an evaluation of the feature states relative to state change detection criteria; and outputting, by the processor, target map data identifying a region of interest on a surface of interest(FIG. 11 illustrates a flowchart of a method for computational localization of fibrillation sources, in accordance with some example implementations (e.g., similar to the illustrations in FIG. 12). In various implementations, the method 1000 (or at least a portion thereof) may be performed by one or more of the computing device 110, an apparatus providing the database 120, an apparatus providing the external software 130, one or more of the user access devices 140, one or more of the sensor devices 150, the access device 165, the computing apparatus 900, other related apparatuses, and/or some portion thereof[0101]. In order to identify the source of VF/AF within a patient, the computational models described herein can be compared against data obtained from the patient. In some aspects, the patient may know that they have VF/AF, but does not know the location of the source of their VF/AF. At this point, the patient may wish to learn the exact origin of their VF/AF (e.g., how bad is the patient's VF/AF and/or their risk of cardiac death), whether an implantable cardioverter-defibrillator (ICD) will be beneficial, whether ablation or surgery will be beneficial for preventing future arrhythmia, what the risks of surgery are, etc. Using the subject matter described herein, the patient may be able to go to a physician who takes non-invasive measurements of the patient, provides the measurement data to a computing apparatus, and receives data to better address the patient's questions and concerns[0077]. In some aspects, a source of VF can be identified based on the fibrillatory source mapping 320. For example, based upon the fibrillatory mapping 320, the computing system 110 can determine that electrical voltage indicative of VF rotates around a particular point/area of pro-arrhythmic substrate, which can be identified as a rotor in this case (e.g., alternatively as focal activation in others), as indicated by the highlighted source 330, illustrated through the white sites in FIG. 3B. In some aspects, the source 330 can be a location of diseased cardiac substrate[0070]).
Regarding claim 19, Krummen discloses the computer-implemented method of claim 18, wherein the region of interest has signal feature values that are similar to signal feature values at a prior treated location which elicited a detected arrhythmia state change(In some implementations, a fibrillatory source map, such as the fibrillatory source map 280 of FIG. 2C and/or the fibrillatory source map 290 of FIG. 2D, for illustrating the location of the VF/AF source can be generated. For example, based upon comparing the VCGs, a determination may be made as to how long a VF/AF source spends in one or more locations. This temporal/percentage information can be used to generate a fibrillatory source map, and/or the map can be overlaid on heart imaging data. Displaying the heart imaging data (e.g., a 3D model) with the fibrillatory source map can allow a medical professional to identify additional characteristics about the VF/AF source(s), and/or to target the source location for radiofrequency, cryogenic, ultrasound, or laser ablation, external beam radiation, revascularization, gene transfer therapy, or other intervention to reduce future arrhythmia burden[0082]).
Regarding claim 20, Krummen discloses the computer-implemented method of claim 18, further comprising: computing, by the processor, a number of feature values for each feature based on respective portions of electrophysiological signals of the electrophysiological data; combining, by the processor, the feature values for each feature to provide the feature signals; and computing, by the processor, the feature states based on a feature signal segment from one of the feature signals(The patient-specific models 194 can include computational models similar to the models in the model library 192, but the models in the patient-specific models 194 can be generated based upon data from actual patients. For example, as illustrated, an endocardial sensor device 150A can be applied to record data from the interior of a heart in a patient 130A. At the same (or approximately the same) time, an EKG sensor 150B can be applied to the exterior of the patient 130A to record EKG readings. Based upon the combination of these readings, one or more patient-specific models 194 can be generated and stored. In some implementations, patient-specific models 194 can additionally or alternatively be based upon CT scan data, MRI scan data, sestamibi scan data, thallium scan data, multi-gated acquisition scan data, fluoroscopy data, x-ray data, echocardiography data, and/or other cardiac imaging data, which can be used to identify the shape, scarring, etc. of the heart of the patient 130N[0042]).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Krummen in view of McCullouch(US 20160262635 A1).
Regarding claim 4, Krummer discloses the one or more non-transitory computer-readable media of claim 2, but fails to explicitly state wherein the feature state change is a first feature state change, and the location is a first location on the surface of interest, and the first feature state change being detected after therapy at a second location on the surface of interest within the patient's body during the treatment, and wherein the state change detector is to output treatment success data predicting a treatment success based on the first and second feature state changes.
However, McCullouch teaches “FIG. 14 illustrates that both coefficient of variation of external work density, as illustrated in FIG. 14(A), and fraction of LV performing negative work, as illustrated in FIG. 14(B), can predict quantitative measures of left-ventricular reverse remodeling; this is a strong indication that these abnormally enlarged hearts have decreased in size with prolonged application of CRT, i.e., a successful therapy.[0174]. in alternative embodiments, provided are compositions, medical devices or products of manufacture, systems, diagnostic tools, and methods, including computer implemented methods, for predicting the response of patients with dyssynchronous heart 10 failure (DHF) to cardiac resynchronization therapy (CRT), comprising: measuring or determining the fraction of the LV/septum performing negative work (MNW); and measuring or determining the coefficient of variation of external work density (COVW), wherein the MNW fraction performing negative work and coefficient of variation COVW (sd/mean) correlated strongly with observed reduction in end-systolic volume after CRT[abstract]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the computerized localization system of Krummen with the predictions of the heart failure diagnosis system of McCullouch. Doing so would disclose variation or change between feature locations and relate the variation to predictions for the patient.
Regarding claim 5, Krummer in view of McCullouch teaches the one or more non-transitory computer-readable media of claim 4, wherein the state change detector is to compute a difference between the first and second feature states, and the difference being indicative of the treatment success(In some variations, the operations can further comprise generating electrocardiogram plots based on the patient's heart, and/or generating the vectorcardiogram based on the electrocardiogram plots. In some variations, the operations can further comprise generating a second computational model for the patient's heart based on the (original) computational model, wherein the second computational model is generated to include a number of fibrillation sources that is less than the one or more fibrillation sources. A side-by-side comparison of the computational model and the second computational mode can be displayed via a user interface. In some aspects, the second computational model can be generated by removing one of the one or more fibrillation sources from the computational model. In some variations, the operations can further include determining a change in fibrillation between the computational model and the second computational model[0009]. If the change in VF/AF exhibited between the composite model and the new model is below a threshold value/percentage, then it can be determined that the removal of the VF/AF source may not be beneficial to the patient[0083]).
Regarding claim 6, Krummen discloses the one or more non-transitory computer-readable media of claim 1, but fails to explicitly state wherein the state change detector: determines an amount of time that a feature state computed based on a respective feature signal of the feature signals maintains a value or deviates from the value by a given amount; and evaluates the determined amount of time relative to a feature state time reference to determine a treatment success of a treatment to the patient.
However, McCullouch teaches “computing the change in these indices of work heterogeneity under varied therapeutic conditions, optionally comprising varied pacing lead locations and varied VV delay times between stimulation of left and right ventricular pacing electrodes,[0070]. (ii) a decrease of at least 0.5 in the coefficient of variation of external work density (COVW) in response to various numbers and locations of pacing leads or various different delay times between stimulation of different pacing allows assessment of which therapeutic parameters (e.g., what numbers and locations of pacing leads or what different delay times between stimulation of different pacing sites) will result in the greatest decrease in absolute COVW[0073])”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the computerized localization system of Krummen with the predictions of the heart failure diagnosis system of McCullouch. Doing so would disclose variation or change between feature locations and relate the variation to predictions for the patient.
Claim(s) 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Krummer in view of McCullouch and further in view of John(US 20200038674 A1).
Regarding claim 7, Krummer in view of McCullouch teaches the one or more non-transitory computer-readable media of claim 6, but fails to explicitly state wherein the state change detector causes the treatment success to be rendered on a display to modify the treatment being applied to the patient.
However, John teaches “At various times prior to, during, or after implantation, the treatment program 300 can be programmed to select or adjust treatment protocols in relation to predetermined times of day and durations (e.g., time since the last stimulation protocol was selected). Additionally, the patient may use a patient programmer 500 to select and adjust the treatment program's protocols 300. The control subsystem 12 can include roving module 300 which contains algorithms and parameters for implementing roving-based treatment, and a roving test module 304, for allowing testing, evaluation of roving test results, selection of successful roving parameter values, and storage of information related to roving test results. The control subsystem 12 can also contain a partial module 42b that is used in the creation, calibration, testing and adjustment of partial signals. The partial module 42b collaborates with partial module 42a of the stimulation subsystem in order to operationally generate the partial signals according to methods described herein[0032]. The method shown in FIG. 12 can be accomplished periodically to ensure that stimulation parameters are effective and advantageous for example, once a week or once a month. Rather than sensed data being evaluated automatically, it is likely that the test results will often be provided to the patient programmer 500 so that these can be evaluated by a user, who will then select successful candidate values[0132]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the computerized localization system of Krummen with the display of stimulation treatment of John. Doing so would disclose the treatment results being displayed directly to the user in order the evaluate treatment success.
Regarding claim 8, Krummen in view of McCullouch and John teaches the one or more non-transitory computer-readable media of claim 7, but Krummen fails to disclose wherein the treatment success is determined based on a proximity of the determined amount of time to the feature state time reference.
However, John teaches “Roving may be used to adjust the parameter values of pulse type signals where a parameter such as duty-cycle is roved over time. The resulting stimulation signals therefore do not have a single frequency or rate of stimulation, but rather alternate between or rove across a range of therapeutic frequencies. These signals can have spectral and temporal profiles which are dynamic and temporally distributed. The signals may contain different spectral content at different moments of time, and energy from one or more spectral bands at particular moments in time[0080]. In step 136 one or more parameter values of the protocol are changed. Parameter values can be, for example, the duration or magnitude of stimulation, or the time between stimulation periods, and stimulation again occurs 137 with parameter value(s) set to the new value(s)[0125]”.
It would be obvious to one of ordinary skill in the art before the effective filing date to configure the computerized localization system of Krummen with the display of stimulation treatment of John. Doing so would disclose the treatment results being displayed directly to the user in order the evaluate treatment success.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA CATHERINE ANTHONY whose telephone number is (703)756-4514. The examiner can normally be reached 7:30 am - 4:30 pm, EST, M-F.
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/MARIA CATHERINE ANTHONY/Examiner, Art Unit 3796
/TAMMIE K MARLEN/Primary Examiner, Art Unit 3796
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