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 § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Robinson (PGPUB: 20210137384 A1) in view of Hundley (PGPUB: 20070236491 A1).
Regarding claims 1, 16, and 19. Robinson teaches a system comprising:
a computing device configured to:
receive a first input identifying a change to a target area for treatment (see Fig. 3, paragraph 93, the adjustment of the target may be done manually or automatically. As additional patients are treated, the previous treatments can inform future treatments and can be used to adjust the target of the current patient (for example, by training the neural network). In another example, the target may be adjusted based on a weighting of input mappings or the confidence score of the combined mappings); and
provide for display an indication of whether the change is accepted based on determining if any of the one or more rules are violated (see Fig. 6, paragraph 130, the confidence score can be determined to quantify the quality and reproducibility of one or more segments or contours for ablation. In some examples, the confidence score can be increased with higher degrees of overlap between imaging groups (e.g., segments to ablate, etc.). In other examples, the confidence score can be decreased with incomplete input mapping, poor quality of input mapping, increased number of VTs, large scar size (e.g., scar greater than ablation), etc. In some examples, the confidence score may be displayed as a numerical value or a percentage (e.g., X of 10, X of 100) and/or categorically (e.g., low, medium/moderate, or high)).
However, Robinson does not expressly teach to determine if a first rule is violated based on the change to the target area for treatment.
Hundley teaches that if the probability is high, then the clinician can be notified to determine if the chemotherapy should be changed (block 612). A clinician (such as an oncologist or other physician) may then decide how to proceed before the next planned active chemo delivery, such as, for example, decrease the dose, change the drug or drug combo, delay the next treatment, prescribe a medicament to help alleviate the condition, or terminate the chemo altogether (perhaps initiating an alternative treatment, such as a radiation treatment (see Fig. 6, paragraph 135).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Robinson by Hundley for providing that if the probability is high, then the clinician can be notified to determine if the chemotherapy should be changed (block 612). A clinician (such as an oncologist or other physician) may then decide how to proceed before the next planned active chemo delivery, such as, for example, decrease the dose, change the drug or drug combo, delay the next treatment, prescribe a medicament to help alleviate the condition, or terminate the chemo altogether (perhaps initiating an alternative treatment, such as a radiation treatment, as to determine if a first rule is violated based on the change to the target area for treatment. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Regarding claims 2, 17, and 20. The combination teaches system of claim 1, wherein the computing device is configured to:
determine that the first rule is not violated; and update the target area based on the change (see Hundley, Fig. 6, paragraph 135, if the probability is determined to be moderate (block 615), then a clinician can determine whether to decrease the dose, alter the chemotherapy regimen, change the chemotherapy drug(s), or increase the monitoring frequency (block 616). If the probability is considered low (block 620), the chemotherapy can continue as planned, and/or a physician may even increase the dose as needed).
Regarding claims 3 and 18. The combination teaches system of claim 1, wherein the computing device is configured to:
determine that the first rule is violated (see Hundley, Fig. 6, paragraph 136, "high" probability typically means that the likelihood that an undesired drop in LVEF (it is contemplated that the undesired drop may correlate to at least about a 5% LVEF drop, and typically (clinically) about 10% or more) will occur if the planned chemotherapy continues is about 75% or greater); and
provide for display an error message based on the violation (see Hundley, Fig. 6, paragraph 136, the histogram results can be provided to a display associated with a clinician's workstation and/or each of the probability calculations and/or results can be provided to the display as well).
Regarding claim 4. The combination teaches system of claim 1,
wherein the image data is at least one of magnetic resonance image data (see Robinson, Fig. 15) and computed tomography image data (see Robinson, Fig. 16).
Regarding claim 5. The combination teaches system of claim 1, wherein the computing device is configured to:
receive a second input; generate target data characterizing the target area (see Hundley, Fig. 6, paragraph 136, "high" probability typically means that the likelihood that an undesired drop in LVEF (it is contemplated that the undesired drop may correlate to at least about a 5% LVEF drop, and typically (clinically) about 10% or more) will occur if the planned chemotherapy continues is about 75% or greater . The term "moderate" means between about 25%-74% probability that the LVEF drop will occur, and the term "low" means that there is less than about a 25% chance that the LVEF drop will occur); and
transmit the target data to a second computing device to treat a patient (see Hundley, paragraph 96, the MRI control system circuit 12 may also assemble and transmit the acquired images to a workstation 20 or other such data processing system for further analysis and/or display. The workstation 20 may be in an MRI suite or may be remote from the MRI suite).
Regarding claim 6. The combination teaches system of claim 1,
wherein the computing device is configured to display an interactive model on a graphical user interface (see Hundley, paragraph 12, systems and/or computer program products for providing physician interactive tools that can be used to evaluate tissue characteristics, including one or more of: cardiotoxicity-induced cardiac injury using voxel/pixel histogram data, identification of injured tissue or alteration of the ratios of native tissue components or chemical or anatomical markers),
wherein the interactive model includes a plurality of segments (see Fig. 13, paragraph 133, a standardized model of the heart 500 can be visually generated with the voxel intensity data such as shown in FIG. 13. The model shown in FIG. 13 is a 17-segment model of the heart that can visually illustrate cardiac status to a clinician), and
wherein the first input identifies a selection of at least one segment of the plurality of segments (see Robinson, paragraph 95, the determined segments can be assigned priorities and/or probabilities. For example, based on the combination of input mappings and risk profile the determined segments could be assigned high, medium or low priorities. In some examples, all of the cardiac arrhythmia target segments can be assigned a priority (e.g., high, medium or low)).
Regarding claim 7. The combination teaches system of claim 6,
wherein the computing device is configured to display notes associated with the at least one segment of the plurality of segments (see Robinson, paragraph 107, the non-image data, such as the 12-lead mappings can be displayed to the user and the user can interact with or click on segments of the cardiac arrhythmia target).
Regarding claim 8. The combination teaches system of claim 6, wherein the first rule is based on a particular combination of the plurality of segments (see Robinson, paragraph 108, the one or more of the cardiac arrhythmia target segments, for each input mapping, can then be combined to determined segment overlap).
Regarding claim 9. The combination teaches system of claim 6,
wherein the first rule is based on the selection of a maximum number of the plurality of segments (see Robinson, paragraph 121, the at least one processor can create an informed ablation plan. The ablation plan can be created using any data available, for example, the ablation plan may include segments with abnormalities, selected segments for ablation, weighting of input mappings, the confidence score, and/or the risk profile).
Regarding claim 10. The combination teaches system of claim 1, wherein the computing device is configured to:
obtain image data for an organ of a patient, wherein the target area for treatment is within the organ (see Robinson, paragraph 132, data representing the outcomes from previously treated patients can be used to enhance the generated image mapping method and the generated decision support module. In an example, over time, the method can predict consistent utilization of invasive catheter ablation for small endocardial lesions in the left ventricular apex in older males. Subsequent outcomes data reveals high rates of toxicity in this population and low rates of toxicity with similar outcomes in patients treated with noninvasive ablation);
generate a segment model based on the organ (see Robinson, Fig. 3); and
display the segment model with an overlay of the image data (see Robinson, paragraph 77, a 3D model of the 17 segments can be generated. In various examples, the model can be generated for the left ventricle, right ventricle, and/or atria. An elliptical cone can be used to generate the 3D model, but any arbitrary ventricle-like or atria-like shape may be used. In at least one example, for each input mapping, the 3D model can be overlaid on the mapping using deformable registration of the model to a left ventricle contour).
Regarding claim 11. The combination teaches system of claim 1, wherein the computing device is configured to:
obtain image data for an organ of a patient, wherein the target area for treatment is within the organ (see Robinson, paragraph 132, data representing the outcomes from previously treated patients can be used to enhance the generated image mapping method and the generated decision support module. In an example, over time, the method can predict consistent utilization of invasive catheter ablation for small endocardial lesions in the left ventricular apex in older males. Subsequent outcomes data reveals high rates of toxicity in this population and low rates of toxicity with similar outcomes in patients treated with noninvasive ablation);
generate a segment model based on the organ (See Robinson, Fig. 3); and
display the image data with an overlay of the segment model (see Robinson, Fig. 1, paragraph 74 and 84, at least one processor can receive one or more input mappings (e.g., of a corresponding ventricle, a corresponding atria, etc.). In some examples, the one or more input mappings can be historical input mappings previously taken and/or newly required input mappings. In some examples, the one or more input mappings can be one or more images from a singular patient; the input mappings can be combined by overlapping segmentation models, combining a segmentation model and 3D geometries, or combinations thereof. In some example, the overlap of contours from the identified abnormality in a 3D geometry can be selected).
Regarding claim 12. The combination teaches system of claim 1, wherein the computing device is configured to:
generate a first digital model of a type of the organ; determine an alignment of the image data to the first digital model (see Robinson, paragraph 77, a 3D model of the 17 segments can be generated. In various examples, the model can be generated for the left ventricle, right ventricle, and/or atria. An elliptical cone can be used to generate the 3D model, but any arbitrary ventricle-like or atria-like shape may be used. In at least one example, for each input mapping, the 3D model can be overlaid on the mapping using deformable registration of the model to a left ventricle contour . In some examples, a free form (b-spline) registration can be used for alignment. Since the segment model is symmetric, anatomical landmarks such as the apex, anterior interventicular groove, posterior interventicular groove, and mitral valve plane can be identified and used as anchor points to align the correct segments in the model to the correct anatomical locations);
generate a second digital model comprising at least a portion of the scanned image and the first digital model (see Robinson, Figs. 14-20, paragraph 208 and 210, the nearby scar was identified as segments 11 and 12. The nearby scar was identified as segments 11 and 12. Ablation was extended back to segment 5. The scar at segments 1 and 2 were not targeted. Segment 6 was not targeted because the ECG was entirely superiorly directed; The mappings from FIGS. 14, 15A, 15B, 16A, 16C, 17, 18A, 18B, 18C, and 19 were input mappings into the method for determining one or more cardiac arrhythmia targets for ablation. FIGS. 20A, 20B, 20C, 20D, 20E, and 20F show the segment abnormalities identified from each of the input mappings (EKG, MRI motion, MRI scar, CT, PET, ECGI, respectively). FIG. 20G is the output of the method defining the one or more cardiac arrhythmia targets, showing the probability of each of the targets); and
store the second digital model in a data repository (see Robinson, paragraph 79, the abnormality can be manually defined on a 12-lead ECG using a segmentation model and can be manually identified on an ECGI, MRI, CT, and/or PET mapping. In one example, the abnormality can be automatically defined on a 12-lead ECG and can be manually identified on an ECGI, MRI, CT, and/or PET mapping. In some examples, each input mapping can be reviewed by an expert individually, and a likelihood of each segment contributing to VT can be scored. These scores can be stored in a database. A target probability defined for each segment s as the weighted average (by weight w) over all input mappings i for each patient p can then be generated and stored in the database).
Regarding claim 13. The combination teaches system of claim 12, wherein the computing device is further configured to provide the second digital model for display (see Hundley, paragraph 26, the systems include: (a) a graphic user interface (GUI) in communication with a display for accepting user input to draw at least one boundary about at least one target region of interest in a left ventricle myocardium in an MRI or CT image of a patient; and (b) a signal processor circuit configured to electronically generate at least one histogram of intensities of voxels/pixels in the MRI or CT image of the at least one region of interest based on boundary data from the boundary drawn with the GUI).
Regarding claim 14. The combination teaches system of claim 12, wherein the computing device is further configured to:
receive a second input identifying an adjustment to the alignment of the image data to the first digital model (see Robinson, paragraph 93, the adjustment of the target may be done manually or automatically. As additional patients are treated, the previous treatments can inform future treatments and can be used to adjust the target of the current patient (for example, by training the neural network). In another example, the target may be adjusted based on a weighting of input mappings or the confidence score of the combined mappings);
adjust the second digital model based on the second input (see Robinson, Fig. 1, paragraph 106, the weighting of input mappings can occur before the mappings are combined, after abnormality identification, after combination, after defining the target, or after adjusting the target. Each input mapping can be given a weight based on one or more factors, for example, quality of scan (e.g., ICD artifact on MRI, etc.), number of input mappings (e.g., number of modalities), clinical relevance (e.g., non-clinical CT induced, etc.), expert acceptance of individual technique, importance of data); and
store the adjusted second digital model in the data repository (see Robinson, paragraph 79, the abnormality can be manually defined on a 12-lead ECG using a segmentation model and can be manually identified on an ECGI, MRI, CT, and/or PET mapping. In one example, the abnormality can be automatically defined on a 12-lead ECG and can be manually identified on an ECGI, MRI, CT, and/or PET mapping. In some examples, each input mapping can be reviewed by an expert individually, and a likelihood of each segment contributing to VT can be scored. These scores can be stored in a database. A target probability defined for each segment s as the weighted average (by weight w) over all input mappings i for each patient p can then be generated and stored in the database).
Regarding claim 15. The combination teaches system of claim 1, wherein the computing device is further configured to:
receive a second input identifying a treatment target area of the organ (see Figs. 14-20, paragraph210, the mappings from FIGS. 14, 15A, 15B, 16A, 16C, 17, 18A, 18B, 18C, and 19 were input mappings into the method for determining one or more cardiac arrhythmia targets for ablation. FIGS. 20A, 20B, 20C, 20D, 20E, and 20F show the segment abnormalities identified from each of the input mappings (EKG, MRI motion, MRI scar, CT, PET, ECGI, respectively). FIG. 20G is the output of the method defining the one or more cardiac arrhythmia targets, showing the probability of each of the targets);
determine a corresponding portion of the second digital model based on the treatment target area of the organ (see Robinson, Fig. 25, paragraph 215, the patient had a large anterior apical scar with two different VTs, both exiting out of two different edges from the scar and interesting sinus rhythm activation that aligns with the VT sites. ECGI agrees with VT1 ECG, but not VT2 ECG. The target treatment decision balanced scar homogenization (large) vs. a more focused approach. The VTs on 12-lead are two different regions, rather far way, requiring a more broad ablation. Full scar homogenization would require segments 7, 13, 14, 15, 16, and 17, while ECGI alone would recommend the lateral edge of the scar, only segments 13, 15, 16); and
regenerate the second digital model to identify the corresponding portion (see Robinson, paragraph 215, The mappings from FIGS. 21A, 21B, 22, 23A, 23B, 24A, 24B, 24C, 24D, 24E, 24F, 25A, and 25B were input mappings into the method for determining one or more cardiac arrhythmia targets for ablation. FIGS. 26A, 26B, 26C, 26D, 26E, 26F, 26G, and 26H show the segment abnormalities identified from each of the input mappings (EKG for VT1, EKG for VT2, CT, PET, ECGI for VT1, ECGI for VT2, ECGI for sinus rhythm scar, and ECGI for sinus rhythm latest activation, respectively). FIG. 26I is the output of the method defining the one or more cardiac arrhythmia targets, showing the probability of each of the targets).
Conclusion
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/XIN JIA/Primary Examiner, Art Unit 2663