Response to Applicant’s Arguments
Applicant’s arguments filed 04/01/2026 have been fully considered, and they are persuasive.
For claims 1-2,7-9,12-14,20-22,24,26-28,30-31 and 49-50, Applicant argues that “the segmentation masks in Richter are not used to generate visual representations of tumor tissues, as recited in Applicant's claim 1. Sjostrand also does not teach or suggest this feature, because Sjostrand similarly relies on the mapping from anatomical images to functional images to identify regions with high intensity, which correspond to regions with high radiopharmaceutical uptake, but may not always correspond to tumor tissues, as evidenced by paragraph [0224] of Sjostrand. For at least the reasons presented herein, Applicant submits the combination of cited references does not teach or suggest ‘generating a visual representation of the tumor tissue of the subject based on the corresponding mask for the tumor tissue,’ as recited in element (D) of claim 1” which is not persuasive. Richter’s segmentation masks represent the classified volumes which include the “hotspots,” or cancerous regions ([0237] - Hotspots may also be classified following their initial detection, e.g., as cancerous or not, and/or assigned likelihood values representing their likelihood of being a metastases. Hotspot classification may be performed by extracting hotspot features (e.g., metrics that describe characteristics of a particular hotspot) and using the extracted hotspot features as a basis for classification, e.g., via a machine learning module); furthermore, Richter teaches the claimed “visual representations of the tumor tissues” in the displayed “anatomical image” (e.g., figures 6-13, [0266] - As shown in FIG. 10A, PET/CT images are obtained 1010, anatomical context is determined 1012 via segmentation of bones and soft tissue and used to obtain measurements (e.g., of lesions and/or risk indices) from the PET image 1014. FIG. 10B shows an example PET/CT image 1016 and FIG. 10C shows example segmentations of bones and soft tissue 1018a and 1018b. As shown in FIG. 10D, segmentation of bone and soft-tissue (e.g., shown in image 1020a) can be used to remove background signal (e.g., from background tissue regions, such as those shown in image 1020b), leaving only desired signal from hotspots indicative of cancerous lesions 1020c; [0268] - One such lesion 1302 is visible in FIGs. 13A - D. Lesions such as lesion 1302 can be detected, for example via a thresholding approach, and classified as PyL™ positive (i.e., indicative of cancer) e.g., as described above. As with segmentation, lesion detection is also rapid. Lesion 1302 was detected in less than 5 seconds).
Applicant’s arguments filed April 1st, 2026 on claim 35 have been fully considered, but they are not persuasive. Applicant argues that “Claims 35 and 50, which are corresponding system and storage medium claims, are nonobvious over the combination of cited references for analogous reasons.” However, claim 35 is directed to a different invention which does not include the allowable feature of claim 1. Accordingly, the claimed invention as represented in the claim 35 does not represent a patentable distinction over the art of record
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 20-22, 24, 26-28, 30-31, 35, and 49-50 are rejected under 35 U.S.C. 103 as being unpatentable over RICHTER et al (WO 2020/144134) in view of SJOSTRAND et al (WO 2019/136349 A3).
As per claim 1, Richter teaches the claimed "method for visualizing a cancer in a subject," the method comprising: "at a computer system comprising one or more processors and a memory coupled to the one or more processors, the memory comprising one or more programs configured to be executed by the one or more processors" (Richter, [0339] - For example, computing resources may include hardware and/or software capable of executing algorithms, computer programs, and/or computer applications), wherein the one or more programs comprise instructions for: "A) obtaining a first set of medical images of a three-dimensional region of interest of the subject, wherein: the first set of medical images was collected at a first time using a first medical imaging modality, the three-dimensional region of interest of the subject comprises a plurality of tissue types, and the plurality of tissue types comprises a tumor tissue and a non-cancerous tissue of the subject" (Richter, [0200] - Examples of anatomical images include, without limitation, x-ray computed tomography (CT) images, magnetic resonance images (MRI), and ultra-sound. Image contrast in anatomical images is a function of physical properties of underlying tissue, such as density, water and fat content. As described in further detail herein, the Al-based segmentation techniques of the present disclosure analyze contrast variations and patterns in anatomical image to identify target 3D VOIs that correspond to different specific target tissue regions; [0274] - When computed for multiple images, collected at different time points, the compute index values can be compared with each other for a particular patient, and the change in index used to evaluate efficacy of a treatment and to make a prognosis of how the index will change in the near future; [0006]-[0009] - An oncologist may use images from a targeted PET or SPECT study of a patient as input in her assessment of whether the patient has a particular disease, e.g., prostate cancer, what stage of the disease is evident, what the recommended course of treatment (if any) would be, whether surgical intervention is indicated, and likely prognosis. The oncologist may use a radiologist report in this assessment. A radiologist report is a technical evaluation of the PET or SPECT images prepared by a radiologist for a physician who requested the imaging study and includes, for example, the type of study performed, the clinical history, a comparison between images, the technique used to perform the study, the radiologist's observations and findings, as well as overall impressions and recommendations the radiologist may have based on the imaging study results...). It is noted that Richter's anatomical images can be collected at any time, including the first time, to be available to the system for process without affecting to the analysis result; moreover, Richter's clinical history (e.g., [0006]) implies a record of past medical events/test records, diagnosed conditions, ...); "B) segmenting the first set of medical images by assigning, for each respective set of one or more pixels in a first plurality of sets of one or more pixels in the set of medical images, a label corresponding to a respective tissue type in the plurality of tissue types based on one or more corresponding pixel values for the respective set of one or more pixels" (Richter, [0215] - A segmentation module that identifies one or more target tissue regions within a particular initial VOI may receive the particular initial VOI as input, and output an integer label for each voxel, with the integer label identifying that voxel as corresponding to one of the particular target tissue regions, or background); "C) generating, for each respective tissue type in the plurality of tissue types, a corresponding mask based on respective sets of one or more pixels assigned the label corresponding to the respective tissue type" (Richter, [0219] - Segmentation maps generated by automated Al-based analysis of anatomical images can be transferred to 3D functional images in order to identify, within the 3D functional image, 3D volumes corresponding to the target VOIs identified in the anatomical image. In particular, in certain embodiments, the individual segmentation masks (of the segmentation map) are mapped from the 3D anatomical image to the 3D functional image. The 3D volumes identified within the 3D functional image can be used for a variety of purposes in analyzing images for assessment of cancer status; see also Sjostrand, Figures 2, 12; [0276] - For example, for a particular category, certain voxels in the one or more auxiliary fine segmentation masks may be identified (e.g., labeled) as belonging to the particular category, but not belong to a set of voxels in the base fine segmentation mask that are identified as belonging to the particular category. These voxels may be added (e.g., by labeling them as such) to the set of voxels identified as belonging to the particular category in the base fine segmentation mask to produce the final merged fine segmentation mask); "D) generating a visual representation of the tumor tissue of the subject based on the corresponding mask for the tumor tissue" (Richter, [0236] - FIG. 5B shows an example process 520 for using the segmentation approaches described herein to detect hotspots representing cancerous lesions. In a first step, a 3D anatomical image is received 522. A one or more target VOIs corresponding to particular target tissue regions are identified within the 3D anatomical image 524 and the identifications of the target VOIs are stitched together 526 to create a segmentation map 534 comprising a plurality of segmentation masks 536. Each segmentation mask of the segmentation map represents a particular target VOI... In particular, one or more specific 3D volumes may correspond to specific tissue regions where cancerous lesions may form, and be analyzed to detect hotspots therein); E) generating a visual representation of a skeleton of the subject within the region of interest based on the first set of medical images" (Richter, [0205] - FIG. 2 shows results of an example segmentation performed on a skeleton of a subject, with different specific bones identified by different colors. The segmentation shown in FIG. 2, performed using a CT image, can be transferred to a functional image in order to identify 3D volumes in the functional image that correspond to the target VOIs identified within the CT image. Other VOIs, such as soft- tissue, may also be identified and used to identify corresponding 3D volumes in a functional image); and "F) displaying the visual representation of the tumor tissue and the visual representation of the skeleton of the subject in a single image" (Richter, [0239] - Hotspots representing lesions may be used to determine risk indices that provide an indication of disease presence and/or state (e.g., a cancer status, similar to a Gleason score) for a patient. For example, metrics such as number of hotspots, a total summed intensity of identified hotspots, area fraction of a particular body part or region (e.g., skeleton) occupied by hotspots, and the like, may be used themselves as, and/or in calculation of, such risk indices. In certain embodiments, regions identified via the segmentation approaches described herein may be used in computation of risk indices, for example in computing metrics such as area fractions). It is noted that Richter does not explicitly teach the representation of the single image "in a same spatial orientation as in the set of medical images, and at a same relative size as in the set of medical images" as claimed. However, for the purpose of comparison, it would have been obvious to show the single image of study "in a same spatial orientation as in the set of medical images, and at a same relative size as in the set of medical images" to aid the recognition of visual cues related to patient's condition.
Applicant argues that “the segmentation masks in Richter are not used to generate visual representations of tumor tissues, as recited in Applicant's claim 1... For at least the reasons presented herein, Applicant submits the combination of cited references does not teach or suggest ‘generating a visual representation of the tumor tissue of the subject based on the corresponding mask for the tumor tissue,’ as recited in element (D) of claim 1” which is not persuasive. Richter’s segmentation masks represent the classified volumes which include the “hotspots,” or cancerous regions ([0237] - Hotspots may also be classified following their initial detection, e.g., as cancerous or not, and/or assigned likelihood values representing their likelihood of being a metastases. Hotspot classification may be performed by extracting hotspot features (e.g., metrics that describe characteristics of a particular hotspot) and using the extracted hotspot features as a basis for classification, e.g., via a machine learning module); furthermore, Richter teaches the claimed “visual representations of the tumor tissues” in the displayed “anatomical image” (e.g., figures 6-13, [0266] - As shown in FIG. 10A, PET/CT images are obtained 1010, anatomical context is determined 1012 via segmentation of bones and soft tissue and used to obtain measurements (e.g., of lesions and/or risk indices) from the PET image 1014. FIG. 10B shows an example PET/CT image 1016 and FIG. 10C shows example segmentations of bones and soft tissue 1018a and 1018b. As shown in FIG. 10D, segmentation of bone and soft-tissue (e.g., shown in image 1020a) can be used to remove background signal (e.g., from background tissue regions, such as those shown in image 1020b), leaving only desired signal from hotspots indicative of cancerous lesions 1020c; [0268] - One such lesion 1302 is visible in FIGs. 13A - D. Lesions such as lesion 1302 can be detected, for example via a thresholding approach, and classified as PyL™ positive (i.e., indicative of cancer) e.g., as described above. As with segmentation, lesion detection is also rapid. Lesion 1302 was detected in less than 5 seconds).
Claim 2 adds into claim 1 "wherein the first set of medical images comprises a computed tomography (CT) scan of the three-dimensional region of the subject" (Richter, [0200] - Examples of anatomical images include, without limitation, x-ray computed tomography (CT) images, magnetic resonance images (MRI), and ultra- sound. Image contrast in anatomical images is a function of physical properties of underlying tissue, such as density, water and fat content).
Claim 20 adds into claim 1 "wherein the displaying F) comprises generating a report comprising the single image comprising the visual representation of the tumor tissue and the visual representation of the skeleton of the subject" (Richter, [0006]- [0009] - An oncologist may use images from a targeted PET or SPECT study of a patient as input in her assessment of whether the patient has a particular disease, e.g., prostate cancer, what stage of the disease is evident, what the recommended course of treatment (if any) would be, whether surgical intervention is indicated, and likely prognosis. The oncologist may use a radiologist report in this assessment. A radiologist report is a technical evaluation of the PET or SPECT images prepared by a radiologist for a physician who requested the imaging study and includes, for example, the type of study performed, the clinical history, a comparison between images, the technique used to perform the study, the radiologist's observations and findings, as well as overall impressions and recommendations the radiologist may have based on the imaging study results... The physician may show the patient his PET/SPECT images and may tell the patient a numerical risk associated with various treatment options or likelihood of a particular prognosis...).
Claim 21 adds into claim 1 "wherein the single image further comprises a visual representation of a reference shape in the single image corresponding to a volume in the three-dimensional region of interest of the subject at a size of the same proportion to the visual representation of the tumor tissue and the visual representation of the skeleton of the subject relative to the set of medical images" (Richter, [0239] - Hotspots representing lesions may be used to determine risk indices that provide an indication of disease presence and/or state (e.g., a cancer status, similar to a Gleason score) for a patient. For example, metrics such as number of hotspots, a total summed intensity of identified hotspots, area fraction of a particular body part or region (e.g., skeleton) occupied by hotspots, and the like, may be used themselves as, and/or in calculation of, such risk indices. In certain embodiments, regions identified via the segmentation approaches described herein may be used in computation of risk indices, for example in computing metrics such as area fractions).
Claim 22 adds into claim 1 "wherein the single image further comprises a visual representation of a reference axial, coronal, or sagittal slice of the three-dimensional region of interest of the subject from the first set of medical images" (Richter, Figure 4) (It is noted that image 404 is a visual representation of a reference axial, coronal, or sagittal slice of the three-dimensional region of interest of the subject); [0206] - As shown in FIG. 4, a machine learning module 402, such as a convolutional neural network (CNN), receives contextual anatomical information (e.g., a CT image or other anatomical imaging modality) 404 along with functional information (e.g., a functional image, such as a SPECT, PET, or other functional image obtained using a particular imaging probe) 406).
Claim 24 adds into claim 20 "wherein the report further comprises one or more measurement for the cancerous tissue" (Richter, [0006] - An oncologist may use images from a targeted PET or SPECT study of a patient as input in her assessment of whether the patient has a particular disease, e.g., prostate cancer, what stage of the disease is evident, what the recommended course of treatment (if any) would be, whether surgical intervention is indicated, and likely prognosis. The oncologist may use a radiologist report in this assessment).
Claim 26 adds into claim 20 "wherein the report further comprises a change in a measurement for the cancerous tissue over time" (Richter, [0006] - A radiologist report is a technical evaluation of the PET or SPECT images prepared by a radiologist for a physician who requested the imaging study and includes, for example, the type of study performed, the clinical history, a comparison between images, the technique used to perform the study, the radiologist's observations and findings, as well as overall impressions and recommendations the radiologist may have based on the imaging study results; [0274] - When computed for multiple images, collected at different time points, the compute index values can be compared with each other for a particular patient, and the change in index used to evaluate efficacy of a treatment and to make a prognosis of how the index will change in the near future).
Claim 27 adds into claim 20 "wherein the report further comprises, for each respective timepoint in a plurality of timepoints, a respective visual representation of the tumor tissue at the respective timepoint" (Richter, [0006]-[0009] - An oncologist may use images from a targeted PET or SPECT study of a patient as input in her assessment of whether the patient has a particular disease, e.g., prostate cancer, what stage of the disease is evident, what the recommended course of treatment (if any) would be, whether surgical intervention is indicated, and likely prognosis. The oncologist may use a radiologist report in this assessment. A radiologist report is a technical evaluation of the PET or SPECT images prepared by a radiologist for a physician who requested the imaging study and includes, for example, the type of study performed, the clinical history, a comparison between images, the technique used to perform the study, the radiologist's observations and findings, as well as overall impressions and recommendations the radiologist may have based on the imaging study results [0274] - When computed for multiple images, collected at different time points, the compute index values can be compared with each other for a particular patient, and the change in index used to evaluate efficacy of a treatment and to make a prognosis of how the index will change in the near future).
Claim 28 adds into claim 20 "wherein the report further comprises a timeline indicating the timing of one or more events associated with the cancer in the subject" (Richter, [0006]-[0009] - An oncologist may use images from a targeted PET or SPECT study of a patient as input in her assessment of whether the patient has a particular disease, e.g., prostate cancer, what stage of the disease is evident, what the recommended course of treatment (if any) would be, whether surgical intervention is indicated, and likely prognosis. The oncologist may use a radiologist report in this assessment. A radiologist report is a technical evaluation of the PET or SPECT images prepared by a radiologist for a physician who requested the imaging study and includes, for example, the type of study performed, the clinical history, a comparison between images, the technique used to perform the study, the radiologist's observations and findings, as well as overall impressions and recommendations the radiologist may have based on the imaging study results... [0274] - When computed for multiple images, collected at different time points, the compute index values can be compared with each other for a particular patient, and the change in index used to evaluate efficacy of a treatment and to make a prognosis of how the index will change in the near future).
Claim 30 adds into claim 20 "wherein the report further comprises a prognosis for the cancer in the subject" (Richter, [0011] - In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality; [0006] - An oncologist may use images from a targeted PET or SPECT study of a patient as input in her assessment of whether the patient has a particular disease, e.g., prostate cancer, what stage of the disease is evident, what the recommended course of treatment (if any) would be, whether surgical intervention is indicated, and likely prognosis. The oncologist may use a radiologist report in this assessment).
Claim 31 adds into claim 20 "wherein the report is displayed in a user interface on a second computer system comprising one or more processors, memory coupled to the one or more processors, and a display" (Richter, [0007] - the process involves having a radiologist perform an imaging study on the patient, analyzing the images obtained, creating a radiologist report, forwarding the report to the requesting physician, having the physician formulate an assessment and treatment recommendation, and having the physician communicate the results, recommendations, and risks to the patient).
Claim 35 claims "a method for visualizing a cancer in a subject, the method comprising: at a computer system comprising one or more processors and a memory coupled to the one or more processors" (Richter, [0339] - For example, computing resources may include hardware and/or software capable of executing algorithms, computer programs, and/or computer applications), the memory comprising one or more programs configured to be executed by the one or more processors, "responsive to receiving a request for a radiology report for a cancer in a subject" (Richter, [0007] - the process involves having a radiologist perform an imaging study on the patient, analyzing the images obtained, creating a radiologist report, forwarding the report to the requesting physician, having the physician formulate an assessment and treatment recommendation, and having the physician communicate the results, recommendations, and risks to the patient): "A) retrieving a first set of medical images of a three-dimensional region of interest of the subject, from a most recent medical imaging evaluation of the cancer performed at a first time point, from a first medical database" (Richter, [0200] - Examples of anatomical images include, without limitation, x-ray computed tomography (CT) images, magnetic resonance images (MRI), and ultra-sound. Image contrast in anatomical images is a function of physical properties of underlying tissue, such as density, water and fat content. As described in further detail herein, the Al-based segmentation techniques of the present disclosure analyze contrast variations and patterns in anatomical image to identify target 3D VOIs that correspond to different specific target tissue regions; [0006]-[0009] - An oncologist may use images from a targeted PET or SPECT study of a patient as input in her assessment of whether the patient has a particular disease, e.g., prostate cancer, what stage of the disease is evident, what the recommended course of treatment (if any) would be, whether surgical intervention is indicated, and likely prognosis. The oncologist may use a radiologist report in this assessment. A radiologist report is a technical evaluation of the PET or SPECT images prepared by a radiologist for a physician who requested the imaging study and includes, for example, the type of study performed, the clinical history, a comparison between images, the technique used to perform the study, the radiologist's observations and findings, as well as overall impressions and recommendations the radiologist may have based on the imaging study results...; [0274] - When computed for multiple images, collected at different time points, the compute index values can be compared with each other for a particular patient, and the change in index used to evaluate efficacy of a treatment and to make a prognosis of how the index will change in the near future).
It is noted that Richter's anatomical images can be collected at any time, including the first time, to be available to the system for process without affecting to the analysis result; moreover, Richter's clinical history (e.g., Richter, [0006]-[0009] - An oncologist may use images from a targeted PET or SPECT study of a patient as input in her assessment of whether the patient has a particular disease, e.g., prostate cancer, what stage of the disease is evident, what the recommended course of treatment (if any) would be, whether surgical intervention is indicated, and likely prognosis. The oncologist may use a radiologist report in this assessment. A radiologist report is a technical evaluation of the PET or SPECT images prepared by a radiologist for a physician who requested the imaging study and includes, for example, the type of study performed, the clinical history, a comparison between images, the technique used to perform the study, the radiologist's observations and findings, as well as overall impressions and recommendations the radiologist may have based on the imaging study results...) implies a record of past medical events/test records, diagnosed conditions, ...; "B) querying a second medical database to identify one or more medical imaging evaluations of the cancer performed prior to the first time point" (Richter, [0007] - The process may also involve repeating the imaging study due to inconclusive results, or ordering further tests based on initial results); and "C) generating a radiology report for the cancer in the subject comprising a first image displaying a visual representation of the cancer and a visual representation of a least a portion of a skeleton for the subject based on the first set of medical images" (Richter, [0205] - FIG. 2 shows results of an example segmentation performed on a skeleton of a subject, with different specific bones identified by different colors. The segmentation shown in FIG. 2, performed using a CT image, can be transferred to a functional image in order to identify 3D volumes in the functional image that correspond to the target VOIs identified within the CT image. Other VOIs, such as soft- tissue, may also be identified and used to identify corresponding 3D volumes in a functional image; [0239] - Hotspots representing lesions may be used to determine risk indices that provide an indication of disease presence and/or state (e.g., a cancer status, similar to a Gleason score) for a patient. For example, metrics such as number of hotspots, a total summed intensity of identified hotspots, area fraction of a particular body part or region (e.g., skeleton) occupied by hotspots, and the like, may be used themselves as, and/or in calculation of, such risk indices. In certain embodiments, regions identified via the segmentation approaches described herein may be used in computation of risk indices, for example in computing metrics such as area fractions).
Applicant’s arguments filed April 1st, 2026 on claim 35 have been fully considered, but they are not persuasive. Applicant argues that “Claims 35 and 50, which are corresponding system and storage medium claims, are nonobvious over the combination of cited references for analogous reasons.” However, claim 35 is directed to a different invention which does not include the allowable feature of claim 1. Accordingly, the claimed invention as represented in the claim 35 does not represent a patentable distinction over the art of record.
Claims 49-50 claim a system and a non-transitory computer-readable storage medium based on the method of claim 1, therefore, they are rejected under a similar rationale.
Claims 7-9, 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over RICHTER et al (WO 2020/144134) in view of SJOSTRAND et al (WO 2019/136349 A3), and further in view of JERMYN et al (Fast segmentation and high-quality three- dimensional volume mesh creation from medical images for diffuse optical tomography) and CHOWA et al (Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network).
Claim 7 adds into claim 1 "wherein generating the visual representation of the tumor tissue comprises generating a corresponding mesh surface for the tumor tissue based on an outer boundary of the corresponding mask for the tumor tissue and smoothing edges of the corresponding mesh surface for the tumor tissue" (Jermyn, 1 Introduction - The spectral measure of the diffuse transport of near-infrared light through soft tissue can provide the ability to image functional tissue information such as hemoglobin oxygenation and water fraction, which can be useful as a noninvasive means of identifying cancer... Numerical approaches allow solutions to be computed for more complex geometries, but require more computational time as well as a discrete representation (volume mesh) of the domain; Chowa, Distance-Based features - Since the benign tumor mesh is smooth and uniform, and angles between the mesh triangles are minimal, the distance-based scores are lower for benign tumors except the centroid distance feature. Figure 5 shows a visualization of mesh feature analysis with the bounding box and the curvature). Thus, it would have been obvious, in view of Sjostrand, Jermyn, and Chowa, to configure Richter's method as claimed by generating a mesh surface for the tumor tissue and smoothing edges based on an outer boundary of the corresponding mask for the tumor tissue. The motivation is visual representing a meshed 3D tumor tissue clusters.
Claim 8 adds into claim 1 "wherein each respective set of one or more pixels in the first plurality of sets of one or more pixels corresponds to a respective voxel in a uniform three-dimensional grid of voxels defined for the set of medical images; and for a respective tissue type in the plurality of tissue types, the corresponding mask comprises a binary indication, for each respective voxel in the three-dimensional grid of voxels, of whether the tissue represented in the voxel is the respective tissue type" (Chowa, Mesh Dataset Generation Using Point-e Network - The regression based model is basically a regression forest-based method to predict the location of a grid point within a 3D space that is essential to compute the SDF., The marching cube algorithm generates triangles of the signed distance point in a voxel grid to approximate the target iso- surface. Finally, the 3D point cloud obtained from the 2D image is converted into a solid mesh; Jermyn, Table 2, User-controlled parameters - Spline distance - controls grid resolution, which will affect computational time). Thus, it would have been obvious, in view of Sjostrand, Jermyn, and Chowa, to configure Richter's method as claimed by generating, in a uniform three-dimensional grid of voxels, the tumor tissue clusters defined for the set of medical images. The motivation is visual representing a meshed 3D tumor tissue clusters on a 3D grid coordinate.
Claim 9 adds into claim 8 "wherein the corresponding mask for the tumor tissue comprises a plurality of groups of non-zero voxels, wherein each respective group of non-zero voxels in the plurality of groups of non-zero voxels is separated from every other respective group of non-zero voxels in the plurality of groups of non-zero voxels by at least one zero voxel" (Richter, [0217] - Second, for each particular label, differently labeled isolated voxels and/or voxel islands that are immediately adjacent to voxels of the largest connected component associated with the particular label are identified and re-assigned the particular label. In certain embodiments, in a third step, any remaining isolated differently labeled voxel islands can then be removed... It is noted that for voxel islands, the surround voxels are zero voxels), and wherein: "generating the visual representation of the tumor tissue comprises generating a corresponding mesh surface for each respective group of non-zero voxels in the plurality of groups of non-zero voxels" (Jermyn, 1 Introduction - The spectral measure of the diffuse transport of near-infrared light through soft tissue can provide the ability to image functional tissue information such as hemoglobin oxygenation and water fraction, which can be useful as a noninvasive means of identifying cancer... Numerical approaches allow solutions to be computed for more complex geometries, but require more computational time as well as a discrete representation (volume mesh) of the domain; Chowa, Figure 4 - islands of the same tissues); or "the visual representation of the tumor tissue excludes representations of respective groups of non-zero voxels having a total volume below a first volume threshold" (Richter, [0217] - The second through fourth steps, in which isolated voxels and/or voxel islands are relabeled based on their surroundings are repeated until convergence - i.e., no change from on iteration to the next. This approach reduces and/or eliminates isolated voxels of a different label from their surrounding thereby mitigating noise in the classification process; Chowa, 2 Materials and Methods - The user has control over element size, quality, and approximation error. For ease of use, these values are set automatically based on the segmentation and medical image information, and no prior knowledge of mesh generation is required to use the tool). Thus, it would have been obvious, in view of Sjostrand, Jermyn, and Chowa, to configure Richter's method as claimed by generating, the tumor tissue clusters in form of islands separated by the zero tissues. The motivation is visual representing distinct meshed 3D tumor tissue clusters as islands separated by a zero tissues on a 3D grid coordinates.
Claim 12 adds into claim 1 "determining a volume for the cancer in the subject based on a volume contained within the mesh surface for the tumor tissue" (Jermyn, 1 Introduction - The spectral measure of the diffuse transport of near-infrared light through soft tissue can provide the ability to image functional tissue information such as hemoglobin oxygenation and water fraction, which can be useful as a noninvasive means of identifying cancer.. Numerical approaches allow solutions to be computed for more complex geometries, but require more computational time as well as a discrete representation (volume mesh) of the domain; Chowa, Figure 4 - Tumor ROIs with their corresponding meshes). Thus, it would have been obvious, in view of Sjostrand, Jermyn, and Chowa, to configure Richter's method as claimed by determining a volume for the cancer in the subject based on a volume contained within the mesh surface for the tumor tissue. The motivation is visual representing a 3D volume tumor clusters on a 3D grid coordinates.
Claim 13 adds into claim 12 "wherein the volume is determined after smoothing edges of the corresponding mesh surface for the tumor tissue" (Jemyn, 2. Material and Method - Once the volumetric mesh has been created, a new feature has been added to allow the users to further optimize the 3-D mesh utilizing the Stellar mesh improvement algorithm. This optimization routine improves tetrahedral meshes so that their worst tetrahedra have high quality, making them more suitable for finite element analysis. Stellar employs a broad selection of improvement operations, including vertex smoothing by nonsmooth optimization, stellar flips and other topological transformations, vertex insertion, and edge contraction; Table 3 - When boundary surfaces are smooth, the termination of the meshing process is guaranteed... Chowa, Distance-Based features - Since the benign tumor mesh is smooth and uniform, and angles between the mesh triangles are minimal, the distance-based scores are lower for benign tumors except the centroid distance feature. Figure 5 shows a visualization of mesh feature analysis with the bounding box and the curvature). Thus, it would have been obvious, in view of Sjostrand, Jermyn, and Chowa, to configure Richter's method as claimed by smoothing edges of the corresponding mesh surface for determining the tumor volume. The motivation is improving the volume calculation process for the cancer tissues using smooth edges by approximation of the 3D volume.
Claim 14 adds into claim 1 "wherein generating the visual representation of the skeleton comprises: determining, for each respective set of one or more pixels in a second plurality of sets of one or more pixels in the set of medical images, a corresponding tissue density" (Richter, [0006] - a clinical history) (It is noted that a clinical history comprises past medical history, a record of past medical events/test records, diagnosed conditions, which provides clues to the diagnosis by revealing patterns and risk factors); and "generating a corresponding mask for the skeleton based on respective sets of one or more pixels in the second plurality of sets of one or more pixels having a tissue density satisfying a set of one or more bone density criteria" (Sjostrand, [0028] - In certain embodiments, step (c) comprises determining, using the first module, a 3D pelvic bone mask that identifies a volume of the 3D anatomical image corresponding to pelvic bones (e.g., one or more (up to all) of a sacrum, a coccyx, a left hip bone, and a right hip bone) of the subject); and "generating a corresponding mesh surface for the skeleton based on an outer boundary of the corresponding mask for the skeleton" (Jermyn, 1 Introduction - The spectral measure of the diffuse transport of near-infrared light through soft tissue can provide the ability to image functional tissue information such as hemoglobin oxygenation and water fraction, which can be useful as a noninvasive means of identifying cancer.. Numerical approaches allow solutions to be computed for more complex geometries, but require more computational time as well as a discrete representation (volume mesh) of the domain). Thus, it would have been obvious, in view of Sjostrand, Jermyn, and Chowa, to configure Richter's method as claimed by generating a corresponding mesh surface for the skeleton based on an outer boundary. The motivation is visual representing a meshed 3D tumor tissue clusters.
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.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHU K NGUYEN whose telephone number is (571)272-7645. The examiner can normally be reached M-F 8-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel F. Hajnik can be reached at (571) 272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PHU K NGUYEN/Primary Examiner, Art Unit 2616
Prosecution Insights