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 .
Status of claims: Claims 1-20 are examined below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/7/2024 was filed and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. An update search found that Valadez et al (US 2015/0023575) in view of DHARMAKUMAR et al (US 2022/0117508) address the Remarks and the Claims. Please see Office below for details.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 8-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Valadez et al (US 2015/0023575) in view of DHARMAKUMAR et al (US 2022/0117508).
Claim 1:
Valadez et al (US 2015/0023575) teaches the following subject matter:
A system (figure 1 and 0003) for medical imaging data normalization for animal studies, the system comprising:
a medical imaging device configured to acquire image data (figure 1 and 0005 detail image data from medical imaging device such as CAT scanner, MRI…etc with image data such as 2D and voxel/3D);
a memory configured to store a standard model and a machine trained network for segmentation of image data (0027-0029 detail information storage for imaging data and atlas (standard model); 0033-0035 detail storage system for collection of digital and advance processing and operation; figure 2 and 0044 further detail atlas as model/standard model that are aligned and then segmented; figure 5 and 0054 detail atlas (standard model) and learning classification (machine trained network)); and
a processor configured to register the image data to the standard model (figure 5 part 506-510 teaches register to region of interest (ROI) of atlas) and segment the registered image data using the machine trained network (figures 5 part 512-514 detail next process of segmenting, where paragraph 0053-0054 further detail segmentation with local classification (or learning/machine learning)), the processor further configured to warp the segmented registered image data to the image data and generate an output image for the subject based on the warped segmented registered image data (FIG. 5, at 512, image segmentation unit 107 inversely maps the registered or warped first ROI to the space of the subject's image data to generate a segmentation mask of the anatomical region).
Valadez et al teaches all the subject matter above, but not the following: for a non-human subject
DHARMAKUMAR et al (US 2022/0117508) teaches
for a non-human subject (0016-0018 teaches medical imaging with Pet and MRI of subject of interest; 0200 detail PET scanning of dog, where 0062 detail further species of dogs such as wolf, as well as other canines in paragraph 0103)
Valadez et al and DHARMAKUMAR et al are both in the field of image analysis, especially normalizing/standardizing medical imaging (DHARMAKUMAR et al paragraph 0262) such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Valadez et al by DHARMAKUMAR et al to include non-human would expanding this approach to pixel-wise assessment of myocardial oxygenation would open the door for testing novel physiological hypotheses, coronary artery disease, understanding could provide new insights that can improve our understanding of how angina develops in patients with microvascular disease and evaluate therapies to alleviate microvascular impairments in oxygenation. Studies of this nature are likely to demand more advanced segmentation and registration approaches so that pixel-wise analysis can be accurately performed as disclosed by DHARMAKUMAR et al in 0236.
Claim 2:
Valadez et al teach:
The system of claim 1, wherein the medical imaging device comprises an MRI device, a CT device, a cone-beam CT, an X-ray device, or a PET device (figure 1 and 0032 detail radiology scanner such as a magnetic resonance (MR) scanner, PET/MR, X-ray or a CT scanner.).
Claim 3:
DHARMAKUMAR et al teach:
The system of claim 1, wherein the non-human subject comprises a canine (0016-0018 detail medical imaging with PET and MRI of subject of interest; 0200 detail PET scanning of dog, where 0062 detail further species of dogs such as wolf, as well as other canines in paragraph 0103).
Claim 4:
DHARMAKUMAR et al teach:
The system of claim 3, wherein the standard model comprises an average anatomical model for a plurality of different breeds of canines (0062 detail further species of dogs such as wolf, as well as other canines in paragraph 0103).
Claim 5:
Valadez et al teach:
The system of claim 1, wherein the processor is configured to register the image data by deforming a scale and a location of the image data to match one or more landmarks shared by the image data and standard model (above detail deform/warp, and figure 5 and 0046 regions corresponding (matching) with landmarks).
Claim 6:
Valadez et al teach:
The system of claim 1, wherein the processor is configured to segment the image data into one or more regions of interest, wherein each region of interest is registered to a respective standard model of a respective region (figure 5 and 0051-0053 detail 510, image segmentation unit 107 non-rigidly registers a first region of interest (ROI) ).
Claim 8:
DHARMAKUMAR et al teach:
The system of claim 1, further comprising: a display configured to display the output image for the non-human subject (0077 detail display map with confidence of BOLD-CMR images, each set of MR data over time for parts of the body such as the heart).
Claim 9:
Valadez et al (US 2015/0023575) teaches the following subject matter:
A computer (0028) implemented method comprising:
acquiring image data (figure 1 and 0005 detail image data from medical imaging device such as CAT scanner, MRI…etc with image data such as 2D and voxel);
registering the image data to a standardized model (figure 5 part 506-510 teaches register to region of interest (ROI) of atlas (standard model));
identifying one or more features in the registered image data using a machine learned model (0054-0056 detail local classification (or learning/machine learning) with registration to local regions); and
providing the one or more features to an operator (0036 detail include a user interface that allows a radiologist or any other skilled user (e.g., physician, technician, operator, scientist, etc.), to manipulate the image data.).
Valadez et al teaches all the subject matter above, but not the following: for a non-human subject.
DHARMAKUMAR et al (US 2022/0117508) teaches:
for a non-human subject (0016-0018 detail medical imaging with Pet and MRI of subject of interest; 0200 detail PET scanning of dog, where 0062 detail further species of dogs such as wolf, as well as other canines in paragraph 0103)
Valadez et al and DHARMAKUMAR et al are both in the field of image analysis, especially normalizing/standardizing medical imaging (DHARMAKUMAR et al paragraph 0262) such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Valadez et al by DHARMAKUMAR et al to include non-human would expanding this approach to pixel-wise assessment of myocardial oxygenation would open the door for testing novel physiological hypotheses, coronary artery disease, understanding could provide new insights that can improve our understanding of how angina develops in patients with microvascular disease and evaluate therapies to alleviate microvascular impairments in oxygenation. Studies of this nature are likely to demand more advanced segmentation and registration approaches so that pixel-wise analysis can be accurately performed as disclosed by DHARMAKUMAR et al in 0236.
Claim 10:
DHARMAKUMAR et al teach:
The computer implemented method of claim 9, wherein the non-human subject is a dog (0018 detail medical imaging with Pet and MRI of subject of interest; 0200 detail PET scanning of dog, where 0062 detail further species of dogs such as wolf, as well as other canines in paragraph 0103).
Claim 11:
DHARMAKUMAR et al teach:
The computer implemented method of claim 10, wherein the standardized model is generated from a plurality of breeds of dogs including multiple size variations (0062 detail further species of dogs such as wolf, as well as other canines in paragraph 0103; 0103 detail reference images for canines, where different canines are different sizes).
Claim 12:
Valadez et al teach:
The computer implemented method of claim 9, wherein the image data is acquired by an MRI device, a CT device, a cone-beam CT, an X-ray device, or a PET device (figure 1 and 0032 detail radiology scanner such as a magnetic resonance (MR) scanner, PET/MR, X-ray or a CT scanner).
Claim 13:
Valadez et al teach:
The computer implemented method of claim 9, wherein registering the image data comprises deformable registration (above detail deform/warp, and figure 5 and 0046 regions corresponding (matching) with landmarks).
Claim 14:
DHARMAKUMAR et al teach:
The computer implemented method of claim 9, wherein the one or more features comprise a location and classification of an organ of the non-human subject (above detail canines (non-human), where 0029 detail organ such as the heart as a whole, portion and section).
Claim 17:
Valadez et al (US 2015/0023575) teaches the following subject matter:
A method (figures 2 and 5 detail flowchart (method)) for generating training data (figure 8 and 0052; claim 8 teaches training local predictor), the method comprising:
acquiring image data (figure 1 and 0005 detail image data from medical imaging device such as CAT scanner, MRI…etc with image data such as 2D and voxel);
deforming the image data to fit a model (FIG. 5, at 512, image segmentation unit 107 inversely maps the registered or warped first ROI to the space of the subject's image data to generate a segmentation mask of the anatomical region);
training a network to generate one or more predictions when input new image data, wherein the training uses the image data, the deformed image data, and respective annotations as training data (0031 detail local predictor to learn and refine registration to shape and generate segmentation from patient-specific and local are view as training data; figure 10-11 and 0055-0057); and
storing the network for use in feature detection for a medical imaging procedure of a subject (0027-0029 detail information storage for imaging data and atlas (standard model); 0033-0035 detail storage system for collection of digital and advance processing and operation; figure 2 and 0044 further detail atlas as model/standard model, where 0031 further detail data that is patient-specific (of a subject)).
Valadez et al teaches all the subject matter above, but not the following: for a non-human subject; a second non-human subject
DHARMAKUMAR et al (US 2022/0117508) teaches:
for a non-human subject (0016-0018 detail medical imaging with Pet and MRI of subject of interest; 0200 detail PET scanning of dog, where 0062 detail further species of dogs such as wolf, as well as other canines in paragraph 0103);
of a second non-human subject (0077 detail animal model (second non-human subject/model/reference/template for registering with) of the same parts with fMRI; 0225 teaches healthy dog model (6-segments))
Valadez et al and DHARMAKUMAR et al are both in the field of image analysis, especially normalizing/standardizing medical imaging (DHARMAKUMAR et al paragraph 0262) such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Valadez et al by DHARMAKUMAR et al to include non-human would expanding this approach to pixel-wise assessment of myocardial oxygenation would open the door for testing novel physiological hypotheses, coronary artery disease, understanding could provide new insights that can improve our understanding of how angina develops in patients with microvascular disease and evaluate therapies to alleviate microvascular impairments in oxygenation. Studies of this nature are likely to demand more advanced segmentation and registration approaches so that pixel-wise analysis can be accurately performed as disclosed by DHARMAKUMAR et al in 0236.
Claim 18:
DHARMAKUMAR et al teach:
The method of claim 17, wherein the first non-human subject and the second non-human subject are different breeds of a same species (0062 detail further species of dogs such as wolf, as well as other canines in paragraph 0103; 0103 detail reference images for canines).
Claim 19:
Valadez et al teach:
The method of claim 17, wherein deforming the image data comprises scaling or warping the image data so that one or more shared landmarks in the image data and model are aligned (above detail deform/warp, and figure 5 and 0046 regions corresponding (matching) with landmarks).
Claim 20:
DHARMAKUMAR et al teaches
The method of claim 17, wherein the image data is acquired using an MRI system (above and 0103 detail MRI imaging of canine) and the medical imaging procedure is for radiation treatment of the non-human subject (0077 detail radiation treatment for human subject/patient, as well as subjects mentioned above such as dogs/canines).
Claims 7 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Valadez et al (US 2015/0023575) in view of DHARMAKUMAR et al (US 2022/0117508) as applied to claim 1 and 9, respectfully above, and further in view of Nguyen et al (2020/0075148).
Claim 7:
Valadez et al and DHARMAKUMAR et al teaches all the subject matter above, but not the following:
The system of claim 1, wherein the output image of the machine trained network is used to adjust a dose of radiation for radiation treatment.
Nguyen et al (2020/0075148) teaches the following subject matter:
The system of claim 1, wherein the output image of the machine trained network is used to adjust a dose of radiation for radiation treatment (0203 detail use of deep neural network for treatment planning by means of dose generation and treatment planner).
Valadez et al and DHARMAKUMAR et al and Nguyen et al are both in the field of image analysis, especially normalizing image/volume of image data (Nguyen et al paragraph 0118-00120 teaches target volume and dimension normalized with neural network in relation to dose) for studies or analysis such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Valadez et al and DHARMAKUMAR et al by Nguyen et al such physician can view the dose distribution pushed toward the desired various critical structure in real time as disclosed by Nguyen et al in 0203.
Claim 15:
Valadez et al and DHARMAKUMAR et al teaches all the subject matter above, but not the following:
The computer implemented method of claim 9, wherein the one or more features are used to generate a plan for radiation treatment.
Nguyen et al (2020/0075148) teaches the following subject matter:
The computer implemented method of claim 9, wherein the one or more features are used to generate a plan for radiation treatment (0203 detail use of system using deep neural network for treatment planning by means of dose generation and treatment planner).
Valadez et al and DHARMAKUMAR et al and Nguyen et al are both in the field of image analysis, especially normalizing image/volume of image data (Nguyen et al paragraph 0118-00120 teaches target volume and dimension normalized with neural network in relation to dose) for studies or analysis such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Valadez et al and DHARMAKUMAR et al by Nguyen et al such physician can view the dose distribution pushed toward the desired various critical structure in real time as disclosed by Nguyen et al in 0203.
Claim 16:
Nguyen et al teaches:
The computer implemented method of claim 15, further comprising: implementing the plan (0203 detail use of system for treatment by means of dose generation and treatment planner).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
LIU et al (US 2021/0366137) teaches DEVICE AND METHOD FOR ALIGNMENT OF MULTI-MODAL CLINICAL IMAGES USING JOINT SYNTHESIS, SEGMENTATION, AND REGISTRATION – figure 3 and 0056 detail The generator model generates a synthesized image from the moving image conditioned on the fixed image; the register model estimates the spatial transformation to align the synthesized image to the fixed image; and the segmentor model estimates segmentation maps of the moving image, the fixed image, and the synthesized image.
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656