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
This action is a non-final rejection
Claims 1-13 are pending
Claims 1-13 are rejected under 35 USC § 101
Claims 1-2, 8-9, 13 are rejected under 35 USC § 102
Claim 3-7, 10-12 are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a foreign priority date of 6-23-2023
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 6-21-2024, and 11-19-2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-13 the claims recite an abstract idea of predicting a pathologic fracture in the proximal femur of an advanced cancer patient.
Independent Claims 1, 8 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 1, 8 recite respectively a device and method for predicting a pathologic fracture in the proximal femur of an advanced cancer patient.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
obtains prediction information about whether a pathologic fracture in the proximal femur will occur within a predetermined period from the time of acquiring the CT image;
belonging to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “predicting a pathologic fracture in the proximal femur of an advanced cancer patient”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “predicting a pathologic fracture in the proximal femur of an advanced cancer patient” (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 1 recites:
a memory for storing one or more instructions; and
a processor for executing the one or more instructions,
wherein the processor processes a CT image including the femur of the cancer patient to generate an input image by executing the one or more instructions,
inputs the input image into a fracture prediction artificial neural network model;
prediction information... which is output by the fracture prediction artificial neural network model.
Claim 8 recites:
receiving an input of a CT image including the femur of the cancer patient;
generating an input image by processing the CT image; and
inputting the input image into a fracture prediction artificial neural network model;
prediction information.. which is output by the fracture prediction artificial neural network model.
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two:
Claim 1 recites:
a memory for storing one or more instructions; and
a processor for executing the one or more instructions,
wherein the processor processes a CT image including the femur of the cancer patient to generate an input image by executing the one or more instructions,
inputs the input image into a fracture prediction artificial neural network model;
prediction information... which is output by the fracture prediction artificial neural network model.
Claim 8 recites:
receiving an input of a CT image including the femur of the cancer patient;
generating an input image by processing the CT image; and
inputting the input image into a fracture prediction artificial neural network model;
prediction information.. which is output by the fracture prediction artificial neural network model.
Amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims 2, 4, 5, 9 recite additional limitations that further define the abstract idea of “predicting a pathologic fracture in the proximal femur of an advanced cancer patient”.
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims 3-7, 10-13 recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims 3-7, 10-13 recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Claim Rejections - 35 USC § 102
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 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 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.
Claims 1-2, 8-9, 13 are rejected by 35 U.S.C. 102(a)(1) as being anticipated by Bregman et.al (WO 2016013004 A1) hereinafter “Bregman”
Regarding claims 1 and 8 Bregman teaches:
a memory for storing one or more instructions; (See at least [Page 18, lines 29-31] via: “...a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention..”) and
a processor for executing the one or more instructions, (See at least [Page 18, line 31] via: “...a processor to carry out aspects of the present invention..”)
receiving an input of a CT image including the femur of the cancer patient (See at least [Claim 1] via: “...receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion...”; in addition see at least [Page 16, line 28-32] via: “...The CT scan may have been ordered for a conventional clinical indication, for example, ... pelvic CT scan...”) The Examiner notes that a pelvic CT scan includes a scan of the proximal femur
wherein the processor processes a CT image including the femur of the cancer patient to generate an input image by executing the one or more instructions, (See at least [Page 6, lines 1-9] via: “...there is provided a computer-implemented method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion..”; in addition see at least [Page 5, line 15-17] via: “...the CT scan is ordered for a conventional clinical indication including at least one member of a group consisting of: low dose CT scan of the chest to screen for lung cancer, CT scan to screen for colon cancer..”; in addition see at least [Page 61, line 9-13] via: “...a tool is provided to detect changes in bones (e.g., the spinal column) characteristic from previous scan. The probability and/or the vector of parameters may enable detection of changes and/or the disease process. Patients with other problems (e.g., cancer) that may cause damage to the bones, and/or old patients may be followed...”; in addition see at least [Page 16, line 28-32] via: “...The CT scan may have been ordered for a conventional clinical indication, for example, ... standard non-contrast CT scan of the abdomen, IV contrast CT scan of the abdomen, oral contrast CT scan of the abdomen, pelvic CT scan...”) The Examiner notes that a pelvic CT scan includes a scan of the proximal femur)
inputs the input image into a fracture prediction artificial neural network model, (See at least [Claim 1] via: “...computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the osteoporotic fracture predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from multiple CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing a signal indicative of the osteoporotic fracture predictive factor..”) and
obtains prediction information about whether a pathologic fracture [in the proximal femur] will occur within a predetermined period from the time of acquiring the CT image which is output by the fracture prediction artificial neural network model. (See at least [Page 2, lines 15-18] via: “... the method further comprises computing an estimate of the probability of the patient being in a certain bone grade state within a predefined time period based on the trained osteoporotic fracture classifier applied to the extracted features..”; in addition see at least [Page 3, line 29-30] via: “...the osteoporotic fracture predictive factor includes a time frame for a risk of developing or having at least one osteoporotic fracture...”; in addition see at least [Page 6, line 30-32 and Page 7, lines 1-3] via: “...the corpus of training images include images of the same patient obtained at different periods in time that are long enough to detect statistically significant changes in at least some of the extracted features, wherein the osteoporotic fracture classifier is trained to predict the risk of developing at least one osteoporotic fracture at a certain future time frame based on the statistically significant changes in the extracted features...”; in addition see at least [Page 15, line 15-25] via: “...the training images include images of the same patient obtained at different periods in time that are long enough to detect statistically significant changes in one or more extracted features related to risk of fracture, for example, multiple CT scans separated by at least about 1 month, or at least about 3 months, or at least about 6 months, or at least about 1 year, or at least about 3 years, or at least about 5 years, or at least about 10 years, or other smaller, intermediate or larger time periods. Such training images may serve as a basis for training the classifier to predict time associated fracture risk (as described herein) based on the changes. Alternatively or additionally, the training set includes multiple scans of patients with shorter time differences, such as when patients are within factors known to accelerate the osteoporosis process, for example, during chemotherapeutic treatment..”; in addition see at least [Page 9, line 5-19] via: “...system for predicting risk of osteoporotic fracture, comprising: a hardware processor; an interface for receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; a memory having stored thereon program modules including code having instructions for execution by the processor, comprising: a bone identification module for processing the imaging data to identify the bone portion; a feature extraction module for extracting features based on the imaging data denoting the identified bone portion; a classifier module for computing an osteoporotic fracture predictive factor indicative of the risk of developing or having at least one osteoporotic fracture in the patient based on the extracted features, the osteoporotic fracture predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from multiple CT scans performed with settings selected for imaging non-osteoporosis related pathology; and an output unit for providing a signal indicative of the osteoporotic fracture predictive factor....”)
Regarding claims 2 and 9 Bregman and Kim teach the invention as claimed and detailed above with respect to claims 1 and 8 respectively. Bregman also teaches:
wherein the CT image is a CT image of the abdomen and pelvis of the cancer patient. (See at least [Page 16, line 28-32] via: “...The CT scan may have been ordered for a conventional clinical indication, for example, ... standard non-contrast CT scan of the abdomen, IV contrast CT scan of the abdomen, oral contrast CT scan of the abdomen, pelvic CT scan...”)
Regarding claim 13 Bregman and Kim teach the invention as claimed and detailed above with respect to claim 8. Bregman also teaches:
A non-transitory computer-readable storage medium storing a program including one or more instructions for performing the method according to claim 8. (See at least [Page 18, lines 29-32 and Page 19, lines 1-6 and Page 12, lines 1-2] via: “...a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.”; in addition see at least [Page 21, line 1-3] via: “...The osteoporotic fracture predictive factor may be displayed on a monitor, stored on a memory (e.g., non-transitory computer readable medium) for future access, and/or forwarded to another computing unit for further processing..”)
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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
non-obviousness.
Claims 3-4 are rejected under 35 U.S.C. 103 as being un-patentable by Bregman, in view of Mu et.al. (US 20180005411 A1) hereinafter “Mu”
Regarding claim 3 Bregman teaches the invention as claimed and detailed above with respect to claim 1. However, Bregmam is silent the following claim that is taught by Mu:
wherein the processor reconstructs the CT image into a radiograph image to generate the input image. (See at least [0001] via: “...generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. More particularly, the present disclosure relates to methods for quickly creating synthetic 2D images, such as digitally reconstructed radiograph (DRR), from a 3D image sometimes represented as a 3D dataset...”; in addition see at least [0044] via: “... If a 3D dataset of the object is available as a CT scan, a simulated X-ray image, a.k.a., a digitally reconstructed radiograph (DRR), can be computed using the 3D CT scan and perspective projection geometry...”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Bregman to incorporate the teachings of Mu. Those in the art would have recognized that Bregman’s teaching regarding predicting risk of osteoporotic fracture in cancer patients could be modified to include Mu’s teaching regarding generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. This combination would be advantageous to radiologists in being able to decipher and visualize 3D CT scans by analyzing a 2D reconstructed image in familiar radiograph format that is easier to analyze.
Regarding claim 4 Bregman teaches the invention as claimed and detailed above with respect to claim 1 and Bregman and Mu teach the invention as claimed and detailed above with respect to claim 3. However, Bregmam is silent the following claim that is taught by Mu:
wherein the CT image is a three-dimensional image and the radiograph image is a two-dimensional image, (See at least [0001] via: “...generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. More particularly, the present disclosure relates to methods for quickly creating synthetic 2D images, such as digitally reconstructed radiograph (DRR), from a 3D image sometimes represented as a 3D dataset...”; in addition see at least [0044] via: “... If a 3D dataset of the object is available as a CT scan, a simulated X-ray image, a.k.a., a digitally reconstructed radiograph (DRR), can be computed using the 3D CT scan and perspective projection geometry...”) and
wherein the processor converts the CT image into the radiograph image through perspective projection. (See at least [0044] via: “... If a 3D dataset of the object is available as a CT scan, a simulated X-ray image, a.k.a., a digitally reconstructed radiograph (DRR), can be computed using the 3D CT scan and perspective projection geometry..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Bregman to incorporate the teachings of Mu. Those in the art would have recognized that Bregman’s teaching regarding predicting risk of osteoporotic fracture in cancer patients could be modified to include Mu’s teaching regarding generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. This combination would be advantageous to radiologists in being able to decipher and visualize 3D CT scans by analyzing a 2D reconstructed image in familiar radiograph format that is easier to analyze.
Claims 5-7, 10-12 are rejected under 35 U.S.C. 103 as being un-patentable by Bregman, in view of Mu, in further view of Kim et.al. (KR 20230137613 A) hereinafter “Kim”
Regarding claim 5 Bregman teaches the invention as claimed and detailed above with respect to claim 1 and Bregman and Mu teach the invention as claimed and detailed above with respect to claim 3. However, Bregmam and Mu are silent the following claim that is taught by Kim:
wherein the processor generates a cropped radiograph image by extracting a part representing the femur of the cancer patient from the reconstructed radiograph image. (See at least [Page 5, lines 35-36] via: “... Figure 4 is an example of crop enhancement of a reference thigh image...”; in addition see at least [Page 5, lines 9-11] via: “...The learning device acquires one reference femur image from the learning DB. The learning device inputs the reference femur image into the learning model. The learning model extracts features from the input image and calculates a probability value for whether there is tumor metastasis in the thigh..”; in addition see at least [Page 2, lines 47-51] via: “... A tumor classification method based on a learning model using femur x-ray images includes the steps of: an analysis device receiving the subject's femur x-ray images; the analysis device extracting an image of one of two femurs from the femur x-ray images; the analysis device inputting the one femur image into a pre-trained learning model; and the analysis device classifying whether the one femur image has a tumor according to the value output by the learning model. )
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Bregman and Mu to incorporate the teachings of Kim. Those in the art would have recognized that Bregman’s teaching regarding predicting risk of osteoporotic fracture in cancer patients could be modified to include Kim’s teaching regarding crop enhancement of a reference thigh image. This combination would be advantageous to radiologists in being able to combine image segmentation (to isolate the femur) with image cropping in order to highlight the femur in order to ease specific analysis of the femur.
Regarding claim 6 Bregman teaches the invention as claimed and detailed above with respect to claim 1 and Bregman, Mu and Kim teach the invention as claimed and detailed above with respect to claim 3&5. However, Bregmam and Mu are silent the following claim that is taught by Kim:
wherein the processor augments the cropped radiograph image to generate a plurality of input images from one of the cropped radiograph images. (See at least [Page 5, lines 35-37] via: “... Figure 4 is an example of crop enhancement of a reference thigh image. The learning device can augment each reference thigh image. Figure 4 is a crop-based augmentation method in which the learning device can generate slightly different images by varying the cropped area in the original image...”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Bregman and Mu to incorporate the teachings of Kim. Those in the art would have recognized that Bregman’s teaching regarding predicting risk of osteoporotic fracture in cancer patients could be modified to include Kim’s teaching regarding crop enhancement and augmentation of a reference thigh image. This combination would be advantageous to radiologists in being able to combine image segmentation (to isolate the femur) with image cropping and augmentation in order to highlight the femur into several regions in order to ease specific analysis of the femur.
Regarding claims 7 and 12 Bregman teaches the invention as claimed and detailed above with respect to claims 1 and 8 respectively. Bregman also teaches:
wherein the fracture prediction artificial neural network model is trained by receiving inputs of a learning input image generated by processing abdomen and pelvis CT images of a plurality of cancer patients, and the determination of whether a pathologic fracture in the proximal femur will occur in each of the plurality of cancer patients within a predetermined period from the time of acquiring the CT images, (See at least [Page 7, line 1-13] via: “...system for predicting risk of osteoporotic fracture, comprising: a hardware processor; an interface for receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; a memory having stored thereon program modules including code having instructions for execution by the processor, comprising: a bone identification module for processing the imaging data to identify the bone portion; a feature extraction module for extracting features based on the imaging data denoting the identified bone portion; a classifier module for computing an osteoporotic fracture predictive factor indicative of the risk of developing or having at least one osteoporotic fracture in the patient based on the extracted features, the osteoporotic fracture predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from multiple CT scans performed with settings selected for imaging non-osteoporosis related pathology; and an output unit for providing a signal indicative of the osteoporotic fracture predictive factor...”; in addition see at least [Page 5, line 5-6] via: “...the CT scan is ordered for a conventional clinical indication including at least one member of a group consisting of: low dose CT scan of the chest to screen for lung cancer, CT scan to screen for colon cancer..”; in addition see at least [Page 33, line 21-24] via: “...a tool is provided to detect changes in bones (e.g., the spinal column) characteristic from previous scan. The probability and/or the vector of parameters may enable detection of changes and/or the disease process. Patients with other problems (e.g., cancer) that may cause damage to the bones, and/or old patients may be followed...”; in addition see at least [Page 10, line 50-53] via: “...The CT scan may have been ordered for a conventional clinical indication, for example, ... standard non-contrast CT scan of the abdomen, IV contrast CT scan of the abdomen, oral contrast CT scan of the abdomen, pelvic CT scan...”) The Examiner notes that a pelvic CT scan includes a scan of the proximal femur.
However, Bregman is silent the following limitation that is taught by Mu
wherein the learning input image is generated by respectively reconstructing the abdomen and pelvis CT images of the plurality of cancer patients into radiograph images, and. (See at least [0001] via: “...generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. More particularly, the present disclosure relates to methods for quickly creating synthetic 2D images, such as digitally reconstructed radiograph (DRR), from a 3D image sometimes represented as a 3D dataset...”; in addition see at least [0044] via: “... If a 3D dataset of the object is available as a CT scan, a simulated X-ray image, a.k.a., a digitally reconstructed radiograph (DRR), can be computed using the 3D CT scan and perspective projection geometry...”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Bregman to incorporate the teachings of Mu. Those in the art would have recognized that Bregman’s teaching regarding predicting risk of osteoporotic fracture in cancer patients could be modified to include Mu’s teaching regarding generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. This combination would be advantageous to radiologists in being able to decipher and visualize 3D CT scans by analyzing a 2D reconstructed image in familiar radiograph format that is easier to analyze.
However, Bregman and Mu are silent the following limitation that is taught by Kim
extracting a part representing the femur of each of the cancer patients from each of the reconstructed radiograph images such that a generated femur cropped image is augmented (See at least [Page 5, lines 35-37] via: “... Figure 4 is an example of crop enhancement of a reference thigh image. The learning device can augment each reference thigh image. Figure 4 is a crop-based augmentation method in which the learning device can generate slightly different images by varying the cropped area in the original image...”; in addition see at least [Page 5, lines 9-11] via: “...The learning device acquires one reference femur image from the learning DB. The learning device inputs the reference femur image into the learning model. The learning model extracts features from the input image and calculates a probability value for whether there is tumor metastasis in the thigh..”; in addition see at least [Page 2, lines 47-51] via: “... A tumor classification method based on a learning model using femur x-ray images includes the steps of: an analysis device receiving the subject's femur x-ray images; the analysis device extracting an image of one of two femurs from the femur x-ray images; the analysis device inputting the one femur image into a pre-trained learning model; and the analysis device classifying whether the one femur image has a tumor according to the value output by the learning model. )
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Bregman and Mu to incorporate the teachings of Kim. Those in the art would have recognized that Bregman’s teaching regarding predicting risk of osteoporotic fracture in cancer patients could be modified to include Kim’s teaching regarding crop enhancement and augmentation of a reference thigh image. This combination would be advantageous to radiologists in being able to combine image segmentation (to isolate the femur) with image cropping and augmentation in order to highlight the femur into several regions in order to ease specific analysis of the femur.
Regarding claim 10 Bregman teaches the invention as claimed and detailed above with respect to claim 8&9. However, Bregmam is silent the following claim that is taught by Mu:
reconstructing the three-dimensional CT image into a two-dimensional radiograph image; (See at least [0001] via: “...generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. More particularly, the present disclosure relates to methods for quickly creating synthetic 2D images, such as digitally reconstructed radiograph (DRR), from a 3D image sometimes represented as a 3D dataset...”; in addition see at least [0044] via: “... If a 3D dataset of the object is available as a CT scan, a simulated X-ray image, a.k.a., a digitally reconstructed radiograph (DRR), can be computed using the 3D CT scan and perspective projection geometry...”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Bregman to incorporate the teachings of Mu. Those in the art would have recognized that Bregman’s teaching regarding predicting risk of osteoporotic fracture in cancer patients could be modified to include Mu’s teaching regarding generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. This combination would be advantageous to radiologists in being able to decipher and visualize 3D CT scans by analyzing a 2D reconstructed image in familiar radiograph format that is easier to analyze.
However, Bregman and Mu are silent the following limitation that is taught by Kim
generating a cropped radiograph image by extracting a part representing the femur of the cancer patient from the reconstructed radiological image; (See at least [Page 5, lines 35-36] via: “... Figure 4 is an example of crop enhancement of a reference thigh image...”; in addition see at least [Page 5, lines 9-11] via: “...The learning device acquires one reference femur image from the learning DB. The learning device inputs the reference femur image into the learning model. The learning model extracts features from the input image and calculates a probability value for whether there is tumor metastasis in the thigh..”; in addition see at least [Page 2, lines 47-51] via: “... A tumor classification method based on a learning model using femur x-ray images includes the steps of: an analysis device receiving the subject's femur x-ray images; the analysis device extracting an image of one of two femurs from the femur x-ray images; the analysis device inputting the one femur image into a pre-trained learning model; and the analysis device classifying whether the one femur image has a tumor according to the value output by the learning model. ) and
augmenting the cropped radiograph image to generate a plurality of input images from one of the cropped radiograph images. (See at least [Page 5, lines 35-37] via: “... Figure 4 is an example of crop enhancement of a reference thigh image. The learning device can augment each reference thigh image. Figure 4 is a crop-based augmentation method in which the learning device can generate slightly different images by varying the cropped area in the original image...”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Bregman and Mu to incorporate the teachings of Kim. Those in the art would have recognized that Bregman’s teaching regarding predicting risk of osteoporotic fracture in cancer patients could be modified to include Kim’s teaching regarding crop enhancement and augmentation of a reference thigh image. This combination would be advantageous to radiologists in being able to combine image segmentation (to isolate the femur) with image cropping and augmentation in order to highlight the femur into several regions in order to ease specific analysis of the femur.
Regarding claim 11 Bregman teaches the invention as claimed and detailed above with respect to claim 8&9 and Bregman, Mu and Kim teach the invention as claimed and detailed above with respect to claim 10. However, Bregmam and Kim are silent the following claim that is taught by Mu:
wherein the three-dimensional CT image is reconstructed into the two-dimensional radiograph image through perspective projection. (See at least [0001] via: “...generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. More particularly, the present disclosure relates to methods for quickly creating synthetic 2D images, such as digitally reconstructed radiograph (DRR), from a 3D image sometimes represented as a 3D dataset...”; in addition see at least [0044] via: “... If a 3D dataset of the object is available as a CT scan, a simulated X-ray image, a.k.a., a digitally reconstructed radiograph (DRR), can be computed using the 3D CT scan and perspective projection geometry...”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Bregman to incorporate the teachings of Mu. Those in the art would have recognized that Bregman’s teaching regarding predicting risk of osteoporotic fracture in cancer patients could be modified to include Mu’s teaching regarding generating two-dimensional (2D) projections (or images) from a previously generated three-dimensional (3D) dataset. This combination would be advantageous to radiologists in being able to decipher and visualize 3D CT scans by analyzing a 2D reconstructed image in familiar radiograph format that is easier to analyze.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Hong (US 20220208384 A1)- METHOD AND APPARATUS OF PREDICTING FRACTURE RISK -teaches: methods of predicting fracture risk, which improve fracture risk prediction by developing a bone radiomics score model based on machine learning. As an embodiment of the present invention, the method of predicting the fracture risk is configured to perform the steps of designing a development set, processing bone images for a plurality of subjects included in the development set, extracting texture features from the bone images, selecting optimal texture features required to predict the fracture risk from the extracted texture features, performing machine learning for the optimal texture features using a training set of the development set, and designing a bone radiomics score model to predict the fracture risk
Conclusion
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
