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 .
Specification
The disclosure is objected to because of the following informalities:
In page 6, line 16, “an image analysis system 10” should read “an image analysis system 12”.
In page 6, line 17, “electronic display 21” should read “electronic display 14”.
In page 18, line 16, “cottage” should read “voltage”.
Appropriate correction is required.
Claim Objections
Claim 6 is objected to because of the following informalities:
In claim 6, line 4, “based om” should read “based on”.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 4, 8, 11-12, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lang (US 2009/0207970, hereinafter “Lang”) in view of Eggermont et al. (Calibration with or without phantom for fracture risk prediction in cancer patients with femoral bone metastases using CT-based finite element models, PLOS ONE, https://doi.org/10.1371/journal.pone.0220564 July 30, 2019, hereinafter “Eggermont”).
Regarding claim 1, Lang discloses A non-transitory computer readable medium comprising instructions … wherein the instruction, when executed by one or more processors, are configured to: (para. [0101], “The computer readable medium on which the program instructions are encoded can be … microprocessors, floppy disks, hard drives, ZIP drives, WORM drives, magnetic tape and optical medium such as CD-ROMs”). Note that: CD-ROMs and hard drives are non-transitory computer readable media, and microprocessors are the processor to execute the instructions. configured to automatically determine bone mineral density from a medical image (para. [0019], “using a computer program to analyze bone mineral density of an x-ray image and comparing and comparing the bone mineral density value obtained from the image with a reference standard or curve”) exhibiting air, fat, and a motion rod, (para. [0107], “the networked x-ray images include accurate reference markers, for example calibration phantoms for assessing bone mineral density of any given x-ray image”; para. [0009], “The internal standard can be, for example, density of a tissue of a human (e.g,. subcutaneous fat, bone, muscle), air surrounding a structure or combinations of tissue and air density”). Note that: (1) the reference markers can be regarded as a motion rod to determine the patient motion or use as a reference material shown in the images; (2) air, fat, and muscle can be shown or exhibited in the x-ray images; and (3) when a software or a program is used to perform a processing instead of manually processing, it is known that the processing is performed automatically through this document.
automatically obtain a bone mineral density equivalent value of the motion rod and (para. [0126], “The methods generally involve simultaneously imaging or scanning the calibration phantom and another material (e.g., bone tissue from a subject for the purpose of quantifying the density of the imaged material (e.g. bone mass)”; para. [0019], “the methods of diagnosing osteoporosis in a subject comprise using a computer program to analyze bone mineral density of an x-ray image and comparing the bone mineral density value obtained from the image with a reference standard or curve, thereby determining if the subject has osteoporosis. Preferably, the x-ray image includes a calibration phantom, for example a calibration phantom as described herein”). Note that: (1) the reference marker can be scanned to have the density values that can be regarded as bone mineral density equivalent value; and (2) air and fat as the reference materials can be scanned in the same way to obtain the corresponding bone density values that can be regarded as the bone mineral density equivalent values of air and fat.
However, Lang fails to disclose, but in the same art of bone mineral density evaluation, Eggermont discloses
automatically identify, from the medical image, intensities of the bone, the motion rod and at least one of: air or fat; (Eggermount, page 4, para. 4, “For the air-fat-muscle calibration, the same nine diaphyseal slices as used for the phantom calibration were selected, using the same protocol for slice selection. The succeeding steps of the air-fat-muscle calibration were completely automated. On the nine selected slices, a square region of interest was defined including the tissue of the right leg and some surrounding air (±1 cm on each side of the leg; Fig 1). Next, a combined histogram of all HU in the volume of interest (i.e. nine slices together) was created to extract the peaks for air, fat and muscle tissue (Fig 2). The mode of the HU around the histogram peak (±50 HU) was calculated, to determine the exact peak in HU for each of the tissues.” para. [0126], “simultaneously imaging or scanning the calibration phantom … for the purpose of quantifying the density of the imaged material”; page 6, Fig. 2: “An example of a histogram of the Hounsfield units within the region of interest, used to extract the peaks for air, fat and muscle. An additional relatively small peak is visible around 1500 HU, indicating the cortical bone of the femur, “
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”). Note that: (1) by automatic histogram method, the intensity peaks (unit: HU) of air, fat, muscle, and bone (as described by “An additional relatively small peak is visible around 1500 HU, indicating the cortical bone of the femur”) are found or identified from the x-ray images, and they can be regarded as the intensities of bone, air, and muscle; and (2) the motion rod as reference marker of materials can be substituted by muscle as reference material so that the intensity of the motion rod can be found or identified in the same way for muscle.
automatically determine a BMD calibration factor based on a relationship between the identified intensities and the BMD equivalent values of the motion rod and the at least one of the air or the fat; and (Eggermont, page 4, para. 4, “the determined HU peaks were linearly fitted to the reference “BMD” values for each patient to obtain the air-fat-muscle calibration function. These values were obtained by phantom calibrating the HU peaks of air, fat and muscle of a randomized subgroup comprising 10 patients scanned on the Philips-1 scanner. Subsequently, we averaged and rounded them, resulting in reference “BMD” values of -840, -80 and 30 for air, fat and muscle, respectively. The linear fits between HU and “BMD” were very good with an average R2 of 1.000±0.000 (slope = 1.194±0.016, intercept = 2.232±12.042)”). Note that: (1) the identified or determined HU peaks of air, fat, muscle is linearly fitted to the reference BMD values (or bone mineral density equivalent value); and (2) the slope and intercept of the linear fits between HU and “BMD” are the BMD calibration factor between the identified intensities and the BMD values. As described above, muscle as a reference material substitutes the motion rod.
automatically determine the BMD of the bone within the medical image based on the BMD calibration factor. (Eggermont, “Quantitative Computed Tomography (QCT) scans can be used to segment patient-specific bone geometries that function as input for the FE models”). Note that: (1) the patient-specific bone geometries can be segmented from the patient images; and (2) using the BMD calibration factor above, the BMD of the bone can be mapped from the HU intensity values of the segmented bone areas in the images.
Lang and Eggermont are in the same field of endeavor, namely bone mineral density evaluation. Before the effective filing date of the claimed invention, it would have been obvious to apply determining a BMD calibration factor with a linear fitting to convert HU intensity value into BMD value, as taught by Eggermont into Lang. The motivation would have been “With the air-fat-muscle calibration, clinical implementation of the FE model as tool for fracture risk assessment will be easier from a practical and financial viewpoint, since FE models can be made using everyday clinical CT scans without the need of concurrent scanning of calibration phantoms.” (Eggermont, page 1, Abstract). The suggestion for doing so would allow to obtain BMD value from x-ray images without scanning of a calibration phantom. Therefore, it would have been obvious to combine Lang and Eggermont.
Regarding claim 2, Lang in view of Eggermont discloses The non-transitory computer readable medium of claim 1, wherein the instructions, when executed by the one or more processors, are further configured to:
automatically obtain a bone mineral density equivalent value of the motion rod, the air, and the fat; (Lang, para. [0126], “The methods generally involve simultaneously imaging or scanning the calibration phantom and another material (e.g., bone tissue from a subject for the purpose of quantifying the density of the imaged material (e.g. bone mass)”; para. [0019], “the methods of diagnosing osteoporosis in a subject comprise using a computer program to analyze bone mineral density of an x-ray image and comparing the bone mineral density value obtained from the image with a reference standard or curve, thereby determining if the subject has osteoporosis. Preferably, the x-ray image includes a calibration phantom, for example a calibration phantom as described herein”). Note that: (1) the reference marker can be scanned to have the density values that can be regarded as bone mineral density equivalent value; and (2) air and fat as the reference materials can be scanned in the same way to obtain the corresponding density values that can be regarded as the bone mineral density equivalent value of air and fat.
automatically identify, from the medical image, intensities of the bone, the motion rod, the air, and the fat; (Eggermount, page 4, para. 4, “For the air-fat-muscle calibration, the same nine diaphyseal slices as used for the phantom calibration were selected, using the same protocol for slice selection. The succeeding steps of the air-fat-muscle calibration were completely automated. On the nine selected slices, a square region of interest was defined including the tissue of the right leg and some surrounding air (±1 cm on each side of the leg; Fig 1). Next, a combined histogram of all HU in the volume of interest (i.e. nine slices together) was created to extract the peaks for air, fat and muscle tissue (Fig 2). The mode of the HU around the histogram peak (±50 HU) was calculated, to determine the exact peak in HU for each of the tissues.” para. [0126], “simultaneously imaging or scanning the calibration phantom … for the purpose of quantifying the density of the imaged material”; page 6, Fig. 2: “An example of a histogram of the Hounsfield units within the region of interest, used to extract the peaks for air, fat and muscle. An additional relatively small peak is visible around 1500 HU, indicating the cortical bone of the femur, “
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”). Note that: (1) by automatic histogram method, the intensity peaks (unit: HU) of air, fat, muscle, and bone (as described by “An additional relatively small peak is visible around 1500 HU, indicating the cortical bone of the femur”) are found or identified from the x-ray images, and they can be regarded as the intensities of bone, air, and muscle; and (2) the motion rod as reference marker of materials can be substituted by muscle as reference material so that the intensity of the motion rod can be found or identified in the same way for muscle.
automatically determine a BMD calibration factor based on a relationship between the identified intensities and a BMD equivalent values of the motion rod, the air, and the fat; and (Eggermont, page 4, para. 4, “the determined HU peaks were linearly fitted to the reference “BMD” values for each patient to obtain the air-fat-muscle calibration function. These values were obtained by phantom calibrating the HU peaks of air, fat and muscle of a randomized subgroup comprising 10 patients scanned on the Philips-1 scanner. Subsequently, we averaged and rounded them, resulting in reference “BMD” values of -840, -80 and 30 for air, fat and muscle, respectively. The linear fits between HU and “BMD” were very good with an average R2 of 1.000±0.000 (slope = 1.194±0.016, intercept = 2.232±12.042)”). Note that: (1) the identified or determined HU peaks of air, fat, muscle is linearly fitted to the reference BMD values (or bone mineral density equivalent value); and (2) the slope and intercept of the linear fits between HU and “BMD” are the BMD calibration factor between the identified intensities and the BMD values. As shown above, muscle substitutes the motion rod as reference material.
automatically determine the BMD of the bone within the medical image based on the BMD calibration factor. (Eggermont, “Quantitative Computed Tomography (QCT) scans can be used to segment patient-specific bone geometries that function as input for the FE models”). Note that: (1) the patient-specific bone geometries can be segmented from the patient images; and (2) using the BMD calibration factor above, the BMD of the bone can be mapped from the HU intensity values of the segmented bone areas in the images.
The motivation to combine Lang and Eggermont given in claim 1 in incorporated here.
Regarding claim 4, Lang in view of Eggermont discloses The non-transitory computer readable medium of claim 1, wherein the BMD equivalent value of the motion rod, the air, and the fat are predetermined. (Eggermont, page4, para. 4, “These values were obtained by phantom calibrating the HU peaks of air, fat and muscle of a randomized subgroup comprising 10 patients scanned on the Philips-1 scanner. Subsequently, we averaged and rounded them, resulting in reference “BMD” values of -840, -80 and 30 for air, fat and muscle, respectively”). Note that: the BMD values the motion rod (substitute by muscle above), the air, and the fat are predefined as 30, -840, and -80.
The motivation to combine Lang and Eggermont given in claim 1 in incorporated here.
Regarding claim 8, Lang in view of Eggermont discloses The non-transitory computer readable medium of claim 1, wherein the motion rod is comprised of aluminum. (Lang, para. [0190], “Any suitable calibration phantom can be used, for example, one that comprises aluminum or other radio-opaque materials.”). Note that: the motion rod used as calibration reference material or reference marker can be of aluminum.
Claim 11 reciting “A computer-implemented method for evaluating a medical image exhibiting air, fat, a bone and a motion rod, wherein computer-implemented method comprises automatically performing the following:” is corresponding to claim 1. Therefore claim 1 is rejected for the same rationale for claim 1.
In addition, Lang in view of Eggermont discloses A computer-implemented method for evaluating a medical image exhibiting air, fat, a bone and a motion rod, wherein computer-implemented method comprises automatically performing the following: (Lang, para. [0009], “the method comprises transmitting an x-ray image from a local computer to a remote computer and obtaining quantitative information from the x-ray image using a computer program.”; para. [0107], “the networked x-ray images include accurate reference markers, for example calibration phantoms for assessing bone mineral density of any given x-ray image”; para. [0009], “The internal standard can be, for example, density of a tissue of a human (e.g,. subcutaneous fat, bone, muscle), air surrounding a structure or combinations of tissue and air density”). Note that: (1) the reference markers can be regarded as a motion rod to determine the patient motion or use as a reference material shown in the images; and (2) air, fat, muscle can be shown or exhibited in the x-ray images.
Claims 12, 14, and 18 are corresponding to claims 2, 4, and 8, respectively. Therefore, claims 12, 14, and 18 are rejected for the same rationale for claims 2, 4, and 8, respectively.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Lang in view of Eggermont and Lee et al. (Phantomless Calibration of CT Scans for Measurement of BMD and Bone Strength — Inter-Operator Reanalysis Precision, Bone. 2017 October ; 103: 325–333. doi:10.1016/j.bone.2017.07.029, hereinafter “Lee”) .
Regarding claim 5, Lang in view of Eggermont discloses The non-transitory computer readable medium of claim 1, wherein the instructions, when executed by the one or more processors, are further configured to:
However, Lang in view of Eggermont fails to disclose, but in the same art of BMD evaluation, Lee discloses
automatically generate a three-dimensional bone model exhibiting the determined BMD of the bone. (Lee, page 17, Figure 2: “Analysis of the spine and hip from a 70-year-old woman. Left: Automatically-placed regions of interest for the BMD analysis of the mid-vertebral trabecular bone (yellow ellipse) and DXA-equivalent areal BMD analysis of the hip, showing the femoral neck (yellow box) and total hip regions of interest (all bone above the yellow line, up to and including the femoral neck). Right: Virtual deformation patterns (magnified for viewing purposes) by finite element analysis for the spine and hip, showing regions of simulated bone tissue failure (colored). The gray colors denote the volumetric BMD values throughout the models. Boundary conditions were applied via virtual layers of plastic (light blue) to evenly distribute the applied loads over the bone surfaces, and simulated a compressive overload of the vertebral body and a sideways fall for the proximal femur”, and “
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”). Note that: On the right in Figure 2, a 3D display of the bone model exhibiting the determined BMD values encoded in a grey level scale.
Lang in view of Eggermont, and Lee, are in the same field of endeavor, namely bone mineral density evaluation. Before the effective filing date of the claimed invention, it would have been obvious to apply generating a 3D bone model exhibiting determined BMD values and a new bone model image, as taught by Lee into Lang in view of Eggermont. The motivation would have been “The gray colors denote the volumetric BMD values throughout the models.” (Lee, page 17, Figure 2’s caption). The suggestion for doing so would allow to generate a 3D model encoded with determined BMD values and a new bone model image. Therefore, it would have been obvious to combine Lang, Eggermont, and Lee.
Regarding claim 6, the combination of Lang, Eggermont, and Lee discloses The non-transitory computer readable medium of claim 1, wherein the instructions, when executed by the one or more processors, are further configured to:
automatically generate a new medical image based on the BMD calibration factor; or automatically calibrate the medical image based om the BMD calibration factor. (Lee, page 17, Figure 2: “Analysis of the spine and hip from a 70-year-old woman. Left: Automatically-placed regions of interest for the BMD analysis of the mid-vertebral trabecular bone (yellow ellipse) and DXA-equivalent areal BMD analysis of the hip, showing the femoral neck (yellow box) and total hip regions of interest (all bone above the yellow line, up to and including the femoral neck). Right: Virtual deformation patterns (magnified for viewing purposes) by finite element analysis for the spine and hip, showing regions of simulated bone tissue failure (colored). The gray colors denote the volumetric BMD values throughout the models. Boundary conditions were applied via virtual layers of plastic (light blue) to evenly distribute the applied loads over the bone surfaces, and simulated a compressive overload of the vertebral body and a sideways fall for the proximal femur”, and “
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”). Note that: (1) On the right in Figure 2, a new medical 3D bone model image is generated exhibiting the determined BDM values that are converted from the image HU intensity values based on the BMD calibration factor; and (2) the CT slice image can be encoded or calibrated with the determined image HU intensity values based on the BMD calibration factor.
The motivation to combine Lang, Eggermont, and Lee given in claim 5 in incorporated here.
Claims 15-16 are corresponding to claims 5-6, respectively. Therefore, claims 15-16 are rejected for the same rationale for claims 5-6, respectively.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lang in view of Eggermont and Brownlee (Train-Test Split for Evaluating Machine Learning Algorithms, Archive.org, https://web.archive.org/web/20231225145754/https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/, hereinafter “Brownlee”) .
Regarding claim 7, Lang in view of Eggermont discloses The non-transitory computer readable medium of claim 1, wherein the instructions, when executed by the one or more processors, are further configured to:
However, Lang in view of Eggermont fails to disclose, but in the art of machine learning algorithm evaluation, Brownlee disclose
automatically compare the intensity and the BMD equivalent value of the one of the air or the fat not used in determining the BMD calibration factor to the relationship; and (Brownlee, page 3, paras. 1-3, “The train-test split is a technique for evaluating the performance of a machine learning algorithm. It can be used for classification or egression problems and can be used for any supervised learning algorithm. The procedure involves taking a dataset and dividing it into two subsets. The first subset is used to fit the model and is referred to as the training dataset. The second subset is not used to train the model; instead, the input element of the dataset is provided to the model, then predictions are made and compared to the expected values. This second dataset is referred to as the test dataset”; page 14, para. 3, “Finally, the model is evaluated on the test set and the performance of the model when making predictions on new data is a mean absolute error of about 2.211 (thousands of dollars)”). Note that: (1) the linear fitting in claim 1 is a machine learning algorithm; (2) all intensity and BMD equivalent values of air and fat of the images can be regarded as a dataset including two parts exclusive to each other: a train dataset for the linear fitting, and a test dataset that is not used for the fitting to determine the BMD calibration factor; (3) after the linear fitting based model has be trained, the test dataset is used to evaluate the performance of the fitting in terms of performance parameters (MAE, mean absolute error). The input of the trained model can be substituted with the intensity value of HU intensity values of air and fat the output can be substitute with the predicted or determined or predicted BMD equivalent value. The output can be compared with the BMD equivalent values of air and fat.
automatically reject the BMD calibration factor as being unacceptable in response to a determination that the comparison exceeds a threshold. (Brownlee, page 14, para. 3, “Finally, the model is evaluated on the test set and the performance of the model when making predictions on new data is a mean absolute error of about 2.211 (thousands of dollars)”). Note that: (1) the mean absolute error for the test dataset can be a performance indicator or metrics for the fitting performance; and (2) it is obvious to one having ordinary skill in the art that: a) the larger the MAE, the worse the fitting performance; b) if the MAE is greater than a value, the fitting or linear regression would be not acceptable to use; and c) the value can be a MAE threshold to reject the BMD calibration factor or not. If the MAE is greater than the threshold value, the factor shall be rejected.
Lang in view of Eggermont, and Brownlee, are in the same field of endeavor, namely machine learning algorithm evaluation. Before the effective filing date of the claimed invention, it would have been obvious to apply splitting the dataset into a training dataset and a test dataset and evaluate the trained model with test dataset in terms of fitting performance metrics, as taught by Brownlee into Lang in view of Eggermont. The motivation would have been “Finally, the model is evaluated on the test set and the performance of the model when making predictions on new data is a mean absolute error of about 2.211 (thousands of dollars).” (Brownlee, page 14, para. 3). The suggestion for doing so would allow to train a regression fitting model with train dataset , and accept or reject the model if the calculated performance metrics is lower than a threshold. Therefore, it would have been obvious to combine Lang, Eggermont, and Brownlee.
Claim 17 is corresponding to claim 7. Therefore, claim 17 is rejected for the same rationale for claim 7.
Claims 9-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lang in view of Eggermont and Chen et al. (Inter-Subject Shape Correspondence Computation From Medical Images Without Organ Segmentation, IEEE Access Volume: 7, 2019, Page(s): 130772-130781, hereinafter “Chen”).
Regarding claim 9, Lang in view of Eggermont discloses The non-transitory computer readable medium of claim 1, wherein the instructions, when executed by the one or more processors, are further configured to:
However, Lang in view of Eggermont fails to disclose, but in the same art of computer graphics, Chen discloses
automatically determine the motion rod in the medical image using a statistical shape model or an active appearance model. (Chen, Abstract, “this paper proposes a novel shape correspondence computation approach which eliminates the need for image segmentation by registering an organ shape template to the training images. This method allows the human expert to specify key anatomical landmarks in the training images to define the correspondence of the crucial landmarks. An intensity-and-landmark-combined strategy is implemented to utilized both the image intensity and expert landmarks to obtain accurate shape correspondence. This method is evaluated for the construction of head anatomy SSM and spine SSM based on computed tomography (CT) images”; page 130774, FIGURE 1: “The workflow of the proposed method (taking the head SSM construction an example)”, “Reference Template”, “Reference Template Volume Image”, and “Subject 1 CT”’s slice images through “Landmark Registration” and “Intensity Registration” to obtain the final “Statistical Shape Model”). Note that: (1) head anatomy can substitute the motion rod here; and (2) user can specify a few landmarks of the motion rod in x-ray images and select a corresponding rod template as input, and the motion rod can be determined or constructed by the proposed SSM without shape segmentation.
Lang in view of Eggermont, and Chen, are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply utilizing a statistic shape model and determining head and spin, as taught by Chen into Lang in view of Eggermont. The motivation would have been “The SSMs constructed using the proposed method demonstrates better shape correspondence accuracy than other state-of-the-arts correspondence methods.” (Chen, Abstract). The suggestion for doing so would allow to determine an object shape with better shape correspondence accuracy. Therefore, it would have been obvious to combine Lang, Eggermont, and Chen.
Regarding claim 10, the combination of Lang, Eggermont, and Chen discloses The non-transitory computer readable medium of claim 1, wherein the instructions, when executed by the one or more processors, are further configured to: automatically determine the bone in the medical image using a statistical shape model or an active appearance model. (Chen, Abstract, “this paper proposes a novel shape correspondence computation approach which eliminates the need for image segmentation by registering an organ shape template to the training images. This method allows the human expert to specify key anatomical landmarks in the training images to define the correspondence of the crucial landmarks. An intensity-and-landmark-combined strategy is implemented to utilized both the image intensity and expert landmarks to obtain accurate shape correspondence. This method is evaluated for the construction of head anatomy SSM and spine SSM based on computed tomography (CT) images”; page 130774, FIGURE 1: “The workflow of the proposed method (taking the head SSM construction an example)”, “Reference Template”, “Reference Template Volume Image”, and “Subject 1 CT”’s slice images through “Landmark Registration” and “Intensity Registration” to obtain the final “Statistical Shape Model”). Note that: (1) head anatomy can substitute bone here; and (2) user can specify a few landmarks of the motion rod in x-ray images and select a corresponding rod template as input, and the bone can be determined or constructed by the proposed SSM without shape segmentation.
The motivation to combine Lang, Eggermont, and Chen given in claim 9 is incorporated here.
Claims 19-20 are corresponding to claims 9-10, respectively. Therefore, claims 19-20 are rejected for the same rationale for claims 9-10, respectively.
Allowable Subject Matter
Claims 3 and 13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding dependent claim 3, in the context of claim as a whole, the prior art either alone or in combination does not teach or suggest the additional elements of: “automatically generate vectors normal to the mesh surface of the bone and towards the skin boundary; automatically identify an intensity distribution of the vectors;”.
Regarding dependent claim 13, in the context of claim as a whole, the prior art either alone or in combination does not teach or suggest the additional elements of: “generating vectors normal to the mesh surface of the bone and towards the skin boundary; identifying an intensity distribution of the vectors;”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Favre et al. (US 2025/0195143 A1) teaches “adding three-dimensional bone density information for the bone matter to the three-dimensional model, simulating a. preparation of the bone to produce a bone surface on the three-dimensional model, simulating placement of the prosthesis in the bone surface”.
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/Biao Chen/
Patent Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
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