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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 30, 2026 has been entered.
Response to Arguments
Applicant’s arguments 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.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 4-5, 7 and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Smith (US 20200294288 A1) in view of Ziabari et al. (US 20220035961 A1) and further in view of Hurley et al. (US 20150324114 A1).
Regarding claim 1, Smith et al. teaches a method of training a neural network (see para [0010]; “the method can further comprise training the convolutional neural network using a set of images that comprises a plurality of paired multiphasic CT images”) and obtained using an imaging technique at less than a full or normal radiation dose (see para [0008]; “reconstructing a high-contrast output CT image from a contrast-enhanced CT image obtained with a low dose of a contrast agent, and/or (iv) reconstructing a low-noise, high-contrast CT image from a CT obtained with low radiation dose and a low dose of a contrast agent”), comprising: receiving an image of an anatomical portion of a subject (see also para [0062]; “the computer system to at least (i) receive a low-dose, contrast-enhanced computed tomography (CT) image captured from a patient”). However, Smith et al. does not teach receiving a CAD model of a surgical implant; generating a first simulated image based on the image and the CAD model, the first simulated image depicting the surgical implant positioned in the anatomical portion of the subject in a pose that corresponds to a pose in which the surgical implant is capable of being implanted into the anatomical portion of the subject; modifying the simulated image to include simulated artifacts from metal, beam hardening, and scatter, to yield a second simulated image corresponding to the first simulated image; and providing the second simulated image to a neural network as an example input and the first simulated image to the neural network as an example output
In the same field of endeavor, Ziabari et al. teaches generating a first simulated image based on the image and the CAD model; modifying the simulated image to include simulated artifacts from metal, beam hardening, and scatter, to yield a second simulated image corresponding to the first simulated image (see para [0008]; “Some of the CT simulated projections include simulated artifacts based on the artifact characterization and some do not. That is, the same simulation is performed twice: once to produce projections with artifacts and once to produce projections without artifacts”, Note; this shows explicit modeling of simulated artifacts in the generated CT data); and providing the second simulated image to a neural network as an example input and the first simulated image to the neural network as an example output (see para [0007]; “A deep convolutional neural network or other artificial intelligence network can be trained on synthetically generated data based on the CAD models to reduce artifacts in CT reconstructed images”, see also para [0008]; “the same simulation is performed twice: once to produce projections with artifacts and once to produce projections without artifacts. These simulations can be used as pairs of inputs to a deep learning network which attempts to learn the non-linear mapping between the projections with artifacts and the projections without artifacts, thereby learning to reduce such artifacts. A deep learning component can also be included that is configured to train a deep learning artifact reduction model based on the CT simulated projections and generate a set of deep learning artifact reduction model parameters. The trained model can be deployed and applied to real CT scan data to reduce artifacts in CT reconstructed images”). Accordingly, it would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify a method reconstructing a full-dose, contrast-enhanced CT image from a low-dose, contrast-enhanced CT image of Smith in view of the use of leveraging computer-aided design (CAD) models, CT simulations, a deep-neutral network high-quality CT reconstructions of Ziabari et al in order to improve the reconstruction quality, thereby enabling better detection of defects (see para [0008]). However, the combination of Smith et al. and Ziabari et al. as a whole does not teach receiving a CAD model of a surgical implant); the first simulated image depicting the surgical implant positioned in the anatomical portion of the subject in a pose that corresponds to a pose in which the surgical implant is capable of being implanted into the anatomical portion of the subject.
In the same field of endeavor, Hurley et al. teaches receiving a CAD model of a surgical implant (see para [0106]; “to model screws, the manipulating engine obtains the length, trajectory and desired radius for each screw and generates a 3D model …emulating a screw”, Note; creating a 3D printed screw for surgical planning); the first simulated image depicting the surgical implant positioned in the anatomical portion of the subject in a pose that corresponds to a pose in which the surgical implant is capable of being implanted into the anatomical portion of the subject (see para [0078]; “the system provides an intuitive mechanism to plan placement of a drill and screws by determining optimal start points, trajectories, sizes and lengths”, see also para [0106]; “In order to model screws, the manipulating engine obtains the length, trajectory and desired radius for each screw and generates a 3D model of a cylinder with a cap, emulating a screw. The modelling engine exports the 3D model for 3D printing”, and para [0015]; “augment the three-dimensional model by applying a virtual screw to the three-dimensional model having a screw trajectory extending from the screw location to an end location perpendicularly into the three-dimensional model from the plane and at the location of the user input action”). Accordingly, it would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify a method reconstructing a full-dose, contrast-enhanced CT image from a low-dose, contrast-enhanced CT image of Smith in view of the use of leveraging computer-aided design (CAD) models, CT simulations, a deep-neutral network high-quality CT reconstructions of Ziabari et al and a system for segmentation and reduction of a three-dimensional model of an anatomical feature of Hurley et al. in order to provide a useful adjunct in the surgical planning of complex fractures (see para [0106]).
Regarding claim 4, the rejection of claim 1 is incorporated herein.
Hurley et al. in the combination of further teach wherein the surgical implant is a screw (see para [0014]; “a system for modeling screw trajectory on a three-dimensional model of an anatomical feature is provided”).
Regarding claim 5, the rejection of claim 1 is incorporated herein.
Ziabari et al. in the combination of further teach wherein the image is generated using a cone-based computed tomography scanner (see para [0017]; “FIG. 3 illustrates a schematic of a cone beam CT system with a turbine blade as the object of interest”).
Regarding claim 7, the rejection of claim 1 is incorporated herein.
Ziabari et al. in the combination of further teach wherein the image comprises a reconstruction of a sinogram (see para [0050]; “method where sinogram (projection) domain deep learning for beam hardening artifact removal or reduction is highlighted… Removing beam hardening artifacts from the projection or sinogram helps to avoid reliance on an initial reconstruction for image domain data can result in a higher quality reconstruction”).
Regarding claim 9, the rejection of claim 1 is incorporated herein.
Smith et al. in the combination of further teach wherein the neural network, as trained, is capable of: receiving a clinical image of the anatomical portion of a patient subject and obtained using the imaging technique at less than the full or normal radiation dose, wherein the clinical image comprises a reconstruction of a low-dose clinical sinogram (see para [0017]; “reconstructing the output image comprises reconstructing a virtual full-dose, contrast-enhanced CT image from the low-dose, contrast-enhanced CT image, the virtual full-dose, contrast-enhanced CT image being reconstructed without sacrificing image quality or accuracy. In one aspect, the method further comprises training the convolutional neural network using a training set of paired low-dose, contrast-enhanced and full-dose, contrast-enhanced CT images, wherein for each pair of low-dose, contrast-enhanced and full-dose, contrast-enhanced CT images within the training set, the low-dose, contrast-enhanced CT image is used as a training input CT image and the associated full-dose, contrast-enhanced CT image is used as a training output CT image”).
Regarding claim 10, the rejection of claim 9 is incorporated herein.
Smith et al. in the combination of further teach further comprising: generating, based on the low-dose clinical sinogram, an up-sampled sinogram having an improved image quality as compared to the low-dose clinical sinogram (see para [0057]; “image reconstruction paradigms generated by the disclosed machine learning methods allows practitioners to reconstruct an equivalent high-resolution contrast-enhanced CT image from a CT image obtained from a patient who was administered at most 80% of the lowest (standard or regulatory approved) concentration of iodinated contrast typically administered in view of the anatomical region to be imaged in an analogous or otherwise healthy counterpart patient”).
Regarding claim 11, the rejection of claim 1 is incorporated herein.
Smith et al. in the combination of further teach wherein: the image comprises a reconstruction of a low-dose sinogram (see [0008]; “reconstructing a high-contrast output CT image from a contrast-enhanced CT image obtained with a low dose of a contrast agent, and/or (iv) reconstructing a low-noise, high-contrast CT image from a CT obtained with low radiation dose and a low dose of a contrast agent”).
Regarding claim 12, the rejection of claim 1 is incorporated herein.
Ziabari et al. in the combination of further teach further comprising: providing training labels to the neural network (see para [0042]; “while the CT projections simulated with CT artifacts can be labeled as input of the AI network. Data pairs of these sets of data can be provided to a deep learning component for it to train a deep learning artificial intelligence model….the CAD model with simulated defects can be labeled as ground truth. In other embodiments, a different model can be provided or defined in the system as ground truth, or the system can be operated without a labeled ground truth. In some embodiments, improved image reconstruction is ultimately provided as the deep learning network learns to map to the ground truth”).
Regarding claim 13, the rejection of claim 12 is incorporated herein.
Ziabari et al. in the combination of further teach wherein the training labels comprise a ground truth image of the anatomical portion of yet another subject having the surgical implant (see para [0040]; “a deep learning model can be trained on the pairs of reconstructed volumes, with artifacts and ground truths derived from the CAD model”, see also para [0058]; “The ground truth can be obtained in a variety of different ways. It may be obtained by utilizing a more reliable source, such as a high resolution image capture or, in the case of simulation, the ground truth may be directly available as a “before” simulation representation”).
Regarding claim 14, the rejection of claim 13 is incorporated herein.
Smith et al. in the combination of further teach wherein the ground truth image is obtained using the full or normal radiation dose (see para [0023]; “used as a training input CT image and the associated full-dose, contrast-enhanced CT image is used as a training output CT image”).
Regarding claim 15, the rejection of claim 14 is incorporated herein.
Smith et al. in the combination of further teach and obtained using less than the full or normal radiation dose (see para [0023]; “used as a training input CT image and the associated full-dose, contrast-enhanced CT image is used as a training output CT image”).
Ziabari et al. in the combination of further teach further comprising: generating, based on the second simulated image and the ground truth image, an algorithm capable of interpolating or otherwise filling in data missing from a second image of the anatomical portion of another subject (see para [0005]; “a projection-based metal-artifact reduction (MAR) algorithm. In general, this prior art approach involves conducting a CT scan of an object, reconstructing the 3D volume, and then segmenting the 3D volume to identify the high-density portions. These portions typically are metal regions. Those identified regions are subtracted from the original measured projection volume and then filled in with interpolation or inpainting. This technique has a number of shortcomings that make it less viable for a number of practical applications. The intermediate steps (e.g. segmentation and interpolation) are limited to specific cases and prone to produce artifacts and errors”).
Regarding claim 16, the rejection of claim 13 is incorporated herein.
Ziabari et al. in the combination of further teach wherein the ground truth image comprises a reconstruction of a ground truth sinogram (see para [0042]; “The deep learning approach can be conducted in the projection/sinogram domain (see FIG. 19) or in the image domain (see FIG. 18)”… the CAD model with simulated defects can be labeled as ground truth. In other embodiments, a different model can be provided or defined in the system as ground truth, ... In some embodiments, improved image reconstruction is ultimately provided as the deep learning network learns to map to the ground truth”).
Regarding claim 17, the rejection of claim 1 is incorporated herein.
Smith et al. in the combination of further teach and obtained using less than the full or normal radiation dose (see para [0023]; “used as a training input CT image and the associated full-dose, contrast-enhanced CT image is used as a training output CT image”).
Ziabari et al. in the combination of further teach further comprising: generating, based on the second simulated image, an algorithm capable of interpolating or otherwise filling in data missing from a second image of the anatomical portion of another subject (see para [0005]; “a projection-based metal-artifact reduction (MAR) algorithm. In general, this prior art approach involves conducting a CT scan of an object, reconstructing the 3D volume, and then segmenting the 3D volume to identify the high-density portions. These portions typically are metal regions. Those identified regions are subtracted from the original measured projection volume and then filled in with interpolation or inpainting. This technique has a number of shortcomings that make it less viable for a number of practical applications. The intermediate steps (e.g. segmentation and interpolation) are limited to specific cases and prone to produce artifacts and errors”).
Regarding claim 18, the rejection of claim 17 is incorporated herein.
Ziabari et al. in the combination of further teach further comprising: providing training labels to the neural network, wherein the training labels comprise a ground truth image of the anatomical portion of yet another subject having the surgical implant (see para [0040]; “a deep learning model can be trained on the pairs of reconstructed volumes, with artifacts and ground truths derived from the CAD model”, see also para [0058]; “The ground truth can be obtained in a variety of different ways. It may be obtained by utilizing a more reliable source, such as a high resolution image capture or, in the case of simulation, the ground truth may be directly available as a “before” simulation representation”).
Regarding claim 19, the rejection of claim 18 is incorporated herein.
Smith et al. in the combination of further teach wherein the ground truth image is obtained using the full or normal radiation dose (see para [0023]; “used as a training input CT image and the associated full-dose, contrast-enhanced CT image is used as a training output CT image”).
Regarding claim 20, the rejection of claim 19 is incorporated herein.
Ziabari et al. in the combination of further teach wherein generating the algorithm comprises: generating the algorithm further based on the ground truth image (see para [0040]; “a deep learning model can be trained on the pairs of reconstructed volumes, with artifacts and ground truths derived from the CAD model”, see also para [0058]; “The ground truth can be obtained in a variety of different ways. It may be obtained by utilizing a more reliable source, such as a high resolution image capture or, in the case of simulation, the ground truth may be directly available as a “before” simulation representation”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. and Ziabari et al. in view of Hurley et al. as applied in claim 1 above, and further in view of Morvan et al. (US 20240339195 A1)
Regarding claim 2, the rejection of claim 1 is incorporated herein. The combination of Smith et al. Ziabari et al. and Hurley et al. as a whole does not teach wherein the image is an image of an anatomical portion of a cadaver.
In the same field of endeavor, Morvan et al. teach wherein the image is an image of an anatomical portion of a cadaver (see para [0998]; “the MR educational content 12706 may comprise one or more virtual elements positioned relative to a physical (e.g., synthetic) anatomical model or anatomy of a cadaver”). Accordingly, it would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify a method reconstructing a full-dose, contrast-enhanced CT image from a low-dose, contrast-enhanced CT image of Smith in view of the use of leveraging computer-aided design (CAD) models, CT simulations, a deep-neutral network high-quality CT reconstructions of Ziabari et al and further in view of reduction of a three-dimensional model of an anatomical feature of Hurley et al. and an information model specifying a first surgical plan for an orthopedic surgery to be performed on a patient of Morvan et al. in order to provide a multi-faceted ecosystem to support surgical joint repair procedures (see para [0998]).
Claims 3, 6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. and Ziabari et al. in view of Hurley et al. as applied in claim 1 above, and further in view of Vu et al. NPL “Evaluation of multislice inputs to convolutional neural networks for medical image segmentation”.
Regarding claim 3. The rejection of claim 1 is incorporated herein.
The combination of Smith et al., Ziabari et al. and hurley et al. does not teach wherein the anatomical portion comprises one or more vertebrae.
In the same field of endeavor Vu et al. teaches wherein the anatomical portion comprises one or more vertebrae (see page 6217, right col. 3rd para; “included the preceding and succeeding axial slice for vertebrae and liver segmentation”). Accordingly, it would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify a method reconstructing a full-dose, contrast-enhanced CT image from a low-dose, contrast-enhanced CT image of Smith in view of the use of leveraging computer-aided design (CAD) models, CT simulations, a deep-neutral network high-quality CT reconstructions of Ziabari et al and further in view of reduction of a three-dimensional model of an anatomical feature of Hurley et al. and evaluation of multislice inputs to convolutional neural networks for medical image segmentation of Vu et al. in order to evaluate model performance (see Abstract).
Regarding claim 6. The rejection of claim 1 is incorporated herein.
Vu et al. in the combination of further teach further comprising repeating the steps of receiving an image, generating a first simulated image, modifying the simulated image, and providing a plurality of times, each time based on a different image (see page 6216, 2nd para; “We additionally design and evaluate a novel, simple approach where the input stack is a volumetric input that is repeatably convolved in 3D to obtain a 2D feature map. This 2D map is in turn fed into a standard 2D network. We conducted experiments using two different CNN backbone architectures and on eight diverse data sets covering different anatomical regions, imaging modalities, and segmentation tasks”). Accordingly, it would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify a method reconstructing a full-dose, contrast-enhanced CT image from a low-dose, contrast-enhanced CT image of Smith in view of the use of leveraging computer-aided design (CAD) models, CT simulations, a deep-neutral network high-quality CT reconstructions of Ziabari et al and further in view of reduction of a three-dimensional model of an anatomical feature of Hurley et al. and evaluation of multislice inputs to convolutional neural networks for medical image segmentation of Vu et al. in order to evaluate model performance (see page 6216, 2nd para).
Regarding claim 8. The rejection of claim 1 is incorporated herein.
Vu et al. in the combination of further teach wherein the image comprises three adjacent image slices of the anatomical portion of the subject (see page 6216, Abstract; “One common alternative, in this study denoted as pseudo-3D, is to use a stack of adjacent slices as input and produce a prediction for at least the central slice”, see also page 6217, right col., 3rd para; “Such a three-slice input was also used…. for the detection of mitotic cells in 4D data (spatial + temporal)”), and the first simulated image is based on a center image slice of the three adjacent image slices (see Fig. 1; “Pseudo-3D uses multiple adjacent slices as input to produce an output of the central slice from the input”, see also page 6217, right col., 3rd para; “one can also incorporate neighboring slices to provide a 3Dcontext to enhance segmentation performance. A common approach to this is to include neighboring slices to a central slice as multiple input image channels” along the selected plane mean" Note; Pseudo-3D implies simulated image). Accordingly, it would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify a method reconstructing a full-dose, contrast-enhanced CT image from a low-dose, contrast-enhanced CT image of Smith in view of the use of leveraging computer-aided design (CAD) models, CT simulations, a deep-neutral network high-quality CT reconstructions of Ziabari et al and further in view of reduction of a three-dimensional model of an anatomical feature of Hurley et al. and evaluation of multislice inputs to convolutional neural networks for medical image segmentation of Vu et al. in order to (Abstract).
Conclusion
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