DETAILED ACTION
Claims 1-14, 16-17 and 20-23 are pending in this application. Claims 15, and 18-19 are cancelled, claims 1-14, 16-17 and 20 are amended, and claims 21-23 are newly added.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/10/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
35 U.S.C. 102(a)
Applicant’s arguments (see Remarks filed 03/13/2026) have been fully considered by the examiner and are persuasive. Applicant argues that Pasha fails to teach the newly amended limitations of claims 1 and 20. The examiner agrees, however in view of the change of scope to the amended claims, a new grounds of rejection is presented and fully discussed below over Pasha and in view of Krishnan.
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 factual inquiries 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 nonobviousness.
1. Claims 1-2, 7-9, 11-13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pasha (US 20210264601 A1) in view of Krishnan (US 20230124879 A1).
Regarding claim 1 Pasha discloses; A medical image processing apparatus comprising processing circuitry configured to:
obtain a respective position for each of a plurality of anatomical landmarks in a three-dimensional space (Pasha, [0039] the image processing method determines landmarks or points from a 3D reconstruction of the spine);
generate a spline based on the positions for the plurality of anatomical landmarks (Pasha, [0050] a 3D spline curve is generated from the vertebrae centroids (landmark points));
[perform spline fitting to the generated spline;
use the spline fitted spline to normalize the positions of the plurality of anatomical landmarks in straightened form to obtain one-dimensional data or two-dimensional data;]
adjust at least one position of the plurality of anatomical landmarks based on the one-dimensional data or the two-dimensional data (Pasha, [0053] the scaling and interpolation method is used to obtain a scaled 2D coordinates of the vertebrae centers, meaning the vertebrae centers being used to generate the curves are adjusted based upon the normalization and interpolation of the 2D data. Given that this is done for each vertebrae centroid, there would be a plurality of landmarks.);
and re-map the adjusted at least one anatomical landmark to the three-dimensional space (Pasha, [0050] the 3D spline curves are generated using the interpolated centroids of the spine, which are determined using the method of [0053] cited above, meaning the interpolation/adjustment of the data in 2D is used to generate the 3D spline curve).
Pasha does not teach; perform spline fitting to the generated spline;
use the spline fitted spline to normalize the positions of the plurality of anatomical landmarks in straightened form to obtain one-dimensional data or two-dimensional data;
However, in the same field of endeavor Krishnan teaches;
perform spline fitting to the generated spline (Krishnan, [0014] a parametric curve is fitted for the spline data);
use the spline fitted spline to normalize the positions of the plurality of anatomical landmarks in straightened form to obtain one-dimensional data or two-dimensional data (Krishnan, [0015] a transform is applied to the parametric curve to obtain a straightened parametric “curve” (2D normalized data), where the parametric curve is a type of spline data);
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The combination of Pasha and Krishnan would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Pasha teaches a method of analyzing spinal alignment, but it does not teach generating a straightened form of the analyzed landmark data. The motivation to add this feature of Krishnan to the system of Pasha is that obtaining a straightened form of the data allows for the anatomical structure to be elongated such that the landmarks may be more accurately or easily assessed. (Krishnan, [0015] and [0030])
Regarding claim 2 the combination of Pasha and Krishnan teaches; The medical image processing apparatus according to claim 1, wherein the plurality of anatomical landmarks correspond to a plurality of vertebrae (Pasha, [0050] a 3D spline curve is generated from the vertebrae centroids (landmark points), the curve would then correspond to the centroids/landmark points).
Regarding claim 7 the combination of Pasha and Krishnan teaches; The medical image processing apparatus according to claim 1, wherein the adjusting of the one or more anatomical landmarks comprises comparing the one-dimensional data or two-dimensional data to a set of one-dimensional or two-dimensional reference positions and adjusting the one-dimensional data or two-dimensional data based on the one-dimensional or two-dimensional reference positions (Pasha,[0027]-[0028] figure 1, the 2D spinal curves pre op and post op are compared based on their cobb angles, [0049] the curves being compared are generated from the spine and may be 2D or 3D, further, [0053] the vertebral centroid coordinates were normalized and scaled based on the average, then interpolation of the 3D coordinates was performed to obtain normalized 2D coordinates, the interpolation/adjustment of the data in 2D is used to generate the 3D spline curve).
Regarding claim 8 the combination of Pasha and Krishnan teaches;The medical image processing apparatus according to claim 7, wherein the one-dimensional or two-dimensional reference positions are mean positions for the plurality of anatomical landmarks in a reference data cohort (Pasha,[0027]-[0028] figure 1, the 2D spinal curves pre op and post op are compared based on their cobb angles, [0049] the curves generated from the spine may be 2D or 3D, further, [0053] the vertebral centroid coordinates were normalized and scaled based on the average, then interpolation of the 3D coordinates was performed to obtain normalized 2D coordinates, the interpolation/adjustment of the data in 2D is used to generate the 3D spline curve).
Regarding claim 9 the combination of Pasha and Krishnan teaches; The medical image processing apparatus according to claim 1, wherein the adjusting of the one or more anatomical landmarks comprises using a calibration to encourage location prediction and discourage proximity of neighbouring landmarks (Pasha, [0050] interpolation of the vertebrae centroids are used to generate spline curves of the spine, further [0053] scaling and interpolation is used to generate multiple curves of the vertebrae to be used in clustering by the model, the examiner is interpreting this to be analogous to the encouragement location prediction as described in pages 18-20 of the applicant’s specification).
Regarding claim 11 the combination of Pasha and Krishnan teaches; The medical image processing apparatus according to claim 1, wherein the normalizing of the three-dimensional positions comprises estimating a scale and normalizing in dependence on the estimated scale (Pasha, [0027]-[0028] figure 1, the 2D spinal curves pre op and post op are compared based on their cobb angles, [0049] the curves generated from the spine may be 2D or 3D, further, [0053] the vertebral centroid coordinates were normalized and scaled based on the average, then interpolation of the 3D coordinates was performed to obtain normalized 2D coordinates, the interpolation/adjustment of the data in 2D is used to generate the 3D spline curve, the normalization and scaling steps are dependent on one another as disclosed in [0053]).
Regarding claim 12 the combination of Pasha and Krishnan teaches; The medical image processing apparatus according to claim 11, wherein the estimating of the scale is based on positions of two or more anchor landmarks (Pasha, [0053] the vertebral centroid coordinates were normalized and scaled based on the average, then interpolation of the 3D coordinates was performed to obtain normalized 2D coordinates, the interpolation/adjustment of the data in 2D is used to generate the 3D spline curve, the normalization and scaling steps are dependent on one another as disclosed in [0053], the scaling is also done based on the vertebral centroids, which are the “anchor points”).
Regarding claim 13 the combination of Pasha and Krishnan teaches; The medical image processing apparatus according to claim 12, wherein a first anchor landmark corresponds to a top spinal vertebra and a second anchor landmark corresponds to a bottom spinal vertebra (Pasha, [0053] the vertebral centroid coordinates were normalized and scaled based on the average, then interpolation of the 3D coordinates was performed to obtain normalized 2D coordinates, the interpolation/adjustment of the data in 2D is used to generate the 3D spline curve, the normalization and scaling steps are dependent on one another as disclosed in [0053], the scaling is also done based on the vertebral centroids, which are the “anchor points”, Figure 3C and figures 4A-4C show the centroid anchor points corresponding to a sequence of vertebra in a spinal alignment, meaning there would be a top and a bottom vertebra).
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(Figure 3C)
Regarding claim 20 the combination of Pasha and Krishnan teaches; A medical image processing method comprising:
obtaining a respective position for each of a plurality of anatomical landmarks in a three-dimensional space (Pasha, [0039] the image processing method determines landmarks or points from a 3D reconstruction of the spine);
generating a spline based on the positions for the plurality of anatomical landmarks (Pasha, [0050] a 3D spline curve is generated from the vertebrae centroids (landmark points));
perform spline fitting to the generated spline (Krishnan, [0014] a parametric curve is fitted for the spline data);
use the spline fitted spline to normalize the positions of the plurality of anatomical landmarks in straightened form to obtain one-dimensional data or two-dimensional data (Krishnan, [0015] a transform is applied to the parametric curve to obtain a straightened parametric “curve” (2D normalized data), where the parametric curve is a type of spline data);
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adjusting at least one of the anatomical landmarks based on the one-dimensional data or two-dimensional data (Pasha, [0053] the scaling and interpolation method is used to obtain a scaled 2D coordinates of the vertebrae centers, meaning the vertebrae centers being used to generate the curves is adjusted based upon the normalization and interpolation of the 2D data.);
and re-mapping the adjusted at least one anatomical landmark to the three-dimensional space (Pasha, [0050] the 3D spline curves are generated using the interpolated centroids of the spine, which are determined using the method of [0053] cited above, meaning the interpolation/adjustment of the data in 2D is used to generate the 3D spline curve).
The combination of Pasha and Krishnan would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Pasha teaches a method of analyzing spinal alignment, but it does not teach generating a straightened form of the analyzed landmark data. The motivation to add this feature of Krishnan to the system of Pasha is that obtaining a straightened form of the data allows for the anatomical structure to be elongated such that the landmarks may be more accurately or easily assessed. (Krishnan, [0015] and [0030])
2. Claims 3- 5, 10, 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Pasha (US 20210264601 A1) in view of Krishnan (US 20230124879 A1) and in further view of Mauldin (US 20210045715 A1).
Regarding claim 3 the combination of Pasha and Krishnan does not disclose; The medical image processing apparatus according to claim 2,
wherein the processing circuitry is further configured to receive volume data including the plurality of vertebrae,
and to detect the plurality of anatomical landmarks in the volume data, thereby to obtain the positions of the anatomical landmarks.
However, in the same field of endeavor, Mauldin discloses; wherein the processing circuitry is further configured to receive volume data including the plurality of vertebrae (Mauldin, [0075]-[0077] 3D models are created from 2D images using features (landmarks) to align the 2D images to create the volumetric model, [0090]-[0091] the 3D model (volumetric model) is generated from vertebral position, size and orientation features),
and to detect the plurality of anatomical landmarks in the volume data, thereby to obtain the positions of the anatomical landmarks (Mauldin, [0090]-[0091] the 3D model (volumetric model) is generated from vertebral position, size and orientation features).
The combination of Pasha, Krishnan, and Mauldin would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The system and method of Pasha and Krishnan teaches a method of reconstructing vertebrae and computing spline curves of the spine. Mauldin teaches a method of generating a volumetric model of the spine to help guide procedures. The motivation for combining the methods of Mauldin with the method of Pasha are that the combination would allow the system of Pasha to generate 3D volumetric models to improving image alignment using 3D volumetric features of the spine for more accurate reconstruction. (Mauldin, [0070]-[0080])
Regarding claim 4 the combination of Pasha, Krishnan, and Mauldin teaches; The medical image processing apparatus according to claim 3, wherein the processing circuitry is further configured to normalize the volume data by creating a curved planar reformat using the three-dimensional spline (Pasha, [0053] spinal height of the 3D model is normalized, in order to generate 2D curves (2D planar model) in each direction for the spine model).
Regarding claim 5 the combination of Pasha, Krishnan, and Mauldin teaches; The medical image processing apparatus according to claim 3, wherein the detecting of the plurality of anatomical landmarks comprises automated landmark detection using a trained model (Pasha, [0032] the classification model predicts the spinal outcome using the 3D shape of the spine as geometrical characteristics).
Regarding claim 10 the combination of Pasha, Krishnan, and Mauldin teaches;The medical image processing apparatus according to claim 4, wherein processing circuitry is further configured to use at least one neural network or other trained model to at least one of (Pasha, [0066] a neural network is trained to segment the models and reconstruct the spine):
perform a registration process to obtain the landmark positions (Mauldin, [0074] the pixels corresponding to specific locations in 3D space are registered to a specific location);
perform the normalization(Pasha, [0053] spinal height of the 3D model is normalized, in order to generate 2D curves (2D planar model) in each direction for the spine model);
or perform the adjusting or remapping of the at least one anatomical landmark (Mauldin, [0076]-[0077] features can be mapped or excluded to reduce positional errors).
The combination of Pasha, Krishnan, and Mauldin would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The system and method of Pasha and Krishnan teaches a method of reconstructing vertebrae and computing spline curves of the spine. Mauldin teaches a method of generating a volumetric model of the spine to help guide procedures, as well as the use of neural networks to register images to reconstruct the spine. The motivation for combining the methods of Mauldin with the method of Pasha are that the combination would allow the system of Pasha to generate 3D volumetric models to improving image alignment using 3D volumetric features of the spine for more accurate reconstruction, further the registration disclosed in Mauldin would allow for more accurate alignment of multiple images during the reconstruction. (Mauldin, [0070]-[0080])
Regarding claim 21, the combination of Pasha, Krishnan and Mauldin teaches; The medical image processing apparatus according to claim 3, wherein the processing circuitry is further configured to transform, based on the spline-fitted spline, the at least one position of the plurality of anatomical landmarks from a coordinate space of the volume data to a reference coordinate space in which a length of a spine is normalized (Krishnan, [0015]-[0017] the spline data is fitted, this fitted data is then used to register and reformat the image volumes on at least one axis to normalize the spinal volume data).
The combination of Pasha, Krishnan and Mauldin would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Pasha teaches a method of analyzing spinal alignment, but it does not teach generating a fitted spline to register the data. The motivation to add this feature of Krishnan to the system of Pasha is that obtaining a fitted spline and then using this fitted spline to adjust the volume data allows for the generation of a fused and elongated volume data which is normalized for accurate anatomical assessment. (Krishnan, [0015]-[0020] and [0030])
Regarding claim 23, the combination of Pasha, Krishnan and Mauldin teaches; The medical image processing apparatus according to claim 3, wherein the processing circuitry is further configured to remap the at least one anatomical landmark of the plurality of anatomical landmarks from a coordinate space in which the at least one position of the at least one anatomical landmark of the plurality of the anatomical landmarks is normalized to a coordinate space of the volume data (Krishnan, [0015]-[0017] the volume data is reformatted/remapped to be fused with the regularized/normalized volume data).
The combination of Pasha, Krishnan and Mauldin would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Pasha teaches a method of analyzing spinal alignment, but it does not teach generating a fitted spline to register the data. The motivation to add this feature of Krishnan to the system of Pasha is that obtaining a fitted spline and then using this fitted spline to adjust the volume data allows for the generation of a fused and elongated volume data which is normalized for accurate anatomical assessment. (Krishnan, [0015]-[0020] and [0030])
3. Claims 6, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Pasha (US 20210264601 A1) in view of Krishnan (US 20230124879 A1) and in further view of Casey (US 11793577 B1).
Regarding claim 6 the combination of Pasha and Krishnan fails to disclose; The medical image processing apparatus according to claim 1, wherein the adjusting of the one or more anatomical landmarks is based on supervised data, wherein the supervised data is data that the plurality of anatomical landmarks and positional information is correlated.
However, in the same field of endeavor Casey teaches; A medical image processing apparatus according to claim 1, wherein the adjusting of the one or more anatomical landmarks is based on supervised data, wherein the supervised data is data that the plurality of anatomical landmarks and positional information is correlated (Casey, column 12 line 60 through column 13 line 20, the data use to generate the surgical model of the spine is done using supervised training/supervised data, column 18 lines 45 through column 19 line 30 details the use of landmarks by the model to map the positions and 3D reconstructions of the vertebrae).
The combination of Pasha, Krishnan, and Casey would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Pasha teaches a method of modeling a spine using a trained model, it however does not teach the use of supervised learning. Casey teaches this deficiency. The motivation for the use of supervised learning as taught by Casey with the system and method of Pasha is that the supervised learning of Casey allows the training data results to be monitored during training, which may increase accuracy. (Casey column 12 line 60 through column 13 line 20)
Regarding claim 16 the combination of Pasha, Krishnan, and Casey teaches;The medical image processing apparatus according to claim 1, wherein the adjusting of the at least one of the anatomical landmarks comprises correcting the at least one of the anatomical landmarks (Casey, column 5 line 62 – column 6 line 30, the 3D model can be aligned to correct the lumbar vertebrae in the model, the correction can be based on either the 3D model landmarks or the 2D multiple models).
The combination of Pasha, Krishnan, and Casey would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Pasha teaches a 3D modeling of the spine to aid in surgery, it however does not explicitly methods of correcting the models. Casey teaches this deficiency, which would improve the system of Pasha by allowing the model to be validated and corrected for accuracy. (Casey columns 5 and 6)
Regarding claim 17 the combination of Pasha, Krishnan, and Casey teaches; The medical image processing apparatus according to claim 1, wherein the adjusting of the at least one of the anatomical landmarks comprises adding at least one of the landmarks, deleting at least one of the landmarks, re-ordering a sequence of the landmarks, or changing a position or label of at least one of the landmarks (Casey, column 5 lines 62 through column 6 lines 20, the model may assess whether any part of the anatomy may be reorganized or repositioned for correction).
The combination of Pasha, Krishnan, and Casey would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Pasha teaches a 3D modeling of the spine to aid in surgery, it however does not explicitly methods of correcting the models. Casey teaches this deficiency, which would improve the system of Pasha by allowing the model to be validated and corrected for accuracy. (Casey columns 5 and 6)
4. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Pasha (US 20210264601 A1) in view of Krishnan (US 20230124879 A1) and Mauldin (US 20210045715 A1), and in further view of Casey (US 11793577 B1)
Regarding claim 14 the combination of Pasha, Krishnan, and Mauldin fails to teach; The medical image processing apparatus according to claim 3, wherein the processing circuitry is further configured to segment one or more anatomical structures in the volume data and to determine a size or volume of the segmented one or more anatomical structures,
and wherein the normalizing of the three-dimensional positions comprises estimating a scale based on the determined size or volume and normalizing in dependence on the estimated scale.
However, in the same field of endeavor, Casey teaches;
wherein the processing circuitry is further configured to segment one or more anatomical structures in the volume data and to determine a size or volume of the segmented one or more anatomical structures (Casey, column 2 line 60 through column 3 line 15, the spine can be segmented to get volumetric segmentations for the spine, further column 8 line 63 through column 9 line 32, the size and volume of the spine are extracted and saved for each patient)
and wherein the normalizing of the three-dimensional positions comprises estimating a scale based on the determined size or volume and normalizing in dependence on the estimated scale (Casey, Column 4 line 37 to line 55, the 3D landmarks are aligned by scaling, translating or rotating based on the 3D volumetric anatomy, the examiner is interpreting this as a scaling factor being estimated, which inherently has to happen for scaling to occur, and the scaling, rotating or translating are all steps of normalization which are dependent on the scaling factor and the volume and landmarks of the 3D anatomy).
The combination of Pasha, Krishnan, Mauldin and Casey would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Pasha teaches a method of 3D spline generation based on images of the spine, but fails to teach volumetric measurements. Mauldin teaches a method of 3D reconstruction of spinal images, but fails to teach a scaling component of these 3D images. Casey remedies the deficiencies of both Pasha and Mauldin, and teaches 3D segmentation and modeling of the spine, as well as scaling and normalization. The addition of these methods combined with the systems of Pasha and Mauldin would allow for accurate generation of 3D models, where the anatomical landmarks are properly normalized for accuracy. (Casey, columns 2-4 and 8-9 as cited above)
5. Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Pasha (US 20210264601 A1) in view of Krishnan (US 20230124879 A1) and Miao (US 20220172350 A1).
Regarding claim 22, the combination of Pasha and Krishnan fails to teach; The medical image processing apparatus according to claim 1, wherein the processing circuitry is further configured to adjust the at least one anatomical landmark of the plurality of anatomical landmarks based on the one-dimensional data, which is obtained by normalizing the spline.
However, in the same field of endeavor of spinal assessment, Miao teaches; wherein the processing circuitry is further configured to adjust the at least one anatomical landmark of the plurality of anatomical landmarks based on the one-dimensional data, which is obtained by normalizing the spline (Miao, [0031] the 3D vertebrae data is rectified to generate a 1D spinal centerline, which is mathematically equivalent to a spline generation, optimization is then performed on the 1D data to normalize it).
The combination of Pasha, Krishnan, and Miao would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The system of Pasha and Krishnan teaches a method of normalizing spinal data and assessing landmarks, however they do not teach generating 1D data from this spinal data. The motivation for the addition of Miao is that generating the 1D data and normalizing the data to generate this 1D data allows for accuracy and anatomical plausibility of the results. (Miao, [0031])
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For a listing of analogous prior art as determined by the examiner, please see the attached PTO-892 Notice of References Cited sheet.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN M ELLIOTT whose telephone number is (703)756-5463. The examiner can normally be reached M-F 8AM-5PM ET.
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/J.M.E./Examiner, Art Unit 2666 /Molly Wilburn/Primary Examiner, Art Unit 2666
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