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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/25/2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/09/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Applicant’s Amendments filed on 02/09/2026 has been entered and made of record.
Currently pending Claim(s)
Independent Claim(s)
Amended Claim(s)
Canceled Claim(s)
1–6, 9–14, 17, 18, and 21–26
1, 6 and 14
1, 2, 4–6, 9–14, 17, 18 and 21–24
7, 8, 15, 16, 19 and 20
In view of applicant Arguments/Remarks and amendment filed on 02/09/2026 with respect to independent claims 1, 6 and 14 under 35 U.S.C 103, claim rejection has been fully considered and the arguments are found to be not persuasive (See(s) Page 7–9), therefore the rejection with respect to U.S.C. 103 still applies.
Applicant’s Reply (February 9, 2026) includes substantive amendments to the claims. This office action has been updated with new grounds of rejection addressing those amendments. Further Applicant’s Arguments/Remarks with respect to independent claims 1, 6 and 14 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection and the arguments are not rejected by cited art Blau (US 20210248779) and Neuser (US 20110129055) as explained in the body of the rejection.
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 non-obviousness.
Claim(s) 1, 3, 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Gliner (US 2022/0165028 A1, hereafter, “Gliner”) in view of Blau (US 20210248779 A1, hereafter, “Blau”) further in view of Neuser et al. (US 20110129055 A1, hereafter, “Neuser”).
Regarding claim 1, Gliner teaches a method for modeling anatomies (See Gliner, [abstract], In one embodiment, a method for generating a three-dimensional (3D) anatomical map, including applying a trained artificial neural network to (a) a set of two-dimensional (2D) fluoroscopic images of a body part of a living subject, and (b) respective first 3D coordinates of the set of 2D fluoroscopic images, yielding second 3D coordinates of the 3D anatomical map), the method comprising:
obtaining a first two-dimensional (2D) fluoroscopy image of an anatomy of a subject (See Gliner, ¶ [0041], Embodiments of the present invention solve the above problems by generating an initial 3D anatomical map from two two-dimensional (2D) fluoroscopic images. ¶ [0057], The fluoroscopic imaging device 37 (FIG. 1) is configured to capture (block 102) multiple sets of 2D fluoroscopic images 54 of respective body parts 56 of respective living subjects 58. Note: Examiner is interpreting two two-dimensional and set as a first and second image);
obtaining a second 2D fluoroscopy image of the anatomy of the subject, the first 2D fluoroscopy image and the second 2D fluoroscopy image capturing the anatomy from different angles (See Gliner, ¶ [0077], In some embodiments, the set of 2D fluoroscopic images 86 includes an anterior posterior (AP) projection of the body part 88 and a left anterior-oblique projection (LAO) of the body part 86, or any other suitable pair of projections. Note: the anterior posterior and anterior-oblique projections are different angles to project the rays);
[obtaining one or more personal characteristics of the subject];
generating a three-dimensional (3D) model of the anatomy based on the first 2D fluoroscopy image, the second 2D fluoroscopy image (See Gliner, ¶ [0044], Once trained the ANN receives two 2D fluoroscopic images ( e.g., anterior-posterior (AP), left anterioroblique (LAO) or any suitable pair of fluoroscopic image projections) and respective 3D coordinates of the two 2D fluoroscopic images as input, and outputs a 3D anatomical map with coordinates in the coordinate system of the magnetic and/or impedance-based location tracking system. The 3D anatomical map may be represented by mesh vertices of a 3D mesh or a 3D point cloud), [the one or more personal characteristics of the subject]; and a neural network trained to reconstruct the 3D model from the first and second 2D fluoroscopy images (See Gliner, ¶ [0044], Once trained the ANN receives two 2D fluoroscopic images ( e.g., anterior-posterior (AP), left anterioroblique (LAO) or any suitable pair of fluoroscopic image projections) and respective 3D coordinates of the two 2D fluoroscopic images as input, and outputs a 3D anatomical map with coordinates in the coordinate system of the magnetic and/or impedance-based location tracking system. The 3D anatomical map may be represented by mesh vertices of a 3D mesh or a 3D point cloud), [wherein the 3D model includes a visual indication of a confidence level for at least a portion of the reconstruction].
However, Gliner fail(s) to teach obtaining one or more personal characteristics of the subject; the one or more personal characteristics of the subject; wherein the 3D model includes a visual indication of a confidence level for at least a portion of the reconstruction.
Blau, working in the same field of endeavor, teaches: one or more personal characteristics of the subject; the one or more personal characteristics of the subject (See Blau, ¶ [0107], The accuracy of the 3D reconstruction of an anatomical object may be improved upon by using a priori information. This a priori information may be the size or the gender of the patient, but also more anatomy-specific information like geometric information about the anatomical object to be reconstructed).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference one or more personal characteristics of the subject; the one or more personal characteristics of the subject based on the method of Blau’s reference. The suggestion/motivation to increase the accuracy of the reconstruction (See Blau, ¶ [0107]).
However, Gliner and Blau fail(s) to teach wherein the 3D model includes a visual indication of a confidence level for at least a portion of the reconstruction.
Neuser, working in the same field of endeavor, teaches: wherein the 3D model includes a visual indication of a confidence level for at least a portion of the reconstruction (See Neuser, ¶ [0040], It is also possible to display, for example on the display device 43 of the computer terminal 42, voxels with different confidence measures by different optical indicators. In this manner the voxel confidence level can be directly indicated to an operator by an additional indicator like a color coding, such that the quality of different parts in the reconstructed volume data or volume slices is immediately evident. In another embodiment for example voxels with a confidence measure corresponding to a confidence exceeding a predetermined threshold (“good voxels”) and/or voxels with a confidence measure corresponding to a confidence falling below a predetermined threshold (“bad voxels”) may be highlighted).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference wherein the 3D model includes a visual indication of a confidence level for at least a portion of the reconstruction based on the method of Neuser’s reference. The suggestion/motivation would have been to provide accurate quality information of reconstructed volume data (See Neuser, ¶ [0008 – 0009]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Blau and Neuser with Gliner to obtain the invention as specified in claim 1.
Regarding claim 3, Gliner teaches the method of Claim 1, wherein the different angles are substantially orthogonal to each other (See Gliner, ¶ [0077], In some embodiments, the set of 2D fluoroscopic images 86 includes an anterior-posterior (AP) projection of the body part 88 and a left anterior-oblique projection (LAO) of the body part 86, or any other suitable pair of projections).
Regarding claim 4, Gliner in view of Gui teaches the method of Claim 1, wherein the confidence level is derived based on one or more probability values obtained from the neural network (See Gliner, ¶ [0074], At a decision block 118, the processing circuitry 22 (FIG. 1) is configured to determine if the difference between the output of the artificial neural network 52 and desired output is small enough. ¶ [0075], If the difference is not small enough (branch 124), the processing circuitry 22 is configured to amend (block 126) parameters (e.g., weights) of the artificial neural network 52 to reduce the difference between the output of the artificial neural network 52 and the desired output of the artificial neural network 52. The difference being minimized in the above example is the overall difference between all the outputs of the artificial neural network 52 and all the desired outputs ( e.g., the 3D coordinates 64 of all of the respective multiple 3D anatomical maps 62). See also [FIG. 4], 116 and 118).
Regarding claim 5, Gliner in view of Blau further in view of Neuser teaches the method of claim 1, [further comprising: displaying the 3D model of the anatomy on a display device; and displaying the visual indication of the confidence level on the display device in conjunction with the 3D model of the anatomy].
However, Gliner and Blau fail(s) to teach further comprising: displaying the 3D model of the anatomy on a display device; and displaying the visual indication of the confidence level on the display device in conjunction with the 3D model of the anatomy.
Neuser, working in the same field of endeavor, teaches: further comprising: displaying the 3D model of the anatomy on a display device; and displaying the visual indication of the confidence level on the display device in conjunction with the 3D model of the anatomy (See Neuser, ¶ [0040], It is also possible to display, for example on the display device 43 of the computer terminal 42, voxels with different confidence measures by different optical indicators. In this manner the voxel confidence level can be directly indicated to an operator by an additional indicator like a color coding, such that the quality of different parts in the reconstructed volume data or volume slices is immediately evident. In another embodiment for example voxels with a confidence measure corresponding to a confidence exceeding a predetermined threshold (“good voxels”) and/or voxels with a confidence measure corresponding to a confidence falling below a predetermined threshold (“bad voxels”) may be highlighted).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference further comprising: displaying the 3D model of the anatomy on a display device; and displaying the visual indication of the confidence level on the display device in conjunction with the 3D model of the anatomy based on the method of Neuser’s reference. The suggestion/motivation would have been to provide accurate quality information of reconstructed volume data (See Neuser, ¶ [0008 – 0009]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Neuser with Gliner and Blau to obtain the invention as specified in claim 5.
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over Gliner (US 2022/0165028 A1, hereafter, “Gliner”) in view of Blau (US 20210248779 A1, hereafter, “Blau”) further in view of Neuser et al. (US 20110129055 A1, hereafter, “Neuser”) and further in view of Landon et al. (CA 3162370 A1, hereafter, “Landon”).
Regarding claim 2, Gliner in view of Blau further in view of Neuser teaches the method of Claim 1, further comprising: [obtaining a third 2D fluoroscopy image of the anatomy, wherein the 3D model of the anatomy is generated further based on the third 2D fluoroscopy image].
However, Gliner, Blau and Neuser fail(s) to teach obtaining a third 2D fluoroscopy image of the anatomy, wherein the 3D model of the anatomy is generated further based on the third 2D fluoroscopy image.
Landon, working in the same field of endeavor, teaches: obtaining a third 2D fluoroscopy image of the anatomy, wherein the 3D model of the anatomy is generated further based on the third 2D fluoroscopy image (See Landon, ¶ [0195], Referring now to FIG. l0A, an illustrative example of a plurality of received 2D images are shown (i.e., 1005A, 1005B, and 1005C) as radiograph "x-ray" images. However, as discussed, various forms of 2D images are contemplated. In further embodiments, the 2D images may comprise fluoroscopy images. ¶ [0215], In some embodiments, one of the 2D images (e.g., AP Femur, AP Tibia, Lateral Tibia, Lateral Femur, etc.) may be selected individually for a direct comparison, as shown in FIG. 13B, wherein the 2D image may be further re-positioned, re-scaled and/or re-oriented to match the 3D bone model to a greater degree. Alternatively, the 3D bone model may be further re-positioned, re-scaled, and/or reoriented to match the 2D image to a greater degree, thus achieving the same relative position, orientation, and scale. This process may be repeated with one or more additional 2D images, such that the 3D model is further adjusted to a best fit).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference obtaining a third 2D fluoroscopy image of the anatomy, wherein the 3D model of the anatomy is generated further based on the third 2D fluoroscopy image based on the method of Landon’s reference. The suggestion/motivation would have been to more efficiently and accurately generate a 3D model (See Landon, [0006–0007, 0179 and 0188]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Landon with Gliner, Blau and Neuser to obtain the invention as specified in claim 2.
Claim(s) 6, 9, 14, 17, 21, 22, 25 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Gliner (US 2022/0165028 A1, hereafter, “Gliner”) in view of Blau (US 20210248779 A1, hereafter, “Blau”) further in view of Neuser et al. (US 20110129055 A1, hereafter, “Neuser”) and further in view of Lisowska et al. (US 20190371439 A1, hereafter, “Lisowska”).
Regarding claim 6, Gliner in view of Blau further in view of Neuser teaches a method comprising:
[obtaining, for each of a plurality of training subjects, a plurality of two-dimensional (2D) images from a computed tomography (CT) scan of the respective training subject, each of the 2D images labeled according to one or more anatomical characteristics associated with the respective training subject and one or more personal characteristics of the respective training subject]; and
training, a neural network to reconstruct a three-dimensional (3D) model of the anatomy for a new subject based on the plurality of 2D images, one or more anatomical characteristics associated with the new subject (See Gliner, ¶ [0044], Once trained the ANN receives two 2D fluoroscopic images ( e.g., anterior-posterior (AP), left anterioroblique (LAO) or any suitable pair of fluoroscopic image projections) and respective 3D coordinates of the two 2D fluoroscopic images as input, and outputs a 3D anatomical map with coordinates in the coordinate system of the magnetic and/or impedance-based location tracking system. The 3D anatomical map may be represented by mesh vertices of a 3D mesh or a 3D point cloud) and [one or more personal characteristics of the new subject, wherein the 3D model includes a visual indication of a confidence level for at least a portion of the reconstruction].
However, Gliner and Blau fail(s) to teach obtaining, for each of a plurality of training subjects, a plurality of two-dimensional (2D) images from a computed tomography (CT) scan of the respective training subject, each of the 2D images labeled according to one or more anatomical characteristics associated with the respective training subject and one or more personal characteristics of the respective training subject; one or more personal characteristics of the new subject; wherein the 3D model includes a visual indication of a confidence level for at least a portion of the reconstruction.
Nesuer, working in the same field of endeavor, teaches: wherein the 3D model includes a visual indication of a confidence level for at least a portion of the reconstruction (See Neuser, ¶ [0023], In step 200, a set of one or more 2D projection image(s) 202 is acquired with a computed tomography x-ray scan. In step 204, a 3D volumetric image 216 is reconstructed from the set 2D projection image(s) 202 using a residual deep learning network).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference wherein the 3D model includes a visual indication of a confidence level for at least a portion of the reconstruction based on the method of Neuser’s reference. The suggestion/motivation would have been to provide accurate quality information of reconstructed volume data (See Neuser, ¶ [0008 – 0009]).
However, Gliner, Blau and Neuser fail(s) to obtaining, for each of a plurality of training subjects, a plurality of two-dimensional (2D) images from a computed tomography (CT) scan of the respective training subject, each of the 2D images labeled according to one or more anatomical characteristics associated with the respective training subject and one or more personal characteristics of the respective training subject; one or more personal characteristics of the new subject.
Lisowska, working in the same field of endeavor, teaches: obtaining, for each of a plurality of training subjects, a plurality of two-dimensional (2D) images from a computed tomography (CT) scan of the respective training subject (See Lisowska, ¶ [0031], The scanner may be configured to obtain two-dimensional or three-dimensional image data in any imaging modality. For example, the scanner 14 may comprise a magnetic resonance (MR or MRI) scanner, CT (computed tomography) scanner), each of the 2D images labeled according to one or more anatomical characteristics associated with the respective training subject and one or more personal characteristics of the respective training subject; one or more personal characteristics of the new subject (See Lisowska, ¶ [0041], The medical images 30 and medical records 32 form a plurality of medical data sets, each data set comprising at least one medical image and associated medical record data. Each data set may comprise metadata concerning the patient, anatomy and/or acquisition);
one or more personal characteristics of the new subject (See Lisowska, ¶ [0041], The medical images 30 and medical records 32 form a plurality of medical data sets, each data set comprising at least one medical image and associated medical record data. Each data set may comprise metadata concerning the patient, anatomy and/or acquisition).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference obtaining, for each of a plurality of training subjects, a plurality of two-dimensional (2D) images from a computed tomography (CT) scan of the respective training subject, each of the 2D images labeled according to one or more anatomical characteristics associated with the respective training subject and one or more personal characteristics of the respective training subject; one or more personal characteristics of the new subject based on the method of Lisowska’s reference. The suggestion/motivation would have been to provide more information on the images for further processing and more accurate diagnosis (See Lisowska, ¶ [0002–0005]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Neuser and Lisowska with Gliner and Blau to obtain the invention as specified in claim 6.
Regarding claim 9, Gliner teaches the method of claim 6, further comprising processing the plurality of 2D images based on a property of an imaging device used to capture the plurality of 2D images (See Gliner, ¶ [0052], A fluoroscopic imaging device 37 has a C-arm 39, an x-ray source 41, an image intensifier module 43 and an adjustable collimator 45. A control processor (not shown), which may be located in the console 24, allows an operator to control the operation of the fluoroscopic imaging device 37, for example by setting imaging parameters, and controlling the collimator 45 to adjust the size and position of the field of view. Note: Examiner is interpreting the imaging parameters as the properties of the imaging device).
Regarding claim 14, claim 14 is rejected the same as claim 6 and the arguments similar to that presented above for claim 6 are equally applicable to the claim 14, and all of the other limitations similar to claim 6 are not repeated herein, but incorporated by reference. Furthermore, Gliner teaches a non-transitory computer readable storage medium to generate a trained neural network usable to reconstruct a three-dimensional model from a set of two or more two-dimensional images, the non-transitory computer readable storage medium having stored thereon instructions that, when executed, cause a processor of a device to at least (See Gliner, ¶ [0081], This software may be downloaded to a device in electronic form, over a network, for example. Alternatively, or additionally, the software may be stored in tangible, non-transitory computer-readable storage media, such as optical, magnetic, or electronic memory. [abstract], a method for generating a three-dimensional (3D) anatomical map, including applying a trained artificial neural network to (a) a set of two-dimensional (2D) fluoroscopic images of a body part of a living subject, and (b) respective first 3D coordinates of the set of 2D fluoroscopic images, yielding second 3D coordinates of the 3D anatomical map).
Regarding claim 17, claim 17 is rejected the same as claim 9 and the arguments similar to that presented above for claim 9 are equally applicable to the claim 17, and all of the other limitations similar to claim 9 are not repeated herein, but incorporated by reference.
Regarding claim 21, Gliner in view of Blau further in view of Neuser teaches the method of claim 1, wherein:
[the one or more personal characteristics of the subject include at least one of an age of the subject, a gender of the subject, or a race of the subject, respectively].
However, Gliner, Blau and Neuser fail(s) to teach the one or more personal characteristics of the subject include at least one of an age of the subject, a gender of the subject, or a race of the subject, respectively.
Lisowska, working in the same field of endeavor, teaches: the one or more personal characteristics of the subject include at least one of an age of the subject, a gender of the subject, or a race of the subject, respectively (See Lisowska, ¶ [0172], This data set contains information about patient age and gender and view position of the scan along with imaging data. Data was removed if the data has an obviously invalid label, for example an age of 404 years old. If there was more than one image per patient, the first scan was selected for training and the other scans were withheld for a further experiment (validation). The whole data set had 30802 patients out of which one image from each of 30000 patients was used for training of the network).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference the one or more personal characteristics of the subject include at least one of an age of the subject, a gender of the subject, or a race of the subject, respectively based on the method of Lisowska’s reference. The suggestion/motivation would have been to provide more information on the images for further processing and more accurate diagnosis (See Lisowska, ¶ [0002–0005]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lisowska with Gliner, Blau and Neuser obtain the invention as specified in claim 21.
Regarding claim 22, Gliner in view of Blau further in view of Neuser and further in view of Lisowska teaches the method of claim 6, wherein:
[the anatomical characteristics include an anatomical structure associated with the training subject's anatomy, and each 2D image from the CT scans is labeled to indicate which of a plurality of known anatomical structures is associated with the training subject].
However, Gliner in view of Blau further in view of Neuser fail(s) to teach the anatomical characteristics include an anatomical structure associated with the training subject's anatomy, and each 2D image from the CT scans is labeled to indicate which of a plurality of known anatomical structures is associated with the training subject.
Lisowska, working in the same field of endeavor, teaches: the anatomical characteristics include an anatomical structure associated with the training subject's anatomy, and each 2D image from the CT scans is labeled to indicate which of a plurality of known anatomical structures is associated with the training subject (See Lisowska, ¶ [0041], The medical images 30 and medical records 32 form a plurality of medical data sets, each data set comprising at least one medical image and associated medical record data. Each data set may comprise metadata concerning the patient, anatomy and/or acquisition. Note: Examiner is interpreting the anatomy metadata as the anatomical structure);
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference the anatomical characteristics include an anatomical structure associated with the training subject's anatomy, and each 2D image from the CT scans is labeled to indicate which of a plurality of known anatomical structures is associated with the training subject based on the method of Lisowska’s reference. The suggestion/motivation would have been to provide more information on the images for further processing and more accurate diagnosis (See Lisowska, ¶ [0002–0005]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lisowska and Gliner, Blau and Neuser to obtain the invention as specified in claim 22.
Regarding claim 25, claim 25 is rejected the same as claim 21 and the arguments similar to that presented above for claim 21 are equally applicable to the claim 25, and all of the other limitations similar to claim 21 are not repeated herein, but incorporated by reference.
Regarding claim 26, claim 26 is rejected the same as claim 22 and the arguments similar to that presented above for claim 22 are equally applicable to the claim 26, and all of the other limitations similar to claim 22 are not repeated herein, but incorporated by reference.
Claim(s) 10, 12, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gliner (US 2022/0165028 A1, hereafter, “Gliner”) in view of Blau (US 20210248779 A1, hereafter, “Blau”) further in view of Neuser et al. (US 20110129055 A1, hereafter, “Neuser”) further in view of Lisowska et al. (US 20190371439 A1, hereafter, “Lisowska”) and further in view of Lotter (US 20230091506 A1, hereafter, “Lotter”).
Regarding claim 10, Gliner in view of Blau further in view of Neuser and further in view of Lisowska teaches the method of claim 9, [wherein the property of the medical system includes an imaging capability of an imaging device, a distance between the imaging device and a known location, or an angle at which the imaging device captures the plurality of 2D images].
However, Gliner, Blau, Neuser and Lisowska fail(s) to teach wherein the property of the medical system includes an imaging capability of an imaging device, a distance between the imaging device and a known location, or an angle at which the imaging device captures the plurality of 2D images.
Lotter, working in the same field of endeavor, teaches: wherein the property of the medical system includes an imaging capability of an imaging device, a distance between the imaging device and a known location, or an angle at which the imaging device captures the plurality of 2D images (See Lotter, ¶ [0057], In some configurations, the process 300 can preprocess the raw images by normalizing pixel values to a predetermined range ( e.g., a range of [127.5, 127.5]) and/or normalizing the height of the images to a predetermined height (e.g., 1750 pixels). Note: Examiner is interpreting the property as preprocessing the raw image to normalize the image for the system (e.g., normalizing pixels for a system)).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference wherein the property of the medical system includes an imaging capability of an imaging device, a distance between the imaging device and a known location, or an angle at which the imaging device captures the plurality of 2D images based on the method of Lotter’s reference. The suggestion/motivation would have been to improve quality of training data and reduce false positives. (See Lotter, ¶ [0003–0006 and 0071]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lotter with Gliner, Blau, Neuser and Lisowska to obtain the invention as specified in claim 10.
Regarding claim 12, Gliner in view of Blau further in view of Neuser and further in view of Lisowska teaches the method of claim 6, wherein the training of the neural network includes:
[deriving a first set of properties from a first plurality of 2D images;
deriving a second set of properties from a second set of the plurality of 2D images;
wherein the neural network is trained further based on the first set of properties and the second set of properties].
However, Gliner, Blau, Neuser and Lisowska fail(s) to teach deriving a first set of properties from a first plurality of 2D images; deriving a second set of properties from a second set of the plurality of 2D images; wherein the neural network is trained further based on the first set of properties and the second set of properties.
Lotter, working in the same field of endeavor, teaches: deriving a first set of properties from a first plurality of 2D images (See Lotter, ¶ [0057], The annotated images can be generated based on a second group of two-dimensional images ( e.g., native 2D mammograms). Each annotated image can include one or more ROis, each ROI including label and a bounding box included in the annotated image. In some aspects, the label can indicate a malignancy type (e.g., normal, benign, or malignant));
deriving a second set of properties from a second set of the plurality of 2D images (See Lotter, ¶ [0059], At 324, the process 300 can receive a second set of annotated images. The annotated images can be generated based on a third group of two-dimensional images (e.g., native 2D mammograms). Each annotated image included in the second set of annotated images can include a 2D image and an image-level label. The image-level can be a binary malignancy label ( e.g., malignant or not malignant)); and
wherein the neural network is trained further based on the first set of properties and the second set of properties (See Lotter, ¶ [0058], The process 300 can train the second neural network to generate ROIs including malignancy type and a bounding box based on the first set of annotated images that are labeled by malignancy type. ¶ [0060], At 328, the process 300 can train the second neural network based on the second set of annotated images).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference deriving a first set of properties from a first plurality of 2D images; deriving a second set of properties from a second set of the plurality of 2D images; wherein the neural network is trained further based on the first set of properties and the second set of properties based on the method of Lotter’s reference. The suggestion/motivation would have been to improve quality of training data and reduce false positives. (See Lotter, ¶ [0003–0006 and 0071]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lotter with Gliner, Blau, Neuser and Lisowska to obtain the invention as specified in claim 12.
Regarding claim 13, Gliner teaches the method of claim 12, wherein the first set of properties includes at least one of: skeletons extracted from the first set of two or more labeled two-dimensional images or key points identifying portions of an anatomy (See Gliner, ¶ [0044], Once trained the ANN receives two 2D fluoroscopic images ( e.g., anterior-posterior (AP), left anterioroblique (LAO) or any suitable pair of fluoroscopic image projections) and respective 3D coordinates of the two 2D fluoroscopic images as input. Note: Examiner is interpreting the 3D coordinates as keypoints and they represent the coordinates of the anatomy).
Regarding claim 18, claim 18 is rejected the same as claim 10 and the arguments similar to that presented above for claim 10 are equally applicable to the claim 18, and all of the other limitations similar to claim 10 are not repeated herein, but incorporated by reference.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Gliner (US 2022/0165028 A1, hereafter, “Gliner”) in view of Blau (US 20210248779 A1, hereafter, “Blau”) further in view of Neuser et al. (US 20110129055 A1, hereafter, “Neuser”) further in view of Lisowska et al. (US 20190371439 A1, hereafter, “Lisowska”) and further in view of Shen et al. (US 20210393229 A1, hereafter, “Shen”).
Regarding claim 11, Gliner in view of Blau further in view of Neuser further in view of Lisowska teaches the method of claim 6, further comprising:
[obtaining a first set of unlabeled 2D images of an anatomy associated with a first CT scan of the CT scans; and
generating the 3D model of the anatomy using the first set of unlabeled 2D images and the neural network].
However, Gliner, Blau, Neuser and Lisowska fail(s) to teach obtaining a first set of unlabeled 2D images of an anatomy associated with a first CT scan of the CT scans; and generating the 3D model of the anatomy using the first set of unlabeled 2D images and the neural network.
Shen, working in the same field of endeavor, teaches: obtaining a first set of unlabeled 2D images of an anatomy associated with a first CT scan of the CT scans (See Shen, ¶ [0023], In step 200, a set of one or more 2D projection image(s) 202 is acquired with a computed tomography x-ray scan. In step 204, a 3D volumetric image 216 is reconstructed from the set 2D projection image(s) 202 using a residual deep learning network); and
generating the 3D model of the anatomy using the first set of unlabeled 2D images and the neural network (See Shen, ¶ [0023], In step 200, a set of one or more 2D projection image(s) 202 is acquired with a computed tomography x-ray scan. In step 204, a 3D volumetric image 216 is reconstructed from the set 2D projection image(s) 202 using a residual deep learning network).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference obtaining a first set of unlabeled 2D images of an anatomy associated with a first CT scan of the CT scans; and generating the 3D model of the anatomy using the first set of unlabeled 2D images and the neural network based on the method of Shen’s reference. The suggestion/motivation would have been to reduce imaging time and radiation dose with sparse and limited sampling (See Shen, ¶ [0002–0005]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Shen with Gliner, Blau, Neuser and Lisowska to obtain the invention as specified in claim 11.
Claim(s) 23 is rejected under 35 U.S.C. 103 as being unpatentable over Gliner (US 2022/0165028 A1, hereafter, “Gliner”) in view of Blau (US 20210248779 A1, hereafter, “Blau”) further in view of Neuser et al. (US 20110129055 A1, hereafter, “Neuser”) further in view of Lisowska et al. (US 20190371439 A1, hereafter, “Lisowska”) and further in view of Haslam et al. (US 11138790 B2, hereafter, “Haslam”).
Regarding claim 23, Gliner in view of Blau further in view of Neuser and further in view of Lisowska teaches the method of claim 22, further comprising:
[determining which of the plurality of known anatomical structures is associated with the anatomy of the subject, wherein the 3D model of the anatomy is generated further based on the determination].
However, Gliner, Blau, Neuser and Lisowska fail(s) to teach determining which of the plurality of known anatomical structures is associated with the anatomy of the subject, wherein the 3D model of the anatomy is generated further based on the determination.
Haslam, working in the same field of endeavor, teaches: determining which of the plurality of known anatomical structures is associated with the anatomy of the subject, wherein the 3D model of the anatomy is generated further based on the determination (See Haslam, [Col. 15, ln. 12–21], FIG. 8 shows a diagram illustrating the Axial3D Automated Pipeline workflow; once the data is received (8.1), the Axial3D automated segmentation will identify the anatomical components within the scan and “label” them accordingly (8.2). The Tissue and Organ Segmentation is then used by the surface generation in order to create the organ volumes (8.3); the volumes are then passed to the conditioning phase (8.4) which will identify the volumes that require printing, the associated correct materials and perform the printer selection).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference to determining which of the plurality of known anatomical structures is associated with the anatomy of the subject, wherein the 3D model of the anatomy is generated further based on the determination based on the method of Haslam’s reference. The suggestion/motivation would have been to automatically generate accurate 3D representation and improve the preoperative planning (See Haslam, [Col. 2, ln. 3–24]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Haslam with Gliner, Blau, Neuser and Lisowska to obtain the invention as specified in claim 23.
Claim(s) 24 is rejected under 35 U.S.C. 103 as being unpatentable over Gliner (US 2022/0165028 A1, hereafter, “Gliner”) in view of Blau (US 20210248779 A1, hereafter, “Blau”) further in view of Neuser et al. (US 20110129055 A1, hereafter, “Neuser”) and further in view of Profio et al. (US 20190231288 A1, hereafter, “Profio”).
Regarding claim 24, Gliner in view of Blau further in view of Neuser teaches The method of claim 1, further comprising:
[comparing the confidence level with a confidence threshold; and
selectively recommending obtainment of one or more additional images associated with the portion of the model based on whether the confidence level is below the confidence threshold].
However, Gliner, Blau and Neuser fail(s) to teach comparing the confidence level with a confidence threshold; and selectively recommending obtainment of one or more additional images associated with the portion of the model based on whether the confidence level is below the confidence threshold.
Profio, working in the same field of endeavor, teaches: comparing the confidence level with a confidence threshold (See Profio, ¶ [0073], In a first example of the method, the method further comprises performing a monitor scan of one or more regions of interest (ROIs) of the patient responsive to the confidence level below the threshold. Note: The examiner is interpreting as the confidence level as the confidence indicator to the threshold); and
selectively recommending obtainment of one or more additional images associated with the portion of the model based on whether the confidence level is below the confidence threshold (See Profio, ¶ [0073], In a first example of the method, the method further comprises performing a monitor scan of one or more regions of interest (ROIs) of the patient responsive to the confidence level below the threshold).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Gliner’s reference comparing the confidence level with a confidence threshold; and selectively recommending obtainment of one or more additional images associated with the portion of the model based on whether the confidence level is below the confidence threshold based on the method of Profio’s reference. The suggestion/motivation would have been to reduce radiation dose and maintaining or improving the image quality (See Profio, ¶ [0002–0005]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Profio with Gliner, Blau and Neuser to obtain the invention as specified in claim 24.
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.
Deleu et al. (US 20200357508 A1) teaches according to one embodiment, receiving information concerning the subject comprises receiving information relating to at least one of the following: the demographic characterization of the subject (including age, gender, race, body mass index, hereditary disorders, etc.), the lifestyle of the subject (such as for example diet, body mass index, smoking or not, sporty or not), the medical history of the subject (such as for example an earlier injury, hereditary disorders, earlier surgeries, etc.), at least one physiological attribute of the subject, quantified functional activities parameters of the subject, information about injured stress tissue, the comorbidity condition of subject, the bone strength characterization of the patient, pain index and annotation, pain location, results of clinical analysis.
Pasha et al. (US 20230115512 A1) teaches a system or method according to at least one embodiment of the present disclosure includes receiving a preoperative image of a patient in a first posture; receiving an intraoperative image of the patient in a second posture; comparing the preoperative image of the patient in the first posture with the intraoperative image of the patient in the second posture; and determining, based on comparing the preoperative image of the patient in the first posture with the intraoperative image of the patient in the second posture, a difference between the first posture and the second posture, the adequate surgical changes are imparted to the second posture to achieve satisfactory surgical outcome.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DION J SATCHER whose telephone number is (703)756-5849. The examiner can normally be reached Monday - Thursday 5:30 am - 2:30 pm, Friday 5:30 am - 9:30 am PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at (571) 272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DION J SATCHER/Patent Examiner, Art Unit 2676
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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