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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/18/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 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.
Claim(s) 1, 4, 8, 14 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et al. US PG-Pub(US 20220011244 A1) in view of Wang et al. US PG-Pub(US 20180068467 A1).
Regarding Claim 1, Goldberg teaches a computed tomography (CT) apparatus(See ¶[0003] where “Computed tomography (CT) uses an X-ray source and a corresponding X-ray detector to scan an object from a number of different positions or angles.”, and Figure 5A.),, comprising: an x-ray source coupled to a source robotic arm(See ¶[0039] where “FIG. 5A illustrates a two-arm robotic scanning system 500 whereby a first robotic arm 502 controls the movement of the X-ray emitter 102 and a second robotic arm 504 controls the movement of the X-ray detector 104.” ); an x-ray detector coupled to a detector robotic arm(See ¶[0039], “a second robotic arm 504 controls the movement of the X-ray detector 104.”) and adapted to output scan data of a volume of interest (VOI) contained in an imaging object(¶[0043], “The robotic scanning system 500, 520 first scans an object to produce an X-ray intensity image 600 of the object being scanned 602 and the markers 604 as illustrated in FIG. 6.”); and a computing device(¶0003], “a computing system performs data processing algorithms on data from the X-ray detector from the scans to reconstruct a 3-dimensional representation of the scanned object.”)
Goldberg does not explicitly teach a motion correction module, comprising software instructions for: estimating a set of geometry-describing parameters comprising the position of the x-ray source, the position of the x-ray detector, and the angular orientation of the x-ray detector based on the scan data and utilizing a locally linear embedding (LLE) motion correction algorithm; generating, via a reconstruction module, reconstructed image data from the estimated geometry-describing parameters; and outputting corrected image data based, at least in part, on the reconstructed image data, to form a corrected image of the VOI.
Wang teaches a motion correction module(See ¶[0073]), comprising software instructions for: estimating a set of geometry-describing parameters(See ¶[0075] where parameter estimation is updated utilizing locally linear embedding, ¶[0079] discloses identifying geometric parameters including SOD, ODD, detector offset, and detector tilt angle) comprising the position of the x-ray source(See ¶[0078]-¶[0079]), the position of the x-ray detector (See ¶[0078]-¶[0079]), and the angular orientation of the x-ray detector(See ¶[0079]) based on the scan data and utilizing a locally linear embedding (LLE) motion correction algorithm(See ¶[0075] where LLE is used alongside a reprojection to correction motion.); generating, via a reconstruction module, reconstructed image data from the estimated geometry-describing parameters(See ¶[0081] where a projection matrix is related to the geometric parameters and used to reconstruct the image.); and outputting corrected image data based, at least in part, on the reconstructed image data, to form a corrected image of the VOI. (See ¶[0081], reconstructed image that is motion compensated is outputted and measured for quality.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Goldberg with Wang in order to use LLE to perform motion correction. One skilled in the art would have been motivated to modify Goldberg in this manner in order to perform geometric calibration and motion correction. (Wang, ¶[0004])
Regarding Claim 4, the combination of Goldberg and Wang teach the apparatus of claim 1, where Goldberg further teaches wherein the source robotic arm and the detector robotic arm are configured to perform a scan along a task specific scan trajectory. (¶[0030]-¶[0031] discloses performing a scan along a certain trajectory to image the patient.)
Regarding Claim 8, claim 8 is considered a storage medium claim substantially corresponding to claim 1. Please see the discussion of claim 1 above for a discussion of similar limitations. Furthermore, Goldberg teaches a non-transitory computer-readable medium storing instructions for causing a computing device (See ¶[0003] where a computing device which inherently would be coupled to a memory or storage medium is used to perform the tasks of image reconstruction.
Regarding Claim 14, claim 14 is considered a method claim substantially corresponding to claim 1. Please see the discussion of claim 1 above for a discussion of similar limitations.
Regarding claim 17, it is substantially similar to claim 4 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 2, 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et al. US PG-Pub(US 20220011244 A1) in view of Wang et al. US PG-Pub(US 20180068467 A1) in view of Koehler et al. US PG-Pub(US 20240081769 A1).
Regarding Claim 2, while the combination of Goldberg and Wang teach the apparatus of claim 1, they do not explicitly teach wherein there are nine geometry-describing parameters per view: the position of the x-ray source is defined by three source coordinates in three-dimensional space (st.sub.x, st.sub.z, st.sub.y), the position of the x-ray detector is defined by three detector coordinates in three-dimensional space (dt.sub.x, dt.sub.z, dt.sub.y), and the angular position of the x-ray detector is defined by three rotation angles (θ.sub.x, θ.sub.y, θ.sub.z), each rotation angle relative to a respective axis of the three-dimensional space.
Koehler teaches wherein there are nine geometry-describing parameters per view(¶[0010] discloses “In one example, up to nine parameters may be used to represent the projection geometry”): the position of the x-ray source is defined by three source coordinates in three-dimensional space (st.sub.x, st.sub.z, st.sub.y) (¶[0010] discloses “up to nine parameters may be used to represent the projection geometry: three for the position of the source” and ¶[0040] also discloses estimating the projection geometry using the position of the x-ray source.), the position of the x-ray detector is defined by three detector coordinates in three-dimensional space (dt.sub.x, dt.sub.z, dt.sub.y) (¶[0010], discloses “up to nine parameters may be used to represent the projection geometry: three for the position of the detector”), and the angular position of the x-ray detector is defined by three rotation angles (θ.sub.x, θ.sub.y, θ.sub.z), each rotation angle relative to a respective axis of the three-dimensional space. ¶[0010], discloses “up to nine parameters may be used to represent the projection geometry: three for the orientation of the detector or detector plane”)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Goldberg and Wang with Koehler in order to measure nine geometric parameters. One skilled in the art would have been motivated to modify Goldberg and Wang in this manner in order to estimate lung volume from radiographic images. (Koehler, ¶[0003])
Regarding Claim 9, it is substantially similar to claim 2 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 15, it is substantially similar to claim 2 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 3, 6-7, 10, 12-13, 16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et al. US PG-Pub(US 20220011244 A1) in view of Wang et al. US PG-Pub(US 20180068467 A1) in view of Koehler et al. US PG-Pub(US 20240081769 A1) in view of Chen et al. ("General rigid motion correction for computed tomography imaging based on locally linear embedding").
Regarding Claim 3, the combination of Goldberg, Wang and Koehler teach the apparatus of claim 2, they do not explicitly teach wherein the LLE motion correction algorithm comprises software instructions for: estimating each of the nine geometry-describing parameters by, for each parameter: generating a sampling grid for the parameter; calculating forward projections corresponding to the samples on the sampling grid; finding the K projections on the projection grid for each scan data point associated with the sampling grid that are the nearest neighbors to the scan data point; optimizing the weights for the K neighbors; and updating the estimated parameter and image reconstruction; and iterating the above steps until a convergence or a specified number of iterations is reached for each parameter.
Chen teaches wherein the LLE motion correction algorithm comprises software instructions for: estimating each of the nine geometry-describing parameters by, for each parameter(See Section 2.2, “Locally Linear Embedding-Based Motion Correction Method”, where Chen discloses calibrating motion parameters to generate reprojected projections from a reconstructed image in densely sampled parametric ranges and update patient motion parameters through LLE): generating a sampling grid for the parameter(Section 2.2 “Locally Linear Embedding-Based Motion Correction Method” discloses “Step 2: Sample the parameter vector densely in the para metric ranges for each projection”); calculating forward projections corresponding to the samples on the sampling grid(Section 2.2 “Locally Linear Embedding-Based Motion Correction Method” discloses “With the sampled parameters, calculate the coordinates of the X-ray source and detector elements, calculate the corresponding system matrices, and reproject for projections ˜ bm.”); finding the K projections on the projection grid for each scan data point associated with the sampling grid that are the nearest neighbors to the scan data point(Section 2.2 “Locally Linear Embedding-Based Motion Correction Method” discloses “Step 3: Find the K nearest neighbors of the original pro jection vector b in the reprojected projections ˜ bm according to the Euclidean distance” ); optimizing the weights for the K neighbors(Section 2.2 discloses “Calculate the weight coefficients in Eq. (8) by solving the following linear equations for the best fit: where C is a local covariance matrix with the K nearest reprojections ˜ bk”, discloses calculating weight coefficients for best fit.); and updating the estimated parameter and image reconstruction(Section 2.2 “Locally Linear Embedding-Based Motion Correction Method” discloses “Step 4: Update the parameter vector with the weight coefficients and the sampled vector ˜ Pk of the K nearest reprojections ˜ bk as XK P ¼ EQ-TARGET;temp:intralink-;e011;326;528 k¼1 wk ˜ Pk: (11) Step 5: Generate the system matrix with the updated motion parameters and perform image reconstruction again.”, parameters are updated and image reconstruction is performed again );
and iterating the above steps until a convergence or a specified number of iterations is reached for each parameter(Section 2.2 where "Step 5: Generate the system matrix with the updated motion parameters and perform image reconstruction again. Steps 2 to 6 need to be repeated until the image quality is sufficiently good by some criteria.".).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Goldberg, Wang and Koehler with Chen in order to incorporate LLE to adjust the motion geometry parameters. One skilled in the art would have been motivated to modify Goldberg, Wang and Koehler in this manner in order to show that the LLE-based patient motion correction is capable of calibrating the six parameters of the patient motion simultaneously, reducing patient motion artifacts significantly. (Chen, Abstract)
Regarding Claim 6, the combination of Goldberg, Wang, Koehler and Chen teach the apparatus of claim 3, where Koehler further teaches wherein the geometry-describing parameters are optimized in the sequence dt.sub.x, dt.sub.z, dt.sub.y, θ.sub.x, θ.sub.y, θ.sub.z, st.sub.x, st.sub.z, st.sub.y. (¶[0010] and ¶[0040] disclose these 9 parameters are used to reconstruct the image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Goldberg, Wang and Chen with Koehler in order to measure nine geometric parameters. One skilled in the art would have been motivated to modify Goldberg, Wang and Chen in this manner in order to estimate lung volume from radiographic images. (Koehler, ¶[0003])
Regarding Claim 7, the combination of Goldberg, Wang, Koehler and Chen teach the apparatus of claim 3, where Chen further teaches wherein for each iteration a sampling space for the sampling grid is reduced while maintaining the same number of samples to generate a finer sample grid having improved searching accuracy. (See Section 4 Discussions and Conclusion, where they talk about “the LLE-based optimization helps reduce the number of sampling points and increase the accuracy of motion correction.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Goldberg, Wang and Koehler with Chen in order to reduce the sampling grid while maintaining the same number of samples. One skilled in the art would have been motivated to modify Goldberg, Wang and Koehler in this manner in order to show that the LLE-based patient motion correction is capable of calibrating the six parameters of the patient motion simultaneously, reducing patient motion artifacts significantly. (Chen, Abstract)
Regarding claim 10, it is substantially similar to claim 3 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 12, it is substantially similar to claim 6 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 13, it is substantially similar to claim 7 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 16, it is substantially similar to claim 3 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 18, it is substantially similar to claim 6 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 19, it is substantially similar to claim 7 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 5 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et al. US PG-Pub(US 20220011244 A1) in view of Wang et al. US PG-Pub(US 20180068467 A1) in view of Jang et al. US PG-Pub(US 20160163101 A1).
Regarding Claim 5, while the combination of Goldberg and Wang teach the apparatus of claim 1, they do not explicitly teach wherein the corrected image has a resolution of at least 50 micrometers (μm).
Jang teaches wherein the corrected image has a resolution of at least 50 micrometers (μm). ¶[0064], “the image reconfigurer 212 may perform meshing to segment the skeletal image into a plurality of images at a target resolution that is a desired high resolution. For example, when a resolution of the skeletal image is about 600 μm and a target resolution is 50 μm, the image reconfigurer 212 may perform meshing in order to segment the skeletal image into 12×12 images.”, the target resolution is 50 micrometers.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Goldberg and Wang with Jang in order to have the corrected image’s resolution at 50 micrometers. One skilled in the art would have been motivated to modify Goldberg and Wang in this manner in order to perform reconstruction to enhance the resolution of a medical image. (Jang, ¶[0003])
Regarding Claim 11, it is substantially similar to claim 5 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAN D HOANG whose telephone number is (571)272-4344. The examiner can normally be reached Monday-Friday 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JOHN M VILLECCO can be reached at 571-272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HAN HOANG/Primary Examiner, Art Unit 2661
Prosecution Insights