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 07/23/2024, 09/20/2024, 09/08/2025, and 6/29/2026 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
Applicant has amended claims 1, 14, and 28. New claims 63-65 are added. No new matter is introduced. Claims 2, 15, 30, and 31 are canceled. Claims 1, 3-14, 16, 28-30, and 63-66 are currently being addressed.
Applicant’s arguments, filed 6/30/2026, with respect to the rejection(s) of claim(s) 1, 14, and 28 under 35 U.S.C 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Jung et. al. (S. Jung, S. Lee, B. Jeon, Y. Jang and H. -J. Chang, "Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography," in IEEE Access, vol. 8, pp. 81849-81863, 2020).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 28-29, 65 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mao (Chinese Patent CN-113689342-A) .
Regarding claim 28, Mao discloses a method for motion correction, which is implemented on a computing device including at least one processor and at least one storage device, comprising: obtaining a plurality of preliminary models of different structures; obtaining a plurality of training samples, wherein the plurality of training samples includes at least one first training sample and at least one second training sample, and each training sample includes a first sample image and a first reference image; and generating a target motion correction model by training each preliminary model of the plurality of preliminary models using the plurality of training samples (Mao, [0108]-[0180], training an initial feature processing model based on a plurality of sample images and a plurality of reference quality features respectively corresponding to the plurality of sample images (the reference quality features serve as labels to which the sample images correspond), samples, comprising: obtaining a plurality of candidate motion correction models by training the plurality of preliminary models using the plurality of training samples, comprising: for the each preliminary model, training the preliminary model according to an iterative operation including one or more iterations, and in at least one of the one or more iterations, the method further comprises: obtaining an updated preliminary model generated in a previous iteration; for each training sample, generating a first sample intermediate image by inputting the first sample image into the updated preliminary model: determining a value of a second loss function based on the first sample intermediate image and the first reference image; and updating the updated preliminary model based on the value of the second loss function, or designating the updated preliminary model as a candidate motion correction model based on the value of the second loss function, wherein the second loss function is a combined loss function including a local loss function associated with a first local region of the first sample intermediate image and a second local region of the first reference image (Mao, [0134]-[0136]), and a dice related loss function associated with a first coronary artery of the first sample intermediate image and a second coronary artery of the first reference image, and the local loss function relates to a mask region: selecting the target motion correction model from the plurality of candidate motion correction models based on a plurality of values of a first loss function corresponding to the plurality of candidate motion (Mao, [0111]-[0116] the processing device may select an image processing model based on a noise type, an artifact type, or a motion type, trained primarily on a plurality of training samples related to the noise type or artifact type).
Taking one sample image as an example, its corresponding reference quality features (i.e., labels) may be mapped in quality features, feature processing models may include convolutional neural network models, support vector machines, naïve Bayes, decision tress, and the like).
Regarding claim 29, Mao further discloses the method of claim 28, wherein the at least one first training sample is associated with at least one image generated by an imaging device, and the at least one second training sample is associated with at least one simulated image (Mao [0143] A history image (a history CT image) corresponding to the imaging object may be used as a sample image, and an image obtained by processing (denoising, removing an artifact, improving resolution, and the processing mode is related to a training target of the image processing model) the sample image may be used as a standard image).
Regarding claim 65, Mao further discloses the method of claim 28, further comprising: performing a preprocessing operation on a value of the local loss function, a value of the dice related loss function, and a value of a global loss function respectively, such that a preprocessed value of the local loss function, a preprocessed value of the dice function, and a preprocessed value of the global loss function are in a same order of magnitude; and determining a value of the combined loss function by a weighted sum of the preprocessed value of the local loss function, the preprocessed value of the dice related loss function, and the preprocessed value of the global loss function (Mao, [0111]-[0116] the processing device may select an image processing model based on a noise type, an artifact type, or a motion type, trained primarily on a plurality of training samples related to the noise type or artifact type).
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, 3-14, 16, 63-64, 66 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mao (Chinese Patent CN-113689342-A) in view of Jung et. al. (S. Jung, S. Lee, B. Jeon, Y. Jang and H. -J. Chang, "Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography," in IEEE Access, vol. 8, pp. 81849-81863, 2020).
Regarding claim 1, Mao discloses a method for motion correction, which is implemented on a computing device including at least one processor and at least one storage device, comprising: obtaining an original image including a motion artifact (Mao, see description, paragraphs [0108]-[0180], an image quality optimization method and system that can improve performance of machine learning models in terms of de-noising, de-artifacting, motion correction, and/or improved resolution, thereby improving effect of medical image processing. The process flow may be executed as set of instructions stored in memory device, processing device including CPU. The method comprises obtaining image to be processed, image to be processed being image containing artifacts and motion.); obtaining a target motion correction model; and generating a target image by removing the motion artifact from the original image using the target motion correction model; (Mao, [0108]-[0180], the determination module may optimize goal according to image quality, the image processing model may be a pre-trained machine learning model. The processing device determines an optimized image for the image based on the output of the image processing model).
However, Mao fails to disclose wherein the original image is a three-dimensional (3D) image including a plurality of 2D layers, and the generating a target image by removing the motion artifact from the original image using the target motion correction model comprises: for each 2D laver of the plurality of 2D layers of the 3D image, obtaining, from the plurality of 2D layers of the 3D image, a plurality of reference layers for the 2D layer; and generating a corrected 2D layer corresponding to the 2D layer by inputting the 2D layer and the plurality of reference layers into the target motion correction model; and generating the target image by combining a plurality of corrected 2D layers corresponding to the plurality of 2D layers of the 3D image.
Jung et. al. teaches wherein the original image is a three-dimensional (3D) image including a plurality of 2D layers, and the generating a target image by removing the motion artifact from the original image using the target motion correction model comprises: for each 2D layer of the plurality of 2D layers of the 3D image, obtaining, from the plurality of 2D layers of the 3D image, a plurality of reference layers for the 2D layer; and generating a corrected 2D layer corresponding to the 2D layer by inputting the 2D layer and the plurality of reference layers into the target motion correction model; and generating the target image by combining a plurality of corrected 2D layers corresponding to the plurality of 2D layers of the 3D image (Jung et. al. I. Introduction, paragraph 6).
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This is important to the claimed invention because as Jung et. al. states, “it is medically impossible to obtain precise corresponding coronary CT images devoid of motion artifacts, which are the ground-truth images required in supervised learning.” Thus, a synthetic image training set is used to generate a plurality of 2D layers that correspond to the 3D image. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Mao and Jung et. al. so that these features are shown in the solution of the claimed invention.
Regarding claim 14, Mao discloses a system for motion correction, comprising: at least one storage device including a set of instructions; and at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to direct the system to perform operations including: obtaining an original image including a motion artifact; obtaining a target motion correction model; and generating a target image by removing the motion artifact from the original image using the target motion correction model (Mao, see description, paragraphs [0108]-[0180]).
However, Mao fails to disclose wherein the original image is a three-dimensional (3D) image including a plurality of 2D layers, and the generating a target image by removing the motion artifact from the original image using the target motion correction model comprises: for each 2D laver of the plurality of 2D layers of the 3D image, obtaining, from the plurality of 2D layers of the 3D image, a plurality of reference layers for the 2D layer; and generating a corrected 2D layer corresponding to the 2D layer by inputting the 2D layer and the plurality of reference layers into the target motion correction model; and generating the target image by combining a plurality of corrected 2D layers corresponding to the plurality of 2D layers of the 3D image.
Jung et. al. teaches wherein the original image is a three-dimensional (3D) image including a plurality of 2D layers, and the generating a target image by removing the motion artifact from the original image using the target motion correction model comprises: for each 2D laver of the plurality of 2D layers of the 3D image, obtaining, from the plurality of 2D layers of the 3D image, a plurality of reference layers for the 2D layer; and generating a corrected 2D layer corresponding to the 2D layer by inputting the 2D layer and the plurality of reference layers into the target motion correction model; and generating the target image by combining a plurality of corrected 2D layers corresponding to the plurality of 2D layers of the 3D image (Jung et. al. I. Introduction, paragraph 6).
This is important to the claimed invention because as Jung et. al. states, “it is medically impossible to obtain precise corresponding coronary CT images devoid of motion artifacts, which are the ground-truth images required in supervised learning.” Thus, a synthetic image training set is used to generate a plurality of 2D layers that correspond to the 3D image. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Mao and Jung et. al. so that these features are shown in the solution of the claimed invention.
Regarding claim 3, Mao further discloses the method of claim 1, wherein the target motion correction model is obtained according to a process including: obtaining a plurality of training samples each of which including a sample image and a reference image, wherein the sample image includes a motion artifact and the reference image is with substantial removal of the motion artifact; and determining the target motion correction model by training, based on the plurality of training samples according to a combined loss function, a preliminary model, wherein the combined loss function includes at least a local loss function, a dice loss function, and a global loss function (Mao, [0111]-[0116] the processing device may select an image processing model based on a noise type, an artifact type, or a motion type, trained primarily on a plurality of training samples related to the noise type or artifact type).
Regarding claim 4, Mao and Jung et. al. discloses the method of claim 3, and Jung further discloses wherein the local loss function is associated with a coronary artery (Jung et. al. B. Generating Synthetic Motion-Corrected Patches using Cross-Phase Style Transfer, paragraph 3, Equations1-4: The corresponding patches extracted from coronary arteries within different phases of a 4D CT ae not just different in terms of the severity of the motion artifacts.).
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This corresponding loss function is critical for noise reduction in the image processing of the clinical content. Thus, it would have been obvious to one skilled in the art prior to the effective filing date to have combined the teachings of Mao and Jung et. al. to include the loss function of Jung et. al. in the solution of the claimed invention.
Regarding claim 5, Mao further discloses the method of claim 1, wherein the target motion correction model is obtained according to a process including: obtaining a plurality of preliminary models of different structures; obtaining a plurality of training samples, wherein the plurality of training samples includes at least one first training sample and at least one second training sample, and each training sample includes a first sample image and a first reference image; and generating the target motion correction model by training each preliminary model of the plurality of preliminary models using the plurality of training samples (Mao, [0108]-[0180], Training an initial feature processing model based on a plurality of sample images and a plurality of reference quality features respectively corresponding to the plurality of sample images (the reference quality features serve as labels to which the sample images correspond). Taking one sample image as an example, its corresponding reference quality features (i.e., labels) may be mapped in quality features, feature processing models may include convolutional neural network models, support vector machines, naïve Bayes, decision tress, and the like).
Regarding claim 6, Mao further discloses the method of claim 5, wherein the obtaining a plurality of training samples comprises: for each first training sample, obtaining the first sample image including a motion artifact; and obtaining the first reference image by removing the motion artifact from the first sample image (Mao, [0111]-[0116] the processing device may select an image processing model based on a noise type, an artifact type, or a motion type, trained primarily on a plurality of training samples related to the noise type or artifact type).
Regarding claim 7, Mao further discloses the method of claim 5, wherein the obtaining a plurality of training samples comprises: for each second training sample, obtaining the first reference image without a motion artifact; and obtaining the first sample image by adding a simulated motion artifact to the first reference image (Mao, [0111]-[0116] the processing device may select an image processing model based on a noise type, an artifact type, or a motion type, trained primarily on a plurality of training samples related to the noise type or artifact type).
Regarding claim 8, Mao further discloses the method of claim 5, wherein the generating the target motion correction model by training each preliminary model of the plurality of preliminary models using the plurality of training samples comprises: obtaining a plurality of candidate motion correction models by training the plurality of preliminary models using the plurality of training samples; and selecting the target motion correction model from the plurality of candidate motion correction models based on a plurality of values of a first loss function corresponding to the plurality of candidate motion correction models (Mao, [0108]-[0180], Training an initial feature processing model based on a plurality of sample images and a plurality of reference quality features respectively corresponding to the plurality of sample images (the reference quality features serve as labels to which the sample images correspond). Taking one sample image as an example, its corresponding reference quality features (i.e., labels) may be mapped in quality features, feature processing models may include convolutional neural network models, support vector machines, naïve Bayes, decision tress, and the like).
Regarding claim 9, Mao further discloses the method of claim 8, wherein the obtaining a plurality of candidate motion correction models by training the plurality of preliminary models using the plurality of training samples comprises: for the each preliminary model, training the preliminary model according to an iterative operation including one or more iterations, and in at least one of the one or more iterations, the method further comprises: obtaining an updated preliminary model generated in a previous iteration; for each training sample, generating a first sample intermediate image by inputting the first sample image into the updated preliminary model; determining a value of a second loss function based on the first sample intermediate image and the first reference image; and updating the updated preliminary model based on the value of the second loss function, or designating the updated preliminary model as a candidate motion correction model based on the value of the second loss function (Mao, [0134]-[0136]).
Regarding claim 10, Mao further discloses the method of claim 8, wherein the selecting the target motion correction model from the plurality of candidate motion correction models based on a plurality of values of a first loss function corresponding to the plurality of candidate motion correction models comprises: obtaining at least one testing sample, wherein the at least one testing sample includes a second sample image and a second reference image; for each candidate motion correction model, generating a second sample intermediate image by inputting the second sample image into the candidate motion correction model; and determining a value of the first loss function based on the second sample intermediate image and the second reference image; and selecting the target motion correction model from the plurality of candidate motion correction models based on the plurality of values of the first loss function corresponding to the plurality of candidate motion correction models (Mao, [0134]-[0138]).
Regarding claim 11, Mao further discloses the method of claim 5, further comprising: obtaining at least one verifying sample, wherein the at least one verifying sample includes a third sample image and a third reference image; and verifying the target motion correction model using the at least one verifying sample (Mao, [0143]-[0145], historical data used as sample data and used as a standard image).
Regarding claim 12, Mao further discloses the method of claim 11, wherein the verifying the target motion correction model using the at least one verifying sample comprises: generating a third sample intermediate image by inputting the third sample image into the target motion correction model; determining a value of a third loss function based on the third sample intermediate image and the third reference image; and in response to determining that the value of the third loss function satisfies a condition, determining the target motion correction model as a verified target motion correction model (Mao, [0146]-[0152] where the plurality of standard images may be converted into a first matrix, a second matrix, and a third matrix, etc. used as input to the intermediate layer of the preliminary image processing model).
Regarding claim 13, Mao and Jung discloses the method of claim 1, and Jung et. al. further discloses wherein the original image is a computed tomography (CT) image of a heart (Jung et. al. Figure 6, A. Extracting Corresponding Coronary Patches from 4D CT).
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It is important to the claimed invention to specify the organ of interest so that the computational model is tailored to the region of interest and is as accurate as possible. Thus, it would have been obvious to one skilled in the art to have combined the teachings of Mao with Jung et. al. so that the coronary artery is the target.
Regarding claim 16, Mao further discloses the system of claim 14, wherein the target motion correction model is obtained according to a process including: obtaining a plurality of training samples each of which including a sample image and a reference image, wherein the sample image includes a motion artifact and the reference image is with substantial removal of the motion artifact; and determining the target motion correction model by training, based on the plurality of training samples according to a combined loss function, a preliminary model, wherein the combined loss function includes at least a local loss function, a dice loss function, and a global loss function (Mao, [0111]-[0116] the processing device may select an image processing model based on a noise type, an artifact type, or a motion type, trained primarily on a plurality of training samples related to the noise type or artifact type).
Regarding claim 63, Mao discloses the method of claim 28. However, Mao fails to disclose and further comprising: extracting a centerline of a coronary artery from the first reference image; determining a mask by performing an expansion operation on the centerline; determining, in the first sample intermediate image, the first local region corresponding to the coronary artery based on the mask; determining, in the first reference image, the second local region corresponding to the coronary artery based on the mask; and determining a value of the local loss function based on a difference between the first local region and the second local region.
Jung et. al. teaches further comprising: extracting a centerline of a coronary artery from the first reference image; determining a mask by performing an expansion operation on the centerline; determining, in the first sample intermediate image, the first local region corresponding to the coronary artery based on the mask; determining, in the first reference image, the second local region corresponding to the coronary artery based on the mask; and determining a value of the local loss function based on a difference between the first local region and the second local region (Jung et. al. III. Results, A. Phantom Study, paragraphs 2-3: Because of the lack of data, we sampled the centerline points M as densely as possible and used seven volumes for training and two volumes for testing.).
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This is important to the claimed invention because the centerline of the coronary artery serves as a reference point as the volume changes over time and motion. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Mao and Jung et. al. so that these features are shown in the solution of the claimed invention.
Regarding claim 64, Mao discloses the method of claim 28. However, Mao fails to disclose further comprising: segmenting a first coronary artery from the first sample intermediate image; segmenting a second coronary artery from the first reference image; and determining a value of the dice related loss function based on the first coronary artery and the second coronary artery.
Jung et. al. teaches further comprising: segmenting a first coronary artery from the first sample intermediate image; segmenting a second coronary artery from the first reference image; and determining a value of the dice related loss function based on the first coronary artery and the second coronary artery (Jung et. al. III. Results, A. Phantom Study, paragraphs 2-3: We evaluated the similarity using the Dice similarity coefficients (DSCs) for overlap of two binary images ranging from zero (no overlap) to one (perfect match). The motion corrected volume is binarized through thresholding and then evaluated with the ground-truth, which is the segmentation mask of the coronary artery within the volume reconstructed at the quiescent heart phase).
This is important to the claimed invention because the dice related loss function removes noise from segmented and overlapping coronary arteries. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Mao and Jung et. al. so that these features are shown in the solution of the claimed invention.
Regarding claim 66, Mao discloses the method of claim 28. However, Mao fails to disclose further comprising: obtaining a plurality of corrected images of an original image; obtaining a reference image corresponding to the initial image; determining a reference rank result by ranking the plurality of corrected images; obtaining an initial loss function; determining an evaluated rank result by ranking, based on the initial loss function and the reference image, the plurality of corrected images; determining the combined loss function by adjusting the initial loss function until an updated evaluated rank result substantially coincides with the reference rank result.
Jung teaches further comprising: obtaining a plurality of corrected images of an original image; obtaining a reference image corresponding to the initial image; determining a reference rank result by ranking the plurality of corrected images; obtaining an initial loss function; determining an evaluated rank result by ranking, based on the initial loss function and the reference image, the plurality of corrected images; determining the combined loss function by adjusting the initial loss function until an updated evaluated rank result substantially coincides with the reference rank result (Jung et. al. Figure 9: Results of motion artifact metrics, P-values were computed by a Wilcoxon rank sum test).
This rank is important to the claimed invention because it determines the effect of the statistical significance of the motion correction compared to the initial reference image. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Mao and Jung et. al. so that these features are shown in the solution of the claimed invention.
Conclusion
Response to Amendment
Examiner has carefully considered the applicant’s amendments and IDS filed on 6/29/2026. After an updated search, new prior art was found to rejection the claims with new grounds of rejection.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 August 7, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
