DETAILED ACTION
Claims 1–20 are still pending in the instant application.
This Office Action is in response to the Applicant’s amendment filed on 08/10/2026.
THIS OFFICE ACTION IS MADE FINAL.
Response to Arguments
In re Applicant’s remarks, Examiner thanks Applicant amended the claimed language to expedite patent prosecution and conducted extensive searches to determine whether the amendment overcomes prior art. In re claims 19 and 20, although Applicant recites additional elements from claims 2–4, these claims only depend on claim 1. Therefore, the last independent claims have arisen new subject matter, i.e., changed the scope of claim. Also, Examiner interpreted that since Applicant uses “or” conjunction in claims 19–20, he has not fully disavowed the claimed language.
In conclusion, Examiner determined that the Applicant’s arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. (emphasis added)
Double Patenting
Claims 1, 7, 9–12, 13–14 and 19–20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 6–12, 17 and 19 of U.S. Patent No. 11,941,805 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are not patentably distinct from the claims of the reference as the differences between them would have been obvious to one of ordinary skill in the art at the time the invention made. A detailed description of the mapping is available in the non-final rejection mailed 11 May 2026, and for brevity, is not repeated herein.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
Claims 1–6 and 17 are rejected under pre-AIA 35 U.S.C. § 103(a) as being unpatentable over Gao et al. (U.S. 11,348,233 B2) in view of Tanaka (U.S. 9,235,916 B2).
Regarding claim 1, Gao discloses a method for image processing, implemented on a computing device having at least one storage device storing a set of instructions, and at least one processor in communication with the at least one storage device, the method comprising:
obtaining a first image of a subject; (Per Fig. 1, Gao’s processing device 120 discloses an initial image. Gao col. 7 line 60 – col. 8 line 33. [t]he processing device 120 may obtain an initial image.)
generating a first intermediate image based on the first image, (Per Fig. 1, Gao teaches that an intermediate image corresponds to the initial image. Ibid. The processing device 120 may obtain an intermediate image corresponding to the initial image.) the first intermediate image including feature information of the first image, (Examiner construes that the feature information as a physical portion in terms of a plurality of pixels. See his col. 17 line 62 – col. 18 line 24. The intermediate image may include a plurality of second pixels or voxels associated with at least a portion of the one or more target objects in the initial image.) wherein the feature information includes edge information of one or more portions of the first image (Per Fig. 11A, Gao discloses that the intermediate image represents a portion of target objects in the initial image. Ibid.) and the edge information reflects edges of the different portions of the first image. (Through Figs. 11A–11B, Gao discloses that the intermediate image contains less information of the target objects: e.g., the first image demonstrates a portion of interfering objects, whereas another image emphasizes detailed information of the target objects. Ibid. [t]he intermediate image (including a coarse representation of the target objects) may include less details than the initial image or a target image that includes a fine representation of the target objects.)
However, Gao fails to specifically disclose generating, based on a weighted fusing of the first intermediate image and at least one reference image, a target image of the subject;
wherein: the at least one reference image includes at least one of the first image or a second intermediate image associated with the first image, the second intermediate image including lower noise than the first image; and
in the weighted fusing, different portions of at least one of the first intermediate image or the at least one reference image have different weights.
In related art, Tanaka discloses generating, based on a weighted fusing of the first intermediate image (a first intermediate image construed as an intermediate composite image) and at least one reference image, (a reference image construed as a noise reduced image) a target image (a target image construed as a final composite image) of the subject; (Per Fig. 9 at step S129, Tanaka discloses weighting coefficient where a noise reduced image and an intermediate composite image C are combined to generate a final composite image E. Tanaka col. 11 lines 43–52. [t]he noise reduced image D and the intermediate composite image C are combined on the basis of the second weighting coefficient w′ that was derived in step S127, and a final composite image E is generated,)
wherein: the at least one reference image includes at least one of the first image (a first image construed as an entire initial reference image) (Per Fig. 11 at step S123, Tanaka’s main control section discloses the entire initial reference image A. Ibid. col. 15 lines 41–47. [t]he main control section 20 carries out spatial noise reduction processing on the entire initial reference image A, and generates the noise reduced image D.)
in the weighted fusing, different portions1 of at least one of the first intermediate image (Per Fig. 5, Tanaka discloses combined regions in the intermediate composite image such that noise reduction is applied. Ibid. col. 12 lines 1–22. [i]mage regions where the reference image A and the non-reference image B are combined (hereinafter also called “combined regions”), and image regions where the reference image A and the non-reference image B are not combined (hereinafter also called “non-combined regions”), arise in the intermediate composite image C)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Tanaka into the teachings of Gao to eliminate non-uniformity while reducing noise effects in a composite image. Ibid. col. 2 lines 24–29.
Regarding claim 2, Gao as modified by Tanaka, discloses obtaining a first image of a subject, wherein the first image includes a region of interest (ROI) (ROI construed as high brightness) and a region of non-interest (non-ROI) (non-ROI construed as low brightness); (Per Fig. 11B, Gao discloses an intermediate image where a portion of interfering objects has a low brightness, whereas the other portion of target objects has a high brightness. Gao col. 36 lines 47–61. [p]ixels or voxels of the at least a portion of the interfering objects may have relatively low brightness, while pixels or voxels of the one or more target objects may have relatively high brightness.)
Regarding claim 3, Gao as modified by Tanaka, discloses the method, wherein: in the weighted fusing, a weight for a first portion of the first intermediate image corresponding to the ROI is no less than a weight for a first portion of the at least on e reference image corresponding to the ROI. (Tanaka’s intermediate composite image generating unit discloses whether the difference of an absolute value is less than or equal to another threshold value such that the reference image and the transformed image are combined. Tanaka col. 23 lines 36–47. [t]he intermediate composite image generating unit sets the weighting coefficient of the reference image with respect to the transformed image such that, the greater the absolute value of the first difference, the greater the weighting coefficient gradually becomes, and carries out combining processing of the reference image and the transformed image.)
Regarding claims 4, 5 and 6, they are rejected in the same manner as claim 3.
Regarding claim 17, Gao as modified by Tanaka, discloses the method, wherein the generating a target image of the subject includes: generating the target image of the subject based on a weighted fusing of the first intermediate image, the first image and the second intermediate image. (Per Fig. 9 at step S129, Tanaka discloses weighting coefficient where a noise reduced image and an intermediate composite image C are combined to generate a final composite image E. Tanaka col. 11 lines 43–52. [t]he noise reduced image D and the intermediate composite image C are combined on the basis of the second weighting coefficient w′ that was derived in step S127, and a final composite image E is generated,)
Claims 19 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Gao in view of Tanaka and further in view of Manhart (U.S. 11,158,030 B2).
Regarding claim 19, Gao discloses a system for imaging processing, comprising:
at least one storage medium including a set of instructions; and (Fig. 1, 130 a storage device)
at least one processor in communication with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to cause the system to perform operations including: (Fig. 1, 120 a processing device)
obtaining a first image of a subject, wherein the first image includes a region of interest (ROI) (ROI construed as high brightness) and a region of non-interest (non-ROI) (non-ROI construed as low brightness); (Per Fig. 11B, Gao discloses an intermediate image where a portion of interfering objects has a low brightness, whereas the other portion of target objects has a high brightness. Gao col. 36 lines 47–61. [p]ixels or voxels of the at least a portion of the interfering objects may have relatively low brightness, while pixels or voxels of the one or more target objects may have relatively high brightness.)
generating a first intermediate image based on the first image, the first intermediate image including feature information of the first image. (Per Fig. 1, Gao teaches that an intermediate image corresponds to the initial image. Ibid. col. 7 line 60 – col. 8 line 33. The processing device 120 may obtain an intermediate image corresponding to the initial image.)
Gao fails to specifically disclose generating, based on a weighted fusing of the first intermediate image and at least one reference image, a target image of the subject;
wherein: the at least one reference image includes at least one of the first image or a second intermediate image associated with the first image, the second intermediate image including lower noise than the first image; and
in the weighted fusing, different portions of at least one of the first intermediate image or the at least one reference image have different weights, wherein in the weighted fusing, a weight for a first portion of the first intermediate image corresponding to the ROI is no less than a weight for a first portion of the at least one reference image corresponding to the ROI.
In related art, Tanaka discloses generating, based on a weighted fusing of the first intermediate image (a first intermediate image construed as an intermediate composite image) and at least one reference image, (a reference image construed as a noise reduced image) a target image (a target image construed as a final composite image) of the subject; (Per Fig. 9 at step S129, Tanaka discloses weighting coefficient where a noise reduced image and an intermediate composite image C are combined to generate a final composite image E. Tanaka col. 11 lines 43–52. [t]he noise reduced image D and the intermediate composite image C are combined on the basis of the second weighting coefficient w′ that was derived in step S127, and a final composite image E is generated,)
wherein: the at least one reference image includes at least one of the first image (a first image construed as an entire initial reference image) (Per Fig. 11 at step S123, Tanaka’s main control section discloses the entire initial reference image A. Ibid. col. 15 lines 41–47. [t]he main control section 20 carries out spatial noise reduction processing on the entire initial reference image A, and generates the noise reduced image D.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Tanaka into the teachings of Gao to eliminate non-uniformity while reducing noise effects in a composite image. Ibid. col. 2 lines 24–29.
Gao as modified by Tanaka, discloses the claimed invention, but fails to specifically disclose in the weighted fusing, different portions of at least one of the first intermediate image or the at least one reference image have different weights, wherein in the weighted fusing, a weight for a first portion of the first intermediate image corresponding to the ROI is no less than a weight for a first portion of the at least one reference image corresponding to the ROI.
In related art, Manhart discloses in the weighted fusing, different portions of at least one of the first intermediate image (Per Fig. 2, Manhart discloses a weighted addition of first intermediate dataset Z2 after reconstructing a stack of tomographic image slices. Manhart col. 7 lines 17–24. The result dataset E is produced by weighted addition 2 of the intermediate datasets Z1 and Z2 using the weighting dataset G.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Manhart into the teachings of Gao and Tanaka to apply a smoothing reconstruction kernel such that low-noise and high-resolution images are provided in tomographic dataset. Ibid. col. 1 lines 42–52.
Regarding claim 20, Gao discloses a non-transitory computer readable medium, comprising at least one set of instructions for image processing, wherein when executed by one or more processors of a computing device, the at least one set of instructions causes the computing device to perform a method, the method comprising:
obtaining a first image of a subject, wherein the first image includes a region of interest (ROI) (ROI construed as high brightness) and a region of non-interest (non-ROI) (non-ROI construed as low brightness); (Per Fig. 11B, Gao discloses an intermediate image where a portion of interfering objects has a low brightness, whereas the other portion of target objects has a high brightness. Gao col. 36 lines 47–61. [p]ixels or voxels of the at least a portion of the interfering objects may have relatively low brightness, while pixels or voxels of the one or more target objects may have relatively high brightness.)
generating a first intermediate image based on the first image, the first intermediate image including feature information of the first image. (Per Fig. 1, Gao teaches that an intermediate image corresponds to the initial image. Ibid. col. 7 line 60 – col. 8 line 33. The processing device 120 may obtain an intermediate image corresponding to the initial image.)
Gao fails to specifically disclose generating, based on a weighted fusing of the first intermediate image and at least one reference image, a target image of the subject;
wherein: the at least one reference image includes at least one of the first image or a second intermediate image associated with the first image, the second intermediate image including lower noise than the first image; and
in the weighted fusing, different portions of at least one of the first intermediate image or the at least one reference image have different weights, wherein in the weighted fusing, a weight for a first portion of the first intermediate image corresponding to the ROI is no less than a weight for a first portion of the at least one reference image corresponding to the ROI.
In related art, Tanaka discloses generating, based on a weighted fusing of the first intermediate image (a first intermediate image construed as an intermediate composite image) and at least one reference image, (a reference image construed as a noise reduced image) a target image (a target image construed as a final composite image) of the subject; (Per Fig. 9 at step S129, Tanaka discloses weighting coefficient where a noise reduced image and an intermediate composite image C are combined to generate a final composite image E. Tanaka col. 11 lines 43–52. [t]he noise reduced image D and the intermediate composite image C are combined on the basis of the second weighting coefficient w′ that was derived in step S127, and a final composite image E is generated,)
wherein: the at least one reference image includes at least one of the first image (a first image construed as an entire initial reference image) (Per Fig. 11 at step S123, Tanaka’s main control section discloses the entire initial reference image A. Ibid. col. 15 lines 41–47. [t]he main control section 20 carries out spatial noise reduction processing on the entire initial reference image A, and generates the noise reduced image D.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Tanaka into the teachings of Gao to eliminate non-uniformity while reducing noise effects in a composite image. Ibid. col. 2 lines 24–29.
Gao as modified by Tanaka, discloses the claimed invention, but fails to specifically disclose in the weighted fusing, different portions of at least one of the first intermediate image or the at least one reference image have different weights, wherein in the weighted fusing, a weight for a second portion of the first intermediate image corresponding to the non-ROI is no larger than a weight for a second portion of the at least one reference image corresponding to the non-ROI.
In related art, Manhart discloses in the weighted fusing, different portions of at least one of the first intermediate image (Per Fig. 2, Manhart discloses a weighted addition of first intermediate dataset Z2 after reconstructing a stack of tomographic image slices. Manhart col. 7 lines 17–24. The result dataset E is produced by weighted addition 2 of the intermediate datasets Z1 and Z2 using the weighting dataset G.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Manhart into the teachings of Gao and Tanaka to apply a smoothing reconstruction kernel such that low-noise and high-resolution images are provided in tomographic dataset. Ibid. col. 1 lines 42–52.
Claims 7–13 are rejected under 35 U.S.C. § 103 as being unpatentable over Gao in view of Tanaka and further in view of Yamamoto et al. (U.S. 9,262,814 B2).
Regarding claim 7, Gao as modified by Tanaka, discloses the claimed invention, but fails to specifically disclose wherein the first intermediate image is generated by extracting the feature information of the first image.
In related art, Yamamoto discloses the method, wherein the first intermediate image is generated by extracting the feature information of the first image. (Per Fig. 1, Yamamoto’s first generator 102 discloses a first intermediate after processing the input image with analysis of frequency component. Yamamoto col. 2 lines 49–67. The first generator 102 generates a first intermediate image by applying inverse conversion of conversion corresponding to the operation of degrading an image in the imaging process (the operation of degrading an original image that should be obtained by imaging) to the input image acquired by the acquirer 101.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Yamamoto into the teachings of Gao and Tanaka to improve image quality preventing artifacts in original images. Ibid. col. 1 lines 45–53.
Regarding claim 8, it has been rejected in the same manner as claim 7.
Regarding claim 9, Gao as modified by Tanaka and Yamamoto, discloses the method, wherein the extracting the feature information of the first image includes:
generating a feature map based on the first image; and (Per Fig. 1, Yamamoto’s second generator 103 discloses a pixel value in the input image. Yamamoto col. 3 line 61 – col. 4 line 11. [t]he second generator 103 generates the second intermediate image by amplifying a pixel value of a pixel that is adjacent to a position corresponding to an edge that represents the contour of a subject in the input image.)
determining the feature information of the first image based on the feature map. (Per Fig. 1, Yamamoto’s second generator 103 discloses a frequency component processing the input image. Ibid. [t]he second generator 103 can generate the second intermediate image including a frequency component that is higher than a frequency component included in the input image acquired by the acquirer 101.)
Regarding claim 10, Gao as modified by Tanaka and Yamamoto, discloses the method, wherein the generating a feature map based on the first image includes:
obtaining a second-order differential value and a pixel mean value of the first image; and (Per Fig. 1, Yamamoto’s second generator 103 discloses a second order differential value of a pixel. Yamamoto col. 3 line 61 – col. 4 line 11. [t]he second generator 103 generates the second intermediate image by amplifying a pixel value of a pixel that is adjacent to a zero-cross point indicating a point at which a second order differential (second derivative) value of a pixel value changes…)
generating the feature map based on the second-order differential value and the pixel mean value. (Per Fig. 1, Yamamoto’s third generator 104 discloses a weighting factor of the second intermediate image. Ibid. col. 4 lines 23–43. The third generator 104 generates a weighting factor which is used in weighted addition (described later) of the first intermediate image and the second intermediate image…)
Regarding claim 11, Gao as modified by Tanaka and Yamamoto, discloses the method, wherein the extracting the feature information of the first image includes: determining the feature information of the first image based on a first machine learning model. (Per Fig. 1, Yamamoto’s second generator 103 discloses a second order differential value of a pixel. Yamamoto col. 3 line 61 – col. 4 line 11. [t]he second generator 103 generates the second intermediate image by amplifying a pixel value of a pixel that is adjacent to a zero-cross point indicating a point at which a second order differential (second derivative) value of a pixel value changes…)
Regarding claim 12, Gao as modified by Tanaka and Yamamoto, discloses the method wherein the second intermediate image is generated by: (Per Fig. 1, Yamamoto’s second generator 103 discloses a second order differential value of a pixel. Yamamoto col. 3 line 61 – col. 4 line 11. [t]he second generator 103 generates the second intermediate image by amplifying a pixel value of a pixel that is adjacent to a zero-cross point indicating a point at which a second order differential (second derivative) value of a pixel value changes…)
Regarding claim 13, it has been rejected in the same manner as claim 10.
Claim 14 is rejected under 35 U.S.C. § 103 as being unpatentable over Gao in view of Tanaka and Yamamoto and further in view of Kim et al. (U.S. 11,449,733 B2).
Regarding claim 14, Gao as modified by Tanaka and Yamamoto, discloses the claimed invention, but fails to specifically disclose the method, wherein the generating the second intermediate image by processing, based on a second machine learning model, the first image includes:
generating, based on an initial image, a third image using the second machine learning model;
determining loss information between the first image and the third image;
updating the second machine learning model based on the loss information; and
generating, based on the first image, the third image using the updated second machine learning model.
In related art, Kim discloses the method, wherein the generating the second intermediate image by processing, based on a second machine learning model, the first image includes:
generating, based on an initial image, a third image using the second machine learning model; (Per Fig. 1, Kim discloses a second learning network model 121 where a generated image 30 and a query image 10 are processed. Kim col. 5 lines 12–20. [t]he device may store a first learning network model 110 and a second learning network model 120 for obtaining the generated image 30 from the query image 10.)
determining loss information between the first image and the third image; (Per Fig. 1, Kim’s device discloses adversarial loss between images. Ibid. col. 5 lines 45–54. The device may acquire adversarial loss that is information about a difference between the query image 10 and the generated image 30 and L2 loss that is information about the feature information 20 of the query image 10 and the feature information 40 of the generated image 30.)
updating the second machine learning model based on the loss information; and (Per Fig. 1, Kim’s device repetitively renders loss information in his learning models. Ibid. The device may train the first and second learning network models 110 and 120 by iteratively performing the series of processes to reduce the adversarial loss and L2 loss…)
generating, based on the first image, the third image using the updated second machine learning model. (Per Fig. 3, Kim’s device discloses a second learning network model where the training images are obtained. Ibid. col. 7 lines 22–32. [t]he device may store first and second learning network models 110 and 120 trained based on source training images 305 for numbers 1 through 5.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kim into the teachings of Gao, Tanaka and Yamamoto to perform a neural network for class recognition. Ibid. col. 1 lines 7–11.
Claim 15 is rejected under 35 U.S.C. § 103 as being unpatentable over Gao in view of Tanaka and further in view of Kim et al. (U.S. 11,449,733 B2).
Regarding claim 15, Gao as modified by Tanaka, discloses the claimed invention, but fails to specifically disclose the method, further comprising updating the target image according to an iterative operation including one or more iterations.
In related art, Kim discloses the method, further comprising updating the target image according to an iterative operation including one or more iterations. (Per Fig. 1, Kim’s device repetitively renders loss information in his learning models. Kim col. 5 lines 45–54. The device may train the first and second learning network models 110 and 120 by iteratively performing the series of processes to reduce the adversarial loss and L2 loss…)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kim into the teachings of Gao and Tanaka to perform a neural network for class recognition. Ibid. col. 1 lines 7–11.
Allowable Subject Matter
Claims 16 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. (emphasis added)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENEDICT LEE whose telephone number is (571)270-0390. The examiner can normally be reached 10:00-17:00 (EST).
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/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
1 In light of specification, Examiner construes different portions as different regions in an image. See Applicant’s Spec ¶150. A portion of an image may refer to a region in the image,
Prosecution Insights