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 .
Status of Claims
Claims 1 – 7, 10 – 12 and 16 – 18 remain pending.
Claims 1, 10 and 16 are Amended.
Claims 8, 9 and 13 – 15 have been canceled.
Claims 17 and 18 are new claims.
Response to Arguments
Applicant's arguments filed June 23, 2026 with respect to claims 1 – 7, 10 – 12 and 16 – 18 have been considered but are moot because the new grounds of rejection necessitated by applicant’s amendment.
Response to Remarks
Applicant argues that Amukotuwa is silent on the following limitations below. Examiner respectfully disagrees for the reasons provided below:
In the Remarks (p. 7) regarding claim 1, applicants assert, “The rejection therefore relies on equating Amukotuwa's "threshold likelihood" and vessel-abnormality output with the subject matter of claim 8 (now cancelled and incorporated into claim 1) operation, but paragraph [0060] does not disclose determining whether to generate, based on the received first X-ray image, "a second X-ray image having the second view of the body part" based on "the probability score" of "the one or more detected pathologies in the first X-ray image." Nor does paragraph [0060] disclose generating that second X-ray image "to further assess the one or more detected pathologies." At most, the cited paragraph describes generating an image that indicates a region and a vessel location in which an abnormality is present”.
Examiner respectfully disagrees because (Amukotuwa in [0041] discloses the first imaging data source 110 can implement computed tomography (CT) imaging techniques. CT imaging techniques implemented by the first imaging data source 110 can include CT-angiography imaging techniques. In various examples, the second imaging data 112 can include thin-slice volumetric data. CT scans combine X-ray technology with computer processing to produce detailed cross-sectional images of the body. Amukotuwa in [0060] discloses, “The one or more spatial data analysis models 202 can determine a likelihood that the image 222 corresponds to an abnormality associated with a given vessel ... determine that the image 222 has at least a threshold likelihood of corresponding to an abnormality of a vessel ... spatial data analysis system 116 can generate an additional image 228. The additional image 228 can indicate the region 224 and also indicate a location 226 of a vessel in which an abnormality is present” wherein threshold likelihood equates to probability score and abnormality equates to pathology. Amukotuwa in [0060] clearly discloses about second image, “the image 222 has at least a threshold likelihood of corresponding to an abnormality ... the spatial data analysis system 116 can generate an additional image 228” wherein additional image 228 equates to second image to detect abnormalities (pathology)).
For the reasons above, the rejections of claims 1 – 16 as established in the last Office
Action (Non-Final, 03/26/2026) are proper and are hereby maintained and incorporated in this Office Action.
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.
Claims 1, 10 and 16 are rejected under 35 U.S.C 103 as being unpatentable over Peng et al. ‘XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors’ (hereinafter Peng) in view of Amukotuwa Patent Application Publication No. US-20220114726-A1 (hereinafter Amukotuwa).
Regarding claim 1, Peng discloses an image processing apparatus, comprising: an input configured to receive a first X-ray image obtained in an image acquisition, wherein the first X-ray image has a first view of a body part of a patient (Peng in [Method, Paragraph – 1] discloses, “The main goal of this work is to synthesis novel views from a frontal view radiograph”. Furthermore, Peng in [3D PriorNet (3DPN), Paragraph – 1] discloses about the receiving the X-ray image as input, “Backprojector produces V T BP from input view radiograph IT, and CT2Xray produces a desired view radiograph IT”); a second view of the body part of the patient using a pre-trained machine-learning model to further assess the one or more detected pathologies, wherein the second view is different from the first view (Peng in Figure. 1 discloses, “From (a) a real radiograph, XraySyn synthesizes (b) a radiograph of novel view. As the view point rotate clockwise in azimuth angle, observe that the heart and the rib bones, as the blue arrows indicate, change accordingly. Additionally, XraySyn obtains (c) the bone structure across all views and can be used to perform (d) bone suppression. Both synthesized views and bone estimation are generated without direct supervision” wherein rotating the view point implies to capturing second view of body part that is different from first view. Additionally, in Peng in [3D PriorNet (3DPN), Paragraph – 2] discloses about 3D U-Net which is a machine learning model); and an output configured to provide the generated second X-ray image (Peng in Figure 2 discloses generated
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as output).
Peng doesn’t disclose about the following limitation as further recited in the claim.
Amukotuwa discloses a processor configured to; detect a presence of one or more pathologies in the first X-ray image; provide a probability score of the one or more detected pathologies in the first X- ray image; determine, based on the probability score, whether to generate, based on the received first X-ray image, a second X-ray image (Amukotuwa in [0041] discloses the first imaging data source 110 can implement computed tomography (CT) imaging techniques. CT imaging techniques implemented by the first imaging data source 110 can include CT-angiography imaging techniques. In various examples, the second imaging data 112 can include thin-slice volumetric data. CT scans combine X-ray technology with computer processing to produce detailed cross-sectional images of the body. Amukotuwa in [0060] discloses, “The one or more spatial data analysis models 202 can determine a likelihood that the image 222 corresponds to an abnormality associated with a given vessel ... determine that the image 222 has at least a threshold likelihood of corresponding to an abnormality of a vessel ... spatial data analysis system 116 can generate an additional image 228. The additional image 228 can indicate the region 224 and also indicate a location 226 of a vessel in which an abnormality is present” wherein threshold likelihood equates to probability score and abnormality equates to pathology. Amukotuwa in [0060] clearly discloses about second image, “the image 222 has at least a threshold likelihood of corresponding to an abnormality ... the spatial data analysis system 116 can generate an additional image 228” wherein additional image 228 equates to second image to detect abnormalities (pathology)).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Amukotuwa into the system of Peng because it would help the system to reduce unnecessary image generation saving resources for the cases that need further analysis.
Summary of Citations (Amukotuwa)
Paragraph [0060]; “The one or more spatial data analysis models 202 can determine a likelihood that the image 222 corresponds to an abnormality associated with a given vessel. In situations where the one or more spatial data analysis models 202 determine that the image 222 has at least a threshold likelihood of corresponding to an abnormality of a vessel that supplies blood to a region of the brain of the individual 108 that is similar to the region 224, the spatial data analysis system 116 can generate an additional image 228. The additional image 228 can indicate the region 224 and also indicate a location 226 of a vessel in which an abnormality is present”.
Summary of Citations (Peng)
[Method, Paragraph – 1]; “The main goal of this work is to synthesis novel views from a frontal view radiograph”.
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Regarding claim 10, Peng discloses an image processing method, the method comprising: receiving a first X-ray image obtained in an image acquisition, wherein the first X-ray image has a first view of a body part of a patient (Peng in [Method, Paragraph – 1] discloses, “The main goal of this work is to synthesis novel views from a frontal view radiograph”. Furthermore, Peng in [3D PriorNet (3DPN), Paragraph – 1] discloses about the receiving the X-ray image as input, “Backprojector produces V T BP from input view radiograph IT, and CT2Xray produces a desired view radiograph IT”); a second view of the body part of the patient using a trained machine-learning model, wherein the second view is different from the first view; and providing the generated second X-ray image (Peng in Figure. 1 discloses, “From (a) a real radiograph, XraySyn synthesizes (b) a radiograph of novel view. As the view point rotate clockwise in azimuth angle, observe that the heart and the rib bones, as the blue arrows indicate, change accordingly. Additionally, XraySyn obtains (c) the bone structure across all views and can be used to perform (d) bone suppression. Both synthesized views and bone estimation are generated without direct supervision” wherein rotating the view point implies to capturing second view of body part that is different from first view. Additionally, in Peng in [3D PriorNet (3DPN), Paragraph – 2] discloses about 3D U-Net which is a machine learning model).
Peng doesn’t disclose about the following limitation as further recited in the claim.
Amukotuwa discloses detecting a presence of one or more pathologies in the first X-ray image; providing a probability score of the one or more detected pathologies in the first X-ray image; determining, based on the probability score, whether to generate, based on the received first X-ray image, a second X-ray image (Amukotuwa in [0041] discloses the first imaging data source 110 can implement computed tomography (CT) imaging techniques. CT imaging techniques implemented by the first imaging data source 110 can include CT-angiography imaging techniques. In various examples, the second imaging data 112 can include thin-slice volumetric data. CT scans combine X-ray technology with computer processing to produce detailed cross-sectional images of the body. Amukotuwa in [0060] discloses, “The one or more spatial data analysis models 202 can determine a likelihood that the image 222 corresponds to an abnormality associated with a given vessel ... determine that the image 222 has at least a threshold likelihood of corresponding to an abnormality of a vessel ... spatial data analysis system 116 can generate an additional image 228. The additional image 228 can indicate the region 224 and also indicate a location 226 of a vessel in which an abnormality is present” wherein threshold likelihood equates to probability score and abnormality equates to pathology. Amukotuwa in [0060] clearly discloses about second image, “the image 222 has at least a threshold likelihood of corresponding to an abnormality ... the spatial data analysis system 116 can generate an additional image 228” wherein additional image 228 equates to second image to detect abnormalities (pathology)).
Regarding claim 16, is a non-transitory computer readable storage medium claim
corresponds to method claim 10. Therefore, the rejection analysis of claim 10 is applied in claim
16.
Claims 2 and 17 is rejected under 35 U.S.C 103 as being unpatentable over Peng in view of Amukotuwa and further in view of Rodin ‘Multitask and Multimodal Neural Network Model for Interpretable Analysis of X-ray Images’ (hereinafter Rodin).
Regarding claim 2, the combination of over Peng and Amukotuwa as a whole teaches claim 1 but fails to teach the further limitations as recited in claim 2. Rodin teaches claims 2 for the same grounds of rejection and motivation established in the Non-Final Office Action of 03/20/2026.
Regarding claim 17, method claim 17 corresponds to apparatus claim 2. Therefore, the rejection analysis and motivation to combine of claim 2 is applicable to claim 17.
Claims 3, 4 and 18 are rejected under 35 U.S.C 103 as being unpatentable over Peng in view of Amukotuwa and further in view of Tan US Patent Publication No. US-11727086-B2 (hereinafter Tan).
Regarding claims 3 and 4, the combination of over Peng and Amukotuwa as a whole teaches claim 1 but fails to teach the further limitations as recited in claims 3 and 4. Tan teaches claims 3 and 4 for the same grounds of rejection and motivation established in the Non-Final Office Action of 03/20/2026.
Regarding claim 18, method claim 18 corresponds to apparatus claim 3. Therefore, the rejection analysis and motivation to combine of claim 3 is applicable to claim 18.
Claim 5 is rejected under 35 U.S.C 103 as being unpatentable over Peng in view of Amukotuwa and Tan and further in view of Chen ‘Generative Adversarial U-Net for Domain-free Medical Image Augmentation’ (hereinafter Chen).
Regarding claim 5, the combination of over Peng, Amukotuwa and Tan as a whole teaches claim 1 but fails to teach the further limitations as recited in claim 5. Chen teaches claim 5 for the same grounds of rejection and motivation established in the Non-Final Office Action of 03/20/2026.
Claims 6, 7, 11 and 12 are rejected under 35 U.S.C 103 as being unpatentable over Peng in view of Amukotuwa and further in view of Karimabadi Patent Application Publication No. WO-2022221712-A1 (hereinafter Karimabadi).
Regarding claims 6, 7, 11 and 12 the combination of over Peng and Amukotuwa as a whole teaches claim 1 but fails to teach the further limitations as recited in claims 6, 7, 11 and 12. Karimabadi teaches claim 6, 7, 11 and 12 for the same grounds of rejection and motivation established in the Non-Final Office Action of 03/20/2026.
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.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAID MUHAMMAD SALEH whose telephone number is (703)756-1684. The examiner can normally be reached M-F 8 am - 5 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached on (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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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
7/08/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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