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 .
Response to Arguments
Applicant's arguments filed 07/30/2026 have been fully considered but they are not persuasive.
Regarding rejections on claims 1, 8-9, applicant argued Huang fails to teach performing image preprocessing on a collection of neuroimages comprising a first structural image, a first nidus image, and a first metabolic image, much less performing image preprocessing on such a collection of neuroimages to acquire a collection of object images comprising a second structural image, a second nidus image, and a second metabolic image. Then applicant argued that Peng fails to teach inputting the claimed collection of object images into a trained three-dimensional convolutional neural network to acquire a position of a nidus of a target object.
However, examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). As primary reference, Huang teach a method for nidus recognition comprising acquiring a first collection of images and preprocessing said first collection of images to acquire a collection of object images for detection lesion for identifying corresponding position information of detected lesion (abstract, Figs. 1-8, and corresponding disclosure). Huang teach acquiring and inputting both PET images and CT images (paragraphs 0059-0060, 0065), wherein CT image is a form of structural image, lesion CT image is nidus image, and PET image is fundamentally a metabolic image. Huang may not expressly disclose a trained three-dimensional convolutional neural network. But Huang does teach processing said collection of images for lesion detection with a pre-trained deep learning network model (paragraphs 0004, 0051, 0065, 0085-0086). Though Huang does not teach using the method on brain images with a trained three-dimensional convolutional neural network for processing, it would have been obvious to one of ordinary skill in the art to recognize such utilization and features are well-known in the art and obvious for implementation detail by design preference. Yet, Peng teach using pretrained convolutional neural networks (paragraphs 0060, 0067-0068, 0130) to process 3D multiple layers of brain/head images (paragraphs 0045, 0055, 0077, 0106). Considering Huang’s teaching on nidus recognition, one of ordinary skill in the art would obviously recognize that Peng’s application with brain images would comprise related collection of neuroimages. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Peng into the method of Huang, to further nidus recognition implementation with trained CNN for 3D input on brain lesion examination by design preference.
Thus, rejections are proper and maintained.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-3, 5, 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (CN116168823) in view of Peng et al. (CN116433976).
To claim 1, Huang teach a method for nidus recognition in a neuroimage (paragraph 0008, obvious as whole body images are acquired), comprising:
determining a collection of neuroimages to be recognized, the collection of neuroimages comprising a first structural image (CT image), a first nidus image (lesion CT image layer), and a first metabolic image (PET image) acquired by capturing images of a target object (paragraphs 0009, 0019-0020, first number of lesion CT image layers contained in the initial training set);
performing image preprocessing on the collection of neuroimages to acquire a collection of object images comprising a second structural image, a second nidus image, and a second metabolic image (paragraphs 0020-0021, 0068-0069);
inputting the collection of object images into a preset network to acquire a position of a nidus of the target object (paragraphs 0058-0062, 0085-0089); and
labeling the position of the nidus on the first structural image based on the position of the nidus of the target object to acquire and display an image of the position of the nidus (paragraphs 0003, 0034, 0052, 0065, 0072, 0086, locate tumor regions and their metastatic lesion regions, identify target lesions, wherein labeling would be obvious when target lesion is identified).
But, Huang do not expressly disclose neuroimages; the preset network being a trained three-dimensional convolutional neural network.
Peng teach inputting captured brain images (paragraphs 0041, 0050-0051) into a trained three-dimensional convolutional neural network (paragraphs 0045, 0055, 0060) for segmentation and target objects identification/classification (paragraphs 0037, 0058).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Peng into the method of Huang, in order to implement machine learning network for preferential application.
To claim 8, Huang and Peng teach an electronic apparatus (as explained in response to claim 1 above).
To claim 9, Huang and Peng teach a non-transitory computer readable storage medium storing computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement a method for nidus recognition in a neuroimage (as explained in response to claim 1 above).
To claim 2, Huang and Peng teach claim 1.
Huang and Peng teach wherein the performing image preprocessing (Huang, paragraph 0079; Peng, paragraphs 0037-0038, 0100, 0115-0116, 0144) on the collection of neuroimages to acquire a collection of object images comprising a second structural image, a second nidus image, and a second metabolic image comprises: performing image segmentation on the first structural image to acquire at least one type of segmented images containing a tissue of the target object (Huang, paragraph 0052); performing image position correction on the first structural image, the first nidus image, and the first metabolic image (Peng, paragraph 0109); extracting the target object from the first structural image, the first nidus image, and the first metabolic image after the image position correction based on the segmented images to acquire a second structural image, a second nidus image, and a second metabolic image; and determining the collection of object images based on the second structural image, the second nidus image, and the second metabolic image (Peng, paragraphs 0080-0083, 0109).
To claim 3, Huang and Peng teach claim 2.
Huang and Peng teach wherein the target object is a brain (Peng, paragraph 0041, human brain structures), and the segmented images comprise a cerebrospinal fluid image, a gray matter image, and a white matter image (Peng, Figs. 1-5, paragraphs 0095-0112, image segmentation, wherein gray matter images and white matter images are acquired; despite lack of teaching cerebrospinal fluid image, CSF image is a well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the method of Huang and Peng for providing additional visual information for evaluation. Hence Official Notice is taken).
To claim 5, Huang and Peng teach claim 2.
Huang and Peng teach wherein the extracting the target object from the first structural image, the first nidus image, and the first metabolic image after the image position correction based on the segmented images to acquire a second structural image, a second nidus image, and a second metabolic image comprises: determining an image mask for characterizing a position of the target object based on at least one of the segmented images; and extracting the target object from the first structural image, the first nidus image, and the first metabolic image after the image position correction, respectively, based on the image mask to acquire the second structural image, the second nidus image, and the second metabolic image (Huang, Fig. 4, paragraphs 0039, 0059; Peng, paragraphs 0084, 0107-0109, 0117-0119).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (CN116168823) in view of Peng et al. (CN116433976) and Peng et al. (US2024/034170, hereafter as Peng2).
To claim 4, Huang and Peng teach claim 2.
But, Huang and Peng do not expressly disclose wherein the performing image position correction on the first structural image, the first nidus image, and the first metabolic image comprises: performing anterior commissure correction, registration, and density standardization on the first structural image, the first nidus image, and the first metabolic image.
Peng teach an operation method of a brain amyloid PET processing system (abstract, Fig. 2), comprises: performing anterior commissure correction (paragraph 0041), registration (paragraphs 0031-0032), and density standardization on the images (paragraphs 0032-0040), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the method of Huang and Peng, in order to further implementation of image preprocessing.
Conclusion
THIS ACTION IS MADE FINAL. 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 ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 August 25, 2026
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