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 .
Claim Rejections - 35 USC § 102
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 1 is rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Shen et al. (WO 2020/113148)(published June 4, 2020)(also published as US PGPUB 2021/0393229 and US Pat. No. 12,023,192)
Regarding claim 1, Shen discloses a CT scan apparatus comprising: a CT scan unit configured to capture N projection images of an electronic part (Shen, p. 2, Lines 17-20), and a CT image generation unit having an artificial intelligence-based AI model configured to receive the N projection images captured by the CT scan unit and output a CT tomography image of the electronic part (Shen, p. 2, Lines 19-24).
Shen further discloses the AI model is generated by learning N learning projection images, a first learning CT tomography image generated by the N learning projection images, and a second learning CT tomography image generated by M learning projection images (here, M > N) as learning data, wherein the AI model learns the second learning CT tomography image as output data. (Shen, p. 9, Line 27 through p. 10 Line 10)
Shen further discloses the AI model learns the N learning projection images and the first learning CT tomography image as input data (Shen, p. 9 Line 27 through p. 10 Line 10); and the CT image generation unit further comprises a preprocessing module configured to process the N projection images to generate a CT tomography image for input and input the CT tomography image to the AI model. (Shen, p. 4 Line 24 through p. 5 Line 6)
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.
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 4 is rejected under 35 U.S.C. 103 as being unpatentable over Shen.
Regarding claim 4, Shen lacks explicit teaching of N is 4, and M is 8 or more.
However, the operating parameters of the neural network, including the number of images required to generate a tomographic image (Shen discloses use of 1, 2, 5, and 10 views, see Shen pp. 3-4) as well as the number of images used to train the machine learning algorithm (Shen discloses a number in the hundreds, see Shen at p. 9) are variables known to affect the outcome of the process, and optimizing those known result-effective variables would be obvious to one of ordinary skill in the art before the filing date of the claimed invention to generate a sufficient dataset for accurate reconstruction while minimizing processing and data utilization resources.
Response to Arguments
Applicant's arguments filed 3/30/2026 have been fully considered but they are not persuasive.
Applicant argues Shen does not teach the features of prior claims 2 and 3, now amended into claim 1. In response Applicant’s attention is directed to the training process discussed in pages 9 and 10 of Shen. Shen discloses “actually measuring a large number of paired X-ray projection and CT images for supervised training” before outlining a technique Shen believes to be superior. Non-preferred disclosures are disclosures. Furthermore, Shen steps from a large number of generated images for training purposes to allowing the neural network to operate on a sparse dataset for operation, thus satisfying the M greater than N limitation.
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 EDWIN C GUNBERG whose telephone number is (571)270-3107. The examiner can normally be reached Monday-Friday, 8:30AM-5:00PM.
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/EDWIN C GUNBERG/Primary Examiner, Art Unit 2884
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