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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/19/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 5 is rejected under 35 U.S.C. 101 as not falling within one of the four statutory categories of invention because the broadest reasonable interpretation of the instant claims in light of the specification encompasses transitory signals. But, transitory signals are not within one of the four statutory categories (i.e. non-statutory subject matter). See MPEP 2106(I). However, claims directed toward a non-transitory computer readable medium may qualify as a manufacture and make the claim patent-eligible subject matter. MPEP 2106(I). Therefore, amending the claims to recite a “non-transitory computer-readable medium” would resolve this issue.
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 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Douglas et al (“Douglas” hereinafter, U.S.P.N. 11,728,035 B1) in view of Li et al (“Li” hereinafter, CN 113095369A, a copy of translation is attached herein).
As per claim 1, Douglas teaches a method for judgment using a deep learning model outputting a predetermined judgment result when an image is input and acquiring the judgement result output from the deep learning model (abstract: Douglas teaches a computerized medical diagnostics system employing AI with a training dataset that is able to input MRI image as shown in figure 17 and output a predetermined judgement result of “no finding, finding or diagnosis based on the training dataset”).
However, Douglas does not explicitly teach “generating a difference image based on the received target image, wherein the difference image is an image whose pixel values are the difference values between a pixel in the target image and one of its surrounding pixels, by the system; converting the target image into frequency domain information, by the system; and inputting the difference image and the frequency domain information into the deep learning model”.
Li teaches a fast MRI reconstruction method and device based on SFT transformation multi-modal fusion. The multi-modal fusion comprises “generating a difference image based on the received target image (, wherein the difference image is an image whose pixel values are the difference values between a pixel in the target image and one of its surrounding pixels, by the system (abstract & paragraph [0056]: S120: under-sampling the T2 image to obtain the under-sampled T2 image. The examiner notes the under-sampling T2 image is the claimed “difference image”, and the obtained MRI image in paragraph [0048] is the claimed “target image”, and the under-sampling processing is based on the difference values between a pixel and its neighboring pixels); converting the target image into frequency domain information (paragraph [0059], S130: the T1 image derived from the obtained MRI image is subjected to Fourier transform), by the system; and inputting the difference image and the frequency domain information into the deep learning model (paragraph [0061]: S140: inputting the under-sampling T2 image and the T1 Fourier transform information into a convolutional neural network for multi-mode fusion to obtain a high resolution image)”.
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to modify Douglas in light of Li’s MRI reconstruction method. One would be motivated to do so because Li’s reconstruction method comprises a multi-mode fusion that is capable of “effectively improving the defect of the traditional method over-smooth picture after the over-smooth; the application of the SFT monitoring mechanism, greatly enhancing the image texture details; the data consistency layer also can avoid the problem that the source image low-frequency information is changed along with the reconstruction” (Li: abstract).
As per claim 2, Douglas in figures 17 and 20 and column 14, lines 38-67, column 15, lines 1-22, teaches segmenting and labeling ROI components in MRI images for neural network training.
As per claim 3, Douglas teaches wherein the target image for judgment is a specific object, and the judgement result is characterized by being based on the material of the object, by the system (as shown in figure 17 of Douglas, the object may be brain, and the material may be a tumor).
As per claim 4, see explanation in claims 1 and 2.
As per claim 5, see explanation in claim 1. The examiner notes both Douglas and Li’s systems are computer-like systems, which inherently includes a non-transitory computer-readable medium.
As per claim 6, see explanation in claim 1. The examine notes both Douglas and Li’s systems are computer-like systems, which inherently includes a processor and a memory.
As per claim 7, see explanation in claim 4.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOM Y LU whose telephone number is (571)272-7393. The examiner can normally be reached Monday - Friday, 9AM - 5PM.
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/TOM Y LU/Primary Examiner, Art Unit 2667