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 § 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 9 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of “a computer-readable storage medium” includes a carrier wave, which is non-statutory subject matter.
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.
Claim(s) 1, 2, 4, and 7-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lazarus et al. (US 2020/0058106 A1), hereinafter referred to as Lazarus. With reference to claims 1, 9 and 10, Lazarus teaches An imaging method using a mobile magnetic resonance apparatus, comprising: randomly sampling an object-to-be-scanned using a mobile magnetic resonance apparatus to acquire encoding of the frequency and phase of the tissue of the object-to-be-scanned, and obtain target K-space data (¶0063, Fig. 1A, 102, ¶0174); inputting the target K-space data and pre-generated denoising reconstruction network parameters into a pre-trained denoising reconstruction network (Fig. 1A, 108, ¶0081); wherein the pre-trained denoising reconstruction network is generated by training based on fully sampled training data (“ground truth” ¶0091, ¶0104); and outputting a magnetic resonance image corresponding to the object-to-be-scanned (Fig. 1A, 114, ¶0088).
With reference to claim 2, Lazarus further teaches the pre-trained denoising reconstruction network is generated according to the following steps, which comprise: using the mobile magnetic resonance apparatus to perform full sampling on multiple scanning objects to acquire the encoding of the frequency and phase of the tissue of each scanning object, and obtain multiple pieces of K-space training data (¶0091); constructing fully sampled training data based on the multiple pieces of K-space training data (¶0091); constructing a target denoising reconstruction network, inputting the fully sampled training data into the target denoising reconstruction network, and outputting a network cost value (¶0093); and generating a pre-trained denoising reconstruction network and pre-generated denoising reconstruction network parameters when the network cost value reaches its minimum (¶0104).
With reference to claim 4, Lazarus further teaches n the constructing a target denoising reconstruction network comprises: constructing a denoising reconstruction network using neural networks; constructing a cost function of the denoising reconstruction network; and mapping the cost function to the denoising reconstruction network to obtain a target denoising reconstruction network; wherein the cost function is
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(¶0098, ∇ is an image gradient, which would be the equivalent of subtracting the feature vector from the reconstructed image). With reference to claim 7, Lazarus further teaches the generating a pre-trained denoising reconstruction network and pre-generated denoising reconstruction network parameters when the network cost value reaches its minimum comprises: when the network cost value does not reach its minimum, backpropagating the network cost value to update the network parameters of the denoising reconstruction network, and continuing the execution of the step of "inputting the fully sampled training data into the target denoising reconstruction network and outputting a network cost value" until the network cost value reaches its minimum and the number of network training reaches a preset number to generate the pre-trained denoising reconstruction network and pre-generated denoising reconstruction network parameters (¶0099, Fig. 1D, ¶0074-¶0076).
With reference to claim 8, performing the method of Claim 1 would require all limitations of the device claim 8.
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(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lazarus as applied to claim 2 above, and further in view of Takeshima et al. (US 2020/0271743 A1), hereinafter referred to as Takeshima. Lazarus teaches all that is required as explained above, however is silent with regards to dividing k-space data for training. Takeshima teaches the constructing fully sampled training data based on the multiple pieces of K-space training data comprises: performing image reconstruction based on each K-space data to obtain the magnetic resonance image of each K-space data; associating each K-space data with its corresponding magnetic resonance image to obtain a K-space image dataset; and randomly dividing the K-space image dataset into equal parts, and determining a preset number of parts of the K-space image dataset as the fully sampled training data (¶0070). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Takeshima with the method of Lazarus so as to improve accuracy and efficiency of training.
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lazarus as applied to claim 1 above, and further in view of Liu et al. (US 2019/0195975 A1), hereinafter referred to as Liu. With reference to claim 5, Lazarus teaches all that is required as explained above however is silent with regards to before the randomly sampling an object- to-be-scanned using a mobile magnetic resonance apparatus, the method further comprises: using a random sampling function to construct a random sampling block of a preset size to obtain a data random collection layer; and setting data collection parameters of the mobile magnetic resonance apparatus as the data random collection layer. Liu teaches before the randomly sampling an object- to-be-scanned using a mobile magnetic resonance apparatus, the method further comprises: using a random sampling function to construct a random sampling block of a preset size to obtain a data random collection layer; and setting data collection parameters of the mobile magnetic resonance apparatus as the data random collection layer (¶0191-0193). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Liu with the method of Lazarus so as to effectively sample the k-space.
With reference to claim 6, Liu further teaches the function of the random sampling block (Fig. 15-A, ¶0191-0193)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY H CURRAN whose telephone number is (571)270-7505. The examiner can normally be reached Monday-Friday, 8am-5pm, EST.
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/GREGORY H CURRAN/Primary Examiner, Art Unit 2852
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