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 .
DETAILED ACTION
Claims 1-20 are pending in this application.
Drawings
The drawings received on 8/14/2023 are accepted for examination purposes.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Objections
Claim 1 is objected to because of the following informalities: Line 9 recites ‘under-sampled FRDs…’ and should recite ‘undersampled FDRs…’. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: Line 11 recites ‘under-sampled FRDs…’ and should recite ‘undersampled FDRs…’. Appropriate correction is required.
Claim 19 is objected to because of the following informalities: Line 8 recites ‘under-sampled FRDs…’ and should recite ‘undersampled FDRs…’. Appropriate correction is required.
Claim 20 is objected to because of the following informalities: Line 1 recites ‘to claim 31’, which is improper dependency. Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities: Paragraph 00105 (line 2) and 00110 (line 8) of Applicant’s Disclosure recited ‘under-sampled FRDs’ and should recite ‘undersampled FDRs’.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 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)(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(s) 1, 3-8, 13-14, 16-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang et al. (US-2025/0005888).
As to Claim 19, Zhang teaches ‘A computerized system comprises a processor that is configured to: obtain an under-sampled frequency domain representation (FDR) of the spatial information, wherein the under-sampled FDR was obtained by sampling an FDR of the spatial information with a sampling mask [Fig 2 (201), par 0040, 0043-0045 – obtain a plurality of pieces of undersampled frequency-domain data, where each radio frequency coil: directly collecting, by the radio frequency coil, undersampled K-space data to be used as a piece of undersampled frequency-domain data; or collecting fully-sampled K-space data by the radio frequency coil, performing undersampling processing by adding a mask in the fully-sampled K-space data, and using the obtained undersampled K-space data as a piece of undersampled frequency-domain data]; implement a machine learning process that once fed with the under- sampled FDR and the sampling mask [par 0040, 0048, 0050 – first image processing network may be a fully convolutional network, image domain information supplement is performed on undersampled frequency-domain data (that is, undersampled K-space data)], reconstructs the spatial information [Fig 2 (202), par 0046-0047 – obtain a plurality of corresponding target restored images, and determine a target reconstructed image based on the plurality of obtained target restored images using a plurality of image processing networks]; wherein the machine learning process was trained using a training data set that comprises training under-sampled FDRs of training spatial information, and one or more training sampling masks that were used to sample FDRs of training spatial information [par 0086-0087, 0094 – performing, based on an undersampled sample data set which is also undersampled K-space data, joint iteration training on a plurality of to-be-trained processing networks that are cascaded to obtain the plurality of trained image processing networks, a plurality of pieces of sample data selected from the sample data set to obtain a plurality of corresponding prediction restored images and a plurality of pieces of corresponding prediction frequency-domain complement data, and determining a prediction reconstructed image based on the plurality of obtained prediction restored images]’.
Further, in regards to claim 1 the computerized system of claim 1 performs the method of claim 1.
Further, in regards to claim 16, the method of claim 1 is fully embodied on the non-transitory computer readable medium of claim 16.
As to Claim 3, Zhang teaches ‘wherein the FDR of the spatial information is magnetic resonance imaging (MRI) information [par 0025-0029 – frequency domain (K-space domain) of a magnetic resonance imaging (MRI) image].
As to Claim 4, Zhang teaches ‘wherein the MRI information is obtained from a single coil of an MRI system [par 0029, 0039 – each individual radio frequency coil receives an MR signal that obtains undersampled K-space data of an MRI image]’.
As to Claim 5, Zhang teaches ‘wherein the MRI information is obtained from multiple coils of an MRI system [par 0029, 0039 – a plurality of radio frequency coils in multi-channel nuclear magnetic resonance imaging]’.
As to Claim 6, Zhang teaches ‘wherein the FDR of the spatial information is a Fourier transform representation of the spatial information [par 0028, 0051 – transforming an image from an image domain (that is, a time domain) to a frequency domain (K-space domain), and is a two-dimensional Fourier transform result of image data of the image domain]’.
As to Claim 7, Zhang teaches ‘wherein the machine learning process is implemented by one or more neural networks [par 0025, 0048 – image restoring network, frequency-domain complement network, and the susceptibility estimation network may be a fully convolutional network]’.
As to Claim 8, Zhang teaches ‘wherein the one or more neural networks comprise a UNET [par 0048 – image restoring network, frequency-domain complement network, and the susceptibility estimation network may be a fully convolutional network such as a U-Net]’.
As to Claim 13, Zhang teaches ‘wherein the training spatial information comprises multiple training spatial information units, wherein at least one of the training spatial information units is related to an object that differs from an object related to the spatial information [par 0026-0028, 0086-0087, 0097 – plurality of pieces of sample data correspond to different radio frequency coils for training based on different images]’.
As to Claim 14, Zhang teaches ‘comprising training the machine learning process using the training data set [par 0086 – performing joint iteration training on a plurality of to-be-training processing networks based on undersampled sample data set]’.
As to Claim 17, Zhang teaches ‘controlling a generation of the spatial information [par 0025-0029, 0129-0131 – storing instructions to perform MRI image reconstruction based on K-space data]’.
As to Claim 18, Zhang teaches ‘controlling a generation of the spatial information by one or more coils of an magnetic resonance imaging (MRI) system [par 0025-0029, 0038, 0129-0131 – storing instructions to perform MRI image reconstruction based on K-space data and radio frequency coils of an MRI system]’.
As to Claim 20, Zhang teaches ‘wherein the computerized system is an MRI system [par 0038 – MRI system]’.
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.
Claim(s) 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Cole et al. (US-2021/0217213).
As to Claim 2, Zhang teaches all of the claimed elements/features as recited in independent claim 1. Zhang does not disclose expressly ‘wherein the one or more training sampling masks are multiple training sampling masks, and wherein at least two of the training sampling masks differ from each other’, although Zhang teaches a plurality of pieces of sample data correspond to different radio frequency coils for training based on different images [par 0026-0028, 0086-0087, 0097].
Cole in the proposed combination teaches ‘wherein the one or more training sampling masks are multiple training sampling masks, and wherein at least two of the training sampling masks differ from each other [Cole et al. (US-2021/0217213): par 0041 – pseudo-random Poisson-disc variable-density sampling masks to fully- sampled k-space]’.
Zhang and Cole are analogous art because they are from the same field of endeavor, namely reconstructing MRI imaging. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include variable-density sampling masks, as taught by Cole. The motivation for doing so would have been to producing higher quality images. Therefore, it would have been obvious to combine Cole with Zhang to obtain the invention as specified in claim 2.
As to Claim 12, Cole in the proposed combination teaches ‘wherein at least one training sampling mask of the one or more training sampling masks differs from the sampling mask [par 0041 – pseudo-random Poisson-disc variable-density sampling masks]’.
Zhang and Cole are analogous art because they are from the same field of endeavor, namely reconstructing MRI imaging. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include variable-density sampling masks, as taught by Cole. The motivation for doing so would have been to producing higher quality images. Therefore, it would have been obvious to combine Cole with Zhang to obtain the invention as specified in claim 12.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Iqbal et al. (US-2022/0101997).
As to Claim 9, Zhang teaches all of the claimed elements/features as recited in dependent claim 7 and independent claim 1. Zhang does not disclose expressly ‘wherein the one or more neural networks comprise a transformer neural network’.
Iqbal teaches ‘wherein the one or more neural networks comprise a transformer neural network [par 0043 – transformer neural network]’.
Zhang and Iqbal are analogous art because they are from the same field of endeavor, namely frequency-domain representations. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include a transformer neural network, as taught by Iqbal. The motivation for doing so would have been to utilizing a different neural network for a health prediction of a user. Therefore, it would have been obvious to incorporate Iqbal with Zhang to obtain the invention as specified in claim 9.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Xuan et al. (Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network).
As to Claim 10, Zhang teaches all of the claimed elements/features as recited in dependent claim 7 and independent claim 1. Zhang does not disclose expressly ‘wherein the one or more neural networks comprise an end-to-end variational network’.
Xuan teaches ‘wherein the one or more neural networks comprise an end-to-end variational network [pg 4, left column, 2nd paragraph – modern MRI reconstruction method like the End-to-End Variational Networks (E2E-VarNet)]’.
Zhang and Xuan are analogous art because they are from the same field of endeavor, namely MRI reconstruction. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include a E2E-VarNet, as taught by Xuan. The motivation for doing so would have been to utilizing a different neural network as a de-aliasing model. Therefore, it would have been obvious to incorporate Xuan with Zhang to obtain the invention as specified in claim 10.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Mosadegh et al. (US-2024/0290487).
As to Claim 11, Zhang teaches all of the claimed elements/features as recited in independent claim 1. Zhang does not disclose expressly ‘wherein the one or more neural networks comprise a sampling mask encoder and an under-sampled FDR encoder’.
Mosadegh teaches ‘wherein the one or more neural networks comprise a sampling mask encoder and an under-sampled FDR encoder [par 0098, 0110-0112 – encoder-decoder global convolutional network]’.
Zhang and Mosadegh are analogous art because they are from the same field of endeavor, namely MRI reconstruction. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include a an encoder-decoder, as taught by Mosadegh. The motivation for doing so would have been to utilizing a different neural network to facilitate decision making. Therefore, it would have been obvious to incorporate Mosadegh with Zhang to obtain the invention as specified in claim 11.
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Chen et al. (US-2023/0367850).
As to Claim 15, Zhang teaches all of the claimed elements/features as recited in independent claim 1. Zhang does not disclose expressly ‘comprising testing the machine learning process using a test data set’.
Chen teaches ‘comprising testing the machine learning process using a test data set [par 0028-0029, 0031 – reserving training data as test data for testing updated ML model]’.
Zhang and Chen are analogous art because they are from the same field of endeavor, namely MRI image reconstruction. Before the effective ling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include testing updated ML model, as taught by Chen. The motivation for doing so would have been to processing complex-valued MRI data. Therefore, it would have been obvious to combine Chen with Zhang to obtain the invention as specified in claim 15.
Conclusion
The prior art made of record
a. US Publication No. 2025/0005888
b. US Publication No. 2021/0217213
c. US Publication No. 2022/0101997
d. Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network
e. US Publication No. 2024/0290487
f. US Publication No. 2023/0367850
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
g. US Publication No. 2023/0408614
h. US Publication No. 2025/0110196
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/MIYA J CATO/Primary Examiner, Art Unit 2681