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 .
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.
Claims 14-20 are 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 they recite a neural network. A neural network is considered to be software per se. See MPEP 2106.03:
As the courts' definitions of machines, manufactures and compositions of matter indicate, a product must have a physical or tangible form in order to fall within one of these statutory categories. Digitech, 758 F.3d at 1348, 111 USPQ2d at 1719. Thus, the Federal Circuit has held that a product claim to an intangible collection of information, even if created by human effort, does not fall within any statutory category. Digitech, 758 F.3d at 1350, 111 USPQ2d at 1720 (claimed “device profile” comprising two sets of data did not meet any of the categories because it was neither a process nor a tangible product). Similarly, software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a “means plus function” limitation) has no physical or tangible form, and thus does not fall within any statutory category. Another example of an intangible product that does not fall within a statutory category is a paradigm or business model for a marketing company. In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1039-40 (Fed. Cir. 2009).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites “[t]he method of claim 1, wherein encoding of the raw data comprises… .” However, there is no reference to encoding of the raw data in claim 1. It is unclear what this limitation is referring to since no step of encoding is set forth prior to this recitation. It is further unclear whether “encoding of the raw data” in claim 7 is a reference to the “raw data encoder” (in at least claim 8) or if this refers to encoding as it is commonly used in the context of MRI acquisition (e.g. phase and frequency encoding of acquired signals during acquisition). For the purposes of further examination, this claim will be interpreted as referring to “encoding” in the conventional MR acquisition context.
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.
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) 1-2, 4-5, 7-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zeller et al. (US 2022/0043091 A1, Feb. 10, 2022) (hereinafter “Zeller”) in view of Ankit, Utkarsh (Transformer Neural Networks: A Step-by-Step Breakdown; builtin.com/artificial-intelligence/transformer-neural-network; Jun. 28, 2022) (hereinafter “Ankit”).
Regarding claims 1 and 14: Zeller discloses receiving raw data of the MRI scan of a patient (fig. 2A, [0077] - data sets D1, D2); and determining, by a neural network, a physiological signal of the patient from the received raw data (fig. 2A, [0078]).
However, Zeller discloses the use of a recurrent neural network (RNN) which uses LSTM modules ([0039]) and does not disclose that the neural network comprises a transformer architecture.
Ankit discloses that RNNs have several disadvantages, including vanishing gradient and slow training times, and that these disadvantages can be overcome by using a transformer architecture (see whole document but particularly section header Long Short-Term Memory).
It would have been prima facie obvious for one having ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method of Zeller by replacing the LSTM-based RNN with a transformer network in view of the teachings of Ankit that a transformer architecture overcomes several known disadvantages of RNNs.
Regarding claims 2 and 15: Zeller further discloses wherein the physiological signal comprises at least one of a respiration curve, an electrocardiogram curve, or a movement curve ([0079], predicted curve r').
Regarding claims 4 and 17: Zeller further discloses modifying, by the neural network, the received raw data taking into account the determined physiological signal ([0079]-[0081]); and outputting the modified raw data (the modified raw data is “output” to the reconstruction process).
Regarding claims 5 and 18: Zeller further discloses receiving sensor data with regard to the physiological signal of the patient and/or with regard to a movement of the patient, wherein the sensor data was recorded by a sensor during creation of the MRI scan, wherein modifying the received raw data further takes into account the received sensor data ([0076], [0079]-[0081]).
Regarding claim 7, as best understood based on limitations which are indefinite: Zeller further discloses wherein encoding of the raw data comprises position encoding and/or an association between a position in k-space and a position in a slice stack of the MRI scan ([0021]).
With respect to claims 8-13 and 19-20: While the claims recite details of the implementation of a known type of neural network (transformer) to a known problem (the motion correction based on physiological signals as disclosed by Zeller), there is no evidence that any of the limitations of these claims are anything more than the natural result of adapting a general transformer neural network framework to solving a specific problem.
See, for example, the description of transformer implementation provided by Ankit as well as the evidence provided by Alammar, Jay (Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention); jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/; May 9, 2018), which provides a detailed breakdown regarding the nature of the input and output to encoder/decoder layers as well as context vectors, and Romano, Nicola (A basic introduction to neural networks – Part 2: Training; www.nicolaromano.net/data-thoughts/training-neural-networks/; retrieved 01/22/2026) which provides a basic overview of supervised training of a neural network including ground-truth training data as well as the application of loss functions for optimization.
In the absence of any evidence to the contrary, the limitations of claims 8-13 and 19-20
are considered to merely be the natural product of implementing the method of Zeller using a transformer architecture as described by Ankit.
Claim(s) 3 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zeller and Ankit as applied to claim 1 above, and further in view of Nguyen et al. (US 2025/0248617 A1, Aug. 7, 2025) (hereinafter “Nguyen”).
Regarding claims 3 and 16: Zeller as modified by Ankit discloses the method of claim 1 and the neural network of claim 14 but are silent on outputting the determined physiological signal.
Nguyen, in the same field of endeavor, discloses outputting a determined physiological signal ([0045]).
It would have been prima facie obvious for one having ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method and neural network of Zeller and Ankit to output the determined physiological signal as taught by Nguyen in order to allow the user to view the data for verification or confirmation purposes.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zeller and Ankit as applied to claim 1 above, and further in view of Zeller (EP 3748387 A1, Dec. 09, 2020) (hereinafter “Zeller ‘387”).
Regarding claim 6: Zeller and Ankit disclose the method of claim 1, but are silent on wherein the raw data comprises temporally sorted k-space lines or Fourier-transformed k-space lines.
Zeller ‘387, in the same field of endeavor, discloses temporal binning (“sorting”) of the k-space data for determining a physiological signal ([0046]-[0048]). Zeller ‘387 further discloses that this binning improves the accuracy of the motion correction ([0047]).
It would have been prima facie obvious for one having ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method of Zeller and Ankit to include temporal binning (“sorting”) of the k-space data as taught by Zeller ‘387 in order to improve the accuracy of the motion correction.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tsai, Tsung-Han, Yz-Heng Lin, and Tsung-Hsien Lin. "Motion artifact correction in mri using gan-based channel attention transformer." 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2023.
Katharopoulos, Angelos, et al. "Transformers are rnns: Fast autoregressive transformers with linear attention." International conference on machine learning. PMLR, 2020.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAROLYN A PEHLKE whose telephone number is (571)270-3484. The examiner can normally be reached 9:00am - 5:00pm (Central Time), Monday - Friday.
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/CAROLYN A PEHLKE/Primary Examiner, Art Unit 3799