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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/14/2026 has been entered.
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, 4-6, 8-15, 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 Lyu (US 2023/0097417 A1, Mar. 30, 2023) (hereinafter “Lyu”) and 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 that processes the received raw data as an input sequence, wherein a positional encoding is applied to the input sequence to generate an input embedding for the transformer architecture, wherein the positional encoding includes information corresponding to a slice position within a slice stack of the MRI scan.
As a preliminary matter, it is noted that transformer architecture is a well-known neural network implementation that is used for a number of tasks including image processing and analysis (as evidenced by Zhao, Ziheng, et al. "K-space transformer for undersampled MRI reconstruction." arXiv preprint arXiv:2206.06947 (2022); Chen, Cheng, et al. "Understanding the brain with attention: A survey of transformers in brain sciences." Brain‐X 1.3 (2023): e29; Jun, Eunji, et al. "Medical transformer: Universal encoder for 3-D brain MRI analysis." IEEE transactions on neural networks and learning systems 35.12 (2023): 17779-17789) where the limitations of at least claim 1 appear to be nothing more than the details of applying a well-known neural network to perform a specific task.
Lyu discloses applying a transformer neural network to the analysis of MRI data, comprising processing received raw data as an input sequence, wherein a positional encoding is applied to the input sequence to generate an input embedding for the transformer architecture, wherein the positional encoding includes information corresponding to a slice position within a slice stack of the MRI scan ([0007]-[0008], [0032]-[0035]). While the specific task of Lyu does not include determining a physiological signal, the implementation of the neural network including formatting the data for input into the neural network appears to be fundamentally the same as the claimed positional encoding.
Further regarding claim 1: 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 as taught by Lyu 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 6: Zeller, Lyu and Ankit disclose the method of claim 1. Lyu further discloses wherein the raw data comprises temporally sorted k-space lines or Fourier-transformed k-space lines ([0033], [0042], [0063]).
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 Lyu (see at least [0007], [0032]-[0038], [0041]-[0050]) and 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 Lyu and 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 Lyu and 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, Lyu 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.
Response to Arguments
Applicant’s arguments with respect to prior art rejection of all pending claims, filed 07/14/2026, are moot in view of the updated grounds of rejection necessitated by amendment. However, in the interest of advancing prosecution, certain of Applicant’s arguments that are germane to the updated grounds of rejection will be addressed.
Applicant argues that because Zeller determines the physiological signal by relying on an external sensor to provide an initial respiratory curve as an input to the neural network and that Zeller does not determine the physiological signal from MRI raw data alone. Applicant further argues that the instant application eliminates the need for external sensors by utilizing a transformer architecture and that Zeller teaches away from this by requiring sensor data.
Examiner respectfully disagrees and notes that, first, nothing in the claims requires the physiological signal to be determined from raw MRI data alone. Applicant is reminded that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Second, the instant application includes the use of external sensor data (see at least claims 5, 9-13, and 18-20 as well as paragraph [0022] of the instant disclosure). It is not clear how the use of sensor data teaches away from a claimed invention that also uses sensor data.
With respect to Applicant’s arguments regarding the rejection of claims 8-13 and 19-20, Applicant provides no evidence that the claims present anything other than the details of the adaptation of a well-known class of neural networks to a specific task.
Conclusion
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
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