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
1. The information disclosure statements (IDS) submitted on 12/18/2025, 07/21/2025, and, 05/14/2025 have been considered by the examiner.
Claim Rejections - 35 USC § 103
2. 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.
3. 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.
4. Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Sommer et al., U.S. Patent No. 11,320,508 B2, and further in view of Yang et al., 2017, “ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI” (pp. 1-14).
Regarding claim 21, Sommer discloses A method for generating magnetic resonance (MR) images, the method comprising:
obtaining, using a reconstruction neural network, multiple sets of one or more initial MR images based on MR data captured during MR imaging of a subject using a magnetic resonance imaging (MRI) system (Sommer teaches in - col. 22, lines 5-13 – “the computer memory 138 is shown as further containing an imaging reconstruction module 162 which contains computer executable code or instructions which enable the processor 130 to control the operation and function of the magnetic resonance imaging system to reconstruct magnetic resonance images. For example, the magnetic resonance imaging data sets 144 may comprise magnetic resonance images reconstructed from the acquired magnetic resonance imaging data 142”. As cited Sommmer discloses reconstructing multiple sets of MR data; but does not explicitly teaches using a neural network to do so. However, examiner here asserts that using a neural network for reconstruction is very well known, and, further cites Yang for evidentiary teachings. Yang teaches “Compressive sensing (CS) is an effective approach for fast Magnetic Resonance Imaging (MRI). It aims at reconstructing MR images from a small number of under-sampled data in k-space, and accelerating the data acquisition in MRI. To improve the current MRI system in reconstruction accuracy and speed, in this paper, we propose two novel deep architectures, dubbed ADMM-Nets in basic and generalized versions. ADMM-Nets are defined over data flow graphs, which are derived from the iterative procedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing a general CS-based MRI model. They take the sampled k-space data as inputs and output reconstructed MR images” (Abstract). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use reconstruction neural network as taught by Yang in the invention of Sommer. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use reconstruction neural network as taught by Yang in the invention of Sommer, in order to improve reconstruction accuracy and speed (see Yang – Abstract as cited before).
The combined invention of Sommer and Yang further discloses “generating an MR image of the subject based on the multiple sets of one or more MR images output by the reconstruction neural network” (see Sommer - col. 27, lines 1-5 – Motion-artifact-corrected magnetic resonance image 802 results from a correction of motion-artifact-corrupted magnetic resonance images 800. The motion-artifact-corrected magnetic resonance image 802 may be provided as directed output by the fully convolution neural network)
wherein generating the MR image comprises providing the multiple sets of one or more MR images to a post-reconstruction neural network that comprises a plurality of neural networks, the plurality of neural networks comprising a first neural network configured to perform alignment among the multiple sets of one or more MR images to correct for motion of the subject during the MR imaging (see Sommer – col. 20, lines 64-66 – “the trained deep learning network 146 may for example comprise a trained deep convolutional neural network and/or a trained fully conventional network” – using plurality of neural networks; col. 21, lines 25-31 – Applying the magnetic resonance imaging data sets 144 to the trained deep learning network 146 may result in magnetic resonance imaging data sets with a reduced motion artifact level, i.e., motion-artifact-corrected magnetic resonance imaging data sets. In this case, the results 148 may comprise for example magnetic resonance imaging data sets with a reduced motion artifact level”).
Double Patenting
5. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
6. Claim 21 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 11,681,000 B2 (herein referred to as Schlemper) in view of Yang et al., 2017, “ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI” (pp. 1-14).
Regarding claim 21, claim 21 has been analyzed and rejected as per claim 8 of Sclemper (claim 8 is combination of subject matter of claim 8 and 1)
Regarding claim 21, Schlemper discloses A method for generating magnetic resonance (MR) images, the method comprising:
obtaining, using a reconstruction neural network, multiple sets of one or more initial MR images based on MR data captured during MR imaging of a subject using a magnetic resonance imaging (MRI) system (see Schlemper – claim 1 - col. 69, line 65- col. 70, line 31 – reconstructing multiple sets of MR data”). As cited Schlemper discloses reconstructing multiple sets of MR data; but does not explicitly teaches using a neural network to do so. However, examiner here asserts that using a neural network for reconstruction is very well known, and, further cites Yang for evidentiary teachings. Yang teaches “Compressive sensing (CS) is an effective approach for fast Magnetic Resonance Imaging (MRI). It aims at reconstructing MR images from a small number of under-sampled data in k-space, and accelerating the data acquisition in MRI. To improve the current MRI system in reconstruction accuracy and speed, in this paper, we propose two novel deep architectures, dubbed ADMM-Nets in basic and generalized versions. ADMM-Nets are defined over data flow graphs, which are derived from the iterative procedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing a general CS-based MRI model. They take the sampled k-space data as inputs and output reconstructed MR images” (Abstract). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use reconstruction neural network as taught by Yang in the invention of Schlemper. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use reconstruction neural network as taught by Yang in the invention of Schlemper, in order to improve reconstruction accuracy and speed (see Yang – Abstract as cited before).
The combined invention of Schlemper and Yang further discloses “generating an MR image of the subject based on the multiple sets of one or more MR images output by the reconstruction neural network” (see Schlemper – claim 1 - col. 70, lines 26-30)
wherein generating the MR image comprises providing the multiple sets of one or more MR images to a post-reconstruction neural network that comprises a plurality of neural networks, the plurality of neural networks comprising a first neural network configured to perform alignment among the multiple sets of one or more MR images to correct for motion of the subject during the MR imaging (see Schlemper – claim 1 - col. 70, lines 4-25; and claim 8 – col. 70, lines 50-58 – a composed transformation is a mathematical tool explicitly used to correct motion artifacts).
7. Claim 21 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 11,344,219 B2 (herein referred to as Schlemper) in view of Yang et al., 2017, “ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI” (pp. 1-14).
Regarding claim 21, claim 21 has been analyzed and rejected as per claim 8 of Sclemper (claim 8 is combination of subject matter of claim 8 and 1)
Regarding claim 21, Schlemper discloses A method for generating magnetic resonance (MR) images, the method comprising:
obtaining, using a reconstruction neural network, multiple sets of one or more initial MR images based on MR data captured during MR imaging of a subject using a magnetic resonance imaging (MRI) system (see Schlemper – claim 1 - col. 69, lines 46-53 – reconstructing multiple sets of MR data”). As cited Schlemper discloses reconstructing multiple sets of MR data; but does not explicitly teaches using a neural network to do so. However, examiner here asserts that using a neural network for reconstruction is very well known, and, further cites Yang for evidentiary teachings. Yang teaches “Compressive sensing (CS) is an effective approach for fast Magnetic Resonance Imaging (MRI). It aims at reconstructing MR images from a small number of under-sampled data in k-space, and accelerating the data acquisition in MRI. To improve the current MRI system in reconstruction accuracy and speed, in this paper, we propose two novel deep architectures, dubbed ADMM-Nets in basic and generalized versions. ADMM-Nets are defined over data flow graphs, which are derived from the iterative procedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing a general CS-based MRI model. They take the sampled k-space data as inputs and output reconstructed MR images” (Abstract). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use reconstruction neural network as taught by Yang in the invention of Schlemper. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use reconstruction neural network as taught by Yang in the invention of Schlemper, in order to improve reconstruction accuracy and speed (see Yang – Abstract as cited before).
The combined invention of Schlemper and Yang further discloses “generating an MR image of the subject based on the multiple sets of one or more MR images output by the reconstruction neural network” (see Schlemper – claim 1 - col. 70, lines 4-7)
wherein generating the MR image comprises providing the multiple sets of one or more MR images to a post-reconstruction neural network that comprises a plurality of neural networks, the plurality of neural networks comprising a first neural network configured to perform alignment among the multiple sets of one or more MR images to correct for motion of the subject during the MR imaging (see Schlemper – claim 1 - col. 69, line 54- col. 70, line 3; and claim 8 – col. 70, lines 27-34 – a composed transformation is a mathematical tool explicitly used to correct motion artifacts).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Manav Seth whose telephone number is (571) 272-7456. The examiner can normally be reached on Monday to Friday from 8:30 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Sumati Lefkowitz, can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manav Seth/
Primary Examiner, Art Unit 2672
September 3, 2026
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