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 Objections
Claim 7 is objected to because of the following informalities: the term “the subfunction” should be amended to “the trained subfunction”. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: the term “the subfunction” should be amended to “the trained subfunction”. Appropriate correction is required.
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 1-12 and 14-18 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) mathematical concepts, calculations, formulas. This judicial exception is not integrated into a practical application because the claim limitations do not have any limitations that amount to significantly more than the judicial exception and do not integrate the method into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because all the limitations are related to mathematical concepts, calculations, formulas. The examiner suggests to add a limitation to the end of the claim that states that the measurement procedure is executed, similar to claim 13.
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.
Claims 1-3 and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Harms (US 2009/0143668), in view of Hansis (US 2020/0008704).
Regarding claim 1, Harms teaches a computer-implemented method for providing a drive sequence for use that is target-region selective, for producing a target excitation state for a process for capturing measurement data from an object under examination using a magnetic resonance device, wherein the drive sequence comprises radiofrequency pulses to be output via transmit channels of a radiofrequency coil arrangement, the method comprising:
before an examination comprising a measurement procedure, precalculating a set of base sequences a reference point of at least one requirement parameter that describes the, and providing the set of base sequences together with the associated reference points at the magnetic resonance device [¶0035, see precalculated spiral gradient waveform and base sequence in library. See also rest of reference.];
providing a measurement procedure value of the at least one requirement parameter at the magnetic resonance device [¶0035, see incremented angle. See also rest of reference.]; and
ascertaining a drive sequence for use for the measurement procedure, wherein when the measurement procedure value for the at least one requirement parameter differs from the reference point, the drive sequence is ascertained from the set of base sequences by interpolation, extrapolation, or interpolation and extrapolation using a derivation algorithm [¶0035, wherein different angles are interpolated/extrapolated. See also rest of reference.].
However, Harms is silent in teaching mutually spaced reference points, one requirement parameter that describes the target excitation state, and the reference point covering a parameter interval for use.
Hansis, which is also in the field of MRI, teaches mutually spaced reference points of at least one requirement parameter that describes the target excitation state [¶0082 and ¶0004. See also rest of reference.], and the reference point covering a parameter interval for use [¶0082. See also rest of reference.]. Hansis further teaches ascertaining a drive sequence for use for the measurement procedure, wherein when the measurement procedure value for the at least one requirement parameter differs from the reference point, the drive sequence is ascertained by interpolation, extrapolation, or interpolation and extrapolation using a derivation algorithm [¶0082, see “Each iteration may be represented as a data point having a pair [parameter value, operating status]. By fitting a smooth function through these data points an optimum value may be found.”].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis because both references are in the field of pulse sequences for MRI and because Hansis teaches it is known in the art that pulse sequence parameters also include parameters related to transmission of RF pulses [Hansis - ¶0082 and ¶0004. See also rest of reference.].
Regarding claim 2, Harms and Hansis teach the limitations of claim 1, which this claim depends from.
Harms further teaches wherein the target region is a slice [See slice disclosed throughout the reference.].
However, Harms is silent in teaching wherein the at least one requirement parameter comprises a slice thickness, a slice orientation, a slice position, or any combination thereof.
Hansis further teaches wherein the target region is a slice, wherein the at least one requirement parameter comprises a slice thickness, a slice orientation, a slice position, or any combination thereof [¶0004. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis because both references are in the field of pulse sequences for MRI and because Hansis teaches it is known in the art that pulse sequence parameters also include parameters related to transmission of RF pulses [Hansis - ¶0082 and ¶0004. See also rest of reference.].
Regarding claim 3, Harms and Hansis teach the limitations of claim 1, which this claim depends from.
However, Harms is silent in teaching wherein the at least one requirement parameter comprises a target flip angle, an energy parameter specifying a desired energy regularization, or the target flip angle and the energy parameter.
Hansis further teaches wherein the at least one requirement parameter comprises a target flip angle, an energy parameter specifying a desired energy regularization, or the target flip angle and the energy parameter [¶0004. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis because both references are in the field of pulse sequences for MRI and because Hansis teaches it is known in the art that pulse sequence parameters also include parameters related to transmission of RF pulses [Hansis - ¶0082 and ¶0004. See also rest of reference.].
Regarding claim 13, the same reasons for rejection as claim 1 also apply to this claim.
Regarding claim 14, the same reasons for rejection as claim 1 also apply to this claim.
Regarding claim 15, the same reasons for rejection as claim 1 also apply to this claim.
Regarding claim 16, the same reasons for rejection as claim 2 also apply to this claim.
Regarding claim 17, the same reasons for rejection as claim 3 also apply to this claim.
Claims 4-5 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Harms, in view of previously cited Hansis, in view of Boulant (US 2018/0252788).
Regarding claim 4, Harms and Hansis teach the limitations of claim 1, which this claim depends from.
However, Harms and Hansis are silent in teaching wherein the set of base sequences are ascertained in an optimization process, based on reference field distribution maps of a cohort of reference objects under examination, or in the optimization process and based on the reference field distribution maps, the reference field distribution maps comprising at least one B0 map and at least one B1 map.
Boulant, which is also in the field of MRI, teaches wherein the set of base sequences are ascertained in an optimization process, based on reference field distribution maps of a cohort of reference objects under examination, or in the optimization process and based on the reference field distribution maps, the reference field distribution maps comprising at least one B0 map and at least one B1 map [See Fig. 2 and corresponding description. See universal pulses. ¶0010. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis with the teachings of Boulant because all references are in the field of configuring pulse sequences for MRI and because Boulant teaches it is known in the art to use field maps to help configure target pulse sequence parameters [Boulant – Fig. 2].
Regarding claim 5, Harms and Hansis teach the limitations of claim 1, which this claim depends from.
However, Harms and Hansis are silent in teaching wherein a plurality of sets of base sequences, each allocated to one cluster of the cohort, are ascertained and provided, the plurality of sets of base sequences comprising the set of base sequences.
Boulant further teaches wherein a plurality of sets of base sequences, each allocated to one cluster of the cohort, are ascertained and provided, the plurality of sets of base sequences comprising the set of base sequences [¶0010-0011. See also Fig. 2. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis with the teachings of Boulant because all references are in the field of configuring pulse sequences for MRI and because Boulant teaches it is known in the art to use field maps to help configure target pulse sequence parameters [Boulant – Fig. 2].
Regarding claim 18, the same reasons for rejection as claim 4 also apply to this claim.
Claims 6 -10 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Harms, in view of previously cited Hansis, in view of Eberhardt (“B1 field map synthesis with generative deep learning used in the design of parallel-transmit RF pulses for ultra-high field MRI”).
Regarding claim 6, Harms and Hansis teach the limitations of claim 1, which this claim depends from.
Harms and Hansis further teach wherein the derivation algorithm for performing the interpolation, the extrapolation, or the interpolation and the extrapolation [Harms - ¶0035, wherein different angles are interpolated/extrapolated. Hansis - ¶0082, see “Each iteration may be represented as a data point having a pair [parameter value, operating status]. By fitting a smooth function through these data points an optimum value may be found.” See also rest of references.].
However, Harms and Hansis are silent in teaching the use of a trained subfunction.
Eberhardt, which is also in the field of MRI, teaches the use of a trained subfunction [See U-net. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis with the teachings of Eberhardt because all references are in the field of configuring pulse sequences for MRI and because Eberhardt teaches it is known in the art to use neural networks to help determine RF pulse parameters for pulse sequences.
Regarding claim 7, Harms, Hansis, and Eberhardt teach the limitations of claim 6, which this claim depends from.
Harms and Hansis are silent in teaching wherein the subfunction comprises a convolutional neural network (CNN).
Eberhardt further teaches wherein the subfunction comprises a convolutional neural network (CNN)[See U-net. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis with the teachings of Eberhardt because all references are in the field of configuring pulse sequences for MRI and because Eberhardt teaches it is known in the art to use neural networks to help determine RF pulse parameters for pulse sequences.
Regarding claim 8, Harms, Hansis, and Eberhardt teach the limitations of claim 7, which this claim depends from.
Harms and Hansis are silent in teaching wherein the CNN is a U-net.
Eberhardt further teaches wherein the CNN is a U-net [See U-net. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis with the teachings of Eberhardt because all references are in the field of configuring pulse sequences for MRI and because Eberhardt teaches it is known in the art to use neural networks to help determine RF pulse parameters for pulse sequences.
Regarding claim 9, Harms, Hansis, and Eberhardt teach the limitations of claim 6, which this claim depends from.
Harms further teaches the set of base sequences.
Harms and Hansis are silent in teaching wherein the subfunction is provided as the result of a training procedure in which the subfunction is trained based on ground truths obtained in a precalculation process also used for the set of sequences.
Eberhardt further teaches wherein the subfunction is provided as the result of a training procedure in which the subfunction is trained based on ground truths obtained in a precalculation process also used for the set of base sequences. [See ground truth. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis with the teachings of Eberhardt because all references are in the field of configuring pulse sequences for MRI and because Eberhardt teaches it is known in the art to use neural networks to help determine RF pulse parameters for pulse sequences.
Regarding claim 10, Harms, Hansis, and Eberhardt teach the limitations of claim 9, which this claim depends from.
Harms and Hansis are silent in teaching wherein the training procedure is performed separately for at least one reference set of reference field distribution maps.
Eberhardt further teaches wherein the training procedure is performed separately for at least one reference set of reference field distribution maps [See B1+ maps. See also rest of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis with the teachings of Eberhardt because all references are in the field of configuring pulse sequences for MRI and because Eberhardt teaches it is known in the art to use neural networks to help determine RF pulse parameters for pulse sequences.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Harms, in view of previously cited Hansis, in view of Liebig (US 2021/0333345).
Regarding claim 11, Harms and Hansis teach the limitations of claim 1, which this claim depends from.
However, Harms and Hansis are silent in teaching wherein field distribution maps comprising a B0 map and at least one B1 map and captured at the object under examination are provided, and the derivation algorithm uses the field distribution maps in addition to the measurement procedure value as input data.
Liebig, which is also in the field of MRI, teaches wherein field distribution maps comprising a B0 map and at least one B1 map and captured at the object under examination are provided, and the derivation algorithm uses the field distribution maps in addition to the measurement procedure value as input data [¶0031 and Fig. 5. See also res of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis with the teachings of Liebig because all references are in the field of configuring pulse sequences for MRI and because Liebig teaches that is known in the art that RF pulses can be determined by correlating field maps to the ideal RF pulse, which can fully automate the designing of ideal RF pulses [Liebig - ¶0066].
Regarding claim 12, Harms, Hansis, and Liebig teach the limitations of claim 11, which this claim depends from.
Hansis further teaches wherein the derivation algorithm comprises, after the interpolation, the extrapolation, or the interpolation and the extrapolation, optimizing the ascertained drive sequence optimizing the ascertained drive sequence for use for optimally achieving the target excitation state [¶0082-0083, wherein multiple variables are optimized. See also rest of reference.].
However, Harms and Hansis are silent in teaching taking into account the field distribution maps in at least one optimization procedure.
Liebig further teaches taking into account the field distribution maps in at least one optimization procedure [¶0031 and Fig. 5. See also res of reference.].
It would have been obvious to a person having ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Harms and Hansis with the teachings of Liebig because all references are in the field of configuring pulse sequences for MRI and because Liebig teaches that is known in the art that RF pulses can be determined by correlating field maps to the ideal RF pulse, which can fully automate the designing of ideal RF pulses [Liebig - ¶0066].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Akcakaya (US 2025/0044389) is considered relevant prior art for teaching a method for designing parallel transmit pulses in MRI using deep learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RISHI R PATEL whose telephone number is (571)272-4385. The examiner can normally be reached Mon-Thurs 7 a.m. - 5 p.m..
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/RISHI R PATEL/Primary Examiner, Art Unit 2858
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