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 .
Response to Arguments
Applicant's arguments filed 04/28/2026 regarding the previous 101 rejections have been fully considered but they are not persuasive.
Applicant argues "accessing a neural network with the computer system, wherein the neural network has been trained on training data in order to learn a mapping from magnetic resonance data to pTx RF pulse waveforms" and "applying the magnetic resonance data to the neural network using the computer system, generating output as pTx RF pulse waveforms" cannot practically be performed in the human mind. Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRIInt'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019). The neural network processing required to generate pTx RF pulse waveforms from magnetic resonance data involves complex mathematical transformations across trained network parameters that are fundamentally beyond human mental capacity.
The examiner respectfully disagrees. Under Prong One, the limitations "accessing a neural network with the computer system, wherein the neural network has been trained on training data in order to learn a mapping from magnetic resonance data to pTx RF pulse waveforms" and “applying the magnetic resonance data to the neural network using the computer system, generating output as pTx RF pulse waveforms”, disclose mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. The steps are performed by a generic computer, which does not overcome the judicial exception. Under Prong Two, the limitations as a whole do integrate the judicial exception into practical exception. Claim 9’s step (a) is mere data gathering, and thus insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). Steps (b) through (d) are limitations related to using the neural network and provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer:
The applicant argues that "[t]he trained deep learning approach is very fast, with an inference time on the order of a few milliseconds (e.g., -2 ms)." Present Application, [0061]. A human mind cannot possibly convert magnetic resonance data to pTx RF pulse waveforms on this time scale (or on any reasonable time scale), further demonstrating that these operations require machine processing and cannot be practically performed mentally.
The examiner respectfully disagrees that this is evidence that the claim (as is) is beyond the judicial exception and the claim as a whole integrates the exception into a practical application. The consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. See MPEP 2106.04(d)(1). The cited [0061] of the applicant’s specification as evidence is not sufficient because the specification also discloses “Additionally, the proposed image domain concatenation at the network input addresses the difficulties that existing deep learning methods have with using multichannel B1+ maps as an input. The trained deep learning approach is very fast, with an inference time on the order of a few milliseconds (e.g., ~2 ms) in an example study.” The emphasized portion is not part of the claim and therefore, the claim does not reflect the reason of the improvement in speed.
Applicant also argues the specification further explains that "the systems and methods described in the present disclosure enable the generation of optimized pTx or other RF pulses quickly using deep learning, but done in a way that still incorporates all the information that would normally be used in solving the optimization problem." Present Application, [0018]. The claimed method thus provides a technological improvement in how MRI systems design RF pulses. Moreover, "the systems and methods described in the present disclosure enable the fast design of parallel transmit ('pTx') RF pulses for MRI, which in some instances may be high-field (e.g., 3 to 7 Tesla) and/or ultrahigh-field ('UHF') MRI." Present Application [0017]. The pTx RF pulse waveforms generated by claim 9 are specifically designed for use with MRI systems, representing a concrete technological application rather than an abstract concept.
The examiner again respectfully disagrees that this is evidence that the claim (as is) is beyond the judicial exception and the claim as a whole integrates the exception into a practical application. [0018] of the applicant’s specification states “In some embodiments, the systems and methods described in the present disclosure implement physics-constrained deep learning ("DL") algorithms for the design of pTx or other RF pulses that explicitly incorporate the physics of the problem and power or other constraints. The physics-constrained optimization problem for designing these pTx or other RF pulses is unrolled for a fixed number of steps such that it has fixed complexity. It is an advantage of the systems and methods described in the present disclosure that these step sizes in this unrolled optimization problem can be learned using deep learning, such as via one or more neural networks. As a result, the optimization problem still incorporates the physics and power constraints, but because of the learned step sizes for the unrolling, the optimization problem can converge on a solution with greater computational efficiency than would otherwise be attainable. Thus, the systems and methods described in the present disclosure enable the generation of optimized pTx or other RF pulses quickly using deep learning, but done in a way that still incorporates all the information that would normally be used in solving the optimization problem, including the encoding matrix and the power constraints.” The emphasized portion is not part of the claim and therefore, the claim does not reflect the reason of the improvement in speed.
The applicant argues that dependent claim 12 recite further technical limitations that reinforce the practical application. For example, claim 12 recites "scout image data comprising scout images acquired with the MRI system," and claim 15 recites "multichannel B1+ map data comprising multichannel B1+ maps,".
The examiner respectfully disagrees. Claims 12 and 15 refer to step (a) in claim 9. Step (a) is receiving already acquired data, which is mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”).
Applicant argues claim 18 recites that "the neural network accessed with the computer system has been trained on training data using a loss function that incorporates a physics- based constraint." As described in the specification, "[t]he physics-based constraint, such as those described above, may be integrated as part of the loss function used during neural network training." Present Application, [0056]. This further demonstrates that the claims are directed to a specific technical implementation rather than an abstract idea.
The examiner respectfully disagrees. This step employs mathematical concepts (e.g., rounding data values) that can be performed mentally. The loss function could also be considered mathematical calculations, which is also a judicial exception and the examiner does not see any further limitations that would amount to significantly more than the judicial exception.
Therefore, the previous 101 rejections stand.
Applicant’s arguments, see applicant arguments/remarks, filed 04/28/2026, with respect to the previous 112 rejection have been fully considered and are persuasive. The previous 112 rejection has been withdrawn.
Applicant's arguments filed 04/28/2026 regarding the previous prior art rejections have been fully considered but they are not persuasive.
Applicant argues that Mirfin does not teach or disclose generating output "as pTx RF pulse waveforms" as recited in claim 9. Applicant goes on to state that an “RF pulse waveform” is a time-domain signal that specifies the amplitude and phase of the RF field as a function of time. Applicant also states the specification describes that "the output pTx pulses waveforms can include RF waveforms for one or more pTx pulses that work best based on the available data encoded in the scout images or other magnetic resonance data." Present Application, [0047]. The claimed method generates complete RF waveforms directly from the neural network, not intermediate design parameters that require additional processing. Because Mirfin does not teach generating output "as pTx RF pulse waveforms" as recited in claim 9(c), Mirfin also cannot teach "storing the pTx RF pulse waveforms for use by the MRI system" as recited in claim 9(d). Mirfin at most stores design parameters that would require additional processing to generate actual waveforms.
The examiner respectfully disagrees. First, the applicant’s specification does not state that an “RF pulse waveform” is a time-domain signal that specifies the amplitude and phase of the RF field as a function of time. In fact, the applicant’s specification teaches that “RF pulse wave forms may be indicative of pTx RF pulses or other RF pulse types, such as water-fat separation RF pulses.” [0007 of applicant’s specification]. Further, the specification also discloses “FIG. 5 is a workflow diagram illustrating an example neural network that can be used to design pTx RF pulses” [0013]. “Described here are systems and method for designing radio frequency ("RF") pulses for use in magnetic resonance imaging ("MRI"). More particularly, the systems and methods described in the present disclosure enable the fast design of parallel transmit ("pTx") RF pulses for MRI, which in some instances may be high-field (e.g., 3 to 7 Tesla) and/or ultrahigh-field ("UHF") MRI. In general, UHF MRI can include MRI systems operating with main magnetic field strengths of 7 T and greater” [0017]. Therefore, RF pulse waveforms include pTx RF pulses. Mirfin teaches “We have proposed a new method for the sub-second design of large-tip-angle parallel transmit pulses based on learning the spatial variations within patient field maps.” Therefore, it is believed that Mirfin teaches RF pulse waveforms.
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 9 and 11-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) method steps that can be performed in a human mind (mental process). This judicial exception is not integrated into a practical application because all the steps involve accessing previously acquired data and converting that data into different information. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are a generic computer. The examiner believes that since the generated pTx RF pulse waveforms are not executed by the MRI, then there is no practical, real-world application. Therefore, the claims disclose an abstract idea. Please see above “response to arguments” section for full explanation/details.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 9, 11, and 14-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mirfin (“Optimisation of parallel transmission radiofrequency pulses using neural networks”).
Regarding claim 9, Mirfin teaches a method for generating parallel transmit (pTx) radio frequency (RF) pulse waveforms for use with a magnetic resonance imaging (MRI) system, the method comprising:
(a) accessing magnetic resonance data with a computer system, wherein the magnetic resonance data have been acquired with an MRI system [See B1+ spatial variations and B1+ maps. See also rest of reference.];
(b) accessing a neural network with the computer system, wherein the neural network has been trained on training data in order to learn a mapping from magnetic resonance data to pTx RF pulse waveforms [See parallel transmit spoke pulses. Discussion section, “We have proposed a new method for the sub-second design of large-tip-angle parallel transmit pulses based on learning the spatial variations within patient field maps.” See Method section. See also rest of reference.];
(c) applying the magnetic resonance data to the neural network using the computer system, generating output as pTx RF pulse waveforms [See parallel transmit spoke pulses. Discussion section, “We have proposed a new method for the sub-second design of large-tip-angle parallel transmit pulses based on learning the spatial variations within patient field maps.” See Method section. See also rest of reference.];
(d) storing the pTx RF pulse waveforms for use by the MRI system [See parallel transmit spoke pulses. Discussion section, “We have proposed a new method for the sub-second design of large-tip-angle parallel transmit pulses based on learning the spatial variations within patient field maps.” See Method section. See also rest of reference.].
Regarding claim 11, Mirfin further teaches wherein the magnetic resonance data accessed with the computer system include image data acquired with the MRI system [See B1+ spatial variations and B1+ maps. See also rest of reference.].
Regarding claim 14, Mirfin further teaches wherein the neural network accessed with the computer system has been trained on training data consistent with the image data in order to learn the mapping from magnetic resonance data to pTx RF pulse waveforms based on field map data encoded in the image data [See Method, Results, and Discussion sections and Figs. 1-2. See also rest of reference.].
Regarding claim 15, Mirfin further teaches wherein the magnetic resonance data accessed with the computer system comprise multichannel B1+ map data comprising multichannel B1+ maps [See B1+ spatial variations and B1+ maps. See also rest of reference.].
Regarding claim 16, Mirfin further teaches wherein the neural network accessed with the computer system has been trained on training data consistent with the multichannel B1+ map data in order to learn the mapping from magnetic resonance data to pTx RF pulse waveforms [See Method, Results, and Discussion sections and Figs. 1-2. See also rest of reference.].
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 10 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Mirfin, in view of Pendse (US 2020/0142057).
Regarding claim 10, Mirfin teaches the limitations of claim 9, which this claim depends from.
Mirfin further teaches the stored pTx RF pulse waveforms [See parallel transmit spoke pulses. Discussion section, “We have proposed a new method for the sub-second design of large-tip-angle parallel transmit pulses based on learning the spatial variations within patient field maps.” See Method section. See also rest of reference.].
However, Mirfin is silent in teaching further comprising generating at least one RF pulse with the MRI system by operating the MRI system based on the stored pTx RF pulse waveforms.
Pendse, which is also in the field of MRI, teaches further comprising generating at least one RF pulse with the MRI system by operating the MRI system based on the stored pTx RF pulse waveforms [claim 7. 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 Mirfin and Pendse because both references are in the field of designing pTx pulses in MRI and Pendse teaches it is known in the art to generate the designed pTx pulses using an MRI to generate MRI images [Pendse - claim 7. See also rest of reference.].
Regarding claim 18, Mirfin teaches the limitations of claim 9, which this claim depends from.
Mirfin is silent in teaching wherein the neural network accessed with the computer system has been trained on training data using a loss function that incorporates a physics-based constraint.
Pendse, which is also in the field of MRI, teaches wherein the neural network accessed with the computer system has been trained on training data using a loss function that incorporates a physics-based constraint [¶0019, ¶0024, ¶0032. 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 Mirfin and Pendse because both references are in the field of designing pTx pulses in MRI and Pendse teaches it is known to predict SAR when designing pTx pulses for patient safety [Pendse - ¶0004, ¶0008, ¶0016-0017, ¶0022, claim 7. See also rest of reference.].
Regarding claim 19, Mirfin and Pendse teach the limitations of claim 18, which this claim depends from.
Mirfin is silent in teaching wherein the physics-based constraint comprises at least one of a specific absorption rate constraint or a power constraint.
Pendse, which is also in the field of MRI, teaches wherein the physics-based constraint comprises at least one of a specific absorption rate constraint or a power constraint [¶0019, ¶0024, ¶0032. 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 Mirfin and Pendse because both references are in the field of designing pTx pulses in MRI and Pendse teaches it is known to predict SAR when designing pTx pulses for patient safety [Pendse - ¶0004, ¶0008, ¶0016-0017, ¶0022, claim 7. See also rest of reference.].
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Mirfin, in view of Blasche (US 2015/0219733).
Regarding claim 12, Mirfin teaches the limitations of claim 11, which this claim depends from.
However, Mirfin is silent in teaching wherein the image data accessed with the computer system include scout image data comprising scout images acquired with the MRI system.
Blasche, which is also in the field of MRI, teaches wherein the image data accessed with the computer system include scout image data comprising scout images acquired with the MRI system [¶0041-0042. 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 Mirfin and Blasche because both references are in the field of determining B1 data for MRI and because Blasche teaches it is known in the art that scout images can be used to select B1 settings [Blasche - ¶0041-0042. See also rest of reference.], which is a goal of Mirfin.
Regarding claim 13, Mirfin and Blasche teaches the limitations of claim 12, which this claim depends from.
However, Mirfin is silent in teaching wherein the scout images comprise anatomical images that depict subject anatomy.
Blasche, which is also in the field of MRI, teaches wherein the scout images comprise anatomical images that depict subject anatomy [¶0041-0042. 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 Mirfin and Blasche because both references are in the field of determining B1 data for MRI and because Blasche teaches it is known in the art that scout images can be used to select B1 settings [Blasche - ¶0041-0042. See also rest of reference.], which is a goal of Mirfin.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over previously cited Mirfin, in view of Luke (“Motion Robust Parallel Transmission Excitation Pulse Design for Ultra-High Field MRI”).
Regarding claim 17, Mirfin teaches the limitations of claim 11, which this claim depends from.
Mirfin is silent in teaching wherein the training data comprise multichannel B1+ maps that are concatenated along a single spatial dimension such that the training data comprise two-dimensional data.
Luke, which is also in the field of MRI, teaches wherein the training data comprise multichannel B1+ maps that are concatenated along a single spatial dimension such that the training data comprise two-dimensional data [Fig. 1, and Methods section, wherein B1+ projections are concatenated. 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 Mirfin and Luke because both references are in the field of determining B1 data for MRI and Luke teaches it is known in the art to concatenate B1 data when performing pulse design [Luke - Fig. 1, and Methods section], similar to Mirfin.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Eman Alkafawi can be reached at 571-272-4448. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RISHI R PATEL/Primary Examiner, Art Unit 2858
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