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 .
Response to Amendment
This office action is in response to the remarks filed on 02/27/2026.
The amendment filed 02/27/2026 has been entered. Claims 1-19 and 48 remain pending in the application, claims 20-47 has been canceled.
The claim interpretation of the term “a feature of susceptibility contrasts” has been withdrawn in light of claim amendments.
The 112(b) rejections have been withdrawn in light of claim amendments.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 11, 15, 17, and 48 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20180321347 A1, hereinafter "Wang '347", of record) in view of Wang et al. (US 20110044524 A1, hereinafter “Wang ‘524”)
Regarding claim 1, Wang ‘347 teaches a method for generating one or more images of an object (MR signal data corresponding to a subject can be transformed into susceptibility-based images that quantitatively depict the structure and/or composition and/or function of the subject [0005]), the method comprising:
obtaining complex magnetic resonance data collected by a magnetic resonance scanner, wherein the complex magnetic resonance data comprises magnitude and phase information regarding the object (para. [0257] discloses obtaining complex MR data include magnitude and phase);
estimating a magnetic susceptibility distribution of the object based on the obtained complex magnetic resonance data (collecting and processing MRI signals of a subject, and reconstructing maps of physical properties intrinsic to the subject (e.g., magnetic susceptibility) [0005]; para. [0258] discloses using MR complex data for a magnetic susceptibility distribution), wherein estimating the magnetic susceptibility distribution of the object comprises:
determining a data fidelity term (data fidelity term [0063]) based on modeling time dependence of the obtained complex magnetic resonance data (complex MRI data disclosed in [0224]- [0249-0250] discloses modeling across time), wherein modeling the time dependence comprises:
using decay rates to model the time dependence of the signal magnitude information ([0249]-[0250] discloses using decay and signal information to model); and
solving a distribution of the decay rates of the object alongside with the magnetic susceptibility distribution by ([0248]-[0250] discloses calculating/solving the distribution of the decay rates):
using a phase dependent on a magnetic susceptibility dipole convolution to model the phase information ([0111] calculation of the data fidelity term using phase information and dipole convolution)
determining a cost function (determining a cost function corresponding to a susceptibility distribution [0343]): comprising the data fidelity term (The cost function includes a data fidelity term [0343]) and multiple regularization terms (two regularization terms are calculated as disclosed in [0178]) involving multiple regions including (two regularization terms are calculated for two different areas as disclosed in [0178]).
Wang ‘347, however, does not teach:
a first region with a first feature of contrasts and a second region with a second feature of contrasts that is different from the first feature of contrasts of the first region
Wang ‘524 is considered analogous to the instant application as “Tool for accurate quantification in molecular mri” is disclosed (title). Wang ‘524 teaches: a first region with a first feature of contrasts and a second region with a second feature of contrasts that is different from the first feature of contrasts of the first region ([0030]-[0032] describes a “second term” which regularizes the susceptibility value in a region where the susceptibility does not vary, i.e. a first region, a region where the susceptibility does vary, i.e. a second region where the feature of susceptibility I is different from the first region).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Wang ‘347 to include a first region with a first feature of contrasts and a second region with a second feature of contrasts that is different from the first feature of contrasts of the first region, as taught by Wang ‘524. Doing so may lead to a slope closer to unity between the estimated and true susceptibility values, as suggested by Wang ‘524 ([0745]).
Regarding claim 2, modified Wang ‘347 teaches the method of claim 1, as discussed above. Wang ‘347 further teaches wherein the multiple regularizations terms involve one or more additional regions with features of contrasts differing from the first feature of contrasts of the first region and the second feature of contrasts of the second region (two regularization terms are calculated for two different areas as disclosed in [0178], one area with a uniform/homogenous susceptibility and one without; [0203] further discloses susceptibility contrasts of three different lesions/regions).
Regarding claim 3, modified Wang ‘347 teaches the method of claim 1, as discussed above. Wang ‘347 further teaches wherein the first feature of contrasts of the first region is lower than the second feature of contrasts of the second region ([0231]-[0232] discloses different three lesions with three different susceptibility contrast; between the three lesion types, both nodular enhancing (beta=−19.6, 95% CI=−23.5 to −15.8, p=<0.0001) and shell enhancing lesions (beta=−13.5, 95% CI=−19.0 to −8.0, p=<0.0001) had significantly lower susceptibility values as compared to those of non-enhancing lesions [0203]).
Regarding claim 4, modified Wang ‘347 teaches the method of claim 1, as discussed above. Wang ‘347 further teaches wherein the multiple regularization terms involve a penalty on a variation of susceptibility values in a region (penalty terms can be formulated using specific tissue structure and shadow structure information. One specific tissue structure is that CSF in the ventricles of the brain is almost pure water with very little cellular contents. Therefore, ventricular susceptibility map should be nearly uniform, and any deviation from uniformity should be regarded as shadow artifacts to be penalized during numerical optimization by incorporating a regularization term [0065]).
Regarding claim 5, modified Wang ‘347 teaches the method of claim 4, as discussed above. Wang ‘347 further teaches wherein the penalty varies with the feature of susceptibility contrasts of the region ([0065], [0129] discloses “adaptive penalization” based off of artifacts/shadows/other contributions in the image which impacts the feature of susceptibility contrasts).
Regarding claim 6, modified Wang ‘347 teaches the method of claim 1, as discussed above. Wang ‘647 further teaches wherein the first feature of contrasts of the first region involves the decay rates of magnitude signals in voxels of the first region ([0324]-[0327] disclose using a magnitude image which includes voxels within the region to obtain water and fat maps taking into account decay rates).
Regarding claim 11, modified Wang ‘347 teaches the method of claim 1, as discussed above. Wang ‘347 further teaches wherein minimizing the cost function is obtained using a preconditioning method (this algorithm will be referred to as minimum local variance (MLV). To reduce error propagation and improve convergence speed, a right preconditioning technique is applied [0298]).
Regarding claim 15, modified Wang ‘347 teaches the method of claim 1, as discussed above. Wang ‘347 further teaches wherein modeling the time dependence of the signal magnitude information and/or the phase information is based on a comparison between the obtained complex magnetic resonance data and a susceptibility modeled complex signal at each echo time (The magnetic field can be determined from the complex MR data by fitting the detected signal as a sum over tissue spectral components, where each component signal characterized by an exponent with a negative real part representing signal decay and an imaginary part representing phase dependent on the magnetic field and the chemical shift (step 830). This fitting for such a complex signal model can be performed on iteratively using numerical optimization [0340]; After determining the magnetic field based on the MR data, the process 800 continues by determining a relationship between the magnetic field and magnetic susceptibility (step 840) [0341]).
Regarding claim 17, modified Wang ‘347 teaches the method of claim 15, as discussed above. Wang ‘347 further teaches wherein the comparison comprises an L1 and/or L2 norm of a difference between the obtained complex magnetic resonance data and the susceptibility modeled complex signal at each echo time ([0073] discloses filtering of the complex magnetic resonance data; [0064] discloses evaluation of an L1 norm for tissue structure information).
Regarding claim 48, Wang ‘347 teaches a system, the system comprising a processor and a non-transitory computer-readable medium having processor-executable instructions stored (The system 900 includes a processor 910, a memory 920, a storage device 930…The processor 910 is capable of processing instructions for execution within the system 900. [0350]) thereon for generating one or more images of an object (MR signal data corresponding to a subject can be transformed into susceptibility-based images that quantitatively depict the structure and/or composition and/or function of the subject [0005]) , wherein the processor-executable instructions, when executed by the processor, facilitate:
obtaining complex magnetic resonance imaging data collected by a magnetic resonance scanner, wherein the complex magnetic resonance imaging data comprises signal magnitude information and phase information regarding the object (para. [0257] discloses obtaining complex MR data include magnitude and phase);
estimating a magnetic susceptibility distribution of the object based on the obtained complex magnetic resonance data (collecting and processing MRI signals of a subject, and reconstructing maps of physical properties intrinsic to the subject (e.g., magnetic susceptibility) [0005]; para. [0258] discloses using MR complex data for a magnetic susceptibility distribution), wherein estimating the magnetic susceptibility distribution of the object comprises:
determining a data fidelity term (data fidelity term [0063]) based on modeling time dependence of the obtained complex magnetic resonance data (complex MRI data disclosed in [0224]- [0249-0250] discloses modeling across time), wherein modeling the time dependence comprises:
using the decay rates to model the time dependence of the signal magnitude information ([0249]-[0250] discloses using decay and signal information to model); and
using a phase dependent on a magnetic susceptibility dipole convolution to model the phase information ([0111] calculation of the data fidelity term using phase information and dipole convolution);
determining a cost function (determining a cost function corresponding to a susceptibility distribution [0343]) and multiple regularization terms (two regularization terms are calculated as disclosed in [0178]) involving multiple regions (two regularization terms are calculated for two different areas as disclosed in [0178])
minimizing the cost function (QSM is based on minimizing a cost function [0063]; minimizing the following cost function [0299]); and
solving a distribution of the decay rates of the object alongside with the magnetic susceptibility distribution by ([0248]-[0250] discloses calculating/solving the distribution of the decay rates):
determining the magnetic susceptibility distribution of the object based on minimizing the cost function ([0299] discloses the steps for magnetic susceptibility distribution based on minimizing the cost function);
generating the one or more images of the object based on the determined magnetic susceptibility distribution of the object(After estimating a magnetic susceptibility distribution of the subject, the process 800 continues by generating one or more images of the subject based on the estimated susceptibility distribution of the subject (step 870) [0344]); and
presenting, on a display device, the one or more images of the object (After estimating a magnetic susceptibility distribution of the subject, the process 800 continues by generating one or more images of the subject based on the estimated susceptibility distribution of the subject (step 870). These images can be electronically displayed on a suitable display device [0344]).
Wang ‘347, however, does not teach:
multiple regularization terms involving multiple regions including a first region with a first feature of contrasts and a second region with a second feature of contrasts different from the first feature of contrasts of the first region.
Wang ‘524 is considered analogous to the instant application as “Tool for accurate quantification in molecular mri” is disclosed (title). Wang ‘524 teaches
multiple regularization terms ([0031]-[0032] disclose two regularization terms, for different areas with varying susceptibilities) involving multiple regions including a first region with a first feature of contrasts and a second region with a second feature of contrasts different from the first feature of contrasts of the first region ([0030]-[0032] describes a “second term” which regularizes the susceptibility value in a region where the susceptibility does not vary, i.e. a first region, a region where the susceptibility does vary, i.e. a second region where the feature of susceptibility is different from the first region).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Wang ‘347 to include multiple regularization terms involving multiple regions including a first region with a first feature of contrasts and a second region with a second feature of contrasts different from the first feature of contrasts of the first region, as taught by Wang ‘524. Doing so may lead to a slope closer to unity between the estimated and true susceptibility values, as suggested by Wang ‘524 ([0745]).
Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang Wang et al. (US 20180321347 A1, hereinafter "Wang '347", of record) in view of Wang et al. (US 20110044524 A1, hereinafter “Wang ‘524”) and Fleming et al (US 2020/0069257 A1, hereinafter “Fleming”, of record).
Regarding claim 7, modified Wang ‘347 teaches the method of claim 1, as discussed above. Wang ‘347, however, does not teach wherein a region is formed by binning a group of voxels sharing a characteristic.
Fleming is considered analogous to the instant application as magnetic resonance imaging is disclosed (abstract).
Fleming teaches a region is formed by binning a group of voxels sharing a characteristic ([0333] discloses binning region values based off of shared relaxation times).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Wang ‘347 to include a region is formed by binning a group of voxels sharing a characteristic, as taught by Fleming. Doing so would allow to tool to monitor soft tissue remodeling non-invasively, as suggested by Fleming ([0333]).
Regarding claim 8, modified Wang ‘347 teaches the method of claim 7, as discussed above. Wang, however, does not teach wherein the characteristic is the distribution of the decay rates. Fleming, however, teaches, wherein the characteristic is the distribution of the decay rates ([0246] and [0269] discloses that a part of the process of binning based on its T2* value are decay functions).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Wang ‘347 to include wherein the characteristic is the distribution of the decay rates. as taught by Fleming. Doing so would allow to tool to monitor soft tissue remodeling non-invasively, as suggested by Fleming ([0333]).
Regarding claim 9, modified Wang ‘347 teaches the method of claim 8, as discussed above. Wang ‘347 further teaches wherein the distribution is a rectangular or Gaussian function (This model assumes a Gaussian distribution and an exchangeable correlation structure to account for the multiple lesions per patient [0197]).
Regarding claim 10, modified Wang ‘347 teaches the method of claim 8, as discussed above. Wang ‘347, however, does not teach, wherein the characteristic is a distribution in space.
Fleming, however, teaches wherein the characteristic is a distribution in space ([0279], [0333] discloses distribution/characteristic within space/voxels).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Wang ‘347 to include wherein the characteristic is a distribution in space, as taught by Fleming. Doing so would allow to tool to monitor soft tissue remodeling non-invasively, as suggested by Fleming ([0333]).
Claims 12-14 rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20180321347 A1, hereinafter "Wang '347", of record) in view of Wang et al. (US 20110044524 A1, hereinafter “Wang ‘524”) and Ye (US 20200008701 A1, hereinafter “Ye”, of record).
Regarding claim 12, modified Wang ‘347 teaches the method of claim 1, as discussed above. Wang further teaches wherein the regularization term is implicitly formed ([0101] discloses forming the regularization term). Wang ‘347, however, does not teach wherein estimating the magnetic susceptibility distribution is realized using a deep neural network. Ye is considered analogous to the instant application as “Systems and methods for magnetic resonance imaging” is disclosed (title).
Ye teaches: estimating the magnetic susceptibility distribution is realized using a deep neural network (target machine learning model may provide a mapping relationship between image data indicating the intensity distribution and a susceptibility distribution relating to a subject. The target machine learning model may be configured to output the susceptibility distribution relating to the subject when the image data are inputted into the target machine learning model based on the mapping relationship. The target machine learning model may be constructed based on a neural network model. Exemplary neural network models may include a back propagation (BP) neural network model, a radial basis function (RBF) neural network model, a deep belief nets (DBN) neural network model, an Elman neural network model, or the like, or a combination thereof [0063]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Wang ‘347 to include estimating the magnetic susceptibility distribution is realized using a deep neural network, as taught by Ye. Doing so would improve image quality, as suggested by Ye ([0003]).
Regarding claim 13, modified Wang ‘347 teaches the method of claim 12, as discussed above. Wang ‘347, however, does not teach wherein the deep neural network is trained with labeled data or unlabeled data, or is untrained.
Ye, however, teaches wherein the deep neural network is trained with labeled data ([0077]-[0078], [0087] discloses the training data, where the training data indicates a specific portion of the body, or a specific sample).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Wang ‘347 to include wherein the deep neural network is trained with labeled data, as taught by Ye. Doing so would improve image quality, as suggested by Ye ([0003]).
Regarding claim 14, modified Wang ‘347 teaches the method of claim 12, as discussed above. Wang, however, does not teach wherein the deep neural network is trained with network weights updated with test data.
Ye, however, teaches wherein the deep neural network is trained with network weights updated with test data ([0096]-[0097] discloses weights between different layers of the neural network model during training).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Wang ‘347 to include wherein the deep neural network is trained with network weights updated with test data, as taught by Ye. Doing so would improve image quality, as suggested by Ye ([0003]).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20180321347 A1, hereinafter "Wang '347", of record) in view of Wang et al. (US 20110044524 A1, hereinafter “Wang ‘524”) and Meineke et al. (US 20190242961 A1, hereinafter “Meineke”, of record).
Regarding claim 16, Wang ‘347 teaches the method of claim 15, as discussed above. Wang, however, does not teach wherein the susceptibility modeled complex signal includes a phase involving echo time and magnetic susceptibility dipole convolution.
Meineke is considered analogous to the instant application as magnetic resonance imaging system is disclosed (abstract). Meineke teaches: wherein the susceptibility modeled complex signal includes a phase involving echo time (phase for echo-times disclosed in [0076], [0078]-[0079], [0089]) and magnetic susceptibility dipole convolution (magnetic susceptibility dipole convolution disclosed in [0092])
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Wang ‘347 to include wherein the susceptibility modeled complex signal includes a phase involving echo time and magnetic susceptibility dipole convolution, as taught in Meineke. Doing so would calculate precise chemical shifts, and to improve the MR fingerprinting, as suggested by Meineke ([0009]).
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20180321347 A1, hereinafter "Wang '347", of record) in view of Wang et al. (US 20110044524 A1, hereinafter “Wang ‘524”), Meineke et al. (US 20190242961 A1, hereinafter “Meineke”, of record) and Ye (US 20190318511 A1, hereinafter “Ye ‘511).
Regarding claim 18, modified Wang ‘347 teaches the method of claim 1, as discussed above. Wang ‘347 further teaches wherein using the phase dependent on the magnetic susceptibility dipole convolution to model the phase information comprises:
calculating a magnetic field experienced by tissue in the object from the obtained complex magnetic resonance data ([0063], [0178], [0192] discloses calculation of the magnetic field experienced by the tissue);
Wang ‘347, however, does not teach:
calculating a phase at each echo time of the obtained complex magnetic resonance data according to the calculated magnetic field and a multiplicative scaling factor; and
performing a comparison involving the calculated phase with the phase of the magnetic susceptibility dipole convolution at each echo time wherein the method further comprises dividing the determined magnetic susceptibility distribution by the multiplicative scaling factor, and wherein generating the one or more images of the object is based on dividing the determined magnetic susceptibility distribution by the multiplicative scaling factor.
Meineke is considered analogous to the instant application as magnetic resonance imaging system is disclosed (abstract). Meineke teaches:
performing a comparison involving the calculated phase with [[a]]the phase of the magnetic susceptibility dipole convolution at each echo time (phase for echo-times disclosed in [0076], [0078]-[0079], [0089]; magnetic susceptibility dipole convolution disclosed in [0092]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Wang ‘347 to include performing a comparison involving the calculated phase with [[a]]the phase of the magnetic susceptibility dipole convolution at each echo time, as taught in Meineke. Doing so would calculate precise chemical shifts, and to improve the MR fingerprinting, as suggested by Meineke ([0009]).
The combined invention still does not teach:
calculating a phase at each echo time of the obtained complex magnetic resonance data according to the calculated magnetic field and a multiplicative scaling factor; and
wherein the method further comprises dividing the determined magnetic susceptibility distribution by the multiplicative scaling factor, and wherein generating the one or more images of the object is based on dividing the determined magnetic susceptibility distribution by the multiplicative scaling factor.
Ye ‘511 is considered analogous to the instant application as “Method and system for determining magnetic susceptibility distribution” is disclosed (title). Ye teaches:
calculating a phase at each echo time of the obtained complex magnetic resonance data (the MR signal received may be a complex signal including a real part and an imaginary part [0099]; ([0084] and [0101] discloses phase and echo-time calculations according to the calculated magnetic field)) according to the calculated magnetic field and a multiplicative scaling factor ([0092], [0133] discloses a scaling factor, used for the distribution map as shown in figs. 9 and 10); and
wherein the method further comprises dividing the determined magnetic susceptibility distribution by the multiplicative scaling factor([0092], [0133] discloses a scaling factor, used for the distribution map as shown in figs. 9 and 10), and wherein generating the one or more images of the object is based on dividing the determined magnetic susceptibility distribution by the multiplicative scaling factor ([0131]-[0133] discloses adjusting magnetic susceptibility by the scaling factor).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Wang ‘347 to include calculating a phase at each echo time of the obtained complex magnetic resonance data according to the calculated magnetic field and a multiplicative scaling factor, and wherein the method further comprises dividing the determined magnetic susceptibility distribution by the multiplicative scaling factor, and wherein generating the one or more images of the object is based on dividing the determined magnetic susceptibility distribution by the multiplicative scaling factor, as taught in Ye ‘511. Doing so would improve the accuracy of the susceptibility map, as suggested by Ye ’511 ([0088]).
Regarding claim 19, modified Wang ‘347 teaches method of claim 18, as discussed above. Wang ‘347, however, does not teach wherein the comparison involving the calculated phase with the phase of the magnetic susceptibility dipole convolution at each echo time comprises calculating an L1 and/or L2 norm of a weighted difference between an exponential factor of the calculated phase and an exponential factor of the phase of the magnetic susceptibility dipole convolution at each echo time.
Meineke teaches:
the comparison involving the calculated phase with the phase of the magnetic susceptibility dipole convolution at each echo time comprises calculating an L1 and/or L2 norm of a weighted difference between an exponential factor of the calculated phase and an exponential factor of the phase of the magnetic susceptibility dipole convolution at each echo time (phase for echo-times disclosed in [0076], [0078]-[0079], [0089]; magnetic susceptibility dipole convolution disclosed in [0092]; [0020]-[0021] discloses weighting values of the MR signal)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Wang ‘347 to include performing a comparison involving the calculated phase with a phase of the magnetic susceptibility dipole convolution at each echo time, as taught in Meineke. Doing so would calculate precise chemical shifts, and to improve the MR fingerprinting, as suggested by Meineke ([0009]).
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
Regarding the 35 USC § 103 rejection of claims 1 and 48, on pages 9-11 of remarks, that the prior art does not teach the amendment “For instance, it is respectfully submitted that Wang '347 and Wang '524 fail to disclose or suggest at least determining a data fidelity term based on modeling time dependence of the obtained complex magnetic resonance data, wherein modeling the time dependence comprises: using
decay to model the time dependence of the signal magnitude information and using a phase dependent on a magnetic susceptibility dipole convolution to model the phase information”, as well as the newly added amendment regarding “solving a distribution of the decay rates of the object alongside with the magnetic susceptibility distribution”. In response, the examiner respectfully disagrees. Newly cited paragraphs [0248]-[0250] of Wang ‘347 disclose solving the solving a distribution of the decay rates of the object alongside with the magnetic susceptibility distribution. Further, paragraphs [0111] of Wang ‘347 disclose calculation of the data fidelity term using phase information and dipole convolution, and paragraph [0257] further discloses obtaining phase and magnitude information of the MR data. Accordingly, this argument is not persuasive and the rejection is maintained.
Regarding the 35 USC 103 rejection of the remaining dependent claims, he applicant argument’s on pages 11-12 are premised upon the assertion that the claims are allowable due to an allowable base claim. The examiner respectfully disagrees for the reasons discussed above.
Conclusion
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/N.B./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798