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 Arguments
Applicant’s arguments, see page 5, filed 3/10/26, with respect to claim 1 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection has been withdrawn.
Applicant's arguments filed 3/10/26 have been fully considered but they are not persuasive.
Regarding applicant’s argument for claim 1, on page 6, that Setsompop does not disclose generating subspace bases from prior signal data, examiner disagrees. Firstly, examiner notes that “prior signal data” does not specifically exclude the obtain k-t space data as such data is obtained previous to the step of generating subspace bases. However, even if this were not the case, Setsompop clearly discloses in paragraphs 54 and 55 that the bases can be extracted from “previously simulated evolution data”. Therefore the argument is overcome and the previous rejection remains.
Regarding applicant’s argument for claim 1, on page 7, that Setsompop does not disclose storing the coefficient maps because the values only exists temporarily, examiner finds the argument moot. Even if applicant’s argument were true (which examiner does not necessarily agree with), calculating a coefficient map in a digital system stores the output, even if only temporarily, as it is further used to reconstruct image data. Current claim language puts no temporal limit on what constitutes “storing”, therefore the argument is overcome and the previous rejection remains.
Claim Rejections - 35 USC § 102
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.
1) Claim(s) 1-4 and 6 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. patent application publication 2019/0369185 by Setsompop et al.
2) Regarding claim 1, Setsompop teaches a method for generating compact signal feature maps from multi-contrast magnetic resonance images, the method comprising:(a) accessing multi-contrast image data with a computer system, wherein the multi- contrast image data comprise a plurality of magnetic resonance images acquired with a magnetic resonance imaging (MRI) system from a subject, wherein the plurality of magnetic resonance images depict multiple different contrast weightings (paragraphs 25 and 39; multi-contrast images are acquired); (b) generating subspace bases from prior signal data using the computer system (figure 7, item 706; paragraph 55; bases are determined from stored data); (c) reconstructing coefficient maps for the subspace bases using a subspace reconstruction framework implemented with the computer system, wherein the subspace reconstruction framework takes as inputs the subspace bases and the multi-contrast image data (paragraphs 56 and 57; equation 1; coefficient map is estimated based on the bases and k-t space data); and (d) storing the coefficient maps as compact signal feature data using the computer system, wherein the compact signal feature data depict similar information as the multi-contrast image data with significantly reduced degrees of freedom relative to the multi-contrast image data (paragraph 58; figure 7, item 712; image is generated from coefficient maps utilizing the subspace constrained data).
3) Regarding claim 2, Setsompop teaches the method of claim 1, wherein the prior signal data comprise previously acquired multi-contrast image data (paragraph 53; previous k-space data is acquired).
4) Regarding claim 3, Setsompop teaches the method of claim 1, wherein the prior signal data comprise simulated multi- contrast image data (figure 7, item 704; data can be simulated).
5) Regarding claim 4, Setsompop teaches the method of claim 1, wherein generating the subspace bases from the prior signal data comprises applying a principal component analysis to the prior signal data and retaining a number of principal components as the subspace bases (paragraph 55; bases extracted using PCA).
6) Regarding claim 6, Setsompop teaches the method of claim 1, wherein the multiple different contrast weightings include at least two of T1-weighting, T2-weighting, T2*-weighting, or fluid attenuation inversion recovery (FLAIR) weighting (paragraph 45; T1, T2 and T2* are at least disclosed).
Claim Rejections - 35 USC § 103
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.
7) Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2019/0369185 by Setsompop et al. as applied to claim 1 above, and further in view of U.S. patent application publication 2017/0360325 by Herbert.
Setsompop does not specifically teach the method of claim 1, wherein generating the subspace bases from the prior signal data comprises applying an independent component analysis to the prior signal data and retaining a number of components as the subspace bases.
Herbert teaches teach the method of claim 1, wherein generating the subspace bases from the prior signal data comprises applying an independent component analysis to the prior signal data and retaining a number of components as the subspace bases (paragraph 33; ICA is utilized to reduce dimensionality of MRI data).
Setsompop and Herbert are combinble because both are from the medical imaging field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Setsompop with Herbert to add ICA. The motivation for doing so would have been to save processing time and storage space. Therefore it would have been obvious to combine Setsompop with Herbert to obtain the invention of claim 5.
8) Claims 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2019/0369185 by Setsompop et al. as applied to claim 1 above, and further in view of U.S. patent application publication 2019/0244348 by Buckler et al.
9) Regarding claim 7, Setsompop does not specifically teach the method of claim 1, further comprising: accessing a machine learning algorithm with the computer system, wherein the machine learning algorithm has been trained on training data to generate tissue feature data based on compact signal feature map data; and generating tissue feature data using the computer system to apply the compact signal feature data extracted from the multi-contrast image data to the machine learning algorithm, generating output as tissue feature data indicative of at least one tissue property of a tissue depicted in the multi-contrast image data.
Buckler teaches the method of claim 1, further comprising: accessing a machine learning algorithm with the computer system, wherein the machine learning algorithm has been trained on training data to generate tissue feature data based on compact signal feature map data (paragraph 188; training can occur on data with collapsed dimensions); and generating tissue feature data using the computer system to apply the compact signal feature data extracted from the multi-contrast image data to the machine learning algorithm, generating output as tissue feature data indicative of at least one tissue property of a tissue depicted in the multi-contrast image data (paragraphs 156, 159 and 296; reduced dimensionality data can be tissue classified by learned models).
Setsompop and Buckler are combinble because both are from the medical imaging field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Setsompop with Buckler to add tissue classification. The motivation for doing so would have been to improve diagnostic accuracy (paragraph 16). Therefore it would have been obvious to combine Setsompop with Buckler to obtain the invention of claim 7.
10) Regarding claim 8, Buckler (as combined with Setsompop in the rejection of claim 7 above) teaches the method of claim 7, wherein the tissue feature data comprise tissue classification data that indicate a classification of the tissue depicted in the multi-contrast image data based on the at least one tissue property (paragraphs 156, 159 and 296; tissue is classified by learned models, data can be multi-contrast as disclosed in paragraph 164 and claim 20).
11) Regarding claim 9, Buckler (as combined with Setsompop in the rejection of claim 7 above) teaches the method of claim 7, wherein the tissue feature data indicate a detection of a tissue feature of the tissue depicted in the multi-contrast image data based on the at least one tissue property (paragraphs 156, 159 and 296; tissue is classified by learned models, data can be multi-contrast as disclosed in paragraph 164 and claim 20).
12) Regarding claim 10, Buckler (as combined with Setsompop in the rejection of claim 7 above) teaches the method of claim 7, wherein the machine learning algorithm is a supervised learning-based machine learning algorithm (paragraph 37; supervised learning can be utilized).
13) Regarding claim 11, Buckler (as combined with Setsompop in the rejection of claim 7 above) teaches the method of claim 7, wherein the machine learning algorithm is an unsupervised learning-based machine learning algorithm (paragraph 37; unsupervised learning can be utilized).
14) Regarding claim 12, Buckler (as combined with Setsompop in the rejection of claim 7 above) teaches the method of claim 7, further comprising displaying the multi-contrast image data to a user together with the tissue feature data (paragraph 161; figure 3; imaging data can be displayed together with tissue data).
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 BENJAMIN O DULANEY whose telephone number is (571)272-2874. The examiner can normally be reached Mon-Fri 10-6.
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BENJAMIN O. DULANEY
Primary Examiner
Art Unit 2676
/BENJAMIN O DULANEY/ Primary Examiner, Art Unit 2683
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