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 with respect to claim(s) 1, 11, 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1-5, 11-15, 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over SIVERSSON (US 2022/0225955) in view of Li (US 2018/0304099).
As to claim 1, SIVERSSON discloses a computer-implemented method for generating one or more synthetic electron density, sED, images (claim 1, 0029), the method comprising:
obtaining a first image of a first imaging modality, the first image depicting an anatomical region of a subject (claim 1, receiving a current set of magnetic resonance, MR, images of the anatomical portion; and
generating, using a trained machine learning model and the first image, a sED image depicting the anatomical region (claim 1, computing current coefficients of the image transfer function by operating the machine-learning model on the current set of MR images; and computing a current synthetic electron density image of the anatomical portion by operating the current coefficients, in accordance with the image transfer function, on the current set of MR images);
wherein the trained machine learning model has been trained using a set of training images comprising a first subset of training images of the first imaging modality, and a second subset of training images in which each training image comprises electron density, ED, information (0009, 0045).
Although SIVERSSON is silent on wherein the ED information comprises values in units of at least one of mass per unit volume, electrons per unit volume, or relative electron density, SIVERSSON discloses, at paragraph 0029, that “synthetic electron density image” refers to any type of image that is computationally generated to contain signal values (“intensity values”) directly related to electron density.
Li teaches that electron density information comprises values in units of at least one of mass per unit volume, electrons per unit volume, or relative electron density (para. 0072).
It would have been obvious to one of ordinary skill in the art to replace synthetic electron density image in SIVERSSON with synthetic electron density image taught by Li since doing so would amount to a simple substitution of one known element for another in order to obtain predictable results, and improve performance.
incorporate Li’s teachings into SIVERSSON since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve reduce computational complexity.
As to claim 2, the combination of SIVERSSON and Li discloses the method of claim 1, wherein the first imaging modality is magnetic resonance, MR, imaging (SIVERSSON, 0039; Li, para. 0072).
As to claim 3, the combination of SIVERSSON and Li discloses the method of claim 1, wherein the sED image comprises a plurality of pixels or voxels each having a value representative of electron density (SIVERSSON, 0032; Li, para. 0072).
As to claim 4, the combination of SIVERSSON and Li discloses the method of claim 1, wherein each of the images in the second subset of training images comprises a plurality of pixels or voxels each having a value representative of electron density (SIVERSSON, 0009, 0032, 0045; Li, para. 0072).
As to claim 5, the combination of SIVERSSON and Li discloses the method of claim 1, wherein the electron density information for each image in the second subset of training images has been generated based on a respective computed tomography, CT, image using at least one CT-ED mapping (SIVERSSON, 0002, 0009, 0029, 0045; Li, para. 0072).
As to claims 11-15, 20, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
As to claim 21, the combination of SIVERSSON and Li discloses the method of claim 1, wherein the ED information comprises values in units of electrons per unit volume (SIVERSSON, 0029; Li, para. 0072).
As to claim 22, the combination of SIVERSSON and Li discloses the method of claim 1, wherein the ED information comprises values in units of relative electron density (SIVERSSON, 0029; Li, para. 0072).
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.
Claim(s) 8-10, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over SIVERSSON (US 2022/0225955) in view of Li (US 2018/0304099) and further in view of RAJAPAKSE (US 2025/0022137).
As to claim 8, the combination of SIVERSSON and Li is silent regarding using generative adversarial network (GAN).
RAJAPAKSE teaches wherein the trained machine learning model is a generative model, wherein the generative model has been trained via a generative adversarial network (GAN) (RAJAPAKSE, 0006, 0029).
It would have been obvious to one of ordinary skill in the art to incorporate RAJAPAKSE’s teachings into the combination of SIVERSSON and Li since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve training process.
As to claim 9, the combination of SIVERSSON, Li and RAJAPAKSE discloses the method of claim 8, wherein the generative model is a first generator model, and the GAN comprises the first generator model and a first discriminator model, the first generator model trained to generate synthesised imaging data that resembles the training images in the second subset of training images based on input training images of the first subset of training images, and the first discriminator model trained to discriminate between the synthesized imaging data generated by the first generative model and the training images in the second subset of training images (RAJAPAKSE, Figs. 1-3, 0006, 0029, 0079-0092) .
As to claim 10, the combination of SIVERSSON, Li and RAJAPAKSE discloses the method of claim 9, wherein the generative adversarial network is a cycle generative adversarial network, CycleGAN, and the CycleGAN further comprises: a second generative model trained to generate synthesised imaging data that resembles the training images in the first subset of training images based on input training images of the second subset of training images, and a second discriminator model trained to discriminate between the synthesized imaging data generated by the second generative model and the training images in the first subset of training images (RAJAPAKSE, Figs. 1-3, 0079-0092).
As to claims 18-19, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
Allowable Subject Matter
Claims 6-7, 16-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art references disclose the claim limitations discussed above, but fails to disclose the combined features required by each of dependent claims 6, 16.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 PHUOC TRAN whose telephone number is (571)272-7399. The examiner can normally be reached 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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