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 9 is objected to because of the following informalities: Claim 9 appears it should depend upon claim 8 as there is no support for “the target loss function” in claim 7. 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.
Claim 20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because given the broadest reasonable interpretation a computer-readable storage medium includes transitory carrier waves per se, which is non-statutory subject matter.
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.
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.
Claim(s) 1-12 and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nezafat et al. (US 2023/0194641 A1) in view of Dillman et al. (US 2023/0237649 A1), hereinafter referred to as Dillman. With reference to claim 1, Nezafat teaches a method implemented on a computing device including at least one processor and a storage device, the method, comprising: obtaining magnetic resonance (MR) images of a subject (Fig. 3, 304); obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model, a second count of the target MR mappings being less than a first count of the MR images (Fig. 3, 310, ¶0044); However is silent with regards to wherein the trained machine learning model includes at least two sub-models, and each sub-model processes at least one of the MR images.
Dillman teaches the trained machine learning model includes at least two sub-models, and each sub-model processes at least one of the MR images (¶0046, ¶0047). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Dillman with the method of Nezafat so as to improve performance of the machine learning model (Dillman, ¶0047).
With reference to claim 2, Nezafat as combined above further teaches the obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model includes: obtaining a relaxation time corresponding to at least one of the MR images; and obtaining the one or more target MR mappings by processing the MR images and the relaxation time corresponding to the at least one of the MR images through the trained machine learning model (¶0037). With reference to claim 3, Nezafat as combined above further teaches the first count is determined based on the second count (¶0046).
With reference to claim 4, Nezafat as combined above further teaches determining the second count based on one or more target quantitative parameters of the subject, each of the one or more target MR mappings corresponding to one of the one or more target quantitative parameters (¶0046).
With reference to claim 5, Nezafat as combined above further teaches the MR images include at least one MR image, a type of a target imaging parameter corresponding to the at least one MR image being the same as a type of one of the one or more target quantitative parameters (¶0046).
With reference to claim 6, Nezafat as combined above further teaches one of the sub-models includes at least one of a fully connected (FC) network, a convolutional neural network (CNN), a recurrent neural network (RNN), or a Transformer (¶0033). With reference to claim 7, Nezafat as combined above further teaches the trained machine learning model is obtained through operations including: obtaining multiple training samples, wherein each training sample of the multiple training samples includes sample MR images and a reference mapping; performing multiple iterations on a preliminary machine learning model based on the multiple training samples to obtain the trained machine learning model; the preliminary machine learning model including at least two sub-models (Nezafat, ¶0033, ¶0037; Dillman ¶0065).
With reference to claim 8, Nezafat as combined above further teaches at least one iteration of the multiple iteration includes: obtaining a predicted mapping by inputting the sample MR images into the preliminary machine learning model; determining a value of a target loss function based on the predicted mapping and the reference mapping; and updating network parameters of the at least two sub-models based on the value of the target loss function (Dillman ¶0065). With reference to claim 9, Nezafat as combined above further teaches the target loss function includes at least two loss terms, and each loss term corresponds to a sub-model (Dillman ¶0065). With reference to claim 10, Nezafat as combined above further teaches at least one of the at least two loss terms includes a weighting factor, the weighting factor being updated when updating network parameters of the at least two sub-models (Dillman ¶0065). With reference to claim 11, Nezafat as combined above further teaches the obtaining the one or more target mappings corresponding to at least a portion of the MR images by processing of the MR images through a trained machine learning network includes: obtaining a first MR mapping corresponding to a target quantitative parameter by processing a first portion of the MR images through a first sub-model; obtaining a second MR mapping corresponding to the target quantitative parameter by processing a second portion of the MR images through a second sub-model; based on weight parameters of the first sub-model and the second sub-model, obtaining a target MR mapping corresponding to the target quantitative parameter by weighting the first MR mapping and the second MR mapping (Dillman ¶0046, ¶0097, Fig. 10).
With reference to claim 12, Nezafat as combined above further teaches the obtaining the one or more target mappings corresponding to at least a portion of the MR images by processing of the MR images through a trained machine learning network includes: obtaining a first MR mapping corresponding to a target quantitative parameter by processing the MR images through a first sub-model; obtaining a second MR mapping corresponding to the target quantitative parameter by processing the MR images through a second sub-model; based on weight parameters of the first sub-model and the second sub-model, obtaining a target MR mapping corresponding to the target quantitative parameter by weighting the first MR mapping and the second MR mapping (Dillman ¶0046, ¶0097, Fig. 10).
With reference to claim 14, Nezafat as combined above further teaches the MR images are acquired in one single scan, and the one or more target MR mappings include at least one of a T1 mapping, a T2 mapping, or a T1rho mapping (¶0044). With reference to claim 15, Nezafat as combined above further teaches the MR images are acquired in one single scan, and the one or more target MR mappings include one of a T1 mapping, a T2 mapping, and a T1rho mapping (¶0033, ¶0044). With reference to claim 16, Nezafat as combined above further teaches obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model includes: inputting T1-weighted MR images acquired in one single scan corresponding to each of the T1 weighted MR images into the trained machine learning model; and generating a T1 mapping by the trained machine learning model (¶0033). With reference to claim 17, Nezafat as combined above further teaches obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model includes: inputting T2-weighted MR images acquired in one single scan corresponding to each of the T2-weighted MR images into the trained machine learning model; and generating a T2 mapping by the trained machine learning model (¶0033).
With reference to claim 18, Nezafat as combined above further teaches obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model includes: inputting T1rho-weighted MR images acquired in one single scan corresponding to each of the T1rho-weighted MR images into the trained machine learning model; and generating a T1rho mapping by the trained machine learning model (¶0035). With reference to claim 19, Nezafat teaches A system for magnetic resonance imaging, comprising: at least one processor and at least one storage, wherein the at least one storage is configured to store computer instructions; and the at least one processor is configured to execute at least a portion of the computer instructions (¶0022) to: obtaining magnetic resonance (MR) images of a subject, at least two of the MR images being acquired by an MRI scanner according to different imaging parameters (Fig. 3, 304, ¶0043); obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model, a second count of the target MR mappings being less than a first count of the MR images (Fig. 3, 310, ¶0044); However is silent with regards to wherein the trained machine learning model includes at least two sub-models, and each sub-model processes at least one of the MR images.
Dillman teaches wherein the trained machine learning model includes at least two sub-models, and each sub-model processes at least one of the MR images (¶0046, ¶0047). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Dillman with the system of Nezafat so as to improve performance of the machine learning model (Dillman, ¶0047).
With reference to claim 20, Nezafat teaches a computer-readable storage medium storing computer instructions, wherein when reading the computer instructions in the storage medium, the computer performs a method (¶0053) including: obtaining magnetic resonance (MR) images of a subject, at least two of the MR images being acquired by an MRI scanner according to different imaging parameters (Fig. 3, 304, ¶0043); obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model, a second count of the target MR mappings being less than a first count of the MR images (Fig. 3, 310, ¶0044); However is silent with regards to wherein the trained machine learning model includes at least two sub-models, and each sub-model processes at least one of the MR images.
Dillman teaches wherein the trained machine learning model includes at least two sub-models, and each sub-model processes at least one of the MR images (¶0046, ¶0047). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Dillman with the computer-readable medium of Nezafat so as to improve performance of the machine learning model (Dillman, ¶0047).
Allowable Subject Matter
Claim 13 is 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 does not disclose or suggest the claimed " first count is equal to 2, and the MR images includes a first MR image corresponding to a first target imaging parameter and a second MR image corresponding to a second target imaging parameter, the one or more target MR mappings corresponding to a target quantitative parameter whose type is same as a type of the first target imaging parameter or the second target imaging parameter" in combination with the remaining claim elements as set forth in claim 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hsiao et al. (US 10,909,681 B2) teach an automated selection of an optimal image from a series of images.
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/GREGORY H CURRAN/Primary Examiner, Art Unit 2852
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