DETAILED ACTION
Response to Amendment
Applicant’s response to the last Office Action, filed on 6/3/2026 has been entered and made of record.
Applicant’s amendments necessitated the new ground of rejection set forth herein; therefore, this action is made Final.
Rejections under 35 USC 112(b) have been added and modified in view of amendments.
Response to Arguments
Applicant's arguments filed on 6/3/2026 have been fully considered but they are not persuasive.
The citations to the Mostapha reference have been updated to reflect newly added claim language. The claims have been amended to, among other changes, require that the data-for-image processed by the post-reconstruction for the post- reconstructed image is the raw data. Examiner nores that the Mostapha reference teaches that post-reconstruction takes place at Fig 2, the output of numeral 220. See initial machine learning based post-reconstruction denoising/altering at the process which begins ¶ 0068 and see also ¶ 0091. This machine learning reconstruction is an iterative reconstruction using raw scan data (k-space measurements) to iteratively arrive at reconstruction. See ¶ 0046-0047 and 0064 and 0066 which show the details of the machine-learning steps and how they are reconstruction steps based on raw scan data.
Examiner also notes that Applicant has not directed arguments to the Enders reference which also clearly teaches the limitations in question. The rejection has been updated to more clearly show how the Enders reference teaches the claims. Enders teaches a system for medical imaging artificial intelligence training in which a default reconstruction takes place and a user evaluates and adjusts the reconstruction to improve the image according to their preferences and to provide training feedback. ¶ 0004-0005 provides an overview while ¶ 0109-0111 teach using default and adjusted reconstruction parameters, ¶ 0149-0150 teaches the user receiving the reconstruction and appraising it, ¶ 0153 shows the evaluation changes by the user. ¶ 0167-0168 teach two embodiments for user-adjusted reconstruction evaluations. In particular, ¶ 0150 shows how the operator provides evaluation/appraisal for training and ¶ 0167 and 0168 both disclose different embodiments for performing post-reconstructions based on stored user parameters and based on stored training data from user evaluations. These post-reconstructions are possible because the raw data is still available to be used again.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: data processing apparatus in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 4-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The independent claims recite, “the reconstruction condition for the post-reconstruction are associated with each other in a database…” It is not clear what the antecedent basis is of the underlined term. In particular, it is not clear if it refers to “the another reconstruction condition different from a reconstruction condition” or the original “the reconstruction condition”. Both of these are “reconstruction conditions” so reciting “the reconstruction condition” does not provide clear antecedent basis.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The meaning of the following language is not clear, “reconstruction condition search method according to claim 8, further comprising: a reconstruction step of reconstructing the image using the raw data prior to wherein the post-reconstruction step and without using the intermediate data.” In particular it is not clear what the phrase “and without using the intermediate data” means in this context. For example, it’s unclear if the claim language is requiring that the “without using the intermediate data” refers to the initial reconstruction step or if it could apply anywhere in the process. The claim language should to make clear how the phrase fits with the claim steps.
Additionally, it is not clear what the antecedent basis of the term “the image” is in claim 10. There are multiple antecedent images.
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, 7, 8, 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mostapha (US PGPub 2023/0095222) in view of Enders (US PGPub 2021/0357689).
Regarding claim 1, Mostapha discloses magnetic resonance imaging apparatus comprising: (¶ 0027 teaches a technique for MR reconstruction that provides an initial reconstruction via a deep learning technique and allows user fine tuning in a post-reconstruction feedback process.)
an imaging unit that collects a nuclear magnetic resonance signal generated by a subject; (¶ 0033)
an operation unit that generates a reconstructed image of the subject by using raw data consisting of the nuclear magnetic resonance signal; (See k-space measurements at ¶ 0033.)
and one or more processors configured to search for a recommended reconstruction condition of the reconstructed image, (¶ 0036 for processor and ¶ 0029 and 0065 for the initial recommended reconstruction conditions.)
the one or more processors including a data-for-image acquisition unit that acquires data-for-image generated by the operation unit, and wherein the one or more processors are further configured to (¶ 0029)
perform post-reconstruction of an image by using the data-for-image under another reconstruction condition different from a reconstruction condition under which the operation unit reconstructs the reconstructed image, (Post-reconstruction takes place at Fig 2, the output of numeral 220. See initial machine learning based post-reconstruction denoising/altering at the process which begins ¶ 0068 and see also ¶ 0091. This machine learning reconstruction is an iterative reconstruction using raw scan data (k-space measurements) to iteratively arrive at reconstruction. See ¶ 0046-0047 and 0064 and 0066 which show the details of the machine-learning steps and how they are reconstruction steps based on raw scan data.)
present a post-reconstructed image reconstructed by the post-reconstruction, receive a user input for the post-reconstructed image and store input data in which the user input and the reconstruction condition for the post-reconstruction are associated with each other in a database, and (¶ 0097 and 0102 teach adjusting the denoising to tune displayed image. ¶ 0041 also teaches the intermediate displaying)
analyze the input data accumulated in the database for the post-reconstruction to determine the recommended reconstruction condition. (See Fig. 2, numerals 240/250 for the analysis and display.)
wherein the data-for-image processed by the post-reconstruction for the post- reconstructed image is the raw data. (As above, post-reconstruction takes place at Fig 2, the output of numeral 220. See initial machine learning based post-reconstruction denoising/altering at the process which begins ¶ 0068 and see also ¶ 0091. This machine learning reconstruction is an iterative reconstruction using raw scan data (k-space measurements) to iteratively arrive at reconstruction. See ¶ 0046-0047 and 0064 and 0066 which show the details of the machine-learning steps and how they are reconstruction steps based on raw scan data.)
In the field of medical imaging post-reconstruction user evaluation Enders teaches performing evaluation of said post-reconstruction image, and Enders also teaches a processor configured to perform post-reconstruction of an image by using the data-for-image under another reconstruction condition different from a reconstruction condition under which the operation unit reconstructs the reconstructed image, present a post-reconstructed image reconstructed by the post-reconstruction, receive a user evaluation for the post-reconstructed image, and store evaluation data in which the user evaluation and the reconstruction condition for the post-reconstruction are associated with each other in a database, and analyze the evaluation data accumulated in the database for the post-reconstruction to determine the recommended reconstruction condition, and wherein the data-for-image processed by the post-reconstruction for the post- reconstructed image is the raw data. (Enders teaches a system for medical imaging artificial intelligence training in which a default reconstruction takes place and a user evaluates and adjusts the reconstruction to improve the image according to their preferences and to provide training feedback. ¶ 0004-0005 provides an overview while ¶ 0109-0111 teach using default and adjusted reconstruction parameters, ¶ 0149-0150 teaches the user receiving the reconstruction and appraising it, ¶ 0153 shows the evaluation changes by the user. ¶ 0167-0168 teach two embodiments for user-adjusted reconstruction evaluations. In particular, ¶ 0150 shows how the operator provides evaluation/appraisal for training and ¶ 0167 and 0168 both disclose different embodiments for performing post-reconstructions based on stored user parameters and based on stored training data from user evaluations. These post-reconstructions are possible because the raw data is still available to be used again.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Mostapha’s medical imaging post-reconstruction user adjustment with Enders’ medical imaging post-reconstruction user evaluation. Mostapha teaches medical imaging post-reconstruction user adjustment with machine learning based on a user adaptive tuning parameters such as denoising parameters, but does not expressly disclose this as an ‘evaluation’. Enders teaches medical imaging post-reconstruction user evaluation in which evaluation of the reconstruction is used for machine learning training. Simply structuring or calling the user feedback an evaluation cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 4, the above combination discloses the magnetic resonance imaging apparatus according to claim 1, wherein generation by the operation unit of the reconstructed image includes transformation processing of the raw data into an image space and correction processing on data in the image space, and the data-for-image acquisition unit acquires data-for-image subjected to the transformation processing as the data-for-image. (As in rejection of claim 1, see raw k-space data at ¶ 0039 and transformation reconstruction at ¶ 0065 and 0068.)
Regarding claim 5, the above combination discloses the magnetic resonance imaging apparatus according to claim 1, wherein the one or more processors are further configured to generate a simulation image using the recommended reconstruction condition, and the simulation image is presented together with the recommended reconstruction condition. (As above, see ¶ 0097 and 0102 which teach adjusting the denoising to tune displayed image as a simulated denoised image.)
Regarding claim 7, the above combination discloses the magnetic resonance imaging apparatus according to claim 1, wherein one or more processors determine the recommended reconstruction condition via statistical processing or machine learning using the evaluation data accumulated in the database. (See rejection of claim 1 regarding both references machine learning.)
Claim 8 is the method corresponding to the apparatus of claim 1. The system necessitates method steps. Remaining limitations are rejected similarly. See detailed analysis above.
Regarding claim 10, the above combination discloses the reconstruction condition search method according to claim 8, further comprising: a reconstruction step of reconstructing the image using the raw data prior to the post-reconstruction step and without using the intermediate data. (¶ 0097 teaches that the post-reconstruction step using the intermediate data by providing a user input value may be omitted.)
Claim 11 is the system corresponding to the apparatus of claim 1. Remaining limitations are rejected similarly. See detailed analysis above.
Claim(s) 6 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mostapha (US PGPub 2023/0095222) in view of Enders (US PGPub 2021/0357689) and Liu (US PGPub 2020/0049785)
Regarding claim 6, the above combination discloses the magnetic resonance imaging apparatus according to claim 1, but not the remaining limitations.
In the field of MR motion-based reconstruction, Liu teaches a body movement determination unit that determines a body movement during imaging, wherein the data-for-image acquisition unit acquires body movement information determined by the body movement determination unit together with the data-for-image, and the one or more processors are further configured to present a reconstruction condition related to the body movement information as the recommended reconstruction condition. (Liu teaches a body movement-based reconstruction at ¶ 0031, 0041, 0043 and 0048.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the above combination’s medical imaging reconstruction adjustment with Liu’s medical imaging motion reconstruction. Liu teaches a body movement-based reconstruction to allow reconstruction to compensate for body motion during imaging, a well-known and widely-used MR technique. Simply performing a reconstruction with some body motion adjustments cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 9, the above combination discloses the reconstruction condition search method according to claim 8, further comprising: a step of receiving body movement information generated during imaging, wherein a suggestion related to a body movement is presented together with the recommended reconstruction condition. (See rejection of claim 6, as above, Liu teaches a body movement-based reconstruction suggestion at steps of ¶ 0031, 0041, 0043 and 0048.)
Conclusion
Based on these facts, 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 extension fee 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Raphael Schwartz whose telephone number is (571)270-3822. The examiner can normally be reached Monday to Friday 9am-5pm CT.
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/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671
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