CTNF 18/839,548 CTNF 83206 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Statues of claim: claims 1-15 are pending below. Information Disclosure Statement 06-52 The information disclosure statement (IDS) submitted on 8/19/2024 was filed and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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. 07-30-05 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. 07-30-06 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: “an image processing module” in claim 1, “an image processing module” in claim 14, “an image processing module” in claim 15. 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 07-30-02 AIA 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 1-15 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. Claims 1, 7, 14 and 15, recited claim element “likelihood” is view as being indefinite for failing to particularly point out and distinctly set parameter(s) on how this is determined or calculated for this probability. Please amend to clarify such that one ordinary skill in the art can implement, determine or code this into a system/device/method such this invention can be replicated. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 14 is 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 claim element recited “computer program product”, where a review of the specification of the instant invention in 0049-0050 detail “…computer program product embodied in one or more computer readable medium(s) having computer executable code embodied thereon. [0050] Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium” include signal that is view as non-statutory subject matter. Please amend to “non-transitory computer readable medium” to overcome rejection. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 9-11 and 13, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sayantan et al (“On hallucinations in tomographic image reconstruction”, IDS) in view of Zhang Jingke et al (Deep Null Space Learning Improves Dataset Recovery for High Frame Rate Synthetic transmit Aperture Imaging, IDS) . Claim 1, similarly claim 14 and 15: Sayantan et al (“On hallucinations in tomographic image reconstruction”, IDS) teaches the following subject matter: A medical system (100, 500) comprising: a medical system (in that the system of deals with magnetic resonance imaging data) comprising: - a memory storing machine executable instructions and an image processing module, wherein the image processing module comprises an image processing neural network portion (section IV.B "Reconstruction Methods": "the data-driven method considered was a U-Net based method") and an artificial structure prediction portion (section IV.D "Computation of Hallucination Maps"]), wherein the image processing module comprises an input configured for receiving magnetic resonance data (section IV.B "Reconstruction Methods": "the initial image estimate that was input to the U-Net was obtained by applying the pseudoinverse on the k-space data"), wherein the image processing neural network portion comprises a first output configured for outputting a corrected magnetic resonance image in response to receiving the magnetic resonance data at the input (section IV.B "Reconstruction Methods": "...learns a mapping from an initial image estimate that contains artifacts due to undersampling to an accurate estimate of the true object."), wherein the artificial structure prediction portion comprises a second output configured to output artificial structure data descriptive of a likelihood of artificial structures in the corrected magnetic resonance image (see in Figs 2 and 4 the (specific) null space hallucination maps), - the image structure prediction neural network portion being trained from a ground truth image collection of magnetic resonance images (section IV.B ibid.), a computational system, wherein execution of the machine executable instructions causes the computational system to: receive the magnetic resonance data (see section IV.B mentioned above); - receive the corrected magnetic resonance image at the first output and the artificial structure data at the second output in response to inputting the magnetic resonance data into the input of the image processing module (see sections IV.B and IV.D mentioned above); and - provide a warning signal depending on the artificial structure data meeting a predetermined criterion (Section VI "Summary and Conclusion": "derive objective figures-of-merit (FOMs) from ensembles of hallucination maps. Furthermore, the probability of occurrence of hallucinations can be potentially quantified from ensembles of hallucination maps"). Sayantan et al do not teach the following: the artificial structure prediction portion is trained from an aggregate ground truth data set that represents global image aspects. Zhang Jingke et al (Deep Null Space Learning Improves Dataset Recovery for High Frame Rate Synthetic transmit Aperture Imaging, IDS) teaches: the artificial structure prediction portion is trained from an aggregate ground truth data set that represents global image aspects (page 221 right column) Sayantan et al and Zhang Jingke et al are both in the field of image analysis, especially use of neural network to learn null-space of inversion data set such that combine outcome is predictable. Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Sayantan et al by Zhang Jingke et al would "enlarging the training dataset to reveal the whole distribution of the data" in order to "ensure the accuracy of null space component recovery" as disclosed by Zhang Jingke et al in page 221 right column. Claims 9-11: Sayantan et al detail in section IV.B "Reconstruction Methods". Sayantan et al discloses image data input to the U-Net (i.e. the claimed image processing neural network portion), but the claimed image processing module may be interpreted as comprising in addition the application of the pseudoinverse in Sayantan et al, thus being inputted with k-space data. Claim 13: Sayantan et al teach: The medical system o f any one of the precedingclaims claim 1, wherein the image processing neural network portion is configured for any at least one of the following: noise removal, artifact correction, motion correction, or de- blurring ,and combinations thereof (section IV.B "Reconstruction Methods") . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 2-3 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. 12-151-08 AIA 07-43 12-51-08 Claim s 4-6 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. 12-151-08 AIA 07-43 12-51-08 Claim s 7 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. 12-151-08 AIA 07-43 12-51-08 Claim s 8 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. 12-151-08 AIA 07-43 12-51-08 Claim 12 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. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gong et al (US 2019/0369191) teaches MRI Reconstruction Using Deep Learning, Generative Adversarial Network And Acquisition Signal Model - data y with a diagnostic imaging apparatus; linearly transforming the undersampled data y to obtain an initial image estimate {tilde over (x)}; applying the initial image estimate {tilde over (x)} as input to a generator network to obtain an aliasing artifact-reduced image x̆ as output of the generator network, where the aliasing artifact-reduced image x̆ is a projection onto a manifold of realistic images of the initial image estimate {tilde over (x)}; and performing an acquisition signal model projection of the aliasing artifact-reduced x̆ onto a space. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSUNG-YIN TSAI whose telephone number is (571)270-1671. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TSUNG YIN TSAI/Primary Examiner, Art Unit 2656 Application/Control Number: 18/839,548 Page 2 Art Unit: 2656 Application/Control Number: 18/839,548 Page 3 Art Unit: 2656 Application/Control Number: 18/839,548 Page 4 Art Unit: 2656 Application/Control Number: 18/839,548 Page 5 Art Unit: 2656 Application/Control Number: 18/839,548 Page 6 Art Unit: 2656 Application/Control Number: 18/839,548 Page 7 Art Unit: 2656 Application/Control Number: 18/839,548 Page 8 Art Unit: 2656 Application/Control Number: 18/839,548 Page 9 Art Unit: 2656 Application/Control Number: 18/839,548 Page 10 Art Unit: 2656
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