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 .
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.
Claim(s) 1-4, 6, 8-13, 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mikhno et al. (US2017/0039706) in view of Fan et al. (“An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN”)
To claim 1, Mikhno teach a method for computing an arterial input function from a region of interest (paragraphs 0074), the method comprising:
a) obtaining a plurality of dynamic image data sets comprising volumetric image data from the region of interest over multiple scanning intervals (paragraphs 0101, 0346, 0348, 0455, 0460, 0520, dynamic/time-series PET imaging data);
b) utilizing an artificial neural network (paragraphs 0086, 0428, 0453, machine learning, which obvious for neural network utilization) to segment the plurality of dynamic image data sets displaying one or more arterial input function(s) (AIF) in the region(s) of interest (paragraphs 0325, 0460, automated segmentation techniques);
c) automatically estimating, using artificial intelligence, an arterial input function based on a plurality of dynamic image data sets combined with one or more-time activity curves (TAC) in the region(s) of interest in target organ(s) (paragraphs 0429, 0455-0456, 0460, 0464, 0479, 0484); and
d) computing a pre-trained predictive pharmacokinetic AI model arterial input function using a time activity curve input associated with the region(s) of interest of the target organ(s) (paragraphs 0083, 0454, 0461, 0485, 0532).
But, Mikhno do not expressly disclose said machine learning being a neural network.
Fan teach a method for computing an arterial input function from a region of interest (abstract), the method comprising: a) obtaining a plurality of dynamic image data sets comprising volumetric image data from the region of interest over multiple scanning intervals (page 2, page 5, Fig. 3); b) utilizing an artificial neural network to segment the plurality of dynamic image data sets displaying one or more arterial input function(s) (AIF) in the region(s) of interest (pages 3-4); c) automatically estimating, using artificial intelligence, an arterial input function based on a plurality of dynamic image data sets in the region(s) of interest in target organ(s); and d) computing a pre-trained predictive AI model arterial input function associated with the region(s) of interest of the target organ(s) (pages 5-9).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Fan into the method of Mikhno, in order to further neural network implementation by design preference.
To claim 19, Mikhno and Fan teach a method for computing an arterial input function (AIF) from a region of interest (ROI) (as explained in response to claim 1 above).
To claim 2, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the artificial neural network in step b) is a self-trained or un-supervised machine learning model (Fan, page 9, right column, unsupervised method).
To claim 3, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the artificial neural network in step b) is selected from the group consisting of multi-layer perceptron (MLP), artificial neural network (ANN), convolutional neural network (CNN) and/or 1D convolutional neural network (1D-CNN), recurrent neural network (RNN), long short-term memory recurrent neural network (LSTM-RNN), gated recurrent unit (GRU) network, generative adversarial networks (GANs), deep machine learning, reinforcement learning algorithm and/or combinations thereof (Fan, abstract, CNN).
To claim 4, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the pre-trained predictive pharmacokinetic AI model in step d) is used to estimate the pharmacokinetic parameters (Mikhno, paragraphs 0101, 0485-0488).
To claim 6, Mikhno and Fan teach claim 4.
Despite lack of disclosure, wherein the pharmacokinetic AI model can be selected from the group consisting of heart, brain, kidneys, lower extremities and/or combinations thereof would have been obviously recognized by one of ordinary skill in the art as a well-known practice in the art (Mikhno, paragraph 0120, teach organs that can be imaged according to the methods described herein include but are not limited to organs such as stomach, lymph nodes, heart, lung, liver, and prostate), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Mikhno and Fan into as specified with claimed feature by design preference, hence Official Notice is taken.
To claim 8, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the image data is characterized by administering Rb-82, O-15, N-13, Cu-62-PTSM, 99m-Tc-Sestamibi, Tl-201, and/or combinations thereof (Mikhno, paragraphs 0010, 0116).
To claim 9, Mikhno and Fan teach claim 1.Mikhno and Fan teach wherein the image-data is characterized by administering Rb-82 in rest and stress PET perfusion imaging to highlight small regional flow defects (Mikhno, paragraph 0455, uses radioactively tagged probes/radioligands for the in vivo quantification of blood flow, obviously perfusion imaging to highlight small regional flow defects is achieved; claimed Rb-82 elution as a radioligands is well-known in the art, which would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate by design preference, hence Official Notice is taken).
To claim 10, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the imaging agent or radionuclide is administered by an automated generation and infusion system and/or intravenous administration of radiopharmaceuticals produced by fission, neutron activation, cyclotron and/or generator (obvious in Mikhno, paragraphs 0006, 0105, 0117, 0303-0331; Fan, page 3).
To claim 11, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the automated radioisotope generation and infusion system comprises Rb-82 elution system (Mikhno, paragraphs 0006-0009, administering radioligand to a subject; claimed Rb-82 elution as a radioligands is well-known in the art, which would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate by design preference, hence Official Notice is taken).
To claim 12, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the pre-trained predictive pharmacokinetic AI model is a self-trained or un-supervised machine learning model (Fan, page 9, right column, unsupervised method).
To claim 13, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the method further comprises using the error of the predicted time activity curves from the observed time activity curves in the region of interest (ROI) for quality assurance (Mikhno, paragraphs 0088-0089, 0428, 0502, 507, 0509, 0514, 0527).
To claim 16, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the method further comprises generating a parametric map using a trained AIF-ROI segmentation (Mikhno, paragraph 0460; Fan, pages 4, 6, 9).
To claim 17, Mikhno and Fan teach claim 1.
Mikhno and Fan teach wherein the method further comprises generating one or more parametric maps using a trained AIF-ROI segmentation in combination with one or more parametric mapping methods (Mikhno, paragraph 0460; Fan, pages 2-6).
To claim 18, Mikhno and Fan teach claim 17.
Mikhno and Fan teach wherein the one or more parametric mapping methods can be selected from the group consisting of nonlinear least squares regression, basis function method, AI-based model for pharmacokinetic modelling or combinations thereof (Mikhno, paragraph 0480, nonlinear least square minimization).
Claim(s) 5, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mikhno et al. (US2017/0039706) in view of Fan et al. (“An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN”) and Shuler et al. (US2007/0015275).
To claim 5, Mikhno and Fan teach claim 4.
Though Mikhno teach one thru three compartment models (paragraphs 0082-0083, 0488), Mikhno and Fan do not expressly disclose wherein the pharmacokinetic modelling can be selected from the group consisting of one, two, three, or four tissue compartment model.
Shuler teach modeling multicompartmental cell culture system (paragraphs 0012, 0107) comprising two-compartment system (paragraphs 0081, 0185), three-compartment system (Fig. 17A, paragraphs 0117, 0198, 0200), and four-compartment system (Fig. 15, paragraphs 0195-0196), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the method of Mikhno and Fan, in order to expand modeling capability.
To claim7, Mikhno, Fan and Shuler teach claim 5.
Mikhno, Fan and Shuler teach wherein the one, two, three, or four tissue compartment model estimates the K1, k2, fractional blood volume, total blood volume and/or combinations thereof (Mikhno, paragraph 0440).
Claim(s) 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mikhno et al. (US2017/0039706) in view of Fan et al. (“An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN”) and Wang et al. (US2022/0286674).
To claim 14, Mikhno and Fan teach claim 13.
Mikhno teach minimizing the sum of squared error (paragraph 0502), but Mikhno and Fan do not expressly disclose wherein the error is mean squared error (MSE) with a threshold value.
Wang teach using mean squared error with a threshold value (paragraph 0066), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the method of Mikhno and Fan, in order to minimize error by design preference.
To claim 15, Mikhno, Fan and Wang teach claim 14.
Mikhno, Fan and Wang teach wherein the mean squared error (MSE) is used to determine the reliability of the region of interest (ROI) and derived AIF (obvious as minimizing error as explained in responses to claims 14 above).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mikhno et al. (US2017/0039706) in view of Fan et al. (“An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN”) and Khorsand et al. (“Assessment of myocardial perfusion by dynamic N-13 ammonia PET imaging: Comparison of 2 tracer kinetic models”).
To claim 20, Mikhno and Fan teach a method for computing an arterial input function (AIF) from a region of interest (ROI) (as explained in response to claim 1 above),
wherein the pre-trained predictive pharmacokinetic AI model is used for estimating the associated parameter to estimate mapping (Mikhno, paragraph 0008; Fan, page 7).
But Mikhno and Fan do not expressly disclose said associated parameter comprising K1, K2 and TBV to estimate myocardial flow reserve (MFR) map and/or coronary flow reserve (CFR) map.
Khorsand teach tracer kinetic model being used for estimating the associated parameter comprising K1, K2 and TBV to estimate myocardial flow reserve (MFR) and/or coronary flow reserve (CFR) (pages 410-417).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Khorsand into the method of Mikhno and Fan, in order to implement assessment of myocardial perfusion by design preference.
Conclusion
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 April 17, 2026
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