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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 3, 2024 and April 25, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 4-9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nam, Hyeong Soo, et al. "Machine-learning-based biochemical characterization of atherosclerotic plaques in intravascular optical coherence tomography–fluorescence lifetime imaging." Diagnostic and Therapeutic Applications of Light in Cardiology 2021. Vol. 11621. SPIE, 2021. (hereinafter Nam).
Regarding independent claim 1, Nam discloses An operation method of an analysis device operated by at least one processor (abstract, “we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features;” time 3:27, “We have recently constructed a multimodal OCT system combined with multispectral FLIm whose specifications are very suitable for intracoronary imaging that requires high speed and high precision imaging.”), the operation method comprising:
receiving a fusion image (time 2:12, “So we decide to combine OCT fluorescence lifetime imaging, which are a robust measurement of the intrinsic biochemical properties of our biological sample;” time 4:30, “So in this study, we investigated to whether first our combined intravascular OCT-FLIm system is able to characterize high risk plaques in vivo in beating coronary arteries.”… time 8:36, “ This movie represent in vivo intracoronary OCT-FLIm imaging result from the atheromatous coronary artery of the swine module. ”); and
classifying tissue components in the fusion image using an artificial intelligence model (slide “research objective” seen below – specifically “tissue characterization”),
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wherein the fusion image includes first information obtained by imaging vascular tissue through an optical coherence tomography device (time 4:55, “ This custom built swept-source OCT system was successfully integrated with the high precision multispectral FLIm system in a fully synchronized manner.”), and second information obtained by imaging the vascular tissue through a fluorescence lifetime imaging device (time 4:55, “ This custom built swept-source OCT system was successfully integrated with the high precision multispectral FLIm system in a fully synchronized manner.”), and
the artificial intelligence model is a model trained to classify tissue components using structural features and fluorescence lifetime image information included in an input image (time 4:30, “a machine learning approach can be successfully integrated with the multispectral OCT-FLIm.;” time 10:09, “We stratified in vivo OCT axial images is based on well-known qualitative OCT characteristics. We found that there are significant differences in fluorescence lifetime signatures between lipid rich, fibrous plaques, and normal wall. In the lipid-rich inflamed plaque in the injured section, the fluorescence lifetime was significantly shortened, especially in the signal poor regions with the diffusion border and bright spot, which are the morphological pictures of lipid-rich inflamed plaque;” time 12:19, “So based on that multispectral FLIm can [INAUDIBLE] plaque components with statistical significance, we introduced the machine learning classifier that transformed the pictures of the multispectral FLIm parameters fluorescence lifetime of channel one, channel two, and intensity ratio into the five plaque component types.”).
Regarding dependent claim 4, the rejection of claim 1 is incorporated herein. Additionally, Nam further discloses wherein the second information includes fluorescence lifetime images of multi-channels mapped to emission light having different wavelengths, and each of the fluorescence lifetime images includes a fluorescence lifetime and a fluorescence intensity acquired in a corresponding one of the channels (time 4:55, “The OCT-FLIm system was constructed based on the custom built swept-source OCT system. The operating wavelength is centered on 1290 nanometers with the bandwidth of 110 nanometer, and the sweeping rate is 120 kilohertz, which allow high resolution and high speed OCT imaging. This custom built swept-source OCT system was successfully integrated with the high precision multispectral FLIm system in a fully synchronized manner.”).
Regarding dependent claim 5, the rejection of claim 1 is incorporated herein. Additionally, Nam further discloses wherein the tissue components include at least one of lipids, macrophages, smooth muscle cells, fibrous plaques, calcium, cholesterol crystals, and normal blood vessel walls (abstract, “Intravascular optical coherence tomography-fluorescence lifetime imaging (OCT-FLIm) provides co-registered structural and biochemical information of atherosclerotic plaques in a label-free manner. For intuitive image interpretation of OCT-FLIm, herein, we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features.”).
Regarding dependent claim 6, the rejection of claim 1 is incorporated herein. Additionally, Nam further discloses further comprising:
estimating an inflammatory response based on quantitative information of macrophages among the tissue components in the fusion image, and classifying tissue containing the macrophages as inflammatory tissue or lipid tissue mixed with inflammation (abstract, “we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features;” macrophages themselves are inflammatory response cells, thus classifying macrophages is classifying the inflammatory regions).
Regarding dependent claim 7, the rejection of claim 1 is incorporated herein. Additionally, Nam further discloses further comprising: detecting atherosclerotic plaques based on the tissue components in the fusion image (time 11:45, “This is the projected results of matching the OCT-FLIm images and the corresponding plaque component. Please note that this analysis is based on [INAUDIBLE] histopathological analysis to demonstrate how histologically identifiable plaque components contribute to OCT-FLIm imaging result to investigate alterations fluorescence lifetime according to OCT morphological pictures. We further statistically analyzed the main FLIm parameter values of each plaque component from the plaque ROIs;” abstract, “ we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features. Using dataset from in vivo atheromatous swine models, the classification accuracy was >92% for each plaque component according the five-fold cross validation. This highly translatable imaging strategy will open a new avenue for clinical intracoronary assessment of high-risk plaques.”)
Regarding dependent claim 8, the rejection of claim 7 is incorporated herein. Additionally, Nam further discloses further comprising: predicting a possibility of rupture of the atherosclerotic plaques based on the tissue components in the fusion image (time 14:03, “ As you can see, normal artery consists of almost 100% of normal tissue, whereas high risk atheromatous artery is highly heterogeneous with a high percentage of lipid and macrophages, which are closely associated with rupture of plaque. … So we can quantitative assess the plaque vulnerability using this OCT-FLIm based biochemical compound characterization. When comparing with these two arteries, below one can be considered as more stable and less vulnerable artery since proportion of components increasing the vulnerability such as lipid and macrophages is lower than that of the upper one;” high risk is read as possibility of rupture being higher).
Regarding dependent claim 9, the rejection of claim 8 is incorporated herein. Additionally, Nam further discloses wherein in the predicting of the possibility of rupture, the possibility of rupture is predicted based on a ratio between tissue components that increase the possibility of rupture and tissue components that contribute to stabilization, among the tissue components in the fusion image (time 14:03, “ As you can see, normal artery consists of almost 100% of normal tissue, whereas high risk atheromatous artery is highly heterogeneous with a high percentage of lipid and macrophages, which are closely associated with rupture of plaque. … So we can quantitative assess the plaque vulnerability using this OCT-FLIm based biochemical compound characterization. When comparing with these two arteries, below one can be considered as more stable and less vulnerable artery since proportion of components increasing the vulnerability such as lipid and macrophages is lower than that of the upper one”).
Claim(s) 10-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Park, Jesung, et al. "A dual-modality optical coherence tomography and fluorescence lifetime imaging microscopy system for simultaneous morphological and biochemical tissue characterization." Biomedical optics express 1.1 (2010): 186-200. (hereinafter Park).
Regarding independent claim 10, Park discloses An operation method of an analysis device operated by at least one processor (Figure 1, “central computer;” page 6, “An OCT/FLIM data acquisition program written in Labview (Labview8.6, National Instruments, Austin, TX) was developed to control the electronics, data acquisition boards and imaging parameters.”), the operation method comprising:
receiving a fusion image (page 6, “co-registered 3-D OCT volume data were acquired after a controlled raster scan with two galvanometers, and saved into the central main computer.”) including first information obtained by imaging vascular tissue through an optical coherence tomography device (page 6, “The program sent synchronized trigger signals to both the FLIM laser and the OCT camera, so that a pixel fluorescence decay acquisition coincides with the corresponding OCT A-line collection;” Figure 1, “OCT subsystem”), and second information obtained by imaging the vascular tissue through a fluorescence lifetime imaging device (page 6, “The program sent synchronized trigger signals to both the FLIM laser and the OCT camera, so that a pixel fluorescence decay acquisition coincides with the corresponding OCT A-line collection;” Figure 1, “FLIM subsystem”);
extracting structural features of the vascular tissue from the first information (page 10, “The OCT volume (2000 (x) x 2000 (y) x 650 (z) µm) in Fig. 4(a) showed a fairly uniform surface, a large core at the middle of the imaged section, and a thick section towards the right side of the volume. Structural evaluation of the artery tissue was performed on the cross-sectional OCT B-scan image (Fig. 4(b))”);
classifying tissue components of the vascular tissue using the structural features and fluorescence lifetime information included in the second information (page 10, “In Fig. 4(f-g) we also show the fluorescence lifetime map from the 390 nm band overlaid over the corresponding OCT volume of the thick cap fibroatheroma, and an ortho-sliced image. In these multimodal images we can clearly see two distinct regions of the plaque showing distinct morphology and biochemical composition, which are in agreement with the histopathological characteristics of a fibroatheroma and a fibrotic lesion.;” page 15, “It is widely accepted that earlier diagnosis of most diseases will decrease the associated morbidity and mortality. OCT and FLIM were identified for combination because of the complementary nature of the OCT morphological images and the FLIM biochemical maps. Following validation experiments with known fluorophores and scattering media we have begun to test our hypothesis for the classification of atherosclerotic plaques and the diagnosis of oral cancer”) ; and
detecting atherosclerotic plaques based on the components of the vascular tissue (Fig. 4. Dual-modal OCT and FLIM maps of an ex vivo calcified human atherosclerotic artery tissue).
Regarding dependent claim 11, the rejection of claim 10 is incorporated herein. Additionally, Park further discloses wherein the second information includes fluorescence lifetime images of multi-channels mapped to emission light having different wavelengths, and each of the fluorescence lifetime images includes a fluorescence lifetime and a fluorescence intensity acquired in a corresponding one of the channels (abstract, “The multispectral FLIM subsystem;” page 3, “Multispectral FLIM, as implemented here, directly measures the fluorescence temporal decay at multiple wavelengths by exciting with a high repetition rate pulsed UV laser and recording the emission on a high-speed microchannel plate photomultiplier tube (MCP-PMT) and high speed digitizer.”).
Regarding dependent claim 12, the rejection of claim 10 is incorporated herein. Additionally, Park further discloses wherein the tissue components include at least one of lipids (page 10, “These fluorescence characteristic resemble the emission of lipids, which is abundant inside the necrotic core”), macrophages, smooth muscle cells, fibrous plaques, calcium, cholesterol crystals, and normal blood vessel walls.
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) 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Nam as applied to claim 1 above, and further in view of Gessert, Nils, et al. "Plaque classification in coronary arteries from ivoct images using convolutional neural networks and transfer learning." arXiv preprint arXiv:1804.03904 (2018). (hereinafter Gessert).
Regarding dependent claim 2, the rejection of claim 1 is incorporated herein. Additionally, Nam discloses wherein the artificial intelligence model includes:
a (Slide, “research objective” see below; “feature extraction, labeling”);
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and a classifier which is trained to receive the structural features output from the (slide, “research objective,” and “tissue characterization;” see also slide “macrophage identification” below),
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and the optical coherence tomography image input to the (OCT data is well known to be able to be acquired in a polar domain (such as in intravascular OCT)).
Nam fails to explicitly disclose as further recited with respect to using a CNN. However, Gessert discloses using a CNN to extract features from an image in the field of plaque detection (page 2, “We employ convolutional neural networks (CNNs) to directly learn relevant features for plaque classification”).
Nam is directed toward “we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIM parameters and convolutional OCT features (abstract).” Gessert is directed toward “automatic plaque classification from entire IVOCT images, the cross-sectional view of the artery, using deep feature learning (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Nam and Gessert are directed toward similar methods of plaque classification. Further. Nam simply states their methodology uses machine learning, while Gessert allows for a specific CNN to be used. CNNs are well understood in the art to be efficient and accurate for image classification tasks. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to utilize the CNN of Gessert in order to output accurate image classification results in an efficient manner.
Regarding dependent claim 3, the rejection of claim 1 is incorporated herein. Additionally, Nam discloses wherein the artificial intelligence model is implemented as (time 4:30, “ in terms of automated and intuitive biochemical characterization to detect the high risk coronary atheroma, a machine learning approach can be successfully integrated with the multispectral OCT-FLIm;” time 15:27, “Our OCT-FLIm catheter imaging incorporated with machine learning classification of biochemical components can provide not only lipid chemogram but quantitative inflammatory burden index;” time 15:59, “our machine learning OCT-FLIm method can detect plaque macrophages with various OCT morphology, even macrophages residing [INAUDIBLE] which can be considered as early flux stages.”).
Nam fails to explicitly disclose as further recited with respect to using a CNN. However, Gessert discloses using a CNN to extract features from an image in the field of plaque detection (page 2, “We employ convolutional neural networks (CNNs) to directly learn relevant features for plaque classification”).
Nam is directed toward “we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIM parameters and convolutional OCT features (abstract).” Gessert is directed toward “automatic plaque classification from entire IVOCT images, the cross-sectional view of the artery, using deep feature learning (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Nam and Gessert are directed toward similar methods of plaque classification. Further. Nam simply states their methodology uses machine learning, while Gessert allows for a specific CNN to be used. CNNs are well understood in the art to be efficient and accurate for image classification tasks. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to utilize the CNN of Gessert in order to output accurate image classification results in an efficient manner.
Claim(s) 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Park as applied to claim 10 above, and further in view of Nam.
Regarding dependent claim 13, the rejection of claim 10 is incorporated herein. Additionally, Park fails to explicitly disclose further comprising:
estimating an inflammatory response based on quantitative information of macrophages among the tissue components of the vascular tissue, and classifying tissue containing the macrophages as inflammatory tissue or lipid tissue mixed with inflammation
However, Nam further discloses further comprising:
estimating an inflammatory response based on quantitative information of macrophages among the tissue components of the vascular tissue, and classifying tissue containing the macrophages as inflammatory tissue or lipid tissue mixed with inflammation (abstract, “we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features;” macrophages themselves are inflammatory response cells, thus classifying macrophages is classifying the inflammatory regions).
Park is directed toward, “We have developed a dual-modality system, incorporating optical coherence tomography (OCT) and fluorescence lifetime imaging microscopy (FLIM), that is capable of simultaneously characterizing the 3- D tissue morphology and its biochemical composition (abstract).” Nam is directed toward, “we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the invention, Park and Nam are directed toward similar methods of endeavor of OCT-FLIM imaging for diagnostic purposes. Further, Nam allows for quantification of inflammatory data through macrophage information (abstract, “we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features”). One of ordinary skill in the art before the effective filing date of the invention would be easily aware that different diseases or states are characterized according to a multitude of features. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate the teaching of Nam to quantify as many different cell types as possible to characterize the complete state of the region, and any diseases that may be present.
Regarding dependent claim 14, the rejection of claim 10 is incorporated herein. Additionally, Park fails to explicitly disclose further comprising: predicting a possibility of rupture of the atherosclerotic plaques based on the tissue components of the vascular tissue.
However, Nam discloses further comprising: predicting a possibility of rupture of the atherosclerotic plaques based on the tissue components of the vascular tissue (time 14:03, “ As you can see, normal artery consists of almost 100% of normal tissue, whereas high risk atheromatous artery is highly heterogeneous with a high percentage of lipid and macrophages, which are closely associated with rupture of plaque. … So we can quantitative assess the plaque vulnerability using this OCT-FLIm based biochemical compound characterization. When comparing with these two arteries, below one can be considered as more stable and less vulnerable artery since proportion of components increasing the vulnerability such as lipid and macrophages is lower than that of the upper one;” high risk is read as possibility of rupture being higher).
Park is directed toward, “We have developed a dual-modality system, incorporating optical coherence tomography (OCT) and fluorescence lifetime imaging microscopy (FLIM), that is capable of simultaneously characterizing the 3- D tissue morphology and its biochemical composition (abstract).” Nam is directed toward, “we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the invention, Park and Nam are directed toward similar methods of endeavor of OCT-FLIM imaging for diagnostic purposes. Further, Nam allows for quantification cells contributing to stabilization or destabilization (abstract, “we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features”). One of ordinary skill in the art before the effective filing date of the invention would be easily aware that many different cell types can relate to tissue stability. Further, tissue stability can lead to rupture, which is a serious health concern. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate the teaching of Nam to quantify as many different cell types as possible to characterize the complete state of the region, and specifically predict any issues as relating to a rupture as early as possible.
Regarding dependent claim 15, the rejection of claim 14 is incorporated herein. Additionally, Nam in the combination further discloses wherein in the predicting of the possibility of rupture, the possibility of rupture is predicted based on a ratio between tissue components that increase the possibility of rupture and tissue components that contribute to stabilization, among the tissue components in the fusion image (time 14:03, “ As you can see, normal artery consists of almost 100% of normal tissue, whereas high risk atheromatous artery is highly heterogeneous with a high percentage of lipid and macrophages, which are closely associated with rupture of plaque. … So we can quantitative assess the plaque vulnerability using this OCT-FLIm based biochemical compound characterization. When comparing with these two arteries, below one can be considered as more stable and less vulnerable artery since proportion of components increasing the vulnerability such as lipid and macrophages is lower than that of the upper one”).
It would be clear to one of ordinary skill in the art that the ratio of unstable cells compared to stable cells would impact the likelihood of rupture. Said differently, more unstable cells means more likelihood of rupture, and the converse also being true. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate the teaching of Nam to characterize the complete state of the region, and specifically determine the cells related to stability or instability to determine rupture potential early.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent No. 9,557,154 to Tearney et al. discloses, “For example, multi-modality imaging data can be processed and fused for further visualization and analysis. FIG. 5 shows an image fusion process for data obtained from the multi-modality catheter system and/or arrangement according to an exemplary embodiment of the present disclosure. As illustrated in FIG. 5, the simultaneous acquisition with rapid helical pull-back scanning, as provided in procedure 510, can be achieved by the exemplary multi-modality imaging system. For example, 3D microstructural information (procedure 520) can be obtained by OFDI and/or OCT procedures, and 2D molecular information (procedure 530) can be obtained by near infra-red fluorescence, fluorescence spectroscopy, Raman spectroscopy, and/or fluorescence lifetime imaging (column 8, line 66).”
The Examiner considered a rejection under 35 U.S.C. 101 with respect to the claims describing an abstract idea. However, upon analysis the Examiner felt that the analysis failed at Step 2, Prong Two because the specification described several improvements to the technological field. See the section titled “Advantageous Effects”, which describes numerous technological advancements/benefits to the claimed invention. See MPEP 2106.04(d) and the Memorandum “Advance notice of change to the MPEP in light of Ex Parte Desjardins” (12/05/25). https://www.uspto.gov/web/offices/pac/mpep/ANC-Desjardins-Memo-12-5-25.pdf
Contact
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/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661