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 03/28/2025 and 04/15/2025 has been considered by the examiner.
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.
Claims 1-6 and 8-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20220114388 A1, published April 14, 2022), from IDS, in view of Nickisch et al. (US 20200402646 A1, published December 24, 2020), from IDS, hereinafter referred to as Li and Nickisch, respectively.
Regarding claim 1, and similarly for claims 10 and 16, Li teaches an image diagnostic system (Fig 1, system 3) comprising:
a catheter insertable into a blood vessel (see para. 0083 – “A catheter-based data collection probe 30 is introduced into the subject 4 and is disposed in the lumen of the particular blood vessel, such as for example, a coronary artery.”);
a memory that stores a program (see para. 0091 – “For example, the software modules 67 can be running on a processor at workstation 85 and the database 90 can be located in the memory of server 50.”); and
a processor configured to execute the program (see para. 0096 – “Various software modules that can include without limitation software, a component thereof, or one or more steps of a software-based or processor executed method can be used in a given embodiment of the disclosure.”) to:
control the catheter to acquire a tomographic image of a blood vessel (see para. 0080 – “Angiography system 20 is configured to noninvasively image the subject 4 such that frames of angiography data, typically in the form of frames of image data, are generated while a pullback procedure is performed using a probe 30 such that a blood vessel in region 25 of subject 4 is imaged using angiography in one or more imaging technologies such as OCT or IVUS, for example.”),
input the acquired image into a computer model to generate information that indicates a plurality of predetermined regions of the blood vessel in the image (Fig. 1F; see para. 0121 – “Once the MLS [machine learning system, computer model] is trained, inputting image data to the neural network is performed to generate a set of image data with predictions, detections, classifications, etc. of the various features/regions of interest. Step 105.”), and
using the generated information, determine the predetermined regions of the blood vessel in the acquired image, and output information indicating the determined regions (see para. 0156 – “An exemplary output Cartesian image of a patient artery that has been classified by an MLS [machine learning system] and one or more related methods is shown in FIGS. 3D and 3E in images B and D.”).
Li teaches inputting an image to a computer model to output information indication a plurality of regions of the blood vessel in the image, but does not explicitly teach also outputting information indicating whether the regions overlap.
Whereas, Nickisch, in an analogous field of endeavor, teaches the information further indicating for each of the predetermined regions whether it overlaps another region, the computer model having been trained with a plurality of tomographic images of blood vessels and a plurality of information each specifying the predetermined regions that can overlap in a corresponding one of the tomograph images (see para. 0012 – “The annotations which are performed for the medical image may result in a segmentation of the medical image into the plurality of regions of interest. The regions of interest may be overlapping or non-overlapping.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified inputting an image to a computer model to output information indication a plurality of regions of the blood vessel in the image, as disclosed in Li, by also outputting information indicating whether the regions overlap, as disclosed in Nickisch. One of ordinary skill in the art would have been motivated to make this modification in order to allow accurate delineation of coronary plaques, the lumen boundary and/or the media-adventitia border, as taught in Nickisch (see para. 0010).
Furthermore, regarding claims 2 and 17, Nickisch further teaches wherein the processor executes the program further to: determine a closed area in the acquired image in which two or more predetermined regions overlap, assign two or more labels corresponding to said two or more predetermined regions to the closed area, and store, in the memory, information that associates the closed area with the assigned labels (see para. 0012 – “The annotations which are performed for the medical image may result in a segmentation of the medical image into the plurality of regions of interest. The regions of interest may be overlapping or non-overlapping.”).
Furthermore, regarding claims 3 and 18, Li further teaches wherein the predetermined regions include at least two of a region inside a stent, a region of a lumen, and a region inside an external elastic membrane (see para. 0104 – “Each region/feature corresponding to lumen L, intima I, plaque Q, adventitia ADV, imaging probe P, media M, and others may be generated by the MLS using a trained NN such as a CNN.”).
Furthermore, regarding claims 4 and 19, Li further teaches wherein the predetermined regions further include at least one of a plaque region, a thrombus region, a hematoma region, and a medical device region (see para. 0104 – “Each region/feature corresponding to lumen L, intima I, plaque Q, adventitia ADV, imaging probe P, media M, and others may be generated by the MLS using a trained NN such as a CNN.”).
Furthermore, regarding claims 5 and 20, Li further teaches a display, wherein the processor executes the program further to control the display to display the information indicating the determined regions (see para. 0122 – “In one embodiment, the method includes displaying final predictive output images from neural network/machine learning system with class/type indicia. Step 108.”).
Furthermore, regarding claim 6, Li further teaches wherein the generated information indicates, for each of pixels of the input image, probabilities that the pixel corresponds to the predetermined regions (see para. 0122 – “In one embodiment, each of the K probability maps for reach of the different K classes/types, are compared on a per pixel basis and assessed such that each pixel for a given image frame is assessed and then a final predictive result is assigned to each pixel.”).
Furthermore, regarding claim 8, Li further teaches wherein the catheter includes:
a first sensor configured to transmit ultrasonic waves and receive the waves reflected by the blood vessel while the catheter is inserted in the blood vessel, and a second sensor configured to emit light and receive the light reflected by the blood vessel while the catheter is inserted in the blood vessel (see para. 0085 – “For example a combination OCT [second sensor] and IVUS [first sensor] data collection probe requires an OCT and IVUS PIU [patient interface unit 35].”), and
the processor executes the program to:
generate an ultrasonic tomographic image of the blood vessel based on the reflected waves received by the first sensor and an optical coherence tomographic image of the blood vessel based on the reflected light received by the second sensor (see para. 0085 – “For example a combination OCT and IVUS data collection probe requires an OCT and IVUS PIU…In this way, a blood vessel of the subject 4 can be imaged longitudinally or via cross-sections.”), and
input the generated images into the computer model to generate the information (Fig. 1F; see para. 0121 – “Once the MLS [machine learning system, computer model] is trained, inputting image data to the neural network is performed to generate a set of image data with predictions, detections, classifications, etc. of the various features/regions of interest. Step 105.”).
Furthermore, regarding claim 9, Li further teaches an angiography apparatus configured to generate an angiographic image of the blood vessel, wherein the catheter includes a marker that can be imaged by the angiography apparatus (see para. 0080 – “Angiography system 20 is configured to noninvasively image the subject 4 such that frames of angiography data, typically in the form of frames of image data, are generated while a pullback procedure is performed using a probe 30 such that a blood vessel in region 25 of subject 4 is imaged using angiography in one or more imaging technologies such as OCT or IVUS, for example.”; see para. 0083 – “The probe 30 typically includes a probe tip, one or more radiopaque markers, an optical fiber, and a torque wire.”).
Furthermore, regarding claim 11, Li further teaches wherein the processor executes the program to assign one or more labels each indicating one of the predetermined regions to a closed area in the tomographic image (see para. 0143 – “The user selected region for annotation in FIG. 3B corresponds to Media as shown by the class identifier selected in FIG. 3A. In this way, any feature/class can be selected for labelling and is stored in memory with the annotations.”).
Furthermore, regarding claim 12, Li further teaches an input device, wherein the processor executes the program to specify the closed area upon input of a designation thereof on the displayed image through the input device (see para. 0143 – “FIGS. 3A and 3B show user interfaces [indput device] for a system suitable for navigating through frames of image data and annotating image data to generate ground truths…In one embodiment, various drawing and editing tools can be used to annotate raw image data. These annotated image can be used to generate ground truth masks with the class or feature of the annotating region being defined and stored in memory using interface 305 of FIG. 3A.”).
Furthermore, regarding claim 13, Li further teaches wherein the GUI components include a plurality of buttons corresponding to the labels (Fig. 3A, “Add Calcium Label”, “Add Media Label”, “Add Lumen Label”, “Add “Feature/Class” Label” buttons on user interface 305).
Furthermore, regarding claim 14, Li further teaches wherein the labels indicate at least two of a stent, a lumen, and an external elastic membrane (see para. 0104 – “Each region/feature corresponding to lumen L, intima I, plaque Q, adventitia ADV, imaging probe P, media M, and others may be generated by the MLS using a trained NN such as a CNN.”).
Furthermore, regarding claim 15, Li further teaches wherein the labels further indicate at least one of a plaque, a thrombus, a hematoma, and a medical device (see para. 0104 – “Each region/feature corresponding to lumen L, intima I, plaque Q, adventitia ADV, imaging probe P, media M, and others may be generated by the MLS using a trained NN such as a CNN.”).
The motivation for claims 2 and 17 was shown previously in claims 1 and 16.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Nickisch, as applied to claim 6 above, and in further view of Shalev et al. (US 20170309018 A1, published October 26, 2017), hereinafter referred to as Shalev.
Regarding claim 7, Li in view of Nickisch teaches all of the elements disclosed in claim 6 above.
Li in view of Nickisch teaches determining probabilities that pixels correspond to the predetermined regions, but does not explicitly teach comparing the probabilities with thresholds to determine the predetermined regions.
Whereas, Shalev, in an analogous field of endeavor, teaches wherein the processor executes the program to compare the probabilities with thresholds to determine the predetermined regions (see para. 0046 – “The plurality of OVR [one-versus-the-rest] SVMs [support vector machines] includes an OVR-lipid (OVR-L) classifier, an OVR-calcium (OVR-C) classifier, and an OVR-fibrous (OVR-F) classifier. A member of the plurality of OVR SVMs classifies a voxel based on a probability threshold and the set of local features.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining probabilities that pixels correspond to the predetermined regions, as disclosed in Li in view of Nickisch, by also comparing the probabilities with thresholds to determine the predetermined regions, as disclosed in Shalev. One of ordinary skill in the art would have been motivated to make this modification in order to improve on conventional methods by more accurately classifying intravascular plaque, as taught in Shalev (see para. 0017).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Singanamalli et al. (US 20210068793 A1, published March 11, 2021) discloses the identification of the organ or region of interest within the organ may be based on reaching a probabilistic threshold of identification based on the output of the neural network.
Sadoughi et al. (US 20210192717 A1, published June 24, 2021) discloses an overlap between two boxes superimposed on an OCT image.
Merritt et al. (US 20220000427 A1, published January 6, 2022) discloses two lesions overlap within the vessel and the lesion may be classified as “multi”.
Kawasaki et al. (US 20090016483 A1, published January 15, 2009) discloses when plaque areas are present on the both sides, namely, in the front and the back of the blood-vessel core line, an overlap area of the plaque areas is displayed in purple.
Berkey (US 20130184584 A1, published July 18, 2013) discloses text labeling may be included overlapping the area of interest (i.e., the lumen) or positioned outside the anatomical structure or area of interest with a line pointing to the area of interest (i.e., the thrombosis).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nyrobi Celestine whose telephone number is 571-272-0129. The examiner can normally be reached on Monday - Thursday, 7:00AM - 5:00PM EST.
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/Nyrobi Celestine/Examiner, Art Unit 3798
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