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 .
Response to Amendment
The amendment of 06/29/2026 has been entered and fully considered by the examiner. No claim is amended. Claims 1-24 are pending in the application with claims 1, 16 and 19 being independent.
Claim Rejections - 35 USC § 101
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.
Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 1, 16, and 19 are directed to an “method” which describes one of the four statutory categories of patentable subject matter, i.e., process.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One:
Claims 1, 16, and 19 recite (“sets forth” or “describes”) the abstract idea of a mental process, substantially as follows: applying the angiography images to a segmentation model configured to generate segmented images of the vessel tree; providing the segmented images to a contrast intensity model and, performing, by the contrast intensity model, a contrast intensity extraction on each of the segmented images to generate a contrast intensity profile of the vessel tree over the sampling time window; providing the contrast intensity profile to a microvasculature health model configured to determine a health of the microvasculature within the vessel tree based on the contrast intensity profile; and determining, using the microvasculature health model, a microvasculature health of microvasculature of the vessel tree.
In claims 1, 16 and 19, the above recited steps can be practically performed in the human mind, with the aid of a pen and paper or with a generic computer, in a computer environment, or merely using the generic computer as a tool to perform the steps. If a person were to visually examine, i.e., perform an observation, of the angiographic images and segment them by pen and paper or with the help of a generic computer, they could determine their health based on experience. There is nothing recited in the claim to suggest an undue level of complexity in how the images are segmented, how the intensity profile is generated or how the health of the vascular tree is determined. Therefore, a person would be able to perform the steps mentally or with a generic computer.
Prong Two: Claims 1, 16, and 19 do not include additional elements that integrate the mental process into a practical application.
This judicial exception is not integrated into a practical application. In particular, the claims recites (1) obtaining angiography images of the vessel tree in the vessel inspection region which have been captured over a sampling time window with a contract agent injected.
The steps in (1) represent merely data gathering or pre-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality with conventionally used tools (see below Step IIB for further details). They don’t positively recite either injection steps, or imaging steps, and merely disclose obtaining images captured.
As a whole, the additional elements merely serve to gather and feed information to the abstract idea and to output a notification based on the abstract idea, while generically implementing it on conventionally used tools. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the estimated bio-information is not outputted in any way such that a practical benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1, 16, and 19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims recite additional steps of obtaining the image data without positively reciting imaging the tissue using injection steps and imaging.
Accordingly, these additional steps amount to no more than insignificant conventional extra-solution activity. Mere insignificant conventional extra-solution activity cannot provide an inventive concept. The claims hence are The not patent eligible.
Dependent Claims
The following dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons:
Defining the type of model used (claims 2-3, 6-8, 13-16, 18, )
Details of segmentation step (claims 4-5).
Details of the intensity profile (claim 22)
The following dependent claims merely further describe the extra-solution activities and therefore, do not amount to significantly more than the judicial exception or integrate the abstract idea into a practical application for similar reasons:
Obtaining additional angiographic images (claims 9-12);
Obtaining additional health data (claim 17);
Using the output of the method (claims 23-24)
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-9, 13-15 and 18-23 are rejected under 35 U.S.C. 103 as being unpatentable over Isgum et al. (U.S. Publication No. 2018/0276817) hereinafter “Isgum” in view of Brauner et al. (U.S. Publication No. 2015/03002584) hereinafter “Brauner”.
Regarding claim 1, Isgum discloses a method of determining microvasculature function of a vessel inspection region [method of Isgum; see abstract, [002] and FIGs. 1-2], the method comprising:
obtaining angiography images of a vessel tree in the vessel inspection region, the angiography images captured over a sampling time window during which a contrast agent has been injected into the vessel tree, [see [0025], [0094], and FIG. 3 of Isgum; step 301] and
applying the angiography images to a segmentation model [neural network is used as a segmentation model; see [0094]] configured to generate segmented images of the vessel tree; [see [0030], [0095] and FIG.3, step 302 disclosing segmenting the images using a neural network (i.e. the segmentation model); see also FIG. 5 for the result of the segmentation]
providing the segmented images to a contrast intensity model [see [0096]; the segmented data is fed to the “machine-learned feature perfusion classification model”; see also [0089] disclosing that the features can be classified using intensity within segments; step 304 in FIG. 3] and,
performing, by the contrast intensity model, a contrast intensity extraction on each of the segmented images to generate features of vessel tree over the sampling time window; [see [0089]; the extraction of the feature vector over multiple segments of the vessel which represent intensity within the segments; the examiner notes that the term “contrast intensity profile” has been interpreted to correspond to the “feature vectors” using the broadest reasonable interpretation]
providing feature to a microvasculature health model [feature-perfusion classification model”] configured to determine a health of the microvasculature within the vessel tree based on the contrast intensity profile; and [see [0097]-[0098]; see FIG. 17 showing the classification model]
determining, using the microvasculature health model, a microvasculature health of microvasculature of the vessel tree. [see FIG. 17 and [0097]; step 306, a predication indicative of a coronary obstruction (i.e. heath of the vessel tree) is outputted by the model]
Isgum does not disclose that the feature generated is a contrast intensity profile of the vasculature.
Brauner, directed towards contrast imaging of the vascular tree in health diagnosis [see abstract of Brauner] further discloses that the feature generated is a contrast intensity profile of the vasculature. [see FIG. 3D and [0131] of Brauner; [0097] further discloses that the imaging can be done using contrast agents which would result in a contrast intensity profile]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum such that the feature being generated is the contrast intensity profile according to the teachings of Brauner in order to determine the center line of the vascular structure and determine its scale (size) and orientation [see [00120] of Brauner]
Regarding claim 2, Isgum further discloses that the segmentation model is a trained machine learning model. [see [0028] of Isgum disclosing a trained CNN neural network]
Regarding claim 3, Isgum further discloses that the trained machine learning model is a neural network. [see [0028] of Isgum disclosing a trained CNN neural network]
Regarding claim 4, Isgum further discloses that the segmentation model is configured to generate the segmented images as two-dimensional (2D) segmented images. [see [0111] of Isgum discloses extraction from 2D coronary tree geometry images using a 2D gaussian operator]
Regarding claim 5, Isgum further discloses that the segmentation model is configured to generate segmented three-dimensional (3D) images of the vessel tree from the angiography images. [see [0020]-[0021], [0103] and [0111] of Isgum discloses extraction from 3D coronary tree geometry images]
Regarding claim 6, Isgum as modified by Brauner discloses all the limitations of claim 1 [see rejection of claim 1]
Brauner further discloses that the contrast intensity model is configured to determine a pixel metric for each segmented image and to determine a rising trend and a falling trend of the pixel metric over the sampling time window for the segmented images, the contrast intensity profile comprising the rising trend and the falling trend. [see FIG. 3D and [0131]; the images show the pixel intensity across the vessel and have a rise and fall trend as can be seen in FIG. 3D]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum such that the contrast intensity model is configured to determine a pixel metric for each segmented image and to determine a rising trend and a falling trend of the pixel metric over the sampling time window for the segmented images, the contrast intensity profile comprising the rising trend and the falling trend according to the teachings of Brauner in order to determine the center line of the vascular structure and determine its scale (size) and orientation [see [00120] of Brauner]
Regarding claim 7, Isgum further discloses that the contrast intensity model is a machine learning model. [see [0028] of Isgum disclosing a trained CNN neural network]
Regarding claim 8, Isgum further discloses that the angiography images comprise images of the vessel tree in both (i) a baseline state [see [0087] of Isgum discloses ultrasound images comprising baseline perfusion state] and (ii) a hyperemic state. [see [0073] of Isgum]
Regarding claim 9, Isgum further discloses that determining, by the contrast intensity model, contrast intensity profiles for (i) angiography images of the vessel tree in the baseline state, [see [0087] of Isgum discloses ultrasound images comprising baseline perfusion state] and (ii) angiography images of the vessel tree in the hyperemic state, [see [0073] of Isgum] and wherein determining the microvasculature health comprises determining at least one of microvasculature resistance reserve [see [0129] of Isgum discloses using microcirculatory resistance and claim 18] or coronary flow reserve [see [0129] of Isgum discloses determining fractional flow reserve (FFR) and claim 18] from the contrast intensity profiles of the baseline and hyperemic states. [see claim 18 and [0073]]
Regarding claim 13, Isgum further discloses that the microvasculature health model comprises a trained machine learning algorithm trained on training angiography images and at least one of (i) index of micro-circulatory resistance data, [see [0129] of Isgum discloses using microcirculatory resistance and claim 18] (ii) coronary flow reserve data, [see [0129] of Isgum discloses determining fractional flow reserve (FFR) and claim 18] and (iii) microvascular resistance reserve data corresponding to the training angiography images, multi-physics simulation data corresponding to the training angiography images, and contrast intensity data. [see [0129] of Isgum discloses determining fractional flow reserve (FFR) and claim 18]
Regarding claim 14, Isgum further discloses that the microvasculature health model is trained to generate at least one of an index of micro-circulatory resistance, a coronary flow reserve value, and a microvascular resistance reserve value for the vessel tree. [see [0129] of Isgum discloses determining fractional flow reserve (FFR) and claim 18]
Regarding claim 15, Isgum further discloses that the microvasculature health model comprises an encoder stage for receiving the contrast intensity profile [see [0100]-[0102] of Isgum] and a multilayer perceptron stage fed by the encoder and trained to generate at least one of a predicted index of microcirculatory resistance[see [0129] of Isgum discloses using microcirculatory resistance and claim 18], a predicted coronary flow reserve value, [see [0129] of Isgum discloses determining fractional flow reserve (FFR) and claim 18] and a predicted microvascular resistance reserve value as an indicator of the microvasculature health of the vessel tree.
Regarding claim 18, Isgum further discloses generating angiography images using a multi-physics model of a contrast injection through a vessel tree, the vessel tree having at least one coronary vessel branching into microvasculature vessels [see [0108] of Isgum disclosing generating a vascular tree], the generated angiography images being binarized,[see [0122]-[0124] of Isgum] and the generated angiography images corresponding to scenarios (i) baseline health microcirculation with baseline values of microvascular resistance [see [0082]; a baseline is determined for the vessel perfusion], (ii) moderate disease microcirculation with moderate values of microvascular resistance, [see [0087]-[0088] of Isgum; the severity of the vessel obstruction is determined] and (iii) severe disease microcirculation with high levels of microvascular resistance; [see [0087]-[0088] of Isgum; the severity of the vessel obstruction is determined] providing the generated angiography images to the contrast intensity model for generating contrast intensity profiles; [see [0096]; the segmented data is fed to the “machine-learned feature perfusion classification model”; see also [0089] disclosing that the features can be classified using intensity within segments; step 304 in FIG. 3] and providing the generated contrast intensity profile to the machine learning model to train the machine learning model to generate the microvasculature health of the vessel tree in the subsequently imaged vessel inspection region. [see [0126] of Isgum discloses training of the machine learning model]
Regarding claim 19, Isgum discloses a method of assessing microvasculature function of a vessel inspection region for predicting a treatment response [method of Isgum; see abstract, [002] and FIGs. 1-2], the method comprising:
obtaining angiography images of a vessel tree in the vessel inspection region, the angiography images captured over a sampling time window during which a contrast agent has been injected into the vessel tree, and [see [0025], [0094], and FIG. 3 of Isgum; step 301]
applying the angiography images to a a segmentation model [neural network is used as a segmentation model; see [0094]] configured to generate segmented images of the vessel tree; [see [0030], [0095] and FIG.3, step 302 disclosing segmenting the images using a neural network (i.e. the segmentation model); see also FIG. 5 for the result of the segmentation]
providing the segmented images to a contrast intensity model and, performing, by the contrast intensity model, a contrast intensity extraction on each of the segmented images to generate a contrast intensity profile of the vessel tree over the sampling time window; see [0096]; the segmented data is fed to the “machine-learned feature perfusion classification model”; see also [0089] disclosing that the features can be classified using intensity within segments; step 304 in FIG. 3]
providing at least a portion of the contrast intensity profile to a microvasculature health model [feature-perfusion classification model”] configured to predict a response of the vessel inspection region to a treatment based on a characteristic of the at least a portion of the contrast intensity profile; and [see [0097]-[0098]; see FIG. 17 showing the classification model]
generate an electronic indication of the predicted response to the treatment. [see FIG. 17 and [0097]; step 306, a predication indicative of a coronary obstruction (i.e. heath of the vessel tree) is outputted by the model]
Isgum does not disclose that the feature generated is a contrast intensity profile of the vasculature.
Brauner, directed towards contrast imaging of the vascular tree in health diagnosis [see abstract of Brauner] further discloses that the feature generated is a contrast intensity profile of the vasculature. [see FIG. 3D and [0131] of Brauner; [0097] further discloses that the imaging can be done using contrast agents which would result in a contrast intensity profile]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum such that the feature being generated is the contrast intensity profile according to the teachings of Brauner in order to determine the center line of the vascular structure and determine its scale (size) and orientation [see [00120] of Brauner]
Regarding claim 20, Isgum as modified by Brauner discloses all the limitations of claim 1 [see rejection of claim 1]
Brauner further discloses that the at least a portion of the contrast intensity profile is a downslope of the contrast intensity profile. [see FIG. 3D and [0131]; the images show the pixel intensity across the vessel and have a rise and fall trend as can be seen in FIG. 3D]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum such that the at least a portion of the contrast intensity profile is a downslope of the contrast intensity profile. according to the teachings of Brauner in order to determine the center line of the vascular structure and determine its scale (size) and orientation [see [00120] of Brauner]
Regarding claim 21, Isgum as modified by Brauner discloses all the limitations of claim 1 [see rejection of claim 1]
Brauner further discloses that the at least a portion of the contrast intensity profile is a portion of the contrast intensity profile isolated from an upslope portion of the contrast intensity profile. [see FIG. 3D and [0131]; the images show the pixel intensity across the vessel and have a rise and fall trend as can be seen in FIG. 3D]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum such that the at least a portion of the contrast intensity profile is a portion of the contrast intensity profile isolated from an upslope portion of the contrast intensity profile according to the teachings of Brauner in order to determine the center line of the vascular structure and determine its scale (size) and orientation [see [00120] of Brauner]
Regarding claim 22, Isgum as modified by Brauner discloses all the limitations of claim 1 [see rejection of claim 1]
Brauner further discloses that the microvasculature health model is configured to predict the response of the vessel inspection region to the treatment based on a downslope of the contrast intensity profile. [see FIG. 3D and [0131]; the images show the pixel intensity across the vessel and have a rise and fall trend as can be seen in FIG. 3D]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum such that the vessel inspection region to the treatment based on a downslope of the contrast intensity profile according to the teachings of Brauner in order to determine the center line of the vascular structure and determine its scale (size) and orientation [see [00120] of Brauner]
Regarding claim 23, Isgum further discloses that the treatment is a medical procedure selected from the group consisting of a bypass procedure [see [0016] and [0096] of Isgum], coronary microvascular intervention, mechanical or ultrasound-based thrombectomy, angiogenesis therapy, stenting, [see [0017] disclosing using stents as a solution] or venous occluder.
Claims 10-12 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Isgum et al. (U.S. Publication No. 2018/0276817) hereinafter “Isgum” in view of Brauner et al. (U.S. Publication No. 2015/03002584) hereinafter “Brauner” as applied to claim 1 above, and further in view of Behar et al (U.S. Publication No. 2017/0372474) hereinafter “Behar”.
Regarding claim 10, Isgum as modified by Brauner discloses all the limitations of claim 1 [see rejection of claim 1]
Isgum as modified by Brauner further discloses providing the second set of segmented images to the contrast intensity model [see [0096]; the segmented data is fed to the “machine-learned feature perfusion classification model”; see also [0089] disclosing that the features can be classified using intensity within segments; step 304 in FIG. 3] and, performing, by the contrast intensity model, the contrast intensity extraction on each of the second set of segmented images [see [0089]; the extraction of the feature vector over multiple segments of the vessel which represent intensity within the segments; the examiner notes that the term “contrast intensity profile” has been interpreted to correspond to the “feature vectors” using the broadest reasonable interpretation]and generating a second contrast intensity profile of the vessel tree over the second sampling time window; ; [see FIG. 3D and [0131] of Brauner; [0097] further discloses that the imaging can be done using contrast agents which would result in a contrast intensity profile] and providing the second contrast intensity profile to the microvasculature health model. [see [0097]-[0098]; see FIG. 17 showing the classification model]
Isgum as modified by Brauner does not disclose obtaining additional angiography images of the vessel tree captured over a second sampling time window during which the contrast agent has been injected into the vessel tree, the additional angiography images captured at a different angle than the angiography images; providing the additional angiography images to the segmentation model to generate a second set of segmented images of the vessel tree;
Koning, directed towards a multi perspective vascular tree image creation [see abstract of Koning] further discloses obtaining additional angiography images of the vessel tree captured over a second sampling time window during which the contrast agent has been injected into the vessel tree, the additional angiography images captured at a different angle than the angiography images; [see [0021]-[0023] of Koning] providing the additional angiography images to the segmentation model to generate a second set of segmented images of the vessel tree; [see [0020] of Koning; the images are fed to a segmentation model]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum as modified by Brauner such that obtaining additional angiography images of the vessel tree captured over a second sampling time window during which the contrast agent has been injected into the vessel tree, the additional angiography images captured at a different angle than the angiography images; providing the additional angiography images to the segmentation model to generate a second set of segmented images of the vessel tree according to the teachings of Koning in order to improve the accuracy of the visualization in various angles using a second image data set from a different angle [see [0007] of Koning]
Regarding claim 11, Isgum as modified by Brauner discloses all the limitations of claim 1 [see rejection of claim 1]
Isgum as modified by Brauner further discloses providing the second set of segmented images to the contrast intensity model and, [see [0096]; the segmented data is fed to the “machine-learned feature perfusion classification model”; see also [0089] disclosing that the features can be classified using intensity within segments; step 304 in FIG. 3] performing, by the contrast intensity model, the contrast intensity extraction on each of the second set of segmented images [see [0089]; the extraction of the feature vector over multiple segments of the vessel which represent intensity within the segments; the examiner notes that the term “contrast intensity profile” has been interpreted to correspond to the “feature vectors” using the broadest reasonable interpretation] and generating a second contrast intensity profile of the vessel tree over the second sampling time window; [see FIG. 3D and [0131] of Brauner; [0097] further discloses that the imaging can be done using contrast agents which would result in a contrast intensity profile]
Koning further discloses obtaining additional angiography images of the vessel tree captured over a second sampling time window during which the contrast agent has been injected into the vessel tree, the additional angiography images captured at a different angle than the angiography images, providing the additional angiography images to the segmentation model to generate a second set of segmented images of the vessel tree [see [0021]-[0023] of Koning disclosing that a second image of the vascular tree is taken at a different angle]; comparing the contrast intensity profile and the second contrast intensity profile to reference contrast intensity profiles to identify one of the contrast intensity profile and the second contrast intensity profile as having a greater correlation to the reference contrast intensity profiles; and providing the one of the identified contrast intensity profile or second contrast intensity profile having the greater correlation to the microvasculature health model. [see [0023]-[0025]; the first and second images are compared ]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum as modified by Brauner such that discloses obtaining additional angiography images of the vessel tree captured over a second sampling time window during which the contrast agent has been injected into the vessel tree, the additional angiography images captured at a different angle than the angiography images, providing the additional angiography images to the segmentation model to generate a second set of segmented images of the vessel tree; comparing the contrast intensity profile and the second contrast intensity profile to reference contrast intensity profiles to identify one of the contrast intensity profile and the second contrast intensity profile as having a greater correlation to the reference contrast intensity profiles; and providing the one of the identified contrast intensity profile or second contrast intensity profile having the greater correlation to the microvasculature health model according to the teachings of Koning in order to improve the accuracy of the visualization in various angles using a second image data set from a different angle [see [0007] of Koning]
Regarding claim 12, Isgum as modified by Brauner discloses all the limitations of claim 1 [see rejection of claim 1]
Koning further disclose that the sampling time window extends from an initial injection of the contrast agent into the vessel tree through washout of the contrast agent from the vessel tree. [see [0021]-[0023] of Koning]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum as modified by Brauner such that the sampling time window extends from an initial injection of the contrast agent into the vessel tree through washout of the contrast agent from the vessel tree according to the teachings of Koning in order to in order to improve the accuracy of the visualization in various angles using a second image data set from a different angle [see [0007] of Koning]
Regarding claim 16, Isgum discloses a computer-implemented method for training a microvasculature health determination system, [method of Isgum; see abstract, [002] and FIGs. 1-2], the method comprising:
obtaining angiography images of a plurality of vessel inspection regions from different subjects, [see [0025], [0094], and FIG. 3 of Isgum; step 301]
obtaining vasculature health data for each of the angiography images;[see [0038] of Isgum]
performing a segmentation on each of the angiography images to generate a segmented image for each angiography image; [see [0030], [0095] and FIG.3, step 302 disclosing segmenting the images using a neural network (i.e. the segmentation model); see also FIG. 5 for the result of the segmentation]
providing the segmented images to a contrast intensity model configured perform a contrast intensity extraction on each of the segmented images to generate a contrast intensity profile of a vessel tree in the vessel inspection region over a sampling time window; see [0096]; the segmented data is fed to the “machine-learned feature perfusion classification model”; see also [0089] disclosing that the features can be classified using intensity within segments; step 304 in FIG. 3] and
providing the contrast intensity profile and the vasculature health data to a machine learning model [feature-perfusion classification model”] to train the machine learning model to generate a microvasculature health of a vessel tree in a subsequently imaged vessel inspection region. [see [0126] of Isgum discloses training of the machine learning model]
Isgum does not disclose that the feature generated is a the angiography images including subsets of angiography images captured over a full contrast agent injection cycle through corresponding vessel inspection regions, the angiography images include subsets of angiography images captured at different perspective views of corresponding vessel inspection regions and that contrast intensity profile of the vasculature.
Koning, directed towards a multi perspective vascular tree image creation [see abstract of Koning] further discloses the angiography images including subsets of angiography images captured over a full contrast agent injection cycle through corresponding vessel inspection regions, the angiography images include subsets of angiography images captured at different perspective views of corresponding vessel inspection regions; [see [0021]-[0023] of Koning]
Brauner, directed towards contrast imaging of the vascular tree in health diagnosis [see abstract of Brauner] further discloses that the feature generated is a contrast intensity profile of the vasculature. [see FIG. 3D and [0131] of Brauner; [0097] further discloses that the imaging can be done using contrast agents which would result in a contrast intensity profile]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum as modified by Brauner such that the angiography images including subsets of angiography images captured over a full contrast agent injection cycle through corresponding vessel inspection regions, the angiography images include subsets of angiography images captured at different perspective views of corresponding vessel inspection regions according to the teachings of Koning in order to in order to improve the accuracy of the visualization in various angles using a second image data set from a different angle [see [0007] of Koning]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum such that the feature being generated is the contrast intensity profile according to the teachings of Brauner in order to determine the center line of the vascular structure and determine its scale (size) and orientation [see [00120] of Brauner]
Regarding claim 17, Isgum further discloses that the vasculature health data comprises at least one of index of micro-circulatory resistance data, [see [0129] of Isgum discloses using microcirculatory resistance and claim 18] (ii) coronary flow reserve data, [see [0129] of Isgum discloses determining fractional flow reserve (FFR) and claim 18], and microvascular resistance reserve data.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Isgum et al. (U.S. Publication No. 2018/0276817) hereinafter “Isgum” in view of Brauner et al. (U.S. Publication No. 2015/03002584) hereinafter “Brauner” as applied to claim 19 above, and further in view of Hageman et al. (US Publication No. 2014/0303013) hereinafter “Hagemean”.
Regarding claim 24, Isgum as modified by Brauner discloses all the limitations of claim 1 [see rejection of claim 1]
Isgum as modified by Brauner does not disclose that the treatment is a pharmacological treatment selected from the group consisting of an antiplatelet agent, an anticoagulant agent, a vasodilator agent, anti-inflammatory agent, a statin, nitroglycerin, calcium channel blockers, beta-blockers, ACE inhibitors, or other anti-anginal medications.
Hageman, directed towards vascular diagnosis and treatment [see abstract of Hageman] further discloses that the treatment is a pharmacological treatment selected from the group consisting of an antiplatelet agent, an anticoagulant agent, a vasodilator agent, anti-inflammatory agent, a statin, nitroglycerin, calcium channel blockers, beta-blockers, ACE inhibitors, or other anti-anginal medications [see [0273] of Hageman listing the medications including anti-inflammatory medication]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Isgum as modified by Brauner such that the treatment is a pharmacological treatment selected from the group consisting of an antiplatelet agent, an anticoagulant agent, a vasodilator agent, anti-inflammatory agent, a statin, nitroglycerin, calcium channel blockers, beta-blockers, ACE inhibitors, or other anti-anginal medications according to the teachings of Min in order to treat vascular symptoms associated with Maculopathy. [see [0003] of Hageman]
Response to Arguments
Applicant's arguments filed 06/29/2026 have been fully considered but they are not persuasive.
Arguments regarding rejection under U.S.C. 101
Regarding Step 2A, prong one of the analysis, the applicant has argued that steps of applying the images to a segmentation model, a contrast intensity model and microvasculature heath model can not be performed in the mind of the person or with a pen and paper as the models in the specification are not capable of being used in the mind.
The examiner respectfully disagrees and notes that even though applications are examined in light of the specification, the specification is not brought into the claims and the limitations of the claims are interpreted in the broadest reasonable interpretation. Here, a trained practitioner’s mind can easily be an equivalent of a trained model which can segment the images into different areas, examine the contrast of the image based on previous training and experience and arrive at a diagnosis of the heath of the microvasculature based on their experience. This is what is being done routinely in a doctor’s office and a person is certainly capable of performing that in their mind. The claims recite the segmentation and contrast modeling in a very high level and general meaning and fail to recite any specific details which would require computation beyond what can be done in a human brain.
The applicant has further argued that the examiner has failed to consider the limitation concerning “obtaining angiographic images” since this can not be a mental process and the claim should be considered as a whole
In response, the examiner respectfully disagrees and notes that all limitations of the claim are considered. However, the limitations concerning the analysis and diagnosis of the data are directed towards an abstract idea while the recited limitation is directed towards feeding information into the diagnosis routine and is considered to be a pre-solutionary activity and is analyzed in step 2B. the examiner further notes that the claim merely requires “obtaining of the angiographic images”. Therefore, the limitation does not include performing angiographical procedures and merely requires acquiring such images. A practitioner could simply obtain printed angiographic images and observe them or acquire their computer files and use them for the following steps of analysis and diagnosis. Therefore, firstly, the recited step can be performed in the human mind (by looking at image data) and further it is a pre-solutionary activity since it gathers data for the following abstract idea steps.
Arguments regarding rejection under U.S.C. 103
Regarding claim 1, the applicant has argued that the claim is directed towards microvasculature function while the prior art used (Isgum and Brauner) are not concerned with such small sized vasculature.
The examiner respectfully disagrees and notes that the primary reference Isgum, clearly discloses in various paragraphs the importance of the study of microvasculature of the myocardium [see for example [0098] and [0117]]. Further, Isgum expressly discloses detecting the index of microcirculatory resistance in order to assess the microvascular integrity [see [0129] of Isgum]. Therefore, not only Isgum is directed towards the study of health of microvasculature, but it also expressly discloses using parameters that are directly use to assess its health.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJAN - SABOKTAKIN whose telephone number is (303)297-4278. The examiner can normally be reached M-F 9 am-5pm CT.
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/MARJAN SABOKTAKIN/Examiner, Art Unit 3797
/MICHAEL J CAREY/Supervisory Patent Examiner, Art Unit 3795
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