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 .
Election/Restrictions
Applicant’s election without traverse of Claims 6-21 drawn to estimating arterial/coronary stenoses, in the reply filed on 1/14/2026 is acknowledged.
Drawings
The drawings were received on 12/08/2023. These drawings are accepted.
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 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process, mathematical calculation and data gathering without significantly more. The claims recite mathematical calculation with no additional limitations because the steps of “estimate features ” involve data used in a calculation. Further, given the broadest reasonable interpretation, the claims recite merely a mental process because the “machine learning model” is undefined in the specifications to be using “machine learning model” without further details. “produce training data” is also merely a mental process because humans can produce training data as well. Further, given the broadest reasonable interpretation, the claims recite merely data gathering step used in calculation because the “analyzing coronary angiograms” acquires data. This judicial exception is not integrated into a practical application because the claimed limitations are merely calculations to arrive at an answer. The processes, given the broadest reasonable interpretation, could potentially be applied using a pen and paper as a mental process. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed limitations do not integrate the mathematical procedure into a practical application.
To distinguish ineligible claims that merely recite a judicial exception from eligible claims that require an implementation of judicial exception, the Supreme Court uses a two-step framework: Step One (Step 2A), determine whether the claims at issue are directed to one of those patent-ineligible concepts; and Step Two (Step 28), if so, ask "what else is there in the claims?" to determine whether the additional elements transform the nature of the claim into a patent eligible application.
The first step I Prong One of Step One (Step 2A) to determine patent eligibility requires the determination of whether the claims at issue are directed to an enumerated patent ineligible concept.
Prong (1) requires the determination of (a) the specific limitations in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea and (b) determining whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated.
The enumerated patent ineligible concepts comprising:
(a) Mathematical Concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity - fundamental economic
principles / practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules/ instructions) and
(c) Mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Claims 18-21 recite a series of steps for estimating features from angiogram images. This judicial exception is not integrated into a practical application because the data gathering step, i.e. analyzing coronary angiograms, do not add a meaningful limitation to the system, method or computer readable medium as it is insignificant extra-solution data gathering activity and is nothing more than generally linking the product to a particular technological environment.
Accordingly, this judicial exception does not integrate the abstract idea into a practical application. Specifically, independent claim 18 only recites limitations directed at mathematical calculation and data gathering. It is not integrated into any practical application. Dependent claims 19-21 are only additional mathematical calculations or data manipulation and data gathering.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 6-8, 12-16 and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Flack et al. (US20240130702, hereinafter “Flack”)
Claim 6. Flack teaches A method for estimating arterial stenoses severity, the method comprising: ([0066] “system and method are able to detect the presence and severity of arterial narrowing (referred to as stenosis),”) classifying a primary anatomic structure ([0087] “The centreline tracker 34 is arranged to detect the four main coronary arteries first—the Left Main (LM), Left Anterior Descending (LAD), Left Circumflex (LCX) and the Right Coronary Artery (RCA)”) of one or more angiogram images of a first patient; ( [0045] “receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient”)
classifying a projection angle of the one or more angiogram images of the first patient; ([0125] “The screen 170 includes a long axis cross sectional view 172 of a coronary artery, and a transverse cross sectional view 174 of the coronary artery that shows inner 70 and outer 72 walls of the vessel.” The long axis cross sectional view and the transverse cross sectional view is understood to be the same as the claimed projection angles in light of instant specifications [0037])
labeling stenoses within the one or more angiogram images ([0091] “the detected coronary arteries have been labelled by the centreline labeller 46”) of the first patient (abstract “CT scan carried out on a patient,”) classified as including a left or right coronary artery; ([0087] “The centreline tracker 34 is arranged to detect the four main coronary arteries first—the Left Main (LM), Left Anterior Descending (LAD), Left Circumflex (LCX) and the Right Coronary Artery (RCA)”)
filtering out certain labels in the one or more angiogram images ([0125] “Using wall selection buttons 175, a user is able to select the inner 70 or outer 72 wall, which causes control points 176 to be displayed that are representative of the shape of the selected wall.”) based on certain classified projection angles; ([0125] “For example, as shown in FIG. 10, a vessel wall editing screen 170 may be used. The screen 170 includes a long axis cross sectional view 172 of a coronary artery, and a transverse cross sectional view 174 of the coronary artery that shows inner 70 and outer 72 walls of the vessel.”)
producing one or more angiogram images of a second patient with corresponding estimated stenoses of the second patient to produce training data; ([0097] “The training data in this example includes inner and outer artery walls and relevant imaging artefacts that have been annotated by medical experts, and covers a wide range of examples of different coronary vessels with varying degrees of disease”)
training a machine learning model with the training data; ([0097] “convolutional neural network (CNN) that is trained using ground truth training data”) and
estimating the arterial stenoses severity of the first patient by running the machine learning model on the filtered and labeled one or more angiogram images of the first patient, ([0127] “the alternative vessel wall editing screen 178 includes a stenosis level drop down box 180 that enables a user to select the appropriate stenosis range 182 for the displayed vessel or for an individual selected lesion.” And [0133] “In an example, a user desires to modify the determined stenosis level of a coronary artery vessel segment from a current level 25%-49% to a new level 1%-24%”) wherein the machine learning model is only run on angiogram images previously labeled as including stenoses. ([0145] “to identify potentially stenotic lesions. After identifying potentially stenotic lesions, the above methodology may be used to increase the stenosis range applicable to the vessel, vessel segment or individual lesion(s). The disease machine learning component may be any suitable machine learning component, such as a U-Net trained neural network.” And figure 13)
Claim 7. Flack teaches The method of claim 6, wherein classifying the primary anatomic structure, ([0087] “The centreline tracker 34 is arranged to detect the four main coronary arteries first—the Left Main (LM), Left Anterior Descending (LAD), Left Circumflex (LCX) and the Right Coronary Artery (RCA)”) classifying the projection angle, ([0100] “wall segmentation are shown in FIGS. 6A to 6C. FIG. 6A shows a ‘long-axis’ view 62 of a portion of an artery that includes coronary artery disease 63, in this example the view 62 reprojected so as to appear linear for ease of reference. FIGS. 6B and 6C show transverse cross-sectional views 66, 76—‘short-axis’ views—of the cardiac artery portion shown in FIG. 6A at different locations along the coronary artery.”) and labeling one or more relevant objects ([0090] “The identification process uses a centreline labelling machine learning component 48, that in this example comprises a supervised classifier, to label the coronary artery centrelines”) is performed using a machine learning technique. ([0085] “centreline tracking machine learning component 36” and [0097] “wall segmenter machine learning component 56 is a supervised volumetric convolutional neural network (CNN)” and [0119] “labelled using a centreline labelling machine learning component, as indicated at step 146.”)
Claim 8. Flack teaches The method of claim 6, further comprising segmenting the coronary artery by classifying each individual pixel of the one or more angiogram images of the first patient as vessel containing pixels and non-vessel containing pixels and omitting non-vessel containing pixels before estimating the arterial stenoses severity of the first patient. ([0139] “the vessel wall segmenter 54 may include an alternate machine wall segmenter machine learning component 56 that is trained to produce lumen masks indicative of an increased level of stenosis” )
Claim 12. Flack teaches The method of claim 6, wherein estimating the arterial stenoses ([0105] “the CAD analysis device 26 is arranged to assess different types of disease including arterial constriction, referred to as ‘stenosis’,”) severity of the first patient is performed ([0071] “quantify and characterise coronary artery disease in the CCTA image data, and produce reports indicative of the analysis.”) on multiple angiogram images previously labeled as including stenoses. ([0071] “coronary artery disease (CAD) analysis device 26 in communication with the data storage device 20 and arranged to analyse CCTA image data stored in the data storage device 20 to identify,”)
Claim 13. Flack teaches The method of claim 12, wherein the multiple angiogram images are consecutive frames of an angiogram. ([0121] “As indicated at steps 152 and 154, the CCTA data is sampled to produce image slice data”)
Claim 14. Flack teaches The method of claim 6, wherein primary anatomic structure of one or more angiogram includes a left coronary artery, a right coronary artery, bypass graft, catheter, pigtail catheter, left ventricle, aorta, radial artery, femoral artery, and/or pacemaker. ([0087] “The centreline tracker 34 is arranged to detect the four main coronary arteries first—the Left Main (LM), Left Anterior Descending (LAD), Left Circumflex (LCX) and the Right Coronary Artery (RCA)” and [0118] “a cardiac region in the CCTA data is then segmented to determine the predicted location of the ascending aorta”)
Claim 15. Flack teaches The method of claim 6, further comprising labeling anatomic coronary artery segments and/or additional angiographically relevant objects within the one or more angiogram images. ([0090] “the key features used by the classification process to label each coronary artery are the end location of the artery centreline remote from the aorta”)
Claim 16. Flack teaches The method of claim 15, wherein the anatomic coronary artery segments includes a proximal right coronary artery (RCA), middle RCA, distal RCA, posterior descending artery, left main artery, proximal left anterior descending artery (LAD), middle LAD, distal LAD, proximal left circumflex (LCX), and/or distal LCX. ([0087] “The centreline tracker 34 is arranged to detect the four main coronary arteries first—the Left Main (LM), Left Anterior Descending (LAD), Left Circumflex (LCX) and the Right Coronary Artery (RCA)—then after the main coronary arteries have been detected, an artery branch detector 44 detects branches on the primary coronary arteries that were not initially identified as viable centrelines.”)
Claim 18. Flack teaches A method of analyzing coronary angiograms, the method comprising: producing one or more coronary angiogram images ( [0045] “receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient”) with a corresponding estimated feature of the one or more coronary angiogram images to produce training data; ([0097] “The training data in this example includes inner and outer artery walls and relevant imaging artefacts that have been annotated by medical experts, and covers a wide range of examples of different coronary vessels with varying degrees of disease”)
training a machine learning model with the training data; ([0097] “convolutional neural network (CNN) that is trained using ground truth training data”) and
running the machine learning model on another one or more coronary angiogram
images to estimate features of the other one or more angiogram images. ([0127] “the alternative vessel wall editing screen 178 includes a stenosis level drop down box 180 that enables a user to select the appropriate stenosis range 182 for the displayed vessel or for an individual selected lesion.” And [0133] “In an example, a user desires to modify the determined stenosis level of a coronary artery vessel segment from a current level 25%-49% to a new level 1%-24%”)
Claim 19. Flack teaches The method of claim 18, wherein the estimated feature comprises coronary stenoses. ([0145] “to identify potentially stenotic lesions. After identifying potentially stenotic lesions, the above methodology may be used to increase the stenosis range applicable to the vessel, vessel segment or individual lesion(s). The disease machine learning component may be any suitable machine learning component, such as a U-Net trained neural network.” And figure 13)
Claim 20. Flack teaches The method of claim 18, wherein the estimated feature comprises anatomic coronary artery segments and/or additional angiographically relevant objects. ([0090] “the key features used by the classification process to label each coronary artery are the end location of the artery centreline remote from the aorta”)
Claim Rejections - 35 USC § 103
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 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 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 17 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Flack et al. (US20240130702, hereinafter “Flack”) and in view of Rongen et al (US20200372674, hereinafter “Rongen”)
Claim 17. Flack teaches The method of claim 15,
Flack does not explicitly teach wherein the additional angiographically relevant objects includes guidewires and/or sternal wires.
Rongen teaches wherein the additional angiographically relevant objects includes guidewires and/or sternal wires. ([0031] “detect at least one wire and/or guide wire in the at least one of the images of the vasculature structure”)
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Flack to have detecting guidewires as taught by Rongen to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to (Rongen et al Abstract “improve the determination of the position of stents in complicated situations.”)
Claim 21. Flack teaches The method of claim 20,
Flack does not explicitly teach wherein the additional angiographically relevant objects includes guidewires and/or sternal wires.
Rongen teaches wherein the additional angiographically relevant objects includes guidewires and/or sternal wires. ([0031] “detect at least one wire and/or guide wire in the at least one of the images of the vasculature structure”)
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Flack to have detecting guidewires as taught by Rongen to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to (Rongen et al Abstract “improve the determination of the position of stents in complicated situations.”)
Allowable Subject Matter
Claims 9-11 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Sutton et al US20190231316 teaches detecting coronary artery obstruction based on an ultrasound image
Zheng et al US20130216110 teaches extracting coronary artery centerlines to determine a quantification of stenosis
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/OWAIS I MEMON/Examiner, Art Unit 2663