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 .
Status of Claims
Claims 1-10 are pending; and Claims 11-20 have been withdrawn from consideration as non-elected claims.
Response to Arguments
Applicant’s arguments presented in Pages 6-8 of its Reply have been found persuasive. Accordingly, the rejections of record under 35 U.S.C. 102(a)(1) and 35 U.S.C. 103 have been withdrawn. New analyses of claims are presented in the sections below.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
a. Determining the scope and contents of the prior art.
b. Ascertaining the differences between the prior art and the claims at issue.
c. Resolving the level of ordinary skill in the pertinent art.
d. 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 1-2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Deep Learning-based Hemodynamic Prediction of Carotid Artery Stenosis Before and After Surgical Treatments” – Published January 10, 2023) in view of Itu et al. (US 2018/0310888 - IDS).
Consider Claim 1, Wang discloses “A method for predicting stroke risk with an artificial intelligence-based medical system” (Wang, Abstract discloses: “Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical
significance in the diagnosis, prevention, and treatment prognosis of ischemic
strokes.” In addition, Wang’s Abstract discloses using deep learning (DL) to implement the mapping of anatomic geometries and computational fluid dynamics (CFD) driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications”. In addition, Section 2.5 in Page 5 discloses “This study is attributed to developing a DL strategy to
implement the mapping of anatomic geometries and CFD driven flow fields to achieve the hemodynamic prediction of 3D carotid artery stenosis (CAS) before and after surgical treatments”), “the method comprising: acquiring values of parameters representing a geometrical shape of a carotid artery (Wang, Page 3, Section 2.2 discloses “The raw CTA data of the carotid arteries for 298 subjects who visited Beijing Friendship Hospital in 2021 and 2022 to examine the cerebral and carotid arteries were collected …”(emphasis added)); ““generating flow information by location within the geometrical shape, the flow information generated as an output of an artificial intelligence in response to input of the values of the parameters to the artificial intelligence” (Wang, Page 5, Section 2.6, wherein the convolutional neural network decoding operation is disclosed and the disclosure is made: “By employing the network with the two matched point clouds to bridge the fluid’s overall cavity and spatial coordinates, the flow field data of velocity
and pressure at each point, can be substantially determined” (emphasis added)); “.”); “and predicting, by an image processor, the stroke risk from the flow information” (Wang, Page 10, Section 4 discloses: “In this study, we proposed a DL strategy for the first time to predict the 3D and unsteady hemodynamics of stenotic carotid arteries before and after surgical treatments (i.e., cavity change). Page 2, Section 1. discloses the relationship between carotid artery stenosis with stroke: “The primary reason for ischemic stroke is the blockage of the common carotid artery (CCA) or internal carotid artery (ICA) induced by atherosclerosis, also known as carotid artery stenosis (CAS), which causes intracranial reduced blood supply.”) Although Wang discloses “The raw CTA data of the carotid arteries for 298 subjects who visited Beijing Friendship Hospital in 2021 and 2022 to examine the cerebral and carotid arteries were collected” (Wang, Page 3, Section 2.2); Wang does not explicitly disclose “acquiring values of parameters representing a geometrical shape of a carotid artery of a patient” (emphasis added). However, in an analogous field of endeavor, Itu discloses extracting patient-specific geometry information from medical images (Itu, Fig. 4:405 and 410 and Paragraph [0056] discloses “Starting at step 405, patient-specific geometry information is extracted from medical images. Next, at step 410, geometric features of the patient-specific vessel tree are extracted” and Paragraph [0066] discloses “For example vascular echocardiography of femoral and carotid arteries may be used to evaluate arterial stiffness, intima/media thickness, etc.” (emphasis added)).
Accordingly, before the effective date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Wang with the teachings of Itu to acquire “values of parameters representing a geometrical shape of a carotid artery of a patient”. One of ordinary skill in the art could have combined these elements in order to provide input to the deep learning model for measurement and prediction of the level of carotid artery stenosis in a patient. Therefore, it would have been obvious to combine Wang and Itu to obtain the invention in Claim 1.
Consider Claim 2, the combination of Wang and Itu discloses “The method of claim 1, wherein acquiring comprises segmenting the geometrical shape from a medical image” (Wang, Page 3, Section 2.2 discloses: “In addition, technicians reconstructed 3D anatomic models by importing the CT images into MIMICS 20.0 (MIMICS, Leuven, Belgium) for arterial segmenting and repairing. Eventually, 280 3D geometric models with no stenosis of carotid bifurcate arteries were built up, and among them 18 heterogeneous cases were excluded due to incomplete information.” In addition, Itu, Paragraph [0006] discloses known method of image segmentation: “The computationally demanding aspect of these CFD models and associated image segmentation process prevents adoption of this technology for real-time applications such as intra-operative guidance of interventions.”) The proposed combination as well as the motivation for combining the Wang and Itu references presented in the rejection of Claim 1, apply to Claim 2 and are incorporated herein by reference. Thus, the method recited in Claim 2 is met by Wang and Itu.
Consider Claim 7, the combination of Wang and Itu discloses “The method of claim 1, wherein generating comprises generating by the artificial intelligence comprising a machine-learned model trained with synthetically generated carotid models with ground truths from computational fluid dynamics or a reduced order model” (Wang, Page 5, Section 2.5 Creation of DL databases. And, Itu, Fig. 3, Offline-Training and Claim 9). The proposed combination as well as the motivation for combining the Wang and Itu references presented in the rejection of Claim 1, apply to Claim 3 and are incorporated herein by reference. Thus, the method recited in Claim 3 is met by Wang and Itu.
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Deep Learning-based Hemodynamic Prediction of Carotid Artery Stenosis Before and After Surgical Treatments” – Published January 10, 2023) in view of Itu et al. (US 2018/0310888 - IDS), and in further view of Gulsun et al. (10,762,637 - IDS).
Consider Claim 3, the combination of Wang and Itu does not explicitly disclose “wherein acquiring comprises acquiring the values of radius and shape for different locations in a slice relative to a center point of the geometrical shape in the slice for different slices along the geometrical shape.” However, in an analogous field of endeavor, Gulsun discloses its trained neural network to provide feature map for image slices throughout the vessel and the distances from the centerline of a vessel (Gulsun, Figs. 2 and 4 and Column 3, lines 1-6).
Accordingly, before the effective date of the instant application, it would have been obvious to one of ordinary skill in the art to combine the combination of Wang and Itu with the teachings of Gulsun to acquire “the values of radius and shape for different locations in a slice relative to a center point of the geometrical shape in the slice for different slices along the geometrical shape”. One of ordinary skill in the art could have combined these elements in order to provide an automated robust and accurate measurements of the status of stenosis of the vessel along its length using a neural network system. Therefore, it would have been obvious to combine Wang, Itu and Gulsun to obtain the invention in Claim 3.
Consider Claim 4, although the combination of Wang and Itu discloses generation of wall sear stress as measure of hemodynamics characteristics in a vessel (Wang, Page 2, right column, lines 3-4 and Itu, Paragraph [0039], wherein the wall shear stress is discloses to be the hemodynamic measures of interest in an artery), it does not explicitly disclose the measurements for the “locations, the locations distributed throughout the geometrical shape.” However, in an analogous field of endeavor, Gulsun discloses its trained neural network to provide feature map for image slices along the centerline of a vessel (Gulsun, Figs. 2 , wherein regions along the vessel shape with plaque are disclosed).
Accordingly, before the effective date of the instant application, it would have been obvious to one of ordinary skill in the art to combine the combination of Wang and Itu with the teachings of Gulsun to acquire “the shear stress for different locations, the locations distributed throughout the geometrical shape.”. One of ordinary skill in the art could have combined these elements in order to provide a thorough and accurate measurements of the status of stenosis of the vessel along its length using a neural network system. Therefore, it would have been obvious to combine Wang, Itu and Gulsun to obtain the invention in Claim 4.
Consider Claim 5, the combination of Wang, Itu, and Gulsun discloses “The method of claim 1, wherein generating the flow information comprises generating velocity, pressure, wall shear stress, shear rate, vorticity, and/or helicity for the locations, the locations distributed throughout the geometrical shape” (Itu, Paragraph [0063] discloses “geometric characteristics determined through the different medical imaging techniques: radius/diameter/area of the vessel, length of the centerline, etc.” and Paragraph [0064], where hemodynamic measures, such as wall stress measurement is disclosed; and Gulsun, Fig. 2). The proposed combination as well as the motivation for combining the Wang, Itu and Gulsun references presented in the rejection of Claim 4, apply to Claim 5 and are incorporated herein by reference. Thus, the method recited in Claim 5 is met by Wang, Itu and Gulsun.
Consider Claim 6, the combination of Wang, Itu and Gulsun discloses “The method of claim 1, wherein generating comprises generating by the artificial intelligence comprising a machine-learned recurrent neural network or transformer” (Itu, Fig. 1:125 and Abstract disclose use of a machine learning algorithm for prediction ACS-related features in a patient; and Gulsun, Fig. 3 and Column 3, lines 51-54 disclose the machine learning neural network to be recurrent neural network).
Accordingly, before the effective date of the instant application, it would have been obvious to one of ordinary skill in the art to combine the combination of Wang and Itu with the teachings of Gulsun to use a recurrent neural network as the machine-learned model. One of ordinary skill in the art could have substituted the machine-learned components and their functions were known in the art, and the results would have been predictable. Therefore, it would have been obvious to combine Wang, Itu and Gulsun to obtain the invention in Claim 6.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Deep Learning-based Hemodynamic Prediction of Carotid Artery Stenosis Before and After Surgical Treatments” – Published January 10, 2023) in view of Itu et al. (US 2018/0310888 - IDS), and in further view of Itu et al. (2016/0148371 – IDS – hereinafter referred to as ITU ‘371).
Consider Claim 8, the combination of Wang and Itu does not explicitly disclose “The method of claim 1, wherein generating comprises assigning an uncertainty for at least one of the parameters, generating different possible flows by the artificial intelligence by perturbation of the values of the at least one parameter as input to the artificial intelligence, and selecting the possible flow as the flow information based on a comparison of the different possible flows to a measurement of flow from medical imaging.” However, in an analogous field of endeavor, Itu ‘371 discloses random or systematic perturbation of properties of models to obtain large number of models for the artificial intelligence system and selection of certain flow information to predict risk of plaque rupture (Itu ‘371, Fig. 2, Abstract, and Paragraphs [0069]-[0070]).
Accordingly, before the effective date of the instant application, it would have been obvious to one of ordinary skill in the art to combine the combination of Wang and Itu with the teachings of Itu ‘371 to generate “different possible flows by the artificial intelligence by perturbation of the values of the at least one parameter as input to the artificial intelligence, and selecting the possible flow as the flow information based on a comparison of the different possible flows to a measurement of flow from medical imaging”. One of ordinary skill in the art could have combined these elements in order to create a larger number of models for training of the neural network. Therefore, it would have been obvious to combine Wang, Itu and Itu ‘371 to obtain the invention in Claim 8.
Consider Claim 9, the combination of Wang, Itu, and Itu ‘371 discloses “The method of claim 1, wherein generating comprises generating the flow information from candidate blood flows generated by the artificial intelligence in response to input of perturbations of the values and selection of the flow information as the candidate blood flow matching a measurement of flow” (Itu ‘371, Fig. 2: 10-14). The proposed combination as well as the motivation for combining the Wang, Itu, and Itu ‘371 references presented in the rejection of Claim 8, apply to Claim 9 and are incorporated herein by reference. Thus, the method recited in Claim 9 is met by Wang, Itu, and Itu ‘371.
Consider Claim 10, the combination of Wang, Itu, and Itu ‘371 discloses “The method of claim 1, wherein the flow information comprises wall shear stress, and wherein predicting comprises predicating the stroke risk as an integral of time averaged wall shear stress for a region of the carotid artery” (Itu ‘371, Paragraph [0140] discloses: Various example metrics include pressure (e.g., average, instantaneous, time-varying, wave-free interval, averaged over a certain sub-interval of a cardiac cycle, or other), flow rate (e.g., average, instantaneous, time-varying, wave-free interval, averaged over a certain sub-interval of a cardiac cycle, or other), wall shear stress (e.g., average, instantaneous, or other), oscillatory shear index, vessel wall strain, vessel wall stress, or any combination of the above defined by any mathematical operator (e.g., addition, subtraction, multiplication, division, integral, derivative, or other (emphasis added). The proposed combination as well as the motivation for combining the Wang, Itu, and Itu ‘371 references presented in the rejection of Claim 8, apply to Claim 10 and are incorporated herein by reference. Thus, the method recited in Claim 10 is met by Wang, Itu, and Itu ‘371.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Siamak HARANDI whose telephone number is (571)270-1832. The examiner can normally be reached on Monday - Friday 9:30 - 6:00 ET.
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/SIAMAK HARANDI/Primary Examiner, Art Unit 2662
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