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
This office action for the 18/940457 application is in response to the communications filed February 02, 2026.
Claims 1 and 16 were amended February 02, 2026.
Claim 13 was cancelled February 02, 2026.
Claims 1-12 and 14-23 are currently pending and considered below.
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 1-12 and 14-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method for assessing cerebral aneurysms, comprising the steps of: collecting imaging and blood flow data of an aneurysm and the attached arteries in a subject; creating a 3D model of the aneurysm and the attached arteries from the imaging data, wherein the 3D model comprises representing the attached arteries as an arterial volume comprising at least one arterial inlet and at least one arterial outlet, applying a fluid governing equation to the arterial volume comprising the Navier Stokes equation, and applying arterial inlet and arterial outlet boundary conditions to the fluid governing equation based on the blood flow data of the attached arteries; generating blood flow simulations with the 3D model and the blood flow data; extracting one or more parameters from the simulations; processing the one or more parameters to generate one or more stress indices; and calculating a risk assessment score for the subject based on a statistical analysis of the one or more parameters and the one or more stress indices. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 2 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the imaging data is collected from at least one of CT Cerebral Angiogram, MRI, MRA, 3D DSA or 4D DSA.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 2 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the blood flow data comprises real-time flow velocity and pressure data from Doppler Ultrasonography.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 3 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the one or more parameters comprise a hemodynamic parameter and/or a morphological parameter.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 4 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the hemodynamic parameter is selected from the group consisting of: Pressure, Pressure Distribution, Flow Velocity, Velocity profile, Wall Shear Stress (WSS), mean maximum WSS (MWSS), mean parent vessel WSS (PTWSS), mean normalized WSS (NWSS), WSS gradient (WSSG), Transverse WSS (TWSS), Aneurysm formation indicator (AFI), Oscillation velocity index (OVI), Gradient oscillatory number (GON), Relative residence time (RRT), or mean oscillatory shear index (OSI).” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 5 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the morphological parameter is selected from the group consisting of: aneurysm size, aspect ratio (AR), size ratio (SR), ellipticity index (EI), undulation index (UI), nonsphericity index (NSI), shape of aneurysm, bottleneck factor (BNF), vessel angle, parent artery diameter, aneurysm neck width, aneurysm height, or aneurysm width.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 6 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the stress indices comprise any of Time Average Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), and Relative Residence Time (RRT).” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 depends from claim 7 and inherits all the limitations of the claim from which it depends. Claim 8 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the step of calculating the risk score comprises a statistical analysis to correlate at least one of the one or more parameters and stress indices with one or more medical factors of the subject.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 9 depends from claim 8 and inherits all the limitations of the claim from which it depends. Claim 9 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the one or more medical factors comprise any of hypertension, sex, smoking, age, medical history, aneurysm type, aneurysm location, rupture status, multiple aneurysms, genetic predisposition affecting the aneurysm condition, injury or trauma to blood vessels, complications from some types of blood infections, blood lipid levels, glucose levels, and indication for diabetes.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 10 depends from claim 9 and inherits all the limitations of the claim from which it depends. Claim 10 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising the step of treating the subject with one or more treatments based on the outcome of the risk assessment score.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 11,
Claim 11 depends from claim 10 and inherits all the limitations of the claim from which it depends. Claim 11 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the treatment comprises implantation of a stent.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 12,
Claim 12 depends from claim 11 and inherits all the limitations of the claim from which it depends. Claim 12 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising the step of assessing the implementation and effectiveness of stent by repeating any previous steps to calculate a treatment assessment score.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 14,
Claim 14 depends from claim 13 and inherits all the limitations of the claim from which it depends. Claim 14 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the step of generating the blood flow simulation comprises providing one or more data inputs from flow velocity and pressure values of the blood flow data.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 15,
Claim 15 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 15 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the statistical analysis is performed in a neural network trained with imaging and blood flow data associated with vessel occlusion.” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“wherein the statistical analysis is performed in a neural network trained with imaging and blood flow data associated with vessel occlusion.” which corresponds to merely using a computer as a tool to perform an abstract idea. Page 8 Lines 28-31 – Page 9 Lines 1-5 describes that the hardware that implements the steps of the abstract idea amounts to a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 16,
Claim 16 is substantially similar to claim 1. Accordingly, claim 16 is rejected for the same reasons as claim 1.
“a non-transitory computer-readable medium with instructions stored thereon, that when executed by a processor perform the steps of:” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“a non-transitory computer-readable medium with instructions stored thereon, that when executed by a processor perform the steps of:” which corresponds to merely using a computer as a tool to perform an abstract idea. Page 8 Lines 28-31 – Page 9 Lines 1-5 describes that the hardware that implements the steps of the abstract idea amounts to a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 17,
Claim 17 is substantially similar to claim 2. Accordingly, claim 17 is rejected for the same reasons as claim 2.
As per claim 18,
Claim 18 is substantially similar to claim 3. Accordingly, claim 18 is rejected for the same reasons as claim 3.
As per claim 19,
Claim 19 is substantially similar to claim 4. Accordingly, claim 19 is rejected for the same reasons as claim 4.
As per claim 20,
Claim 20 is substantially similar to claim 5. Accordingly, claim 20 is rejected for the same reasons as claim 5.
As per claim 21,
Claim 21 is substantially similar to claim 6. Accordingly, claim 21 is rejected for the same reasons as claim 6.
As per claim 22,
Claim 22 is substantially similar to claim 7. Accordingly, claim 22 is rejected for the same reasons as claim 7.
As per claim 23,
Claim 23 is substantially similar to claim 15. Accordingly, claim 23 is rejected for the same reasons as claim 15.
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 (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 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 1-12 and 14-23 are rejected under 35 U.S.C. 103 as being unpatentable over Hall et al. (US 2009/0067568; herein referred to as Hall) in view of Choi et al. (US 2022/0230312; herein referred to as Choi) in further view of Aben et al. (US 2022/0164950; herein referred to as Aben).
As per claim 1,
Hall teaches a method for assessing cerebral aneurysms, comprising the steps of: collecting imaging and blood flow data of an aneurysm and the attached arteries in a subject:
(Paragraph [0017] of Hall. The teaching describes that according to an aspect of the invention, there is provided a method of assessing rupture risk of an aneurysm from time-resolved images including injecting a contrast enhancing agent into a patient with an aneurysm, acquiring a sequence of 2D X-ray images of said aneurysm over a cardiac cycle, extracting linear or area measurements of said aneurysm from said sequence of images, and correlating the extracted aneurysm measurement values to clinically known values to assess a rupture risk of said aneurysm.)
Hall further teaches creating a 3D model of the aneurysm and the attached arteries from the imaging data:
(Paragraph [0023] of Hall. The teaching describes segmenting said aneurysm in each image of each of said image sequences and interpolating 2D segmented outlines into a 3D shape of said aneurysm.)
Hall further teaches generating blood flow simulations with the 3D model and the blood flow data; extracting one or more parameters from the simulations; and processing the one or more parameters to generate one or more stress indices:
(Paragraph [0031] of Hall. The teaching describes creating a mathematical model of the walls of the aneurysm, and of blood flow through said aneurysm, running said mathematical model wherein changes in the aneurysm model due to blood pressure changes are observed, comparing the modeled behavior of said aneurysm with corresponding measured behavior of an aneurysm, and refining parameters characterizing said aneurysm wall model and blood flow model, wherein rupture risk is assessed in terms of a stress and strain on a wall of said aneurysm.)
Hall does not explicitly teach calculating a risk assessment score for the subject based on a statistical analysis of the one or more parameters and the one or more stress indices.
However, Choi teaches calculating a risk assessment score for a subject based on a statistical analysis of one or more parameters and one or more stress indices:
(Paragraphs [0024], [0042], and [0048] and Claim 38 of Choi. The teaching describes a system that receives patient information (e.g., 3D cardiac imaging, patient demographics, and history) and provides a patient-specific and location-specific risk score for the pathogenesis of CAD. In one embodiment, an exemplary feature vector for determining CAD is generated in step 414 may include presence of aortic aneurysm and general risk factors of CAD, such as smoking, diabetes, hypertension, abdominal obesity, dietary habits, family history of CAD, etc. Method 400 may then include associating the feature vector with the presence or absence of plaque at each point of the patient-specific geometric model (step 416). Method 400 may involve continuing to perform the above steps 412, 414, 416, for each of a plurality of points in the patient-specific geometric model (step 418), and for each of any number of patients on which a machine learning algorithm may be based (step 420). The disclosure of claim 38 in this publication describes that the supervised machine learning algorithm is selected from a group consisting of a support vector machine, a neural network, a Bayesian classifier, and a Tree Ensemble, which involve statistical analyses of one or more parameters and stress indices relating to CAD.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the cardiovascular risk assessment system of Hall, the cardiovascular risk prediction system of Choi. Paragraph [0004] of Choi teaches that the disclosed methods of modeling a patient’s cardiovascular system are advantageous in predicting the future health of the patient. One of ordinary skill in the art would have added to the teaching of Hall, the teaching of Choi based on this incentive without yielding unexpected results.
The combined teaching of Hall and Choi does not explicitly teach wherein the 3D model comprises representing the attached arteries as an arterial volume comprising at least one arterial inlet and at least one arterial outlet, applying a fluid governing equation to the arterial volume comprising the Navier Stokes equation, and applying arterial inlet and arterial outlet boundary conditions to the fluid governing equation based on the blood flow data of the attached arteries.
However, Aben teaches a 3D model comprises representing the attached arteries as an arterial volume comprising at least one arterial inlet and at least one arterial outlet, applying a fluid governing equation to the arterial volume comprising the Navier Stokes equation, and applying arterial inlet and arterial outlet boundary conditions to the fluid governing equation based on the blood flow data of the attached arteries:
(Paragraphs [0113]-[0115] of Aben. The teaching describes a method that determines pressure parameters of the vessel of interest. Based on the 3D reconstruction, as a result of step 102, and the obstruction region as defined by step 103, the pressure drop is calculated by applying physical laws including viscous resistance and separation loss effects present in coronary flow behavior, The patient specific aortic pressure is preferably obtained from the measured end-diastolic and end-systolic pressure using the guiding catheter as illustrated by 1102. The guiding catheter is placed in the coronary ostium and the aortic pressure is measured by connecting a transducer. From the measured aortic pressure trace, the end-diastolic and end-systolic pressure can be calculated as for instance a weighted average of both end-diastolic and end-systolic pressure. Alternatively, the patient specific aorta pressure can be measured at the brachial artery using a pressure cuff measurement. From the 3D reconstruction, the diameter or cross-section area graph is extracted as shown by 1103 and together with the obstruction region (1105), the pressure-drop or vFFR (vessel FFR) is calculated along the 3D reconstruction by the method. Furthermore, the vFFR or the pressure-drop can be visualized as a color map on the surface of the 3D reconstruction using a corresponding color or grey value as can be seen by 1106. In the color map the green color represents a low pressure-drop or high vFFR as red represents a high pressure-drop or low vFFR. Since wall shear stress cannot be measured directly in vessels, we need to compute through solving the equations that describe the motion of fluids: the Navier-Stokes equations. The most widely used method to solve these complex equations is called computational fluid dynamics (CFD) which computes the velocity distribution within a volumetric geometry, which is in our case a vessel, given the appropriate input data (boundary conditions). Based on this velocity information, the local wall shear stress distribution in vessel along its surface can be derived and is further described by the flowchart of FIG. 12. The first step of FIG. 12, at 1201, one or more processors create a 3D volume mesh as illustrated by step 1201. For example, the 3D reconstruction can be converted into a 3D volume mesh by filling of the 3D reconstruction, as a result of step 102, with grid points which represent the volume of the blood within the 3D reconstruction. These grid points form the basis for the volumetric mesh elements, which are required to compute velocity from the governing equations for blood flow as further described by step 1203. In this meshing procedure, the individual grid spacing, or mesh size, is determined by the complexity of the vessel of interest. Generally, a finer grid spacing is required in the regions where large changes in the velocity profiles are anticipated such as at the luminal vessel wall. This implies that smaller volumetric elements are often used at the vessel wall (also called boundary mesh layer), while larger volumetric elements are admissible in the central part of the vessel, where the smaller changes in the velocity profiles are present. At high curvature or at narrow vessel segments, changes in the velocity profiles are also anticipated resulting that these regions will also benefit from smaller volumetric elements. In case the 3D reconstruction represents a bifurcation or a vessel tree, the region in which the vessel splits up smaller elements are preferred especially at the ostial side of the bifurcation. To increase the computational speed of the CFD calculations, as performed by step 1203, the element size and shape of the volume mesh can thus be varied throughout the vessel of interest. A multitude of methods are described in existing art that are able to achieve this. FIG. 14 provides an example of the output of step 1201, in which 1401 illustrates the 3D volume mesh, with finer elements near the obstruction, and 1402 shows the inlet of the vessel in which the boundary layers are visible (1403). Optionally, the proximal part of the 3D volume mesh and/or the distal ends of the 3D volume mesh can be extended with a certain length as for instance three time the local diameter (3D mesh extension). This will ensure a smooth transition with respect to the boundary conditions as described by step 1202 and that the imposed flow is fully developed when the velocity enters the vessel of interest which will benefit the CFD computation as further described by step 1203.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the 3D modeling of the combined teaching of Hall and Choi, the 3D modeling techniques of Aben. Paragraph [0114] of Aben describes that the modeling used to generate this 3D modeling is widely used and known in the art. One of ordinary skill in the art in possession of the combined teaching of Hall and Choi would have looked to Aben and combined its elements based on this understanding of routine usage and ability to indirectly calculate wall sheer stress in blood vessels. One of ordinary skill in the art would have added to the combine teaching of Hall and Choi, the teaching of Aben based on this understanding and incentive without yielding unexpected results.
As per claim 2,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 1.
Hall further teaches wherein the imaging data is collected from at least one of CT Cerebral Angiogram, MRI, MRA, 3D DSA or 4D DSA:
(Paragraph [0051] of Hall. The teaching describes that the dynamic behavior of the aneurysm can be measured as described above in connection with FIGS. 1 to 3, although the 3D DSA produced by the method of FIG. 3 is useful in this context.)
As per claim 3,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 2.
The combined teaching of Hall further teaches wherein the blood flow data comprises real-time flow velocity and pressure data from Doppler Ultrasonography:
(Paragraphs [0040] and [0051]of Hall. The teaching describes used herein, the term “image” refers to multi-dimensional data composed of discrete image elements (e.g., pixels for 2-D images and voxels for 3-D images). The image may be, for example, a medical image of a subject collected by computer tomography, magnetic resonance imaging, ultrasound, or any other medical imaging system known to one of skill in the art. As the blood pressure changes, a change in the vascular model can be observed at step 506. The model will start pulsating with a periodic pressure change.)
(Paragraph [0053] and [0060] of Choi. The teaching describes determining velocity changes with ultrasound.)
As per claim 4,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 3.
Hall further teaches wherein the one or more parameters comprise a hemodynamic parameter and/or a morphological parameter:
(Paragraphs [0040] and [0051]of Hall. The teaching describes used herein, the term “image” refers to multi-dimensional data composed of discrete image elements (e.g., pixels for 2-D images and voxels for 3-D images). The image may be, for example, a medical image of a subject collected by computer tomography, magnetic resonance imaging, ultrasound, or any other medical imaging system known to one of skill in the art. As the blood pressure changes, a change in the vascular model can be observed at step 506. The model will start pulsating with a periodic pressure change.)
As per claim 5,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 4.
Hall further teaches wherein the hemodynamic parameter is selected from the group consisting of: Pressure, Pressure Distribution, Flow Velocity, Velocity profile, Wall Shear Stress (WSS), mean maximum WSS (MWSS), mean parent vessel WSS (PTWSS), mean normalized WSS (NWSS), WSS gradient (WSSG), Transverse WSS (TWSS), Aneurysm formation indicator (AFI), Oscillation velocity index (OVI), Gradient oscillatory number (GON), Relative residence time (RRT), or mean oscillatory shear index (OSI):
(Paragraphs [0040] and [0051]of Hall. The teaching describes used herein, the term “image” refers to multi-dimensional data composed of discrete image elements (e.g., pixels for 2-D images and voxels for 3-D images). The image may be, for example, a medical image of a subject collected by computer tomography, magnetic resonance imaging, ultrasound, or any other medical imaging system known to one of skill in the art. As the blood pressure changes, a change in the vascular model can be observed at step 506. The model will start pulsating with a periodic pressure change.)
As per claim 6,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 5.
Hall further teaches wherein the morphological parameter is selected from the group consisting of: aneurysm size, aspect ratio (AR), size ratio (SR), ellipticity index (EI), undulation index (UI), nonsphericity index (NSI), shape of aneurysm, bottleneck factor (BNF), vessel angle, parent artery diameter, aneurysm neck width, aneurysm height, or aneurysm width:
(Paragraph [0008] of Hall. The teaching describes that as tension increases, risk of rupture increases. Increased pressure, such as that due to systemic hypertension, and increased aneurysm size aggravate wall tension and therefore increase the risk of rupture.)
As per claim 7,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 6.
Choi further teaches wherein the stress indices comprise any of Time Average Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), and Relative Residence Time (RRT):
(Paragraph [0039] of Choi. The teaching describes that the step of obtaining one or more estimates of biophysical hemodynamic characteristics of the patient (step 408) may include obtaining a list of one or more estimates of biophysical hemodynamic characteristics from computational fluid dynamics analysis, such as wall-shear stress, oscillatory shear index, particle residence time, Reynolds number, Womersley number, local flow rate, and turbulent kinetic energy, etc.)
As per claim 8,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 7.
Choi further teaches wherein the step of calculating the risk score comprises a statistical analysis to correlate at least one of the one or more parameters and stress indices with one or more medical factors of the subject:
(Paragraphs [0024], [0042], and [0048] and Claim 38 of Choi. The teaching describes a system that receives patient information (e.g., 3D cardiac imaging, patient demographics, and history) and provides a patient-specific and location-specific risk score for the pathogenesis of CAD. In one embodiment, an exemplary feature vector for determining CAD is generated in step 414 may include presence of aortic aneurysm and general risk factors of CAD, such as smoking, diabetes, hypertension, abdominal obesity, dietary habits, family history of CAD, etc. Method 400 may then include associating the feature vector with the presence or absence of plaque at each point of the patient-specific geometric model (step 416). Method 400 may involve continuing to perform the above steps 412, 414, 416, for each of a plurality of points in the patient-specific geometric model (step 418), and for each of any number of patients on which a machine learning algorithm may be based (step 420). The disclosure of claim 38 in this publication describes that the supervised machine learning algorithm is selected from a group consisting of a support vector machine, a neural network, a Bayesian classifier, and a Tree Ensemble, which involve statistical analyses of one or more parameters and stress indices relating to CAD.)
As per claim 9,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 8.
Choi further teaches wherein the one or more medical factors comprise any of hypertension, sex, smoking, age, medical history, aneurysm type, aneurysm location, rupture status, multiple aneurysms, genetic predisposition affecting the aneurysm condition, injury or trauma to blood vessels, complications from some types of blood infections, blood lipid levels, glucose levels, and indication for diabetes:
(Paragraphs [0024], [0042], and [0048] and Claim 38 of Choi. The teaching describes a system that receives patient information (e.g., 3D cardiac imaging, patient demographics, and history) and provides a patient-specific and location-specific risk score for the pathogenesis of CAD. In one embodiment, an exemplary feature vector for determining CAD is generated in step 414 may include presence of aortic aneurysm and general risk factors of CAD, such as smoking, diabetes, hypertension, abdominal obesity, dietary habits, family history of CAD, etc. Method 400 may then include associating the feature vector with the presence or absence of plaque at each point of the patient-specific geometric model (step 416). Method 400 may involve continuing to perform the above steps 412, 414, 416, for each of a plurality of points in the patient-specific geometric model (step 418), and for each of any number of patients on which a machine learning algorithm may be based (step 420). The disclosure of claim 38 in this publication describes that the supervised machine learning algorithm is selected from a group consisting of a support vector machine, a neural network, a Bayesian classifier, and a Tree Ensemble, which involve statistical analyses of one or more parameters and stress indices relating to CAD.)
As per claim 10,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 9.
The combined teaching of Hall and Choi further teaches further comprising the step of treating the subject with one or more treatments based on the outcome of the risk assessment score:
(Paragraphs [0024], [0042], and [0048] and Claim 38 of Choi. The teaching describes a system that receives patient information (e.g., 3D cardiac imaging, patient demographics, and history) and provides a patient-specific and location-specific risk score for the pathogenesis of CAD. In one embodiment, an exemplary feature vector for determining CAD is generated in step 414 may include presence of aortic aneurysm and general risk factors of CAD, such as smoking, diabetes, hypertension, abdominal obesity, dietary habits, family history of CAD, etc. Method 400 may then include associating the feature vector with the presence or absence of plaque at each point of the patient-specific geometric model (step 416). Method 400 may involve continuing to perform the above steps 412, 414, 416, for each of a plurality of points in the patient-specific geometric model (step 418), and for each of any number of patients on which a machine learning algorithm may be based (step 420). The disclosure of claim 38 in this publication describes that the supervised machine learning algorithm is selected from a group consisting of a support vector machine, a neural network, a Bayesian classifier, and a Tree Ensemble, which involve statistical analyses of one or more parameters and stress indices relating to CAD.)
(Paragraph [0010] of Choi. The teaching describes that X-ray C-arms are routinely used in medicine to acquire images for diagnostic assessment of a patient's vascular structures, and for guidance of interventional therapeutic procedures such as stent placement or coiling of aneurysms.)
As per claim 11,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 10.
The combined teaching of Hall and Choi further teaches wherein the treatment comprises implantation of a stent:
(Paragraphs [0024], [0042], and [0048] and Claim 38 of Choi. The teaching describes a system that receives patient information (e.g., 3D cardiac imaging, patient demographics, and history) and provides a patient-specific and location-specific risk score for the pathogenesis of CAD. In one embodiment, an exemplary feature vector for determining CAD is generated in step 414 may include presence of aortic aneurysm and general risk factors of CAD, such as smoking, diabetes, hypertension, abdominal obesity, dietary habits, family history of CAD, etc. Method 400 may then include associating the feature vector with the presence or absence of plaque at each point of the patient-specific geometric model (step 416). Method 400 may involve continuing to perform the above steps 412, 414, 416, for each of a plurality of points in the patient-specific geometric model (step 418), and for each of any number of patients on which a machine learning algorithm may be based (step 420). The disclosure of claim 38 in this publication describes that the supervised machine learning algorithm is selected from a group consisting of a support vector machine, a neural network, a Bayesian classifier, and a Tree Ensemble, which involve statistical analyses of one or more parameters and stress indices relating to CAD.)
(Paragraph [0010] of Choi. The teaching describes that X-ray C-arms are routinely used in medicine to acquire images for diagnostic assessment of a patient's vascular structures, and for guidance of interventional therapeutic procedures such as stent placement or coiling of aneurysms.)
As per claim 12,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 11.
The combined teaching of Hall and Choi further teaches further comprising the step of assessing the implementation and effectiveness of stent by repeating any previous steps to calculate a treatment assessment score:
(Paragraph [0017] of Hall. The teaching describes that according to an aspect of the invention, there is provided a method of assessing rupture risk of an aneurysm from time-resolved images including injecting a contrast enhancing agent into a patient with an aneurysm, acquiring a sequence of 2D X-ray images of said aneurysm over a cardiac cycle, extracting linear or area measurements of said aneurysm from said sequence of images, and correlating the extracted aneurysm measurement values to clinically known values to assess a rupture risk of said aneurysm.)
(Paragraph [0042] of Hall. The teaching describes that angiography is the use of fluoroscopy to view the cardiovascular system. An iodine-based contrast is injected into the bloodstream and observed as it travels around. Since liquid blood and the vessels are not very dense, a contrast with high density, such as iodine, is used to view the vessels using X-rays. Angiography can be used to find aneurysms, leaks, blockages, new vessel growth, and placement of catheters and stents. This means that after a stent is placed, the same system can assess the risk of the same patient and determine a treatment effectiveness based on the changed risk)
(Paragraphs [0024], [0042], and [0048] and Claim 38 of Choi. The teaching describes a system that receives patient information (e.g., 3D cardiac imaging, patient demographics, and history) and provides a patient-specific and location-specific risk score for the pathogenesis of CAD. In one embodiment, an exemplary feature vector for determining CAD is generated in step 414 may include presence of aortic aneurysm and general risk factors of CAD, such as smoking, diabetes, hypertension, abdominal obesity, dietary habits, family history of CAD, etc. Method 400 may then include associating the feature vector with the presence or absence of plaque at each point of the patient-specific geometric model (step 416). Method 400 may involve continuing to perform the above steps 412, 414, 416, for each of a plurality of points in the patient-specific geometric model (step 418), and for each of any number of patients on which a machine learning algorithm may be based (step 420). The disclosure of claim 38 in this publication describes that the supervised machine learning algorithm is selected from a group consisting of a support vector machine, a neural network, a Bayesian classifier, and a Tree Ensemble, which involve statistical analyses of one or more parameters and stress indices relating to CAD.)
As per claim 14,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 13.
The combined teaching of Hall and Choi wherein the step of generating the blood flow simulation comprises providing one or more data inputs from flow velocity and pressure values of the blood flow data:
(Paragraph [0031] of Hall. The teaching describes creating a mathematical model of the walls of the aneurysm, and of blood flow through said aneurysm, running said mathematical model wherein changes in the aneurysm model due to blood pressure changes are observed, comparing the modeled behavior of said aneurysm with corresponding measured behavior of an aneurysm, and refining parameters characterizing said aneurysm wall model and blood flow model, wherein rupture risk is assessed in terms of a stress and strain on a wall of said aneurysm.)
(Paragraphs [0040] and [0051]of Hall. The teaching describes used herein, the term “image” refers to multi-dimensional data composed of discrete image elements (e.g., pixels for 2-D images and voxels for 3-D images). The image may be, for example, a medical image of a subject collected by computer tomography, magnetic resonance imaging, ultrasound, or any other medical imaging system known to one of skill in the art. As the blood pressure changes, a change in the vascular model can be observed at step 506. The model will start pulsating with a periodic pressure change.)
(Paragraph [0053] and [0060] of Choi. The teaching describes determining velocity changes with ultrasound.)
As per claim 15,
The combined teaching of Hall, Choi and Aben teaches the limitations of claim 1.
Choi further teaches wherein the statistical analysis is performed in a neural network trained with imaging and blood flow data associated with vessel occlusion.:
(Paragraphs [0024], [0042], and [0048] and Claim 38 of Choi. The teaching describes a system that receives patient information (e.g., 3D cardiac imaging, patient demographics, and history) and provides a patient-specific and location-specific risk score for the pathogenesis of CAD. In one embodiment, an exemplary feature vector for determining CAD is generated in step 414 may include presence of aortic aneurysm and general risk factors of CAD, such as smoking, diabetes, hypertension, abdominal obesity, dietary habits, family history of CAD, etc. Method 400 may then include associating the feature vector with the presence or absence of plaque at each point of the patient-specific geometric model (step 416). Method 400 may involve continuing to perform the above steps 412, 414, 416, for each of a plurality of points in the patient-specific geometric model (step 418), and for each of any number of patients on which a machine learning algorithm may be based (step 420). The disclosure of claim 38 in this publication describes that the supervised machine learning algorithm is selected from a group consisting of a support vector machine, a neural network, a Bayesian classifier, and a Tree Ensemble, which involve statistical analyses of one or more parameters and stress indices relating to CAD.)
As per claim 16,
Claim 16 is substantially similar to claim 1. Accordingly, claim 16 is rejected for the same reasons as claim 1.
As per claim 17,
Claim 17 is substantially similar to claim 2. Accordingly, claim 17 is rejected for the same reasons as claim 2.
As per claim 18,
Claim 18 is substantially similar to claim 3. Accordingly, claim 18 is rejected for the same reasons as claim 3.
As per claim 19,
Claim 19 is substantially similar to claim 4. Accordingly, claim 19 is rejected for the same reasons as claim 4.
As per claim 20,
Claim 20 is substantially similar to claim 5. Accordingly, claim 20 is rejected for the same reasons as claim 5.
As per claim 21,
Claim 21 is substantially similar to claim 6. Accordingly, claim 21 is rejected for the same reasons as claim 6.
As per claim 22,
Claim 22 is substantially similar to claim 7. Accordingly, claim 22 is rejected for the same reasons as claim 7.
As per claim 23,
Claim 23 is substantially similar to claim 15. Accordingly, claim 23 is rejected for the same reasons as claim 15.
Response to Arguments
Applicant's arguments filed February 02, 2026 have been fully considered.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that none of the limitations of the alleged abstract idea relate to “managing personal behavior or relationships or interactions between people” or “a list of rules or instructions that a human person can follow in the course of their personal behavior”.
The Examiner respectfully disagrees. The steps of the abstract idea are abstract because each of the limitations of the abstract idea recite a list of rules or instructions that a human person can perform in the course of their personal behavior. A human can collect imaging and blood flow data of an aneurysm and the attached arteries of a subject. A human can create a 3D model of the aneurysm wherein the 3D model comprises representing wherein the 3D model comprises representing the attached arteries as an arterial volume comprising at least one arterial inlet and at least one arterial outlet, applying a fluid governing equation to the arterial volume comprising the Navier Stokes equation, and applying arterial inlet and arterial outlet boundary conditions to the fluid governing equation based on the blood flow data of the attached arteries. This limitation in particular, in addition to the others, is mere mathematical calculations in the analysis steps that a human is more than capable of performing. A human can generate blood flow simulations, extract one or more parameters, process the one or more parameters, and calculate a risk assessment score. In particular, claim 1 requires absolutely no technology whatsoever to conduct the steps of the abstract idea. The Examiner is left with understanding that this process can be performed by anything or anyone, including a person. The high level of generality of the steps of the abstract idea does not provide any sufficient evidence to conclude that the steps of the abstract idea are precluded from being performed by a human. Even if the steps of claim were being implemented on a computer, as is the case with claim 16, this is a merely apply it level recitation of the abstract idea to an computer. Accordingly, claims 1 and 16 recite an abstract idea under Step 2A Prong 1.
The Applicant further argues that the Navier-Stokes equation is not claimed in the abstract, but rather applied to a patient-specific arterial volume.
The Examiner respectfully disagrees. The Navier-Stokes equation per se is abstract. We know this because the Navier-Stokes equation pertains to mathematics and the field of mathematics is inherently abstract. A patient-specific arterial volume is abstract. We know this because a volume of an object is merely information and information is inherently abstract. This volume being constrained by subject measured physiological boundary conditions is also merely information. Generating blood flow simulations from which physical stress indices are extracted to compute a clinical risk assessment score is abstract. We know this because the calculations are merely driven by mathematical calculations. There is nothing here to suggest that the Navier-Stokes equation is being applied to anything but abstract elements. There is nothing tangible to be gained by the combination of these elements. We know this because the only thing gained by the claimed invention is a risk assessment score which is merely information. Accordingly, this argument is not persuasive.
The Applicant further argues that the pending claims provide a practical application of computational fluid dynamic which improves the field of cerebrovascular medicine.
The Examiner respectfully disagrees. The end result of the pending claims do not provide any tangible effect to the field of cerebrovascular medicine. The claimed risk score, at best, merely presents information to a user calculating these metrics. Any effect on the field of cerebrovascular medicine are completely dependent on factors outside of the claimed invention. We know this because the invention does not require any sort of actionable particular treatment or prophylaxis of a particular medical condition. The claims as written merely pertain to the field of medical information computation and there is no evidence that computational efficiency is being improved, especially with the calculations being dependent on such routine equations such as the Navier-Stokes equation.
The Applicant further argues that the claimed steps, when executed in order are not performed in the abstract, but rather performed sequentially to produce a tangible clinical risk assessment.
The Examiner respectfully disagrees for the reasons indicated above.
The Applicant further argues that the claimed invention is not merely applied to a computer. Rather, the claimed invention provides a technical improvement in the way of aneurysm risk assessment.
The Examiner respectfully disagrees for the reasons indicated above.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 103 are rendered moot in light of the new combination of references used in the current rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H. CHOI can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
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