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 .
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 1,3-11, 13-23, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea including mathematical calculations without significantly more. The claims 1,3-11, 13-23 recite a method and system for determining Wall Shear Stress (WSS) with 4D flow Magnetic Resonance Imaging (MRI), the system comprising: a processor configured for: receiving 4D MRI flow data; calculating each of a velocity gradient and a pressure field from the 4D MRI flow data; correcting the velocity gradient, thereby producing a corrected velocity gradient, wherein correcting the velocity gradient comprises generating a spatial gradient of the pressure field wherein the spatial gradient of the pressure field is employed to correct the velocity gradient based on the conservation of mass (COM) and the conservation of linear momentum (COLM); and determining a WSS from the corrected velocity gradient; as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but presumable recitation of generic computer components.
That is, other than presumably reciting “processor”, nothing in the claim element precludes the step from practically being performed in the mind. For example “receiving 4D MRI flow data” in the context of this claim encompasses the user conducting a basic data gathering step. The user could manually also “calculating each of a velocity gradient and a pressure field from the 4D MRI flow data; correcting the velocity gradient, thereby producing a corrected velocity gradient, wherein correcting the velocity gradient comprises generating a spatial gradient of the pressure field wherein the spatial gradient of the pressure field is employed to correct the velocity gradient based on the conservation of mass (COM) and the conservation of linear momentum (COLM); and determining a WSS from the corrected velocity gradient” since these are mainly mathematical concepts that use known mathematical equations such as COM and COLM.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the imitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform the above noted steps. The processor is recited at a high-level of generality (i.e., as a generic system performing a generic calculation function) such that it amounts no more than mere instructions to apply the exception using a processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the determining steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
As for the depending claim(s), they are also rejected under 35 USC 101 at least for the similar
reasons noted above as they are directed to abstract ideas and does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,3-8, 11, 13-18, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Juniper et al (GB 2613415 A; hereinafter referred to as Juniper) in view of Zhang et al ( J. Zhang et al., “4d flow MRI pressure estimation using velocity measurement-error-based weighted least-squares,” IEEE Transactions on Medical Imaging, vol. 39, no. 5, pp. 1668–1680, May 2020; hereinafter referred to as Zhang)
Regarding Claim 1, Juniper discloses a method for determining Wall Shear Stress (WSS) with 4D flow Magnetic Resonance Imaging (MRI) (“The present invention relates to processing of Magnetic Resonance Velocimetry (MRV) data, including for example to reconstruction of noisy or incomplete data. In particular, techniques described herein relate to compression and decompression of flow fields extracted from such data.” [PG.1], “MRV images are acquired, having poor quality (SNR ---6), intended for reconstruction/segmentation, and images of higher quality (SNR > 30) that serve as the ground truth… The reconstructed images and the segmented (smoothed) domain are used to estimate the posterior distribution of the wall shear rate and compare it with the ground truth” [Pgs 24-25], Magnetic Resonance Velocimetry is known in the art as being a form of 4D Flow MRI), the method comprising:
receiving 4D MRI flow data (“ receiving MRV data encoding information relating to a fluid velocity field, u*, within a subset of the MRV data forming a region, 12, enclosed by a boundary, ao, the boundary including at least a physical boundary portion, F, and optionally further including an inlet portion, fl, and/or an outlet portion, Fo” [Pg. 5]);
calculating each of a velocity gradient from the 4D MRI flow data (“(c) calculating a model fluid velocity field, u°, using the initial values as inputs to a Navier-Stokes problem, and solving for the velocity field within and at the boundary;” [Pg. 5]);
correcting the velocity gradient, thereby producing a corrected velocity gradient (“ (e) determining a generalised gradient of //with respect to each of the unknown parameters in the set fx); (f) for each of the unknown parameters in the set (4, using the generalised gradient for that parameter, evaluated at the initial value of all of the parameters, to determine a direction in which perturbing each parameter reduces the magnitude of _7; and thereby determining improved values for each of the unknown parameters in the set {x); and (g) reconstructing the MRV data by outputting the improved values from step (f) and calculating the reconstructed velocity, u,” [Pg. 5]);
wherein correcting the velocity gradient comprises generating a pressure field set employed to correct the velocity gradient based on the conservation of mass (COM) and the conservation of linear momentum (COLM) (“In this section results are presented of reconstruction and segmentation of noisy flow images by solving the inverse Navier-Stokes problem (2.45) using the algorithms described above. The reconstructed velocity field is then used to estimate the wall shear rate on the reconstructed boundary.” [Pg. 41], it is known in the art that the Navier Stokes equation includes using a pressure gradient as well as COM and COLM, see EQN 2.45 below).
PNG
media_image1.png
75
427
media_image1.png
Greyscale
and determining a WSS from the corrected velocity gradient (“In this section results are presented of reconstruction and segmentation of noisy flow images by solving the inverse Navier-Stokes problem (2.45) using the algorithms described above. The reconstructed velocity field is then used to estimate the wall shear rate on the reconstructed boundary.” [Pg. 41]).
Juniper does not specifically disclose calculating a pressure field, and generating a spatial gradient of the pressure field.
However, in a similar field of endeavor, Zhang teaches calculating a pressure field (“This work introduces a 4D flow magnetic resonance imaging (MRI) pressure reconstruction method which employs weighted least-squares (WLS) for pressure integration. Pressure gradients are calculated from the velocity fields,” [Abstract],
and generating a spatial gradient of the pressure field (“ a weighted least-squares (WLS) reconstruction method for spatial integration of pressure gradients is introduced in this work.“ [Introduction], the pressure gradients calculated in Zhang would be substituted into the Navier Stokes equation used in Juniper to correct the velocity gradients.
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper as outlined above with calculating a pressure field, and generating a spatial gradient of the pressure field as taught by Zhang, because it improves the accuracy of reconstructed pressure fields [Introduction]
Regarding Claim 3, Juniper discloses all limitations noted above except that the pressure field was calculated using a weighted approach with weights given as a function of physical and measurement variables: w(swall )=w min+(w max-w_min) swall/s_(wall,max) , with wmax/w_min =10; where s_wall means distance from the blood vessel wall.
However, Zhang teaches that the pressure field was calculated using a weighted approach with weights given as a function of physical and measurement variables: w(swall )=w min+(w max-w_min) swall/s_(wall,max) , with wmax/w_min =10; where s_wall means distance from the blood vessel wall. (“Pressure gradients are calculated from the velocity fields, and velocity errors are estimated from the velocity divergence for incompressible flow. Pressure gradient errors are estimated by propagating the velocity errors through Navier-Stokes momentum equation. A weight matrix is generated based on the pressure gradient errors, then employed for pressure reconstruction.” [Abstract], “Fig 6 (b) shows the pressure and velocity error distributions as a function of Y (spanwise direction) for the case of 𝜆=33% with a velocity error level of 9.9%. WLS improved the pressure accuracy significantly in regions with lower velocity error level (near the walls). Fig 6 (c) compares the statistical distributions of the pressure error magnitudes by the two methods for the same case. The medians of |𝜖𝑝,𝑊𝐿𝑆| and |𝜖𝑝,𝑃𝑜𝑖𝑠𝑠𝑜𝑛| were 4.2% and 6.8%, respectively.” [B. 2D Pulsatile flow], see Fig. 6 for range of spanwise values taken).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper as outlined above with calculating a pressure field, and generating a spatial gradient of the pressure field as taught by Zhang, because it improves the accuracy of reconstructed pressure fields [Introduction]
Regarding Claim 4, Juniper discloses that the corrected velocity gradient was determined by subtracting velocity gradient errors (Vu=Vut+eVu) estimated from: COLM:
PNG
media_image2.png
20
380
media_image2.png
Greyscale
) COM: e-(Vu-eVu )=0 (“As before, Figure 4h shows the error as a function of iteration number. Velocity slices are drawn for ten equidistant cross-sections (labelled with the letters A to J) for both the reconstructed image (Figure 4i) and the ground truth (Figure 4j). Figures 4i and 4j plot slices of the reconstructed velocity and the ground truth velocity respectively. The light grey background lines represent the noisy input data, while the darker grey foreground lines represent the reconstruction.” [Pg. 45], “Using the input parameters of Table 2, the algorithm manages to reconstruct the noisy velocity image and reduce segmentation errors in just six iterations, with total reconstruction error E. = 5.94% The results for the low SNR MRV images are presented in Figure 8, showing axial u; in Figure 8a, ur* in Figure 8d, the axial and radial reconstruction velocity fields in Figures 8b and 8e respectively and the axial and radial discrepancy terms a."?(u; -Su;) and o-u-.1(u; -Su;) in Figures 8c and 8” [49], as mentioned in previous Claims Juniper uses COM and COLM equations when reconstructing velocity gradients).
Regarding Claim 5, Juniper discloses that the method uses the 4D MRI flow data in a whole region of interest (ROI) (“receiving MRV data encoding information relating to a fluid velocity field, u*, within a subset of the MRV data forming a region, 12, enclosed by a boundary, ao, the boundary including at least a physical boundary portion, F, and optionally further including an inlet portion, fl, and/or an outlet portion, Fo; and wherein the region _0 is a subset of an imaging region, /, over which the MRV data encodes information” [Pg. 5]).
Regarding Claim 6, Juniper discloses that the pressure field is reconstructed by integrating a pressure gradient estimated from a velocity and the velocity gradient in the whole ROI (“receiving MRV data encoding information relating to a fluid velocity field, u*, within a subset of the MRV data forming a region, 12, enclosed by a boundary, ao, the boundary including at least a physical boundary portion, F, and optionally further including an inlet portion, fl, and/or an outlet portion, Fo; and wherein the region _0 is a subset of an imaging region, /, over which the MRV data encodes information “ [Pg.5], “In this section results are presented of reconstruction and segmentation of noisy flow images by solving the inverse Navier-Stokes problem (2.45) using the algorithms described above. The reconstructed velocity field is then used to estimate the wall shear rate on the reconstructed boundary.” [Example], it is known in the art that the Navier Stokes equation includes using a pressure gradient as well as COM and COLM, see EQN 2.45 below).
PNG
media_image1.png
75
427
media_image1.png
Greyscale
Regarding Claim 7, Juniper discloses that the reconstructed pressure field gradient is employed with additional regularization from a divergence-free constraint to correct the velocity gradient (“The goal in this invention is to infer the unknown parameters of the Navier-Stokes problem (2.1) such that the model velocity u approximates the noisy measured velocity u* in the covariance-weighted L2-metric defined by é In the general case, the unknown model parameters of (2.1) are the shape of (i.e. exactly where the boundary constraining the fluid is located), the kinematic viscosity v, and the boundary conditions gi, go.” [Pg. 26]).
PNG
media_image3.png
243
453
media_image3.png
Greyscale
Regarding Claim 8, Juniper discloses that the WSS is estimated based on the corrected near-wall velocity gradient (“In this section results are presented of reconstruction and segmentation of noisy flow images by solving the inverse Navier-Stokes problem (2.45) using the algorithms described above. The reconstructed velocity field is then used to estimate the wall shear rate on the reconstructed boundary.” [Example]).
Regarding Claim 11, Juniper discloses a system for determining Wall Shear Stress (WSS) with 4D flow Magnetic Resonance Imaging (MRI) (“The present invention relates to processing of Magnetic Resonance Velocimetry (MRV) data, including for example to reconstruction of noisy or incomplete data. In particular, techniques described herein relate to compression and decompression of flow fields extracted from such data.” [PG.1], “MRV images are acquired, having poor quality (SNR ---6), intended for reconstruction/segmentation, and images of higher quality (SNR > 30) that serve as the ground truth… The reconstructed images and the segmented (smoothed) domain are used to estimate the posterior distribution of the wall shear rate and compare it with the ground truth” [Pgs 24-25], “An input module 404 for receiving user input is also supplied, which can allow a user to adjust parameters and to control the operation of the system 400. Processor 402 is provided to perform the calculations.” [Pg. 62] Magnetic Resonance Velocimetry is known in the art as being a form of 4D Flow MRI), the system comprising a processor configured to:
receive 4D MRI flow data (“ receiving MRV data encoding information relating to a fluid velocity field, u*, within a subset of the MRV data forming a region, 12, enclosed by a boundary, ao, the boundary including at least a physical boundary portion, F, and optionally further including an inlet portion, fl, and/or an outlet portion, Fo” [Pg. 5]);
calculate each of a velocity gradient from the 4D MRI flow data (“(c) calculating a model fluid velocity field, u°, using the initial values as inputs to a Navier-Stokes problem, and solving for the velocity field within and at the boundary;” [Pg. 5]);
correct the velocity gradient, thereby producing a corrected velocity gradient (“ (e) determining a generalised gradient of //with respect to each of the unknown parameters in the set fx); (f) for each of the unknown parameters in the set (4, using the generalised gradient for that parameter, evaluated at the initial value of all of the parameters, to determine a direction in which perturbing each parameter reduces the magnitude of _7; and thereby determining improved values for each of the unknown parameters in the set {x); and (g) reconstructing the MRV data by outputting the improved values from step (f) and calculating the reconstructed velocity, u,” [Pg. 5]);
wherein correcting the velocity gradient comprises generating a pressure field set employed to correct the velocity gradient based on the conservation of mass (COM) and the conservation of linear momentum (COLM) (“In this section results are presented of reconstruction and segmentation of noisy flow images by solving the inverse Navier-Stokes problem (2.45) using the algorithms described above. The reconstructed velocity field is then used to estimate the wall shear rate on the reconstructed boundary.” [Pg. 41], it is known in the art that the Navier Stokes equation includes using a pressure gradient as well as COM and COLM, see EQN 2.45 below).
PNG
media_image1.png
75
427
media_image1.png
Greyscale
and determine a WSS from the corrected velocity gradient (“In this section results are presented of reconstruction and segmentation of noisy flow images by solving the inverse Navier-Stokes problem (2.45) using the algorithms described above. The reconstructed velocity field is then used to estimate the wall shear rate on the reconstructed boundary.” [Pg. 41]).
Juniper does not specifically disclose calculating a pressure field, and generating a spatial gradient of the pressure field.
However, in a similar field of endeavor, Zhang teaches calculating a pressure field (“This work introduces a 4D flow magnetic resonance imaging (MRI) pressure reconstruction method which employs weighted least-squares (WLS) for pressure integration. Pressure gradients are calculated from the velocity fields,” [Abstract],
and generating a spatial gradient of the pressure field (“ a weighted least-squares (WLS) reconstruction method for spatial integration of pressure gradients is introduced in this work.“ [Introduction], the pressure gradients calculated in Zhang would be substituted into the Navier Stokes equation used in Juniper to correct the velocity gradients.
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper as outlined above with calculating a pressure field, and generating a spatial gradient of the pressure field as taught by Zhang, because it improves the accuracy of reconstructed pressure fields [Introduction]
Regarding Claim 13, Juniper discloses all limitations noted above except that the pressure field was calculated using a weighted approach with weights given as a function of physical and measurement variables: w(swall )=w min+(w max-w_min) swall/s_(wall,max) , with wmax/w_min =10; where s_wall means distance from the blood vessel wall.
However, Zhang teaches that the pressure field was calculated using a weighted approach with weights given as a function of physical and measurement variables: w(swall )=w min+(w max-w_min) swall/s_(wall,max) , with wmax/w_min =10; where s_wall means distance from the blood vessel wall. (“Pressure gradients are calculated from the velocity fields, and velocity errors are estimated from the velocity divergence for incompressible flow. Pressure gradient errors are estimated by propagating the velocity errors through Navier-Stokes momentum equation. A weight matrix is generated based on the pressure gradient errors, then employed for pressure reconstruction.” [Abstract], “Fig 6 (b) shows the pressure and velocity error distributions as a function of Y (spanwise direction) for the case of 𝜆=33% with a velocity error level of 9.9%. WLS improved the pressure accuracy significantly in regions with lower velocity error level (near the walls). Fig 6 (c) compares the statistical distributions of the pressure error magnitudes by the two methods for the same case. The medians of |𝜖𝑝,𝑊𝐿𝑆| and |𝜖𝑝,𝑃𝑜𝑖𝑠𝑠𝑜𝑛| were 4.2% and 6.8%, respectively.” [B. 2D Pulsatile flow], see Fig. 6 for range of spanwise values taken).
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper as outlined above with calculating a pressure field, and generating a spatial gradient of the pressure field as taught by Zhang, because it improves the accuracy of reconstructed pressure fields [Introduction]
Regarding Claim 14, Juniper discloses that the corrected velocity gradient was determined by subtracting velocity gradient errors (Vu=Vut+eVu) estimated from: COLM:
PNG
media_image2.png
20
380
media_image2.png
Greyscale
) COM: e-(Vu-eVu )=0 (“As before, Figure 4h shows the error as a function of iteration number. Velocity slices are drawn for ten equidistant cross-sections (labelled with the letters A to J) for both the reconstructed image (Figure 4i) and the ground truth (Figure 4j). Figures 4i and 4j plot slices of the reconstructed velocity and the ground truth velocity respectively. The light grey background lines represent the noisy input data, while the darker grey foreground lines represent the reconstruction.” [Pg. 45], “Using the input parameters of Table 2, the algorithm manages to reconstruct the noisy velocity image and reduce segmentation errors in just six iterations, with total reconstruction error E. = 5.94% The results for the low SNR MRV images are presented in Figure 8, showing axial u; in Figure 8a, ur* in Figure 8d, the axial and radial reconstruction velocity fields in Figures 8b and 8e respectively and the axial and radial discrepancy terms a."?(u; -Su;) and o-u-.1(u; -Su;) in Figures 8c and 8” [49], as mentioned in previous Claims Juniper uses COM and COLM equations when reconstructing velocity gradients).
Regarding Claim 15, Juniper discloses that the method uses the 4D MRI flow data in a whole region of interest (ROI) (“receiving MRV data encoding information relating to a fluid velocity field, u*, within a subset of the MRV data forming a region, 12, enclosed by a boundary, ao, the boundary including at least a physical boundary portion, F, and optionally further including an inlet portion, fl, and/or an outlet portion, Fo; and wherein the region _0 is a subset of an imaging region, /, over which the MRV data encodes information” [Pg. 5]).
Regarding Claim 16, Juniper discloses that the pressure field is reconstructed by integrating a pressure gradient estimated from a velocity and the velocity gradient in the whole ROI (“receiving MRV data encoding information relating to a fluid velocity field, u*, within a subset of the MRV data forming a region, 12, enclosed by a boundary, ao, the boundary including at least a physical boundary portion, F, and optionally further including an inlet portion, fl, and/or an outlet portion, Fo; and wherein the region _0 is a subset of an imaging region, /, over which the MRV data encodes information “ [Pg.5], “In this section results are presented of reconstruction and segmentation of noisy flow images by solving the inverse Navier-Stokes problem (2.45) using the algorithms described above. The reconstructed velocity field is then used to estimate the wall shear rate on the reconstructed boundary.” [Example], it is known in the art that the Navier Stokes equation includes using a pressure gradient as well as COM and COLM, see EQN 2.45 below).
PNG
media_image1.png
75
427
media_image1.png
Greyscale
Regarding Claim 17, Juniper discloses that the reconstructed pressure field gradient is employed with additional regularization from a divergence-free constraint to correct the velocity gradient (“The goal in this invention is to infer the unknown parameters of the Navier-Stokes problem (2.1) such that the model velocity u approximates the noisy measured velocity u* in the covariance-weighted L2-metric defined by é In the general case, the unknown model parameters of (2.1) are the shape of (i.e. exactly where the boundary constraining the fluid is located), the kinematic viscosity v, and the boundary conditions gi, go.” [Pg. 26]).
PNG
media_image3.png
243
453
media_image3.png
Greyscale
Regarding Claim 18, Juniper discloses that the WSS is estimated based on the corrected near-wall velocity gradient (“In this section results are presented of reconstruction and segmentation of noisy flow images by solving the inverse Navier-Stokes problem (2.45) using the algorithms described above. The reconstructed velocity field is then used to estimate the wall shear rate on the reconstructed boundary.” [Example]).
Regarding Claim 21, Juniper discloses all limitations noted above except that the reconstructed pressure gradient is calculated with weights which increase linearly with increasing distance from a wall.
However, in a similar field of endeavor, Zhang teaches except that the reconstructed pressure gradient is calculated with weights which increase linearly with increasing distance from a wall (“In this study we introduced a method which uses weighted least-squares for pressure integration. By assigning lower weights to less accurate velocity measurements and thus pressure gradient values, the WLS method reduces the effects of noisy measurements during the spatial integration, and improves the accuracy of the reconstructed pressure… WLS reduced pressure errors in the near-wall regions significantly as the greater errors were more confined around the centerline. ” [Discussion], weights would inherent increase the further away from the wall due to the measurements being more error prone)
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper as outlined above with the reconstructed pressure gradient is calculated with weights which increase linearly with increasing distance from a wall as taught by Zhang, because reduces the effects of noisy measurements [Discussion]
Regarding Claim 22, Juniper discloses all limitations noted above except that the reconstructed pressure gradient is calculated with weights which increase linearly with increasing distance from a wall.
However, in a similar field of endeavor, Zhang teaches except that the reconstructed pressure gradient is calculated with weights which increase linearly with increasing distance from a wall (“In this study we introduced a method which uses weighted least-squares for pressure integration. By assigning lower weights to less accurate velocity measurements and thus pressure gradient values, the WLS method reduces the effects of noisy measurements during the spatial integration, and improves the accuracy of the reconstructed pressure… WLS reduced pressure errors in the near-wall regions significantly as the greater errors were more confined around the centerline. ” [Discussion], weights would inherent increase the further away from the wall due to the measurements being more error prone)
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper as outlined above with the reconstructed pressure gradient is calculated with weights which increase linearly with increasing distance from a wall as taught by Zhang, because reduces the effects of noisy measurements [Discussion]
Claims 9-10, & 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Juniper in view of Zhang as applied to Claims 1 and 11 above, and further in view of Mohd et al (M. A. Mohd Adib, S. Ii, Y. Watanabe, and S. Wada, “Minimizing the blood velocity differences between phase-contrast magnetic resonance imaging and computational fluid dynamics simulation in cerebral arteries and aneurysms,” Medical & Biological Engineering & Computing, vol. 55, no. 9, pp. 1605–1619, Feb. 2017.; hereinafter referred to as Mohd)
Regarding Claim 9, Juniper in view of Zhang discloses all limitations noted above except that the WSS is used to analyze physiological remodeling of a blood vessel wall .
However, in a similar field of endeavor, Mohd teaches that the WSS is used to analyze physiological remodeling of a blood vessel wall (“Hemodynamic values such as wall shear stress (WSS) are important factors in the growth and rupture of a cerebral aneurysm” [Effect of boundary treatments on wall shear stress (WSS)], see Fig. 11 for remodeling using V-optimized).
PNG
media_image4.png
663
964
media_image4.png
Greyscale
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper in view of Zhang as outlined above with the WSS is used to analyze physiological remodeling of a blood vessel wall as taught by Mohd, because it allows to depict a more realistic flow distribution in the carotid bifurcations [Introduction].
Regarding Claim 10, Juniper in view of Zhang discloses all limiations noted above except results of the analysis of the physiological remodeling of a blood vessel wall provides an indication on at least one of growth or rupture of the blood vessel wall.
However, in a similar field of endeavor, Mohd teaches that results of the analysis of the physiological remodeling of a blood vessel wall provides an indication on at least one of growth or rupture of the blood vessel wall (“Hemodynamic values such as wall shear stress (WSS) are important factors in the growth and rupture of a cerebral aneurysm” [Effect of boundary treatments on wall shear stress (WSS)], see Fig. 11 for remodeling using V-optimized).
PNG
media_image4.png
663
964
media_image4.png
Greyscale
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper in view of Zhang as outlined above with results of the analysis of the physiological remodeling of a blood vessel wall provides an indication on at least one of growth or rupture of the blood vessel wall as taught by Mohd, because it allows to depict a more realistic flow distribution in the carotid bifurcations [Introduction].
Regarding Claim 19, Juniper in view of Zhang discloses all limitations noted above except that the WSS is used to analyze physiological remodeling of a blood vessel wall .
However, in a similar field of endeavor, Mohd teaches that the WSS is used to analyze physiological remodeling of a blood vessel wall (“Hemodynamic values such as wall shear stress (WSS) are important factors in the growth and rupture of a cerebral aneurysm” [Effect of boundary treatments on wall shear stress (WSS)], see Fig. 11 for remodeling using V-optimized).
PNG
media_image4.png
663
964
media_image4.png
Greyscale
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper in view of Zhang as outlined above with the WSS is used to analyze physiological remodeling of a blood vessel wall as taught by Mohd, because it allows to depict a more realistic flow distribution in the carotid bifurcations [Introduction].
Regarding Claim 20, Juniper in view of Zhang discloses all limiations noted above except results of the analysis of the physiological remodeling of a blood vessel wall provides an indication on at least one of growth or rupture of the blood vessel wall.
However, in a similar field of endeavor, Mohd teaches that results of the analysis of the physiological remodeling of a blood vessel wall provides an indication on at least one of growth or rupture of the blood vessel wall (“Hemodynamic values such as wall shear stress (WSS) are important factors in the growth and rupture of a cerebral aneurysm” [Effect of boundary treatments on wall shear stress (WSS)], see Fig. 11 for remodeling using V-optimized).
PNG
media_image4.png
663
964
media_image4.png
Greyscale
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Juniper in view of Zhang as outlined above with results of the analysis of the physiological remodeling of a blood vessel wall provides an indication on at least one of growth or rupture of the blood vessel wall as taught by Mohd, because it allows to depict a more realistic flow distribution in the carotid bifurcations [Introduction].
Response to Arguments
Applicant's arguments filed 02/13/2026 have been fully considered but they are not persuasive.
Regarding Claims 1, 3-11, & 13-22 the applicant argues the following:
As has already been admitted in the Office Action, Juniper does not disclose calculating a pressure field and generating a spatial gradient of the pressure field. Accordingly, Juniper cannot possibly disclose or suggest a method for determining Wall Shear Stress (WSS) comprising calculating a velocity gradient and a pressure field from 4D MRI flow data, and correcting the velocity gradient, thereby producing a corrected velocity gradient. Correcting the velocity gradient comprises generating a spatial gradient of the pressure field wherein the spatial gradient of the pressure field is employed to correct the velocity gradient.
As reiterated here, Zhang also clearly does not disclose or suggest spatial gradients of any kind, and therefore does not disclose or suggest a method for determining Wall Shear Stress (WSS) which includes the step of correcting a velocity gradient by generating a spatial gradient of the pressure field wherein the spatial gradient of the pressure field is employed to correct the velocity gradient.
Zhang is limited to reporting a weighted least-squares (WLS) reconstruction method for integration of pressure gradients (Zhang at Introduction). Zhang discloses pressure reconstruction by determining velocity errors and pressure gradient errors and constructing a weight matrix based on those errors (Zhang at Abstract).
However, Zhang does not state or reference spatial gradients of pressure fields at all and therefore cannot disclose or suggest employing the spatial gradient of the pressure field to correct the velocity gradient.
Importantly, Zhang expresses reports that there are "several limitations of the WLS pressure reconstruction method," including the following:
The error estimation algorithm employed in this study can only be applied to incompressible flows as the divergence-free assumption is invalid for compressible flows. In addition, the algorithms for error estimation and pressure gradient calculation are only applicable to velocity data which fully resolves the gradients along all dimensions. For 3D flows, volumetric data with all 3 velocity components are required. 2D planar velocity data or 3 velocity components captured on a 2D plane measured from 3D flow would not be sufficient because the velocity gradient perpendicular to the measurement plane is not resolvable...Another limitation of WLS is that the velocity data need to be temporally and spatially resolved to ensure accurate derivative evaluation. The pressure in small vessel branches... cannot be estimated due to the insufficient number of voxels for numerical difference. Zhang at Discussion and Conclusions, Page 16.
Therefore, Zhang himself points out limitations to the WLS method, which are caused by insufficiencies in existing velocity data. Zhang describes limitations to ascertaining pressure from unresolved, or uncorrected, velocity data with the WLS method. Critically, Zhang does not propose any solutions to these limitations and therefore cannot possibly disclose or suggest the claimed elements of calculating a pressure field and generating a spatial gradient of the pressure field as steps for correcting a velocity gradient.
Accordingly, Zhang also does not disclose or suggest a method for determining Wall Shear Stress (WSS) comprising calculating a velocity gradient and a pressure field from 4D MRI flow data, and correcting the velocity gradient, thereby producing a corrected velocity gradient. Correcting the velocity gradient comprises generating a spatial gradient of the pressure field wherein the spatial gradient of the pressure field is employed to correct the velocity gradient.
Claim 1 further recites determining a WSS from the corrected velocity gradient.
Therefore, the claims are not obvious in view of any combination of the cited art, because no combination of the cited art discloses or suggests calculating a pressure field, and generating a spatial gradient of a pressure field wherein the spatial gradient of the pressure field is employed to correct the velocity gradient.
Applicant respectfully requests withdrawal of the 35 U.S.C. § 103 rejections.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
It is first noted that Juniper teaches the following in regards to Claim 1 and Claim 11;
receiving MRV data which is known in the art as being 4D MRI flow data (“the method comprising the steps of: (a) receiving MRV data encoding information relating to a fluid velocity field, u*, within a subset of the MRV data forming a region, 12, enclosed by a boundary, ao, the boundary including at least a physical boundary portion, F,” [Pg. 5 Lines 2-5];
calculating each of a velocity gradient and a pressure field from the 4D MRI flow data (
PNG
media_image5.png
210
692
media_image5.png
Greyscale
[Pg. 33], “the method includes outputting values of p, the pressure field, and the improved values for the unknown parameters in the set (4 as part of step (g).” [Pg. 16 Lines 21-22]);
correcting the velocity gradient, thereby producing a corrected velocity gradient, wherein correcting the velocity gradient comprises generating a spatial gradient of the pressure field wherein the spatial gradient of the pressure field is employed to correct the velocity gradient based on the conservation of mass (COM) and the conservation of linear momentum (COLM) (
PNG
media_image6.png
372
752
media_image6.png
Greyscale
[Pg. 9],
PNG
media_image7.png
511
708
media_image7.png
Greyscale
; [Pg. 14-15], “The methods described herein formulate and solve a generalized inverse Navier-Stokes problem for the joint reconstruction and segmentation of noisy flow velocity images. This amounts to a reduction of the total scanning time. In some examples shown below, there is a reduction in scanning time by a factor of 27, for example. At the same time, the method provides additional knowledge about the physics of the flow (e.g. pressure), and addresses the shortcomings of MRV (low spatial resolution and partial volume effect) that hinder the accurate estimation of wall shear stresses using existing methods. The images derived by the methods disclosed herein may be further post-processed in order to either reveal obscured flow patterns or to extract a quantity of interest (e.g. pressure, wall shear stress, etc.).” [Pg. 4 Lines 25-34]
and determining a WSS from the corrected velocity gradient (
PNG
media_image8.png
285
731
media_image8.png
Greyscale
[Pg. 43].
That is to say Juniper teaches a system of using 4D MRI flow data that caluylates both a velocity gradient and a pressure field, wherein the velocity gradient is coniuouisly uopdated using an Oseen problem which uses a pressure gradient as a variable. Finally the reconstructed velocity is used to determine Wall Shear Stress in the system.
Zhang strengthens Juniper by providing a more explicit sequential pressure calculation (“This work introduces a 4D flow magnetic resonance imaging (MRI) pressure reconstruction method which employs weighted least-squares (WLS) for pressure integration. Pressure gradients are calculated from the velocity fields, and velocity errors are estimated from the velocity divergence for incompressible flow. Pressure gradient errors are estimated by propagating the velocity errors through Navier-Stokes momentum equation. A weight matrix is generated based on the pressure gradient errors, then employed for pressure reconstruction. “ [Abstract].
Motivation to combine can be found in both references attempting to resolve the same issues which is a reduction in noise from acquired 4D MRI data to provide better measurements (“This is important because MRV data is time consuming to obtain and obtaining low noise data is yet more time consuming as many scans must be made and averaged. By providing an efficient and reliable reconstruction algorithm, the overall time to acquire low-noise MRV data can be reduced substantially.” [Juniper Pg. 6 Lines 22-26], “However, several error sources and limitations inherent to in vivo 4D flow MRI result in unreliable pressure fields. The setting of velocity encoding (venc) parameter for a 4D flow acquisition is determined by the maximum velocity expected in the region of interest. Velocity greater than the venc leads to velocity aliasing, while higher venc settings lead to increased noise which affects 4D flow measurements in low velocity regions [9]. Artifacts such as concomitant gradient fields and eddy currents affect the accuracy of measured phase differences [10]. The partial volume effect and intravoxel dephasing are also common sources of systematic errors, especially for voxels near lumen boundaries [11]. For in vivo measurements, the limited scan time results in decreased spatiotemporal resolution and increased image artifacts [10]. Thus, a robust algorithm is needed to accurately reconstruct the pressure field from 4D flow MRI.” [Zhang SECTION I. Introduction])
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN MALDONADO whose telephone number is 703-756-1421. The examiner can normally be reached 8:00 am-4:00 pm PST M-Th 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, Christopher Koharski can be reached on (571) 272-7230. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Steven Maldonado/
Patent Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797
Prosecution Insights