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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/19/2025 has been entered.
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 with respect to claim(s) 1, 3-11, & 13-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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