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 § 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 of this title, 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.
Claims 1-2, 6, 13-14, 18, 22 are rejected under 35 U.S.C. 103 as being unpatentable over Müller et al ("Particle-based fluid simulation for interactive applications.”, ACM, 2003) in view of Green et al (US8947430).
Regarding Claim 1. Müller teaches An image processing method, comprising:
acquiring a density image and a fluid shape image (Müller, abstract, the paper proposed an interactive method based on Smoothed Particle Hydrodynamics (SPH) to simulate fluids with free surfaces. The method is an extension of the SPH-based technique by Desbrun to animate highly deformable bodies. We gear the method towards fluid simulation by deriving the force density fields directly from the Navier-Stokes equation and by adding a term to model surface tension effects. In contrast to Eulerian grid-based approaches, the particle-based approach makes mass conservation equations and convection terms dispensable which reduces the complexity of the simulation. In addition, the particles can directly be used to render the surface of the fluid. We propose methods to track and visualize the free surface using point splatting and marching cubes-based surface reconstruction. Our animation method is fast enough to be used in interactive systems and to allow for user interaction with models consisting of up to 5000 particles.
Page 5, col 2, par 4, The water in the glass shown in Fig. 3 is sampled with 2200 particles. An external rotational force field causes the fluid to swirl. The first image (a) shows the individual particles. For the second image (b), point splatting was used to render the free surface only. In both modes, the animation runs at 20 frames per second on a 1.8 GHz Pentium IV PC with a GForce 4 graphics card. The most convincing results are produced when the iso surface of the color field is visualized using the marching cubes algorithm as in image (c).
Therefore, the first image Fig 3(a) is equivalent to density image, while the second image Fig 3(b) is equivalent to shape image.);
blurring the density image based on the fluid shape image to obtain a blurred image (Müller, page 2, col 1, par 6 - page 2 col 2, par 1-2, Smoothed Particle Hydrodynamics (SPH) is an interpolation method for particle systems. With SPH, field quantities that are only defined at discrete particle locations can be evaluated anywhere in space. For this purpose, SPH distributes quantities in a local neighborhood of each particle using radial symmetrical smoothing kernels. According to SPH, a scalar quantity A is interpolated at location r by a weighted sum of contributions from all particles:
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where j iterates over all particles, mj is the mass of particle j, rj its position, pj the density and Aj the field quantity at rj.
Page 3, col 2, par 3-4, We model surface tension forces (not present in Eqn. (7)) explicitly based on ideas of Morris12. Molecules in a fluid are subject to attractive forces from neighboring molecules. Inside the fluid these intermolecular forces are equal in all directions and balance each other. In contrast, the forces acting on molecules at the free surface are unbalanced. The net forces (i.e. surface tension forces) act in the direction of the surface normal towards the fluid. They also tend to minimize the curvature of the surface. The larger the curvature, the higher the force. Surface tension also depends on a tension coefficient σ which depends on the two fluids that form the surface.
Therefore, the radial symmetrical smoothing kernels calculates the force at the surface of the fluid. The method considered the unbalanced force at the surface of the fluid and calculates the tension accordingly. The calculation is similar to blur the interposed region between fluid and the non-fluid region, so that the edge of the fluid is rendered more realistic.);
Müller fails to explicitly teach, however, Green teaches a transition region (Green, abstract, the invention describes A method for rendering a particle-based fluid surface includes generating a depth image of a plurality of particles which form a fluid surface, and smoothing the depth image to generate a smoothed depth image. From the smoothed depth image, a smoothed surface position and a smoothed surface normal for each of a plurality of pixels included within the smoothed depth image is determined, and a shaded surface of the fluid is rendered as a function of the smoothed surface positions and the smoothed surface normals.
Col 2, line 28-37, FIG. 1 illustrates a first exemplary method for rendering a particle-based fluid surface in accordance with the present invention. The method 100 commences at operation 102 where an image of the fluid surface is generated. At 104, a noise texture of the fluid surface is generated. At 106, a background image, corresponding to scene data located view-wise behind the fluid surface is generated. At 108, the fluid surface image, the noise texture, and the background image are employed to form a final image of the fluid surface. The final image may be displayed or written to a render target.
Col 3, line 59-67, Operation 206 represents a process, whereby the depth image of the fluid surface generated in operation 204 is smoothed. Smoothing the depth image of the fluid surface hides the spherical nature of the particles so that the surface does not appear unnaturally blobby or jelly-like. In one embodiment of this process, the smoothing operation includes Gaussian filtering, whereby a convolution of a Gaussian kernel is applied to the depth image provided in eq. (2).
Col 6, line 43-67, … Additive blending (realized by the summation operation) is used to account for the accumulated amount of fluid at each position. Optionally, shading of the fluid is performed using a different exponential falloff for each color channel, so that the color of the fluid varies with the thickness. The thickness values may be used in generating the shaded fluid surface in operation 102, as described herein.
Col 7, line 21-29, FIG. 4 illustrates a further exemplary embodiment of operation 104 in which a noise texture for a fluid surface is generated. At 402, a noise kernel is generated for each particle of the fluid surface image generated in 102. At 404, the noise kernels are summed to compute a noise texture of the fluid surface. Exemplary, the fluid surface image for which the noise texture is generated is the depth image described above in operation 204, or the smoothed depth image described in operation 206.
Therefore, the shaded surface of the fluid is equivalent to transition region of the fluid simulation.).
Müller and Green are analogous art because they both teach method of particle-based fluid simulation. Green further teaches method of computing a noise texture of the fluid surface. Therefore, it would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention, to modify the particle-based fluid simulation method (taught in Müller), to further use the noise kernels to computer a noise texture of the fluid surface (taught in Green), so as to prevent patterns from becoming visibly apparent (Green, col 7, line 21-47).
The combination of Müller and Green further teaches determining a transition region, a fluid region and an ordinary region in the blurred image based on a preset density threshold range (Müller, Page 4, col 1, par 1-4, The surface of the fluid can be found by using an additional field quantity which is 1 at particle locations and 0 everywhere else. This field is called color field in the literature. For the smoothed color field we get:
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The gradient field of the smoothed color field
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yields the surface normal field pointing into the fluid and the divergence of n measures the curvature of the surface.
To distribute the surface traction among particles near the surface and to get a force density we multiply by a normalized scalar field δs = |n| which is non-zero only near the surface. For the force density acting near the surface we get
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Evaluating n/|n| at locations where |n| is small causes numerical problems. We only evaluate the force if |n| exceeds a certain threshold.
The region at the surface of the fluid is the transition region, in which the force on each side of the fluid particles are unbalanced. The unbalanced force is not evaluated is the force doesn’t exceed a certain threshold.);
rendering the transition region, the fluid region and the ordinary region to generate a target image (Müller, page 5, col 1, par 3, 4. Surface Tracking and Visualization, The color field cS and its gradient field n = ∇cS defined in section 3.3 can be used to identify surface particles and to compute surface normals. We identify a particle i as a surface particle if
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where l is a threshold parameter. The direction of the surface normal at the location of particle i is given by
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.
Page 5, col 2, par 4, The water in the glass shown in Fig. 3 is sampled with 2200 particles. An external rotational force field causes the fluid to swirl. The first image (a) shows the individual particles. For the second image (b), point splatting was used to render the free surface only. In both modes, the animation runs at 20 frames per second on a 1.8 GHz Pentium IV PC with a GForce 4 graphics card. The most convincing results are produced when the iso surface of the color field is visualized using the marching cubes algorithm as in image (c).
Page 5, col 2, par 5, The image sequence shown in Fig. 4 demonstrates interaction with the fluid. Through mouse motion, the user generates an external force field that cause the water to splash. The free surface is rendered using point splatting while isolated particles are drawn as single droplets.).
Regarding Claim 2. The combination of Müller and Green further teaches The image processing method of claim 1, wherein, the acquiring a density image and a fluid shape image, comprises:
acquiring the density image and the fluid shape image according to a preset drawing algorithm and drawing data corresponding to the drawing algorithm (Müller, page 2, col 1, par 6 - page 2 col 2, par 1-3, Smoothed Particle Hydrodynamics (SPH) is an interpolation method for particle systems. With SPH, field quantities that are only defined at discrete particle locations can be evaluated anywhere in space. For this purpose, SPH distributes quantities in a local neighborhood of each particle using radial symmetrical smoothing kernels. According to SPH, a scalar quantity A is interpolated at location r by a weighted sum of contributions from all particles:
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where j iterates over all particles, mj is the mass of particle j, rj its position, pj the density and Aj the field quantity at rj.
The function W(r,h) is called the smoothing kernel with core radius h. Since we only use kernels with finite support, we use h as the radius of support in our formulation. If W is even (i.e. W(r,h) =W(−r,h)) and normalized, the interpolation is of second order accuracy. The kernel is normalized if
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Page 3, col 2, par 3-4, We model surface tension forces (not present in Eqn. (7)) explicitly based on ideas of Morris12. Molecules in a fluid are subject to attractive forces from neighboring molecules. Inside the fluid these intermolecular forces are equal in all directions and balance each other. In contrast, the forces acting on molecules at the free surface are unbalanced. The net forces (i.e. surface tension forces) act in the direction of the surface normal towards the fluid. They also tend to minimize the curvature of the surface. The larger the curvature, the higher the force. Surface tension also depends on a tension coefficient σ which depends on the two fluids that form the surface.
Therefore, equation (1)-(8) describes how to calculate force density fields for each particle. Those equations are equivalent to the drawing algorithms for the density image.).
Regarding Claim 6. The combination of Müller and Green further teaches The image processing method of claim 1, wherein, the blurring the density image based on the fluid shape image to obtain a blurred image, comprises:
by taking a pixel in the fluid shape image as a central point, sampling pixel positions in the density image corresponding to the central point to acquire a target pixel,
performing calculation based on the weight value and density value of the central point and the weight value and the target density value of the target pixel to acquire a blurred value of the central point, and
acquiring the blurred image based on the blurred value of each central point (Müller, Müller, page 2, col 1, par 6 - page 2 col 2, par 1-3, Smoothed Particle Hydrodynamics (SPH) is an interpolation method for particle systems. With SPH, field quantities that are only defined at discrete particle locations can be evaluated anywhere in space. For this purpose, SPH distributes quantities in a local neighborhood of each particle using radial symmetrical smoothing kernels. According to SPH, a scalar quantity A is interpolated at location r by a weighted sum of contributions from all particles:
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where j iterates over all particles, mj is the mass of particle j, rj its position, pj the density and Aj the field quantity at rj.
The function W(r,h) is called the smoothing kernel with core radius h. Since we only use kernels with finite support, we use h as the radius of support in our formulation. If W is even (i.e. W(r,h) =W(−r,h)) and normalized, the interpolation is of second order accuracy. The kernel is normalized if
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).
Claim 13 is similar in scope as Claim 1, and thus is rejected under same rationale.
Claim 14 is similar in scope as Claim 1, and thus is rejected under same rationale.
Claim 18 is similar in scope as Claim 6, and thus is rejected under same rationale.
Claim 22 is similar in scope as Claim 6, and thus is rejected under same rationale.
Allowable Subject Matter
Claims 3-5, 7-11, 17, 19-21, 23 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reason for the indication of allowable subject matter:
Regarding Claim 3, it recites “The image processing method of claim 2, wherein, the acquiring the density image and the fluid shape image according to a preset drawing algorithm and drawing data corresponding to the drawing algorithm, comprises:
performing point drawing according to a preset first drawing algorithm and a preset first fluid particle density value to generate a plurality of first fluid particles,
superimposing density values corresponding to the plurality of first fluid particles based on the position of each first fluid particle to obtain a density image,
performing point drawing according to a preset second drawing algorithm and a preset constant value to generate a plurality of second fluid particles, and
superimposing density values corresponding to the plurality of second fluid particles based on the position of each second fluid particle to obtain a fluid shape image.” in the context of Claim 3.
The prior arts of record either alone or in combination fails to teach or suggest the above quoted limitation of Claim 3. Therefore, Claim 3 is allowable over prior art.
Regarding Claim 4. it recites “The image processing method of claim 2, wherein, the acquiring the density image and the fluid shape image according to a preset drawing algorithm and drawing data corresponding to the drawing algorithm, comprises:
performing point drawing according to a preset third drawing algorithm and a preset second fluid particle density value to generate a plurality of third fluid particles,
superimposing density values corresponding to the plurality of third fluid particles based on the position of each third fluid particle, to draw image data in a first designated channel of the target image as the density image,
performing point drawing according to a preset third drawing algorithm and a preset constant value to generate a plurality of fourth fluid particles, and
superimposing the plurality of fourth fluid particles based on the position of each fourth fluid particle, to draw image data in a second designated channel of the target image as the fluid shape image.” in the context of Claim 4.
The prior arts of record either alone or in combination fails to teach or suggest the above quoted limitation of Claim 4. Therefore, Claim 4 is allowable over prior art.
Regarding Claim 5, it recites ”The image processing method of claim 1, wherein, the acquiring a density image and a fluid shape image, comprises:
based on a preset image generation model, setting and inputting different image generation parameters into the image generation model to generate the density image and the fluid shape image.” in the context of Claim 5.
The prior arts of record either alone or in combination fails to teach or suggest the above quoted limitation of Claim 5. Therefore, Claim 5 is allowable over prior art.
Regarding Claim 7, it recites “The image processing method of claim 1, wherein, the sampling pixel positions in the density image corresponding to the central point to acquire a target pixel, comprises:
acquiring pixel positions in the density image corresponding to the central point,
acquiring a diagonal for the pixel positions, wherein the sampling is performed on the diagonal to acquire the target pixels.” in the context of Claim 7.
The prior arts of record either alone or in combination fails to teach or suggest the above quoted limitation of Claim 7. Therefore, Claim 7 is allowable over prior art.
Regarding Claim 8, recites “The image processing method of claim 6, further comprising:
acquiring an original density value corresponding to each target pixel in the density image,
performing calculation on the original density value corresponding to each target pixel with a preset coefficient to obtain a target density value corresponding to each target pixel.” in the context of Claim 8.
The prior arts of record either alone or in combination fails to teach or suggest the above quoted limitation of Claim 8. Therefore, Claim 8 is allowable over prior art.
Regarding Claim 9, it recites “The image processing method of claim 6, wherein, the blurring the density image based on the fluid shape image to obtain a blurred image comprises: by taking a pixel in the fluid shape image as a central point, acquiring a plurality of pixels in the density image, averaging the density values of the central point and the plurality of pixels to obtain a blurred value of the central point, and acquiring a blurred image based on the blurred value of each central point.” in the context of Claim 9.
The prior arts of record either alone or in combination fails to teach or suggest the above quoted limitation of Claim 9. Therefore, Claim 9 is allowable over prior art.
Regarding Claim 10, It recites “The image processing method of claim 6, wherein, the determining a transition region, a fluid region and an ordinary region in the blurred image based on a preset density threshold range, comprises:
acquiring a maximum density value and a minimum density value in a preset density threshold range,
comparing the density value of each pixel in the blurred image with the maximum density value and the minimum density value, respectively,
setting the density value of a pixel whose density value is greater than the maximum density value as a first numerical value, setting the density value of a pixel whose density value is greater than the minimum density value as a second numerical value, and calculating the density value of a pixel whose density value is less than the maximum density value and greater than the minimum density value through a preset calculation formula; wherein the first numerical value is greater than the second numerical value,
acquiring pixels with density values less than the first value and greater than the second value as the transition region in the blurred image, pixels with density values less than the second value as the ordinary region, and pixels with density values greater than the first value as the fluid region.” in the context of Claim 10.
The prior arts of record either alone or in combination fails to teach or suggest the above quoted limitation of Claim 10. Therefore, Claim 10 is allowable over prior art.
Regarding Claim 11, it recites “The image processing method of claim 1, wherein the rendering the transition region, the fluid region and the ordinary region to generate a target image, comprises:
performing rendering by setting different color mixing according to data corresponding to the transition region, the fluid region and the common region, to generate the target image.” in the context of Claim 11.
The prior arts of record either alone or in combination fails to teach or suggest the above quoted limitation of Claim 11. Therefore, Claim 11 is allowable over prior art.
Claim 17 recites similar limitations as discussed above with regard to claims 2, 3, 4. Therefore, claim 17 is allowable over prior art.
Claim 19 recites similar limitations as discussed above with regard to claim 10. Therefore, claim 19 is allowable over prior art.
Claim 20 recites similar limitations as discussed above with regard to claim 11. Therefore, claim 20 is allowable over prior art.
Claim 21 recites similar limitations as discussed above with regard to claims 2, 3, 4. Therefore, claim 21 is allowable over prior art.
Claim 23 recites similar limitations as discussed above with regard to claim 11. Therefore, claim 23 is allowable over prior art.
Conclusion
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/Xin Sheng/Primary Examiner, Art Unit 2619