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 .
Response to Amendment
This action is in response to the amendment filed on 16th March, 2026. Claims 1, 11, and 16 have been amended. Claims 1-20 remain rejected in the application.
Response to Arguments
Applicant's arguments with respect to Claims 1, 11, and 16 filed on 16th March, 2026, with respect to the rejection under 35 U.S.C. § 103, regarding that the prior art does not teach the limitation(s): "representing one or more objects in a scene using a geometric mesh approximating a plurality of volumetric particles having respective volumes associated with corresponding portions of the one or more objects" and "determining a response value of the at least one volumetric particle corresponding to the intersection of the ray" have been fully considered, but are moot because of new grounds for rejection. It has now been taught by the combination of Wu and Weidlich.
Regarding arguments to Claims 2-10, 12-15, and 17-20, they directly/indirectly depend on independent Claims 1, 11, and 16 respectively. Applicant does not argue anything other than independent Claims 1, 11, and 16. The limitations in those claims, in conjunction with combination, was previously established as explained.
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.
Claims 1, 5-7, 9-11, 13-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20240176931 A1, previously cited), hereinafter referenced as Wu, in view of Weidlich et al. (US 20220398800 A1), hereinafter referenced as Weidlich.
Regarding Claim 1, Wu discloses a computer-implemented method (Wu, [0044]: teaches a method for real-time volumetric rendering of dynamic translucent particles), comprising:
representing one or more objects in a scene using a geometric mesh approximating a plurality of volumetric particles having respective volumes associated with corresponding portions of the one or more objects (Wu, [0044]: teaches a graphics processing pipeline that renders dynamic particles <read on volumetric particles> for real-time applications, such as simulations or video games; FIG. 2 teaches using a density volume <read on geometric mesh> to approximate the generated dynamic particles <read on objects> in the scene; [0070]: teaches graphics processing pipeline 200 converting the dynamic particles into density volume 203, where the density volume represents a density distribution of the respective dynamic particles distributed in a 3D space, such as simulated translucent materials (e.g., snow, ash, dust, etc.) being represented as particles having a radius and position in 3D space <read on respective volumes>, which can then be converted to a density value (or volume texture) that represents the density of the material in a 3D grid <read on corresponding portions of objects>; [0071]: teaches the graphics processing pipeline 200 then precomputing light distribution within the density volume 205, where "the light distribution represents a light value for each grid point within the density volume using ray marching from a light source"; Note: it should be noted that the particles are being interpreted as volumetric particles as each particle has its own radius and position; additionally, the particles correspond to the 3D grid, which is interpreted to be corresponding to each 3D grid point);
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determining an intersection of a ray, cast for a selected view with respect to the scene, with at least a portion of the geometric mesh corresponding to at least one of the volumetric particles (Wu, [0074]: teaches rendering dynamic particles in real-time using ray marching 207, where "rendering is performed by computing pixel color values determined using ray marching <read on determining ray intersection> toward a viewpoint position <read on ray cast for selected view>, the density volume, and the precomputed light distribution"; FIG. 2 teaches converting the generated dynamic particles to a density volume <read on geometric mesh corresponding to volumetric particles>);
determining a response value of the at least one volumetric particle corresponding to the intersection of the ray (Wu, [0092]: teaches "the pixel color values are computed by generating a ray for each pixel from the camera position, sampling the generated rays at sections that intersect with the density volume, retrieving a light value <read on response value> from the light distribution at each sample, and computing a scattered radiance value at each sample based on the retrieved light values and computing the pixel color values based on the scattered radiance values"); and
[[using the response value to determine a pixel value for an image of the scene to be rendered from the selected view.]]
However, Wu does not expressly disclose
using the response value to determine a pixel value for an image of the scene to be rendered from the selected view.
Weidlich discloses
using the response value to determine a pixel value for an image of the scene to be rendered from the selected view (Weidlich, [0067]: teaches a rendering engine taking in object position and lighting data as input <read on response value> to compute each pixel color value in each frame <read on image>; [0068]: teaches the rendering engine performing "ray tracing wherein a pixel color value is determined by computing which objects lie along a ray traced in the scene space from the camera viewpoint <read on selected view> through a point or portion of the camera view plane that corresponds to that pixel"; [0073]: teaches the rendering engine 950 using scene data to render CGI imagery, where "the camera viewpoint is not explicit, but can be determined from a viewing frustum").
Weidlich is analogous art with respect to Wu because they are from the same field of endeavor, namely performing ray tracing to render volumetric scenes. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a rendering engine that performs ray tracing to determine pixel color value based on ray intersection hits of objects in scene space as taught by Weidlich into the teaching of Wu. The suggestion for doing so would allow the system to determine what to render from a given viewpoint and the resulting ray intersection values, thereby yielding predictable results. Therefore, it would have been obvious to combine Weidlich with Wu.
Regarding Claim 11, it recites the limitations that are similar in scope to Claim 1, but in at least one processor. As shown in the rejection, Wu and Weidlich discloses the limitations of Claim 1. Additionally, Wu discloses at least one processor (Wu, [0113]: teaches processors executing code) comprising:
processing logic to (Wu, [0113]: teaches the processors being discrete logic circuitry <read on processing logic>):…
Thus, Claim 11 is met by Wu according to the mapping presented in the rejection of Claim 1, given the computer-implemented method corresponds to at least one processor.
Regarding Claim 5, the combination of Wu and Weidlich discloses the computer-implemented method of Claim 1. Additionally, Wu further discloses wherein
the volumetric particles represent different colors for different view directions (Wu, [0074]: teaches rendering dynamic particles in real-time using ray marching 207, where "rendering is performed by computing pixel color values determined using ray marching toward a viewpoint position <read on different colors for different view directions>, the density volume, and the precomputed light distribution").
Regarding Claims 6 and 13, the combination of Wu and Weidlich discloses the computer-implemented method and the at least one processor of Claims 1 and 11 respectively. Additionally, Wu further discloses wherein the volumetric particles correspond to local three-dimensional functions including at least one of
a linear function,a Lagrangian function,a Gaussian distribution function,a Gaussian kernel, ora Gabor kernel (Wu, [0070]: teaches converting the dynamic particles into a density volume 203, where the density volume represents the density distribution <read on Gaussian distribution functions> of the respective dynamic particles distributed in 3D space; Note: although "Gaussian distribution function" is not expressly stated, one skilled in the art would understand that it is a defined as a continuous random variable characterized by its mean (µ) and standard deviation (σ); the density distribution is being interpreted as random a density mesh approximation of dynamic particles that best encompasses said particles).
Regarding Claims 7 and 14, the combination of Wu and Weidlich discloses the computer-implemented method and the at least one processor of Claims 1 and 11 respectively. Additionally, Wu further discloses
determining that the ray intersects a plurality of semi-transparent volumetric particles (Wu, FIG. 2 teaches rendering dynamic particles in real-time using ray marching to determine ray intersection; [0070]: teaches converting the dynamic particles into density volume 203 to represent simulated translucent materials, such as snow, ash, and dust <read on semi-transparent volumetric particles>; Note: it should be noted that translucent materials, such as snow, ash, and dust, are not fully transparent and is thus being interpreted as being semi-transparent); and
determining the pixel value, corresponding to the ray, based in part upon response values from one or more of the intersected semi-transparent volumetric particles up to at least a transmissive threshold (Wu, [0092]: teaches "the pixel color values are computed by generating a ray for each pixel from the camera position, sampling the generated rays at sections that intersect with the density volume, retrieving a light value <read on response values> from the light distribution at each sample, and computing a scattered radiance value at each sample based on the retrieved light values and computing the pixel color values based on the scattered radiance values <read on transmissive threshold>"; Note: it should be noted that although "transmissive threshold" is not explicitly stated, one skilled in the art would understand that a limit must be imposed on calculating light scatter to avoid performance penalties, which is interpreted to be a transmissive threshold).
Regarding Claim 9, the combination of Wu and Weidlich discloses the computer-implemented method of Claim 1. Additionally, Wu further discloses wherein
determining the intersection of the ray is accelerated using hardware acceleration (Wu, [0106]: teaches to further accelerate computation of the graphics processing pipeline <read on hardware acceleration>, "multiple processing circuitries (e.g., GPUs) may be used in the rendering process"; [0074]: teaches the graphics processing pipeline 200 teaches taking ray intersection samples to determine pixel color values from ray marching calculations <read on determining intersection of ray>).
Regarding Claim 10, the combination of Wu and Weidlich discloses the computer-implemented method of Claim 1. Additionally, Wu further discloses
generating the image of the scene to be provided to an operation relating to at least one of robotics, automotive navigation, realistic synthetic image generation, or synthetic image relighting (Wu, [0109]: teaches the real-time volumetric rendering of dynamic particles producing realistic simulations of high-density natural materials <read on realistic synthetic image generation>, such as snow).
Regarding Claims 15 and 20, the combination of Wu and Weidlich discloses the computer-implemented method and the system of Claims 1 and 16 respectively. Additionally, Wu further discloses wherein the at least one processor is comprised in at least one of:
a system for performing simulation operations;a system for performing simulation operations to test or validate autonomous machine applications;a system for performing digital twin operations;a system for performing light transport simulation;a system for rendering graphical output;a system for performing deep learning operations;a system implemented using an edge device;a system for generating or presenting virtual reality (VR) content;a system for generating or presenting augmented reality (AR) content;a system for generating or presenting mixed reality (MR) content;a system incorporating one or more Virtual Machines (VMs);a system implemented at least partially in a data center;a system for performing hardware testing using simulation;a system for synthetic data generation;a system for performing generative AI operations;a system for performing one or more operations using a large language model (LLM);a system for performing one or more operations using a vision language model (VLM);a collaborative content creation platform for 3D assets; ora system implemented at least partially using cloud computing resources (Wu, [0069]: teaches the graphics processing pipeline 200 generating dynamic snow particles from a physically-based simulation 201 <read on system for performing simulation operations>).
Regarding Claim 16, Wu discloses a system (Wu, [0045]: teaches a content generation system 100) comprising:
one or more processors to determine pixel values for an image of a scene to be rendered from a [[specified]] view by, in part, casting a plurality of rays corresponding to the [[specified]] view (Wu, [0066]: teaches a processing unit 127 <read on processors> including a control component 198 that renders the dynamic particles in real-time by computing pixel color values determined using ray marching toward a viewpoint position <read on ray casting from view>, the density volume, and the light distribution, where an output representation of the dynamic particles <read on image of scene> is generated based on the rendering) and
determining intersections of the plurality of rays with a mesh of volumetric particles having respective volumes representing corresponding portions of one or more objects in the scene (Wu, [0074]: teaches rendering dynamic particles in real-time using ray marching 207, where "rendering is performed by computing pixel color values determined using ray marching <read on determining ray intersection> toward a viewpoint position, the density volume, and the precomputed light distribution"; FIG. 2 teaches converting the generated dynamic particles to a density volume <read on mesh corresponding to objects>; [0070]: teaches graphics processing pipeline 200 converting the dynamic particles into density volume 203, where the density volume represents a density distribution of the respective dynamic particles distributed in a 3D space, such as simulated translucent materials (e.g., snow, ash, dust, etc.) being represented as particles having a radius and position in 3D space <read on respective volumes>, which can then be converted to a density value (or volume texture) that represents the density of the material in a 3D grid <read on corresponding portions of objects>; [0071]: teaches the graphics processing pipeline 200 then precomputing light distribution within the density volume 205, where "the light distribution represents a light value for each grid point within the density volume using ray marching from a light source"; Note: it should be noted that the particles are being interpreted as volumetric particles as each particle has its own radius and position; additionally, the particles correspond to the 3D grid, which is interpreted to be corresponding to each 3D grid point; furthermore, "volumetric particles" and "objects in scene" are being interpreted as similar terms),
the pixel value corresponding to a given ray calculated using response values of one or more volumetric particles intersected by the ray (Wu, [0092]: teaches "the pixel color values are computed by generating a ray for each pixel from the camera position, sampling the generated rays at sections that intersect with the density volume <read on volumetric particles>, retrieving a light value <read on response value> from the light distribution at each sample, and computing a scattered radiance value at each sample based on the retrieved light values and computing the pixel color values based on the scattered radiance values").
However, Wu does not expressly disclose
one or more processors to determine pixel values for an image of a scene to be rendered from a specified view by, in part, casting a plurality of rays corresponding to the specified view.
Weidlich discloses
one or more processors to determine pixel values for an image of a scene to be rendered from a specified view by, in part, casting a plurality of rays corresponding to the specified view (Weidlich, [0073]: teaches scene data including locations of several articulated characters, background objects, lighting, etc. specified in a 2D, 3D, or other dimensional space (such as 2.5-dimensional space, pseudo-3D spaces, etc.) along with locations of a camera viewpoint and view place <read on specified view> from which to render imagery).
Weidlich is analogous art with respect to Wu because they are from the same field of endeavor, namely performing ray tracing to render volumetric scenes. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a rendering engine that performs ray tracing to determine pixel color value based on ray intersection hits of objects in scene space as taught by Weidlich into the teaching of Wu. The suggestion for doing so would allow the system to determine what to render from a given viewpoint and the resulting ray intersection values, thereby yielding predictable results. Therefore, it would have been obvious to combine Weidlich with Wu.
Regarding Claim 18, the combination of Wu and Weidlich discloses the system of Claim 16. Additionally, Wu further discloses wherein
casting of the plurality of rays is accelerated using hardware acceleration (Wu, [0106]: teaches to further accelerate computation of the graphics processing pipeline <read on hardware acceleration>, "multiple processing circuitries (e.g., GPUs) may be used in the rendering process"; [0074]: teaches the graphics processing pipeline 200 teaches taking ray intersection samples <read on casting rays> to determine pixel color values from ray marching calculations).
Claims 2, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20240176931 A1, previously cited), hereinafter referenced as Wu, in view of Weidlich et al. (US 20220398800 A1), hereinafter referenced as Weidlich as applied to Claims 1, 11, and 16 above respectively, and further in view of Cline et al. (US 5226113 A, previously cited), hereinafter referenced as Cline.
Regarding Claims 2 and 12, the combination of Wu and Weidlich discloses the computer-implemented method of Claim 1. The combination of Wu and Weidlich does not expressly disclose the limitations of Claim 2; however, Cline discloses wherein
the volumetric particles are two- or three- or more dimensional particles having anisotropic factors along different dimensions (Cline, [Col. 4, Lines 46-60]: teaches "the data projection is scaled and any anisotropy <read on anisotropic factors> between the object space and the image plane is removed by only a single set of calculations, after back-projection is complete," where "the apparent dimensions of each voxel <read on 3D particles> are going to change as the effective elevation angles .psi. and .gamma. change <read on different dimensions>").
Cline is analogous art with respect to Wu, in view of Weidlich because they are from the same field of endeavor, namely volumetric ray tracing. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to scale data projection to remove anisotropy between object space and the image plane based on effective elevation angles as taught by Cline into the teaching of Wu, in view of Weidlich. The suggestion for doing so would result in corrected height data of the surfaces of volumetric objects, which would allow for more accurate ray-traced light calculations. Therefore, it would have been obvious to combine Cline with Wu, in view of Weidlich.
Regarding Claim 19, the combination of Wu and Weidlich discloses the system of Claim 16. The combination of Wu and Weidlich does not expressly disclose the limitations of Claim 19; however, Cline discloses wherein
the volumetric particles are three-dimensional particles having anisotropic factors along different dimensions (Cline, [Col. 4, Lines 46-60]: teaches "the data projection is scaled and any anisotropy <read on anisotropic factors> between the object space and the image plane is removed by only a single set of calculations, after back-projection is complete," where "the apparent dimensions of each voxel <read on 3D particles> are going to change as the effective elevation angles .psi. and .gamma. change <read on different dimensions>").
Cline is analogous art with respect to Wu, in view of Weidlich because they are from the same field of endeavor, namely volumetric ray tracing. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to scale data projection to remove anisotropy between object space and the image plane based on effective elevation angles as taught by Cline into the teaching of Wu, in view of Weidlich. The suggestion for doing so would result in corrected height data of the surfaces of volumetric objects, which would allow for more accurate ray-traced light calculations. Therefore, it would have been obvious to combine Cline with Wu, in view of Weidlich.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20240176931 A1, previously cited), hereinafter referenced as Wu, in view of Weidlich et al. (US 20220398800 A1), hereinafter referenced as Weidlich, and further in view of Cline et al. (US 5226113 A, previously cited), hereinafter referenced as Cline as applied to Claim 2 above respectively, and further in view of Bigos et al. (US 20220309736 A1, previously cited), hereinafter referenced as Bigos.
Regarding Claim 3, the combination of Wu, Weidlich, and Cline discloses the computer-implemented method of Claim 2. Additionally, Wu further discloses
generating the plurality of volumetric particles [[based in part on a plurality of two-dimensional images obtained for a plurality of views of the scene]] (Wu, FIG. 2 teaches generating dynamic particles in step 201).
However, the combination of Wu, Weidlich, and Cline does not expressly disclose
generating the plurality of volumetric particles based in part on a plurality of two-dimensional images obtained for a plurality of views of the scene.
Bigos discloses
generating the plurality of volumetric particles based in part on a plurality of two-dimensional images obtained for a plurality of views of the scene (Bigos, [0089]: teaches generating camera viewpoint images <read on 2D images> of a scene based on the materials in the scene (e.g., material properties such as light response properties)).
Bigos is analogous art with respect to the combination of Wu, Weidlich, and Cline because they are from the same field of endeavor, namely accurate light distribution for 3D scenes. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network model that reproduces accurate ray-traced lighting of object surfaces as taught by Bigos into the combined teaching of Wu, Weidlich, and Cline. The suggestion for doing so would allow for the neural network to be trained on training images that used ray marching, which results in faster and more accurate data output, thereby resulting in a rendering system with improved performance. Therefore, it would have been obvious to combine Bigos with the combination of Wu, Weidlich, and Cline.
Regarding Claim 4, the combination of Wu, Weidlich, Cline, and Bigos discloses the computer-implemented method of Claim 3. The combination of Wu, Weidlich, and Cline does not expressly disclose the limitations of Claim 4; however, Bigos discloses wherein
the selected view is different from any of the plurality of views for which the plurality of two-dimensional images is obtained (Bigos, [0089]: teaches generating camera viewpoint images <read on 2D images of differing views> of a scene based on the materials in the scene (e.g., material properties such as light response properties)).
Bigos is analogous art with respect to the combination of Wu, Weidlich, and Cline because they are from the same field of endeavor, namely accurate light distribution for 3D scenes. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network model that reproduces accurate ray-traced lighting of object surfaces as taught by Bigos into the combined teaching of Wu, Weidlich, and Cline. The suggestion for doing so would allow for the neural network to be trained on training images that used ray marching, which results in faster and more accurate data output, thereby resulting in a rendering system with improved performance. Therefore, it would have been obvious to combine Bigos with the combination of Wu, Weidlich, and Cline.
Claim 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20240176931 A1, previously cited), hereinafter referenced as Wu, in view of Weidlich et al. (US 20220398800 A1), hereinafter referenced as Weidlich as applied to Claims 1 and 16 above respectively, and further in view of Zobel et al. (US 20230216999 A1, previously cited), hereinafter referenced as Zobel.
Regarding Claim 8, the combination of Wu and Weidlich discloses the computer-implemented method of Claim 1. The combination of Wu and Weidlich does not expressly disclose the limitations of Claim 8; however, Zobel discloses wherein
the view corresponds to a distorted or moving virtual camera with rolling shutter (Zobel, [0204]: teaches imaging system 200 performing "lens distortion correction (LDC) and/or rolling shutter correction (RSC) to the image to reduce any distortion from the lens and/or rolling shutter," where imaging system 200 "takes an input and reprojects the perspective to a new location in the environment" and eliminate wobbling of the camera <read on distorted virtual camera>).
Zobel is analogous art with respect to Wu, in view of Weidlich because they are from the same field of endeavor, namely rendering 3D scenes and environments. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a 3D stabilization engine that performs image correction as taught by Zobel into the teaching of Wu, in view of Weidlich. The suggestion for doing so would allow the system account for errors when rendering light-accurate scenes, thereby yielding predictable results. Therefore, it would have been obvious to combine Zobel with Wu, in view of Weidlich.
Regarding Claim 17, the combination of Wu and Weidlich discloses the system of Claim 16. The combination of Wu and Weidlich does not expressly disclose the limitations of Claim 17; however, Zobel discloses wherein
the specified view corresponds to a distorted virtual camera (Zobel, [0204]: teaches imaging system 200 performing "lens distortion correction (LDC) and/or rolling shutter correction (RSC) to the image to reduce any distortion from the lens and/or rolling shutter," where imaging system 200 "takes an input and reprojects the perspective to a new location in the environment" and eliminate wobbling of the camera <read on distorted virtual camera>).
Zobel is analogous art with respect to Wu, in view of Weidlich because they are from the same field of endeavor, namely rendering 3D scenes and environments. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a 3D stabilization engine that performs image correction as taught by Zobel into the teaching of Wu, in view of Weidlich. The suggestion for doing so would allow the system account for errors when rendering light-accurate scenes, thereby yielding predictable results. Therefore, it would have been obvious to combine Zobel with Wu, in view of Weidlich.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Desbrun et al. (US 20160307359 A1) discloses enabling particle-based fluid simulation; and
Hecht (US 9189883 B1) discloses rendering scenes that comprise of one or more volumes viewed along a ray from a virtual camera.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARL TRUONG whose telephone number is (703)756-5915. The examiner can normally be reached 10:30 AM - 7:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at (571) 272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.D.T./Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
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