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 § 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 1-21 are rejected under 35 U.S.C. 103 as being unpatentable by Marrs et al. (U.S. 2019/0318454 A1) in view of Cerny et al. (U.S. 2021/0166338 A1).
Regarding Claim 1, Marrs discloses a method (Marrs, [0016] “FIG.1, a method 100”, comprising:
processing, by a computer system (Marrs, [0009] “FIG. 4B, a processing system implemented using the PPU”, rendering data associated with an object of interest in a multidimensional digital environment displayed on a view space (Marrs [0031] “FIG. 2, the PPU 200 is a graphics processing unit (GPU) configured to implement a graphics rendering pipeline for processing three-dimensional (3D) graphics data” and [0088] “The model data defines each of the objects that may be visible on a display. The application then makes an API call to the driver kernel that requests the model data to be rendered and displayed” and [0017] “complex pixels within a scene to be rendered are identified, the complex pixels may each include visible pixels that are partially covered by one or more instances of geometry (e.g., one or more polygons, etc.) within the scene” Marrs teaches a processing system renders a 3D graphics data associated with object of interest that includes visible pixels covered by instances of geometry in a multidimensional digital environment (a 3D scene) displayed on a view space ;
determining a pixel count of the object of interest (Marrs, [0020] “the pixel identification pass may identify and mark the complex pixels (e.g., the visible pixels within the scene that are partially covered by one or more instances of geometry) and [0021] “the pixel identification pass may also count, for each pixel, a number of instances of geometry that touch the pixel. This may be stored as a primitive count for the pixel” Marrs teaches determining a pixel count of the object of interest, e.g., the visible pixels within the scene.
determining a total number of pixels in the view space (Marrs, [0147] After this pass, a 2D surface is obtained that contains non-zero values for the pixels that have some partially visible primitives and require further treatment for visibility. The values in those locations indicate the number of primitives that can potentially be visible in those pixels. One can keep track of total number of primitives covering all the pixels of interest by incrementing the unordered access view (UAV) counter for the same UAV” Marrs teaches determining a total number of pixels in the view space by the access view counter.
However, Marrs does not explicitly teach generating a screen coverage metric based on a ratio between the pixel count of the object of interest and the total number of pixels in the view space.
Cerny teaches generating a screen coverage metric based on a ratio between the pixel count of the object of interest and the total number of pixels in the view space (Cerny, [0045] “an image 210 for a wide FOV display to have pixel densities that are smaller at edge regions 212, 214, 216, 218 than at center regions 215 and smaller at corner regions 211, 213, 217 and 219 than at the edge regions 212, 214, 216, 218 as shown in FIG. 2D” and Fig. 3. [0049] The window of the view plane 320 lying within the viewing volume 314, One or more objects 318 which lie within the view volume would be projected to the screen space viewing window 322” and [0056] “the triangle 527 overlaps two different zones in the screen space, i.e., zones Z5 and Z6. FIG. 5B also depicts how the triangle 527 is transformed from homogeneous coordinates to screen space pixel coordinates differently for the different zones, due to the different zones having different screen space transform parameters. Thus the total pixel area rendered is reduced” Cerny teaches generating a screen coverage metric e.g., metric of different pixel densities in each region 211-219, Fig. 2D, based on the pixel count of the object of interest and the total number of pixels in the view space e.g., the triangle 527 only overlaps two different zones Z5, Z6. Thus, the total pixel area rendered is reduced (ratio of rendered pixels of object of interest) the total number of pixels in the view space, Fig. 5B.
Marrs and Cerny are combinable because they are from the same field of endeavor, system and method for image processing and try to solve similar problems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made for modifying the method of Marrs to combine with (as taught by Cerny) in order to because Cerny can provide generating a screen coverage metric e.g., metric of different pixel densities in each regions 211-219, Fig. 2D, based on the pixel count of the object of interest and the total number of pixels in the view space e.g., the triangle 527 only overlaps two different zones Z5, Z6. Thus the total pixel area rendered is reduced (ratio of rendered pixels of object of interest) the total number of pixel in the view space, Fig. 5B (Cerny, [0014], Fig. 2, [0056], Fig. 6, [0078]). Doing so, it may provide developers with more flexibility and greater control over the rendering process (Cerny, [0013]).
Regarding Claim 2, a combination of Marrs and Cerny discloses the method of claim 1, wherein the object of interest is identified using at least one of color segmentation, depth information, object identifiers (Marrs, [0138] Complex pixels can be identified by analyzing the depth and primitive ID buffers” Marrs teaches the object of interest (pixels of object) can be identified using the primitive ID/object identifier, or shader-based differentiation.
Regarding Claim 3, a combination of Marrs and Cerny discloses the method of claim 1, wherein the pixel count of the object of interest is determined using at least one of single-pass rendering (Marrs, [0031] “the PPU 200 is a graphics processing unit (GPU) configured to implement a graphics rendering pipeline for processing three-dimensional (3D) graphics data” and [0116] “anti-aliasing may therefore be performed by combining single-pass deferred GPU rasterization and GPU ray tracing” Marrs teaches using at least one single-pass rendering by a GPU, multi-pass rendering, post-processing analysis, or computational sampling techniques.
Regarding Claim 4, a combination of Marrs and Cerny discloses the method of claim 1, wherein the view space includes at least one of a viewport displaying the multidimensional digital environment, a selected portion of a rendered scene, a secondary camera (Marrs, [0031] “a graphics rendering pipeline for processing three-dimensional (3D) graphics data in order to generate two-dimensional (2D) image data for display on a display device” and [0096] “The viewport SCC stage 550 performs viewport scaling, culling, and clipping of the geometric primitives. Each surface being rendered to is associated with an abstract camera position. The camera position represents a location of a viewer looking at the scene and defines a viewing frustum that encloses the objects of the scene” Marrs teaches the view space includes a viewport displaying by a camera on the multidimensional digital environment (2D, 3D), or a computationally inferred screen space representation.
Regarding Claim 5, a combination of Marrs and Cerny discloses the method of claim 1, wherein the screen coverage metric is used to determine whether the object of interest meets a visibility threshold (Marrs, [0017] “complex pixels within a scene to be rendered are identified, the complex pixels may each include visible pixels that are partially covered by one or more instances of geometry (e.g., one or more polygons, etc.) within the scene” and [0229] “where the renderer traces some high minimum number (e.g., 64) of samples per pixel and then traces additional batches of samples within that pixel until a maximum threshold or stable distribution is achieved” Marrs teaches determine whether the objects of interest (the visible pixels cover instances of geometry) meet the visibility threshold (a maximum threshold or stable distribution is achieved).
Regarding Claim 6, a combination of Marrs and Cerny discloses the method of claim 1, wherein the rendering data is obtained using at least one of a secondary camera, an alternative rendering pass (Marrs, [0019] the conservative rasterization may be implemented over a plurality of passes of the one or more instances of geometry within the scene. For example, a first pass of the conservative rasterization may include a depth pre-pass” and [0020] Further still, a second pass of the conservative rasterization may include a pixel identification pass” Marrs teaches an alternative rendering pass such as a pixel identification pass, or a non-visible computational rendering process.
Regarding Claim 7, the method of claim 1, Marrs does not explicitly teach wherein the screen coverage metric is updated dynamically at predetermined time intervals or in response to changes in the rendering of the multidimensional digital environment.
However, Cerny teaches the screen coverage metric is updated dynamically at predetermined time intervals or in response to changes in the rendering of the multidimensional digital environment (Cerny, Fig. 1, [0041] “When rendering the scene conventionally, these regions have the same number of pixels and the time spent rendering equal sized regions on the screen is approximately the same” and [0070] an efficient graphics rendering process may need to render the entire object uniquely at one or more stages of the rendering pipeline, FIGS. 7A-7C corresponds to the number of times that the object needs to be rendered” Cerny teaches the screen coverage metric (Figs 7A-7C) is updated dynamically at predetermine time intervals corresponds to the number of times that the object needs to be rendered of the multidimensional digital environment.
Marrs and Cerny are combinable see rationale in claim 1.
Regarding Claim 8, a combination of Marrs and Cerny discloses a non-transitory computer readable medium (Marrs, [0084] “The memory 440, the , possible examples of computer-readable media”) comprising instructions (Marrs, [0031] “a set of instructions configured to be executed by the PPU 200”), which when executed by a processing system executes a method, comprising:
processing rendering data associated with an object of interest in a multidimensional digital environment displayed on a view space;
determining a pixel count of the object of interest;
determining a total number of pixels in the view space;
generating a screen coverage metric based on a ratio between the pixel count of the object of interest and the total number of pixels in the view space.
Claim 8 is substantially similar to claim 1 and is rejected based on similar analyses.
Regarding Claim 9, a combination of Marrs and Cerny discloses the non-transitory computer readable medium of claim 8, wherein the object of interest is identified using at least one of color segmentation, depth information, object identifiers, or shader-based differentiation.
Claim 9 is substantially similar to claim 2 and is rejected based on similar analyses.
Regarding Claim 10, a combination of Marrs and Cerny discloses the non-transitory computer readable medium of claim 8, wherein the pixel count of the object of interest is determined using at least one of single-pass rendering, multi-pass rendering, post-processing analysis, or computational sampling techniques.
Claim 10 is substantially similar to claim 3 and is rejected based on similar analyses.
Regarding Claim 11, a combination of Marrs and Cerny discloses the non-transitory computer readable medium of claim 8, wherein the view space includes at least one of a viewport displaying the multidimensional digital environment, a selected portion of a rendered scene, a secondary camera, or a computationally inferred screen space representation.
Claim 11 is substantially similar to claim 4 and is rejected based on similar analyses.
Regarding Claim 12, a combination of Marrs and Cerny discloses the non-transitory computer readable medium of claim 8, wherein the screen coverage metric is used to determine whether the object of interest meets a visibility threshold.
Claim 12 is substantially similar to claim 5 and is rejected based on similar analyses.
Regarding Claim 13, a combination of Marrs and Cerny discloses the non-transitory computer readable medium of claim 8, wherein the rendering data is obtained using at least one of a secondary camera, an alternative rendering pass, or a non-visible computational rendering process.
Claim 13 is substantially similar to claim 6 and is rejected based on similar analyses.
Regarding Claim 14, a combination of Marrs and Cerny discloses the non-transitory computer readable medium of claim 8, wherein the screen coverage metric is updated dynamically at predetermined time intervals or in response to changes in the rendering of the multidimensional digital environment.
Claim 14 is substantially similar to claim 7 and is rejected based on similar analyses.
Regarding Claim 15, a combination of Marrs and Cerny discloses a system (Marrs, [0009] “a processing system implemented using the PPU”), comprising:
a memory device; a processing system coupled to the memory device (Marrs, [0033] “The PPU 200 may also be connected to a local memory comprising a number of memory devices 204. In an embodiment, the local memory may comprise a number of dynamic random access memory (DRAM) devices”, the processing system configured to:
process rendering data associated with an object of interest in a multidimensional digital environment displayed on a view space;
determine a pixel count of the object of interest;
determine a total number of pixels in the view space;
generate a screen coverage metric based on a ratio between the pixel count of the object of interest and the total number of pixels in the view space.
Claim 15 is substantially similar to claim 1 and is rejected based on similar analyses.
Regarding Claim 16, a combination of Marrs and Cerny discloses the system of claim 15, wherein the object of interest is identified using at least one of color segmentation, depth information, object identifiers, or shader-based differentiation.
Claim 16 is substantially similar to claim 2 and is rejected based on similar analyses.
Regarding Claim 17, a combination of Marrs and Cerny discloses the system of claim 15, wherein the pixel count of the object of interest is determined using at least one of single-pass rendering, multi-pass rendering, post-processing analysis, or computational sampling techniques.
Claim 17 is substantially similar to claim 3 and is rejected based on similar analyses.
Regarding Claim 18, a combination of Marrs and Cerny discloses the system of claim 15, wherein the view space includes at least one of a viewport displaying the multidimensional digital environment, a selected portion of a rendered scene, a secondary camera, or a computationally inferred screen space representation.
Claim 18 is substantially similar to claim 4 and is rejected based on similar analyses.
Regarding Claim 19, a combination of Marrs and Cerny discloses the system of claim 15, wherein the screen coverage metric is used to determine whether the object of interest meets a visibility threshold.
Claim 19 is substantially similar to claim 5 and is rejected based on similar analyses.
Regarding Claim 20, a combination of Marrs and Cerny discloses the system of claim 15, wherein the rendering data is obtained using at least one of a secondary camera, an alternative rendering pass, or a non-visible computational rendering process.
Claim 20 is substantially similar to claim 6 and is rejected based on similar analyses.
Regarding Claim 21, a combination of Marrs and Cerny discloses the system of claim 15, wherein the screen coverage metric is updated dynamically at predetermined time intervals or in response to changes in the rendering of the multidimensional digital environment.
Claim 21 is substantially similar to claim 7 and is rejected based on similar analyses.
Conclusion
The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure Chong et al. (U.S. 2020/0193686 A1) and Gardella et al. (U.S. 2011/0169822 A1).
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/KHOA VU/Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
