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, 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 1-3, 8, 10-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (U.S. Patent Application Publication No. 2024/0104824), referred herein as Gupta, in view of Kozlowski et al. (U.S. Patent Application Publication No. 2023/0281906), referred herein as Kozlowski.
Regarding claim 1, Gupta teaches one or more processors comprising: one or more circuits to (fig 9, system 900, processor 910):
identify an object and a refractive surface of a simulated scene (figs 1A and 1C; paragraph 34, lines 1-7; paragraph 36, lines 1-14 and the last 8 lines; an object and refractive surface are identified in a simulated scene);
generate, based at least on one or more samples of light paths in the simulated scene, a transformation data structure for the object relative to the refractive surface (paragraph 38, lines 1-16; paragraph 46, lines 1-11 and the last 4 lines; paragraphs 51 and 52; paragraph 54, lines 1-10; transformation data structures are generated for the object relative to the surface based on light paths in the simulated scene);
determine, based at least on the transformation data structure, a position in the simulated scene depicted by an image representing the object as appearing behind the refractive surface (figs 1A and 1C; paragraph 36, lines 1-14 and the last 8 lines; paragraph 38; paragraph 41, lines 1-17; positions for the object in the scene are determined based on the transformation data structure, where the object appears behind the surface); and
process a frame of the simulated scene according to the position in the simulated scene depicted by the image representing the object (paragraph 36, lines 1-14; paragraph 41, lines 1-17; paragraphs 45 and 48; a frame of the scene is processed according to the determined positions).
Gupta does not explicitly teach generating a motion vector to process the simulated scene.
However, in a similar field of endeavor, Kozlowski teaches a system configured to identify an object and a refractive surface of a simulated scene, and determine positions of objects in the scene based on samples of light paths in the scene, where the objects appear behind the refractive surface (figs 1; paragraphs 14 and 16; paragraphs 22, 23, and 24), and further configure to process a frame of the simulated scene based at least on a motion vector generated according to the positions (paragraph 14; paragraph 27; paragraphs 36 and 37).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the motion vectors of Kozlowski with the frame processing of Gupta because this helps reduce artifacts to improve image quality, while also reducing processing requirements, which is especially beneficial in ray tracing techniques such as that in Gupta, (see, for example, Kozlowski, paragraphs 3 and 36).
Regarding claim 2, Gupta in view of Kozlowski teaches the one or more processors of claim 1, wherein the frame is a first frame, and the position in the simulated scene depicted by the image is a first position of a first image, and wherein the one or more circuits are to: determine a second position in the simulated scene depicted by a second image representing the object in a second frame using a second transformation data structure; and generate a motion vector for one or more pixels of the second frame based at least on a position of the object in the first frame and the second transformation data structure (Gupta, paragraph 38, the last 11 lines; paragraph 39; paragraph 41, lines 1-17; paragraphs 48 and 52; Kozlowski, paragraphs 23 and 25; paragraph 33; paragraphs 39 and 43; paragraphs 46 and 48; the motivation to combine is similar to that discussed above in the rejection of claim 1).
Regarding claim 3, Gupta in view of Kozlowski teaches the one or more processors of claim 2, wherein the position of the object is a first position of the object, and wherein the one or more circuits are to: generate the second transformation data structure according to a second position of the object in the simulated scene, the second position of the object being a different position from the first position of the object (Gupta, figs 1A and 1C; paragraphs 38 and 39; paragraphs 48 and 52; Kozlowski, paragraphs 23 and 25; paragraph 33; paragraphs 39 and 43; paragraphs 46 and 48; the motivation to combine is similar to that discussed in the rejection of claim 1).
Regarding claim 8, Gupta in view of Kozlowski teaches the one or more processors of claim 1, wherein the refractive surface is a first refractive surface, and the simulated scene comprises a second refractive surface positioned between the object and the first refractive surface, and wherein the one or more circuits are to: generate a second transformation data structure for the second refractive surface; and render the frame of the simulated scene according to the position of the image determined using the transformation data structure and the second transformation data structure (Gupta, figs 1A and 1C; paragraph 36, lines 1-14 and the last 8 lines; paragraph 38; paragraph 41, lines 1-17; paragraphs 45 and 48; Kozlowski, figs 1; paragraphs 23 and 25; paragraphs 33 and 34; paragraphs 43 and 44; paragraph 46; the motivation to combine is similar to that discussed above in the rejection of claim 1).
Regarding claim 10, Gupta in view of Kozlowski teaches the one or more processors of claim 1, wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational Al operations; a system for performing generative Al operations using a large language model (LLM); a system for performing generative Al operations using a video language model (VLM); a system for performing generative Al operations using a multimodal language model; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system using or deploying one or more inference microservices; a system that incorporates one or more machine learning models deployed in a service or microservice along with an operating system (OS)-level virtualization package (e.g., a container); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (Gupta, paragraph 29; Kozlowski, paragraphs 14 and 15; paragraph 29; claim 13; the motivation to combine is similar to that discussed above in the rejection of claim 1).
Regarding claim 11, Gupta teaches a system, comprising: one or more processors configured to (fig 9, system 900, processor 910):
generate, for a first frame of a simulated scene, a first transformation data structure for an object positioned relative to a refractive surface in the simulated scene (paragraph 38, lines 1-16; paragraph 46, lines 1-11 and the last 4 lines; paragraphs 51 and 52; paragraph 54, lines 1-10; transformation data structures are generated for the object relative to the surface based on light paths in the simulated scene);
determine, using the first transformation data structure, a first position in the simulated scene depicted by an image representing the object as appearing behind the refractive surface in the first frame (figs 1A and 1C; paragraph 36, lines 1-14 and the last 8 lines; paragraph 38; paragraph 41, lines 1-17; positions for the object in the scene are determined based on the transformation data structure, where the object appears behind the surface);
generate, for a second frame of the simulated scene, a second transformation data structure for an object positioned relative to the refractive surface in the simulated scene, determine, using the second transformation data structure, a second position in the simulated scene depicted by the image representing the object as appearing behind the refractive surface in the second frame (figs 1A and 1C; paragraphs 38 and 39; paragraph 41, lines 1-17; paragraphs 48 and 52; a second transformation data structure is generated for a subsequent frame to determine the position of the object in the same manner as the first frame); and
generate a rendering for the object based at least on the first position and the second position (paragraph 36, lines 1-14; paragraph 38, the last 11 lines; paragraph 39; paragraph 41, lines 1-17; paragraphs 45 and 48; a rendering for the object is generated according to the determined positions).
Gupta does not explicitly teach generating a motion vector to process the simulated scene.
However, in a similar field of endeavor, Kozlowski teaches a system configured to identify an object and a refractive surface of a simulated scene, and determine positions of objects in the scene based on samples of light paths in the scene, where the objects appear behind the refractive surface (figs 1; paragraphs 14 and 16; paragraphs 22, 23, and 24), and further configure to process a frame of the simulated scene based at least on a motion vector generated according to the positions (paragraph 14; paragraph 27; paragraphs 36 and 37).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the motion vectors of Kozlowski with the frame processing of Gupta because this helps reduce artifacts to improve image quality, while also reducing processing requirements, which is especially beneficial in ray tracing techniques such as that in Gupta, (see, for example, Kozlowski, paragraphs 3 and 36).
Regarding claim 12, Gupta in view of Kozlowski teaches the system of claim 11, wherein the one or more processors are to: render the second frame based at least on the motion vector (Gupta, paragraph 36, lines 1-14; paragraph 38, the last 11 lines; paragraph 39; paragraph 41, lines 1-17; paragraphs 45 and 48; Kozlowski, paragraphs 23 and 25; paragraph 33; paragraphs 39 and 43; paragraphs 46 and 48; the motivation to combine is similar to that discussed above in the rejection of claim 1).
Regarding claim 13, Gupta in view of Kozlowski teaches the system of claim 12, wherein the one or more processors are to: render the second frame using temporal anti-aliasing calculated using the motion vector (Kozlowski, paragraphs 13 and 14; paragraphs 20 and 27; paragraphs 36 and 37; the motivation to combine is similar to that discussed above in the rejection of claim 1).
Regarding claim 14, Gupta in view of Kozlowski teaches the system of claim 11, wherein the one or more processors are to: determine the first transformation data structure and the second transformation data structure using a light transport simulation process (Gupta, paragraph 29; paragraphs 48 and 52; Kozlowski, paragraphs 14 and 15; the motivation to combine is similar to that discussed above in the rejection of claim 1).
Regarding claim 17, the limitations of this claim substantially correspond to the limitations of claim 10; thus they are rejected on similar grounds.
Regarding claims 18-20, the limitations of these claims substantially correspond to the limitations of claims 1-3, respectively; thus they are rejected on similar grounds as their corresponding claims.
Claims 4-6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta, in view of Kozlowski, and further in view of Fenney et al. (U.S. Patent Application Publication No. 2023/0252718), referred herein as Fenney.
Regarding claim 4, Gupta in view of Kozlowski teaches the one or more processors of claim 1, wherein the one or more circuits are to: generate the transformation data structure based at least on the refractive surface (Gupta, paragraph 38, lines 1-16; paragraph 46, lines 1-11 and the last 4 lines; paragraphs 51 and 52; paragraph 54, lines 1-10).
Gupta in view of Kozlowski does not explicitly teach determining a distance to a center point of the refractive surface from a local region of the refractive surface and basing the data structure on the center point.
However, in a similar field of endeavor, Fenney teaches a system configured to identify objects and refractive surfaces and generate data structures based on light paths in a simulated scene (paragraphs 2 and 3; paragraphs 98 and 100), and further configured to determine a distance to a center point of the refractive surface from a local region of the refractive surface, and base the data structure, in part, on the center point (fig 8D; paragraphs 140 and 147).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the center point data structure processing of Fenney with the data structure processing of Gupta in view of Kozlowski because this helps optimize the ray tracing procedures by reducing processing load and increasing processing efficiency (see, for example, Fenney, paragraph 17; paragraph 144)
Regarding claim 5, Gupta in view of Kozlowski, further in view of Fenney teaches the one or more processors of claim 4, wherein the one or more circuits are to: determine the distance to the center point according to a curvature of the local region of the refractive surface (Fenney, fig 8D; paragraphs 140 and 147; Gupta, paragraph 60, lines 1-11; Kozlowski, paragraph 40; the motivations to combine are similar to those discussed above in the rejections of claims 1 and 4).
Regarding claim 6, Gupta in view of Kozlowski, further in view of Fenney teaches the one or more processors of claim 5, wherein at least one light path sample intersects the refractive surface at an intersection point, and wherein the one or more processors are to: determine the curvature of the local region according to one or more vertices of the refractive surface proximate to the intersection point (Gupta, figs 1A and 1C; paragraph 38, lines 1-16; paragraph 46, lines 1-11 and the last 4 lines; paragraph 52; paragraph 54, lines 1-10; paragraph 60, lines 1-11; paragraph 61; Kozlowski, paragraphs 16 and 18; paragraph 40; the motivation to combine is similar to that discussed above in the rejection of claim 1).
Regarding claim 15, the limitations of this claim substantially correspond to the limitations of claim 4; thus they are rejected on similar grounds.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta, in view of Kozlowski, and further in view of Kryachko (U.S. Patent Application Publication No. 2018/0012392), referred herein as Kryachko.
Regarding claim 7, Gupta in view of Kozlowski teaches the one or more processors of claim 1, wherein the one or more circuits are to: generate the transformation data structure according to the refractive surface (Gupta, figs 1A and 1C; paragraph 38, lines 1-16; paragraph 46, lines 1-11 and the last 4 lines; paragraphs 51 and 52; paragraph 54, lines 1-10; Kozlowski, paragraphs 16 and 18; paragraph 40).
Gupta in view of Kozlowski does not explicitly teach utilizing a refractive index of the refractive surface.
However, in a similar field of endeavor, Kryachko teaches a system configured to identify objects and refractive surfaces and generate transformation data structures based on light paths in a simulated scene (figs 2C and 10A; paragraph 112; paragraph 120), and further configured to utilize a refractive index of the refractive surface to generate the transformation data structure (paragraphs 65 and 66; paragraph 112; paragraph 129).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the refraction index processing of Kryachko with the transformation processing of Gupta in view of Kozlowski because this improves the refractive surface approximation, thereby improving the overall image quality output by the processors (see, for example, Kryachko, paragraphs 9, 10, and 11)
Regarding claim 16, the limitations of this claim substantially correspond to the limitations of claim 7; thus they are rejected on similar grounds.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta, in view of Kozlowski, and further in view of Moller (U.S. Patent Application Publication No. 2008/0018732), referred herein as Moller.
Regarding claim 9, Gupta in view of Kozlowski teaches the one or more processors of claim 1, wherein the one or more circuits are to: generate the position of the image further based at least on an operation.
Gupta in view of Kozlowski does not explicitly teach utilizing a perspective division operation.
However, in a similar field of endeavor, Moller teaches a system configured to identify objects and refractive surfaces and generate object renderings based on light paths in a simulated scene utilizing transformation data structures (paragraph 20; paragraph 95; paragraph 175), and further configured to utilize a perspective division operation to generate object positions (paragraphs 175 and 198).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the perspective division of Moller with the object position processing of Gupta in view of Kozlowski because this may provide accurate scene rendering with reduced artifacts, thereby improving output image quality and viewing (see, for example, paragraph 91; paragraph 206).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jenkins (U.S. Patent No. 6,028,608); System and method of perception-based image generation and encoding.
Jenkins (U.S. Patent No. 6,057,847); System and method of image generation and encoding using primitive reprojection.
Jenkins (U.S. Patent No. 6,111,582); System and method of image generation and encoding using primitive reprojection.
Kho (U.S. Patent Application Publication No. 2013/0027394); Apparatus and method of multi-view rendering.
Mendez (U.S. Patent Application Publication No. 2018/0033191); Graphics processing systems.
Gruen (U.S. Patent Application Publication No. 2019/0318533); Realism of scenes involving water surfaces during rendering.
Muthler (U.S. Patent Application Publication No. 2021/0390755); Ray tracing hardware acceleration with alternative world space transforms.
Muthler (U.S. Patent Application Publication No. 2021/0390760); Ray tracing hardware acceleration for supporting motion blur and moving/deforming geometry.
Panteleev (U.S. Patent Application Publication No. 2022/0189109); Adaptive temporal image filtering for rendering realistic illumination.
Mcallister (U.S. Patent Application Publication No. 2023/0252716); Generation of tight world space bounding regions.
Muthler (U.S. Patent No. 11,663,770); Hardware-based techniques applicable for ray tracing for efficiently representing and processing an arbitrary bounding volume.
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Hu et al; Reverse Ray Tracing For Transformation Optics; OSA; 2015.
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DAVID T. WELCH
Primary Examiner
Art Unit 2613
/DAVID T WELCH/Primary Examiner, Art Unit 2613
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