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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
MPEP 2106 III provides a flowchart for the subject matter eligibility test for product and processes. The claim analysis following the flowchart is as follows:
Regarding Claim 1:
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes. It recites a method, which is a process.
Step 2A, Prong One: Does the claim recite an abstract idea, law of nature, or nature phenomenon?
Yes.
Claim 1 recites: A method comprising: traversing a bounding volume hierarchy (“BVH”) to identify an object within a search area in a scene; determining a distance of the identified object to a center point of the search area; updating or maintaining the search area based on the distance; and continuing traversal of the BVH.
Traversing a bounding hierarchy, determining a distance, updating or maintaining a search area and further continuing traversal the hierarchy can all be done mentally and/or though mathematical relationships and calculations because hierarchy traversing, computing a distance, and updating or maintaining a search area are mental processes/mathematical concepts of mathematical relationships and calculations.
Step 2A, Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
Claim 1 does not recite any additional elements.
Therefore, this judicial exception is not integrated into a practical application because no additional elements are recited to integrate the abstract ideas into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above, no additional elements are recited in claim 1.
Therefore, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, claim 1 is not eligible subject matter under 35 USC 101.
Regarding claim 2, it depends on claim 1 with additional limitation ”wherein traversing the BVH comprises eliminating nodes not included within the search area from consideration.”. Eliminating nodes in traversing involves mental process. Therefore, claim 2 recites abstract idea without additional elements. Similar to the discussion above with respect to claim 1, no additional elements are recited to integrate the abstract idea into practical application or amount to significantly more than the abstract idea.
Therefore, claim 2 is not eligible subject matter under USC 101.
Regarding claim 3, it depends on claim 1 with additional limitation “wherein traversing the BVH comprises traversing to nodes included within the search area.”. Traversing nodes in a hierarchy structure involves mental process. Therefore, claim 3 recites abstract idea without additional elements. Similar to the discussion above with respect to claim 1, no additional elements are recited to integrate the abstract idea into practical application or amount to significantly more than the abstract idea.
Therefore, claim 3 is not eligible subject matter under USC 101.
Regarding claim 4, it depends on claim 1 with additional limitation “wherein the distance is a Chebyshev distance.”. Computing Chebyshev distance involves mathematical equation calculation. Therefore, claim 4 recites abstract idea without additional elements. Similar to the discussion above with respect to claim 1, no additional elements are recited to integrate the abstract idea into practical application or amount to significantly more than the abstract idea.
Therefore, claim 4 is not eligible subject matter under USC 101.
Regarding claim 5, it depends on claim 1 with additional limitation “wherein updating or maintaining the search area based on the distance comprises comparing the search area to the distance and, if the distance is smaller than the search area and a sufficient number of objects have been found in the search area, shrink the search area to align with the distance.”. Comparing distance, checking if distance is smaller and the number of objects found and reducing the search area involve mathematical concepts of relationship and calculation. Therefore, claim 5 recites abstract idea without additional elements. Similar to the discussion above with respect to claim 1, no additional elements are recited to integrate the abstract idea into practical application or amount to significantly more than the abstract idea.
Therefore, claim 5 is not eligible subject matter under USC 101.
Regarding claim 6, it depends on claim 1 with additional limitation “wherein traversing the BVH further includes traversing to an instance node.”. Traversing to an instance node in a hierarchy structure involves mental process. Therefore, claim 6 recites abstract idea without additional elements. Similar to the discussion above with respect to claim 1, no additional elements are recited to integrate the abstract idea into practical application or amount to significantly more than the abstract idea.
Therefore, claim 6 is not eligible subject matter under USC 101.
Regarding claim 7, it depends on claim 6 with additional limitation “wherein traversing to the instance node comprises performing an instance transform on extents of the search area.”. Computing an instance transform involves mathematical equation calculation. Therefore, claim 7 recites abstract idea without additional elements. Similar to the discussion above with respect to claim 6, no additional elements are recited to integrate the abstract idea into practical application or amount to significantly more than the abstract idea.
Therefore, claim 7 is not eligible subject matter under USC 101.
Regarding claim 8, it depends on claim 1 with additional limitation “wherein continuing traversal of the BVH comprises repeating the traversing, determining, and updating steps until a termination criterion is met.”. Traversing in a hierarchy structure until a condition is met involves mental process, can be implemented by a human. Therefore, claim 8 recites abstract idea without additional elements. Similar to the discussion above with respect to claim 1, no additional elements are recited to integrate the abstract idea into practical application or amount to significantly more than the abstract idea.
Therefore, claim 8 is not eligible subject matter under USC 101.
Regarding claim 9, it depends on claim 8 with additional limitation “wherein the termination criterion comprises a situation in which the BVH has been fully traversed.”. Determining if all nodes have been visited in a hierarchy structure involves mental process, can be done by a human. Therefore, claim 9 recites abstract idea without additional elements. Similar to the discussion above with respect to claim 8, no additional elements are recited to integrate the abstract idea into practical application or amount to significantly more than the abstract idea.
Therefore, claim 9 is not eligible subject matter under USC 101.
Claims 10-18 recite similar limitations discussed above with respect to claims 1-
9 respectively but with additional elements of a system with a memory and a processor. The memory and processor are generic computer component that do not integrate the abstract ideas recites in these claims into practical application or amount to significantly more (see MPEP 2106.05(a), (b), and (f)).
Claims 19 and 20 recite similar limitations discussed above with respect to
claims 1-2 respectively but with additional elements of a system with a first digital circuit; and a second digital circuit, wherein the first digital circuit is configured to transmit a search query to the second digital circuit, and the second digital circuit is configured to execute the search query by performing operations including.
The first digital circuit can be implemented as a CPU or human brain, and the second digital circuit can be implemented as a GPU, both are generic computer component that do not integrate the abstract ideas recites in these claims into practical application or amount to significantly more (see MPEP 2106.05(a), (b), and (f)).
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 5, 8-10, 14 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL Zhu et al. (“RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing”), hereinafter as Zhu, in view of NPL Nagarajan et al. (“RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search”), hereinafter as Nagarajan.
Regarding claim 1, Zhu teaches A method (Zhu Page 1, Left Column, First paragraph, “This paper proposes to formulate neighbor search as a ray tracing problem and leverage the dedicated ray tracing hardware in recent GPUs for acceleration.”) comprising: traversing a bounding volume hierarchy (“BVH”) to identify an object within a search area in a scene (Zhu teaches a query point Q, and traversing a BVH to decide the neighboring points of Q, further teaches a search radius of r, Page 4, Right Column, First Paragraph, “the algorithm above can be mapped as a ray tracing problem. This is done by building a BVH from the AABBs of all the points and casting a ray originated from 𝑄. Step 1 essentially uses the ray to traverse the BVH and discards points whose AABBs do not intersect the ray. Step 2 is a ray-primitive intersection test, where the primitives are spheres. Step 1 is completely accelerated by the RT cores, and Step 2 can be implemented as an IS shader in OptiX.”);
determining a distance of the identified object to a center point of the search area (Zhu Page 4, Left Column, Last paragraph, “Step 2 (Sphere test): Otherwise, we further test whether 𝑄 resides in the sphere, by comparing the distance between 𝑄 and the sphere center 𝑃 with 𝑟 . If successful, we can record 𝑃 as a neighbor of 𝑄”);
updating or maintaining the search area based on the distance (Zhu teaches maintaining the search area until finding k neighbors, page 5, left column, first paragraph, “Lines 3-6 show that we generate an AABB for each point and build the BVH. All the AABBs have the same width (twice the search radius). Lines 17-18 show that each query is mapped to a ray. When the maximum neighbor count 𝐾 is met, the IS shader calls the AH shader to terminate the ray.”); ……
Zhu is not relied on for the below claim language ……and continuing traversal of the BVH. Nagarajan teaches ……and continuing traversal of the BVH (Nagarajan teaches changing search area and further teaches refitting BVH until finding k nearest neighbors, Page 5, Right Column, First Paragraph, “we remove all the points that have
found their 𝑘 neighbors from our dataset so that the next iteration will only consider points that have not found their neighbors yet. For points that have not found all 𝑘 neighbors, we expand the neighbor search space by incrementing the radius of spheres and re-fitting the bounding boxes around the spheres for the BVH, as shown in Lines 10 and 11. We continue this process till 𝐷 = {∅} and all points have found their 𝑘 nearest neighbors.”).
Zhu and Nagarajan are in the same field of endeavor, namely neighboring objects query based on BVH ray tracing. Nagarajan teaches an iterative approach to find k nearest neighbors to improve efficiency (Nagarajan Page 6, Left Column, Second paragraph, “With a large starting radius (e.g., enough to find at least k neighbors for 99% of the points), many points will find too many candidate neighbors in their initial search, and hence will waste time sorting those candidates to find the k closest. By resolving those points with smaller radii, TrueKNN also reduces sorting time. Indeed, as we show in Section 5.5.1, TrueKNN’s benefits are not only because outliers force large query radii for fixed-radius search. Even in settings where we do not attempt to resolve outlier points, TrueKNN outperforms fixed-radius kNNS”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Nagarajan with the method of Zhu to improve efficiency.
Regarding claim 5, Zhu in view of Nagarajan teaches The method of claim 1, and further teaches wherein updating or maintaining the search area based on the distance comprises comparing the search area to the distance and, if the distance is smaller than the search area and a sufficient number of objects have been found in the search area, shrink the search area to align with the distance (Zhu teaches using a smaller radius for AABB, Page 7, Left Column, Third Paragraph, “it is possible to use smaller AABBs for a query if its 𝐾 neighbors can be found within a smaller radius. We use an iterative method over a uniform grid to identify the proper AABB size for a query. This is illustrated in Figure 10a.”, Page 11, Right Column, Third Paragraph, “in building a BVH for a query (or a query partition) one could use an AABB size smaller than what is strictly required. Using a smaller AABB would reduce the number of neighbors returned but also provide performance gains, since performance is sensitive to AABB size as established in Section 3.2.2.”).
Regarding claim 8, Zhu in view of Nagarajan teaches The method of claim 1, and further teaches wherein continuing traversal of the BVH comprises repeating the traversing, determining, and updating steps until a termination criterion is met (Nagarajan teaches a termination criterion of finding all k nearest neighbors, page 5, Left Column, Last paragraph, “We use the minimum neighbor distance returned by RandomSample as our start radius (𝑟𝑎𝑑𝑖𝑢𝑠) in Line 1. In Line 3,we call RT-kNNS with our dataset (D) and radius (radius) to find all points within a fixed neighborhood of p. We then check if the previously chosen radius was sufficient for all points to find 𝑘 neighbors in Line 5. In Line 6, we remove all the points that have found their 𝑘 neighbors from our dataset so that the next iteration will only consider points that have not found their neighbors yet. For points that have not found all 𝑘 neighbors, we expand the neighbor search space by incrementing the radius of spheres and re-fitting the bounding boxes around the spheres for the BVH, as shown in Lines 10 and 11. We continue this process till 𝐷 = {∅} and all points have found their 𝑘 nearest neighbors.”).
Zhu and Nagarajan are in the same field of endeavor, namely neighboring objects query based on BVH ray tracing. Nagarajan teaches an iterative approach to find k nearest neighbors to improve efficiency (Nagarajan Page 6, Left Column, Second paragraph, “With a large starting radius (e.g., enough to find at least k neighbors for 99% of the points), many points will find too many candidate neighbors in their initial search, and hence will waste time sorting those candidates to find the k closest. By resolving those points with smaller radii, TrueKNN also reduces sorting time. Indeed, as we show in Section 5.5.1, TrueKNN’s benefits are not only because outliers force large query radii for fixed-radius search. Even in settings where we do not attempt to resolve outlier points, TrueKNN outperforms fixed-radius kNNS”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Nagarajan with the method of Zhu to improve efficiency.
Regarding claim 9, Zhu in view of Nagarajan teaches The method of claim 8, and further teaches wherein the termination criterion comprises a situation in which the BVH has been fully traversed (Zhu teaches terminating the traversal process when the entire tree visited, Page 2, Right Column, Fourth paragraph, “When the ray reaches a leaf node, we test the ray against all the enclosed primitives and record the closet hit point (so far). This process continues until we have traversed the entire tree, at which point the closet hit is reported.”).
Regarding claim 10, it recites similar limitations of claim 1 but in a system form. The rationale of claim 1 rejection is applied to reject claim 10. In addition, Zhu teaches A system comprising: a memory configured to store at least a portion of a bounding volume hierarchy (“BVH”); and a processor configured to perform operations comprising (Zhu teaches using BVH as the data structure, inherently teaches a memory storing the data structure, further teaches using GPU as the processor, Page 3, Left Column, Third Paragraph, “While using BVH to prune the search space is work-efficient, tree traversal is irregular, exhibiting frequent control-flow and memory divergences (e.g., per-thread stack management). Nvidia’s recent Turing (and later) GPU architecture is equipped with dedicated hardware, i.e., the RT cores, to accelerate BVH tree traversal”, Page 1, Left Column, First paragraph, “This paper argues that neighbor search can be made both work-efficient and hardware-friendly — by using Bounding Volume Hierarchy (BVH) tree as the basic data structure. This design decision allows us to formulate neighbor search as a ray tracing problem (Section 3.1), which, critically, has dedicated hardware support (for BVH traversal) in recent GPUs such as Nvidia’s Turing (and later) architecture.”).
Regarding claim 14, claim 14 has similar limitations as claim 5, therefore it is rejected under the same rationale as claim 5.
Regarding claim 17, claim 17 has similar limitations as claim 8, therefore it is rejected under the same rationale as claim 8.
Regarding claim 18, claim 18 has similar limitations as claim 9, therefore it is rejected under the same rationale as claim 9.
Regarding claim 19, it recites similar limitations of claim 1 but in a system form. The rationale of claim 1 rejection is applied to reject claim 19. In addition, Zhu teach A system comprising: a first digital circuit; and a second digital circuit, wherein the first digital circuit is configured to transmit a search query to the second digital circuit, and the second digital circuit is configured to execute the search query by performing operations including (Zhu teaches a query scheduling optimization algorithm, inherently teaches a first digital circuit implementing the algorithm, further teaches GPU as the second digital circuit, Page 1, Right Column, Last Paragraph, “We introduce two optimizations, query scheduling and query partitioning, that mitigate the bottlenecks and effectively exploit the ray tracing hardware.”, Page 8, Right Column, 8th paragraph, “We implement our algorithm in OptiX 7.1; the entire program is compiled with nvcc V11.4.48. We evaluate the performance on two Turing GPUs: a RTX 2080Ti (68 RT cores, 4352 CUDA cores, 11 GB GDDR6) and a RTX 2080 (46 RT cores, 2944 CUDA cores, 8 GB GDDR6).”).
Claim(s) 2-3, 11-12 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL Zhu et al. (“RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing”), hereinafter as Zhu, in view of NPL Nagarajan et al. (“RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search”), hereinafter as Nagarajan, further in view of IDS NPL Evangelou et al. (“Fast Radius Search Exploiting Ray-Tracing Frameworks”), hereinafter as Evangelou.
Regarding claim 2, Zhu in view of Nagarajan teaches The method of claim 1, but is not relied on for the below claim language wherein traversing the BVH comprises eliminating nodes not included within the search area from consideration. Evangelou teaches wherein traversing the BVH comprises eliminating nodes not included within the search area from consideration (Evangelou Page 8, First Paragraph, “In the same spirit, we can exploit a BVH containing samples sj , each associated with a bound of radius ~r, for k-nearest-neighbors searches with a truncation distance ~r, beyond which all queried samples are rejected. Although this truncation comes out of necessity rather than choice, since ~r affects the BVH construction and query performance and cannot be infinite, many practical algorithms already employ a truncation strategy to reject outliers and boost convergence rate.”).
Zhu, Nagarajan and Evangelou are in the same field of endeavor, namely neighboring object searching based on ray tracing hardware. Evangelou teaches truncated k-nn search method to improve efficiency. Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Evangelou with the method of Zhu and Nagarajan to improve efficiency.
Regarding claim 3, Zhu in view of Nagarajan teaches The method of claim 1, but not relied on for the below claim language wherein traversing the BVH comprises traversing to nodes included within the search area. Evangelou teaches wherein traversing the BVH comprises traversing to nodes included within the search area (Evangelou teaches removing all nodes out of bound of radius, indicating traversing to nodes within the radius, Page 9, Listing 2, “const float3 diff = sample.pos - ray_origin; const float t = dot(diff, diff); if(t < payload.query.radius * payload.query.radius)” and Page 8, First Paragraph, “In the same spirit, we can exploit a BVH containing samples sj , each associated with a bound of radius ~r, for k-nearest-neighbors searches with a truncation distance ~r, beyond which all queried samples are rejected.”).
Zhu, Nagarajan and Evangelou are in the same field of endeavor, namely neighboring object searching based on ray tracing hardware. Evangelou teaches truncated k-nn search method to improve efficiency. Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Evangelou with the method of Zhu and Nagarajan to improve efficiency.
Regarding claim 11, claim 11 has similar limitations as claim 2, therefore it is rejected under the same rationale as claim 2.
Regarding claim 12, claim 12 has similar limitations as claim 3, therefore it is rejected under the same rationale as claim 3.
Regarding claim 20, claim 20 has similar limitations as claim 2, therefore it is rejected under the same rationale as claim 2.
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL Zhu et al. (“RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing”), hereinafter as Zhu, in view of NPL Nagarajan et al. (“RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search”), hereinafter as Nagarajan, further in view of NPL Mandarapu et al. (“Arkade: k-Nearest Neighbor Search With Non-Euclidean Distances using GPU Ray Tracing”), hereinafter as Mandarapu.
Regarding claim 4, Zhu in view of Nagarajan teaches The method of claim 1, but is not relied on for the below claim language wherein the distance is a Chebyshev distance. Mandarapu teaches wherein the distance is a Chebyshev distance (Mandarapu Page 1, Right Column, First paragraph, “𝑘-Nearest Neighbor Search (𝑘NN) is the problem of finding points similar to a query point based on a desired distance function. Several commonly used distance functions include Euclidean distance (𝐿2 norm), Manhattan distance (𝐿1 norm), Chebyshev distance (𝐿∞ norm)”, Page 6, Right Column, Fifth paragraph, “For example, consider the 𝐿∞ distance and 𝑟 = 1. By the inclusion property, it is possible to construct a finite-sized sphere 𝑆 of radius 𝑟 ′ =√2 according to the 𝐿2 norm such that all the points that are within a distance 𝑟 according to 𝐿∞ norm will fall inside sphere 𝑆.”).
Zhu, Nagarajan and Mandarapu are in the same field of endeavor, namely neighboring object searching based on ray tracing hardware. Mandarapu teaches a method to use Chebyshev distance in kNN with ray tracing hardware to improve performance (Mandarapu Page 1, Left Column, Abstract, “We propose and implement two reductions to support 𝑘NN for a broad range of distances other than the Euclidean distance: ……With our reductions, we observe that 𝑘NN search time speedups range between 1.6x-200x and 1.3x-33.1x over various state-of-the art GPU shader core and RT core baselines,”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Mandarapu with the method of Zhu and Nagarajan to improve performance.
Regarding claim 13, claim 13 has similar limitations as claim 4, therefore it is rejected under the same rationale as claim 4.
Claim(s) 6-7 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL Zhu et al. (“RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing”), hereinafter as Zhu, in view of NPL Nagarajan et al. (“RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search”), hereinafter as Nagarajan, further in view of Laine et al. (US 20200051315 A1), hereinafter as Laine.
Regarding claim 6, Zhu in view of Nagarajan teaches The method of claim 1, but is not relied on for the below claim language wherein traversing the BVH further includes traversing to an instance node. Laine teaches wherein traversing the BVH further includes traversing to an instance node (Laine paragraph [0169] “The BVH, as described above, includes a hierarchically arranged (e.g., in a tree structure) set of bounding volumes, with each volume encompassing a three dimensional portion of a scene. Leaf nodes of the BVH tree may include ranges of geometric primitives (e.g., triangles), items, item ranges or instance nodes.”).
Zhu, Nagarajan and Laine are in the same field of endeavor, namely query based BVH ray tracing. Laine teaches the instance node and instance transformation with BVH to improve efficiency (Laine paragraph [0060] “Objects replicated many times in the scene at different positions, orientations and scales can be represented in the scene as instance nodes which associate a bounding box and leaf node in the world space BVH with a transformation that can be applied to the world-space ray to transform it into an object coordinate space, and a pointer to an object-space BVH. This avoids replicating the object space BVH data multiple times in world space, saving memory and associated memory accesses. The instance transform increases efficiency”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Laine with the method of Zhu and Nagarajan to improve efficiency.
Regarding claim 7, Zhu in view of Nagarajan and Laine teach The method of claim 6, and further teach wherein traversing to the instance node comprises performing an instance transform on extents of the search area (Laine paragraph [0154] “instancing of BVH nodes, help control changes to the BVH when scene changes occur. When instanced objects change position and/or orientation without changing shape, we only need to change the instance node's transformation coefficients and bounding box (and any ancestor complete bounding boxes that fail to bound the new bounding box for the instance node). This is less work than transforming each primitive and inserting it into a world-space BVH.”).
Zhu, Nagarajan and Laine are in the same field of endeavor, namely query based BVH ray tracing. Laine teaches the instance node and instance transformation with BVH to improve efficiency (Laine paragraph [0060] “Objects replicated many times in the scene at different positions, orientations and scales can be represented in the scene as instance nodes which associate a bounding box and leaf node in the world space BVH with a transformation that can be applied to the world-space ray to transform it into an object coordinate space, and a pointer to an object-space BVH. This avoids replicating the object space BVH data multiple times in world space, saving memory and associated memory accesses. The instance transform increases efficiency”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Laine with the method of Zhu and Nagarajan to improve efficiency.
Regarding claim 15, claim 15 has similar limitations as claim 6, therefore it is rejected under the same rationale as claim 6.
Regarding claim 16, claim 16 has similar limitations as claim 7, therefore it is rejected under the same rationale as claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Nagarajan et al. (“RT-DBSCAN: Accelerating DBSCAN using Ray Tracing Hardware”) teaches a method to translate fixed-radius nearest neighbor queries to ray tracing queries based on RT hardware.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOMING WEI whose telephone number is (571)272-3831. The examiner can normally be reached M-F 8:00-5:00.
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/XIAOMING WEI/Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
