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 .
Information Disclosure Statement
The information disclosure statement (IDS) was filed on 10/08/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6, 16, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wei et. al., hereafter Wei (CN No. 115937448-A) in view of Ye et. al. (Ye, Yutong, Xiang Lian, and Mingsong Chen. "Efficient exact subgraph matching via gnn-based path dominance embedding." Proceedings of the International Conference on Very Large Data Bases. Vol. 17. No. 7. Very Large Data Base Endowment Inc., 2024. (Year: 2024)) .
As per claim 1, Wei teaches “An apparatus, comprising: a processing system that includes one or more processors and one or more memories coupled with the one or more processors, the processing system configured to cause the apparatus to: (See page 3 last 3 paragraphs “According to a fifth aspect, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method provided in accordance with the present disclosure” Wei)
“obtain a source point cloud comprising a plurality of source points;
obtain a candidate point cloud comprising a plurality of candidate points;” (See page 3 paragraph 6 “determining a first characteristic point set of the source point cloud according to the geometric information and the semantic information of the source point cloud; the second determining module is used for determining a second feature point set of the target point cloud according to the geometric information and the semantic information of the target point cloud; a third determining module, configured to determine a set of associated point pairs according to the first feature point set and the second feature point set, where the set of associated point pairs includes candidate associated point pairs, and the candidate associated point pairs include the first feature point and the second feature point that are associated with each other;” Wei)
“form one or more… subgraphs, wherein each… subgraph of the one or more… subgraphs corresponds to a respective source point in the source point cloud and comprises the respective source point and one or more other source points associated with the respective source point;” (See page 8 paragraph 6, Examiner interprets the initial relation graph as the source subgraph and each candidate point pair vertex as a the one or more subgraphs of the source. “The method comprises the steps of taking candidate associated point pairs as observation data, calculating a translation invariant relation (also called translation invariant observation) and a rotation invariant relation (rotation invariant observation) between any two candidate associated point pairs, and constructing an initial relation graph by taking each candidate associated point pair as a vertex and the translation invariant relation as an edge; adjusting the initial relation graph according to the rotation invariant relation and the rotation invariant relation to obtain a target relation graph; and determining the candidate associated point pair represented by the vertex of the maximum complete subgraph in the target relational graph as a target associated point pair. And finally, solving a rotation matrix R and a translation vector t by using a target associated point pair through a robust optimization method.” Wei)
“generate… source vector… , wherein each source vector embedding of the source vector embeddings corresponds to respective… subgraph of the one or more… subgraphs;” (See Page 7 paragraph 1 “For example, the first feature point and the second feature point may be associated according to a spatial distance between the first feature vector and the second feature vector, and the first feature point and the second feature point respectively characterized by the first feature vector and the second feature vector having a spatial distance smaller than a preset value (e.g., 0.1 meter) are determined as candidate associated point pairs, so as to obtain an associated point pair set.” Wei)
form one or more candidate subgraphs, wherein each candidate subgraph of the one or more candidate subgraphs corresponds to a respective candidate point in the candidate point cloud and comprises the respective candidate point and one or more other candidate points associated with the respective candidate point; (See page 8 paragraph 6, Examiner interprets the initial relation graph as the source subgraph and each candidate point pair vertex as a the one or more subgraphs of the source. “The method comprises the steps of taking candidate associated point pairs as observation data, calculating a translation invariant relation (also called translation invariant observation) and a rotation invariant relation (rotation invariant observation) between any two candidate associated point pairs, and constructing an initial relation graph by taking each candidate associated point pair as a vertex and the translation invariant relation as an edge; adjusting the initial relation graph according to the rotation invariant relation and the rotation invariant relation to obtain a target relation graph; and determining the candidate associated point pair represented by the vertex of the maximum complete subgraph in the target relational graph as a target associated point pair. And finally, solving a rotation matrix R and a translation vector t by using a target associated point pair through a robust optimization method.” Wei)
“generate, with the GNN, candidate vector…, wherein each candidate vector… of the candidate vector… corresponds to a respective candidate subgraph of the one or more candidate subgraphs; and” (See Page 7 paragraph 1, the point pairs correspond to the subgraphs “For example, the first feature point and the second feature point may be associated according to a spatial distance between the first feature vector and the second feature vector, and the first feature point and the second feature point respectively characterized by the first feature vector and the second feature vector having a spatial distance smaller than a preset value (e.g., 0.1 meter) are determined as candidate associated point pairs, so as to obtain an associated point pair set.” Wei)
“for each source vector… of the source vector… :
determine a corresponding matching candidate vector embedding of the candidate vector embeddings based on a comparison of distances between the source vector… and each of the candidate vector embeddings; and” (See Page 7 paragraphs 1-2, the point pairs correspond to the subgraphs “For example, the first feature point and the second feature point may be associated according to a spatial distance between the first feature vector and the second feature vector, and the first feature point and the second feature point respectively characterized by the first feature vector and the second feature vector having a spatial distance smaller than a preset value (e.g., 0.1 meter) are determined as candidate associated point pairs, so as to obtain an associated point pair set.” In paragraph 2, it shows a comparison of the source vector (first feature vector of the source point cloud) and the candidate vectors (when associated as candidates), the closest. “For example, KD (K-dimensional index tree) trees are respectively established for a first feature vector of the source point cloud and a second feature vector point of the target point cloud; then traversing each node (first characteristic vector) in the KD tree of the source point cloud, and searching a second characteristic vector which is closest to the node in the target point cloud; and traversing each node (second characteristic vector) in the KD tree of the target point cloud, and searching a first characteristic vector which is closest to the node in the source point cloud. And when the first characteristic vector and the second characteristic vector are nearest neighbors, determining a first characteristic point and a second characteristic point respectively characterized by the first characteristic vector and the second characteristic vector as candidate associated point pairs, thereby obtaining an associated point pair set” Wei)
“determine the respective candidate point of the respective candidate subgraph associated with the corresponding matching candidate vector… is a respective target point of the respective source point of the respective… subgraph associated with the source vector… .” (See page 8 paragraph 6, the candidate associated point pair (which is the pair as a vertex, which is a subgraph) is determined. They are each respectively associated with the source points, source subgraph and source vectors. “The method comprises the steps of taking candidate associated point pairs as observation data, calculating a translation invariant relation (also called translation invariant observation) and a rotation invariant relation (rotation invariant observation) between any two candidate associated point pairs, and constructing an initial relation graph by taking each candidate associated point pair as a vertex and the translation invariant relation as an edge; adjusting the initial relation graph according to the rotation invariant relation and the rotation invariant relation to obtain a target relation graph; and determining the candidate associated point pair represented by the vertex of the maximum complete subgraph in the target relational graph as a target associated point pair. And finally, solving a rotation matrix R and a translation vector t by using a target associated point pair through a robust optimization method.” The target associated point pair is the selected point pair from the candidates. The association is based on the vectors as seen on Page 7 paragraphs 1-2. Wei), however Wei does not teach “form one or more source subgraphs, wherein each source subgraph of the one or more source subgraphs…” and “generate, with a graph neural network (GNN)… vector embeddings… respective source subgraph of the one or more source subgraphs;”
Ye teaches “form one or more source subgraphs, wherein each source subgraph of the one or more source subgraphs…” and “generate, with a graph neural network (GNN)… vector embeddings… respective source subgraph of the one or more source subgraphs;” (See page 8 section 4.4 GNN-Based Subgraph Matching Algorithm paragraphs 1-5, examiner interprets the query graphs as the source subgraph that is used to find candidate subgraphs, which utilizes vector embeddings of the query graph. “In this subsection, we illustrate the exact subgraph matching algorithm by traversing the indexes over GNN-based path embeddings in Algorithm 3. Specifically, given a query graph 𝑞, we first obtain all query paths of length 𝑙 in a set 𝑄 from the query plan 𝜑 (line 1). Then, for each query path 𝑝𝑞∈𝑄, we generate path embedding vectors 𝑜(𝑝𝑞), 𝑜′(𝑝𝑞) (via multi-GNNs ), and 𝑜0 (𝑝𝑞) (via 𝑀𝑗) (lines 2-5)… retrieve path candidate sets for each query path 𝑝 𝑞∈𝑄 (lines6-28). Finally, we refine candidate paths and join the matched paths to obtain / return subgraphs 𝑔∈S that are isomorphic to 𝑞 (lines29-30). Refinement. After finding all candidate paths in 𝑝𝑞.𝑐𝑎𝑛𝑑_𝑙𝑖𝑠𝑡 for each query path 𝑝𝑞∈𝑄, we will assemble these paths (with overlapping vertexIDs) into candidate subgraphs to be refined and return the actual matching subgraph answers in S (lines29-30).” See also page 1 column 2 paragraphs 3-4 “In this case, the project manager can specify this query graph 𝑞 and issue a subgraph matching query over the collaboration network 𝐺 to obtain candidate teams matching with 𝑞 (e.g., subgraph 𝑔 isomorphic to 𝑞, circled in Figure 1(a))” Ye also utilizes a GNN. See page 1 abstract and page 2 section Our contributions columns 1-2. Ye)
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 teachings of Wei with the teachings of Ye to find candidate subgraphs from source subgraphs by utilizing vector embeddings generated the GNN. The modification would have been motivated by the desire to have an effective and efficient framework by performing effective pruning to guarantee no false dismissals by not missing any queries for subgraph matching, it also increases processing efficiency when performing subgraph matching, therefore it is an improvement, as suggested by Ye (See page 12 section 8 Conclusions “In this paper, we propose a novel GNN-based path embedding (GNN PE) framework for efficient processing of exact subgraph matching queries over a large-scale data graph. We carefully design GNN models to encode paths (and their surrounding 1-hop neighbors) in the data graph into embedding vectors, where subgraph rela tionships are strictly reflected by vector dominance constraints in the embedding space. The resulting embedding vectors can be used for efficient exact subgraph matching without false dismissals. Extensive experiments have been conducted to show the efficiency and effectiveness of our proposed GNN-PE approach over both real and synthetic graph data sets.” Ye)
Claim 11 is rejected under the same analysis as claim 1.
Claim 20 is rejected under the same analysis as claim 1. (See page 3 last paragraph, “According to a seventh aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform a method provided according to the present disclosure” )
As per claim 6, Wei in view of Ye already teaches “the apparatus of claim 1, wherein: the one or more other source points associated with the respective source point are k-nearest neighbors of the respective source point; and the one or more other candidate points associated with the respective candidate point are k-nearest neighbors of the respective candidate point.” (See Page 7 paragraphs 1-2, the point pairs are associated based on closeness to their neighbors, which are determined from k-nearest neighbors correspond to the subgraphs “For example, the first feature point and the second feature point may be associated according to a spatial distance between the first feature vector and the second feature vector, and the first feature point and the second feature point respectively characterized by the first feature vector and the second feature vector having a spatial distance smaller than a preset value (e.g., 0.1 meter) are determined as candidate associated point pairs, so as to obtain an associated point pair set.” In paragraph 2, it shows a comparison of the source vector (first feature vector of the source point cloud) and the candidate vectors (when associated as candidates), the closest. “For example, KD (K-dimensional index tree) trees are respectively established for a first feature vector of the source point cloud and a second feature vector point of the target point cloud; then traversing each node (first characteristic vector) in the KD tree of the source point cloud, and searching a second characteristic vector which is closest to the node in the target point cloud; and traversing each node (second characteristic vector) in the KD tree of the target point cloud, and searching a first characteristic vector which is closest to the node in the source point cloud. And when the first characteristic vector and the second characteristic vector are nearest neighbors, determining a first characteristic point and a second characteristic point respectively characterized by the first characteristic vector and the second characteristic vector as candidate associated point pairs, thereby obtaining an associated point pair set.” See also page 9 paragraphs 7-14 as it shows vertices which are determined on closeness to their neighbors as well. “For the target relationship graph shown in fig. 4B, a maximum group screening method may be adopted to screen out the target associated point pairs. The graph with edges between any two vertices in the graph containing a plurality of vertices is called a clique, and the maximum clique refers to a sub-clique with the largest number of vertices in the graph and is also called a maximum complete subgraph. For example, as shown in fig. 4B, the subgraph composed of the vertices 410, 420, 440, 450, 460 is the largest complete subgraph in the target relationship graph, and the candidate associated point pairs represented by the vertices of the largest complete subgraph are target associated point pairs. I.e., the candidate associated point pairs represented by vertices 410, 420, 440, 450, 460, respectively, are target associated point pairs” Wei)
Claim 16 is rejected under the same analysis as claim 6.
Claims 2-3 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Wei in view of Ye and further in view of Jiang et. al. (Jiang, Tao, et al. "Point Cloud based Motion State Estimation Method for Autonomous Driving." 2023 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2023. (Year: 2023)) .
As per claim 2, Wei in view of Ye already teaches “the apparatus of claim 1, wherein the processing system is configured to cause the apparatus to: for each of one or more source points of the plurality of source points… based on a source point of the one or more source points and the respective target point of the source point.”, however Wei in view of Ye does not teach “generate a respective scene flow vector”.
Jiang teaches “generate a respective scene flow vector” (See page 1 abstract “This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases.” See also page 4 column 2 paragraph 4 “… when considering the endpoint error (EPE3D) of foreground points in the scene flow, we converted the motion vectors into a three-dimensional scene flow to describe the flow direction of each point in the source frame. The results obtained from comparing this with the current literature on scene flow are presented in table II” Jiang)
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 teachings of Wei with the teachings of Ye and Jiang to generate scene flow vectors from source points. The modification would have been motivated by the desire to have faster vector estimation, more efficient embedding and better performance, therefore it is an improvement, as suggested by Jiang (See page 1 abstract “This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases. In our experiments conducted on the KITTI dataset, the proposed method demonstrates superior performance compared to existing scene flow estimation methods. Specifically, without considering motion segmentation errors, the error in the motion direction is only 0.0363 m/s, showcasing better performance. Additionally, our method achieves an error of 0.076 m for three-dimensional endpoint error (EPE3D), showcasing distinct advantages over current scene flow networks.” See also page 2 column 2 paragraph 2 “In this paper, we propose a method for fast motion vector estimation applicable to motion segmented point clouds. By processing the output of motion segmentation applied to laser point clouds, the motion vector estimation task can be efficiently embedded after motion segmentation. Compared to traditional detection and tracking methods, our algorithm leverages temporal sequence information from the point cloud. While performing spatial clustering, the method utilizes the preserved temporal information for instance association. The predicted motion vectors are obtained by combining the clustered displacement differences and the fusion results of the object matches.” Jiang )
Claim 12 is rejected under the same analysis as claim 2.
As per claim 3, Wei in view of Ye and Jiang already teaches “the apparatus of claim 2, wherein, for each of the one or more source points…between the source vector embedding associated with the source point of the one or more source points and the candidate vector embedding associated with the respective target point of the source point.”, however Jiang also teaches “…to generate the respective scene flow vector the processing system causes the apparatus to optimize an objective function constrained by a respective distance…” (See page 2 section II. Method paragraph 2 “ ICP(Iterative Closest Point ）algorithm is an iterative optimization algorithm used for point cloud registration. Its purpose is to align two or more point cloud datasets by finding the optimal transformation matrix (translation and rotation) that minimizes the distance between corresponding points in space. The algorithm proceeds by initializing with a reference point cloud and a target point cloud, then iteratively finding the closest point correspondences between them. The calculated transformation matrix is continually refined using least-squares estimation to minimize the distance error between matched point pairs…”. They would have been obvious to combine for the same reasons as claim 2. Jiang) (On reference Wei see also page 10 paragraphs 1-4, it shows an optimization function which also utilizes ICP (Iterative closest point). Wei)
Claim 13 is rejected under the same analysis as claim 3.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wei in view of Ye and further in view of Jiang and Murayama et. al. (US Pub. No. 20190026921 A1).
As per claim 4, Wei in view of Ye and Jiang already teaches “the apparatus of claim 2, wherein the processing system is configured to cause the apparatus to estimate… candidate point for a first source point of the one or more source points based on the first source point and the respective scene flow vector of the first source point.”, however Wei in view of Ye and Jiang does not teach “occluded candidate point”
Murayama teaches “occluded candidate point” (See paragraphs 32, 50, 75, 92 and 99 “0032] The analyzing unit 201 analyzes a subject in the measuring point candidate position and determines whether there is an occlusion region in at least any of the measuring point candidate position and a position within a prescribed range from the measuring point candidate position. ” “[0075] As in the measuring device 1 in the first embodiment, the measuring device 2 accepts an input of a measuring point candidate position to the first image being a left viewpoint image as an initial reference image, configures the measuring point candidate position, and determines whether there is an occlusion region around the measuring point candidate position (S201 to S203). Then, a reference image according to the result of S203 is selected (S204).” If the candidate point is in an occlusion region, it is an occluded candidate point. Murayama)
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 teachings of Wei with the teachings of Ye, Jiang and Murayama estimate occluded candidate points based on the source point and flow scene vector. The modification would have been motivated by the desire to find occluded objects by occluded points from occlusion regions without decreasing accuracy and without increasing computing processing, therefore it is an improvement, as suggested by Murayama (See paragraphs 10 and 13 “[0010] One aspect of the present invention has been made in view of the above-mentioned points, and an object thereof is to provide a calculating device capable of preventing a decrease in calculating accuracy due to configuration of a measuring point in or around an occlusion region without excessively increasing the amount of computing processing.” “[0013] According to each of the aspects of the present invention, an effect of preventing a decrease in calculating accuracy due to configuration of a measuring point in or around an occlusion region without excessively increasing the amount of computing processing is achieved.” Murayama)
Claim 14 is rejected under the same analysis as claim 4.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wei in view of Ye and further in view of Sarkar et. al. (Sarkar, Rishov, et al. "FlowGNN: A dataflow architecture for real-time workload-agnostic graph neural network inference." 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 2023. (Year: 2023)) .
As per claim 5, Wei in view of Ye already teaches “the apparatus of claim 1, wherein the GNN..”, however Wei in view of Ye does not teach “is a GNNFlow model”.
Sarkar teaches “is a GNNFlow model” (See page 1 abstract “Prior art focuses on accelerating specific classes of GNNs, such as Graph Convolutional Networks (GCN), but lacks generality to support a wide range of existing or new GNN models. Furthermore, most works rely on graph pre-processing to exploit data locality, making them unsuitable for real-time applications. To address these limitations, in this work, we propose a generic dataflow architecture for GNN acceleration, named FlowGNN, which is generalizable to the majority of message-passing GNNs. The contributions are three-fold. First, we propose a novel and scalable dataflow architecture, which generally supports a wide range of GNN models with message-passing mechanism… ” )
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 teachings of Wei with the teachings of Ye and Sarkar to utilize a GNN flow model. The modification would have been motivated by the desire to have a generalizable GNN, provide real-time processing and faster GNN performance, therefore it is an improvement, as suggested by Sarkar (See page 1 abstract “Prior art focuses on accelerating specific classes of GNNs, such as Graph Convolutional Networks (GCN), but lacks generality to support a wide range of existing or new GNN models. Furthermore, most works rely on graph pre-processing to exploit data locality, making them unsuitable for real-time applications. To address these limitations, in this work, we propose a generic dataflow architecture for GNN acceleration, named FlowGNN, which is generalizable to the majority of message-passing GNNs. The contributions are three-fold. First, we propose a novel and scalable dataflow architecture, which generally supports a wide range of GNN models with message-passing mechanism… The contributions are three-fold. First, we propose a novel and scalable dataflow architecture, which generally supports a wide range of GNN models with message-passing mechanism. The architecture features a configurable dataflow optimized for simultaneous computation of node embedding, edge embedding, and message passing, which is generally applicable to all models. We also propose a rich library of model specific components. Second, we deliver ultra-fast real-time GNN inference without any graph pre-processing, making it agnostic to dynamically changing graph structures. Third, we verify our architecture on the Xilinx Alveo U50 FPGA board and measure the on-board end-to-end performance. We achieve a speed-up of up to 24–254× against CPU (6226R) and 1.3–477× against GPU (A6000) (with batch sizes 1 through 1024); we also outperform the SOTA GNN accelerator I-GCN by 1.26× speedup and 1.55× energy efficiency over four datasets.” Sarkar)
Claim 15 is rejected under the same analysis as claim 5.
Claims 7-10 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wei in view of Ye and further in view of Weijing et. al. (Shi, Weijing, and Raj Rajkumar. "Point-gnn: Graph neural network for 3d object detection in a point cloud." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020. (Year: 2020)).
As per claim 7, Wei in view of Ye already teaches “the apparatus of claim 1, wherein the source point cloud and the candidate point cloud each comprise”, however Wei in view of Ye does not teach “three-dimensional coordinates associated with state values.”
Weijing teaches “three-dimensional coordinates associated with state values.” (See page 3 section 3.1 Graph Construction paragraph 1 “Formally, we define a point cloud of N points as a set P ={p1,...,pN}, where pi = (xi,si) is a point with both 3D coordinates xi ∈ R3 and the state value si ∈ Rk a k length vector that represents the point property. The state value si can be the reflected laser intensity or the features which encode the surrounding objects. Given a point cloud P, we construct a graph G = (P,E) by using P as the vertices and connecting a point to its neighbors within a fixed radius r, i.e.” )
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 teachings of Wei with the teachings of Ye and Weijing for the point clouds to utilize three-dimensional coordinates associated with state values . The modification would have been motivated by the desire to have more efficient processing and better accuracy, therefore it is an improvement, as suggested by Weijing (See page 1 abstract “Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, we propose an auto-registration mechanism to reduce translation variance, and also design a box merging and scoring operation to combine detections from multiple vertices accurately. Our experiments on the KITTI benchmark show the proposed approach achieves leading accuracy using the point cloud alone and can even surpass fusion-based algorithms.” See also page 3 section 3.1 Graph Construction paragraph 2 “The construction of such a graph is the well-known fixed radius near-neighbors search problem. By using a cell list to find point pairs that are within a given cut-off distance, we can efficiently solve the problem with a runtime complexity of O(cN) where c is the max number of neighbors within the radius [1].” Weijing)
Claim 17 is rejected under the same analysis as claim 7.
As per claim 8, Wei in view of Ye and Weijing already teaches “the apparatus of claim 7, wherein the state values comprise at least one of: a reflected laser intensity, a previous flow value, or a color pixel value.” (See page 3 section 3.1 Graph Construction paragraph 1 “Formally, we define a point cloud of N points as a set P ={p1,...,pN}, where pi = (xi,si) is a point with both 3D coordinates xi ∈ R3 and the state value si ∈ Rk a k length vector that represents the point property. The state value si can be the reflected laser intensity or the features which encode the surrounding objects. Given a point cloud P, we construct a graph G = (P,E) by using P as the vertices and connecting a point to its neighbors within a fixed radius r, i.e.” Weijing)
Claim 18 is rejected under the same analysis as claim 8.
As per claim 9, Wei in view of Yu already teaches “the apparatus of claim 1, wherein the one or more other source points associated with the respective source point are within… from the respective source point.”, however Wei in view of Yu does not teach “within a radius…”
Weijing teaches “within a radius…” (See page 3 section 3.1 Graph Construction paragraphs 1-4 “Formally, we define a point cloud of N points as a set P ={p1,...,pN}, where pi = (xi,si) is a point with both 3D coordinates xi ∈ R3 and the state value si ∈ Rk a k length vector that represents the point property. The state value si can be the reflected laser intensity or the features which encode the surrounding objects. Given a point cloud P, we construct a graph G = (P,E) by using P as the vertices and connecting a point to its neighbors within a fixed radius r, i.e… The construction of such a graph is the well-known fixed radius near-neighbors search problem. By using a cell list to find point pairs that are within a given cut-off distance, we can efficiently solve the problem with a runtime complexity of O(cN) where c is the max number of neighbors within the radius [1]… To preserve the information within the original point cloud, we encode the dense point cloud in the initial state value si of the vertex. More specifically, we search the raw points within a r0 radius of each vertex and use the neural network on sets to extract their features. ” See also abstract. Weijing)
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 teachings of Wei with the teachings of Ye and Weijing for the points being within a radius. The modification would have been motivated by the desire to have more efficient processing and better accuracy, therefore it is an improvement, as suggested by Weijing (See page 1 abstract “Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, we propose an auto-registration mechanism to reduce translation variance, and also design a box merging and scoring operation to combine detections from multiple vertices accurately. Our experiments on the KITTI benchmark show the proposed approach achieves leading accuracy using the point cloud alone and can even surpass fusion-based algorithms.” See also page 3 section 3.1 Graph Construction paragraph 2 “The construction of such a graph is the well-known fixed radius near-neighbors search problem. By using a cell list to find point pairs that are within a given cut-off distance, we can efficiently solve the problem with a runtime complexity of O(cN) where c is the max number of neighbors within the radius [1].” Weijing)
Claim 19 is rejected under the same analysis as claim 9.
As per claim 10, Wei in view of Ye already teaches “the apparatus of claim 1, wherein the one or more other candidate points associated with the respective candidate point are within… from the respective candidate point.”, however Wei in view of Yu does not teach “within a radius…”
Weijing teaches “within a radius…” (See page 3 section 3.1 Graph Construction paragraphs 1-4 “Formally, we define a point cloud of N points as a set P ={p1,...,pN}, where pi = (xi,si) is a point with both 3D coordinates xi ∈ R3 and the state value si ∈ Rk a k length vector that represents the point property. The state value si can be the reflected laser intensity or the features which encode the surrounding objects. Given a point cloud P, we construct a graph G = (P,E) by using P as the vertices and connecting a point to its neighbors within a fixed radius r, i.e… The construction of such a graph is the well-known fixed radius near-neighbors search problem. By using a cell list to find point pairs that are within a given cut-off distance, we can efficiently solve the problem with a runtime complexity of O(cN) where c is the max number of neighbors within the radius [1]… To preserve the information within the original point cloud, we encode the dense point cloud in the initial state value si of the vertex. More specifically, we search the raw points within a r0 radius of each vertex and use the neural network on sets to extract their features. ” See also abstract. Weijing)
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 teachings of Wei with the teachings of Ye and Weijing for the points being within a radius. The modification would have been motivated by the desire to have more efficient processing and better accuracy, therefore it is an improvement, as suggested by Weijing (See page 1 abstract “Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, we propose an auto-registration mechanism to reduce translation variance, and also design a box merging and scoring operation to combine detections from multiple vertices accurately. Our experiments on the KITTI benchmark show the proposed approach achieves leading accuracy using the point cloud alone and can even surpass fusion-based algorithms.” See also page 3 section 3.1 Graph Construction paragraph 2 “The construction of such a graph is the well-known fixed radius near-neighbors search problem. By using a cell list to find point pairs that are within a given cut-off distance, we can efficiently solve the problem with a runtime complexity of O(cN) where c is the max number of neighbors within the radius [1].” Weijing)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN J MENDEZ MUNIZ whose telephone number is (703)756-5672. The examiner can normally be reached M-F, 8AM - 5PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer can be reached at (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DYLAN JOHN MENDEZ MUNIZ/Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675
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