CTNF 18/963,119 CTNF 100773 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 06-52 The information disclosure statement (IDS) submitted on 27 November 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 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-7, 9 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Svoboda (US 20220277579) in view of Zisheng (A new extracting algorithm of k nearest neighbors searching for point clouds) . Regarding claim 1, Zisheng teaches a decoding method, comprising: Determining a reconstructed point set based on a reconstructed point cloud, wherein the reconstructed point set comprises at least one point (Abstract, For any point in the model, its initial nearest neighbors can be extracted from its reverse neighborhood using an inner product of two related vectors other than direct Euclidean distance calculations and comparisons. The initial neighbors can be its full or partial set of the all nearest neighbors ); While Zisheng fails to disclose the following, Svoboda teaches: Inputting geometry information and a reconstructed value of an attribute to-be-processed of a point in the reconstructed point set into a preset network model, and determining a processed value of the attribute to-be-processed of the point in the reconstructed point set based on the preset network model (Paragraph 4, a computer-implemented method of characterizing a person's hand geometry includes inputting a three-dimensional (3D) point cloud of the person's hand into a clustered dynamic graph convolutional neural network (clustered DGCNN), and processing the 3D point cloud, with a shared network portion of the clustered DGCNN, to create a processed version of the three-dimensional point cloud); and Determining a processed point cloud corresponding to the reconstructed point cloud according to the processed value of the attribute to-be-processed of the point in the reconstructed point set (Paragraph 4, a computer-implemented method of characterizing a person's hand geometry includes inputting a three-dimensional (3D) point cloud of the person's hand into a clustered dynamic graph convolutional neural network (clustered DGCNN), and processing the 3D point cloud, with a shared network portion of the clustered DGCNN, to create a processed version of the three-dimensional point cloud). Svoboda and Zisheng are both considered to be analogous to the claimed invention because they are in the same field of point clouds. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zisheng to incorporate the teachings of Svoboda and input geometry information and a reconstructed attribute value to a network model to determine a processed value. Doing so would allow for using a known way to effectively reconstruct point cloud information. Method claim 12 and CRM claim 20 correspond to method claim 1. Therefore, claims 12 and 20 are rejected for the same reasons as above. Note: Svoboda teaches the additional limitation performing encoding and reconstruction according to an original point cloud to obtain a reconstructed point cloud (Paragraph 4, a computer-implemented method of characterizing a person's hand geometry includes inputting a three-dimensional (3D) point cloud of the person's hand into a clustered dynamic graph convolutional neural network (clustered DGCNN), and processing the 3D point cloud, with a shared network portion of the clustered DGCNN, to create a processed version of the three-dimensional point cloud). Svoboda and Zisheng are both considered to be analogous to the claimed invention because they are in the same field of point clouds. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zisheng to incorporate the teachings of Svoboda and use encoding and reconstruction to transform an original point cloud to a reconstructed point cloud. Doing so would allow for using a known way to effectively transmit and reconstruct point cloud information. Regarding claim 2, the combination of Zisheng and Svoboda teaches the method of claim 1, wherein determining the reconstructed point set based on the reconstructed point cloud comprises: Determining a key point from the reconstructed point cloud (Zisheng, Abstract, a query point); and Performing extraction on the reconstructed point cloud according to the key point to determine the reconstructed point set, wherein the key point and the reconstructed point set have a correspondence (Zisheng, Abstract, The nearest neighbors of a reverse nearest neighborhood are proposed to use in extracting nearest points of a query point). Method claim 13 corresponds to method claim 2. Therefore, claim 13 is rejected for the same reasons as used above. Regarding claim 3, the combination of Zisheng and Svoboda teaches the method of claim 2. While the combination as previously presented fails to disclose the following, Svoboda further teaches: Determining the key point by performing farthest point sampling (FPS) on the reconstructed point cloud (Paragraph 106, each point cloud is subsampled using Furthest Point Sampling (FPS) to some number of points). Svoboda and Zisheng are both considered to be analogous to the claimed invention because they are in the same field of point clouds. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zisheng to incorporate the teachings of Svoboda and use FPS to determine the key point. Doing so would allow for using the method of choice as it represents the original shape of the point cloud in the most complete way compared to other subsampling algorithms (Svoboda, Paragraph 106). Method claim 14 corresponds to method claim 3. Therefore, claim 14 is rejected for the same reasons as used above. Regarding claim 4, the combination of Zisheng and Svoboda teaches the method of claim 2, wherein performing extraction on the reconstructed point cloud according to the key point to determine the reconstructed point set comprises: Performing K nearest neighbors (KNN) search in the reconstructed point cloud according to the key point, to determine a neighbor point corresponding to the key point (Abstract, The nearest neighbors of a reverse nearest neighborhood are proposed to use in extracting nearest points of a query point, avoiding repetitive Euclidean distance calculation in an extracting process for saving time and memories); and Determining the reconstructed point set based on the neighbor point corresponding to the key point (Abstract, The initial neighbors can be its full or partial set of the all nearest neighbors). Method claim 15 corresponds to method claim 4. Therefore, claim 15 is rejected for the same reasons as used above. Regarding claim 5, the combination of Zisheng and Svoboda teaches the method of claim 4, wherein performing KNN search in the reconstructed point cloud according to the key point to determine the neighbor point corresponding to the key point comprises: Based on the key point, searching for a first preset number of candidate points in the reconstructed point cloud through KNN search (Zisheng, 1. Introduction, a kNN searching algorithm needs to (1) calculate distances between the query point and any others in the data set); Calculating a distance between the key point and each of the first present number of candidate points (Zisheng, 1. Introduction, a kNN searching algorithm needs to (1) calculate distances between the query point and any others in the data set), and determining a second preset number of smaller distances from the obtained first preset number of distances (Zisheng, 1. Introduction, a kNN searching algorithm needs to (1) calculate distances between the query point and any others in the data set); and Determining the neighbor point corresponding to the key point according to candidate points corresponding to the second preset number of distances, wherein the second present number is smaller than or equal to the first preset number (Zisheng, 1. Introduction, a kNN searching algorithm needs to (1) calculate distances between the query point and any others in the data set). Note: The KNN algorithms allows for a variable number of nearest neighbors to be searched. It would be obvious to a person of ordinary skill in the art to run the algorithm twice and adapt the value of K the second time to be smaller than or equal to the first run. Method claim 16 corresponds to method claim 5. Therefore, claim 16 is rejected for the same reasons as used above. Regarding claim 6, the combination of Zisheng and Svoboda teaches the method of claim 4, wherein determining the reconstructed point set based on the neighbor point corresponding to the key point comprises: Determining the reconstructed point set according to the key point and the neighbor point corresponding to the key point (Zisheng, Abstract, The nearest neighbors of a reverse nearest neighborhood are proposed to use in extracting nearest points of a query point, avoiding repetitive Euclidean distance calculation in an extracting process for saving time and memories. For any point in the model, its initial nearest neighbors can be extracted from its reverse neighborhood using an inner product of two related vectors other than direct Euclidean distance calculations and comparisons. The initial neighbors can be its full or partial set of the all nearest neighbors). Method claim 17 corresponds to method claim 6. Therefore, claim 17 is rejected for the same reasons as used above. Regarding claim 7, the combination of Zisheng and Svoboda teaches the method of claim 2. While the combination as previously presented fails to disclose the following, Svoboda further teaches: Determining the number of points in the reconstructed point cloud (Paragraph 106, each point cloud is subsampled using Furthest Point Sampling (FPS) to some number of points (e.g., 4096)); and Determining the number of key points (Paragraph 106, each point cloud is subsampled using Furthest Point Sampling (FPS) to some number of points (e.g., 4096)) according to the number of points in the reconstructed point cloud and the number of points in the reconstructed point set (Zisheng, 1. Introduction, choose the most closest k points). Svoboda and Zisheng are both considered to be analogous to the claimed invention because they are in the same field of point clouds. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zisheng to incorporate the teachings of Svoboda and determine the number of points in the reconstructed point cloud and determine the number of key points. Doing so would allow for processing the point cloud as full resolution point clouds are often too big as inputs to a deep learning model (e.g., more than 100,000 points) (Svoboda, Paragraph 106). Method claim 18 corresponds to method claim 7. Therefore, claim 18 is rejected for the same reasons as used above. Regarding claim 9, the combination of Zisheng and Svoboda teaches the method of claim 2, wherein determining the processed point cloud corresponding to the reconstructed point cloud according to the processed value of the attribute to-be-processed of the point in the reconstructed point set comprises: When the key point is a plurality of key points, performing extraction on the reconstructed point cloud according to the plurality of key points to obtain a plurality of reconstructed point sets (Zisheng, Abstract, The nearest neighbors of a reverse nearest neighborhood are proposed to use in extracting nearest points of a query point, avoiding repetitive Euclidean distance calculation in an extracting process for saving time and memories); While the combination as presented previously fails to disclose the following, Svoboda further teaches: Determining a target set corresponding to the reconstructed point set according to the processed value of the attribute to-be-processed of the point in the reconstructed point set (Paragraph 7, The method further includes computing a similarity score by comparing the generated shape parameters associated with the person's hand to a corresponding set of shape parameters associated with an earlier scanned hand on a cluster-by-cluster basis and determining whether the person's hand matches the earlier scanned hand based on whether the similarity score meets or exceeds a threshold value); After determining target sets corresponding to the plurality of reconstructed point sets, determining the processed point cloud by performing fusion according to the plurality of target sets obtained (Paragraph 4, Each pre-defined cluster corresponds to a unique part of a hand's surface). Note: Svoboda teaches different clusters of points that correspond to unique parts of a hand’s surface, and those clusters can be combined to generated larger surfaces or the entire hand. Svoboda and Zisheng are both considered to be analogous to the claimed invention because they are in the same field of point clouds. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zisheng to incorporate the teachings of Svoboda and determine a target set corresponding to the reconstructed point set according to the processed value of the attribute and perform fusion according to the plurality of target sets obtained. Doing so would allow for processing the smaller point clouds individually and combining them as needed. Method claim 19 corresponds to method claim 9. Therefore, claim 19 is rejected for the same reasons as used above . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Zisheng in view of Svoboda as applied to claims 1-7, 9 and 12-20 and further in view of Liu (US 20240013341) . Regarding claim 8, the combination of Svoboda and Zisheng teaches the method of claim 7. While the combination fails to disclose the following, Liu teaches: Determining a first factor (Paragraph 58, upsampling process 502 is performed on input original sparse point cloud feature N×F (i.e., the feature value of the input point cloud) to obtain k times of an initial dense point cloud feature kN×F (i.e., the first feature value)); Calculating a product of the number of points in the reconstructed point cloud and the first factor (Paragraph 58, upsampling process 502 is performed on input original sparse point cloud feature N×F (i.e., the feature value of the input point cloud) to obtain k times of an initial dense point cloud feature kN×F (i.e., the first feature value)); and Determining the number of key points according to the product and the number of points in the reconstructed point set (Paragraph 58, upsampling process 502 is performed on input original sparse point cloud feature N×F (i.e., the feature value of the input point cloud) to obtain k times of an initial dense point cloud feature kN×F (i.e., the first feature value)). Liu and the combination of Zisheng and Svoboda are both considered to be analogous to the claimed invention because they are in the same field of point clouds. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Zisheng and Svoboda to incorporate the teachings of Liu and upsample the point cloud. Doing so would allow for effectively reconstructing the encoded point cloud . 07-21-aia AIA Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Zisheng in view of Svoboda as applied to claims 1-7, 9 and 12-20 and further in view of Sugio (US 20210105458) . Regarding claim 10, the combination of Zisheng and Svoboda teaches the method of claim 9. While the combination fails to disclose the following, Sugio teaches: When at least two of the plurality of target sets comprise a processed value of an attribute to-be-processed of a first point, calculating the mean value of the obtained at least two processed values to determine a processed value of the attribute to-be-processed of the first point in the processed point cloud (Paragraph 163, when the total number of prediction modes is five, and the number of peripheral three-dimensional points is two, an average value of the attribute information on the peripheral three-dimensional points and the attribute values of the items two peripheral three-dimensional points are assigned to three prediction modes); When none of the plurality of target sets comprises the processed value of the attribute to-be-processed of the first point, determining a reconstructed value of the attribute to-be-processed of the first point in the reconstructed point cloud as the processed value of the attribute to-be-processed of the first point in the processed point cloud (Paragraph 164, When there is a prediction mode to which no predicted value is assigned, for example, if the three-dimensional data encoding device and the three-dimensional data decoding device assign different predicted values to the prediction mode, the three-dimensional data decoding device cannot accurately decode the attribute information items of the encoded three-dimensional data. To avoid this, a new predicted value is assigned to the prediction mode to which no predicted value is assigned); Note: Sugio teaches an example of different values being assigned to an attribute. A person of ordinary skill in the art would be able to use the original attribute value in the reconstructed point cloud value if there are no target sets containing the attribute value. Sugio and the combination of Zisheng and Svoboda are both considered to be analogous to the claimed invention because they are in the same field of point clouds. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Zisheng and Svoboda to incorporate the teachings of Sugio and calculate the mean value for a to-be-processed attribute if there are at least two values or use the first point’s value if there are no values. Doing so would allow for effectively reconstructing the encoded point cloud . 07-21-aia AIA Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Zisheng in view of Svoboda as applied to claims 1-7, 9 and 12-20 and further in view of Lee (US 20250094674) and further in view of Qiao (US 20220165364) . Regarding claim 11, the combination of Zisheng and Svoboda teaches the method of claim 1. While the combination fails to disclose the following, Lee teaches: In the preset network model, obtaining a graph structure of the point in the reconstructed point set by performing graph construction (Paragraph 60, produce a graph based on the isometric mesh. For instance, the isometric mesh may be represented as a graph and/or 3D point cloud to work with a GNN or GNNs) based on the reconstructed value of the attribute to-be-processed of the point in the reconstructed point set additionally with geometry information of the point in the reconstructed point set (Paragraph 20, point clouds may be utilized to represent 3D objects and/or 3D object geometry), and determining the processed value of the attribute to-be-processed of the point in the reconstructed point set by performing graph convolution (Paragraph 74, convolution on the graph). Lee and the combination of Zisheng and Svoboda are both considered to be analogous to the claimed invention because they are in the same field of point clouds. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Zisheng and Svoboda to incorporate the teachings of Lee and construct a graph based on the reconstructed values and geometry information and perform graph convolution. Doing so would allow for effectively reconstructing the encoded point cloud. While the combination of Zisheng, Svoboda, and Lee fails to disclose the following, Qiao teaches: Graph attention mechanism on the graph structure of the point in the reconstructed point set (Paragraph 82, Certain embodiments provide the results using a multi-head graph attention mechanism and/or a performer attention mechanism and residual blocks that greatly improve the representation capacity of the model, to learn complex chemical environments). Qiao and the combination of Zisheng, Svoboda, and Lee are both considered to be analogous to the claimed invention because they are in the same field of point clouds. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Zisheng, Svoboda, and Lee to incorporate the teachings of Qiao and perform graph attention mechanism on the graph structure. Doing so would allow for greatly improving the representation capacity of the model, to learn complex chemical environments (Qiao, Paragraph 82). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SNIGDHA SINHA whose telephone number is (571)272-6618. The examiner can normally be reached Mon-Fri. 12pm-8pm. 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, Jason Chan can be reached at 571-272-3022. 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. /SNIGDHA SINHA/Examiner, Art Unit 2619 /JASON CHAN/Supervisory Patent Examiner, Art Unit 2619 Application/Control Number: 18/963,119 Page 2 Art Unit: 2619 Application/Control Number: 18/963,119 Page 3 Art Unit: 2619 Application/Control Number: 18/963,119 Page 4 Art Unit: 2619 Application/Control Number: 18/963,119 Page 5 Art Unit: 2619 Application/Control Number: 18/963,119 Page 6 Art Unit: 2619 Application/Control Number: 18/963,119 Page 7 Art Unit: 2619 Application/Control Number: 18/963,119 Page 8 Art Unit: 2619 Application/Control Number: 18/963,119 Page 9 Art Unit: 2619 Application/Control Number: 18/963,119 Page 10 Art Unit: 2619 Application/Control Number: 18/963,119 Page 11 Art Unit: 2619 Application/Control Number: 18/963,119 Page 12 Art Unit: 2619 Application/Control Number: 18/963,119 Page 13 Art Unit: 2619
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