DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 3rd 2026 has been entered.
Status of Claims
3. Claims 1, 9-12, 16, 24-27, 31, 39-42, 46, and 54-57 have been amended.
4. Claims 8, 23, 38, and 53 have been cancelled.
5. Claims 2-7, 13-15, 17-22, 28-30, 31-37, 43-45, 57-52, and 58-60 are as previously presented.
6. Claims 61-64 have been newly added.
Claim Rejections - 35 USC § 112
7. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
8. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
9. Claim 64 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 64, there does not seem to be support for “selecting an entropy decoding context based on whether a previously decoded point was predicted using vertical prediction” within the specification. Paragraph 78, 80, and 86 of the specification mentions entropy encoding, but do not discuss entropy decoding or selecting an entropy decoding context based on whether a previously decoded point was predicted using vertical prediction.
Claim Rejections - 35 USC § 103
10. 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.
11. 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.
12. Claims 1-7, 9-12, 16-22, 24-27, 31-37, 39-42, 46-52, 54-57 are rejected under 35 U.S.C. 103 as being unpatentable over Mammou (US 20210312670 A1), hereinafter Mammou, in view of Park (US 20230394712 A1), hereinafter Park.
Regarding claim 1, Mammou teaches an apparatus configured to decode point cloud data, the apparatus comprising: a memory; and one or more processors coupled to the memory (paragraph 199), the one or more processors configured to cause the apparatus to determine a vertical predictor for a current point of the point cloud data (Fig. 2A-2B, paragraph 70-72, generating a prediction tree containing ancestor nodes used to predict child nodes, wherein ancestor nodes are interpreted as a vertical predictor, which suggests generating the prediction tree determines the vertical predictors); wherein to determine the vertical predictor for the current point, the one or more processors are further configured to cause the apparatus to: determine a pivot azimuth value (Fig. 8A, paragraph 102-107, wherein points generated from a LIDAR system can have parameter ϕ , which is defined as its azimuth angle); scale the pivot azimuth value to obtain a first pivot azimuth index (paragraph 108-113, wherein parameterizing ϕ into tilde over ϕ is interpreted as scaling the pivot azimuth value to obtain a first azimuth index); determine a second azimuth index based on the first pivot azimuth index (paragraph 132-134, wherein determining the azimuth value of a current point based on the azimuth value of a previous point is interpreted as determining a second azimuth index); determine a predictor azimuth index based on the second azimuth index (paragraph 132-135, wherein the predictor relationship to find the azimuth of a node based on another azimuth value is interpreted as a predictor azimuth index); and determine the vertical predictor based on the predictor azimuth index (paragraph 72-74, wherein predicting a node based on ancestor nodes based the ancestor node’s prediction technique suggests including predicting a node based off of the ancestor node’s azimuth values, wherein the ancestor node is interpreted as the vertical predictor) and perform predictive geometry decoding (paragraph 92, wherein the prediction tree consists of compressed geometry information; Fig. 6, paragraph 96-97, wherein decoding a prediction tree of a point cloud is interpreted as predictive geometry decoding) using the vertical predictor to decode a position of the current point (Fig. 2B, paragraph 72-74, using a prediction tree to predict a node using its ancestor node is interpreted as decoding the current point, and the ancestor node being used as a predictor is interpreted as a vertical predictor; paragraph 74-81, wherein the prediction tree is used to predict the position of the current point).
Mammou does not teach determining a pivot laser ID for a current point of the point cloud data; determining a vertical predictor wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID.
Park teaches determining a pivot laser ID for a current point of the point cloud data (Fig. 19, paragraph 281-282, classifying points of point cloud data acquired by a laser of the same laser ID interpreted as determining a pivot laser ID for points in the point cloud data); determining a vertical predictor wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID (paragraph 283, wherein points arranged in layers based on their laser ID implies that a first and second point with different laser IDs can be neighbors, and can be used as a predictor).
It would be obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Mammou to incorporate the teachings of Park for this apparatus of decoding point cloud data by determining vertical predictors based off of laser IDs. Both inventions discuss processing point cloud data, and specifically point cloud data generated from LiDAR devices. Both also similarly discuss generating a geometric predictive tree to predict the positions of data points in the point cloud in order to reduce the latency and computational complexity of the encoding and decoding process. As both references discuss analogous art of predictive decoding of point cloud data using neighboring points as predictors, it would have been obvious to combine them.
Regarding claim 2, Mammou in view of Park discloses the apparatus of claim 1. Additionally, Mammou teaches the apparatus of claim 1, wherein to determine the pivot laser ID, the one or more processors are further configured to cause the apparatus to: determine the pivot laser ID to be a laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as the laser ID, and can have a parent node with the same index) of a previously decoded point decoded before the current point (Fig. 6, paragraph 96-99, decoding the properties child node based on its parent node, wherein the parent node is interpreted as a previously decoded point decoded before its child node, and wherein the prediction tree can be formed based on properties of a LiDAR system, which can include laser IDs).
Regarding claim 3, Mammou in view of Park discloses the apparatus of claim 1. Additionally, Mammou teaches the apparatus of claim 1, wherein to determine the pivot laser ID, the one or more processors are further configured to cause the apparatus to: determine the pivot laser ID to be a laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as the laser ID) of the current point (Fig. 6, paragraph 96-99, wherein the prediction tree can be formed based on properties of a LiDAR system, which can include laser IDs, and decoding the properties of a root node not based on other points is interpreted as determining the laser ID based on the current point).
Regarding claim 4, Mammou in view of Park discloses the apparatus of claim 1. Additionally, Mammou teaches the apparatus of claim 1, wherein the one or more processors are further configured to cause the apparatus to: determine a predictor laser ID list based on the pivot laser ID, wherein the predictor laser ID list includes a plurality of laser IDs, including the second laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as a laser ID, and the prediction tree is interpreted as a list of predictor laser IDs, which can include the pivot laser ID and a first and second laser ID).
Regarding claim 5, Mammou in view of Park discloses the apparatus of claim 4. Additionally, Mammou teaches the apparatus of claim 4, wherein the predictor laser ID list includes at least one laser ID above the pivot laser ID and at least one laser ID below the pivot laser ID (Fig. 9, paragraph 151-153, wherein laser emitter index is interpreted as a laser IDs, and the prediction tree is interpreted as a list of predictor laser IDs, and where a given node can have a parent or child with a different laser ID which is interpreted as having a laser ID above or below the pivot laser ID).
Regarding claim 6, Mammou in view of Park discloses the apparatus of claim 5. Additionally, Mammou teaches the apparatus of claim 5, wherein to determine the vertical predictor, the one or more processors are further configured to cause the apparatus to: determine a respective vertical predictor for each of the plurality of laser IDs in the predictor laser ID list (Fig. 9, paragraph 151-153, creating a prediction tree where nodes can include laser ID information; Fig. 2B, paragraph 72-74, wherein parent nodes are interpreted as vertical predictors and implies each node in the tree has a respective vertical predictor); determine the vertical predictor to be a first respective vertical predictor associated with the laser ID directly above the pivot laser ID (Fig. 9, paragraph 151-153, wherein a parent node can have a different laser emitter index, which is interpreted as a vertical predictor directly above the pivot laser ID); or determine, based on the first respective vertical predictor being unavailable, the vertical predictor to be a second respective vertical predictor associated with the laser ID directly below the pivot laser ID; or determine, based on the first respective vertical predictor being unavailable and the second respective vertical predictor being unavailable, that no vertical predictor is available (Fig. 2B, paragraph 72-73, wherein a root node having no predictions is interpreted as having no vertical predictor available).
Regarding claim 7, Mammou in view of Park discloses the apparatus of claim 4. Additionally, Mammou teaches the apparatus of claim 4, wherein to determine the predictor laser ID list, the one or more processors are further configured to cause the apparatus to: determine whether any laser IDs in the predictor laser ID list are outside a laser ID range; and remove any laser IDs from the predictor laser ID list that are outside the laser ID range (Fig. 3, paragraph 85-87, wherein selecting nodes to add to the prediction tree based on their properties, which can include laser IDs, is interpreted as determining if laser IDs for a predictor laser ID list fit a given predictor criteria, and implies excluding nodes based on their laser IDs which is interpreted as equivalent to removing them).
Regarding claim 9, Mammou in view of Park discloses the apparatus of claim 1. Additionally, Mammou teaches the apparatus of claim 1, wherein to determine the pivot azimuth value, the one or more processors are further configured to cause the apparatus to: determine the pivot azimuth value from one of an azimuth of a previously decoded point (Fig. 6, paragraph 92-99, wherein the prediction tree can consist of compressed geometry information from a LIDAR system, which include their azimuth value, and decoding a child node based off of the information of their parent node is interpreted as determining the azimuth value based on the azimuth of a previously decoded point), an azimuth of the current point (Fig. 6, paragraph 96-99, wherein the prediction tree can be formed based on properties of a LiDAR system, which can include azimuths, and decoding the properties of a root node not based on other points is interpreted as determining the pivot azimuth based on the current point), an estimate of an azimuth of the previously decoded point (paragraph 145-150, wherein points and parameters can be encoded with lossy compression, which implies decoding an estimation of parameters such as azimuths), or an estimate of an azimuth of the current point.
Regarding claim 10, Mammou in view of Park discloses the apparatus of claim 1. Additionally, Mammou teaches the apparatus of claim 1, wherein to scale the pivot azimuth value, the one or more processors are further configured to cause the apparatus to: receive a vertical predictor azimuth scale value (paragraph 108-114, wherein quantization parameters are interpreted as vertical predictor azimuth scale values); and scale the pivot azimuth value using the vertical predictor azimuth scale value to obtain the first pivot azimuth index (paragraph 108-110, wherein tilde over ϕ is interpreted as the first pivot azimuth index).
Regarding claim 11, Mammou in view of Park discloses the apparatus of claim 1. Additionally, Park teaches the apparatus of claim 1, wherein to determine the vertical predictor based on the predictor azimuth index, the one or more processors are further configured to cause the apparatus to: determine the vertical predictor to be a previously coded point that has the second laser ID and an azimuth index equal to the predictor azimuth index (Fig. 16-17, Fig. 21, paragraph 248-252, wherein a predictive tree can be encoded based on a point’s azimuth value, and laser IDs and azimuths can both be encoded separately, which suggests that a parent node in the predictive tree, which is interpreted as a vertical predictor, can be a previously coded point with a second laser ID and the same azimuth index).
Regarding claim 12, Mammou in view of Park discloses the apparatus of claim 1. Additionally, Mammou teaches the apparatus of claim 1, wherein the pivot azimuth value corresponds to an azimuth of a previously coded point (Fig. 6, paragraph 92-99, wherein the prediction tree can consist of compressed geometry information from a LIDAR system, which include their azimuth value, and decoding a child node based off of the information of their parent node is interpreted as the azimuth value corresponding to azimuth of a previously coded point), and wherein to determine the predictor azimuth index based on the second azimuth index, the one or more processors are further configured to cause the apparatus to: set the predictor azimuth index to equal to a smallest index value that is greater than a value of the second azimuth index (paragraph 130-135, wherein δϕ is defined as a fixed constant corresponding to the rotational speed of the LIDAR system, which suggests that tilde over ϕ, which is interpreted as the predictor azimuth index, is the smallest possible value greater than the value of a second azimuth index) for which there is a point that is associated with the second laser ID (paragraph 130-135, wherein the predictive tree being an encoding of one or more parameters includes parameters from a LIDAR system such as laser ID, which suggests the current point and previous point can have different associated azimuth index value and laser IDs).
Regarding claim 16, Mammou teaches an apparatus configured to encode point cloud data, the apparatus comprising: a memory; and one or more processors coupled to the memory (paragraph 199), the one or more processors configured to cause the apparatus to determine a vertical predictor for a current point of the point cloud data (Fig. 2A-2B, paragraph 70-72, generating a prediction tree containing ancestor nodes used to predict child nodes, wherein ancestor nodes are interpreted as a vertical predictor, which suggests generating the prediction tree determines the vertical predictors); wherein to determine the vertical predictor for the current point, the one or more processors are further configured to cause the apparatus to: determine a pivot azimuth value (Fig. 8A, paragraph 102-107, wherein points generated from a LIDAR system can have parameter ϕ , which is defined as its azimuth angle); scale the pivot azimuth value to obtain a first pivot azimuth index (paragraph 108-113, wherein parameterizing ϕ into tilde over ϕ is interpreted as scaling the pivot azimuth value to obtain a first azimuth index); determine a second azimuth index based on the first pivot azimuth index (paragraph 132-134, wherein determining the azimuth value of a current point based on the azimuth value of a previous point is interpreted as determining a second azimuth index); determine a predictor azimuth index based on the second azimuth index (paragraph 132-135, wherein the predictor relationship to find the azimuth of a node based on another azimuth value is interpreted as a predictor azimuth index); and determine the vertical predictor based on the predictor azimuth index (paragraph 72-74, wherein predicting a node based on ancestor nodes based the ancestor node’s prediction technique suggests including predicting a node based off of the ancestor node’s azimuth values, wherein the ancestor node is interpreted as the vertical predictor) and perform predictive geometry encoding (paragraph 92, wherein the prediction tree compressing spatial information such as geometry is interpreted as predictive geometry encoding) using the vertical predictor to encode a position of the current point (Fig. 2B, paragraph 72-74, using a prediction tree to predict a node using its ancestor node is interpreted as encoding the current point, and the ancestor node being used as a predictor is interpreted as a vertical predictor; paragraph 74-81, wherein the prediction tree is used to predict the position of the current point).
Mammou does not teach determining a pivot laser ID for a current point of the point cloud data; determining a vertical predictor wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID.
Park teaches determining a pivot laser ID for a current point of the point cloud data (Fig. 19, paragraph 281-282, classifying points of point cloud data acquired by a laser of the same laser ID interpreted as determining a pivot laser ID for points in the point cloud data); determining a vertical predictor wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID (paragraph 283, wherein points arranged in layers based on their laser ID implies that a first and second point with different laser IDs can be neighbors, and can be used as a predictor).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 17, Mammou in view of Park discloses the apparatus of claim 16. Additionally, Mammou teaches the apparatus of claim 16, wherein to determine the pivot laser ID, the one or more processors are further configured to cause the apparatus to: determine the pivot laser ID to be a laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as the laser ID, and can have an ancestor node with the same index) of a previously encoded point encoded before the current point (Fig. 2A, paragraph 70-74, encoding nodes predicted from ancestor nodes, where ancestor nodes are interpreted as a previously encoded point encoded before the current node, and wherein the prediction tree can be formed based on spatial and/or attribute information, which can include laser IDs).
Regarding claim 18, Mammou in view of Park discloses the apparatus of claim 16. Additionally, Mammou teaches the apparatus of claim 16, wherein to determine the pivot laser ID, the one or more processors are further configured to cause the apparatus to: determine the pivot laser ID to be a laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as the laser ID) of the current point (Fig. 2A, paragraph 70-74, wherein the prediction tree can be formed based on properties of a LiDAR system, which can include laser IDs, and encoding the properties of a root node not based on other points is interpreted as determining the laser ID based on the current point).
Regarding claim 19, Mammou in view of Park discloses the apparatus of claim 16. Additionally, Mammou teaches the apparatus of claim 16, wherein the one or more processors are further configured to cause the apparatus to: determine a predictor laser ID list based on the pivot laser ID, wherein the predictor laser ID list includes a plurality of laser IDs, including the second laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as a laser ID, and the prediction tree is interpreted as a list of predictor laser IDs, which can include the pivot laser ID and a first and second laser ID).
Regarding claim 20, Mammou in view of Park discloses the apparatus of claim 19. Additionally, Mammou teaches the apparatus of claim 19, wherein the predictor laser ID list includes at least one laser ID above the pivot laser ID and at least one laser ID below the pivot laser ID (Fig. 9, paragraph 151-153, wherein laser emitter index is interpreted as a laser IDs, and the prediction tree is interpreted as a list of predictor laser IDs, and where a given node can have a parent or child with a different laser ID which is interpreted as having a laser ID above or below the pivot laser ID).
Regarding claim 21, Mammou in view of Park discloses the apparatus of claim 20. Additionally, Mammou teaches the apparatus of claim 20, wherein to determine the vertical predictor, the one or more processors are further configured to cause the apparatus to: determine a respective vertical predictor for each of the plurality of laser IDs in the predictor laser ID list (Fig. 9, paragraph 151-153, creating a prediction tree where nodes can include laser ID information; Fig. 2B, paragraph 72-74, wherein parent nodes are interpreted as vertical predictors and implies each node in the tree has a respective vertical predictor); determine the vertical predictor to be a first respective vertical predictor associated with the laser ID directly above the pivot laser ID (Fig. 9, paragraph 151-153, wherein a parent node can have a different laser emitter index, which is interpreted as a vertical predictor directly above the pivot laser ID); or determine, based on the first respective vertical predictor being unavailable, the vertical predictor to be a second respective vertical predictor associated with the laser ID directly below the pivot laser ID; or determine, based on the first respective vertical predictor being unavailable and the second respective vertical predictor being unavailable, that no vertical predictor is available (Fig. 2B, paragraph 72-73, wherein a root node having no predictions is interpreted as having no vertical predictor available).
Regarding claim 22, Mammou in view of Park discloses the apparatus of claim 19. Additionally, Mammou teaches the apparatus of claim 19, wherein to determine the predictor laser ID list, the one or more processors are further configured to cause the apparatus to: determine whether any laser IDs in the predictor laser ID list are outside a laser ID range; and remove any laser IDs from the predictor laser ID list that are outside the laser ID range (Fig. 3, paragraph 85-87, wherein selecting nodes to add to the prediction tree based on their properties, which can include laser IDs, is interpreted as determining if laser IDs for a predictor laser ID list fit a given predictor criteria, and implies excluding nodes based on their laser IDs which is interpreted as equivalent to removing them).
Regarding claim 24, Mammou in view of Park discloses the apparatus of claim 23. Additionally, Mammou teaches the apparatus of claim 23, wherein to determine the pivot azimuth value, the one or more processors are further configured to cause the apparatus to: determine the pivot azimuth value from one of an azimuth of a previously encoded point (Fig. 2A, paragraph 70-74, wherein the prediction tree can consist of compressed geometry information from a LIDAR system, which include their azimuth value, and encoding a child node based off of the information of their parent node is interpreted as determining the azimuth value based on the azimuth of a previously decoded point), an azimuth of the current point (Fig. 2A, paragraph 70-74, wherein the prediction tree can be formed based on spatial information which can include azimuths, and encoding the properties of a root node not based on other points is interpreted as determining the pivot azimuth based on the current point), an estimate of an azimuth of the previously encoded point (paragraph 145-150, wherein points and parameters can be encoded with lossy compression, which suggests encoding an estimation of parameters such as azimuths), or an estimate of an azimuth of the current point.
Regarding claim 25, Mammou in view of Park discloses the apparatus of claim 16. Additionally, Mammou teaches the apparatus of claim 16, wherein to scale the pivot azimuth value, the one or more processors are further configured to cause the apparatus to: scale the pivot azimuth value using a vertical predictor azimuth scale value to obtain the first pivot azimuth index (paragraph 108-114, wherein quantization parameters are interpreted as vertical predictor azimuth scale values, and tilde over ϕ is interpreted as a first pivot azimuth value); and encode a syntax element indicating the vertical predictor azimuth scale value (paragraph 129, wherein quantization parameters qr, qç, qθ and qϕ are interpreted as syntax elements indicating the vertical predictor azimuth scale value).
Regarding claim 26, Mammou in view of Park discloses the apparatus of claim 16. Additionally, Park teaches the apparatus of claim 16, wherein to determine the vertical predictor based on the predictor azimuth index, the one or more processors are further configured to cause the apparatus to: determine the vertical predictor to be a previously coded point that has the second laser ID and an azimuth index equal to the predictor azimuth index (Fig. 16-17, Fig. 21, paragraph 248-252, wherein a predictive tree can be encoded based on a point’s azimuth value, and laser IDs and azimuths can both be encoded separately, which suggests that a parent node in the predictive tree, which is interpreted as a vertical predictor, can be a previously coded point with a second laser ID and the same azimuth index).
Regarding claim 27, Mammou in view of Park discloses the apparatus of claim 16. Additionally, Mammou teaches the apparatus of claim 16, wherein the pivot azimuth value corresponds to an azimuth of a previously coded point (Fig. 6, paragraph 92-99, wherein the prediction tree can consist of compressed geometry information from a LIDAR system, which include their azimuth value, and decoding a child node based off of the information of their parent node is interpreted as the azimuth value corresponding to azimuth of a previously coded point), and wherein to determine the predictor azimuth index based on the second azimuth index, the one or more processors are further configured to cause the apparatus to: set the predictor azimuth index equal to a smallest index value that is greater than a value of the second azimuth index (paragraph 130-135, wherein δϕ is defined as a fixed constant corresponding to the rotational speed of the LIDAR system, which suggests that tilde over ϕ, which is interpreted as the predictor azimuth index, is the smallest possible value greater than the value of a second azimuth index) for which there is a point that is associated with the second laser ID (paragraph 130-135, wherein the predictive tree being an encoding of one or more parameters includes parameters from a LIDAR system such as laser ID, which suggests the current point and previous point can have different associated azimuth index value and laser IDs).
Regarding claim 30, Mammou in view of Park discloses the apparatus of claim 16. Additionally, Mammou teaches the apparatus of claim 16, further comprising a sensor configured to capture the point cloud data (Fig. 11, paragraph 178, sensor can perform a 3D reconstruction to generate a point cloud).
Regarding claim 31, Mammou teaches a method for decoding point cloud data, the method comprising: determining a vertical predictor for a current point of the point cloud data (Fig. 2A-2B, paragraph 70-72, generating a prediction tree containing ancestor nodes used to predict child nodes, wherein ancestor nodes are interpreted as a vertical predictor, which suggests generating the prediction tree determines the vertical predictors); wherein to determine the vertical predictor for the current point, the one or more processors are further configured to cause the apparatus to: determining a pivot azimuth value (Fig. 8A, paragraph 102-107, wherein points generated from a LIDAR system can have parameter ϕ , which is defined as its azimuth angle); scaling the pivot azimuth value to obtain a first pivot azimuth index (paragraph 108-113, wherein parameterizing ϕ into tilde over ϕ is interpreted as scaling the pivot azimuth value to obtain a first azimuth index); determining a second azimuth index based on the first pivot azimuth index (paragraph 132-134, wherein determining the azimuth value of a current point based on the azimuth value of a previous point is interpreted as determining a second azimuth index); determining a predictor azimuth index based on the second azimuth index (paragraph 132-135, wherein the predictor relationship to find the azimuth of a node based on another azimuth value is interpreted as a predictor azimuth index); and determining the vertical predictor based on the predictor azimuth index (paragraph 72-74, wherein predicting a node based on ancestor nodes based the ancestor node’s prediction technique suggests including predicting a node based off of the ancestor node’s azimuth values, wherein the ancestor node is interpreted as the vertical predictor) and performing predictive geometry decoding (paragraph 92, wherein the prediction tree consists of compressed geometry information; Fig. 6, paragraph 96-97, wherein decoding a prediction tree of a point cloud is interpreted as predictive geometry decoding) using the vertical predictor to decode a position of the current point (Fig. 2B, paragraph 72-74, using a prediction tree to predict a node using its ancestor node is interpreted as decoding the current point, and the ancestor node being used as a predictor is interpreted as a vertical predictor; paragraph 74-81, wherein the prediction tree is used to predict the position of the current point).
Mammou does not teach determining a pivot laser ID for a current point of the point cloud data; determining a vertical predictor wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID.
Park teaches determining a pivot laser ID for a current point of the point cloud data (Fig. 19, paragraph 281-282, classifying points of point cloud data acquired by a laser of the same laser ID interpreted as determining a pivot laser ID for points in the point cloud data); determining a vertical predictor wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID (paragraph 283, wherein points arranged in layers based on their laser ID implies that a first and second point with different laser IDs can be neighbors, and can be used as a predictor).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 32, Mammou in view of Park discloses the apparatus of claim 31. Additionally, Mammou teaches the method of claim 1, wherein determining the pivot laser ID comprises: determining the pivot laser ID to be a laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as the laser ID, and can have a parent node with the same index) of a previously decoded point decoded before the current point (Fig. 6, paragraph 96-99, decoding the properties child node based on its parent node, wherein the parent node is interpreted as a previously decoded point decoded before the child node, and wherein the prediction tree can be formed based on properties of a LiDAR system, which can include laser IDs).
Regarding claim 33, Mammou in view of Park discloses the apparatus of claim 31. Additionally, Mammou teaches the method of claim 31, wherein determining the pivot laser ID comprises: determining the pivot laser ID to be a laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as the laser ID) of the current point (Fig. 6, paragraph 96-99, wherein the prediction tree can be formed based on properties of a LiDAR system, which can include laser IDs, and decoding the properties of a root node not based on other points is interpreted as determining the laser ID based on the current point).
Regarding claim 34, Mammou in view of Park discloses the method of claim 31. Additionally, Mammou teaches the method of claim 31, further comprising: determining a predictor laser ID list based on the pivot laser ID, wherein the predictor laser ID list includes a plurality of laser IDs, including the second laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as a laser ID, and the prediction tree is interpreted as a list of predictor laser IDs, which can include the pivot laser ID and a first and second laser ID).
Regarding claim 35, Mammou in view of Park discloses the method of claim 34. Additionally, Mammou teaches the method of claim 34, wherein the predictor laser ID list includes at least one laser ID above the pivot laser ID and at least one laser ID below the pivot laser ID (Fig. 9, paragraph 151-153, wherein laser emitter index is interpreted as a laser IDs, and the prediction tree is interpreted as a list of predictor laser IDs, and where a given node can have a parent or child with a different laser ID which is interpreted as having a laser ID above or below the pivot laser ID).
Regarding claim 36, Mammou in view of Park discloses the method of claim 35. Additionally, Mammou teaches the method of claim 35, wherein determining the vertical predictor, further comprises: determining a respective vertical predictor for each of the plurality of laser IDs in the predictor laser ID list (Fig. 9, paragraph 151-153, creating a prediction tree where nodes can include laser ID information; Fig. 2B, paragraph 72-74, wherein parent nodes are interpreted as vertical predictors and implies each node in the tree has a respective vertical predictor); determining the vertical predictor to be a first respective vertical predictor associated with the laser ID directly above the pivot laser ID (Fig. 9, paragraph 151-153, wherein a parent node can have a different laser emitter index, which is interpreted as a vertical predictor directly above the pivot laser ID); or determining, based on the first respective vertical predictor being unavailable, the vertical predictor to be a second respective vertical predictor associated with the laser ID directly below the pivot laser ID; or determining, based on the first respective vertical predictor being unavailable and the second respective vertical predictor being unavailable, that no vertical predictor is available (Fig. 2B, paragraph 72-73, wherein a root node having no predictions is interpreted as having no vertical predictor available).
Regarding claim 37, Mammou in view of Park discloses the method of claim 34. Additionally, Mammou teaches the method of claim 34, wherein determining the predictor laser ID list, further comprises: determining whether any laser IDs in the predictor laser ID list are outside a laser ID range; and remove any laser IDs from the predictor laser ID list that are outside the laser ID range (Fig. 3, paragraph 85-87, wherein selecting nodes to add to the prediction tree based on their properties, which can include laser IDs, is interpreted as determining if laser IDs for a predictor laser ID list fit a given predictor criteria, and implies excluding nodes based on their laser IDs which is interpreted as equivalent to removing them).
Regarding claim 39, Mammou in view of Park discloses the method of claim 31. Additionally, Mammou teaches the method of claim 31, wherein determining the pivot azimuth value comprises: determining the pivot azimuth value from one of an azimuth of a previously decoded point (Fig. 6, paragraph 92-99, wherein the prediction tree can consist of compressed geometry information from a LIDAR system, which include their azimuth value, and decoding a child node based off of the information of their parent node is interpreted as determining the azimuth value based on the azimuth of a previously decoded point), an azimuth of the current point (Fig. 6, paragraph 96-99, wherein the prediction tree can be formed based on properties of a LiDAR system, which can include azimuths, and decoding the properties of a root node not based on other points is interpreted as determining the pivot azimuth based on the current point), an estimate of an azimuth of the previously decoded point (paragraph 145-150, wherein points and parameters can be encoded with lossy compression, which implies decoding an estimation of parameters such as azimuths), or an estimate of an azimuth of the current point.
Regarding claim 40, Mammou in view of Park discloses the method of claim 31. Additionally, Mammou teaches the method of claim 31, wherein scaling pivot azimuth value comprises: receiving a vertical predictor azimuth scale value (paragraph 108-114, wherein quantization parameters are interpreted as vertical predictor azimuth scale values); and scaling the pivot azimuth value using the vertical predictor azimuth scale value to obtain the first pivot azimuth index (paragraph 108-110, wherein tilde over ϕ is interpreted as the first pivot azimuth index).
Regarding claim 41, Mammou in view of Park discloses the method of claim 31. Additionally, Park teaches the method of claim 31, wherein determining the vertical predictor based on the predictor azimuth index comprises: determining the vertical predictor to be a previously coded point that has the second laser ID and an azimuth index equal to the predictor azimuth index (Fig. 16-17, Fig. 21, paragraph 248-252, wherein a predictive tree can be encoded based on a point’s azimuth value, and laser IDs and azimuths can both be encoded separately, which suggests that a parent node in the predictive tree, which is interpreted as a vertical predictor, can be a previously coded point with a second laser ID and the same azimuth index).
Regarding claim 42, Mammou in view of Park discloses the method of claim 31. Additionally, Mammou teaches the method of claim 31, wherein the pivot azimuth value corresponds to an azimuth of a previously coded point (Fig. 6, paragraph 92-99, wherein the prediction tree can consist of compressed geometry information from a LIDAR system, which include their azimuth value, and decoding a child node based off of the information of their parent node is interpreted as the azimuth value corresponding to azimuth of a previously coded point), and wherein to determine the predictor azimuth index based on the second azimuth index, the one or more processors are further configured to cause the apparatus to: set the predictor azimuth index equal to a smallest index value that is greater than a value of the second azimuth index (paragraph 130-135, wherein δϕ is defined as a fixed constant corresponding to the rotational speed of the LIDAR system, which suggests that tilde over ϕ, which is interpreted as the predictor azimuth index, is the smallest possible value greater than the value of a second azimuth index) for which there is a point that is associated with the second laser ID (paragraph 130-135, wherein the predictive tree being an encoding of one or more parameters includes parameters from a LIDAR system such as laser ID, which suggests the current point and previous point can have different associated azimuth index value and laser IDs).
Regarding claim 46, Mammou teaches a method for encoding point cloud data, the method comprising: determining a vertical predictor for a current point of the point cloud data (Fig. 2A-2B, paragraph 70-72, generating a prediction tree containing ancestor nodes used to predict child nodes, wherein ancestor nodes are interpreted as a vertical predictor, which suggests generating the prediction tree determines the vertical predictors); wherein to determine the vertical predictor for the current point, the one or more processors are further configured to cause the apparatus to: determining a pivot azimuth value (Fig. 8A, paragraph 102-107, wherein points generated from a LIDAR system can have parameter ϕ , which is defined as its azimuth angle); scale the pivot azimuth value to obtain a first pivot azimuth index (paragraph 108-113, wherein parameterizing ϕ into tilde over ϕ is interpreted as scaling the pivot azimuth value to obtain a first azimuth index); determining a second azimuth index based on the first pivot azimuth index (paragraph 132-134, wherein determining the azimuth value of a current point based on the azimuth value of a previous point is interpreted as determining a second azimuth index); determining a predictor azimuth index based on the second azimuth index (paragraph 132-135, wherein the predictor relationship to find the azimuth of a node based on another azimuth value is interpreted as a predictor azimuth index); and determining the vertical predictor based on the predictor azimuth index (paragraph 72-74, wherein predicting a node based on ancestor nodes based the ancestor node’s prediction technique suggests including predicting a node based off of the ancestor node’s azimuth values, wherein the ancestor node is interpreted as the vertical predictor) and performing predictive geometry encoding (paragraph 92, wherein the prediction tree compressing spatial information such as geometry is interpreted as predictive geometry encoding) using the vertical predictor to encode a position of the current point (Fig. 2B, paragraph 72-74, using a prediction tree to predict a node using its ancestor node is interpreted as encoding the current point, and the ancestor node being used as a predictor is interpreted as a vertical predictor; paragraph 74-81, wherein the prediction tree is used to predict the position of the current point).
Mammou does not teach determining a pivot laser ID for a current point of the point cloud data; determining a vertical predictor wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID.
Park teaches determining a pivot laser ID for a current point of the point cloud data (Fig. 19, paragraph 281-282, classifying points of point cloud data acquired by a laser of the same laser ID interpreted as determining a pivot laser ID for points in the point cloud data); determining a vertical predictor wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID (paragraph 283, wherein points arranged in layers based on their laser ID implies that a first and second point with different laser IDs can be neighbors, and can be used as a predictor).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 47, Mammou in view of Park discloses the method of claim 46. Additionally, Mammou teaches the method of claim 46, wherein determining the pivot laser ID comprises: determining the pivot laser ID to be a laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as the laser ID, and can have an ancestor node with the same index) of a previously encoded point decoded before the current point (Fig. 2A, paragraph 70-74, encoding nodes predicted from ancestor nodes, where ancestor nodes are interpreted as a previously encoded point decoded before the current point, and wherein the prediction tree can be formed based on spatial and/or attribute information, which can include laser IDs).
Regarding claim 48, Mammou in view of Park discloses the method of claim 46. Additionally, Mammou teaches the method of claim 46, wherein determining the pivot laser ID comprises: determining the pivot laser ID to be a laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as the laser ID) of the current point (Fig. 2A, paragraph 70-74, wherein the prediction tree can be formed based on properties of a LiDAR system, which can include laser IDs, and encoding the properties of a root node not based on other points is interpreted as determining the laser ID based on the current point).
Regarding claim 49, Mammou in view of Park discloses the apparatus of claim 46. Additionally, Mammou teaches the method of claim 46, further comprising: determining a predictor laser ID list based on the pivot laser ID, wherein the predictor laser ID list includes a plurality of laser IDs, including the second laser ID (Fig. 9, paragraph 151-153, wherein the laser emitter index is interpreted as a laser ID, and the prediction tree is interpreted as a list of predictor laser IDs, which can include the pivot laser ID and a first and second laser ID).
Regarding claim 50, Mammou in view of Park discloses the method of claim 49. Additionally, Mammou teaches the method of claim 49, wherein the predictor laser ID list includes at least one laser ID above the pivot laser ID and at least one laser ID below the pivot laser ID (Fig. 9, paragraph 151-153, wherein laser emitter index is interpreted as a laser IDs, and the prediction tree is interpreted as a list of predictor laser IDs, and where a given node can have a parent or child with a different laser ID which is interpreted as having a laser ID above or below the pivot laser ID).
Regarding claim 51, Mammou in view of Park discloses the method of claim 50. Additionally, Mammou teaches the method of claim 50, wherein determining the vertical predictor comprises: determining a respective vertical predictor for each of the plurality of laser IDs in the predictor laser ID list (Fig. 9, paragraph 151-153, creating a prediction tree where nodes can include laser ID information; Fig. 2B, paragraph 72-74, wherein parent nodes are interpreted as vertical predictors and implies each node in the tree has a respective vertical predictor); determining the vertical predictor to be a first respective vertical predictor associated with the laser ID directly above the pivot laser ID (Fig. 9, paragraph 151-153, wherein a parent node can have a different laser emitter index, which is interpreted as a vertical predictor directly above the pivot laser ID); or determining, based on the first respective vertical predictor being unavailable, the vertical predictor to be a second respective vertical predictor associated with the laser ID directly below the pivot laser ID; or determining, based on the first respective vertical predictor being unavailable and the second respective vertical predictor being unavailable, that no vertical predictor is available (Fig. 2B, paragraph 72-73, wherein a root node having no predictions is interpreted as having no vertical predictor available).
Regarding claim 52, Mammou in view of Park discloses the method of claim 49. Additionally, Mammou teaches the method of claim 49, wherein determining the predictor laser ID list comprises: determining whether any laser IDs in the predictor laser ID list are outside a laser ID range; and remove any laser IDs from the predictor laser ID list that are outside the laser ID range (Fig. 3, paragraph 85-87, wherein selecting nodes to add to the prediction tree based on their properties, which can include laser IDs, is interpreted as determining if laser IDs for a predictor laser ID list fit a given predictor criteria, and implies excluding nodes based on their laser IDs which is interpreted as equivalent to removing them).
Regarding claim 54, Mammou in view of Park discloses the method of claim 46. Additionally, Mammou teaches the method of claim 46, wherein determining the pivot azimuth value comprises: determining the pivot azimuth value from one of an azimuth of a previously encoded point (Fig. 2A, paragraph 70-74, wherein the prediction tree can consist of compressed geometry information from a LIDAR system, which include their azimuth value, and encoding a child node based off of the information of their parent node is interpreted as determining the azimuth value based on the azimuth of a previously decoded point), an azimuth of the current point (Fig. 2A, paragraph 70-74, wherein the prediction tree can be formed based on spatial information which can include azimuths, and encoding the properties of a root node not based on other points is interpreted as determining the pivot azimuth based on the current point), an estimate of an azimuth of the previously encoded point (paragraph 145-150, wherein points and parameters can be encoded with lossy compression, which suggests encoding an estimation of parameters such as azimuths), or an estimate of an azimuth of the current point.
Regarding claim 55, Mammou in view of Park discloses the method of claim 46. Additionally, Mammou teaches the method of claim 46, wherein scaling the pivot azimuth value comprises: scaling the pivot azimuth value using an vertical predictor azimuth scale value to obtain the first pivot azimuth index (paragraph 108-114, wherein quantization parameters are interpreted as vertical predictor azimuth scale values, and tilde over ϕ is interpreted as a first pivot azimuth value); and encoding a syntax element indicating the vertical predictor azimuth scale value (paragraph 129, wherein quantization parameters qr, qç, qθ and qϕ are interpreted as syntax elements indicating the vertical predictor azimuth scale value).
Regarding claim 56, Mammou in view of Park discloses the method of claim 46. Additionally, Park teaches the method of claim 46, wherein determining the vertical predictor based on the predictor azimuth index comprises: determining the vertical predictor to be a previously coded point that has the second laser ID and an azimuth index equal to the predictor azimuth index (Fig. 16-17, Fig. 21, paragraph 248-252, wherein a predictive tree can be encoded based on a point’s azimuth value, and laser IDs and azimuths can both be encoded separately, which suggests that a parent node in the predictive tree, which is interpreted as a vertical predictor, can be a previously coded point with a second laser ID and the same azimuth index).
Regarding claim 57, Mammou in view of Park discloses the method of claim 46. Additionally, Mammou teaches the method of claim 46, wherein the pivot azimuth value corresponds to an azimuth of a previously coded point (Fig. 6, paragraph 92-99, wherein the prediction tree can consist of compressed geometry information from a LIDAR system, which include their azimuth value, and decoding a child node based off of the information of their parent node is interpreted as the azimuth value corresponding to azimuth of a previously coded point), and wherein determining the predictor azimuth index based on the second azimuth index comprises: setting the predictor azimuth index equal to a smallest index value that is greater than a value of the second azimuth index (paragraph 130-135, wherein δϕ is defined as a fixed constant corresponding to the rotational speed of the LIDAR system, which suggests that tilde over ϕ, which is interpreted as the predictor azimuth index, is the smallest possible value greater than the value of a second azimuth index) for which there is a point that is associated with the second laser ID (paragraph 130-135, wherein the predictive tree being an encoding of one or more parameters includes parameters from a LIDAR system such as laser ID, which suggests the current point and previous point can have different associated azimuth index value and laser IDs).
Regarding claim 60, Mammou in view of Park discloses the method of claim 46. Additionally, Mammou teaches the method of claim 46, further comprising: capturing the point cloud data (Fig. 11, paragraph 178, sensor performing a 3D reconstruction to generate a point cloud interpreted as capturing the point cloud data).
12. Claims 13-15, 28-29, 43-35, 58-59 are rejected under 35 U.S.C. 103 as being unpatentable over Mammou in view of Park as applied to claims 1, 16, 31, 46 above, and further in view of Sugio (US 12294740 B2), hereinafter Sugio.
Regarding claim 13, Mammou in view of Park discloses the apparatus of claim 1. Additionally, Sugio teaches the apparatus of claim 1, wherein to decode the current point using the vertical predictor and predictive geometry decoding, the one or more processors are further configured to cause the apparatus to: decode residual coordinate values for the current point (Fig. 87, Col. 60 lines 37-49, encoded prediction residual), wherein the residual coordinate values are in a spherical domain and include a residual radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value); determine coordinate values of the vertical predictor (Fig. 87, Col. 60 lines 37-52, wherein the calculated predicted value of a first three-dimensional point is interpreted as coordinates of a vertical predictor), wherein the coordinate values of the vertical predictor are in the spherical domain and include a vertical predictor radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value); and add the coordinate values of the vertical predictor to the residual coordinate values in order to obtained a reconstructed current point (Fig. 87, Col. 60 lines 52-58, calculating first geometry information of the first three-dimensional point by adding the predicted value and the prediction residual interpreted as obtaining a reconstructed point), wherein to add the coordinate values of the vertical predictor to the residual coordinate values, the one or more processors are further configured to cause the apparatus to add the residual radius to the vertical predictor radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value, and suggests adding the predicted value and prediction residuals together involve adding radius values together).
It would have been obvious before the effective filing date of the claimed invention to have modified Mammou in view of Park to have incorporated the teachings of Sugio for this apparatus for encoding and decoding a point cloud by combining coordinate values of residuals and vertical predictors. Both Mammou and Park discuss processing point cloud data, and specifically point cloud data generated from LiDAR devices, and similarly discuss generating a predictive tree from the point cloud in order to reduce the latency and computational complexity of the encoding and decoding process. Furthermore, Sugio also discusses processing point cloud data including point cloud data generated from LiDAR devices, as well as generating a prediction tree from the point cloud for the purposes of more efficiently compressing data. As all three references discuss similar inventive and advantageous solutions to the same problem or processing LiDAR point cloud data, it would have been obvious to combine these references.
Regarding claim 14, Mammou in view of Park and further in view of Sugio discloses the apparatus of claim 13. Additionally, Mammou teaches the apparatus of claim 13, wherein the one or more processors are further configured to cause the apparatus to: convert the reconstructed current point from the spherical domain to a Cartesian domain (paragraph 141-144, wherein reconstructed (r̃, tilde over ϕ, i) parameters are defined as a point in the spherical domain; paragraph 123, wherein Cartesian coordinates (x̂, ŷ, ẑ) are reconstructed from spherical domain coordinates).
Regarding claim 15, Mammou in view of Park and further in view of Sugio discloses the apparatus of claim 13. Additionally, Mammou teaches the apparatus of claim 13, further comprising a display configured to present imagery based on the reconstructed current point (Fig. 11, paragraph 179, rendering a decoded point cloud in a 3-D application).
Regarding claim 28, Mammou in view of Park discloses the apparatus of claim 16. Additionally, Sugio teaches the apparatus of claim 16, wherein to encode the current point using the vertical predictor and predictive geometry encoding, the one or more processors are further configured to cause the apparatus to: determine coordinate values of the current point (Fig. 81, Col. 55 line 52 – Col. 56 line 11, wherein the geometry information of the node is interpreted as coordinate values of the current point), wherein the coordinate values of the current point are in a spherical domain and include a radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value); determine coordinate values of the vertical predictor (Fig. 81, Col. 55 line 52 – Col. 56 line 11, wherein the calculated predicted value is interpreted as the coordinate values of the vertical predictor), wherein the coordinate values of the vertical predictor are in the spherical domain and include a vertical predictor radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value); subtract the coordinate values of the vertical predictor from the coordinate values of the current point to obtain residual values for the current point (Fig. 81, Col. 55 line 52 – Col. 56 line 11, calculating prediction residual as the difference between the predicted value and geometry information of the node), wherein to subtract the coordinate values of the vertical predictor from the coordinate values of the current point, the one or more processors are further configured to cause the apparatus to subtract the vertical predictor radius from the radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value, and suggests finding the difference between the predicted value and geometry information involves subtracting their radiuses); and encode the residual values (Fig. 81, Col. 55 line 52 – Col. 56 line 11, encoding node information and prediction residual).
The motivation to combine would be the same as that set forth in claim 13.
Regarding claim 29, Mammou in view of Park and further in view of Sugio discloses the apparatus of claim 28. Additionally, Mammou teaches the apparatus of claim 28, wherein the one or more processors are further configured to cause the apparatus to: convert the current point from a Cartesian domain to the spherical domain to obtain the coordinate values of the current point (Fig. 8A, paragraph 106-108, converting a current point from Cartesian domain to a spherical coordinate system).
Regarding claim 43, Mammou in view of Park discloses the method of claim 41. Additionally, Sugio teaches the method of claim 41, wherein to decode the current point using the vertical predictor and predictive geometry decoding comprises: decoding residual coordinate values for the current point (Fig. 87, Col. 60 lines 37-49, encoded prediction residual), wherein the residual coordinate values are in a spherical domain and include a residual radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value); determining coordinate values of the vertical predictor (Fig. 87, Col. 60 lines 37-52, wherein the calculated predicted value of a first three-dimensional point is interpreted as coordinates of a vertical predictor), wherein the coordinate values of the vertical predictor are in the spherical domain and include a vertical predictor radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value); and adding the coordinate values of the vertical predictor to the residual coordinate values in order to obtained a reconstructed current point (Fig. 87, Col. 60 lines 52-58, calculating first geometry information of the first three-dimensional point by adding the predicted value and the prediction residual interpreted as obtaining a reconstructed point), wherein to add the coordinate values of the vertical predictor to the residual coordinate values, the one or more processors are further configured to cause the apparatus to add the residual radius to the vertical predictor radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value, and suggests adding the predicted value and prediction residuals together involve adding radius values together).
The motivation to combine would be the same as that set forth for claim 13.
Regarding claim 44, Mammou in view of Park and further in view of Sugio discloses the method of claim 43. Additionally, Mammou teaches the method of claim 43, further comprising: converting the reconstructed current point from the spherical domain to a Cartesian domain (paragraph 141-144, wherein reconstructed (r̃, tilde over ϕ, i) parameters are defined as a point in the spherical domain; paragraph 123, wherein Cartesian coordinates (x̂, ŷ, ẑ) are reconstructed from spherical domain coordinates).
Regarding claim 45, Mammou in view of Park and further in view of Sugio discloses the method of claim 43. Additionally, Mammou teaches the method of claim 43, further comprising displaying imagery based on the reconstructed current point (Fig. 11, paragraph 179, rendering a decoded point cloud in a 3-D application).
Regarding claim 58, Mammou in view of Park discloses the method of claim 46. Additionally, Sugio teaches the method of claim 46, wherein encoding the current point using the vertical predictor and predictive geometry encoding comprises: determining coordinate values of the current point (Fig. 81, Col. 55 line 52 – Col. 56 line 11, wherein the geometry information of the node is interpreted as coordinate values of the current point), wherein the coordinate values of the current point are in a spherical domain and include a radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value); determining coordinate values of the vertical predictor (Fig. 81, Col. 55 line 52 – Col. 56 line 11, wherein the calculated predicted value is interpreted as the coordinate values of the vertical predictor), wherein the coordinate values of the vertical predictor are in the spherical domain and include a vertical predictor radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value); subtracting the coordinate values of the vertical predictor from the coordinate values of the current point to obtain residual values for the current point (Fig. 81, Col. 55 line 52 – Col. 56 line 11, calculating prediction residual as the difference between the predicted value and geometry information of the node), wherein to subtract the coordinate values of the vertical predictor from the coordinate values of the current point, the one or more processors are further configured to cause the apparatus to subtract the vertical predictor radius from the radius (Fig. 54, Col. 36 line 61 – Col. 37 line 56, wherein the geometry information of encoded points can be polar coordinates, which is interpreted as being coordinates in a spherical domain and having a radius value, and suggests finding the difference between the predicted value and geometry information involves subtracting their radiuses); and encoding the residual values (Fig. 81, Col. 55 line 52 – Col. 56 line 11, encoding node information and prediction residual).
The motivation to combine would be the same as that set forth for claim 13.
Regarding claim 59, Mammou in view of Park and further in view of Sugio discloses the method of claim 58. Additionally, Mammou teaches the method of claim 58, further comprising: converting the current point from a Cartesian domain to the spherical domain to obtain the coordinate values of the current point (Fig. 8A, paragraph 106-108, converting a current point from Cartesian domain to a spherical coordinate system).
Allowable Subject Matter
13. Claims 61-63 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
14. Applicant's arguments filed March 3rd have been fully considered but they are not persuasive.
Applicant argues that neither Mammou (US 20210312670 A1), hereinafter Mammou, nor Park (US 20230394712 A1), hereinafter Park, nor any combination herein discloses "determining a vertical predictor for a current point of the point cloud data, wherein the vertical predictor is based on a second point having a second laser ID different than the pivot ID". Additionally, applicant argues that it would not be obvious to combine the Mammou and Park references, as both Mammou and Park teach two different forms of prediction tree based geometry predictions.
Examiner respectfully disagrees. Examiner replies that in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. Additionally, Examiner replies that, during patent examination, the pending claims must be given their broadest reasonable interpretation consistent with the specification. See MPEP § 2111. Also, it is improper to import claim limitations from the specification. See MPEP § 2111.01(II). Park discloses determining a vertical predictor using a coding process for geometric encoding, as seen in Fig. 15 and paragraph 234, wherein geometric point cloud data can be encoded using a predictive tree, and a predicted value of a point can be calculated using predictive modes.
Additionally, paragraph 236 of Park explicitly teaches geometry encoding, using a position predictive mode based on configuring a position predictive tree with reference to neighbor points. This interpreted as using neighbor points as a predictor for the current point, where those neighbor points are interpreted as a vertical predictor. Furthermore, paragraphs 282-283 of Park teaches where points are arranged in layers based on their laser ID, which suggests that a first and second point with different laser IDs can be neighbors, including a point and its neighbor point used as a vertical predictor. Additionally, Mammou similarly teaches a vertical predictor in a predictive tree, as seen in Fig. 2B, paragraph 72-74, wherein using a prediction tree to predict a node using its ancestor node is interpreted as decoding the current point using its ancestor node as a vertical predictor, and in paragraph 74-81 wherein the prediction tree is used to predict the position of a current point. In conclusion, as both applications teach using vertical predictors for geometric encoding, as well as methods for encoding a point cloud using a predictive tree, the examiner maintains the rejection for claim 1 and its dependent claims.
In conclusion, the rejections set forth in the previous Office Action are shown to have been proper, and the claims are rejected above. To the extent that new citations and parenthetical remarks can be considered new grounds of rejection, such new grounds are necessitated by applicant’s amendments to the claim.
Conclusion
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN W YICK whose telephone number is (571)272-4063. The examiner can normally be reached M-F 8-5.
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/JORDAN WAN YICK/Examiner, Art Unit 2612
/Said Broome/Supervisory Patent Examiner, Art Unit 2612