DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) filed on December 23, 2024, has been considered by the examiner.
Specification
The disclosure is objected to because it contains multiple embedded hyperlinks and/or other form of browser-executable code (see paragraphs [0021-0023], [0064], and [0147]) . Applicant is required to delete the embedded hyperlinks and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The disclosure is objected to because of the following informalities:
In paragraph [0031], “n information processing device” should read “An information processing device”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a “normal vector prediction section” in Claim 1,
a “prediction residual generation section” in Claim 1,
a “prediction residual encoding section” in Claim 1,
a “geometry encoding section” in Claim 2,
a “ geometry decoding section” in Claim 2,
an “attribute encoding section” in Claim 7,
an “attribute decoding section” in Claim 7,
a “selector section” in Claim 11,
a “intra prediction section” in Claim 12
and a “normal vector decoding section” in Claim 16.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 15-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US Pub No 20250124601), hereinafter Park.
As to Claim 1, Park teaches an information processing device (see paragraph [0438], “FIG. 28 illustrates a normal information encoder according to embodiments”) comprising:
a normal vector prediction section (see Fig. 28, ‘Normal Information Predictor’)
configured to predict a yet-to-be-encoded normal vector of an encoding target point (see paragraph (see paragraph [0443], “For the normal information, prediction of the current vertex normal information may be performed”)
on a basis of encoded information different from the yet-to-be-encoded normal vector obtained through an encoding process of point cloud data (see paragraph [0444], “For the normal information, prediction of the current vertex normal information may be performed based on the reconstructed geometry information and/or the reconstructed neighboring vertex normal information”, wherein ‘reconstructed geometry’, refers to encoded and decoded geometry information of a point cloud (see paragraph [0278], “The geometry reconstruction or geometry reconstructor 16005 restores (reconstructs) the geometry information based on the decompressed geometry image, the decompressed occupancy map, and/or the decompressed auxiliary patch information. For example, the geometry changed in the encoding process may be reconstructed”),
and derive a prediction value of the yet-to-be- encoded normal vector, in the encoding process (see paragraph [0443], “For the normal information, prediction of the current vertex normal information may be performed based on the reconstructed geometry information and/or the reconstructed neighboring vertex normal information”, and see paragraph [0441], “the normal information is a real number”);
a prediction residual generation section configured to generate a prediction residual that is a difference between the prediction value and the yet-to- be-encoded normal vector (see paragraph [0444], “According to an embodiment, residual normal information, which is a residual from the original normal information from the original normal information, may be quantized by a residual normal information quantizer.”);
and a prediction residual encoding section configured to encode the prediction residual (see paragraph [0445], “The residual normal information may be entropy encoded by a normal information entropy encoder to generate a bitstream”).
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because Park explicitly motivates doing so at least in paragraphs [0364], including “Embodiments can be modified and combined”, otherwise motivating combining the geometry encoding and reconstruction taught in paragraph [0278] with the normal prediction taught in a later embodiment.
As to Claim 2, Park teaches a geometry encoding section (see Fig. 1, point cloud video encoder 10002)
configured to encode a geometry of the point cloud data as the encoded information (see paragraph [0195], “The point cloud encoder 10002 according to embodiments may generate a geometry image. The geometry image refers to image data including geometry information about a point cloud”);
and a geometry decoding section (see Fig. 1, point cloud video decoder 10008),
configured to decode the geometry that has been encoded (see paragraph [0074], “The point cloud video decoder 10007 decodes the received point cloud video data. The decoder according to the embodiments may perform a reverse process of encoding according to the embodiments”, where a reverse process would involve decoding the geometry image received from the point cloud encoder),
wherein the normal vector prediction section derives the prediction value on a basis of the geometry that has been decoded (see paragraph [0444], “For the normal information, prediction of the current vertex normal information may be performed based on the reconstructed geometry information and/or the reconstructed neighboring vertex normal information”, wherein ‘reconstructed geometry’, refers to encoded and decoded geometry information of a point cloud).
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because Park explicitly motivates doing so at least in paragraphs [0364], including “Embodiments can be modified and combined”, otherwise motivating combining the geometry decoding and reconstruction taught in an earlier embodiment with the normal prediction taught in a later embodiment.
As to Claim 15, Park teaches an information processing method (see Abstract, “A point cloud data transmission method according to embodiments”) comprising the same steps recited in Claim 1. Therefore, the rejection and rationale are analogous to that of Claim 1.
As to Claim 16, Park teaches an information processing device (see Fig. 28) comprising:
a normal vector prediction section (see Fig. 28,’Normal Information Predictor’)
configured to predict a yet-to-be-encoded normal vector of an encoding target point (see paragraph (see paragraph [0443], “For the normal information, prediction of the current vertex normal information may be performed”)
on a basis of encoded information different from the yet-to-be-encoded normal vector obtained through an encoding process of point cloud data (see paragraph [0444], “For the normal information, prediction of the current vertex normal information may be performed based on the reconstructed geometry information and/or the reconstructed neighboring vertex normal information”, wherein ‘reconstructed geometry’, refers to encoded and decoded geometry information of a point cloud (see paragraph [0278], “The geometry reconstruction or geometry reconstructor 16005 restores (reconstructs) the geometry information based on the decompressed geometry image, the decompressed occupancy map, and/or the decompressed auxiliary patch information. For example, the geometry changed in the encoding process may be reconstructed”),
and derive a prediction value of the yet-to-be- encoded normal vector, in the encoding process (see paragraph [0043], “For the normal information, prediction of the current vertex normal information may be performed based on the reconstructed geometry information and/or the reconstructed neighboring vertex normal information”, and see paragraph [0441], “the normal information is a real number”);
by decoding a prediction residual that has been encoded (see paragraph [0535], “The normal information decoder receives a parsed normal information bitstream and performs entropy decoding and dequantization to generate reconstructed residual normal information”)
and adding the prediction value to the prediction residual (see paragraph [0537], “Reconstructed normal information may be generated by adding the predicted normal information and the reconstructed residual normal information”).
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because Park explicitly motivates doing so at least in paragraphs [0364], including “Embodiments can be modified and combined”, otherwise motivating combining the geometry decoding and reconstruction taught in an earlier embodiment with the normal prediction taught in a later embodiment.
As to Claim 17, Park teaches a geometry decoding section (see Fig. 1, point cloud video decoder 10008),
configured to decode the geometry that has been encoded (see paragraph [0074], “The point cloud video decoder 10007 decodes the received point cloud video data. The decoder according to the embodiments may perform a reverse process of encoding according to the embodiments”, where a reverse process would involve decoding the geometry image received from the point cloud encoder),
wherein the normal vector prediction section derives the prediction value on a basis of the geometry that has been decoded (see paragraph [0444], “For the normal information, prediction of the current vertex normal information may be performed based on the reconstructed geometry information and/or the reconstructed neighboring vertex normal information”, wherein ‘reconstructed geometry’, refers to encoded and decoded geometry information of a point cloud).
As to Claim 20, Nakagami teaches an information processing method (see Abstract, “A point cloud data transmission method according to embodiments”) which comprises the same steps recited in Claim 19. Therefore, the rejection and rationale are analogous to that of Claim 20.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US Pub No 20250124601), hereinafter Park in view of Lin et al (CN Pub No 106934853), hereinafter Lin.
As to Claim 3, Park teaches a geometry encoding section configured to encode a geometry of the point cloud data as the encoded information (see paragraph [0195], “The point cloud encoder 10002 according to embodiments may generate a geometry image. The geometry image refers to image data including geometry information about a point cloud”);
Park fails to teach that the normal vector prediction section derives the prediction value on a basis of analysis of an octree of the geometry that has been encoded. However, in an analogous art of image compression, Lin teaches a method for encoding point cloud data (see paragraph [0013], “The invention can compress point cloud data”, wherein compressing is a well-known term in the art for encoding) which comprises:
encoding geometry data of a point cloud using an octree (see paragraph [0023 -0024], “compressing the point cloud picture by adopting an octree method to obtain a compressed point cloud picture.. carrying out space position coding on the compressed point cloud picture obtained in the step one to obtain a space neighbor relation between points”, wherein the ‘position information’ is the geometry data of the point cloud),
and deriving a normal value prediction on a basis of analysis of an octree (see paragraph [0056]-[0057], “the normal vector of the intersection point coordinate is obtained according to a least square method by utilizing the space neighbor relation between the points.. from the analytic geometry.. a plane fitting plane is obtained by a least square method, and a normal vector of the intersection point is estimated”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the octree positional encoding taught by Lin with the residual encoding taught by Park. The motivation for doing so would be to rapidly obtain a normal value prediction. Lin teaches in paragraph [0079], “The invention can compress point cloud data on the premise of not changing the surface shape of the automobile workpiece, simultaneously establishes a neighbor topological relation, and can rapidly obtain the normal vector of the surface of the workpiece by combining a least square method. In the prior art, the calculation of the whole spray painting track planning needs 3-5 minutes, and only needs less than one minute by adopting the method.” Thus, it would have been obvious to combine the octree compression taught by Lin with the teachings of Park in order to obtain the invention as claimed in Claim 3.
As to Claim 4, Park fails to teach that the normal vector prediction section derives the prediction value on a basis of map information indicating a point adjacent to the encoding target point in the octree structure.
However, Lin teaches that a normal vector prediction can be obtained on a basis of map information indicating a point adjacent to the encoding target point in the octree structure (see paragraph [0056]-[0057], “the normal vector of the intersection point coordinate is obtained according to a least square method by utilizing the space neighbor relation between the points.. from the analytic geometry.. a plane fitting plane is obtained by a least square method, and a normal vector of the intersection point is estimated”, and see paragraph [0056]-[0057], “the normal vector of the intersection point coordinate is obtained according to a least square method by utilizing the space neighbor relation between the points.. from the analytic geometry.. a plane fitting plane is obtained by a least square method, and a normal vector of the intersection point is estimated”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the octree positional encoding taught by Lin with the residual encoding taught by Park. The motivation for doing so would be to rapidly obtain a normal value prediction (see Lin, paragraph [0079]). Thus, it would have been obvious to combine the octree compression taught by Lin with the teachings of Park in order to obtain the invention as claimed in Claim 4.
As to Claim 18, Park teaches a geometry decoding section configured to decode a geometry of the point cloud data that has been encoded as the encoded information (see paragraph [0074]).
Park fails to teach that the normal vector prediction section derives the prediction value on a basis of analysis of an octree of the geometry.
However, Lin teaches deriving a normal value prediction on a basis of analysis of an octree ((see paragraph [0056]-[0057], “the normal vector of the intersection point coordinate is obtained according to a least square method by utilizing the space neighbor relation between the points.. from the analytic geometry.. a plane fitting plane is obtained by a least square method, and a normal vector of the intersection point is estimated”, and see paragraph [0056]-[0057], “the normal vector of the intersection point coordinate is obtained according to a least square method by utilizing the space neighbor relation between the points.. from the analytic geometry.. a plane fitting plane is obtained by a least square method, and a normal vector of the intersection point is estimated”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the octree positional encoding taught by Lin with the residual encoding taught by Park. The motivation for doing so would be to rapidly obtain a normal value prediction (see Lin, paragraph [0079]). Thus, it would have been obvious to combine the octree compression taught by Lin with the teachings of Park in order to obtain the invention as claimed in Claim 18.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US Pub No 20250124601), hereinafter Park in view of Lin et al (CN Pub No CN 106934853), hereinafter Lin, and further in view of Saiki et al. (JP Pub No 2015081831), hereinafter Saiki.
As to Claim 5, Park fails to teach wherein the normal vector prediction section derives the prediction value on a basis of table information based on the octree structure.
Lin teaches obtaining a normal vector prediction from an octree structure, but fails to teach the prediction value is determined on a basis of table information based on the octree structure. However, in an analogous art, Saiki teaches a method of encoding 3D data using an octree structure (see paragraph [0060], “A non-volatile storage device 1100 for storing octree data 1106 created from a three-dimensional geometric map”)
Which can be used to obtain a normal value prediction (see paragraph [0060], “In addition, as described above, the surface-normal look-up table 1108 is a table in which the normal direction of each point on the wall in the three-dimensional space or on the surface of a fixed or semi-fixed constantly exists target object, and is used”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the lookup table taught by Saiki with the data compression method taught by Park and the octree taught by Lin. The motivation for doing so would be to decrease processing time, and increase real-time performance of the system. Saiki teaches in paragraph [0178], “The normal direction of the obstruction surface is obtained from a predetermined lookup table 1108 created by the normal estimation of the surface in offline processing. For all points in the 3-dimensional geometric map, the normal direction is pre-calculated to aid in the real-time performance of the system.” Thus, it would have been obvious to combine the lookup table taught by Saiki with the teachings of Park and Lin in order to obtain the invention as claimed in Claim 5.
Claims 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US Pub No 20250124601), hereinafter Park, in view of Nakagami et al. (WO Pub No 2020145143), hereinafter Nakagami.
As to Claim 7, Park fails to explicitly teach an attribute decoding section configured to decode the attribute that has been encoded, wherein the normal vector prediction section derives the prediction value on a basis of the attribute that has been decoded.
However, in an analogous art, Nakagami teaches an attribute encoding section (see paragraph [0085] , “The attribute information encoding unit 105 acquires the interpolated attribute information supplied from the interpolation processing unit 104…the attribute information encoding unit 105 encodes the interpolated attribute information to generate encoded data”),
and a prediction section which obtains a prediction value on a basis of the attribute that has been decoded (see paragraphs [0242-0243], “The prediction unit 404 predicts attribute information supplied from the point cloud generation unit 403, and generates a prediction value of the attribute information”) Nakagami teaches that the attribute information may be normal vector information (see paragraph [0019], “The attribute information includes, for example, color information, reflectance information, normal information, and the like”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the attribute encoding section taught by Nakagami with the teachings of Park. The motivation for doing so would be to easily encode dense data. Nakagami teaches in paragraph [0026], “Thus, since the attribute information can be processed in a dense state, it is not necessary to classify the presence or absence of the neighboring point when processing is performed by using the attribute information of the neighboring point, and encoding/decoding can be performed more easily”). Thus, it would have been obvious to combine the attribute encoding taught by Nakagami with the teachings of Park in order to obtain the invention as claimed in Claim 7.
As to Claim 19, Park fails to teach an attribute decoding section configured to decode an attribute of the point cloud data that has been encoded as the encoded information, wherein the normal vector prediction section derives the prediction value on a basis of the attribute that has been decoded.
However, in an analogous art, Nakagami teaches an attribute encoding section (see paragraph [0103] , “As illustrated in Fig.10, the decoding device 130 includes a position information decoding unit 131, an attribute information decoding unit 132,”),
configured to decode an attribute of the point cloud data that has been encoded as the encoded information (see paragraph [0105], “The attribute information decoding unit 132 decodes encoded data of the attribute information”),
and a prediction section which obtains the prediction value on a basis of the attribute that has been decoded (see paragraphs [0242-0243], “The prediction unit 404 predicts attribute information supplied from the point cloud generation unit 403, and generates a prediction value of the attribute information”) Nakagami teaches that the attribute information may be normal vector information (see paragraph [0019], “The attribute information includes, for example, color information, reflectance information, normal information, and the like”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the attribute decoding section taught by Nakagami with the teachings of Park. The motivation for doing so would be to easily encode and decode dense data (see Nakagami, paragraph [0026]). Thus, it would have been obvious to combine the attribute encoding taught by Nakagami with the teachings of Park in order to obtain the invention as claimed in Claim 19.
Claim 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US Pub No 20250124601), hereinafter Park in view of Nakagami et al. (WO Pub No 2020145143), hereinafter Nakagami, and further in view of Tatoglu (A. Tatoglu and K. Pochiraju, "Point cloud segmentation with LIDAR reflection intensity behavior," 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA, 2012, pp. 786-790), hereinafter Tatoglu.
As to Claim 8, Park in view of Nakagami teaches a prediction section which obtains the prediction value on a basis of the attribute that has been decoded (see Nakagami, paragraphs [0242-0243], “The prediction unit 404 predicts attribute information supplied from the point cloud generation unit 403, and generates a prediction value of the attribute information”).
Furthermore, Nakagami teaches that the attribute encoding section which encodes data (see paragraph [0085] , “The attribute information encoding unit 105 acquires the interpolated attribute information supplied from the interpolation processing unit 104. As described in the 1 the embodiment, the attribute information encoding unit 105 encodes the interpolated attribute information to generate encoded data”),
including information such as normal vector information and reflectance information (see paragraph [0019], “The attribute information includes, for example, color information, reflectance information, normal information, and the like”),
Park in view of Nakagami fails to expressly teach that the normal vector prediction section derives the prediction value on a basis of the reflectance. However, in an analogous art, Tatoglu teaches a method for analyzing point cloud data (see Abstract, pg. 786, “Light Detection and Ranging (LIDAR) scans are increasingly being used for 3D map construction and reverse engineering…In this paper, we present techniques to model the intensity of the laser reflection return from a point during LIDAR scanning”),
which comprises obtaining normal vector information from reflectance information (see pg. Section II, pg. 786, “Many time of flight LIDAR scanners provide the laser reflection intensity observed by the photo-detector. The intensity of reflection return depends, in general, upon the distance to the object, the angle between the surface normal”, and see Section VI., pg. 790, “We are currently pursuing robust determination of surface normals from point cloud data, which leads to an accurate determination of the viewing angle for each point”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the reflectance information taught by Tatoglu with the teachings of Park and Nakagami. The motivation for doing so would be to use reflectance data to better enhance point cloud analysis. Tatoglu teaches on Section I, pg. 786, ”Utilizing reflection intensity maps to segment materials or structures could improve the overall performance of different types of applications including automatic cruise control, agricultural analysis, weather analysis, hazardous area analysis, terrain type detection etc.. However, the reflection intensity depends upon several factors including the distance to the object, view angle and the surface characteristics”. Thus, it would have been obvious to combine the reflectance analysis taught by Tatoglu with the teachings of Pan and Nakagami in order to obtain the invention as claimed in Claim 8.
As to Claim 9, Park in view of Nakagami teaches the attribute that has been decoded includes information related to reflection (see paragraph [0019], “The attribute information includes, for example, color information, reflectance information, normal information, and the like”),,
However, Park in view of Nakagami fails to explicitly teach obtaining information related to a reflection model, and the normal vector prediction section derives the prediction value on a basis of the reflection model. However, Tatoglu teaches obtaining reflectance information related to a reflection model (see Section II, pg. 786, “We examine the reflection intensity data in a typical 3D point cloud to characterize the diffuse and specular reflectivity characteristics of the scanned surface”, and see Section II, pg. 787, “In this analysis we consider Blinn-Phong [12] model for analysis that incorporates both the Lambertian (diffuse) and specular reflection components as well as Gaussian [13] and Beckmann [14] models to model specular reflection properties on textured surfaces”),
and predicting a normal on a basis of a reflection model (see Section II, pg. 787, “The Lambertian reflection model defines the diffuse reflection for the dull, matte surfaces. The amount of intensity reflected by these types of materials is independent of the relationship between the view angle and the surface normal. We model the reflection intensity model” and see Section VI., pg. 790, “We are currently pursuing robust determination of surface normals from point cloud data, which leads to an accurate determination of the viewing angle for each point”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the reflectance information taught by Tatoglu with the teachings of Park and Nakagami. The motivation for doing so would be to use reflectance data to better enhance point cloud analysis (see Section I, pg. 786). Thus, it would have been obvious to combine the reflectance analysis taught by Tatoglu with the teachings of Pan and Nakagami in order to obtain the invention as claimed in Claim 9.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US Pub No 20250124601), hereinafter Park in view of Nakagami et al. (WO Pub No 2020145143), hereinafter Nakagami, and further in view of Zeng (J. Zeng et al., "Deep Surface Normal Estimation With Hierarchical RGB-D Fusion," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 6146-6155), hereinafter Zeng.
As to Claim 10, Park in view of Nakagami fails to teach that the normal vector prediction section derives the prediction value by using a neural network that outputs the prediction value on a basis of a captured image.
However, in an analogous art, Zeng teaches a method for predicting surface normal for 3D point
by inputting a captured image into a neural network (see pg. 6146, Abstract, “In this paper, a hierarchical fusion network with adaptive feature re-weighting is proposed for surface normal estimation from a single RGB-D image”, wherein the hierarchical fusion network is the ‘neural network’, and the RGB-D image is the ‘captured image’).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the fusion network taught by Zeng with the image encoding method taught by Park and Nakagami. The motivation for doing so would be to use the captured image data for enhanced normal prediction. Zeng teaches on pg. 6146, Section 1., “This motivates us to combine the advantages of color and depth inputs while compensating for the deficiency of each other in the task of normal estimation. Specifically, the RGB information is utilized to fill the missing pixels in depth; meanwhile the depth clue is merged into RGB results to enhance sharp edges and correct erroneous estimation, resulting in a complete normal map with fine details.” Thus, it would have been obvious to combine the neural network taught by Zeng with the teachings of Park and Nakagami in order to obtain the invention as claimed in Claim 10.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US Pub No 20250124601), hereinafter Park in view of Nakagami et al. (WO Pub No 2020145143A1), hereinafter Nakagami, and further in view of Hur et al. (US Pub No 20220084164).
As to Claim 11, Park teaches a normal vector prediction section which derives a prediction value based on a geometry of the point cloud data decoded (see paragraph [0444], “For the normal information, prediction of the current vertex normal information may be performed based on the reconstructed geometry information and/or the reconstructed neighboring vertex normal information”, wherein ‘reconstructed geometry’, refers to encoded and decoded geometry information of a point cloud).
Park fails to explicitly teach obtaining a prediction value based on an attribute of the point cloud data. However, in an analogous art, Nakagami teaches a prediction section which generates a prediction value on a basis of encoded attribute data (see paragraphs [0242-0243], “The prediction unit 404 predicts attribute information supplied from the point cloud generation unit 403, and generates a prediction value of the attribute information”) Nakagami teaches that the attribute information may be normal vector information (see paragraph [0019], “The attribute information includes, for example, color information, reflectance information, normal information, and the like”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the attribute encoding section taught by Nakagami with the teachings of Park. The motivation for doing so would be to easily encode dense data (see Nakagami, paragraph [0026]). Thus, it would have been obvious to combine the attribute encoding taught by Nakagami.
Neither Park nor Nakagami teaches obtaining a plurality of the prediction values and a selector section configured to select at least one of a plurality of the prediction values. However, in an analogous art, Hur teaches obtaining a plurality of prediction values for encoded data (see paragraph [0323], “FIG. 19 is a diagram illustrating another embodiment of the method of selecting a prediction mode for attribute encoding”, and see paragraph [0328], “According to an embodiment, the number of predictor candidates generated in step S7004 is greater than 1”),
and a selector section (see Fig. 21, ‘Attribute information prediction unit’),
configured to select at least one of a plurality of the prediction values (see paragraph [0329], “When predictor candidates are generated in step 57004, a process of selecting an optimal predictor is performed by applying a rate-distortion optimization (RDO) procedure”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the selector section taught by Hur with the normal prediction sections taught by Park and Nakagami. The motivation for doing so would be to obtain a prediction value that best reduces rate-distortion costs (see Hur, paragraph [0329]). Thus, it would have been obvious to combine the prediction section taught by Hur with the teachings of Park and Nakagami in order to obtain the invention as claimed in Claim 11. .
As to Claim 12, Park fails to teach an intra prediction section configured to derive a second prediction value of the yet-to-be-encoded normal vector through intra prediction based on a normal vector of a point adjacent to the encoding target point.
However, Nakagami teaches that attribute data can be predicted through attribute data of adjacent points (see paragraphs [0269- 0270], “The prediction unit 434 searches for a point (adjacent to the point) to be processed corresponding to the attribute information to be processed based on the position information generated in step S431. In step S435, the prediction unit 434 derives a prediction value of the attribute information of the point to be processed by using the attribute information of the neighboring point searched in step S434”). Nakagami further teaches that attribute data may include normal data (see paragraph [0019], “The attribute information includes, for example, color information, reflectance information, normal information, and the like”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the attribute encoding section taught by Nakagami with the teachings of Park. The motivation for doing so would be to easily encode dense data (see Nakagami, paragraph [0026). Thus, it would have been obvious to combine the intra code prediction taught by Nakagami with the tea.
Neither Park nor Nakagami teaches a selector section configured to select at least one of the prediction value or the second prediction value, wherein the prediction residual generation section generates the prediction residual on a basis of at least one of the prediction value or the second prediction value.
However, Hur teaches obtaining a plurality of prediction values for encoded data (see paragraph [0323], “FIG. 19 is a diagram illustrating another embodiment of the method of selecting a prediction mode for attribute encoding”, and see paragraph [0328], “According to an embodiment, the number of predictor candidates generated in step S7004 is greater than 1”),
and a selector section (see Fig. 21, ‘Attribute information prediction unit’),
configured to select at least one of a plurality of the prediction values (see paragraph [0329], “When predictor candidates are generated in step 57004, a process of selecting an optimal predictor is performed by applying a rate-distortion optimization (RDO) procedure”)
wherein the prediction residual generation section generates the prediction residual on a basis of the selected prediction value (see paragraph [0347], “The prediction mode set for each point through the above-described process and the residual attribute value in the set prediction mode are output to the residual attribute information quantization processor”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the selector section taught by Hur with the normal prediction sections taught by Park and Nakagami. The motivation for doing so would be to reduce the residual and thus enhance encoding. Hur teaches in paragraph [0036], “ a predictor candidate corresponding to a neighbor point having the best similar attribute value among the similar attribute values of the neighbor points registered in the point to the attribute value of the point may be set as a base predictor. Thereby, the size of the residual attribute value of the point may be reduced.” Thus, it would have been obvious to combine the prediction section taught by Hur with the teachings of Park and Nakagami in order to obtain the invention as claimed in Claim 11.
Claims 13 is rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US Pub No 20250124601), hereinafter Park in view of Nakagami et al. (WO Pub No 2020145143), hereinafter Nakagami, in view of Hur et al. (US Pub No 20220084164), hereinafter Hur, and further in view of Kim et al. (US Pub No 20110211640), hereinafter Kim.
As to Claim 13, Park in view of Nakagami and Hur fails to explicitly teach the selector section sets a flag indicating a result of the selection, and the prediction residual encoding section encodes the flag.
However, in an analogous art, Kim teaches a method for encoding vector data (see Abstract, “A method and an apparatus for encoding a motion vector”),
which comprises generating predictions for a vector and selecting a candidate vector (see paragraph [0008], “a prediction candidate selector for selecting one or more motion vector prediction candidates”),
and setting a flag indicating a result of the selection (see paragraph [0047], “Further, the encoder 150 may generate and encode a prediction candidate identification flag for identifying the motion vector prediction candidate determined as the predicted motion vector, and then additionally include the prediction candidate identification flag in the bit-stream”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the selection flag taught by Kim with the normal vector prediction and encoding taught by Park, Nakagami, Hur. The motivation for doing so would be to provide the flag to the decoder so that the decoder can identify which motion vector prediction was selected. Kim teaches in paragraph [0057], “The prediction candidate selection flag encoder 250 generates and encodes a prediction candidate selection flag for identifying which motion vector prediction candidate is selected”). Thus, it would have been obvious to combine the flag taught by Kim with the teachings of Park, Nakagami, Hur in order to obtain the invention as claimed in Claim 13.
Claims 14 is rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US Pub No 20250124601), hereinafter Park in view of Nakagami et al. (WO Pub No 2020145143), hereinafter Nakagami, in view of Hur et al. (US Pub No 20220084164), hereinafter Hur, and further in view of Zhu et al. (US Pub No 20240087176), hereinafter Zhu.
As to Claim 14, Park in view of Nakagami fails to teach the prediction residual generation section generates the prediction residual by using a result of combining the prediction value and the second prediction value.
Hur teaches generating multiple prediction values (see paragraphs [0242-0243]), but fails to teach combining a first and second prediction value. However, in an analogous, Zhu teaches a method for decoding a point cloud (see Abstract, “A point cloud decoding method is provided”),
which comprises obtaining a residual by combining prediction values (see paragraph [0039], “It is a prediction algorithm in FIG. 1. It is to select attribute information of one or more points by using a proximity relationship between geometric information or attribute information, obtain final prediction attribute information by means of weighted averaging, and encode prediction residual information”) . Thus, it would have been obvious to combine the prediction combination taught by Tsukuba with the normal vector prediction and encoding taught by Park, Nakagami, and Hu. The motivation for doing so would doing so would be to improve the accuracy of attribute encoding. Zhu teaches in paragraph [0137], “In this way, an attribute decoding mode suitable for performing attribute prediction on the target point cloud group may be determined, thereby improving accuracy and prediction efficiency of performing attribute prediction on each point in the target point cloud group.” Thus, it would have been obvious to combine the teachings of Park, Nakagami, Hur, and Zhu in order to obtain the invention as claimed in Claim 14.
Allowable Subject Matter
Claim 6 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.
The following is a statement of reasons for the indication of allowable subject matter: None of the cited references teach wherein the normal vector prediction section sets, as the prediction value, a normal of a triangular face of the geometry that has been encoded at a level of the octree having predetermined resolution, and the triangular face of the geometry is a face to be subjected to a trisoup decoding process at a time of decoding.
Lin (cited in the rejections of Claims 3-4 and 18) teaches using an octree to encode the geometry of point cloud data, but fails to teach performing trisoup decoding.
The closest prior art of the record, Graziosi et al.( Graziosi D, Nakagami O, et al., “An overview of ongoing point cloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC)”, APSIPA Transactions on Signal and Information Processing, 2020) teaches that a surface may be approximated using triangular faces using the trisoup method. However, Graziosi fails to explicitly teach obtaining a normal vector from the triangular face of the geometry. Furthermore, Graziosi fails to explicitly teach an octree having predetermine resolution.
Alexiou et al. (Alexiou E, et al., “A comprehensive study of the rate-distortion performance in MPEG point cloud compression”, APSIPA Transactions on Signal and Information Processing, 2019) teaches using an octree to encode geometry data of image data, and teaches applying trisoup encoding with an octree (see Fig. 3). Alexiou further teaches approximating normal vectors using a plane-fitting algorithm. However, Alexiou fails to explicitly teach obtaining a normal vector from the triangular face of the geometry.
Zhu (cited in the rejection of Claim 14) teaches using an octree to encode geometric data, and that trisoup encoding may be used with octrees (see paragraph [0034]). However, Zhu fails to teach obtaining a normal vector prediction from a triangular face.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schwarz et al. (WO Pub No 2020254719) teaches encoding a normal vector by predicting a normal value, and the calculating a residual between the predicted normal value and the actual normal value.
Yuan et al. (US Pub No 20240037800) teaches a method of encoding point cloud data which comprises obtaining a first prediction value for attribute data, and then using the first prediction value to calculate a second prediction value for attribute data.
Gao et al. (US Pub No 20210306664) teaches a method of encoding point cloud data which comprises performing geometry encoding and attribute encoding. Gao further teaches using an octree to encode geometry data of a point cloud.
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/S.T./ Examiner, Art Unit 2664
/JENNIFER MEHMOOD/ Supervisory Patent Examiner, Art Unit 2664
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