DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 7-8, 10-15 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over, , in view of NPL Nguyen et al. (“Toward Optimal Real-time Dynamic Point Cloud Streaming over Bandwidth-constrained Networks”), hereinafter as Nguyen, in view of NPL Garcia et al. (“Textured Splat-Based Point Clouds for Rendering in Handheld Devices”), hereinafter as Garcia, and further in view of Sugio et al. (US 20240394926 A1), hereinafter as Sugio.
Regarding claim 1, Nguyen teaches A method (Nguyen Page 1, Right Column, Fourth Paragraph, “To optimize user experience, our method encodes each point cloud into multiple versions with different LoD”) comprising: receiving a request to access a point cloud from a client device (Nguyen Page 2, Right Column, Third paragraph, “The general architecture of our proposed framework is shown in Fig. 1 which consists of a client running on the user device (e.g.,VR/AR headset) and a server. The client connects to the server over a bandwidth-constrained network such as Wifi or 5G…… The receiver is responsible for receiving point cloud data sent from the server in real-time. The renderer’s task is to render the received point clouds on the user device’s screen.”), ……
determining one or more performance parameters that limit an amount of point cloud data that the client device is able to receive or process in a given time (Nguyen Page 2, Figure 1, Right Column, Third paragraph, “The system monitor’s first task is to estimate the client’s available network bandwidth. Its second task is to send the estimated bandwidth, the Model View Projection matrix (MVP)[22], device’s screen size to the server.”);
selecting different sets of …… for different views of the point cloud that satisfy the one or more performance parameters based on a cumulative amount of data encoded within the different sets of …… being equal to or less than the amount of point cloud data that the client device is able to receive or process in the given time (Nguyen teaches the network bandwidth in Mbps as the performance parameter in a second, a visibility computation component based on the model view projection matrix of the client, further teaches a dynamic programming based solution to select different sets of LoD versions for point cloud data, Page 4, Left Column, First Paragraph, “therefore, the task of finding the optimal version selection 𝜒𝑠 can be formulated as finding the path that passed all 𝑀 layers while having the highest cumulative value with the total cost is lower than the available network bandwidth 𝑅𝑎”, Page 3, Left Column, First paragraph, “The visibility computation component calculates the estimated screen area of each point cloud using the convex hull, MVP matrix and screen size. Then, considering the client’s estimated network bandwidth, screen area, and metadata of each LoD version, the LoD version selection selects the appropriate LoD version for each point cloud”, Fourth paragraph, “we use the bit rate of a version after compression to represent its cost 𝐶(𝑚, 𝑛)”) …… and providing the different sets of …… to the client device in response to the request to access the point cloud (Nguyen Page 3, Left Column, First paragraph, “considering the client’s estimated network bandwidth, screen area, and metadata of each LoD version, the LoD version selection selects the appropriate LoD version for each point cloud using the proposed method described in 3.3.1 and 3.3.2. Finally, the sender transmits the selected LoD versions to the client.”).
Nguyen fails to teach the point cloud comprising a plurality of points that are distributed across a three-dimensional (3D) space to generate a 3D model, wherein each point of the plurality of points is defined with a position in the 3D space and a plurality of visual characteristics that are presented at the position;…… optimized splats …… optimized splats …… wherein each set of optimized splats from the different sets of optimized splats comprises at least a first optimized splat corresponding to a first lossy encoding of a first visual characteristic from the plurality of visual characteristics defined for a set of points from the plurality of points in a particular view from the different views and a second optimized splat corresponding to a second lossy encoding of a second visual characteristic from the plurality of visual characteristics defined for the set of points…… optimized splats;
Garcia teaches the point cloud comprising a plurality of points that are distributed across a three-dimensional (3D) space to generate a 3D model, wherein each point of the plurality of points is defined with a position in the 3D space and a plurality of visual characteristics that are presented at the position (Garcia teaches the 3D position, color and depth attribute for point cloud data, Page 227, Left Column, Second Paragraph, “especially since the emergence of low-cost devices like RGB-D sensors capable of generating colored 3D point clouds easily and promptly”, Page 229, Left Column, First paragraph, “laser scanners or by manual modeling, that is, with reasonably accurate 3D positions and colors.”);…… optimized splats …… optimized splats …… wherein each set of optimized splats from the different sets of optimized splats comprises at least a first optimized splat corresponding to a …… of a first visual characteristic from the plurality of visual characteristics defined for a set of points from the plurality of points in a particular view from the different views (Garcia teaches a base splat model to generate the optimized splats, splats can be merged, the color of the splats are computed as a weighted average, the normal of the splats are defined pointing outwards from the inside of the model based on point-to-camera information, Page 228, Right Column, Second Paragraph, “The first approximation of the splat model is obtained by replacing each point (which is a mere isolated 3D sample) with a surface patch that adapts better to the shape of the original object.”, Third paragraph, “For each point pi ∈ IR3 in the input cloud (0 ≤ i < M, M being the total number of points), its neighbors within a distance r, Nr(pi), are identified and undergo Principal Component Analysis (PCA) [Jolliffe 1986] to estimate the normal and tangential directions of the local surface……The resulting splat is therefore modeled as an ellipse”, Page 228, Right Column, Last Paragraph, “Once the tangent and normal directions are computed, their orientation must be properly set for a correct operation of the texturing step. Splat normals must point outwards from the inside of the model, and here is where the point-to-camera pointers come into play”, and Page 229, Left Column, Third Paragraph, “The resulting splat center, major axis, normal vector and ellipse eccentricity (and color, if no texturing is applied next) are computed as a weighted average among all the affected splats, using their respective areas as the weighting coefficients.”) and a second optimized splat corresponding to a …… of a second visual characteristic from the plurality of visual characteristics defined for the set of points …… optimized splats (Garcia’s teaching of a base splat model can also be used to generate a second optimized splats, Page 228, Right Column, Second Paragraph, “The first approximation of the splat model is obtained by replacing each point (which is a mere isolated 3D sample) with a surface patch that adapts better to the shape of the original object.”, Third paragraph, “For each point pi ∈ IR3 in the input cloud (0 ≤ i < M, M being the total number of points), its neighbors within a distance r, Nr(pi), are identified and undergo Principal Component Analysis (PCA) [Jolliffe 1986] to estimate the normal and tangential directions of the local surface……The resulting splat is therefore modeled as an ellipse”, and Page 229, Left Column, Third Paragraph, “The resulting splat center, major axis, normal vector and ellipse eccentricity (and color, if no texturing is applied next) are computed as a weighted average among all the affected splats, using their respective areas as the weighting coefficients.”);
Nguyen and Garcia are in the same field of endeavor, namely adaptive point cloud transmission. Garcia teaches a textured splats based approach to improve efficiency and accuracy (Garcia Page 230, Left Column, Fourth paragraph, “Splat-based models are not only more visually pleasing than point based ones: their size, which does matter for both storage and transmission, especially for handheld devices, is typically less than 10% in terms of number of primitives.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Garcia with the level of detail method of Nguyen to further improve efficiency and accuracy.
Nguyen in view of Garcia fail to teach …… first lossy encoding…… second lossy encoding. Sugio teaches …… first lossy encoding…… second lossy encoding (Sugio paragraph [0975] “The three-dimensional data encoding device may add, to the header of the bitstream or the like, information that indicates which of the reversible (Lossless) encoding and the irreversible (lossy) encoding has been used…… When lossless_enable_flag=0, the three-dimensional data decoding device decodes the irreversibly encoded bitstream by applying the inverse RAHT.”, paragraph [0009] “A three-dimensional data encoding method according to one aspect of the present disclosure includes: transforming pieces of attribute information of three-dimensional points included in point cloud data into coefficient values; and encoding the coefficient values to generate a bitstream”).
Nguyen, Garcia and Sugio are in the same field of endeavor, namely adaptive point cloud transmission. Sugio teaches a point cloud encoding and decoding method to improve accuracy and efficiency (Sugio paragraph [0011] “The present disclosure provides a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, or a three-dimensional data decoding device that is capable of improving accuracy.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Sugio with the method of Nguyen and Garcia to further improve efficiency and accuracy.
Regarding claim 2, Nguyen in view of Garcia and Sugio teach The method of claim 1, wherein determining the one or more performance parameters comprises: and further teach monitoring a performance of a network path that establishes network connectivity to the client device (Nguyen Figure 1, the system monitor sends the network resource information from the client to the server); and determining the amount of point cloud data that the client device is able to receive or process in the given time based on the performance of the network path (Nguyen teaches network bandwidth measured in Mbps, Figure 3a, Page 6, Left Column, Second Paragraph, “As shown in Fig. 3a, when the bandwidth is 360 Mbps, the LM-based solution can improve the PSNR value by 4.8 dB compared to
the Hybrid method, and by 11.3 dB compared to the Equal method. As the available bandwidth increases, the difference in PSNR between the proposed method and the Hybrid method diminishes, and their performance becomes almost similar when the bandwidth exceeds 600 Mbps. This is because the available network resource is sufficient to support the higher LoD version of individual point clouds.”).
Regarding claim 7, Nguyen in view of Garcia and Sugio teach The method of claim 1, wherein providing the different sets of optimized splats comprises: and further teach streaming the different sets of optimized splats over a data network to the client device without streaming data associated with each point of the plurality of points (Nguyen teaches streaming LoD version of multiple point clouds from server to client, Figure 1, Page 3, Left Column, First Paragraph, “the sender transmits the selected LoD versions to the client…… In particular, four versions are generated with numbers of points equal to {𝑁𝑝 , 𝑁𝑝 /4, 𝑁𝑝 /16, 𝑁𝑝 /64}, where 𝑁𝑝 is the number of points in the original point cloud”, Right Column, Second Paragraph, “To this end, the LoD version selection problem in multiple dynamic point cloud streaming can be formulated as follows. Given the available network resource 𝑅 of the client, the estimated screen area coverage of individual point clouds, find an LoD version 𝑛𝑚 for each point cloud 𝑚 to optimize the overall value or objective function OV, which is defined as a weighted sum of individual point clouds’ values”).
Regarding claim 8, Nguyen in view of Garcia and Sugio teach The method of claim 1, wherein providing the different sets of optimized splats comprises: and further teach distributing the different sets of optimized splats to a rendering engine of the client device for generation of a lossy 3D representation of the 3D model (Nguyen teaches generating a mean opinion score for the impaired video, implicitly teaches the lossy 3D representation of the 3D model, Page 2, Right Column, Third Paragraph, “The renderer’s task is to render the received point clouds on the user device’s screen.”, Figure 4, Page 6, Left Column, Third Paragraph, “Specifically, the participant sees the reference video (generated with original point clouds) and the impaired video side by side…… The MOS of the proposed and reference methods for two scenes are shown in Fig. 4. For Scene 1, the DP-based solution can improve the MOS by 0.9 ∼ 1.5 compared to the Hybrid method, and by 1.4 ∼ 3.3 compared to the
Equal method.).
Regarding claim 10, Nguyen in view of Garcia and Sugio teach The method of claim 1 further comprising: and further teach receiving a prioritized list of visual characteristics that provides a higher priority to the first visual characteristic than the second visual characteristic (Sugio teaches a quantization parameter based on priority of each attribute, paragraph [0658] “The three-dimensional data encoding device may encode, as attribute information, a quantization parameter used for encoding of each attribute information …… the quantization parameter can be changed for each attribute information. For example, if the quantization parameter of attribute information having higher priority is set to be smaller, and the quantization parameter of attribute information having lower priority is set to be greater”); and wherein selecting the different sets of optimized splats comprises: selecting the first optimized splat that encodes the first visual characteristic with less data reduction and quality loss than the second optimized splat encoding of the second visual characteristic based on the prioritized list of visual characteristics (Sugio teaches greater quantization parameter with more data reduction, and smaller quantization parameter with less data reduction, paragraph [0658] “ if the quantization parameter of attribute information having higher priority is set to be smaller, and the quantization parameter of attribute information having lower priority is set to be greater, the total code amount can be reduced while preserving the attribute information having higher priority.”, paragraph [0653] “For example, if the cumulative code amount is greater than the value of the desired code amount×TH1, the three-dimensional data encoding device can set the value of the quantization parameter to be greater, in order to reduce the actual code amount. If the cumulative code amount is smaller than the value of the desired code amount×TH3, the three-dimensional data encoding device can set the value of the quantization parameter to be smaller, in order to increase the actual code amount.”).
Nguyen, Garcia and Sugio are in the same field of endeavor, namely adaptive point cloud transmission. Sugio teaches a point cloud encoding and decoding method to improve accuracy and efficiency (Sugio paragraph [0011] “The present disclosure provides a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, or a three-dimensional data decoding device that is capable of improving accuracy.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Sugio with the method of Nguyen and Garcia to further improve efficiency and accuracy.
Regarding claim 11, Nguyen in view of Garcia and Sugio teach The method of claim 1, wherein selecting the different sets of optimized splats comprises: and further teach adjusting an amount of data reduction that is associated with each optimized splat in the different sets of optimized splats based on the one or more performance parameters (Nguyen teaches choosing different LoD version of point cloud, which means adjusting the amount of data reduction, based on the network bandwidth, the Figure 3, Page 6, Left Column, Second Paragraph, “As shown in Fig. 3a, when the bandwidth is 360 Mbps, the LM-based solution can improve the PSNR value by 4.8 dB compared to the Hybrid method, and by 11.3 dB compared to the Equal method. As the available bandwidth increases, the difference in PSNR between the proposed method and the Hybrid method diminishes, and their performance becomes almost similar when the bandwidth exceeds 600 Mbps. This is because the available network resource is sufficient to support the higher LoD version of individual point clouds.”).
Regarding claim 12, Nguyen in view of Garcia and Sugio teach The method of claim 1, and further teach wherein the plurality of visual characteristics comprise two or more of a color visual characteristic, a roughness visual characteristic, a reflectivity visual characteristic, and a transparency visual characteristic (Sugio teaches the color and reflectance attributes as the visual characteristics, paragraph [0602] “when the three-dimensional data decoding device decodes color and reflectance as attribute information, the three-dimensional data decoding device may decode the result of encoding of color and the result of encoding of reflectance in the order thereof in the bitstream.”).
Nguyen, Garcia and Sugio are in the same field of endeavor, namely adaptive point cloud transmission. Sugio teaches a point cloud encoding and decoding method to improve accuracy and efficiency (Sugio paragraph [0011] “The present disclosure provides a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, or a three-dimensional data decoding device that is capable of improving accuracy.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Sugio with the method of Nguyen and Garcia to further improve efficiency and accuracy.
Regarding claim 13, Nguyen in view of Garcia and Sugio teach The method of claim 1, and further teach wherein the first optimized splat comprises a single primitive that replaces a definition of two or more points from the set of points having a common value for the first visual characteristic, and wherein the single primitive is defined with the common value for the first visual characteristic (Garcia teaches a measurement of flatness as the common value of the first visual characteristic, points and splats can be merged based on the flatness number, Page 2, Right Column, Third Paragraph, “For each point pi ∈ IR3 in the input cloud (0 ≤ i < M, M being the total number of points), its neighbors within a distance r, Nr(pi), are identified and undergo Principal Component Analysis (PCA) [Jolliffe 1986] to estimate the normal and tangential directions of the local surface …… The resulting splat is therefore modeled as an ellipse……Besides, a qualitative measure of the flatness of the region around the point can be obtained through the eigenvalue magnitudes as fi = (√ λ1 + √ λ2)/( √ λ1 + √ λ2 + √ λ3). fi = 1 (λ3 = 0) corresponds to a completely planar area, and fi = 2/3 (λ1 = λ2 = λ3) to a perfectly isotropic point set. This flatness measure determines which splats can be efficiently merged with their neighbors to generate larger splats to fill potential holes in under sampled regions.”).
Nguyen, Garcia and Sugio are in the same field of endeavor, namely adaptive point cloud transmission. Garcia teaches a textured splats based approach to improve efficiency and accuracy (Garcia Page 230, Left Column, Fourth paragraph, “Splat-based models are not only more visually pleasing than point based ones: their size, which does matter for both storage and transmission, especially for handheld devices, is typically less than 10% in terms of number of primitives.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Garcia with the level of detail method of Nguyen and Sugio to further improve efficiency and accuracy.
Regarding claim 14, it recites similar limitations of claim 1 but in a system form. The rationale of claim 1 rejection is applied to reject claim 14. In addition, Nguyen teaches A distribution system comprising: one or more hardware processors configured to: (Nguyen Figure 1, Page 2, Right Column, Third Paragraph, “The general architecture of our proposed framework is shown in Fig. 1 which consists of a client running on the user device (e.g.,VR/AR headset) and a server.”).
Regarding claim 15, claim 15 has similar limitations as claim 2, therefore it is rejected under the same rationale as claim 2.
Regarding claim 19, claim 19 has similar limitations as claim 10, therefore it is rejected under the same rationale as claim 10.
Regarding claim 20, it recites similar limitations of claim 1 but in a non-transitory computer-readable medium form. The rationale of claim 1 rejection is applied to reject claim 20. In addition, Sugio teaches A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a distribution system, cause the distribution system to perform operations comprising (Sugio paragraph [0176] “It is to be noted that these general or specific aspects may be implemented as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be implemented as any combination of a system, a method, an integrated circuit, a computer program, and a recording medium.” And paragraph [1079] “Moreover, in the above embodiments, the structural components may be implemented as dedicated hardware or may be realized by executing a software program suited to such structural components. Alternatively, the structural components may be implemented by a program executor such as a CPU or a processor reading out and executing the software program recorded in a recording medium such as a hard disk or a semiconductor memory.”):
Nguyen, Garcia and Sugio are in the same field of endeavor, namely adaptive point cloud transmission. Sugio teaches a point cloud encoding and decoding method to improve accuracy and efficiency (Sugio paragraph [0011] “The present disclosure provides a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, or a three-dimensional data decoding device that is capable of improving accuracy.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Sugio with the method of Nguyen and Garcia to further improve efficiency and accuracy.
Claim(s) 3 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over, , in view of NPL Nguyen et al. (“Toward Optimal Real-time Dynamic Point Cloud Streaming over Bandwidth-constrained Networks”), hereinafter as Nguyen, in view of
NPL Garcia et al. (“Textured Splat-Based Point Clouds for Rendering in Handheld Devices”), hereinafter as Garcia, and further in view of Sugio et al. (US 20240394926 A1), hereinafter as Sugio, and Evans et al. (US 20140198097 A1), hereinafter as Evans.
Regarding claim 3, Nguyen in view of Garcia and Sugio teach The method of claim 1, wherein determining the one or more performance parameters comprises: but fail to teach identifying one or more hardware resources of the client device used to render the point cloud; and determining the amount of point cloud data that the client device is able to receive or process in the given time based on a rendering performance associated with the one or more hardware resources. Evans teaches identifying one or more hardware resources of the client device used to render the point cloud (Evans paragraph [0051] “The role of both the Polygonal 3D API 225 and the Point Cloud 3D API 230 pushes data across CPU/GPU Boundary 240 for rasterization via the GPU instruction stream 280. The GPU is responsible for moving the 3D object information in object space into image space.”); and determining the amount of point cloud data that the client device is able to receive or process in the given time based on a rendering performance associated with the one or more hardware resources (Evans teaches recalculate the LOD factor based on the usage of GPU, further teaches the frame rate as the amount of point cloud data per second, paragraph [0061] “Scene objects can enter and leave the view, requiring a recalculation of the LOD factor 420. Other considerations in alternative embodiments can include the processor and GPU utilization levels, the frame rate, and changes to application rendering requirements.”)
Nguyen, Garcia, Sugio and Evans are in the same field of endeavor, namely adaptive point cloud transmission. Evans teaches a dynamic level of detail based point cloud rendering method to improve accuracy and efficiency (Evans Abstract “The novel point cloud dynamic level of detail techniques can be employed to optimize or otherwise improve the rendering efficiency of rendering point cloud objects.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Evans with the method of Nguyen, Garcia and Sugio to further improve efficiency and accuracy.
Regarding claim 16, claim 16 has similar limitations as claim 3, therefore it is rejected under the same rationale as claim 3.
Allowable Subject Matter
Claims 4-6, 9, 17 and 18 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:
Regarding claims 4 and 17, the closest prior of Garcia teaches using textured splats to represent point. However, Garcia fails to teach the combined limitation below as a whole, “wherein the different plurality of optimized splats that have been generated for the particular view comprises the first optimized splat and the second optimized splat in the different sets of optimized splats, a third optimized splat corresponding to a different lossy encoding of the first visual characteristic than the first optimized splat, and a fourth optimized splat corresponding to a different lossy encoding of the second visual characteristic than the second optimized splat”. Furthermore, no prior art of record either alone or in combination teaches the above limitation as a whole. Therefore, claims 4 and 17 are considered to allowable.
Claims 5 and 6 contain allowable subject matter because they depend on claim 4 that contains allowable subject matter.
Regarding claims 9 and 18, the closest prior art of Oh et al. (US 20230334703 A1), hereinafter as Oh, teaches transmitting and encoding only necessary part of the point cloud data, removing the unnecessary part of the data (Oh paragraph [0245] “Each bitstream according to the embodiments may be composed of slices. Regardless of layer information or LoD information, the geometry data bitstream and the attribute data bitstream may each be configured as one slice and delivered. In this case, when only a part of the layer or LoD is to be used, operations of 1) decoding the bitstream, 2) selecting only a desired part and removing unnecessary parts, and 3) performing encoding again based on only the necessary information should be performed.”, and paragraph [0225] “Regarding the method/device according to the embodiments, described herein is a method for efficiently supporting selective decoding of a part of data when the selective decoding is needed due to receiver performance or transmission speed in transmitting and receiving point cloud data. The proposed method proposes a method to select information needed or eliminate unnecessary information in the bitstream unit by dividing the geometry and attribute data delivered in units of data into semantic units such as geometry octree and level of detail (LoD).”). However, Oh fails to teach the combined limitation below as a whole, “receiving a list of visual characteristics that are supported by the client device; and wherein selecting the different sets of optimized splats comprises: including one optimized splat from a plurality of optimized splats that encodes at least one visual characteristic from the list of visual characteristics in each view of the different views as part of the different sets of optimized splats; and excluding, from the different sets of optimized splats, any optimized splat from the plurality of optimized splats that encodes a visual characteristic not in the list of visual characteristics”.
Furthermore, no prior art of record either alone or in combination teaches the above limitation as a whole. Therefore, claims 9 and 18 are considered to allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Hosseini et al. (“Dynamic Adaptive Point Cloud Streaming”) teaches DASH-PC, an adaptive bandwidth-efficient and view-aware point cloud streaming system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOMING WEI whose telephone number is (571)272-3831. The examiner can normally be reached M-F 8:00-5:00.
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/XIAOMING WEI/Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611