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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/28/2026 has been entered.
Response to Amendment
This communication is in response to the action filed on 01/28/2026.
Claims 1, 6, 9, 14, and 17 are currently amended. Claims 4 and 12 are canceled. Claims 1-3, 5-11, and 13-20 are pending.
Response to Arguments
Applicant’s arguments filed on 01/28/2026 on pages 8-11, under REMARKS with respect to 35
U.S.C. 102 and 103 claim rejections to claims 1-20 have been fully considered and are persuasive. The rejections to the claims have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US 11,252,435 B2.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 non-obviousness.
Claims 1-2, 9-10, and 17-18 are rejected under 35 § U.S.C. 103 as being obvious over US 11,627,314 B2 to TOURAPIS et al (hereinafter “TOURAPIS”) in view of US 11,252,435 B2 to DIVORRA ESCODA et al. (hereinafter “ESCODA”).
As per claim 1, TOURAPIS discloses a method for geometry filtering for mesh compression (a computing system and corresponding method for performing a geometric filtering strategy for mesh compression of an encoder configured to compress attribute and/or spatial information for a point cloud or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud; abstract; figs 1, 5c-5d; column 3, lines 1-21; column 21, lines 60-67), the method being executed by at least one processor (the computing system comprises a computer processor and memory component in order to store and execute the image processing method; column 77, lines 54-64), the method comprising: receiving a coded bitstream associated with a mesh, wherein the coded bitstream comprises boundary information associated with the mesh (an encoded bitstream is received via the computing system and the bit stream is associated with a point cloud of a 3D mesh and the bit stream with accompanying point cloud comprise boundary points (information) associated with the 3D mesh/point cloud and boundary points are detected in order to identify occupied object space and nearest neighbors in the point cloud and generate an occupancy map; column 16, lines 31-55; column 14, lines 24-46; column 31, lines 1-26); determining one or more boundary vertices associated with the mesh based on the boundary information (based on the boundary points vertices associated with points {P(j(0)) and so on… and are nearest neighbor points and act as vertices associated with the boundary points identified; column 16, lines 31-55; column 14, lines 24-46; column 31, lines 1-26); and where the connection degree is a measure of a number of vertices traversed from a non-boundary vertex to a boundary vertex (the prior art states “each iteration, the points of point cloud PC may be traversed and every vertex may be associated with the direction D (k) that maximizes” which includes a measure (maximum) of every vertex (boundary vertex) traversed of non-boundary vertex (point cloud points) and therefore each iteration is interpreted to comprise a connection degree as the maximum valuer produced; fig 12A-B; column 13, line 36 - column 14, line 60; column 76, lines 22-60); and compressing the filtered geometry map using a video codec into a plurality of single channel or multiple channel images (at step 220 of fig 2a the occupancy map is input to occupancy map compression module 220 which compresses the occupancy map and is done via a video codec to reconstruct the point cloud information and distribute the data via a network interface 1540 comprising the computer system and other devices attached to a network and the network includes storage area networks such as Fibre Channel SANs; fig 2A; column 11, lines 1-39; column 45, lines 34-45; column 79, line 65 – column 80, line 14). TOURAPIS fails to disclose generating a filtered geometry map associated with the mesh based on a connection degree greater than or equal to one and less than a predetermined threshold using a filtering algorithm and a quantization step size of a vertex, where the one or more boundary vertices associated with the mesh and one or more non-boundary vertices have different quantization step sizes.
ESCODA discloses generating a filtered geometry map associated with the mesh based on a connection degree greater than or equal to one and less than a predetermined threshold using a filtering algorithm and a quantization step size of a vertex (a computing system adapted for geometric encoding and decoding schemes using a geometric partitions strategy acting as the mesh by defining each partition as an image block and an array of image blocks would substantially comprise a mesh, the mesh is based on pixels labeled in each partition and labeled based on an adjustable and definable threshold of a pixel image feature, pixels may be labeled as "partial surface", with a label different from those of Partition 1 and 0 if they meet or do not meet the threshold, this threshold is used by the filtering model which includes filters applied as Discrete Cosine Transform DCT In-loop de-blocking filtering adapts filter strength depending on the encoded video data; figs 8-11; column 10, line 40-column 13, line 64; column 22, line 36-column 23, line 30),where the one or more boundary vertices associated with the mesh and one or more non-boundary vertices have different quantization step sizes (each parameter of each point (x,y) can be used as model parameters and are determined to be part of the mesh or not part of the partition in mesh based on a feature threshold which is adjustable and a quantization step size is applied and the step sizes are different and are applied as delta p and delta theta are the selected quantization “parameter precision” steps an offset in the selected values can be established, the quantized indices for theta and p, are the information transmitted to code the partitions (geometric mesh) shape; column 12, line 30-column 13, line 64; column 14, lines 30-64; column 17, line 60-column 18, line 67).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have a filtering algorithm and a quantization step size of a vertex of ESCODA reference. The Suggestion/motivation for doing so would have been to provide the ability to use parameter precision adjustments according to quantization step size which can be adapted according to some high-level syntax, such as the sequence, picture, and/or slice level as suggested at column 13, lines 58-64 of ESCODA which further increases the adaptability of the geometric mesh provided. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine ESCODA with TOURAPIS to obtain the invention as specified in claim 1.
As per claim 2, TOURAPIS in view of ESCODA discloses the method of claim 1. Modified TOURAPIS further discloses wherein the filtering algorithm further comprises one or more filtering parameters (the algorithm comprises a plurality of adjustable parameters; fig 6D; column 12, lines 29-64; column 14, lines 50-60; column 58, lines 1-18), the one or more filtering parameters comprising filtering strength factors, filtering iteration numbers, weighting factors, or number of neighboring vertices (the filtering parameters are easily adjustable see fig 6 flow chart and include strength level of the filtering module; fig 6D; column 12, lines 29-64; column 14, lines 50-60; column 58, lines 1-18).
As per claim 9, TOURAPIS discloses a device for geometry filtering for mesh compression (a computing system and corresponding method for performing a geometric filtering strategy for mesh compression of an encoder configured to compress attribute and/or spatial information for a point cloud or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud; abstract; figs 1, 5c-5d; column 3, lines 1-21), the device comprising: at least one memory configured to store program code (the computing system comprised system memory 1520 to store things such as data, instruction, programs, code and various other information related to the computing process to be performed; column 79, lines 26-64); and at least one processor configured to read the program code and operate as instructed by the program code (the computing system further comprises connected to system memory 1520 system processor 1510 to execute said stored information relating to the computing method performed; column 79, lines 26-64), the program code including: first receiving code configured to cause the at least one processor to receive a coded bitstream associated with a mesh, wherein the coded bitstream comprises boundary information associated with the mesh (an encoded bitstream is received via the computing system and the bit stream is associated with a point cloud of a 3D mesh and the bit stream with accompanying point cloud comprise boundary points (information) associated with the 3D mesh/point cloud and boundary points are detected in order to identify occupied object space and nearest neighbors in the point cloud and generate an occupancy map; column 16, lines 31-55; column 14, lines 24-46; column 31, lines 1-26); first determining code configured to cause the at least one processor to determine one or more boundary vertices associated with the mesh based on the boundary information (based on the boundary points vertices associated with points {P(j(0)) and so on… and are nearest neighbor points and act as vertices associated with the boundary points identified; column 16, lines 31-55; column 14, lines 24-46; column 31, lines 1-26); and where the connection degree is a measure of a number of vertices traversed from a non-boundary vertex to a boundary vertex (the prior art states “each iteration, the points of point cloud PC may be traversed and every vertex may be associated with the direction D (k) that maximizes” which includes a measure (maximum) of every vertex (boundary vertex) traversed of non-boundary vertex (point cloud points) and therefore each iteration is interpreted to comprise a connection degree as the maximum valuer produced; fig 12A-B; column 13, line 36 - column 14, line 60; column 76, lines 22-60); and first compressing code configured to cause the at least one processor to compress the filtered geometry map using a video codec into a plurality of single channel or multiple channel images (at step 220 of fig 2a the occupancy map is input to occupancy map compression module 220 which compresses the occupancy map and is done via a video codec to reconstruct the point cloud information and distribute the data via a network interface 1540 comprising the computer system and other devices attached to a network and the network includes storage area networks such as Fibre Channel SANs; fig 2A; column 11, lines 1-39; column 45, lines 34-45; column 79, line 65 – column 80, line 14). TOURAPIS fails to disclose first generating code configured to cause the at least one processor to generate a filtered geometry map associated with the mesh based on a connection degree greater than or equal to one and less than a predetermined threshold using a filtering algorithm and a quantization step size of a vertex, where the one or more boundary vertices associated with the mesh and one or more non-boundary vertices have different quantization step sizes.
ESCODA discloses first generating code configured to cause the at least one processor to generate a filtered geometry map associated with the mesh based on a connection degree greater than or equal to one and less than a predetermined threshold using a filtering algorithm and a quantization step size of a vertex (a computing system and computing method adapted for geometric encoding and decoding schemes using a geometric partitions strategy acting as the mesh by defining each partition as an image block and an array of image blocks would substantially comprise a mesh, the mesh is based on pixels labeled in each partition and labeled based on an adjustable and definable threshold of a pixel image feature, pixels may be labeled as "partial surface", with a label different from those of Partition 1 and 0 if they meet or don not meet the threshold, this threshold is used by the filtering model which includes filters applied as Discrete Cosine Transform DCT In-loop de-blocking filtering adapts filter strength depending on the encoded video data; figs 8-11; column 10, line 40-column 13, line 64; column 22, line 36-column 23, line 30), where the one or more boundary vertices associated with the mesh and one or more non-boundary vertices have different quantization step sizes (each parameter of each point (x,y) can be used as model parameters and are determined to be part of the mesh or not part of the partition in mesh based on a feature threshold which is adjustable and a quantization step size is applied and the step sizes are different and are applied as delta p and delta theta are the selected quantization “parameter precision” steps an offset in the selected values can be established, the quantized indices for theta and p, are the information transmitted to code the partitions (geometric mesh) shape; column 12, line 30-column 13, line 64; column 14, lines 30-64; column 17, line 60-column 18, line 67).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have where the one or more boundary vertices associated with the mesh and one or more non-boundary vertices have different quantization step sizes of ESCODA reference. The Suggestion/motivation for doing so would have been to provide the ability to use parameter precision adjustments which can be adapted according to some high-level syntax, such as the sequence, picture, and/or slice level as suggested at column 13, lines 58-64 of ESCODA. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine ESCODA with TOURAPIS to obtain the invention as specified in claim 9.
As per claim 10, TOURAPIS in view of ESCODA discloses the device of claim 9. Modified TOURAPIS further discloses wherein the filtering algorithm further comprises one or more filtering parameters, the one or more filtering parameters comprising filtering strength factors (the algorithm comprises a plurality of adjustable parameters; fig 6D; column 12, lines 29-64; column 14, lines 50-60; column 58, lines 1-18), filtering iteration numbers, weighting factors, or number of neighboring vertices (the filtering parameters are easily adjustable see fig 6 flow chart and include strength level of the filtering module; fig 6D; column 12, lines 29-64; column 14, lines 50-60; column 58, lines 1-18).
As per claim 17, TOURAPIS discloses a non-transitory computer-readable medium storing instructions (a computing system and corresponding method for performing a geometric filtering strategy for mesh compression of an encoder configured to compress attribute and/or spatial information for a point cloud or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud the computing system comprised system memory 1520 to store things such as data, instruction, programs, code and various other information related to the computing process to be performed; abstract; figs 1, 5c-5d; column 3, lines 1-21; column 79, lines 26-64), the instructions comprising: one or more instructions that, when executed by one or more processors of a device for geometry filtering for mesh compression (the computing system further comprises connected to system memory 1520 system processor 1510 to execute said stored information relating to the computing method performed of mesh compression; fig 12B; column 79, lines 26-64), cause the one or more processors to: receive a coded bitstream associated with a mesh, wherein the coded bitstream comprises boundary information associated with the mesh an encoded bitstream is received via the computing system and the bit stream is associated with a point cloud of a 3D mesh and the bit stream with accompanying point cloud comprise boundary points (information) associated with the 3D mesh/point cloud and boundary points are detected in order to identify occupied object space and nearest neighbors in the point cloud and generate an occupancy map; column 16, lines 31-55; column 14, lines 24-46; column 31, lines 1-26); determine one or more boundary vertices associated with the mesh based on the boundary information (based on the boundary points vertices associated with points {P(j(0)) and so on… and are nearest neighbor points and act as vertices associated with the boundary points identified; column 16, lines 31-55; column 14, lines 24-46; column 31, lines 1-26); and where the connection degree is a measure of a number of vertices traversed from a non-boundary vertex to a boundary vertex (the prior art states “each iteration, the points of point cloud PC may be traversed and every vertex may be associated with the direction D (k) that maximizes” which includes a measure (maximum) of every vertex (boundary vertex) traversed of non-boundary vertex (point cloud points) and therefore each iteration is interpreted to comprise a connection degree as the maximum valuer produced; fig 12A-B; column 13, line 36 - column 14, line 60; column 76, lines 22-60); and compress the filtered geometry map using a video codec into a plurality of single channel or multiple channel images (at step 220 of fig 2a the occupancy map is input to occupancy map compression module 220 which compresses the occupancy map and is done via a video codec to reconstruct the point cloud information and distribute the data via a network interface 1540 comprising the computer system and other devices attached to a network and the network includes storage area networks such as Fibre Channel SANs; fig 2A; column 11, lines 1-39; column 45, lines 34-45; column 79, line 65 – column 80, line 14). TOURAPIS fails to disclose generate a filtered geometry map associated with the mesh based a connection degree greater than or equal to one and less than a predetermined threshold using a filtering algorithm and a quantization step size of a vertex, where the one or more boundary vertices associated with the mesh and one or more non- boundary vertices have different quantization step sizes.
ESCODA discloses generate a filtered geometry map associated with the mesh based a connection degree greater than or equal to one and less than a predetermined threshold using a filtering algorithm and a quantization step size of a vertex (a computing system and computing method adapted for geometric encoding and decoding schemes using a geometric partitions strategy acting as the mesh by defining each partition as an image block and an array of image blocks would substantially comprise a mesh, the mesh is based on pixels labeled in each partition and labeled based on an adjustable and definable threshold of a pixel image feature, pixels may be labeled as "partial surface", with a label different from those of Partition 1 and 0 if they meet or don not meet the threshold, this threshold is used by the filtering model which includes filters applied as Discrete Cosine Transform DCT In-loop de-blocking filtering adapts filter strength depending on the encoded video data; figs 8-11; column 10, line 40-column 13, line 64; column 22, line 36-column 23, line 30), where the one or more boundary vertices associated with the mesh and one or more non- boundary vertices have different quantization step sizes (each parameter of each point (x,y) can be used as model parameters and are determined to be part of the mesh or not part of the partition in mesh based on a feature threshold which is adjustable and a quantization step size is applied and the step sizes are different and are applied as delta p and delta theta are the selected quantization “parameter precision” steps an offset in the selected values can be established, the quantized indices for theta and p, are the information transmitted to code the partitions (geometric mesh) shape; column 12, line 30-column 13, line 64; column 14, lines 30-64; column 17, line 60-column 18, line 67).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have one or more non- boundary vertices have different quantization step sizes of ESCODA reference. The Suggestion/motivation for doing so would have been to provide the ability to use parameter precision adjustments which can be adapted according to some high-level syntax, such as the sequence, picture, and/or slice level as suggested at column 13, lines 58-64 of ESCODA. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine ESCODA with TOURAPIS to obtain the invention as specified in claim 17.
As per claim 18, TOURAPIS in view of ESCODA discloses the non-transitory computer-readable medium of claim 17. Modified TOURAPIS further discloses wherein the filtering algorithm further comprises one or more filtering parameters, the one or more filtering parameters comprising filtering strength factors (the algorithm comprises a plurality of adjustable parameters; fig 6D; column 12, lines 29-64; column 14, lines 50-60; column 58, lines 1-18), filtering iteration numbers, weighting factors, or number of neighboring vertices (the filtering parameters are easily adjustable see fig 6 flow chart and include strength level of the filtering module; fig 6D; column 12, lines 29-64; column 14, lines 50-60; column 58, lines 1-18).
Claims 3, 5-8, 11, 13-16, 19-20 are rejected under 35 § U.S.C. 103 as being obvious over US 11,627,314 B2 to TOURAPIS et al (hereinafter “TOURAPIS”) in view of US 11,252,435 B2 to DIVORRA ESCODA et al. (hereinafter “ESCODA”) in view of WO 2021/062044 A1 to MAMOU et al. (hereinafter “MAMOU”).
As per claim 3, TOURAPIS in view of ESCODA discloses the method of claim 2. Modified TOURAPIS fails to disclose wherein the filtering strength factors comprise a first filtering strength factor associated with boundary vertices and a second filtering strength factor associated with non-boundary vertices.
MAMOU discloses wherein the filtering strength factors comprise a first filtering strength factor associated with boundary vertices and a second filtering strength factor associated with non-boundary vertices (the filter 244 is an adaptive filter and is able to by user selection of more sophisticated filters (higher strength/first strength factor) that would result in the desired luminance value, further weighting values of filters may be applied during filtering according to the normal difference and the system tracks non-boundary vertices and performs boundary stitching on boundary vertices and would be adapted to apply a second filter with new weights to said stitched vertices; paragraphs [0103], [0207], [0217], [0416], [0432]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify TOURAPIS to have wherein the filtering strength factors comprise a first filtering strength factor associated with boundary vertices and a second filtering strength factor associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to provide various weighted filter values/strengths in order to improve performance of up sampling and down sampling steps as suggested by MAMOU paragraph [0217]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with modified TOURAPIS to obtain the invention as specified in claim 3.
As per claim 5, TOURAPIS in view of ESCODA discloses the method of claim 2. Modified TOURAPIS fails to disclose wherein the filtering iteration numbers comprise a first filtering iteration number associated with boundary vertices and a second filtering iteration number associated with non-boundary vertices.
MAMOU discloses wherein the filtering iteration numbers comprise a first filtering iteration number associated with boundary vertices and a second filtering iteration number associated with non-boundary vertices (an iterative segment refinement (filtering) procedure is ren on segments of the geometric point cloud in order to further refine data at step 348 to generate a sequence of point cloud refined segments 356, further the number of iterations is determined by user selection of the N value of iterations to be performed on the vertices found and includes boundary stitched vertices as well as standard non-stitched vertices, see step d of paragraph 0191; fig 3A, 3F; paragraphs [0109], [0154], [0191]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify TOURAPIS to have wherein the filtering iteration numbers comprise a first filtering iteration number associated with boundary vertices and a second filtering iteration number associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to provide for any number N of iteration steps to be performed as desired for the desired outcome in order to reach no further change in the image output as suggested by step d, of paragraph [0191] of MAMOU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with modified TOURAPIS to obtain the invention as specified in claim 5.
As per claim 6, TOURAPIS in view of ESCODA, in view of MAMOU discloses the method of claim 5. Modified TOURAPIS fails to disclose wherein the filtering iteration numbers are based on a respective quantization step size.
ESCODA discloses wherein the filtering iteration numbers are based on a respective quantization step size (a quantization step size is applied and the step sizes are different and are applied as delta p and delta theta are the selected quantization “parameter precision” steps an offset in the selected values can be established, the quantized indices for theta and p, are the information transmitted to code the partitions (geometric mesh) shape and includes for each cycle/iteration run in-loop filtering for artifacts reduction; column 12, line 30-column 13, line 64; column 14, lines 8-19).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify TOURAPIS to have a respective quantization step size of ESCODA reference. The Suggestion/motivation for doing so would have been to provide the ability to use parameter precision adjustments according to quantization step size which can be adapted according to some high-level syntax, such as the sequence, picture, and/or slice level as suggested at column 13, lines 58-64 of ESCODA which further increases the adaptability of the geometric mesh provided. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine ESCODA with modified TOURAPIS to obtain the invention as specified in claim 6.
As per claim 7, TOURAPIS in view of ESCODA discloses the method of claim 2. Modified TOURAPIS fails to disclose wherein the weighting factors comprise a first weighting factor associated with boundary vertices and a second weighting factor associated with non-boundary vertices.
MAMOU discloses wherein the weighting factors comprise a first weighting factor associated with boundary vertices and a second weighting factor associated with non-boundary vertices (the filter 244 is an adaptive filter and is able to by user selection of more sophisticated filters that would result in the desired luminance value, further weighting values of filters may be applied during filtering according to the normal difference and the system tracks non-boundary vertices and performs boundary stitching on boundary vertices and would be adapted to apply a second filter with new weights to said stitched vertices; paragraphs [0103], [0207], [0217], [0416], [0432]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify TOURAPIS to have wherein the weighting factors comprise a first weighting factor associated with boundary vertices and a second weighting factor associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to various weighted filter values/strengths in order to improve performance of up sampling and down sampling steps as suggested by MAMOU paragraph [0217]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with modified TOURAPIS to obtain the invention as specified in claim 7.
As per claim 8, TOURAPIS in view of ESCODA discloses the method of claim 2. Modified TOURAPIS fails to disclose wherein the number of neighboring vertices comprise a first number of neighboring vertices associated with boundary vertices and a second number of neighboring vertices associated with non-boundary vertices.
MAMOU discloses wherein the number of neighboring vertices comprise a first number of neighboring vertices associated with boundary vertices and a second number of neighboring vertices associated with non-boundary vertices (the computing system as seen in paragraph 0191 applies a smoothing filter procedure based on nearest neighbor value Q in reference frame RF provided in an equation in the cited paragraph, wherein Q is an adjustable value and the smoothing operation may be applied to vertices and boundary stitched vertices respectively with any desired Q “nearest neighbor” value; paragraphs [0191], [0194]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify TOURAPIS to have wherein the number of neighboring vertices comprise a first number of neighboring vertices associated with boundary vertices and a second number of neighboring vertices associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to provide the user the ability to implement an optimization strategy by applying various smoothing filter parameters and adjusting those parameter values including the value of “Q” in this context relating to nearest neighbor pixels as suggested by MAMOU at paragraph [0194]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with modified TOURAPIS to obtain the invention as specified in claim 8.
As per claim 11, TOURAPIS discloses the device of claim 10. TOURAPIS fails to disclose wherein the filtering strength factors comprise a first filtering strength factor associated with boundary vertices and a second filtering strength factor associated with non-boundary vertices.
MAMOU discloses wherein the filtering strength factors comprise a first filtering strength factor associated with boundary vertices and a second filtering strength factor associated with non-boundary vertices (the filter 244 is an adaptive filter and is able to by user selection of more sophisticated filters (higher strength/first strength factor) that would result in the desired luminance value, further weighting values of filters may be applied during filtering according to the normal difference and the system tracks non-boundary vertices and performs boundary stitching on boundary vertices and would be adapted to apply a second filter with new weights to said stitched vertices; paragraphs [0103], [0207], [0217], [0416], [0432]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have wherein the filtering strength factors comprise a first filtering strength factor associated with boundary vertices and a second filtering strength factor associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to provide various weighted filter values/strengths in order to improve performance of up sampling and down sampling steps as suggested by MAMOU paragraph [0217]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with TOURAPIS to obtain the invention as specified in claim 11.
As per claim 13, TOURAPIS discloses the device of claim 10. TOURAPIS fails to disclose wherein the filtering iteration numbers comprise a first filtering iteration number associated with boundary vertices and a second filtering iteration number associated with non-boundary vertices.
MAMOU discloses wherein the filtering iteration numbers comprise a first filtering iteration number associated with boundary vertices and a second filtering iteration number associated with non-boundary vertices (an iterative segment refinement (filtering) procedure is ren on segments of the geometric point cloud in order to further refine data at step 348 to generate a sequence of point cloud refined segments 356, further the number of iterations is determined by user selection of the N value of iterations to be performed on the vertices found and includes boundary stitched vertices as well as standard non-stitched vertices, see step d of paragraph 0191; fig 3A, 3F; paragraphs [0109], [0154], [0191]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have wherein the filtering iteration numbers comprise a first filtering iteration number associated with boundary vertices and a second filtering iteration number associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to provide for any number N of iteration steps to be performed as desired for the desired outcome in order to reach no further change in the image output as suggested by step d, of paragraph [0191] of MAMOU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with TOURAPIS to obtain the invention as specified in claim 13.
As per claim 14, TOURAPIS in view of ESCODA discloses the device of claim 13. TOURAPIS fails to disclose [claim limitations not covered].
ESCODA discloses wherein the filtering iteration numbers are based on a respective quantization step size (a quantization step size is applied and the step sizes are different and are applied as delta p and delta theta are the selected quantization “parameter precision” steps an offset in the selected values can be established, the quantized indices for theta and p, are the information transmitted to code the partitions (geometric mesh) shape and includes for each cycle/iteration run in-loop filtering for artifacts reduction; column 12, line 30-column 13, line 64; column 14, lines 8-19).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have filtering iteration numbers are based on a respective quantization step size of ESCODA reference. The Suggestion/motivation for doing so would have been to provide the ability to use parameter precision adjustments according to quantization step size which can be adapted according to some high-level syntax, such as the sequence, picture, and/or slice level as suggested at column 13, lines 58-64 of ESCODA which further increases the adaptability of the geometric mesh provided. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine ESCODA with TOURAPIS to obtain the invention as specified in claim 14.
As per claim 15, TOURAPIS discloses the device of claim 10. TOURAPIS fails to disclose wherein the weighting factors comprise a first weighting factor associated with boundary vertices and a second weighting factor associated with non-boundary vertices.
MAMOU discloses wherein the weighting factors comprise a first weighting factor associated with boundary vertices and a second weighting factor associated with non-boundary vertices (the filter 244 is an adaptive filter and is able to by user selection of more sophisticated filters that would result in the desired luminance value, further weighting values of filters may be applied during filtering according to the normal difference and the system tracks non-boundary vertices and performs boundary stitching on boundary vertices and would be adapted to apply a second filter with new weights to said stitched vertices; paragraphs [0103], [0207], [0217], [0416], [0432]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have wherein the weighting factors comprise a first weighting factor associated with boundary vertices and a second weighting factor associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to provide various weighted filter values/strengths in order to improve performance of up sampling and down sampling steps as suggested by MAMOU paragraph [0217]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with TOURAPIS to obtain the invention as specified in claim 15.
As per claim 16, TOURAPIS discloses the device of claim 10. TOURAPIS fails to disclose [wherein the number of neighboring vertices comprise a first number of neighboring vertices associated with boundary vertices and a second number of neighboring vertices associated with non-boundary vertices.
MAMOU discloses wherein the number of neighboring vertices comprise a first number of neighboring vertices associated with boundary vertices and a second number of neighboring vertices associated with non-boundary vertices (the computing system as seen in paragraph 0191 applies a smoothing filter procedure based on nearest neighbor value Q in reference frame RF provided in an equation in the cited paragraph, wherein Q is an adjustable value and the smoothing operation may be applied to vertices and boundary stitched vertices respectively with any desired Q “nearest neighbor” value; paragraphs [0191], [0194]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have wherein the number of neighboring vertices comprise a first number of neighboring vertices associated with boundary vertices and a second number of neighboring vertices associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to provide the user the ability to implement an optimization strategy by applying various smoothing filter parameters and adjusting those parameter values including the value of “Q” in this context relating to nearest neighbor pixels as suggested by MAMOU at paragraph [0194]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with TOURAPIS to obtain the invention as specified in claim 16.
As per claim 19, TOURAPIS discloses the non-transitory computer-readable medium of claim 18. TOURAPIS fails to disclose wherein the filtering strength factors comprise a first filtering strength factor associated with boundary vertices and a second filtering strength factor associated with non-boundary vertices.
MAMOU discloses wherein the filtering strength factors comprise a first filtering strength factor associated with boundary vertices and a second filtering strength factor associated with non-boundary vertices (the filter 244 is an adaptive filter and is able to by user selection of more sophisticated filters (higher strength/first strength factor) that would result in the desired luminance value, further weighting values of filters may be applied during filtering according to the normal difference and the system tracks non-boundary vertices and performs boundary stitching on boundary vertices and would be adapted to apply a second filter with new weights to said stitched vertices; paragraphs [0103], [0207], [0217], [0416], [0432]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have wherein the filtering strength factors comprise a first filtering strength factor associated with boundary vertices and a second filtering strength factor associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to provide various weighted filter values/strengths in order to improve performance of up sampling and down sampling steps as suggested by MAMOU paragraph [0217]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with TOURAPIS to obtain the invention as specified in claim 19.
As per claim 20, TOURAPIS discloses the non-transitory computer-readable medium of claim 18. TOURAPIS fails to disclose wherein the filtering iteration numbers comprise a first filtering iteration number associated with boundary vertices and a second filtering iteration number associated with non-boundary vertices.
MAMOU discloses wherein the filtering iteration numbers comprise a first filtering iteration number associated with boundary vertices and a second filtering iteration number associated with non-boundary vertices (an iterative segment refinement (filtering) procedure is ren on segments of the geometric point cloud in order to further refine data at step 348 to generate a sequence of point cloud refined segments 356, further the number of iterations is determined by user selection of the N value of iterations to be performed on the vertices found and includes boundary stitched vertices as well as standard non-stitched vertices, see step d of paragraph 0191; fig 3A, 3F; paragraphs [0109], [0154], [0191]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify TOURAPIS to have wherein the filtering iteration numbers comprise a first filtering iteration number associated with boundary vertices and a second filtering iteration number associated with non-boundary vertices of MAMOU reference. The Suggestion/motivation for doing so would have been to provide for any number N of iteration steps to be performed as desired for the desired outcome in order to reach no further change in the image output as suggested by step d, of paragraph [0191] of MAMOU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MAMOU with TOURAPIS to obtain the invention as specified in claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
Progressive Coding of 3-D Graphic Models – Li et al.
US 2007/0242894 A1
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/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677