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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1, 15 and 20 recite the limitation "the determined symmetry error for each vertex" in line 10, 11 and 11, respectively. There is insufficient antecedent basis for this limitation in the claim. For the purpose of examination, a “symmetry error for each vertex” recited in the previous limitation will be used antecedent basis.
Claims 1, 15 and 20 recite the limitation "first symmetry error threshold" in line 12, 13 and 12, respectively. There is insufficient antecedent basis for this limitation in the claim. For the purpose of examination, “a first error threshold” recited in the previous limitation will be used antecedent basis.
Claim Interpretation
Exemplary claim 1 recites “… a plurality of vertices in the mesh… determining each vertex from the plurality of vertices having a symmetry error larger than a first error threshold; performing a clustering process on the plurality of vertices based on the determined symmetry error for each vertex such that each vertex having a symmetry error larger than the first symmetry error threshold is clustered together in one or more clusters”. Let’s denote:
the “mesh” as M={v0, v1, …vN} with N>1, wherein vn with n ∈ [0,N] are “the plurality of vertices”;
the “symmetry error” for each vertex in M as a function errsym(vn);
“the first error threshold” as th1;
“each vertex from the plurality of vertices having a symmetry error larger than a first error threshold” ∈Mth1={va, vb …} ⊆ M wherein errsym(vn)>th1 for n∈{a, b, …}; and
“each vertex having a symmetry error larger than the first symmetry error threshold is clustered together in one or more clusters” as klstr(vn) for n∈{a, b, …}.
For the purpose of Examination, the limitations “determining each vertex from the plurality of vertices having a symmetry error larger than a first error threshold; performing a clustering process on the plurality of vertices based on the determined symmetry error for each vertex such that each vertex having a symmetry error larger than the first symmetry error threshold is clustered together in one or more clusters” will be interpreted as “clustering together in one or more clusters each vertex from the plurality of vertices having a symmetry error larger than a first error threshold”.
Claim Rejections - 35 USC § 103
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Patricio Simari et al. [Folding meshes: Hierarchical mesh segmentation based on planar symmetry] in view of Zeyun Shi et al. [Symmetry and Orbit Detection via Lie-Algebra Voting].
Regarding claim 1, Patricio teaches:
1. A method of encoding a mesh (i.e. Meshes representing real world objects, both artist-created and scanned, contain a high level of redundancy due to (possibly approximate) planar reflection symmetries, either global or localized to different subregions… The method, inspired by techniques in Computer Vision, has foundations in robust statistics and is resilient to structured outliers which are present in the form of the non-symmetric regions of the data- Abstract), the method comprising:
determining a global symmetry plane of the mesh (i.e. plane of maximum symmetry- page 4, ¶6) that divides the mesh into a first side and a second side(i.e. This paper presents two principal contributions. Firstly, we introduce a method capable of detecting global as well as local approximate planar symmetries in 3D meshes- page 2, ¶3…given a plane p- page 3, ¶6…For a body which exhibits planar symmetry it is known that its plane of symmetry is perpendicular to a principal axis and contains the object’s center of mass [MIK92]. This lets us solve for the current plane of maximum symmetry in a closed form manner by considering the center of mass m and weighted covariance matrix C relative to the weights wi…We compute the eigenvectors of C and consider the three planes determined by these vectors and m. For each of these planes we compute the distances di and associated costs ρi retaining the one of minimum sum cost- page 4, ¶6-7);
determining, using the global symmetry plane, whether the mesh is one of fully symmetric (i.e. b) Detected global symmetry plane- fig. 1… In the case of global symmetry, this region should be the entire mesh- page 3, ¶6… Note that in the case of a global symmetry, this set will be empty- page 5, ¶2), partially symmetric, and asymmetric (i.e. Figure 7 further illustrates the symmetry detection approach. In the woman, we firstly find the dominant partial symmetry that includes her body and legs- page 7, ¶3) based on a statistical calculation performed on a plurality of vertices in the mesh (i.e. Given a mesh, we wish to find a connected region S of faces that exhibit planar symmetry within a tolerance parameter e. In the case of global symmetry, this region should be the entire mesh. We approach the problem as a model fitting scenario, in which the model consists of the sought plane, and the connected region of symmetry- page 4, ¶6-7…The GM estimator exhibits excellent behavior in rejecting structured outliers with the appropriate choice of the scale factor σ- page 4, ¶3… During these initial iterations we set σ = 1.4826•median(di), which is a popular estimate of scale [FP02], not letting it fall below 2e to avoid instability- page 4, ¶9);
based on the determination that the mesh is partially symmetric:
dividing the mesh into a plurality of sub-meshes (i.e. and segmenting the mesh into the symmetric and remaining regions- Abstract);
determining whether each sub-mesh (i.e. local symmetry- fig. 7) is one of fully symmetric (i.e. The algorithm is then applied to the remaining regions to find other local symmetries, as well as recursively to half of the symmetric region to find nested symmetries. This process leads to a hierarchical folding tree representation of the object where geometry is only stored in tree leaves. The original surface can then be reconstructed from its folding tree in a bottom-up fashion by reflecting symmetric geometry and reconnecting segmentation boundaries- page 2, ¶2… Proceeding to the remaining regions, we find the local symmetries of the head and arms. Analogously, in the dragon, we find the symmetry of its body and then legs, head and arms. In the bull, we detect the local symmetry of its body including the back left leg, then symmetries in the other three legs and the head and finally another weak nested symmetry found in the middle of its body. We detect symmetries on the body, and separately in head and the ears of the bunny and also find two other weak nested symmetries in its body. Lastly, in the octopus, its head is found to be symmetric, also containing a nested symmetry, and multiple local symmetries are found in different parts of its tentacles- page 7, ¶3, fig. 7), partially symmetric, and asymmetric; and
performing symmetry coding on each sub-mesh from the plurality of sub-meshes that is determined to be fully symmetric (i.e. Our work differs from most approaches for 3D symmetry detection in meshes in that we aim at the robust detection of not only global but also local reflection symmetries, i.e. those present only in parts of a 3D mesh, and we exploit these symmetries in order to achieve mesh compression by eliminating faces implied by the discovered symmetries- page 2, ¶5…The structure encodes the non-redundant regions of the original mesh as well as the reflection planes and is created by the recursive application of the detection method- Abstract…The original surface can then be reconstructed from its folding tree in a bottom-up fashion by reflecting symmetric geometry and reconnecting segmentation boundaries- page 2, ¶2… we have presented a new compact representation of meshes, called folding trees, which represent the original mesh by only encoding the non-redundant regions as well as the planes of symmetry and can be used to recover the original object through unfolding- page 7, ¶5…The elimination of faces which are repeated in redundant areas of global and local symmetries leads to new mesh compression schemes that can be used for mesh storage, processing, and transmission- page 7, ¶6).
However, Patricio does not teach explicitly:
determining each vertex from the plurality of vertices having a symmetry error larger than a first error threshold; performing a clustering process on the plurality of vertices based on the determined symmetry error for each vertex such that each vertex having a symmetry error larger than the first symmetry error threshold is clustered together in one or more clusters; based on the clustering process.
In the same field of endeavor, Zeyun teaches:
determining each vertex from the plurality of vertices having a symmetry error (i.e. the final expression of this regularized distance as equation 10- page 221, ¶11) larger than a first error threshold (i.e. lower bound α
ω
2+γs2- page 222, ¶13); performing a clustering process on the plurality of vertices based on the determined symmetry error for each vertex such that each vertex having a symmetry error larger than the first symmetry error threshold is clustered together in one or more clusters; based on the clustering process (i.e. Since symmetry and orbit detection relies on finding local accumulation of samples, any pair of transformations that are sufficiently dissimilar should be efficiently culled instead of being submitted to an exact distance computation. We obtain a speedup of a factor 3 by first checking the following lower bound on the distance defined in Eq. (10): α
ω
2+γs2≤
‖
δ
‖
A
If this lower bound is beyond a given large threshold, we can safely skip further computations for this pair during clustering- page 222, ¶13).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Patricio with the teachings of Zeyun to improve upon existing voting-based symmetry detection techniques by leveraging the Lie group structure of geometric transformations (Zeyun- Abstract).
Regarding claim 8, Patricio and Zeyun teach all the limitations of claim 1 and Patricio further teaches:
wherein the dividing the mesh into a plurality of sub-meshes based on the clustering process comprises:
determining a boundary for each cluster (i.e. We present characteristic results, concerning mesh com- pression, the depth of the hierarchical segmentation, average reconstruction error as a percentage of the bounding box di- agonal, as well as running times in table 1. Figure 6 shows initial and reconstructed meshes, complementing the results of figures 1 and 5, as well as illustrating all folding plane po- sitions and the folding trees- page 7, ¶3);
generating a cutting plane for each determined boundary such that the each cutting plane separates two adjacent clusters in a direction perpendicular to the global symmetry plane (i.e. In the chair model, we find the vertical plane of global symmetry then each cushion, which was a separate connected component, was folded through three perpendicular planes- page 7, ¶3).
Claims 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Patricio Simari et al. [Folding meshes: Hierarchical mesh segmentation based on planar symmetry] in view of Zeyun Shi et al.[Symmetry and Orbit Detection via Lie-Algebra Voting] and further in view of Michael F. Deering [US 6525722 B1].
Regarding claim 15, Patricio teaches:
15. A method of encoding a mesh (i.e. Meshes representing real world objects, both artist-created and scanned, contain a high level of redundancy due to (possibly approximate) planar reflection symmetries, either global or localized to different subregions… The method, inspired by techniques in Computer Vision, has foundations in robust statistics and is resilient to structured outliers which are present in the form of the non-symmetric regions of the data- Abstract), the method comprising:
wherein the mesh is divided by a global symmetry plane (i.e. plane of maximum symmetry- page 4, ¶6) into a first side and a second side (i.e. This paper presents two principal contributions. Firstly, we introduce a method capable of detecting global as well as local approximate planar symmetries in 3D meshes- page 2, ¶3…given a plane p- page 3, ¶6…For a body which exhibits planar symmetry it is known that its plane of symmetry is perpendicular to a principal axis and contains the object’s center of mass [MIK92]. This lets us solve for the current plane of maximum symmetry in a closed form manner by considering the center of mass m and weighted covariance matrix C relative to the weights wi…We compute the eigenvectors of C and consider the three planes determined by these vectors and m. For each of these planes we compute the distances di and associated costs ρi retaining the one of minimum sum cost- page 4, ¶6-7);
wherein the mesh is determined to be, using the global symmetry plane, whether one of fully symmetric (i.e. b) Detected global symmetry plane- fig. 1… In the case of global symmetry, this region should be the entire mesh- page 3, ¶6… Note that in the case of a global symmetry, this set will be empty- page 5, ¶2), partially symmetric, and asymmetric (i.e. Figure 7 further illustrates the symmetry detection approach. In the woman, we firstly find the dominant partial symmetry that includes her body and legs- page 7, ¶3) based on a statistical calculation performed on a plurality of vertices in the mesh (i.e. Given a mesh, we wish to find a connected region S of faces that exhibit planar symmetry within a tolerance parameter e. In the case of global symmetry, this region should be the entire mesh. We approach the problem as a model fitting scenario, in which the model consists of the sought plane, and the connected region of symmetry- page 4, ¶6-7…The GM estimator exhibits excellent behavior in rejecting structured outliers with the appropriate choice of the scale factor σ- page 4, ¶3… During these initial iterations we set σ = 1.4826•median(di), which is a popular estimate of scale [FP02], not letting it fall below 2e to avoid instability- page 4, ¶9);
wherein based on the determination that the mesh is partially symmetric:
the mesh is divided into a plurality of sub- meshes (i.e. and segmenting the mesh into the symmetric and remaining regions- Abstract);
each sub- mesh (i.e. local symmetry- fig. 7) is determined to be one of fully symmetric (i.e. The algorithm is then applied to the remaining regions to find other local symmetries, as well as recursively to half of the symmetric region to find nested symmetries. This process leads to a hierarchical folding tree representation of the object where geometry is only stored in tree leaves. The original surface can then be reconstructed from its folding tree in a bottom-up fashion by reflecting symmetric geometry and reconnecting segmentation boundaries- page 2, ¶2… Proceeding to the remaining regions, we find the local symmetries of the head and arms. Analogously, in the dragon, we find the symmetry of its body and then legs, head and arms. In the bull, we detect the local symmetry of its body including the back left leg, then symmetries in the other three legs and the head and finally another weak nested symmetry found in the middle of its body. We detect symmetries on the body, and separately in head and the ears of the bunny and also find two other weak nested symmetries in its body. Lastly, in the octopus, its head is found to be symmetric, also containing a nested symmetry, and multiple local symmetries are found in different parts of its tentacles- page 7, ¶3, fig. 7), partially symmetric, and asymmetric; and
symmetry coding is performed on each sub-mesh from the plurality of sub-mesh that is determined to be fully symmetric(i.e. Our work differs from most approaches for 3D symmetry detection in meshes in that we aim at the robust detection of not only global but also local reflection symmetries, i.e. those present only in parts of a 3D mesh, and we exploit these symmetries in order to achieve mesh compression by eliminating faces implied by the discovered symmetries- page 2, ¶5…The structure encodes the non-redundant regions of the original mesh as well as the reflection planes and is created by the recursive application of the detection method- Abstract…The original surface can then be reconstructed from its folding tree in a bottom-up fashion by reflecting symmetric geometry and reconnecting segmentation boundaries- page 2, ¶2… we have presented a new compact representation of meshes, called folding trees, which represent the original mesh by only encoding the non-redundant regions as well as the planes of symmetry and can be used to recover the original object through unfolding- page 7, ¶5…The elimination of faces which are repeated in redundant areas of global and local symmetries leads to new mesh compression schemes that can be used for mesh storage, processing, and transmission- page 7, ¶6).
However, Patricio does not teach explicitly:
each vertex from the plurality of vertices having a symmetry error larger than a first error threshold is determined; a clustering process is performed on the plurality of vertices based on the determined symmetry error for each vertex such that each vertex having a symmetry error larger than the first symmetry error threshold is clustered together in one or more clusters; based on the clustering process.
In the same field of endeavor, Zeyun teaches:
each vertex from the plurality of vertices having a symmetry error (i.e. the final expression of this regularized distance as equation 10- page 221, ¶11)larger than a first error threshold (i.e. lower bound α
ω
2+γs2- page 222, ¶13)is determined; a clustering process is performed on the plurality of vertices based on the determined symmetry error for each vertex such that each vertex having a symmetry error larger than the first symmetry error threshold is clustered together in one or more clusters; based on the clustering process(i.e. Since symmetry and orbit detection relies on finding local accumulation of samples, any pair of transformations that are sufficiently dissimilar should be efficiently culled instead of being submitted to an exact distance computation. We obtain a speedup of a factor 3 by first checking the following lower bound on the distance defined in Eq. (10): α
ω
2+γs2≤
‖
δ
‖
A
If this lower bound is beyond a given large threshold, we can safely skip further computations for this pair during clustering- page 222, ¶13).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Patricio with the teachings of Zeyun to improve upon existing voting-based symmetry detection techniques by leveraging the Lie group structure of geometric transformations (Zeyun- Abstract).
However, Patricio and Zeyun do not teach explicitly:
generating a bitstream comprising the mesh.
In the same field of endeavor, Michael teaches:
generating a bitstream comprising the mesh (i.e. At step 250, a binary output stream is generated by first outputting Huffman table initialization, after which the vertices are traversed in order. Appropriate tags and Δ's are output for all values. In another embodiment, customized table portions may be output (i.e., the tables need not remain fixed for all objects). This is described in "Optimized Geometry Compression for Real-time Rendering" by Michael Chow in the proceedings of IEEE Visualization 97- Col 22, line 60-67).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Patricio and Zeyun with the teachings of Michael to reduce the overall size of the graphics data even further (Michael- col 3 line 66-67 and col 4 line 1-17).
Regarding claim 20, Patricio teaches:
20. A method of decoding a mesh (i.e. Meshes representing real world objects, both artist-created and scanned, contain a high level of redundancy due to (possibly approximate) planar reflection symmetries, either global or localized to different subregions… The method, inspired by techniques in Computer Vision, has foundations in robust statistics and is resilient to structured outliers which are present in the form of the non-symmetric regions of the data- Abstract), the method comprising:
receiving a bitstream comprising the mesh;
wherein the mesh is divided by a global symmetry plane (i.e. plane of maximum symmetry- page 4, ¶6) into a first side and a second side (i.e. This paper presents two principal contributions. Firstly, we introduce a method capable of detecting global as well as local approximate planar symmetries in 3D meshes- page 2, ¶3…given a plane p- page 3, ¶6…For a body which exhibits planar symmetry it is known that its plane of symmetry is perpendicular to a principal axis and contains the object’s center of mass [MIK92]. This lets us solve for the current plane of maximum symmetry in a closed form manner by considering the center of mass m and weighted covariance matrix C relative to the weights wi…We compute the eigenvectors of C and consider the three planes determined by these vectors and m. For each of these planes we compute the distances di and associated costs ρi retaining the one of minimum sum cost- page 4, ¶6-7);
wherein the mesh is determined to be, using the global symmetry plane, whether one of fully symmetric (i.e. b) Detected global symmetry plane- fig. 1… In the case of global symmetry, this region should be the entire mesh- page 3, ¶6… Note that in the case of a global symmetry, this set will be empty- page 5, ¶2), partially symmetric, and asymmetric (i.e. Figure 7 further illustrates the symmetry detection approach. In the woman, we firstly find the dominant partial symmetry that includes her body and legs- page 7, ¶3) based on a statistical calculation performed on a plurality of vertices in the mesh (i.e. Given a mesh, we wish to find a connected region S of faces that exhibit planar symmetry within a tolerance parameter e. In the case of global symmetry, this region should be the entire mesh. We approach the problem as a model fitting scenario, in which the model consists of the sought plane, and the connected region of symmetry- page 4, ¶6-7…The GM estimator exhibits excellent behavior in rejecting structured outliers with the appropriate choice of the scale factor σ- page 4, ¶3… During these initial iterations we set σ = 1.4826•median(di), which is a popular estimate of scale [FP02], not letting it fall below 2e to avoid instability- page 4, ¶9);
wherein based on the determination that the mesh is partially symmetric:
the mesh is divided into a plurality of sub- meshes (i.e. and segmenting the mesh into the symmetric and remaining regions- Abstract);
each sub- mesh (i.e. local symmetry- fig. 7) is determined to be one of fully symmetric (i.e. The algorithm is then applied to the remaining regions to find other local symmetries, as well as recursively to half of the symmetric region to find nested symmetries. This process leads to a hierarchical folding tree representation of the object where geometry is only stored in tree leaves. The original surface can then be reconstructed from its folding tree in a bottom-up fashion by reflecting symmetric geometry and reconnecting segmentation boundaries- page 2, ¶2… Proceeding to the remaining regions, we find the local symmetries of the head and arms. Analogously, in the dragon, we find the symmetry of its body and then legs, head and arms. In the bull, we detect the local symmetry of its body including the back left leg, then symmetries in the other three legs and the head and finally another weak nested symmetry found in the middle of its body. We detect symmetries on the body, and separately in head and the ears of the bunny and also find two other weak nested symmetries in its body. Lastly, in the octopus, its head is found to be symmetric, also containing a nested symmetry, and multiple local symmetries are found in different parts of its tentacles- page 7, ¶3, fig. 7), partially symmetric, and asymmetric; and
symmetry coding is performed on each sub-mesh from the plurality of sub-mesh that is determined to be fully symmetric(i.e. Our work differs from most approaches for 3D symmetry detection in meshes in that we aim at the robust detection of not only global but also local reflection symmetries, i.e. those present only in parts of a 3D mesh, and we exploit these symmetries in order to achieve mesh compression by eliminating faces implied by the discovered symmetries- page 2, ¶5…The structure encodes the non-redundant regions of the original mesh as well as the reflection planes and is created by the recursive application of the detection method- Abstract…The original surface can then be reconstructed from its folding tree in a bottom-up fashion by reflecting symmetric geometry and reconnecting segmentation boundaries- page 2, ¶2… we have presented a new compact representation of meshes, called folding trees, which represent the original mesh by only encoding the non-redundant regions as well as the planes of symmetry and can be used to recover the original object through unfolding- page 7, ¶5…The elimination of faces which are repeated in redundant areas of global and local symmetries leads to new mesh compression schemes that can be used for mesh storage, processing, and transmission- page 7, ¶6).
However, Patricio does not teach explicitly:
each vertex from the plurality of vertices having a symmetry error larger than a first error threshold is determined; a clustering process is performed on the plurality of vertices based on the determined symmetry error for each vertex such that each vertex having a symmetry error larger than the first symmetry error threshold is clustered together in one or more clusters; based on the clustering process.
In the same field of endeavor, Zeyun teaches:
each vertex from the plurality of vertices having a symmetry error (i.e. the final expression of this regularized distance as equation 10- page 221, ¶11)larger than a first error threshold (i.e. lower bound α
ω
2+γs2- page 222, ¶13)is determined; a clustering process is performed on the plurality of vertices based on the determined symmetry error for each vertex such that each vertex having a symmetry error larger than the first symmetry error threshold is clustered together in one or more clusters; based on the clustering process(i.e. Since symmetry and orbit detection relies on finding local accumulation of samples, any pair of transformations that are sufficiently dissimilar should be efficiently culled instead of being submitted to an exact distance computation. We obtain a speedup of a factor 3 by first checking the following lower bound on the distance defined in Eq. (10): α
ω
2+γs2≤
‖
δ
‖
A
If this lower bound is beyond a given large threshold, we can safely skip further computations for this pair during clustering- page 222, ¶13).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Patricio with the teachings of Zeyun to improve upon existing voting-based symmetry detection techniques by leveraging the Lie group structure of geometric transformations (Zeyun- Abstract).
However, Patricio and Zeyun do not teach explicitly:
generating a bitstream comprising the mesh.
In the same field of endeavor, Michael teaches:
generating a bitstream comprising the mesh (i.e. At step 250, a binary output stream is generated by first outputting Huffman table initialization, after which the vertices are traversed in order. Appropriate tags and Δ's are output for all values. In another embodiment, customized table portions may be output (i.e., the tables need not remain fixed for all objects). This is described in "Optimized Geometry Compression for Real-time Rendering" by Michael Chow in the proceedings of IEEE Visualization 97- Col 22, line 60-67).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Patricio and Zeyun with the teachings of Michael to reduce the overall size of the graphics data even further (Michael- col 3 line 66-67 and col 4 line 1-17).
Allowable Subject Matter
Claims 2-7, 9-14 and 16-19 are 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.
Additional Prior Art Listing
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Non-Patent Literature:
Dilip Mathew Thomas et al. “Multiscale Symmetry Detection in Scalar Fields by Clustering Contours”.
Xu Kai et al. “Partial intrinsic reflectional symmetry of 3D shapes”.
Mark Pauly et al. “Discovering structural regularity in 3D geometry”.
Conclusion
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CLIFFORD HILAIRE
Primary Examiner
Art Unit 2488
/CLIFFORD HILAIRE/Primary Examiner, Art Unit 2488