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 .
Allowable Subject Matter
Claims 3-4, 6-11, 14-16, 18 and 20 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.
The following is a statement of reasons for the indication of allowable subject matter:
With regards to claims 3 and 18, several of the features of these claims were known in the art as evidenced by Wysocki et al, “COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION”, which discloses obtaining features (e.g., “Semantic segmentation”; “probability of each class P(B)”) representing voxels in a tree structure (“Octree”), wherein feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed at p. 3, sec. 3.2 and FIG. 3; see, also: p.2, sec. 3 and FIG. 2; p. 4, sec. 3.5. Wysocki discloses determining an occupancy probability (e.g., “existence probability score Pex”) of the current voxel based on the feature (e.g., “Semantic segmentation”; “probability of each class P(B)”) representing voxels in a tree structure (“Octree”) at pp. 3-4, sec. 3.3-3.5. Wysocki discloses decoding occupancy information (e.g., “static” or “dynamic voxels”) of voxels in the tree structure, wherein whether the current voxel is occupied or not is decoded based on the occupancy probability (e.g., “existence probability score Pex”) for the current voxel at p. 4, sec.3.5; to wit: “[S]tatic, occupied voxels (yellow) are building-related; dynamic, unoccupied voxels (gray) represent moving objects, such as pedestrians or cars… Voxels are deemed as static if the existence probability score Pex is greater than the static threshold probability: Pex >= Pstatic; otherwise, voxels represent the dynamic state. Points within dynamic voxels are relabeled to the other class…” See, also, pp.2-3, sec. 3 and FIG. 2. Wysocki discloses reconstructing the point cloud based on the occupancy information at p. 4, sec. 3.5 (“Points within dynamic voxels are relabeled to the other class and are backprojected to the input point cloud. The static voxels obtain the point class that scores the greatest probability P(B) within a voxel (Figure 7). Static voxels with semantics are projected onto the fac¸ade, forming the points comparison texture map layer with labels corresponding to the classes…”) However, Wysocki does not disclose entropy decoding the feature.
With regards to claim 4, several of the features of this claim were known in the art as evidenced by Wysocki et al, “COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION”, which discloses decoding occupancy information (e.g., “static” or “dynamic voxels”) of voxels in the tree structure, wherein whether the current voxel is occupied or not is decoded based on the occupancy probability (e.g., “existence probability score Pex”) for the current voxel at p. 4, sec.3.5; to wit: “[S]tatic, occupied voxels (yellow) are building-related; dynamic, unoccupied voxels (gray) represent moving objects, such as pedestrians or cars… Voxels are deemed as static if the existence probability score Pex is greater than the static threshold probability: Pex >= Pstatic; otherwise, voxels represent the dynamic state. Points within dynamic voxels are relabeled to the other class…” See, also, pp.2-3, sec. 3 and FIG. 2. However, Wysocki does not disclose the decoding occupancy information comprises: parsing one or more syntax elements indicative of occupancy information of the current voxel, wherein whether the current voxel is occupied is decoded losslessly by arithmetic decoding the one or more syntax elements based on the occupancy probability.
With regards to claim 6, several of the features of this claim were known in the art as evidenced by Wysocki et al, “COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION”, which discloses obtaining features (e.g., “Semantic segmentation”; “probability of each class P(B)”) representing voxels in a tree structure (“Octree”), wherein feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed at p. 3, sec. 3.2 and FIG. 3; see, also: p.2, sec. 3 and FIG. 2; p. 4, sec. 3.5. However, Wysocki does not disclose the obtaining features further comprises: obtaining another feature generating a first feature based on the another feature and the feature of voxels in the tree structure, wherein the occupancy probability of the current voxel is determined based on the first feature.
With regards to claim 7, this claim depends from claim 6 and therefore incorporates the features of that claim that were found allowable.
With regards to claim 8, several of the features of this claim were known in the art as evidenced by Wysocki et al, “COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION”, which discloses obtaining features (e.g., “Semantic segmentation”; “probability of each class P(B)”) representing voxels in a tree structure (“Octree”), wherein feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed at p. 3, sec. 3.2 and FIG. 3; see, also: p.2, sec. 3 and FIG. 2; p. 4, sec. 3.5. However, Wysocki does not disclose the obtaining features further comprises: obtaining another feature generating a set of hyperprior parameters based on the another feature, wherein the features of voxels in a tree structure are decoded based on the set of hyperprior parameters, and wherein the occupancy probability of the current voxel being occupied is determined based on the decoded feature.
With regards to claim 9, several of the features of this claim were known in the art as evidenced by Wysocki et al, “COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION”, which discloses reconstructing the point cloud based on the occupancy information at p. 4, sec. 3.5 (“Points within dynamic voxels are relabeled to the other class and are backprojected to the input point cloud. The static voxels obtain the point class that scores the greatest probability P(B) within a voxel (Figure 7). Static voxels with semantics are projected onto the fac¸ade, forming the points comparison texture map layer with labels corresponding to the classes…”) However, Wysocki does not disclose a coarser portion of the point cloud data is decoded losslessly and a finer portion is decoded by lossy compression, the lossy compression comprising: decoding features for the remaining portion of the point cloud data and reconstructing the remaining portion of point cloud data based on the features for the remaining portion.
With regards to claims 10-11, these claims depend from claim 9 and therefore incorporate the features of that claim that were found allowable.
With regards to claims 14 and 20, several of the features of these claims were known in the art as evidenced by Wysocki et al, “COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION”, which discloses encoding occupancy information (e.g., “static” or “dynamic voxels”) of voxels in the tree structure, wherein whether the current voxel is occupied or not is encoded based on the occupancy probability (e.g., “existence probability score Pex”) for the current voxel at p. 4, sec.3.5; to wit: “[S]tatic, occupied voxels (yellow) are building-related; dynamic, unoccupied voxels (gray) represent moving objects, such as pedestrians or cars… Voxels are deemed as static if the existence probability score Pex is greater than the static threshold probability: Pex >= Pstatic; otherwise, voxels represent the dynamic state. Points within dynamic voxels are relabeled to the other class…” See, also, pp.2-3, sec. 3 and FIG. 2. As a matter of claim construction, the broadest reasonable interpretation of the term “encode” includes its ordinary meaning as converting something into a coded form. In this case, the voxels are coded as “static” or “dynamic.” However, Wysocki does not disclose entropy encoding the feature.
With regards to claim 15, several of the features of this claim were known in the art as evidenced by Wysocki et al, “COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION”, which discloses encoding occupancy information (e.g., “static” or “dynamic voxels”) of voxels in the tree structure, wherein whether the current voxel is occupied or not is encoded based on the occupancy probability (e.g., “existence probability score Pex”) for the current voxel at p. 4, sec.3.5; to wit: “[S]tatic, occupied voxels (yellow) are building-related; dynamic, unoccupied voxels (gray) represent moving objects, such as pedestrians or cars… Voxels are deemed as static if the existence probability score Pex is greater than the static threshold probability: Pex >= Pstatic; otherwise, voxels represent the dynamic state. Points within dynamic voxels are relabeled to the other class…” See, also, pp.2-3, sec. 3 and FIG. 2. As a matter of claim construction, the broadest reasonable interpretation of the term “encode” includes its ordinary meaning as converting something into a coded form. In this case, the voxels are coded as “static” or “dynamic.” However, Wysocki does not disclose the encoding occupancy information comprises: entropy encoding one or more syntax elements indicative of occupancy information of the current voxel, wherein whether the current voxel is occupied is encoded losslessly by arithmetic encoding the one or more syntax elements based on the occupancy probability.
With regards to claim 16, several of the features of this claim were known in the art as evidenced by Wysocki et al, “COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION”, which discloses encoding occupancy information (e.g., “static” or “dynamic voxels”) of voxels in the tree structure, wherein whether the current voxel is occupied or not is encoded based on the occupancy probability (e.g., “existence probability score Pex”) for the current voxel at p. 4, sec.3.5; to wit: “[S]tatic, occupied voxels (yellow) are building-related; dynamic, unoccupied voxels (gray) represent moving objects, such as pedestrians or cars… Voxels are deemed as static if the existence probability score Pex is greater than the static threshold probability: Pex >= Pstatic; otherwise, voxels represent the dynamic state. Points within dynamic voxels are relabeled to the other class…” See, also, pp.2-3, sec. 3 and FIG. 2. As a matter of claim construction, the broadest reasonable interpretation of the term “encode” includes its ordinary meaning as converting something into a coded form. In this case, the voxels are coded as “static” or “dynamic.” However, Wysocki does not disclose a coarser portion of the point cloud data is encoded losslessly and a finer portion is encoded by lossy compression, the lossy compression comprising: encoding features for the remaining portion of the point cloud data.
Other prior art considered and hereby made of record includes:
Hornung et al, “OctoMap: an efficient probabilistic 3D mapping framework basedon octrees” which discloses an octree map compression method that keeps the 3Dmodels compact.
Sengupta et al, “Semantic Octree: Unifying Recognition, Reconstruction and Representation via an Octree Constrained Higher Order MRF”, which discloses embedding an octree into a hierarchical robust PN Markov Random Field. This allows us to jointly infer the multi-resolution 3D volume along with the object-class labels, all within the constraints of an octree data-structure. The octree representation is chosen as this datastructure is efficient for further processing such as dynamic updates, data compression, and surface reconstruction.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5, 12-13, 17 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wysocki et al, “COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION”.
With regards to claim 1, Wysocki discloses obtaining features (e.g., “Semantic segmentation”; “probability of each class P(B)”) representing voxels in a tree structure (“Octree”), wherein feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed at p. 3, sec. 3.2 and FIG. 3; see, also: p.2, sec. 3 and FIG. 2; p. 4, sec. 3.5.
Wysocki discloses determining an occupancy probability (e.g., “existence probability score Pex”) of the current voxel based on the feature (e.g., “Semantic segmentation”; “probability of each class P(B)”) representing voxels in a tree structure (“Octree”) at pp. 3-4, sec. 3.3-3.5
Wysocki discloses decoding occupancy information (e.g., “static” or “dynamic voxels”) of voxels in the tree structure, wherein whether the current voxel is occupied or not is decoded based on the occupancy probability (e.g., “existence probability score Pex”) for the current voxel at p. 4, sec.3.5; to wit: “[S]tatic, occupied voxels (yellow) are building-related; dynamic, unoccupied voxels (gray) represent moving objects, such as pedestrians or cars… Voxels are deemed as static if the existence probability score Pex is greater than the static threshold probability: Pex >= Pstatic; otherwise, voxels represent the dynamic state. Points within dynamic voxels are relabeled to the other class…” See, also, pp.2-3, sec. 3 and FIG. 2. As a matter of claim construction, the broadest reasonable interpretation of the term “decode” includes its ordinary meaning as converting something into an intelligible form. In this case, the occupancy probabilities are converted into an intelligible form.
Wysocki discloses reconstructing the point cloud based on the occupancy information at p. 4, sec. 3.5 (“Points within dynamic voxels are relabeled to the other class and are back-projected to the input point cloud. The static voxels obtain the point class that scores the greatest probability P(B) within a voxel (Figure 7). Static voxels with semantics are projected onto the fac¸ade, forming the points comparison texture map layer with labels corresponding to the classes…”)
With regards to claim 2, Wysocki inherently discloses the set of voxels includes one or more voxels in a child level of, or in a same level as, the current voxel in the tree structure at pp. 3-4, sec. 3.3, when it discloses use of an “octree structure” as evidenced by Wikipedia, “Octree” at p. 1.
With regards to claim 5, Wysocki inherently discloses the tree structure (“Octree”) contains levels 0, 1, , N, wherein the obtaining features of voxels at pp. 3-4, sec. 3.3, when it discloses use of an “octree structure” as evidenced by Wikipedia, “Octree” at p. 1. Wysocki further discloses determining an occupancy probability (e.g., “existence probability score Pex”) of the current voxel being occupied based on the feature (e.g., “Semantic segmentation”; “probability of each class P(B)”), and the decoding occupancy information (e.g., “static” or “dynamic voxels”) are performed for each of levels 1 to N at pp. 3-4, secs. 3.3-3.5.
With regards to claim 12, Wysocki discloses the tree structure is an octree structure at pp. 3-4, sec. 3.3.
With regards to claim 13, Wysocki discloses obtaining features (e.g., “Semantic segmentation”; “probability of each class P(B)”) representing voxels in a tree structure (“Octree”), wherein feature for a current voxel is obtained from at least a set of voxels that are still to be encoded at p. 3, sec. 3.2 and FIG. 3; see, also: p.2, sec. 3 and FIG. 2; p. 4, sec. 3.5.
Wysocki discloses determining an occupancy probability (e.g., “existence probability score Pex”) of the current voxel based on the feature (e.g., “Semantic segmentation”; “probability of each class P(B)”) at pp. 3-4, sec. 3.3-3.5.
Wysocki discloses encoding occupancy information (e.g., “static” or “dynamic voxels”) of voxels in the tree structure, wherein whether the current voxel is occupied or not is encoded based on the occupancy probability (e.g., “existence probability score Pex”) for the current voxel at p. 4, sec.3.5; to wit: “[S]tatic, occupied voxels (yellow) are building-related; dynamic, unoccupied voxels (gray) represent moving objects, such as pedestrians or cars… Voxels are deemed as static if the existence probability score Pex is greater than the static threshold probability: Pex >= Pstatic; otherwise, voxels represent the dynamic state. Points within dynamic voxels are relabeled to the other class…” See, also, pp.2-3, sec. 3 and FIG. 2. As a matter of claim construction, the broadest reasonable interpretation of the term “encode” includes its ordinary meaning as converting something into a coded form. In this case, the voxels are coded as “static” or “dynamic.”
With regards to claim 17, Wysocki implicitly discloses performing its method using processors and at least one memory coupled to the one or more processors at p. 1 et seq. One of ordinary skill in the art would infer from the subject of the article, i.e., image analysis, that the disclosed method was intended to be performed upon a computer.
The steps performed by the apparatus of this claim are anticipated by Wysocki for the same reasons as were provided in the discussion of claim 1, which recites a method performing these same steps.
With regards to claim 19, Wysocki implicitly discloses performing its method using processors and at least one memory coupled to the one or more processors at p. 1 et seq. One of ordinary skill in the art would infer from the subject of the article, i.e., image analysis, that the disclosed method was intended to be performed upon a computer.
The steps performed by the apparatus of this claim are anticipated by Wysocki for the same reasons as were provided in the discussion of claim 13, which recites a method performing these same steps.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID F DUNPHY whose telephone number is (571)270-1230. The examiner can normally be reached 9 am - 5 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID F DUNPHY/Primary Examiner, Art Unit 2673
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