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 .
Response to Arguments
Applicant’s arguments with respect to claims 1, 7 and 9 have been considered but they are not persuasive.
Applicant asserts that Facciolo does not disclose the claimed combination of clustering and per-cluster pair selection as recited in claim 1. Facciolo does not define N image clusters to which images are distributed according to each image's incidence and azimuth viewing angles, nor does it later select the best stereo pairs per cluster under the constraint that both images of a pair must belong to the same cluster. Accordingly, Facciolo lacks the explicit steps (i) of distributing images into clusters by their incidence and azimuth angles and (ii) of selecting the best stereo pairs per cluster with the stated membership rule. (p. 4-5 of Remarks).
Examiner notices that Facciolo discloses “we built a correlation matrix (Figure 6) between the measures and some descritptors computed from the RPC models of the images. The three most relevant are: angle between the views, maximum incidence angle, and time difference between the two images. To understand how these variables affect the completeness we partitioned this 3-parameter space and computed the average completeness for each cell as shown in Figure 7.…Based on these observations we propose a simple heuristic for sorting the image pairs. We prioritize the pairs forming angles from 5 to 45 degrees, with maximum incidence angle below 40 degrees” at p. 1545; “We estimate the height modes at each point by applying the k-medians clustering with increasing number of clusters (1 to 8) until the clusters have a span inferior to a predefined precision… and the proposed clustering-based strategy (denoted as k-medians)” at p. 1547; “We propose a heuristic to select the best image pairs from a large collection, and we observe that the optimal result is obtained by keeping only few well-chosen pairs from the large set of all possible pairs” at p. 1549; see also Figure 3-4 and 7. In Fig 7, Facciolo discloses average completeness of image pairs can be distributed into different clusters according to the incidence and viewing angles as shown below.
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Here, the image pairs can be distributed to different clusters based on angle criteria, such as, incidence angle and viewing angle. It is obvious that the criteria can be set for any other angles or parameters. Therefore, the argued limitation can be read by the teaching of Facciolo.
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.
Claims 1, 4-7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Facciolo et al. (Automatic 3D Reconstruction from Multi-Date Satellite Images, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops) in view of Raif et al. (US 9,430,872 B2).
As to Claim 1, Facciolo teaches A method for selecting stereo pairs of satellite or aerial images to generate elevation data for an area of interest (Facciolo, p. 1544), said method being computer- implemented and comprising a phase of selecting eligible stereo pairs from an initial set of images representing the area of interest, followed by a phase of ranking the selected stereo pairs according to their quality, said method being characterised in that it further comprises a phase of defining N image clusters, where N is an integer greater than or equal to 2, the images of the set being distributed into different clusters according to the incidence and azimuth viewing angles thereof, and a phase of selecting the best stereo pairs per cluster on the basis of the ranking established during the ranking phase and on the fact that for a stereo pair to belong to a cluster, both of the images in said pair must belong to said cluster, wherein the combination of the phases of defining clusters and of selecting the best pairs per cluster allows the number of stereo pairs to be minimized while ensuring that the area of interest is covered by the viewing angles (Facciolo discloses “Here, we propose a new incremental method that selects and aggregates only a small fraction of all the pairs. Our method consists of three stages (Figure 3): 1. Pair selection. We propose a heuristic for sorting all the possible image pairs so that the first pairs of the list yield results with higher completeness measure. 2. Stereo matching. For each selected image pair a 3D point cloud is computed by stereo matching and triangulation. Note that each point cloud is computed independently with no need for bundle adjustment. 3. Alignment and fusion. The triangulated point cloud computed from each selected image pair is projected into a geographic grid and registered with the others… The proposed fusion strategy accounts for this by favoring the elevation modes closer to the ground… 2.1. Selection of Image Pairs… So, given a set of multi-date images, we want a criterion for sorting all the pairs according to their quality (defined by the completeness measure), and process only the first elements of this list.” at p. 1544; “we built a correlation matrix (Figure 6) between the measures and some descritptors computed from the RPC models of the images. The three most relevant are: angle between the views, maximum incidence angle, and time difference between the two images. To understand how these variables affect the completeness we partitioned this 3-parameter space and computed the average completeness for each cell as shown in Figure 7.…Based on these observations we propose a simple heuristic for sorting the image pairs. We prioritize the pairs forming angles from 5 to 45 degrees, with maximum incidence angle below 40 degrees” at p. 1545; “We estimate the height modes at each point by applying the k-medians clustering with increasing number of clusters (1 to 8) until the clusters have a span inferior to a predefined precision… and the proposed clustering-based strategy (denoted as k-medians)” at p. 1547; “We propose a heuristic to select the best image pairs from a large collection, and we observe that the optimal result is obtained by keeping only few well-chosen pairs from the large set of all possible pairs” at p. 1549; see also Figure 3-4 and 7. In Fig 7, Facciolo discloses average completeness of image pairs can be distributed into different clusters according to the incidence and viewing angles as shown below.
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It is obvious that the image clustering can also be distributed based on a similar criteria. For example, Raif discloses “At 146, the images are clustered and a performance prediction is made for each cluster. In some embodiments, a first pass is made based on a metadata-based performance prediction and a subset of image clusters is identified. That subset of image clusters then receives the more computationally intensive correlator-based performance evaluation” in C6L39-44; “In the example method shown in FIG. 6 above, subsets of five images are combined into clusters and evaluated based on one or more types of performance scores. Clusters formed from subsets of N images, where N is an integer greater than two, can also be evaluated using performance scores as described above” in C8L27-32. Here, Raif’s type of performance scores can be incidence angle and azimuth viewing angle so that the image pairs with one cluster may have similar property.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Facciolo with the teaching of Raif so that one or more image clusters is identified and passed to be used for 3D modeling (Raif, C6L45-46).
As to Claim 4, Facciolo in view of Raif teaches The method according to claim 1, wherein the phase of selecting the best pairs per cluster consists of selecting the same number of pairs per cluster (Facciolo discloses “Figure 10 compares the results obtained by the median and the proposed clustering-based strategy (denoted as k-medians)” at p.1547. Here, Facciolo discloses an example of 50 best pairs as shown in Fig 10. There is no disclosed criticality to the number of pairs in a cluster. It is rendered obvious as a design choice (see MPEP 2144.04) that would have no impact on the function or results of the claimed invention.)
As to Claim 5, Facciolo in view of Raif teaches The method according to claim 1, wherein the best pairs selected during the phase of selecting the best pairs per cluster are aggregated into a final list (Facciolo discloses “1. Pair selection. We propose a heuristic for sorting all the possible image pairs so that the first pairs of the list yield results with higher completeness measure” at p. 1544; “This motivates our choice of fusing only the first 50 pairs, instead of fusing them all (2162 in this case) as in [23]” at p. 1548; “We propose a heuristic to select the best image pairs from a large collection” at p. 1549. Here, the number of best selected pairs is determined in a final list, for example, 50 best pairs selected by the heuristic criterion as shown in Fig 14.)
Claim 6 recites similar limitations as claim 1 but in a method form. Therefore, the same rationale used for claim 1 is applied.
Claim 7 recites similar limitations as claim 1 but in a system form. Therefore, the same rationale used for claim 1 is applied.
Claim 9 recites similar limitations as claim 1 but in a non-transitory terminal-readable medium form. Therefore, the same rationale used for claim 1 is applied.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Facciolo in view of Raif and Tao et al. (CN 105654548 A).
As to Claim 3, Facciolo in view of Raif teaches The method according to claim 1, wherein the cluster definition phase is carried out by an affinity propagation algorithm (Facciolo discloses “Our method uses a local affine camera approximation and thus focuses on the 3D reconstruction of small areas” in Abstract; “and the proposed clustering-based strategy (denoted as k-medians)” at p.1547. Tao further discloses “In the embodiment, the Affinity Propagation (AP) algorithm is used for initial clustering, and the AP clustering algorithm can adaptively determine the number of data clusters, and has a wider application range and more powerful processing of complex data” at p.9.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Facciolo and Raif with the teaching of Tao so as to use affinity propagation algorithm for initial clustering and has a wider application range and more powerful processing of complex data (Tao, p. 9).
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/Weiming He/
Primary Examiner, Art Unit 2611