baDETAILED 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 Objections
Claims 7 and 12 are objected to because of the following informalities:
Claim 7 is objected to because of the following informalities: Claim 7, line 2 discloses “approx.” The examiner construes this to mean “approximately.” Appropriate correction is required.
Claim 7 is objected to because of the following informalities: Claim 7, line 2 discloses “8.72*10-5” The examiner construes this to mean “
8.72
×
10
5
.” Appropriate correction is required.
Claim 12 is objected to because of the following informalities: Claim 12, lines 1-2 disclose “wherein a step of generating a premium value based on the payout.” The examiner construes this as a typographical error and recommends amending to disclose “wherein a step of generating a premium value is based on the payout.” Appropriate correction is required.
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.
Claims 1-14 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite. Claim 1, lines 16-18 discloses “generating equally spaced network points over said geographic and/or topographic area providing a mesh network of network points having a definable mesh size and covering the whole geographic and/or topographic area.” It is unclear how a network point of a mesh networking system may be “generated.” For the purposes of the present examination, “taking measurements of … equally spaced network points” is construed. However, further clarification is required.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite. Claim 1, lines 3-4 disclose “a topographic and/or geographic area.” Claim 1, line 10 discloses “a geographic and/or topographic area.” The two disclosures indicate the same limitations in opposite order. It is unclear if “a topographic and/or geographic area” is the same “a geographic and/or topographic area.” For the purposes of the present examination, they are construed the same. However, further clarification is required.
Claim 1 recites the limitation "the data structure" in line 11. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “the predefined data structure” is construed. However, further clarification is required.
Claim 1 recites the limitation "the whole geographic and/or topographic area" in line 18. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “a whole geographic and/or topographic area” is construed. However, further clarification is required.
Claim 1 recites the limitation "the total number" in line 23. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “a total number” is construed. However, further clarification is required.
Claims 2-12 are rejected by virtue of their dependence from claim 1.
Claim 2 recites the limitation "the geographic area" in line 8. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “the geographic and/or topographic area” is construed. However, further clarification is required.
Claim 2 recites the limitation "the geographic area" in lines 11-12. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “the geographic and/or topographic area” is construed. However, further clarification is required.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite. Claim 4, lines 1-2 disclose “wherein the network points are regularly spaced within the mesh network with a pre-definable spacing.” Claim 1, from which Claim 4 depends, discloses in line 16 discloses “generating equally spaced network points.” It is unclear how a network point may be both equally spaced and regularly spaced with a pre-definable spacing. Further clarification is required.
Claims 5-7 are rejected by virtue of their dependence from claim 4.
The term “essentially” in claim 5 is a relative term which renders the claim indefinite. The term “essentially” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The limitation "essentially" makes it unclear how much deviation from 0.005 x 0.005 deg is allowed. Further clarification is required.
Claim 6 recites the limitation "the corner points" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “corner points” is construed. However, further clarification is required.
Claim 7 is rejected by virtue of its dependence from claim 6.
The term “approx.” in claim 7 is a relative term which renders the claim indefinite. The term “approx.” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what deviation may be construed from “approx. 8.72*10-5 radian.” Further clarification is required.
Claim 7 recites the limitation "the dimensional m x n blocks" in line 1. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “the two dimensional m x n blocks” is construed. However, further clarification is required.
Claim 12 recites the limitation "the payout coverage" in line 2. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “a payout coverage” is construed. However, further clarification is required.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite. Claim 13, line 12 discloses the central ground station comprises. Claim 13, lines 17-18 disclose “a meshed network structure of network points generated with equally spaced network points.” It is unclear how a central ground station may comprise a meshed network structure of “generated” network points.” For the purposes of the present examination, “a meshed network structure of network points with equally spaced network points over said geographic and/or topographic area generating measurements…” is construed. However, further clarification is required.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite. Claim 13, line 6 discloses “said geographic area.” It is unclear if “said geographic area” is the same “a geographic and/or topographic area” of Claim 13, lines 4-5. For the purposes of the present examination, they are construed the same. However, further clarification is required.
Claim 13 recites the limitation "the data structure" in line 5. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “the predefined data structure” is construed. However, further clarification is required.
Claim 13 recites the limitation "the whole" in line 19. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “a whole” is construed. However, further clarification is required.
Claim 13 recites the limitation "the total number of network points" in lines 21-22. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “a total ” is construed. However, further clarification is required.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite. Claim 13, line 20 discloses “an occurrence.” It is unclear if “an occurrence” is the same “a measured occurrence” of Claim 13, line 2. For the purposes of the present examination, they are construed the same. However, further clarification is required.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite. Claim 13, line 22 discloses “after the occurrence.” It is unclear if “the occurrence” of the cited limitation is the same “measured occurrence” Claim 13, line 2. For the purposes of the present examination, they are construed the same. However, further clarification is required.
Claim 14 is rejected by virtue of its dependence from Claim 13.
Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite. Claim 14, line 3 discloses “the geographic area.” It is unclear if “the geographic area” of the cited limitation is the same “geographic and/or topographic area” of claim 13, lines 4-5. For the purposes of the present examination, they are construed the same. However, further clarification is required.
Claim 14 recites the limitation "the adjustable damage-cover structure" in line 4. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “an adjustable damage-cover structure” is construed. However, further clarification is required.
Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite. Claim 14, line 6 discloses “the geographic area.” It is unclear if “the geographic area” of the cited limitation is the same “geographic and/or topographic area” of claim 13, lines 4-5. For the purposes of the present examination, they are construed the same. However, further clarification is required.
Claim 14 recites the limitation "the flood-exposed physical object" in lines 8-9. There is insufficient antecedent basis for this limitation in the claim. For the purposes of the present examination, “a flood-exposed physical object” is construed. However, further clarification is required.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 12 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 12 depends from claim 1, and discloses wherein a premium value is assessed based upon a payout coverage associated with a measurement. None of the limitations to include a payout coverage or a measurement are assessed in independent claim 1, from which claim 12 depends and therefore fails to limit the subject matter of the claim upon which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
EXAMINER COMMENT
Jiang, X., Liang, S., He, X., Ziegler, A., Lin, P., Pan, M., Wang, D., Zou, J., Hao, D., Mao, G., Zeng, Y., Yin, J., Feng, L., Miao, C., Wood, E., & Zeng, Z. (2021, August). Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning. ScienceDirect, hereinafter Jiang, is regarded as the closest prior art to the invention of claims 1 and 13. With regard to Claim 1, Jiang discloses measuring by satellite-based and/or airplane-based aerial remote sensing devices of the optical-based measuring system, optical imaging sensory data and transmitting said optical imaging sensor data via a data transmission link to a central ground station, (Jiang, e.g., see fig. 1C illustrating SAR imaging, which is construed as optical imaging sensory data; see also pg. 38, col. 1 disclosing Sentinel-1A and Sentinel-1B are dual-satellite systems with SAR sensors launched in 2014 and 2016, respectively. In this study, we used the GEE cloud platform and C-band SAR data, acquired by Sentinel-1 in wide-swath (IW) mode at 10 m resolution on July 2020 for the flooded Yangtze River study area. The IW-mode SAR imagery is composited by dual-polarization with vertical transmit and vertical receive (VV) and vertical transmit and horizontal receive (VH). Here, we used the VV-polarized data from all available IW-mode SAR imagery datasets for flood mapping for their accuracy and detecting floods. The 32-bit sentinel-1 level-1 Ground Range Detected (GRD) product was pre-processed in GEE using the Sentinels Application Platform (SNAP), which provides software packages needed for manipulation of the images; examiner notes that Google Cloud’s globally distributed data-center infrastructure GEE is construed as a central ground station. For optical validation, we randomly selected 2,000 sample points in the study area, equally divided between flooded and non-flooded locations. We used the Planet satellite imagery from July 2020 to interpret sample pixels. Planet constellations provide 3m spatial resolution imagery and daily autonomous collection globally).
capturing a geographic and/or topographic area to be covered by a predefined data structure of a flood map generator, the data structure at least comprising definable area parameters, capturing geographic location and/or geographic extent of said geographic and/or topographic area, and generating a flood map by the flood map generator based on the transmitted optical imaging sensor data using the predefined data structure, (Jiang, e.g., see rejection as applied above to pg. 38, col. 1 disclosing a 10 m resolution on July 2020 for the flooded Yangtze River study area; see also figs. 6-7 illustrating a geographic and/or topographic area to be covered with a definable area perimeter; see also pg. 38, col. 2, section 2.1.2 to col. 2, section 2.2.1 disclosing land cover data, acquired from the 10-m resolution global land cover map were used to determine the land types inundated by flooding. With its high-resolution and satisfactory overall accuracy, the FROM-GLC10 includes ten landcover types: cropland, forest, grassland, shrubs, wetland, tundra, impervious surface, bare land, and snow/ice. The dataset was generated using the random forest classification algorithm in GEE by incorporating the FROM-GLC30 land cover data and Sentinel-2images. Inspired by Deep Embedded Clustering (DEC), we developed the adaptive unsupervised Felz-CNN segment system (AUFCS) to perform unsupervised flood mapping based on SAR images; see also pg. 41, col. 2 – pg. 42, col. 1 of section 2.3 disclosing we focused the algorithm testing on the middle and lower reaches of the Yangtze River. The headwaters of the Yangtze River suffer from heavy rains that generate yearly floods, especially in the middle and lower reaches. In this study, we implemented our model for flood events detection from July to August 2020 in the middle and lower reaches of the Yangtze River, a total of 1,140,300
k
m
2
(fig. 6). Operational 6-day revisit imagery was available for this area; see also pg. 42, col. 1, section 3.1 disclosing the blue colour in our flood inundation map shows the extent of the water body before the flood, and the red colour indicates the flooded area. The total flooded area was 13,461.81
k
m
2
in July 2020, including seven provinces in the middle and lower reaches of the Yangtze River basin. Based on a pre-disaster classified land cover dataset for the Yangtze River basin and post disaster SAR images, we determine that the most affected landcovers were croplands (9,430.36
k
m
2
), forest (1,513.75
k
m
2
) , and residential areas (1,397.5
k
m
2
). These areas equate to 2.3%, 0.32%, and 1.79% of these three land covers (fig. 8). More than 80% of the flooded area was on farmland and residential areas, where most damages occurred; see also figs. 9-13; see also pg. 42, col. 1 – pg. 43, col. 2 disclosing our map shows that Poyang Lake (fig. 9) and the Huaihe River basin (fig. 10) were the most extensively flooded areas (a total area of 3,566,47
k
m
2
). The Dongting Lake basin (fig. 11) was the second most affected feature, with a total flood inundation area of 1,244.61
k
m
2
. A 350.09-
k
m
2
region in the lower reaches of the mainstream was also severely flooded (fig. 12). IN addition, floods were concentrated in Huoqiu, Shouxian, and Lujiang counties in southern Anhui Province, Poyang, Xinjian, and Yugan counties in the northern Jiangxi Province (fig. 13. The cropland areas that were most severely affected by floods were in Poyang county (449.32
k
m
2
), Jiangxi Province, accounting for 4.67% of all croplands damaged. Feidong county Anhui Province (11.71
k
m
2
), which accounted for 0.81% of the total residential area affected by a flood, was the most severely affected. The detailed disaster area statistics for the county are shown in Table 2).
wherein a grid of grid cells over geographic and/or topographic area is defined by each grid cell having a network point as a centroid and wherein the geographic and/or topographic area is completely covered by the grid cells of the grid. (Jiang, e.g., see rejection as applied above; see also fig. 3 illustrating a proposed algorithm for training the proposed CNN network; see also fig. 4 illustrating a diagram of merging super-pixels; see also pg. 38, col. 2, section 2.2.1 disclosing inspired by Deep Embedded Clustering (DBC), we developed the adaptive unsupervised Felz-CNN segment system (AUFCS) to perform unsupervised flood mapping based on SAR images. The DEC algorithm defines a parametric nonlinear mapping from data space X to a low-dimensional feature space Z and clusters objects in low-dimensional space. Our algorithm focuses on unsupervised image segmentation and consists of phases for pre-classifying pixels and merging super-pixels. The SAR image is first over-segmented using the super-pixel segmentation algorithm to generate category labels. The super-pixels are then used as area units and are clustered and merged using CNN to extract water bodies. CNN has characterized the ability to learn representations of data with multiple levels of abstraction with reasonable accuracy; see also pg. 38, col. 2, section 2.2.2 disclosing to generate super-pixels, we use the graph-based Felzenswalb and Huttenlocher (Felz) segmentation algorithm to pre-segment the SAR image. The primary purpose of segmentation is to divide the image into specific regions with unique properties from which objects of interest can be extracted (e.g., standing water); see also pg. 38, col. 2 – col. 41, col, 1, section 2.2.3 disclosing we use the Stochastic Gradient Descent method to update the weight parameters of the convolutional layer to determine the category merging method by calculating the cross-entropy loss between the output
x
n
'
of the neural network and the
C
n
'
label obtained from a pre-classification phase. The algorithm repeats the pre-post iteration process T times to get the final prediction of the clustering label
C
n
'
. The essential components of the CNN are mainly composed of the input layer and the three-layer convolution module (fig. 3). The input of the model comes from the original SAR image, which is 1,500 x 1,500 x 1 dimensional matrix. A 3 x 3 convolution kernel (filter) is used to scan for convolution from left to right and from top to bottom. The essential components of the CNN are mainly composed of the input layer and the three-layer convolution module (Fig. 3). We used the all-zero padding method to make full use of the edge information on the SAR image. The algorithm solves the image segmentation problem involving the expression
x
n
∈
Z
n
=
1
N
represents the category to which each pixel belongs that is generated by the function
c
n
=
f
x
n
, which assigns each pixel to one of the k groups based on k-means clustering. When function f and eigenvector
x
n
are given, the latter category of each pixel is obtained via the equation above. In addition, when
c
n
is given, the above equation is converted to supervised classification, and the parameters of functions f and eigenvectors
x
n
are updated through the gradient descent optimization algorithm. To separate the regions (inner-class distance),
c
n
is predicted by introducing the following two criteria: (1) pixels with similar characteristics are assigned the same label; (2) consecutive pixels in space are assigned the same label. The first criterion ensures that pixels with similar spectral structure and object texture features are allocated to the same set. We prioritized the cluster labels in the area blocks by adding the spatial relation of the pixels as constrains. The flow diagram of the algorithm for merging the super-pixels is shown in fig. 4).
aggregating, after a measured occurrence of a flood event, for an affected area of said geographic and/or topographic area the total number of grid cells within the affected area, measuring, after the occurrence of the flood event, the affected area of said geographic and/or topographic area based on measuring a flooding at each grid cell within the affected area based on the flood map, wherein grid cells measured as flooded are contributing to the measured affected area while grid cells measured as not flooded are contributing to the area measured as not affected, and (Jiang, e.g., see rejection as applied above, especially with regard to fig. 4; see also abstract disclosing the algorithm consists of three phases (i) super-pixel generation, (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation; see also fig. 4 illustrating a diagram of merging super-pixel. (a) super-pixel-based segmentation algorithm provides the initial fine-grained category labels. (b) Convolutional network yields a p-dimensional feature map, with the largest value being the corresponding pixel label. (c) category with the highest frequency in each category is identified, and all pixels in the cluster are recorded in this category; see also pg. 41, col. 2, section 2.3 disclosing we focused the algorithm testing on the middle and lower reaches of the Yangtze River. The headwaters of the Yangtze River suffer from heavy rains that generate yearly floods, especially in the middle and lower reaches; see also fig. 7 illustrating flood mapping in the middle and lower reaches of the Yangtze River basin in July; see also figs. 9-13 illustrating further flood mapping; and see also pg. 42, col. 1, section 3.1 disclosing the blue colour in our flood inundation map shows the extent of the water body before the flood, and the red colour indicates the flooded area. The total flooded area was 13,461.81
k
m
2
in July 2020, including seven provinces in the middle and lower reaches of the Yangtze River basin. The most severely flooded areas were located in the proximity of large lakes, rivers, canals, and low-lying regions (figs. 6 and 7). Based on a pre-disaster classified land cover dataset for the Yangtze River basin and post-disaster SAR images, we determine that the most affected landcovers were croplands, forest, and residential areas. These areas equate to 2.3%, 0.32%, and 1.79% of these three land covers (fig. 8). Our map shows that Poyang Lake (Fig. 9 and the Huaihe River basin (Fig. 10) were the most extensively flooded areas (a total area of 3,566,47
k
m
2
). The Dongting Lake basin (Fig. 11) was the second most affected feature, with a total flood inundation area of 1,244.61
k
m
2
. A 350.09-
k
m
2
region in the lower reaches of the mainstream was also severely flooded (Fig. 12). In addition, floods were concentrated in Huoqiu, Shouxian, and Luijiang counties in southern Anhui Province, Poyang, Xinjian, and Yugan counties in the northern Jiangxi Province (Fig. 13). The detailed disaster area statistics for the county are shown in Table 2; examiner notes Table 2, columns to include Cropland, Forest, Grassland, Wetland, Residential Areas, and Bare Land provide the measurements of the occurrence of the flood event; see also fig. 14 illustrating randomly selected 2,000 uniformly distributed verification points based on the daily sequence of Planet imagery, among which 1,000 points were the flooded and non-flooded locations, and see also pg. 43, col. 2 – pg. 45, col. 1, section 3.2 disclosing in the validation of our Felz-CNN algorithm used 1,00 flooded and 1,00 non-flooded points (fig. 14) identified on the inundation map. This analysis shows that we achieved high overall classification accuracy (92.85%) and a good overall kappa coefficient (85.70%) The proportion of corrected extracted flooded areas is greater with a smaller misclassification error, and the ration of extracted non-flooded regions in the ground truth is also greater with a small leakage error).
measuring the flooding extent value and/or flooding impact measure value of the affected area based on the grid cells measured as flooded to the total number of grid cells of the geographic and/or topographic area. (Jiang, e.g., see rejection as applied above, particularly to Table 2, column Total Area disclosing a flooding extent value, and also to fig. 13 illustrating a flood extent of Pre-Flood to Inundation over cropland, forest, grassland, wetland, impervious surface, and bareland; examiner notes column “Total Area” is construed as the flooding extent value and/or flooding impact measure value of the affected area; see also Table 3 disclosing a confusion matrix for flood extraction; see also Table 4 disclosing comparisons of satellite resource, algorithm, accuracy and efficiency between this study and other studies using SAR and optical satellite imagery; see also pg. 45, col. 1 disclosing the user accuracy and corresponding to an error of commission, can be as high as 95.53%, and the producer accuracy, indicating the quality of category of training set pixels, also reaches a high level of 92.85% (Table 3). In addition, the proportion of corrected extracted flooded areas is greater with a smaller misclassification error, and the ratio of extracted non-flooded regions in the ground truth is also greater with a smaller leakage error. We also compared the overall, producer and user accuracies of the proposed algorithm with other algorithms using SAR satellite imagery to detect flood inundation (Table 4) including the following: the Radar Produced Inundation Diary (RAPID), Hierarchical Split-based Approach (HSBA), Automated Surface Water Extraction (ASWE), Prototype system, Multi-Modal change detection, Harmonic analysis, and Fuzzy systems. Our algorithm achieves the same accuracy level as these previous traditional pixel-based and object oriented classification algorithms).
Jiang, however, may not be relied upon as explicitly disclosing generating equally spaced network points over said geographic and/or topographic area providing a meshed network of network points having a definable mesh size and covering the whole geographic and/or topographic area.
In fact, utilizing data provided from equally spaced network points over said geographic and/or topographic area providing a mesh network of network points having a definable mesh size and covering the whole geographic and/or topographic area is not obvious as a combinable variant.
US 12,392,927 B1 to Kakde et al., hereinafter Kakde, discloses utilizing a simulation model to determine how many network points comprised of flood sensors are required over a minimal geographic area in order to optimize sensor placement, wherein the network points are neither equally spaced, nor covering the whole geographic area; e.g., see figs. 15C and 15F and associated disclosure.
US 2013/0250811 A1 to Vasseur et al., hereinafter Vasseur, discloses a reactive routing network which is implicitly equally spaced; e.g., see figs. 3G-3J and associated disclosure, but is not placed over a geographic and/or topographic area, wherein the definable mesh size covers the whole geographic and topographic area.
US 2013/0013206 A1 to Guha et al., hereinafter Guha, discloses analyzing a distributed sensor network for meteorological parameter data to assess spatial and temporal variations of a meteorological parameter. Although Guha discloses a distributed network overlaid on a geographic area; e.g., see figs. 3-4, the network points are neither equally spaced, nor cover the entirely of the geographic area.
Therefore, Jiang, taken alone or in combination, does not teach or fairly suggest: generating equally spaced network points over said geographic and/or topographic area providing a meshed network of network points having a definable mesh size and covering the whole geographic and/or topographic area., taken in combination with the other limitations of claim.
Conclusion
Claims 1 and 13 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 2022/0156636 A1 to Albrecht et al. relates to efficient flood waters analysis from spatio-temporal data fusion and statistics.
US 2022/0003893 A1 to Ding et al. relates to a forecast operation method for lowering reservoir flood limited water level considering forecast uncertainty.
US 2021/0278564 A1 to Liu et al. relates to dynamic flood risk data management.
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/E.S.V./Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863