CTNF 18/682,938 CTNF 95316 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims Objections Claim 2 is objected to because of the following informalities: Claim language “from a statistic of the elevation indicated in the elevation data on a plurality of points in a boundary area of the subject divided area” should read “from a statistic of the elevation indicated in the elevation data on a plurality of points in [[a]] the boundary area of the subject divided area” in order to provide the appropriate antecedent basis. Claim 3 is objected to because of the following informalities: Claim language “the processing circuitry estimates the elevation at each point in the interior of the subject divided area” should read “the processing circuitry estimates the elevation at each point in [[the]] interior of the subject divided area” in order to provide the appropriate antecedent basis. Appropriate correction is requested. Claim Rejections – 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below. Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process/product. The below claim is considered to be a statutory category (product). Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belongs to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity. Independent Claim 1 is copied below, with the limitations belonging to an abstract idea highlighted in bold; the remaining limitations are ‘’additional elements’’. A flood depth estimation apparatus comprising: processing circuitry: to acquire inundated area data indicating an inundated area in a subject area, inundation estimation data indicating for the subject area, a boundary where a water level changes when inundation occurs, and elevation data indicating an elevation at each point in the subject area; to divide the inundated area indicated in the acquired inundated area data, into one or more divided areas based on the boundary indicated in the inundation estimation data ; for each of the one or more divided areas obtained by the division to calculate the water level in a subject divided area , from the elevation indicated in the elevation data on a boundary area of the subject divided area ; and for the each point, to calculate a flood depth at a subject point, from the elevation indicated in the elevation data on the subject point, and the calculated water level for the divided area including the subject point . Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that when recited as such in a claim limitation covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations), certain methods of organizing human activity, and mental processes (concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion). For example, the limitations of “ to divide the inundated area indicated in the acquired inundated area data, into one or more divided areas based on the boundary indicated in the inundation estimation data” ; “ for each of the one or more divided areas obtained by the division to calculate the water level in a subject divided area , from the elevation indicated in the elevation data on a boundary area of the subject divided area” , and “ to calculate a flood depth at a subject point, from the elevation indicated in the elevation data on the subject point, and the calculated water level for the divided area including the subject point ” are treated by the Examiner as belonging to mathematical concepts grouping. Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. The additional element in Claim 1 of “processing circuitry” is recited in generality and do not recite particular machines applying or being used by the abstract idea. The “processing circuitry” is a generic computer used to facilitate the application of the judicial exception. The preamble of Claim 1 : “A flood depth estimation apparatus comprising” is a generically recited preamble. The additional element in Claim 1 of “to acquire inundated area data indicating an inundated area in a subject area, inundation estimation data indicating for the subject area, a boundary where a water level changes when inundation occurs, and elevation data indicating an elevation at each point in the subject area” is treated as an extra solution activity recited in generality (e.g., mere data gathering). Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B. Step 2B of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2A, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea. The above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis). Therefore, claim 1 is rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more. The independent claim, therefore, is not patent eligible. Similar limitations comprise the abstract ideas of independent Claims 5-8. With regards to the dependent claims, Claims 2-4 and 9-10 provide additional features/steps which are either part of an expanded abstract idea of the independent claims and/or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record. The dependent claims are, therefore, also ineligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1, 3-6, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over US20190318440A1 to Wani et al. (hereinafter Wani) in view of JP5403726 (B2) to Satoshi et al. (hereinafter Satoshi) . Regarding Claim 1: Wani discloses: “ A flood depth estimation apparatus comprising :” (para 0039 – “ Presented herein is a flood analysis system , also referred to as a flood management system, with versatile tools to prepare for flooding ”) “ processing circuitry :” (para 0044 – “ a system includes a memory comprising instructions and one or more computer processors ”): “ to acquire inundated area data indicating an inundated area in a subject area ” (Fig. 25; para 0185 – “ as new live data 2510 (i.e. inundated area data , added by examiner) is obtained, the live data 2510 may be input to the river routing model 1908 and the flood inundation model 1910 . Additionally, the weather information 1904 may also be updated for the flood monitor 1906 . The live data 2510 may be of different types, such as satellite imagery of the region or readings from the stream gauges. There are about 2,000 gauge stations inside of the US, which are located along the rivers and provide real-time water-level readings (i.e. in a subject area , added by examiner)”; see also para 0184); “ inundation estimation data indicating for the subject area, a boundary where a water level changes when inundation occurs ” (Fig. 13, inundation map 1302; para 0122 – “ The different color areas, as indicated by the different shadings, show the water depth in the different blocks . The darker lines show the waterways , and the colors in the map indicate the water depth (i.e. boundary where a water level when inundation occurs , added by examiner)”; see also paras 0126 and 0130), and “ elevation data indicating an elevation at each point in the subject area ” (para 0167 – “ The input 2202 includes one or more of the following: the inflow and outflow of water for each of the cells (e.g., the flow 1916 ); a mesh based on a Digital Elevation Model (DEM), which represents the elevation of the surface , and shapefiles for roads, coasts, buildings, and critical infrastructure; topography/bathmetry of the region ; and other parameters (e.g., roughness of the surface ).”; see also para 0168); “ to divide the inundated area indicated in the acquired inundated area data, into one or more divided areas based on the boundary indicated in the inundation estimation data ” (para 0157 – “ The inundation runoff maps 1914 include a map which is divided into grid cells (i.e. inundated area is divided into one or more areas, a.k.a. cells, added by examiner ) corresponding to the same grid cells as the input weather data. ”; para 0191 – “FIG. 26 is a flowchart of a method 2600 , according to some example embodiments, for a flood monitoring and management system. Operation 2602 is for accessing, by one or more processors of a flood analysis system , weather information for a geographical region that is divided into cells .”; para 0233; para 0242 – water level); “ for each of the one or more divided areas obtained by the division to calculate the water level in a subject divided area, from the elevation indicated in the elevation data on a boundary area of the subject divided area ” (para 0198 – “ calculating the predicted water depth in each sub-cell further comprises generating a mesh of the geographical region, the mesh being a division of the geographical region into sub-cells that cover the geographical region without overlap; and identifying water level at each sub-cell of the mesh based on an elevation and a type of surface of the sub-cel l ”). Wani does not specifically disclose: “ for the each point, to calculate a flood depth at a subject point, from the elevation indicated in the elevation data on the subject point, and the calculated water level for the divided area including the subject point ”. However, Satoshi discloses: “for the each point, to calculate a flood depth at a subject point, from the elevation indicated in the elevation data on the subject point, and the calculated water level for the divided area including the subject point” (para 0015 – “ The processor is characterized by having it perform the following steps: a fourth step of calculating the water level by adding the inundation depth at the first point to the elevation of the ground in the section including the first point ; a fifth step of calculating the water level of the sections included in the inundation area that do not include the first point by spatially interpolating the water level calculated by the second and fourth steps; a sixth step of calculating the inundation depth of the sections by subtracting the elevation of the ground in the sections from the water level of the sections calculated by the fifth step ; and a seventh step of outputting the calculated inundation depth of the sections ”). 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 flood estimation apparatus, disclosed by Wani, as taught by Satoshi, in order to calculate the flood depth at a subject point accurately and promptly to save lives. Regarding Claim 3: Wani/Satoshi combination discloses the flood depth estimation apparatus according to claim 1. Wani further discloses: “ wherein the processing circuitry estimates the elevation at each point in the interior of the subject divided area by filling the elevation indicated in the elevation data for the boundary area of the subject divided area toward the interior of the subject divided area ” (Fig. 23; para 0168 – “ The DEM defines land elevation data in the area . The shapefiles define the f ootprints for elements in the area, such as buildings, roads, and the coastline ”; para 0172 – “ FIG. 23 illustrates an example of the flood inundation model 1910 , according to some example embodiments. The flood inundation model 1910 combines an elevation of sample points in a map 2302 , a road shapefile 2304 , and a building shapefile 2306 to calculate a mesh 2314 ”; para 0174 – “ The flood inundation model 1910 keeps information for each sub-cell, such as land elevation and surface characteristics (e.g., roughness) . The size of each sub-cell may vary within the mesh 2314 , as certain areas may be more important than others, so the important areas will have smaller sub-cells than less important areas that will have bigger sub-cells. Therefore, there is more information available on the important areas than on the areas that are not as relevant ”), and “ calculates from the estimated elevation at the each point in the interior, the water level at the each point in the interior ” (para 0198 – “ calculating the predicted water depth in each sub-cell further comprises generating a mesh of the geographical region, the mesh being a division of the geographical region into sub-cells that cover the geographical region without overlap; and identifying water level at each sub-cell of the mesh based on an elevation and a type of surface of the sub-cell ”). Wani does not specifically disclose: “ the processing circuitry calculates from the elevation indicated in the elevation data on the subject point and the water level at the subject point, the flood depth at the subject point ”. However, Satoshi discloses: “ the processing circuitry calculates from the elevation indicated in the elevation data on the subject point and the water level at the subject point, the flood depth at the subject point ” (para 0015 – “ The processor is characterized by having it perform the following steps: a fourth step of calculating the water level by adding the inundation depth at the first point to the elevation of the ground in the section including the first point ; a fifth step of calculating the water level of the sections included in the inundation area that do not include the first point by spatially interpolating the water level calculated by the second and fourth steps; a sixth step of calculating the inundation depth of the sections by subtracting the elevation of the ground in the sections from the water level of the sections calculated by the fifth step ; and a seventh step of outputting the calculated inundation depth of the sections ”). 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 flood estimation apparatus, disclosed by Wani, as taught by Satoshi, in order to calculate the flood depth at a subject point accurately and promptly to save lives. Regarding Claim 4: Wani/Satoshi combination discloses the flood depth estimation apparatus according to claim 1. Wani further discloses: “ wherein the processing circuitry generates the inundated area data from an input image which is remote sensing data acquired by performing detection on the subject area ” (para 0185 – “ as new live data 2510 is obtained, the live data 2510 may be input to the river routing model 1908 and the flood inundation model 1910 . Additionally, the weather information 1904 may also be updated for the flood monitor 1906 . The live data 2510 may be of different types, such as satellite imagery (i.e. input image , added by examiner) of the region or readings from the stream gauges ”). Regarding Claim 5: Wani discloses: “ A flood depth estimation method comprising :” (para 0043 – “the method includes operations for generating a flood inundation map showing the predicted water depth at each sub-cell in the geographical region ”) “ acquiring inundated area data indicating an inundated area in a subject area, inundation estimation data indicating for the subject area, a boundary where a water level changes when inundation occurs, and elevation data indicating an elevation at each point in the subject area ” (Fig. 25; para 0185 – “ as new live data 2510 (i.e. inundated area data , added by examiner) is obtained, the live data 2510 may be input to the river routing model 1908 and the flood inundation model 1910 . Additionally, the weather information 1904 may also be updated for the flood monitor 1906 . The live data 2510 may be of different types, such as satellite imagery of the region or readings from the stream gauges. There are about 2,000 gauge stations inside of the US, which are located along the rivers and provide real-time water-level readings (i.e. in a subject area , added by examiner)”; see also para 0184); “ dividing the inundated area indicated in the inundated area data, into one or more divided areas based on the boundary indicated in the inundation estimation data ” (para 0157 – “ The inundation runoff maps 1914 include a map which is divided into grid cells (i.e. inundated area is divided into one or more areas, a.k.a. cells, added by examiner ) corresponding to the same grid cells as the input weather data. ”; para 0191 – “FIG. 26 is a flowchart of a method 2600 , according to some example embodiments, for a flood monitoring and management system. Operation 2602 is for accessing, by one or more processors of a flood analysis system , weather information for a geographical region that is divided into cells .”; para 0233; para 0242 – water level); “ for each of the one or more divided areas, calculating the water level in a subject divided area, from the elevation indicated in the elevation data on a boundary area of the subject divided area ” (para 0198 – “ calculating the predicted water depth in each sub-cell further comprises generating a mesh of the geographical region, the mesh being a division of the geographical region into sub-cells that cover the geographical region without overlap; and identifying water level at each sub-cell of the mesh based on an elevation and a type of surface of the sub-cel l ”). Wani does not specifically disclose: “ for the each point, calculating a flood depth at a subject point, from the elevation indicated in the elevation data on the subject point, and the water level calculated for the divided area including the subject point ”. However, Satoshi discloses: “ for the each point, calculating a flood depth at a subject point, from the elevation indicated in the elevation data on the subject point, and the water level calculated for the divided area including the subject point ” (para 0015 – “ The processor is characterized by having it perform the following steps: a fourth step of calculating the water level by adding the inundation depth at the first point to the elevation of the ground in the section including the first point ; a fifth step of calculating the water level of the sections included in the inundation area that do not include the first point by spatially interpolating the water level calculated by the second and fourth steps; a sixth step of calculating the inundation depth of the sections by subtracting the elevation of the ground in the sections from the water level of the sections calculated by the fifth step ; and a seventh step of outputting the calculated inundation depth of the sections ”). 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 flood estimation apparatus, disclosed by Wani, as taught by Satoshi, in order to calculate the flood depth at a subject point accurately and promptly to save lives. Regarding Claim 6: Wani discloses: “ A non-transitory computer readable medium storing a flood depth estimation program causing a computer to function as a flood depth estimation apparatus to execute :” (para 0045 – “ a non-transitory machine-readable storage medium includes instructions that, when executed by a machine, cause the machine to perform operations comprising: … generating a prediction of inflow and outflow of water between cells; calculating, for a plurality of sub-cells of each cell in the geographical region, a predicted water depth in each sub-cell based on the prediction of the inflow and outflow between cells and a hydraulic model; generating a flood inundation map showing the predicted water depth at each sub-cell in the geographical region .”) “ a data acquisition process to acquire inundated area data indicating an inundated area in a subject area” (Fig. 25; para 0185 – “ as new live data 2510 (i.e. inundated area data , added by examiner) is obtained, the live data 2510 may be input to the river routing model 1908 and the flood inundation model 1910 . Additionally, the weather information 1904 may also be updated for the flood monitor 1906 . The live data 2510 may be of different types, such as satellite imagery of the region or readings from the stream gauges. There are about 2,000 gauge stations inside of the US, which are located along the rivers and provide real-time water-level readings (i.e. in a subject area , added by examiner)”; see also para 0184); “inundation estimation data indicating for the subject area, a boundary where a water level changes when inundation occurs” (Fig. 13, inundation map 1302; para 0122 – “ The different color areas, as indicated by the different shadings, show the water depth in the different blocks . The darker lines show the waterways , and the colors in the map indicate the water depth (i.e. boundary where a water level when inundation occurs , added by examiner)”; see also paras 0126 and 0130), and “elevation data indicating an elevation at each point in the subject area ” (para 0167 – “ The input 2202 includes one or more of the following: the inflow and outflow of water for each of the cells (e.g., the flow 1916 ); a mesh based on a Digital Elevation Model (DEM), which represents the elevation of the surface , and shapefiles for roads, coasts, buildings, and critical infrastructure; topography/bathmetry of the region ; and other parameters (e.g., roughness of the surface ).”; see also para 0168); “ an inundated area dividing process to divide the inundated area indicated in the inundated area data acquired by the data acquisition process, into one or more divided areas based on the boundary indicated in the inundation estimation data ” (para 0157 – “ The inundation runoff maps 1914 include a map which is divided into grid cells (i.e. inundated area is divided into one or more areas, a.k.a. cells, added by examiner ) corresponding to the same grid cells as the input weather data. ”; para 0191 – “FIG. 26 is a flowchart of a method 2600 , according to some example embodiments, for a flood monitoring and management system. Operation 2602 is for accessing, by one or more processors of a flood analysis system , weather information for a geographical region that is divided into cells .”; para 0233; para 0242 – water level); “ for each of the one or more divided areas obtained by the division by the inundated area dividing process, a water level calculation process to calculate the water level in a subject divided area, from the elevation indicated in the elevation data on a boundary area of the subject divided area ” (para 0198 – “ calculating the predicted water depth in each sub-cell further comprises generating a mesh of the geographical region, the mesh being a division of the geographical region into sub-cells that cover the geographical region without overlap; and identifying water level at each sub-cell of the mesh based on an elevation and a type of surface of the sub-cel l ”). Wani does not specifically disclose: “ for the each point, a depth calculation process to calculate a flood depth at a subject point, from the elevation indicated in the elevation data on the subject point, and the water level calculated by the water level calculation process for the divided area including the subject point ”. However, Satoshi discloses: “for the each point, a depth calculation process to calculate a flood depth at a subject point, from the elevation indicated in the elevation data on the subject point, and the water level calculated by the water level calculation process for the divided area including the subject point” (para 0015 – “ The processor is characterized by having it perform the following steps: a fourth step of calculating the water level by adding the inundation depth at the first point to the elevation of the ground in the section including the first point ; a fifth step of calculating the water level of the sections included in the inundation area that do not include the first point by spatially interpolating the water level calculated by the second and fourth steps; a sixth step of calculating the inundation depth of the sections by subtracting the elevation of the ground in the sections from the water level of the sections calculated by the fifth step ; and a seventh step of outputting the calculated inundation depth of the sections ”). 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 flood estimation apparatus, disclosed by Wani, as taught by Satoshi, in order to calculate the flood depth at a subject point accurately and promptly to save lives. Regarding Claim 10: Wani/Satoshi combination discloses the flood depth estimation apparatus according to claim 3. Wani further discloses: “ wherein the processing circuitry generates the inundated area data from an input image which is remote sensing data acquired by performing detection on the subject area ” (para 0185 – “ as new live data 2510 is obtained, the live data 2510 may be input to the river routing model 1908 and the flood inundation model 1910 . Additionally, the weather information 1904 may also be updated for the flood monitor 1906 . The live data 2510 may be of different types, such as satellite imagery (i.e. input image , added by examiner) of the region or readings from the stream gauges ”) . 07-21-aia AIA Claim s 2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Wani in view of Satoshi and in further view of CN113834547A to Ke et al. (hereinafter Ke) . Regarding Claim 2: Wani/Satoshi combination discloses the flood depth estimation apparatus according to claim 1. Wani further discloses: “ wherein the processing circuitry calculates the water level in the overall subject divided area ” (para 0043 – “ the method includes operations for generating a prediction of inflow and outflow of water between cells, and for calculating, for a plurality of sub-cells of each cell in the geographical region, a predicted water depth in each sub-cell based on the prediction of the inflow and outflow between cells and a hydraulic model ”). Wani does not specifically disclose: “ from a statistic of the elevation indicated in the elevation data on a plurality of points in a boundary area of the subject divided area ”. However, Ke discloses: “ from a statistic of the elevation indicated in the elevation data on a plurality of points in a boundary area of the subject divided area ” (Fig.5; para 0089 – “ the elevation value of the quadratic polynomial vertex is used as the expected elevation value, and the fitting standard error RMSE is used as the error. For unreasonable cases, the median is used as the expected elevation value (i.e. statistic of the elevation , see Specification at para 0029, added by examiner), and the mean absolute error MAE is used as the error ”). 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 flood estimation apparatus, disclosed by Wani/Satoshi combination, as taught by Ke, in order to obtain the elevation data of higher accuracy using the statistic of the elevation. Regarding Claim 9: Wani/Satoshi/Ke combination discloses the flood depth estimation apparatus according to claim 2. Wani further discloses: “ wherein the processing circuitry generates the inundated area data from an input image which is remote sensing data acquired by performing detection on the subject area ” (para 0185 – “ as new live data 2510 is obtained, the live data 2510 may be input to the river routing model 1908 and the flood inundation model 1910 . Additionally, the weather information 1904 may also be updated for the flood monitor 1906 . The live data 2510 may be of different types, such as satellite imagery (i.e. input image , added by examiner) of the region or readings from the stream gauges ”) . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wani in view of US2018373993 to Petty (hereinafter Petty) . Regarding Claim 7: Wani discloses: “ A flood depth estimation apparatus comprising ” (para 0039 – “ Presented herein is a flood analysis system , also referred to as a flood management system, with versatile tools to prepare for flooding ”) “ processing circuitry ” (para 0044 – “ a system includes a memory comprising instructions and one or more computer processors ”) “ to take an input image or inundated area data indicating an inundated area in a subject area ” (para 0125 – “ The new data may include many types of weather and situation data, such as updated weather maps, updated screen gauge readings, satellite imagery , reports from people on the field, news, social media, etc. As the new data comes in, the simulation is updated, including updating the map 1402 ”; para 0185 – “ as new live data 2510 is obtained, the live data 2510 may be input to the river routing model 1908 and the flood inundation model 1910 . Additionally, the weather information 1904 may also be updated for the flood monitor 1906 . The live data 2510 may be of different types, such as satellite imagery of the region or readings from the stream gauges ”; para 0186 – “ Once the live data 2510 is received, the flood analysis system 1902 updates the flood inundation maps 1912 in real time to have a current vision of the flood predictions. Thus, the flood analysis system 1902 is a live system that can quickly adapt to changing conditions and updated information ”). Wani does not explicitly disclose: “ which is remote sensing data acquired by performing detection on the subject area, elevation data indicating an elevation at each point in the subject area, and inundation estimation data indicating for the subject area, a boundary where a water level changes when inundation occurs, as input, and to estimate a flood depth at the each point, using a learned model that outputs the flood depth at the each point ”. However, Petty discloses: “ which is remote sensing data acquired by performing detection on the subject area, elevation data indicating an elevation at each point in the subject area” (para 0044 – “ Other input information that can be provided to the flood forecasting system at step 604 can include remote sensing data. Examples of remote sensing data that could be used include, but are not limited to, elevation data, image data ( for identifying inundation, for example), as well as other kinds of data ”) , and “inundation estimation data indicating for the subject area, a boundary where a water level changes when inundation occurs, as input” (para 0043 – “ Using the measured streamflow information from step 602 , along with possibly other input information, the system updates flood forecast information in step 604 ”; Fig. 6, step 604 – “ Update flood forecast information (interpreted as inundation estimation data , added by examiner) using the measured streamflow data ”) , and “to estimate a flood depth at the each point, using a learned model that outputs the flood depth at the each point ” (para 0018 – “ the embodiments use artificial intelligence (machine learning) to improve both the accuracy and availability (in time) of synthetic streamflow data ”; para 0017 – “ Examples of streamflow data include, but are not limited to: water level surface elevation (or gage height or stream stage), water depth ”). 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 flood estimation apparatus, disclosed by Wani, as taught by Petty, in order to obtain more accurate inundation data including using the machine learning model . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Petty in view of Wani . Regarding Claim 8: Petty discloses: “ A training apparatus comprising ” (para 0031 – “ the correlations determined in the first phase are used along with additional historical streamflow data to train a predictive model (i.e., a machine learning model) to accurately predict streamflow information from one source based on streamflow data from other sources in the streamgage network ”) “ which is remote sensing data acquired by performing detection on the subject area, elevation data indicating an elevation at each point in the subject area” (para 0044 – “ Other input information that can be provided to the flood forecasting system at step 604 can include remote sensing data. Examples of remote sensing data that could be used include, but are not limited to, elevation data, image data ( for identifying inundation, for example), as well as other kinds of data ”) , and “inundation estimation data indicating for the subject area, a boundary where a water level changes when inundation occurs, as learning data” (para 0043 – “ Using the measured streamflow information from step 602 , along with possibly other input information, the system updates flood forecast information in step 604 ”; Fig. 6, step 604 – “ Update flood forecast information (interpreted as inundation estimation data , added by examiner) using the measured streamflow data ”; para 0031 – “ during a second phase, the correlations determined in the first phase are used along with additional historical streamflow data to train a predictive model (i.e., a machine learning model) to accurately predict streamflow information from one source based on streamflow data from other sources in the streamgage network. In other words, the correlation data generated during the first phase becomes part of the training data (i.e. learning data , added by examiner) that is used to train the predictive model during the second phase ”) , and “ to generate a learned model that outputs a flood depth at the each point ” (para 0018 – “ the embodiments use artificial intelligence (machine learning) to improve both the accuracy and availability (in time) of synthetic streamflow data ”; para 0017 – “Examples of streamflow data include, but are not limited to: water level surface elevation (or gage height or stream stage), water depth ”). Petty does not explicitly disclose: “ processing circuitry to use an input image or inundated area data indicating an inundated area in a subject area ”. However, Wani discloses: “ processing circuitry” (para 0044 – “ a system includes a memory comprising instructions and one or more computer processors ”) “ to use an input image or inundated area data indicating an inundated area in a subject area ” (para 0125 – “ The new data may include many types of weather and situation data, such as updated weather maps, updated screen gauge readings, satellite imagery , reports from people on the field, news, social media, etc. As the new data comes in, the simulation is updated, including updating the map 1402 ”; para 0185 – “ as new live data 2510 is obtained, the live data 2510 may be input to the river routing model 1908 and the flood inundation model 1910 . Additionally, the weather information 1904 may also be updated for the flood monitor 1906 . The live data 2510 may be of different types, such as satellite imagery of the region or readings from the stream gauges ”; para 0186 – “ Once the live data 2510 is received, the flood analysis system 1902 updates the flood inundation maps 1912 in real time to have a current vision of the flood predictions. Thus, the flood analysis system 1902 is a live system that can quickly adapt to changing conditions and updated information ”). 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 flood estimation apparatus, disclosed by Petty, as taught by Wani, in order to obtain more accurate inundation data including using the real-time image data to obtain the accurate and timely flood information. Conclusion 07-96 The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US20210018317 to Noma et al. (hereinafter Noma) discloses road surface water depth calculation device. US20200149888A1 to Long et al. (hereinafter Long) discloses method and device for monitoring water volume change, computer device and storage medium. US20200103530A1 to Cheng et al. (hereinafter Cheng) discloses method for extracting elevation control point with assistance of satellite laser altimetry data. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lyudmila Zaykova-Feldman whose telephone number is (469)295-9269. The examiner can normally be reached 8:30am - 5:30pm, Monday through Friday. 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, Arleen M. Vazquez can be reached on 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /LYUDMILA ZAYKOVA-FELDMAN/Examiner, Art Unit 2857 /LINA CORDERO/Primary Examiner, Art Unit 2857 Application/Control Number: 18/682,938 Page 2 Art Unit: 2857 Application/Control Number: 18/682,938 Page 3 Art Unit: 2857
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