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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a storage unit in claims 1, 8, 11 and 18; a simulation setting unit in claims 1-5; a flood information generation unit in claims 1 and 6-8; and a simulation output unit in claims 1 and 10.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
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.
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim recites a program and the body of the claim recites computer program steps related to implementing steps of a method, which are nothing more than just programmed instruction to be performed by a system. Therefore, the steps/elements recited in claim 21 are non-statutory because a computer program per se, i.e., the descriptions or expressions of the program, are not physical “things” and thus do not fall into either a process, machine, manufacture and/or composition of matter category and makes it ineligible subject matter under 35 USC § 101. In contrast, a claimed non-transitory computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer, which permits the computer program’s functionality to be realized, and is thus statutory.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6-7, 10-13, 16-17 and 20-21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wani et al. (Pub. No.: US 2019/0316309 A1) hereinafter Wani.
Regarding claim 1, Wani discloses an apparatus for urban flood simulation (Paragraph 37 teaches that example methods, systems, and computer programs are directed to a flood monitoring and management system. Additionally, FIG. 2 and paragraph 56 teach that FIG. 2 is a block diagram illustrating a flood analysis system 200, according to some example embodiments and FIG. 31 and paragraph 247 teach that FIG. 31 is a block diagram illustrating an example of a machine 3100 upon which one or more example process embodiments described herein may be implemented or controlled. In alternative embodiments, the machine 3100 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 3100 may operate in the capacity of a server machine, a client machine, or both in server-client network environments.), the apparatus comprising:
a storage unit configured to store a digital elevation model of a city, city design information and precipitation information (Paragraph 249 teaches that the machine 3100 may additionally include a mass storage device (e.g., drive unit) 3116, a signal generation device 3118 (e.g., a speaker), a network interface device 3120, and one or more sensors 3121, such as a Global Positioning System (GPS) sensor, compass, accelerometer, biometric sensor, or other sensor. Paragraph 167 teaches that 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). Lastly, paragraph 63 teaches that hydrology considers quantifying surface water flow and solute transport. Some of the methods for measuring flow, once water has reached a river, include stream gauge and tracer techniques. Precipitation is one of the parameters used in hydrology. Precipitation can be measured in various ways, such as by a disdrometer for precipitation characteristics at a fine time scale; radar for cloud properties, rain rate estimation, and hail and snow detection; rain gauge for routine accurate measurements of rain and snowfall; and satellite for rainy area identification, rain rate estimation, land cover and use, and soil moisture.),
and at least one computer program configured to perform a method for urban flood simulation (Paragraph 250 teaches that the mass storage device 3116 may include a machine-readable medium 3122 on which is stored one or more sets of data structures or instructions 3124 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein.);
a simulation setting unit configured to generate simulation zone information and water flow information using the digital elevation model of the city and the city design information (Paragraph 110 teaches that a flood input interface 1106, including map 1102, provides different options for setting up the one or more flood simulations to be used under the selected weather scenario. Flooding events, rainfall fields, and climate scenarios may utilize data (e.g., a rainfall map) from past flooding events. Paragraph 167 teaches that 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) and paragraph 59 teaches that the geographic data 208 includes information about the geography of an area, such as elevation, type of groundcover (e.g., pavement, grass, or rock), waterways, water-flow obstacles, etc.);
a flood information generation unit configured to generate flood-related information using the simulation zone information, the water flow information, and the precipitation information (FIG. 2 and paragraph 56 teach that FIG. 2 is a block diagram illustrating a flood analysis system 200, according to some example embodiments. The flood analysis system 200 includes a flood estimation module 202 that generates flood estimation maps 216. The flood analysis system 200 may be utilized when a storm is coming to predict possible flooding, and may also be utilized for preparing for future events by analyzing flood risk and evaluating the effectiveness of possible mitigation measures. Additionally, paragraph 57 teaches that in one example embodiment, the flood estimation module 202 utilizes several inputs, including weather data 204, historical data 206, and geographic data 208 to generate the flood estimation maps 216. The weather data 204 includes weather prediction data, such as the weather data generated by the National Weather Service, but any other source of weather information may also be utilized. The weather data 204 may include rainfall estimates by area, satellite pictures, weather warnings, etc.)
and a simulation output unit configured to visualize and display the flood-related information (FIG. 10 and paragraph 104 teach that FIG. 10 is a diagram illustrating a user interface 1000 for a flood-risk map 1002 simulation, according to some example embodiments. The user interface 1000 is for the simulations module that calculates the flood-risk map. The flood-risk map 1002 provides a color-coded risk indicator for a scenario identified for the simulation.).
Regarding claim 2, Wani discloses everything claimed as applied above (see claim 1), in addition, Wani discloses wherein the simulation setting unit generates connection relationship information of elements constituting at least one drainage facility and location information of each drainage facility using the digital elevation model of the city and the city design information (Paragraph 58 teaches that the historical data 206 includes historical weather-related data as well as flooding data. The historical data 206 may then identify levels of rainfall at different times for a given location (e.g., a city, an area code, a region, a county, etc.), as well as flood levels and the places where flooding occurred and paragraph 162 teaches that the river routing model 1908 generates the flow 1916 for each grid cell, which includes the inflow and outflow of the grid cells, where the inflow and outflow refer to the amount of water that comes in or comes out of the grid cell, respectively.).
Regarding claim 3, Wani discloses everything claimed as applied above (see claim 2), in addition, Wani discloses wherein the simulation setting unit calculates a slope of a ground surface using an elevation value of the city extracted from the digital elevation model and generates information on a direction of water flow using the slope of the ground surface (Paragraph 105 teaches that the simulation may take into account different inputs and use different models, such as a model that predicts risk based on climate change. The flood-risk map 1002 predicts what may happen in the future when storms happen in the area by showing the inundation areas, up to block-by-block-resolution inundation maps, which include water levels, water depth at each location, and water velocity (including direction and actual flow speed).).
Regarding claim 6, Wani discloses everything claimed as applied above (see claim 1), in addition, Wani discloses wherein the flood information generation unit generates information related to an expected flood pattern according to at least one precipitation pattern using the simulation zone information, the water flow information, and the precipitation information, and generates the flood-related information by further using the information related to the expected flood pattern (Paragraph 108 teaches that in some cases, the manager may also define one or more weather patterns and then run the simulations to create the flood-risk map.).
Regarding claim 7, Wani discloses everything claimed as applied above (see claim 6), in addition, Wani discloses wherein the simulation zone information further includes content related to watershed characteristics of the city (Paragraph 61 teaches that hydrology is the scientific study of the movement, distribution, and quality of water, including the water cycle, water resources, and environmental watershed sustainability. Using various analytical methods and scientific techniques, hydrology analyzes data to help solve water-related problems such as environmental preservation, natural disasters, and water management.),
the content related to the watershed characteristics of the city includes at least one of an infiltration rate of water into soil, outflow of water from the ground surface per unit time, and a time taken for water to reach an outflow point (Paragraph 62 teaches that a phenomenon related to hydrology is infiltration. Infiltration is the process by which water enters the soil. Some of the water is absorbed, and the rest percolates down to the water table. The infiltration capacity, the maximum rate at which the soil can absorb water, depends on several factors. The layer that is already saturated provides a resistance that is proportional to its thickness, while that plus the depth of water above the soil provides the driving force (hydraulic head). Dry soil can allow rapid infiltration by capillary action, which diminishes as the soil becomes wet. Ground compaction reduces the porosity and the pore sizes. Further, surface cover increases capacity by retarding runoff, reducing compaction and other processes.),
and the flood information generation unit generates the information related to the expected flood pattern by further using the content related to the watershed characteristics (Paragraph 60 teaches that the inputs are used by hydrological models 210 and hydraulic models 212 (also referred to herein as hydrodynamic models), and data analysis 214 is performed on the different results from the models to generate the flood estimation maps 216.).
Regarding claim 10, Wani discloses everything claimed as applied above (see claim 1), in addition, Wani discloses wherein when an amount of water accumulated at least one point included in the simulation zone exceeds a preset threshold, the simulation output unit sets the point as a flood risk zone and displays a degree of flood risk in stages according to the amount of water accumulated (Paragraph 42 teaches that additionally, city planners may utilize the flood analysis system to generate flood risk maps that identify which areas in the map are more likely to be flooded in the future. The city planners may also create plans to reduce damages, such as by elevating structures or buying land to change its use, and see the cost-benefit analysis of those plans and paragraph 188 teaches that in some example embodiments, when the live data 2510 is received, a check is made to determine if the live data 2510 (e.g., water depth in some location) is different from the estimated values beyond a predetermined threshold. If the data difference is below the threshold, then the model may not be updated. However, if the data difference is greater than the threshold, a new simulation is performed to update the flood inundation maps 1912. Lastly, paragraph 164 teaches that it is to be noted that the flood monitor 1906 and the river routing model 1908 are executed frequently in the background. The flood inundation model 1910 is executed once a signal is received from the river routing model 1908 indicating that the time series at one or more places has exceeded the flood stage. In other words, the flood inundation model 1910 is executed when there is a flood condition.).
Regarding claim 11, the method steps correspond to and are rejected similarly to the apparatus steps of claim 1 (see claim 1 above). In addition, Wani discloses a method for urban flood simulation (FIGS. 26, 28, and 30 and paragraph 37 teach example methods, systems, and computer programs are directed to a flood monitoring and management system.).
Regarding claim 12, the method steps correspond to and are rejected similarly to the apparatus steps of claim 2 (see claim 2 above).
Regarding claim 13, the method steps correspond to and are rejected similarly to the apparatus steps of claim 3 (see claim 3 above).
Regarding claim 16, the method steps correspond to and are rejected similarly to the apparatus steps of claim 6 (see claim 6 above).
Regarding claim 17, the method steps correspond to and are rejected similarly to the apparatus steps of claim 7 (see claim 7 above).
Regarding claim 20, the method steps correspond to and are rejected similarly to the apparatus steps of claim 10 (see claim 10 above).
Regarding claim 21, Wani discloses everything claimed as applied above (see claim 11), in addition, Wani discloses a computer program written to perform each step of the method for urban flood simulation according to claim 11 on a computer and recorded on a computer-readable recording medium (Paragraph 37 teaches example methods, systems, and computer programs are directed to a flood monitoring and management system and paragraph 248 teaches that examples, as described herein, may include, or may operate by, logic, a number of components, or mechanisms. Circuitry is a collection of circuits implemented in tangible entities that include hardware (e.g., simple circuits, gates, logic, etc.). Circuitry membership may be flexible over time and underlying hardware variability. Circuitries include members that may, alone or in combination, perform specified operations when operating. In an example, hardware of the circuitry may be immutably designed to carry out a specific operation (e.g., hardwired). In an example, the hardware of the circuitry may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a computer-readable medium physically modified (e.g., magnetically, electrically, by moveable placement of invariant massed particles, etc.) to encode instructions of the specific operation.).
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wani in view of Jamieson et al. (Pub. No.: US 2025/0156611 A1) hereinafter Jamieson.
Regarding claim 8, discloses everything claimed as applied above (see claim 1), however, Wani fails to disclose wherein the storage unit further stores an artificial neural network model that generates water outflow quantity and water discharge quantity information of at least one drainage facility using the simulation zone information, the water flow information, and the precipitation information as input values.
Jamieson discloses wherein the storage unit further stores an artificial neural network model that generates water outflow quantity and water discharge quantity information of at least one drainage facility using the simulation zone information, the water flow information, and the precipitation information as input values (Paragraph 24 teaches that embodiments of the invention utilize ML to build a surrogate model and obtain deluge flood maps without having solve equations. The ML model is trained on simulations (surfaces and associated flood maps) from public domain sources. Subsequent to training the ML model, a surface is passed to the ML algorithm, and the ML outputs flood maps that are similar to prior art traditional deluge flood maps. Embodiments of the invention further provide for interactive drainage where users can place/move ponds and swales on a surface and paragraph 35 teaches that embodiments of the invention (via PBDL 110) leverage the inputs and outputs of a full simulation to train a Convolutional Neural Network to approximate the results of the simulation but provide results much faster than if running the full simulation. This would allow users to quickly iterate through designs and get an idea of the impact of their changes. In addition, embodiments of the invention are able to provide dynamic results showing the evolution of water flow over time (15 timesteps to be more precise).). Since Wani teaches an apparatus that can store information related to floods that impact simulation zones, water flow and precipitation of cities and Jamieson teaches an apparatus that can store neural network training information related to flooding and water flow and simulate predicted water information related to buildings and/or overflows and floods, it would have been obvious to a person having ordinary skill in the art to have combined the functions together so that any of the water and flood information being stored previously, could also then be stored for potentially training data for an artificial neural network.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wani to incorporate the functions of Jamieson so that any of the stored water/flood information could be stored and used to help train a artificial neural network, which would help improve the overall speed of generating a simulation of water flowing through a city model and improve visual accuracy by using a properly trained model to generate the flow of the water.
Furthermore, Wani in view of Jamieson disclose and the flood information generation unit inputs the simulation zone information, the water flow information, and the precipitation information to the artificial neural network model (FIG. 2A and paragraph 36 of Jamieson teach that FIG. 2A illustrates a convolutional neural network (CNN) as a predictive model utilized in accordance with one or more embodiments of the invention. As illustrated, ground models 202 were used as input (i.e., into input layers 204A). Such ground models may consist of LIDAR data from ground topography or other publicly available ground model data (e.g., from government sources). In addition, suitable parameters were specified. Additionally, paragraph 38 of Jamieson teaches that each model 204 in the sequence takes the output of the previous model as its input. This allows the models 204 to learn and predict the evolution of stormwater overland flow over time. As used herein, stormwater overland flow refers to the rain landing on a surface and following the land's topography to find low spots and form flooding hotspots and paragraph 64 of Jamieson teaches that at step 1206, a new input that consists of new ground surface data is obtained in a first format.),
and obtains the water outflow quantity and the water discharge quantity of the at least one drainage facility from the artificial neural network model to generate the flood-related information (Paragraph 67 of Jamieson teaches that at step 1214, the grid is processed in/by the CNN to generate the CNN output. The output generation may include the display of the stormwater overland flow map.).
Regarding claim 18, the method steps correspond to and are rejected similarly to the apparatus steps of claim 8 (see claim 8 above).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wani in view of Jamieson as applied to claims 8 and 18 above, and further in view of Zhao et al. (Pub. No.: US 2020/0226224 A1), hereinafter Zhao.
Regarding claim 9, Wani in view of Jamieson disclose everything claimed as applied above (see claim 8), however, Wani in view of Jamison fail to disclose wherein the artificial neural network model includes a Navier-Stokes equation expressed by the following Mathematical Formula as a portion of a loss function: ρ(δu/δt)+(u ⃗ ×
∇
) u ⃗)= -
∇
p+ μ
∇
2 u ⃗ + f .
Zhao discloses wherein the artificial neural network model includes a Navier-Stokes equation expressed by the following Mathematical Formula as a portion of a loss function: ρ(δu/δt)+(u ⃗ ×
∇
) u ⃗)= -
∇
p+ μ
∇
2 u ⃗ + f (FIG.2 and paragraph 24 of Zhao teach that FIG. 2 shows a first velocity and a second velocity according to an embodiment of the present invention. The second simulation section 130 can calculate the second velocity using the Navier Stokes equations. In an embodiment, the second simulation section 130 can calculate a second velocity V.sub.t at a current time step t by considering a first force term 201 and a second force term 202 based on a second velocity V.sub.t−1 at a previous time step t−1. Additionally, paragraph 26 of Zhao teaches that the first simulation section 120 can calculate a first velocity by using one or more portions of the Navier Stokes equations. In an embodiment, the first simulation section 120 can calculate a first velocity u.sub.t at a current time step t by considering only the second force term 202 based on a second velocity V.sub.t−1 at a previous time step t−1. FIG. 2 indicates that the first velocity u.sub.t is calculated based on only some of the forces (e.g., external force) acting on the fluid in the Navier Stokes equations.). Since Wani in view of Jamieson teach a neural network for storing, training and simulating various water/flood related information and Zhao teaches training and simulating water and fluids using neural networks and incorporates a very similar Navier-Stokes equation to its training process, it would have been obvious to a person having ordinary skill in the art to combine the functions together so that a Navier-Stokes equation could also be incorporated in any fluid/water simulation training method.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wani in view of Jamieson to incorporate the functions of Zhao so that additional training equations like a Navier-Stokes equation could be incorporated into the neural network training process which would then provide more accurate movements of water and help in potentially providing possible design improvements to a user designing a city or facility’s pipelines by ensuring that the movement of water is simulated as accurately as possible.
Regarding claim 19, the method steps correspond to and are rejected similarly to the apparatus steps of claim 9 (see claim 9 above).
Allowable Subject Matter
Claims 4, 5, 14 and 15 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 reason for the indication of allowable subject matter:
Claim 4 would be allowable for disclosing wherein the simulation setting unit further extracts an angle value of a pipe installed in the at least one drainage facility from the city design information, and calculates a degree (Fi) of water flowing to an adjacent point using the following Mathematical Formula: Fij = (Hi - Hj)/dij x P x cos(θ) .
The most relevant arts searched do not teach the dependent claim 4 cited limitations of “The apparatus according to claim 3, wherein the simulation setting unit further extracts an angle value of a pipe installed in the at least one drainage facility from the city design information, and calculates a degree (Fi) of water flowing to an adjacent point using the following Mathematical Formula: Fij = (Hi - Hj)/dij x P x cos(θ) -Hi: elevation at point i -Hj: elevation at point j -dij: distance between two points -P: drainage characteristic coefficient -θ: pipe angle”.
Claim 5 would be allowable for disclosing wherein the simulation setting unit calculates a discharge capacity (Dout) of water in the at least one drainage facility using the following Mathematical Formula using information on the pipe installed in the at least one drainage facility from the city design information: Dout = Dm x (1 - a(Rp/Wp)).
The most relevant arts searched do not teach the dependent claim 5 cited limitations of “The apparatus according to claim 2, wherein the simulation setting unit calculates a discharge capacity (Dout) of water in the at least one drainage facility using the following Mathematical Formula using information on the pipe installed in the at least one drainage facility from the city design information: Dout = Dm x (1 - a(Rp/Wp)) -Dm: basic discharge capacity of drainage facility -a: friction coefficient inside pipe -Rp: number of turns of pipe -Wp: width of pipe”.
Regarding claim 14, the method steps correspond to and contain similarly allowable subject matter and reasoning to the apparatus steps of claim 4 (see claim 4 above).
Regarding claim 15, the method steps correspond to and contain similarly allowable subject matter and reasoning to the apparatus steps of claim 5 (see claim 5 above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Goto et al. (Pub. No.: US 2026/0051163 A1) discloses estimation model generating apparatuses for estimating information related to the flow rate of rivers and other water sources.
Pescarmona et al. (U.S. Patent: #11,348,014 B2) discloses methods for implementing and training artificial intelligence-based watershed hydrology analysis and management systems for evaluating risks in different drainage systems and stations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to George Renze whose telephone number is (703)756-5811. The examiner can normally be reached Monday-Friday 9:00am - 6:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao Wu can be reached at (571) 272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.R./Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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