DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s response to the Non-final Office Action dated 11/05/2025, filed with the office on 02/04/2026, has been entered and made of record.
Response to Amendment
In light of Applicant’s amendment of the claims, the 35 U.S.C. 112(b) rejections of record are withdrawn.
In light of Applicant’s amendment of claim 17, the claim objection of record with respect to claim 17 is withdrawn.
Status of Claims
Claims 1-7 and 10-17 are pending. Claims 1-7, 10-12 and 15-17 are amended. Claims 8 and 9 are cancelled.
Response to Arguments
Applicant's arguments filed on February 04, 2026 with respect to rejection of claims under 35 U.S.C. 101 has been fully considered; but they are not found persuasive. Specifically, in page 10 of its reply, Applicant argues in third paragraph that AI related claims should not be analyzed with a “high level of generality” and improvements to continual learning and model efficiency constitute technological improvements, not abstract idea. Examiner respectfully disagrees. Although the claim language recites a trained model outputting an output, the claim language lacks the details regarding the structure and function of the trained model. Simply taking an input and outputting an output using a model is generic and considered insignificant extra solution activity. Additionally, the claims do not recite any automatic operations that cannot be practically performed in a human mind in order to integrate the abstract idea into a practical application. Therefore, the arguments are not found persuasive. Accordingly, the rejection of claims under 35 U.S.C. 101 is maintained.
Applicant’s amendment of independent Claims 1, 15 and 16, which has altered the scope of the claims of the instant application, has necessitated the new ground(s) of rejection presented in this office action with respect to claims of the instant application. Accordingly, in response to Applicant’s arguments that are merely directed to the amended portion of the claims, new analyses have been presented below, which make Applicant’s arguments moot.
Consequently, THIS ACTION IS MADE FINAL.
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.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 15, and 16 respectively recite an apparatus, a system and a method for image-based damage assessment. With respect to analysis of independent claim 1, 15, and 16:
Step 1:
With regard to Step 1, the instant claims are directed to an apparatus, a system and a method and therefore, the claim is directed to one of the statutory categories of invention.
Step 2A, Prong One:
With regard to 2A, Prong One, the limitations of “acquire an image including a plurality of buildings”, “extract a first plurality of disaster buildings”, “calculate a number of the extracted plurality of first disaster buildings”, “provide at least a part of first disaster information”, “cut out an image of a region of the plurality of buildings from the image”, “discriminate whether or not each of the plurality of buildings of the cut out image is one of the extracted first plurality of disaster buildings”, “inputting the cut out image of the region of the plurality of buildings to a first trained model”, “extract a second plurality of disaster buildings”, “calculate a number of the extracted second plurality of disaster buildings”, “provide at least a part of second disaster information…to a second terminal associated with the second disaster cause”, and “choose one of the first terminal and the second terminal” as drafted, recite an abstract idea, such as a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind of a person, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). That is, an analyst reviewing the acquired image may identify a cause of the first disaster, count the number of affected first buildings in the acquired image, relay the information to a first terminal, cut out a region with buildings from the acquired image, determine whether each of the buildings are among the affected first buildings, input the cut out image to a model to get an output, identify a second cause of a second disaster in the acquired image, count the number of affected second buildings, relay the information to a second terminal and choose the first terminal or the second terminal to provide the information related to the number of affected first or second buildings associated with the first or second disaster. This is the concept that falls under the grouping of abstract ideas mental processes, i.e., a concept performed in the human mind, evaluation, judgement, and/or opinion of an analyst.
Step 2A, Prong Two:
The 2019 PEG defines the phrase “evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception”. Therefore, additional elements, or a combination of additional elements in the claim, are required to apply, rely on, or use the judicial exception. In the instant case, the additional elements/limitations in the claims, i.e., at least one processor and a trained model merely regarded as adding insignificant extra-solution activities to the judicial exception, and do not apply, rely on, or use the judicial exception as an indication of integration of the judicial exception into a practical application. Accordingly, the above-mentioned additional elements/limitations do not integrate the abstract idea into a practical application; and therefore, the claim recites an abstract idea.
Step 2B:
Because the claims fail under Step 2A, the claims are further evaluated under Step 2B. The claims herein do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as discussed above with respect to integration of the abstract idea into practical application, the additional elements/limitations to perform the steps, amount to no more than insignificant extra-solution activity. Mere command instructions to apply an exception using generic components cannot provide an inventive concept. Therefore, claims 1, 15 and 16 are not patent eligible.
Further, with regard to dependent claims 2-7, 10-14 and 17 viewed individually, these additional steps, under their broadest reasonable interpretation, cover performance of the limitations as an abstract idea, and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
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.
Claims 1-7, 10-14, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Richardson et al. (US 2021/0383481 A1) in view of translated Japanese patent document Koichi et al. (JP 6795901 B2) and in further view of Rodolico et al. (US 2018/0047265 A1).
Regarding claim 1, Richardson teaches, A disaster information processing apparatus comprising: (Richardson, ¶0040: “a plurality of devices 306a-306n equipped with systems for capturing aerial imagery”) at least one processor; and at least one memory that stores a command to be executed by the at least one processor, (Richardson, ¶0040: “having at least one processor and memory for executing the computer instructions and methods described above”) wherein the at least one processor is configured to: acquire an image (Richardson, ¶0004: “process aerial images to extract data about structures present in the aerial images”) including a plurality of buildings; (Richardson, ¶0021: “structure or property feature, including but not limited to, roofs, walls, buildings, awnings, house”) extract a first plurality of disaster buildings that have suffered from a disaster (Richardson, ¶0033: “open a map view of all affected properties”) due to a first disaster cause (Richardson, ¶0042: “detect, extract, and categorize structure data arising from a wide variety of events (both weather-related and non-weather-related), such as wildfires, lightning, arson, hurricanes, hailstorms, tornadoes”) from the acquired image; (Richardson, ¶0022: “detect, extract, and categorize structure data from aerial imagery… for a given region of interest”) calculate a number of the extracted first plurality of disaster buildings; provide at least a part of first disaster information, (Richardson, ¶0008: “a table including a list of properties within the ROI, and attributes associated with each property”; also see Fig. 5) which is related to the extracted first plurality of disaster buildings and includes the calculated number of the extracted first plurality of disaster buildings, (Richardson, ¶0032: “process the post-catastrophe aerial imagery associated with the property, using the damage detection subsystem 18d (see FIG. 1), to precisely determine the extent of the damage to the specific property”) (provide information) ; cut out an image of a region of the plurality of buildings from the image; (Richardson, ¶0025: “The region can be of interest to the user because of one or more structures present in the region. The geospatial ROI can be represented as a polygon bounded by latitude and longitude coordinates”) discriminate whether or not each of the plurality of buildings of the cut out image is one of the extracted first plurality of disaster buildings (Richardson, ¶0028: “machine learning subsystem 18a can process aerial images (e.g., retrieved from aerial imagery database 12) using machine learning algorithms to automatically determine and extract attributes”) by inputting the cut out image of the region of the plurality of buildings to a first trained model, wherein the first trained model outputs, in a case in which the image of the plurality of buildings is given as input, (Richardson, ¶0028: “using machine learning algorithms to automatically determine and extract attributes (e.g., roof type, area, slope, material, eave height, etc.) of one or more structures within the ROI”) whether or not a disaster cause of each of the plurality of buildings of the input image is the first disaster cause; (Richardson, ¶0042: “systems/methods of the present disclosure could be utilized to detect, extract, and categorize structure data arising from… wildfires, lightning, arson, hurricanes, hailstorms, tornadoes, etc”). However, Richardson does not explicitly teach, a first terminal associated with the first disaster cause and extract a second plurality of disaster buildings that have suffered from a disaster due to a second disaster cause different from the first disaster cause from the acquired image; calculate a number of the extracted second plurality of disaster buildings; provide at least a part of second disaster information, which is related to the extracted second plurality of disaster buildings and includes the calculated number of the extracted second plurality of disaster buildings, to a second terminal associated with the second disaster cause, the second terminal being different from the first terminal; and according to disaster cause, choose one of the first terminal and the second terminal to be provided with the at least the part of the first disaster information or the at least the part of the second disaster information.
In an analogous field of endeavor, Kunio teaches, extract a second plurality of disaster buildings that have suffered from a disaster due to a second disaster cause different from the first disaster cause (Koichi, page 5, ¶01: “a fire in a building is caused by the collapse of the building due to an earthquake. Here, the attribute of the building is a fireproof classification of the building”) from the acquired image; (Koichi, page 4, ¶02: “height and area of the building can be calculated from, for example, an aerial photograph”) calculate a number of the extracted second plurality of disaster buildings; (Koichi, page 2, ¶08: “A collapse number calculation means for calculating the number of collapsed buildings”) provide at least a part of second disaster information, which is related to the extracted second plurality of disaster buildings and includes the calculated number of the extracted second plurality of disaster buildings, (Koichi, page 2, ¶08: “A collapse number calculation means for calculating the number of collapsed buildings, and a collapse risk ranking calculation means for calculating the risk rank for building collapse in the evaluation area in the evaluation target area based on the number of building collapses per unit area”) to a second terminal associated with the second disaster cause, (Koichi, page 8, ¶03: “The communication unit 28 is configured by an appropriate interface, and is used by the CPU 32 to exchange data with an external server or the like via a wireless or wired communication line”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Richardson using the teachings of Koichi to introduce counting collapsed buildings. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of computing and reporting the number of collapsed buildings after an earthquake. Therefore, it would have been obvious to combine the analogous arts Richardson and Koichi to obtain the above-described limitations in claim 1. However, the combination of Richardson and Kunio does not explicitly teach, (provide information) to a first terminal associated with the first disaster cause and the second terminal being different from the first terminal; and according to disaster cause, choose one of the first terminal and the second terminal to be provided with the at least the part of the first disaster information or the at least the part of the second disaster information.
In an analogous field of endeavor, Rodolico teaches, (provide information) to a first terminal associated with the first disaster cause (Rodolico, ¶0111: “authority that is to be contacted (e.g., e-mail the local police, e-mail the local fire department”) and the second terminal being different from the first terminal; (Rodolico, ¶0110: “the earthquake authority may be contacted”) and according to disaster cause, choose one of the first terminal and the second terminal (Rodolico, ¶0110: “determining whether to report the seismic event and/or determining which authority to contact may be based on any of properties of the seismic event determined from the analysis performed”) to be provided with the at least the part of the first disaster information or the at least the part of the second disaster information. (Rodolico, ¶0111: “message may include the one or more notifications received at step 701. A phone call may be initiated… provides a description of the seismic event (e.g., strength, length of time, number of premises effected”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Richardson in view of Kunio using the teachings of Rodolico to introduce communicating with different terminals. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of reporting an information to a terminal associated with the appropriate authority. Therefore, it would have been obvious to combine the analogous arts Richardson, Kunio and Rodolico to obtain the invention in claim 1.
Regarding claim 2, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 1, wherein the at least one processor is configured to: calculate the number of the extracted first plurality of disaster buildings for each area; (Richardson, ¶0022: “processor 20 executes system code 16 which can detect, extract, and categorize structure data from aerial imagery and determine the likelihood of potential property damage due to a weather event for a given region of interest”) and provide at least a part of the first disaster information for each area, which includes the number of the extracted first plurality of disaster buildings calculated for each area to the first terminal. (Richardson, ¶0029: “generate a spreadsheet, table, database, or the like (see, e.g., FIG. 5) that includes a list of properties and weather information, property machine learning attributes (e.g., roof type, area, slope, material, eave height, etc.), and a likelihood of property damage associated with each property or structure within a given region of interest”).
Regarding claim 3, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 2, wherein the at least one processor is configured to: acquire information on the first terminal for each area, (Richardson, ¶0025: “after a weather event has occurred and potential property damage has been sustained. In step 102, the system 10 collects (e.g., receives, downloads, etc.) data on properties based on a geospatial region of interest”) which is associated with the first disaster cause; and provide at least a part of the first disaster information for each area to the first terminal associated with each area. (Rodolico, ¶0111: “message may include the one or more notifications… provides a description of the seismic event (e.g., strength, length of time, number of premises effected, identification of the geographic area”).
The proposed combination as well as the motivation for combining Richardson, Kunio and Rodolico references presented in the rejection of claim 1, apply to claim 3 and are incorporated herein by reference. Thus, the apparatus recited in claim 3 is met by Richardson, Kunio and Rodolico.
Regarding claim 4, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 2, wherein the at least one processor is configured to: display the area on a display to be selectable by a user; (Richardson, ¶0025: “the bounds can be determined from a selection made by the user (e.g., in a geospatial mapping interface”) and provide at least a part of the first disaster information on the area selected by the user to the first terminal associated with the area selected by the user. (Richardson, ¶0026: “After the user provides the geospatial ROI, aerial images associated with the geospatial ROI can be obtained from the aerial image”).
Regarding claim 5, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 2, wherein the at least one processor is configured to: acquire area region information corresponding to the acquired image; (Richardson, ¶0025: “region can be of interest to the user because of one or more structures present in the region. The geospatial ROI can be represented as a polygon bounded by latitude and longitude coordinates”) and acquire the first disaster information for each area by using the acquired area region information. (Richardson, ¶0031: “property damage associated with each property or structure within a given region of interest. The data package can also include a visualization of this information”).
Regarding claim 6, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 1, wherein the at least one processor is configured to display at least a part of the first disaster information on a display. (Richardson, ¶0008: “display a table including a list of properties within the ROI, and attributes associated with each property… can also display a project map showing the ROI”).
Regarding claim 7, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 1, wherein the at least one processor is configured to: acquire building region information corresponding to the acquired image; (Richardson, ¶0037: “a visual breakdown of the data generated by the system … the geographical locations from the property”) and extract the plurality of buildings from the acquired image by using the acquired building region information. (Richardson, ¶0022: “processor 20 executes system code 16 which can detect, extract, and categorize structure data from aerial imagery… for a given region of interest”).
Regarding claim 10, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 1, wherein the at least one processor is configured to: extract each of disaster buildings that have suffered from a disaster due to each of a plurality of disaster causes from the acquired image; (Richardson, ¶0020: “extracting, and categorizing structure data from imagery and determining the likelihood of potential property damage due to a major weather event (e.g., hurricane, hailstorm, and the like”) calculate a number of the extracted disaster buildings for each disaster cause; and provide at least a part of disaster information for each disaster cause, which is related to the extracted disaster buildings (Richardson, ¶0033: “a filter button 124 for sorting the projects based on, for example, the date of the weather event, the type of weather event”) and includes the calculated number of the extracted disaster buildings for each disaster cause, (Richardson, ¶0008: “a table including a list of properties within the ROI, and attributes associated with each property”; also see Fig. 5) to a third terminal which is different from the first terminal and is associated with each disaster cause. (Rodolico, ¶0110: “authorities may be contacted (e.g., the local police”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Richardson in view of Kunio and in further view of Rodolico using the additional teachings of Rodolico to introduce notifying a terminal associated with a different authority. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of reporting damage information to an insurance underwriting company. Therefore, it would have been obvious to combine the analogous arts Richardson, Kunio and Rodolico to obtain the invention in claim 10.
Regarding claim 11, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 10, wherein the at least one processor is configured to: discriminate whether or not each of the plurality of buildings included in the image has suffered from a disaster; (Richardson, ¶0038: “the project map 180, which visually convey information to the user. The layers are designed to toggle on or off to provide the most detailed view possible for the user”’ also see Fig. 7 and Fig. 8) and extract each of the disaster buildings that has suffered from a disaster due to each disaster cause from the plurality of buildings discriminated as having suffered from a disaster. (Richardson, ¶0039: “a property information window 212, including for example, property identification number… hail size hail percentage, wind speed gusts, and sustained wind speeds”).
Regarding claim 12, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 11, wherein the at least one processor is configured to: cut out an image of a region (Richardson, ¶0025: “The region can be of interest to the user because of one or more structures present in the region. The geospatial ROI can be represented as a polygon bounded by latitude and longitude coordinates”) of the plurality of buildings from the image; (Richardson, ¶0022: “detect, extract, and categorize structure data from aerial imagery… for a given region of interest”) and acquire whether or not each of the plurality of buildings of the cut out image has suffered from a disaster by inputting the cut out image of the region of the plurality of buildings to a second trained model, and the second trained model outputs, (Richardson, ¶0028: “the system 10 collects information from the machine learning subsystem 18a. The machine learning subsystem 18a can process aerial images (e.g., retrieved from aerial imagery database 12) using machine learning algorithms to automatically determine and extract attributes (e.g., roof type, area, slope, material, eave height, etc.) of one or more structures within the ROI. Various machine learning algorithms will be known to those of skill in the art for determining the attributes of the structures within the ROI, based on aerial images”) in a case in which the image of the plurality of buildings is given as input, whether or not each of the plurality of buildings of the input image has suffered from a disaster. (Richardson, ¶0029: “automatically predict the likelihood that property damage has been sustained from the weather event… property machine learning attributes (e.g., roof type, area, slope, material, eave height, etc.), and a likelihood of property damage associated with each property or structure within a given region of interest”)
Regarding claim 13, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 1, wherein the first disaster cause is a fire, (Richardson, ¶0042: “a wide variety of events (both weather-related and non-weather-related), such as wildfires, lightning, arson”) and the first terminal is associated with a fire station. (Conboy, ¶0167: “send a notification to the local fire department”).
The proposed combination as well as the motivation for combining Richardson, Kunio and Rodolico references presented in the rejection of claim 1, apply to claim 13 and are incorporated herein by reference. Thus, the apparatus recited in claim 13 is met by Richardson, Kunio and Rodolico.
Regarding claim 14, Richardson in view of Kunio and in further view of Rodolico teaches, The disaster information processing apparatus according to claim 1, wherein the image is an aerial image captured from a flying object or a satellite image captured from an artificial satellite. (Richardson, ¶0040: “capturing aerial imagery and weather data. For example, the camera devices can include, but are not limited to, a unmanned aerial vehicle 306a, an airplane 306b, and a satellite 306n”).
Regarding claim 16, it recites a method with steps corresponding to the elements in apparatus recited in claim 1. Therefore, the recited steps of method claim 16 are mapped to the proposed combination in the same manner as the corresponding elements of apparatus claim 1. Additionally, the rationale and motivation to combine Richardson, Kunio and Rodolic presented in rejection of claim 1, apply to this claim.
Regarding claim 17, Richardson in view of Kunio and in further view of Rodolico teaches, A non-transitory, computer-readable tangible recording medium on which a program for causing, when read by a computer, the computer to execute (Richardson, ¶0023: “The system 10 includes system code 16 (i.e., non-transitory, computer-readable instructions) stored on a computer-readable medium and executable by the processor 20 or one or more computer systems”) the disaster information processing method according to claim 16 is recorded. (Richardson, ¶0040: “processor and memory for executing the computer instructions and methods described above”).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Nishioka (US 20200221012 A1) in view of Richardson et al. (US 2021/0383481 A1) in further view of translated Japanese patent document Koichi et al. (JP 6795901 B2) and still in further view of Rodolico et al. (US 2018/0047265 A1).
Regarding claim 15, Nishioka teaches, A disaster information processing system comprising: a first terminal (Nishioka, ¶0052: “process of transmitting an emergency signal in the terminal device 10”) including at least one first processor, (Nishioka, ¶0007: “terminal device includes: a reception unit”) and at least one first memory that stores a command to be executed by the at least one first processor; (Nishioka, ¶0035: “the control unit 38 outputs a video recording start instruction”) a second terminal (Nishioka, ¶0021: “the second terminal device 10b”) including at least one second processor, (Nishioka, ¶0007: “terminal device includes: a reception unit”) and at least one second memory that stores a command to be executed by the at least one second processor (Nishioka, ¶0035: “the control unit 38 outputs a video recording start instruction”) a server including at least one third processor, (Nishioka, ¶0003: “the server activates a fixed monitoring camera”) and at least one third memory that stores a command to be executed by the at least one third processor; (Nishioka, ¶0035: “the control unit 38 outputs a video recording start instruction”) and a fourth terminal including at least one fourth processor, (Nishioka, ¶0007: “terminal device includes: a reception unit”) and at least one fourth memory that stores a command to be executed by the at least one fourth processor, (Nishioka, ¶0035: “the control unit 38 outputs a video recording start instruction”) wherein the at least one fourth processor is configured to: acquire an image (Nishioka, ¶0035: “imaging unit 72 captures a video in accordance with the video recording instruction and outputs imaged data”). However, Nishioka does not explicitly teach, including a plurality of buildings; extract an image of a region of the plurality of buildings from the acquired image; and provide the extracted image of the region of the plurality of buildings to the server, the at least one third processor is configured to: acquire the image of the region of the plurality of buildings provided from the fourth terminal; extract a first plurality of disaster buildings that have suffered from a disaster due to a first disaster cause from the acquired image of the region of the plurality of buildings, calculate a number of the extracted first plurality of disaster buildings; provide at least a part of first disaster information, which is related to the extracted first plurality of disaster buildings and includes the calculated number of the extracted first plurality of disaster buildings, to the terminal associated with the first disaster cause; cut out an image of a region of the plurality of buildings from the image; discriminate whether or not each of the plurality of buildings of the cut out image is one of the extracted first plurality of disaster buildings by inputting the cut out image of the region of the plurality of buildings to a first trained model, wherein the first trained model outputs, in a case in which the image of the plurality of buildings is given as input, whether or not a disaster cause of each of the plurality of buildings of the input image is the first disaster cause; extract a second plurality of disaster buildings that have suffered from a disaster due to a second disaster cause different from the first disaster cause from the acquired image of the region of the plurality of buildings; calculate a number of the extracted second plurality of disaster buildings; provide at least a part of second disaster information, which is related to the extracted second plurality of disaster buildings and includes the calculated number of the extracted second plurality of disaster buildings, to the second terminal associated with the second disaster cause; and according to disaster cause, choose one of the first terminal and the second terminal to be provided with the at least the part of the first disaster information or the at least the part of the second disaster information the at least one first processor is configured to: acquire at least a part of the first disaster information provided from the server; and display at least a part of the first disaster information on a first display, and the at least one second processor is configured to: acquire at least a part of the second disaster information provided from the server; and display at least a part of the second disaster information on a second display.
In an analogous field of endeavor, Richardson teaches, including a plurality of buildings; (Richardson, ¶0004: “process aerial images to extract data about structures present in the aerial images”) extract an image of a region of the plurality of buildings from the acquired image; (Richardson, ¶0042: “detect, extract, and categorize structure data arising from a wide variety of events (both weather-related and non-weather-related), such as wildfires, lightning, arson, hurricanes, hailstorms, tornadoes”) and provide the extracted image of the region of the plurality of buildings (Richardson, ¶0022: “detect, extract, and categorize structure data from aerial imagery… for a given region of interest”) to the server, the at least one third processor is configured to: (Richardson, ¶0040: “a plurality of internal servers 302a-302n having at least one processor and memory for executing the computer instructions”) acquire the image of the region of the plurality of buildings provided from the fourth terminal; (Richardson, ¶0040: “a plurality of storage servers 304a-304n for receiving and storing aerial imagery and weather data”) extract a first plurality of disaster buildings that have suffered from a disaster due to a first disaster cause from the acquired image (Richardson, ¶0042: “detect, extract, and categorize structure data arising from… wildfires, lightning, arson, hurricanes, hailstorms, tornadoes, etc”) of the region of the plurality of buildings, (Richardson, ¶0025: “geospatial ROI can be represented as a polygon bounded by latitude and longitude coordinates”) calculate a number of the extracted first plurality of disaster buildings; (Richardson, ¶0008: “a table including a list of properties within the ROI, and attributes associated with each property”; also see Fig. 5) provide at least a part of first disaster information, which is related to the extracted first plurality of disaster buildings and includes the calculated number of the extracted first plurality of disaster buildings, (Richardson, ¶0032: “process the post-catastrophe aerial imagery associated with the property, using the damage detection subsystem 18d (see FIG. 1), to precisely determine the extent of the damage to the specific property”) cut out an image of a region of the plurality of buildings from the image; (Richardson, ¶0025: “The region can be of interest to the user because of one or more structures present in the region. The geospatial ROI can be represented as a polygon bounded by latitude and longitude coordinates”) discriminate whether or not each of the plurality of buildings of the cut out image is one of the extracted first plurality of disaster buildings (Richardson, ¶0028: “machine learning subsystem 18a can process aerial images (e.g., retrieved from aerial imagery database 12) using machine learning algorithms to automatically determine and extract attributes”) by inputting the cut out image of the region of the plurality of buildings to a first trained model, wherein the first trained model outputs, in a case in which the image of the plurality of buildings is given as input, (Richardson, ¶0028: “using machine learning algorithms to automatically determine and extract attributes (e.g., roof type, area, slope, material, eave height, etc.) of one or more structures within the ROI”) whether or not a disaster cause of each of the plurality of buildings of the input image is the first disaster cause; (Richardson, ¶0042: “systems/methods of the present disclosure could be utilized to detect, extract, and categorize structure data arising from… wildfires, lightning, arson, hurricanes, hailstorms, tornadoes, etc”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Nishioka using the teachings of Richardson to introduce obtaining aerial imagery of disaster buildings. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of assessing the impact of the disaster based on the obtained image. Therefore, it would have been obvious to combine the analogous arts Nishioka and Richardson to obtain the above-described limitations in claim 15. However, the combination of Nishioka and Richardson does not explicitly teach, terminal associated with the first disaster cause and extract a second plurality of disaster buildings that have suffered from a disaster due to a second disaster cause different from the first disaster cause from the acquired image of the region of the plurality of buildings; calculate a number of the extracted second plurality of disaster buildings; provide at least a part of second disaster information, which is related to the extracted second plurality of disaster buildings and includes the calculated number of the extracted second plurality of disaster buildings, to the second terminal associated with the second disaster cause; and according to disaster cause, choose one of the first terminal and the second terminal to be provided with the at least the part of the first disaster information or the at least the part of the second disaster information the at least one first processor is configured to: acquire at least a part of the first disaster information provided from the server; and display at least a part of the first disaster information on a first display, and the at least one second processor is configured to: acquire at least a part of the second disaster information provided from the server; and display at least a part of the second disaster information on a second display.
In another analogous field of endeavor, Kunio teaches, extract a second plurality of disaster buildings that have suffered from a disaster due to a second disaster cause (Koichi, page 2, ¶08: “A collapse number calculation means for calculating the number of collapsed buildings”) different from the first disaster cause from the acquired image of the region of the plurality of buildings; (Koichi, page 5, ¶01: “a fire in a building is caused by the collapse of the building due to an earthquake”) calculate a number of the extracted second plurality of disaster buildings; (Koichi, page 2, ¶08: “A collapse number calculation means for calculating the number of collapsed buildings”) provide at least a part of second disaster information, which is related to the extracted second plurality of disaster buildings and includes the calculated number of the extracted second plurality of disaster buildings, (Koichi, page 2, ¶08: “A collapse number calculation means for calculating the number of collapsed buildings, and a collapse risk ranking calculation means for calculating the risk rank for building collapse in the evaluation area in the evaluation target area based on the number of building collapses per unit area”) to the second terminal associated with the second disaster cause; (Koichi, page 8, ¶03: “The communication unit 28 is configured by an appropriate interface, and is used by the CPU 32 to exchange data with an external server or the like via a wireless or wired communication line”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Nishioka in view of Richardson using the teachings of Koichi to introduce counting collapsed buildings. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of computing and reporting the number of collapsed buildings after an earthquake. Therefore, it would have been obvious to combine the analogous arts Nishioka, Richardson and Koichi to obtain the above-described limitations in claim 15. However, the combination of Nishioka, Richardson and Kunio does not explicitly teach, terminal associated with the first disaster cause and according to disaster cause, choose one of the first terminal and the second terminal to be provided with the at least the part of the first disaster information or the at least the part of the second disaster information the at least one first processor is configured to: acquire at least a part of the first disaster information provided from the server; and display at least a part of the first disaster information on a first display, and the at least one second processor is configured to: acquire at least a part of the second disaster information provided from the server; and display at least a part of the second disaster information on a second display.
In still another analogous field of endeavor, Rodolico teaches, terminal associated with the first disaster cause (Rodolico, ¶0111: “authority that is to be contacted (e.g., e-mail the local police, e-mail the local fire department”) and according to disaster cause, choose one of the first terminal and the second terminal (Rodolico, ¶0110: “determining whether to report the seismic event and/or determining which authority to contact may be based on any of properties of the seismic event determined from the analysis performed”) to be provided with the at least the part of the first disaster information or the at least the part of the second disaster information (Rodolico, ¶0111: “message may include the one or more notifications received at step 701. A phone call may be initiated… provides a description of the seismic event (e.g., strength, length of time, number of premises effected”) the at least one first processor is configured to: acquire at least a part of the first disaster information provided from the server; (Rodolico, ¶0019: “The push notification server 105 may generate push notifications to deliver data”) and display at least a part of the first disaster information on a first display, (Rodolico, ¶0043: “portal server 318 may be a computing device capable of providing a web portal through which users may view, on any connected display device”) and the at least one second processor is configured to: acquire at least a part of the second disaster information provided from the server; (Rodolico, ¶0019: “the local office 103 may include a variety of servers 105-107”; and ¶0020: “application server may perform various security system functions including storing remotely security camera footage, storing past event history”) and display at least a part of the second disaster information on a second display. (Rodolico, ¶0043: “portal server 318 may be a computing device capable of providing a web portal through which users may view, on any connected display device”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Nishioka in view of Richardson in further view of Kunio using the teachings of Rodolico to introduce communicating with different terminals. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of reporting an information to a terminal associated with the appropriate authority. Therefore, it would have been obvious to combine the analogous arts Nishioka, Richardson, Kunio and Rodolico to obtain the invention in claim 15.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/MEHRAZUL ISLAM/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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