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 .
Priority
This application is a continuation of U.S. Patent Application 17/121,515 “Classification of Wildfire Danger”, filed 12/14/2020 which claims priority to Provisional 62/948,153, filed 12/13/2019. A Notice of Allowance was granted on 11/30/2023. The priority applications are incorporated herein by reference in its entirety for all purposes.
Status of the Claims
Claims 1-18 are pending in the instant patent application.
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.
Regarding Claims 1-6, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1-6 are directed to the abstract idea of wildfire damage prediction.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites initializing an area with macro fire conflagration characteristics that express propensity of parts of the area to accommodate ignition and/or movement of fire; performing a first fire conflagration analysis for the area to simulate numerous randomly distributed ignitions and fire spread conditions using the macro fire conflagration characteristics to generate macro fire conflagration scores (MAFC scores) that summarize results of the simulated ignitions and fire spread for the parts of the area; initializing the area with notional structures and individually or collectively encoded site characteristics reflecting at least site vegetation, fireproof construction rating, and firefighting capabilities of organization(s) expected to respond to fires approaching the notional structures; performing a second fire conflagration analysis to simulate numerous randomly distributed ignitions, fire spread conditions and resulting damage to the notional structures, including average annual losses to generate micro fire conflagration scores (MIFC scores) that summarize vulnerability to fire conflagration.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can performed in the human mind (including an observation, evaluation, judgment, opinion). Furthermore, dependent claims 3-5 recite Mathematical Concepts due to the mathematical relationships/calculations taking place.
Accordingly, the claim recites an abstract idea and dependent claims 2-6 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim does not have an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes elements that are not directed to the abstract idea under 2A. These elements include the generic computing elements described in the Applicant’s specification in at least 0040. These elements do not amount to more than the abstract idea because it is aa generic computer performing generic functions.
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 7-12, they are directed to a non-transitory computer readable medium, however the claims are directed to a judicial exception without significantly more. Claims 7-12 are directed to the abstract idea of wildfire damage prediction.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 7, claim 7 recites initializing an area with macro fire conflagration characteristics that express propensity of parts of the area to accommodate ignition and/or movement of fire; performing a first fire conflagration analysis for the area to simulate numerous randomly distributed ignitions and fire spread conditions using the macro fire conflagration characteristics to generate macro fire conflagration scores (MAFC scores) that summarize results of the simulated ignitions and fire spread for the parts of the area; initializing the area with notional structures and individually or collectively encoded site characteristics reflecting at least site vegetation, fireproof construction rating, and firefighting capabilities of organization(s) expected to respond to fires approaching the notional structures; performing a second fire conflagration analysis to simulate numerous randomly distributed ignitions, fire spread conditions and resulting damage to the notional structures, including average annual losses to generate micro fire conflagration scores (MIFC scores) that summarize vulnerability to fire conflagration.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can performed in the human mind (including an observation, evaluation, judgment, opinion). Furthermore, dependent claims 9-11 recite Mathematical Concepts due to the mathematical relationships/calculations taking place.
Accordingly, the claim recites an abstract idea and dependent claims 8-12 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a memory and a plurality of processors. The memory and plurality of processors are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 7 includes elements that are not directed to the abstract idea under 2A. These elements include a memory, plurality of processors and the generic computing elements described in the Applicant’s specification in at least 0040. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 7 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 13-18, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 13-18 are directed to the abstract idea of wildfire damage prediction.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 13, claim 13 recites initializing an area with macro fire conflagration characteristics that express propensity of parts of the area to accommodate ignition and/or movement of fire; performing a first fire conflagration analysis for the area to simulate numerous randomly distributed ignitions and fire spread conditions using the macro fire conflagration characteristics to generate macro fire conflagration scores (MAFC scores) that summarize results of the simulated ignitions and fire spread for the parts of the area; initializing the area with notional structures and individually or collectively encoded site characteristics reflecting at least site vegetation, fireproof construction rating, and firefighting capabilities of organization(s) expected to respond to fires approaching the notional structures; performing a second fire conflagration analysis to simulate numerous randomly distributed ignitions, fire spread conditions and resulting damage to the notional structures, including average annual losses to generate micro fire conflagration scores (MIFC scores) that summarize vulnerability to fire conflagration.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can performed in the human mind (including an observation, evaluation, judgment, opinion). Furthermore, dependent claims 15-17 recite Mathematical Concepts due to the mathematical relationships/calculations taking place.
Accordingly, the claim recites an abstract idea and dependent claims 14-18 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a memory and one or more processors. The memory and one or more processors are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 13 includes elements that are not directed to the abstract idea under 2A. These elements include a memory, one or more processors and the generic computing elements described in the Applicant’s specification in at least 0040 of 17/121,515. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 13 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
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.
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.
Claim(s) 1-4, 6-10, 12-16 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drake et al. (US 2014/0244318 A1) in view of Chapin et al. (US 2011/0295624 A1) in view of Tohidi et al. (US 2020/0155882 A1) further in view of Green et al. (US 2016/0055595 A1).
Regarding Claim 1, Drake teaches the limitations of Claim 1 which state
initializing an area with macro fire conflagration characteristics that express propensity of parts of the area to accommodate ignition and/or movement of fire (Drake: Para 0098 via FIG. 48 is a diagram of the location-based risk algorithm. This location-based risk value is determined by computing a multitude of wildfire risk factors known to impact a site's potential for wildfire ignition. The location-based risk model is built using the 40 Scott and Burgan fire behavior fuel models. Landscape fuel models include, but are not limited to: elevation, slope, aspect, fuel model, canopy cover, canopy base height, canopy height, and canopy bulk density. Fuel landscape files are loaded into fire behavior analysis and mapping software (e.g., FLAMMAP.TM. at http://www.firemodels.org/index.php/national-systems/flammap) to produce outputs of predicted flame length and crown fire);
performing a first fire conflagration analysis for the area to simulate numerous randomly distributed ignitions and fire spread conditions using the macro fire conflagration characteristics to generate macro fire conflagration scores (MAFC scores) that summarize results of the simulated ignitions and fire spread for the parts of the area (Drake: Para 0098 via FIG. 48 is a diagram of the location-based risk algorithm. This location-based risk value is determined by computing a multitude of wildfire risk factors known to impact a site's potential for wildfire ignition. The location-based risk model is built using the 40 Scott and Burgan fire behavior fuel models. Landscape fuel models include, but are not limited to: elevation, slope, aspect, fuel model, canopy cover, canopy base height, canopy height, and canopy bulk density. Fuel landscape files are loaded into fire behavior analysis and mapping software (e.g., FLAMMAP.TM. at http://www.firemodels.org/index.php/national-systems/flammap) to produce outputs of predicted flame length and crown fire);
initializing the area with notional structures and individually or collectively (Drake: Para 0014 via selecting properties for which an assessment needs to be completed; providing a mobile device on which is installed an application for collecting wildfire hazard assessment data; applying a site-based risk algorithm, to the collected wildfire hazard assessment data to calculate a site-based risk total; using a location-based risk algorithm to generate a location-based risk value, wherein, the location-based risk value is determined by computing a multitude of wildfire risk factors known to impact a site's potential for wildfire ignition; and using a level of service algorithm to multiply the site-based risk total by the location-based risk value to generate a level of service score).
However, Drake does not explicitly disclose the limitations of Claim 1 which state encoded site characteristics reflecting at least site vegetation, fireproof construction rating, and firefighting capabilities of organization(s) expected to respond to fires approaching the notional structures.
Chapin though, with the teachings of Drake, teaches of
encoded site characteristics reflecting at least site vegetation, fireproof construction rating, and firefighting capabilities of organization(s) expected to respond to fires approaching the notional structures (Chapin: Para 0050-0052 via FIG. 3 illustrates another exemplary flow chart of a method 300 for determining a fire risk score that includes extracting data from a plurality of data sources, wherein the data includes at least real estate values (from Zillow.com, for example), map data (Yahoo maps, Google maps, etc. for determining/retrieving a driving distance and driving time from a fire station to a building), National Fire Incident Reporting System data, city and municipal building code data, insurance claim data, state fire marshal data and building data, the building data including at least an address of a building, an age of the building, a size of the building, an indication whether or not sprinklers are present in the building, and an indication whether one or more smoke detectors in the building are remotely monitored (block 302). The method 300 may also assign weights to the extracted building data (block 304) and identify one or more fire stations near an address of the building (block 306). The method 300 may then obtain from a map database or determine, based on retrieved map data, a driving time from the fire stations to the building (block 308), perform a statistical regression analysis to: at least the driving time from the one or more fire stations to the building, the age of the building and the size of the building to determine an expected loss to the building in the event of a fire (block 310), and calculate a fire risk score for the building to assist an insurance company assess a risk of insuring the building (block 312). The fire risk score calculation system 100 may use a combination of historical fire incident data from sources such as the NFIRS, and NFPA analysis reports. The model may be a regression model to predict an Expected Loss Index (ELI), and Maximum Loss Index (MLI) using key variables such as the driving distance from fire stations, age of the home, and size of the home. The Indices may be modulated using various factors that influence fire growth in a home. These variables may include, for example, driving distances from nearby fire stations, the age of the building and the size of the building. The factors may include, for example, the presence or absence of fire sprinklers, whether smoke detectors in the building are remotely monitored, the location of the building including the number of nearby fire losses, a percentage of loss distribution and a distance to the three nearest fire stations, whether or not the ceiling in the basement is covered with drywall and the extent that is covered, and the NEC code adoption year).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Drake with the teachings of Chapin in order to have encoded site characteristics reflecting at least site vegetation, fireproof construction rating, and firefighting capabilities of organization(s) expected to respond to fires approaching the notional structures. The motivations behind this being to incorporate the teachings of determining fire risk scores and determining risk reduction actions to prevent or mitigate such events as taught by Chapin. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
In addition, Drake does not explicitly teach the limitation of Claim 1 which states performing a second fire conflagration analysis to simulate numerous randomly distributed ignitions, fire spread conditions and resulting damage to the notional structures.
Tohidi though with the teachings of Drake/Chapin, teaches of
performing a second fire conflagration analysis to simulate numerous randomly distributed ignitions, fire spread conditions and resulting damage to the notional structures (Tohidi: Para 0152-0153, 0160 via Fire forecasting is a high-resolution, near-live forecasting tool designed to equip emergency-response managers and fire departments with information about the estimated evolution of the fire. In some example embodiments, fire forecasting combines physical modeling of wildfire spread with fire machine-learning situational awareness capability to provide reliable forecasts. One objective of fire modeling is to model fire behavior in hopes of producing answers to the following questions: “How quickly and in which direction does the fire spread?”, “How much heat does the fire generate?”, “How high is the flame?”, and so forth. Fire modeling also estimates the effects of fire, such as ecological and hydrological changes, fuel consumption, tree mortality, and amount and rate of generated emissions from the smoke plume…fire models accept a point of ignition as the initial condition of the rasterized fire spread. However, due to the associated uncertainties with the early location of the fire, this point of ignition may be changed such that the current model accepts multiple ignition points as well as multiple ignition sites, also known as the scar-site. By leveraging this capability, the fire forecasting benefits from the most accurate early estimates of the fire location as its initial conditions. The outcomes of the fire monitoring products are evaluated based on the metrics and previous forecast simulations. After pre-processing the fire perimeters through both shape and time-dependent metrics, the results are imported as scar-site information 1814 to the fire model 1820).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Drake/Chapin with the teachings of Tohidi in order to have performing a second fire conflagration analysis to simulate numerous randomly distributed ignitions, fire spread conditions and resulting damage to the notional structures. The motivations behind this being to incorporate the teachings of monitoring and modeling fires as taught by Tohidi. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Drake does not explicitly disclose the limitation of Claim 1 which states including average annual losses to generate micro fire conflagration scores (MIFC scores) that summarize vulnerability to fire conflagration.
Green though, with the teachings of Drake/Chapin/Tohidi, teaches of
including average annual losses to generate micro fire conflagration scores (MIFC scores) that summarize vulnerability to fire conflagration (Green: Para 0165 via several different associated adjustment scores may be used to adjust the average annual loss. In some embodiments, a cap may be applied to the adjustment scores. For example, adjusted average annual loss=average annual loss*(1+adjustment cap*sum (associated adjustment scores)/maximum possible sum of the adjustment scores) As an example (e.g., as seen in FIG. 19), a flood risk rating may be assigned as follows: if the property point is impacted by 2-10 year flood, the flood risk rating may be 6.0 (there may be some repetitive loss associated with the property point); if the property point is impacted by 10-50 year flood, the flood risk rating may be 5.0 (there may be some potential repetitive loss associated with the property point); if the property point is impacted by 50-100-year flood, the flood risk rating may be 4.0; if the property point is impacted by 100-200 year flood, the flood risk rating may be 3.0; if the property point is impacted by 200-500-year flood, the flood risk rating may be 2.0; if the property point is impacted by 500-1000 year flood, the flood risk rating may be 1.0; if the property point is not impacted by flood, the flood risk rating may be 0).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Drake/Chapin/Tohidi with the teachings of Green in order to have including average annual losses to generate micro fire conflagration scores (MIFC scores) that summarize vulnerability to fire conflagration. The motivations behind this being to incorporate the teachings of risk analysis as taught by Green. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 2, the combination of Drake/Chapin/Tohidi/Green teaches the limitations of Claim 2 which state
using a Voronoi or other graph partitioning algorithm on at least one processor to group the parts into first regions that have shared propensities to accommodate ignition and/or movement based on the MAFC scores (Drake: Para 0172 via FIG. 35 is an example of the geographic location-based risk mapping. This view allows a user to see a spectrum of fire risk based on geographic location alone. Users start by seeing an overview of the entire West, with fire graphics depicting map locations where an active fire is burning. Zooming in brings an area map into closer view, and a user can drag the area of the map he warns to see into the center of the screen. The user can also enter a longitude and latitude, zip code, city, or fire name into a search dialogue in order to navigate to a given location);
using a Voronoi or other graph partitioning algorithm on at least one processor to group the parts of the first regions into second regions based on the MIFC scores (Drake: Para 0101-0103, 0172, 0181 via FIG. 12 is a diagram of the level of service algorithm. The site-based risk total (see FIG. 46) is multiplied by the location-based risk value (see FIG. 48) to yield the level of service score. The result of this calculation is a level of service score that can be used by the insurer client to define a number of things, including how the wildfire risk assessment provider services the data included in the report (i.e., auto-completion of wildfire risk assessment reporting vs. customized wildfire risk report write-up), how underwriting policies are written/priced, and what kind of wildfire response actions are taken by wildfire risk assessment providers. Notably, the client can customize level of service risk, value thresholds to define how the wildfire risk assessment provider processes data. The resulting level of service score falls into scoring categories--for instance, low, moderate, and high--to determine a course of action to be taken by the wildfire risk assessment provider staff, as predetermined by the client. Report output is returned directly to the users mobile device application in the education use case, and/or to the insurance client office in the underwriting use case. Typically, properties with the lowest level of service score receive a Level 1 service designation, which auto-generates scoring and recommendation language. Notably, no wildfire risk assessment provider staff risk verification or write-up occurs at this level. Level 2 service includes wildfire risk assessment provider staff analysis of the data that has been collected, including photo and note analysis, map analysis for location-based risk verification, summary write-up of property risk factors and recommendations, and recommendations for "Yes" conditions which--when completed--are likely to reduce wildfire risk on a given property. Level 3 service includes wildfire risk assessment provider staff analysis as outlined by Level 2 service but also goes further to include phone or written communication with wildfire risk assessment provider staff to clarify risk scenario/scoring, advise on mitigation actions needed, and answer questions associated with the report…FIG. 35 is an example of the geographic location-based risk mapping. This view allows a user to see a spectrum of fire risk based on geographic location alone. Users start by seeing an overview of the entire West, with fire graphics depicting map locations where an active fire is burning. Zooming in brings an area map into closer view, and a user can drag the area of the map he warns to see into the center of the screen. The user can also enter a longitude and latitude, zip code, city, or fire name into a search dialogue in order to navigate to a given location… FIG. 38 is the updated wildfire risk, indicator screen, which is a view that can be seen both on the mobile application device (education users) and a web interface (for client users). This screen is updated daily to show wildfire status across a given geographic area, and potential wildfire risk values fall into four possible categories (see FIG. 51). Users are able to zoom in on this view (see FIG. 39) and once the user reaches a certain view magnification threshold, an option to export properties included in the zoom appears);
wherein the MAFC and the MIFC scores can be applied to a site based on its location within the first and second regions (Drake: Para 0170-0173 via FIG. 34 is an example of the updated wildfire risk screenshot with a view set to only show active fires. This functionality allows users to browse current fire activity in a particular geographic map zoom and then navigate more deeply to learn about a given fire. In this example, users typically start by seeing an overview of the entire western United States, with fire graphics depicting map locations where an active fire is burning. Zooming in brings an area map into closer view, and touching any of the fire graphics brings up detailed information for the given fire. Additionally, the user can enter a longitude and latitude, zip code, city, or fire name into a search dialogue to bring up information regarding a given fire or fire location. Once an active fire has been selected, the user receives detail in the term of a daily fire situation report and map. This situation report gathers information about a given wildfire's spread, expected growth/direction, areas a affected and areas threatened. Additionally, the user can read basic summary data about a given fire, including size, date(s) of activity, percent containment, etc. FIG. 35 is an example of the geographic location-based risk mapping. This view allows a user to see a spectrum of fire risk based on geographic location alone. Users start by seeing an overview of the entire West, with fire graphics depicting map locations where an active fire is burning. Zooming in brings an area map into closer view, and a user can drag the area of the map he warns to see into the center of the screen. The user can also enter a longitude and latitude, zip code, city, or fire name into a search dialogue in order to navigate to a given location. A spectrum of colors indicating severity of threat allows users to see geographic fire risk broken down by various wildfire factors, including fire history, predominant vegetative fuel type, topography, climate, etc. This information would need to be multiplied by site data (site-based risk assessment) to obtain the full picture of a given property's risk).
Regarding Claim 3, the combination of Drake/Chapin/Tohidi/Green teaches the limitations of Claim 3 which state
further including applying a regression analysis to fit the MAFC scores and the MIFC scores to the average annual losses experienced by the notional structures (Chapin: Para 0051-0052, 0057 via The method 300 may also assign weights to the extracted building data (block 304) and identify one or more fire stations near an address of the building (block 306). The method 300 may then obtain from a map database or determine, based on retrieved map data, a driving time from the fire stations to the building (block 308), perform a statistical regression analysis to: at least the driving time from the one or more fire stations to the building, the age of the building and the size of the building to determine an expected loss to the building in the event of a fire (block 310), and calculate a fire risk score for the building to assist an insurance company assess a risk of insuring the building (block 312). The fire risk score calculation system 100 may use a combination of historical fire incident data from sources such as the NFIRS, and NFPA analysis reports. The model may be a regression model to predict an Expected Loss Index (ELI), and Maximum Loss Index (MLI) using key variables such as the driving distance from fire stations, age of the home, and size of the home. The Indices may be modulated using various factors that influence fire growth in a home. These variables may include, for example, driving distances from nearby fire stations, the age of the building and the size of the building. The factors may include, for example, the presence or absence of fire sprinklers, whether smoke detectors in the building are remotely monitored, the location of the building including the number of nearby fire losses, a percentage of loss distribution and a distance to the three nearest fire stations, whether or not the ceiling in the basement is covered with drywall and the extent that is covered, and the NEC code adoption year… FIG. 5 illustrates an exemplary Expected Loss Index (ELI). The Expected Loss Index is an averaged property loss index. The ELI is a function of Driving Time, T.sub.2=t.sub.2+t.sub.3; age of home (Age); and Size of home (Size). Thus, ELI=f(T.sub.2, Age, Size). When Driving Time.ltoreq.T.sub.2,0, ELI=ELI.sub.o. When T.sub.2,0.gtoreq.T.sub.2>T.sub.2,0, ELI=ELI.sub.o+DxT.sup.a.times.Age.sup.b.times.Size.sup.c, where D, a, b, and c are determined using, for example, a multi-variate regression analysis. Those of ordinary skill in the art will readily recognize that other methods of regression analysis or statistical computations may be applied. When T.sub.2>T.sub.2,1, ELi=ELi.sub.max).
Regarding Claim 4, the combination of Drake/Chapin/Tohidi/Green teaches the limitations of Claim 4 which state
further including fitting a macro coefficient to a second or higher order term of the MAFC scores and a micro coefficient to a first order term of the MIFC scores (Chapin: Para 0051-0052, 0057 via The method 300 may also assign weights to the extracted building data (block 304) and identify one or more fire stations near an address of the building (block 306). The method 300 may then obtain from a map database or determine, based on retrieved map data, a driving time from the fire stations to the building (block 308), perform a statistical regression analysis to: at least the driving time from the one or more fire stations to the building, the age of the building and the size of the building to determine an expected loss to the building in the event of a fire (block 310), and calculate a fire risk score for the building to assist an insurance company assess a risk of insuring the building (block 312). The fire risk score calculation system 100 may use a combination of historical fire incident data from sources such as the NFIRS, and NFPA analysis reports. The model may be a regression model to predict an Expected Loss Index (ELI), and Maximum Loss Index (MLI) using key variables such as the driving distance from fire stations, age of the home, and size of the home. The Indices may be modulated using various factors that influence fire growth in a home. These variables may include, for example, driving distances from nearby fire stations, the age of the building and the size of the building. The factors may include, for example, the presence or absence of fire sprinklers, whether smoke detectors in the building are remotely monitored, the location of the building including the number of nearby fire losses, a percentage of loss distribution and a distance to the three nearest fire stations, whether or not the ceiling in the basement is covered with drywall and the extent that is covered, and the NEC code adoption year… FIG. 5 illustrates an exemplary Expected Loss Index (ELI). The Expected Loss Index is an averaged property loss index. The ELI is a function of Driving Time, T.sub.2=t.sub.2+t.sub.3; age of home (Age); and Size of home (Size). Thus, ELI=f(T.sub.2, Age, Size). When Driving Time.ltoreq.T.sub.2,0, ELI=ELI.sub.o. When T.sub.2,0.gtoreq.T.sub.2>T.sub.2,0, ELI=ELI.sub.o+DxT.sup.a.times.Age.sup.b.times.Size.sup.c, where D, a, b, and c are determined using, for example, a multi-variate regression analysis. Those of ordinary skill in the art will readily recognize that other methods of regression analysis or statistical computations may be applied. When T.sub.2>T.sub.2,1, ELi=ELi.sub.max).
Regarding Claim 6, the combination of Drake/Chapin/Tohidi/Green teaches the limitations of Claim 6 which state
further assigning the notional structures with individually or collectively encoded site characteristics for each of the notional structures reflecting proximity to fire hydrant (Chapin: Para 0051, 0059 via The method 300 may also assign weights to the extracted building data (block 304) and identify one or more fire stations near an address of the building (block 306). The method 300 may then obtain from a map database or determine, based on retrieved map data, a driving time from the fire stations to the building (block 308), perform a statistical regression analysis to: at least the driving time from the one or more fire stations to the building, the age of the building and the size of the building to determine an expected loss to the building in the event of a fire (block 310), and calculate a fire risk score for the building to assist an insurance company assess a risk of insuring the building (block 312)… Modulation may also be applied for any address location (ZIP specific) based upon historic fire incidents and proximity to fire stations).
Regarding Claims 7-10 and 12, they are analogous to Claims 1-4 and 6 and are rejected for the same reasons (see also Tohidi: Para 0265).
Regarding Claims 13-16 and 18, they are analogous to Claims 1-4 and 6 and are rejected for the same reasons (see also Tohidi: Para 0265).
Claim(s) 5, 11 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drake et al. (US 2014/0244318 A1) in view of Chapin et al. (US 2011/0295624 A1) in view of Tohidi et al. (US 2020/0155882 A1) in view of Green et al. (US 2016/0055595 A1) further in view of Pierre et al. (US 2011/0153368 A1).
Regarding Claim 5, while the combination of Drake/Chapin/Tohidi/Green teaches the limitations of Claim 1, they do not teach the limitations of Claim 5 which state wherein calculation of the average annual losses during second fire conflagration analysis is restricted to processing fire conflagration events of a higher severity.
Pierre though, with the teachings of Drake/Chapin/Tohidi/Green, teaches of
wherein calculation of the average annual losses during second fire conflagration analysis is restricted to processing fire conflagration events of a higher severity (Pierre: Para 0021 via provide a risk analysis application with web based CAT modeling capabilities. The application components use a modular approach to overcome technology barriers for end user acceptance. The model simulations are based on unique combinations of scientifically accepted concepts and formulas which provides for more accurate risk analysis when compared to other risk analysis applications. The following models, not to be taken in a limiting sense, may be simulated: models associated with wind (tropical cyclone, severe weather wind, tornado), seismic (earthquake), pyrotechnic (wildfire, volcano, terrorism, fire following earthquake), hydro (flood, tsunami, ruptured dam), blast (terrorism, meteor, over pressure), environmental (air quality-terrorism, air quality-wildfire, nuclear radiation), falling debris (hail, volcano), and pilled debris (volcano ash, landslide)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Drake/Chapin/Tohidi/Green with the teachings of Pierre in order to have wherein calculation of the average annual losses during second fire conflagration analysis is restricted to processing fire conflagration events of a higher severity. The motivations behind this being to incorporate the teachings of providing an analysis tool for dynamic management of catastrophic exposure as taught by Pierre. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claims 11 and 17, they are analogous to Claim 5 and are rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Maddox et al. (US 2020/0134733 A1)
Billman et al. (US 2014/0257862 A1)
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/T.E.S./ Examiner, Art Unit 3625
/BETH V BOSWELL/ Supervisory Patent Examiner, Art Unit 3625
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