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 .
DETAILED ACTION
Status of the Claims
The following office action in response to the application filed on 11/15/2024.
Claims 1-20 were previously presented.
Therefore, claims 1-20 are pending and addressed below.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are directed to a method, a system, a non-transitory computer readable medium which is a process, machine, manufacturer or composition of matter and thus statutory category of invention (Step 1: YES).
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. The claim recites the limitations of “…obtaining building data representative of attributes of the building; obtaining based upon the building data, roof data associated with the building, climate region data associated with the building, historical weather data associated with the building, and historical hail data associated with the building, wherein the historical hail data includes at least one of video data, image data, or audio data of a hail event and at least one hail characteristic of the hail event disposed at the building; monitor video, image, or audio signals proximate the building; detect an occurrence of the hail event by detecting a hail event signature within the video, image, or audio signals, the hail event signature including a set of signal characteristics forming a pattern indicative of an occurrence of hail events; responsive to detecting the occurrence of the hail event, collect the at least one of the video data, the image data, or the audio data of the hail event in real time; analyze the at least one of the video data, the image data, or the audio data of the hail event to estimate the at least one hail characteristic of the hail event, the at least one hail characteristic including at least one of a direction of the hail event, a size of the hail event, a density of the hail event, elevations of structure exposed to the hail event, or a duration of the hail event; and transmit the at least one of the video data, the image data, or the audio data of the hail event and the at least one hail characteristic of the hail event; generating base-line probable roof loss confidence score data based upon the building data, the roof data, the historical weather data, the historical hail data, and the climate region data; determining a current roof condition of the roof of the building based upon at least one of the building or roof data; and determining the predicted level of roof damage to the roof of the building in a future interval based upon the base-line probable roof loss confidence score and the current roof condition, including determining the predicted levels of roof damage to the roof of the building for each of one or more predicted future specific environmental events associated with corresponding predicted sets of characteristics of the predicted future specific environmental events”. These recited limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of fundamental economic principles or practices (including insurance; assessing and determining roof damage of a building) but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of fundamental economic principles or practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional limitations (besides those that recite the abstract idea) include the presence in the method claim of a monitoring device and one or more processors that are recited at a high level of generality to perform the functions of “obtaining…building data; obtaining… roof, climate region, historical data associated with the building…; monitor …video, image, or audio signals…; detect …an occurrence of the hail event … by detecting …a hail event signature within the video, image, or audio signals, the hail event signature…; collect… the at least one of the video data, the image data, or the audio data of the hail event; analyze …the video data, the image data, or the audio data…to estimate …the hail characteristic…; and transmit …the video data, the image data, or the audio data; generating… base-line probable roof loss confidence score data …; determining …a current roof condition of the roof…; and determining… the predicted level of roof damage to the roof of the building in a future interval…”, such that it amounts no more than mere instructions to apply the exception using the generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a particular application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception or amount to an inventive concept. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of the one or more processors that are recited at a high level of generality to perform the functions of the monitoring device and the one or more processors that are recited at a high level of generality to perform the functions of “obtaining…building data; obtaining… roof, climate region, historical data associated with the building…; monitor …video, image, or audio signals…; detect …an occurrence of the hail event …… by detecting …a hail event signature within the video, image, or audio signals, the hail event signature…; collect… the at least one of the video data, the image data, or the audio data of the hail event; analyze …the video data, the image data, or the audio data…to estimate …the hail characteristic…; and transmit …the video data, the image data, or the audio data; generating… base-line probable roof loss confidence score data …; determining …a current roof condition of the roof…; and determining… the predicted level of roof damage to the roof of the building in a future interval…”, above amounts to mere instructions to apply the exception using the generic computer components. When viewing the additional elements either individually or as an ordered combination, the claim as a whole does not amount to significantly more than the judicial exception because the claim does not include improvements to another technology or technical field, improvements to the function of the computer itself, and does not provide meaningful limitations beyond general linking the use of an abstract idea to a particular technological environment. In effect, the additional limitations add the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer. Mere instructions to apply an exception using the generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Independent claim 12 is rejected based on the reasoning applicable to claim 1. Thus, the claim is not patent-eligible.
Dependent claims 2-11 and 13-16 are dependent on claims 1 and 12. Therefore, claims 2-11 and 13-16 are directed to the same abstract idea of claims 1 and 12. Claims 2-11 and 13-16 further recite the limitations that merely refer back to further details of the abstract idea. In addition, the additional limitations (besides those that recite the abstract idea) of the processor, the cost of roof damage predicting module, the confidence score computing device, the policy parameter adjusting module and the level of roof damage predicting module included in the dependent claims 7 and 13-15 that are all recited at a high level of generality to perform the functions of “adjusting… one or more parameters of an insurance policy for insuring the roof or the building based upon the predicted level of roof damage” (claim 7); “determine …the predicted cost of roof damage to the roof of the building based upon the predicted level of roof damage to the roof of the building” (claim 13); “adjust… policy parameters of an insurance policy associated with the roof of the building based upon the predicted level of roof damage” (claim 14); and “implements… a probability function to determine… the predicted level of roof damage to the roof of the building, wherein a contribution of a first term of the probability function is weighted via a first weighting variable relative to a second term of the probability function” (claim 15), such that it amounts no more than mere instructions to apply the exception using the generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The dependent claims 2-11 and 13-16 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception or amount to an inventive concept. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to nothing more than an instruction to “apply it” with the judicial exception. In addition, the additional limitations (besides those that recite the abstract idea) of the processor, the cost of roof damage predicting module, the confidence score computing device, the policy parameter adjusting module and the level of roof damage predicting module included in the dependent claims 7 and 13-15 that are all recited at a high level of generality to perform the functions of “adjusting… one or more parameters of an insurance policy for insuring the roof or the building based upon the predicted level of roof damage” (claim 7); “determine …the predicted cost of roof damage to the roof of the building based upon the predicted level of roof damage to the roof of the building” (claim 13); “adjust… policy parameters of an insurance policy associated with the roof of the building based upon the predicted level of roof damage” (claim 14); and “implements… a probability function to determine… the predicted level of roof damage to the roof of the building, wherein a contribution of a first term of the probability function is weighted via a first weighting variable relative to a second term of the probability function” (claim 15), above amounts to mere instructions to apply the exception using the generic computer components. When viewing the additional elements either individually or as an ordered combination, the claim as a whole does not amount to significantly more than the judicial exception because the claim does not include improvements to another technology or technical field, improvements to the function of the computer itself, and does not provide meaningful limitations beyond general linking the use of an abstract idea to a particular technological environment. In effect, the additional limitations add the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer. Mere instructions to apply an exception using the generic computer component cannot provide an inventive concept. Thus, when considering the combination of elements and the claimed as a whole, the dependent claims 2-11 and 13-16 are not patent eligible.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. The claim recites the limitations of “…receive building data that is representative of attributes of the building; receive roof data associated with the building based upon the building data; receive historical weather data associated with the building based on the building data; receive historical hail data associated with the building based on the building data; generate base-line probable roof loss confidence score data associated with the building based on the building data, the roof data, the historical weather data, and the historical hail data; determine current roof condition data associated with the building based on at least one of the building data or the roof data; and determine the predicted level of roof damage to the roof of the building based upon the base-line probable roof loss confidence score and the current roof condition”. These recited limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of fundamental economic principles or practices (including insurance; assessing and determining roof damage of a building) but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of fundamental economic principles or practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The additional limitations (besides those that recite the abstract idea) include the presence in the method claim of a processor, a building data receiving module, a roof data receiving module, a weather data receiving module, a hail data receiving module, a base-line probable roof loss confidence score data generation module, a current roof condition determining module and a level of roof damage predicting module that are recited at a high level of generality to perform the functions of “…receive …building data…; receive …roof data…; receive …historical weather data; receive …historical hail data; generate …base-line probable roof loss confidence score data; determine… current roof condition data; and determine… the predicted level of roof damage to the roof of the building…”, such that it amounts no more than mere instructions to apply the exception using the generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a particular application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception or amount to an inventive concept. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of the processor, the building data receiving module, the roof data receiving module, the weather data receiving module, the hail data receiving module, the base-line probable roof loss confidence score data generation module, the current roof condition determining module and the level of roof damage predicting module that are recited at a high level of generality to perform the functions of “…receive …building data…; receive …roof data…; receive …historical weather data; receive …historical hail data; generate …base-line probable roof loss confidence score data; determine… current roof condition data; and determine… the predicted level of roof damage to the roof of the building…”, above amounts to mere instructions to apply the exception using the generic computer components. When viewing the additional elements either individually or as an ordered combination, the claim as a whole does not amount to significantly more than the judicial exception because the claim does not include improvements to another technology or technical field, improvements to the function of the computer itself, and does not provide meaningful limitations beyond general linking the use of an abstract idea to a particular technological environment. In effect, the additional limitations add the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer. Mere instructions to apply an exception using the generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Dependent claims 18-20 are dependent on claim 17. Therefore, claims 18-20 are directed to the same abstract idea of claim 17. Claims 18-20 further recite the limitations that merely refer back to further details of the abstract idea. In addition, the additional limitations (besides those that recite the abstract idea) of the processor, the cost of roof damage predicting module, the policy parameter adjusting module and the level of roof damage predicting module
included in the dependent claims 18 and 19 that are all recited at a high level of generality to perform the functions of “determine…a predicted cost of roof damage to the roof of the building …; and adjust …policy parameters based on the base-line probable roof loss confidence score data, the current roof condition data, and the predicted level of roof damage” (claim 18); and “implement … a probability function to determine… the predicted level of roof damage to the roof of the building, wherein a contribution of a first term of the probability function is weighted via a first weighting variable relative to a second term of the probability function” (claim 19), such that it amounts no more than mere instructions to apply the exception using the generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The dependent claims 18-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception or amount to an inventive concept. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to nothing more than an instruction to “apply it” with the judicial exception. In addition, the additional limitations (besides those that recite the abstract idea) of the processor, the cost of roof damage predicting module, the policy parameter adjusting module and the level of roof damage predicting module included in the dependent claims 18 and 19 that are all recited at a high level of generality to perform the functions of “determine…a predicted cost of roof damage to the roof of the building …; and adjust …policy parameters based on the base-line probable roof loss confidence score data, the current roof condition data, and the predicted level of roof damage” (claim 18); and “implement … a probability function to determine… the predicted level of roof damage to the roof of the building, wherein a contribution of a first term of the probability function is weighted via a first weighting variable relative to a second term of the probability function” (claim 19), above amounts to mere instructions to apply the exception using the generic computer components. When viewing the additional elements either individually or as an ordered combination, the claim as a whole does not amount to significantly more than the judicial exception because the claim does not include improvements to another technology or technical field, improvements to the function of the computer itself, and does not provide meaningful limitations beyond general linking the use of an abstract idea to a particular technological environment. In effect, the additional limitations add the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer. Mere instructions to apply an exception using the generic computer component cannot provide an inventive concept. Thus, when considering the combination of elements and the claimed as a whole, the dependent claims 18-20 are not patent eligible.
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 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 AIA 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 of this title, 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 17-20 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Jagannathan (2021/0133891) and further in view of Kroger (2017/0039307).
As per claim 17, Jagannathan teaches a non-transitory computer-readable medium storing computer-readable instructions that, when executed by a processor, cause the processor to determine a predicted level of roof damage to a roof of a building by implementing (via the system related to roof condition evaluation and risk scoring, and more specifically, to systems and methods that can evaluate building roof conditions to determine information useful in property insurance underwriting and pricing, as well as insurance portfolio assessment, see abstract and paragraph 2):
a building data receiving module that causes the processor to receive building data that is representative of attributes of the building; a roof data receiving module that causes the processor to receive roof data associated with the building based upon the building data (via the system may include an interface configured to receive at least one input regarding the building, roofing system, location of the building roofing, location specific weather data, historical building performance data, or data extracted from imagery {... }, see paragraph 5);
a weather data receiving module that causes the processor to receive historical weather data associated with the building based on the building data; a hail data receiving module that causes the processor to receive historical hail data associated with the building based on the building data (via the system may include an interface configured to receive at least one input regarding the building, roofing system, location of the building roofing, location specific weather data, historical building performance data, or data extracted from imagery {... }, see paragraphs 3-5 and Fig.1);
a base-line probable roof loss confidence score data generation module that causes the processor to generate base-line probable roof loss confidence score data associated with the building based on the building data, the roof data, {...}, and the historical hail data (via the system may include an interface configured to receive at least one input regarding the building, roofing system, location of the building roofing system, location-specific weather data, historical building performance data, or data extracted from imagery. The system includes a roof condition risk scoring engine configured to receive the input through the interface. The roof condition risk scoring engine is programmed to apply the input to a model and transform the input into an indicator indicating a probability of loss associated with the roofing system replacement or reconstruction cost. The probability can be scaled into a roof condition risk score (e.g., a numeric score, a grade, a quality rating, etc., see paragraphs 5, 10 and Fig.1);
a current roof condition determining module that causes the processor to determine current roof condition data associated with the roof of the building based on at least one of the building data or the roof data; and a level of roof damage predicting module that causes the processor to determine the predicted level of roof damage to the roof of the building based upon the base-line probable roof loss confidence score and the current roof condition (via the system includes a roof condition risk scoring engine configured to receive the input through the interface. The roof condition risk scoring engine is programmed to apply the input to a model and transform the input into an indicator indicating a probability of loss associated with the roofing system replacement or reconstruction cost. The probability can be scaled into a roof condition risk score (e.g., a numeric score, a grade, a quality rating, etc.), including determining predicted levels of roof damage to the roof of the building for each of one or more predicted specific events associated with corresponding predicted sets of characteristics of the predicted specific events (via the roof condition model may then be used to evaluate/determine the roof condition of a specific building. Similar information, or characteristics, used in creating the model can be used to evaluate a roof condition of the building. This information (each of one or more predicted specific events associated with corresponding predicted sets of characteristics of the predicted specific events) includes, for example, location of the building (e.g., street address, etc.), building elevation, occupant maintenance behavior, consumer financial and location-level demographics data, historical weather information for location of the building (e.g., hail, hail size, hail duration, hail direction (e.g., sideway, etc.), wind, lightning, storms, tornadoes, heat index, snowfall, humidity, frequency, etc.), age of the building, age of roof (e.g., years since roof was replaced), building code compliance, builder information, maintenance events, vegetation, roof slope, roof pitch, roof direction, roof shape (e.g., whether roof is gabled, etc.), type of roof, roof covering material type (e.g., steel, tin, tile, clay, slate, built-up tar and gravel, architectural shingles, wood shakes, asphalt shingles, etc.), roof dimension, image of roof, whether any insurance claims were made on the roofs, and cost of the claims (e.g., replacement cost of roof, repair cost of roof, etc.). Other information and/or other combinations of information may be used. For example, several small storms can cause aggregate damage that might go undetected compared to a single large hail storm (e.g., a property owner is less likely to detect and repair damage caused by multiple small hail storms compared to damage caused by large hail storms). The roof model can take into account information such as, e.g., hail size, hail storm frequency, and the other data described herein to produce a likelihood of loss for a particular building roof. The risk scores can be associated with a corresponding qualitative risk rating (e.g., a risk score of 1 indicates the roof condition is “very good”), see paragraphs 5, 27-54, Fig.1-Fig.4).
Jagannathan does not explicitly teach the limitations wherein the historical weather data and the climate region data.
However, Kroger teaches these limitations wherein the historical weather data and the climate region data (via the weather event modeling data may include historical and climate data e.g., {precipitation volume "over time", water height, water velocity, tides, wave height, radar cross sections, atmospheric pressure, and rainfall, seismic data, and the like. In some cases, weather event modeling data 102 is prioritized to remove or diminish the contributory value of conflicting results as part of optional quality control procedures. In these cases, data that is more trusted is biased or otherwise weighted over data that is less trusted. For example, in situ data from a trusted sensor may be biased over radar data. As another example, human-observed data may biased over electronically collected sensor data or vice versa, see paragraph 90).
It would have been obvious to one having ordinary skill in the art just before the effective filing date of the claimed invention to modify the system taught by Jagannathan to substitute the historical weather data and the climate region data as taught in Kroger above. This is common to the same field of endeavor of systems and methods for roof condition evaluation and risk scoring. The combination amounts at least to combining prior art elements according to known methods to yield predictable results. As such, there exist a need to facilitate a better system and method for generating a base-line probable roof loss confidence score data based upon the historical weather data and the climate region data as taught in Kroger at least at paragraph 90 and abstract.
As per claim 18, Jagannathan teaches a cost of roof damage predicting module that causes the processor to determine a predicted cost of roof damage to the roof of the building based upon the predicted level of roof damage to the roof of the building; and a policy parameter adjusting module that causes the processor to adjust policy parameters based on the base-line probable roof loss confidence score data, the current roof condition data, and the predicted level of roof damage (via the system may include an interface configured to receive at least one input regarding the building, roofing system, location of the building roofing system, location-specific weather data, historical building performance data, or data extracted from imagery. The system includes a roof condition risk scoring engine configured to receive the input through the interface. The roof condition risk scoring engine is programmed to apply the input to a model and transform the input into an indicator indicating a probability of loss associated with the roofing system replacement or reconstruction cost, see paragraphs 5 and 19).
As per claim 19, Jagannathan teaches wherein the level of roof damage predicting module causes the processor to implement a probability function to determine the predicted level of roof damage to the roof of the building (via the indicator produced by the model may indicate a probability of loss tied to roof replacement cost reconstruction cost or a new adjusted replacement/reconstruction cost of the roof, e.g., the probability that the roof of the building will need to be repaired and/or replaced and the potential repair and/or replacement cost, e.g., prediction of cost, of repairing or replacing the roof, wherein a contribution of a first term of the probability function {is weighted via a first weighting variable} relative to a second term of the probability function (no missing shingles vs. missing shingles [weighting]} {... }. In one embodiment, the image processing module 135 is configured to determine various other roof characteristics {... }, whether there is evidence of prior damage, e.g., hail damage, etc. The image processing module 135 provides indicators of each of the roof characteristics determined, and the indicators can be used by the roof condition risk scoring engine 130 with the model to determine the roof risk score {determining predicted level of roof damage}, see paragraphs 30 and 36).
Jagannathan does not explicitly teach the limitations wherein a contribution of a first term of the probability function is weighted via a first weighting variable relative to a second term of the probability function.
However, Kroger teaches these limitations wherein a contribution of a first term of the probability function is weighted via a first weighting variable relative to a second term of the probability function (via weather event modeling data may include historical and climate data e.g., (precipitation volume "over time", water height, water velocity, tides, wave height, radar cross sections, atmospheric pressure, and rainfall, seismic data, and the like. In some cases, weather event modeling data 102 is prioritized (e.g., weighted) to remove or diminish the contributory value of conflicting results as part of optional quality control procedures. In these cases, data that is more trusted is biased or otherwise weighted over data that is less trusted. For example, in situ data from a trusted sensor may be biased over radar data. As another example, human-observed data may biased over electronically collected sensor data or vice versa, see paragraph 90).
It would have been obvious to one having ordinary skill in the art just before the effective filing date of the claimed invention to modify the system taught by Jagannathan to substitute wherein the first term of the probability function is weighted via a first weighting variable relative to a second term of the of the function, as taught in Kroger above. This is common to the same field of endeavor of systems and methods for roof condition evaluation and risk scoring. The combination amounts at least to combining prior art elements according to known methods to yield predictable results. As such, there exist a need to facilitate a better system and method wherein a first term of the probability function is weighted via a first weighting variable relative to a second term of the of the function, as taught in Kroger above as taught in Kroger at least at paragraph 90 and Abstract.
As per claim 20, Jagannathan teaches wherein the current roof condition of the roof of the building indicates a current level of damage to the roof based upon a damage and repair history of the roof (via the roof condition model may then be used to evaluate the roof condition of a specific building. Similar information, or characteristics, used in creating the model can be used to evaluate a roof condition of the building. This information includes historical weather information for location of the building (e.g., hail, hail size, hail duration, hail direction (e.g., sideway, etc.), wind, lightning, storms, tornadoes, heat index, snowfall, humidity, frequency, etc.), age of the building, age of roof (e.g., years since roof was replaced). The system retrieve information regarding pre-existing damage to the roofing system (e.g., hail damage), information regarding historical hail events that occurred at the building location since the current roof was installed, including, e.g., frequency of hailstorms, number of hailstorms, hailstone diameter, etc., information regarding pre-existing wind damage. The system may retrieve and/or obtain information regarding historical wind events that occurred at the building location since the current roof was installed, other historic catastrophic events (e.g., tornados, hurricanes, thunderstorm events, etc.) at the building location since the current roof was installed from, see paragraphs 27-44, Fig.1-Fig.4).
Notes: Regarding claims 1-16, Jagannathan (2021/0133891) teaches a method and a system related to determine an indicator of probability or risk of at least one of a roof of a building needing to be repaired, a roof of a building needing to be replaced, and an insurance claim being made by a holder of an insurance policy insuring the roof of a building. The system may include an interface configured to receive at least an input regarding a building or the location of the building, and additional information regarding the building. The system includes a roof condition risk scoring engine configured to receive the input received through the interface. The roof condition risk scoring engine is programmed to transform the input based on the additional information regarding the building and based on a model into an indicator indicating the probability or risk that the roof of the building will require repair or replacement during a time period, or that a holder of an insurance policy insuring the roof of the building will make a claim on the policy. A roof condition risk scoring engine includes an interface configured to receive an input regarding a building from a first source. The roof condition risk scoring engine is configured to establish a communication link with a second source to obtain information regarding the building from a second source based on the information provided by the first source. The roof condition risk scoring engine is configured to output an indicator indicating the determined condition of roof risk. Kroger (2017/0039307) teaches a property damage estimate method may be summarized as including collecting meteorological data, geospatial data, or both meteorological and geospatial data from a plurality of sensors disparately situated in a defined geographic area, the collecting occurring before, during, and in some cases after a determined significant weather event; providing geospatial property attribute information for each of a plurality of real property structures within the defined geographic area; estimating, with a super-computing capable device, a magnitude and duration of significant weather event forces at points associated with each of the plurality of real property structures according to at least one significant weather event model to produce at least one model output data set; applying data from the at least one model output data set to the geospatial property attribute information; and based on application of model output data set data to the geospatial property attribute information, automatically estimating damage to the plurality of real property structures. At least some of the plurality of sensors may include a light detection and ranging (LiDAR) circuit. The at least one significant weather event model may include processing at least one of terrain, atmospheric, and bathymetric model data with a finite element model. The finite element model may include a Triangular Irregular Network (TIN) finite element model representing ground and bathymetric surfaces. The Triangular Irregular Network (TIN) finite element model may represent ground cover and surface roughness. At least one significant weather event force may be a flood level. The at least one significant weather event model may include a hydrodynamic flood inundation model. Labrie et al. (2015/0073864) teaches methods and systems for building facet analysis and applying property repair guidelines to that analysis. In particular, but not by way of limitation, the present invention relates to systems and methods for intelligently creating a set of guidelines and applying it to a building repair analysis to ensure building codes and other construction requirements and building repair waste requirements are met. Additionally, it relates to systems and methods for using aerial CAD data, insurance and building code guidelines, weather data, and inspection data for intelligently making repair decisions for building facets.
However, the combination of prior arts of record would be hind-sight reasoning to combine the individual elements disclosed in the prior art in order to achieve Applicant's claimed invention. Thus, claims 1-16 are defined over the prior arts.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claim 1 of the instant application is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,182,873 (co-pending U.S. Application No. 17/356,764). Both claim 1 of the instant application and claim 1 of U.S. Patent No. 12,182,873 are directed to a method for determining a predicted level of roof damage to a roof of a building. Thus, claim 1 of U.S. Patent No. 12,182,873 teaches or suggests all of the limitations of claim 1 of the instant application. However, claim 1 of U.S. Patent No. 12,182,873 also contains additional limitations not found in claim 1 of the instant application, such as the limitations “…by executing a building data receiving module…; …by executing a roof data receiving module…; …by executing a weather data receiving module …; …by executing a hail data receiving module…; …by executing a climate region data receiving module …; provided by a smart home device…; generating, by executing a base-line probable roof loss confidence score data generation module…; determining, by executing a current roof condition determining module…; and determining, by executing a level of roof damage predicting module…”. Accordingly, claim 1 of U.S. Patent No. 12,182,873 is directed to a species of claim 1 of the current application (see MPEP § 804(II)(B)(2)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the additional limitations of claim 1 of U.S. Patent No. 12,182,873 so that the roof damage of the building would be determined more efficiently.
Claim 12 of the instant application is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of U.S. Patent No. 12,182,873 (co-pending U.S. Application No. 17/356,764). Both claim 12 of the instant application and claim 11 of U.S. Patent No. 12,182,873 are directed to a system for determining a predicted level of roof damage to a roof of a building. Thus, claim 11 of U.S. Patent No. 12,182,873 teaches or suggests all of the limitations of claim 12 of the instant application. However, claim 11 of U.S. Patent No. 12,182,873 also contains additional limitations not found in claim 12 of the instant application, such as the limitations “…the confidence score computing device, including a processor and a memory having stored thereon: a building data receiving module that, when executed by the processor of the confidence score computing device, causes the processor of the confidence score computing device to receive building data from a building computing device, wherein the building data is representative of attributes of the building; a roof data receiving module that, when executed by the processor of the confidence score computing device, causes the processor of the confidence score computing device to receive roof data from a roof computing device; a weather data receiving module that, when executed by the processor of the confidence score computing device, causes the processor of the confidence score computing device to receive historical weather data from a weather computing device based on the building data; a hail data receiving module that, when executed by the processor of the confidence score computing device, causes the processor of the confidence score computing device to receive historical hail data from a hail computing device based on the building data, wherein the historical hail data includes the at least one of the video data, the photograph data, or the audio data of the hail event and the at least one hail characteristic of the hail event provided by the smart home device; a climate zone data receiving module that, when executed by the processor of the confidence score computing device, causes the processor of the confidence score computing device to receive climate zone data from a climate zone computing device based on the building data; a base-line probable roof loss confidence score data generation module that, when executed by the processor of the confidence score computing device, causes the processor of the confidence score computing device to generate base-line probable roof loss confidence score data for the building based on the building data, the roof data, the historical weather data, the historical hail data, and the climate zone data; a current roof condition determining module that, when executed by the processor of the confidence score computing device, causes the processor of the confidence score computing device to determine current roof condition data for the roof of the building based on at least one of the building or roof data; and a level of roof damage predicting module that, when executed by the processor of the confidence score computing device, causes the processor of the confidence score computing device to determine the predicted level of roof damage to the roof of the building...”. Accordingly, claim 11 of U.S. Patent No. 12,182,873 is directed to a species of claim 1 of the current application (see MPEP § 804(II)(B)(2)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the additional limitations of claim 11 of U.S. Patent No. 12,182,873 so that the roof damage of the building would be determined more efficiently.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
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/TIEN C NGUYEN/Primary Examiner, Art Unit 3694
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