Systems and methods for automating property assessment using probable roof loss confidence scores

US12182873B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-12182873-B2
Application numberUS-202117356764-A
CountryUS
Kind codeB2
Filing dateJun 24, 2021
Priority dateJun 24, 2021
Publication dateDec 31, 2024
Grant dateDec 31, 2024

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  2. Abstract

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  5. First independent claim

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Abstract

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Systems and methods are provided for automating property assessment using probable roof loss confidence scores. More particularly, the systems and methods may generate a base-line probable roof loss confidence score based upon buildings, roof, weather, hail, and climate data. The systems and methods may predict a specific event and a set of characteristics of the specific event. The systems and methods may predict a level of roof damage and a cost of roof damage based upon the predicted level of roof damage. The systems and methods may adjust a risk-related factor, and may further adjust policy parameters based upon the risk-related factor. A probable roof loss confidence score may be generated, as well as property insurance claims data or property insurance loss mitigation data based upon probable roof loss confidence score data.

First claim

Opening claim text (preview).

What is claimed is: 1. A computer-implemented method for determining a predicted level of roof damage to a roof of a building, comprising: obtaining, by executing a building data receiving module on a processor, building data representative of attributes of the building; obtaining, by executing a roof data receiving module on the processor, roof data associated with the building based upon the building data; obtaining, by executing a weather data receiving module on the processor, historical weather data associated with the building based upon the building data; obtaining, by executing a hail data receiving module on the processor, historical hail data associated with the building based upon the building data, wherein the historical hail data includes at least one of video data, photograph data, or audio data of a hail event and at least one hail characteristic of the hail event provided by a smart home device disposed at the building, wherein the smart home device is configured to: monitor audio signals proximate the building; detect an occurrence of the hail event by detecting an audio signature within the audio signals, the audio signature including a set of audio 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 photograph data, or the audio data of the hail event in real time; analyze the at least one of the video data, the photograph 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 photograph data, or the audio data of the hail event and the at least one hail characteristic of the hail event to the processor; obtaining, by executing a climate region data receiving module on the processor, climate region data associated with the building based upon the building data; generating, by executing a base-line probable roof loss confidence score data generation module on the processor, 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, by executing a current roof condition determining module on the processor, a current roof condition of the roof of the building based upon at least one of the building or roof data; and determining, by executing a level of roof damage predicting module on the processor, 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. 2. The computer-implemented method of claim 1 , wherein the current roof condition includes a remaining roof lifespan. 3. The computer-implemented method of claim 1 , wherein determining the predicted level of roof damage comprises determining a predicted cost of roof damage to the roof of the building, and determining the predicted cost of roof damage is further based upon the attributes of the building. 4. The computer-implemented method of claim 1 , further comprising adjusting a risk-related factor associated with an insurance policy relating to the roof based upon the predicted level of roof damage to the roof of the building. 5. The computer-implemented method of claim 1 , wherein determining the predicted level of roof damage comprises implementing 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. 6. The computer-implemented method of claim 5 , wherein the first and second terms of the probability function are each based upon at least one of the building data, the roof data, the weather data, the hail data, or the climate region data. 7. The computer-implemented method of claim 1 , further comprising adjusting, by the processor, one or more parameters of an insurance policy for insuring the roof or the building based upon the predicted level of roof damage. 8. The computer-implemented method of claim 1 , wherein the attributes of the building include at least one of: a geographic location of the building, a building orientation relative to geographic cardinal directions, height of the building, number of stories of the building, tree cover over or around the building, location and height of structures surrounding the building, or elevation of terrain surrounding the building. 9. The computer-implemented method of claim 1 , wherein the hail data is representative of attributes of historical hail that has impacted a geographic area that includes a geographic location of the building, and wherein the attributes of the hail include at least one of: a physical characteristic of the hail, a size of the hail, a shape of the hail, a density of the hail, a hardness of the hail, a range of hail sizes produced by a storm, or a resistance to flexing of the hail. 10. The computer-implemented method of claim 1 , wherein the climate region data is representative of at least one of: a climate associated with a geographic location of the building; a humidity associated with a geographic location of the building, a temperature associated with a geographic location of the building, a moisture associated with a geographic location of the building, or whether a geographic location of the building is associated with a marine climate. 11. A system for determining a predicted level of roof damage to a roof of a building, the system comprising: a smart home device disposed at the building configured to: monitor audio signals proximate the building; detect an occurrence of a hail event by detecting an audio signature within the audio signals, the audio signature including a set of audio characteristics forming a pattern indicative of an occurrence of hail events; responsive to detecting the occurrence of the hail event, collect at least one of video data, photograph data, or audio data of the hail event in real time; analyze the at least one of the video data, the photograph data, or the audio data of the hail event to estimate 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 photograph data, or the audio data of the hail event and the at least one hail characteristic of the hail event to a confidence score computing device; and 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,

Assignees

Inventors

Classifications

  • Probabilistic graphical models, e.g. probabilistic networks · CPC title

  • Product appraisal · CPC title

  • Real estate · CPC title

  • Machine learning · CPC title

  • G06Q40/08Primary

    Insurance · CPC title

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What does patent US12182873B2 cover?
Systems and methods are provided for automating property assessment using probable roof loss confidence scores. More particularly, the systems and methods may generate a base-line probable roof loss confidence score based upon buildings, roof, weather, hail, and climate data. The systems and methods may predict a specific event and a set of characteristics of the specific event. The systems and…
Who is the assignee on this patent?
State Farm Mutual Automobile Insurance Co
What technology area does this patent fall under?
Primary CPC classification G06Q40/08. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Dec 31 2024 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 12 related publications on this page (citations in our corpus or others sharing the same primary CPC).