Abstract: Apparatuses, systems and methods are provided for performing insurance property loss mitigation processes based on an event driven probable roof loss confidence score based on a particular weather event, the event driven probable roof loss confidence score associated with a roof of a building. An example method may include receiving a base-line probable roof loss confidence score for the building. The method may further include receiving weather data associated with the particular weather event, that occurred in the geographic area of the building, and hail data associated with the particular weather event. The method also may include estimating at least one attribute of the one or more attributes, and generating the event driven probable roof loss confidence score based on the base-line probable roof loss confidence score, the weather data, the hail data, and the at least one estimated attribute.

Abstract: An impairment analysis (“IA”) computer system for alerting a first driver of a first vehicle to a driving hazard posed by a second vehicle operated by a second driver is provided. The IA computer system is associated with the first vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) receive second vehicle data including second driver data and second vehicle condition data, where the second vehicle data is collected by a plurality of sensors included on the first vehicle; (ii) analyze the second vehicle data by applying a baseline model to the second vehicle data; (iii) determine that the second vehicle poses a driving hazard to the first vehicle based upon the analysis; and/or (iv) generate an alert signal based upon the determination that the second vehicle poses a driving hazard to the first vehicle.

Abstract: An impairment analysis (“IA”) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

Abstract: Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

Abstract: Apparatuses, systems and methods are provided for generating enterprise data relating to roof damage associated with weather and hail data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the base-line probable roof loss confidence scores. The apparatuses, systems and methods may generate probable roof loss confidence score data based upon the base-line probable roof loss confidence scores, weather event data and hail event data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the probable roof loss confidence score.

Abstract: An impairment analysis (“IA”) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

Abstract: Apparatuses, systems and methods are provided for generating enterprise data relating to roof damage associated with weather and hail data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the base-line probable roof loss confidence scores. The apparatuses, systems and methods may generate probable roof loss confidence score data based upon the base-line probable roof loss confidence scores, weather event data and hail event data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the probable roof loss confidence score.

Abstract: Apparatuses, systems and methods are provided for performing insurance property loss mitigation processes based on an event driven probable roof loss confidence score based on a particular weather event, the event driven probable roof loss confidence score associated with a roof of a building. An example method may include receiving a base-line probable roof loss confidence score for the building. The method may further include receiving weather data associated with the particular weather event, that occurred in the geographic area of the building, and hail data associated with the particular weather event. The method also may include estimating at least one attribute of the one or more attributes, and generating the event driven probable roof loss confidence score based on the base-line probable roof loss confidence score, the weather data, the hail data, and the at least one estimated attribute.

Abstract: An impairment analysis (“IA”) computer system for alerting a first driver of a first vehicle to a driving hazard posed by a second vehicle operated by a second driver is provided. The IA computer system is associated with the first vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) receive second vehicle data including second driver data and second vehicle condition data, where the second vehicle data is collected by a plurality of sensors included on the first vehicle; (ii) analyze the second vehicle data by applying a baseline model to the second vehicle data; (iii) determine that the second vehicle poses a driving hazard to the first vehicle based upon the analysis; and/or (iv) generate an alert signal based upon the determination that the second vehicle poses a driving hazard to the first vehicle.

Abstract: 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.

Abstract: Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

Abstract: An impairment analysis (“IA”) computer system for detecting a driver or vehicle impairment is provided. The IA computer system is associated with a host vehicle, and includes a plurality of sensors, and at least one processor in communication with the plurality of sensors and at least one memory device. The at least one processor is programmed to: (i) interrogate a target vehicle via the plurality of sensors by scanning at least one of the target vehicle and a target driver; (ii) receive sensor data including at least one of target driver data and target vehicle condition data; (iii) analyze the sensor data to determine whether at least one of lane drift and vehicle speed deviation for the target vehicle exceeds a respective threshold; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) direct collision avoidance action based upon the detection.

Abstract: An impairment analysis (“IA”) computer system for detecting a driver or vehicle impairment to facilitate vehicle collision avoidance is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate a target vehicle via the plurality of sensors by scanning at least one of the target vehicle and a target driver of the target vehicle; (ii) receive sensor data including at least one of target driver data and target vehicle condition data; (iii) analyze the sensor data by determining at least one outlier within the sensor data; (iv) detect an impairment of at least one of the target driver and the target vehicle based upon the analysis; and/or (v) direct corrective action based upon the detected impairment.

Abstract: An impairment analysis (“IA”) computer system for alerting a first driver of a first vehicle to a driving hazard posed by a second vehicle operated by a second driver is provided. The IA computer system is associated with the first vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) receive second vehicle data including second driver data and second vehicle condition data, where the second vehicle data is collected by a plurality of sensors included on the first vehicle; (ii) analyze the second vehicle data by applying a baseline model to the second vehicle data; (iii) determine that the second vehicle poses a driving hazard to the first vehicle based upon the analysis; and/or (iv) generate an alert signal based upon the determination that the second vehicle poses a driving hazard to the first vehicle.

Abstract: An impairment analysis (“IA”) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

Abstract: In a computer-implemented method of roofing material identification, image data corresponding to one or more images of roofing materials may be received. The image data may be processed to determine pertinent characteristics of the roofing materials. The determined characteristics and a characteristics database storing data indicative of associations between a plurality of roofing material products and characteristics of the plurality of roofing material products, may be used to identify a roofing material product associated with the pertinent characteristics. An indication of the identified roofing material product may be provided (e.g., to facilitate claim processing).

Abstract: In a computer-implemented method of roofing material identification, image data corresponding to one or more images of roofing materials may be received. The image data may be processed to determine pertinent characteristics of the roofing materials. The determined characteristics and a characteristics database storing data indicative of associations between a plurality of roofing material products and characteristics of the plurality of roofing material products, may be used to identify a roofing material product associated with the pertinent characteristics. An indication of the identified roofing material product may be provided (e.g., to facilitate claim processing).

Abstract: A method for identifying siding includes receiving particular texture data associated with a physical texture of a particular siding sample, accessing a reference database including reference texture data associated with physical texture of multiple different reference siding samples, performing an automated texture data analysis by analyzing the particular texture data associated with the particular siding sample and the reference texture data associated with different reference siding samples, and automatically identifying at least one reference siding sample that matches the particular siding sample based at least on the automated texture data analysis. The particular texture data associated with the physical texture of the particular siding sample may comprise a digital image, such as a photographic or scanned image of the particular siding sample, or a photographic or scanned image of an ink image physically transferred from the particular siding sample onto a substrate.

Abstract: A method for identifying siding includes receiving particular texture data associated with a physical texture of a particular siding sample, accessing a reference database including reference texture data associated with physical texture of multiple different reference siding samples, performing an automated texture data analysis by analyzing the particular texture data associated with the particular siding sample and the reference texture data associated with different reference siding samples, and automatically identifying at least one reference siding sample that matches the particular siding sample based at least on the automated texture data analysis. The particular texture data associated with the physical texture of the particular siding sample may comprise a digital image, such as a photographic or scanned image of the particular siding sample, or a photographic or scanned image of an ink image physically transferred from the particular siding sample onto a substrate.