Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: A method and system may use computer vision techniques and machine learning analysis to automatically identify a user's biometric characteristics. A user's client computing device may capture a video of the user. Feature data and movement data may be extracted from the video and applied to statistical models for determining several biometric characteristics. The determined biometric characteristic values may be used to identify individual health scores and the individual health scores may be combined to generate an overall health score and longevity metric. An indication of the user's biometric characteristics which may include the overall health score and longevity metric may be displayed on the user's client computing device.

Abstract: A method and system may use machine learning analysis of audio data to automatically identify a user's biometric characteristics. A user's client computing device may capture audio of the user. Feature data may be extracted from the audio and applied to statistical models for determining several biometric characteristics. The determined biometric characteristic values may be used to identify individual health scores and the individual health scores may be combined to generate an overall health score and longevity metric. An indication of the user's biometric characteristics which may include the overall health score and longevity metric may be displayed on the user's client computing device.

Abstract: Systems and methods for real-time detection and mitigation anomalous behavior of a remote vehicle are provided, e.g., vehicle behavior that is consistent with distracted or unexpectedly disabled driving. On-board and off-board sensors associated with a subject vehicle may monitor the subject vehicle's environment, and behavior characteristics of a remote vehicle operating within the subject vehicle's environment may be determined based upon collected sensor data. The remote vehicle's behavior characteristics may be utilized to detect or determine the presence of anomalous behavior, which may be anomalous for the current contextual conditions of the vehicles' environment. Mitigating actions for detected remote vehicle anomalous behaviors may be suggested and/or automatically implemented at the subject vehicle and/or at proximate vehicles to avoid or reduce the risk of accidents, injury, or death resulting from the anomalous behavior. In some situations, authorities may be notified.

Abstract: Methods, systems, apparatus, and non-transitory computer readable media are described for detecting system outages using application event logs. Various aspects may include obtaining several prior application event logs where the status of the system is known at the time the application event logs were recorded. Additionally, various aspects may include determining characteristics of prior application event logs which were recorded during a system outage, and/or determining characteristics of prior application event logs which were recorded while the system was available. When current application event logs are obtained where the status of the system is unknown at the time the current application event logs are recorded, various aspects include comparing the current application event logs to the prior application event logs to determine that a system outage has occurred based upon the comparison.

Abstract: Methods and systems for processing machine accelerated and augmented customer data using a Web-Scale Data Fabric (WSDF). According to embodiments, the data may be received as data transfer objects from a set of business operations client applications. The data transfer objects may be analyzed using complex event processing (CEP) and, based on the analyzing, rules specific to the business operations client application may be applied. The methods and systems may semantically classify text specific to the business operations client application. A federated database (FD) may archive the receive data transfer objects as well as analysis data specific to the business operations client application.

Abstract: Systems and methods for real-time detection and mitigation anomalous behavior of a remote vehicle are provided, e.g., vehicle behavior that is consistent with distracted or unexpectedly disabled driving. On-board and off-board sensors associated with a subject vehicle may monitor the subject vehicle's environment, and behavior characteristics of a remote vehicle operating within the subject vehicle's environment may be determined based upon collected sensor data. The remote vehicle's behavior characteristics may be utilized to detect or determine the presence of anomalous behavior, which may be anomalous for the current contextual conditions of the vehicles' environment. Mitigating actions for detected remote vehicle anomalous behaviors may be suggested and/or automatically implemented at the subject vehicle and/or at proximate vehicles to avoid or reduce the risk of accidents, injury, or death resulting from the anomalous behavior. In some situations, authorities may be notified.

Abstract: A method and system may use computer vision techniques and machine learning analysis to automatically identify a user's biometric characteristics. A user's client computing device may capture a video of the user. Feature data and movement data may be extracted from the video and applied to statistical models for determining several biometric characteristics. The determined biometric characteristic values may be used to identify individual health scores and the individual health scores may be combined to generate an overall health score and longevity metric. An indication of the user's biometric characteristics which may include the overall health score and longevity metric may be displayed on the user's client computing device.

Abstract: Methods and systems for displaying a user interface that warns a driver that a vehicle is located within or is near a geographical area (and/or intersection) associated with a higher than average risk of animal-vehicle and/or vehicle-vehicle collisions are provided. According to certain aspects, an electronic device may access a database that identifies a plurality of high risk areas, including areas associated with prior vehicle accidents. The electronic device may display a virtual road map, as well as an icon indicating the vehicle's current location and a plurality of visual indications of high-risk areas. When the electronic device detects that the current location of the vehicle is within and/or approaching a high-risk area, the electronic device may then warn the driver about the higher than average risk of experiencing a vehicle collision.

Abstract: Methods, systems, apparatus, and non-transitory computer readable media are described for detecting system outages using application event logs. Various aspects may include obtaining several prior application event logs where the status of the system is known at the time the application event logs were recorded. Additionally, various aspects may include determining characteristics of prior application event logs which were recorded during a system outage, and/or determining characteristics of prior application event logs which were recorded while the system was available. When current application event logs are obtained where the status of the system is unknown at the time the current application event logs are recorded, various aspects include comparing the current application event logs to the prior application event logs to determine that a system outage has occurred based upon the comparison.

Abstract: Methods and systems for displaying a user interface that warns a driver that a vehicle is located within or is near a geographical area (and/or intersection) associated with a higher than average risk of animal-vehicle and/or vehicle-vehicle collisions are provided. According to certain aspects, an electronic device may access a database that identifies a plurality of high risk areas, including areas associated with prior vehicle accidents. The electronic device may display a virtual road map, as well as an icon indicating the vehicle's current location and a plurality of visual indications of high-risk areas. When the electronic device detects that the current location of the vehicle is within and/or approaching a high-risk area, the electronic device may then warn the driver about the higher than average risk of experiencing a vehicle collision.

Abstract: Methods and systems for analyzing environment data to determine whether a vehicle operator is at an elevated risk for an animal collision are provided. According to certain aspects, an insurance provider may assess elevated risk according to various factors and, if it is determined that the vehicle operator is at an elevated risk for an animal collision, the insurance provider may generate a warning and wirelessly communicate the warning to the vehicle operator. The factors analyzed may include past accident, driver characteristic, weather, calendar, time of day, animal, seasonal, and/or other information. The vehicle operator may be notified of the risk and optionally presented with tips to mitigate the risk. The vehicle operator may be notified of the risk by a mobile device and/or the vehicle, such as from a vehicle communication and control system. Animal collision avoidance functionality may be used to adjust insurance premiums, rates, or rewards.

Abstract: Methods and systems for displaying a user interface that warns a driver that a vehicle is located within or is near a geographical area (and/or intersection) associated with a higher than average risk of animal-vehicle and/or vehicle-vehicle collisions are provided. According to certain aspects, an electronic device may access a database that identifies a plurality of high risk areas, including areas associated with prior vehicle accidents. The electronic device may display a virtual road map, as well as an icon indicating the vehicle's current location and a plurality of visual indications of high-risk areas. When the electronic device detects that the current location of the vehicle is within and/or approaching a high-risk area, the electronic device may then warn the driver about the higher than average risk of experiencing a vehicle collision.

Abstract: Methods and systems for processing machine accelerated and augmented customer data using a Web-Scale Data Fabric (WSDF). According to embodiments, the data may be received as data transfer objects from a set of business operations client applications. The data transfer objects may be analyzed using complex event processing (CEP) and, based on the analyzing, rules specific to the business operations client application may be applied. The methods and systems may semantically classify text specific to the business operations client application. A federated database (FD) may archive the receive data transfer objects as well as analysis data specific to the business operations client application.

Abstract: Methods and systems for displaying a user interface that warns a driver that a vehicle is located within or is near a geographical area (and/or intersection) associated with a higher than average risk of animal-vehicle and/or vehicle-vehicle collisions are provided. According to certain aspects, an electronic device may access a database that identifies a plurality of high risk areas, including areas associated with prior vehicle accidents. The electronic device may display a virtual road map, as well as an icon indicating the vehicle's current location and a plurality of visual indications of high-risk areas. When the electronic device detects that the current location of the vehicle is within and/or approaching a high-risk area, the electronic device may then warn the driver about the higher than average risk of experiencing a vehicle collision.

Abstract: Methods and systems for displaying a user interface that warns a driver that a vehicle is located within or is near a geographical area (and/or intersection) associated with a higher than average risk of animal-vehicle and/or vehicle-vehicle collisions are provided. According to certain aspects, an electronic device may access a database that identifies a plurality of high risk areas, including areas associated with prior vehicle accidents. The electronic device may display a virtual road map, as well as an icon indicating the vehicle's current location and a plurality of visual indications of high-risk areas. When the electronic device detects that the current location of the vehicle is within and/or approaching a high-risk area, the electronic device may then warn the driver about the higher than average risk of experiencing a vehicle collision.

Abstract: Methods, systems, apparatus, and non-transitory computer readable media are described for detecting system outages using application event logs. Various aspects may include obtaining several application event logs where the status of the system is known at the time the application event logs were recorded. Additionally, various aspects may include determining characteristics of application event logs which were recorded during a system outage, and/or determining characteristics of application event logs which were recorded while the system was available. Based upon the characteristics, various aspects include training using various machine learning techniques. When current application event logs are obtained where the status of the system is unknown at the time the current application event logs are recorded, various aspects include using the training data to determine a likelihood that a system outage has occurred based upon the obtained current application event logs.