Abstract: The following generally relates to using Augmented Reality (AR) to enhance the in-vehicle experience. In some examples, AR techniques are applied to provide AR indications of vehicle safety indicia to alert vehicle occupants to information that may not otherwise be perceptible. In other example, AR techniques are applied to provide emergency vehicle warnings to improve the likelihood of safe responses thereto. In yet other examples, AR techniques are applied to generated personalized outdoor displays, for example, to ameliorate conditions that may impair safe operation of a vehicle.

Abstract: A method and system may survey a property using aerial images captured from an unmanned aerial vehicle (UAV), a manned aerial vehicle (MAV) or from a satellite device. The method may include identifying a commercial property for a UAV to perform surveillance, and directing the UAV to hover over the commercial property and capture aerial images at predetermined time intervals. Furthermore, the method may include receiving the aerial images of the commercial property captured at the predetermined time intervals, detecting a surveillance event at the commercial property, generating a surveillance alert, and transmitting the surveillance alert to an electronic device associated with an owner of the commercial property.

Abstract: Described methods and systems user to manually activate or engage the pump from a remote location and/or to monitor the state of the sump pump (e.g., the water level, the pump condition, pipe conditions, etc.) when located at a remote location relative to the sump pump. A sump pump system may implement sensors configured to detect motion or acceleration of a sensor disposed in water in the sump basin or disposed on a sump pump or pipe. The sump pump system may analyze data from these sensors to identify diagnostic metrics or values that are transmitted to a user's user interface device, thereby notifying a user of conditions such as a stuck impeller, a blocked pipe, a dry pumping pump, etc.

Abstract: The following generally relates to using Augmented Reality (AR) to enhance the in-vehicle experience. In some examples, AR techniques are applied to provide AR indications of vehicle safety indicia to alert vehicle occupants to information that may not otherwise be perceptible. In other example, AR techniques are applied to provide emergency vehicle warnings to improve the likelihood of safe responses thereto. In yet other examples, AR techniques are applied to generated personalized outdoor displays, for example, to ameliorate conditions that may impair safe operation of a vehicle.

Abstract: A method may include receiving sensor data of a sensor associated with a vehicle occupied by a user, the sensor data indicating that the vehicle is involved in an accident. The method may further include determining one or more potential injuries sustained by occupants of the vehicle during the accident and transmitting an indication of the one or more injuries to a mobile device of the user. Still further, the method may including prompting the user to provide a response confirming or modifying the one or more potential injuries and updating the one or more potential injuries based on the response. The method may also include generating at least one insurance claim form for the accident based on the one or more potential injuries.

Abstract: The following relates generally to artificial intelligence (AI)-based responses to social media posts. In some embodiments, one or more processors may be configured to: (1) receive, via a chatbot, a social media post; (2) categorize, via the chatbot, the social media post; (3) determine, via the chatbot, based upon the categorization, an entity to contact; (4) build, via the chatbot, based upon the determined entity to contact, a response to the social media post; and/or (5) send, via the chatbot, the response to the entity.

Abstract: The following relates generally to AI-based review of insurance claims complaints. In some embodiments, one or more processors: (1) receive, via a chatbot, an insurance claim complaint; (2) categorize, via the chatbot, the insurance claim complaint by determining a category of the insurance claim complaint, the category comprising a tone category or a policy category; (3) build, via the chatbot, a complaint report including information of the insurance claim complaint and an indication of the category; and/or (5) send, via the chatbot, the complaint report to an insurance complaint administrator computing device.

Abstract: The following relates generally to artificial intelligence (AI)-based communication with an insurance customer. In some embodiments, one or more processors: receive, via a chatbot (or voicebot or other bot), a question in a first language; translate, via the chatbot, the question from the first language to a second language; determine, via the chatbot, an answer to the translated question, the answer being determined in the second language; translate, via the chatbot, the answer from the second language to the first language; and/or present, via the chatbot, the answer in the first language to an insurance customer.

Abstract: Methods and systems for alerting a user of crime-prone locations are disclosed herein. In some embodiments, the method comprises: (1) receiving, by one or more processors from a user device, location data of the user device and a destination input by the user; (2) determining, by the one or more processors, based upon the location data, that the user device is approaching the destination; (3) loading, by the one or more processors, a crime density map surrounding the destination; (4) determining, by the one or more processors, based upon the crime density map, at least one non-crime-prone parking spot, wherein the at least one non-crime-prone parking spot meets at least one predetermined criterion; and/or (5) presenting, by the one or more processors via the user device, an indication of the at least one non-crime-prone parking spot.

Abstract: Methods and systems for alerting a user of accident-prone locations are disclosed herein. In some embodiments, the method comprises: (1) receiving, by one or more processors and from a user device associated with the user, location data and motion data of the user device; (2) determining, by the one or more processors, based upon the motion data, that the user has started a trip; (3) loading, by the one or more processors, an accident density map surrounding a location of the user device based upon the location data; (4) determining, by the one or more processors, based upon the accident density map, at least one accident-prone location within a scope of the accident density map, wherein the at least one accident-prone location meets at least one predetermined criterion; and/or (5) presenting, by the one or more processors via the user device, an indication of the at least one accident-prone location.

Abstract: Systems and methods are described for using data collected by unmanned aerial vehicles (UAVs) to generate insurance claim estimates that an insured individual may quickly review, approve, or modify. When an insurance-related event occurs, such as a vehicle collision, crash, or disaster, one or more UAVs are dispatched to the scene of the event to collect various data, including data related to vehicle or real property (insured asset) damage. With the insured's permission or consent, the data collected by the UAVs may then be analyzed to generate an estimated insurance claim for the insured. The estimated insurance claim may be sent to the insured individual, such as to their mobile device via wireless communication or data transmission, for subsequent review and approval. As a result, insurance claim handling and/or the online customer experience may be enhanced.

Abstract: The following generally relates to using Augmented Reality (AR) to enhance pedestrian navigation. In some examples, AR techniques are applied to provide AR indications of a location of a group member that includes two or more AR devices. In these examples, AR techniques may be applied to determine a relative position of the group member and/or provide navigational guidance to the group member. In other examples, AR techniques are applied to provide AR-assisted pedestrian guidance. In these examples, AR techniques may be applied to present an AR display that includes information for a point of interest along a route.

Abstract: A sump pump system may implement adaptive learning and machine learning techniques to facilitate improved control of sump pumps. A sump pump system may implement the described techniques to generate, train, and/or implement a machine learning model that is capable of predicting or estimating one or more conditions of the sump pump system (e.g., water level in the basin, motor malfunction, stuck impeller, geyser effect, blocked outlet pipe, faulty level sensor/switch, faulty bearing, failure to engage pump at high-water mark, etc.) based on one or more detected input variables (e.g., acceleration or vibration patterns detected in water, on a pump, or on a pipe; capacitance values of water; audio signatures; electrical signatures, such as power or current draw; pump motor rotation speed; water pressure signatures or values, such as those detected at the bottom of a sump basin; etc.).

Abstract: In a computer-implemented method and system for capturing the condition of a structure, the structure is scanned with an unmanned aerial vehicle (UAV). Data collected by the UAV corresponding to points on a surface of a structure is received and a 3D point cloud is generated for the structure, where the 3D point cloud is generated based at least in part on the received UAV data. A 3D model of the surface of the structure is reconstructed using the 3D point cloud.

Abstract: Various techniques are described utilizing one or more unmanned aerial vehicles (UAVs, or “drones”) for various disaster and/or catastrophe-related purposes. UAVs may collect data in an attempt to predict the occurrence and/or extent of a catastrophe and/or to mitigate the impact of a catastrophe before and, if not at that time, once it has occurred. The UAVs may perform various tasks such that the damage to property caused by a catastrophe (or potential catastrophe) may be eliminated or mitigated. A UAV may receive a flight path based on an energy consumption related condition and operate based on the flight path. Operating based on the flight path includes docking the UAV with a power supply device for charging a power source of the UAV.

Abstract: A method may include receiving sensor data of a sensor associated with a vehicle occupied by a user, the sensor data indicating that the vehicle is involved in an accident. The method may further include determining one or more potential injuries sustained by occupants of the vehicle during the accident and transmitting an indication of the one or more injuries to a mobile device of the user. Still further, the method may including prompting the user to provide a response confirming or modifying the one or more potential injuries and updating the one or more potential injuries based on the response. The method may also include generating at least one insurance claim form for the accident based on the one or more potential injuries.

Abstract: Apparatuses, systems and methods are provided for generating customized code. The apparatuses, systems and methods may cause a chatbot to generate customized code. The apparatuses, systems and methods may integrate the customized code in an insurance application. The customized code, when executed by one or more processors, may cause the one or more processors to collect user information from a user device, determine whether a change of insurance policy for the user is required, and responsive to determining that a change of insurance policy is required, automatically update the insurance policy in the insurance application based upon the required change.

Abstract: Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, a solution to fix the error. The apparatuses, systems and methods may analyze the solution to determine a number of at least one of (i) a set of steps or (ii) a set of interactions required by the solution. The apparatuses, systems and methods may, responsive to determining that the number exceeds a predetermined threshold, fix the error by implementing the solution with respect to the target code.

Abstract: Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, whether there is a solution to fix the error. The apparatuses, systems and methods may, responsive to determining that there is a solution to fix the error, cause the ML chatbot to (i) fix the error, and/or (ii) present the error and/or the solution to a user.

Abstract: A computer system may include one or more processors configured to: (1) obtain insurance claim information; (2) generate, via an ML chatbot (or voice bot), the customized presentation based upon the insurance claim information; and/or (3) provide, via the ML chatbot, the customized presentation to a user device, such as a visual depiction and/or a verbal description of the customized presentation.