Abstract: Systems and methods for analyzing documentation for assessing potential fraudulent user submissions are provided. According to certain aspects, a server computer may receive an initial set of documentation descriptive of damage to a property asset, and may analyze the initial set of documentation to determine whether additional documentation is needed. The server computer may initiate a communication channel with a user device via which the additional documentation may be submitted, and the server computer may similarly analyze the additional documentation to determine a likelihood of fraud. The server computer may process the user submission accordingly.

Abstract: A charging system can include an external power source. The external power source can include a first groove. The external power source also can include a second groove. The second groove can be separate from the first groove. The charging system also can include a housing. The housing can include an internal power source configured to be wirelessly charged by the external power source that is removably coupled to the housing. The housing also can include a component configured to draw energy from the internal power source. Other embodiments are disclosed.

Abstract: A ring includes an inner surface configured to contact a finger of a user when the ring is worn by the user. The ring can include a first power source configured to be wirelessly charged by a second power source that is (i) removably coupled along a circumference of the inner surface of the ring, and (ii) disposed at an outer surface of the ring. The ring also can include a sensor configured to draw energy from the first power source and sense an activity of the user wearing the ring.

Abstract: A computer-implemented method can include receiving vehicle image data of a vehicle captured by a portable imaging gate apparatus, wherein the portable imaging gate apparatus includes a plurality of frames, and wherein at least one frame of the plurality of frames is adjustable to accommodate vehicles of different sizes. The computer-implemented method can also include analyzing the vehicle image data to identify one or more vehicle features. The computer-implemented method can further include determining a first vehicle feature from the one or more vehicle features. The computer-implemented method can additionally include updating a risk evaluation based at least on the first vehicle feature. Other embodiments are disclosed.

Abstract: A computer-implemented method including receiving an image for evaluation, the image includes video and/or still image data; analyzing the image to extract profile features using a pattern recognition or a facial recognition algorithm, an object recognition, and/or an object character recognition, the profile features include first profile features related to a vehicle and second profile features related to an image verification indicator, the second profile features include one or more features related to an identity of a user; generating an image verification score including a first comparison and a second comparison of at least a portion of one or more of the first profile features and at least a portion of one or more of the second profile features, respectively, and the image verification indicator; and updating a vehicle profile when the image verification score satisfies a threshold of similarity based on the comparisons.

Abstract: A method for predicting risk exposure can include receiving a set of data collected via a smart ring. The method also can include analyzing, via a trained machine learning (ML) model, the set of data collected via the smart ring to determine whether the set of data collected via the smart ring (a) represents an impairment pattern or (b) correlates to a high-risk pattern, wherein at least one of the impairment pattern or the high-risk pattern correlates to a risk exposure. The method for predicting risk exposure further can include generating a notification to alert a user of the risk exposure. Other embodiments are disclosed.

Abstract: A computer-implemented method can include receiving first telematics data associated with a first real-life trip of a real-life vehicle operated by a user. The computer-implemented method can also include determining how proficiently the user operated the real-life vehicle when the user encountered one or more real-life occurrences during the first real-life trip. The computer-implemented method can further include generating one or more first virtual occurrences to be encountered by a virtual character during a first virtual trip. The computer-implemented method can additionally include receiving second telematics data associated with a second real-life trip of the real-life vehicle operated by the user. The computer-implemented method can also include determining how proficiently the user operated the real-life vehicle when the user encountered the one or more real-life occurrences during the second real-life trip.

Abstract: A system for displaying information via a smart ring device can include a smart ring device. The smart ring device also can include a processor disposed within a ring band. The processor can be configured to collect data to identify one or more conditions. The one or more conditions can include one or more of a biometric of a user of the smart ring device, a state of the user of the smart ring device, or a condition of an environment of the user of the smart ring device. The smart ring device further can include a display at a surface of the ring band. The processor can be configured to cause the display to present the information indicative of the one or more conditions. The processor also can be configured to determine whether a termination condition has been met for the one or more conditions.

Abstract: A computer-implemented method for determining risk scores using machine learning based on sensor data. The method includes receiving sensor data and demographic data for a user, determining a pattern of behavior based on the sensor data and demographic data, identifying a plurality of test groups based on other users that exhibit similar patterns of behavior to each other where sensor data from other users' electronic devices are received, identifying a test group corresponding to the user from among the plurality of test groups, determining a risk score using a trained risk analyzer machine learning model trained with historical sensor data, historical demographic data, and feedback data indicative of incidents, identifying risk mitigation strategies based on the risk score, refining the risk mitigation strategies using an iterative machine learning process as additional feedback data is collected, and retrieving risk mitigation communications from a database when the risk score exceeds a threshold.

Abstract: A computer-implemented method including receiving trip data of one or more trips of a driver from one or more sensors, dividing the trip data into a plurality of trip data segments based on a predetermined distance, transforming the plurality of trip data segments into a multi-dimensional graphical representation beyond two dimensions by generating a two-dimensional graphical representation using relative longitude and latitude coordinates extracted from the trip data as indexes and adding depth to each point of the two-dimensional graphical representation to form a high-depth image-like tensor, and determining predicted driving behaviors of the driver by inputting the multi-dimensional graphical representation into a prediction model to extract features indicative of driving behaviors. Other embodiments are described.

Abstract: A telematics analysis computing device for monitoring driving behavior of a driver of a vehicle, the telematics analysis computing device comprising one or more processors in communication with a memory device comprising computer instructions that, when run on the one or more processors, cause the one or more processors to execute operations: receiving telematics data collected from one or more electronic devices of one or more users; analyzing the telematics data to determine (a) one or more stop locations and (b) one or more driving behaviors of the one or more users at the one or more stop locations; generating, based on analyzing the telematics data, safety scores for the one or more stop locations; generating a route based on a request received from an electronic device of the driver; identifying, along the route, as generated, at least one of the one or more stop locations having a respective safety score of the safety scores that is below a predetermined threshold; and transmitting a notification to th

Abstract: A system for displaying information via a smart ring can include a ring band. The system for displaying information via a smart ring also can include at least one processor disposed within the ring band. The at least one processor disposed within the ring can be configured to identify a user of the smart ring. The at least one processor disposed within the ring further can be configured to analyze the data to identify a condition. The system for displaying information via a smart ring further can include a display disposed at least partially in, at least partially on, or coupled to the ring band and communicatively coupled to the processor. Furthermore, the at least one processor disposed within the ring can be configured to cause the display to present information indicative of the condition. Other embodiments are disclosed.

Abstract: A computer-implemented method to determine a device of a driver including: receiving, by a computing device and from a first user device, first telematics data; receiving, by the computing device and from a second user device, second telematics data; determining, using a trained machine learning model, patterns in the first and second telematics data; determining, using the patterns in the first and second telematics data, relative positions of the first user device and the second user device; determining a predicted position of the first user device relative to the second user device; and identifying that the first user device or the second user device is associated with the driver of a vehicle based at least in part upon the predicted position of the first user device relative to the second user device. Other embodiments are described.

Abstract: A ring clip includes a body. The ring clip also can include a groove disposed within the body and extending along a length of the body between a first end and a second end of the ring clip. The groove can be configured to receive a ring having a conventional ring shape. The body of the ring clip can be removable from the ring. The ring clip further can include a sensor configured to collect information from a user. The first portion of an outside surface of the body can include a location that is outside of the groove. The first portion of the outside surface of the body can include a location that is not configured to contact a finger of the user when the ring clip is coupled to the ring and when the ring is worn on the finger of the user. Other embodiments are disclosed herein.

Abstract: A ring device for displaying information includes a ring band. The ring device also can include a processor located within the ring band. The processor can be configured to analyze data to identify one or more conditions. The ring device further can include a projector module located at least partially within the ring band. The projector module can be communicatively coupled to the processor. The ring device further can include a user input unit. The user input unit can include one or more sensors located at least partially within the ring band. The one or more sensors can be configured to receive a user input. The user input unit also can be communicatively coupled to the processor. The processor can be configured to cause the projector module to present the information indicative of the one or more conditions, as identified. Other embodiments are disclosed.

Abstract: A computer-implemented method can include: receiving, by a computing device, driving data of a vehicle indicative of a driving automation system (DAS) operating the vehicle; receiving, by the computing device, historical system DAS rating data for a vehicle type of the vehicle; analyzing, by the computing device, a historical DAS score associated with the vehicle type with a DAS score for the vehicle to calculate a DAS rating for the vehicle; and adjusting, by the computing device, the DAS operation of the vehicle based upon the DAS rating for the vehicle. Other embodiments are described.

Abstract: A charging system can include a housing. The housing can include an inner surface configured to contact a finger of a user when the housing is worn by the user. The housing also can include an internal power source configured to be wirelessly charged by a removable power source that is removably coupled to the housing. The housing further can include a component configured to draw energy from the internal power source. The charging system also can include a first LED activated in response to a first activity at the removable power source. The charging system further can include a second LED activated in response to a second activity at the removable power source. The second LED can be different from the first LED. The second activity can be different from the first activity. Other embodiments are disclosed.

Abstract: Method, system, device, and non-transitory computer-readable medium for data management.

Abstract: A computer-implemented method includes a computer-implemented method can include receiving telematics data comprising information related to one or more driving behaviors of a user. The telematics data is received from one or more sensors located in a vehicle when the user is operating the vehicle. The method also can include determining one or more user characteristics of the user based at least on the telematics data. The method additionally can include selecting an advertisement from a plurality of advertisements based at least on the one or more user characteristics. Other embodiments are described.