Abstract: A charging system can include a housing. The housing can include an inner surface, an internal power source, and a component configured to draw energy from the internal power source. The charging system also can include a charging source that is removably coupled to the housing. The charging source can include a visual output disposed at the charging source. The visual output can operate at a first flashing rate to indicate the internal power source of the housing is not charging, and the visual output can operate at a second flashing rate to indicate the internal power source of the housing is charging. Other embodiments are disclosed.

Abstract: A computer-implement method can include receiving, from two or more sensors associated with a mobile device of a user, a first set of activity data of the user. The computer-implemented method can also include identifying by a first artificial neural network, one or more metrics from the first set of activity data. The one or more metrics can be predictive of a health indicator of the user. The computer-implemented method can further include causing a configuration of the two or more sensors, based at least on the one or more metrics. Causing the configuration of the two or more sensors can include causing a deactivation of at least one of the two or more sensors. A remaining one or more of the two or more sensors can remain activated. The computer-implemented method can additionally include receiving from the remaining one or more of the two or more sensors, a second set of activity data of the user. Other embodiments are disclosed.

Abstract: A charging system can include a housing. The housing can include a controller. The housing also can include a power source configured to power the controller. The charging system also can include a wireless charger configured to transfer energy to the power source. The wireless charger can include a first non-flashing visual output at a first brightness. The wireless charger also can include a second non-flashing visual output at a second brightness. Other embodiments are disclosed.

Abstract: A method for managing risk can include receiving, from a sensor, data indicative of a light exposure pattern. The method also can include analyzing, via a machine learning (ML) model, the data to predict a level of risk exposure for a user. The ML model can be trained with training data correlating light exposure patterns with risk patterns. The method further can include comparing the level of risk exposure, as predicted, to a threshold. The method additionally can include, in response to the level of risk exposure, as predicted, exceeding the threshold, determining a remediation action to reduce the level of risk exposure for the user. and communicating the remediation action to the user via an output device. Other embodiments are disclosed herein.

Abstract: A computer-implemented method including detecting, prior to an activity, a first cortisol level of a user from one or more sensors worn by the user. The method also can include predicting, using a machine learning (ML) model, a level of risk for the user performing the activity prior to the activity based at least on the first cortisol level of the user. The method additionally can include transmitting a first notification prior to the activity. The first notification provides the user with instructions for a remediating action based at least on the level of risk for the user performing the activity.

Abstract: A method can include acquiring, by a sensor of a sleep detecting device, a set of user data indicative of sleep activity of a user. The method further can include acquiring data indicative of an activity of the user. The method also can include predicting, based at least on the set of user data and the activity of the user, a level of risk exposure for the activity of the user. The method also can include in response to predicting, generating an output indicative of the level of risk exposure of the user. Other embodiments are disclosed herein.

Abstract: A method for predicting risk exposure can include sensing, by a sensor of a ring worn by a user, a set of data. The sensor can be integrated with a microfluidic device. The method for predicting risk exposure further can include determining, by a trained machine learning (ML) model, a risk score based at least on the set of data. The method for predicting risk exposure also can include determining, by a trained ML model, a remediating action to reduce the risk score. The method for predicting risk exposure also can include, in response to determining the risk score, generating a notification to alert the user of the risk score and the remediating action. Other embodiments are disclosed.

Abstract: A computer-implemented method includes generating one or more virtual occurrences to be encountered by the virtual character. The computer implemented method further includes determining one or more virtual outcomes associated with the one or more virtual occurrences. The computer implemented method further includes generating a virtual trip comprising the one or more virtual occurrences with the one or more virtual outcomes associated with the one or more virtual occurrences. The computer implemented method further includes determining a predicted change in a vehicle condition of a virtual vehicle. The computer implemented method further includes updating the character profile of the virtual character. The computer implemented method further includes transmitting for display, the character profile, as updated, and the vehicle condition, as updated, to an electronic device of a user. Other embodiments are disclosed.

Abstract: A method can include collecting, by one or more sensors, biometric data of a user. The method also can include performing, by one or more processors, an authentication operation which can include determining whether the biometric data of the user matches a biometric signature for a known user. The authentication operation further can include authenticating the user by updating a record to indicate that the user has been identified and authenticated as the known user. The method also can include performing operations by the one or more processors. The operations can include generating a private key from a seed valued stored in a profile of the known user. The operations further can include digitally signing transaction data using the private key. The operations also can include providing the known user with access to a source of information while the known user is wearing the housing. Other embodiments are disclosed.

Abstract: A system for displaying information can include a ring band. The system for displaying the information further can include a processor disposed within the 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 ring band, a state of the user of the ring band, or a condition of an environment. The system for displaying the information also can include a display coupled to the ring band and communicatively coupled to the processor. The processor can be configured to cause the display to present the information indicative of the one or more conditions, as identified. The processor further can be configured to determine whether a termination condition has been met for the one or more conditions, as identified. Other embodiments are disclosed herein.

Abstract: A computer-implemented method for operating a computing device including: receiving telematics data from one or more sensors of an electronic device of a user; assessing driving behavior of the user based on the telematics data; generating a personalized digital training program for the user based at least on the driving behavior of the user; generating one or more driving scores for the user based on performance of the user on the personalized digital training program; and outputting a driving credential for the user based on the one or more driving scores. Other embodiments are described.

Abstract: A ring device can include a ring band. A ring device also can include a processor located within the ring band and configured to analyze data to identify an occurrence of one or more conditions. A ring device further can include one or more sensors located at least partially within the ring band. The one or more sensors can include a sensor configured to receive a user input from a user of the ring device. The user input can include a hand gesture. The one or more sensors can be communicatively coupled to the processor. The processor can be configured to determine whether the user input satisfies the one or more conditions. After the processor determines that the user input satisfies the one or more conditions, the processor can control presentation of information displayed at an output device. Other embodiments are disclosed herein.

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 to determine a device of a driver including receiving, from a first user device of a first user in a vehicle, first telematics data, receiving from a second user device of a second user in the vehicle, second telematics data, determining, using a trained machine learning model, patterns in the first and second telematics data, determining relative positions of the first user device and the second user device by analyzing the telematics data to identify that one of the devices experiences a change in vertical acceleration before the other, determining a delay between the changes in vertical acceleration, determining a front-to-rear positioning of the first user device relative to the second user device based on the delay, determining a predicted position of the first user device relative to the second user device, and identifying the driver of the vehicle based upon the predicted position. Other embodiments are described.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: comparing, by an electronic device, at least one parameter of a set of telematics data associated with a vehicle and at least one additional parameter of an additional set of telematics data associated with one or more other vehicles within a threshold distance of the vehicle to determine a difference between the at least one parameter and the at least one additional parameter; and displaying, by the electronic device, the difference to the operator of the vehicle to notify the operator of the vehicle about the difference. Other embodiments are described.

Abstract: A method for revoking access to personal information. The method can include obtaining personal data of a user. The method further can include storing the personal data in a digital wallet. The method also can include determining that at least a portion of the personal data in the digital wallet is subject to a data security risk. The method additionally can include transmitting for display on a device of the user, an alert regarding the data security risk and a control to revoke access to the at least the portion of the personal data. Other embodiments are disclosed.

Abstract: A telematics analysis computing device for monitoring driving behavior of a driver of a vehicle 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 including aggregating real-time telematics data from multiple electronic devices of multiple drivers of multiple vehicles based at least in part upon a common location for the multiple drivers, determining, in real time, one or more outliers in the real-time telematics data, determining, based at least in part upon the one or more outliers, that at least one driver poses an accident risk for one or more other drivers, and transmitting a real-time notification to one or more electronic devices associated with the one or more other drivers at the common location. Other embodiments are described.

Abstract: A credit score prediction (CSP) computing device comprising at least one processor in communication with a memory device. The at least one processor can be configured to receive current user data and current vehicle telematics data associated with a first user. The at least one processor further can be configured to apply a model, as trained, to the current user data and the current vehicle telematics data to predict a credit score associated with the first user. The model can be trained using a training dataset. The at least one processor also can be configured to determine a probability of error associated with the credit score, as predicted, based upon a number of records in the current vehicle telematics data. The probability of error can be lower when the number of records in the current vehicle telematics data is higher.

Abstract: The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed stress patterns. A smart ring, wearable on a user's finger, continuously monitors user's physiological and behavioral parameters indicative of being under stress. This stress data, representing stress patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed stress patterns. The user can be warned of this risk to prevent them from driving or to encourage them to use an appropriate stress coping strategy before or during driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while exposed to high risk due to deteriorated psychological or physiological conditions stemming from being under stress.