Abstract: A charging system can include a removable power source housing. The removable power source housing can be devoid of being worn on a finger of a user. The removable power source housing can include a first LED activated in response to a first activity at the removable power source housing. The removable power source housing can include a second LED activated in response to a second activity at the removable power source housing. The second activity can be different from the first activity. The removable power source housing can include a third LED activated in response to a third activity at the removable power source housing. The third activity can be different from the first activity and the second activity. The charging system also can include a ring housing. The ring housing can be configured to be removably coupled to the removable power source housing. Other embodiments are disclosed.

Abstract: A method can include providing a machine learning model based in part on a user profile for a user. The method can also include transmitting the machine learning model, as compressed, to a mobile user device. The method can further include generating, on the mobile user device, a personalized machine learning model by training the machine learning model, as compressed, based in part on dynamic data of the user. The method can additionally include determining, by the personalized machine learning model on the mobile user device, a risk score of the user. Other embodiments are described.

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 method can include receiving telematics data from one or more sensors of an electronic device of a user. The method can also include determining a respective skill level for each of a plurality of driving skills of the user based on the telematics data. The method can further include determining, from among the plurality of driving skills, a driving skill for the user to improve. The method can additionally include generating personalized feedback for the user. The personalized feedback is for improving the driving skill. The method can also include transmitting the personalized feedback to a mobile device of the user for display to the user. Other embodiments are disclosed.

Abstract: A method for feature extraction from telematics data. The method includes obtaining telematics data for a plurality of trips for one or more drivers for a policy. The method also includes generating, using an automatic feature extraction encoder, respective latent representation embeddings each of the plurality of trips for the one or more drivers for the policy. The method additionally includes combining the respective latent representation embeddings for the plurality of trips to generate a combined embedding representation for the policy. The method further includes generating an output for the policy, using a supervised machine-learning model, based on inputs to the supervised machine-learning model comprising the combined embedding representation. Other embodiments are described.

Abstract: A method can include receiving a state of a tire of a vehicle, and collecting telematics data for an operator of the vehicle. The method can further include determining an expected state of the tire, and transmitting, for display on a user device, a message regarding the expected state of the tire. The method can also include repeating the collecting, the determining, and the transmitting. The method can further include receiving a current state of the tire. The method can additionally include determining whether the current state of the tire is within a predetermined threshold of the expected state of the tire. The method can also include, when the current state of the tire is determined to not be within the predetermined threshold of the expected state of the tire, resetting the expected state of the tire to be the current state of the tire. Other embodiments are disclosed.

Abstract: A computing system for generating a list of possessions associated with a user may be provided. The computing system may include a processor in communication with a memory device, and the processor may be configured to receive image data, analyze the image data to identify at least one possession, update a user profile to include the identified at least one possession, determine additional information is needed to accurately estimate a value associated with the at least one possession, generate and transmit an additional information prompt to a user computing device, receive, from the user computing device, the additional information, estimate, using the additional information, the value associated with the at least one possession, and update the user profile to include the estimated value associated with the at least one possession stored in association with the at least one possession.

Abstract: A computer-implemented method for health assessment includes providing an artificial neural network trained with training data. The method also includes receiving activity data comprising personal activity metrics corresponding to a target person, and determining, by the artificial neural network, a health indicator of the target person based upon the activity data corresponding to the target person. The method further includes outputting the health indicator of the target person to an electronic device for communication of the health indicator to a user of the electronic device. Other embodiments are disclosed.

Abstract: A computer-implemented method can include receiving a first set of telematics data collected by one or more sensors associated with a vehicle of a user. The computer-implemented method can also include determining a driving tip to be provided to the user based at least on the first set of telematics data. The computer-implemented method can further include generating a treatment for providing the driving tip to the user. The computer-implemented method can additionally include providing the driving tip to the user in accordance with the treatment. The computer-implemented method can also include receiving a second set of telematics data collected by the one or more sensors after providing the driving tip. The computer-implemented method can further include determining whether the user complied with the driving tip based at least on the second set of telematics data. The computer-implemented method can additionally include updating the machine learning model. Other embodiments are described.

Abstract: A computer-implemented method can include receiving, from one or more sensors, operation data associated with operation of a vehicle. The computer-implemented method can also include updating one or more operation ratings based upon at least a portion of the operation data. The computer-implemented method can further include processing, based at least on the one or more operation ratings, as updated, one or more virtual movements simulating a performance level of the one or more operation ratings for a virtual vehicle to undertake in a virtual trip, wherein the performance level is a metric associated with at least one updated operation ratings. The computer-implemented method can additionally include transmitting for displaying, in a user interface, visual data indicative of a summary of the virtual trip based on the one or more virtual movements, wherein the summary comprises at least a score associated with the virtual trip. Other embodiments are described.

Abstract: A method performed by a housing can include collecting, by one or more sensors of the housing, a biometric data of a user while the user is wearing the housing. The method performed by the housing also can include performing, by one or more processors, an authentication operation. The authentication operation can include analyzing the biometric data of the user. The authentication operation also can include comparing the biometric data of the user to a biometric signature for a known user to determine whether the biometric data of the user matches the biometric signature for the known user. The authentication operation further can include authenticating the user as the known user when the biometric data of the user is determined to match the biometric signature for the known user. Other embodiments are disclosed.

Abstract: A computer-implemented method including receiving a first set of sensor data associated with a user from one or more first IoT devices of the user related to a first domain. The method also can include receiving a second set of sensor data associated with the user from one or more second IoT devices of the user related to a second domain. The method additionally can include analyzing the first set of sensor data associated with the user from the one or more first IoT devices of the user related to the first domain and the second set of sensor data associated with the user from the one or more second IoT devices of the user related to the second domain to identify one or more risks of the user. The method further can include generating one or more risk scores associated with the user based on the one or more risks of the user.

Abstract: A method can include: aggregating, from multiple sources into a data hub, vehicle-related data for a vehicle; performing an analysis of the vehicle-related data; generating a predictive maintenance recommendation based on the analysis of the vehicle-related data; transmitting the predictive maintenance recommendation to a mobile electronic device for display on a graphical user interface of the mobile electronic device to a user of the vehicle; receiving a reward to incentivize a recommended usage of the vehicle based on the analysis of the vehicle-related data; transmitting the reward to the mobile electronic device for display on the graphical user interface; automating a vehicle ownership task for the vehicle based on the analysis of the vehicle-related data; and transmitting the vehicle ownership task to the mobile electronic device for display on the graphical user interface. Other embodiments are disclosed.

Abstract: A method can include receiving first data regarding roads, a vehicle, and a user of the vehicle; receiving modeled data for the vehicle, wherein the modeled data is generated at least by applying at least one predictive model to the first data; receiving a destination; receiving a first location; generating potential routes for the user to operate the vehicle along the roads from the first location to the destination; generating risk assessments for the potential routes based on the first data; selecting an identified route of the potential routes; and transmitting the identified route to an electronic device of the user. Other embodiments are disclosed.

Abstract: Method and system for determining a status of a mobile device of a user are disclosed. For example, the method includes receiving first sensor data at a first time from an application installed on a mobile device of a user, determining a first location of the mobile device, immediately subsequent to receiving the first sensor data at the first time, receiving second sensor data at a second time from the application, the second time following the first time by a time interval, determining a second location of the mobile device, determining a distance between the first location and the second location, determining whether the distance corresponding to the time interval exceeds a predetermined threshold, and determining whether a trip log indicative of at least one trip during the time interval is received from the application.

Abstract: A computer-implemented method for predicting fuel consumption efficiency. The method can include receiving vehicle telematics data from a client device. The method can also include analyzing historical trip data including past commute patterns and vehicle route data based on the vehicle telematics data. The method can further include analyzing, using a trained machine learning model, the vehicle telematics data to determine one or more past user driving features. The method can additional include receiving at least one of traffic congestion information, terrain information, or weather condition information for one or more future vehicle trips. The method can also include predicting, using the trained machine learning model, a future fuel consumption efficiency of the user during a predetermined future period of time. The method can further include transmitting the future fuel consumption efficiency, as predicted, for one or more routes to the client device for display. Other embodiments are described.

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.