Abstract: A computer-implemented method for training a virtual character, the computer-implemented method: receiving telematics data associated with one or more real trips; determining, based at least in part upon the telematics data, skill points associated with real skills detected during the one or more real trips; generating, based at least in part upon a virtual character profile for the virtual character, one or more virtual occurrences to be encountered by the virtual character; determining, based at least in part upon virtual ratings associated with the skill points and the virtual character profile, one or more virtual outcomes associated with the one or more virtual occurrences; updating the virtual character profile based at least in part upon the one or more virtual outcomes; and providing a reward based upon the one or more virtual outcomes. Other descriptions are included.

Abstract: A computer-implemented method for identifying occupants of a vehicle, the computer-implemented method including: receiving, using a sensor associated with a first user device of a first vehicle occupant in the vehicle, an echo signal associated with a first ping signal emitted by the first user device; generating a first relative positioning map based upon the echo signal; and determining that the first vehicle occupant is a driver or a passenger of the vehicle based at least in part upon the first relative positioning map.

Abstract: Method, system, device, and non-transitory computer-readable medium for predicting trip data. In one aspect, a computer-implemented method includes: obtaining a first set of trip data associated with a first set of vehicular trips operated by a vehicle operator during a first time period, the first set of trip data including a first set of telematics data and a first set of travel conditions; obtaining a second set of travel conditions associated with a second set of vehicular trips associated with a second time period separate from the first time period; and determining a second set of telematics data associated with a target vehicular trip based at least in part upon the first set of telematics data, the first set of travel conditions, and the second set of travel conditions.

Abstract: Method and system for transforming telematics data for agnostic use. For example, a computer-implemented method includes: receiving, from a first telematics system of a first type, telematics data in a first form, the telematics data being associated with a vehicle; and transforming, via a prediction model, the telematics data into a first prediction in a second form, the second form being: different from the first form, and the form which data collected, processed, stored, or transmitted by a second telematics system of a second type would be, the second type being different from the first type.

Abstract: Method, system, device, and non-transitory computer-readable medium for data management. In some examples, a computer-implemented method can include: receiving personal data sets associated with vehicle operators; receiving, via sensors, sensor data sets associated with the vehicle operators, wherein the sensors comprise at least one of a Global Positioning Systems (GPS) sensor configured to provide a geographic location, an accelerometer configured to provide at least a linear or angular acceleration, or a gyroscope configured to provide orientation sensor data; receiving, from a requesting party of marketplace participants, an information request for a target operator profile associated with a target data profile selected from data profiles of the vehicle operators; and transmitting, in response to the information request, the target operator profile to the requesting party.

Abstract: A computer-implemented method of generating a route can include: identifying, by one or more processors, one or more indications of calmness associated with one or more route segments between an origin location and a destination location by analyzing sensor data associated with the one or more route segments; determining one or more calmness scores associated with the one or more route segments based upon the one or more indications of calmness, as identified, associated with the one or more route segments; and generating, by the one or more processors, a route between the origin location and the destination location, wherein the route is selected based at least in part upon the one or more calmness scores associated with the one or more route segments. Other embodiments are described.

Abstract: A computer system for generating a public transportation schedule for users is provided.

Abstract: A computer-implemented method for operating a computing device including: receiving, by one or more processors and from one or more sensors, sensor data corresponding to a user; receiving, by the one or more processors, demographic data corresponding to the user; determining a pattern of behavior of the user based on the sensor data and the demographic data; and determining, using a trained risk analyzer machine learning model, a risk score of the user based at least in part upon a test group associated with the pattern of behavior of the user. Other descriptions are enclosed.

Abstract: A method and system is provided for providing local business-based recommendations or search results to visitors. In response to a request for a digital map of a geographic area, the system determines whether a user is familiar with the geographic area based on the user's location history. In response to determining that the user is unfamiliar with the geographic area and therefore is a visitor of the geographic area, the system provides recommendations, suggestions, or search results to the user which includes local businesses which are of interest to the user. The local businesses may include local businesses which are related to a geographic search query provided by the user, or may include local businesses recommended by the system according to the time of day, time of year, events within the geographic area such as events stored in the user's virtual calendar, reviews of the local businesses, etc.

Abstract: Method and system for verifying a reliability of sensor data received from a mobile device of a user are disclosed. For example, the method includes receiving first sensor data collected and/or generated by one or more sensors of the mobile device from an application installed on a mobile device of a user at a first time, receiving second sensor data collected and/or generated by the one or more sensors of the mobile device from the application at a second time, determining whether the mobile device is in a vehicle that the user is driving during a time interval based at least upon the first sensor data and the second sensor data, and in response to the mobile device not being in the vehicle that the user is driving during the time interval, transmitting a notification to the mobile device indicating that the application does not work properly.

Abstract: Telematics systems and methods are described for generating interactive animated guided user interfaces (GUIs). A telematics cloud platform is configured to receive vehicular telematics data from a telematics device onboard a vehicle. A GUI value compression component determines, based on the vehicular telematics data, a plurality of GUI position values and a plurality of corresponding GUI time values. A geospatial animation app receives the plurality of GUI position values and the plurality of corresponding GUI time values. The geospatial animation app implements an interactive animated GUI that renders a plurality of geospatial graphics or graphical routes on a geographic area map via a display device. The geospatial graphics or graphical routes are rendered to have different visual forms based on differences between respective GUI position values and corresponding GUI time values.

Abstract: A computer-implemented method for improving compression of predictive models includes generating an unlabeled simulated data set by expanding an initial data set, and generating a labeled data set by predicting the unlabeled, simulated data set using a complex model to output a plurality of labels. The method also includes training a relatively simple neural network using the labeled data set.

Abstract: A system for a telematics data marketplace for a plurality of users including one or more processors. The one or more processors can be configured to perform certain operations. The operations can include receiving a request from a provider. The request can include a data specification. The operations also can include searching user data associated with the plurality of users based on the data specification to generate a search result. The user data can be associated with the plurality of users including telematics data. The operations additionally can include identifying one or more users of the plurality of users based at least in part upon the search result. The operations further can include transmitting a prompt to the one or more users. The prompt can indicate that the provider requests access to user data associated with the one or more users. The operations additionally can include receiving a response to the prompt from the one or more users. Other embodiments are described.

Abstract: A computer-implemented method implemented by one or more processors for assessing accident events, the computer-implemented method including: determining, based on first sensor data captured by one or more first sensors associated with a first vehicle, that an accident event occurred; and after determining that the accident event occurred: sending, to an electronic device associated with a second vehicle, a request for second sensor data captured by one or more second sensors of the electronic device associated with the second vehicle; receiving, from the electronic device, the second sensor data; analyzing the second sensor data to determine that the second sensor data is related to the accident event; and aggregating the first sensor data with the second sensor data. Other embodiments are described.

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 computer implemented including: collecting, via one or more sensors, a first set of operator data associated with a first group of vehicle operators during a first time period; determining, for each vehicle operator of the first group of vehicle operators and using one or more trained machine learning models based at least in part upon the first set of operator data, a first set of telematics inferences, the one or more trained machine learning models being trained using training data sets comprising sensor data associated with a second group of vehicle operators to predict telematics inferences; collecting, via the one or more sensors, a second set of operator data associated with the first group of vehicle operators during a second time period; determining, for each vehicle operator of the first group of vehicle operators and based on the second set of operator data, a second set of telematics inferences; determining, for each vehicle operator of the first group of vehicle operators, one or more match eva

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 machine learning model is manufactured by a process including retrieving training data, minimizing a loss function, wherein the training data may include labeled or unlabeled data, the machine learning model generating a prediction. A machine learning training/operation server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to retrieve training data, input a training input, analyze the training input to generate a prediction, generate a loss score, and store the trained machine learning model. A method for training a machine learning model includes receiving training data, inputting a training input, analyzing the training input, generating a loss score, and storing the trained machine learning model.

Abstract: A system for displaying information indicative of driving conditions, to a driver, using a smart ring are disclosed. An exemplary system includes a smart ring with a ring band having a plurality of surfaces including an inner surface, an outer surface, a first side surface, and a second side surface. The system further includes a processor, configured to obtain data from a communication module within the ring band, or from one or more sensors disposed within the ring band. The obtained data are representative of information indicative of one or more driving conditions to be displayed to the driver. The smart ring also includes a projector module display disposed on at least one of the plurality of surfaces, and configured to present information indicative of the one or more driving conditions.

Abstract: Systems, methods, and techniques for assessing driver safety based on noise levels or sounds associated with the interior of a vehicle are provided. Noise levels or sounds associated with the interior of a vehicle over a first interval of time may be compared to indications of vehicle motion over the first interval of time to identify noise levels or sounds at times when the indications of vehicle motion indicate unsafe driving. The identified noise levels or sounds may be analyzed to determine noise levels, sources, and/or characteristics of sounds associated with unsafe driving. Noise levels or sounds associated with the interior of the vehicle over a second interval of time may be analyzed to determine instances in which the noise levels, sources, and/or characteristics of sounds associated with unsafe driving occur over the second interval of time. Alerts may be generated for vehicle operators based on the determined instances.