Abstract: A system and method for analyzing risk and providing risk mitigation instructions. The system receives analyzes sensor data and other data corresponding to a user to determine a test group. The system uses the test group to determine a risk score, and, subsequently, a risk mitigation strategy. Machine learning techniques are implemented to refine how the test group, risk score, and mitigation are each selected.

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 and method for analyzing risk and providing risk mitigation instructions. The system receives analyzes sensor data and other data corresponding to a user to determine a test group. The system uses the test group to determine a risk score, and, subsequently, a risk mitigation strategy. Machine learning techniques are implemented to refine how the test group, risk score, and mitigation are each selected.

Abstract: A distributed ledger operated by a group of network participants according to a set of consensus rules stores vehicle condition data and generated vehicle condition reports. The vehicle condition data may be received directly from a vehicle or an associated mobile device and the vehicle condition report may be accessed by an authorized user.

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 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 computer-implemented method for providing feedback to drivers of vehicles is disclosed. The method comprises receiving an image corresponding to a vehicle indicating driving behavior data associated with a user, The driving behavior data are indicative of wear and tear on the vehicle or fuel efficiency for the vehicle and the driving behavior data indicative of an impact associated with the user on longevity of the vehicle. Based at least in part upon analyzing the image and determining a driving performance metric associated with the user feedback associated with the user is generated and provided to a mobile device associated with the user for presentation at the mobile device.

Abstract: A distributed ledger operated by a group of network participants according to a set of consensus rules stores vehicle condition data and generated vehicle condition reports, The vehicle condition data may be received directly from a vehicle or an associated mobile device and the vehicle condition report may be accessed by an authorized user.

Abstract: A distributed ledger operated by a group of network participants according to a set of consensus rules stores vehicle condition data and generated vehicle condition reports, The vehicle condition data may be received directly from a vehicle or an associated mobile device and the vehicle condition report may be accessed by an authorized user.

Abstract: A system and method for analyzing risk and providing risk mitigation instructions. The system receives analyzes sensor data and other data corresponding to a user to determine a test group. The system uses the test group to determine a risk score, and, subsequently, a risk mitigation strategy. Machine learning techniques are implemented to refine how the test group, risk score, and mitigation are each selected.

Abstract: A system and method for analyzing risk and providing risk mitigation instructions. The system receives analyzes sensor data and other data corresponding to a user to determine a test group. The system uses the test group to determine a risk score, and, subsequently, a risk mitigation strategy. Machine learning techniques are implemented to refine how the test group, risk score, and mitigation are each selected.