Abstract: Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to utilizing partially available data associated with driver and/or driving behaviors to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

Abstract: Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to utilizing partially available data associated with driver and/or driving behaviors to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

Abstract: Systems and methods in accordance with aspects of this disclosure may be provided to determine and calculate an overall driving risk index value corresponding to a driver and scene configuration. The overall driving risk index value may provide driving risk modeling and estimation at a personalized driving level. In some cases, the overall driving risk index value may be determined using a risk-predictive modeling system with weighting and machine learning and may include one or more of: a driver score system, a driver-contextual risk score system, and a conflict index system.

Abstract: Implementations claimed and described herein provide systems and methods for behavior assessment for an individual. In one implementation, user data for the individual is obtained from one or more digital sources. Categorized user data is created by transforming the user data into a platform independent format. The categorized user data is associated with a plurality of content-based bins. One or more behavioral insight categories are determined from the categorized user data. A plurality of behavioral metrics is determined based on the one or more behavioral insight categories and the categorized user data. A personality profile for the individual is generated by converting the plurality of behavioral metrics into one or more scores. A risk assessment for the individual is generated based on the personality profile.

Abstract: Implementations described and claimed herein provide systems and methods for risk assessment. In one implementation, a telematics-centric driving risk value is generated for a specific individual by determining one or more demographic segments corresponding to the specific individual and calculating one or more risk factor values associated with the one or more demographic segments using telematics data. A telematics-weighted personalized risk value is generated by: determining one or more telematics metrics from the telematics data; calculating a telematics persona risk value based on the one or more telematics metrics; calculating a behavioral persona risk value based on one or more behavioral metrics; calculating a household persona risk value based on one or more household metrics; and calculating a finance persona risk value based on one or more finance metrics.

Abstract: Systems and methods in accordance with aspects of this disclosure may be provided to determine and calculate an overall driving risk index value corresponding to a driver and scene configuration. The overall driving risk index value may provide driving risk modeling and estimation at a personalized driving level. In some cases, the overall driving risk index value may be determined using a risk-predictive modeling system with weighting and machine learning and may include one or more of: a driver score system, a driver-contextual risk score system, and a conflict index system.

Abstract: Methods, computer-readable media, systems, and/or apparatuses for providing offer and insight generation functions are provided. For instance, user input may be received requesting generation of an offer. In response to receiving the request, an application may be transmitted to a device, such as a mobile device of a user. In some examples, the application may be executed by the device and may facilitate establishing a communication session with a third party system, identifying and extracting data from the third party system, and transmitting the extracted data to an entity for evaluation. In some examples, evaluation by the entity may include generating one or more insights, outputs and the like. In some arrangements, the evaluation may be performed using machine learning and, in some examples, may be performed in real-time or near real-time.

Abstract: Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to utilizing partially available data associated with driver and/or driving behaviors to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

Abstract: Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to monitoring driver and/or driving behaviors in view of an exhaustive list of variables to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

Abstract: Systems and methods in accordance with aspects of this disclosure may be provided to determine and calculate an overall driving risk index value corresponding to a driver and scene configuration. The overall driving risk index value may provide driving risk modeling and estimation at a personalized driving level. In some cases, the overall driving risk index value may be determined using a risk-predictive modeling system with weighting and machine learning and may include one or more of: a driver score system, a driver-contextual risk score system, and a conflict index system.

Abstract: Methods, computer-readable media, systems, and/or apparatuses for providing offer and insight generation functions are provided. For instance, user input may be received requesting generation of an offer. In response to receiving the request, an application may be transmitted to a device, such as a mobile device of a user. In some examples, the application may be executed by the device and may facilitate establishing a communication session with a third party system, identifying and extracting data from the third party system, and transmitting the extracted data to an entity for evaluation. In some examples, evaluation by the entity may include generating one or more insights, outputs and the like. In some arrangements, the evaluation may be performed using machine learning and, in some examples, may be performed in real-time or near real-time.

Abstract: Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to monitoring driver and/or driving behaviors in view of an exhaustive list of variables to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

Abstract: Interactive diagnostic modeling evaluator (i-DME) for repairing a diagnostic matrix of a system. The diagnostic modeling evaluator cause steps to be performed comprising: evaluating a first diagnostic performance of a diagnostic unit using supervised data that includes one or more instances of a fault of a system; proposing a repair to the diagnostic unit based on the evaluated diagnostic performance; evaluating a second diagnostic performance of the diagnostic unit having the proposed repair; and upon determining that the second diagnostic performance meets a performance metric criterion, repairing the diagnostic unit according to the proposed repair.