Allstate Insurance Company

Anuradha Kodali

Sunil Kumar Chintakindi

Regina Madigan

Howard Hayes

Timothy W Gibson

Aleksandr Likhterman

Soton Ayodele Rosanwo

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.

Personalized driving risk modeling and estimation system and methods

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.

Exhaustive driving analytical systems and modelers

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.

Data processing system with machine learning engine to provide output generation functions

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.

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Fremont, CA, United States of America

Anuradha Kodali