Allstate Insurance Company

Symbol Technologies, LLC

Motorola Corporation

Motorola Solutions, Inc.

Diamond 18 Ltd.

Arity International Limited

Soren K Lundsgaard

Joy A Thomas

Dana Ferguson

Jared S Snyder

Eric D Huls

Grady Irey

Frank Kazas

Tara Cavallaro Kozlowski

David J Krug

Ryan Dunn

Anthony Pham

Michael F Korus

Surong Zeng

Puneet Batta

Douglas A Kuhlman

Wei Mao

Edgardo L Promenzio

Giuseppe Mario Di Prizio

Yi-Cheng Liu

James Fagioli

Nick J Grivas

Nicholas S Dade

John K Hefferan

Niels C Lundsgaard

Mohsin S Bhally

Tom Lee

Jorge R Lopez Danieluk

Frédéric Nguyen

Systems and methods are disclosed for guidance, control, and testing of autonomous vehicle features and a driver's response thereto. The system may activate a plurality of autonomous driving features of an autonomous vehicle. In response to a determination to initiate a driving test, the system may generate an indication to a driver of the autonomous vehicle of the initiation of the driving test and may deactivate or adjust parameters of one or more of the plurality of autonomous driving features. The system may receive, from one or more sensors of the autonomous vehicle or one or more sensors of a mobile computing device within the autonomous vehicle, driving data associated with the autonomous vehicle. Based on the driving data associated with the autonomous vehicle, the system may determine the driver's response time and actions taken by the driver during the driving test. Moreover, in response to a determination to end the driving test, the system may reactivate the one or more of the plurality of autonomous driving features previously deactivated or may readjust previously adjusted parameters. In some aspects, based on the driver's response time and actions taken by the driver during the driving test, a drive score may be generated for the driver.

Data processing system for guidance, control, and testing autonomous vehicle features and driver response

One or more location analysis computing devices, methods, and computer-readable media are disclosed herein for determining the position of a mobile device (smartphone, tablet computer) within an interior of a vehicle. The position of the mobile device may be calculated by detecting changes in accelerometer data. The accelerometer data may first need to be translated to determine corresponding axes, since the device may not be right side up (e.g., in a pocket). The vehicle may travel over road discontinuities such as bumps, and calculating the position of the mobile device may be based on the different magnitude and angle resulting from a first tire and a second tire hitting the bump. Data from vehicle sensors or other mobile device sensors may also be used in the calculating. Once the position is determined, commands may be sent to the mobile device to deactivate certain functionality, or to a remote server for further processing.

Detection of mobile device location within vehicle using vehicle based data and mobile device based data

Aspects of the disclosure relate to multicomputer processing of vehicle operational data from telematics devices and other sources with centralized event control. An event control computing platform may receive vehicle operational data from a telematics device associated with a user. Subsequently, the event control computing platform may identify, based on the received data, whether at least one criterion associated with the user has been satisfied. If the received data indicates that the at least one criterion associated with the user has been satisfied, then the event control computing platform may generate a command configured to cause a change to a subunit of user data and then may transmit the generated command to a subunit provisioning server.

Multicomputer processing of user data with centralized event control

Aspects of the disclosure relate to a system and method for electronically monitoring and using data indicative of driver characteristics based on sensors. For example, the system may determine an origin associated with a user, a destination associated with the user, and a desired arrival time at the destination. A driving characteristic computing device of the system may determine an estimate for a duration of a trip from the origin to the destination. Based on the estimate for the duration and the desired arrival time at the destination, the driving characteristic computing device may determine a recommended departure time from the origin for the user. Based on sensor data, the system may determine a departure time from the origin for the user. The driving characteristic computing device may compare the departure time to the recommended departure time, and a value of a property of insurance for the user may be determined based on the comparing.

System for monitoring and using data indicative of driver characteristics based on sensors

Aspects of the disclosure relate to using machine learning for optimized call routing. A computing platform may receive requests to establish a voice call session. Based on corresponding phone numbers, the computing platform may identify demographic information for corresponding clients. Using a machine learning model and based on the demographic information and representative performance data, the computing platform may score potential client-representative combinations to indicate likelihoods of a successful outcome resulting from establishing a voice call session between the respective client and representative. Scoring the potential client-representative combinations may be based on fall off rates, indicating changes in representative effectiveness as hold time increases. The computing platform may adjust the scores based on a historical frequency of interaction between each representative and clients corresponding to the identified demographic information. Based on the adjusted scores, the computing platform may select at least one of the potential client-representative combinations.

Machine learning system for routing optimization based on historical performance data

Methods, systems, and apparatuses for presentation of advertising content during presentation of media content and based on data collected from vehicle sensors and a mobile device. Media content, such as stored songs, podcasts, or videos, may be presented to a user. Sensor data corresponding to operation of a vehicle may be collected, and a risk score based on operation of the vehicle may be determined. Based on the risk score, an advertising tier may be determined. The scope of media content available to a user and the frequency of presentation of advertising content during presentation of such media content may be based on the risk score. For example, safe driving behaviors may result in rewards which permit access to more media content, and/or safe driving behaviors may result in fewer advertisements being presented to a user on an in-vehicle display.

Modulation of advertisement display based on vehicle operation data

An improved media streaming device and methods are disclosed herein for receiving and modifying a streaming signal to adjust the rate of occurrence of advertisements based on sensor data collected from various sensors associated with a vehicle and a mobile device found within an interior of a vehicle. The improved media streaming device may receive a risk score based on the collected sensor data, and may determine an advertisement tier based on the risk score. Further, the improved media streaming device may select targeted advertisement content based on at least the sensor data, the calculated risk score, the advertisement tier, and other contextual attributes relating to the driver and/or the vehicle. The improved media streaming device may then modify the streaming signal to inject the selected advertisement at the frequency prescribed by the advertisement tier.

Systems and methods for modulating advertisement frequencies in streaming signals based on vehicle operation data

Growing community of inventors

Inverness, IL, United States of America

Soren K Lundsgaard