Allstate Insurance Company

Motorola Corporation

United Services Automobile Association (usaa)

Motorola Mobility LLC

Blackberry Corporation

Motorola Solutions, Inc.

United States Automobile Association (usaa)

Google Technology Holdings LLC

Surender Kumar

Jason Tyler Griffin

Howard Hayes

Timothy W Gibson

Mark V Slusar

Regina Madigan

Sunil Kumar Chintakindi

John Rugel

Clint J Marlow

Ryan Mark Briggs

Mark L Shaughnessy

James Earl Mathis

Matei Stroila

Faramak Vakil

Michael D Kotzin

Ravi Durairaj

Nolan Serrao

Avinash Joshi

Nathan Lee Post

Vidya Narayanan

Anthony R Metke

Randy L Ekl

Mark E Faga

Mark Paxman Warnick

Mario Frank DeRango

Whay Chiou Lee

Jeanie Graciela Lopez

Samuel Glazer

Elena Marie Carrasco

Donald Nathaniel Holloway, Iii

Neil Patrick Adams

Andre Rene Buentello

Jose L Romero, Jr

Jeffrey D Bonta

Thomas Casey Hill

Alexander Sherkin

Sivakumar Muthuswamy

Stacy Callaway Huggar

Philip Peter Ramirez

Peter Stanforth

Michael D Sasuta

Thomas V D'Amico

Rajeev Agrawal

William Daniel Farmer

Narayanan Venkitaraman

Mark A Birchler

Bashar Jano

Matthew Olenak

Chris Wiklund

Tim Gibson

Avani Patel

Araba Appiagyei-Dankah

Veljko Krunic

Soton Ayodele Rosanwo

Dragan Boscovic

Ramandeep Ahuja

Brian Stricker

Roberto Virgillio Jolliffe

Younuskhan Mohamed Iynoolkhan

Chanakykumar Bhavsar

Will Kerns Maney, Jr

Shayla Leigh Callis

Ivan Ortiz

William Polisson

Breanna Nicole Allerkamp

Peggy K Matson

David Michael Schlittler

Bhavesh G Patel

Nicolette De Guia

Gregory A Dertz

Mario F Derango

Madhusudhana Rao Abburi

Susan L Lukasik

Brian Katz

Neetu Jain

Moises Norena

Nilesh Malpekar

Yogesh Bhatt

Sydney Ann Conrad-Cook

Marco Fratti

Randy L Eki

John Bradley Moats

Jon Dwain McLaughlin

Ryan G Campbell

Michael Osborn

Ashley Greenwell

David Border

Jorge Caraballo Falcon

Charles Peavie

Ann Conway

Gary J Aitkenhead

Dragan M Boscovic

Susan Lucille Lukasik

A computing system is configured to trigger a safety driver assessment and obtain driving conditions for a route using a variety of sensors and databases. The computing system is also configured to receive driver information and determine a safety driving test based on the driver information and driving conditions. During the safety driving test, the computing system is configured to measure, via the variety of sensors of an autonomous vehicle, driving responses of a driver operating the autonomous vehicle. Based on measured driving responses, the computing system is configured to determine a safety driving score and save the safety driving score in a safety test record.

Safety driver assessment and dynamic autonomous driving

A transactional method and system of incentivizing task performance using token-based exchange for electronic payment authentications. The system and method are configured to reward a person with electronic tokens for tasks that are performed and verified by the system. These tokens can then be exchanged by the user for completing purchases via online retailers or other electronic payment-based services. Verification can involve the use of natural language processing and image comparison algorithms to determine whether a particular task has been successfully completed.

System and method for incentivizing task performance using token-based authentication of electronic transactions

Systems and methods are disclosed for monitoring or affecting movement of a vehicle using a traffic device. An event data source may have a processor and/or a transceiver. The event data source may transmit, via the transceiver and to a vehicle and infrastructure computing device, information indicative of an event affecting a portion of road. The vehicle and infrastructure computing device may comprise a vehicle and infrastructure control computer. The vehicle and infrastructure computing device may receive, from the event data source, the information indicative of the event affecting the portion of road. The computing device may determine one or more traffic devices associated with the portion of road and configured to control traffic for the portion of road. Based on the information indicative of the event affecting the portion of road, the computing device may send, to the one or more traffic devices associated with the portion of road, instructions to change one or more characteristics of the one or more traffic devices.

Electrical data processing system for monitoring or affecting movement of a vehicle using a traffic device

Aspects of the disclosure relate to computing platforms that utilize improved mitigation analysis and policy management techniques to improve onboarding security. A computing platform may initiate an onboarding process with a mobile device. The computing platform may receive one or more onboarding verification inputs indicative of a user identity. The computing platform may send commands directing a mitigation analysis and output generation platform to analyze the onboarding verification inputs. The computing platform may receive a mitigation output indicating a degree of correlation between the one or more received onboarding verification inputs and expected onboarding verification inputs. In response to determining that the mitigation output exceeds a predetermined mitigation threshold, the computing platform may send mitigation interface information and one or more commands directing the mobile device to display an onboarding mitigation interface that prompts for one or more additional onboarding verification inputs indicative of the user identity.

Onboarding platform for performing dynamic mitigation analysis

Methods, computer-readable media, systems, and/or apparatuses for evaluating movement data to identify a user as a driver or non-driver passenger are provided. In some examples, movement data may be received from a mobile device of a user. The movement data may include sensor data including location data, such as global positioning system (GPS) data, accelerometer and/or gyroscope data, and the like. Additional data may be retrieved from one or more other sources. For instance, additional data such as usage of applications on the mobile device, public transportation schedules and routes, image data, vehicle operation data, and the like, may be received and analyzed with the movement data to determine whether the user of the mobile device was a driver or non-driver passenger of the vehicle. Based on the determination, the data may be deleted in some examples or may be further processed to generate one or more outputs.

Multi-computer processing system for dynamically executing response actions based on movement data

A transactional method and system of incentivizing task performance using token-based exchange for digital media access. The system and method are configured to reward a person with electronic tokens for tasks that are performed and verified by the system. These tokens can then be exchanged by the user for access to desired digital media. Verification can involve the use of natural language processing and image comparison algorithms to determine whether a particular task has been successfully completed.

System and method for incentivizing task performance using token-based digital media access

Methods, computer-readable media, systems and apparatuses for determining a blind corner navigational score based on real-time or near real-time navigational analysis using sensor data, digital image data, and a map database are discussed. In some arrangements, detection of a blind sensor may be performing using sensor data, digital image data, and navigational data from a map database system. In at least some arrangements, a warning signal or a vehicle control signal may be transmitted to a vehicle in response to a determination that the blind corner navigational score is above a threshold. In at least some arrangements, route correction and/or route modification based on an upcoming blind corner may be performed if a blind corner navigational score is above a threshold.

Automated vehicle control and guidance based on real-time blind corner navigational analysis

Methods, computer-readable media, software, and apparatuses may determine that an expected vehicle demand will exceed an expected supply in a vehicle sharing application. In order to meet the demand, one or more users may be contacted with a request to provide a vehicle for sharing on a particular date. A machine learning algorithm may be used in determining that the expected vehicle demand will exceed the expected vehicle supply.

Vehicle sharing optimization

A system may be configured to detect an emergency condition at a premises; dispatch one or more autonomous drones to a location associated with the emergency condition; receive from the one or more autonomous drones, sensor data associated with the emergency condition; generate, based on the sensor data, a plan identifying an evacuation route for safely evacuating the premises; and transmit an instruction that causes the one or more autonomous drones to indicate, to one or more persons in a vicinity of the emergency condition, the evacuation route. The system may further detect, based on the sensor data, one or more safe areas in the premises, and the evacuation route may pass through at least one of the one or more safe areas.

Emergency incident detection, response, and mitigation using autonomous drones

Aspects of the disclosure relate to using machine learning methods for customized output generation. A computing platform may train a model (using historical data) by classifying the historical data by a trip context, a device interaction context, and physical condition context, or a personality context, and training models using the classified historical data. The computing platform may monitor a data source system to collect new data, which may include information about multiple drivers. The computing platform may generate, by inputting the new data into the model, a customized driving output for a first driver, where the customized driving output is based at least in part on information about a second driver. The computing platform may send, to a computing device, the customized driving output and commands directing the computing device to display the customized driving output, which may cause the computing device to display the customized driving output.

Using context based machine learning for generation of customized driving outputs

A system including a processor and memory may provide for automatically activating or deactivating a feature of a fleet vehicle. For example, one or more fleet vehicles may include one or more of a global-positioning system, a speed governor, electronically-controlled brakes, an electronically-controlled accelerator, a speed limiter, or an on-board computer with a processor and memory. One or more features may be activated by a local or remote computing device or system. For example, a system may determine one or more recommended routes between two or more locations. The system may track a fleet vehicle's progress along a route, and activate a feature of the fleet vehicle based on the fleet vehicle following or not following the recommended route. For example, the system may cause activation of a speed limiter on the fleet vehicle, disable the fleet vehicle, and/or activate or deactivate autonomous features of the fleet vehicle.

Fleet vehicle feature activation

Methods, computer-readable media, software, systems and apparatuses may receive, from a user device, notification of a user enrolling in a privacy incident protection application, receive, from the user device, user account information associated with one or more user accounts of the user, where the user account information includes a plurality of contextual settings, determine a risk footprint associated with the user based on the user account information, monitor the one or more user accounts, receive an indication of an incident based on monitoring the one or more user accounts and based on the risk footprint, and transmit an incident notification to a data server provider associated with the incident. The incident notification may include instructions to perform a mitigation action associated with the incident.

Context based privacy risk footprint and incident protection

Implementations configure hologram interactions with vehicle passengers. A hologram manager can configure a hologram to interact with vehicle passengers, alleviate danger, and/or guide vehicle passengers to safety. The hologram manager can select an input modality for the hologram according to sensed conditions. For example, the hologram manager can determine a state of impairment for a passenger using sensed data and machine learning model(s). The hologram manager can then select an input modality according to the state of impairment. The generated hologram can be configured by the hologram manager to interact with the passenger using the selected input modality. For example, an instruction manager can generate a set of instructions using sensed and/or gathered information, and the hologram manager can configure the hologram to provide the instructions to the passenger using the input modality.

Configuring hologram interactions with vehicle passengers

Event-based connected vehicle control and response systems, methods, and apparatus are disclosed. An example method comprises identifying the occurrence of an event, storing first data corresponding to apparatus operation for a first threshold amount of time prior to the event, during the occurrence of the event, and for a second threshold amount of time after the event, determining whether a responsive object is involved in or near the event, in response to determining that the responsive object is involved in or near the event, transmitting the first data to the responsive object, and receiving, from the responsive object, second data, analyzing the first data and the second data to determine a party at-fault for the event, aggregating the first data and second data into an event report, and causing, automatically, a response to be initiated through an entity associated with the party at-fault.

Event-based connected vehicle control and response systems

Systems and apparatuses for receiving data from a plurality of sensors and using the data, as well as other data, to generate a parking recommendation for an autonomous vehicle and instruct the autonomous vehicle to travel to the recommended parking location are provided. Data may be received from a plurality of sensors within a first autonomous vehicle, as well as from other vehicles and/or structures. Historical parking data associated with the first autonomous vehicle may also be extracted. In some examples, an expected future trip of the first autonomous vehicle may be determined. The system may then evaluate the data to generate a parking recommendation for the first autonomous vehicle. The system may generate and transmit instructions for traveling from a current location to the recommended parking location and may cause the first autonomous vehicle to travel to the recommended parking location.

Generating and transmitting parking instructions for autonomous and non- autonomous vehicles

Growing community of inventors

Palatine, IL, United States of America

Surender Kumar