State Farm Mutual Automobile Insurance Company

Crown Packaging Technology, Inc.

Crown Cork & Seal Technologies Corporation

Brian Mark Fields

Steven C Cielocha

Steve Roberson

Todd Binion

Aaron Scott Chan

Mohamed A Wazeer

Ronny S Bryant

Carol Marie Csanda

Stacey Gorsuch

Torri Wollenschlager

Shawn C Bennett

Weixin Wu

Richard A Novak

Chien Che Huang

Craig M Main

Terry Quakenbush

Stephen A Kohaus

Kristopher Keith Gaudin

Leo Nelson Chan

Jacob J Alt

Bryan Allen Plummer

Scott T Christensen

Justin Davis

Joe Harr

Nathan Wilhelm Baumann

Laura Anne Uphoff

Jibo He

Giles Roll

John Adrian Nepomuceno

Aaron C Williams

Lee Marvin John Assam

Nicholas R Baker

Jufeng James Peng

Stephen R Prevatt

Steve Cielocha

Jeremy Myers

J Lynn Wilson

Steve J Balbach

Abhishek Harish

Hyunji Lim

Matthew James Waughtel

Parker Andrew Brown

Nathan Lee Tofte

Joseph P Harr

Kenneth Jason Sanchez

Benjamin F Bowne

Matthew Eric Riley, Sr

David W Thurber

Duane Lee Marzinzik

Martin John Watson

Andrew Robert Lockley

Bryan T Wilkerson

Matthew Christopher Jon Donovan

Vicki King

Nick U Christopulos

Kelly S Minter

Kurtis C Houdek

Neeraj Jain

Heidi Brown-McCreery

Andrea Lee Rhoades

Jufeng “James” Peng

Christopher Paul Ramsey

Mark E Clauss

Kyle Pott

Duane Christiansen

Matthew R Baker

Matthew Zettinger

Kerry Cote

Michael L Bernico

Matthew S Megyese

Michael Harris, Jr

Brendan Keane

Steven Joseph Balbach

Mihir Dilip Angal

Samuel C Weeks

Thasphon Chuenchujit

Kelsey Catherine Yoshie Stiles

Allison Lynne Diaz

Alisha Deshmukh

Julian Coltea

Akash Duseja

Mark O'Flaherty

James P Rodriguez

Garry Richard Chant

Jason Freeck

Michael Harris

Audrey Schwartz

Michael DiBenedetto

Anna Zarkoob

Vivek Doshi

Ben Tucker

Joseph Robert Brannan

Nicholas U Christopulos

Christina P Mullen

Laura Jane McGirr

Eleanor Rachel Ann Hyde

Gregory L Hayward

Shawn M Call

Jaime Skaggs

Jessica Lynn Shull

According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driver—the autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated. Insurance discounts may be provided for autonomous vehicles having this safety functionality.

Autonomous vehicle control assessment and selection

An autonomous vehicle (AV) computing device including at least one processor may be provided. The at least processor may be programmed to (i) receive a proposed trip including a destination location and a departure time, (ii) determine environmental conditions data based on the destination location and the departure time, (iii) retrieve current software ecosystem data for the AV, (iv) retrieve aggregated data for a plurality of AVs, the aggregated data including a plurality of correlations, each correlation including a) an interaction between at least one software application and at least one environmental condition and b) an adverse performance outcome associated with the interaction, (v) compare the environmental conditions data for the proposed trip and the current software ecosystem data for the AV to the plurality of correlations to identify an adverse performance outcome, and (vi) execute a remedial action to avoid the adverse performance outcome.

Systems and methods for evaluating autonomous vehicle software interactions for proposed trips

The following generally relates to using Augmented Reality (AR) to enhance pedestrian navigation. In some examples, AR techniques are applied to provide AR indications of a location of a group member that includes two or more AR devices. In these examples, AR techniques may be applied to determine a relative position of the group member and/or provide navigational guidance to the group member. In other examples, AR techniques are applied to provide AR-assisted pedestrian guidance. In these examples, AR techniques may be applied to present an AR display that includes information for a point of interest along a route.

Systems and methods for pedestrian guidance via augmented reality

A computer-implemented method includes receiving a vehicle condition query via a computer network and retrieving condition data corresponding to a vehicle from a vehicle condition database. Further, the method includes determining a condition of the vehicle based on a collective analysis of the condition data, where the condition of the vehicle includes a market value of the vehicle and an overall quality level of the vehicle, and receiving a geographic location of a computing device. Still further, the method includes generating a vehicle condition report, where the vehicle condition report includes an interactive image of a first set of one or more visual condition descriptors indicative of the condition of the vehicle. Moreover, the method includes customizing the vehicle condition report according to the climate associated with the geographic location for the computing device, and communicating the vehicle condition report to the second computing device.

Systems and methods to assess the condition of a vehicle

Systems and methods for an artificial intelligence (AI)/machine learning (ML) chatbot for intelligent negotiation of terms is disclosed herein. An exemplary computing system includes one or more processors; and a memory storing executable instructions thereon. When executed by the one or more processors, these instructions may cause the one or more processors to: receive, from a first contracting party during a negotiating period, a plurality of prospective terms for a contractual agreement; and determine, via an AI chatbot, whether the plurality of prospective terms are acceptable terms based on a plurality of parameters indicated by a second contracting party. Responsive to determining that the plurality of prospective terms are unacceptable terms, the instructions may further cause the processors to generate, via the AI chatbot, a plurality of counter terms based on the plurality of parameters; and transmit the plurality of counter terms to the first contracting party.

AI/ML chatbot for negotiations

Vehicles may have the capability to navigate according to various levels of autonomous capabilities, the vehicle having a different set of autonomous competencies at each level. In certain situations, the vehicle may shift from one level of autonomous capability to another. The shift may require more or less driving responsibility from a human operator. Sensors inside the vehicle collect human operator parameters to determine an alertness level of the human operator. An alertness level is determined based on the human operator parameters and other data including historical data or human operator-specific data. Notifications are presented to the user based on the determined alertness level that are more or less intrusive based on the alertness level of the human operator and on the urgency of an impending change to autonomous capabilities. Notifications may be tailored to specific human operators based on human operator preference and historical performance.

Manual control re-engagement in an autonomous vehicle

In a network of autonomous or semi-autonomous vehicles, an alert may be triggered when one of the vehicles switches from autonomous to manual mode. The alert may be communicated to nearby autonomous vehicles so that drivers of those vehicles may become aware of a potentially unpredictable manual driver nearby. Drivers of autonomous vehicles who may have become disengaged (e.g., sleeping, reading, talking, etc.) during autonomous driving may become re-engaged upon noticing the alert. A re-engaged driver may choose to switch his/her own vehicle from autonomous to manual mode in order to appropriately react to an unpredictable nearby manual driver. In additional or alternative embodiments, the alert may be triggered or intensified when indications of impairment of a nearby driver or malfunction of a nearby vehicle are detected.

Driver re-engagement system

A system for detecting water damage to a structure may include a plurality of energy-harvesting sensors on or within the structure, each sensor being configured to (i) generate sensor data indicative of one or more characteristics of air, construction materials, and/or water within the structure and (ii) generate power for operating the sensor responsively to an external stimulus other than an electrical power source. One or more processors of the system receive sensor data generated by the energy-harvesting sensors, detecting water damage to the structure by analyzing at least the sensor data, and cause an indication of the detected water damage to be presented to a user.

Systems and methods for detecting water damage using energy-harvesting sensors

The following generally relates to using Augmented Reality (AR) to enhance the in-vehicle experience. In some examples, AR techniques are applied to provide AR indications of vehicle safety indicia to alert vehicle occupants to information that may not otherwise be perceptible. In other example, AR techniques are applied to provide emergency vehicle warnings to improve the likelihood of safe responses thereto. In yet other examples, AR techniques are applied to generated personalized outdoor displays, for example, to ameliorate conditions that may impair safe operation of a vehicle.

Systems and methods for in-vehicle driver assistance via augmented reality

A computer-implemented method of assessing driver capability for operating an autonomous vehicle includes identifying a human operator of the autonomous vehicle, the human operator being associated with a driving capability profile. The method further includes signaling to the human operator with the signal, the signal requesting a response from the human operator. The method also includes updating, based on the response from the human operator to the signal, the driving capability profile to indicate a level of skill of the human operator at responding to the signal.

Assessing driver ability to operate an autonomous vehicle

In a computer-implemented method of assessing driving performance using route scoring, driving data indicative of operation of a vehicle while the vehicle was driven on a driving route may be received. Road infrastructure data indicative of one or more features of the driving route may also be received. A route score for the driving route may be calculated using the road infrastructure data, and a driving performance score for a driver of the vehicle may be calculated using the driving data and the route score for the driving route. Data may be sent to a client device via a network to cause the client device to display the driving performance score and/or a ranking based on the driving performance score, and/or the driving performance score may be used to determine a risk rating for the driver of the vehicle.

Route scoring for assessing or predicting driving performance

A system and method for measuring a driver's actual driving behaviors (e.g., acceleration, deceleration) in a manual driving mode to determine their preferred driving style, and then causing an autonomous or semi-autonomous vehicle to operate itself, within limits, in accordance with the drivers' driving style when operating in a self-driving mode, thereby providing a more familiar and comfortable driving experience for the driver. Data is collected on the actual driving behavior, any pre-existing data is accessed on the actual driving behavior, and the collected data and the pre-existing data are combined. A custom control is then created based upon the combined data, and the custom control is applied to manage the self-driving behavior of the autonomous or semi-autonomous vehicle in a self-driving mode. Additional data continues to be collected on the actual driving behavior, and the custom control is adjusted based upon the collected additional data.

Managing self-driving behavior of autonomous or semiautonomous vehicle based upon actual driving behavior of driver

The following relates generally to artificial intelligence (AI)-based responses to social media posts. In some embodiments, one or more processors may be configured to: (1) receive, via a chatbot, a social media post; (2) categorize, via the chatbot, the social media post; (3) determine, via the chatbot, based upon the categorization, an entity to contact; (4) build, via the chatbot, based upon the determined entity to contact, a response to the social media post; and/or (5) send, via the chatbot, the response to the entity.

Chatbot for reviewing social media

Systems and methods for enhanced outdoor displays via augmented reality

Systems and approaches relate to, inter alia, identifying customer opportunities. The systems and approaches may monitor a plurality of routes traversed by a vehicle. The systems and approaches may then access historical route data for the plurality travel routes. The systems and approaches may further calculate a risk index based on the historical route data, and determine at least one customer opportunity based on the calculated risk index.

Technology for analyzing previous vehicle usage to identify customer opportunities

Growing community of inventors

Normal, IL, United States of America

Brian Mark Fields