State Farm Mutual Automobile Insurance Company

Timothy J Nickel

Michael Shawn Jacob

William Guthrie

Brian William Batronis

Robert D Rariden

Frank Matthew McCully

Benjamin D Schappaugh

Scott T Christensen

Jaime Skaggs

Tim G Sanidas

Alexander Cardona

James P Rodriguez

Thomas Hilton Jannusch

Pamela Rearden

David Dohrmann

Karthikeyan Srinivasan

Techniques for identifying faults in a collaborative computing system including a plurality of disparate, geographically separated computing systems are described herein. An intelligent monitoring (IM) server computing system may receive data from the plurality of computing devices and may monitor the health of the collaborative computing system. The IM server computing system may analyze the data and identify one or more faults associated with a portion of the collaborative system (e.g., an associated computing device, platform, network, etc.). In some examples, the IM server computing system may be configured to identify potential future faults associated with the portion of the collaborative system. Based on the fault, the IM server computing device may determine an action to take to remedy the fault and/or prevent the potential future fault. The IM server computing device may either automatically perform the action or send a notification to the associated computing system to perform the action.

Error monitoring and prevention in computing systems based on future fault prediction and historical values of a network characteristic

Systems and methods for determining the effectiveness of vehicle safety features are provided. Vehicle build information (VBI) for vehicles manufactured by a plurality of OEMs may be obtained. The VBI may contain OEM-specific terminology for smart safety features associated with each vehicle. The obtained VBI may be analyzed to generate an ontology model mapping each feature to any OEM-specific terminology associated with the feature. The ontology model may be applied to the VBI to generate translated VBI for each vehicle, such that the OEM-specific terminology associated with each feature is replaced with OEM-agnostic terminology for the feature. Vehicle accident record information may be obtained for each vehicle, including, e.g., the number, frequency, severity, etc. of accidents associated with each vehicle. Using the OEM-agnostic terminology for each feature associated with each vehicle and the vehicle accident information for each vehicle, an effectiveness score associated with each feature may be calculated.

Systems and methods of determining effectiveness of vehicle safety features

Techniques for providing recommendations of actions for a user to perform to improve efficiency in user interactions with a user interface of a user computing device. An intelligent monitoring (IM) computing system may receive data from a user computing device and may determine user interactions associated with the user interface. The user interactions may include selections, an order associated with the selections, times associated with the selections, and/or other data corresponding to user interaction with a user interface. The IM computing device may be configured to determine a fault (e.g., inefficiency) associated with the user interactions and determine an action for the user to perform to correct the fault. The IM computing device may cause a notification including the action to surface on a display of the user computing device, such as to inform the user of a means by which they can improve efficiency of the user interactions.

Intelligent user interface monitoring and alert

Error monitoring and prevention in computing systems based on determined trends and routing a data stream over a second network having less latency

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Bloomington, IL, United States of America

Timothy J Nickel