State Farm Mutual Automobile Insurance Company

Michael Shawn Jacob

Nathan Lee Tofte

Brian N Harvey

Rosemarie Geier Grant

Nathan Wilhelm Baumann

Roxane Lyons

Joshua David Lillie

Timothy W Ryan

Shawn M Call

Roger D Schmidgall

Kyle Pott

Michael Jeffrey Aviles

Lee C Drinan

Kevin William Payne

Andrew Reeser

Stacy L Kennedy

Lisa Ann Frey

Eric Haefli

Oscar Allan Arulfo

Leah Garcia

Jeffrey Wilson Stoiber

Sunish Shreenarayan Menon

Rajiv C Shah

Phillip Sangpil Moon

Erin Ann Olander

Jeffrey Kinsey

Lucas Allen

Jeffrey David Hevrin

Sripriya Sundararaman

James Dykeman

Manali Desai

Kevin Payne

Ellakate LeFebre

Scott T Christensen

Jackie O Jordan, Ii

Shawn C Bennett

Gregory Dwayne Carter

William Guthrie

Timothy J Nickel

Brian William Batronis

Robert D Rariden

Frank Matthew McCully

Benjamin D Schappaugh

Kalpana Aravabhumi

EllaKate LeFebre

Joshua Robert Brown

Jack Weekes

Lisa A Frey

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

This disclosure provides systems for generating customized datasets. An example dataset generator mines contacts from multiple members of an enterprise, applies a pattern recognition process to determine attributes of the contacts aligned with objectives of the enterprise, stores the contacts and associated attributes in a relational data structure, and through a machine learning process, generates a customized contact dataset of the contacts with attributes relevant to a user request, an event, or a notification received. The example dataset generator may also generate an action steps dataset for the user. The dataset generator may aggregate contacts from calls, chats, emails, SMS messages, and video feeds of an enterprise, apply a pattern recognition process to determine attributes of the contacts, and through a machine learning process, partition and distribute the contacts and associated action steps on respective dashboards of multiple customer service representatives of the enterprise.

Systems for generating customized datasets

Systems and methods are described for analyzing information relating to technical events associated with client devices in an organization (e.g., hardware and/or software malfunctions or performance inefficiencies originating at a client device or elsewhere in an organizational computing system). Particularly, machine learning techniques may use one or more trained artificial neural networks to classify technical events. Classifications of technical events may include, for example, causes of technical events, identifications of other affected devices, and/or steps for resolving technical events. Additionally, systems and methods are described for analyzing information relating to organizational ideas conceived of by client device users within the organization.

Systems and methods for analyzing a technical event

A method and system may survey a property using aerial images captured from an unmanned aerial vehicle (UAV), a manned aerial vehicle (MAV) or from a satellite device. The method may include identifying a commercial property for a UAV to perform surveillance, and directing the UAV to hover over the commercial property and capture aerial images at predetermined time intervals. Furthermore, the method may include receiving the aerial images of the commercial property captured at the predetermined time intervals, detecting a surveillance event at the commercial property, generating a surveillance alert, and transmitting the surveillance alert to an electronic device associated with an owner of the commercial property.

Method and system for assessing damage to infrastructure

Systems and methods are described for using data collected by unmanned aerial vehicles (UAVs) to generate insurance claim estimates that an insured individual may quickly review, approve, or modify. When an insurance-related event occurs, such as a vehicle collision, crash, or disaster, one or more UAVs are dispatched to the scene of the event to collect various data, including data related to vehicle or real property (insured asset) damage. With the insured's permission or consent, the data collected by the UAVs may then be analyzed to generate an estimated insurance claim for the insured. The estimated insurance claim may be sent to the insured individual, such as to their mobile device via wireless communication or data transmission, for subsequent review and approval. As a result, insurance claim handling and/or the online customer experience may be enhanced.

Unmanned aerial vehicle (UAV) data collection and claim pre-generation for insured approval

A building monitoring computer system for monitoring building integrity may be provided. Various types of sensors may be embedded throughout or within certain portions of different types of building or construction material making up the building, such as within roofing, foundation, or structural materials. The sensors may be in wireless communication with a home controller. The sensors may be water, moisture, temperature, vibration, or other types of sensors, and may detect unexpected or abnormal conditions within the home. The sensors and/or home controller may transmit alerts to a mobile device of the home owner associated with the unexpected condition, and/or that remedial actions may be required to repair the home or mitigate further damage to the home. The sensor data may also be communicated to an insurance provider remote server to facilitate the insurance provider communicating insurance-related recommendations, updating insurance policies, or preparing insurance claims for review for home owners.

Systems and methods for monitoring building health

Various techniques are described utilizing one or more unmanned aerial vehicles (UAVs, or 'drones') for various disaster and/or catastrophe-related purposes. UAVs may collect data in an attempt to predict the occurrence and/or extent of a catastrophe and/or to mitigate the impact of a catastrophe before and, if not at that time, once it has occurred. The UAVs may perform various tasks such that the damage to property caused by a catastrophe (or potential catastrophe) may be eliminated or mitigated. A UAV may receive a flight path based on an energy consumption related condition and operate based on the flight path. Operating based on the flight path includes docking the UAV with a power supply device for charging a power source of the UAV.

Disaster damage analysis and loss mitigation implementing unmanned aerial vehicles (UAVs)

Unmanned aerial vehicles (UAVs) may facilitate insurance-related tasks. UAVs may actively be dispatched to an area surrounding a property, and collect data related to property. A location for an inspection of a property to be conducted by a UAV may be received, and one or more images depicting a view of the location may be displayed via a user interface. Additionally, a geofence boundary may be determined based on an area corresponding to a property boundary, where the geofence boundary represents a geospatial boundary in which to limit flight of the UAV. Furthermore, a navigation route may be determined which corresponds to the geofence boundary for inspection of the property by the UAV, the navigation route having waypoints, each waypoint indicating a location for the UAV to obtain drone data. The UAV may be directed around the property using the determined navigation route.

Insurance underwriting and re-underwriting implementing unmanned aerial vehicles (UAVs)

Unmanned aerial vehicles (UAVs) may facilitate the generation of a virtual reconstruction model of a vehicle collision. UAVs may collect data (including images) related to the vehicle collision, such as with the insured's permission, which may be received by an external computing device associated with the insurer and utilized to perform a photogrammetric analysis of the images to determine vehicle impact points, the road layout at the scene of the collision, the state of the traffic light at the scene of the collision, the speeds and directions of vehicles, etc. This data may be used to generate a virtual reconstruction model of the vehicle collision. An insurer may use the virtual reconstruction model to perform various insurance-related tasks, such as allocating fault to drivers or autonomous vehicles involved in the vehicle collision, and adjustment of insurance pricing based upon the fault allocation.

Accident reconstruction implementing unmanned aerial vehicles (UAVs)

A task management platform generates an interactive display tasks based on multi-team activity data of different geographic locations across a plurality of distributed guided user interfaces (GUIs). Additionally the task management platform uses a distributed machine-learning based system to determine a suggested task item for a remote team based on multi-team activity data of different geographic locations.

Task management user interface (GUI)

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

Artificial intelligence systems and methods configured to predict team management decisions

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

Growing community of inventors

Le Roy, IL, United States of America

Michael Shawn Jacob