State Farm Mutual Automobile Insurance Company

Tim G Sanidas

Michael L Bernico

Michael K Cook

Taylor Griffin Smith

V Rao Kanneganti

Lynn Calvo

Jason Matthew White

Milton Mowery

Christopher Zimmer

Alex Anton

Joseph David Albright

Jeff Perschall

Matthew E Vose

Scott T Christensen

Jaime Skaggs

Alexander Cardona

James P Rodriguez

Timothy J Nickel

Karthikeyan Srinivasan

Thomas Hilton Jannusch

Pamela Rearden

Jonathan Guidry

David Dohrmann

Jason O'Donnell

Doug Dewey

Edward P Matesevac

Garrick Douglas Gabbrants

Robert T Trefzger

Ramakrishna Duvvuri

Mike Fields

Chris Kawakita

Anthony Nathan Noel

Systems and methods are described for generating efficient iterative electronic recommendation structures. In various aspects, one or more processors aggregate a plurality of ratings vectors, where each ratings vector is associated with a vector type and contains one or more content ranking metrics associated with one or more users. The one or more processors generate similarity pairing values from the plurality of ratings vectors, where each similarity pairing value is based on a similarity mapping between a first ratings vector and a second ratings vector. The one or more processors further generate an electronic recommendation structure based on the similarity pairing values, where the electronic recommendation structure includes a bi-directional look-up interface that is configured to return a bi-directional recommendation value after receiving a lookup request for either the vector type of the first ratings vector or the vector type of the second ratings vector.

Systems and methods for generating efficient iterative recommendation structures

A life plan management (LPM) computing device for connecting consumers with producers is provided. The LPM computing device may be in communication with a client computing device and may include a processor. The processor may be programmed to i) receive user input including at least one goal, ii) analyze the at least one goal to determine a plurality of categories, iii) store the plurality of categories, iv) analyze at least one of the plurality of categories to determine a plurality of producers, v) store the plurality of producers, vi) generate an interactive life map including the at least one goal, and enabling the consumer to access the plurality of producers by navigating the life map to select the at least one goal and the at least one category, vii) and cause the interactive life map to be displayed on the client computing device.

Systems and methods for life plan generation and management

A multi-sided match making ('MMM') computer system including at least one processor in communication with at least one memory device for matching consumers to providers is provided. The at least one processor is configured to: (i) receive registration data from a user, (ii) receive user data from at least one of a sensor and a mobile device associated with the user, (iii) analyze the registration data and the user data, (iv) determine a need based upon the analyzed registration and user data, (v) transmit the determined need to at least one caregiver associated with the user, and (vi) match the user to at least one provider based upon the determined need, wherein the provider is at least one of the caregiver, another caregiver, and a service, and wherein the provider is able to meet the determined need for the user.

Multi-sided match making platforms

Systems and methods are described for providing user offers based on efficient iterative recommendation structures. In various aspects, a server invokes a bi-directional look-up interface via a lookup request, where the bi-directional look-up interface is exposed via an electronic recommendation structure. The lookup request causes the bi-directional look-up interface to return a bi-directional recommendation value. The bi-directional recommendation value indicates a likelihood of a first user selecting a first offer or a second offer. The bi-directional recommendation value is transmitted via a computer network to a client device associated with the first user upon a determination that the likelihood meets or exceeds a recommendation threshold. The client device is operative to display at least one of the first offer or the second offer.

Systems and methods for providing user offers based on efficient iterative recommendation structures

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

Methods and systems for processing machine accelerated and augmented customer data using a Web-Scale Data Fabric (WSDF). According to embodiments, the data may be received as data transfer objects from a set of business operations client applications. The data transfer objects may be analyzed using complex event processing (CEP) and, based on the analyzing, rules specific to the business operations client application may be applied. The methods and systems may semantically classify text specific to the business operations client application. A federated database (FD) may archive the receive data transfer objects as well as analysis data specific to the business operations client application.

Implementation of a web-scale data fabric

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Bloomington, IL, United States of America

Tim G Sanidas