State Farm Mutual Automobile Insurance Company

John Andrew Schirano

Erin Ann Olander

Alexander Cardona

Jeremy Carnahan

John Ryan Hailey

Joseph P Harr

Jane A Scott

Kenneth A Blocker

Sondra Mulford

John Chizmar

Dana C Hunt

Michael Stine McGraw

Vicki King

Bryan R Nussbaum

Mike McGraw

Michael Stine Mcgraw

A feature mapping computer system configured to (i) receive a localized image including a photo depicting a driving environment and location data associated with the photo, (ii) identify, using an image recognition module, a roadway feature depicted in the photo, (iii) generate, using a photogrammetry module, a point cloud based upon the photo and the location data, wherein the point cloud comprises a set of data points representing the driving environment in a three dimensional ('3D') space, (iv) localize the point cloud by assigning a location to the point cloud based upon the location data, and (v) generate an enhanced base map that includes a roadway feature.

Systems and methods for enhanced base map generation

Provided herein is an engagement and care support platform ('ECSP') computer system including at least one processor in communication with at least one memory device for facilitating senior user engagement. The processor is programmed to: (i) register a user through an application, (ii) register a caregiver associated with the user through the application, (iii) generate a senior profile based upon user personal and scheduling data, (iv) build a daily interactive user interface that reflects the senior profile, (v) display the daily interactive user interface at a first client device associated with the user, (vi) cause the first client device to initiate a daily interaction prompt to the user, (vii) determine whether any user interaction was received in response to the daily interaction prompt, and (viii) transmit a daily update message to a second client device associated with the caregiver, including an indication of whether any user interaction was received.

Alert systems for senior living engagement and care support platforms

Methods, systems, and computer readable media for predictively determining a risk of damage to a property are provided. To determine the risk, a high resolution virtual model of a region that includes the property is obtained. The virtual model is imported into a simulation environment. One or more of the simulation parameters are set based on historic weather data for the region. For example, each parameter may be associated with a probability distribution derived based on the historic weather data that is sampled prior to executing the simulation. One or more simulations are executed in accordance with the sampled inputs to simulate the likely weather patterns the property will experience. The result of the simulation is analyzed to determine the predicted risk of damage to the property.

Systems and methods for predictive modeling via simulation

A home cost analysis server for executing a customized home search may include a processor programmed to define a user budget constraint, receive a list of available homes, and determine an initial monthly cost for each home. The processor may also identify one or more additional home costs based on historical insurance claim data. The processor may also display available homes that satisfy the user budget constraint.

Systems and methods for executing a customized home search

A home cost analysis server is configured to train an image processing program to identify features of homes, receive user input including a prospective home, and access a first database storing metadata and images associated with homes, including the prospective home, available for purchase. The server is also configured to input images of the prospective home to the machine-learned image processing program, which outputs a feature of the prospective home, access a second database storing historical ancillary costs, and perform a lookup in the second database to retrieve comparable historical ancillary costs associated homes having a comparable feature to the outputted feature. The server is further configured to analyze the metadata associated with the prospective home, the outputted feature, and the comparable historical ancillary costs to determine ancillary home costs associated with the prospective home, and display the ancillary home costs and an overall monthly cost for the prospective home.

Systems and methods for identifying ancillary home costs

A home cost analysis server for executing a customized home search may include a processor programmed to define a user's budget constraint and a level of flexibility thereof, including a threshold difference from the budget constraint, retrieve a list of homes and public listing information associated with each home, and determine a monthly cost associated with each home. The processor may also isolate a first subset of homes having a monthly cost that satisfies the budget constraint, and a second subset of homes having a monthly cost within the threshold difference of the budget constraint, and display the first and second subsets along with respective first and second graphical indicators, as well as a third graphical indicator of a first adjustable expense data element, the third graphical indicator recommending reducing the first adjustable data element to move at least one first home from the second subset into the first subset.

A feature mapping computer system configured to (i) receive a first localized image including a first photo and a first location; (ii) receive a second localized image including a second photo and a second location; (iii) identify a roadway feature depicted in both the first and second photos; (iv) generate, using a photogrammetry module, a point cloud based upon the first and second photos and first and second locations; (v) generate a localized point cloud by assigning a location to the point cloud based upon at least one of the first and second locations; and (vi) generate an enhanced base map that includes a roadway feature by embedding an indication of the identified roadway feature onto the localized point cloud.

A home cost analysis server for identifying one or more hidden home costs may include a processor programmed to receive user input including at least one prospective home. The processor may access a first external database storing metadata associated with a plurality of homes available for purchase, the plurality of homes including the prospective home, and access a second external database storing historical ancillary costs associated with a plurality of homes in a geographic area surrounding the prospective home. The processor may analyze metadata associated with the prospective home and the historical ancillary costs to determine ancillary home costs associated with the prospective home, and cause to be displayed the ancillary home costs at the user computing device using at least one graphical indicator. As a result, the prospective homebuyer may have additional education and insight into the potential costs of owning a home.

Systems and methods for identifying hidden home maintenance costs

A home cost analysis server for executing a customized home search may include a processor programmed to receive user input including a geographic area and a budget, and define a budget constraint. The processor may access an external database storing a list of available homes in the geographic area, and analyze metadata associated with each available home on the list to determine a monthly home cost associated with each available home. The processor may also determine a significant home maintenance cost associated with each available home based upon the metadata and the geographic area, and calculate a total cost associated with each available home. The processor may further isolate a subset of the available homes having the total cost associated therewith that satisfies the budget constraint, and cause to be displayed the subset of available homes along with a graphical indicator that associates the total cost with the budget constraint.

Growing community of inventors

Bloomington, IL, United States of America

John Andrew Schirano