Michael L Bernico, 47Benicia, CA

2023006, Mar 2, 2023

Nov 8, 2022

17/983273

- Bloomington IL, US
Michael Bernico - Bloomington IL, US
Peter Laube - Bloomington IL, US
Utku Pamuksuz - Champaign IL, US
Jeffrey S. Myers - Normal IL, US
Edward W. Breitweiser - Boomington IL, US

G06V 40/10
G06V 20/40
A61B 5/00
G06K 9/62

A method and system may use machine learning analysis of audio data to automatically identify a user's biometric characteristics. A user's client computing device may capture audio of the user. Feature data may be extracted from the audio and applied to statistical models for determining several biometric characteristics. The determined biometric characteristic values may be used to identify individual health scores and the individual health scores may be combined to generate an overall health score and longevity metric. An indication of the user's biometric characteristics which may include the overall health score and longevity metric may be displayed on the user's client computing device.

2023003, Feb 2, 2023

Oct 11, 2022

17/963397

- Bloomington IL, US
Kenneth J. Sanchez - San Francisco CA, US
Michael L. Bernico - Bloomington IL, US

STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY - BLOOMINGTON IL

G06N 3/04
G06N 3/08

A method of training and using a machine learning model that controls for consideration of undesired factors which might otherwise be considered by the trained model during its subsequent analysis of new data. For example, the model may be a neural network trained on a set of training images to evaluate an insurance applicant based upon an image or audio data of the insurance applicant as part of an underwriting process to determine an appropriate life or health insurance premium. The model is trained to probabilistically correlate an aspect of the applicant's appearance with a personal and/or health-related characteristic. Any undesired factors, such as age, sex, ethnicity, and/or race, are identified for exclusion. The trained model receives the image (e.g., a “selfie”) of the insurance applicant, analyzes the image without considering the identified undesired factors, and suggests the appropriate insurance premium based only on the remaining desired factors.

2021037, Dec 2, 2021

Aug 13, 2021

17/402312

- BLOOMINGTON IL, US
Jennifer Malia Andrus - Seattle WA, US
Holly Lambert - Roswell GA, US
Daniel J. Green - Bloomington IL, US
Michael Bernico - Bloomington IL, US
Bradley A. Sliz - Deerfield IL, US
He Yang - The Colony TX, US

STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY - BLOOMINGTON IL

G06T 7/73
G06Q 30/02
G06Q 10/10
G06Q 40/08

A system and computer-implemented method for facilitating a user of a mobile device obtaining image data of damage to a vehicle for damage assessment includes capturing image data of a vehicle with the mobile device. The mobile device may include an orientation model for capturing the image data. The captured image data is analyzed, and a determination is made of the orientations of the images of the captured image data. In addition, a determination is made as to whether the captured image data can be used for the damage assessment. The captured image data may then be transmitted to a damage estimator computing device for estimating an amount of damage to the vehicle.

2021035, Nov 11, 2021

Jun 15, 2018

16/010101

- Bloomington IL, US
Jennifer Malia Andrus - Seattle WA, US
Shane Tomlinson - Bloomington IL, US
Daniel J. Green - Bloomington IL, US
Michael Bernico - Bloomington IL, US
Bradley A. Sliz - Deerfield IL, US
He Yang - The Colony TX, US

State Farm Mutual Automobile Insurance Company - Bloomington IL

G06Q 40/08
G06N 99/00
G06Q 30/02
G06K 9/00
G06K 9/66
G06T 7/00

A system and computer-implemented method for processing image data of a vehicle to identify one or more damaged parts includes receiving the image data of the vehicle from a policyholder. The image data is processed to determine whether the image data includes images of one or more damaged parts of the vehicle. One or more damaged parts are identified. A parts database is used to receive data corresponding to the identified one or more damaged parts. A parts list is generated including the identified one or more damaged parts and the data corresponding to the identified one or more damaged parts.

2021004, Feb 11, 2021

Oct 27, 2020

17/081656

- Bloomington IL, US
Jennifer Malia Andrus - Seattle WA, US
Shane Tomlinson - Bloomington IL, US
Daniel J. Green - Bloomington IL, US
Michael Bernico - Bloomington IL, US
Bradley A. Sliz - Deerfield IL, US
He Yang - The Colony TX, US

G06K 9/00
G06Q 30/02
G06Q 40/08
G06K 9/62
G06T 7/73

A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

2020034, Nov 5, 2020

Jun 15, 2018

16/009983

- Bloomington IL, US
Jennifer Malia Andrus - Seattle WA, US
Shane Tomlinson - Bloomington IL, US
Daniel J. Green - Bloomington IL, US
Michael Bernico - Bloomington IL, US
Bradley A. Sliz - Deerfield IL, US
He Yang - The Colony TX, US

State Farm Mutual Automobile Insurance Company - Bloomington IL

G06K 9/00
G06Q 30/02
G06Q 40/08
G06K 9/62
G06T 7/73

A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

2014027, Sep 18, 2014

Mar 7, 2014

14/201046

- Bloomington IL, US
Tim G. Sanidas - Bloomington IL, US
Jeff Perschall - Normal IL, US
Michael Bernico - Bloomington IL, US
Michael K. Cook - Carlock IL, US
Lynn Calvo - Fayetteville GA, US
V. Rao Kanneganti - Swedesboro NJ, US

STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY - Bloomington IL

G06Q 40/08
G06F 17/30

705 4, 707769, 707741

Methods and systems for processing data, such as insurance data, using a Web-Scale Data Fabric (WSDF). According to embodiments, a stream ingestion hardware component can ingest messages related to an actionable event and send data objects to an in-memory data store based on the messages. The in-memory data store can retrieve insurance policy information that may be applicable to the actionable event and store the insurance policy information in cache memory. A search-based application interfaces with the in-memory data store to search for and retrieve data associated with the insurance policy information, from which a user or administrator may process or otherwise access the information. The systems and methods can further initiate insurance claim processing as well as enrich the data using various techniques to enable real-time search.

2014028, Sep 18, 2014

Mar 7, 2014

14/201325

- Bloomington IL, US
Tim G. Sanidas - Bloomington IL, US
Jeff Perschall - Normal IL, US
Michael Bernico - Bloomington IL, US
Michael K. Cook - Carlock IL, US
Lynn Calvo - Fayetteville GA, US
V. Rao Kanneganti - Swedesboro NJ, US

H04L 29/08

709202

Methods and systems for processing business operations transactions and associated augmented customer data using a Web-Scale Data Fabric (WSDF). According to embodiments, a plurality of computer servers are configured for economical large scale computation and data storage with resilience despite underpinning commodity hardware failure and grow-shrink capacity changes of nodes and associated interconnectivity. The servers communicate with direct attached storage (DAS) and include a co-processor coupled for computation capacity. The servers can connect to an external computer network (ECN) for external client input and output, as well as other functionalities such as physical-to-virtual network connectivity mapping and maintaining resilient storage of data received from the ECN or computationally derived from the received data.