State Farm Mutual Automobile Insurance Company

Blueowl, LLC

Quanata, LLC

Kenneth Jason Sanchez

Aaron Scott Chan

Theobolt N Leung

Micah Wind Russo

Eric Dahl

Michael L Bernico

Jeffrey S Myers

Vinay Kumar

Brian Mark Fields

Steven J Harris

Gareth Finucane

John Adrian Nepomuceno

Patricia Grealish-Rust

Holger Struppek

Tina Fang

Scott Howard

John Minichiello

Matthew R Baker

Matthew Zettinger

Steve Roberson

Bennett Smith

Matthew Christopher Jon Donovan

Taylor Michael Thiel

Kayla Drozd

Gina Taylor

Saivikas Thalluri

Gil Tamari

Jie Yan

Blake Konrardy

Mohamed A Wazeer

Christina P Mullen

Utku Pamuksuz

Callum Brook

Jeremy Shawn Fogg

Lynn Splittstoesser

Muhammad Ibrahim Saeed

Narendra Anilal

William Cho Hseih

Chase Southwood

Richard Zhou

Sukeerthi Khadri

Sanjay Jayaswal

Yue Liu

Dirk Patrick Maloney

Scott T Christensen

Anuj Dahiya

Michael Sungjun Kim

Yuhao Zhou

Yuetong Xing

Daniel Parks

Keely Renwick

Mary Piontkowski

Abhinav Rai

Shashwat Gupta

Sanjiv Kumar

Vibhakar V S Vemulapati

Harshit Agarwal

Pravita Tolanavar

Jackie O Jordan, Ii

Justin Davis

Sripriya Sundararaman

Aaron Shimer

Aditya Subramanian

Fang Fang

Michael Kim

Ben Blaisdell

Vibhakar Vemulapati

Simon Reed

Ashley Anne Pettit

Mary Kay Gonet Quist

A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device programmed to: (i) receive driving event records, each driving event record including phone usage by a user, wherein a driving event record is labeled as an actual distracted driving event or a passenger event, (ii) divide the driving event records into at least two clusters based at least in part upon common features and the labels of each driving event record by processing the plurality of driving event records with a semi-supervised machine learning algorithm, (iii) generate a trained model based at least in part upon the at least two clusters, (iv) process a new driving event using the trained model, (v) assign the new driving event to one of the clusters using the trained model, and/or (vi) determine whether the new driving event is an actual distracted driving event or a passenger event.

Systems and methods for identifying distracted driving events using semi-supervised clustering

A smart ring clip includes a body with a groove for receiving a portion of a ring, the ring contacting the body, and the body including a flexible material. One or more of a battery, a charging unit, a processing unit, a user input unit, a communication unit, a memory unit, at least one sensor unit, and an output unit is one of attached to or disposed within the body. The body is secured, such as clipped, to the ring by the groove and is removable from the ring upon movement of the body in a direction away from the ring.

Smart ring clip and method of manufacture

Method, system, device, and non-transitory computer-readable medium for classifying a vehicle trip. In one aspect, a computer-implemented method includes: obtaining a user classification associated with an unlabeled vehicular trip; obtaining a first set of historic telematics data associated with work; obtaining a second set of historic telematics data associated with personal use; training a classification model based at least in part upon the first set of historic telematics data and the second set of historic telematics data; obtaining a set of unlabeled telematics data associated with the unlabeled vehicular trip; identifying and comparing a first set of baseline operation features, a second set of baseline operation features, a set of representative operation features; classifying the unlabeled vehicular trip; and validating the user classification based at least in part upon the user classification and the classification made using the classification model.

Systems and methods for validating a vehicular trip classification as for personal use or for work based upon similarity in operation features

Apparatuses, systems and methods are provided for determining vehicle driver distractions. More particularly, apparatuses, systems and methods are provided for determining distractions at particular geographic locations.

Apparatuses, systems, and methods for determining vehicle operator distractions at particular geographic locations

A smart ring device includes a ring band having a plurality of surfaces. The plurality of surfaces can include an inner surface and an outer surface opposite to the inner surface. The inner surface can be configured to contact a user when the ring band is worn by the user. The smart ring device further can include a processor disposed within the ring band. The processor can be configured to determine an indication of one or more driving conditions. The smart ring device also can be communicatively coupled to the processor. The smart ring device also can be configured to receive an input from the user. The processor further can be configured to cause the smart ring device to provide, to the user of the smart ring device, an output including the indication of the one or more driving conditions. Other embodiments are disclosed.

Non-visual outputs for a smart ring

A charging system can include a housing. The housing can include an inner surface. The housing also can include an internal power source. The housing further can include a component configured to draw energy from the internal power source. The charging system also can include a removable charging source. The removable charging source can be removably coupled to the housing. The removable charging source can include first and second registration structures at different locations of a perimeter of a portion of the removable charging source. The first and second registration structures can align the inner surface of the housing with the removable charging source. Other embodiments are disclosed.

Smart ring charger with registration structures

A method includes receiving telematics data comprising information related to one or more driving behaviors of the user. The method also includes processing one or more user interface features based at least in part upon the telematics data. The method further includes generating a web page for the user. Other embodiments are disclosed herein.

Systems and methods for generating personalized landing pages for users

A charging system can include a housing. The housing can include a controller. The housing also can include a power source configured to power the controller. The charging system also can include a wireless charger configured to transfer energy to the power source. The wireless charger can include a first indicator. The first indicator can be configured to provide a first non-flashing visual output at a first brightness level and indicative of a first charging status of the power source. The first indicator also can be configured to provide a second non-flashing visual output at a second brightness level indicative of a second charging status of the power source. Other embodiments are disclosed.

Environment-integrated smart ring charger

A system serializing and deserializing models configured to (i) store a first model, wherein the first model includes a plurality of functionalities; (ii) generate a human-readable document based on the first model, wherein the human-readable document describes the first model; (iii) generate a second model based on the human-readable document, wherein the second model includes the plurality of functionalities; (iv) train the second model; (v) generate a new human-readable document based on the trained second model; and (vi) generate an updated second model based on the new human-readable document.

Systems and methods for automatically serializing and deserializing models

Method and system for presenting in-game objectives in a virtual game. For example, the method includes determining first real-world driving characteristics based upon first real-world telematics data of a first real-world user, determining second real-world driving characteristics based upon second real-world telematics data of a second real-world user, generating a shared virtual map based upon the first real-world driving characteristics and the second real-world driving characteristics, generating a shared in-game objective based upon the shared virtual map, presenting the shared in-game objective to a first virtual character associated with the first real-world driving characteristics of the first real-world user and a second virtual character associated with the second real-world driving characteristics of the second real-world user, and allowing the first virtual character and the second virtual character to accomplish the shared in-game objective in the shared virtual map.

Systems and methods for presenting shared in-game objectives in virtual games

Method, system, device, and non-transitory computer-readable medium for classifying a vehicle trip. In one aspect, a computer-implemented method includes: obtaining a user classification associated with an unlabeled vehicular trip; obtaining a first set of historic device interaction data associated with work; obtaining a second set of historic device interaction data associated with personal use; training a classification model based at least in part upon the first set of historic device interaction data and the second set of historic device interaction data; obtaining a set of unlabeled device interaction data associated with the unlabeled vehicular trip; identifying and comparing a first set of baseline device interaction features, a second set of baseline device interaction features, a set of representative device interaction features; classifying the unlabeled vehicular trip; and validating the user classification based at least in part upon the user classification and the classification made using the classification model.

Systems and methods for validating a vehicular trip classification as for personal use or for work based upon similarity in device interaction features

Systems and methods detecting onboard sensor tampering are disclosed. According to embodiments, data captured by interior sensors within a vehicle may be analyzed to determine an indication that the activity of the vehicle operator either cannot be sufficiently detected or cannot be sufficiently identified using the captured data (e.g., that the captured data may be compromised). A date and time associated with the indication may be recorded, and a vehicle operator associated with the indication may be identified. A possible cause for the compromised data may be diagnosed, and notification may be generated indicating that the activity of the vehicle operator either cannot be sufficiently detected or cannot be sufficiently identified, and/or the possible cause. Additionally, a recommendation for restoring sensor functionality may be generated for the vehicle operator based the possible cause.

Technology for detecting onboard sensor tampering

A method for predicting risk exposure includes receiving a particular set of data acquired via a sensor. The method for predicting risk exposure further can include analyzing, via a machine learning (ML) model, the particular set of data. The analyzing can include determining that the particular set of data represents a particular light exposure pattern corresponding to a light exposure pattern correlated with a risk pattern. The ML model can be trained with a first set of data and a second set of data to identify a correlation between the light exposure pattern and the risk pattern. The method for predicting risk exposure also can include predicting a risk exposure for a user based on the particular set of data. The method for predicting risk exposure also can include providing a notice indicating the risk exposure.

Smart ring system for monitoring UVB exposure levels and using machine learning technique to predict high risk driving behavior

A computer-implemented method implemented by one or more processors for assessing accident events, the computer-implemented method including: determining, based on first sensor data captured by one or more first sensors associated with a first vehicle, that an accident event occurred; and after determining that the accident event occurred: sending, to an electronic device associated with a second vehicle, a request for second sensor data captured by one or more second sensors of the electronic device associated with the second vehicle; receiving, from the electronic device, the second sensor data; analyzing the second sensor data to determine that the second sensor data is related to the accident event; and aggregating the first sensor data with the second sensor data. Other embodiments are described.

Technology for assessing accident events

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.

Method of controlling for undesired factors in machine learning models

Growing community of inventors

San Francisco, CA, United States of America

Kenneth Jason Sanchez