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

Tina Fang

Holger Struppek

Scott Howard

John Minichiello

Matthew R Baker

Matthew Zettinger

Steve Roberson

Bennett Smith

Matthew Christopher Jon Donovan

Taylor Michael Thiel

Kayla Drozd

Blake Konrardy

Mohamed A Wazeer

Christina P Mullen

Utku Pamuksuz

Callum Brook

Jeremy Shawn Fogg

Lynn Splittstoesser

Gina Taylor

Saivikas Thalluri

Jie Yan

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

Gil Tamari

Vibhakar V S Vemulapati

Harshit Agarwal

Pravita Tolanavar

Jackie O Jordan, Ii

Justin Davis

Sripriya Sundararaman

Aaron Shimer

Aditya Subramanian

Fang Fang

Ben Blaisdell

Vibhakar Vemulapati

Simon Reed

Ashley Anne Pettit

Mary Kay Gonet Quist

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

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: building, using a machine learning algorithm, a transit model based at least in part upon telematics data associated with a user; generating, based at least in part upon the transit model, a dynamic transit route for the user; calculating, based at least in part upon the telematics data associated with the user, a potential benefit comprising an amount of fuel cost savings for the user, reduced travel time for the user, insurance savings, or environmental pollution reduction for the user; generating a notification comprising the dynamic transit route and the potential benefit; and transmitting the notification to a mobile device of the user. Other embodiments are described.

Systems and methods for generating dynamic transit routes

A smart ring includes a housing configured to be worn by a user. The smart ring can further include a power source disposed within the housing. The smart ring also can include a charging circuit disposed within the housing. The charging circuit can include a harvesting element configured to charge the power source. The harvesting element can be configured to convert mechanical energy to electrical energy. The smart ring also can include a device at least partially disposed within the housing. The device can be configured to draw energy from the power source. Other embodiments are disclosed.

Harvesting energy for a smart ring via piezoelectric charging

A computer system for determining personalized loss event valuations is provided. The computer system includes one processor in communication with at least one memory device, and the at least one processor is programmed to: (i) receive user data from the user computer device, wherein the user data include data related to a user's personal information and personal property, (ii) utilize a trained machine learning model to determine a first loss valuation associated with a first loss event based at least in part upon the user data, (iii) utilize the trained machine learning model to determine a second loss valuation associated with a second loss event based at least in part upon the user data, wherein the first loss event and the second loss event are different loss events, and/or (iv) generate a loss event policy that includes the first loss valuation and the second loss valuation.

Systems and methods for determining personalized loss valuations for a loss event

Systems and methods for improving vehicular safety are provided. According to certain aspects, an electronic device may receive and analyze image data depicting an individual located within a vehicle. The electronic device may also access certain data related to a condition of the individual, and may accordingly determine whether the individual is fit to operate the vehicle. If the individual is unfit to operate the vehicle, the electronic device may alter autonomous operation of the vehicle.

Apparatuses, systems and methods for improving operation of autonomous vehicles

Method and system for granting in-game resources for a telematics-based game. In some examples, a computer-implemented method includes: generating, based on a character profile, one or more virtual occurrences to be encountered by a virtual character; determining, based on a plurality of virtual ratings of the virtual character, one or more outcomes associated with the one or more virtual occurrences; determining, based on a user's driving performance during one or more real trips, a first quantity of a first game resource at least for purchasing outcome-modifying items, the outcome-modifying items being exclusively purchasable using the first game resource; upon receiving the user's selection to use a first outcome-modifying item: updating the one or more outcomes according to a predetermined adjustment; and determining, based on the updated one or more outcomes, a second quantity of a second game resource at least for purchasing character cosmetic upgrades, unlocking in-game items, and unlocking regions of a virtual map.

Systems and methods for training and applying virtual occurrences and granting in-game resources to a virtual character using telematics data of one or more real trips

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: determining a first real-world user based at least in part upon first real-world telematics data associated with the first real-world user; generating one or more first virtual experiences based at least in part upon one or more first real-world driving behaviors; generating a first virtual character; and presenting the one or more first virtual experiences in a virtual game to the first real-world user, wherein the one or more first virtual experiences include one or more first virtual obstacles to be encountered by the first virtual character in the virtual game based at least in part upon the one or more first real-world driving behaviors. Other embodiments are described.

Systems and methods for generating virtual experiences for a virtual game

Method, system, device, and non-transitory computer-readable medium for data management. In some examples, a computer-implemented method includes: collecting a plurality of personal data sets and a plurality of sensor data sets; for each vehicle operator of the plurality of vehicle operators: generating and continually updating an operator profile, one or more telematics inferences, a data profile; and listing and continually updating the data profile onto a telematics marketplace; receiving a plurality of conditional bids for a target operator profile associated with a target data profile, each conditional bid of the plurality of conditional bids including one or more conditional payments and one or more payment conditions; determining, for each conditional bid of the plurality of conditional bids, a predicted bid-generated profit or a predicted bid-generated revenue; determining a winning bid and an associated winning bidder; and transmitting the target operator profile to the winning bidder.

Systems and methods for managing vehicle operator profiles based on telematics inferences via an auction telematics marketplace with a bid profit predictive model

Method and system for validating planting of trees. For example, the method includes receiving a first image depicting a tree, receiving a second image depicting an environment where the tree is to be planted, receiving a third image depicting the tree having been planted in the environment, selecting and encoding a first patch of the first image that depicts the tree, selecting and encoding a second patch of the second image that depicts the environment, selecting and encoding a third patch of the third image that depicts both the tree and the environment, and comparing the encoded patches to determine whether the tree has been planted in the environment.

Systems and methods for validating planting of trees

Method, system, device, and non-transitory computer-readable medium for managing vehicle operator profiles based on telematics inferences via a telematics marketplace. In some examples, a computer-implemented method includes: collecting a plurality of personal data sets continually; collecting a plurality of sensor data sets continually via one or more tensing modules; for each vehicle operator of the plurality of vehicle operators: generating and continually updating an operator profile including the personal data set: determining and continually updating one or more telematics inferences using a universal predictive model based at least in part upon the sensor data set, the universal predictive model having weights and biases hidden to marketplace participants; generating and continually updating a data profile including the one or more telematics inferences; and listing and continually updating the data profile onto a telematics marketplace to be accessible by a plurality of marketplace participants.

Systems and methods for managing vehicle operator profiles based on universal telematics inferences via a telematics marketplace

A computer implemented including: collecting, via one or more sensors, a first set of operator data associated with a first group of vehicle operators during a first time period; determining, for each vehicle operator of the first group of vehicle operators and using one or more trained machine learning models based at least in part upon the first set of operator data, a first set of telematics inferences, the one or more trained machine learning models being trained using training data sets comprising sensor data associated with a second group of vehicle operators to predict telematics inferences; collecting, via the one or more sensors, a second set of operator data associated with the first group of vehicle operators during a second time period; determining, for each vehicle operator of the first group of vehicle operators and based on the second set of operator data, a second set of telematics inferences; determining, for each vehicle operator of the first group of vehicle operators, one or more match evaluations based at least in part upon the first set of telematics inferences and the second set of telematics inferences; and modifying one or more weights of the one or more trained machine learning models based at least in part upon the one or more match evaluations. Other embodiments are described.

Systems and methods for match evaluation based on change in telematics inferences via a telematics marketplace

Method and system for determining whether a user of a mobile device is a driver of a vehicle. For example, the method includes receiving telematics data and device interaction data collected by the mobile device during vehicle trip segments, analyzing the telematics data to determine first driving events of a predetermined type during the vehicle trip segments, determining second driving events of the predetermined type during which the user interacts with the mobile device by correlating the telematics data and the device interaction data, calculating a ratio of the number of the second driving events to the number of the first driving events, and determining whether or not the user of the mobile device is the driver of the vehicle during the vehicle trip segments by comparing the ratio to a predetermined threshold.

Systems and methods for determining a vehicle driver based on mobile device usage during high attention driving events

A computer-implemented method of generating a scenic route including: receiving, by one or more processors, a user request from a user for directions from an origin location to a destination location; identifying, by the one or more processors, one or more route segments of one or more routes between the origin location and the destination location based on at least telemetry data and point of interest (POI) data; determining, by the one or more processors, one or more respective scenic views proximate to the one or more respective scenic points; receiving, by the one or more processors, a selection of at least one of the one or more respective scenic views; and transmitting, by the one or more processors, navigation directions to the user for at least one route of the one or more routes. Other embodiments are described.

Systems and methods for generating scenic routes

Growing community of inventors

San Francisco, CA, United States of America

Kenneth Jason Sanchez