Abstract: Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

Abstract: A computer system for generating an insurance policy for a user includes a processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations including: (i) receiving, from a wearable electronic user device, biometric data associated with a user; (ii) analyzing, based upon a plurality of rules, the biometric data; (iii) determining a health score associated with the user, based in part upon the analysis of the biometric data, wherein the health score represents a likelihood that the user will maintain a level of health for a predefined period of time; (iv) retrieving terms and conditions for an insurance policy from a database based upon the health score; and (v) generating, based upon the determining, an insurance policy for the user based upon the terms and conditions.

Abstract: Aspects of distributed ledger technology are leveraged to verify subrogation settlements. In particular, two parties to a subrogation claim provide cryptographic hashes to a subrogation demand smart contract stored at an address on a blockchain. The subrogation demand smart contract determines that the parties have reached an agreement by determining that the cryptographic hashes match. A settlement amount from the subrogation claim may be appended to a set of settlement amounts to determine a net settlement amount to facilitate a single payment between the parties on a periodic basis, such as daily, to alleviate the need for the parties to send or receive a payment for each individual settlement amount.

Abstract: A computer system for generating an insurance policy for a user includes a processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations including: (i) receiving, from a wearable electronic user device, biometric data associated with a user; (ii) analyzing, based upon a plurality of rules, the biometric data; (iii) determining a health score associated with the user, based in part upon the analysis of the biometric data, wherein the health score represents a likelihood that the user will maintain a level of health for a predefined period of time; (iv) retrieving terms and conditions for an insurance policy from a database based upon the health score; and (v) generating, based upon the determining, an insurance policy for the user based upon the terms and conditions.

Abstract: Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

Abstract: Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

Abstract: Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

Abstract: Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

Abstract: A computer system for generating an insurance policy for a user includes a processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations including: (i) receiving, from a wearable electronic user device, biometric data associated with a user; (ii) analyzing, based upon a plurality of rules, the biometric data; (iii) determining a health score associated with the user, based in part upon the analysis of the biometric data, wherein the health score represents a likelihood that the user will maintain a level of health for a predefined period of time; (iv) retrieving terms and conditions for an insurance policy from a database based upon the health score; and (v) generating, based upon the determining, an insurance policy for the user based upon the terms and conditions.

Abstract: Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

Abstract: A computer system for analyzing biometric data of a user collected from a plurality of user devices and used to generate an insurance policy for the user includes a processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations including: (i) receiving, from a wearable electronic user device, biometric data associated with a user; (ii) analyzing, based upon a plurality of rules, the biometric data; (iii) determining a health score associated with the user, based in part upon the analysis of the biometric data, wherein the health score represents a likelihood that the user will maintain a level of health for a predefined period of time; (iv) retrieving terms and conditions for an insurance policy from a database based upon the health score; and (v) generating, based upon the determining, an insurance policy for the user based upon the terms and conditions.

Abstract: Aspects of distributed ledger technology are leveraged to verify subrogation settlements. In particular, two parties to a subrogation claim provide cryptographic hashes to a subrogation demand smart contract stored at an address on a blockchain. The subrogation demand smart contract determines that the parties have reached an agreement by determining that the cryptographic hashes match. A settlement amount from the subrogation claim may be appended to a set of settlement amounts to determine a net settlement amount to facilitate a single payment between the parties on a periodic basis, such as daily, to alleviate the need for the parties to send or receive a payment for each individual settlement amount.

Abstract: Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

Abstract: Aspects of distributed ledger technology are leveraged to verify subrogation settlements. In particular, two parties to a subrogation claim provide cryptographic hashes to a subrogation demand smart contract stored at an address on a blockchain. The subrogation demand smart contract determines that the parties have reached an agreement by determining that the cryptographic hashes match. A settlement amount from the subrogation claim may be appended to a set of settlement amounts to determine a net settlement amount to facilitate a single payment between the parties on a periodic basis, such as daily, to alleviate the need for the parties to send or receive a payment for each individual settlement amount.

Abstract: Aspects of distributed ledger technology are leveraged to verify subrogation settlements. In particular, two parties to a subrogation claim provide cryptographic hashes to a subrogation demand smart contract stored at an address on a blockchain. The subrogation demand smart contract determines that the parties have reached an agreement by determining that the cryptographic hashes match. A settlement amount from the subrogation claim may be appended to a set of settlement amounts to determine a net settlement amount to facilitate a single payment between the parties on a periodic basis, such as daily, to alleviate the need for the parties to send or receive a payment for each individual settlement amount.

Abstract: A system configured for facilitating LLLT on a head of a user includes an LLLT system and a head-mounting device. The LLLT system includes a laser diode lead configured to output laser light suitable for LLLT and a laser controller configured to provide power to and control operation of the laser diode lead. The head-mounting device is wearable on a head of a user, and the head-mounting device comprising an attachment point. The attachment point is configured to secure the laser diode lead to direct laser light output by the laser diode lead toward a treatment area on a head of a user.

Abstract: A computer system for analyzing biometric data of a user collected from a plurality of user devices and used to generate an insurance policy for the user includes a processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations including: (i) receiving, from a wearable electronic user device, biometric data associated with a user; (ii) analyzing, based upon a plurality of rules, the biometric data; (iii) determining a health score associated with the user, based in part upon the analysis of the biometric data, wherein the health score represents a likelihood that the user will maintain a level of health for a predefined period of time; (iv) retrieving terms and conditions for an insurance policy from a database based upon the health score; and (v) generating, based upon the determining, an insurance policy for the user based upon the terms and conditions.