Abstract: A method includes training artificial neural network models; analyzing first data to determine vehicle imperatives; and generating a respective action value by analyzing actions using the of trained artificial neural network models. A non-transitory computer readable medium containing program instructions that when executed, cause a computer system to: train artificial neural network models; analyze first data to determine vehicle imperatives; and generate a respective action value by analyzing actions using the of trained artificial neural network models. A computing system includes: a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: train artificial neural network models; analyze first data to determine vehicle imperatives; and generate a respective action value by analyzing actions using the of trained artificial neural network models.

Abstract: In a method for applying penalties or incentives to a driver of a rented vehicle, an indication that the driver has agreed to terms for renting the vehicle from the vehicle owner is received, with the terms including the potential application of penalties or incentives to the driver based on driving behavior. Telematics data, indicative of operation of the rented vehicle by the driver during a period of time, is received. By analyzing the telematics data, one or more driving behaviors of the driver during the time period is/are identified. For each driving behavior, a corresponding state of an environment of the rented vehicle when the driving behavior occurred is determined. One or more penalties or incentives are caused to be applied to the driver, based on the driving behavior(s) and the corresponding state(s) of the environment of the rented vehicle.

Abstract: Embodiments described herein generate a first set of prompts, the first set of prompts configured to prompt a vehicle owner for a first set of answers used to learn preferred vehicle renter characteristics; receive the first set of answers; generate, based upon the first set of answers, a second set of prompts, the second set of prompts configured to prompt the owner for a second set of answers used to learn additional preferred vehicle renter characteristics; receive the second set of answers; predict user preference value(s) of a profile of the owner based upon the second set of answers, wherein the user preference value(s) define criteria for sharing a vehicle associated with the profile with vehicle renters who satisfy the criteria; apply the criteria to potential vehicle renters; and cause an indication of the vehicle to be displayed only to the potential vehicle renters who satisfy the criteria.

Abstract: Embodiments described herein, inter alia, receive telematics data collected over a period of time, wherein the telematics data is indicative of operation of a vehicle by a potential renter during the period of time; identify, upon analyzing the telematics data, driving behavior(s) of the renter during the period of time; determine, for each driving behavior, a corresponding state of an environment of the vehicle when the driving behavior occurred; determine renter eligibility value(s) for the renter based on the driving behavior(s) and the corresponding state of an environment of the vehicle; compare the renter eligibility value(s) to user preference value(s) of a profile of a vehicle owner, wherein the user preference value(s) define one or more criteria for vehicle renters with whom the vehicle can be shared; and cause an indication of the vehicle associated with the profile to be displayed only if the renter satisfies the criteria.

Abstract: A method includes training artificial neural network models; analyzing first data to determine vehicle imperatives; and generating a respective action value by analyzing actions using the of trained artificial neural network models. A non-transitory computer readable medium containing program instructions that when executed, cause a computer system to: train artificial neural network models; analyze first data to determine vehicle imperatives; and generate a respective action value by analyzing actions using the of trained artificial neural network models. A computing system includes: a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: train artificial neural network models; analyze first data to determine vehicle imperatives; and generate a respective action value by analyzing actions using the of trained artificial neural network models.

Abstract: Processes for the preparation of glycosylated steviol glycoside compositions useful as sweeteners and flavor modifiers in food and beverage products and the like are improved by the use of basic conditions before, during and/or after an enzyme-catalyzed glycosylation of a steviol glycoside composition.

Abstract: A computer-implemented method in a mobile computing device for tracking health and usage of electric vehicle (EV) batteries using Quick Response (QR) codes (or NFC or RFID tags) is provided. The method may include (1) capturing, by a camera associated with a mobile computing device, an image of a tag affixed to an EV; (2) analyzing the image of the tag affixed to the EV; (3) identifying, by the one or more processors of the mobile computing device, the EV based upon analyzing the image of the tag affixed to the EV; (4) determining vehicle battery data associated with a rechargeable battery that powers the identified EV; (5) determining based upon the vehicle battery data associated with the rechargeable battery that powers the identified EV, a battery status indication corresponding to the identified EV; and/or (6) providing, via a user interface, the battery status indication corresponding to the identified EV.

Abstract: A computer-implemented method in a mobile computing device for tracking health and usage of electric vehicle (EV) batteries using Quick Response (QR) codes (or NFC or RFID tags) is provided. The method may include (1) capturing, by a camera associated with a mobile computing device, an image of a tag affixed to an EV; (2) analyzing the image of the tag affixed to the EV; (3) identifying, by the one or more processors of the mobile computing device, the EV based upon analyzing the image of the tag affixed to the EV; (4) determining vehicle battery data associated with a rechargeable battery that powers the identified EV; (5) determining based upon the vehicle battery data associated with the rechargeable battery that powers the identified EV, a battery status indication corresponding to the identified EV; and/or (6) providing, via a user interface, the battery status indication corresponding to the identified EV.

Abstract: Embodiments described herein generate a first set of prompts, the first set of prompts configured to prompt a vehicle owner for a first set of answers used to learn preferred vehicle renter characteristics; receive the first set of answers; generate, based upon the first set of answers, a second set of prompts, the second set of prompts configured to prompt the owner for a second set of answers used to learn additional preferred vehicle renter characteristics; receive the second set of answers; predict user preference value(s) of a profile of the owner based upon the second set of answers, wherein the user preference value(s) define criteria for sharing a vehicle associated with the profile with vehicle renters who satisfy the criteria; apply the criteria to potential vehicle renters; and cause an indication of the vehicle to be displayed only to the potential vehicle renters who satisfy the criteria.

Abstract: In a method for applying penalties or incentives to a driver of a rented vehicle, an indication that the driver has agreed to terms for renting the vehicle from the vehicle owner is received, with the terms including the potential application of penalties or incentives to the driver based on driving behavior. Telematics data, indicative of operation of the rented vehicle by the driver during a period of time, is received. By analyzing the telematics data, one or more driving behaviors of the driver during the time period is/are identified. For each driving behavior, a corresponding state of an environment of the rented vehicle when the driving behavior occurred is determined. One or more penalties or incentives are caused to be applied to the driver, based on the driving behavior(s) and the corresponding state(s) of the environment of the rented vehicle.

Abstract: Embodiments of extended reality (XR) methods and systems for handling home-related information are disclosed. In one embodiment, a computer-implemented method may include (1) obtaining one or more XR preferences for a party; (2) presenting, using one or more devices associated with the party instructions constructed to guide the party to move throughout or around a real property, and prompts constructed to direct the party to capture data representing one or more of the real property or belongings; (3) determining, by one or more processors processing the captured data, asset data representing one or more of the real property or the belongings; and/or (4) presenting, in a virtualized environment in accordance with the party's XR preferences via an XR device, one or more visual depictions of the determined asset data such that the party or an insurance representative can at least one of view, modify, or approve the asset data.

Abstract: Embodiments of extended reality (XR) methods and systems for processing vehicle-related insurance claims are disclosed. In one embodiment, a computer-implemented method may include: (i) obtaining, using one or more XR devices, first data representing insured party XR preferences, and second data related to a damage-causing event for a vehicle; (ii) creating, using one or more processors and based upon the second data, a virtual reconstruction of the damage-causing event; and/or (iii) presenting, in a virtualized environment in accordance with the XR preferences via an XR device, the virtual reconstruction such that the insured party or an insurance representative may view, alter, and/or approve the virtual reconstruction.

Abstract: A method of facilitating first mile/last mile transfer of a vehicle includes analyzing a first route using a first trained machine learning model and a second route using a second trained machine learning model to determine, respectively, an incentive to a first user and a cost to a second user; causing a first message including the incentive to be displayed to the first user via an electronic device of the first user; and causing a second message including the cost to be displayed to the second user via an electronic device of the second user.

Abstract: A method includes training artificial neural network models; analyzing first data to determine vehicle imperatives; and generating a respective action value by analyzing actions using the of trained artificial neural network models. A non-transitory computer readable medium containing program instructions that when executed, cause a computer system to: train artificial neural network models; analyze first data to determine vehicle imperatives; and generate a respective action value by analyzing actions using the of trained artificial neural network models. A computing system includes: a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: train artificial neural network models; analyze first data to determine vehicle imperatives; and generate a respective action value by analyzing actions using the of trained artificial neural network models.

Abstract: A method of facilitating a fleet exchange includes retrieving a fleet and retrieving a set of workers, analyzing the fleet and the set of workers using smart scheduling to generate allocations, and displaying the allocations to the workers. In another aspect, a non-transitory computer readable medium includes program instructions that when executed, cause a computer system to receive vehicle allocation request, retrieve worker information and a worker calendar, generate an allocation, and display the allocation in a display device. A computing system for facilitating fleet exchange includes one or more processors and a memory including computer executable instructions that, when executed by the one or more processors, cause the computing system to retrieve a fleet and a set of workers, analyze the fleet and the set of workers to generate allocations, and display the allocations in a respective computing device of the workers.

Abstract: A method of facilitating a fleet exchange includes retrieving a fleet and retrieving a set of workers, analyzing the fleet and the set of workers using smart scheduling to generate allocations, and displaying the allocations to the workers. In another aspect, a non-transitory computer readable medium includes program instructions that when executed, cause a computer system to receive vehicle allocation request, retrieve worker information and a worker calendar, generate an allocation, and display the allocation in a display device. A computing system for facilitating fleet exchange includes one or more processors and a memory including computer executable instructions that, when executed by the one or more processors, cause the computing system to retrieve a fleet and a set of workers, analyze the fleet and the set of workers to generate allocations, and display the allocations in a respective computing device of the workers.

Abstract: A method of causing car sharing platform user to perform vehicle actions includes training a model by analyzing historical data; analyzing a first data set to determine vehicle requirements comprising respective actions that include respective action values; displaying the actions; receiving user acceptance of the actions; analyzing a second data set to identify changes to the requirements indicating completed actions; and releasing the respective generated action value for the completed actions.

Abstract: A wearable medical device assembly for therapeutic treatment of a person includes: a wearable medical device configured to be applied over a portion of the person; a first anchor and a second anchor each comprising an adhesive configured to affix the respective anchor to the person's skin; and an attachment system that selectively attaches the wearable medical device to the first anchor and the second anchor, wherein the attachment system permits the wearable medical device to be detached from, and re-attached to, one or both of the anchors while both of the anchors remain affixed to the person's skin

Abstract: Systems and methods relating to usage of multimodal transportation systems are disclosed. Such systems and methods may identify available local transportation modes for a user, such as vehicle-sharing, ridesharing, rental vehicles, taxicabs, owned vehicles, or public transit options. The available transportation modes may be compared, and recommendations may be presented to the user. Routes may be identified, compared, or recommended to a user, and scheduling or ticket purchasing may be facilitated. With user permission, user transportation data may be collected via a smartphone to identify user transportation patterns and preferences, thereby improving recommendations regarding and assessment of user transportation. Information regarding risks or other relevant factors associated with various transportation modes may be assessed for a user based upon typical characteristics of user transportation choices over a plurality of transportation scenarios, which may be indicated by a user transportation profile.

Abstract: In a method for applying penalties or incentives to a driver of a rented vehicle, an indication that the driver has agreed to terms for renting the vehicle from the vehicle owner is received, with the terms including the potential application of penalties or incentives to the driver based on driving behavior. Telematics data, indicative of operation of the rented vehicle by the driver during a period of time, is also received. By analyzing the telematics data, one or more driving behaviors of the driver during the time period is/are identified. One or more characteristics of the rented vehicle are also determined. One or more penalties or incentives are caused to be applied to the driver, based on the driving behavior(s) and the one or more characteristics of the rented vehicle.