Abstract: A computer system for sump pump monitoring and repair service provider notification may include one or more processors configured to: detect that a sump pump is faulty, transmit a prompt for service quotes to a machine learning (ML) chatbot to cause the ML chatbot to: request sump pump replacement or repair services from one or more service providers, receive cost estimates from the one or more repair service providers, receive schedule availability from the one or more repair service providers, receive, from the ML chatbot, the cost estimates and the schedule availability, and communicate the cost estimates and/or the schedule availability to a user associated with the sump pump.

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: 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.

Abstract: Embodiments described herein receive telematics data collected over a period of time, wherein the telematics data is indicative of operation of a vehicle by an owner of the vehicle during the period of time; analyze the telematics data to identify driving behavior(s) of the owner during the period of time; predict one or more user preference values of a vehicle-sharing platform profile of the owner based on the identified one or more driving behaviors, wherein the one or more user preference values define one or more criteria for vehicle renters with whom the first vehicle can be shared; apply the one or more criteria to a potential vehicle renter; and cause an indication of the first vehicle of the owner to be displayed via a mobile device of the potential renter only if the potential vehicle renter satisfies the one or more criteria.

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 of extended reality (XR) methods and systems for handling home-related information are disclosed. A method may include obtaining XR preference(s) for a party, and presenting, using at least an XR device, an AR environment to the party, at least in part by overlaying, on real-world content viewed using the XR device, instructions that guide the party and prompts that direct the party to capture data. The method may also include determining, by processing the captured data, asset data representing one or more of the real property or the belongings, and presenting, in a virtual reality (VR) environment that includes a first avatar of the party and a second avatar of an insurance representative, in accordance with the party's XR preferences, one or more visual depictions of the determined asset data such that the party or the insurance representative can view and modify the asset data.

Abstract: Systems and methods are described for performing analysis of parametric events. The method may include: (1) measuring, by one or more processors, an initial composition for an area via one or more sensors associated with the area; (2) using a trained machine learning algorithm, a likelihood of a trigger activation for a parametric event for a user, wherein the calculating includes: (a) predicting a total composition fluctuation for the area, (b) calculating a predicted composition change from the initial composition for the area based upon the total composition fluctuation, and (c) calculating the likelihood of the trigger activation, wherein the trigger activation occurs when the predicted composition change from the initial composition for the area reaches a predetermined threshold value; and (3) an estimated loss for the user based at least upon the likelihood of the trigger activation.

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 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 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 including 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: 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 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: 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: Systems and methods are described for analyzing user data to generate a dialogue output. The method may include: (1) receiving, by one or more processors, an indication of a user identity for a user at a generative artificial intelligence (AI) model; (2) retrieving, by the one or more processors and based upon at least the user identity, user data from one or more publicly accessible sources; (3) determining, by the one or more processors and based upon at least the user data, one or more personalization characteristics associated with at least an information retention rate for the user via the generative AI model; and (4) generating, by the one or more processors and based upon at least the one or more personalization characteristics, a personalized dialogue output (or visual or virtual output for display) for the user via the generative AI model.

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: 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: Embodiments described herein receive telematics data collected over a period of time, wherein the telematics data is indicative of operation of a vehicle by an owner of the vehicle during the period of time; analyze the telematics data to identify driving behavior(s) of the owner during the period of time; predict one or more user preference values of a vehicle-sharing platform profile of the owner based on the identified one or more driving behaviors, wherein the one or more user preference values define one or more criteria for vehicle renters with whom the first vehicle can be shared; apply the one or more criteria to a potential vehicle renter; and cause an indication of the first vehicle of the owner to be displayed via a mobile device of the potential renter only if the potential vehicle renter satisfies the one or more criteria.

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.