Proposed is an automated telematics-based system (1) for score-driven operations associated with motor vehicles (41, . . . , 45) or transportation means of passengers or goods and based on a dynamic, telematics-based data aggregation, and method thereof, for automated risk-transfer related to complex peer-to-peer lending schemes, especially related to vehicles and car sharing schemes and sharing economy transportation schemes related to risks associated with damages to third parties. The telematics-based system (1) comprises telematics devices (411, . . . , 415) associated with the plurality of motor vehicles (41, . . . , 45), wherein the telematics devices (411, . . . , 415) comprise a wireless connection (42101-42108) to a central, expert-system-based circuit (11). The telematics devices (411, . . . , 415) are connected via interfaces (421, . . . , 425) to the sensors and/or measuring devices (401, . . . , 405) and/or an on-board diagnostic system (431, . . . , 435) and/or an in-car interactive device (441, . . . , 445), wherein the telematics devices (411, . . . , 415) capture usage-based (31) and/or user-based (32) and/or operational (33) telematics data (3) of the motor vehicle (41, . . . , 45) and/or user (321, 322, 323). In response to an emitted shadow request (109) individualized risk-transfer profiles (114) based upon the dynamically generated variable scoring parameters (1011, . . . , 1013) are transmitted from a first risk-transfer systems (11) to the corresponding motor vehicle (41, . . . , 45) and issued by means of a user unit (461, . . . , 465) of the motor vehicle (41, . . . , 45) for selection by the driver of the motor vehicles (41, . . . , 45). In return of issuing an individualized risk-transfer profile (114) over said user unit (461, . . . , 465), payment-transfer parameters are transmitted from the first risk-transfer system (11) to the provider of the telematics-based system (1).

Proposed is an automated telematics-based system (1) for score-driven operations associated with motor vehicles (41, . . . , 45) or transportation means of passengers or goods and based on a dynamic, telematics-based data aggregation, and method thereof, for automated risk-transfer related to complex peer-to-peer lending schemes, especially related to vehicles and car sharing schemes and sharing economy transportation schemes related to risks associated with damages to third parties. The telematics-based system (1) comprises telematics devices (411, . . . , 415) associated with the plurality of motor vehicles (41, . . . , 45), wherein the telematics devices (411, . . . , 415) comprise a wireless connection (42101-42108) to a central, expert-system-based circuit (11). The telematics devices (411, . . . , 415) are connected via interfaces (421, . . . , 425) to the sensors and/or measuring devices (401, . . . , 405) and/or an on-board diagnostic system (431, . . . , 435) and/or an in-car interactive device (441, . . . , 445), wherein the telematics devices (411, . . . , 415) capture usage-based (31) and/or user-based (32) and/or operational (33) telematics data (3) of the motor vehicle (41, . . . , 45) and/or user (321, 322, 323). In response to an emitted shadow request (109) individualized risk-transfer profiles (114) based upon the dynamically generated variable scoring parameters (1011, . . . , 1013) are transmitted from a first risk-transfer systems (11) to the corresponding motor vehicle (41, . . . , 45) and issued by means of a user unit (461, . . . , 465) of the motor vehicle (41, . . . , 45) for selection by the driver of the motor vehicles (41, . . . , 45). In return of issuing an individualized risk-transfer profile (114) over said user unit (461, . . . , 465), payment-transfer parameters are transmitted from the first risk-transfer system (11) to the provider of the telematics-based system (1).

A method for remote damage assessment of a vehicle is provided. The present disclosure relates to the technical field of artificial intelligence, in particular to the technical field of image and text recognition. An implementation solution is: performing data collection on a target vehicle to determine damage information of the target vehicle; obtaining call content of an insurance claiming call for the target vehicle, and extracting accident-related information from the call content, wherein the accident-related information includes named entities in the call content and a relationship between the named entities; and determining a first fraud probability corresponding to the target vehicle at least based on the damage information and the accident-related information.

Systems and methods are disclosed for providing an experience subscription service. The systems and methods include operations for identifying, by an experience-based subscription service, a given experience-related resource available for access on a future experience date; obtaining cost information for the given experience-related resource; determining an amortized subscription value for a subscriber of the experience-based subscription service, the amortized subscription value representing a subscription cost accumulated over repeated time periods; computing a suspension amount as a function of the cost information of the given experience-related resource and the amortized subscription value; receiving, on a booking date, a request from the subscriber to reserve the given experience-related resource; and in response to receiving the request, preventing the subscriber from reserving a second experience-related resource during a suspension period that begins on the booking date and terminates after a period of time determined by the suspension amount.

A method and system for managing electric vehicle (EV) distributed energy resource(s) (DER) are disclosed. A DER analytics engine may receive electricity consumption data of a plurality of sites from corresponding electricity meters of the plurality of sites, detect EV charging information based at least in part on the electricity consumption data, obtain EV telematics data of EVs associated with the EV charging information, reconcile the EV charging information and the EV telematics data, and generate, based on the reconciled EV charging information and the EV telematics data, models for at least one of continuous EV load prediction, electrical vehicle supply equipment (EVSE detection), and/or optimization for at least one of aggregated load, load per feeder, or maximum revenue for time-of-use tiers.

Aspects described provide systems and methods that relate generally to image analysis and, more specifically, identifying individual components and elements in an image. The systems and methods include a valuation application executing one or more application program interfaces (APIs) communicating with one or more websites via a network, where the user is prompted to enter information and/or take pictures or videos of their vehicle that they would like to sell. The valuation application utilizes a machine learning model to identify and value the various vehicle components within the images and videos. Based on the machine learning model, the valuation application identifies each component according to the images and videos and performs a search to determine the value of the components identified. The valuation application tabulates and summarizes the vehicle component resale values and resell information for the user to view.

A method of training a machine learning model to determine an item margin is provided. The method includes monitoring a first value for a first item having attributes and monitoring a first value for a second type of item having attributes where an attribute of the first attributes is the same as an attribute of the second attributes. The method also includes determining a first margin based on the first values. The first attributes, the second attributes, and the first margin are input as training data for the machine learning model where the machine learning model is trained with the training data. The monitoring operations for the first item and the second item are repeated to obtain a second value for the first and second items. Furthermore, the trained machine learning model is applied to the second values to determine a second margin.

Apparatuses, systems and methods are provided for generating enterprise data relating to roof damage associated with weather and hail data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the base-line probable roof loss confidence scores. The apparatuses, systems and methods may generate probable roof loss confidence score data based upon the base-line probable roof loss confidence scores, weather event data and hail event data. The apparatuses, systems and methods may determine aspects of a proposed service related to roof damage (e.g., damage extent, repair estimates, or repair timing) based upon the enterprise data and the probable roof loss confidence score.

An information processing device is provided which includes a first acquisition unit (214) configured to acquire a control command that is inputted by a first user and corresponds to presentation unit information for designating a presentation unit for presenting a tactile stimulus by a tactile presentation device and mode information for designating a mode of the tactile stimulus; a generation unit (218) configured to generate a tactile control signal for presenting the tactile stimulus to the presentation unit in accordance with the control command; and a first distribution unit (222) configured to distribute the tactile control signal to the tactile presentation device worn on a body of a second user.

Disclosed is a quotation method executed by a computer, comprising: obtaining structure parameters and electrical parameters of a product (S101); constructing an external view of the product by using the structure parameters of the product, and performing similarity comparison on the external view of the product and the external view of a historical product to obtain an appearance similarity sorting (102); performing similarity comparison on the electrical parameters of the product and the electrical parameters of the historical product to obtain an electrical parameter similarity sorting (103); on the basis of the cost weights of a structural member and an electrical component and the appearance similarity sorting and the electrical parameter similarity sorting, obtaining a comprehensive sorting which is based on the structure parameters and the electrical parameters (S104); and determining, based on the comprehensive sorting, a bill of materials of the product, and calculating, based on the bill of the materials of the product, the product quotation (105).