Implementations include classifying vehicle trips as similar to previous trips based on location information of a vehicle received from a location device. Unique tile identifiers of the trip, each corresponding to a geographic area and the location information, may be determined and used to generate a fingerprint of the trip. The derived trip fingerprint of the trip information may be compared to stored fingerprints of one or more previously received trips to determine if the new trip is similar to one or more of the previous trips. In one instance, information or data of a new trip may be adjusted based on previous trip data. For example, aspects of the new trip or the previous trip may be updated with information or data of a previous trip fi the new trip and the previous trip are similar.
System, methods, and apparatuses provide customizable product features by receiving, from a computing device associated with a user, an indication of user interest in a product, identifying, based on a first profile associated with the user, a first attribute, determining one or more second profiles that comprise the first attribute, determining one or more features, of the product, that are associated with the one or more second profiles, determining, for each of the one or more features, a utility value, simulating, based on strategic alternatives data and the utility value for each of the one or more features, demand for the one or more features, determining, based on the simulated demand, that a subset of the one or more features satisfies a need state associated with the user, and sending, to the computing device, a product package including the subset of the one or more features.
Systems, methods, computer-readable media, and apparatuses for evaluating device usage and generating one or more outputs based on the device usage are provided. For instance, data from one or more sensors within a user personal mobile device may be received and processed to determine movement associated with the device. In addition, an amount of usage (e.g., hours, minutes, etc.) associated with the device may be received. In some examples information regarding the device or user of the device may be received. In some examples, application usage and/or types of applications used may also be received. This data may be processed and a likelihood of damage to the device may be determined. Based on this likelihood, one or more outputs may be determined.
Aspects of the disclosure relate to using ultrasonic or other types of signals to determine a distance between a transmitter and one or more mobile devices. The distance may be used to facilitate travel on foot or in a vehicle. One aspect disclosed provides a computing platform that may receive ultrasonic sensing data associated with mobile devices in a vehicle from a signal transmitter. Unique identifiers of the mobile devices may be determined. Based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each mobile device in the vehicle may be determined. The computing platform may use a machine learning classifier to determine that a particular occupant is a driver in the vehicle based on the relative distance.
G08G 1/0967 - Systèmes impliquant la transmission d'informations pour les grands axes de circulation, p. ex. conditions météorologiques, limites de vitesse
G01S 11/14 - Systèmes pour déterminer la distance ou la vitesse sans utiliser la réflexion ou la reradiation utilisant des ondes ultrasonores, sonores ou infrasonores
5.
DRIVER PASSENGER DETECTION USING ULTRASONIC SENSING
Aspects of the disclosure relate to using ultrasonic or other types of signals to detect a driver in a vehicle. A computing platform may receive ultrasonic sensing data associated with mobile devices in the vehicle from a signal transmitter. Unique identifiers of the mobile devices may be determined. Based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each mobile device in the vehicle may be determined. The computing platform may use a machine learning classifier to determine that a particular occupant is a driver in the vehicle based on the relative distance.
B60W 40/08 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conducteurs ou aux passagers
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p. ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons
H04W 4/70 - Services pour la communication de machine à machine ou la communication de type machine
6.
SYSTEM AND METHOD FOR ROADSIDE ASSISTANCE PROVIDER SELECTION BASED ON DEMAND
Implementations include providing assistance services, and more specifically for selecting a roadside assistance provider from a plurality of providers using one or more predictive models of aspects of a roadside assistance request. Selection of the roadside assistance provider may be based on ranking of roadside assistance providers associated with a service area, the ranking based on one or more predicted output values from one or more predictive models of aspects of a roadside assistance service, such as estimated arrival time and an estimated probability of acceptance of the request by a roadside assistance provider. One or more of the predicted output values may be adjusted based on a boost value as determined boost value predictive model to increase a likelihood that a selected roadside assistance provider accepts a request to provide the assistance.
Systems and methods are provided for automatically changing or updating insurance coverage using a computer device. An insurance analyzer may receive data associated with an insurance user. The insurance analyzer may determine types of insurance coverage available and coverage amounts associated with the received user data. Based on the received data the insurance analyzer may automatically activate insurance coverage based on determined types of insurance available and coverage amounts. The insurance analyzer may also deactivate insurance coverage or change insurance coverage amounts based on determined user activity.
Implementations described herein provide systems and methods for mitigating climate risk. In one implementation, a risk factor is determined based on one or more predictive climate models. The risk factor is applied to a specific home, and an effect of the risk factor on the specific home is determined over a period of time. A mitigating action personalized to the specific home based on the risk factor is performed.
Disclosed are systems, apparatuses, methods, and computer readable medium for a climate risk assessment system. A disclosed climate risk assessment system can include a machine learning (ML) model or other neural network that is capable of understanding unstructured data to build loss models that can estimate effects of climate change and risks associated with building based on the effects of climate change. A method of the climate risk assessment system includes: receiving an unstructured document; identifying a property associated with the document based on geographical information extracted from the unstructured document; identifying at least one modified building property associated the unstructured document; updating a data structure corresponding to the building to include the at least one modified building property; and updating a loss model associated with the property based on the data structure.
Implementations claimed and described herein provide systems and methods for determining driving attributes using telematics data. The systems and methods use telematics data generated via a telematics device disposed in a vehicle. One or more driving attributes associated with a vehicle operator and/or the vehicle based on the telematics data are determined by the system. The one or more driving attributes associated with the vehicle operator and/or the vehicle are denormalized and aggregated. Furthermore, the aggregated driving attributes are communicated with a provider computing device in response to a request for the aggregated driving attributes.
11.
SYSTEM AND METHOD FOR ROADSIDE ASSISTANCE PROVIDER SELECTION USING PREDICTIVE MODELS
Implementations include providing assistance services, and more specifically for selecting a roadside assistance provider from a plurality of providers using one or more predictive models of aspects of a roadside assistance request. Selection of the roadside assistance provider may be based on ranking of roadside assistance providers associated with a service area, the ranking based on one or more predicted output values from one or more predictive models of aspects of a roadside assistance service, such as estimated arrival time and an estimated probability of acceptance of the request by a roadside assistance provider. The predictive models or other components of the roadside assistance system may be machine¬ learning and adaptable based on historical data and feedback information of responses and rankings to prior roadside assistance requests.
A device may obtain data associated with an event and process the data via an evacuation prediction and management engine to obtain an evacuation prediction. When the evacuation prediction indicates that an evacuation order is likely to be issued within a period of time based on the event occurring, the device transmits, via the evacuation prediction and management engine, an evacuation service offering to a user device. Upon a user of the user device confirming acceptance of the evacuation service offering, the device receives a payment for the user for the evacuation service offering. When the event or evacuation order occurs and based on the payment for the evacuation service offering, the device automatically initiates a payment to a user account.
H04W 4/90 - Services pour gérer les situations d’urgence ou dangereuses, p. ex. systèmes d’alerte aux séismes et aux tsunamis
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
Implementations described herein provide systems and methods for mitigating climate risk. In one implementation, an environmental forecast factor is determined based on one or more predictive climate models. The environmental forecast factor can be, for example, an environmental condition applied to a specific home. A severity of the environmental forecast factor on the specific home can be determined over a period of time. A financial instrument can be created that invests in an asset that is inversely related to the environmental forecast factor over the period of time, where the financial instrument is predicted to appreciate in value to at least the severity of the environmental forecast factor.
Implementations described herein provide systems, methods, and devices for vehicle monitoring and control based on advanced driving assistance system (ADAS) feature usage. The systems, methods, and devices include a driving data collection system for collecting driving-related data from an original equipment manufacturer (OEM) server and/or a mobile device. The OEM server receives the ADAS-related data from the vehicle and sends the ADAS-related data to the vehicle monitoring and control platform. Systems also include a vehicle/driver operation assessment system which uses one or more first deep-learning models to generate vehicle operation behavior values from the ADAS-related data. Furthermore the systems include a dynamic risk control model which uses one or more second deep-learning models to generate target outputs based on the vehicle operation behavior values. The target outputs include modification or control of the vehicle operations, one or more alerts, and/or a pricing variable.
B60W 30/08 - Anticipation ou prévention de collision probable ou imminente
H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons
15.
DETERMINING DRIVER AND VEHICLE CHARACTERISTICS BASED ON AN EDGE-COMPUTING DEVICE
Methods, computer-readable media, software, and apparatuses may collect, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data including data indicative of driving characteristics associated with an operation of the vehicle. The edge-computing device may analyze, based on a machine learning model, characteristics of the vehicle driving event data. The edge-computing device may, based on the machine learning model, determine at least one of: a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics, and the information may be displayed via a graphical user interface to a user in the vehicle.
G06N 5/04 - Modèles d’inférence ou de raisonnement
B60W 30/095 - Prévision du trajet ou de la probabilité de collision
B60W 40/04 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conditions ambiantes liés aux conditions de trafic
B60W 40/06 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conditions ambiantes liés à l'état de la route
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
One or more devices in an accident detection and recovery computing system may be configured to determine that vehicle accidents have occurred, collect and analyze accident characteristics and other related data, and providing customized accident recovery services. Mobile computing devices, alone or in combination with vehicle-based systems and external devices, may detect accidents or receive accident indication data. After determining that an accident has occurred, mobile computing devices and/or vehicle-based systems may be configured to determine accident characteristics, retrieve vehicle data and vehicle occupant data from one or external servers, determine the damages or potential damages resulting from the accident, and determine one or more accident recovery options or recommendations based on the accident damages. Various user interface screens may be generated and displayed via the user's mobile device and/or a vehicle-based display device to provide the user with accident information, damages, and recovery options or recommendations.
G01P 3/00 - Mesure de la vitesse linéaire ou angulaireMesure des différences de vitesses linéaires ou angulaires
G01P 15/00 - Mesure de l'accélérationMesure de la décélérationMesure des chocs, c.-à-d. d'une variation brusque de l'accélération
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
H04W 4/02 - Services utilisant des informations de localisation
17.
SYSTEMS, METHODS, AND DEVICES FOR GREENHOUSE GAS MONITORING AND CONTROL
Implementations described herein provide systems, methods, and devices for greenhouse gas monitoring and control. The systems, methods, and devices include a device detection system which identifies device identifier(s) corresponding to device(s) associated with a user. The device(s) include at least one of an electric vehicle, a home device, a power system device, or a power system device. Upon detecting the device(s), a user behavior analyzer receives energy consumption-related data from the device(s) resulting from energy consumption behavior of the user. The system determines, based on the energy consumption-related data, one or more behavior metrics of the user. A reduction integration generator identifies an energy consumption reduction integration corresponding to the one or more behavior metrics (e.g., a device operational parameter modification, a behavioral modification, a credit, and so forth), and integrates the energy consumption reduction integration to facilitate a reduction of energy consumption for the user.
Systems and methods provide for a computerized system for quoting home owners insurance and providing a more consultative way of delivering insurance quotes and insurance quote information. The system may present insurance consumers with an automated process of asking questions and receiving feedback. Based on the feedback, the system may provide insurance options and explanations of those options enabling consumers to make a decision that best fits their personal situation. For example, systems and methods are directed to determining and providing a deductible that fits a user based on the user's tolerance for risk and cash position. The system may also provide a description of the types of risks and damages that are covered by particular insurance coverages. The system may also provide an analysis of the insurance obtained by similarly situated individuals. The system may also provide descriptions of insurance features.
The disclosure provides an early notification system to alert a driver of an approaching unsafe autonomous or semi-autonomous driving zone so that a driver may switch vehicle to a non-autonomous driving mode and navigate safely through the identified location. In response, to a determination of an upcoming unsafe autonomous or semi-autonomous driving zone, the driver or system may take appropriate actions in response to the early notification.
B60W 10/04 - Commande conjuguée de sous-ensembles de véhicule, de fonction ou de type différents comprenant la commande des ensembles de propulsion
B60W 10/30 - Commande conjuguée de sous-ensembles de véhicule, de fonction ou de type différents comprenant la commande d'équipements auxiliaires, p. ex. des compresseurs pour la climatisation ou des pompes à huile
B60W 30/08 - Anticipation ou prévention de collision probable ou imminente
Systems and methods are provided for obtaining data annotations from a crowdsourced group of individuals. The individuals can be provided with a set of data describing damage to an item and a variety of annotations can be applied to the data. In a variety of embodiments, multiple individuals can review the same claim and a final claim outcome can be determined based on the multiple reviews. In many embodiments, machine classifiers can process the set of data to identify particular features within the data. Scoring data can be generated, based on annotations provided by other individuals and/or machine classifiers that reflects the adjuster's skill at identifying features within the data and annotating the data. Claims can be assigned to individuals based on the score assigned to the individual.
One or more devices in a driving data analysis system may be configured to receive and execute a driving data analysis software application. One or more servers may provide a driving data analysis software application to various driving data analysis devices, such as mobile user devices and/or on-board vehicle systems. The driving data analysis devices may be configured to execute the driving data analysis software application, to receive/collect various driving data, analyze the driving data, and determine eligibility for one or more insurance offers based on the analysis of the driving data. For insurance offers based on driving data, insurance offer vouchers may be generated by the driving data analysis device and transmitted to an insurance provider server to redeem the insurance offer.
Aspects of the disclosure relate to computing platforms that utilize improved machine learning techniques for dynamic device quality evaluation. A computing platform may receive driving data from a mobile device. Using the driving data, the computing platform may compute a plurality of driving metrics, which may include: a geopoint expectation rate score, a trips per day rank score, a consecutive geopoint time difference score, a global positioning system (GPS) accuracy rating score, and a distance between consecutive trips score. By applying a machine learning model to the plurality of driving metrics, the computing platform may compute a device evaluation score, indicating a quality of the driving data received from the mobile device. Based on the device evaluation score, the computing platform may set flags, which may be accessible by a driver score generation platform, causing the driver score generation platform to perform an action with regard to the mobile device.
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
A system may receive information indicating a service area of a shared mobility service, calculate information indicating one or more of risk or revenue associated with the provision of shared mobility services in the service area, determine an adjusted service area, wherein the adjusted service area is associated with one or more of a reduction in risk or an increase in revenue, and transmit information indicating the adjusted service area to a device associated with the shared mobility service.
Systems, apparatuses, and methods for providing roadside assistance services are described herein. The system may include network computing devices and computing devices associated with user vehicles and service vehicles. The system may predict incoming requests for roadside services, and may allocate service providers among various geographical regions and/or time slots to handle the incoming requests. The system may receive a request for a roadside service from a user. The system may select an appropriate service provider to assist the user, and may assign the service request to the selected service provider.
A driving analysis server may be configured to receive vehicle operation data from vehicle sensors and telematics devices of a first vehicle, and may use the data to identify a potentially high-risk or unsafe driving behavior by the first vehicle. The driving analysis server also may retrieve corresponding vehicle operation data from one or more other vehicles, and may compare the potentially high-risk or unsafe driving behavior of the first vehicle to corresponding driving behaviors in the other vehicles. A driver score for the first vehicle may be calculated or adjusted based on the comparison of the driving behavior in the first vehicle to the corresponding driving behaviors in the other vehicles.
Methods, computer-readable media, software, and apparatuses provide a system that may facilitate communications between drivers who share a route. The system may allow communications to be sent from one driver to another driver and allow drivers to post queries to other drivers sharing a route. Computing devices in the vehicle may collect route data for the system to evaluate and to use in identifying drivers sharing a route.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
One or more driving analysis computing devices in a driving analysis system may be configured to analyze driving data, determine driving behaviors, and calculate driver scores based on driving data transmitted using vehicle-to-vehicle (V2V) communications. Driving data from multiple vehicles may be collected by vehicle sensors or other vehicle-based systems, transmitted using V2V communications, and then analyzed and compared to determine various driving behaviors by the drivers of the vehicles. Driver scores may be calculated or adjusted based on the determined driving behaviors of vehicle drivers, and also may be calculated or adjusted based on other the driver scores of nearby vehicles.
G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
G08G 1/01 - Détection du mouvement du trafic pour le comptage ou la commande
G09B 19/16 - Conduite des véhicules ou autres moyens de transport
G08G 1/0967 - Systèmes impliquant la transmission d'informations pour les grands axes de circulation, p. ex. conditions météorologiques, limites de vitesse
A provider computing system includes a claims database storing claim statements for a plurality of claims, the claim statements including a plurality of claim variable, an orchestration circuit storing computer-executable instructions embodying one or more machine learning models, at least one processor, and at least one memory, the at least one memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations. The operations include receive a first claim statement corresponding to a new claim; cause, by the orchestration circuit, the one or more machine learning models to parse the first claim statement; determine a first claim variable from the first claim statement; generate, by the orchestration circuit, a response including a request for a second claim statement; and transmit the request for a second claim statement.
Systems and methods provide for an automated system for analyzing damage and processing claims associated with an insured item, such as a vehicle. An enhanced claims processing server may analyze damage associated with the insured item using photos/video transmitted to the server from a user device (e.g., a mobile device). The mobile device may receive feedback from the server regarding the acceptability of submitted photos/video, and fi the server determines that any of the submitted photos/video is unacceptable, the mobile device may capture additional photos/video until all of the data are deemed acceptable. To aid in damage analysis, the server may also interface with various internal and external databases storing reference images of undamaged items and cost estimate information for repairing previously analyzed damages to similar items. Further still, the server may generate a payment for compensating a claimant for repair of the insured item.
System, apparatuses, computer-implemented methods, and computer-readable media executable by insurance system servers and user computing devices for receiving requests for insurance products are provided. In order to determine one or more factors of the insurance product or policy, the system may use body characteristics of the customer or potential customer, such as height, weight, body mass index, and the like. In some examples, this information may be determined from one or more images provided by the user. For instance, one or more images of the customer or potential customer may be captured and transmitted to the system for processing. Based on the received images, the system may determine various body characteristics of the user and may use that information to determine one or more policy factors for the insurance product or policy, such as premium, coverage, term, type of policy, or the like.
A system including a processor and memory may provide for automated support communications, such as communications with individuals who need assistance. Automated communications may use one or more factors to determine how to adjust communications according to the needs of a user. For example, automated communications may be adjusted based on, e.g., a keyword used by a user in the user's communications, or a location associated with the user's mobile device or user vehicle. Automated communications may be adjusted in timing, frequency, or content. One or more external events (e.g., phone call, dispatch request, additional automated communication) may be triggered based on the automated communications.
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
H04M 1/72421 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens de soutien local des applications accroissant la fonctionnalité pour donner accès à des services d’urgence les fonctions de service d’urgence étant activées automatiquement, p. ex. par détection d’une condition d’alarme
H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons
32.
FEEDBACK LOOP IN MOBILE DAMAGE ASSESSMENT AND CLAIMS PROCESSING
Systems and methods provide for an automated system for analyzing damage and processing claims associated with an insured item, such as a vehicle. An enhanced claims processing server may analyze damage associated with the insured item using photos/video transmitted to the server from a user device (e.g., a mobile device). The mobile device may receive feedback from the server regarding the acceptability of submitted photos/video, and if the server determines that any of the submitted photos/video is unacceptable, the mobile device may capture additional photos/video until all of the data are deemed acceptable. To aid in damage analysis, the server may also interface with various internal and external databases storing reference images of undamaged items and cost estimate information for repairing previously analyzed damages to similar items. Further still, the server may generate a payment for compensating a claimant for repair of the insured item.
Subscription services, namely, providing trade-in and upgrade options for smart devices; online retail store services featuring smart devices for buying and selling; online auction services featuring bidding and purchasing of smart devices
34.
DATA PROCESSING SYSTEM WITH MACHINE LEARNING ENGINE TO PROVIDE OUTPUT GENERATING FUNCTIONS
Systems, methods, computer-readable media, and apparatuses for identifying and executing one or more interactive condition evaluation tests to generate an output are provided. In some examples, user information may be received by a system and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. A user interface may be generated including instructions for executing the identified tests. Upon initiating a test, data may be collected from one or more sensors in the computing device. The data collected may be transmitted to the system and may be processed using one or more machine learning datasets to generate an output.
G06V 30/194 - Références réglables par une méthode adaptative, p. ex. par apprentissage
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
35.
DATA PROCESSING SYSTEM WITH MACHINE LEARNING ENGINE TO PROVIDE OUTPUT GENERATING FUNCTIONS
Methods, apparatuses, systems, and computer-readable media for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing user behavior data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, user behavior data may be collected and processed using one or more machine learning datasets. The sensor data, the user behavior data, and other data may be used together to generate an output.
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
36.
REAL TIME RISK ASSESSMENT AND OPERATIONAL CHANGES WITH SEMI-AUTONOMOUS VEHICLES
A route risk mitigation system and method using real-time information to improve the safety of vehicles operating in semi-autonomous or autonomous modes. The method mitigates the risks associated with driving by assigning real-time risk values to road segments and then using those real-time risk values to select less risky travel routes, including less risky travel routes for vehicles engaged in autonomous driving over the travel routes. The route risk mitigation system may receive location information, real-time operation information, (and/or other information) and provide updated associated risk values. In an embodiment, separate risk values may be determined for vehicles engaged in autonomous driving over the road segment and vehicles engaged in manual driving over the road segment.
Methods, computer-readable media, systems and apparatuses for home maintenance monitoring and rewarding users for completion of various home maintenance tasks are provided. The systems may receive data associated with a home, such as a plurality of systems and/or devices associated with the home. Based on the received information, the systems may generate a maintenance task list for the home including a plurality of maintenance tasks. Data related to maintenance performed on the home may be received and, based on the received maintenance data, a determination may be made as to whether one or more tasks on the maintenance task list have been completed. Upon completion of a task, a reward, such as points, discounts on future maintenance, insurance premium discounts, etc., may be deposited in an account of the user. In some arrangements, the points may be redeemed as payment on future maintenance, payment on insurance premiums, or the like.
G06Q 30/0217 - Remises ou incitations, p. ex. coupons ou rabais impliquant une contribution sur des produits ou des services en échange d’une incitation ou d’une récompense
G06Q 10/20 - Administration de la réparation ou de la maintenance des produits
38.
DATA PROCESSING SYSTEM WITH MACHINE LEARNING ENGINE TO PROVIDE OUTPUT GENERATION FUNCTIONS
Methods, computer-readable media, systems, and/or apparatuses are provided for providing offer and insight generation functions. User input requesting an offer or insight may be received and an image of a photographic identification of a user may be requested. The image of the photographic identification may be captured and stored. A self-captured image of the user may be captured (e.g., via an image capture device of the computing device) and compared to an image of a user from the photographic identification. Responsive to determining that the images match, displaying an instruction to capture a vehicle identification number. The vehicle identification number may be captured. Data, including location data, may be extracted and an archive including the extracted data may be generated and the data may be transmitted to an entity computing system for processing. The entity computing system may evaluate the data and generate one or more insights and/or outputs.
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
39.
METHODS AND SYSTEMS FOR AN INTELLIGENT TECHNICAL DEBT HELPER BOT
Methods and systems for an intelligent technical debt helper may include receiving, via a processor, a level of technical debt associated with a technical debt of a computer program code and determining, via the processor, whether the level of technical debt is greater than a technical debt threshold. The method may also include generating, using an artificial intelligence neural network model communicatively coupled to the processor and based on the computer program code, an automated code recommendation to address the technical debt of the computer program code when the level of technical debt is greater than the technical debt threshold.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06Q 10/101 - Création collaborative, p. ex. développement conjoint de produits ou de services
Aspects of the disclosure relate to using machine learning for customized communication output generation. A computing platform may receive lead information from an enterprise user device. Based on the lead information, the computing platform may generate lead management information indicating a communications that should be sent to an individual corresponding to the lead information, where the communications correspond to a plurality of communication types and are each sent on a particular date indicated in the lead management information. The computing platform may send the lead management information and one or more commands directing a communication storage system to select and send each of the communications to the individual on the particular date corresponding to each of the communications, which may cause a client device corresponding to the individual to display each of the communications on the particular date corresponding to each of the communications.
Aspects of the disclosure relate to a system and method for cryptographically protecting data transferred between spatially distributed computing devices. An intermediary database may be used to facilitate the protected data transfer and/or record the data transfers. A first computing device may transfer, to the intermediary database, encrypted data that may be securely transferred to other computing devices. A second computing device may generate a GUI used to view data available from the intermediary database. Once data is selected by the second device, the second device may transfer a key (or other encryption mechanism) to the first device. The first computing device may encrypt the data using the received key and transmit the encrypted data to the intermediary database. The intermediary database may transmit the encrypted data to the second computing device, and the second computing device may decrypt and use the data.
Aspects of the disclosure relate to computing platforms that apply augmented reality techniques for vehicle diagnostics. A computing platform may receive images of a vehicle. By applying image recognition and machine learning algorithms to the images of the vehicle, the computing platform may identify the vehicle. The computing platform may identify schematics corresponding to the vehicle. Using the schematics, the computing platform may generate x-ray image information corresponding to the vehicle. The computing platform may send the images, the x-ray image information, and commands directing an enterprise user device to display an −x ray image, which may cause the enterprise user device to: modify the images of the vehicle based on the x-ray image information, and display an x-ray vehicle interface depicting a portion of the vehicle that: is not visible in the images, but would be visible fi an exterior portion of the vehicle was displaced.
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
Aspects of the disclosure relate to multicomputer processing of vehicle operational data from telematics devices and other sources with centralized event control. An event control computing platform may receive vehicle operational data from a telematics device associated with a user. Subsequently, the event control computing platform may identify, based on the received data, whether at least one criterion associated with the user has been satisfied. If the received data indicates that the at least one criterion associated with the user has been satisfied, then the event control computing platform may generate a command configured to cause a change to a subunit of user data and then may transmit the generated command to a subunit provisioning server.
B60R 16/023 - Circuits électriques ou circuits de fluides spécialement adaptés aux véhicules et non prévus ailleursAgencement des éléments des circuits électriques ou des circuits de fluides spécialement adapté aux véhicules et non prévu ailleurs électriques pour la transmission de signaux entre des parties ou des sous-systèmes du véhicule
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
H04L 67/12 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p. ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance
H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons
H04W 12/00 - Dispositions de sécuritéAuthentificationProtection de la confidentialité ou de l'anonymat
44.
AUTONOMOUS DRIVING ALGORITHM EVALUATION AND IMPLEMENTATION
Methods and systems for autonomous driving algorithm evaluation are described herein. A computing device may receive, via telematics sensors associated with a vehicle, telematics data corresponding to one or more trips taken by the vehicle during a period of time. Portions of the telematics data corresponding to use of an autonomous driving algorithm may be determined. One or more performance metrics of the autonomous driving algorithm may be determined based on the portions of the telematics data corresponding to use of the autonomous driving algorithm. The one or more performance metrics may be compared to one or more other performance metrics, such as those corresponding to other autonomous driving algorithms. An autonomous vehicle score may be assigned to the autonomous driving algorithm. Based on the autonomous vehicle score, an indication of a second autonomous driving algorithm may be sent to the vehicle.
B60W 50/08 - Interaction entre le conducteur et le système d'aide à la conduite
B60W 50/00 - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier
B60W 50/04 - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier pour surveiller le fonctionnement du système d'aide à la conduite
45.
INTELLIGENT SYSTEMS AND METHODS FOR CLICK PRICE GENERATION
ii based on a multiplication of the plurality of respective factor solutions; and generating a click price based on at least the probability and a target acquisition cost of an entity.
Intelligent disturbance detection systems and methods of use to capture a disturbance via an application tool on a mobile smart device remote from the user, extract features from the disturbance, compare the extracted features to disturbance labels of a disturbance set in a comparison by a disturbance detection neural network model of the application tool, generate a disturbance label when the extracted features match the disturbance label in the comparison, train the model to generate a custom disturbance label associated with the extracted features when the extracted features do not match the one or more disturbance labels in the comparison, and generate an automatic alert via the mobile smart device to transmit an identification of the disturbance to the user based on the disturbance label, the custom disturbance label, or combinations thereof.
Systems and methods for intelligent click price generation may include solving for a plurality of coefficients of a plurality of respective factors, wherein each coefficient is an exponent in an equation; determining a factor value for each factor, wherein the factor value for each factor is between 0 and 1; setting a respective factor exponent for each factor value for each factor to 0 or 1 based on a truth determination of a factor whether to include each factor; solving for a plurality of respective factor solutions based on each factor value for each factor set to a power of each respective factor exponent; determining a probability of a conversion associated with an imminent click for an individual i based on a multiplication of the plurality of respective factor solutions; and generating a click price based on at least the probability and a target acquisition cost of an entity.
Systems and methods in accordance with embodiments of the invention can analyze a variety of software applications, modify the software applications, and/or automatically deploy the software applications to a distributed computing system. Distributed computing systems can provide software applications in a scalable, low cost manner that can be dynamically scaled to demand. Software deployment systems in accordance with embodiments of the invention can automatically process software applications to determine the suitability of the software application to be deployed to a distributed computing system and/or modify the software application for deployment. A variety of machine classifiers can be used to score various aspects of a software application to identify portions of the application for modification and/or suitability for deployment.
H04L 67/00 - Dispositions ou protocoles de réseau pour la prise en charge de services ou d'applications réseau
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p. ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
49.
COLLISION PREDICTION BASED ON DIFFERENT TYPES OF IMPACT
Implementations claimed and described herein provide systems and methods for generating a prediction for a likelihood of a collision based on a respective collision prediction algorithm for a particular type of impact. In one implementation, determining, by a collision prediction algorithm, a prediction score based on a subset of variables associated with the movement of a mobile device. The prediction score associated with a likelihood that the movement is associated with a particular type of impact associated with the first collision prediction algorithm. A prediction that the movement is not association with the first type of impact when the prediction score is below a threshold score is outputted.
G08G 1/01 - Détection du mouvement du trafic pour le comptage ou la commande
B60R 21/013 - Circuits électriques pour déclencher le fonctionnement des dispositions de sécurité en cas d'accident, ou d'accident imminent, de véhicule comportant des moyens pour détecter les collisions, les collisions imminentes ou un renversement
Aspects of the disclosure relate to computing platforms that utilize machine learning to reduce false positive/negative collision output generation. A computing platform may apply machine learning algorithms on received data to generate a collision output. In response to generating the collision output indicating a collision, the computing platform may identify a data collection location. If the data collection location is within a predetermined radius of a false positive collection location, the computing platform may modify the collision output to indicate a non-collision. If the data collection location is not within the predetermined radius, the computing platform may compute a score using telematics data and compare the score to a predetermined threshold. If the score does not exceed the predetermined threshold, the computing platform may modify the collision output to indicate a non-collision. If the score exceeds the predetermined threshold, the computing platform may affirm the collision output indicating a collision.
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
B60R 21/00 - Dispositions ou équipements sur les véhicules pour protéger les occupants ou les piétons ou pour leur éviter des blessures en cas d'accidents ou d'autres incidents dus à la circulation
Systems and methods for intelligent caller to agent assignment receive requests to establish a voice call session for a plurality of callers, identify demographic information, score a caller-agent combination to generate a plurality of scores, determine the caller-agent combination with a highest score of the plurality of scores, and assign a first agent of the caller-agent combination with the highest score to the voice call session for a caller, and when a first agent is concurrently assigned to another voice call session for another caller, execute one or more iterations until (i) the first agent is assigned to one of the voice call session for the caller and the another voice call session for the another caller and (ii) a second agent of another caller-agent combination is assigned to the other of the voice call session for the caller and the another voice call session for the another caller.
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
52.
SYSTEMS AND METHODS OF DISPLAYING DRIVING DATA USING TELEMATICS DATA
Implementations claimed and described herein provide systems and methods for generating a display of driving data using telematics data. The systems and methods use telematics data generated via a telematics device disposed in a vehicle. One or more driving attributes associated with a vehicle operator and/or the vehicle based on the telematics data are determined by the system. The one or more driving attributes associated with the vehicle operator and/or the vehicle are compared to one or more driving attributes associated with one or more connected users, where the one or more driving attributes associated with one or more connected users are received from one or more databases. Furthermore, a user interface is generated that presents a result of the comparison.
Aspects of the disclosure provide a computer-implemented-method and system for providing a food delivery service to a person's residence. To execute these methods, the delivery system may utilize (a) information regarding the location of a person ordering food from a restaurant or a food orderer, (b) the location of the restaurant, and (c) the location of potential third party delivery agents. These food delivery service systems may include restaurants of any type or food and/or retail food stores, such as grocery stores, pharmacies, drug stores, retail stores, discount retail stores, etc. The food delivery service systems may further utilize historical information and/or insurance information to enhance the food delivery services.
Aspects relate to a machine learning system implementing an evolutionary boosting machine. The system may initially select randomized feature sets for an initial generation of candidate models. Evolutionary algorithms may be applied to the system to create later generations of the cycle, combining and mutating the feature selections of the candidate models. The system may determine optimal number of boosting iterations for each candidate model in a generation by building boosting iterations from an initial value up to a predetermined maximum number of boosting iterations. When a final generation is achieved, the system may evaluate the optimal model of the generation. If the optimal boosting iterations of the optimal model does not meet solution constraints on the optimal boosting iterations, the system may adjust a learning rate parameter and then proceed to the next cycle. Based on termination criteria, the system may determine a resulting/final optimal mode.
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06F 18/2115 - Sélection du sous-ensemble de caractéristiques le plus significatif en évaluant différents sous-ensembles en fonction d'un critère d'optimisation, p. ex. la séparabilité des classes, la sélection en avant ou l’élimination en arrière
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06N 3/126 - Algorithmes évolutionnaires, p. ex. algorithmes génétiques ou programmation génétique
Systems and methods are disclosed for determining a distraction level of a driver. A real-time driver analysis computer may receive sensor data from one or more driver analysis sensors. The real-time driver analysis computer may analyze the sensor data to determine a distraction level of a driver. Based on the distraction level, the real-time driver analysis computer may send one or more control signal to the vehicle and output one or more alerts to a mobile device associated with the driver.
B60W 40/08 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conducteurs ou aux passagers
B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p. ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
56.
NATURAL LANGUAGE PROCESSING FOR ROADSIDE ASSISTANCE
Implementations claimed and described herein provide systems and methods for submitting a roadside assistance request. The systems and methods use natural language communication to determine an intent. The intent is determined by the system and used to generate and submit the roadside assistance request.
Implementations claimed and described herein provide systems and methods for responding to a query associated with a roadside assistance request. The systems and methods use a chatbot to identify a category of the query and generate a response. The response is sent to a user device for display.
Methods, computer-readable media, software, and apparatuses may assist in assessing proportional fault in an automobile accident involving an automobile having one or more autonomous features. An expected behavior of an autonomous feature is compared to an observed outcome of an accident and a fault proportion between a human driver and the autonomous feature may be determined, based on the comparison.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06N 5/04 - Modèles d’inférence ou de raisonnement
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
59.
MULTI-COMPUTER SYSTEM FOR DYNAMICALLY DETECTING AND IDENTIFYING HAZARDS
Systems, methods, computer-readable media, and apparatuses for providing hazard detection and broadcast functions are provided. In some examples, sensor data may be captured by a mobile device, vehicle, or the like. The data may be used to detect a hazard, identify a type of hazard, and the like. One or more users or groups of users for notification of the hazard may be identified and one or more notifications may be transmitted to users within the group.
G08G 1/01 - Détection du mouvement du trafic pour le comptage ou la commande
G08G 1/0967 - Systèmes impliquant la transmission d'informations pour les grands axes de circulation, p. ex. conditions météorologiques, limites de vitesse
60.
DYNAMIC DRIVING METRIC OUTPUT GENERATION USING COMPUTER VISION METHODS
Aspects of the disclosure relate to dynamic driving metric output platforms that utilize improved computer vision methods to determine vehicle metrics from video footage. A computing platform may receive video footage from a vehicle camera. The computing platform may determine that a reference marker in the video footage has reached a beginning and an end of a road marker based on brightness transitions, and may insert time stamps into the video accordingly. Based on the time stamps, the computing platform may determine an amount of time during which the reference marker covered the road marking. Based on a known length of the road marking and the amount of time during which the reference marker covered the road marking, the computing platform may determine a vehicle speed. The computing platform may generate driving metric output information, based on the vehicle speed, which may be displayed by an accident analysis platform. Based on known dimensions of pavement markings the computing platform may obtain the parameters of the camera (e.g., focal length, camera height above ground plane and camera tilt angle) used to generate the video footage and use the camera parameters to determine the distance between the camera and any object in the video footage.
G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments
G06T 7/50 - Récupération de la profondeur ou de la forme
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c.-à-d. étalonnage de caméra
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
An aerial image system may generate images of structures providing views that might otherwise be difficult to obtain. These images may be used to determine various characteristics of the structure, which may then be processed for a variety of tasks including product marketing, claim processing and post-claim analysis. The images may be analyzed by the aerial image system or by a ground-based processing system to determine the structural characteristics such as roof age, roof type and roof pitch. In some examples, image analysis techniques such as edge detection and contrast determination may assist in determining the requisite data. Additionally or alternatively, the processing system may direct or control one or more image capture devices to obtain desired images.
Methods, computer-readable media, software, systems and apparatuses may retrieve, via a computing device and over a network, information related to one or more characteristics of a particular application or service deployed in a computing environment. The particular application or service may be associated with a class of applications or services based on the information. A type of personal data collected may be determined for each application or service in the associated class. For the particular application or service, a risk metric indicative of a type of personal data collected by the particular application or service in relation to the type of personal data collected by other applications or services in the associated class may be determined. An additional application or service with a lower risk than the particular application or service may be recommended.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06F 16/28 - Bases de données caractérisées par leurs modèles, p. ex. des modèles relationnels ou objet
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G06F 21/64 - Protection de l’intégrité des données, p. ex. par sommes de contrôle, certificats ou signatures
Methods and systems disclosed herein describe a canonical model that sets forth a standardized schema that represents data entities and their relationships as a logical data structure across multiple business units. A schema validator module may validate application-specific schema against the canonical module. Additionally, legacy and/or existing applications may be upgraded, through a transformation module, to comply with the canonical model. The transformation module may transform application-specific data and/or information to the standardized schema, and vice versa, to ensure that legacy and/or existing applications may communicate with applications and/or processing engines that comply with the standardized schema.
Implementations claimed and described herein provide systems and methods for determining driving attributes using telematics data. The systems and methods use telematics data generated via a telematics device disposed in a vehicle. One or more driving attributes associated with a vehicle operator and/or the vehicle based on the telematics data are determined by the system. The one or more driving attributes associated with the vehicle operator and/or the vehicle are denormalized and aggregated. Furthermore, the aggregated driving attributes are communicated with a provider computing device in response to a request for the aggregated driving attributes.
Systems and apparatuses are provided for receiving a request for roadside assistance from a mobile device. The request may include at least one image of a vehicle for which assistance is being requested. The image may be analyzed to determine a roadside assistance issue corresponding to the request. A service may be determined for the roadside assistance issue, and a first roadside assistance provider available to provide the service to the vehicle with the roadside assistance issue may be identified. A communication may be transmitted to a computing device of the first roadside assistance provider specifying the service. Further, a notification is generated and transmitted to the mobile device.
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
G06Q 10/0875 - Énumération ou classification des pièces, des fournitures ou des services, p. ex. nomenclatures
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
Implementations claimed and described herein provide systems and methods for generating a display of driving data using telematics data. The systems and methods use telematics data generated via a telematics device disposed in a vehicle. One or more driving attributes associated with a vehicle operator and/or the vehicle based on the telematics data are determined by the system. The one or more driving attributes associated with the vehicle operator and/or the vehicle are compared to one or more driving attributes associated with one or more connected users, where the one or more driving attributes associated with one or more connected users are received from one or more databases. Furthermore, a user interface is generated that presents a result of the comparison.
G07C 5/02 - Enregistrement ou indication du temps de circulation, de fonctionnement, d'arrêt ou d'attente uniquement
B60W 40/10 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés au mouvement du véhicule
B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
67.
Intelligent structural protection systems and methods
Systems and methods for deployment of a protective component, generation of a customized design for the protective component, or combinations thereof are associated with a structure comprising a portion, a neural network model, processor(s), and memory storing machine readable instructions. When executed for deployment, the neural network model predicts the likelihood of the occurrence of the natural event in the geographic area within the time frame as high as defined by when the likelihood is above a threshold, and deploys the protective component for protecting the portion of the structure when the likelihood is high. For customized design, the neural network model is used to access dimension and weather data associated with a structure and weather data to generate the customized design of the protective component for the structure.
G01W 1/10 - Dispositifs pour la prévision des conditions météorologiques
G01S 19/14 - Récepteurs spécialement adaptés pour des applications spécifiques
G05B 13/02 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
Methods, systems, and computer-readable media, and apparatuses for optimizing policy underwriting and insurance policy coverage are described herein. Insurance information related to a new homeowner insurance policy may be retrieved from one or more insurance system databases. The insurance information may comprise an insurance policy premium. An unrestrained rating plan premium may be determined based on a plurality of premium components. The plurality of premium components may comprise at least one multiplicative factor and at least one additive factor determined from the insurance information. An underwriting score may be determined based on the unrestrained rating plan premium and the insurance policy premium. In response to determining that the underwriting score fails to meet a predetermined underwriting score threshold, the new homeowner insurance policy may be rejected.
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
69.
PROCESSING SYSTEM HAVING MACHINE LEARNING ENGINE FOR PROVIDING CUSTOMIZED USER FUNCTIONS
Systems and apparatuses for generating customized user output are provided. The system may collect sensor data, associated with the user, from a variety of sources. The system may use the sensor data to generate a customized user output. The system may analyze the sensor data, and determine, based on the sensor data and the customized user output, one or more user recommendation outputs. The system may update the customized user output based on additional or subsequent sensor data, and/or based on whether or not the user recommendation output was completed, as determined from subsequent sensor data.
Apparatuses, systems, and methods are provided for the logical configuration of vehicle control systems based on driver profiles. A vehicle control computer may identify driving behavior of a driver of a vehicle through vehicle operation data provided by one or more of vehicle sensors, a telematics device, and a mobile device. Based on the driving behavior, the vehicle control computer may develop a first driving profile for the driver of the vehicle. The vehicle control computer transmit the first driving profile to a remote server storing driving profiles of a plurality of users. The vehicle control computer may download a second driving profile associated with a different driver from the remote server. The vehicle control computer may configure vehicle operations based off of the second driving profile associated with the different driver and may actuate vehicle operation based on the configuration.
Systems, apparatuses, methods discussed herein provide insurance for individuals who provide a ride hailing application to a mobile device configured to receive telematics data indicating an accident of the vehicle associated with the mobile device and to send metadata comprising an indication of interaction with the ride hailing application by the driver during the time of the accident as indicated by the telematics data. Based on the metadata, a period of use of the plurality of periods of use of the ride hailing application during which the accident occurred may be determined and a notification to the mobile device based on the determination that the driver was interacting with the ride hailing application may be sent to the mobile device. Further, one or more operational states of the ride hailing application or a device or system may be updated or altered based on the transmitted notification.
Methods, computer-readable media, systems and apparatuses for home maintenance monitoring and rewarding users for completion of various home maintenance tasks are provided. The systems may receive data associated with a home, such as a plurality of systems and/or devices associated with the home. Based on the received information, the systems may generate a maintenance task list for the home including a plurality of maintenance tasks. Data related to maintenance performed on the home may be received and, based on the received maintenance data, a determination may be made as to whether one or more tasks on the maintenance task list have been completed. Upon completion of a task, a reward, such as points, discounts on future maintenance, insurance premium discounts, etc., may be deposited in an account of the user. In some arrangements, the points may be redeemed as payment on future maintenance, payment on insurance premiums, or the like.
G06Q 30/0217 - Remises ou incitations, p. ex. coupons ou rabais impliquant une contribution sur des produits ou des services en échange d’une incitation ou d’une récompense
G06Q 10/20 - Administration de la réparation ou de la maintenance des produits
Systems, methods, apparatuses and computer-readable media for receiving data associated with a structure or home are presented. Data may be received from an insurance provider associated with the structure, from publicly available sources and/or from a user. A template of items to evaluate associated with the structure may be generated. User input may be received associated with one or more of the items and a determination may be made as to whether a potential issue exists with respect to the item. Other aspects relate to receiving data associated with a plurality of items from the template and prioritizing potential issues identified for each item. Other aspects relate to providing options or images to a user for selection. The user may select an option or image based on the similarity to the item in the structure, which may prompt additional options or images to be provided to the user for selection.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
74.
AUTONOMOUS VEHICLE CONTROL SYSTEMS WITH COLLISION DETECTION AND RESPONSE CAPABILITIES
Aspects of the disclosure relate to controlling an autonomous vehicle to respond to a detected collision. An autonomous vehicle control system may receive sensor data associated with an autonomous vehicle in which the autonomous vehicle control system is installed. The autonomous vehicle control system may analyze the sensor data in real-time as the sensor data is received and may detect an occurrence of a collision involving the autonomous vehicle. In response to detecting the occurrence of the collision, the autonomous vehicle control system may generate claim information based on the sensor data and may process the claim information based on at least one insurance profile maintained by the autonomous vehicle control system. Then, the autonomous vehicle control system may generate a claim notification based on processing the claim information and may send the claim notification to a vehicle management computer system.
B60R 21/00 - Dispositions ou équipements sur les véhicules pour protéger les occupants ou les piétons ou pour leur éviter des blessures en cas d'accidents ou d'autres incidents dus à la circulation
B60R 21/0136 - Circuits électriques pour déclencher le fonctionnement des dispositions de sécurité en cas d'accident, ou d'accident imminent, de véhicule comportant des moyens pour détecter les collisions, les collisions imminentes ou un renversement réagissant à un contact effectif avec un obstacle
B60W 30/00 - Fonctions des systèmes d'aide à la conduite des véhicules routiers non liées à la commande d'un sous-ensemble particulier, p. ex. de systèmes comportant la commande conjuguée de plusieurs sous-ensembles du véhicule
G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
75.
Natural Language Processing Platform For Automated Event Analysis, Translation, and Transcription Verification
Aspects of the disclosure relate to computing platforms that utilize improved natural language processing techniques for claims processing. A computing platform may receive an audio file and determine one or more utterance segments based on the audio file. The computing platform may generate and send one or more audio chunks based on the one or more utterance segments to an audio transcription system. The computing platform may receive one or more text segments in response and may generate an audio transcription file accordingly. The computing platform may assign a category to each word in the audio transcription file, and may send user interface information to a user device along with one or more commands directing the user device to generate a user interface based on the user interface information, which may cause the user device to display the user interface.
The Board Of Trustees Of The Leland Stanford Junior University (USA)
Inventeur(s)
Aragon, Juan Carlos
Phillips, Derek
Madigan, Regina
Chintakindi, Sunil
Kochenderfer, Mykel
Abrégé
Aspects of the disclosure relate to a dynamic distance estimation output platform that utilizes improved computer vision and perspective transformation techniques to determine vehicle proximities from video footage. A computing platform may receive, from a visible light camera located in a first vehicle, a video output showing a second vehicle that is in front of the first vehicle. The computing platform may determine a longitudinal distance between the first vehicle and the second vehicle by determining an orthogonal distance between a center-of-projection corresponding to the visible light camera, and an intersection of a backside plane of the second vehicle and ground below the second vehicle. The computing platform may send, to an autonomous vehicle control system, a distance estimation output corresponding to the longitudinal distance, which may cause the autonomous vehicle control system to perform vehicle control actions.
G05B 13/02 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/20 - ScènesÉléments spécifiques à la scène dans les scènes de réalité augmentée
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p. ex. véhicules ou piétonsReconnaissance des objets de la circulation, p. ex. signalisation routière, feux de signalisation ou routes
Systems, devices, and methods provide an augmented reality visualization of a real world accident scene. The system may comprise an augmented reality visualization device in communication with a display interface. The display interface may be configured to present a graphical user interface including an accident scene that corresponds to a real world location. The augmented reality visualization device (and/or system) may comprise one or more data stores configured to store accident scene information corresponding to the real world location, such as vehicles, motorcycles, trees, road objects, etc. Additionally, the one or more data stores may also store participant information corresponding to details about other multiple participants, such as witnesses, other drivers, and/or police officers.
Aspects of the disclosure relate to an autonomous vehicle evaluation system that performs continuous evaluation of the actions, strategies, preferences, margins, and responses of an autonomous driving control system. A computing platform may receive sensor data from one or more autonomous vehicle sensors, manufacturer computing platform, or V2X computing platform. Based on this sensor data, the computing platform may determine one or more driving patterns. Based on a primary context corresponding to the one or more driving patterns, the computing platform may group the one or more driving patterns. The computing platform may determine a driving pattern degradation output indicating degradation corresponding to the one or more grouped driving patterns, and the computing platform may send the driving pattern degradation output to an autonomous driving system, which may cause the autonomous driving system to take corrective action accordingly.
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p. ex. utilisant des pilotes automatiques
G05D 1/227 - Transfert de la commande entre la commande à distance et la commande embarquéeTransfert de la commande entre plusieurs dispositions de commande à distance
G05D 1/617 - Sécurité ou protection, p. ex. définition de zones de protection autour d’obstacles ou évitement de zones dangereuses
Systems including one or more sensors, coupled to a vehicle, may detect sensor information and provide the sensor information to another computing device for processing. A system includes one or more sensors, coupled to a vehicle and configured to detect sensor information, and a computing device configured to communicate with one or more mobile sensors to receive the mobile sensor information, communicate with the one or more sensors to receive the sensor information, and analyze the sensor information and the mobile sensor information to identify one or more risk factors.
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
B60W 30/095 - Prévision du trajet ou de la probabilité de collision
B60W 40/04 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conditions ambiantes liés aux conditions de trafic
B60W 40/06 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conditions ambiantes liés à l'état de la route
B60W 40/08 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conducteurs ou aux passagers
Intelligent risk detection systems and methods include one or more processors, one or more memory components communicatively coupled to the one or more processors, and machine readable instructions that cause the system to determine one or more risk factors with respect to a monitored item based on one or more sensors, determine, via a risk model, a risk score indicative of a risk of a danger event based on the one or more risk factors, detect whether the risk score is above a threshold risk score, generate a risk event detection when the risk score is above the threshold risk score, transmit a risk detection alert based on generation of the risk event detection, activate a risk mitigation activation sensor, and determine whether an updated risk score is below the threshold risk score such that the risk of the danger event is determined to be mitigated.
System and methods are disclosed for determining properties of vehicle insurance policies for vehicles that engage in autonomous driving. Vehicle driving data, an autonomous driving system quality rating, and/or other information may be used to determine, for example, a premium, a deductible, a coverage term, and/or a coverage amount of an automobile insurance policy of an automobile that engages in autonomous driving. In addition, vehicle driving data and/or other information may be used to determine a distance-based autonomous driving insurance factor based at least in part on a distance traveled by the vehicle while the vehicle was engaged in autonomous driving.
A computer-assisted method for determining, such as by a computing device, a success rate for a presentation page included in a plurality of insurance presentations is described herein. Based on the success rate for the presentation page, a display of a recommendation for an insurance agent to include the presentation page in an insurance presentation for a customer may be generated. The insurance agent may select an option to include the presentation in the insurance presentation, and the selection may be received at a computing device. The insurance presentation may then be generated to include the presentation page.
Systems, methods, apparatuses, and computer-readable media for receiving data from one or more sensors associated with one or more home devices of a user, such as appliances, home systems, etc., receiving data from one or more sensors associated with a vehicle of the user, and/or receiving data associated with a lifestyle of the user are presented. In some examples, the data may aggregated and analyzed to assess risk associated with the user in order to determine or adjust an insurance rate, premium, and/or incentive.
A driving analysis server may be configured to receive vehicle operation data from vehicle sensors, and may use the data to identify a potentially high-risk or unsafe driving event by the vehicle. The driving analysis server also may receive corresponding image data, video, or object proximity data from the vehicle or one or more other data sources, and may use the image, video, or proximity data to analyze the potentially high-risk or unsafe driving event. A driver score for the vehicle or driver may be calculated or adjusted based on the analysis of the data and the determination of one or more causes of the driving event.
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
85.
Safe Hand-Off Between Human Driver and Autonomous Driving System
Methods, computer-readable media, software, and apparatuses may determine whether a human driver, or an autonomous driving system, should be in control of a vehicle in response to a detection of an unexpected event. A decision may be made to pass control from the autonomous driving system to the human driver, if it is determined that the human driver can handle the unexpected event more safely than the autonomous vehicle.
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p. ex. utilisant des pilotes automatiques
G05D 1/81 - Transfert de la commande entre la commande automatique embarquée et la commande manuelle embarquée
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
Systems and methods for messaging a user include one or more processors, one or more memory components, and machine readable instructions that cause the AI system to perform at least the following when executed by the one or more processors: request a consent input from a user, receive the consent input from the user, send a message to a mobile device of the user, responsive to the user selecting an option of being connected to the agent, and responsive to the user not selecting the option of being connected to the agent, and send one or more further messages presenting the user with the option of being connected to the agent on a pre-determined schedule until (i) a pre-determined duration of time expires or (ii) the user selects the option of being connected to the agent within the pre-determined duration of time.
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
G06K 19/06 - Supports d'enregistrement pour utilisation avec des machines et avec au moins une partie prévue pour supporter des marques numériques caractérisés par le genre de marque numérique, p. ex. forme, nature, code
H04M 1/72436 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens de soutien local des applications accroissant la fonctionnalité avec des moyens interactifs de gestion interne des messages pour la messagerie textuelle, p. ex. services de messagerie courte [SMS] ou courriels
H04W 4/14 - Services d'envoi de messages courts, p. ex. SMS ou données peu structurées de services supplémentaires [USSD]
87.
DATA PROCESSING SYSTEM COMMUNICATING WITH A MAP DATA PROCESSING SYSTEM TO DETERMINE OR ALTER A NAVIGATION PATH BASED ON ONE OR MORE ROAD SEGMENTS
Systems and methods are disclosed for determining a probability of an adverse event that will occur in a future. The system may determine a geographical location of a vehicle based on data indicating the geographical location of the vehicle received from the vehicle and sensed by a vehicle sensor of the vehicle; determine an environment of the geographical location of the vehicle; determine vehicle attributes of the vehicle; link the environment of the geographical location of the vehicle and the vehicle attributes; and based on a link between the environment of the geographical location of the vehicle and the vehicle attributes, determine a probability of an adverse event that will occur in a future.
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
H04W 4/44 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons pour la communication entre véhicules et infrastructures, p. ex. véhicule à nuage ou véhicule à domicile
88.
SUBSCRIPTION-BASED AND EVENT-BASED CONNECTED VEHICLE CONTROL AND RESPONSE SYSTEMS
Event-based and subscription-based connected vehicle control and response systems, methods, and apparatus are disclosed. An example method comprises receiving first data acquired by a first plurality of vehicle operation sensors of a first vehicle from a first computing device associated with a first subscriber, identifying an occurrence of an event involving the first vehicle, requesting second data acquired by one or more responsive object sensors in a vicinity of the event, determining a causation of the event, generating a fault distribution, and transmitting the fault distribution to the computing device of the first subscriber.
B60W 30/09 - Entreprenant une action automatiquement pour éviter la collision, p. ex. en freinant ou tournant
G06Q 30/0207 - Remises ou incitations, p. ex. coupons ou rabais
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
G08G 1/01 - Détection du mouvement du trafic pour le comptage ou la commande
Methods, computer-readable media, systems and apparatuses for determining a blind corner navigational score based on real-time or near real-time navigational analysis using sensor data, digital image data, and a map database are discussed. In some arrangements, detection of a blind sensor may be performing using sensor data, digital image data, and navigational data from a map database system. In at least some arrangements, a warning signal or a vehicle control signal may be transmitted to a vehicle in response to a determination that the blind corner navigational score is above a threshold. In at least some arrangements, route correction and/or route modification based on an upcoming blind corner may be performed if a blind corner navigational score is above a threshold.
G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
90.
Intelligent prediction systems and methods for conversational outcome modeling frameworks for sales predictions
An intelligent prediction system includes one or more processors, one or more memory components, and machine-readable instructions that cause the intelligent prediction system to: receive text data comprising a plurality of speaker turn segments of a transcription of a conversation, each speaker turn segment of the plurality of speaker turn segments representative of a turn in the conversation, the plurality of speaker turn segments collectively representative of the conversation up to a point of time, generate a point in time bind probability based on a speaker turn segment bind probability of a speaker turn segment at the point in time and memory data associated with the plurality of segments up to the point in time, and generate a speaker turn segment impact score at the point in time by subtracting an immediately preceding point in time bind probability from the point in time bind probability.
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06F 17/18 - Opérations mathématiques complexes pour l'évaluation de données statistiques
G06Q 30/0202 - Prédictions ou prévisions du marché pour les activités commerciales
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
91.
Vehicle diagnostic platform using augmented reality for damage assessment
Aspects of the disclosure relate to computing platforms that apply augmented reality techniques for vehicle diagnostics. A computing platform may receive images of a vehicle. By applying image recognition and machine learning algorithms to the images of the vehicle, the computing platform may identify the vehicle. The computing platform may identify schematics corresponding to the vehicle. Using the schematics, the computing platform may generate x-ray image information corresponding to the vehicle. The computing platform may send the images, the x-ray image information, and commands directing an enterprise user device to display an x-ray image, which may cause the enterprise user device to: modify the images of the vehicle based on the x-ray image information, and display an x-ray vehicle interface depicting a portion of the vehicle that: is not visible in the images, but would be visible if an exterior portion of the vehicle was displaced.
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
Aspects of the present disclosure generally relate to systems and methods for trip data processing, and more specifically, for on-demand generation of a user score from trip data. One example method for user score generation generally includes receiving input data from one or more sensors indicating driving characteristics during one or more trips by a user, extracting, via a feature extraction system, one or more features associated with each trip of the one or more trips upon receive input data for the trip, and storing the one or more features in a features database. The method may also include retrieving, from the features database, the one or more features in response a request for generation of a user score, and generating the user score based on the one or more features retrieved from the features database.
Methods, computer-readable media, software, and apparatuses are provided to assist a user and vendor in completing an online trusted transaction. Trusted vendor websites are verified and user identities are confirmed through a cyber-security safe logon credentialing system. The vendor can be confident that the user identity has been verified to be who they say they are and the user can be confident that they are using a trusted verified vendor website.
Implementations include providing assistance services, and more specifically for selecting a roadside assistance provider from a plurality of providers using one or more predictive models of aspects of a roadside assistance request. Selection of the roadside assistance provider may be based on ranking of roadside assistance providers associated with a service area, the ranking based on one or more predicted output values from one or more predictive models of aspects of a roadside assistance service, such as estimated arrival time and an estimated probability of acceptance of the request by a roadside assistance provider. One or more of the predicted output values may be adjusted based on a boost value as determined boost value predictive model to increase a likelihood that a selected roadside assistance provider accepts a request to provide the assistance.
Implementations include providing assistance services, and more specifically for selecting a roadside assistance provider from a plurality of providers using one or more predictive models of aspects of a roadside assistance request. Selection of the roadside assistance provider may be based on ranking of roadside assistance providers associated with a service area, the ranking based on one or more predicted output values from one or more predictive models of aspects of a roadside assistance service, such as estimated arrival time and an estimated probability of acceptance of the request by a roadside assistance provider. The predictive models or other components of the roadside assistance system may be machine-learning and adaptable based on historical data and feedback information of responses and rankings to prior roadside assistance requests.
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
G06Q 30/0282 - Notation ou évaluation d’opérateurs commerciaux ou de produits
Aspects of the present disclosure generally relate to systems and methods for trip data processing, and more specifically, for on-demand generation of a user score from trip data. One example method for user score generation generally includes receiving input data from one or more sensors indicating driving characteristics during one or more trips by a user, extracting, via a feature extraction system, one or more features associated with each trip of the one or more trips upon receive input data for the trip, and storing the one or more features in a features database. The method may also include retrieving, from the features database, the one or more features in response a request for generation of a user score, and generating the user score based on the one or more features retrieved from the features database.
A method, apparatus, and system for collecting and evaluating powered vehicle operation utilizing on-board diagnostic components and location determining components or systems. The invention creates one or more databases whereby identifiable behavior or evaluative characteristics can be analyzed or categorized. The evaluation can include predicting likely future events. The database can be correlated or evaluated with other databases for a wide variety of uses.
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
G07C 5/02 - Enregistrement ou indication du temps de circulation, de fonctionnement, d'arrêt ou d'attente uniquement
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
Disclosed herein is a traveling-based insurance rating system comprising a telematics device associated with a vehicle having one or more sensors arranged therein, a mobile device associated with a passenger of the vehicle, and a server computer comprising hardware, including a processor and memory. The server computer may receive driving data of a driver of the vehicle from the one or more sensors via the telematics device. The server computer may then identify one or more values of the driving data to be within one or more predetermined ranges and classify the driving data as a trip of the passenger. Based on the driving data from the trip, the server computer may determine one or more driving behaviors of the driver, calculate an insurance rating for the passenger of the vehicle based on the one or more driving behaviors of the driver, and transmit the insurance rating to the mobile device.
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
H04W 4/02 - Services utilisant des informations de localisation
H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons
H04W 8/18 - Traitement de données utilisateur ou abonné, p. ex. services faisant l'objet d'un abonnement, préférences utilisateur ou profils utilisateurTransfert de données utilisateur ou abonné
Systems, methods, apparatuses and computer-readable media for receiving data associated with a structure or home are presented. Data may be received from an insurance provider associated with the structure, from publicly available sources and/or from a user. A template of items to evaluate associated with the structure may be generated. User input may be received associated with one or more of the items and a determination may be made as to whether a potential issue exists with respect to the item. Other aspects relate to receiving data associated with a plurality of items from the template and prioritizing potential issues identified for each item. Other aspects relate to providing options or images to a user for selection. The user may select an option or image based on the similarity to the item in the structure, which may prompt additional options or images to be provided to the user for selection.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
Systems, methods apparatuses, and computer-readable media for analyzing vehicle accident claim information from a vehicle that is deemed a total loss, as well as historical data, to determine whether one or more parts from the vehicle are available for reuse is presented. In some examples, aspects may also relate to determining whether the parts available for reuse should be stored by the entity using the system (e.g., the insurance company) for future use in the repair of other vehicles, such as other vehicles insured by the insurance company, or should be sold. The determination to store or sell the part may be based, at least in part, on historical data of the entity.
