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.
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 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
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.
9.
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 for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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.
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.
G07C 5/00 - Registering or indicating the working of vehicles
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
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 - Measuring linear or angular speedMeasuring differences of linear or angular speeds
G01P 15/00 - Measuring accelerationMeasuring decelerationMeasuring shock, i.e. sudden change of acceleration
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
H04W 4/02 - Services making use of location information
15.
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 - Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
B60W 10/30 - Conjoint control of vehicle sub-units of different type or different function including control of auxiliary equipment, e.g. air-conditioning compressors or oil pumps
B60W 30/08 - Predicting or avoiding probable or impending collision
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 - Registering or indicating the working of vehicles
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
H04W 4/029 - Location-based management or tracking services
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 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
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.
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 - Speech classification or search using natural language modelling
H04M 1/72421 - User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality for supporting emergency services with automatic activation of emergency service functions, e.g. upon sensing an alarm
H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
30.
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
32.
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 - References adjustable by an adaptive method, e.g. learning
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
33.
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 - Movements or behaviour, e.g. gesture recognition
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
34.
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.
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.
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 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
H04W 4/029 - Location-based management or tracking services
38.
Lead management platform for targeted output generation
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 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or 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 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided forArrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric for transmission of signals between vehicle parts or subsystems
G07C 5/00 - Registering or indicating the working of vehicles
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
H04W 12/00 - Security arrangementsAuthenticationProtecting privacy or anonymity
42.
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.
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 - Network arrangements or protocols for supporting network services or applications
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
47.
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 - Detecting movement of traffic to be counted or controlled
B60R 21/013 - Electrical circuits for triggering safety arrangements in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over
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 - Registering or indicating the working of vehicles
B60R 21/00 - Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
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.
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.
G06F 18/2115 - Selection of the most significant subset of features by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming
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 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06V 20/59 - Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
54.
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 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
57.
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.
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.
G06T 7/80 - Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
G07C 5/00 - Registering or indicating the working of vehicles
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
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 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
61.
SYSTEMS AND METHODS OF DETERMINING DRIVING ATTRIBUTES
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.
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.
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 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06Q 10/0875 - Itemisation or classification of parts, supplies or services, e.g. bill of materials
G06T 7/70 - Determining position or orientation of objects or cameras
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 - Registering or indicating driving, working, idle, or waiting time only
B60W 40/10 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to vehicle motion
B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
65.
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 - Devices for predicting weather conditions
G01S 19/14 - Receivers specially adapted for specific applications
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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.
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.
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 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
72.
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 - Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
B60R 21/0136 - Electrical circuits for triggering safety arrangements in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over responsive to actual contact with an obstacle
B60W 30/00 - Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
G08G 1/00 - Traffic control systems for road vehicles
73.
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)
Inventor
Aragon, Juan Carlos
Phillips, Derek
Madigan, Regina
Chintakindi, Sunil
Kochenderfer, Mykel
Abstract
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 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/20 - ScenesScene-specific elements in augmented reality scenes
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
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.
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.
B60W 40/08 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
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 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
83.
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.
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 - Selection of displayed objects or displayed text elements
G06K 19/06 - Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
H04M 1/72436 - User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality with interactive means for internal management of messages for text messaging, e.g. short messaging services [SMS] or e-mails
H04W 4/14 - Short messaging services, e.g. short message service [SMS] or unstructured supplementary service data [USSD]
85.
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 - Location-based management or tracking services
H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
86.
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 - Taking automatic action to avoid collision, e.g. braking and steering
G06Q 30/0207 - Discounts or incentives, e.g. coupons or rebates
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
G08G 1/01 - Detecting movement of traffic to be counted or controlled
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.
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
88.
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.
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 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or 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.
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 - Registering or indicating the working of vehicles
G07C 5/02 - Registering or indicating driving, working, idle, or waiting time only
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
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 - Registering or indicating the working of vehicles
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
H04W 4/02 - Services making use of location information
H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
H04W 8/18 - Processing of user or subscriber data, e.g. subscribed services, user preferences or user profilesTransfer of user or subscriber data
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 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
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.
Systems, apparatuses, and methods for providing roadside assistance services are described herein. The system can include network computing devices and computing devices associated with vehicles and service vehicles. Requests for roadside services can be automatically assigned to an available service provider. Service providers can be ranked based on a variety of criteria and requests can be assigned based on the ranking. The ranking of service providers can be adjusted based on the performance of the service provider. If a service provider takes too long to respond to a request, the request can be automatically declined and an alternative service provider can be selected.
Intelligent voice response systems and methods may include one or more machine readable instructions stored in a memory that cause a processor to receive an automated input including at least two of the following: a vehicle metric of the vehicle, a driving score of a user of the vehicle, a driving time during a trip of the vehicle, a geographical location of the vehicle, an adverse weather event within a predetermined distance of the vehicle, a historical driving route of the vehicle, a predicted driving route of the vehicle within a first predetermined period of time, or a sound within a predetermined distance of the vehicle. An action may be generated and implemented and reception of an affirmative response from the user may be determined. An alert may be generated to the user based on the affirmative response.
B60Q 9/00 - Arrangement or adaptation of signal devices not provided for in one of main groups
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
