Methods, computer-readable media, systems and apparatuses for determining and implementing hazard unit based insurance policies are presented. A user may input a preliminary navigational route. The preliminary navigational route may be parsed into a plurality of road segments. Sensor data may be received from one or more databases. The sensor data may provide information associated with driving behaviors of the user, environmental conditions of the routes on which the vehicle has traveled, and the like. A road segment hazard score may be calculated for each road segment based in part on the sensor data. A total route hazard score may be calculated based on the road segment hazard score calculated for each road segment. The total route segment score may be transmitted to a real-time vehicular service exchange. One or more bids may be received from one or more computing devices of the real-time vehicular service exchange. A bid from the one or more received bids may be selected to insure the vehicle as it travels along the preliminary navigational route.
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 3/56 - Devices characterised by the use of electric or magnetic means for comparing two speeds
G01P 15/00 - Measuring accelerationMeasuring decelerationMeasuring shock, i.e. sudden change of acceleration
G01P 15/04 - Measuring accelerationMeasuring decelerationMeasuring shock, i.e. sudden change of acceleration by making use of inertia forces for indicating maximum value
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
3.
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
Systems and methods are provided for evaluating aggregate risk exposure of insurance policies associated with a geographical area. An insurance system may include an aggregate risk management system, which calculates an aggregate risk exposure of insurance policies associated with a geographical area, and compares the aggregate risk exposure with a maximum tolerable risk exposure for the geographical area. Based on the comparison, the aggregate risk management system determines whether more insurance policies may be issued associated with the geographical area. The aggregate risk management system may issue a block at an insurance system to prevent new insurance policies associated with a geographical area based on the aggregate risk exposure for that geographical area.
Systems and methods are disclosure for using sensors to deliver educational content to vehicle users during critical events. One method comprises: receiving, by a first computing device having at least one processor and from a user device of a vehicle user via a wireless data connection, a notification of a critical event for a vehicle of the vehicle user and a vehicle identification of the vehicle; receiving, from the user device via the first wireless data connection, user input soliciting educational content to remedy the critical event; determining, based on the received user input, a first set of search parameters; for each of the search parameters in the first set of search parameters, selecting educational content for a first list of educational content from a second list of educational content; and displaying, on the user device, the first list of educational content based on the first set of search parameters.
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
6.
ASSESSING PROPORTIONAL FAULT IN AN AUTOMOBILE ACCIDENT
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.
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
A method, system and computer-readable medium are provided for performing a property inspection using aerial images, the method including the steps of receiving an indication of a request from a user to receive a quote regarding a property, identifying the property, retrieving one or more aerial images associated with the property, extracting information regarding the property from the one or more aerial images and providing an insurance decision for the property to the user according to the extracted information from the one or more aerial images in response to the request from the user.
Systems and methods are disclosed for receiving and transmitting accelerometer data and/or usage data, and analyzing the data to detect movement or usage of the device within a vehicle. A device, such as a mobile device, may detect a device movement event or a device usage event associated with the device. Based on the detection of the device movement event or the device usage event, a time associated with the event may be stored. The device may determine whether another event associated with the device occurs within a threshold amount of time from the time associated with the event. Based on a determination of whether the other event occurs within the threshold amount of time, the device may determine an event session associated with the device. The event session may comprise an instantaneous event or a continuous event. Data indicative of the event session may be transmitted to a server.
H04M 1/72409 - User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality by interfacing with external accessories
H04M 1/72463 - User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions to restrict the functionality of the device
9.
SYSTEMS AND METHODS FOR GENERATING A USER INTERFACE
Implementations claimed and described herein provide systems and methods for generating an user interface in response to a request associated with a product or service. The systems and methods use one or more machine learning models to generate the user interface. The user interface is transmitted to a user device for display.
An interaction voice response apparatus and method includes obtaining, from a chat bot, interaction data from an interaction with a user and based on a prompt, generating, with a large language model communicatively coupled to the chat bot and directed by the prompt, content based on the interaction data from the interaction with the user corresponding to data fields in a format defined by the prompt, wherein the content comprises direct extractions directly extracted from the interaction data, inferences deduced from the interaction data, or combinations thereof, generating, with the large language model, deduction flag indications, wherein a positive deduction flag indication of the deduction flag indications is generated when the content comprises an inference of the inferences, and outputting a data set comprising at least one next intent recommendation as a deduction based on the content and the indications in the format.
Systems and apparatuses for using machine learning to generate a safety output are provided. In some examples, data may be received from a plurality of sources, may be analyzed and one or more machine learning datasets may be generated based on the analyzed data. In some arrangements, data may be received from one or more vehicles. The vehicles may be autonomous, semi-autonomous, or non-autonomous, and/or configured to operate in one or more of those modes. The data may be evaluated based on the one or more machine learning datasets to determine a safety output associated with the data. The safety output may then be used to classify the data and/or to generate one or more instructions for operation of an autonomous vehicle. The instruction(s) may be transmitted to the autonomous vehicle and may modify operation of the vehicle (e.g., to improve safety associated with the vehicle).
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
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
A method for determining the performance metric of a function may include interpolating the performance metric of the function relative to a known performance metric of a reference function. The performance metric of the function may be interpolated based on a first difference in a performance of the function measured by applying a first machine learning model and a performance of the function measured by applying a second machine learning model. The performance metric of the function may be further interpolated based on a second difference in a performance of the reference function measured by applying the first machine learning model and a performance of the reference function measured by applying the second machine learning model. The function may be deployed to a production system if the performance metric of the function exceeds a threshold value. Related systems and articles of manufacture, including computer program products, are also provided.
Systems and methods are disclosed for guidance, control, and testing of autonomous vehicle features and a driver's response thereto. The system may activate a plurality of autonomous driving features of an autonomous vehicle. In response to a determination to initiate a driving test, the system may generate an indication to a driver of the autonomous vehicle of the initiation of the driving test and may deactivate or adjust parameters of one or more of the plurality of autonomous driving features. The system may receive, from one or more sensors of the autonomous vehicle or one or more sensors of a mobile computing device within the autonomous vehicle, driving data associated with the autonomous vehicle. Based on the driving data associated with the autonomous vehicle, the system may determine the driver's response time and actions taken by the driver during the driving test. Moreover, in response to a determination to end the driving test, the system may reactivate the one or more of the plurality of autonomous driving features previously deactivated or may readjust previously adjusted parameters. In some aspects, based on the driver's response time and actions taken by the driver during the driving test, a drive score may be generated for the driver.
Methods, computer-readable media, software, and apparatuses may determine that an expected vehicle demand will exceed an expected supply in a vehicle sharing application. In order to meet the demand, one or more users may be contacted with a request to provide a vehicle for sharing on a particular date. A machine learning algorithm may be used in determining that the expected vehicle demand will exceed the expected vehicle supply.
Implementations claimed and described herein provide systems and methods for predicting demand associated with a product or service. The systems and methods use a machine learning model to determine a potential consumer is likely to purchase a product or a service. A notification is generated based on the determination that the potential consumer is likely to purchase the product or the service.
Implementations claimed and described herein provide systems and methods for responding to a query associated with a product or service. The systems and methods use a machine learning model to generate a recommendation and a user interface. The recommendation is transmitted to a user device for display via the user interface.
Implementations described herein provide systems, methods, and devices for vehicle occupant detection based on wireless transmission(s) from one or more wireless transmitter devices (e.g., beacons) located in the vehicle. The wireless transmitter devices can be standalone devices, such as portable low energy beacons, and/or the wireless transmitter devices can be integrated into other components of the vehicle, such as an on-board dash computer or a door handle. The systems disclosed herein receive the wireless transmission and determine transmission metrics associated with the transmission, such as a signal strength value or a transmission angle. A driver status determination and/or a passenger status determination is based on the transmission metrics. A data file converter generates a driver status data file representing the driver status determination and/or the passenger status determination. The driver status data file is sent to an application for which the driver status data file is configured.
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations using radio waves
G01S 1/04 - Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmittersReceivers co-operating therewith using radio waves Details
H04W 4/48 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for in-vehicle communication
H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
Implementations claimed and described herein provide systems and methods for responding to a query associated with a product or service. The systems and methods use a machine learning model to generate a recommendation and a user interface. The recommendation is transmitted to a user device for display via the user interface.
Implementations claimed and described herein provide systems and methods for predicting demand associated with a product or service. The systems and methods use a machine learning model to determine a potential consumer is likely to purchase a product or a service. A notification is generated based on the determination that the potential consumer is likely to purchase the product or the service.
As described herein, a base model based on imbalanced data may be selected for a machine learning process associated with a specific application. A first false positive error rate may be generated based on the selected base model. A plurality of imbalanced data sets may be generated based on the imbalanced data associated with the base model. A plurality of models may be generated based on the generated plurality of imbalanced data sets. A subset of the outputs of the plurality of models may be ensembled and a second false positive error rate may be generated based on the ensembled output of the subset of the plurality of models. The second false positive error rate may be determined to be less than the first false positive error rate.
Aspects of the disclosure relate to computing platforms that utilize machine learning to perform output generation based on intent identification. The computing platform may train intent orchestration models (e.g., intent identification, output generation, or communication channel) using historical data. The computing platform may data corresponding to an individual. Based on the data, the computing platform may select intent identification models, and may use them to identify an intent. Based on the intent of the individual, the computing platform may select engagement output generation models, and may use them to generate a customer engagement output. The computing platform may use a communication channel model to identify a communication channel. The computing platform may send commands directing display of the customer engagement output, which may cause a user device to display the customer engagement output using the communication channel.
An agent interaction apparatus, systems, and methods include obtaining streaming interaction data contemporaneously generated from an interaction with a user; determining, with a large language model, an intent expressed in the streaming interaction data; generating a prompt comprising one or more inquiries based on the intent expressed in the streaming interaction data; generating, from the streaming interaction data fed into the large language model and directed by the prompt, text corresponding to one or more responses from the user to the one or more inquiries; generating, with the large language model, at least one guidance request based on an absence of a response to, a need for clarification of, or supplemental information to request for at least one of the one or more inquiries based on the one or more responses from the user; and outputting the at least one guidance request within a guidance section of an interface.
Implementations described herein provide systems, methods, and devices for vehicle monitoring and control based on advanced driving assistance system (ADAS) feature usage. The systems, methods, and devices include a driving data collection system for collecting driving-related data from an original equipment manufacturer (OEM) server and/or a mobile device. The OEM server receives the ADAS-related data from the vehicle and sends the ADAS-related data to the vehicle monitoring and control platform. Systems also include a vehicle/driver operation assessment system which uses one or more first deep-learning models to generate vehicle operation behavior values from the ADAS-related data. Furthermore the systems include a dynamic risk control model which uses one or more second deep-learning models to generate target outputs based on the vehicle operation behavior values. The target outputs include modification or control of the vehicle operations, one or more alerts, and/or a pricing variable.
B60W 40/02 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to ambient conditions
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
In one aspect, a method includes receiving a telematics data associated with a vehicle collected from one or more data sources and determining, using a machine-learning model trained to identify high-risk driving behaviors using telematics data, one or more predictions based on the telematics data. A prediction of the one or more predictions is associated with a current time. The method may further include generating a time-based report of the one or more predictions. The time-based report identifies instances of the one or more predictions that reach a threshold value.
Methods and systems for entity assignment to assign an entity of a plurality of entities to a lead resource of at least two lead resources may include receiving a score for each entity of the plurality of entities. The method may further include determining a ranking of the plurality of entities based upon the score for each entity and receiving a distance between each entity of the plurality of entities and each lead resource of the at least two lead resources, wherein the plurality of entities are greater in number than the at least two lead resources. The method may also include applying an optimization algorithm based on the ranking and the distance between each entity and each lead resource and updating the optimization algorithm in real-time until each entity of the plurality of entities is paired to one of the at least two lead resources.
Aspects of the disclosure relate to a dynamic processing system for roadside service control and output generation. A computing platform may receive, from a client device, video content corresponding to a disabled vehicle, which may include geotagging information corresponding to a location of the disabled vehicle. Based on the video content and the geotagging information, the computing platform may determine a provider output indicating a potential service provider for assisting with the disabled vehicle. The computing platform may send, to the client device, an indication of the provider output. In response to receiving an indication that the potential service provider is acceptable, the computing platform may send a request to dispatch a driver of the potential service provider to the location of the disabled vehicle.
G07C 5/00 - Registering or indicating the working of vehicles
G06Q 10/20 - Administration of product repair or maintenance
G06Q 50/40 - Business processes related to the transportation industry
G08G 1/00 - Traffic control systems for road vehicles
G08G 1/137 - Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles within the vehicle the indicator being in the form of a map
27.
GEOGRAPHICAL RISK HEATMAP BASED ON VARIABLE RISK FACTORS
Implementations described herein provide systems and methods for generating a geographical heatmap based on variable factors. One implementation can include aggregating a plurality of data associated with a geographical location, generating a respective asset for a respective region within the geographical location, associating a respective subset of the plurality of data with the respective asset, determining a respective first type score for a respective first type data, determining a respective second type score for a respective second type data, generating a respective overall score based on the respective first type score and the respective second type score, constructing a map including each of the respective assets, and displaying, as a heatmap, the map with each respective asset.
A system for computer vision data acceptability analysis and methods of use to receive a plurality of computer vision data comprising data processed via a computer vision model with one or more rules, generate one or more metrics for each of the plurality of computer vision data based on the one or more rules, compare a compared metric of the one or more metrics for each of the plurality of computer vision data to an acceptability threshold, determine the computer vision data to be acceptable when the compared metric associated with the computer vision data is equal to or above the acceptability threshold, generate an overall acceptability score for the plurality of computer vision data, and automatically generate feedback for computer vision model processing based on the overall acceptability score to improve acceptability.
Implementations described herein provide systems and methods for generating a geographical heatmap based on variable factors. One implementation can include aggregating a plurality of data associated with a geographical location, generating a respective asset for a respective region within the geographical location, associating a respective subset of the plurality of data with the respective asset, determining a respective first type score for a respective first type data, determining a respective second type score for a respective second type data, generating a respective overall score based on the respective first type score and the respective second type score, constructing a map including each of the respective assets, and displaying, as a heatmap, the map with each respective asset.
A system includes a privacy vault storing user-associated contents. The vault also stores access permissions defined for third-parties with whom the user has a sharing relationship. An access permission defines, for at least one third party, procurement and utilization policies for vault contents accessed by the third-party. The system may access a user account to recover user-associated contents stored by the accessed account and stores the recovered contents in the privacy vault. The system receives a request from a third-party to access identified contents stored in the privacy vault and determines if the contents are procurable by the third party based on an access permission defined, in the privacy vault, for the third-party. The system provides procurable contents to the third party along with indication of any constraints on the contents defined by utilization policies of the access permission defined for the third party.
Methods and systems disclosed herein describe a universal access layer that allows a plurality of applications to obtain data and/or information from a plurality of heterogeneous data stores. The universal access layer may include one or more application data objects to validate requests, transform a format of the request, determine which data stores comprise the requested data and/or information, encrypt the request, combine responses into a single response, and retransform the response prior to sending it to the requesting application. By using the universal access layer, applications may improve the speed with which they access data and/or information from the plurality of heterogeneous data stores.
Implementations claimed and described herein provide systems and methods for generating a driving behavior assessment using telematics data. The systems and methods use different types of telematics data generated via different data connections. Vehicle behavior telematics data is generated using a first type of connection with a vehicle (e.g., using an onboard diagnostics (OBD) device) and personal mobility telematics data is generated using a second type of connection via a mobile device associated with a vehicle operator. One or more driving attributes associated with the vehicle operator are determined by the system based on at least one of the vehicle behavior telematics data or the personal mobility telematics data. Scoring factors are calculated based on the one or more driving attributes. Furthermore, a policy level rate structure for an insurance policy can be generated based on the one or more scoring factors.
Apparatuses, systems, and methods are provided for the utilization of vehicle control systems to cause a vehicle to take preventative action responsive to the detection of a near short term adverse driving scenario. A vehicle control system may receive information corresponding to vehicle operation data and ancillary data. Based on the received vehicle operation data and the received ancillary data, a multi-dimension risk score module may calculate risk scores associated with the received vehicle operation data and the received ancillary data. Subsequently, the vehicle control systems may cause the vehicle to perform at least one of a close call detection action and a close call detection alert to lessen the risk associated with the received vehicle operation data and the received ancillary data.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
B60Q 1/08 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to illuminate the way ahead or to illuminate other areas of way or environments the devices being headlights adjustable, e.g. remotely-controlled from inside vehicle automatically
B60Q 1/46 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for giving flashing caution signals during drive, other than signalling change of direction, e.g. flashing the headlights
B60Q 1/50 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating other intentions or conditions, e.g. request for waiting or overtaking
B60Q 9/00 - Arrangement or adaptation of signal devices not provided for in one of main groups
B60R 25/00 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles
B60R 25/10 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles actuating a signalling device
B60T 7/12 - Brake-action initiating means for automatic initiationBrake-action initiating means for initiation not subject to will of driver or passenger
B60T 7/18 - Brake-action initiating means for automatic initiationBrake-action initiating means for initiation not subject to will of driver or passenger operated by remote control, i.e. initiating means not mounted on vehicle operated by wayside apparatus
B60T 7/22 - Brake-action initiating means for automatic initiationBrake-action initiating means for initiation not subject to will of driver or passenger initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle
B60W 50/08 - Interaction between the driver and the control system
G05B 15/02 - Systems controlled by a computer electric
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
Systems and methods are disclosed for generating vehicle insurance rates based on driver-independent variables and/or driver-dependent variables. Vehicle insurance rates may additionally or alternatively be based on changes in the level of autonomy of vehicles. In some embodiments, a density of vehicles near a target vehicle may be tracked. Vehicle insurance rates may be determined based on the vehicle density. Furthermore, systems and methods are disclosed for analyzing a driver's use of autonomous vehicle features and/or the driver's maintenance of the autonomous vehicle. The driver may also be taught certain driving skills by enabling vehicle teaching features. The driver's response to these teaching features may be monitored, and a reward or recommendation may be generated and provided to the driver based on the driver's response.
Aspects of the disclosure relate to using computer vision methods for asset evaluation. A computing platform may receive historical images of a plurality of properties and corresponding historical inspection results. Using the historical images and historical inspection results, the computing platform may train a roof waiver model (which may be a computer vision model) to output inspection prediction information directly from an image. The computing platform may receive a new image corresponding to a particular residential property. Using the roof waiver model, the computing platform may analyze the new image to output of a likelihood of passing inspection. The computing platform may send, to a user device and based on the likelihood of passing inspection, inspection information indicating whether or not a physical inspection should be performed and directing the user device to display the inspection information, which may cause the user device to display the inspection information.
A multi-stop route selection system may include a telematics device associated with a vehicle having one or more sensors arranged therein, a mobile device, and a server computer. The server computer may receive driving data of a driver of the vehicle and a vehicle location from the telematics device, determine one or more driving behaviors of the driver based on the driving data, receive data regarding a calendar of the driver from the mobile device, identify a plurality of appointments in the calendar, determine a route comprising multiple destinations for the driver based on the vehicle location, the one or more driving behaviors, and the plurality of appointments, transmit the route to the mobile device, receive a request to add a new destination to the route from the mobile device, generate a modified route comprising the new destination, and transmit the modified route for the driver to the mobile device.
G01C 21/36 - Input/output arrangements for on-board computers
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 4/029 - Location-based management or tracking services
H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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]
Systems and methods in accordance with embodiments of the invention can proactively determine if a vehicle has stopped during a trip and calculate a likelihood that the vehicle is in need of roadside assistance. Information can be collected from a variety of devices, such as mobile phones, including the vehicle's location, the type of road, passing vehicles, and/or ambient noise. The likelihood of needing roadside assistance can be determined based on a configurable probability that the vehicle is experiencing a roadside event. The arrangements described herein provide for receiving and processing data in real-time to efficiently and accurately detect stopped vehicles, determine whether the vehicle is stopped for an urgent or non-urgent situation reason, and provide assistance accordingly.
G07C 5/00 - Registering or indicating the working of vehicles
G01C 21/00 - NavigationNavigational instruments not provided for in groups
G01C 21/28 - NavigationNavigational instruments not provided for in groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
G01S 19/00 - Satellite radio beacon positioning systemsDetermining position, velocity or attitude using signals transmitted by such systems
G01S 19/25 - Acquisition or tracking of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS
A method, medium, and apparatus for allowing evaluation of property, such as damaged property, remotely and efficiently. A mobile computing device may be used to conduct bilateral communication between a client and an agent for evaluating property. Systems and methods may be used to efficiently automate intake of communications and intelligently load-balance resources for handling calls.
Implementations claimed and described herein provide systems and methods for generating instructions for a mobile electric vehicle (EV) charging station to meet an EV at a particular time and place. In one implementation, EV trip data including a remaining range of the EV and an intended route of the EV is collected to determine a range of locations that the EV can stop at along its route without running out of power. Instructions to one of the locations are generated for a mobile EV charging station that is a best fit for arriving at the particular location and for the EV to reach the same location.
Methods and systems disclosed herein describe deploying a plurality of microservices to calculate an insurance rate. The plurality of microservices may operate in parallel to calculate a plurality of partial rates that are combined (e.g., added up) to determine the insurance rate. During the calculating steps, one or more rating factors may be cached and/or stored. The plurality of microservices may reduce the time and/or resources required by a processor to calculate an insurance rate, while the stored rating factors may be displayed to the user to provide greater transparency into how the insurance rate was calculated.
Aspects of the disclosure relate to computing platforms that utilize improved mitigation analysis and policy management techniques to improve onboarding security. A computing platform may determine that a predetermined period of time has elapsed since finalizing an onboarding process. The computing platform may receive spot check verification inputs indicative of a user identity and may direct a mitigation analysis and output generation platform to analyze the spot check verification inputs. The computing platform may receive an indication of a correlation between the spot check verification inputs and expected spot check verification inputs. In response to determining that the correlation exceeds a predetermined threshold, the computing platform may determine that an additional verification test should be conducted, and may direct the mobile device to display an interface that prompts for additional onboarding verification inputs.
Methods, systems, and apparatuses are described for engaging autonomous driving algorithms based on driver frustration levels and vehicle conditions. A frustration level of a driver of a vehicle may be determined using one or more sensors. Based on a determination that the frustration level satisfies a threshold, one or more automated driving algorithms which may be engaged by the vehicle to improve the safety of the driver may be determined. The threshold may be based on a road segment traveled by the vehicle, the user, or similar considerations. Based on a determination that the frustration level satisfies a threshold, engagement of the one or more automated driving algorithms may be caused.
Aspects of the disclosure relate to enhanced processing systems for providing dynamic driving metric outputs using improved machine learning methods. A computing platform may receive sensor data from vehicle sensors. The computing platform may generate a pattern deviation output corresponding to an output of a sensor data analysis model, an actual outcome associated with a lowest TTC value, and driving actions that occurred over a prediction horizon corresponding to the pattern deviation output. The computing platform may cluster the pattern deviation outputs to maximize a ratio of inter-cluster variance to intra-cluster variance. The computing platform may train a long short term memory (LSTM) for each cluster, and may verify consistency of the pattern deviation outputs in the respective clusters. After verifying the consistency of the pattern deviation outputs in each cluster, the computing platform may modify the sensor data analysis model to reflect pattern deviation outputs associated with verified consistency.
Implementations include classifying vehicle trips as similar to previous trips based on location information of a vehicle received from a location device. Unique tile identifiers of the trip, each corresponding to a geographic area and the location information, may be determined and used to generate a fingerprint of the trip. The derived trip fingerprint of the trip information may be compared to stored fingerprints of one or more previously received trips to determine if the new trip is similar to one or more of the previous trips. In one instance, information or data of a new trip may be adjusted based on previous trip data. For example, aspects of the new trip or the previous trip may be updated with information or data of a previous trip fi the new trip and the previous trip are similar.
System, methods, and apparatuses provide customizable product features by receiving, from a computing device associated with a user, an indication of user interest in a product, identifying, based on a first profile associated with the user, a first attribute, determining one or more second profiles that comprise the first attribute, determining one or more features, of the product, that are associated with the one or more second profiles, determining, for each of the one or more features, a utility value, simulating, based on strategic alternatives data and the utility value for each of the one or more features, demand for the one or more features, determining, based on the simulated demand, that a subset of the one or more features satisfies a need state associated with the user, and sending, to the computing device, a product package including the subset of the one or more features.
Systems, methods, computer-readable media, and apparatuses for evaluating device usage and generating one or more outputs based on the device usage are provided. For instance, data from one or more sensors within a user personal mobile device may be received and processed to determine movement associated with the device. In addition, an amount of usage (e.g., hours, minutes, etc.) associated with the device may be received. In some examples information regarding the device or user of the device may be received. In some examples, application usage and/or types of applications used may also be received. This data may be processed and a likelihood of damage to the device may be determined. Based on this likelihood, one or more outputs may be determined.
Aspects of the disclosure relate to using ultrasonic or other types of signals to determine a distance between a transmitter and one or more mobile devices. The distance may be used to facilitate travel on foot or in a vehicle. One aspect disclosed provides a computing platform that may receive ultrasonic sensing data associated with mobile devices in a vehicle from a signal transmitter. Unique identifiers of the mobile devices may be determined. Based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each mobile device in the vehicle may be determined. The computing platform may use a machine learning classifier to determine that a particular occupant is a driver in the vehicle based on the relative distance.
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.
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.
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 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.
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
G07C 5/00 - Registering or indicating the working of vehicles
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
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.
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.
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.
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
60.
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 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.
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
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]
75.
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.
Arranging subscriptions for trade-in and upgrade programs for smart devices; online retail store services featuring smart devices; online auction services featuring bidding and purchasing of smart devices
77.
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
78.
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
79.
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
83.
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
87.
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.
Various aspects of the subject technology relate to systems, methods, and machine-readable media for investigating an insurance claim. A system may be configured to perform operations including establishing a video conferencing session with a mobile device associated with an insurance product and providing a user interface configured to provide a claims adjuster with remote control of a set of functions on the mobile device associated with the insurance product during the video conferencing session. The operations further include receiving, via the user interface, instructions for requesting the mobile device to perform at least one function in the set of functions on the mobile device and transmitting, to the mobile device, a request to perform the at least one function on the mobile device.
H04N 21/414 - Specialised client platforms, e.g. receiver in car or embedded in a mobile appliance
H04N 21/4788 - Supplemental services, e.g. displaying phone caller identification or shopping application communicating with other users, e.g. chatting
91.
INTELLIGENT SYSTEMS AND METHODS FOR CLICK PRICE GENERATION
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]
93.
Glare detection systems and methods for automated vehicular control
Aspects of the present disclosure describe systems, methods, and devices for automated vehicular control based on glare detected by an optical system of a vehicle. In some aspects, automated control includes controlling the operation of the vehicle itself, a vehicle subsystem, or a vehicle component based on a level of glare detected. According to some examples, controlling the operation of a vehicle includes instructing an automatically or manually operated vehicle to traverse a selected route based on levels of glare detected or expected along potentials routes to a destination. According to other examples, controlling operation of a vehicle subsystem or a vehicle component includes triggering automated responses by the subsystem or the component based on a level of glare detected or expected. In some additional aspects, glare data is shared between individual vehicles and with a remote data processing system for further analysis and action.
G08G 1/0967 - Systems involving transmission of highway information, e.g. weather, speed limits
B60W 40/02 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to ambient conditions
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G01W 1/10 - Devices for predicting weather conditions
G08G 1/01 - Detecting movement of traffic to be counted or controlled
G08G 1/09 - Arrangements for giving variable traffic instructions
G08G 1/0968 - Systems involving transmission of navigation instructions to the vehicle
94.
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
