Abstract:
Proposed is an automated system (1000), phone distraction detector and method for automatically detecting phone distraction events in a moving vehicle (202), caused by a driver of the moving vehicle (202) by using a smartphone (102). An Internet of Things (IoT) server (104) is communicatively coupled to the smartphone (102). The smartphone (102) comprises one or more sensors (118, 120) built into the smartphone (102), wherein sensory data of the one or more sensors (118, 120) are captured by the sensors (118, 120) of the smartphone (102) during a vehicle trip and are transmitted to the Internet of Things (IoT) server (104), and wherein the sensor data comprises one or more chunks of data streams associated with an IoT service from the smartphone (l02). The sensory data are processed and analyzed by the server (104) to identify one or more phone handling events associated with the smartphone (102) during the vehicle trip (202). One or more phone handling events associated with the smartphone (102) are aggregated into one or more macro events by the processing means of the server (104). The aggregated one or more macro events are processes by the server (104) based on a set of preconfigured rules to determine a phone distraction event, wherein the set of preconfigured rules are based on predefined thresholds to determine the phone distraction event.
Abstract:
Proposed is an automated system (1) providing an automatable long-term care risk mitigation structure associated with immediate care needs of risk-exposed individuals in case of becoming a long-term care-receiver. The system (1) provides an automated multi-step trigger and defection cycle (9/91, 92, 93, 94, 95, 96, 97) based on sensory measuring data for risk-exposed individuals (4) thereby mitigating possibly occurring long-term care needs by means of the automated system based on a defected sufficient state of disability (2), possibly predetermined up-front amount parameters (8211) and an assigned care equity ratio associated with an asset (3). The state of disability (2) of the risk-exposed individual (4) is measurable by a definable set of risk-exposed individual measuring parameters (10).
Abstract:
Proposed is an electronic risk measuring and scoring system (100), in particular for mobile telematics devices (10) and method thereof. In particular, an electronic risk measuring and scoring system (100) which measures an ADAS risk score measure measuring the impact of ADAS features (200) to the accident risk associated with a motor vehicles (10), and which rates and calibrates a risk-transfer user-specifically thereby capturing the impact of ADAS features (200) in measures of risk-transfer claims frequency and severity.
Abstract:
Proposed is a method and system for automated transportation mode recognition based on sensory data measured by a plurality of sensors (102) of a cellular mobile device (10) of a user (6/61 /62), the plurality of sensors (102) at least comprising an accelerometer (1025) and a gyroscope (1026), the plurality of sensors (102) being connected to a monitoring mobile node application (101) of the mobile device (10), wherein the mobile device (10) measures time series of sensory parameter values based on measuring parameters obtained from the sensors (102), the measuring parameters comprise time series of sensory parameter values of a 3-axis accelerometer as sensor (102) and time series of sensory parameter values of GPS-based speed measurements of a GPS receiver as sensor (102), and wherein the measured time series of sensory parameter values trigger the automated transportation mode recognition as input feature values to a gradient boosting machine-learning classifier, the transportation modes at least comprising the modes public transportation and/or motorcycle and/or cycling and/or train and/or tram and/or plane and/or car and/or skiing and/or boat, and the transportation mode recognition generating a transport mode label for a transport mode movement pattern of a trip.
Abstract:
Proposed is a method and system for identifying and/or classifying an occupant of a vehicle based on sensory data measured by a plurality of sensors of a cellular mobile device of the occupant, the plurality of sensors at least comprising an accelerometer and a gyroscope. The mobile device measures gravitational acceleration movement sensory data by means of the accelerometer based on measuring parameters obtained from the accelerometer, wherein vehicle entering or exiting movement patterns of the user are detected from the acceleration movement sensory data at least comprising pattern for base axis and degree of rotation associated with a vehicle entrance or exit of the user. The detected vehicle entering or exiting movement patterns of the user trigger as input features the recognition of a vehicle entering or exiting movement of the user by performing a decision-tree classification on the input features to rule out whether the user entered or exited from a left or right side of the vehicle.
Abstract:
Proposed is a digital platform (1) providing an automated, multi-channel, end-to-end risk-transfer product configuration process (2) for configuring, launching and processing of customized reinsurance risk-transfer structures, wherein an automated risk-transfer product placement is provided by the system (1) as a first online channel comprising a parameter-driven, rule-based underwriting process (21) for creating a portfolio of customized second-tier structures, wherein an automated claim handling (22) is provided by the system (1) as a second online channel, and wherein an automated accounting (23) is provided by the system (1) as a third online channel. For capturing of risk information parameters (2144), a product configurator (214) comprises a machine-based exposure data intelligence (215) enabled to automatically identify unique risks of objects (12) based on a precise location of the objects (12). The system (1) further comprises a graphical user interface of a portfolio analytics framework (6) providing a dynamic representation (61) of a portfolio structure (14). By means of the metric simulation engine (10), the dynamic representation of the portfolio structure (14) provides forward- and backward-looking insights to the user thereby enabling portfolio steering by identification of critical areas of the portfolio (14) and impacts of possible changes to the underwriting before going to launch it in the market.
Abstract:
Proposed is an event generator (10) generating risk events (2) for clash-quantifying, multi-risk assessment systems (1) with automated assessment of multi-risk exposures induced by the generated risk events (2), liability catastrophes (21) and casualty accumulations (22). A plurality of affected units (3) are subject to the risk exposure of the occurring risk events (2) caused by one or a plurality of causing liability risk exposed units (4). The event generator performs a scenario selection by means of a scenario selector (104) selecting relevant LLC/ULC/ELC scenarios (1001) from the scenarios (1001) of a data structure (101). Based on the selected scenarios (10001), concrete events (1002) are generated by means of the event generator (10).
Abstract:
Proposed are a measuring apparatus(1) and a measuring method for automated traffic and driving pattern recognition and location-dependent measurement and forecast of absolute and relative risks for car accidents based on exclusively non-insurance related measuring data and based on automated traffic pattern recognition and associated with the traffic and driving pattern providing a high degree of temporal and spatial resolution. The proposed apparatus(1) provides a grid- based (2121, 2122, 2123, 2124), technically new way of automation of automated traffic and driving pattern recognition and risk-prediction related to motor accidents using environment based factors (elevation, road network, traffic data, weather conditions including socio-economic factors)that are impacting motor traffic and are location dependent received from appropriate measuring devices (41,…,45). In this way, predictions of the accident risk for arbitrary areas can be provided. The measuring apparatus(1) is calibrated by comparing features of areas or road segments with the number and type of accidents that have measured or registered there, linking the features and accident data e.g. using the disclosed machine learning techniques.
Abstract:
Proposed is an automated, self-adaptive, machine-learningtelematics- based system (1) for score-driven operations associated with motor vehicles (41,…,45) or transportation means of passengers or goods and based on a dynamic, telematics- based data aggregation, and method thereof, for automated risk-transfer related to complex peer-to-peer lending schemes, especially related to vehicles and car sharing schemes and sharing economy transportation schemes related to risks associated with damages to third parties. The telematics-based system (1) comprises telematics devices (411,…,415) associated with the plurality of motor vehicles (41,…,45), wherein the telematics devices (411,…,415) comprise a wireless connection (42101-42108) to a central, expert-system-based circuit (11). The telematics devices (411,…,415) are connected via interfaces (421,…,425) to the sensors and/or measuring devices (401, …, 405) and/or an on-board diagnostic system (431,…,435) and/or an in-car interactive device (441,…,445), wherein the telematics devices (411,…,415) capture usage-based (31) and/or user-based (32) and/or operational (33) telematics data (3) of the motor vehicle (41,…,45) and/or user (321, 322, 323). In response to an emitted shadow request (109) individualized risk-transfer profiles (114) based upon the dynamically generated variable scoring parameters (1011,…,1013) are transmitted from a first risk-transfer systems (11) to the corresponding motor vehicle (41,…,45) and issued by means of a user unit (461,…,465) of the motor vehicle (41,…,45) for selection by the driver of the motor vehicles (41,…,45). In return of issuing an individualized risk-transfer profile (114) over said user unit (461,…,465), payment-transfer parameters are transmitted from the first risk-transfer system (11) to the provider of the telematics-based system (1).
Abstract:
Proposed is a secure key management, peer-to-peer transmission system (6), and method thereof, based on a controlled, double-tier cryptographic key structure (2), providing a closed cryptosystem for secure content distribution and further processing within a provided, secured network environment (11). Individual, user-specific data (331) are captured by means of capturing device (33) associated with a user network node (3). Based on the individual, user-specific data (331), data services (101) are requested from and provided to the user network node (3), by means of a data consumer network node (4).The captured individual, user-specific data (331) are transmitted from the user network node (3) to a central, P2P transmission system (1) and are processed by a non-storage-based processing unit (10), providing the requested data service (101) of the data consumer network node (4) requested by the user network node (3). A first cryptographic key (21) is generated by the non-storage-based processing unit (10), wherein service response data (211) of the requested service (101) are encrypted, by means of the first cryptographic key (21), to single encrypted service response data (212) and transmitted to the user network node (3), in response to the requested service (101). The received single encrypted service response data (212) are double encrypted, by means of a generated second cryptographic key (22), to double encrypted service response data (221) by the user network node (3) and transmitted back and stored in the central, P2P transmission system (1). The first cryptographic key (21) is transmitted and/or made accessible to the user network node (3), if predefined authorization-parameters (102) are triggered, by means of the central, P2P transmission system (1).