“Fully Integrated And Embedded Measuring System Directed To A Score-Indexing Parameter Essentially Based On Directly Measured Connected Motor Vehicle Sensory Data And Method Thereof” in Patent Application Approval Process (USPTO 20240013310): Swiss Reinsurance Company Ltd.
2024 JAN 25 (NewsRx) -- By a
This patent application is assigned to
The following quote was obtained by the news editors from the background information supplied by the inventors: “Motor vehicles, such as automobiles and other types of vehicles used for transportation are usually regarded as technically essential to many activities for a large proportion of the population. Since such vehicles can be subject to damage or destruction by the occurrence of physically impacting events, as an accident event. and can cause personal injury and/or property damage during those events that are largely unpredictable, they have been objects of risk transfer technology and insurance systems against the impact of the occurrence of such accident events impacting a physical damage measurable and indexable by providing a monetary loss measure representing the level of physical damage. Most countries even mandatory require a risk-cover for possibly occurring accident events as pre-condition of licensing for operation of a vehicle while many vehicles are further covered against the replacement cost for resolving the impact of the damage to the vehicle, itself. However, the rise of telematics-supported usage-based insurance (UBI) has introduced a new technical based era in the world of vehicle risk-transfer and measurements.
“The development of many technologies such as arrays of air bags and computerized engine regulation, light weight and/or impact absorbing materials and parts and exhaust gas treatment which support enhanced safety and reduced environmental impact of the use of such vehicles have become more and more common and the inclusion of such technologies in currently produced vehicles has even been mandated by regulation while their inclusion has often raised the cost of vehicles to a significant degree and thus the loss associated with a possible physical damage. Any such increase in cost, of course, increases the potential loss to be covered by the risk-transfer system and risk-transfer premiums based principally on vehicle cost have increased accordingly to the point of compromising the ability of some vehicle users to procure and maintain adequate risk-transfer/insurance. Therefore, risk-transfer providers have sought to potentially reduce premiums based on the driving records of users and prior claims. However, driving records can contain only historical information and are usually insufficiently complete to accurately reflect the driving habits of particular vehicle users and thus may not accurately measure and predict actual driving habits or the increase or reduction of the transferred risk to the risk-transfer system.
“However, in the recent years, digital transformation has led to an increased interest in using and utilizing smart and/or real-time measuring technology (as e.g. wearables or telematics) in various technical risk-transfer approaches, something that has come to challenge the traditional purely statistical insurance structures.
“In general, there are three broad categories of change related to the opportunities of new technology. The first two are related to how new technologies (chatbots, Robo-advisors etc.) and automated data processing and analytics change how individuals and insurers interact with each other and how risk-transfer can be applied for improving the efficiency of the risk-transfer processes, for example by automation. The last category of change is related to the fact that new technologies can create opportunities for insurers to modify their existing risk-transfer structures toward becoming more agile and tailor-made and being relied on technical means and technical methods. The opportunities with new technology, especially the last category, have gained much attention from the insurance industry as this provides the insurer with an increased ability to meet the new customer demand and the new competitive environment.
“Furthermore, through these new technologies and the new working methods that follow, risk-transfer technology has an increased opportunity to meet the needs of the users in a whole new way. Connected devices, such as smart watches, and advanced data processing and analysis, allow to offer individuals “smart insurances” and “smart risk-transfers”; technology-based insurance processes tailored to the individuals’ needs and lifestyles. Smart insurances, based on the individual’s needs and lifestyle will become increasingly important for insurers to create a strong relationship and loyalty to the policyholders and thus stay competitive on a market that is becoming increasingly affected by the digitalization.
“Especially, in the world of automobile insurance, the development of telematics-supported usage-based insurance (UBI) has ushered a new era of risk-transfer. Vehicle telematics, integrated navigation, and computer and mobile communication technology used to directly monitor driving behavior allow insurers to actually measure and use true causal risk factors to accurately assess risks and develop precise UBI rating and measurement. Furthermore, with premiums accurately reflecting true risks, policyholders are incentivized to adopt risk-minimizing behaviors with benefits accruing not only to consumers and risk-transfer providers. These benefits are propelling the insurance technology to quickly expand the availability of telematics-based UBI structures.
“In particular, in the effort to obtain more current measuring data from which driving habits can be assessed in regard to accident-related risk exposure under a risk-transfer policy, various systems have been proposed to measure and aggregate data concerning operation of respective vehicles on a substantially real-time basis. Such data can then be processed to provide a more accurate measurement and/or assessment of driving habits and the relative risks, i.e. the individual provability of the occurrence of a physical accident event with a physical impact, that may be projected from such driving habits and/or environmental context. Many, if not most, of the technical arrangements that have been proposed to perform such a function provide for collection of information only upon the occurrence of events such as excessive longitudinal or lateral acceleration that are perceived to be correlated with a risk of an actual impact and are, hence, very coarse-grained in the information provided. Further, generation of events that cause reportage may not accurately reflect the true, actually measured risk incident to particular qualities of individual driving habits and, moreover, may not allow such information to be optimally current. Unfortunately, making collected data more fine-grained by altering thresholds of vehicle operation condition events which will cause an event to be reported goes along with a strongly increased vehicle operating conditions to be reported and transmitted as well as with an increased storage and processing of increased amounts of collected vehicle operating condition data. Further, such increased volume of collected data due to alteration of reporting thresholds will be incrementally less and less correlated with the actual risk.”
There is additional background information. Please visit full patent to read further.”
In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventor’s summary information for this patent application: “It is one object of the present invention to provide a fully integrated and embedded measuring system and method directed, inter alia, to measure a holistic score-indexing measuring parameter in real-time, which is essentially based on directly (real-time or near-real-time) measured connected motor vehicle sensory data. The invention shall provide a unified data-capturing and measuring solution based primarily on the data produced directly by the vehicle.
“According to the present invention, these objects are achieved, particularly, with the features of the independent claims. In addition, further advantageous embodiments can be derived from the dependent claims and the related descriptions.
“According to the present invention, the above-mentioned objects for an automated, fully embedded, machine-learning-based measuring system for measuring a risk-indexing measurand essentially based on directly measured connected motor vehicle sensory data of a plurality of motor vehicles with associated telematics devices are achieved in that the telematics devices comprises one or more wireless connections to a data transmission network, and at least one interface for connection with at least one vehicle’s data transmission bus and/or a plurality of interfaces for connection with sensors and/or measuring devices, wherein the telematics devices capture telematics data comprising vehicle features and usage parameter values and driver behavior parameter values and contextual and trip-related parameter values of the motor vehicle and/or driver, in that the system comprises a data pre-processing module identifying representative signals by filtering for relevant signals and a data exploration module for associating and interpreting the filtered signals in their context, and a dimensionality reduction module reducing the signals to signals having a significance in respect to the risk-indexing measurand indicative of a risk of the occurrence of an accident event having an impact to the vehicle and/or driver, and in that for the measuring of the risk-indexing measurand, the data pre-processing module and/or the data exploration module and/or the dimensionality reduction module are based on a set of machine-learning structures, transmitting their output values as input to a risk-score generator generating the risk-indexing measurand. The measuring of the risk-indexing measurand can e.g. be at least based on measuring the contribution given by (i) a vehicle component capturing which vehicle systems are present and activated or deactivated, (ii) a driver component at least capturing harsh maneuvers and/or excess of speed and/or risky behaviors and/or distraction, and (iii) a contextual component, the telematics data being enriched with additional data layers to capture location-based risks. The risk-indexing measurand capturing the risk of the occurrence of an accident event can e.g. provide a measure of riskiness of a driver driving a certain vehicle in a certain context in a certain way. The invention has, inter alia, e.g. the advantage it creates a unified electronic system based primarily on the data produced directly by the vehicle. These very punctual and rich data stem from a change in status of the vehicle at any point in time, due to i) the actions/reaction of the driver, ii) the context in which the driver is driving or, better, the way he perceives and feels the surroundings, iii) the way the car adapts to internal and external conditions, including the driver. The richness and reliability of these signals is leveraged to provide a comprehensive measuring and scoring system that covers different and complementary angles: (a) Vehicle component: which systems are present and activated/deactivated; (b) Driver component: harsh maneuvers, excess of speed, risky behaviors, distraction, etc.; © Contextual component: vehicle data are enriched with additional layers to evaluate location-based risks. These components are put together ab-initio by the system, namely at any point in time the system measures and/or creates a score that is measure for the riskiness of a driver, driving a certain vehicle in a certain context in a certain way. It should be noted that the term “riskiness”, as used herein, is understood as a physical measurand measuring the probability and/or likelihood of being involved in an occurring real-world accident event with a measurable impact strength (resulting in a measurable physical damage) to the vehicle. The inventive system relies on a set of advanced ML to identify representative signals (i.e., filter out what is not needed), disentangles components of value (i.e., a signal can be representative of a maneuver or set of maneuvers in a certain context) and combines only those components that can be insurance meaningful. That is, the system relies completely on technical electronic means and measuring devices/sensors allowing to put is measurands on physically measured quantities. The inventive system has further e.g. the advantage that it is enabled to combine signals which are representative of the vehicles (or another monitored object) performance/characteristics, of the behavior of the driver and context. The measuring data can come directly from measuring devices and/or sensors of the vehicle, from the mobile phone, from a cloud connected device (e.g. a connected vehicle) etc. Further, the inventive score-based measuring system can e.g. be realized as an edge-based solution with different electronic scoring modules e.g. embedded in the infotainment system of the vehicle (or another object), or on the mobile phone.
“In an embodiment variant, the system can e.g. comprise a telematics aggregation engine capturing and aggregating the telematics data by a telematics-driven core aggregator with telematics data-driven triggers generating telematics data sets, wherein the capturing of the telematics data is triggered by detecting a change of a status of the vehicle at a point in time, the status being given by the values of the vehicle features and usage parameter and driver behavior parameter and contextual and trip-related parameter at said point in time, and wherein the change of the status is induced due to actions and/or reaction of the driver and/or due to the context in which the driver is driving and the way the driver perceives and feels the surroundings, respectively, and/or the way the vehicle adapts to internal and external conditions including the behavior of the driver. The aggregated telematics data sets can e.g. comprise a plurality of processed risk-related or risk-transfer-related (insurance) attributes, wherein the attribute values capturing characteristics of the vehicle driving by the driver having a significance in respect to the risk-indexing measurand indicative of a risk of the occurrence of an accident event having an impact to the vehicle and/or driver and/or characteristics having a significance in respect to a risk-transfer from the driver to a risk-transfer system.
“In a further embodiment variant, further the risk-score generator can e.g. be based on one or more machine-learning structures for generating the risk-indexing measurand.
“In another embodiment variant, the system comprises further a tariffmeter generating dynamically a variable tariff value for a risk-transfer from a certain driver to an automated risk-transfer system in respect to an aggregated risk exposure of transferred risks to said automated risk-transfer system from the vehicles. A driver’s tariff value can e.g. be generated starting from the base tariff value by dynamically varying the base tariff value based on the measured vehicle features and usage parameter values and driver behavior parameter values and contextual and trip-related parameter values in respect to their measured frequency and severity at a certain time. The tariffmeter can e.g. comprise a tariff indicator dynamically indicating the present driver’s tariff value at least indicating a not adjusted base tariff value and/or a slightly adjusted base tariff value and/or a reduced base tariff value and/or an increased base tariff value.
“As an embodiment variant, the telematics devices can e.g. be connected to an on-board diagnostic system and/or an in-car interactive device and/or a monitoring cellular mobile node application.
“In an embodiment variant, the machine-learning based system can e.g. comprise one or more risk-transfer systems to provide risk-transfers based on risk transfer parameters from at least some of the motor vehicles to the risk-transfer systems, wherein the risk-transfer systems comprise a plurality of payment transfer modules configured to receive and store monetary payment parameters associated with risk-transfer of risk exposures of said motor vehicles for pooling of their risks.
“Finally, in an embodiment variant, the aggregated essentially directly measured connected motor vehicle sensory data of a plurality of motor vehicles can e.g. be enriched by sensory data of a mobile device of the driver, the mobile device at least comprising a smart phone or a cellular mobile phone associatable with the specific driver.”
There is additional summary information. Please visit full patent to read further.”
The claims supplied by the inventors are:
“1. An automated, fully embedded, machine-learning-based measuring system for measuring a risk-indexing measurand based on directly measured connected motor vehicle sensory data of a plurality of motor vehicles with associated telematics devices, the telematics devices comprising one or more wireless connections to a data transmission network, and at least one interface for connection with at least one vehicle’s data transmission bus and/or a plurality of interfaces for connection with sensors and/or measuring devices, wherein the telematics devices capture telematics and/or sensory data comprising vehicle features and usage parameter values and driver behavior parameter values and contextual and trip-related parameter values of the motor vehicle and/or driver, the system comprising: processing circuitry configured to implement a data pre-processing module identifying representative signals by filtering for relevant signals and providing data cleaning by removing noisy data and outliers from a measured telematics and/or sensory data of the connected motor vehicle, wherein the data pre-processing module monitors and automatically detects different patterns of missing data, which can be detected by the data pre-processing module using threshold triggers and pattern recognition for detecting distribution characteristics of the missing data of a processed data set, and triggers a suitable imputation processing for the data set, a data exploration module for associating and interpreting the filtered signals in their context, and a dimensionality reduction module reducing the signals to signals having a significance in respect to the risk-indexing measurand measuring a risk as a probability value for the occurrence of an accident event having a physical impact with a measurable damage to the vehicle and/or driver, wherein the number of variables to be used as input to an accident risk modelling structure is reduced using feature selection and feature extraction means, the feature selection means selecting prominent variables using correlation-based feature selection and principal component analysis-based feature extraction, and the feature extraction means transforming high dimensional data into fewer dimensions to be used in the modelling process, and wherein for the measuring of the risk-indexing measurand, the data pre-processing module and/or the data exploration module and/or the dimensionality reduction module are based on a set of machine-learning structures, transmitting their output values as input to a risk-score generator, the risk-score generator generating the risk-indexing measurand, and comprising a predictive accident risk modelling structure at least comprising, as predictive accident risk modelling structures, multiple linear regression and/or REPTree and/or random tree and/or multilayer perceptron.
“2. The automated, fully embedded, machine-learning-based measuring system according to claim 1, wherein the measuring of the risk-indexing measurand is at least based on measuring the contribution given by (i) a vehicle component capturing which vehicle systems are present and activated or deactivated, (ii) a driver component at least capturing harsh maneuvers and/or excess of speed and/or risky behaviors and/or distraction, and (iii) a contextual component, the telematics data being enriched with additional data layers to capture location-based risks.
“3. The automated, fully embedded, machine-learning-based measuring system according to claim 1, wherein the risk-indexing measurand capturing the risk of the occurrence of an accident event provides a measure of riskiness of a driver driving a certain vehicle in a certain context in a certain way.
“4. The automated, fully embedded, machine-learning-based measuring system according to claim 1, wherein the system comprises a telematics aggregation engine capturing and aggregating the telematics data by a telematics-driven core aggregator with telematics data-driven triggers generating telematics data sets, wherein the capturing of the telematics data is triggered by detecting a change of a status of the vehicle at a point in time, the status being given by the values of the vehicle features and usage parameter and driver behavior parameter and contextual and trip-related parameter at said point in time, and wherein the change of the status is induced due to actions and/or reaction of the driver and/or due to the context in which the driver is driving and the way the driver perceives and feels the surroundings, respectively, and/or the way the vehicle adapts to internal and external conditions including the behavior of the driver.
“5. The automated, fully embedded, machine-learning-based measuring system according to claim 4, wherein the aggregated telematics data sets comprise a plurality of processed risk-related or risk-transfer-related (insurance) attributes, and wherein the attribute values capturing characteristics of the vehicle driving by the driver having a significance in respect to the risk-indexing measurand indicative of a risk of the occurrence of an accident event having an impact to the vehicle and/or driver and/or characteristics having a significance in respect to a risk-transfer from the driver to a risk-transfer system.
“6. The automated, fully embedded, machine-learning-based measuring system according to claim 1, wherein further the risk-score generator is based on one or more machine-learning structures for generating the risk-indexing measurand.
“7. The automated, fully embedded, machine-learning-based measuring system according to claim 1, wherein the system comprises further a tariffmeter generating dynamically a variable tariff value for a risk-transfer from a certain driver to an automated risk-transfer system in respect to an aggregated risk exposure of transferred risks to said automated risk-transfer system from the vehicles.
“8. The automated, fully embedded, machine-learning-based measuring system according to claim 7, wherein a driver’s tariff value is generated starting from the base tariff value by dynamically varying the base tariff value based on the measured vehicle features and usage parameter values and driver behavior parameter values and contextual and trip-related parameter values in respect to their measured frequency and severity at a certain time.
“9. The automated, fully embedded, machine-learning-based measuring system according to claim 8, wherein the tariffmeter comprises a tariff indicator dynamically indicating the present driver’s tariff value at least indicating a not adjusted base tariff value and/or a slightly adjusted base tariff value and/or a reduced base tariff value and/or an increased base tariff value.
“10. The automated, fully embedded, machine-learning-based measuring system according to claim 1, wherein the telematics devices are connected to an on-board diagnostic system and/or an in-car interactive device and/or a monitoring cellular mobile node application.
“11. The automated, fully embedded, machine-learning-based measuring system according to claim 1, wherein machine-learning based system comprises one or more risk-transfer systems to provide risk-transfers based on risk transfer parameters from at least some of the motor vehicles to the risk-transfer systems, and wherein the risk-transfer systems comprise a plurality of payment transfer modules configured to receive and store monetary payment parameters associated with risk-transfer of risk exposures of said motor vehicles for pooling of their risks.
“12. The automated, fully embedded, machine-learning-based measuring system according to claim 1, wherein the aggregated essentially directly measured connected motor vehicle sensory data of a plurality of motor vehicles are enriched by sensory data of a mobile device of the driver, the mobile device at least comprising a smart phone or a cellular mobile phone associatable with the specific driver.”
There are additional claims. Please visit full patent to read further.
URL and more information on this patent application, see: DI LILLO, Luigi. Fully Integrated And Embedded Measuring System Directed To A Score-Indexing Parameter Essentially Based On Directly Measured Connected Motor Vehicle Sensory Data And Method Thereof.
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