Patent Application Titled “Risk Based Automotive Insurance Rating System” Published Online (USPTO 20190347739)

2019 DEC 03 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventor Fuchs, Gil Emanuel (Nes Tziona, IL), filed on July 29, 2019, was made available online on November 14, 2019.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “There is a need in the automotive insurance industry to accurately predict the risk of claims being made and the costliness of claims being made and adjusting the insurance rate charged to an individual or for a vehicle accordingly. The more accurate the prediction, the lower the premiums can become, making the insurer more competitive and presumably profitable, and/or the insurer may choose to not insure individuals or vehicles of the perceived greatest risk or smallest profit potential.

“It is known in the art to base premiums on such thing as the geographic area where a driver lives, or potentially the area s/he drives through on a regular basis. It is also known to further evaluate rates based on the historical location and frequency of accidents, crime rates, traffic flow and/or claims made in the vicinity of geographic area used as a rating territory. It is further known to adjust the rates based on the drivers past driving history with respect to insurance claims and driving record.

“One of the many problems with existing insurance risk rating systems is that they are too granular or non-specific. For example, typically a geographic area for rating would be based on the address of the owner of the vehicle. This would mean that all the residents of a given area or neighborhood would be lumped into the same rate category. These rates could be adjusted for factors such as the type of car being insured on how expensive claims are for that particular type of car in the area of interest, however this type of rating system generally does not take into account the areas typically driven through on a regular basis by the driver.

“Another issue with current insurance risk rating systems is that assumptions made in the systems may not be valid. For example, most would agree that if a person obeys the traffic laws, then that person’s driving risk would be less. This may not be the case and the embodiments of the present invention make no such assumptions.

“There is a need in the industry to have a vehicle insurance risk system based on one or several parameters that are spatially referenced with respect to the transportation network the vehicles travel on and further based on the driving habits of individual drivers that are insured or desire insurance. Knowing when and where a driver drives and knowing the historical risk associated with driving a given route at a particular time, a formula can be derived to predict risk for individual driver which in turn can be used to set rates. Because the parameters related to driving/insurance risk and driving habits of a given driver are associated with transportation system elements in the present invention, a more refined model of risk is possible than for insurance risk solutions in the art. Determining premiums based on a single point or region (for example a residential address) does not take into account where a person drives on a regular basis.

“As the correlation between one or more risk parameters and insurance risk may vary over time and may vary regionally, it may be needed to statistically analyze the parameters used in a model and continually change them over time. In addition, historical parameters used may lose relevance with time and will need to be retired or withdrawn from the determination of risk--relying on more recent data.

“Real time information (while the insured is driving) may be much more relevant to risk. For example, if the road is icy, the likelihood of making a claim is potentially higher, than if the only information available is that is likely to be icy at the timeframe when driving.

“With a dynamic rating system that is continually updated and also has a real-time component, it is further possible to compel drivers to adjust driving habits based on the real-time information to reduce the risk. For example, if a particular route is known to be icy, and the course the driver is taking is being monitored, and the monitoring system further suggest an alternate non-icy route, then the driver can be rewarded for avoiding risky conditions by a reduced premium, or by monthly rebate checks or similar.

“Real-time information can come from a variety of sources such as wireless acquired weather information and traffic reports. This information can further be statistically aggregated to produce historical weather/traffic risk information likelihood indices that are spatially and temporally indexed. Metadata associated with the historical information can then be used to cull older information and continually update the indices with the latest information. Also continuous, real time, accumulation of accident reports with root causes can be helpful to asses and distribute that risk across the total driving space of some geographic region.

“Glossary:

“Driver Insurance Risk: The probability that an insured will make a claim and for how much given a variety of measured factors. It could also refer simply to the probability of being in an accident.

“Transportation Network: A system of road, streets, paths, sidewalks, trails, waterways or other ways that a vehicle or pedestrian travels along. A transportation network can be subdivided by the type of vehicle or pedestrian that is intended to be used for. For example, roads and streets may be used by cars, trucks and busses. Trails and sidewalks may be used by pedestrians and perhaps bicycles. Transportation networks are generally stored in a Geographic information System that documents the location and interaction of various components of the transportation network. Attribution is also associated with the various components of the network.

“Element: Is a distinct component of a transportation network that has an associated geographic coordinate/s. Examples of elements are road segments where the road begins and ends at an intersection; an intersection between two or more roads; or the boundary of a lake.

“Attribution: Attribution associated with a transportation network includes any piece of information that can be related to a spatially referenced element or component of the transportation network. Examples are such things as speed limits, number of lanes, connections between components, or type of vehicle that can traverse the component. Attribution, in addition to being spatially referenced may have a temporal (time) component expressed as, for example, time of day, time of week, or time of year. An example of this is the speed limit in a school zone.

“Metadata: Metadata is a special kind of attribution associated with the quality of components of transportation network. Metadata can be associated with individual geographic components, attribution or the source of the geography or attribution. Metadata may be associated with precision or accuracy of the components or source. Metadata may have a component that list the age of the source.

“Index: two meaning are used: 1) With respect to a hazard index, this is another way of stating the probability that some hazardous incident could occur on a given road segment, but stating it in a more granular fashion rather than percentage, for example, High, Medium or Low. In addition an index can represent one or more values used to multiply or otherwise adjust up or down a baseline value. For example if a prospective insured base premium is $100, discounts and/or increases to the base may be applied by multiplying the base by a crash index, a driver age index, a safe driving index or a single index that is based on a number of parameters.

“2) With respect to a database, if an attribute of a database entry allows selecting or sorting of the database elements, then it is referred to as an index. For example, to get a list of all the accidents that occur on the weekend, then you would select accident that have a day of week attribute that is either Saturday or Sunday.

“Maneuver/Complex Maneuver: A maneuver is an attribute associated with an action that can be either perform or not performed and which is associated with one or more components of a transportation network. For example, a no-left-turn at an intersection is an example of a prohibited maneuver. A complex maneuver is generally associated with more than one component of a transportation network--for example, what is known as a Michigan Left Turn, in which a vehicle desires to turn left at an intersection, but in order to do this has to turn right, cross one or more lanes, then cross a median on an avenue, then turn left, is a complex maneuver.

“Parameters: Any factor that may be directly or indirectly be related to insurance risk.

“Geocode: Process of taking a street address and determining a geo-referenced coordinate usually a latitude and longitude and further determining the associated transportation segment associated to the street address.

“Snapping: Refers to the process of finding the nearest transportation segment (via perpendicular distance) to a given geo-spatial coordinate location.

“Multivariate Analysis: A class of statistical analysis used to determine the relevance of one or more parameters in predicting an outcome and used to build a predictive function base on one or more of the analyzed parameters. In this case the outcome is the prediction of insurance risk or driving hazard assessment. An example of a multivariate analysis is an Artificial Neural Network (or simply a neural network). Another example is any form of machine learning.

“Threshold: In multivariate analysis, several factors contribute to the predictive model. Some factors can be more relevant or more influential than others. For example the number of accidents in the past along a particular road segment, may be a better predictor of insurance risk of driving that segment than the average vehicle speed along the segment. However a relative weighting of the two parameters may predict better than either one used singly. So if a predictive model, when using a particular factor in the prediction, does not perform appreciably better than if the factor was not incorporated in the model, the factor can be removed from consideration. When this happens is when the difference in the two predictions is less than a preset threshold value.

“Accident Count: The number of accidents that occur for a given element of the transportation network over a given time. This may be further subdivided based on weather conditions and/or time of day, time of week or based on other attributes that may influence accident occurrence.

“Incident: A single occurrence of a measured parameter. For example an individual accident report is an incident of the parameter accidents; a recorded speed of an individual driver along a segment of road is an incident of speed of travel for that segment.

“Granularity: This term is used to refer to the specificity of either an attribute or index. For example, if an accident count is based simply on the transportation element it took place on, it is less granular than if the accident count is based on the location (element) and the time.

“Insurance Risk: This term is used collectively for all embodiments of the present invention to encompass the desired outcome of an insurance risk model. Examples of desired output are the probability of: having an accident, making an insurance claims, or making an insurance claim within defined monetary limits.

“Crowd Sourced: Information that is gathered from voluntary (or otherwise) information that is contributed to a website or webservice via an internet link. This information can be anything from verbal reports concerning traffic, to GPS trails that observe a drivers location and speed in real-time, which can then subsequently be used to update maps and other information pertaining to traffic or hazard.

“Outside Sourced: all sourcing of risk factor information that are not part of vehicle tracking and sensor analysis. This can include crowd sourcing, police reports, accident reports from insurance and/or police, weather from weather bureaus or crowd sourced, pavement conditions from highway departments or state government, traffic data from published or crowd sourced services and many others.

“Statistically Significant: refers to a minimum amount of information that can be used to achieve acceptable predictions of risk or hazard. For example if a predictive function relies heavily on a variable such as the average speed of vehicle passage for each road segment, then wherever there is no information concerning the average speed for any segment, then an average speed would have to be assumed. You could default to the speed limit for example. The more road segments that have an estimated average speed, the poorer the prediction of risk will be. A threshold needs to be in place to exclude information that is below a pre-defined value of percent coverage.

“Statistical Relevance: in any form of multivariate analysis, one or more measurements or parameters are used to predict an outcome. In this case an outcome is the risk associated with driving along a transportation element. In the process of developing the prediction function, it may be found that removal of certain parameters or measurements from the predictive function, does not appreciably change the prediction. A threshold can be set, pertaining to how much a specific parameter influences the prediction and if the correlation between an actual outcome and the predicted outcome does not improve about the threshold, then the parameter can be dropped from consideration. This is not to say that it could not be re-introduced when more or better data is available, or used in other geographic areas.

“Sensor derivative: Sensors that are incorporated in a vehicle or are within a vehicle (accelerometers in a smartphone where the smartphone is in the vehicle for example) can have the output evaluated and turned into a parameter. For example if an accelerometer indicates rapid acceleration in the direction of the front of the vehicle and a tire spin sensor records an event, this may be registered as a sensor derivative called dangerous acceleration. If there is a rapid acceleration to the left followed by a rapid acceleration to the right, this may be registered as a dangerous lane change event.

“Below are examples of elements of a vehicle insurance risk database. Some or all of these elements may be used to develop a risk model or risk indices.

“Standard GIS road network including: Road Segments Geography typically stored as a series of end nodes locations, and a series of shape points (internal points that define the location of the segment) or as a geometric function.

“Attributes Stored relative to a node or the segment as a whole (Road segments typical have an end node at the intersection with another road segment or a political boundary or a geographic feature.) Intersections Geography may be stored as either a singularity or a series of point and lines which make up a complex intersection (such as a highway cloverleaf) Attributes are stored that are associated with the intersection and/or the connecting segments Maneuvers (including complex maneuvers) Geography usually stored as a reference to one or more geographic components that make up the maneuver Attribution Examples (all attributes may have multiple values base on time and may also have metadata associate with them): For Segments: Speed limit/Actual Speed Driven Accident Count Historical Traffic Flow/count Historical Weather Information Number of Lanes Vehicle Type Access Street Side Parking Elevation/Change in Elevation Railroad crossing Political Boundaries Parking Areas Historical Data Crimes associated with a location (snapped to road segment or intersection); time of data; time of year Accidents: type of accident (solo or collision); location, direction of travel; date, time of day; type of vehicle; weather; driver record Previous Claims: location; type of claim (accident; vandalism; car-jacking); amount of claim; type and age of vehicle.

“Police citations: location, type Weather: ice, temperature, wind, pressure, snow, rain, flooding”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventor’s summary information for this patent application: “A primary object of the present invention is a method to develop a database comprising parameters that are related to insurance risk and/or driving hazard to be used for vehicle insurance rating and/or pricing and furthermore, where the parameters are related to transportation network elements.

“Another object of the invention is to determine which parameters or combination of parameters best predicts insurance risk for individual drivers or individual vehicles.

“A further object of the present invention is a maintenance and update method for the above mentioned databases.

“Yet another object of the present invention is to track and parameterize the driving habits of individual drivers and to compare those driving habits to historical parameters and habits of other driver in order to predict individual insurance risk or driving hazard.

“It is a further object of the present invention to influence the driving habits of individual drivers by suggesting safer routes or driving habits and to reward or penalize individual driver based on their utilization or lack of utilization of suggestions.

“It is an object of the present invention to develop a system that comprises a database, software and hardware to predict insurance risk or driving hazard, to mitigate insurance risk or driving hazard while individuals are driving and to set insurance premiums based on the database and real-time input.

“It is an object of the present invention to develop an insurance rating system based on accident counts for individual elements of a transportation network and how frequently a driver travels elements with accident risk.

“It is an object of this invention to display driving hazard or insurance risk relative to transportation segments on a map of a transportation network.

“It is an object of this to route from an origin to a destination taking into account hazards and risk data from the hazard/risk database.”

The claims supplied by the inventors are:

“1. A computer-implemented method for vehicle navigation incorporating insurance risk-based routing, comprising: compiling a risk database in a non-transitory storage including historical information comprising a plurality of indications of historical vehicle and driver activities and risk factors wherein the historical information is geo-referenced to transportation elements; developing, by a processor executing stored instructions, a statistical predictive relationship to estimate an initial insurance risk as a function of the historical information for said transportation elements, wherein an anticipated accuracy of the predictive statistical relationship is also presented with a prediction of insurance risk and wherein the anticipated accuracy is based on metadata associated with the historical information for the transportation segments used in the prediction; monitoring and recording in a non-transitory storage at least one of the vehicle or specific driver activity including driving habits, time and frequency of the at least one of the vehicle or specific driver traversing individual transportation elements; receiving information regarding location and time of vehicle operation or location and time where the driver is driving, and using said location and time information as input to the statistical predictive relationship executed on the processor to provide a modified insurance risk estimate for said transportation elements; acquiring and storing in a non-transitory storage additional geo-referenced risk factors from outside sources; refining the statistical predictive relationship on the processor by incorporating the recorded at least one of the vehicle and specific driver activity and the additional geo-referenced risk factors into the risk database and re-developing the statistical predictive relationship; determining, using the statistical predictive relationship executed on the processor, a further modified insurance risk estimate for said transportation elements based on at least one of adding new risk factors as statistically significant amounts of data becomes available for the new risk factors and removing risk factors from the predictive model as the impact on the predictive relationship goes below a statistical threshold; storing said further modified insurance risk estimates for said transportation elements in the risk database; receiving, at a processor through a navigation device, a routing request associated with a specific vehicle for route guidance for the vehicle from a start to a destination; determining, by a processor executing stored instructions, possible routes across transportation elements and receiving real-time hazard information for each determined possible route; comparing, by a processor, each determined possible route to the risk database and calculating relative risk for each said possible route based on the modified insurance risk estimate associated with the transportation elements contained in the possible route and received real-time hazard information; and presenting, through the navigation device, one or more of said possible routes with its calculated relative risk.

“2. The method of claim 1, wherein the risk factors for each transportation element comprise at least one of: accident counts; traffic density; number of driving citations, and number of insurance claims.

“3. The method of claim 2, wherein the risk factors are indexed by one or more of: time of day, time of week, and severity of the accident in terms of vehicle damage or passenger injury, type of traffic citation and cost of insurance claims.

“4. The method of claim 1, wherein the only risk factor is the number of traffic accidents per transportation segment, said risk factor is further indexed by at least one of time of day and day of week.

“5. The method of claim 1, wherein additional risk factors comprise at least one of the type of vehicle, driver demographics, weather information and pavement conditions.

“6. The method of claim 1, wherein the statistical predictive relationship is developed using one of a neural network or machine learning.

“7. The method of claim 1, wherein each type of historic information is based on a plurality of disparate sources and wherein the information from the disparate sources is merged using consistent units of measurement and parameterized into consistent ranges of measure.

“8. The method of claim 7, wherein at least one of the disparate sources contains information geo-referenced to an address and that address is geocoded and snapped to a transportation segment.

“9. The method of claim 1, wherein the determined insurance risk associated with transportation segments is productized by the processor and stored in non-transitory storage as attribution associated with a transportation map.

“10. The method of claim 1, wherein the insurance risk is collectively determined for a plurality of routes from an origin to a destination and wherein route selection is at least in part based on minimizing the collective risk.

“11. The method of claim 10, wherein if a driver follows a determined route that has a minimized collective risk, the driver is provided a discount on insurance premiums.

“12. The method of claim 1, wherein additional risk factors comprise at least one of, traffic conditions, accident occurrences, detours, and weather information wherein the additional factors are received in real-time and used to determine an immediate risk of driving.

“13. The method of claim 12, wherein if the immediate risk of driving exceeds a threshold, and the driver delays travel until such time as the immediate risk of driving is less, the driver is rewarded with reduced insurance premiums.

“14. The method of claim 12, wherein the received real-time information is utilized in a route determination wherein route selection is at least in part based on minimizing collective risk of driving along the route.

“15. The method of claim 1, wherein the recorded activity comprises historical routes taken by the specific driver or vehicle and the frequency those routes are taken, and said method further comprises: determining while the vehicle is in motion if it is likely that the vehicle is traveling along a frequented route; upon finding that a likely route is being taken, calculating alternate routes to the destination of the currently traveled route in order to determine if the alternate route has a lower risk factor; and upon determining that a lower risk factor route is available, presenting that route to the driver.

“16. The method of claim 15, wherein if the driver takes the present lower risk route, the driver receives a discount on the driver’s insurance premium.

“17. The method of claim 1, wherein the insurance premium is periodically adjusted based on the collective exposure to risk for a given period of time.

“18. The method of claim 1, wherein the predictive function varies geographically at least by one of the weighting of risk factors and the risk factors that are actually incorporated into the model.

“19. The method of claim 1, wherein the historical information and the risk factors consist entirely of sensor output and derivative of the sensor output from sensors contained within and that are part of the vehicle.

“20. A computer-implemented vehicle navigation system incorporating insurance risk-based routing, comprising: at least one processor and associated memory from which instructions are executed by said at least one processor; a database module maintained in memory and executed by the processor to compile a database of historical information comprising a plurality of indications of vehicle and driver activities and risk factors, wherein the historical information is geo-referenced to transportation elements and wherein the risk factors assigned for each transportation element comprise each of accident counts, traffic density, number of driving citations, and number of insurance claims; a monitoring and recording module executed by said at least one processor configured to monitor and record in memory at least one of the vehicle and specific driver activity including both driving habits and when and how often the at least one of the vehicle and driver traverses individual transportation elements; a communications module executed by said at least one processor configured to acquire additional geo-referenced risk factors from outside sources; an insurance risk estimator executed by said at least one processor configured to develop a statistical predictive relationship to estimate insurance risk as a function of the historical information received from the database module for each transportation element, refine the statistical predictive relationship by incorporating both the recorded at least one of the vehicle and specific driver activity and additional geo-referenced risk factors into the database of historical information and re-developing the statistical predictive relationship, and at least one of adding new risk factors as statistically significant amounts of data become available for the new risk factors and removing risk factors from the predictive model as the impact on the predictive relationship goes below a statistical threshold; a route calculator executed by said at least one processor configured to calculate possible routes across transportation elements in response to a routing request, compare each calculated possible route to the risk database, and calculate a relative risk for each said possible route based on the modified insurance risk estimate associated with the transportation elements contained in the possible route; and a navigation device including at least one said at least one processor and associated memory, and a GPS unit, said navigation device configured to receive a routing request associated with a specific vehicle or driver for route guidance for the vehicle or driver from a start to a destination and to present to a user one or more of said possible routes, each with its calculated relative risk.”

For more information, see this patent application: Fuchs, Gil Emanuel. Risk Based Automotive Insurance Rating System. Filed July 29, 2019 and posted November 14, 2019. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220190347739%22.PGNR.&OS=DN/20190347739&RS=DN/20190347739

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