The monitored use of a vehicle provides accurate and reliable data
that can be used to determine the insurable risk of a vehicle operator.
What is disclosed is a system and method for monitoring vehicle
operation and using the collected data to calculate a driver score.
The driver score can then be applied to ascertain the risk of insuring
a particular driver, as well as being used as a tool for defining
or adjusting the terms of an insurance policy for an insured driver.
The collection of data such as the times the vehicle is operated,
the locations the vehicle is operated and the speeds or other characteristics
of how the vehicle is operated can all be used to calculate the
driver score. By installing a vehicle monitor within a vehicle and
extracting this or similar data, more accurate and profitable insurance
policies can be developed.
1. A method of determining a cost of automobile insurance in a predictive
manner based upon monitoring, recording and communicating data representative
of operator and vehicle driving characteristics, the method comprising:
monitoring the activity of the vehicle for a first period of time;
calculating a driver score based at least in part on the vehicle
activity during the first period of time; and applying the driver
score to determine the future terms of an insurance policy for the
2. A method of determining a cost of automobile insurance in a
predictive manner based upon monitoring, recording and communicating
data representative of operator and vehicle driving characteristics,
the method comprising: installing a vehicle monitor within a vehicle;
monitoring the activity of the vehicle for a first period of time;
calculating a driver score based at least in part on the vehicle
activity during the first period of time; applying the driver score
to determine the terms of an insurance policy to be issued for the
insured vehicle; monitoring the activity of the vehicle for a subsequent
period of time; adjusting the driver score based at least in part
on the vehicle activity during the subsequent period of time; and
modifying the terms of the insurance policy on a forward going basis.
3. The method of claim 2, wherein vehicle monitor includes a wireless
interface and the step of calculating a driver score further comprises
the steps of: wirelessly transmitting data obtained from the monitoring
step to a central system; and the central system calculating the
driver score based at least in part on the transmitted data.
4. The method of claim 3, wherein the step of calculating a driver
score further comprises the steps of: identifying time of day classes
in which the vehicle can be utilized; determining the amount of
driving time that the vehicle is used in each of the time of day
classes; calculating a time of day weighted value based on the amount
of driving time that the vehicle is used in each of the time of
day classes and claim propensities for the time of day classes;
applying the time of day weighted value in the calculation of the
driver score; identifying geographical sub-areas in which the vehicle
can be utilized; determining the amount of driving time that the
vehicle is used in each of the geographical sub-areas; calculating
an area weighted value based on the amount of driving time that
the vehicle is used in each of the geographical sub-areas and claim
propensities for the geographical sub-areas; applying the area weighted
value in the calculation of the driver score; identifying speed
classes in which the vehicle can be utilized; determining the frequency
at which the vehicle is used in each of the speed classes; calculating
an offset value based on the frequency at which the vehicle is used
in each of the speed classes; and applying the offset value in the
calculation of the driver score.
5. The method of claim 2, wherein the step of monitoring the vehicle
during the first period of time further comprises the steps of:
identifying times during the first period of time at which the vehicle
was operated; identifying geographical sub-areas in which the vehicle
was operated during the first time period; and identifying the speeds
at which the vehicle was operated during the first period of time.
6. The method of claim 5, wherein the step of calculating a driver
score further comprises the steps of: applying the identified times,
geographical sub-areas and speeds in the calculation of the driver
7. A system for calculating a driver score and applying the driver
score in the determination of the terms of an insurance policy to
be issued, the system comprising: a recording system that is installable
within a vehicle; a GPS interface that is couple to the recording
system; a vehicle bus interface that is couple to the recording
system; a transmitter coupled to the recording system for transmitting
vehicle operation data obtained by the recording system through
the GPS interface and the vehicle bus interface; a receiver that
is communicatively coupled to the transmitter for receiving the
vehicle operation data; and a central system that is coupled to
the receiver and operable to: calculate a driver score based at
least in part on the vehicle operation data; and apply the driver
score determine the terms of the insurance policy.
8. The system of claim 7, wherein the transmitter and the receiver
are communicatively coupled over a wireless interface.
9. The system of claim 7, wherein the wireless interface is a cellular
10. The system of claim 7, wherein the wireless interface is a
11. The system of claim 7, wherein the vehicle operation data comprises:
times at which the vehicle is operated; locations in which the vehicle
is operated; and speeds at which the vehicle is operated.
12. The system of claim 11, further comprising a back end processor
that is coupled to the central system and is operable to provide
the central system with claim propensity data related to time, locations
and vehicle speeds.
CROSS-REFERENCE TO RELATED APPLICATIONS
 This application is a continuation in part of the U.S. patent
application that was filed on Sep. 8, 2004 and assigned Ser. No.
BACKGROUND OF THE INVENTION
 The present invention is directed towards data acquisition
and processing of information related to various driver characteristics
and, more particularly to collecting driver characteristic data
and generating and driver score based on the collected driver characteristic
data. The driver score can then be applied in the calculation of
insurance premiums or risk analysis.
 The insurance industry can be likened to an evening at a
Las Vegas Black Jack table. The casino has picked the game and established
the rules in such a manner that statistically over a period of time,
the casino will win. Sure, some individual tourist will walk away
with hundreds or thousands of dollars; however, compared to the
number of visitors that leave tens, hundreds, thousands, and even
tens or hundreds of thousands of dollars behind, these infrequent
winners are negligible. This is quite evident upon staying at one
of the casinos and viewing the elaborate decorations, the granite
tiling in the bathrooms, the reduced pricing for food and of course,
the open bar for active gamblers.
 How does this relate to the insurance industry? Similar
to the odds setters in Las Vegas, insurance companies have their
own odds setters. The odds setters in the insurance industry include
highly compensated and highly educated and trained actuarial scientists.
The actuarial scientists acquire and analyze large amounts of varied
data that is even remotely related to the calculation of insurance
risks, and apply the results of this analysis in the calculation
of insurance premiums. The task faced by the actuarial scientists
is to derive insurance premiums for a large domain of individuals
that in the long run, will result in the amount of premiums collected
by the insurance company to be significantly larger than the amount
of required insurance payouts.
 Traditionally, the insurance industry generates individual
policies that are more likely than not to be profitable to the insurance
company. The various aspects of the policies include premiums, deductibles,
exclusions, liability limitations, etc. The policies are developed
based on various characteristics of the individual seeking the policy,
the characteristics of the general populous, and the characteristics
of categories of the general populous that may be applicable.
 In the automotive insurance industry, the data related to
the various characteristics of the individual are gathered through
the use of standard forms, personal interviews, obtaining the applicant's
public motor vehicle driving record maintained by governmental agencies
or a combination of any of these methods. This data results in a
classification of the applicant to a broad actuarial class for which
insurance rates are assigned based upon the empirical experience
of the insurer. Many factors are relevant to such classification
in a particular actuarial class. These factors can include age,
sex, marital status, vehicle type, vehicle color, location of residence,
driving record including accidents, past insurance claims, at fault
accidents, types of losses covered, liability levels desired, inclusion
of uninsured motorists, inclusion of comprehensive coverage, inclusion
of collision coverage, deductibles, etc. Some of these classifications
can be further sub-divided into additional sub-classes, such as
age ranges, and vehicle types (i.e., trucks, sports cars, sedans).
 Similar to the goal of the Las Vegas Black Jack table attracting
patrons, the insurance companies need to provide competitive pricing
of their insurance policies. However, the insurance companies walk
a fine line between offering competitive pricing while maintaining
viable operating profits. Thus, insurance companies continually
seek ways in which to provide competitive pricing without compromising
their profit margins. Presently, some insurance companies address
this need by providing discounts and surcharges for some types of
use of the vehicle, equipment on the vehicle, and type of driver.
For instance, the insurance company my add surcharges if the vehicle
is being used for business. Likewise, the insurance company may
provide discounts for vehicles that include airbags, antilock brakes,
and theft deterrent devices, or if the driver has a good driving
record or is a good student.
 However, the insurance industry is faced with significant
problems based on their current methodologies. For instance, the
information obtained by the insurance company is time constrained.
As and example, an insured party may live in a large city when obtaining
the policy and subsequently move to the suburbs. Or the insured
party may change jobs and consequently have a drastic change in
the number of miles traveled during an insurance policy period.
Unless the insured party notifies the insurance company regarding
the address change, the expected mileage change or other such parameters,
the insured party may end up paying a higher premium than would
otherwise be available. Thus, the insurance company is vulnerable
to churn based on lower premiums that may be offered by a competitor.
In addition, the information collected by the insurance company
may not be verifiable, and even existing public records may include
limited or erroneous information. Thus, there is a need in the art
for a more reliable and non-time sensitive mechanism for collection
of information regarding the insured party.
 Techniques have been suggested for addressing this problem
in the art, such as the use of vehicle operating data recording
systems. Such systems reside within a vehicle, measure various operating
parameters, and report the information to a central recording system.
In addition, the use of wireless or radio transmission of the data
to the central recording system has also been suggested. However,
there are no methods of applying this information in the insurance
industry in an effort to improve the competitive nature of the insurance
policy offerings. Thus, there is a need in the art for a method
to identify pertinent vehicle operation information to be collected
and to apply the collected information in a manner to generate a
score that identifies the risks or insurability of a driver.
BRIEF SUMMARY OF THE INVENTION
 The present invention addresses these needs in the art,
as well as other needs that are not herein identified, by providing
a system and method for monitoring the use of a vehicle and calculating
a driver score based on the monitored use. The driver score can
then be applied in a variety of manners to achieve a variety of
results, including but not limited to, determining or adjusting
the terms of an insurance policy, such as changing the premium,
the deductibles, the exclusions, the duration or the like. More
specifically, a vehicle monitor is installed or coupled to a vehicle
to be monitored. The vehicle monitor collects data from various
sensors to identify vehicle operation data. Based at least in part
on the vehicle operation data, a driver score is calculated and
then the driver score is applied in setting or modifying the terms
of the insurance policy either on a retroactive basis or on a forward
 In one embodiment of the invention, the vehicle monitor
may be used to determine a driver score that serves as input for
calculating the terms of a new insurance policy. In another embodiment,
the vehicle monitor may be used to determine a driver score that
serves as input for modifying the terms of an existing insurance
policy. In another embodiment, the driver score can be used to determine
whether a party qualifies for insurance.
 The vehicle monitor may operate to collect a variety of
information or operating parameters including the times during which
the vehicle is operated, the geographic areas or sub-areas within
which the vehicle is operated and the speeds at which the vehicle
is operated. Other parameters could also be monitored by the vehicle
monitor and all or only subsets of this information may be used
in the determination of the driver score.
 The determination of the driver score can be accomplished
by the vehicle monitor, by a central system or by a combination
of both. In addition to the driver score, other extrinsic data such
as claim propensities, vehicle types, driver records and demographics
may also be used in determining or adjusting the terms of the insurance
policy. In addition, this extrinsic data may also be applied in
the calculation of the driver score.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING
 FIG. 1 is a block diagram of an environment suitable for
various embodiments of the present invention.
 FIG. 2 is a mapping diagram of a geographic region that
is divided into sub-areas that illustrates the second parameter--where
the vehicle is used.
 FIG. 3 is a flow diagram illustrating the steps involved
in an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
 The present invention is directed towards acquiring performance
and usage data through various sensors and monitors within and without
a vehicle, utilizing the performance and usage data to generate
a driver score, and then utilizing the driver score in the calculations
of insurance premiums or rating factors. In general, the present
invention includes at least four distinct aspects. These aspects
include: (1) the methods and devices utilized in the acquisition
of performance and usage data; (2) the types of performance and
usage data collected and the treatment of the ranges of the data
values; (3) the method to calculate the drivers score based at least
in part on the performance and usage data; and (4) the application
of the driver score in the calculation of insurance premiums, rating
factors, risk analysis, etc.
 FIG. 1 is a block diagram of an environment suitable for
various embodiments of the present invention. Three vehicles 111-113
are shown, for illustrative purposes, operating within the environment.
Each of the vehicles is equipped with a data collection and recording
system 140 but the details are only shown with respect to one of
the vehicles 111. The data collection and recording system is shown
as including two data collection interfaces: a GPS interface 120
and a vehicle bus interface 130. It should be understood that the
present invention is not limited to these two interfaces nor are
these two interfaces required for the present invention. Other interfaces
are also anticipated such as weather information interfaces, clock
interface, or other similar interfaces. The vehicle bus interface
130 can acquire information such as the speed of the vehicle, state
of the windshield wipers, state of the lights (on, off, fog lights,
brights, etc.), amount of pressure applied to the brakes, motion
through the use of an accelerometer, time of day, temperature, vehicle
maintenance, operation of equipment within the vehicle such as radios,
cellular telephones, DVD players or the like, the volume at which
audio equipment is operated, and the identity of the driver based
on the entry of an identification number, seat settings, weight
or the like, status of seat belts, number of passengers, etc. The
GPS interface 120 can acquire information such as the location of
the vehicle, time of day, direction of motion, speed of the vehicle,
etc. A recording system 140 collects information from the data collection
interfaces and either stores the information locally, transmits
the information through transmitter 150, or applies processing to
the information prior to either storing or transmitting the information.
For instance, in an exemplary embodiment of the present invention,
the system may only operate to collect time of day, location and
speed information. In such an embodiment, the data recording system
140 operates to filter the data available from the data collection
interfaces and only provide the necessary information to the central
system. In an alternative embodiment, the data recording system
140 may operate to transmit all available information and a central
system 170 operates to filter out the unnecessary information.
 The data from the various vehicles 111-113 is received by
a receiver 160 and then provided to a central system 170. The central
system can perform processing on the received data, either alone
or in conjunction with back end processing 180. The back end processing
180 may include input from actuarial scientist or other data collection
and processing systems.
 The data collected for the various vehicles may be transferred
to the central system using a variety of different technologies
and those skilled in the art will understand the benefits and limitations
of each such technology. For instance, the invention may be embodied
within an environment that uses wireless technology to periodically
transmit collected data to the central system 170. The wireless
technology may include pager technology or cellular technology conforming
to any of a variety of past, existing or future technologies including
FLEX, REFLEX, POCSAG, AMPS, NAMPS, TDMA, CDMA, GSM, GPRS or the
like. Alternatively, the system may store the data and only transmit
it when requested. In yet another embodiment, the data recording
system 140 may store the data for later retrieval. Such later retrieval
could be accomplished through a local wireless system, such as blue
tooth, INFRARED, FM, AM, or I.E.E.E. 802.11 technology, or through
a physical wired technology or even through the use of a memory
card, storage media or print out.
 Once the data is received by the central system 170, the
data is used to generate a driver score. The driver score is based
at least in part on the data collected by the vehicles and provided
to the central system 170. However, additional data that is received
independent from the data collection systems in the individual vehicles
could also be used in calculating the driver score. This information
may include the traditional information that has been collected
by insurance companies for years as is listed in the background
section, or may include other information such as satellite tracking
of the vehicle, cellular signal tracking of the vehicle, weather
information, mapping information, hazardous road condition information,
or the like.
 The driver score is basically a value that encompasses a
variety of parameters. The driver score reflects a qualitative view
of the driving characteristics for a particular vehicle or a combination
of a vehicle and driver. Depending on the parameters that are used
to calculate the driver score, the driver score can reflect various
characteristics. In the preferred embodiment, the driver score operates
to establish a risk level associated with insuring a particular
driver. Other uses of the driver score may include, but are not
limited to, verifying the accuracy of information provided to an
insurance company, verifying compliance of a teenaged driver within
guidelines established by his or her parents, verify compliance
of teenaged drivers with local/regional laws such as curfew and
number of passengers, etc.
 Advantageously, an insurance company can offer a product
embodying aspects of this invention to its customers and offer a
discount based on the inclusion of the product. The customer can
further agree to be bound by restrictions to gain other discounts.
For instance, an insured party can agree to maintain within the
speed limit to obtain a premium discount in exchange for allowing
the insurance company the ability to actively monitor compliance.
The present invention can also be utilized as a theft deterrent,
similar to a LO-JACK type system in that the location of the vehicle
can be monitored.
 In the preferred embodiment, the driver score reflects an
insurance risk and is used to either increase or decrease an insurance
premium or otherwise modify the terms of an insurance policy.
Driver Score Example
 The present invention can be illustrated through the use
of an exemplary embodiment that bases the driver score on the following
information: when the vehicle is in use, where the vehicle is used,
and how the vehicle is used.
 Table 1 illustrates a simple heuristic that can be applied
to determine a weighted score reflecting the first parameter--when
the vehicle is in use. TABLE-US-00001 TABLE 1 Normal Peak Time of
Day Traffic Traffic Risk Traffic Weighted Score Risk Factor 0.60
1.40 2.50 Driver A 20% 75% 5% 1.295 Driver B 80% 20% 0% 0.760 Driver
C 20% 20% 60% 1.900 (capped at 50%) 1.650
 Various sensors or collection interfaces could be used to
determine the time of day that a vehicle is operated such as through
the GPS system, the vehicle bus, or through notifying the central
system through a wireless interface. Regardless of the technique
used, the time of day operational characteristics of a vehicle can
be determined over a period of time and continually updated over
time. The actual times that the vehicle is operated can be recorded
by the recording system 140 and reported to the central system 170
or categories of times can be reported. Table 1 shows one technique
to breakdown the operation of a vehicle within three time-categories,
normal traffic, peak traffic and risk traffic. For instance peak
traffic could include the times between 7:00-9:00 AM and 4:00-7:00
PM, risk traffic could include late night driving, such as between
11:00 PM to 4:00 AM and normal traffic would include the remainder.
It will be appreciated that these categories are for illustrative
purposes only and the present invention is equally applicable to
other sets of categories. For instance, one or more of the following
categories could be added to or substitute any of the already listed
categories: weekend, particular day of the week, morning rush, evening
rush, holiday travel, lunch time rush, garaged, parked, Sunday morning,
Friday/Saturday evening, etc.
 The second block in the left most column of Table 1 defines
a risk factor for each of the listed time categories. The values
listed in this table define a risk factor that is associated with
driving during the identified time periods. This information can
be derived using various techniques such as empirical data or information
that is obtained from actuarial tables published by insurance companies.
The risk factors can be based on a national average or could be
regionally based as well.
 Table 1 lists driving characteristics for three vehicles
or drivers (Driver A, B and C). The driving characteristics provide
a percentage of driving time that the vehicle is operated, or the
driver operates a vehicle during the listed time categories.
 Based on the risk factor and the driving characteristics,
a weighted score, as shown in Table 2, is calculated by multiplying
the percentage of time that a vehicle is operated in a particular
category by the risk factor associated with that category and then
summing the products for each of the categories. For the provided
example, Driver A's weighted score is determined as follows: TABLE-US-00002
TABLE 2 Claim % of Time-Category Propensity Time Products Normal
Traffic 0.6 * 20% 0.12 Peak Traffic 1.4 * 75% 1.05 Low Traffic 2.5
* 5% 0.125 Weighted Score 1.295
 Driver B has more of a tendency to drive during normal traffic
(80%) and thus, has a much lower weighted score of 0.76. Driver
C has a tendency to drive late at night in the risk traffic category
and thus has a weighted score of 1.9. Thus, Driver C has the highest
weighted score. If it is desired not to penalize a driver that happens
to be assigned to night shift work, one technique to alleviate an
adverse affect based on Driver C's weighted score would be to apply
a cap. For instance, if the late night percentage is capped at 50%,
then the weighted score for Driver C drops to 1.65. This illustrates
how the driver score can be flexible and fair by basing the data
on more than just the actually measured data. For instance, if the
driver score is being utilized by an insurance company to determine
premium rates, the insurance company may decide not to penalize
a night shift worker simply because his job forces him to travel
within a higher risk time period.
 It should be understood that this example is provided for
illustrative purposes only and that the present invention may use
other techniques to calculate such a weighted score. For instance,
rather than percentages of time, the actual number of hours averaged
over a period of time, such as a day, week, month or quarter could
be utilized. In addition, the application of risk factors to the
various time categories can be adjusted based on a variety of factors,
some of which may include, but are not necessarily required, are
type of vehicle, driver's record, population of the area, etc.
 FIG. 2 is a mapping diagram of a geographic region that
is divided into sub-areas that illustrates the second parameter--where
the vehicle is used.
 The region includes 5 sub-areas A-E. The sub-areas can be
defined based on any of a variety of techniques including zip codes,
area codes, counties, states, cellular cells, longitude and latitude,
traffic density, population, road density, or any of a variety of
other techniques of combinations of techniques. Regardless of the
technique used to sub-divide a region, risk factor data for the
region can be obtained and applied in the determination of a weighted
score for this parameter. Table 3 illustrates a simple heuristic
that can be applied to determine a weighted score reflecting the
second parameter--where the vehicle is used. TABLE-US-00003 TABLE
3 Rural Suburb Metro Rural Metro Weighted Area Streets Streets Streets
H'way H'way Score Risk Factor 0.55 1.75 2.20 1.55 1.35 Driver A
15% 20% 30% 15% 20% 1.5950 Driver B 70% 15% 10% 5% 0% 0.9450 Driver
C 15% 0% 15% 70% 0% 1.4975
 The risk factor data for each region identifies a driving
risk associated with that region. Thus, in the example provided,
a high risk factor indicates that the area has a higher probability
of resulting in an incident, such as a traffic accident, when a
vehicle is operated in the area. Similar to the time of day calculations
in Table 1, the risk factor values are multiplied by the percentage
of time that the vehicle/driver is within that region or sub-area
and then the products are summed to obtain the weighted score.
 Again, the use of percentages is just an example and other
criteria could also be applied such as accumulative hours over a
period of time, average number of hours over a period of time, number
of miles driven in the particular area, or the like.
 In an alternative embodiment, the tables used to calculate
a weighted score based on time of day and area can be combined into
a multi-dimensional table. Thus, each of the sub-areas in the region
could include a time of day table that includes different risk factors
based on sub-area and time of day. For instance, the area surrounding
a subway station may have a high risk factor during peak traffic
but a very low risk factor during normal traffic. Thus, those skilled
in the art will appreciate that various techniques can be applied
to calculate the weighted scores and the examples provided in this
description are simply to illustrate calculation of a value that
rates driver characteristics. However, certain aspects of the selection
of parameters and assignment of risk factors and techniques to calculate
the score that are disclosed herein are also considered novel.
 Table 4 illustrates a simple heuristic that can be applied
to determine a weighted score reflecting the third parameter--how
the vehicle is used. This example shows one alternative for calculating
the driver score, or elements of the driver score by using an offset
rather than a weighted score. TABLE-US-00004 TABLE 4 Limited Speed
Highway Streets Access Penalty Offset Speed limit .+-. 5 mph +0.015
+0.025 +0.02 Speed limit .+-. 15 mph +0.05 +0.10 +0.08 Driver A
10/4 4/2 4/1 0.81 Driver B 2/1 8/0 4/2 0.52 Driver C 5/0 5/0 4/0
 The illustrated heuristic identifies offsets to be added
to the weighted scores calculated in accordance with the first two
parameters. The offset is based on ranges of miles per hour centered
on the speed limit and the types of roadways being traveled. For
instance, a set of offsets are provided for the highways, streets,
and limited access roadways for speeds that are 5 mph above or below
the posted speed limit and speeds that are 15 mph above or below
the posted speed limit. This particular configuration is once again
provided as an example only and the present invention is not limited
to this particular configuration. For example, one set of offsets
could also be used when the vehicle is a particular threshold below
the speed limit and another set of offsets could be used when the
vehicle is above the posted speed limit. In addition, the structure
defined in Table 4 is set up as a penalty system. An award system
could also be established to subtract offsets from the score based
on conforming to the speed limit.
 The values entered for Driver A, Driver B and Driver C illustrate
an alternative method to the percentages used in the previous examples.
In this example, the propensity of the driver on a scale of 0 to
10 is listed for the various conditions. This number could also
represent a frequency over a period of time--for instance over a
given period of time, Driver A will be over the speed limit by more
than 5 mph 10 times and over the speed limit by more than 15 mph
4 times. For each occurrence, the offset is added for the particular
driver. Thus, for Driver A, the total offset penalty of 0.81 is
calculated as follows: 10*0.015+4*0.05+4*0.025+2*0.1+4*0.02+1*0.08=0.81
 As previously mentioned, the examples that have been provided
are for illustrative purposes only and other factors and weighting
systems could also be incorporated into the present invention and
the present invention is not limited to any particular arrangement.
The main focus of the present invention is to provide a means for
calculating a driver score that is based on various operational
parameters. In the example provided, these parameters have included
when the vehicle is in use, where the vehicle is used and how the
vehicle is used.
 Once the various parameters have been determined and the
weighted scores and penalties calculated, then the driver score
can be determined. For the illustrated example, the driver score
is simply the sum of the "when" and "where"
parameters plus the penalty or offset determined by the "how"
parameters. Table 5 illustrates the calculation of the driver score
for Driver A, Driver B and Driver C. Alternatively, the driver score
could be calculated in different manners, such as multiplying the
weighted score for the "when" with the weighted score
for the "where" and than adding in the offsets. It will
be appreciated that the particular technique employed, although
novel in and of itself, in no way limits other aspects of the present
invention. TABLE-US-00005 TABLE 5 Driver A Driver B Driver C Time
of day (When) 1.295 0.760 1.150 Area (Where) 1.5950 0.9450 1.4975
Speed (How) 0.81 0.52 0.28 Driver Score 3.7 2.225 2.9275
 Thus, in the illustrated example, Driver A has a driver
score of 3.7, Driver B has a driver score of 2.225 and Driver C
has a driver scored of 2.9275. Based on the particular parameters
and structure of the provided examples, in this situation Driver
A is a higher risk driver than Driver B or Driver C. The driver
score can then be used in a variety of manners. For instance, the
driver score could be used as one of several parameters entered
into the calculation of an automobile insurance premium or, as an
offset or adjustment to an automobile insurance premium. Such use
can be applied retroactively to provide a rebate of previously paid
insurance premiums but, more preferably is applied in accordance
with a predictive model to identify insurance premiums on a forward
looking basis. For instance, a particular policy holder may agree
to a monitored program with the expectation that if his or her driver
score is good, the future premiums of the insurance policy may be
reduced. Likewise, the insurance provider may be able to increase
the insurance premiums on a forward going basis if the individual's
driver score is bad. Similarly, a new customer may be require to
submit to a monitoring period prior to the issuance of a policy.
Thus, the insurance company can gain an assessment of the risk associated
with a new customer and issue a policy that is commensurate with
those risks. The driver score could also be used for providing discounts
or rate adjustments for life and/or health insurance. Other uses
for the driver score may include, but are not limited to State tax
credits, purchase price discounts or rebates for automobiles, discounts
for extended warranties, discounts for vehicle registration, access
to High Occupancy Vehicle (HOV) lanes or the like.
 One objective of the modeling is to create tools that are
statistically predictive of accidents. For instance, volumes of
GPS and accelerometer data, as well as any other information that
is available, e.g. driver's age, years driving experience, etc.
are analyzed. This data will be correlated to vehicle accident data
looking for relationships that are statistically valid indicators
of accident likelihood. GIS data that includes posted speed limits,
type of street, etc. may also be included. The information analyzed,
in an exemplary embodiment, includes at least the following:
 Vehicle location--This information has a focus on the route
followed and type of street where the driving takes place.
 Speed of vehicle--This information is particularly relative
to posted speed limits.
 Time of day--This information focuses on when driving takes
 Length of time driving--This information focuses on the
 Acceleration--This information has an emphasis on instances
outside the "normal" range.
 Deceleration--This information has an emphasis on instances
that are outside the "normal" range.
 Lateral g-forces--This information focuses on where excessive
turning speed could result in turnovers.
 Seat belt usage--This information relates to whether a seat
belt is used and the percentage of time it is used compared to driving
 Each of these data elements, and more, are noted for each
driver. Each driver's incidence of accidents is noted also. Profiles
are developed of the values of these data elements that are more
likely to be present for those drivers that have accidents. The
profiles are reduced to a single number, a score, that is reflective
of each driver's relative likelihood of having an accident.
 The creation of the score is through the use of generalized
linear models. Use of these models not only provides the multivariate
correlation of the GPS/GIS/accelerometer data to accidents, but
it eliminates the "overlap" of the data elements as well.
For example, if drivers with low seat belt usage percentages also
tend to be the drivers that exceed the speed limit, the analysis
does not result in low seat belt usage and exceeding the speed limit
having high relative risk factors without further adjustment. Essentially,
the relative risk factors for seat belt usage assuming a "standard"
distribution of driving speeds is determined. And the risk factors
for exceeding the speed limit are determined assuming a "standard"
distribution of seat belt usage.
 As a result of the modeling performed by this invention,
the variation in driver accident propensity is explained by each
data element and can be identified. In addition, the data elements
can be ranked according to the amount of variation in risk each
 Drivers with scores that indicate they are much more likely
to have an accident than others can be identified and dealt with
appropriately. Often additional training or other remedial activities
can improve a driver's habits and, hopefully, prevent an accident
from occurring. The scores associated with each driver need to be
updated periodically to reflect possible changes in accident likelihood.
Noting the deterioration in a driver's score and providing the necessary
intervention quickly again is expected to prevent accidents from
 Table 6 illustrates one method of applying the driver score.
In this example, the driver score is used to select a rating factor.
The rating factor is a multiplier to the insurance premium derived
using other available rating mechanisms. TABLE-US-00006 TABLE 6
Driver Score Rating Factor 0.0 to 0.9 0.85 1.0 to 1.75 0.90 1.76
to 2.49 0.95 2.50 to 3.19 1.00 3.20 to 3.59 1.05 3.60 to 3.99 1.10
4.0 to 4.29 1.15 4.30+ 1.30
 In accordance with Table 6 and the calculated driver scores,
Driver A would have a rating factor of 1.10, Driver B would have
a rating factor of 0.95 and Driver C would have a rating factor
of 1.00. Thus, in this example, based on the rating factors, Driver
A's premium would be increased by 10% based on his driver score,
Driver B's premium would be reduced by 5% and Driver C's premium
would not be adjusted.
 Thus, the present invention has been described by way of
example as a system that includes a vehicle based component and
a central component. The vehicle based component collects usage
data through one or more interfaces and then provides the usage
data to the central system either by means of wireless transmission
or other methods. The central system then calculates a driver score
based at least in part on the usage data received, as well as claim
propensity information. Finally, the driver score can be applied
in adjusting the premium of an insurance policy or other terms and
conditions of the policy.
 FIG. 3 is a flow diagram illustrating the steps involved
in an embodiment of the present invention. The process begins at
step 310 where a new vehicle is selected for driver score based
insurance. At step 310 the new vehicle is initialized. This process
can include a variety of tasks, such as but not limited to nor requiring,
installation of the monitoring and recording system into the vehicle,
provisioning the system including provisioning of any wireless communication
systems, entry of user data into the central system and verification
of operation. These tasks can include gathering initial information
about the driver, the vehicle, the topographical area in which the
vehicle is operated, the identification of what drivers will be
utilizing the vehicle, matching the identification of the monitoring
and recording system with the drivers, etc.
 Once the system is initialized, the monitoring and recording
system begins to monitor the vehicle activity for a first period
of time 320. The data collected can be provided to the central system
either on-line in real-time, periodically over a wireless interface,
or through physically docking the vehicle with the central system
either locally or remotely. The first period of time can vary depending
on the particular embodiment but generally is sufficiently long
to obtain data that is an accurate portrayal of the vehicle activity.
Logically an entire year would seem like a valid period when calculating
a driver score for insurance premium purposes but realistically,
this would not be practical. Thus, a shorter period of time that
encompasses enough variants in the individuals schedule should suffice.
For instance, a two to four week period of time may be sufficient
if during that period of time, no extreme conditions occur, such
as the driver going on vacation, the driver taking an extended road
trip or the vehicle being in the shop.
 Once the first period of time has been satisfied, the system
can operate to generate the driver score 330. As previously described,
the driver score may include a variety of parameters with various
weights applied to the parameters. Several examples have been previously
provided, each of which may contain novel aspects of the invention,
yet do not operate to limit the generality of the invention to utilize
various other parameters, combinations of parameters and the application
of various weighting factors.
 Once the driver score is determined, if the vehicle or user
is currently uninsured 340, the processing continues at step 350
where the driver score is applied in the selection and definition
of an insurance policy. On the other hand, if the vehicle or user
is already insured, processing continues at step 360 where the terms
of the insurance policy can be adjusted. In steps 350 and 360, the
typical application of the driver score is in the adjustment of
the insurance premium, however, other adjustments or term settings
could also be made, such as but not limited to, changing deductibles,
changing exclusions, changing the duration of the policy, etc.
 After the completion of steps 350 or 360, processing continues
at step 370 where the vehicle activity continues to be monitored.
At step 370, the monitoring process continues for a second duration
of time. The second duration of time can be as insignificant as
seconds or fractions of seconds or, could be substantial such as
days, weeks, etc. Preferably, the second period of time is less
in duration than the first period of time but this is not a requirement.
 Upon completion of the second period of time, the driver
score is then adjusted at step 380. The adjusted driver score is
then reapplied in step 360 for adjusting the terms of the insurance
policy. Thus, the driver score and the terms of the insurance policy
can be continually updated as the system collects further information
about the vehicle activity.
 In an alternate embodiment, an insurance policy can simply
be issued to an insured party at premiums and terms calculated in
the normal fashion. Subsequent premiums and terms can then be adjusted
over time by employing the monitoring and driver score calculation
aspects of the present invention.
 The present invention has been described using detailed
descriptions of embodiments thereof that are provided by way of
example and are not intended to limit the scope of the invention.
The present invention can be implemented as a process that runs
within a variety of system environments or as an entire system including
various components. The described embodiments comprise different
features, not all of which are required in all embodiments of the
invention. Some embodiments of the present invention utilize only
some of the features, aspects or possible combinations of the features
or aspects. Variations of embodiments of the present invention that
are described and embodiments of the present invention comprising
different combinations of features noted in the described embodiments
will occur to persons of the art.