Patent Document

TECHNICAL FIELD 
       [0001]    The present disclosure relates generally to vehicle longevity. 
       BACKGROUND 
       [0002]    Advertising claims require logic to back up the claim. Calculations of vehicle longevity are useful, for example, to support advertising claims. However, although claims are backed by hard numbers, certain previously used methods of calculating vehicle longevity claims include subjective steps. Due to the subjective steps, previously used methods of calculating vehicle longevity are not easily repeatable, the results of the methods are less objective, and it is difficult to project the accuracy of support for longevity claims into the future. 
         [0003]    In addition, previously used methods use a model for calculating longevity that applies different weights to different model years. However, the weights are difficult to determine. For example, for recently sold vehicles, it is difficult to determine a weight that reflects longevity and is not influenced by exogenous factors such as fire or flood. 
       SUMMARY 
       [0004]    The present technology relates to systems and methods for calculating vehicle longevity. The methods use steps that are repeatable and thus the results of the methods are objective and can include projections of how long a longevity claim can be supported into the future. 
         [0005]    The present technology identifies critical stages in the vehicle aging process; uses objective and scalable process for validating longevity-based advertising claims; and provides uncertainty estimates as well as shelf life for proposed claims. 
         [0006]    Other aspects of the present invention will be in part apparent and in part pointed out hereinafter. 
     
    
     
       DESCRIPTION OF THE DRAWINGS 
         [0007]      FIG. 1  illustrates schematically a system including a computing architecture, according to an embodiment of the present disclosure. 
           [0008]      FIG. 2  illustrates a method, according to an embodiment of the present disclosure. 
           [0009]      FIG. 3  illustrates is a chart illustrating an aggregated vehicle in operation (VIO) residual time series. 
           [0010]      FIG. 4  is a chart illustrating a first make VIO residual time series. 
           [0011]      FIG. 5  is a bar graph illustrating values of vehicle longevity. 
       
    
    
       [0012]    The figures are not necessarily to scale and some features may be exaggerated or minimized, such as to show details of particular components. In some instances, well-known components, systems, materials or methods have not been described in detail in order to avoid obscuring the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure. 
       DETAILED DESCRIPTION 
       [0013]    As required, detailed embodiments of the present disclosure are disclosed herein. The disclosed embodiments are merely examples that may be embodied in various and alternative forms, and combinations thereof. As used herein, for example, “exemplary,” and similar terms, refer expansively to embodiments that serve as an illustration, specimen, model or pattern. 
         [0014]    While the present technology is described primarily herein in connection with longevity of trucks, the technology is not limited to such vehicles or products. The concepts can be used in a wide variety of applications, such as in connection with aircraft, marine craft, farm equipment, construction equipment, major home appliances, and other. 
         [0015]    According to one embodiment,  FIG. 1  shows a system  10  configured to perform methods such as the method  100  shown in  FIG. 2 .  FIG. 1  illustrates schematically features of the system  10 . The system  10  includes a computing unit  30 . The computing unit  30  includes a processor  40  for controlling and/or processing data, input/output data ports  42 , and a memory  50 . Connecting infrastructure within the system  10 , such as one or more data buses and wireless transceivers, is not shown in detail in order to simplify the figures. 
         [0016]    The processor could be multiple processors, which could include distributed processors or parallel processors in a single machine or multiple machines. The processor could include virtual processor(s). The processor could include a state machine, application specific integrated circuit (ASIC), programmable gate array (PGA) including a Field PGA, or state machine. When a processor executes instructions to perform “operations,” this could include the processor performing the operations directly and/or facilitating, directing, or cooperating with another device or component to perform the operations. 
         [0017]    The memory  50  can include a variety of computer-readable media, including volatile media, non-volatile media, removable media, and non-removable media. The term “computer-readable media” and variants thereof, as used in the specification and claims, includes storage media. Storage media includes volatile and/or non-volatile, removable and/or non-removable media, such as, for example, RAM, ROM, EEPROM, flash memory or other memory technology, CDROM, DVD, or other optical disk storage, magnetic tape, magnetic disk storage, or other magnetic storage devices or any other medium that is configured to be used to store information that can be accessed by the processor  40 . 
         [0018]    While the memory  50  is illustrated as residing proximate the processor  40 , it should be understood that at least a portion of the memory can be a remotely accessed storage system, for example, a server on a communication network, a remote hard disk drive, a removable storage medium, combinations thereof, and the like. Thus, any of the data, applications, and/or software described below can be stored within the memory and/or accessed via network connections to other data processing systems (not shown) that may include a local area network (LAN), a metropolitan area network (MAN), or a wide area network (WAN), for example. 
         [0019]    The memory  50  includes several categories of software and data used in the computing unit  30  including applications  60 , a database  70 , an operating system  80 , and input/output device drivers  90 . 
         [0020]    The operating system  80  may be any operating system for use with a data processing system. The input/output device drivers  90  may include various routines accessed through the operating system  80  by the applications to communicate with devices, and certain memory components. The applications  60  can be stored in the memory  50  and/or in a firmware (not shown) as executable instructions, and can be executed by the processor  40 . 
         [0021]    The applications  60  include various programs that, when executed by the processor  40 , implement the various features of the computing unit  30 . The applications  60  include applications described in further detail with respect to exemplary methods. The applications  60  are stored in the memory  50  and are configured to be executed by the processor  40 . 
         [0022]    The term “application,” or variants thereof, is used expansively herein to include routines, program modules, programs, components, data structures, algorithms, and the like. Applications can be implemented on various system configurations, including single-processor or multiprocessor systems, minicomputers, mainframe computers, personal computers, hand-held computing devices, microprocessor-based, programmable consumer electronics, combinations thereof, and the like. 
         [0023]    The applications  60  may use data stored in the database  70 . The database  70  includes static and/or dynamic data used by the applications  60 , the operating system  80 , the input/output device drivers  90  and other software programs that may reside in the memory  50 . 
         [0024]    It should be understood that  FIG. 1  and the description above are intended to provide a brief, general description of a suitable environment in which the various aspects of some embodiments of the present disclosure can be implemented. While the description refers to computer-readable instructions, embodiments of the present disclosure also can be implemented in combination with other program modules and/or as a combination of hardware and software in addition to, or instead of, computer readable instructions. 
         [0025]      FIG. 2  shows an exemplary method  100  that facilitates analyzing vehicle longevity, according to an embodiment of the present disclosure. It should be understood that the steps of the method  100  are not necessarily presented in any particular order and that performance of some or all the steps in an alternative order is possible and is contemplated. The steps have been presented in the demonstrated order for ease of description and illustration. Steps can be added, omitted and/or performed simultaneously without departing from the scope of the appended claims. 
         [0026]    It should also be understood that the illustrated method  100  can be ended at any time. In certain embodiments, some or all steps of this process, and/or substantially equivalent steps are performed by execution of computer-readable instructions stored or included on a computer readable medium, such as the memory  50  of the computing unit  30  described above, for example. 
         [0027]    Referring to  FIG. 2 , the method  100  begins  102  and flow proceeds to blocks  104 ,  106 ,  108 ,  110 ,  112 ,  114 ,  116 ,  118 . Blocks  106 ,  108  are associated with computer executable instructions for identifying a threshold separation point based on an aggregated VIO residual time series. The separation point represents a point in time where vehicles are failing due to aging. Blocks  110 ,  112 ,  114 ,  116  are associated with computer executable instructions for calculating a current longevity value based on a make VIO residual time series and the threshold separation point. Block  118  is associated with computer executable instructions for calculating a future longevity value for based on a make VIO residual time series and an adjusted time window. 
         [0028]    In block  104 , the processor  40  accesses an aggregated data set  200  and make data sets  202 ,  204 ,  206 ,  208  that are stored in the memory  50 . 
         [0029]    As used herein, the aggregated data set  200  is associated with a segment of vehicles. For purposes of teaching, a segment of vehicles is a category of vehicles. For example, a segment of vehicles is full-size pickup trucks, mid-size pickup trucks, or any other category of vehicles. 
         [0030]    The aggregated data set  200  is an aggregation of data based on the make data sets  202 ,  204 ,  206 ,  208 . The make data sets  202 ,  204 ,  206 ,  208  are associated with different makes (e.g., the various makes  502 ,  504 ,  506 ,  508  shown in  FIG. 5 ) of the vehicles in the segment of vehicles. For example, a make in a segment is full-size pickup trucks for a single manufacturer. 
         [0031]    Referring momentarily to  FIG. 4 , the make data sets  202 ,  204 ,  206 ,  208  each include a vehicle in operation (VIO) residual time series for a respective make  502 ,  504 ,  506 ,  508 , referred to as a make VIO residual time series (first make VIO residual time series  402  associated with first make  502  shown in  FIG. 4 ). For example, each VIO residual (e.g., data point) in the first make VIO residual time series  402  is a VIO residual of the respective make for a model year (MY). A VIO residual, calculated as a percentage (%), for a model year and make, is the number of vehicles that remain in operation divided by the total historical number of vehicles in operation. 
         [0032]    Similarly, referring momentarily to  FIG. 3 , the aggregated data set  200  includes a vehicle in operation (VIO) residual time series for all of makes  502 ,  504 ,  506 ,  508  in a segment, referred to as an aggregated VIO residual time series  300 . For example, each VIO residual (e.g., data point) in the aggregated VIO residual time series  300  is a VIO residual for a model year (MY), calculated as a percentage (%), and is the number of vehicles that remain in operation for all of makes  502 ,  504 ,  506 ,  508  in the segment divided by the total historical number of vehicles in operation for all makes  502 ,  504 ,  506 ,  508  in the segment. 
         [0033]    The data is screened for data anomalies and outliers. For example, a VIO residual that is higher than 100% is a data anomaly. 
         [0034]    In alternative embodiments, the aggregated product data set is associated with a product group and is an aggregation of individual product data sets (e.g., a lower level of granularity with respect to the aggregated product data). 
         [0035]    Referring to  FIGS. 2 and 3 , at the block  106 , the aggregated VIO residual time series  300  is generated at a particular point in time (e.g., end of June, 2014). The y-axis is VIO residual (%) and the x-axis is time (t) with model years indicated. As described above, each VIO residual (e.g., data point) in the aggregated VIO residual time series  300  is calculated based on a sum of the number of vehicles in that remain in operation for all of makes  502 ,  504 ,  506 ,  508  in the segment divided by a sum of the total historical number of vehicles in operation for all makes  502 ,  504 ,  506 ,  508  in the segment. 
         [0036]    In  FIG. 3 , a VIO residual of close to 100% is the VIO residual of model year 2013 (e.g., close to 100% of model year 2013 vehicles in this segment are on the road at the end of June 2014). That VIO residual gradually decreases moving in a direction toward later model years. The 1998 model year has a VIO residual of about 25%. 
         [0037]    The aggregated VIO residual time series  300  does not decrease at a constant rate as the model year decreases. Rather, in general, the aggregated VIO residual time series  300  decreases at a slower rate over the more recent model years (e.g., those model years closer to 2013) and decreases at a faster rate over the older model years (e.g., those model years closer to 1998). 
         [0038]    To illustrate this, a first constant slope line  310  is shown overlaying the aggregated VIO residual time series  300  of the more recent years and a second constant slope line  312  is shown overlaying the aggregated VIO residual time series  300  of the older model years. The first constant slope line  310  fits the aggregated VIO residual time series  300  for a first stage time period  320  and the second constant slope line  312  fits the aggregated VIO residual time series  300  for a second stage time period  322 . 
         [0039]    The aggregated VIO residual time series  300  in the first stage time period  320  represents attrition during the earlier years of the life of the vehicle and the aggregated VIO residual time series  300  in the second stage time period  322  represents attrition during the later years of the life of the vehicle. The slope of the second constant slope line  312  is approximately five times the slope of the first constant slope line  310 . The change in slope represents different stages of attrition related to vehicle aging. Accordingly, the age-related vehicle attrition is more likely in the second stage time period  322  than in the first stage time period  320 . 
         [0040]    For example, the first stage time period  320  may be approximately the first ten years of the life of the vehicle. Here, all makes may perform similarly in terms of longevity. Attrition in the first stage time period  320  may be driven by exogenous factors such as flooding, fires, or accidents rather than the age of the vehicle. In other words, vehicle age is not the primary driver of vehicle attrition. As used herein, exogenous factors are those which are unrelated to vehicle durability. 
         [0041]    The first-stage time period  320  and the second-stage time period  322  may be assumed to abut one another, separated by a separation point  330 . Because there is generally not an exact point where the slope changes (e.g., the change is gradual), a range of separation points are possible (e.g., optimal values for separation points). 
         [0042]    The separation point  330  may be any point within a range of separation points  340 , as described in further detail below. The range of separation points  340  is bounded by a lower separation boundary  342  and an upper separation boundary  344 . 
         [0043]    At the block  108 , the range of separation points  340  is determined by identifying the lower separation boundary  342  and the upper separation boundary  344 . A method, such as piecewise linear regression, is used to identify the lower separation boundary  342  and the upper separation boundary  344 . The following equations are used with piecewise linear regression: 
         [0000]        y=b   0   +b   1   *x  for  x≦c    
         [0000]        y= ( b   0   +b   1   *c )+ b   2 *( x−c ) for  x&gt;c    
         [0000]    where y is the VIO residual, x is the time or model year, c is the separation point, b 0  is the intercept at the y-axis, b 1  is the slope of the line  312 , and b 2  is the slope of the line  310 . 
         [0044]    First, test values for separation point (element  330  in  FIG. 3  and variable c in equations) are selected. The test values for separation point  330  (c) can be determined, for example, by selecting a range of values around the bend in the aggregated VIO residual time series  300 . 
         [0045]    A test value in the range of test values for separation point  330  (c) is used as an input to the equations, thereby defining each of the first stage time period  320  (e.g., c to 2103) and the second stage time period  322  (e.g., 1998 to c). 
         [0046]    A linear regression analysis is performed to fit a line (e.g., line  312 ) to the aggregated VIO residual time series  300  in the second stage time period  322 , generating constants b 0 , b 1 . A linear regression analysis is performed to fit a line (e.g., line  310 ) to the aggregated VIO residual time series  300  in the first stage time period  320 , using constants b 0 , b 1  and generating constant b 2 . 
         [0047]    Each test value for separation point  330  (c) is in the range of separation points  340  if the variables b 0 , b 1 , b 2  that result from each linear regression analysis fit a line (e.g., lines  310 ,  312 ) to the aggregated VIO residual time series  300  in a respective stage time period, for example, with a statistical confidence level greater or equal to 95% (or another suitable confidence level). Particularly, a test value for separation point  330  (c) is one of the lower separation boundary  342  and the upper separation boundary  344  if the variables b 0 , b 1 , b 2  that result from each linear regression analysis fit a line (e.g., lines  310 ,  312 ) to the aggregated VIO residual time series  300  in a respective stage time period with a statistical confidence level approximately equal to 95%. 
         [0048]    Each of the values in the range of separation points  340  objectively represents a structural break, which is also referred to as breakpoint, bend, kink, or flexion point. In other words, the range of separation points  340  includes values for separation point  330  (c) for which there is a 95% confidence level that the value is acceptable for use as the separation point  330  (c). 
         [0049]    Using the values for the range of separation points  340  and a make VIO residual time series (e.g., the first make VIO residual time series  402 ), longevity is quantified for the make (e.g., first make  502 ). 
         [0050]    At the block  110  of the method  100  of  FIG. 2 , the second stage time period  322  is defined by a value in the range of separation points  340 . Referring to  FIG. 4 , a value for the separation point  330  (c) defines an end of the second stage time period  322 . For example, using the different values of the separation boundaries  342 ,  344  as the value of the separation point  330  changes the width of the second stage time period  322 . 
         [0051]    At the block  112 , the first make VIO residual time series  402  is numerically integrated over the second stage time period  322  to calculate a value for the first make longevity  512 . In other words, a first area  412  under the first make VIO residual time series  402  is calculated. The first area  412  represents the average VIO residual in the second stage time period  322 . The average VIO residual in the second stage time period  322  represents the first make longevity  512  of the first make  502 . The first area  412  represents vehicle longevity because more area indicates more vehicles survived. 
         [0052]    By integrating only over the second stage time period  322 , the weight placed on longevity in the first stage time period  320  is essentially zero. In other words, the years between the current date (e.g., 2013) and the separation point  330  are given weight of zero. 
         [0053]    Referring momentarily to  FIG. 5 , a range of values for a first make longevity  512  are calculated. Because the range of separation points  340  includes values for separation point  330  (c) for which there is a 95% confidence level that the value is acceptable for use as the separation point  330  (c), the calculated range of values for the first make longevity  512  are similarly acceptable to a 95% confidence level. The value of the lower separation boundary  342  is used to calculate a lower first make longevity boundary  522  and the upper separation boundary  344  is used to calculate an upper first make longevity boundary  532 . 
         [0054]    Using the value for the lower separation boundary  342  to define the second stage time period  322 , the first make VIO residual time series  402  is integrated over the second stage time period  322  to calculate a value for the lower first make longevity boundary  522 . Using the value for the upper separation boundary  344  to define the second stage time period  322 , the first make VIO residual time series  402  is integrated over the second stage time period  322  to calculate a value for the upper first make longevity boundary  532 . 
         [0055]    The blocks  110 ,  112  are repeated for each make  504 ,  506 ,  508  resulting in values for second make longevity  514 , third make longevity  516 , fourth make longevity  518 . For example, a value in the range of separation points  340  is used to define the second stage time period  322  and a respective one of a second make VIO residual time series, a third make VIO residual time series, and a fourth make VIO residual time series is integrated over the second stage time period  322 . 
         [0056]    Similarly, using values of the lower separation boundary  342  and the upper separation boundary  344  to define the second stage time period  322 , a respective one of the second make VIO residual time series, the third make VIO residual time series, and the fourth make VIO residual time series is integrated over the second stage time period  322  to calculate the lower second make longevity boundary  524 , the lower third make longevity boundary  526 , the lower fourth make longevity boundary  528 , the upper second make longevity boundary  534 , the upper third make longevity boundary  536 , and the upper fourth make longevity boundary  528 . 
         [0057]    Referring to  FIG. 5 , at the block  114 , an object  500  is generated. Here, the object  500  is a bar graph visually displaying the values for make longevity  512 ,  514 ,  516 ,  518 , each with its respective lower make longevity boundary  522 ,  524 ,  526 ,  528  and upper make longevity boundary  532 ,  534 ,  536 ,  538 , for each of the makes  502 ,  504 ,  506 ,  508 . The boundaries of the values for longevity can be compared to determine a statistically significant (to a 95% confidence level) difference in values for make longevity  512 ,  514 ,  516 ,  518 . For example, the lower second make longevity boundary  524  is above the upper third make longevity boundary  536  so it is clear that the value of second make longevity  514  is greater than the value of third make longevity  516 . 
         [0058]    At the block  118 , a value of longevity for a future year is calculated based on current make VIO residual timelines. Using the current make VIO residual time series, the second stage time period  322  is adjusted to approximate a future year, and the current make VIO residual time series is numerically integrated over the adjusted second stage time period  322  to calculate the future value of longevity. 
         [0059]    By calculating future values of longevity for different makes, the makes can be compared as above into the future to estimate how long a current longevity claim will be valid. 
         [0060]    One approach of adjusting the second stage time period  322  for each future year is to slide the second stage time period  322  forward in time one year while keeping the same width (by dropping the oldest years and adding a newest year). This approach mimics what happens as vehicles age as the oldest model year in the first stage time period  320  becomes the newest model year in the second stage time period  322 . This approach assumes that any differences in the first stage time period  320  persist into the second stage time period  322 . For example, a make is a certain percentage better in a model year that is currently in the first stage time period  320 , it is assumed that the make stays that much better when that model year becomes part of the second stage time period  322 . 
         [0061]    A second approach is to expand the second stage time period  322  by, for each future year, adding a newest year to the second stage time period  322 . The newest year is one year more than the current separation point  330 . 
         [0062]    A third approach is to shrink the second stage time period  322  by, for each future year, dropping the oldest year in the second stage time period  322 . This approach assumes that there is industry parity after the separation point  330  and the only years that make a difference in longevity are the years that remain in the second stage time period  322 . 
         [0063]    Various embodiments of the present disclosure are disclosed herein. The above-described embodiments are merely exemplary illustrations of implementations set forth for a clear understanding of the principles of the disclosure. Variations, modifications, and combinations may be made to the above-described embodiments without departing from the scope of the claims. All such variations, modifications, and combinations are included herein by the scope of this disclosure and the following claims.

Technology Category: g