Patent Publication Number: US-7912795-B2

Title: Automated predictive modeling of business future events based on transformation of modeling variables

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a continuation of, and claims priority to U.S. Ser. No. 11/615,703 filed Dec. 22, 2006 now U.S. Pat. No. 7,720,782, entitled “AUTOMATED PREDICTIVE MODELING OF BUSINESS FUTURE EVENTS BASED ON HISTORICAL DATA”, which is incorporated by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention generally relates to statistical modeling and analysis. More particularly, the invention relates to development of predictive models. 
     2. Background Art 
     Statistical modeling and analysis replaced the use of rule-based decision making during the last decade. Predictive modeling is a form of statistical analysis that is increasingly being used in customer management, underwriting, assessment of business patterns, customer loyalty, product portfolio performances, pricing variations, and so forth. Predictive modeling involves development of mathematical constructs that enable reliable prediction of future events or measurements based on historical information. The results may further be exploited for decision-making, which is related to the profitability of an organization. 
     Prediction of future events or measurements of a problem under investigation is performed by analyzing modeling variables. The modeling variables are related to different attributes and characteristics of the problem. The number of modeling variables utilized for predictive modeling has grown exponentially over the past few years. In some cases, the number of modeling variables may be up to 10,000 or even more. This leads to increased time and resource requirements for predictive modeling. 
     Further, it is essential to identify the relationship between a dependent variable and the modeling variables. The manual development of predictive models makes the identification difficult and leads to inclusion of redundant modeling variables. The inclusion of redundant modeling variables may lead to incorrect parameter estimation, increased computation time, confounding interpretations, and increased time requirement for building a predictive model. The manual development may also require more time. 
     Given the foregoing, what is needed is a method to reduce time requirements for predictive modeling. Further, the method should develop predictive models without manual intervention. The method should also enable manual modification and verification of the developed predictive models. 
     BRIEF SUMMARY OF THE INVENTION 
     The present invention meets the needs identified above by providing a method, system and computer program product for predictive analysis. 
     An advantage of the present invention is that it performs an automatic development of predictive models for a plurality of modeling variables. 
     Another advantage of the present invention is that it automatically performs a plurality of transformations for the plurality of modeling variables. 
     Another advantage of the present invention is that it automatically performs a selection of the transformed modeling variables. 
     Yet another advantage of the present invention is that it automatically performs a regression of the selected variables. 
     Still another advantage of the present invention is that it performs a preparation of a predictive model. 
     The invention presents a method, system and computer program product for automatically developing predictive models, for a plurality of modeling variables. The plurality of modeling variables is transformed and a transformation is selected, based on a transformation rule. A clustering of the transformed modeling variables is performed to create variable clusters. The variable clusters so created are checked for adequate representation of all the different attributes that should be present in a given model. This ensures a proper representation of different types of variables and a reduction of the modeling bias in the later steps of the model building process. Thereafter, a set of variables is selected from variable clusters, based on a selection rule. A regression of the set of variables is performed for determining prediction variables. A predictive model is then prepared utilizing the prediction variables. The preparation of the predictive model may also include modification of the predictive model, review of transformations of the modeling variables, and validation of the predictive model. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES 
       The features and advantages of the present invention will become more apparent from the detailed description set forth below, when taken in conjunction with the drawings, in which like reference numbers indicate identical or functionally similar elements. Additionally, the left-most digit of a reference number identifies the drawing in which the reference number first appears. 
         FIG. 1  is a block diagram of an exemplary system for developing predictive models, in accordance with an embodiment of the invention. 
         FIG. 2  is a flowchart illustrating a process for developing predictive models, in accordance with an embodiment of the invention. 
         FIG. 3  is a block diagram of an exemplary system for developing predictive models, in accordance with an alternative embodiment of the invention. 
         FIG. 4  is a flowchart illustrating a process for generating a plurality of transformations, in accordance with an embodiment of the invention. 
         FIG. 5  is a flowchart illustrating a process for selecting a transformation, in accordance with an embodiment of the invention. 
         FIG. 6  is a block diagram of an exemplary computer system that is useful for implementing the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     I. Overview 
     The present invention is directed to a method, system and computer program product for developing predictive models. Predictive models are developed automatically for a plurality of modeling variables. A plurality of transformations is generated for each of the plurality of modeling variables. A transformation is selected for each of the plurality of modeling variables, based on a transformation rule. A clustering of the transformed modeling variables is performed to create variable clusters. A set of variables are selected from the variable clusters, based on a selection rule. A regression of the set of variables is performed for determining prediction variables. A predictive model is then prepared utilizing these prediction variables. The preparation of the predictive model may also include modification of the predictive model, review of the plurality of transformations, and validation of the predictive model. 
     The present invention is now described in more detail herein in terms of the above-mentioned exemplary embodiment. This is for convenience only and is not intended to limit the application of the present invention. In fact, after reading the following description, it will be apparent to one skilled in the relevant art(s) how the following invention can be implemented in alternative embodiments, e.g., in the various software and hardware platforms being used, the mode of implementation of the invention, the end use of the invention, etc. 
     II. System 
       FIG. 1  is a block diagram providing an overview of an exemplary system  100 . In an embodiment of the invention, system  100  performs development of predictive models utilizing a plurality of modeling variables. System  100  includes a transformation module  102 , a selection module  104 , a regression module  106 , and a model preparation module  108 . 
     System  100  may be supported by a statistical analysis software. In accordance with an embodiment of the invention, the statistical analysis software may be a SAS® Statistical Software, available from SAS Institute Inc., Cary, N.C. The SAS® Statistical Software assists users in developing predictive models. 
     Transformation module  102  automatically transforms a plurality of modeling variables. 
     Examples of transformations include linear, logarithmic, square root, and so forth. Transformation module  102  generates a plurality of transformations for each of the plurality of modeling variables. A transformation is selected for each of the plurality of modeling variables by transformation module  102 , based on a transformation rule. 
     Selection module  104  automatically selects a set of variables from the transformed modeling variables, based on a selection rule. Selection module  104  performs a clustering of the transformed modeling variables for creating variable clusters. The set of variables is selected from the variable clusters based on the selection rule. 
     Regression module  106  performs an automatic regression of the set of variables. The regression is performed for determining prediction variables. The prediction variables are used in preparing the predictive model. 
     Model preparation module  108  prepares the predictive model based on the prediction variables. Model preparation module  108  also enables a user to perform modification of the predictive model, review of the plurality of transformations of the plurality of modeling variables, and validation of the predictive model. 
     III. Process 
       FIG. 2  is a flowchart illustrating a process  200  for developing predictive models, in accordance with an embodiment of the invention. In various embodiments of the invention, process  200  is implemented by using a statistical analysis software. 
     At step  202 , the information required for developing predictive models is entered. In an embodiment of the invention, the information includes a plurality of modeling variables, a dependent variable, and a dataset for the plurality of modeling variables. In an embodiment of the invention, the dependent variable may be a bivariate variable. In an alternative embodiment of the invention, the dependent variable may be a continuous variable. 
     At step  204 , the plurality of modeling variables are transformed. The transformation is performed to obtain a linear relationship of each of the plurality of modeling variables, in relation to the dependent variable. In an embodiment of the invention, a plurality of transformations may be generated for each modeling variable. In an embodiment of the invention, the transformations are stored in a memory device for future reference. 
     A transformation is selected for each modeling variable, from the plurality of transformations, based on a transformation rule. In an embodiment of the invention, the transformation rule is based on a correlation between the modeling variable and the dependent variable. The transformation rule is also based on a proportion of a range of the modeling variable utilized, and a proportion of a range of the dependent variable that is explained by the modeling variable. 
     At step  206 , a set of variables is selected from the transformed modeling variables. In an embodiment of the invention, a clustering of the transformed modeling variables is performed to create variable clusters. The set of variables is selected from the variable clusters, based on a selection rule. In an embodiment of the invention, one variable is selected from each variable cluster. 
     In an embodiment of the invention, the selection rule is based on a correlation between a transformed variable and the dependent variable, and a proportion of a range of the dependent variable explained by the transformed variable. 
     In an alternative embodiment of the invention, the selection rule is based on a log-likelihood difference. The loglikehood difference is the difference between two model-fit statistics, one being derived by utilizing an intercept model, and the other being derived by utilizing an intercept-plus-covariate model. 
     At step  208 , a regression of the set of variables is performed. The regression is performed for determining prediction variables. In an embodiment of the invention, a stepwise regression is performed. 
     In an embodiment of the invention, a logistic regression may be performed. In an alternative embodiment of the invention, an Ordinary Least Squares (OLS) regression may be performed. 
     At step  210 , a predictive model is prepared by utilizing the prediction variables obtained from regression. In an embodiment of the invention, the preparation of the predictive model includes reviewing the transformations of the plurality of modeling variables and validating the predictive model. In an embodiment of the invention, the preparation of the predictive models further includes a modification of the predictive model. 
     IV. Example Implementation 
       FIG. 3  is a block diagram providing a detailed view of an exemplary system  300 . System  300  is supported by the SAS® Statistical Software as described above. 
     System  300  includes an input module  302 , a value substitution module  304 , a transformation module  306 , a selection module  308 , a regression module  310 , and a model preparation module  312 . 
     Input module  302  enables entering information required for developing predictive models. In an embodiment of the invention, the information includes a plurality of modeling variables, a dependent variable, and a dataset for the modeling variables. In an embodiment of the invention, the information is input manually. In an alternative embodiment of the invention, the information is entered automatically. 
     In an embodiment of the invention, the dependent variable is a bivariate variable. In an alternative embodiment of the invention, the dependent variable is a continuous variable. 
     Value substitution module  304  performs a substitution of values in the dataset for the modeling variables. In an embodiment of the invention, the substitution may be performed for at least one of missing values, invalid values, and/or zero values. 
     In an embodiment of the invention, the missing values for a modeling variable may be substituted by the minimum value of the modeling variable in the dataset. In an alternative embodiment of the invention, the missing values may be substituted by a replacement value. The replacement value is computed automatically based on the relationship between the dependent variable and the modeling variable. The relationship may be derived by excluding values of the dependent variable that correspond to the missing values. 
     In an embodiment of the invention, the invalid values may be substituted by the minimum value of the modeling variable in the dataset. In an alternative embodiment of the invention, the invalid values may be substituted by the maximum value of the modeling variable in the dataset. In another alternative embodiment of the invention, the invalid values may be substituted by a replacement value. The replacement value is computed automatically based on the relationship between the dependent variable and the modeling variable. The relationship may be derived by excluding values of the dependent variable that correspond to the invalid values. 
     In an embodiment of the invention, the zero values may be substituted by the minimum value of the modeling variable in the dataset. In an alternative embodiment of the invention, the zero values may be substituted by a replacement value. The replacement value is computed automatically based on the relationship between the dependent variable and the modeling variable. The relationship may be derived by excluding values of the dependent variable that correspond to the zero values. 
     Transformation module  306  automatically transforms the plurality of modeling variables. In various embodiments of the invention, the transformation includes generation of bivariate ranks and plots. 
     In an embodiment of the invention, a plurality of bivariate ranks and plots are generated between the dependent variable and each of the modeling variables. In an embodiment of the invention, the plurality of bivariate ranks and plots may be stored for future reference. 
     In an embodiment of the invention, the plurality of bivariate ranks and plots are utilized for splitting the dataset for each modeling variable into a plurality of buckets. In an embodiment of the invention, the dataset for each modeling variable is split into, for example, twenty equal-sized buckets. In an alternative embodiment of the invention, the dataset for each modeling variable is split into a user-specified number of equal-sized buckets. The equal-sized buckets are utilized for generating a plurality of transformations for the modeling variables. The generation of a plurality of transformations is described in conjunction with  FIG. 4 . 
     In an embodiment of the invention, for each modeling variable, a transformation may be selected from the plurality of transformations, based on a transformation rule. 
     In an embodiment of the invention, the transformation rule for a modeling variable is based on a correlation between a modeling variable and a dependent variable, Corr; a proportion of a range of the modeling variable utilized, RangeM; and a proportion of a range of the dependent variable that is explained by the modeling variable, RangeExp. In an alternative embodiment of the invention, the transformation rule is based on a log-likelihood difference. 
     In an embodiment of the invention, the transformation rule leads to the selection of the transformation which has the highest value of a transformation selection statistic, TranStat. In an embodiment of the invention, TranStat is given by the following equation:
 
TranStat=Corr 2 *RangeExp*√{square root over (Range M )}  1
 
     Corr may be calculated as a linear correlation between the dependent variable and the modeling variable. In an embodiment of the invention, Corr is equal to a Pearson product-moment correlation coefficient. 
     In an alternative embodiment of the invention, three transformations may be selected, based on the transformation rule. The selection of the transformation is further described in conjunction with  FIG. 5 . 
     Selection module  308  automatically selects a set of variables from the transformed modeling variables, based on a selection rule. Selection module  308  includes a variable clustering module  314  and a variable selection module  316 . 
     Variable clustering module  314  performs a clustering of the transformed modeling variables. The clustering is performed to create variable clusters. The transformed modeling variables in each variable cluster are correlated amongst themselves. The transformed modeling variables in a variable cluster are less correlated with the variables in other variable clusters. The clustering is supported by a variable clustering algorithm. 
     In an embodiment of the invention, the variable clustering algorithm is PROC VARCLUS. The PROC VARCLUS variable clustering algorithm is supported by the SAS® Statistical Software. Additional information about the SAS® Statistical Software and about PROC VARCLUS is available at http://www.sas.com. The algorithm divides the modeling variables into either disjoint or hierarchical variable clusters. Each variable cluster is associated with a linear combination of the modeling variables present in the variable cluster. The linear combination may either be a first principal component or a centroid component. The first principal component is a weighted average of the modeling variables that explains the maximum possible variance. The centroid component is an unweighted average of the modeling variables. The algorithm maximizes the sum of variances across variable clusters to obtain the resulting variable clusters for selection of a set of variables. 
     Variable selection module  316  selects the set of variables from the variable clusters, based on a selection rule. In an embodiment of the invention, one variable may be selected from each variable cluster. 
     In an embodiment of the invention, the selection rule is based on a correlation between a transformed modeling variable and the dependent variable, CorrTran, and the proportion of the range of the dependent variable explained by the transformed modeling variable, RangeTran. In an alternative embodiment of the invention, the selection rule is based on a log-likelihood difference. 
     In an embodiment of the invention, one variable is selected from one variable cluster based on the selection rule. The selection rule leads to the selection of a variable, which has the highest value of a variable selection statistic, SelectStat, in the variable cluster. 
     In an embodiment of the invention, SelectStat is given by the following equation:
 
SelectStat=CorrTran*RangeTran  2
 
     CorrTran may be calculated as a linear correlation between the dependent variable and the transformed variable. In an embodiment of the invention, CorrTran is equal to a Pearson product-moment correlation coefficient. 
     Regression module  310  performs a regression of the set of variables selected by variable selection module  316 . The regression is performed for determining prediction variables. 
     In an embodiment of the invention, stepwise regression is performed for determining the prediction variables. 
     In an embodiment of the invention, an OLS regression is performed. The OLS regression is supported by a PROC REG regression algorithm provided by SAS® Statistical Software. In an alternative embodiment of the invention, a logistic regression is performed. The logistic regression is supported by a PROC LOGISTIC regression algorithm provided by SAS® Statistical Software. 
     In an embodiment of the invention, the regression provides a partial R-square value, a regression coefficient, a Student&#39;s t-test value, a p-value, and a Variable Inflation Factor (VIF), as an output for the set of selected modeling variables. The partial R-square value measures the marginal contribution of a variable upon an inclusion of the variable in the model. The Student&#39;s t-test value and the p-value denote the statistical significance of the variable. The VIF denotes redundancy of a modeling variable in the model. For example, a high value of VIF denotes that the modeling variable is correlated to at least one other modeling variable in the model, and the modeling variable is therefore redundant and removed from the model. 
     Model preparation module  312  enables a preparation of a predictive model. The predictive model may be prepared by using the prediction variables, and the regression coefficients from regression module  310 . 
     In an embodiment of the invention, modification of the predictive model is performed manually. The modification of the predictive model may be performed by adding or removing the predictive variables. 
     In an alternative embodiment of the invention, model preparation module  312  enables a manual review of the plurality of transformations generated by transformation module  306 . 
     In an alternative embodiment of the invention, model preparation module  312  enables a validation of the predictive model.  FIG. 4  is a flowchart illustrating a process for generating a plurality of transformations, in accordance with an embodiment of the invention. 
     At step  402 , the dataset for a modeling variable is split into a set of equal-sized buckets. In an embodiment of the invention, the dataset is split, for example, into twenty equal-sized buckets. The bivariate ranks and plots, as described in conjunction with  FIG. 3 , are utilized for splitting the dataset. 
     At step  404 , some counters are initialized. In an embodiment of the invention, the counters may be a first bucket number (FB), a last bucket number (LB), and a total number of buckets (NOB). For example, in case the total number of buckets is twenty, then a value ‘one’ is assigned to FB, a value ‘20’ is assigned to NOB, and the value stored in NOB is assigned to LB. 
     At step  406 , the set of buckets is truncated to generate a truncated dataset. In an embodiment of the invention, the set of equal-sized buckets is truncated from FB to LB. Further, the remaining buckets are temporarily discarded from the set of buckets. For example, in case the values stored in FB, LB, and NOB are equal to 1, 18 and 20 respectively, then the set of buckets is truncated from 1 to 18 to obtain a set of 18 buckets. Further, the buckets with numbers 19 and 20 are temporarily discarded from the set of buckets. 
     At step  408 , the transformation statistic is calculated and stored. The transformation statistic is calculated after applying a transformation to the set of buckets. In an embodiment of the invention, the transformation may be, for example, a linear, logarithmic or square root transformation. In an embodiment of the invention, TranStat is calculated for the truncated dataset, as described in conjunction with  FIG. 3 . Further, the transformation statistic is stored along with the transformation for the set of buckets. 
     At step  410 , it is checked whether the value stored in FB is equal to half of the value stored in NOB. In an alternative embodiment of the invention, the value stored in FB may be compared with a predetermined proportion of NOB. If the condition is true, then the generation of the transformations for the modeling variable is stopped. If the condition is false, then the generation of the transformations for the modeling variable is continued. 
     At step  412 , it is checked whether the positive difference between the values stored in FB and LB is greater than half of the value stored in NOB. If the condition is false, then step  416  is performed, else the process is continued. 
     At step  414 , the value stored in LB is decreased by one. Thereafter, step  406  is performed. 
     At step  416 , the value stored in FB is increased by one. Further, the value stored in NOB is assigned to LB. Thereafter, step  406  is performed. 
       FIG. 5  is a flowchart illustrating a process for selecting a transformation, in accordance with an embodiment of the invention. In one embodiment, the process from step  502  to step  524  is carried out for all the modeling variables. 
     At step  502 , one transformation is selected for each modeling variable from the plurality of transformations. In an embodiment of the invention, the transformations generated in  FIG. 4  may be sorted in a descending order, based on the transformation statistic. In an embodiment of the invention, the transformations are sorted based on TranStat. After the sorting, the transformation with the highest value of TranStat is selected. Further, depending on the truncation buckets for the selected transformation, some counters are initialized. For example, if the selected transformation includes a set of 16 buckets with FB=3 and LB=18, then a value of three is assigned to the lower bucket number, LBN, and a value of  18  is assigned to the upper bucket number, UBN. 
     At step  504 , it is checked whether the value stored in UBN is greater than (NOB−2). If the condition is false, then transfer the control to step  512 , else the process is continued. 
     At step  506 , two conditions are checked, i.e., the value of LBN and the type of transformation. If either the value stored in LBN is not less than three or the transformation is not logarithmic, then step  510  is performed. Otherwise, the process is continued. 
     At step  508 , the set of buckets is modified. Each of the upper two buckets and the lower two buckets are split into twenty equal-sized buckets. The set of buckets is then truncated for LBN+2 and UBN−2. For example, in case NOB is equal to twenty, then the lower two buckets and the upper two buckets will be discarded from the set of buckets. 
     The lower two buckets are then replaced with twenty buckets obtained from splitting the lower two buckets. The upper two buckets are replaced with twenty buckets obtained by splitting the upper two buckets. The value stored in LBN, UBN, and NOB is reinitialized, based on the modification of the set of buckets. Thereafter, step  520  is performed. 
     At step  510 , the set of buckets is modified. The lower two buckets are split into twenty equal-sized buckets. The set of buckets is truncated for (UBN−2). The lower two buckets are replaced with the twenty buckets obtained by splitting the lower two buckets. The values stored in LBN, UBN, and NOB are reinitialized based on the modification of the set of buckets. Thereafter, step  520  is performed. 
     At step  512 , two conditions are checked, i.e., the value of LBN, and the type of transformation. If either the value stored in LBN is not less than three or the transformation is not logarithmic, then step  516  is performed. Otherwise, the process is continued. 
     At step  514 , the set of buckets is modified. The upper two buckets are split into twenty equal-sized buckets. The set of buckets is truncated for LBN+2 and the upper two buckets are replaced with the twenty buckets obtained by splitting the upper two buckets. The value stored in LBN, UBN, and NOB is reinitialized, based on the modification of the set of buckets. Thereafter, step  520  is performed. 
     At step  516 , it is checked if the positive difference between UBN and LBN is equal to half of the value stored in NOB. If the condition is false, then step  524  is performed. Otherwise, the process is continued. 
     At step  518 , and the set of buckets is modified. In an embodiment of the invention, the set of buckets from LBN to UBN are split into twenty equal-sized buckets. 
     At step  520 , the transformations are generated for the modified set of buckets. The transformations are generated by performing steps  406  to  416 , as described in conjunction with  FIG. 4 . The counters are reinitialized by assigning the value stored in LBN to FB and by assigning the value stored in UBN to LB. 
     At step  522 , one transformation is selected for the transformed modeling variable. In an embodiment of the invention, transformations may be sorted in a descending order based on the transformation statistic. In an embodiment of the invention, transformations are sorted, based on TranStat. After the sorting, the transformation with the highest value of TranStat is selected. 
     At step  524 , the selected transformation for the transformed modeling variable is stored. 
       FIG. 6  is a block diagram of an exemplary computer system that is useful for implementing the invention. 
     The present invention, i.e., system  100 , process  200 , system  300  or any part(s) or function(s) thereof, may be implemented by using hardware, software or a combination thereof, and may be implemented in one or more computer systems or other processing systems. However, manipulations performed by the present invention are often referred to in terms such as adding or comparing, which are commonly associated with the mental operations performed by a human operator. This capability of a human operator is unnecessary, or undesirable, in most cases, in any of the operations described herein, which form part of the present invention. On the contrary, all the operations are automated operations. Machines useful for performing the operations of the present invention include general purpose digital computers or similar devices. An example of a computer system  600  is shown in  FIG. 6 . 
     Computer system  600  includes one or more processors such as processor  602 . Processor  602  is connected to a communication infrastructure  604 , such as a communication bus, a cross-over bar or a network. Various software embodiments are described in terms of this exemplary computer system. After reading this description, it will become apparent to a person skilled in the relevant art(s) as to how the invention can be implemented by using other computer systems and/or architectures. 
     Computer system  600  can include a display interface  606  that forwards graphics, text, and other data from communication infrastructure  604  (or from a frame buffer that is not shown) for display on a display unit  608 . 
     Computer system  600  also includes a main memory  610 , preferably a random access memory (RAM), and may also include a secondary memory  612 . Secondary memory  612  may include, for example, a hard disk drive  614  and/or a removable storage drive  616  representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. Removable storage drive  616  reads from and/or writes to a removable storage unit  618  in a well-known manner. Removable storage unit  618  represents a floppy disk, a magnetic tape, an optical disk, etc., which is read by and written to by removable storage drive  616 . As will be appreciated, removable storage unit  618  includes a computer-usable storage medium with stored computer software and/or data. 
     In alternative embodiments, secondary memory  612  may include other similar devices, enabling computer programs or other instructions to be loaded into computer system  600 . Such devices may include, for example, a removable storage unit and an interface. Examples of these devices may include a program cartridge and a cartridge interface such as those found in video game devices, a removable memory chip such as an erasable programmable read-only memory (EPROM), or a programmable read only memory (PROM)) and an associated socket, as well as other removable storage units and interfaces, which enable software and data to be transferred from the removable storage unit to computer system  600 . 
     Computer system  600  may also include a communications interface  620 , which enables software and data to be transferred between computer system  600  and external devices. Examples of communications interface  620  may include a modem, a network interface such as an Ethernet card, a communications port, a Personal Computer Memory Card International Association (PCMCIA) slot and card, etc. Software and data transferred via communications interface  620  are in the form of signals  624 , which may be electronic, electromagnetic, optical or other signals that are capable of being received by communications interface  620 . These signals  624  are provided to communications interface  620  via a communications path  622  (e.g. channel). This communications path  622  carries signals  624  and may be implemented by using a wire or cable, fiber optics, a telephone line, a cellular link, a radio frequency (RF) link, and other communications channels. 
     In this document, the terms ‘computer program medium’ and ‘computer-usable medium’ are used to generally refer to media such as removable storage drive  616 , a hard disk installed in hard disk drive  614 , and signals  624 . These computer program products provide software to computer system  600 . The invention is directed at such computer program products. 
     Computer programs (also referred to as computer control logic) are stored in main memory  610  and/or secondary memory  612 . These computer programs may also be received via communications interface  620 . Such computer programs, when executed, enable computer system  600  to perform the features of the present invention, as discussed herein. In particular, the computer programs, when executed, enable processor  602  to perform the features of the present invention. Accordingly, such computer programs act as the controllers of computer system  600 . 
     In an embodiment where the invention is implemented by using software, the software may be stored in a computer program product and loaded into computer system  600  by using removable storage drive  616 , hard disk drive  614  or communications interface  620 . The control logic (software), when executed by processor  602 , causes processor  602  to perform the functions of the invention, as described herein. 
     In another embodiment, the invention is implemented primarily in hardware, using, for example, hardware components such as application-specific integrated circuits (ASICs). Implementation of the hardware state machine, to perform the functions described herein, will be apparent to persons skilled in the relevant art(s). 
     In yet another embodiment, the invention is implemented by using a combination of both hardware and software. 
     V. Conclusion 
     While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example and not limitation. It will be apparent to persons skilled in the relevant art(s) that various changes in form and detail can be made therein, without departing from the spirit and scope of the present invention. Thus, the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents. 
     In addition, it should be understood that the figures illustrated in the attachments, which highlight the functionality and advantages of the present invention, are presented for exemplary purposes only. The architecture of the present invention is sufficiently flexible and configurable, such that it may be utilized (and navigated) in ways other than that shown in the accompanying figures. 
     Further, the purpose of the foregoing Abstract is to enable the U.S. Patent and Trademark Office and the public generally, and especially the scientists, engineers and practitioners in the art who are not familiar with patent or legal terms or phraseology, to determine quickly by a cursory inspection the nature and essence of the technical disclosure of the application. The Abstract is not intended to be limiting to the scope of the present invention in any way.