Patent Publication Number: US-2010111288-A1

Title: Time to answer selector and advisor for call routing center

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is related to U.S. Ser. No. 12/021,251, filed Jan. 28, 2008, which is hereby incorporated by reference in its entirety for all purposes. 
    
    
     BACKGROUND 
     1. Field 
     The present invention relates generally to the field of routing phone calls and other telecommunications in a contact center system. 
     2. Related Art 
     The typical contact center consists of a number of human agents, with each assigned to a telecommunication device, such as a phone or a computer for conducting email or Internet chat sessions, that is connected to a central switch. Using these devices, the agents are generally used to provide sales, customer service, or technical support to the customers or prospective customers of a contact center or a contact center&#39;s clients. 
     Typically, a contact center or client will advertise to its customers, prospective customers, or other third parties a number of different contact numbers or addresses for a particular service, such as for billing questions or for technical support. The customers, prospective customers, or third parties seeking a particular service will then use this contact information, and the incoming caller will be routed at one or more routing points to a human agent at a contact center who can provide the appropriate service. Contact centers that respond to such incoming contacts are typically referred to as “inbound contact centers.” 
     Similarly, a contact center can make outgoing contacts to current or prospective customers or third parties. Such contacts may be made to encourage sales of a product, provide technical support or billing information, survey consumer preferences, or to assist in collecting debts. Contact centers that make such outgoing contacts are referred to as “outbound contact centers.” 
     In both inbound contact centers and outbound contact centers, the individuals (such as customers, prospective customers, survey participants, or other third parties) that interact with contact center agents using a telecommunication device are referred to in this application as a “caller.” The individuals acquired by the contact center to interact with callers are referred to in this application as an “agent.” 
     Conventionally, a contact center operation includes a switch system that connects callers to agents. In an inbound contact center, these switches route incoming callers to a particular agent in a contact center, or, if multiple contact centers are deployed, to a particular contact center for further routing. In an outbound contact center employing telephone devices, dialers are typically employed in addition to a switch system. The dialer is used to automatically dial a phone number from a list of phone numbers, and to determine whether a live caller has been reached from the phone number called (as opposed to obtaining no answer, a busy signal, an error message, or an answering machine). When the dialer obtains a live caller, the switch system routes the caller to a particular agent in the contact center. 
     BRIEF SUMMARY 
     Systems and methods of the present invention can be used to improve or optimize the routing of callers to agents in a contact center. According to one aspect of the present invention an interface is provided for use with an inbound or outbound call routing center for routing callers based on performance of agents and/or pattern matching algorithms between callers and agents. In one example, the interface includes a graphical element (e.g., a selector, slider, text field, or the like) for setting and adjusting a time limit for a time to answer or connect the caller to an agent. For instance, increasing the time limit allows a contact routing system to hold a caller until the best matching agent becomes free. If the time limit is exceeded, the contact routing system operates to route the caller to the best matching agent of those that are available at that time. 
     Increasing the time to answer and connect a caller, however, may have detrimental effects on the probability of revenue generation or customer satisfaction (not to mention that the caller may simply hang-up or abandon the call). Accordingly, in one example, the interface is further operable to display an estimated effect of the time limit on at least one outcome variable. For instance, the interface is operable to display an estimated revenue generation, cost, customer satisfaction, first call resolution, cancellation, or other outcome variables of the pattern matching algorithm(s) of the system based on a particular time limit for routing the caller. The outcome variables may be estimated based on past call history data, stored algorithms, look-up tables, or the like. Further, the interface may be operable to display an estimated change in the at least one outcome variable if the wait time is changed. For instance, if a contact center user wishes to increase the time limit by two seconds, the interface is operable to display an estimated change in one or more outcome variables. In one example, determining estimates of outcome variables includes evaluating a past time period of the same (or similar) set of agents and constructing a distribution of agent/caller pairs. Using each pair, an expected success rate can be computed via the pattern matching algorithm and applied to current information to estimate probably outcomes (e.g., with respect to one or more of sales, cost, customer satisfaction, etc.). 
     According to another aspect, a method is provided for routing a caller to an agent, the method including comparing caller data associated with a caller with agent data associated with a set of agents utilizing a pattern matching algorithm. The method further includes causing the caller to be routed to the best matching agent of the set of agents per the pattern matching algorithm if available and if not available holding the caller until the best matching agent becomes available or a time limit for holding the caller is reached. If the time limit is reached, the caller is routed to the best available agent at that time. The method further includes displaying of a graphical element to a user for adjusting the time limit. The method may further display the estimated effect of the time limit or an adjustment thereto on one or more outcome variables of the pattern matching algorithm. 
     According to another aspect, an apparatus is provided comprising logic for comparing caller data associated with a caller with agent data associated with a set of agents utilizing a pattern matching algorithm. If the best matching agent per the pattern matching algorithm is not available the apparatus is operable to hold the caller until a time limit is reached and cause the caller to be routed to the best available agent of the set of agents if the time limit is exceeded. Further, the apparatus is operable to cause the display of a graphical element with an interface for adjusting the time limit. 
     Many of the techniques described may be implemented in hardware, firmware, software, or combinations thereof. In one example, the techniques are implemented in computer programs executing on programmable computers that each includes a processor, a storage medium readable by the processor (including volatile and nonvolatile memory and/or storage elements), and suitable input and output devices. Program code is applied to data entered using an input device to perform the functions described and to generate output information. The output information is applied to one or more output devices. Moreover, each program is preferably implemented in a high level procedural or object-oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram reflecting the general setup of a contact center operation. 
         FIG. 2  illustrates an exemplary routing system having a routing engine according to one example. 
         FIG. 3A  illustrates an exemplary routing engine for routing callers based on performance based routing and/or pattern matching algorithms. 
         FIG. 3B  illustrates an exemplary method for matching callers and agents using caller data and agent data in a pattern matching algorithm. 
         FIG. 4  illustrates an exemplary interface having a graphic element for adjusting a time to answer limit for a call routing system. 
         FIG. 5  illustrates an exemplary interface having a graphic element for adjusting a time to answer limit for a call routing system, and further operable to display estimated effects on output variables for a selected time to answer limit. 
         FIG. 6  illustrates a typical computing system that may be employed to implement some or all processing functionality in certain embodiments of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The following description is presented to enable a person of ordinary skill in the art to make and use the invention, and is provided in the context of particular applications and their requirements. Various modifications to the embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Moreover, in the following description, numerous details are set forth for the purpose of explanation. However, one of ordinary skill in the art will realize that the invention might be practiced without the use of these specific details. In other instances, well-known structures and devices are shown in block diagram form in order not to obscure the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein. 
     While the invention is described in terms of particular examples and illustrative figures, those of ordinary skill in the art will recognize that the invention is not limited to the examples or figures described. Those skilled in the art will recognize that the operations of the various embodiments may be implemented using hardware, software, firmware, or combinations thereof, as appropriate. For example, some processes can be carried out using processors or other digital circuitry under the control of software, firmware, or hard-wired logic. (The term “logic” herein refers to fixed hardware, programmable logic and/or an appropriate combination thereof, as would be recognized by one skilled in the art to carry out the recited functions.) Software and firmware can be stored on computer-readable storage media. Some other processes can be implemented using analog circuitry, as is well known to one of ordinary skill in the art. Additionally, memory or other storage, as well as communication components, may be employed in embodiments of the invention. 
     According to one aspect of the present invention, a displayed computer interface for setting and changing a limit time for holding a caller (or “time to answer” limit) is described. Increasing the time may allow for the best or a better matching agent per a performance based, pattern matching algorithm, or computer model to become available for connection with the caller. The time limit can be altered in real time or at a predetermined time in the future, and estimated effects of the time limit or changes thereto on outcomes of the system may be displayed to a user. Initially, exemplary call routing systems and methods utilizing performance based, pattern matching algorithms, and/or computer models are described for routing callers to available agents. This description is followed by exemplary interfaces and methods for setting and adjusting a time to answer limit to allow the call routing system to find an agent match for a caller. 
       FIG. 1  is a diagram reflecting the general setup of a contact center operation  100 . The network cloud  101  reflects a specific or regional telecommunications network designed to receive incoming callers or to support contacts made to outgoing callers. The network cloud  101  can comprise a single contact address, such as a telephone number or email address, or multiple contract addresses. The central router  102  reflects contact routing hardware and software designed to help route contacts among call centers  103 . The central router  102  may not be needed where there is only a single contact center deployed. Where multiple contact centers are deployed, more routers may be needed to route contacts to another router for a specific contact center  103 . At the contact center level  103 , a contact center router  104  will route a contact to an agent  105  with an individual telephone or other telecommunications equipment  105 . Typically, there are multiple agents  105  at a contact center  103 , though there are certainly embodiments where only one agent  105  is at the contact center  103 , in which case a contact center router  104  may prove to be unnecessary. 
       FIG. 2  illustrates an exemplary contact center routing system  200  (which may be included with contact center router  104  of  FIG. 1 ). Broadly speaking, routing system  200  is operable to match callers and agents based, at least in part, on agent performance, pattern matching algorithms or computer models using caller data and/or agent data, and the like. Routing system  200  may include a communication server  202  and a routing engine  204  (referred to at times as “SatMap” or “Satisfaction Mapping”) for receiving and matching callers to agents. 
     Routing engine  204  may operate in various manners to match callers to agents based on performance data of agents, pattern matching algorithms, and computer models, which adapt over time based on the performance or outcomes of previous caller-agent matches. In one example, the routing engine  204  includes a neural network based adaptive pattern matching engine. Various other exemplary pattern matching and computer model systems and methods which may be included with content routing system and/or routing engine  204  are described in U.S. Ser. No. 12/021,251, filed Jan. 28, 2008, and U.S. Ser. No. U.S. patent application Ser. No. 12/202,091, filed Aug. 29, 2008, both of which are hereby incorporated by reference in their entirety. Of course, it will be recognized that other performance based or pattern matching algorithms and methods may be used alone or in combination with those described here. 
     Routing system  200  may further include other components such as collector  206  for collecting caller data of incoming callers, data regarding caller-agent pairs, outcomes of caller-agent pairs, agent data of agents, and the like. Further, routing system  200  may include a reporting engine  208  for generating reports of performance and operation of the routing system  200 . Various other servers, components, and functionality are possible for inclusion with routing system  200 . Further, although shown as a single hardware device, it will be appreciated that various components may be located remotely from each other (e.g., communication server  202  and routing engine  204  need not be included with a common hardware/server system or included at a common location). Additionally, various other components and functionality may be included with routing system  200 , but have been omitted here for clarity. 
       FIG. 3A  illustrates detail of exemplary routing engine  204 . Routing engine  204  includes a main mapping engine  304 , which receives caller data and agent data from databases  310  and  312 . In some examples, routing engine  204  may route callers based solely or in part on performance data associated with agents. In other examples, routing engine  204  may make routing decisions based solely or in part on comparing various caller data and agent data, which may include, e.g., performance based data, demographic data, psychographic data, and other business-relevant data. Additionally, affinity databases (not shown) may be used and such information received by routing engine  204  for making routing decisions. 
     Routing engine  204  further includes or is in communication with timing heuristic logic  308 , which determines the time to answer limit, e.g., how long a caller is held as routing engine  204  determines and waits for the best matching agent for routing the caller. For instance, in a case where the best matching agent is busy (e.g., with another call, post call processing, or the like), increasing the time to answer increases the chance that the best (or a better matching agent) becomes available for routing relative to the then available agent(s). In other words, if a caller is held for a longer period of time, the best matching agent may become available for the caller. Of course, if a caller is held too long it may have a negative effect on revenue or customer satisfaction, for example. In one example, described herein, the caller wait time is adjustable by a user and further provides an estimation to the user as to the effects on cost, revenue, customer satisfaction, and the like. 
     In one example, routing engine  204  further includes or is in communication with one or more neural network engines  306 . Neural network engines  306  may receive caller and agent data directly or via routing engine  204  and operate to match and route callers based on pattern matching algorithms and computer models generated to increase the changes of desired outcomes. Further, as indicated in  FIG. 3A , call history data (including, e.g., caller-agent pair outcomes with respect to cost, revenue, customer satisfaction, etc.) may be used to retrain or modify the neural network engine  306  periodically, e.g., daily or weekly. 
     Exemplary methods for routing callers to agents include rating agents on performance, comparing agent data and caller data and matching per a pattern matching algorithm, creating computer models to predict outcomes of agent-caller pairs, or combination thereof.  FIG. 3B  illustrates an exemplary method for increasing the chances of an optimal interaction by combining agent grades (which may be determined from grading or ranking agents on desired outcomes), agent demographic data, agent psychographic data, and other business-relevant data about the agent (individually or collectively referred to in this application as “agent data”), along with demographic, psychographic, and other business-relevant data about callers (individually or collectively referred to in this application as “caller data”). Agent and caller demographic data can comprise any of: gender, race, age, education, accent, income, nationality, ethnicity, area code, zip code, marital status, job status, credit score, and the like. Agent and caller psychographic data can comprise any of introversion, sociability, desire for financial success, film and television preferences, and the like. 
     In one example, a method for operating a contact center includes determining caller data associated with a caller (e.g., a caller on hold), determining agent data associated with a set of agents (e.g., available and busy agents), comparing the agent data and the caller data (e.g., via a pattern matching algorithm), and matching the caller to an agent to increase the chance of an optimal interaction. In particular, at  322 , caller data (such as a caller demographic or psychographic data) is identified for a caller. One way of accomplishing this is by retrieving caller data from available databases by using the caller&#39;s contact information as an index. Available databases include, but are not limited to, those that are publicly available, those that are commercially available, or those created by a contact center or a contact center client. In an outbound contact center environment, the caller&#39;s contact information is generally known beforehand. In an inbound contact center environment, the caller&#39;s contact information can be retrieved by examining the caller&#39;s CallerID information or by requesting this information of the caller at the outset of the contact, such as through entry of a caller account number or other caller-identifying information. Other business-relevant data such as historic purchase behavior, current level of satisfaction as a customer, or volunteered level of interest in a product may also be retrieved from available databases. 
     At  324 , agent data for a set of agents is identified or determined. One method of determining agent demographic or psychographic data can involve surveying agents at the time of their employment or periodically throughout their employment. Such a survey process can be manual, such as through a paper or oral survey, or automated with the survey being conducted over a computer system, such as by deployment over a web-browser. In some example, the method uses agent grades, demographic, psychographic, and other business-relevant data, along with caller demographic, psychographic, and other business-relevant data, other embodiments of the exemplary methods and systems can eliminate one or more types or categories of caller or agent data to reduce the time to answer, computing power, or storage necessary. 
     The agent data and caller data may then be compared at  326 . For instance, the agent data and caller data can be passed to a computational system for comparing caller data to agent data for each agent-caller pair, i.e., the caller data and agent data is compared in a pair-wise fashion for each potential routing decision. In one example, the comparison is achieved by passing the agent and caller data to a pattern matching algorithm to create a computer model that matches each caller with each agent and estimates the probable outcome of each matching along a number of optimal interactions, such as the generation of a sale, the duration of contact, or the likelihood of generating an interaction that a customer finds satisfying. 
     The pattern matching algorithm to be used in the exemplary methods and system can comprise any correlation algorithm, such as a neural network algorithm or a genetic algorithm. To generally train or otherwise refine the algorithm, actual contact results (as measured for an optimal interaction) are compared against the actual agent and caller data for each contact that occurred. The pattern matching algorithm can then learn, or improve its learning of, how matching certain callers with certain agents will change the chance of an optimal interaction. In this manner, the pattern matching algorithm can then be used to predict the chance of an optimal interaction in the context of matching a caller with a particular set of caller data, with an agent of a particular set of agent data. Preferably, the pattern matching algorithm is periodically refined as more actual data on caller interactions becomes available to it, such as periodically training the algorithm every night after a contact center has finished operating for the day. 
     The pattern matching algorithm may create or use a computer model reflecting the predicted chances of an optimal interaction for each agent and caller matching. Preferably, the computer model will comprise the predicted chances for a set of optimal interactions for every agent that is logged in to the contact center as matched against every available caller. Alternatively, the computer model can comprise subsets of these, or sets containing the aforementioned sets. For example, instead of matching every agent logged into the contact center with every available caller, examples can match every available agent with every available caller, or even a narrower subset of agents or callers. Likewise, the present invention can match every agent that ever worked on a particular campaign—whether available or logged in or not—with every available caller. Similarly, the computer model can comprise predicted chances for one optimal interaction or a number of optimal interactions and weight them in various fashions. 
     A computer model can also comprise a suitability score for each matching of an agent and a caller. The suitability score can be determined by taking the chances of a set of optimal interactions as predicted by the pattern matching algorithm, and weighting those chances to place more or less emphasis on a particular optimal interaction as related to another optimal interaction. The suitability score can then be used in the exemplary methods and systems to determine which agents should be connected to which callers. 
     Based on the pattern matching algorithm and/or computer model, the method further includes determining the agent having the best match to the caller at  328 . As will be understood, the best matching agent may depend on the pattern matching algorithm, computer model, and desired output variables and weightings selected by a particular call center. The caller is then routed to the best matching agent at  330  if the agent is available within the time limit set for the time to answer. If the time limit is exceeded, however, the method and system may operate to route the caller to the then best matching agent, i.e., the best matching agent of available agents, as determined by the system. In some examples, the system may generate a ranking of agents for a particular caller and connect to the caller to the highest ranking available agent when the time limit is reached. 
     According to another aspect of the exemplary systems and methods described, a visual computer interface and printable reports may be provided to the contact center or their clients to allow them to, in a real-time or a past performance basis, monitor the statistics of agent to caller matches, measure the optimal interactions that are being achieved versus the interactions predicted by the computer model, as well as any other measurements of real time or past performance using the methods described herein. A visual computer interface for changing the time to answer (as well as the weighting on an optimal interaction) can also be provided to the contact center or the contact center client, such that they can, as discussed herein, monitor or change the time to answer as well as view an estimated effect on outcome variables. 
       FIG. 4  illustrates an exemplary interface  400  having a graphic element  402  for adjusting a time to answer limit for callers. It will be recognized that interface  400  may be displayed within a browser page, portal page, or standalone user interface for a contact center routing system. Additionally, various other information and functionality may be included with interface  400 , but is omitted here for clarity. 
     In this example, interface  400  displays a report of call center performance broken down by different agents at  410  (and which may also be broken down by time periods or display various graphs, which are not shown). Interface  400  further includes settings for desired outcomes of different variables of the pattern matching algorithms and computer models being used for routing callers to agents at  404 . In particular, sliders  404  include sliders for adjusting the weightings of revenue, cost, and customer satisfaction in the call center routing algorithms and computer models (note that the value for the three sliders equals 100). Various weighting methods and algorithms are described, for example, in copending U.S. patent application Ser. No. 12/202,091, filed Aug. 29, 2008, which is incorporated herein by reference in its entirety. Of course, various other pattern matching algorithms, computer models, and weighting methods for adjusting the desired outcomes are possible and contemplated. 
     Interface  400  further includes slider  402 , which operates to adjust the time to answer limit relative to a Service Level Agreement (“SLA”) time limit. For instance, an SLA may dictate a time to answer limit of two seconds. In this example, if slider  402  is set to 100 the system determines the best matching agent for a caller and then waits (or holds the caller) for the SLA limit if the best matching agent is not available and routes the caller to the best-then available agent when the limit is exceeded. Note, if the best then available agent is available, the system routes the caller prior to the time to answer limit. If slider  402  is set to 50, however, the system might double the time to answer limit. If slider  402  is set to zero, the system may wait without regard to the SLA time limit until the best matching agent is available. In other examples, slider  402  may include actual times without reference to an SLA limit or the like. For example, slider  402  can range from a minimum wait time (e.g., 0.1 seconds) to a maximum time (e.g., 1 minute, 5 minutes, or more). 
     Slider  402  may operate to change the wait time in response to click-and-drag operations with a mouse, input to a key board (e.g., arrow keys, numerical entries, and so on). Further, the “slider” may be replaced with an entry field for manually entering times or values, up-and-down arrows for adjusting the time, and so on. 
     As described, increasing the wait time allows time for the best or a better matching agent to become free for connection with the caller. Increasing the time to answer, however, may lead to changes in various outcomes of the call such as revenue, cost, and customer satisfaction. For example, increasing the time to answer time may negatively impact the customer satisfaction outcome of the call as well as increase the change of the caller abandoning the call. Accordingly, and in one example, interface  400  includes an SLA advisor  420 , which when selected provides various information on the selected wait time and/or changes in the wait time with respect to outcome variables, such as the estimated effect of changing the wait time on revenue, cost, and customer satisfaction. 
       FIG. 5  illustrates exemplary interface  401  having a graphic element  402  for adjusting a time to answer limit for callers and operable for illustrating estimated effects on output variables for a set or adjusted time to answer limit. In particular, at  520  a user may adjust the wait time (or value) by selecting the arrows or inputting a number directly. As the input time is changed, the estimated effect to selected outcome variables is shown at  522 . In this instance illustrating to a user that changing the time from 80 to 70 results in a decrease in revenue, increase in cost, and decrease in customer satisfaction. When the user is satisfied with the change or setting of the time the user may select “set” at  520 . 
     Various estimation methods and algorithms for estimating outcome variables are described, for example, in copending U.S. provisional Patent application Ser. No. 61/084,201, filed on Jul. 28, 2008, and which is incorporated herein by reference in its entirety. In one example, the estimate includes evaluating a past time period of the same (or similar) set of agents and constructing a distribution of agent/caller pairs. Using each pair, an expected success rate can be computed via the pattern matching algorithm and applied to current information to estimate current performance (e.g., with respect to one or more of sales, cost, customer satisfaction, etc.). Accordingly, taking historical call data and agent information the algorithm can compute estimates of changing the time limit with the pattern matching algorithm. It is noted that a comparable time (e.g., time of day, day of the week etc.) for the historical information may be important as performance will likely vary with time. In other examples, stored algorithms or look-up tables may be used to estimate the change in output variables. 
     Many of the techniques described here may be implemented in hardware or software, or a combination of the two. Preferably, the techniques are implemented in computer programs executing on programmable computers that each includes a processor, a storage medium readable by the processor (including volatile and nonvolatile memory and/or storage elements), and suitable input and output devices. Program code is applied to data entered using an input device to perform the functions described and to generate output information. The output information is applied to one or more output devices. Moreover, each program is preferably implemented in a high level procedural or object-oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. 
     Each such computer program is preferably stored on a storage medium or device (e.g., CD-ROM, hard disk or magnetic diskette) that is readable by a general or special purpose programmable computer for configuring and operating the computer when the storage medium or device is read by the computer to perform the procedures described. The system also may be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner. 
       FIG. 6  illustrates a typical computing system  600  that may be employed to implement processing functionality in embodiments of the invention. Computing systems of this type may be used in clients and servers, for example. Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. Computing system  600  may represent, for example, a desktop, laptop or notebook computer, hand-held computing device (PDA, cell phone, palmtop, etc.), mainframe, server, client, or any other type of special or general purpose computing device as may be desirable or appropriate for a given application or environment. Computing system  600  can include one or more processors, such as a processor  604 . Processor  604  can be implemented using a general or special purpose processing engine such as, for example, a microprocessor, microcontroller or other control logic. In this example, processor  604  is connected to a bus  602  or other communication medium. 
     Computing system  600  can also include a main memory  608 , such as random access memory (RAM) or other dynamic memory, for storing information and instructions to be executed by processor  604 . Main memory  608  also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor  604 . Computing system  600  may likewise include a read only memory (“ROM”) or other static storage device coupled to bus  602  for storing static information and instructions for processor  604 . 
     The computing system  600  may also include information storage system  610 , which may include, for example, a media drive  612  and a removable storage interface  620 . The media drive  612  may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. Storage media  618  may include, for example, a hard disk, floppy disk, magnetic tape, optical disk, CD or DVD, or other fixed or removable medium that is read by and written to by media drive  612 . As these examples illustrate, the storage media  618  may include a computer-readable storage medium having stored therein particular computer software or data. 
     In alternative embodiments, information storage system  610  may include other similar components for allowing computer programs or other instructions or data to be loaded into computing system  600 . Such components may include, for example, a removable storage unit  622  and an interface  620 , such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units  622  and interfaces  620  that allow software and data to be transferred from the removable storage unit  618  to computing system  600 . 
     Computing system  600  can also include a communications interface  624 . Communications interface  624  can be used to allow software and data to be transferred between computing system  600  and external devices. Examples of communications interface  624  can include a modem, a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port), a PCMCIA slot and card, etc. Software and data transferred via communications interface  624  are in the form of signals which can be electronic, electromagnetic, optical or other signals capable of being received by communications interface  624 . These signals are provided to communications interface  624  via a channel  628 . This channel  628  may carry signals and may be implemented using a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of a channel include a phone line, a cellular phone link, an RF link, a network interface, a local or wide area network, and other communications channels. 
     In this document, the terms “computer program product,” “computer-readable medium” and the like may be used generally to refer to physical, tangible media such as, for example, memory  608 , storage media  618 , or storage unit  622 . These and other forms of computer-readable media may be involved in storing one or more instructions for use by processor  604 , to cause the processor to perform specified operations. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system  600  to perform features or functions of embodiments of the present invention. Note that the code may directly cause the processor to perform specified operations, be compiled to do so, and/or be combined with other software, hardware, and/or firmware elements (e.g., libraries for performing standard functions) to do so. 
     In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into computing system  600  using, for example, removable storage media  618 , drive  612  or communications interface  624 . The control logic (in this example, software instructions or computer program code), when executed by the processor  604 , causes the processor  604  to perform the functions of the invention as described herein. 
     It will be appreciated that, for clarity purposes, the above description has described embodiments of the invention with reference to different functional units and processors. However, it will be apparent that any suitable distribution of functionality between different functional units, processors or domains may be used without detracting from the invention. For example, functionality illustrated to be performed by separate processors or controllers may be performed by the same processor or controller. Hence, references to specific functional units are only to be seen as references to suitable means for providing the described functionality, rather than indicative of a strict logical or physical structure or organization. 
     The above-described embodiments of the present invention are merely meant to be illustrative and not limiting. Various changes and modifications may be made without departing from the invention in its broader aspects. The appended claims encompass such changes and modifications within the spirit and scope of the invention.