COMPUTER SYSTEM AND METHOD FOR AUTOMATICALLY OPTIMIZING SELECTION OF MEDIA UNITS

Aspects of the subject disclosure may include, for example, identifying an initial set of content schedule constraints including an initial plurality of values for two or more of price, available inventory, target audience, media content, number of impressions, placement dates, and placement times; generating a preliminary advertising schedule based on the initial set of content schedule constraints; calculating reach and average frequency based on the preliminary advertising schedule, resulting in a first calculated reach and a first calculated average frequency; updating one or more of the initial plurality of values of the initial set of content schedule constraints based on the first calculated reach and the first calculated average frequency, the updating resulting in an updated set of content schedule constraints, the updated set of content schedule constraints having one or more values of only some constituent content schedule constraints thereof differ from one or more values of corresponding content schedule constraints of the initial set of content schedule constraints; and generating a first updated advertising schedule based on the updated set of content schedule constraints. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a computer system and method for automatically optimizing selection of media units.

BACKGROUND

“Linear television” refers to television (TV) that is watched with the originally broadcast advertising inserted in ad breaks, including live pre-recorded and video on demand (VOD). This is in contrast to dynamically inserted advertising, also referred to as addressable. The TV advertisement marketplace consists of buyers (e.g., TV advertising agencies) and sellers (e.g., TV content producers and distributors). A linear TV advertising campaign includes a series of video clips (each of which is referred to as a “creative”) placed in available time slots that sellers sell to buyers. Three related quantities describe, at a high level, the effectiveness of a linear TV advertising campaign: impressions, reach, and frequency. For example, if 10 people watch a particular creative, and each such person watched that creative two times over the course of an advertising campaign, then the campaign will have a reach of 10 people, collect 20 impressions, and have an average frequency equal to 2. The mathematical relationship between these three quantities is:

The quantity “impressions” may be replaced by another quantity that typically is referred to as ratings, which are the ratio of impressions to the estimated size of the target segment (also referred to as the “universe estimate”), expressed as a percentage. In the example above, if the estimated size of the target segment is 1,000, then the rating is 2 (100×20/1,000). In the description herein, impressions and ratings may be used interchangeably unless otherwise noted.

An advertiser (buyer) typically is interested in maximizing impressions, subject to a number of constraints, such as budget, dates, times of day, separation between transmission times, and combination of TV networks, channels and programs, among others. Since these constraints specify absolute or relative minimum and maximum limits on the fraction of the budget to spend on, or impressions to collect from various potential ad placements, they effectively limit the maximum value of impressions for a particular TV campaign. Maximizing impressions within these constraints can however deliver excessive frequency, with some persons being exposed a very large number of times to the advertising. This may make those viewers turn away from the advertised product or service, and thereby achieve a result that is the opposite of that desired and intended by the advertiser. The ideal outcome, from the advertiser's perspective, is one which balances reach and average frequency. In some cases this may mean maximizing impressions, in some cases this may mean maximizing reach, and in other cases this may mean finding a solution somewhere between these.

Achieving such a balance between reach and average frequency efficiently and in a wide range of cases is a challenging problem.

DETAILED DESCRIPTION

In various embodiments, a computerized system automatically optimizes an advertising campaign in a variety of ways, such as optimizing for the ratings of the advertising campaign or by optimizing for the reach of the advertising campaign. Such automated optimization enables advertisers to achieve their goal, subject to the constraints of the advertising campaign (such as the budget and target networks of the advertising campaign).

For example, various embodiments can perform optimization which:a. Optimizes for reach only. Such optimization can be performed, for example, when the main goal of the user (e.g., buyer) is to obtain a proposal that satisfies all of the provided constraints (such as any of the constraints described above) while maximizing target reach, regardless of the total number of rating points.b. Optimizes for both reach and ratings. Such optimization can be performed, for example, when the user (e.g., buyer) is interested in obtaining a high reach but also in keeping ratings as high as possible.c. Optimizes for ratings only. This is the traditional goal of maximizing the total number of times an advertisement is watched. Again, this optimization can be done while simultaneously respecting budget and desired allocation constraints.

In various embodiments, users can employ disclosed mechanisms to perform optimization according to one or more of the optimization goals (a, b, c) described above. It may be reasonable, for example, for the user to select the “reach only” optimization goal first. The reach obtained by applying the “reach only” optimization goal, however, is usually higher than the reach obtained by applying the “reach and ratings” optimization goal, although at the cost of fewer rating points than the “reach and ratings” optimization goal. A user may, therefore, wish to employ mechanisms (such as described herein) to perform multiple optimization goals (e.g., a, b and/or c above), to compare the results and/or to select one of the optimization goals based on the user's preference for reach in comparison to ratings.

Referring toFIG. 1, a dataflow diagram is shown of a system100for optimizing the number of impressions in an advertising campaign according to one embodiment. Referring toFIG. 2, a flowchart is shown of a method200performed by the system100ofFIG. 1according to one embodiment. The system100ofFIG. 1and the method200ofFIG. 2are applicable to both the “reach only” and “reach and ratings” goals described above.

The method200initializes an iteration count I to zero (FIG. 2, operation201).

The system100includes a constraint initialization module102, which initializes and outputs a set of constraints104(see also,FIG. 2, operation202). The constraints104can include one or more constraints, such as one or more of any of the constraints disclosed herein. In one example, the set of constraints that this constraint initialization module102outputs are those that neither the seller nor the buyer entered as part of the campaign's requirements. In another example, the set of constraints that this constraint initialization module102outputs can be automatically manipulated by the system100in operation114. The constraint initialization module102can initialize the set of constraints104in any of a variety of ways. For example, the constraint initialization module102can receive input from a user (not shown) which specifies both the constraint(s) to be included in the constraints104and a corresponding value for each such constraint. For example, the user input can indicate that the constraints104are to include a budget constraint having a value of $500,000. In response to receiving such input, the constraint initialization module102can include, in the constraints104, the constraint(s) and corresponding value(s) specified by the input. This is merely an example of a way in which the constraint initialization module102can initialize the constraints104. As other examples, the constraint initialization module102can iterate over a set of constraints and corresponding values, use machine learning to automatically generate constraints and corresponding values based on historical data that characterize successful campaigns, or any combination thereof. Although the constraints104may be referred to herein in the plural, it should be understood that the constraints104may include only a single constraint.

The system100also includes a scheduler106, which receives the constraints104as input, and which generates a schedule108as output that maximizes impressions and satisfies all of the constraints104(see also,FIG. 2, operation204). A variety of techniques that the scheduler106can use to generate the schedule108are described in detail elsewhere herein.

The system100also includes a reach calculation module110, which receives the schedule108as input, and which calculates a reach112based on the schedule108(see also,FIG. 2, operation206). The reach calculation module110can calculate the reach112at the network-daypart level (though other groupings are also possible). For example, the reach calculation module110can calculate a separate value of the reach112for each of a plurality of network-daypart combinations. As this implies, the reach112can be a plurality of reaches for the schedule108. The reach calculation module112calculates average frequency in addition to reach. Therefore, any reference herein to the reach calculation module112calculating reach should be understood to apply equally to the reach calculation module112calculating both reach and average frequency.

The system100also includes a constraint control module114, which receives the constraints104and the reach112and average frequency as inputs, and which modifies the constraints104based on the other inputs to produce modified constraints116as output (see also,FIG. 2, operation208). The output of the constraint control module114can include a list of the constraints (which can, for example, be identified by their constraint IDs) whose upper bounds need to be updated, and the magnitudes of the updated upper bounds. The constraint control module114can perform the indicated updates on the constraints104to produce the modified constraints116.

Referring now toFIG. 2, the method200determines whether the value of the iteration count I is greater than or equal to some predetermined maximum number of iterations N (seeFIG. 2, operation210). If I>=N, then the method200terminates (seeFIG. 2operation212). Otherwise (i.e., if I<N), the method200returns to operation204, in which the scheduler106produces a new schedule based on the modified constraints116, and the remaining operations206-210repeat until I>=N. As this implies, the method200can produce any number of schedules (some or all of which can be different from each other in any of a variety of ways), can produce any number of values of reach (some or all of which can be different from each other in any of a variety of ways), and can produce any number of sets of constraints (some or all of which can be different from each other in any of a variety of ways).

Note that instead of iterating N times, the method200can instead iterate until at least some predetermined amount of time has passed since the method200began. This can be implemented, for example, by initializing a timer in operation201instead of a counter, and by determining, in operation210, whether the current value of the timer (which can increase in value in correspondence with the passage of time) is greater than or equal to some predetermined maximum amount of time.

As yet another example, the method200can instead terminate when the rate of change in the reach112falls below some predetermined threshold value, where the rate of change in the reach112can be measured, for example, by the difference in value of the reach112from one iteration of the method200to the next (over two or more such iterations).

Regardless of the termination criterion that is used, the most recently produced schedule, or the “best” schedule according to some qualitative and quantitative criteria, or a subset of all the produced schedules by the system100and method200when the method200terminates is output by the system100and method200as their final output.

A wide variety of scheduling techniques are known in the art, and the scheduler106can use any of a variety of techniques. In addition, however, in various embodiments, the scheduler106uses improved techniques which enable the system100and method200to significantly improve the resulting schedule108, as measured by the coverage of the advertising in the schedule108, where “coverage” refers to the number of persons or homes that fall within the desired target that are exposed to at least one airing of the schedule108.

In general, the scheduler106implements a scheduling algorithm which executes a loop in which only some of the constraints104differ from one iteration of the loop to the next. Various embodiments can make this feature as efficient as possible by indexing the right hand sides of the constraints104(in particular upper limits). The constraints (within the constraints104) that need modification may not be contained in the initial formulation of the problem (e.g., may not be part of the set of the user-provided constraints), but may instead be added to the constraints104by the reach optimization process based on the contents of the cartesian product of the inventory and the campaigns being scheduled.

As a specific example, each of the constraints104can include the following parameters, each of which can have a corresponding value:a. Constraint ID: any data that acts as a unique identifier of the constraint.b. Campaign ID: a unique identifier for the schedule.c. Inventory label: data that identifies a feature of the inventory. For example, the name of the network and/or daypart to which the constraint applies. In various embodiments, the scheduler106will only apply the constraint to network-daypart combinations specified by the inventory label.d. Inventory value: this is the value of the inventory feature. In the case of a network-daypart constraint, this label could take values of the form “Network X—Prime time”.e. Measurement: the constraint's measurement. This is the units the constraint values are measured in. For example, a constraint can determine the maximum number of dollars to spend on a network, or it could set the minimum number of target segment impressions that are desired from a certain show, or it could specify the number of advertisements placed during a certain daypart. In the context of an embodiment of a method described herein, the measurement of the constraints under the algorithm's control can be based on the target segment impressions, the target being a segment of the population defined in terms of demographics (or some other behavioral or attitudinal classifications).f. Target value: the ideal value for the inventory that matches the inventory label and value in terms of the measurement units.g. Target minimum: the minimum value for the inventory that matches the inventory label and value in terms of the measurement units.h. Target maximum: the maximum value for the inventory that matches the inventory label and value in terms of the measurement units.i. Relative to: If this constraint is relative to another constraint (e.g., “of the dollars spent on network X, which may not be known in advance, allocate 25% during daytime”), then this parameter points to the other constraint. Otherwise, this parameter has a null value.j. Elasticity flag: this is a binary value which indicates whether the constraint's minimum and maximum values may be violated by the schedule108that the scheduler106generates based on the constraints104. As the name implies, elastic constraints' limits “stretch” just enough to accommodate the allocation of impression/dollars but not more than what is needed.

The set of parameters listed above is merely an example and does not constitute a limitation. Various embodiments can use constraints having parameters other than those listed above.

In various embodiments, the constraint control module114can only make modifications to the constraints104which relax those constraints104. In various embodiments, the scheduler106can place a non-integer number of advertisements in a single slot in intermediate iterations of the method200, but not in the final iteration of the schedule108that is output by the method200(i.e., such a final iteration of the schedule108only contains integer numbers of placements in the slots in the schedule108in such embodiments).

As described above, although various embodiments may optimize for different goals, the system100and method200shown and described in connection withFIGS. 1 and 2are applicable to optimization for reach and/or ratings optimization goals, although the process of optimizing for the various goals may differ somewhat in their execution paths, as will be illustrated by the following examples.

First, consider an example in which the set of constraints104under the control of the method200is represented by the following table, which also shows the reach and average frequency decomposition:

The constraint control module114can calculate, based on a table such as the one above, the mean average frequency from the values in the columns target_impressions, and target_reach, e.g., mean_avg_frequency=(2700+2340+7000)/(1000+1300+2000)=2.8.

The constraint control module114can calculate, for each network-daypart in a table such as the one above, a maximum number of impressions (max_target_imps_at_mean) targeting a frequency equal to the mean_avg_frequency. The following table illustrates an example of the results of such calculations of the value of the column max_target_imps_at_mean, which involves, for each network-daypart (i.e., row in the table below), dividing the value of target_impressions by the value of mean_avg_frequency (which is equal to 2.8 in this example):

The constraint control module114can, for each network-daypart, calculate the corresponding reach proportion, which involves, for each network-daypart (i.e., row in the table above), dividing the value of target_reach for that network-daypart by the sum of all of the values of target_reach in the table:

The constraint control module114can, for each network-daypart, calculate a proportional number of impressions collected at the same rate as reach, by multiplying the sum of all values of target_impressions in the table by the value of reach_proportion for that network-daypart. This quantity will be referred to herein as max_prop_target_imps.

The constraint control module114can determine which of the constraints104to control in the current iteration. This can be performed, for example, by selecting the network-dayparts for which the value of avg_frequency>mean_avg_frequency. An example of this is shown in the table below, in which network-daypart 4 is selected for constraint control because its avg_frequency is greater than the value of mean_avg_frequency:

The next step, which produces as output a list of constraints (e.g., constraint IDs) to be modified by the constraint control module114and the new values of such constraints, can vary depending on whether the system100and method200are optimizing for reach only or for both reach and ratings. In either case, the list of constraints to be modified can be generated in the manner described above.

If the system100and method200are optimizing for reach only, then the constraint control module114can calculate the new maximum limit of the selected constraints by choosing the maximum of max_target_imps_at_mean and max_prop_target_imps for each of the selected constraints. For example, using the values in the tables above, this would result in the following new maximum values for the selected constraints (i.e., network-daypart 4 in the example above):

If the system100and method200are optimizing for both reach and ratings, then the constraint control module114can calculate the new maximum limit of the selected constraints by: (1) selecting the constraint that has not been modified in any way during the previous iterations (from among the constraints selected above) with the maximum average frequency and (2) setting the new max_value of that constraint to be equal to the value of max_target_imps_at_mean.

Regardless of which of these two methods are used to select the new maximum values of the modified constraints116in the next iteration of the method200, the method200can then continue to the next iteration in the manner described above.

The final output of the system100and method200is the version of the schedule108which has the highest reach, regardless of the iteration of the method200at which that version of the schedule108is found. As this implies, the version of the schedule108that is output by the system100and method200may not be the version of the schedule108that is produced in the final iteration of the method200. As this further implies, during each iteration of the method200, the system100and method200can compute the full reach of the schedule108in addition to computing separate reaches for each network-daypart in the schedule108.

Referring now toFIG. 3A, various steps of a method3000according to an embodiment are shown. As seen in thisFIG. 3A, step3002comprises identifying an initial set of content schedule constraints including an initial plurality of values for two or more of price, available inventory, target audience, media content, number of impressions, placement dates, and placement times. Next, step3004comprises generating a preliminary advertising schedule based on the initial set of content schedule constraints. Next, step3006comprises calculating reach and average frequency based on the preliminary advertising schedule, resulting in a first calculated reach and a first calculated average frequency. Next, step3008comprises updating one or more of the initial plurality of values of the initial set of content schedule constraints based on the first calculated reach and the first calculated average frequency, the updating resulting in an updated set of content schedule constraints, the updated set of content schedule constraints having one or more values of only some constituent content schedule constraints thereof differ from one or more values of corresponding content schedule constraints of the initial set of content schedule constraints. Next, step3010comprises generating a first updated advertising schedule based on the updated set of content schedule constraints.

Referring now toFIG. 3B, various steps of a method3100according to an embodiment are shown. As seen in thisFIG. 3B, step3102comprises initializing a set of content schedule constraints including two or more of price, available inventory, target audience, media content, number of impressions, placement dates, and placement times, wherein the initializing results in an initial set of content schedule constraints having a plurality of initial values. Next, step3104comprises generating a first advertising schedule based on the plurality of initial values of the initial set of content schedule constraints. Next, step3106comprises determining, based on the first advertising schedule, a first reach and a first average frequency. Next, step3108comprises modifying one or more of the plurality of initial values of the initial set of content schedule constraints based on the first reach and the first average frequency, the modifying resulting in a modified set of content schedule constraints, the modified set of content schedule constraints having one or more values of only some constituent content schedule constraints thereof differ from one or more values of corresponding content schedule constraints of the initial set of content schedule constraints. Next, step3110comprises generating a second advertising schedule based on the modified set of content schedule constraints.

Referring now toFIG. 3C, various steps of a method3200according to an embodiment are shown. As seen in thisFIG. 3C, step3202comprises identifying, by a processing system including a processor, a set of content schedule constraints including a plurality of values for two or more of price, available inventory, target audience, media content, number of impressions, placement dates, and placement times. Next, step3204comprises determining, by the processing system, an advertising schedule based on the set of content schedule constraints. Next, step3206comprises calculating reach and average frequency based on the advertising schedule, resulting in a calculated reach and a calculated average frequency. Next, step3208comprises updating one or more of the plurality of values of the set of content schedule constraints based on the calculated reach and the calculated average frequency, the updating resulting in an updated set of content schedule constraints, the updated set of content schedule constraints having one or more values of only some constituent content schedule constraints thereof differ from one or more values of corresponding content schedule constraints of an immediately prior set of content schedule constraints. Next, step3210comprises generating an updated advertising schedule based on the updated set of content schedule constraints. Next, step3212comprises iterating the calculating, the updating, and the generating, wherein the calculating is iteratively performed based on each immediately prior updated advertising schedule, wherein the updating is iteratively performed based on each immediately prior calculated reach and each immediately prior calculated average frequency, and wherein the generating is iteratively performed based on each immediately prior updated set of content schedule constraints.

As described herein, various embodiments can provide for optimizing for ratings and then optimizing for reach.

As described herein, various embodiments can provide for iterating (e.g., changing constraints and/or changing constraint values).

As described herein, various embodiments can provide for: (a) initial selection of advertisements across different networks/dayparts; (b) identifying where there is waste (e.g., too many advertisements and/or too much frequency); (c) optimizing—for example, put (e.g., daypart units) somewhere else to increase reach; (d) iterating (b), (c) and/or (d).

In one embodiment, a method comprises:(A) identifying a set of content schedule constraints including at least one of price, available inventory, target audience, media content, number of impressions, and placement dates and/or times;(B) using a non-linear selection method to automatically generate a selection of an advertising schedule based on the set of content schedule constraints, comprising:(B)(1) using a scheduler to execute a scheduling algorithm in a loop in which only some of the set of content schedule constraints differ between iterations of the loop, thereby generating a preliminary schedule;(B)(2) calculating reach and average frequency based on the preliminary schedule; and(B)(3) updating the set of content schedule constraints based on the calculated reach and average frequency.

In one example, (B) above comprises optimizing the advertising schedule for both reach and ratings.

In another example, (B) above comprises optimizing the advertising schedule for reach only.

In another example, (B) above comprises optimizing the advertising schedule for ratings only.

One advantage of various embodiments is that such embodiments can deliver a key advertising requirement, namely improved coverage of an advertising target. Various embodiments satisfy this requirement automatically in a real world application, using a media owner's actual available inventory, and selecting the schedule's units in minutes. The size of the inventory (often ˜tens of thousands of possible ad placements) and the number of constraints targeted by a campaign (often ˜thousands of constraints) can make the computations described herein impractical via mental/manual methods.

As described herein, various embodiments can provide a computerized system that automatically optimizes an advertising campaign in a variety of ways, such as optimizing for the ratings of the advertising campaign or by optimizing for the reach of the advertising campaign. Automated optimization provided by various embodiments enables advertisers to achieve their goal, subject to the constraints of the advertising campaign, such as the budget and target networks of the advertising campaign

It is to be understood that although particular embodiments have been described, such embodiments are provided as illustrative only. Various other embodiments, including but not limited to the following, are also within the scope of the claims. For example, elements and components described herein may be further divided into additional components or joined together to form fewer components for performing the same functions.

Any of the functions disclosed herein may be implemented using means for performing those functions. Such means include, but are not limited to, any of the components disclosed herein, such as the computer-related components described below.

The techniques described herein may be implemented, for example, in hardware, one or more computer programs tangibly stored on one or more computer-readable media, firmware, or any combination thereof. The techniques described herein may be implemented in one or more computer programs executing on (or executable by) a programmable computer including any combination of any number of the following: a processor, a storage medium readable and/or writable by the processor (including, for example, volatile and non-volatile memory and/or storage elements), an input device, and an output device. Program code may be applied to input entered using the input device to perform the functions described and to generate output using the output device.

Various embodiments include features which are only possible and/or feasible to implement with the use of one or more computers, computer processors, and/or other elements of a computer system. Such features are either impossible or impractical to implement mentally and/or manually. For example, various embodiments can automatically apply a variety of scaling factors to a unit CPM distribution using a multi-step process that would be impossible or impractical to implement mentally and/or manually. More generally, the problem of scheduling television advertisements belongs to a class of problems known to be computationally hard (see, e.g., “The complexity of scheduling TV commercials,” Electronic Notes in Theoretical Computer Science, Vol. 40, pages 162-185 (2001), Klemens Hagele and Colm Dunlaing and Soren Riis). A human, therefore, would find it overwhelming and, as a practical matter, impossible to schedule (using mental and/or manual processes only) a large number of television advertisements within the applicable temporal and other constraints.

Any claims herein which affirmatively require a computer, a processor, a memory, or similar computer-related elements, are intended to require such elements, and should not be interpreted as if such elements are not present in or required by such claims. Such claims are not intended, and should not be interpreted, to cover methods and/or systems which lack the recited computer-related elements. For example, any method claim herein which recites that the claimed method is performed by a computer, a processor, a memory, and/or similar computer-related element, is intended to, and should only be interpreted to, encompass methods which are performed by the recited computer-related element(s). Such a method claim should not be interpreted, for example, to encompass a method that is performed mentally or by hand (e.g., using pencil and paper). Similarly, any product claim herein which recites that the claimed product includes a computer, a processor, a memory, and/or similar computer-related element, is intended to, and should only be interpreted to, encompass products which include the recited computer-related element(s). Such a product claim should not be interpreted, for example, to encompass a product that does not include the recited computer-related element(s).

Each computer program within the scope of the claims below can be implemented in any programming language, such as assembly language, machine language, a high-level procedural programming language, or an object-oriented programming language. The programming language can, for example, be a compiled or interpreted programming language.

Each such computer program can be implemented in a computer program product tangibly embodied in a machine-readable storage device for execution by a computer processor. Method steps can be performed by one or more computer processors executing a program tangibly embodied on a computer-readable medium to perform functions by operating on input and generating output. Suitable processors include, by way of example, both general and special purpose microprocessors. Generally, the processor receives (reads) instructions and data from a memory (such as a read-only memory and/or a random access memory) and writes (stores) instructions and data to the memory. Storage devices suitable for tangibly embodying computer program instructions and data include, for example, all forms of non-volatile memory, such as semiconductor memory devices, including EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROMs. Any of the foregoing can be supplemented by, or incorporated in, specially-designed ASICs (application-specific integrated circuits) or FPGAs (Field-Programmable Gate Arrays). A computer can generally also receive (read) programs and data from, and write (store) programs and data to, a storage medium such as an internal disk or a removable disk. Various embodiments can be used in conjunction with any digital print engine or marking engine, display monitor, or other raster output device capable of producing color or gray scale pixels on paper, film, display screen, or other output medium.

Any data disclosed herein can be implemented, for example, in one or more data structures tangibly stored on a medium. Various embodiments can store such data in such data structure(s) and read such data from such data structure(s).

Various embodiments can be implemented via computer-executable instructions that can run on one or more computers, and/or in combination with other program modules and/or as a combination of hardware and software.

Various embodiments can be implemented using various computer system configurations, comprising single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

Various embodiments can be implemented in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

Referring now toFIG. 4, an example computing environment can comprise a computer402, the computer402comprising a processing unit404, a system memory406and a system bus408. The system bus408couples system components including, but not limited to, the system memory406to the processing unit404. The processing unit404can be any of various commercially available processors. Dual microprocessors and other multiprocessor architectures can also be employed as the processing unit404.