Patent Publication Number: US-7899673-B2

Title: Automatic pruning of grammars in a multi-application speech recognition interface

Description:
BACKGROUND 
     One of the forefronts of computing technology is speech recognition. With computerized applications controlling many aspects of daily activities from word processing to controlling appliances, providing speech recognition based interfaces for such applications is a high priority of research and development for many companies. Even web site operators and other content providers are deploying voice driven interfaces for allowing users to browse their content. The voice interfaces commonly include “grammars” that define valid utterances (words, terms, phrases, etc.) that can occur at a given state within an application&#39;s execution. The grammars are fed to a speech recognition system and used to interpret the user&#39;s voice entry. 
     Speech recognition systems have inherent challenges such as distinction of grammar over background noise, reliability of recognition for larger sizes of the grammar, and the like. Another challenge for speech recognition systems is multi-application environments. While operating systems are known, which provide a multi-tasking capability thereby allowing multiple application programs to run essentially simultaneously, coordinating control of multiple applications through a single speech recognition interface presents many hurdles. As the number of active applications along with their associated list of commands increases, and the combined grammar expands, the system may become less usable, because the application grammars may have an increasing tendency to compete with each other. 
     It is with respect to these and other considerations that the present invention has been made. 
     SUMMARY 
     This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended as an aid in determining the scope of the claimed subject matter. 
     Embodiments are directed to managing a plurality of applications through a speech recognition interface. Applications may be registered as they are activated and assigned priority levels. Each registered application may then request registration of a set of commands associated with that application. A subset of commands among the set requested for registration by the application may be selected for use by the speech recognitions interface. The selected commands may then be assigned command priority levels. The speech recognition system may interpret voice input and provide recognized commands based on the application and command priority structure(s). 
     These and other features and advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. It is to be understood that both the foregoing general description and the following detailed description are explanatory only and are not restrictive of aspects as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an example computing operating environment; 
         FIG. 2  illustrates a networked system where example embodiments may be implemented; 
         FIG. 3  illustrates an example architecture of a speech recognition system with grammar pruning according to embodiments; 
         FIG. 4  illustrates a conceptual diagram of processes and sub-processes of an implementation of a speech recognition system with grammar pruning; 
         FIG. 5  illustrates two example stages of speech recognition based control in a multi-application environment; and 
         FIG. 6  illustrates a logic flow diagram for a process of using grammar pruning in a speech recognition system. 
     
    
    
     DETAILED DESCRIPTION 
     As briefly described above, a speech recognition system in a multi-application environment may be enhanced by adding grammar pruning based on assignment of priorities at application and command levels. In the following detailed description, references are made to the accompanying drawings that form a part hereof, and in which are shown by way of illustrations specific embodiments or examples. These aspects may be combined, other aspects may be utilized, and structural changes may be made without departing from the spirit or scope of the present disclosure. The following detailed description is therefore not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents. 
     Referring now to the drawings, aspects and an exemplary operating environment will be described.  FIG. 1  and the following discussion are intended to provide a brief, general description of a suitable computing environment in which the invention may be implemented. While the embodiments will be described in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a personal computer, those skilled in the art will recognize that aspects may also be implemented in combination with other program modules. 
     Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that embodiments may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. 
     Embodiments may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. 
     With reference to  FIG. 1 , one example system for implementing the embodiments includes a computing device, such as computing device  100 . In a basic configuration, the computing device  100  typically includes at least one processing unit  102  and system memory  104 . Computing device  100  may include a plurality of processing units that cooperate in executing programs. Depending on the exact configuration and type of computing device, the system memory  104  may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.) or some combination of the two. System memory  104  typically includes an operating system  105  suitable for controlling the operation of a networked personal computer, such as the WINDOWS® operating systems from MICROSOFT CORPORATION of Redmond, Wash. The system memory  104  may also include one or more software applications such as program modules  106 , speech recognition engine  122 , grammar module  124 , and voice controlled application(s)  126 . 
     Speech recognition engine  122  and grammar module  124  may work in a coordinated manner as part of a voice interface system in managing voice controlled application(s)  126 . As described below in more detail, speech recognition engine  122  and grammar module  124  may reduce grammar contention, grammar overlap, and the like, by assigning and managing application- and command-level priorities. Voice controlled application(s)  126  may be any program that receives speech recognition based input such as a word processing program, a navigation program, a media player control program, a communication program, and the like. Speech recognition engine  122  and grammar module  124  may be an integrated part of voice controlled application  126  or separate applications. Speech recognition engine  122 , grammar module  124 , and voice controlled application(s)  126  may communicate between themselves and with other applications running on computing device  100  or on other devices. Furthermore, either one of speech recognition engine  122 , grammar module  124 , and voice controlled application(s)  126  may be executed in an operating system other than operating system  105 . This basic configuration is illustrated in  FIG. 1  by those components within dashed line  108 . 
     The computing device  100  may have additional features or functionality. For example, the computing device  100  may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in  FIG. 1  by removable storage  109  and non-removable storage  110 . Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory  104 , removable storage  109  and non-removable storage  110  are all examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device  100 . Any such computer storage media may be part of device  100 . Computing device  100  may also have input device(s)  112  such as keyboard, mouse, pen, voice input device, touch input device, etc. Output device(s)  114  such as a display, speakers, printer, etc. may also be included. These devices are well known in the art and need not be discussed at length here. 
     Furthermore, input devices  112  may receive input from other input devices such as audio input devices  113  in some specific implementations. For example, a speech recognition system with grammar pruning may receive training data from microphones directly connected to the computing device  100  or from recording devices that provide pre-recorded audio data. 
     The computing device  100  may also contain communication connections  116  that allow the device to communicate with other computing devices  118 , such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection  116  is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. The term computer readable media as used herein includes both storage media and communication media. 
     Referring to  FIG. 2 , a networked system where example embodiments may be implemented, is illustrated. System  200  may comprise any topology of servers, clients, Internet service providers, and communication media. Also, system  200  may have a static or dynamic topology. The term “client” may refer to a client application or a client device employed by a user to perform operations associated with an adaptation system. While the adaptation system may include many more components, relevant ones are discussed in conjunction with this figure. 
     Speech recognition service  202 , database server  204 , grammar service  206 , and application servers  208  and  209  may also be one or more programs or a server machine executing programs associated with the adaptation system tasks. Similarly, data sources may include one or more data stores, input devices, and the like. 
     A speech recognition based system may be run on a server or a client machine connected to network(s)  210  and use speech recognition service  202  to convert audio (voice) input to textual commands. Speech recognition service  202  may use a generic language model and data for customizing the language model from data sources  212 - 214 ,  216 , and  218  directly or through database server  204 . The data sources may include data stores where generic language models and other customization data may be stored or user devices for providing direct input such as audio training data in speech recognition system. As such, data sources may also encompass client devices  222 - 228  for providing input to speech recognition service  202 . Data sources may include SQL servers, databases, non multi-dimensional data sources, file compilations, data cubes, and the like. Database server  204  may manage the data sources. 
     Based on the language model data and grammar pruning feedback from the grammar service  206 , speech recognition service  202  is configured to provide voice controlled applications on client devices  222 ,  224 ,  226 , and  228  or application servers  208  and  209  textual commands in response to voice input by a user. 
     Grammar service  206  may provide a grammar pruning service for a speech recognition interface of the speech recognition service  202 . In a multi-application environment, the speech recognition interface may get overloaded with a large number of commands for different applications, and possibly their synonyms. Furthermore, one or more applications may use the same word or phrase for different commands. Thus grammar conflict could be a problem in a multi-application environment. By registering applications and their associated commands, assigning priorities to the registered applications and commands, grammar service  206  may enable an efficient operation of the speech recognition interface in coordinating multiple applications. 
     Users may interact with speech recognition service  202  and/or application servers  208  and  209  from client devices  222 ,  224 ,  226 , and  228  over network(s)  210 . In some embodiments, users may run applications that consume the commands provided by speech recognition service  202  in the individual client devices (or applications). In other embodiments, such applications may be controlled by application servers  208  and  209  and interface with the users client devices  222 ,  224 ,  226 , and  228  over network(s)  210 . 
     Network(s)  210  may include a secure network such as an enterprise network, or an unsecure network such as a wireless open network. Network(s)  210  provide communication between the nodes described above. By way of example, and not limitation, network(s)  210  may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. 
     Many other configurations of computing devices, applications, data sources, data distribution and analysis systems may be employed to implement a speech recognition system with grammar pruning. 
       FIG. 3  illustrates example architecture  300  of a speech recognition system with grammar pruning. Architecture  300  may comprise any topology of processing systems, storage systems, source systems, and configuration systems. Architecture  300  may also have a static or dynamic topology. 
     In supporting multiple speech-enabled applications to run simultaneously, an operating system may face a number of challenges such as “grammar overlap” or “grammar proximity.” Grammar overlap is the likelihood of two or more programs designed to use the same recognition keyword for different actions receiving the same command, because each application&#39;s grammar may be designed separately. Grammar proximity refers to two programs using recognition keywords that sound alike, making speech recognition task more difficult and error-prone. Another issue that arises in a speech enabled multi-application environment is interface overloading. For example, a top level speech menu of a system may support six applications (A, B, C, D, E, F) with each application contributing 10 keywords to the top level menu. This would result in the top level menu of the speech recognition interface having to support 60 keywords (commands). As the number of applications (and features) increases and the combined grammar expands, the system may become less usable, because the application grammars may have an increasing tendency to compete with each other. 
     According to some embodiments, grammar contention (grammar overlap and proximity) as well as interface overload may be substantially reduced by enabling an application to define “importance” levels for its global grammar elements (commands). For example, more common commands may be given a higher “importance” level than others. This enables the speech recognition system to automatically prune the global grammar as applications get added to the system, while giving the applications the authority to declare which commands are most important to their users. 
     An operating system with multiple speech enabled applications may provide an example implementation scenario for some embodiments. The operating system may typically control a number of applications including a word processing application, a spreadsheet application, a media player application, and the like. Each application may insert several common commands into the top level menu of the speech recognition interface (e.g. “open document”, “save”, “open sheet”, “enter formula”, “play”, “pause”, “dial”), each with an “importance level” specified. These commands could be considered short cuts. Initially, only two of the applications may be present in the operating system. The operating system could decide to expose all grammar elements in the menu, regardless of their importance level, because the total number of commands is reasonably small. However, if the end user later installs or activates another two applications, the top-level menu could become too complex. At that point, the operating system may decide to prune certain grammar elements from the top level menu, so that only those with “high” importance remain. In addition, the operating system may track the frequency of use of each command, to ensure that a frequently used command remains in the top-level menu, even if the application specified its importance as “low.” 
     Furthermore, a speech recognition system, according to embodiments, may also enable an application to specify an “importance” level for alternative grammar elements (e.g. synonyms for a particular command). This enables the system to prune synonyms from the combined global grammar, to avoid recognition conflicts. According to other embodiments, the “importance” levels may be used in combination with a current “priority” level of the active applications, so that a “foreground” application provides more commands than a “background” application. Below is an example of how a list of alternatives may be expressed in a mark-up language: 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                   
                 &lt;command id=“initiate phone call” importance=“1”&gt; 
               
               
                   
                  &lt;one-of&gt; 
               
               
                   
                   &lt;item importance=“1”&gt; call &lt;/item&gt; 
               
               
                   
                   &lt;item importance=“1”&gt; dial &lt;/item&gt; 
               
               
                   
                   &lt;item importance=“2”&gt; phone &lt;/item&gt; 
               
               
                   
                   &lt;item importance=“3”&gt; contact &lt;/item&gt; 
               
               
                   
                   &lt;item importance=“3”&gt; buzz &lt;/item&gt; 
               
               
                   
                  &lt;/one-of&gt; 
               
               
                   
                 &lt;/command&gt;. 
               
               
                   
               
            
           
         
       
     
     Now referring to  FIG. 3 , audio subsystem  302  begins the process of speech recognition by converting sound input to audio signals. This block is typically implemented as signal processors that are well known in the art. The audio signal is provided to speech recognition engine  304 , which converts the audio signal to textual data. 
     Speech engine  304  may comprise a number of components such as a language modeling module, a training module, a language model customization module, and the like. Speech recognition engine  304  recognizes words, phrases, and the like, based on customized language and acoustic models and provides textual versions of the audio utterances. 
     Grammar service  306 , according to embodiments, performs the task of managing recognized text before it is provided to applications  1  through N ( 308 ). As shown in the figure, multiple applications may consume the recognized text. This may lead to one or more of the problems described above. To reduce grammar contention and interface overload, grammar service  306  may prune the grammar elements available at any given time using a registration and prioritization process. 
     The registration and prioritization process essentially assigns priorities or importance levels to elements (commands) associated with each application. Grammar service  306  acts as an arbiter of competing speech enabled applications using the same interface. 
     Applications  1  through N ( 308 ) represent any speech enabled application including, but not limited to, word processing applications, presentation applications, browsing applications, navigational assistance applications, media player applications, radio/TV tuner applications, communication applications, and the like. Operating system  310  provides a framework for the different components of the speech recognizing system to work within. While the illustration shows all components and the applications under one operating system, various portions of architecture  300  may be executed in different operating systems. 
     Components of the speech recognizing system such as grammar service  306  may be loaded into a server, executed over a distributed network, executed in a client device, and the like. Furthermore, the components described above are for illustration purposes only, and do not constitute a limitation on the embodiments. A speech recognizing system with automatic grammar pruning may be implemented using fewer or additional components in various orders. Individual components may be separate applications, or part of a single application. The speech recognition system or its components may include individually or collectively a user interface such as a web service, a Graphical User Interface (GUI), and the like. 
       FIG. 4  illustrates a conceptual diagram of processes and sub-processes of an implementation of a speech recognition system with grammar pruning. Speech recognition systems rely on a number of statistical models to capture information about speech. Often, the language and/or acoustic models are adapted for a particular user allowing the system to capture the user&#39;s speaking style and vocabulary. Textual or audio data may be available (e.g. the user&#39;s archived e-mails, stored documents, and the like) to be used by the speech recognition application to learn about the words the user tends to use and the way they tend to use them. 
     On the speech recognition portion of operations, user  412  provides voice input to signal processing  402 , which converts the sound signals to audio signals. Feature extraction process  404  determines recognizable portions of audio signals that can be converted to textual data. Learning environment  406  is a part of any speech recognition system where training data  408  and language models  410  are used to enhance conversion of audio signals to textual data. Language models may be updated based on feedback from learning environment  406  as well. Speech recognition  414  is the process, where textual data is produced for software applications. In some embodiments, speech recognition may include parsing of commands and data based on input from grammar service  420 , which maintains and uses a global command list for multiple applications. In other embodiments, this functionality may be performed within grammar service  420 . 
     As mentioned above, grammar service operations may begin with parsing of commands and data ( 426 ) from the recognized text. Prior to this step, however, grammar service  420  may go through a registration process  422  of applications and commands. The registration of commands may include a selection process, where the grammar service allows a predetermined number of commands among all commands submitted by the registered applications to maintain a maximum size for its global command list. The registration process may be followed by an assignment of priorities process  424 , where each command is assigned a priority or importance level based on a status or type of the associated application, a suggested priority level by the application, a number of already registered applications and/or commands, available system resources, a category of commands, and the like. The priority levels may even be assigned or modified externally based on credentials of a user, system administrator, or an application. The prioritized global command list is then used in parsing the commands from the recognized text. The grammar service may then provide the parsed application-specific commands to individual applications in process  428 . 
     In some embodiments, grammar service  420  may register synonyms for registered commands. Depending on a number of active applications and available system resources, the number of registered synonyms may also be pruned by the grammar service. 
     Optionally, the grammar service may build a secondary “subgrammar” to provide access to the commands that have been pruned from the unified grammar. To accomplish this, a unique identifier may be determined for each application and saved along with an importance level of the application and a grammar command that identifies that application. The grammar service may then include the application&#39;s grammar command in its unified grammar, such that this grammar command can access a “submenu” (also built by the grammar service) that contains the full list of functions. For example, consider an application called “media player” that implements a number of functions such as “play”, “stop”, “pause”, “next”, “previous”, and so on. Initially, the grammar service may attempt to place all of the commands in the unified grammar. If the grammar service is forced to prune commands, it may create an additional command called “media player” which provides access to a submenu of all the pruned commands associated with the “media player” application. 
       FIG. 5  illustrates two example stages of speech recognition based control in a multi-application environment. The multi-application environment is an example in-car-control system that manages various applications in an automobile. 
     In the first phase of diagram  500 , control application  502  is arranged to provide a speech recognition interface and control two applications, navigation assistance application  504  and satellite radio tuner application  506 . Each application provides a list of five commands to be registered and used by the control application. For example, navigation assistance application  504  may provide commands “plan route”, “directions”, “location”, “map”, and “zoom” as designated by reference numeral  505 . Satellite radio tuner application  506  may provide the commands “station”, “scan”, “volume”, “seek”, and “set station” as designated by reference numeral  507 . Based on the lists of global commands provided by both applications, control application  502  selects and prioritizes five commands for the common interface. The commands, designated by reference numeral  503 , are “plan route”, “directions”, “station”, “scan”, and “volume.” 
     In the second phase, two additional applications are activated: media player application  510  and hands-free-phone application  512 . As described previously, the new applications submit their global commands as well. According to some embodiments, not each application has to submit an equal number of commands. As shown in the example diagram, the additional applications submit three commands each. For example, media player application  510  submits commands “play”, “change disk”, and “skip” as designated by reference numeral  511 . Hands-free-phone application  512  submits the commands “dial”, “store number”, and “volume” as designated by reference numeral  513 . Control application  502  reassesses the list of commands, selects a new set of commands to be included in a revised list of five top level commands  503   a , and assigns priorities to the new list of commands. The top priority command “plan route” remains in first place. The previously second command “directions” is dropped from the list. In its place, the satellite radio command “station” is moved to second place. Third command is “play” from media player application  510 . Fourth command “change disk” is also from media player application  510 . Finally, the fifth command “dial” is from hands-free-phone application  512 . 
     The speech recognition and grammar pruning processes, components, examples, and architecture discussed in  FIGS. 3 through 5  are for illustration purposes only. Embodiments are not limited to the example applications, modules, or processes. Managing speech recognition in a multi-application environment may be provided in many other ways using the principles described herein. 
       FIG. 6  illustrates a logic flow diagram for a process of using grammar pruning in a speech recognition system. Process  600  may be implemented in a component of a speech recognition system such as the grammar service  306  of  FIG. 3 . 
     Process  600  begins with operation  602 , where the grammar service receives request(s) for registration from one or more applications and registers the application(s) acting as an arbiter of competing speech interfaces. The grammar service essentially gives the applications the authority to declare which commands are most important to their users by defining importance levels for their global grammar elements (commands). Processing advances from operation  602  to operation  604 . 
     At operation  604 , priorities or importance levels are assigned to registered applications. The priority levels may be assigned based on a type or status of an application. For example, communication applications may be assigned higher priority levels than entertainment applications. The priority levels may also be assigned so that a “foreground” application provides more commands than a “background” application. The assigned application priority levels may then be used in selecting and prioritizing commands from each application for the global command list. Processing proceeds from operation  604  to operation  606 . 
     At operation  606 , the grammar service receives a list of commands to be registered from each registered application. This step is basically a brokering operation, where the grammar service begins to determine which commands should be allowed and in what order in the global command list. The registered applications may provide suggested priority levels for their submitted commands. Processing moves from operation  606  to operation  608 . 
     At operation  608 , the grammar service selects allowed commands for the global command list. The grammar service may use a heuristic to determine a maximum allowable size of the global command list, also referred to as unified grammar. The size can be a constant maximum value chosen at implementation or a heuristic chosen by the grammar service at run-time based on an analysis of the available computing resources, application status, and the like. If the unified list is larger than the allowed maximum size, the grammar service may start removing grammar elements beginning with those set at the lowest importance level. 
     In selecting allowed commands, the grammar service may prune “conflicts” from the unified grammar. A “conflict” is defined as two grammar elements (commands) that are identical, or that are grammatically similar enough to create problems with speech recognition. The grammar service may perform an item-by-item comparison of all grammar elements, comparing every grammar element to every other, using a comparison method based on a metric of “grammatical distance” or the number of distinctive phonetic characteristics that can be used to differentiate the two grammar components. If a conflict is found, the grammar service may use the importance level assigned to each grammar element to eliminate those with lower importance. The grammar service may also use the priority assigned to each software application to resolve conflicts between grammar elements from different applications that are set to the same importance level. 
     Additionally, the grammar service may use application status to select grammar elements to be included in the global command list. For example, if certain functionalities are not available for an active application, such as phone service being not available despite the hands-free-phone application being active, the grammar service may lower the priorities or remove the commands associated with that application from the global command list. Processing advances from operation  608  to operation  610 . 
     At operation  610 , the grammar service assigns priority levels to the selected commands. As mentioned above, the priorities or importance levels may be assigned based on a status or a type of the associated application, a suggested priority level by the application, a number of already registered applications and/or commands, available system resources, a category of commands, and the like. The priority levels may also be assigned or modified externally based on credentials. For example, a system administrator or a user may assign or modify the priorities depending on their permission levels (a user may be allowed to modify priority levels for entertainment or office applications, but not system or communication applications). Furthermore, certain applications may also be given the authority to force a change in the importance levels of their commands based on predetermined permission levels. Processing moves from operation  610  to operation  612 . 
     At operation  612 , recognized text is received from a speech recognition engine. The recognized text may include raw data and commands. In some embodiments, the speech recognition engine may perform parsing functions and provide only the commands to the grammar service. In other embodiments, the grammar service may parse the commands as shown in operation  614 . While parsing the commands, the grammar service may use the prioritized global grammar list and/or any submenus if they exist. Processing moves from operation  614  to operation  616 . 
     At operation  616 , the commands are provided to relevant applications for execution. As described previously, the global command list may be updated dynamically based on a number of active applications, application status, system resources status, and the like. After operation  616 , processing moves to a calling process for further actions. 
     The operations included in process  600  are for illustration purposes. Managing multiple voice controlled applications by using grammar pruning may be implemented by similar processes with fewer or additional steps, as well as in different order of operations using the principles described herein. 
     The above specification, examples and data provide a complete description of the manufacture and use of the composition of the embodiments. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims and embodiments.