Patent Publication Number: US-8990080-B2

Title: Techniques to normalize names efficiently for name-based speech recognition grammars

Description:
BACKGROUND 
     A name-based speech grammar provides data to a speech recognition system on how to recognize names. A name-based speech grammar may be used, for example, by a speech recognition system in a mobile device, such as a smart phone, to allow the user of the device to perform an action related to a name. One component of name-based speech grammar generation is name normalization. Name normalization may be performed to determine the pronunciation of a name. Some names may have several possible pronunciations. For example, “conference room  123 ” may be pronounced as “conference room one hundred twenty three”, “conference room one twenty three”, or “conference room one two three.” Name normalization can add substantial time and processing resources to name-based speech grammar generation. It is with respect to these and other considerations that the present improvements have been needed. 
     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. 
     Various embodiments are generally directed to techniques to normalize name-based speech recognition grammars. Some embodiments are particularly directed to techniques to normalizing name-based speech recognition grammars more efficiently by caching, and on a per-culture basis. In one embodiment, for example, a technique may comprise receiving a name for normalization, during name processing for a name-based speech grammar generating process. A normalization cache may be examined to determine if the name is already in the cache in a normalized form. When the name is not already in the cache, the name may be normalized and added to the cache. When the name is in the cache, the normalization result may be retrieved and passed to the next processing step. Caching normalization results may greatly reduce the time and processing expense of name normalization and grammar generation. Other embodiments are described and claimed. 
     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  illustrates an embodiment of a first system for generating and using a name-based speech grammar. 
         FIG. 2  illustrates an embodiment of a second system for generating and using a name-based speech grammar. 
         FIG. 3  illustrates an embodiment of a grammar builder. 
         FIG. 4  illustrates an embodiment of a logic flow to normalize names during speech grammar generation. 
         FIG. 5  illustrates an embodiment of a logic flow to determine when to rebuild a normalization cache. 
         FIG. 6  illustrates an embodiment of a computing architecture. 
         FIG. 7  illustrates an embodiment of a communications architecture. 
     
    
    
     DETAILED DESCRIPTION 
     Speech recognition is an increasingly useful tool for interacting with computing devices, in particular, mobile devices such as smart phones. Speech recognition may be computationally intensive and may use large amounts of storage for speech grammars. Consequently, many mobile devices send speech recognition tasks to a remote device, such as a speech recognition server. The mobile device may receive the recognized result as text and/or another format usable by the mobile device. This may allow the mobile device to, for example, look up a contact phone number or an e-mail address for the user without making the user navigate to a search input and type in the contact name. This type of service uses, at least, a name-based speech grammar that allows the speech recognizer to recognize a name from a speech input, and provide the name in a way that the mobile device can locate the name in a contact list. The embodiments are not limited to this context. 
     Various embodiments are directed to techniques to improve efficiency in building a name-based speech grammar, in particular, during a name normalization process. Name normalization is expensive, computationally, and takes a lot of time. In a given culture, e.g. American English, or France French, many names occur frequently. Embodiments take advantage of this reoccurrence by caching normalization results. When a name-based speech grammar (NSG) is being built for a set of names, a normalization cache may be checked for the name being processed. When the name is in the normalization cache, the normalization step can be avoided by using the cached normalization result. As a result, the embodiments can improve efficiency and NSG grammar generation time for a client services system. 
       FIG. 1  illustrates a block diagram for a system  100  for generating and using a name-based speech grammar. In one embodiment, for example, the system  100  may comprise a computer-implemented system  100  having multiple components, such as a client access server  110  and client devices  120 - 1 ,  120 - a  (collectively, client devices  120 ). As used herein the terms “system” and “component” are intended to refer to a computer-related entity, comprising either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be implemented as a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers as desired for a given implementation. The embodiments are not limited in this context. 
     In the illustrated embodiment shown in  FIG. 1 , the system  100  may be implemented with one or more electronic devices. Examples of an electronic device may include without limitation a mobile device, a personal digital assistant, a mobile computing device, a smart phone, a cellular telephone, a handset, a one-way pager, a two-way pager, a messaging device, a computer, a personal computer (PC), a desktop computer, a laptop computer, a notebook computer, a handheld computer, a server, a server array or server farm, a web server, a network server, an Internet server, a work station, a mini-computer, a main frame computer, a supercomputer, a network appliance, a web appliance, a distributed computing system, multiprocessor systems, processor-based systems, consumer electronics, programmable consumer electronics, television, digital television, set top box, wireless access point, base station, subscriber station, mobile subscriber center, radio network controller, router, hub, gateway, bridge, switch, machine, or combination thereof. Although the system  100  as shown in  FIG. 1  has a limited number of elements in a certain topology, it may be appreciated that the system  100  may include more or less elements in alternate topologies as desired for a given implementation. 
     In various embodiments, the system  100  may comprise a client access server  110 . Client access server  110  may include one or more devices that include applications and data to provide one or more services to client applications and devices. In an embodiment, client access server  110  provides a service that may receive and respond to speech information. In an embodiment, client access server  110  may provide, for example, e-mail service, telephone service, voice mail service, personal information management service, calendaring service, contact management service, and so forth. The embodiments are not limited in this context. 
     In an embodiment, client access server  110  may be implemented with a cloud computing model. In a cloud computing model, applications and services may be provided as though the applications and data were on a local device, without having to install the applications and/or store the data on a local device. However, the applications and/or data storage may be implemented across many devices, servers, and data stores, accessible over a communication interface from a local device. In a cloud computing model, client access server  110  may be physically embodied on one or more servers, and in one or more physical locations. Client access server  110  may be a sub-component of a larger cloud computing implementation of a group of services. Regardless of physical configuration, client access server  110  may appear, logically, as one device or system to external entities, such as client devices  120 . 
     In an embodiment, client access server  110  may include a request handler  112 . Request handler  112  may receive requests for data and/or services from a client device  120 . Request handler  112  may be a web browser application. Request handler  112  may be an application program interface (API). Request handler  112  may be capable of receiving a request such as a hypertext protocol (HTTP) request. In an embodiment, the request may include a request for speech recognition. 
     In an embodiment, client access server  110  may include client data  114 . Client data  114  may include any data related to providing a service to a client. Client data  114  may include, for example, mailbox data for providing an e-mail service. Client data  114  may include, without limitation, contact information, calendar information, voicemail information, and so forth. 
     In an embodiment, client access server  110  may include a speech recognizer  116 . Speech recognizer  116  may be an application or component that receives speech in the form of audio speech data  102  and converts the audio speech data  102  into a text representation of the speech. Speech recognizer  116  may refer to one or more speech grammars  140  to perform the recognition process. The embodiments are not limited to these examples. 
     In an embodiment, client access server  110  may include a grammar builder  118 . Grammar builder  118  may generate the one or more speech grammars  140 . In an embodiment, one of the speech grammars  140  generated by grammar builder  118  may be a name-based speech grammar (NSG). An NSG may be generated from name data  150 . Grammar builder  118  is described further with respect to  FIG. 3 . In an embodiment, multiple speech grammars  140  may be generated by grammar builder  118  and used by speech recognizer  116 . A speech grammar  140  may be generated for each different culture represented in name data  150 , for example. That is, there may be one speech grammar  140  for English, one speech grammar  140  for French, and so forth. In particular, there may be separate NSGs for each culture, as names are often specific to one culture. 
     Name data  150  may include a set of names that may be relevant to a client of client access server  110 . For example, name data  150  may include a corporate directory, professional contact list, or personal contact list. In addition to names of people, name data  150  may also include, for example, location names, street names, city names, e-mail addresses, conference room names, and so forth. Name data  150  may also include numbers. Numbers may be used alone, for example in a phone number, or may be components of names, for example “Conference Room  123 .” Numbers, as names, can be particularly resource-intensive to normalize, as there can be many variants of a number in speech. The number “ 123 ”, for example, may be spoken, in English, as “one hundred twenty three”, “one two three”, and “one twenty three.” A speech recognizer would have to be able to convert any of these variants into the correct number, and the speech grammar would have to contain a pattern for each variant. In an embodiment, name data  150  may be in a format that is used by the applications that use name data  150 , such as an e-mail application or contact application, and not, for example, in a speech grammar format. 
     In various embodiments, the system  100  may comprise client devices  120 - 1 , and  120 - a , where a represents a positive integer. Client devices  120  may include any electronic devices capable of receiving voice information and communicating with client access server  110 . The voice information may be received from a user through a microphone, or may be an audio file stored on client device  120 . Client devices  120  may include applications (not shown) that may communicate with client access server  110  to receive or send data, and perform various functions. Such an application may include an e-mail client application, a calendar application, a contact management application, and so forth. 
     The components of client access server  110  shown in  FIG. 1 , e.g.  112 ,  114 ,  116 ,  118 , speech grammar  140  and name data  150  may be located on one server or may be distributed across a plurality of servers and data stores. The embodiments are not limited to these examples. 
       FIG. 2  illustrates a block diagram of a system  200  for generating and using a name-based speech grammar. System  200  may be similar to system  100 . Request handler  212  and client data  214  may be representative of request handler  112  and client data  114 , respectively. Speech recognizer  216  and grammar builder  218  may be representative of speech recognizer  116  and grammar builder  218 , respectively. Clients  220  may be representative of clients  120 . 
     In system  200 , client access server  210 - 1  may be separate from a client services server  230 . Client access server  210 - 1  may still receive requests for client data  214 . Client access server  210 - 1  may be, for example, an email server provided by a first business entity. Services, however, such as speech recognition services, may be provided from a different source, e.g. client services server  230 . System  200  may include additional client access servers  210 - b , where b represents any positive integer. The additional client access servers  210 - b  may be provided by other entities, such as another business, a government agency, an academic entity and so forth. 
     Client services server  230  may provide services including speech recognition to multiple, unrelated clients such as client access server  210 - 1  and  210 - b . In an embodiment, client services server  230  may construct speech grammars  240  from name data  250  compiled from multiple sources. In an embodiment, name data  250  may be received from the multiple sources and stored with client services server  230 , either consolidated into one data store, or in separate logical data stores for each separate entity. In an embodiment, name data  250  may be provided on-the-fly to client services server  230  for the generation of speech grammars  240  without being stored by client services server  230  beyond the speech grammar  240  generation. 
     In an embodiment, grammar builder  218  may generate one NSG  242  for all of client services server  230 &#39;s client entities such as client access servers  210 - 1 ,  201 - b . In an embodiment, grammar builder  218  may generate separate NSGs  242  specific to each client entity. In an embodiment, grammar builder  218  may generate one normalization cache (not shown) that contains normalization results for all of the client entities. In an embodiment, grammar builder  218  may build separate normalization caches for each client entity. The embodiments are not limited to these examples. 
       FIG. 3  illustrates a block diagram of a grammar builder  300 . Grammar builder  300  may be a representative example of grammar builder  118 ,  218 . Grammar builder  300  may include one or more components, such as name processing modules  310 , and a name normalizer  320 . The functions of grammar builder  300  may be implemented with more or other components and are not limited to this example. 
     In various embodiments, grammar builder  300  may include name processing modules  310 . Name processing modules  310  may include one or more modules to perform various steps in generating a name-based speech grammar, not including a name normalization step. Name processing modules  310  may include pre-normalization steps and/or post-normalization steps. A pre-normalization step may be, for example, speech grammar filter list processing. A speech grammar filter list may include a list of patterns, and each pattern may include a regular expression. When a name from name data  150 ,  250  matches a regular expression in a pattern, the name may be transformed as specified in the pattern&#39;s output. For example, one pattern may match names that include a bracketed description. The pattern may output the name with the bracketed expression removed. Additional or alternate name processing modules  310  may be included as needed for name-based speech grammar generation. 
     In various embodiments, grammar builder  300  may include name normalizer  320 . Name normalizer  320  may perform name normalization for the building of a name-based speech grammar. Name normalization may start with name data  150 ,  250 , e.g. a corporate employee directory, and determine pronunciations of a name. 
     Name normalizer  320  may, in some embodiments, read a name from name data  150 ,  250 . In an embodiment, name normalizer  320  may split the name into component parts, e.g. words, such as first name, last name, middle name, middle initial, and suffix. When name data  150 ,  250  also includes other types of data, such as addresses and room numbers, those “names” may also be split into component parts, such as street or room number, conference room name, street name, city name, state name and so forth. In an embodiment, name data  150 ,  250  may include names already in a component form when name normalizer  320  reads a name from name data  150 ,  250 . 
     In an embodiment, name normalizer  320  may receive a name from a pre-normalization processing step from a name processing module  310 . Name normalizer  320  may call or execute one or more sets of instructions, such as functions, routines, applets, scripts and so forth, to perform the name normalization. 
     In an embodiment, name normalizer  320  may first check a normalization cache  330 - 1  to see if the currently selected name or name component has already been normalized and placed in normalization cache  330 - 1 . When the currently selected name has already been normalized, it may be in stored in normalization cache  330 - 1  as a normalization result  332 - 1 . When the name is not present in normalization results  332 - 1 , name normalizer  320  may proceed with the normalization process, after which the now-normalized currently selected name may be placed in normalization cache  330 - 1 . 
     In an embodiment, normalization cache  330  may include a mapping of a name to a normalization result. The normalization results  332  may include the normalized name. In an embodiment, the normalization results  332  may instead or additionally include a Boolean value for a name, where the Boolean value indicates whether a name has multiple pronunciations. In some embodiments, names having multiple pronunciations may be excluded from the name-based speech grammar in order to restrict the size of the grammar. 
     In an embodiment, grammar builder  300  may comprise, or maintain, multiple normalization caches  330 - c , where c represents a positive integer, one normalization cache for each language culture for which a name-based speech grammar is needed. Names tend to re-occur on a per-culture basis. For example, “John” occurs frequently in English, “Jean” occurs frequently in French, and “Jose” occurs frequently in Spanish. Each normalization cache  330 - 1 ,  330 - c  may have, therefore, its own set of normalization results  332 - 1 ,  332 - c , respectively. 
     In an embodiment, a normalization cache  330 - 1  may be persisted on a memory beyond the generation of one name-based speech grammar. That is, it may be stored on a non-volatile memory to be available for subsequent grammar building processes. In an embodiment, a normalization cache  330 - 1  may be persisted in memory, typically volatile memory, for the duration of the generation of one (or one set of) name-based speech grammar(s), but may be discarded at the end of the generation process. 
     Normalization results  332  may be provided to a next step in the name-based speech grammar generation process, to one of name processing modules  310 . At the end of the generation process, a name-based speech grammar  340 - 1 ,  340 - c  may be generated for each culture existing in name data  150 ,  250 . 
     The components of grammar builder  300 , such as name processing modules  310  and name normalizer  320 , may be communicatively coupled via various types of communications media. The components  310 ,  320  may coordinate operations between each other. The coordination may involve the uni-directional or bi-directional exchange of information. For instance, the components  310 ,  320  may communicate information in the form of signals communicated over the communications media. The information can be implemented as signals allocated to various signal lines. In such allocations, each message is a signal. Further embodiments, however, may alternatively employ data messages. Such data messages may be sent across various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces. 
     Operations for the above-described embodiments may be further described with reference to one or more logic flows. It may be appreciated that the representative logic flows do not necessarily have to be executed in the order presented, or in any particular order, unless otherwise indicated. Moreover, various activities described with respect to the logic flows can be executed in serial or parallel fashion. The logic flows may be implemented using one or more hardware elements and/or software elements of the described embodiments or alternative elements as desired for a given set of design and performance constraints. For example, the logic flows may be implemented as logic (e.g., computer program instructions) for execution by a logic device (e.g., a general-purpose or specific-purpose computer). 
       FIG. 4  illustrates one embodiment of a logic flow  400 . The logic flow  400  may be representative of some or all of the operations executed by one or more embodiments described herein. Logic flow  400  may represent a normalization process during name-based speech grammar generation. 
     In the illustrated embodiment shown in  FIG. 4 , the logic flow  400  may receive a name for normalization at block  402 . For example, name normalizer  320  may receive a name from another name processing module  310 , or from name data  150 ,  250 . The name may be a component of a name, a full name, a person&#39;s name, a number, an e-mail address, an internal address, a telephone number, a street address, a city name, a country name, a title, a nickname, and so forth. In an embodiment, the name may be specific to, or associated with, a culture. 
     The logic flow  400  may determine whether the name is in a normalization cache at block  404 . For example, name normalizer  320  may check normalization cache  330 - 1  to see if the name is present in the normalization cache in normalized form. 
     The logic flow  400  may retrieve the normalization result from the normalization cache at block  406 , when the name is in the normalization cache. For example, name normalizer  320  may read the normalization result from normalization results  332 - 1 . 
     The logic flow  400  may normalize the name and add the normalization result to the normalization cache at block  408  when the name was not in the normalization cache. For example, name normalizer  320  may proceed with normalization, for example, by calling or executing one or more sets of instructions to normalize the name. Once the name is normalized, it may be added to normalization cache  330 - 1  as a normalization result  332 - 1 . Name normalizer  320  may also retain the normalization result to pass to a next process. 
     The logic flow  400  may provide the normalization result to the next process in a name-based speech generation process at block  410 . For example, name normalizer  320  may provide the normalization result, either retrieved from the normalization cache  330  or just normalized, to a post-normalization name processing module  310 . 
     The logic flow  400  may determine whether there are additional names to normalize at block  412 . Name normalizer  320  may receive another name from a pre-normalization name processing module  310 , and may repeat logic flow  400  beginning at block  402 . When name normalizer  320  stops receiving names, meaning that there are no additional names to normalize, logic flow  400  may end at block  414 . 
     Further name processing and speech grammar generation processes make occur after block  414  to produce a name-based speech grammar for a culture (not shown). In an embodiment, logic flow  400  may be repeated for each culture that has names to be normalized for a culture-specific name-based speech grammar. 
     In an embodiment, a normalization cache  330 - b  may not have enough storage space allocated to it to store every normalization result from name data  150 ,  250 . In such a case, name normalizer  320  may only write normalization results to normalization cache  330  when a name occurs more frequently in a culture. Name normalizer  320  may keep track of the relative frequency of occurrence of the names, and may only store names that occur above a certain threshold frequency. In an embodiment, all normalization results may be stored until the normalization cache is full, at which point the lower frequency names may be overwritten with the more frequently occurring names when needed. 
     In an embodiment, grammar builder  300  may be tasked with generating or regenerating a speech grammar on a daily basis, or at some other periodic interval. 
       FIG. 5  illustrates one embodiment of a logic flow  500 . The logic flow  500  may be representative of some or all of the operations executed by one or more embodiments described herein. Logic flow  500  may represent a process of determining when to update normalization cache  330 . 
     In an embodiment, logic flow  500  may determine whether an expiration date for a normalization cache has expired in block  502 . In an embodiment, normalization cache  330  may be assigned an expiration date, for example, by name normalizer  320  or by grammar builder  300 . In an embodiment, grammar builder  300  may be tasked with generating or regenerating a speech grammar on a daily basis, or at some other periodic interval, so the expiration date may be set, for example, to be a longer period than the grammar generating interval, in order to maximize the benefits of using the normalization cache. 
     In an embodiment, when the normalization cache has expired, logic flow  500  may rebuild the normalization cache in block  504 . In an embodiment, this may occur during the process of building a speech grammar. The contents of the normalization cache may be effectively deleted, for example, by clearing, or zeroing, the cache, or by marking all of the storage bits used for the normalization cache as available for overwriting. The embodiments are not limited to these examples. 
     In an embodiment, when the normalization cache has not expired, or when an expiration date is not used, logic flow  500  may compare the current name data to the name data that was previously normalized in block  506 . For example, the current copy of a corporate directory may be compared to an archived copy of the corporate directory from when the normalization cache was last generated. 
     In an embodiment, when the difference in the two versions of the name data exceeds a threshold, in block  508 , logic flow  500  may rebuild the normalization cache in block  504 . For example, when a corporation has a group of newly hired and/or newly laid-off employees, the corporate directory may change. At a threshold of, for example, five or ten percent difference, the normalization cache may be rebuilt. Otherwise, the normalization cache is not rebuilt and logic flow  500  ends at block  510 . 
     In some embodiments, determining when to rebuild the normalization cache may depend just on an expiration date, e.g. blocks  502  and  504  alone. In some embodiments, determining when to rebuild the normalization cache may depend just on a different threshold, e.g. blocks  506 ,  508  and  504  alone. The embodiments are not limited to these examples. 
     In an embodiment, generating a speech grammar for 450,000 names and twenty-six different cultures without using a normalization cache took about thirty hours. When a persistent normalization cache was used with the same input data, the time needed to generate the speech grammar was only about 30 minutes. When a normalization cache that was generated on-the-fly, that is, not persisted beyond the normalization process, was used with the same input data, the time needed to generate the speech grammar was about eleven hours. 
       FIG. 6  illustrates an embodiment of an exemplary computing architecture  600  suitable for implementing various embodiments as previously described. The computing architecture  600  includes various common computing elements, such as one or more processors, co-processors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input/output (I/O) components, and so forth. The embodiments, however, are not limited to implementation by the computing architecture  600 . 
     As shown in  FIG. 6 , the computing architecture  600  comprises a processing unit  604 , a system memory  606  and a system bus  608 . The processing unit  604  can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures may also be employed as the processing unit  604 . The system bus  608  provides an interface for system components including, but not limited to, the system memory  606  to the processing unit  604 . The system bus  608  can be any of several types of bus structure that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. 
     The system memory  606  may include various types of memory units, such as read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, ovonic memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, or any other type of media suitable for storing information. In the illustrated embodiment shown in  FIG. 6 , the system memory  606  can include non-volatile memory  610  and/or volatile memory  612 . A basic input/output system (BIOS) can be stored in the non-volatile memory  610 . 
     The computer  602  may include various types of computer-readable storage media, including an internal hard disk drive (HDD)  614 , a magnetic floppy disk drive (FDD)  616  to read from or write to a removable magnetic disk  618 , and an optical disk drive  620  to read from or write to a removable optical disk  622  (e.g., a CD-ROM or DVD). The HDD  614 , FDD  616  and optical disk drive  620  can be connected to the system bus  608  by a HDD interface  624 , an FDD interface  626  and an optical drive interface  628 , respectively. The HDD interface  624  for external drive implementations can include at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies. 
     The drives and associated computer-readable storage media provide volatile and/or nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For example, a number of program modules can be stored in the drives and memory units  610 ,  612 , including an operating system  630 , one or more application programs  632 , other program modules  634 , and program data  636 . The one or more application programs  632 , other program modules  634 , and program data  636  can include, for example, grammar builder  118 ,  218 ,  300 , name processing modules  310 , name normalizer  320  and speech recognizer  116 ,  216 . 
     A user can enter commands and information into the computer  602  through one or more wire/wireless input devices, for example, a keyboard  638  and a pointing device, such as a mouse  640 . Other input devices may include a microphone, an infra-red (IR) remote control, a joystick, a game pad, a stylus pen, touch screen, or the like. These and other input devices are often connected to the processing unit  604  through an input device interface  642  that is coupled to the system bus  608 , but can be connected by other interfaces such as a parallel port, IEEE 1394 serial port, a game port, a USB port, an IR interface, and so forth. 
     A monitor  644  or other type of display device is also connected to the system bus  608  via an interface, such as a video adaptor  646 . In addition to the monitor  644 , a computer typically includes other peripheral output devices, such as speakers, printers, and so forth. 
     The computer  602  may operate in a networked environment using logical connections via wire and/or wireless communications to one or more remote computers, such as a remote computer  648 . The remote computer  648  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  602 , although, for purposes of brevity, only a memory/storage device  650  is illustrated. The logical connections depicted include wire/wireless connectivity to a local area network (LAN)  652  and/or larger networks, for example, a wide area network (WAN)  654 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network, for example, the Internet. 
     When used in a LAN networking environment, the computer  602  is connected to the LAN  652  through a wire and/or wireless communication network interface or adaptor  656 . The adaptor  656  can facilitate wire and/or wireless communications to the LAN  652 , which may also include a wireless access point disposed thereon for communicating with the wireless functionality of the adaptor  656 . 
     When used in a WAN networking environment, the computer  602  can include a modem  658 , or is connected to a communications server on the WAN  654 , or has other means for establishing communications over the WAN  654 , such as by way of the Internet. The modem  658 , which can be internal or external and a wire and/or wireless device, connects to the system bus  608  via the input device interface  642 . In a networked environment, program modules depicted relative to the computer  602 , or portions thereof, can be stored in the remote memory/storage device  650 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used. 
     The computer  602  is operable to communicate with wire and wireless devices or entities using the IEEE 802 family of standards, such as wireless devices operatively disposed in wireless communication (e.g., IEEE 802.7 over-the-air modulation techniques) with, for example, a printer, scanner, desktop and/or portable computer, personal digital assistant (PDA), communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone. This includes at least Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. Wi-Fi networks use radio technologies called IEEE 802.7x (a, b, g, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wire networks (which use IEEE 802.3-related media and functions). 
       FIG. 7  illustrates a block diagram of an exemplary communications architecture  700  suitable for implementing various embodiments as previously described. The communications architecture  700  includes various common communications elements, such as a transmitter, receiver, transceiver, radio, network interface, baseband processor, antenna, amplifiers, filters, and so forth. The embodiments, however, are not limited to implementation by the communications architecture  700 . 
     As shown in  FIG. 7 , the communications architecture  700  comprises includes one or more clients  702  and servers  704 . The clients  702  may implement the client devices  120 ,  220 , and in some embodiments, client access server  210 . The servers  704  may implement the server systems for client access server  110 ,  210  and client services server  230 . The clients  702  and the servers  704  are operatively connected to one or more respective client data stores  708  and server data stores  710  that can be employed to store information local to the respective clients  702  and servers  704 , such as cookies and/or associated contextual information. 
     The clients  702  and the servers  704  may communicate information between each other using a communication framework  706 . The communications framework  706  may implement any well-known communications techniques, such as techniques suitable for use with packet-switched networks (e.g., public networks such as the Internet, private networks such as an enterprise intranet, and so forth), circuit-switched networks (e.g., the public switched telephone network), or a combination of packet-switched networks and circuit-switched networks (with suitable gateways and translators). The clients  702  and the servers  704  may include various types of standard communication elements designed to be interoperable with the communications framework  706 , such as one or more communications interfaces, network interfaces, network interface cards (NIC), radios, wireless transmitters/receivers (transceivers), wired and/or wireless communication media, physical connectors, and so forth. By way of example, and not limitation, communication media includes wired communications media and wireless communications media. Examples of wired communications media may include a wire, cable, metal leads, printed circuit boards (PCB), backplanes, switch fabrics, semiconductor material, twisted-pair wire, co-axial cable, fiber optics, a propagated signal, and so forth. Examples of wireless communications media may include acoustic, radio-frequency (RF) spectrum, infrared and other wireless media. One possible communication between a client  702  and a server  704  can be in the form of a data packet adapted to be transmitted between two or more computer processes. The data packet may include a cookie and/or associated contextual information, for example. 
     Various embodiments may be implemented using hardware elements, software elements, or a combination of both. Examples of hardware elements may include devices, components, processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), memory units, logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. Examples of software elements may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints, as desired for a given implementation. 
     Some embodiments may comprise an article of manufacture. An article of manufacture may comprise a storage medium to store logic. Examples of a storage medium may include one or more types of computer-readable storage media capable of storing electronic data, including volatile memory or non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writeable or re-writeable memory, and so forth. Examples of the logic may include various software elements, such as software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. In one embodiment, for example, an article of manufacture may store executable computer program instructions that, when executed by a computer, cause the computer to perform methods and/or operations in accordance with the described embodiments. The executable computer program instructions may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, and the like. The executable computer program instructions may be implemented according to a predefined computer language, manner or syntax, for instructing a computer to perform a certain function. The instructions may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language. 
     Some embodiments may be described using the expression “one embodiment” or “an embodiment” along with their derivatives. These terms mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. 
     Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. These terms are not necessarily intended as synonyms for each other. For example, some embodiments may be described using the terms “connected” and/or “coupled” to indicate that two or more elements are in direct physical or electrical contact with each other. The term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. 
     It is emphasized that the Abstract of the Disclosure is provided to comply with 37 C.F.R. Section 1.72(b), requiring an abstract that will allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein,” respectively. Moreover, the terms “first,” “second,” “third,” and so forth, are used merely as labels, and are not intended to impose numerical requirements on their objects. 
     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.