Predicting textual candidates

Embodiments are provided to predict and suggest one or more candidates. Words, acronyms, compound words, phrases, and other textual and symbolic representations can be predicted and suggested to a user as part of an input process or other user operation. In an embodiment, a number of textual candidates can be predicted based in part on user input and data stored in a store component. The number of predicted textual candidates can be suggested to a user as a number of suggested textual candidates. Embodiments enable a user to select an appropriate textual candidate from the number of suggested textual candidates, while reducing a number of associated user operations.

BACKGROUND

Computer-implemented word prediction methods attempt to reduce the amount of manual input required by application users. For example, e-mail applications, word processing applications, browser applications, searching applications, etc. use various word prediction algorithms when attempting to predict and suggest words based on a user's input to a respective application. Some word prediction algorithms can (correctly or incorrectly) interpret user input and automatically suggest a corresponding word.

Accordingly, a word prediction method can, in some instances, provide a more efficient and satisfactory experience by accurately predicting and suggesting words to a user. However, some word prediction methods can also operate to disrupt and aggravate users (e.g. displaying an erroneous word list, obstructing the user's work, etc.), thereby detracting from the value of the underlying prediction algorithms. Moreover, current word prediction methods are rarely configured to predict and suggest compound words or phrases, even when the words or phrases are frequently used. Correspondingly, current word prediction methods can be adversely affected by the accuracy and robustness of the underlying word prediction algorithms.

SUMMARY

Embodiments are provided to predict and suggest one or more candidates to a user. In an embodiment, one or more appropriate textual candidates can be predicted and suggested to a user based in part on the user's input, textual context, and/or communication. In one embodiment, one or more textual candidates (e.g. words, acronyms, compound words, phrases, and/or other textual and symbolic representations) can be predicted and suggested based in part on user input and/or a prediction algorithm. The prediction algorithm is configured to predict one or more textual candidates, including next words and/or phrases, using an n-gram probability model and the user input, including previously stored user input. A suggestion component is configured to suggest a number of appropriate textual candidates based on the one or more predicted textual candidates to a user.

DETAILED DESCRIPTION

Embodiments are provided to predict and suggest one or more appropriate candidates to a user. For example, a number of words, acronyms, compound words, phrases, and other textual and symbolic representations can be predicted and suggested to a user as part of an input process (word processing, text messaging, digital communication, etc.) or other operation. In an embodiment, a number of textual candidates can be predicted based in part on user input and data stored in a store component. The number of predicted textual candidates can be suggested to the user as a number of suggested textual candidates. Embodiments enable a user to select an appropriate candidate from the number of suggested candidates in fewer operations (e.g. fewer keystrokes, taps, scrolls, etc.).

In an embodiment, a software application having executable instructions is configured to predict and suggest one or more appropriate textual candidates (e.g. words, acronyms, compound words, and/or phrases) based in part on a textual context or communication. The application is configured to predict and suggest one or more textual candidates based in part on user input, including prior user input, and a prediction algorithm. In one embodiment, the prediction algorithm is configured to predict one or more textual candidates using an n-gram probability model and ranking features, but is not so limited.

FIG. 1is a block diagram of a system100, under an embodiment. In an embodiment, the system100is configured to predict and suggest one or more appropriate candidates, such as textual candidates (e.g. words, acronyms, compound words, and/or phrases) based in part on a user operation and/or input, candidate context, and/or a user communication. For example, the system100can operate to predict and suggest a following or next word, subsequent word, compound word, acronym, and/or phrase. In one embodiment, the system100can be configured as a software application including executable instructions and other functionality which operates to automatically suggest and/or complete a word, compound word and/or phrase based on user input and other stored data.

As shown inFIG. 1, the system100includes a prediction component102configured to predict a textual candidate. The prediction component102can operate to predict a number of textual candidates based in part on user input, textual or candidate context, and/or a user communication, but is not so limited. For example, the prediction component102can operate to predict a word, acronym, compound word, phrase, and/or other textual or symbolic representation based in part on a user's input to a device, system, etc. using the system100. The prediction component102can use stored input and/or dictionaries in conjunction with current user input to predict one or more appropriate textual candidates to a user, as described below.

In one embodiment, the prediction component102is configured to predict a number of words (e.g. next word, following word, or subsequent word), acronyms, compound words, and/or phrases based in part on user input and a prediction algorithm104that includes an n-gram probability model. The prediction algorithm104can use data stored in a store component106to ascertain a temporal correlation and other pertinent associations for appropriate candidates. In an embodiment, the store component106included stored text, which a user has entered, word by word.

The prediction algorithm104can operate to pick up words from the store component106as candidates and ascertain the temporal correlation and other pertinent associations for these candidates. In one embodiment, the term “word” refers to a string of character(s) that does not include a “separator character”, but is not so limited. A separator character refers to a character that is used to divide a string of characters into distinct words. Example separator characters include, but are not limited to, spaces, commas, quotes, and exclamation points. As further example, “MSOFFICE” and “OFFICE10” are words but “Microsoft Office” is not a “word” because it contains the space separator character. Furthermore, the system100can predict web and e-mail addresses, such as “www.123-we.net” and “dc8@aol.com” for example.

Moreover, the prediction algorithm104, including the n-gram probability model, can be used to predict the most probable textual candidates in the current textual context. Accordingly, the system100can predict a number of textual candidates given a current user's context. For example, suppose that a user has typed “Microsoft Windows M”. The prediction component102can use “Microsoft Windows” as preceding words and the letter “M” (as what the user is currently typing) to generate predictive candidates, as described below.

In one embodiment, the prediction algorithm104includes:

w represents a word in the store component, wherein words are stored to the store component based on user input or operation (w1is the first word stored in the store component, w2is the second word stored in the store component, . . . and wnis the word to be predicted and stored (e.g. stored at a particular location in the store such as a memory location or bin that corresponds with a more recent entry)).

P(wn) represents the probability of wn.

P(wn|wmn−1) represents the probability of a word wngiven a preceding sequence wmn−1.

PML( ) represents a function to calculate the maximum likelihood estimate of the probability.

C(wmn) represents a number of times that a sequence of words wm. . . wnexists in a store component.

kirepresents an interpolation coefficient, where Σiki=1 (kivalues can be determined by a parameter tuning process which is configured to minimize the amount of keystrokes required by a user).

n represents a number of candidates (e.g. words) in the store component.

N represents a number that specifies the length of the word sequence the prediction algorithm104uses to predict a number of candidates. That is, N is a number of preceding words that the prediction algorithm uses PLUS 1 (which corresponds with the candidate associated with the current user operation (e.g. typing, inking, or otherwise inputting). For example, if N is equal to 2, the prediction algorithm104uses the previous word only when predicting a candidate. That is, if N=2 and a user has input “Windows Mobile”, the prediction algorithm104uses “Mobile”, but not “Windows”, to predict the next candidate. Accordingly, if N=3, the prediction algorithm104uses “Windows Mobile” when predicting a candidate, such as the next word and/or phrase, as described below. As N is increased, the prediction can be made to be more context-sensitive. N and/or kican be modified and tailored to a particular implementation to provide a desired balance between accuracy and performance.

The prediction component102can use user input as a string for searching through the store component102to locate appropriate candidates. However, if there is no user input after the last separator character, such as a white space, in one embodiment, the prediction component102uses the last word of a sequence of preceding words and searches for matches with the last word. The prediction component102can return candidates that are next to or follow the last word of the preceding word sequence.

With continuing reference toFIG. 1, the system100also includes a suggestion component108configured to suggest one or more appropriate textual candidates to a user based on candidates that are predicted by the prediction component102. For example, the suggestion component108can suggest one or more words, acronyms, compound words, and/or phrases to a user as predicted by the prediction component102and/or after filtering, ranking, or otherwise limiting the number of suggested textual candidates that are ultimately presented to a user. The suggestion component108can thereby suggest the most probable textual candidates based in part on the user input history as stored in the store component106in conjunction with the current textual context and/or user input.

In an embodiment, the suggestion component108is configured to use ranking information associated with the number of predicted candidates to remove certain predicted candidates before suggesting appropriate textual candidates to the user. By using the ranking information, the suggestion component108can limit the number of suggested textual candidates that are ultimately presented to the user. Accordingly, by limiting the number of suggested candidates, the suggestion component108is configured to suggest a number of predicted textual candidates in a manner that does not unduly interfere with the user's work or current focus. In one embodiment, the suggestion component108can locate or otherwise position suggested textual candidates or a suggested candidate user-interface based on a caret position and/or available space on screen. The suggestion component108is configured to suggest one or more predicted textual candidates so that a user can readily select a suggested textual candidate while continuing to focus on a current project.

The system100also includes a collection component110that is configured to collect textual and other information based in part on user input, but is not so limited. The collection component110can collect textual and symbolic information, such as words, acronyms, compound words, and/or phrases. The collected information can then be stored in the store component106and used when predicting and suggesting one or more appropriate textual candidates. In one embodiment, the collection component110can operate to store user input as a text stream, word by word, along with a tree structured index. The tree structured index can be used when searching the store component106for certain data. For example, if a user enters text that consists of multiple words, the collection component110is configured to separate the text into individual words and store the words as a text stream in the store component106. Thus, textual and symbolic representations can be stored by the collection component110according to some desired configuration, chronological order for example, in the store component106.

As described above, if a user enters a text, the collection component110is configured to separate the text into individual words or character strings and store these words word by word in the store component106. In an embodiment, the collection component will not omit any word and stores all words even if these words are duplicated with existing information in the store component106. Correspondingly, the order of stored words and textual representations can be quickly and efficiently identified, including identifying when (e.g. a temporal association) and how often (e.g. a frequency, number of times, etc.) a word and/or phrase has been used. The store component106can also be used to identify a particular context for a word and/or phrase. In an embodiment, the collection component110associates identification information with each textual entry for the store component106. For example, an identification (ID) number can be associated with each storage bin or memory location of the store component106(seeFIG. 2). In one embodiment, as textual information is stored in the store component106, information can be moved or re-located from one location (e.g. bin) to another location and a corresponding ID can be used to determine when textual information was entered and/or a particular context associated with the stored textual information.

The system100can use frequency information, count information, and/or temporal status, that are identified by using information in the store component106, in conjunction with one or more thresholds (e.g. ranking thresholds) when determining which candidates to suggest. Other identifiers can be associated with words and/or phrases and stored in the store component106to provide additional information regarding entries stored therein.

Accordingly, the store component106includes textual information such as words, including frequently used words, acronyms, compound word, phrases and/or other textual information, but is not so limited. According to a desired implementation, the store component106can include textual and other information. In alternate embodiments, the store component106can be configured as part of external system memory or a file system for example. In one embodiment, the store component106is configured as a ring buffer, wherein the collection component110stores textual information to the ring buffer based on an index association. As described below, the prediction component102can use information stored in the store component106to predict textual candidates, such as a next word, compound word, and/or a phrase for example.

As described above, in an embodiment, the prediction component102includes a prediction algorithm104including an n-gram probability model. The prediction algorithm104and the n-gram probability model can be used to model candidate (e.g. word) sequences and other statistical observations. The prediction algorithm104can use the statistical properties of n-grams during the modeling process, wherein each n-gram can be based on n words. The prediction component102can use information in the store component106to identify frequently used words, frequently used compound words, and/or frequently used phrases when predicting and suggesting a particular candidate such as a next word, compound word, and/or phrase for example.

As an example, suppose that a user frequently types the phrase “Windows Mobile” into an ultra-mobile computing device. The prediction component102can use the information of the store component106to determine frequent entries, recent entries, and other associations (e.g. probabilities, ranking, etc.) to predict the term “Mobile” once the user has typed “Windows”. Correspondingly, the number of user operations can be reduced since the user does not need to type “m” or “mo” to get “Mobile” after typing “Windows”. That is, the system100is configured to provide textual candidates without having the user type a portion of a desired candidate. A user can thereafter select a candidate that is predicted and suggested by the system100(e.g. clicking or highlighting a candidate, pressing an assigned key, etc).

As further example, and as shown inFIG. 2, the store component106includes the depicted textual entries as collected and stored by the collection component110based on user operations. That is, the collection component110operates to store textual information in associated memory locations or bins associated with the store component106. As shown inFIG. 2, each memory location is associated with a corresponding identification (ID) number (i.e. memory location1corresponds with ID=1, memory location2corresponds with ID=2, and so on).

In one embodiment, increasing ID numbers are associated with more recent entries in the store component106. For example, and with continuing reference toFIG. 2, the word “Windows” in memory location having ID=1 is an older entry as compared to the “Windows” entry in the memory location having an ID=5. Correspondingly, the word “Windows” in memory location having ID=5 is an older entry as compared to the “Windows” entry in the memory location having an ID=30. And, the word “Windows” in memory location having ID=30 is an older entry as compared to the “Windows” entry in the memory location having an ID=34. In another embodiment, decreasing ID numbers correspond to more recent entries in the store component106. As described below, the system100can use temporal and other information, associated with each ID for example, to rank and/or limit the number of predicted and/or suggested textual candidates.

Continuing the example, and using equation (1) where the coefficient kiequals 1/n, assume that the preceding words on a user's display are “includes Windows” and the user has typed the letter “m”. First, the prediction component102can search through the store component106to locate words that begin with “m” as shown in Table 1 below.

The prediction component102can then calculate the probability of words that begin with “m”. Thus, the probability of “mobile” can be calculated as:

where n is the total number of words in the store component106. The probability of other words can also be calculated as shown in Table 2 below:

As described above, if only preceding words are available (i.e. no current input, such as “m” or “mo” for example), the prediction component102can set the n value equal to the number of words that were located and returned by the prediction component102after performing a search to locate candidates in the store component106. For example, assume that there is no input string but preceding words include “includes Windows”. If user has typed “includes Windows 5.0” before, it's likely that user will type “5.0” again. Even if user has not typed “includes Windows 5.0” before but typed “Windows 5.0”, it may still be likely that user will type “5.0” again. Correspondingly, the system100is configured to search for “Windows”, which is the most recent or latest preceding word.

As shown inFIG. 2, words that are next to “Windows” are “Mobile” and “Media”. Thus, “Mobile” and “Media” are considered by the system100as candidates. Also, there are 4 occurrences of “Windows” according to this example. Thus, the system100can set n equal to 4. Also, C(Windows)=4. Next, the system100is configured to search for “includes” that is located in front of and adjacent to “Windows”. That is, the system100is configured to calculate C(“includes Windows”) or C(“includes Windows” AND a potential candidate).

Since there is one occurrence of “includes Windows Media”, C(includes Windows Media)=1. Since there is one occurrence of “includes Windows”, C(includes Windows)=1. Since there is one occurrence of “Windows Media”, C(Windows Media)=1. Lastly, since there are four occurrences of “Windows”, C(Windows)=4. Thus, if the preceding words are “includes Windows”, the probability of “media” can be calculated as follows:

In one embodiment, the prediction component102can predict a number of phrases by appending one or more appropriate words after performing a prefix search to locate pertinent words in the store component106. For example, assume the prediction component102has returned the word list of Table 3 after performing a prefix search based on a user's input.

Using the list of Table 3, the prediction component102can operate to create the following phrases as shown in Table 4 below.

After creating the phrases, the prediction component102can list unique phrases, calculate a count of the unique phrases, and remove phrases having a count less than or equal to a certain threshold (e.g. removal phrases associated with a threshold equal to 1). In the example above, the prediction component102will retain “mobile 5.0” since the count is 2, as shown by Tables 5 and 6 below.

The prediction component102can continue to append one or more subsequent words to the created phrase list to create longer phrases. In one embodiment, the prediction component102can be configured to not append subsequent words if the last word is a separator character, such as a sentence break for example. In another embodiment, the prediction component102can limit phrases to not contain more than W words. For example, if W=2, a phrase “Windows Media” can be predicted but “Windows Media Player” will not be predicted. The W value can be controlled using a setting, such as a registry setting for example, described below.

The prediction component102can calculate phrase probability using a predicting algorithm that includes the following:

C(wn) is the number of times the candidate (e.g. word, phrase, etc.) wnis observed in the store component106.

C(wnn+N) is the number of times the sequence of candidates wn. . . wn+Nis observed in the store component106. For example, the probability of “mobile 5.0” can be calculated as follows:

The prediction component102can also be configured to merge any unique word and/or phrase lists and sort by probability. For example, as shown in Table 7 below, the prediction component102has merged unique words and phrases to create a merged list, and also has sorted the merged list by probability.

The prediction component102can be further configured to merge the raw word/phrase lists, including the ID information. Thereafter, the prediction component102can sort the list by ID in some desired order (e.g. descending, ascending, etc.). For example, as shown in Table 8 below, the prediction component102has merged the raw word/phrase lists described above and sorted the list by ID in descending order.

The prediction component102can thereafter remove shorter words and/or phrases according to a desired implementation. In one embodiment, the prediction component102can operate to remove words and/or phrases having a length that is less than Y. The Y value can also be controlled by using a setting, such as a registry setting for example as described below. The prediction component102can also remove a word and/or phrase that is the same as the input string.

Once the prediction component102has removed any shorter words and/or phrases, the prediction component102can then eliminate less probable candidates. In one embodiment, the prediction component102is configured to select the top Z candidates in the unique list. For each candidate, the prediction component102searches for a match in the raw list and selects a match having the largest ID, as shown by Table 9 below.

Again, the prediction component102can be configured to retain the top Z candidates and eliminate others. The Z value can also be controlled by using a setting, such as a registry setting for example, as described below. In one embodiment, the prediction component102will prioritize an item that has a higher ID if multiple items have the same probabilities, as shown in Table 10 below.

As described above, the prediction component102can also adjust candidate probability based on the candidate ID. In one embodiment, the prediction component102operates to sort candidates by ID in an ascending order as shown in Table 11 below. If the ID is the same for multiple candidates, a longer candidate is given priority.

In an embodiment, the prediction component102is further configured to adjust the probability based on rank as follows:
Padj(Wn)=m1×P(Wn)+m2×f(Rank)  (3)

where m1+m2=1, and m1and m2are variables that can be configured to provide the desired candidates. For example, m1and m2can be modified to achieve minimum number of user key strokes required to obtain a desired candidate;

The following provides an example of the probability calculation for the word “mobile” assuming that m1=m2=½.

The remaining probability calculation results are shown in Table 12 below.

Thereafter, the prediction component102can sort the candidates by the adjusted probability, as shown in Table 13 below. As described above, if the probability is same, the longer candidate will be given priority.

As described above, ki, mi, W, X, Y and Z values can be set using a number of registry settings for example.

FIG. 3is a flow diagram illustrating the prediction and suggestion of a candidate, such as a textual, symbolic, or other representative candidate for example, under an embodiment. The components ofFIG. 1will be used in describing the flow diagram, but the embodiment is not so limited. At300, a user operation occurs. For example, a user may be using an application associated with a handheld device to correspond with another user by keying input such as a character, cursor, enter, etc.

At302, the system100determines if the user operation is associated with a separator character or operation, such as an input associated with a space for example. For example, the system100can determine if the user operation terminates a word or a meaningful number of characters. If the system100determines that the user operation is associated with a separator character, at304, the collection component110collects information associated with the user operation and stores the information in the store component106. For example, the collection component110can operate to collect and store the text a user has entered as part of a textual communication operation.

The flow then proceeds to306where the prediction component106can determine the probabilities of multiple candidates, including next or subsequent words and phrases for example. As described above, the prediction component106can use data in the store component106and/or user input to determine a candidate probability. At308, a limiting procedure, such as a ranking algorithm, can be used by the system100to limit the number of candidates that are to be suggested to the user. At310, after any limiting procedure, the remaining candidates can be suggested or recommended to the user by the suggestion component108. The flow returns to300and the system100awaits further user input.

If the user operation is not associated with a separator character, the flow proceeds to312and the system100determines if a candidate has been selected. If the user has selected a candidate (e.g. mouse click, cursor keys, etc.), at314the system100operates to provide the selected candidate to the user and stores the candidate in the store component106at304. If the user has not selected a candidate at312, the flow proceeds to316and the system100determines whether to perform a prediction operation. In one embodiment, a prediction will not be performed based on certain user operations or input. For example, the system100will not make a prediction if a user press a down arrow key, an ESC key, a Print Screen key, a Home key, an End key, an Insert key, certain mouse clicks, etc.

If a prediction operation is not required, the flow returns to300. If a prediction operation is required, the flow proceeds to306and the prediction component106can determine the probabilities of multiple candidates. At308, a limiting procedure can be used by the system100to limit the number of candidates that are to be suggested to the user. At310, after any limiting procedure, the remaining candidates can be suggested or recommended to the user by the suggestion component108. Thereafter, the flow returns to300and the system100awaits further user input.

In an embodiment, the system100includes an auto-correct feature. If the auto-correct feature is enabled and a preceding word was corrected, the system100uses the corrected word to predict and suggest candidates based thereon. In one embodiment, the system100shows next word suggestions as predicted once a user finishes typing a word and presses a space (or some other auto-correct trigger keys). To provide suggestions at this time, the system100passes the preceding words to the prediction component102which can operate to return words and/or phrases that are likely to follow.

According to an embodiment, the system100can be configured as a software application that includes instructions that when executed are configured to predict and suggest a textual candidate, such as a word, acronym, compound word, and/or phrase based in part on user input to a host system running the software application. The software application can be implemented as part of the functionality of an ultra-mobile portable computing device, such as a WINDOWS Mobile-based Pocket PC, to predict and suggest words, compound words and/or phrases based in part on user input to the device.

In such an implementation, the suggestion component108can be configured to display a candidate window with appropriate textual suggestion(s) without substantially interfering with the user's focus. For example, the suggestion component108can position or otherwise locate suggested textual candidates that enable a user to quickly select a candidate without unduly obstructing the user's view. In an embodiment, the components of the system can be configured to capture, predict, and suggest ink-based input as well as typed input.

As described above, the system100can be configured as a software application (seeFIG. 4prediction and suggestion application24) that can be executed on a client computing device, such as an ultra-mobile computing device for example. According to such an embodiment, a textual store25can be stored locally (e.g. system memory12ofFIG. 4), wherein textual information can be written and/or accessed from the textual store25accordingly.

The system100can be employed in a variety of computing environments and applications. For example, the system100can used with computing devices having networking, security, and other communication components configured to provide communication functionality with other computing and/or communication devices. Such computing devices include desktop computers, laptop computers, tablet computers, handheld devices (e.g. smart phone, ultra-mobile personal computer, etc.), and/or other communication devices.

The embodiments described herein can be used with a number of applications, systems, and other devices and are not limited to any particular implementation or architecture. Also, while certain data structures, component features, and predictive functionality has been described herein, the embodiments are not so limited and can include more complex data structures, features, and other functionality. Accordingly, the embodiments and examples described herein are not intended to be limiting and other embodiments are available.

Exemplary Operating Environment

Referring now toFIG. 4, the following discussion is intended to provide a brief, general description of a suitable computing environment in which embodiments of the invention may be implemented. While the invention will be described in the general context of program modules that execute in conjunction with program modules that run on an operating system on a personal computer, those skilled in the art will recognize that the invention may also be implemented in combination with other types of computer systems and program modules.

Referring now toFIG. 4, an illustrative operating environment for embodiments of the invention will be described. As shown inFIG. 4, computer2comprises a general purpose desktop, laptop, handheld, tablet, or other type of computer capable of executing one or more application programs. The computer2includes at least one central processing unit8(“CPU”), a system memory12, including a random access memory18(“RAM”), a read-only memory (“ROM”)20, a textual store25, and a system bus10that couples the memory to the CPU8. A basic input/output system containing the basic routines that help to transfer information between elements within the computer, such as during startup, is stored in the ROM20.

The computer2further includes a mass storage device14for storing an operating system32, application programs, such as a prediction and suggestion application24, and other program modules. The mass storage device14is connected to the CPU8through a mass storage controller (not shown) connected to the bus10. The mass storage device14and its associated computer-readable media provide non-volatile storage for the computer2. Although the description of computer-readable media contained herein refers to a mass storage device, such as a hard disk or CD-ROM drive, it should be appreciated by those skilled in the art that computer-readable media can be any available media that can be accessed or utilized by the computer2.

According to various embodiments, the computer2may operate in a networked environment using logical connections to remote computers through a network4, such as a local network, the Internet, etc. for example. The computer2may connect to the network4through a network interface unit16connected to the bus10. It should be appreciated that the network interface unit16may also be utilized to connect to other types of networks and remote computing systems. The computer2may also include an input/output controller22for receiving and processing input from a number of input types, including a keyboard, mouse, pen, stylus, finger, and/or other means. Similarly, an input/output controller22may provide output to a display, a printer, or other type of output device. Additionally, a touch screen can serve as an input and an output mechanism.

As mentioned briefly above, a number of program modules and data files may be stored in the mass storage device14and RAM18of the computer2, including an operating system32suitable for controlling the operation of a networked personal computer, such as the WINDOWS operating systems from MICROSOFT CORPORATION of Redmond, Wash. for example. The mass storage device14and RAM18may also store one or more program modules. In particular, the mass storage device14and the RAM18may store other application programs, such as a word processing application28, an inking application30, e-mail application34, drawing application, etc.

It should be appreciated that various embodiments of the present invention can be implemented (1) as a sequence of computer implemented acts or program modules running on a computing system and/or (2) as interconnected machine logic circuits or circuit modules within the computing system. The implementation is a matter of choice dependent on the performance requirements of the computing system implementing the invention. Accordingly, logical operations including related algorithms can be referred to variously as operations, structural devices, acts or modules. It will be recognized by one skilled in the art that these operations, structural devices, acts and modules may be implemented in software, firmware, special purpose digital logic, and any combination thereof without deviating from the spirit and scope of the present invention as recited within the claims set forth herein.