Patent Publication Number: US-8984187-B2

Title: Handheld electronic device with text disambiguation allowing dynamic expansion of input key associations

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of U.S. patent application Ser. No. 12/619,998, filed on Nov. 17, 2009, which is a continuation of U.S. patent application Ser. No. 11/216,523, filed on Aug. 31, 2005 (now U.S. Pat. No. 7,644,209), both of which are expressly incorporated herein by reference. 
    
    
     BACKGROUND 
     1. Field 
     The invention relates generally to handheld electronic devices and, more particularly, to a handheld electronic device having a reduced keyboard and an input disambiguation function, and also relates to an associated method. 
     2. Description of the Related Art 
     Numerous types of handheld electronic devices are known. Examples of such handheld electronic devices include, for instance, personal data assistants (PDAs), handheld computers, two-way pagers, cellular telephones, and the like. Many handheld electronic devices also feature wireless communication capability, although many such handheld electronic devices are stand-alone devices that are functional without communication with other devices. 
     Such handheld electronic devices are generally intended to be portable, and thus are of a relatively compact configuration in which keys and other input structures often perform multiple functions under certain circumstances or may otherwise have multiple aspects or features assigned thereto. With advances in technology, handheld electronic devices are built to have progressively smaller form factors yet have progressively greater numbers of applications and features resident thereon. As a practical matter, the keys of a keypad can only be reduced to a certain small size before the keys become relatively unusable. In order to enable text entry, however, a keypad must be capable of entering all twenty-six letters of the Latin alphabet, for instance, as well as appropriate punctuation and other symbols. 
     One way of providing numerous letters in a small space has been to provide a “reduced keyboard” in which multiple letters, symbols, and/or digits, and the like, are assigned to any given key. For example, a touch-tone telephone includes a reduced keypad by providing twelve keys, of which ten have digits thereon, and of these ten keys eight have Latin letters assigned thereto. For instance, one of the keys includes the digit “2” as well as the letters “A”, “B”, and “C”. Other known reduced keyboards have included other arrangements of keys, letters, symbols, digits, and the like. Since a single actuation of such a key potentially could be intended by the user to refer to any of the letters “A”, “B”, and “C”, and potentially could also be intended to refer to the digit “2”, the input generally is an ambiguous input and is in need of some type of disambiguation in order to be useful for text entry purposes. 
     In order to enable a user to make use of the multiple letters, digits, and the like on any given key, numerous keystroke interpretation systems have been provided. For instance, a “multi-tap” system allows a user to substantially unambiguously specify a particular character on a key by pressing the same key a number of times equivalent to the position of the desired character on the key. For example, on the aforementioned telephone key that includes the letters “ABC”, and the user desires to specify the letter “C”, the user will press the key three times. While such multi-tap systems have been generally effective for their intended purposes, they nevertheless can require a relatively large number of key inputs compared with the number of characters that ultimately are output. 
     Another exemplary keystroke interpretation system would include key chording, of which various types exist. For instance, a particular character can be entered by pressing two keys in succession or by pressing and holding first key while pressing a second key. Still another exemplary keystroke interpretation system would be a “press-and-hold/press-and-release” interpretation function in which a given key provides a first result if the key is pressed and immediately released, and provides a second result if the key is pressed and held for a short period of time. While they systems have likewise been generally effective for their intended purposes, such systems also have their own unique drawbacks. 
     Another keystroke interpretation system that has been employed is a software-based text disambiguation function. In such a system, a user typically presses keys to which one or more characters have been assigned, generally pressing each key one time for each desired letter, and the disambiguation software attempt to predict the intended input. Numerous such systems have been proposed, and while many have been generally effective for their intended purposes, shortcomings still exist. 
     It would be desirable to provide an improved handheld electronic device with a reduced keyboard that seeks to mimic a QWERTY keyboard experience or other particular keyboard experience. Such an improved handheld electronic device might also desirably be configured with enough features to enable text entry and other tasks with relative ease. 
     SUMMARY 
     In view of the foregoing, an improved handheld electronic device includes a keypad in the form of a reduced QWERTY keyboard and is enabled with disambiguation software. As a user enters keystrokes, the device provides output in the form of a default output and a number of variants from which a user can choose. The output is based largely upon the frequency, i.e., the likelihood that a user intended a particular output, but various features of the device provide additional variants that are not based solely on frequency and rather are provided by various logic structures resident on the device. The device enables editing during text entry and also provides a learning function that allows the disambiguation function to adapt to provide a customized experience for the user. The device is also configured to enable the entry and learning of new characters, as well as new words that comprise the new characters. 
     Accordingly, an aspect is to provide an improved handheld electronic device and an associated method, with the handheld electronic device including a reduced keyboard that seeks to simulate a QWERTY keyboard experience or another particular keyboard experience. 
     Another aspect is to provide an improved handheld electronic devices and an associated method that provide a text input disambiguation function. 
     Another aspect is to provide an improved handheld electronic device and an associated method that employ a disambiguation function which, responsive to an ambiguous input, provides a number of proposed outputs according to relative frequency. 
     Another aspect is to provide an improved handheld electronic device and an associated method that provide a number of proposed outputs that can be based upon relative frequency and/or can result from various logic structures resident on the device. 
     Another aspect is to provide an improved handheld electronic device and an associated method that enable a custom experience by a user based upon various learning features and other features. 
     Another aspect is to provide an improved handheld electronic device and an associated method that employ a disambiguation function and that enable the entry and learning of new characters, as well as the entry and learning of new words that comprise the new characters. 
     Another aspect is to provide an improved handheld electronic device and an associated method, wherein the handheld electronic device includes an input apparatus which facilitates the selection of variants with relative ease. 
     Another aspect is to provide an improved handheld electronic device and an associated method that employ a disambiguation function to disambiguate text input from a reduced QWERTY keyboard or other keyboard and that allow editing of the text input. 
     Accordingly, an aspect is to provide an improved method of enabling input into a handheld electronic device of a type including an input apparatus, an output apparatus, and a memory. The memory has stored therein a number of linguistic elements, an alphabet, and a number of objects, with at least some of the linguistic elements each being in the alphabet. The number of objects comprise a number of language objects, and at least some of the language objects each comprise a number of the linguistic elements in the alphabet. The input apparatus includes a plurality of input members, with at least some of the input members each having a number of the linguistic elements assigned thereto. The general nature of the method can be stated as including detecting as a first input an input of a new language object comprising at least a first new linguistic element different than the linguistic elements in the alphabet, and storing the at least a first new linguistic element. The method further includes detecting as a second input an ambiguous input comprising a number of actuations of a number of the input members, generating a number of prefix objects each having a number of the linguistic elements of the input members of the ambiguous input, with at least one of the prefix objects including the at least a first new linguistic element and, for each prefix object of at least some of the prefix objects, seeking a corresponding language object that corresponds with the prefix object. 
     Another aspect is to provide an improved handheld electronic device, the general nature of which can be stated as including a processor apparatus and an input apparatus. The processor apparatus comprises a processor and a memory. The memory has stored therein a number of linguistic elements, an alphabet, and a number of objects. At least some of the linguistic elements each are in the alphabet. The number of objects comprise a number of language objects. At least some of the language objects each comprise a number of the linguistic elements in the alphabet. The input apparatus includes a plurality of input members. At least some of the input members each have a number of the linguistic elements assigned thereto. The alphabet is expandable to have added thereto a number of additional linguistic elements upon entry of a new language object having at least a first linguistic element not already in the alphabet. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A full understanding can be gained from the following Description of the Preferred Embodiment when read in conjunction with the accompanying drawings in which: 
         FIG. 1  is a top plan view of an improved handheld electronic device in accordance with the invention; 
         FIG. 2  is a schematic depiction of the improved handheld electronic device of  FIG. 1 ; 
         FIG. 2A  is a schematic depiction of a portion of the handheld electronic device of  FIG. 2 ; 
         FIGS. 3A and 3B  depict an exemplary flowchart depicting certain aspects of a disambiguation function that can be executed on the handheld electronic device of  FIG. 1 ; 
         FIG. 4  is another exemplary flowchart depicting certain aspects of a disambiguation function that can be executed on the handheld electronic device by which certain output variants can be provided to the user; 
         FIGS. 5A and 5B  depict another exemplary flowchart depicting certain aspects of the learning method that can be executed on the handheld electronic device; 
         FIG. 6  is another exemplary flowchart depicting certain aspects of a method by which various display formats that can be provided on the handheld electronic device; 
         FIG. 6A  are another exemplary flowchart depicting certain aspects of the method that can be executed on the handheld electronic device; 
         FIG. 7  is an exemplary output during a text entry operation; 
         FIG. 8  is another exemplary output during another part of the text entry operation; 
         FIG. 9  is another exemplary output during another part of the text entry operation; 
         FIG. 10  is another exemplary output during another part of the text entry operation; 
         FIG. 11  is an exemplary output on the handheld electronic device during another text entry operation; 
         FIG. 12  is an exemplary output that can be provided in an instance when the disambiguation function of the handheld electronic device has been disabled; 
         FIG. 13  is an exemplary depiction of a map file stored on the handheld electronic device; 
         FIG. 14  is an exemplary depiction of an alphabet stored on the handheld electronic device; 
         FIG. 15A  is an exemplary output during another text entry operation; 
         FIG. 15B  is another exemplary output during another part of the another text entry operation; 
         FIG. 15C  is another exemplary output during another part of the another text entry operation; 
         FIG. 15D  is another exemplary output during another part of the another text entry operation; and 
         FIG. 16  is an exemplary depiction of an alphabet stored on the handheld electronic device. 
     
    
    
     Similar numerals refer to similar parts throughout the specification. 
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
     An improved handheld electronic device  4  is indicated generally in  FIG. 1  and is depicted schematically in  FIG. 2 . The exemplary handheld electronic device  4  includes a housing  6  upon which are disposed an input apparatus  8 , an output apparatus  12 , and a processor apparatus  14 . The processor apparatus  14  includes a processor  16  and a memory  20 , and additionally includes a number of routines depicted generally with the numeral  22  for the processing of data. The processor  16  may be, for instance, and without limitation, a microprocessor (μP) and is responsive to inputs from the input apparatus  8  and provides output signals to the output apparatus  12 . The processor  16  also interfaces with the memory  20 . Examples of handheld electronic devices are included in U.S. Pat. Nos. 6,452,588 and 6,489,950, which are incorporated by record herein. 
     As can be understood from  FIG. 1 , the input apparatus  8  includes a keypad  24  and a thumbwheel  32 . As will be described in greater detail below, the keypad  24  is in the exemplary form of a reduced QWERTY keyboard including a plurality of keys  28  that serve as input members. It is noted, however, that the keypad  24  may be of other configurations, such as an AZERTY keyboard, a QWERTZ keyboard, or other keyboard arrangement, whether presently known or unknown, and either reduced or not reduced. As employed herein, the expression “reduced” and variations thereof in the context of a keyboard, a keypad, or other arrangement of input members, shall refer broadly to an arrangement in which at least one of the input members has assigned thereto a plurality of linguistic elements such as, for example, characters in the set of Latin letters, whereby an actuation of the at least one of the input members, without another input in combination therewith, is an ambiguous input since it could refer to more than one of the plurality of linguistic elements assigned thereto. As employed herein, the expression “linguistic element” and variations thereof shall refer broadly to any element that itself can be a language object or from which a language object can be constructed, identified, or otherwise obtained, and thus would include, for example and without limitation, characters, letters, strokes, ideograms, phonemes, morphemes, digits, and the like. As employed herein, the expression “language object” and variations thereof shall refer broadly to any type of object that may be constructed, identified, or otherwise obtained from one or more linguistic elements, that can be used alone or in combination to generate text, and that would include, for example and without limitation, words, “shortcuts”, symbols, ideograms, and the like. An example of one type of shortcut might be the use of the expression “BTW” to refer as a shortcut to the phrase “by the way”, it being the intention that the person reading the message will understand the shortcut “BTW” to mean “by the way.” 
     The system architecture of the handheld electronic device  4  advantageously is organized to be operable independent of the specific layout of the keypad  24 . Accordingly, the system architecture of the handheld electronic device  4  can be employed in conjunction with virtually any keypad layout substantially without requiring any meaningful change in the system architecture. It is further noted that certain of the features set forth herein are usable on either or both of a reduced keyboard and a non-reduced keyboard. 
     The keys  28  are disposed on a front face of the housing  6 , and the thumbwheel  32  is disposed at a side of the housing  6 . The thumbwheel  34  can serve as another input member and is both rotatable, as is indicated by the arrow  34 , to provide selection and/or navigational inputs to the processor  16 , and also can be pressed in a direction generally toward the housing  6 , as is indicated by the arrow  38 , to provide another selection input to the processor  16 . 
     Among the keys  28  of the keypad  24  are a &lt;NEXT&gt; key  40  and an &lt;ENTER&gt; key  44 . The &lt;NEXT&gt; key  40  can be pressed to provide a selection input to the processor  16  and provides substantially the same selection input as is provided by a rotational input of the thumbwheel  32  when certain routines are active on the handheld electronic device  4 . Since the &lt;NEXT&gt; key  40  is provided adjacent a number of the other keys  28  of the keypad  24 , the user can provide a selection input to the processor  16  substantially without moving the user&#39;s hands away from the keypad  24  during a text entry operation. As will be described in greater detail below, the &lt;NEXT&gt; key  40  additionally and advantageously includes a graphic  42  disposed thereon, and in certain circumstances the output apparatus  12  also displays a displayed graphic  46  thereon to identify the &lt;NEXT&gt; key  40  as being able to provide a selection input to the processor  16 . In this regard, the displayed graphic  46  of the output apparatus  12  is substantially similar to the graphic  42  on the &lt;NEXT&gt; key and thus identifies the &lt;NEXT&gt; key  40  as being capable of providing a desirable selection input to the processor  16 . 
     As can further be seen in  FIG. 1 , many of the keys  28  include a number of linguistic elements  48  disposed thereon. As employed herein, the expression “a number of” and variations thereof shall refer broadly to any quantity, including a quantity of one. In the exemplary depiction of the keypad  24 , many of the keys  28  include two linguistic elements  48  disposed thereon, such as including a first linguistic element  52  and a second linguistic element  56  thereon. 
     One of the keys  28  of the keypad  24  includes as the characters  48  and as the first and second linguistic elements  52  and  56  thereof the letters “Q” and “W”, and an adjacent key  28  includes as the characters  48  and as the first and second linguistic elements  52  and  56  thereof the letters “E” and “R”. It can be seen that the arrangement of the characters  48  on the keys  28  of the keypad  24  is generally of a QWERTY arrangement, albeit with many of the keys  28  including two of the characters  28 . 
     The output apparatus  12  includes a display  60  upon which can be provided an output  64 . An exemplary output  64  is depicted on the display  60  in  FIG. 1 . The output  64  includes a text component  68  and a variant component  72 . The variant component  72  includes a default portion  76  and a variant portion  80 . The display also includes a caret  84  that depicts generally where the next input from the input apparatus  8  will be received. 
     The text component  68  of the output  64  provides a depiction of the default portion  76  of the output  64  at a location on the display  60  where the text is being input. The variant component  72  is disposed generally in the vicinity of the text component  68  and provides, in addition to the default proposed output  76 , a depiction of the various alternate text choices, i.e., alternates to the default proposed output  76 , that are proposed by an input disambiguation function in response to an input sequence of key actuations of the keys  28 . 
     As will be described in greater detail below, the default portion  76  is proposed by the disambiguation function as being the most likely disambiguated interpretation of the ambiguous input provided by the user. The variant portion  80  includes a predetermined quantity of alternate proposed interpretations of the same ambiguous input from which the user can select, if desired. The displayed graphic  46  typically is provided in the variant component  72  in the vicinity of the variant portion  80 , although it is understood that the displayed graphic  46  could be provided in other locations and in other fashions without departing from the present concept. It is also noted that the exemplary variant portion  80  is depicted herein as extending vertically below the default portion  76 , but it is understood that numerous other arrangements could be provided without departing from the concept of the invention. 
     Among the keys  28  of the keypad  24  additionally is a &lt;DELETE&gt; key  86  that can be provided to delete a text entry. As will be described in greater detail below, the &lt;DELETE&gt; key  86  can also be employed in providing an alternation input to the processor  16  for use by the disambiguation function. 
     The memory  20  is depicted schematically in  FIG. 2A . The memory  20  can be any of a variety of types of internal and/or external storage media such as, without limitation, RAM, ROM, EPROM(s), EEPROM(s), and the like that provide a storage register for data storage such as in the fashion of an internal storage area of a computer, and can be volatile memory or nonvolatile memory. The routines  22  can be in any of a variety of forms such as, without limitation, software, firmware, and the like. As will be explained in greater detail below, the routines  22  include a disambiguation routine that provides the aforementioned disambiguation function as an application, as well as other routines. 
     As can be understood from  FIG. 2A , the memory  20  additionally includes data stored and/or organized in a number of tables, sets, lists, and/or otherwise. Specifically, the memory  20  includes a generic word list  88 , a new words database  92 , and a frequency learning database  96 . The memory  20  additionally has stored therein another data source  99  and a map file  49 , both of which are described elsewhere herein in greater detail. 
     Stored within the various areas of the memory  20  are a number of language objects  100  and frequency objects  104 . The language objects  100  generally are each associated with an associated frequency object  104 . The language objects  100  include, in the present exemplary embodiment, a plurality of word objects  108  and a plurality of N-gram objects  112 . The word objects  108  are generally representative of complete words within the language or custom words stored in the memory  22 . For instance, if the language stored in the memory is, for example, English, generally each word object  108  would represent a word in the English language or would represent a custom word. 
     Associated with substantially each word object  108  is a frequency object  104  having frequency value that is indicative of the relative frequency within the relevant language of the given word represented by the word object  108 . In this regard, the generic word list  88  includes a corpus of word objects  108  and associated frequency objects  104  that together are representative of a wide variety of words and their relative frequency within a given vernacular of, for instance, a given language. The generic word list  88  can be derived in any of a wide variety of fashions, such as by analyzing numerous texts and other language sources to determine the various words within the language sources as well as their relative probabilities, i.e., relative frequencies, of occurrences of the various words within the language sources. 
     The N-gram objects  112  stored within the generic word list  88  are short strings of characters within the relevant language typically, for example, one to three characters in length, and typically represent word fragments within the relevant language, although certain of the N-gram objects  112  additionally can themselves be words. However, to the extent that an N-gram object  112  also is a word within the relevant language, the same word likely would be separately stored as a word object  108  within the generic word list  88 . As employed herein, the expression “string” and variations thereof shall refer broadly to an object having one or more characters or components, and can refer to any of a complete word, a fragment of a word, a custom word or expression, and the like. 
     In the present exemplary embodiment of the handheld electronic device  4 , the N-gram objects  112  include 1-gram objects, i.e., string objects that are one character in length, 2-gram objects, i.e., string objects that are two characters in length, and 3-gram objects, i.e., string objects that are three characters in length, all of which are collectively referred to as N-grams  112 . Substantially each N-gram object  112  in the generic word list  88  is similarly associated with an associated frequency object  104  stored within the generic word list  88 , but the frequency object  104  associated with a given N-gram object  112  has a frequency value that indicates the relative probability that the character string represented by the particular N-gram object  112  exists at any location within any word of the relevant language. The N-gram objects  112  and the associated frequency objects  104  are a part of the corpus of the generic word list  88  and are obtained in a fashion similar to the way in which the word object  108  and the associated frequency objects  104  are obtained, although the analysis performed in obtaining the N-gram objects  112  will be slightly different because it will involve analysis of the various character strings within the various words instead of relying primarily on the relative occurrence of a given word. 
     The present exemplary embodiment of the handheld electronic device  4 , with its exemplary language being the English language, includes twenty-six 1-gram N-gram objects  112 , i.e., one 1-gram object for each of the twenty-six letters in the Latin alphabet upon which the English language is based, and further includes  676  2-gram N-gram objects  112 , i.e., twenty-six squared, representing each two-letter permutation of the twenty-six letters within the Latin alphabet. 
     The N-gram objects  112  also include a certain quantity of 3-gram N-gram objects  112 , primarily those that have a relatively high frequency within the relevant language. The exemplary embodiment of the handheld electronic device  4  includes fewer than all of the three-letter permutations of the twenty-six letters of the Latin alphabet due to considerations of data storage size, and also because the 2-gram N-gram objects  112  can already provide a meaningful amount of information regarding the relevant language. As will be set forth in greater detail below, the N-gram objects  112  and their associated frequency objects  104  provide frequency data that can be attributed to character strings for which a corresponding word object  108  cannot be identified or has not been identified, and typically is employed as a fallback data source, although this need not be exclusively the case. 
     In the present exemplary embodiment, the language objects  100  and the frequency objects  104  are maintained substantially inviolate in the generic word list  88 , meaning that the basic language corpus remains substantially unaltered within the generic word list  88 , and the learning functions that are provided by the handheld electronic device  4  and that are described below operate in conjunction with other object that are generally stored elsewhere in memory  20 , such as, for example, in the new words database  92  and the frequency learning database  96 . 
     The new words database  92  and the frequency learning database  96  store additional language objects  100  and associated frequency objects  104  in order to provide to a user a customized experience in which words and the like that are used relatively more frequently by a user will be associated with relatively higher frequency values than might otherwise be reflected in the generic word list  88 . More particularly, the new words database  92  includes word objects  108  that are user-defined and that generally are not found among the word objects  108  of the generic word list  88 . Each word object  108  in the new words database  92  has associated therewith an associated frequency object  104  that is also stored in the new words database  92 . The frequency learning database  96  stores word objects  108  and associated frequency objects  104  that are indicative of relatively more frequent usage of such words by a user than would be reflected in the generic word list  88 . As such, the new words database  92  and the frequency learning database  96  provide two learning functions, that is, they together provide the ability to learn new words as well the ability to learn altered frequency values for known words. 
     In addition to the word objects  108  and associated frequency objects  104  stored in the new words database  92 , the new words database  92  has an alphabet  47  stored therein. The alphabet  47  includes a listing of all of the characters that are comprised by the word objects  108  in the new words database  92 . 
       FIGS. 3A and 3B  depict in an exemplary fashion the general operation of certain aspects of the disambiguation function of the handheld electronic device  4 . Additional features, functions, and the like are depicted and described elsewhere. 
     An input is detected, as at  204 , and the input can be any type of actuation or other operation as to any portion of the input apparatus  8 . A typical input would include, for instance, an actuation of a key  28  having a number of characters  48  thereon, or any other type of actuation or manipulation of the input apparatus  8 . 
     Upon detection at  204  of an input, a timer is reset at  208 . The use of the timer will be described in greater detail below. 
     The disambiguation function then determines, as at  212 , whether the current input is an operational input, such as a selection input, a delimiter input, a movement input, an alternation input, or, for instance, any other input that does not constitute an actuation of a key  28  having a number of characters  48  thereon. If the input is determined at  212  to not be an operational input, processing continues at  216  by adding the input to the current input sequence which may or may not already include an input. 
     Many of the inputs detected at  204  are employed in generating input sequences as to which the disambiguation function will be executed. An input sequence is build up in each “session” with each actuation of a key  28  having a number of characters  48  thereon. Since an input sequence typically will be made up of at least one actuation of a key  28  having a plurality of characters  48  thereon, the input sequence will be ambiguous. When a word, for example, is completed the current session is ended an a new session is initiated. 
     An input sequence is gradually built up on the handheld electronic device  4  with each successive actuation of a key  28  during any given session. Specifically, once a delimiter input is detected during any given session, the session is terminated and a new session is initiated. Each input resulting from an actuation of one of the keys  28  having a number of the characters  48  associated therewith is sequentially added to the current input sequence. As the input sequence grows during a given session, the disambiguation function generally is executed with each actuation of a key  28 , i.e., and input, and as to the entire input sequence. Stated otherwise, within a given session, the growing input sequence is attempted to be disambiguated as a unit by the disambiguation function with each successive actuation of the various keys  28 . 
     Once a current input representing a most recent actuation of the one of the keys  28  having a number of the characters  48  assigned thereto has been added to the current input sequence within the current session, as at  216  in  FIG. 3A , the disambiguation function generates, as at  220 , substantially all of the permutations of the characters  48  assigned to the various keys  28  that were actuated in generating the input sequence. In this regard, the “permutations” refer to the various strings that can result from the characters  48  of each actuated key  28  limited by the order in which the keys  28  were actuated. The various permutations of the characters in the input sequence are employed as prefix objects. 
     For instance, if the current input sequence within the current session is the ambiguous input of the keys “AS” and “OP”, the various permutations of the first character  52  and the second character  56  of each of the two keys  28 , when considered in the sequence in which the keys  28  were actuated, would be “SO”, “SP”, “AP”, and “AO”, and each of these is a prefix object that is generated, as at  220 , with respect to the current input sequence. As will be explained in greater detail below, the disambiguation function seeks to identify for each prefix object one of the word objects  108  for which the prefix object would be a prefix. 
     The method of the invention also determines, as at  222 , whether or not the input field into which language is being entered is a “special” input field. In this regard, a special input field is one to which particular stored data can be of particular relevance, and such particular stored data and is therefore sought to be obtained first before obtaining other data. In effect, therefore, the method can, for instance, provide proposed output results that are particularly suited to the input field. As such, the output results are more likely to be the results desired by the user than otherwise might be the case if all of the data sources were searched in the usual fashion to provide proposed disambiguation results. If the input field is determined by the method to be special, a special flag is set and processing is transferred, as at  226 , for further processing, as at  604  in  FIG. 6A , as will be discussed in greater detail below. 
     If, however, the input field is determined as at  222  to not be special, processing continues at  224 . For each generated prefix object, the memory  20  is consulted, as at  224 , to identify, if possible, for each prefix object one of the word objects  108  in the memory  20  that corresponds with the prefix object, meaning that the sequence of letters represented by the prefix object would be either a prefix of the identified word object  108  or would be substantially identical to the entirety of the word object  108 . Further in this regard, the word object  108  that is sought to be identified is the highest frequency word object  108 . That is, the disambiguation function seeks to identify the word object  108  that corresponds with the prefix object and that also is associated with a frequency object  104  having a relatively higher frequency value than any of the other frequency objects  104  associated with the other word objects  108  that correspond with the prefix object. 
     It is noted in this regard that the word objects  108  in the generic word list  88  are generally organized in data tables that correspond with the first two letters of various words. For instance, the data table associated with the prefix “CO” would include all of the words such as “CODE”, “COIN”, “COMMUNICATION”, and the like. Depending upon the quantity of word objects  108  within any given data table, the data table may additionally include sub-data tables within which word objects  108  are organized by prefixes that are three characters or more in length. Continuing onward with the foregoing example, if the “CO” data table included, for instance, more than 256 word objects  108 , the “CO” data table would additionally include one or more sub-data tables of word objects  108  corresponding with the most frequently appearing three-letter prefixes. By way of example, therefore, the “CO” data table may also include a “COM” sub-data table and a “CON” sub-data table. If a sub-data table includes more than the predetermined number of word objects  108 , for example a quantity of 256, the sub-data table may include further sub-data tables, such as might be organized according to a four letter prefixes. It is noted that the aforementioned quantity of 256 of the word objects  108  corresponds with the greatest numerical value that can be stored within one byte of the memory  20 . 
     Accordingly, when, at  224 , each prefix object is sought to be used to identify a corresponding word object  108 , and for instance the instant prefix object is “AP”, the “AP” data table will be consulted. Since all of the word objects  108  in the “AP” data table will correspond with the prefix object “AP”, the word object  108  in the “AP” data table with which is associated a frequency object  104  having a frequency value relatively higher than any of the other frequency objects  104  in the “AP” data table is identified. The identified word object  108  and the associated frequency object  104  are then stored in a result register that serves as a result of the various comparisons of the generated prefix objects with the contents of the memory  20 . 
     It is noted that one or more, or possibly all, of the prefix objects will be prefix objects for which a corresponding word object  108  is not identified in the memory  20 . Such prefix objects are considered to be orphan prefix objects and are separately stored or are otherwise retained for possible future use. In this regard, it is noted that many or all of the prefix objects can become orphan object if, for instance, the user is trying to enter a new word or, for example, if the user has mis-keyed and no word corresponds with the mis-keyed input. 
     Once the result has been obtained at  224 , the disambiguation function determines, as at  228 , whether artificial variants should be generated. In order to determine the need for artificial variants, the process at  228  branches, as at  230 , to the artificial variant process depicted generally in  FIG. 4  and beginning with the numeral  304 . The disambiguation function then determines, as at  308 , whether any of the prefix objects in the result correspond with what had been the default output  76  prior to detection of the current key input. If a prefix object in the result corresponds with the previous default output, this means that the current input sequence corresponds with a word object  108  and, necessarily, the previous default output also corresponded with a word object  108  during the previous disambiguation cycle within the current session. 
     The next point of analysis is to determine, as at  310 , whether the previous default output was made the default output because of a selection input, such as would have causes the setting of a flag, such as at  254  of  FIG. 3B , discussed in greater detail below. In the event that the previous default output was not the result of a selection input, no artificial variants are needed, and the process returns, as at  312 , to the main process at  232 . However, if it is determined at  310  that the previous default output was the result of a selection input, then artificial variants are generated, as at  316 . 
     More specifically, each of the artificial variants generated at  316  include the previous default output plus one of the characters  48  assigned to the key  28  of the current input. As such, if the key  28  of the current input has two characters, i.e., a first character  52  and a second character  56 , two artificial variants will be generated at  316 . One of the artificial variants will include the previous default output plus the first character  52 . The other artificial variant will include the previous default output plus the second character  56 . 
     However, if it is determined at  308  that none of the prefix objects in the result correspond with the previous default output, it is next necessary to determine, as at  314 , whether the previous default output had corresponded with a word object  108  during the previous disambiguation cycle within the current session. If the answer to the inquiry at  314  is no, it is still necessary to determine, as at  318 , whether the previous default output was made the default output because of a selection input, such as would have causes the setting of the flag. In the event that the previous default output was not the result of a selection input, no artificial variants are needed, and the process returns, as at  312 , to the main process at  232 . However, if it is determined at  318  that the previous default output was the result of a selection input, then artificial variants are generated, as at  316 . 
     On the other hand, if it is determined that the answer to the inquiry at  314  is yes, meaning that the previous default output had corresponded with a word object, but with the current input the previous default output combined with the current input has ceased to correspond with any word object  108 , then artificial variants are generated, again as at  316 . 
     After the artificial variants are generated at  316 , the method then determines, as at  320 , whether the result includes any prefix objects at all. If not, processing returns, as at  312 , to the main process at  232 . However, if it is determined at  320  that the result includes at least a first prefix object, meaning that the current input sequence corresponds with a word object  108 , processing is transferred to  324  where an additional artificial variant is created. Specifically, the prefix object of the result with which is associated the frequency object  104  having the relatively highest frequency value among the other frequency objects  104  in the result is identified, and the artificial variant is created by deleting the final character from the identified prefix object and replacing it with an opposite character  48  on the same key  28  of the current input that generated the final character  48  of the identified prefix object. In the event that the specific key  28  has more than two characters  48  assigned thereto, each such opposite character  48  will be used to generate an additional artificial variant. 
     Once the need for artificial variants has been identified, as at  228 , and such artificial variants have been generated, as in  FIG. 4  and as described above, processing continues, as at  232 , where duplicate word objects  108  associated with relatively lower frequency values are deleted from the result. Such a duplicate word object  108  could be generated, for instance, by the frequency learning database  96 , as will be set forth in greater detail below. If a word object  108  in the result matches one of the artificial variants, the word object  108  and its associated frequency object  104  generally will be removed from the result because the artificial variant will be assigned a preferred status in the output  64 , likely in a position preferred to any word object  108  that might have been identified. 
     Once the duplicate word objects  108  and the associated frequency objects  104  have been removed at  232 , the remaining prefix objects are arranged, as at  236 , in an output set in decreasing order of frequency value. The orphan prefix objects mentioned above may also be added to the output set, albeit at positions of relatively lower frequency value than any prefix object for which a corresponding word object  108  was found. It is also necessary to ensure that the artificial variants, if they have been created, are placed at a preferred position in the output set. It is understood that artificial variants may, but need not necessarily be, given a position of preference, i.e., assigned a relatively higher priority or frequency, than prefix objects of the result. 
     If it is determined, as at  240 , that the flag has been set, meaning that a user has made a selection input, either through an express selection input or through an alternation input of a movement input, then the default output  76  is considered to be “locked,” meaning that the selected variant will be the default prefix until the end of the session. If it is determined at  240  that the flag has been set, the processing will proceed to  244  where the contents of the output set will be altered, if needed, to provide as the default output  76  an output that includes the selected prefix object, whether it corresponds with a word object  108  or is an artificial variant. In this regard, it is understood that the flag can be set additional times during a session, in which case the selected prefix associated with resetting of the flag thereafter becomes the “locked” default output  76  until the end of the session or until another selection input is detected. 
     Processing then continues, as at  248 , to an output step after which an output  64  is generated as described above. More specifically, processing proceeds, as at  250 , to the subsystem depicted generally in  FIG. 6  and described below. Processing thereafter continues at  204  where additional input is detected. On the other hand, if it is determined at  240  that the flag had not been set, then processing goes directly to  248  without the alteration of the contents of the output set at  244 . 
     The handheld electronic device  4  may be configured such that any orphan prefix object that is included in an output  64  but that is not selected with the next input is suspended. This may be limited to orphan prefix objects appearing in the variant portion  80  or may apply to orphan prefix objects anywhere in the output  64 . The handheld electronic device  4  may also be configured to similarly suspend artificial variants in similar circumstances. A reason for such suspension is that each such orphan prefix object and/or artificial variant, as appropriate, may spawn a quantity of offspring orphan prefix objects equal to the quantity of characters  48  on a key  28  of the next input. That is, each offspring will include the parent orphan prefix object or artificial variant plus one of the characters  48  of the key  28  of the next input. Since orphan prefix objects and artificial variants substantially do not have correspondence with a word object  108 , spawned offspring objects from parent orphan prefix objects and artificial variants likewise will not have correspondence with a word object  108 . Such suspended orphan prefix objects and/or artificial variants may be considered to be suspended, as compared with being wholly eliminated, since such suspended orphan prefix objects and/or artificial variants may reappear later as parents of a spawned orphan prefix objects and/or artificial variants, as will be explained below. 
     If the detected input is determined, as at  212 , to be an operational input, processing then continues to determine the specific nature of the operational input. For instance, if it is determined, as at  252 , that the current input is a selection input, processing continues at  254 . At  254 , the word object  108  and the associated frequency object  104  of the default portion  76  of the output  64 , as well as the word object  108  and the associated frequency object  104  of the portion of the variant output  80  that was selected by the selection input, are stored in a temporary learning data register. Additionally, the flag is set. Processing then returns to detection of additional inputs as at  204 . 
     If it is determined, as at  260 , that the input is a delimiter input, processing continues at  264  where the current session is terminated and processing is transferred, as at  266 , to the learning function subsystem, as at  404  of  FIG. 5A . A delimiter input would include, for example, the actuation of a &lt;SPACE&gt; key  116 , which would both enter a delimiter symbol and would add a space at the end of the word, actuation of the &lt;ENTER&gt; key  44 , which might similarly enter a delimiter input and enter a space, and by a translation of the thumbwheel  32 , such as is indicated by the arrow  38 , which might enter a delimiter input without additionally entering a space. 
     It is first determined, as at  408 , whether the default output at the time of the detection of the delimiter input at  260  matches a word object  108  in the memory  20 . If it does not, this means that the default output is a user-created output that should be added to the new words database  92  for future use. In such a circumstance processing then proceeds to  412  where the default output is stored in the new words database  92  as a new word object  108 . Additionally, a frequency object  104  is stored in the new words database  92  and is associated with the aforementioned new word object  108 . The new frequency object  104  is given a relatively high frequency value, typically within the upper one-fourth or one-third of a predetermined range of possible frequency values. 
     In this regard, frequency objects  104  are given an absolute frequency value generally in the range of zero to 65,535. The maximum value represents the largest number that can be stored within two bytes of the memory  20 . The new frequency object  104  that is stored in the new words database  92  is assigned an absolute frequency value within the upper one-fourth or one-third of this range, particularly since the new word was used by a user and is likely to be used again. 
     With further regard to frequency object  104 , it is noted that within a given data table, such as the “CO” data table mentioned above, the absolute frequency value is stored only for the frequency object  104  having the highest frequency value within the data table. All of the other frequency objects  104  in the same data table have frequency values stored as percentage values normalized to the aforementioned maximum absolute frequency value. That is, after identification of the frequency object  104  having the highest frequency value within a given data table, all of the other frequency objects  104  in the same data table are assigned a percentage of the absolute maximum value, which represents the ratio of the relatively smaller absolute frequency value of a particular frequency object  104  to the absolute frequency value of the aforementioned highest value frequency object  104 . Advantageously, such percentage values can be stored within a single byte of memory, thus saving storage space within the handheld electronic device  4 . 
     Upon creation of the new word object  108  and the new frequency object  104 , and storage thereof within the new words database  92 , processing is transferred to  420  where the learning process is terminated. Processing is then returned to the main process, as at  204 . 
     If at  408  it is determined that the word object  108  in the default output  76  matches a word object  108  within the memory  20 , processing then continues at  416  where it is determined whether the aforementioned flag has been set, such as occurs upon the detection of a selection input, and alternation input, or a movement input, by way of example. If it turns out that the flag has not been set, this means that the user has not expressed a preference for a variant prefix object over a default prefix object, and no need for frequency learning has arisen. In such a circumstance, processing continues at  420  where the learning process is terminated. Processing then returns to the main process at  254 . 
     However, if it is determined at  416  that the flag has been set, the processor  20  retrieves from the temporary learning data register the most recently saved default and variant word objects  108 , along with their associated frequency objects  104 . It is then determined, as at  428 , whether the default and variant word objects  108  had previously been subject of a frequency learning operation. This might be determined, for instance, by determining whether the variant word object  108  and the associated frequency object  104  were obtained from the frequency learning database  96 . If the default and variant word objects  108  had not previously been the subject of a frequency learning operation, processing continues, as at  432 , where the variant word object  108  is stored in the frequency learning database  96 , and a revised frequency object  104  is generated having a frequency value greater than that of the frequency object  104  that previously had been associated with the variant word object  108 . In the present exemplary circumstance, i.e., where the default word object  108  and the variant word object  108  are experiencing their first frequency learning operation, the revised frequency object  104  may, for instance, be given a frequency value equal to the sum of the frequency value of the frequency object  104  previously associated with the variant word object  108  plus one-half the difference between the frequency value of the frequency object  104  associated with the default word object  108  and the frequency value of the frequency object  104  previously associated with the variant word object  108 . Upon storing the variant word object  108  and the revised frequency object  104  in the frequency learning database  96 , processing continues at  420  where the learning process is terminated and processing returns to the main process, as at  254 . 
     If it is determined at  428  that that default word object  108  and the variant word object  108  had previously been the subject of a frequency learning operation, processing continues to  436  where the revised frequency value  104  is instead given a frequency value higher than the frequency value of the frequency object  104  associated with the default word object  108 . After storage of the variant word object  108  and the revised frequency object  104  in the frequency learning database  96 , processing continues to  420  where the learning process is terminated, and processing then returns to the main process, as at  254 . 
     With further regard to the learning function, it is noted that the learning function additionally detects whether both the default word object  108  and the variant word object  104  were obtained from the frequency learning database  96 . In this regard, when word objects  108  are identified, as at  224 , for correspondence with generated prefix objects, all of the data sources in the memory are polled for such corresponding word objects  108  and corresponding frequency objects  104 . Since the frequency learning database  96  stores word objects  108  that also are stored either in the generic word list  88  or the new words database  96 , the word object  108  and the associated frequency object  104  that are obtained from the frequency learning database  96  typically are duplicates of word objects  108  that have already been obtained from the generic word list  88  or the new words database  96 . However, the associated frequency object  104  obtained from the frequency learning database  96  typically has a frequency value that is of a greater magnitude than that of the associated frequency object  104  that had been obtained from the generic word list  88 . This reflects the nature of the frequency learning database  96  as imparting to a frequently used word object  108  a relatively greater frequency value than it otherwise would have in the generic word list  88 . 
     It thus can be seen that the learning function indicated in  FIGS. 5A and 5B  and described above is generally not initiated until a delimiter input is detected, meaning that learning occurs only once for each session. Additionally, if the final default output is not a user-defined new word, the word objects  108  that are the subject of the frequency learning function are the word objects  108  which were associated with the default output  76  and the selected variant output  80  at the time when the selection occurred, rather than necessarily being related to the object that ultimately resulted as the default output at the end of the session. Also, if numerous learnable events occurred during a single session, the frequency learning function operates only on the word objects  108  that were associated with the final learnable event, i.e., a selection event, an alternation event, or a movement event, prior to termination of the current session. 
     With further regard to the identification of various word objects  108  for correspondence with generated prefix objects, it is noted that the memory  22  can include a number of additional data sources  99  in addition to the generic word list  88 , the new words database  92 , and the frequency learning database  96 , all of which can be considered linguistic sources. An exemplary other data source  99  is depicted in  FIG. 2A , it being understood that the memory  22  might include any number of other data sources  99 . The other data sources  99  might include, for example, an address database, a speed-text database, or any other data source without limitation. An exemplary speed-text database might include, for example, sets of words or expressions or other data that are each associated with, for example, a character string that may be abbreviated. For example, a speed-text database might associate the string “br” with the set of words “Best Regards”, with the intention that a user can type the string “br” and receive the output “Best Regards”. 
     In seeking to identify word objects  108  that correspond with a given prefix object, the handheld electronic device  4  may poll all of the data sources in the memory  22 . For instance the handheld electronic device  4  may poll the generic word list  88 , the new words database  92 , the frequency learning database  96 , and the other data sources  99  to identify word objects  108  that correspond with the prefix object. The contents of the other data sources  99  may be treated as word objects  108 , and the processor  20  may generate frequency objects  104  that will be associated such word objects  108  and to which may be assigned a frequency value in, for example, the upper one-third or one-fourth of the aforementioned frequency range. Assuming that the assigned frequency value is sufficiently high, the string “br”, for example, would typically be output to the display  60 . If a delimiter input is detected with respect to the portion of the output having the association with the word object  108  in the speed-text database, for instance “br”, the user would receive the output “Best Regards”, it being understood that the user might also have entered a selection input as to the exemplary string “br”. 
     The contents of any of the other data sources  99  may be treated as word objects  108  and may be associated with generated frequency objects  104  having the assigned frequency value in the aforementioned upper portion of the frequency range. After such word objects  108  are identified, the new word learning function can, if appropriate, act upon such word objects  108  in the fashion set forth above. 
     Again regarding  FIG. 3A , when processing proceeds to the filtration step, as at  232 , and the duplicate word objects  108  and the associated frequency objects  104  having relatively lower frequency values are filtered, the remaining results may include a variant word object  108  and a default word object  108 , both of which were obtained from the frequency learning database  96 . In such a situation, it can be envisioned that if a user repetitively and alternately uses one word then the other word, over time the frequency objects  104  associated with such words will increase well beyond the aforementioned maximum absolute frequency value for a frequency object  104 . Accordingly, if it is determined that both the default word object  108  and the variant word object  108  in the learning function were obtained from the frequency learning database  96 , instead of storing the variant word object  108  in the frequency learning database  96  and associating it with a frequency object  104  having a relatively increased frequency value, instead the learning function stores the default word object  108  and associates it with a revised frequency object  104  having a frequency value that is relatively lower than that of the frequency object  104  that is associated with the variant word object  108 . Such a scheme advantageously avoids excessive and unnecessary increases in frequency value. 
     If it is determined, such as at  268 , that the current input is a movement input, such as would be employed when a user is seeking to edit an object, either a completed word or a prefix object within the current session, the caret  84  is moved, as at  272 , to the desired location, and the flag is set, as at  276 . Processing then returns to where additional inputs can be detected, as at  204 . 
     In this regard, it is understood that various types of movement inputs can be detected from the input device  8 . For instance, a rotation of the thumbwheel  32 , such as is indicated by the arrow  34  of  FIG. 1 , could provide a movement input, as could the actuation of the &lt;NEXT&gt; key  40 , or other such input, potentially in combination with other devices in the input apparatus  8 . In the instance where such a movement input is detected, such as in the circumstance of an editing input, the movement input is additionally detected as a selection input. Accordingly, and as is the case with a selection input such as is detected at  252 , the selected variant is effectively locked with respect to the default portion  76  of the output  64 . Any default output  76  during the same session will necessarily include the previously selected variant. 
     In the context of editing, however, the particular displayed object that is being edited is effectively locked except as to the character that is being edited. In this regard, therefore, the other characters of the object being edited, i.e., the characters that are not being edited, are maintained and are employed as a context for identifying additional word objects  108  and the like that correspond with the object being edited. Were this not the case, a user seeking to edit a letter in the middle of a word otherwise likely would see as a new output  64  numerous objects that bear little or no resemblance to the characters of the object being edited since, in the absence of maintaining such context, an entirely new set of prefix objects including all of the permutations of the characters of the various keystrokes of the object being edited would have been generated. New word objects  108  would have been identified as corresponding with the new prefix objects, all of which could significantly change the output  64  merely upon the editing of a single character. By maintaining the other characters currently in the object being edited, and employing such other characters as context information, the user can much more easily edit a word that is depicted on the display  60 . 
     In the present exemplary embodiment of the handheld electronic device  4 , if it is determined, as at  252 , that the input is not a selection input, and it is determined, as at  260 , that the input is not a delimiter input, and it is further determined, as at  268 , that the input is not a movement input, in the current exemplary embodiment of the handheld electronic device  4  the only remaining operational input generally is a detection of the &lt;DELETE&gt; key  86  of the keys  28  of the keypad  24 . Upon detection of the &lt;DELETE&gt; key  86 , the final character of the default output is deleted, as at  280 . At this point, the processing generally waits until another input is detected, as at  284 . It is then determined, as at  288 , whether the new input detected at  284  is the same as the most recent input that was related to the final character that had just been deleted at  280 . If so, the default output  76  is the same as the previous default output, except that the last character is the opposite character of the key actuation that generated the last character. Processing then continues to  292  where learning data, i.e., the word object  108  and the associate frequency object  104  associated with the previous default output  76 , as well as the word object  108  and the associate frequency object  104  associated with the new default output  76 , are stored in the temporary learning data register and the flag is set. Such a key sequence, i.e., an input, the &lt;DELETE&gt; key  86 , and the same input as before, is an alternation input. Such an alternation input replaces the default final character with an opposite final character of the key  28  which generated the final character  48  of the default output  76 . The alternation input is treated as a selection input for purposes of locking the default output  76  for the current session, and also triggers the flag which will initiate the learning function upon detection of a delimiter input at  260 . 
     If it turns out, however, that the system detects at  288  that the new input detected at  284  is different than the input immediately prior to detection of the &lt;DELETE&gt; key  86 , processing continues at  212  where the input is determined to be either an operational input or an input of a key having one or more characters  48 , and processing continues thereafter. 
     It is also noted that when the main process reaches the output stage at  248 , an additional process is initiated which determines whether the variant component  72  of the output  64  should be initiated. Processing of the additional function is initiated from  248  at element  504  of  FIG. 6 . Initially, the method at  508  outputs the text component  68  of the output  64  to the display  60 . Further processing determines whether or not the variant component  72  should be displayed. 
     Specifically, it is determined, as at  512 , whether the variant component  72  has already been displayed during the current session. If the variant component  72  has already been displayed, processing continues at  516  where the new variant component  72  resulting from the current disambiguation cycle within the current session is displayed. Processing then returns to a termination point at  520 , after which processing returns to the main process at  204 . If, however, it is determined at  512  that the variant component  72  has not yet been displayed during the current session, processing continues, as at  524 , to determine whether the elapsed time between the current input and the immediately previous input is longer than a predetermined duration. If it is longer, then processing continues at  516  where the variant component  72  is displayed and processing returns, through  520 , to the main process, as at  204 . However, if it is determined at  524  that the elapsed time between the current input and the immediately previous input is less than the predetermined duration, the variant component  72  is not displayed, and processing returns to the termination point at  520 , after which processing returns to the main process, as at  204 . 
     Advantageously, therefore, if a user is entering keystrokes relatively quickly, the variant component  72  will not be output to the display  60 , where it otherwise would likely create a visual distraction to a user seeking to enter keystrokes quickly. If at any time during a given session the variant component  72  is output to the display  60 , such as if the time between successive inputs exceeds the predetermined duration, the variant component  72  will continue to be displayed throughout that session. However, upon the initiation of a new session, the variant component  72  will be withheld from the display if the user consistently is entering keystrokes relatively quickly. 
     As mentioned above, in certain circumstances certain data sources can be searched prior to other data sources if the input field is determined, as at  222 , to be special. For instance, if the input field is to have a particular type of data input therein, and this particular type of data can be identified and obtained, the disambiguated results will be of a greater degree of relevance to the field and have a higher degree of correspondence with the intent of the user. For instance, a physician&#39;s prescription pad typically includes blank spaces into which are inserted, for instance, a patient&#39;s name, a drug name, and instructions for administering the drug. The physician&#39;s prescription pad potentially could be automated as an application on the device  4 . During entry of the patient&#39;s name, the data source  99  that would most desirably be searched first would be, for instance, a data source  99  listing the names and, for instance, the contact information for the doctor&#39;s patients. Similarly, during entry of the drug name, the data source  99  that would most desirably be searched first would be the data source  99  listing, for instance, names of drugs. By searching these special data sources first, the relevance of the proposed disambiguated results is higher since the results are more likely to be what is intended by the user. If the method obtains an insufficient quantity of results in such a fashion, however, additional results can be obtained in the usual fashion from the other data sources. 
     As can be seen in  FIG. 6A , after processing is transferred to  604  from the main process, the method searches, as at  608 , for word objects  108  and frequency objects  104  in whatever data source  99  is determined to correspond with or have some relevance to the special input field. The input field typically will inform the operating system of the device  4  that it typically receives a particular type of input, and the operating system will determine which data source  99  will be searched first in seeking disambiguation results. 
     The disambiguation results obtained from the special, i.e., predetermined, data source  99  are then filtered, as at  612 , to eliminate duplicate results, and the quantity of remaining results are then counted, as at  616 , to determine whether the quantity is less than a predetermined number. If the answer to this inquiry is “no”, meaning that a sufficient quantity of results were obtained from the particular data source  99 , processing is transferred, as at  620 , to the main process at  236 . 
     On the other hand, if it is determined at  616  that insufficient disambiguation results were obtained from the predetermined data source  99 , addition results typically will desirably be obtained. For instance, in such a circumstance processing continues, as at  624 , to processing at which the prefix results are arranged in order of decreasing frequency value into a special output set. A special flag is set, as at  628 , that indicates to the method that the additional disambiguation results that are about to be obtained from the other data sources of the device  4  are to appended to the end of the special output set. Processing is transferred, as at  630 , back to the main process at  224 , after which additional disambiguation results will be sought from the other data sources on the device  4 . With the special flag being set, as at  628 , the results that were obtained from the predetermined data source are to be listed ahead of the additional results obtained from the remaining data sources, even if the additional results are associated with relatively higher frequency values than some of the results from the predetermined data source. The method could, however, be applied in different fashions without departing from the concept of the invention. 
     An exemplary input sequence is depicted in FIGS.  1  and  7 - 11 . In this example, the user is attempting to enter the word “APPLOADER”, and this word presently is not stored in the memory  20 . In  FIG. 1  the user has already typed the “AS” key  28 . Since the data tables in the memory  20  are organized according to two-letter prefixes, the contents of the output  64  upon the first keystroke are obtained from the N-gram objects  112  within the memory. The first keystroke “AS” corresponds with a first N-gram object  112  “S” and an associated frequency object  104 , as well as another N-gram object  112  “A” and an associated frequency object  104 . While the frequency object  104  associated with “S” has a frequency value greater than that of the frequency object  104  associated with “A”, it is noted that “A” is itself a complete word. A complete word is always provided as the default output  76  in favor of other prefix objects that do not match complete words, regardless of associated frequency value. As such, in  FIG. 1 , the default portion  76  of the output  64  is “A”. 
     In  FIG. 7 , the user has additionally entered the “OP” key  28 . The variants are depicted in  FIG. 7 . Since the prefix object “SO” is also a word, it is provided as the default output  76 . In  FIG. 8 , the user has again entered the “OP” key  28  and has also entered the “L” key  28 . It is noted that the exemplary “L” key  28  depicted herein includes only the single character  48  “L”. 
     It is assumed in the instant example that no operational inputs have thus far been detected. The default output  76  is “APPL”, such as would correspond with the word “APPLE”. The prefix “APPL” is depicted both in the text component  68 , as well as in the default portion  76  of the variant component  72 . Variant prefix objects in the variant portion  80  include “APOL”, such as would correspond with the word “APOLOGIZE”, and the prefix “SPOL”, such as would correspond with the word “SPOLIATION”. 
     It is particularly noted that the additional variants “AOOL”, “AOPL”, “SOPL”, and “SOOL” are also depicted as variants  80  in the variant component  72 . Since no word object  108  corresponds with these prefix objects, the prefix objects are considered to be orphan prefix objects for which a corresponding word object  108  was not identified. In this regard, it may be desirable for the variant component  72  to include a specific quantity of entries, and in the case of the instant exemplary embodiment the quantity is seven entries. Upon obtaining the result at  224 , if the quantity of prefix objects in the result is fewer than the predetermined quantity, the disambiguation function will seek to provide additional outputs until the predetermined number of outputs are provided. In the absence of artificial variants having been created, the additional variant entries are provided by orphan prefix objects. It is noted, however, that if artificial variants had been generated, they likely would have occupied a place of preference in favor of such orphan prefix objects, and possibly also in favor of the prefix objects of the result. 
     It is further noted that such orphan prefix objects may actually be offspring orphan prefix objects from suspended parent orphan prefix objects and/or artificial variants. Such offspring orphan prefix objects can be again output depending upon frequency ranking as explained below, or as otherwise ranked. 
     The orphan prefix objects are ranked in order of descending frequency with the use of the N-gram objects  112  and the associated frequency objects  104 . Since the orphan prefix objects do not have a corresponding word object  108  with an associated frequency object  104 , the frequency objects  104  associated with the various N-gram objects  112  must be employed as a fallback. 
     Using the N-gram objects  112 , the disambiguation function first seeks to determine if any N-gram object  112  having, for instance, three characters is a match for, for instance, a final three characters of any orphan prefix object. The example of three characters is given since the exemplary embodiment of the handheld electronic device  4  includes N-gram objects  112  that are an exemplary maximum of the three characters in length, but it is understood that if the memory  22  included N-gram objects four characters in length or longer, the disambiguation function typically would first seek to determine whether an N-gram object having the greatest length in the memory  22  matches the same quantity of characters at the end of an orphan prefix object. 
     If only one prefix object corresponds in such a fashion to a three character N-gram object  112 , such orphan prefix object is listed first among the various orphan prefix objects in the variant output  80 . If additional orphan prefix objects are matched to N-gram objects  112  having three characters, then the frequency objects  104  associated with such identified N-gram objects  112  are analyzed, and the matched orphan prefix objects are ranked amongst themselves in order of decreasing frequency. 
     If it is determined that a match cannot be obtained with an N-gram object  112  having three characters, then two-character N-gram objects  112  are employed. Since the memory  20  includes all permutations of two-character N-gram objects  112 , a last two characters of each orphan prefix object can be matched to a corresponding two-character N-gram object  112 . After such matches are achieved, the frequency objects  104  associated with such identified N-gram objects  112  are analyzed, and the orphan prefix objects are ranked amongst themselves in descending order of frequency value of the frequency objects  104  that were associated with the identified N-gram objects  112 . It is further noted that artificial variants can similarly be rank ordered amongst themselves using the N-gram objects  112  and the associated frequency objects  104 . 
     In  FIG. 9  the user has additionally entered the “OP” key  28 . In this circumstance, and as can be seen in  FIG. 9 , the default portion  76  of the output  64  has become the prefix object “APOLO” such as would correspond with the word “APOLOGIZE”, whereas immediately prior to the current input the default portion  76  of the output  64  of  FIG. 8  was “APPL” such as would correspond with the word “APPLE.” Again, assuming that no operational inputs had been detected, the default prefix object in  FIG. 9  does not correspond with the previous default prefix object of  FIG. 8 . As such, the first artificial variant “APOLP” is generated and in the current example is given a preferred position. The aforementioned artificial variant “APOLP” is generated by deleting the final character of the default prefix object “APOLO” and by supplying in its place an opposite character  48  of the key  28  which generated the final character of the default portion  76  of the output  64 , which in the current example of  FIG. 9  is “P”, so that the aforementioned artificial variants is “APOLP”. 
     Furthermore, since the previous default output “APPL” corresponded with a word object  108 , such as the word object  108  corresponding with the word “APPLE”, and since with the addition of the current input the previous default output “APPL” no longer corresponds with a word object  108 , two additional artificial variants are generated. One artificial variant is “APPLP” and the other artificial variant is “APPLO”, and these correspond with the previous default output “APPL” plus the characters  48  of the key  28  that was actuated to generate the current input. These artificial variants are similarly output as part of the variant portion  80  of the output  64 . 
     As can be seen in  FIG. 9 , the default portion  76  of the output  64  “APOLO” no longer seems to match what would be needed as a prefix for “APPLOADER”, and the user likely anticipates that the desired word “APPLOADER” is not already stored in the memory  20 . As such, the user provides a selection input, such as by scrolling with the thumbwheel  32 , or by actuating the &lt;NEXT&gt; key  40 , until the variant string “APPLO” is highlighted. The user then continues typing and enters the “AS” key. 
     The output  64  of such action is depicted in  FIG. 10 . Here, the string “APPLOA” is the default portion  76  of the output  64 . Since the variant string “APPLO” became the default portion  76  of the output  64  (not expressly depicted herein) as a result of the selection input as to the variant string “APPLO”, and since the variant string “APPLO” does not correspond with a word object  108 , the character strings “APPLOA” and “APPLOS” were created as an artificial variants. Additionally, since the previous default of  FIG. 9 , “APOLO” previously had corresponded with a word object  108 , but now is no longer in correspondence with the default portion  76  of the output  64  of  FIG. 10 , the additional artificial variants of “APOLOA” and “APOLOS” were also generated. Such artificial variants are given a preferred position in favor of the three displayed orphan prefix objects. 
     Since the current input sequence in the example no longer corresponds with any word object  108 , the portions of the method related to attempting to find corresponding word objects  108  are not executed with further inputs for the current session. That is, since no word object  108  corresponds with the current input sequence, further inputs will likewise not correspond with any word object  108 . Avoiding the search of the memory  20  for such nonexistent word objects  108  saves time and avoids wasted processing effort. 
     As the user continues to type, the user ultimately will successfully enter the word “APPLOADER” and will enter a delimiter input. Upon detection of the delimiter input after the entry of “APPLOADER”, the learning function is initiated. Since the word “APPLOADER” does not correspond with a word object  108  in the memory  20 , a new word object  108  corresponding with “APPLOADER” is generated and is stored in the new words database  92 , along with a corresponding new frequency object  104  which is given an absolute frequency in the upper, say, one-third or one-fourth of the possible frequency range. In this regard, it is noted that the new words database  92  and the frequency learning database  96  are generally organized in two-character prefix data tables similar to those found in the generic word list  88 . As such, the new frequency object  104  is initially assigned an absolute frequency value, but upon storage the absolute frequency value, if it is not the maximum value within that data table, will be changed to include a normalized frequency value percentage normalized to whatever is the maximum frequency value within that data table. 
     As a subsequent example, in  FIG. 11  the user is trying to enter the word “APOLOGIZE”. The user has entered the key sequence “AS” “OP” “OP” “L” “OP”. Since “APPLOADER” has now been added as a word object  108  to the new words database  92  and has been associated with frequency object  104  having a relatively high frequency value, the prefix object “APPLO” which corresponds with “APPLOADER” has been displayed as the default portion  76  of the output  64  in favor of the variant prefix object “APOLO”, which corresponds with the desired word “APOLOGIZE.” Since the word “APOLOGIZE” corresponds with a word object  108  that is stored at least in the generic word list  88 , the user can simply continue to enter keystrokes corresponding with the additional letters “GIZE”, which would be the letters in the word “APOLOGIZE” following the prefix object “APOLO”, in order to obtain the word “APOLOGIZE”. Alternatively, the user may, upon seeing the output  64  depicted in  FIG. 11 , enter a selection input to affirmatively select the variant prefix object “APOLO”. In such a circumstance, the learning function will be triggered upon detection of a delimiter symbol, and the word object  108  that had corresponded with the character string “APOLO” at the time the selection input was made will be stored in the frequency learning database  92  and will be associated with a revised frequency object  104  having a relatively higher frequency value that is similarly stored in the frequency learning database  92 . 
     An additional feature of the handheld electronic device  4  is depicted generally in  FIG. 12 . In some circumstances, it is desirable that the disambiguation function be disabled. For instance, when it is desired to enter a password, disambiguation typically is relatively more cumbersome than during ordinary text entry. As such, when the system focus is on the component corresponding with the password field, the component indicates to the API that special processing is requested, and the API disables the disambiguation function and instead enables, for instance, a multi-tap input interpretation system. Alternatively, other input interpretation systems could include a chording system or a press-and-hold/press-and-release interpretation system. As such, while an input entered with the disambiguation function active is an ambiguous input, by enabling the alternative interpretation system, such as the exemplary multi-tap system, each input can be largely unambiguous. 
     As can be understood from  FIG. 12 , each unambiguous input is displayed for a very short period of time within the password field  120 , and is then replaced with another output, such as the asterisk. The character “R” is shown displayed, it being understood that such display is only for a very short period of time. 
     As can be seen in FIGS.  1  and  7 - 11 , the output  64  includes the displayed graphic  46  near the lower end of the variant component  72 , and that the displayed graphic  46  is highly similar to the graphic  42  of the &lt;NEXT&gt; key  40 . Such a depiction provides an indication to the user which of the keys  28  of the keypad  24  can be actuated to select a variant output. The depiction of the displayed graphic  46  provides an association between the output  64  and the &lt;NEXT&gt; key  40  in the user&#39;s mind. Additionally, if the user employs the &lt;NEXT&gt; key  40  to provide a selection input, the user will be able to actuate the &lt;NEXT&gt; key  40  without moving the user&#39;s hands away from the position the hands were in with respect to the housing  6  during text entry, which reduces unnecessary hand motions, such as would be required if a user needed to move a hand to actuate the thumbwheel  32 . This saves time and effort. 
     It is also noted that the system can detect the existence of certain predefined symbols as being delimiter signals if no word object  108  corresponds with the text entry that includes the symbol. For instance, if the user desired to enter the input “one-off”, the user might begin by entering the key sequence “OP” “BN” “ER” “ZX” “OP”, with the “ZX” actuation being intended to refer to the hyphen symbol disposed thereon. Alternatively, instead of typing the “ZX” key the user might actuate an &lt;ALT&gt; entry to unambiguously indicate the hyphen. 
     Assuming that the memory  20  does not already include a word object  108  of “one-off”, the disambiguation function will detect the hyphen as being a delimiter input. As such, the key entries preceding the delimiter input will be delimited from the key entries subsequent to the delimiter input. As such, the desired input will be searched as two separate words, i.e., “ONE” and “OFF”, with the hyphen therebetween. This facilitates processing by more narrowly identifying what is desired to be searched. 
     It is noted that layout of the characters  48  disposed on the keys  28  in  FIG. 1  is an exemplary character layout that would be employed where the intended primary language used on the handheld electronic device  4  was, for instance, English. Other layouts involving these characters  48  and/or others can be used depending upon the intended primary language and any language bias in the makeup of the language objects  100 . 
     The map file  49  depicted in  FIG. 2A  is depicted in greater detail in  FIG. 13 . The map file  49  is a table that includes an indication of the keys  28  and the characters  48  assigned thereto. As can be seen in  FIG. 13 , many of the keys  28  have characters  48  assigned thereto in addition to those characters  48  that are depicted in  FIG. 1  as being disposed on the keys  28 . For example, the map file  49  indicates that the &lt;UI&gt; key  28  has assigned thereto the letters “Y” and “I”, and such letters are indicated in  FIG. 1  as being characters  48  disposed on the &lt;UI&gt; key  28 .  FIG. 13  further indicates that the &lt;UI&gt; key  28  additionally has assigned thereto the characters  48  Ú, Ù, Û, Ü, Í, Ì, Î, and Ï. It is noted that for the sake of simplicity the characters  48  are depicted in  FIGS. 13-16  as being capital letter characters. It is further noted, however, that the characters  48  could additionally include lower case letter characters or other characters without departing from the present concept. 
     While the keys  28  have assigned thereto the characters  48  depicted in the map file  49 , not all of the characters  48  necessarily are active on the handheld electronic device  4 . That is, even though the characters  48  U, I, Ú, Ù, Û, Ü, Í, Ì, Î, and Ï are assigned to the &lt;UI&gt; key  28 , not all of these characters  48  are automatically employed in, for instance, the generation of prefix objects for the purpose of disambiguating an ambiguous input. An active character  48  is a character  48  that is assigned to a key  28  and that is automatically considered by the processor apparatus  14  to be a possible intended result of actuating the key  28  during a text entry procedure. 
     The characters  48  that are active on the handheld electronic device  4  are included in an alphabet  45 , such as is depicted in  FIG. 14 , that is stored in the memory  20 . In the present exemplary embodiment, the alphabet  45  includes a static portion  51  that is stored as a part of the generic word list  88  and a modifiable portion  47  that is stored as a part of the new words database  92 . The modifiable portion  47  of the alphabet  45  is advantageously configured to allow the addition to the alphabet of characters  48  from the map file  49  that are not, for instance, already included in the static portion  51  of the alphabet  45 . 
     It can be seen that at least some of the characters  48  in the map file  49  are in the alphabet  45 . As a general matter, the language objects  100  stored in the memory  20  are comprised of characters  48  in the alphabet  45 . 
     Upon the detection of an ambiguous input, the processor apparatus  14  consults the map file  49  to identify the set of characters  48  that are assigned to the keys  28  of the ambiguous input. The set of characters  48  from the map file  49  are then compared with the alphabet  45  to identify the characters  48  in the set that are also in the alphabet  45 . Stated otherwise, the map file  49  provides a listing of all of the characters  48  assigned to the keys  28  of the ambiguous input, and the alphabet  45  allows the identification of the characters  48  that are active on the handheld electronic device  4 . In comparing the set of characters  48  from the map file  49  with those of the alphabet  45 , the set of characters  48  typically will be compared with both the static portion  51  and the modifiable portion  47  of the alphabet  45  to obtain all active characters  48 . 
     As a general matter, the static portion  51  is unchangeable and reflects the various characters  48  of which the language objects  100  in the generic word list  88  are comprised. The static portion  51  thus is indicative of the various characters  48  that typically would be considered to be valid characters in the language of the generic word list  88 . For instance, the language of the generic word list  88  maybe be English, such as might be indicated by a relatively large proportion of English words being reflected as language objects  100  stored in the generic word list  88 . The resultant static portion  51  of the alphabet  45  thus might comprise the twenty-six Latin letters. 
     The modifiable portion  47  of the alphabet  45  generally reflects the additional characters  48  that are not already a part of the static portion  51  and that, for instance, comprise the characters  48  in one or more of the language objects  100  in, for instance, the new words database  92 . In the exemplary alphabet  45  depicted in  FIG. 14 , the modifiable portion  47  thereof is indicated as including the character  48  “É”. For instance, the user may have previously entered the new language object  100  “SOUFFLÉ”. Upon entry of the new language object  100  “SOUFFLÉ”, the character  48  “É” would have been added to the modifiable portion  47  of the alphabet  45 . In such a fashion, the character  48  “É” has been made an active character  48  on the handheld electronic device  4 . 
     An exemplary text entry procedure is indicated in  FIGS. 15A-15D . If it assumed that the alphabet  45  is that depicted generally in  FIG. 14 , an ordinary actuation, i.e., a press-and-release actuation, of the key  28 &lt;UI&gt; will result in an output such as that depicted generally in  FIG. 15A . That is, the character  48  “I” will be displayed as a text component  68  and as a default portion  76  of a variant component  72 . The character  48  “U” is depicted as being the variant portion  80  of the variant component  72 . 
     If the user is seeking to enter the language object  100  “ÜBER”, neither of the characters  48  “I” and “U” in the variant component  72  of  FIG. 15A  will be an acceptable first character. The user can, however, display the set of characters  48  from the map file  49  that are assigned to the key  28 &lt;UI&gt; by actuating the key  28 &lt;UI&gt; with a press-and-hold actuation and by performing a scrolling operation with the thumbwheel  32 . Such an output is depicted generally in  FIG. 15B , it being noted that only a portion of the set of characters  48  is depicted in the variant component  72 , with the graphic  46  being depicted in the variant component  72  as indicating the existence of additional variants in the forms of other characters  48  from the map file  49  that are assigned to the key  28 &lt;UI&gt;. 
     In  FIG. 15B , the character  48  “Ï” is depicted as being the default portion  76  of the variant component  72 , and is additionally depicted as being the text component  68 .  FIG. 15C  depicts that the user has entered a navigational input, such as by scrolling the thumbwheel  32  or actuating the &lt;NEXT&gt;  40  sufficiently that the character  48  “Ü” is highlighted and is displayed as the text component  68 . 
     In order the complete the entry of the new language object  100  “ÜBER”, the user will thereafter need to actuate the keys  28 &lt;BN&gt;, &lt;ER&gt;, and &lt;ER&gt;, although since a language object  100  for the word “ÜBER” is not already stored in the memory  20 , the user likely will have to expressly enter the additionally characters  48  of “ÜBER”, such as with the use of scrolling among the variants  80  after some of the keystrokes. Upon entry, for example, of the new language object “ÜBER”, the character  48  “Ü” is added to the modifiable portion  47  of the alphabet  45 , as is depicted generally in  FIG. 16 , and a language object  100  for “ÜBER” has been added to the new words database  92 . 
     The character  48  “Ü” has thus been made an active character  48  on the handheld electronic device  4 . Accordingly, future entry of the word “ÜBER” will advantageously be much easier for the user since “Ü” has been made an active character  48  on the handheld electronic device  4  and thus will automatically be employed by the processor apparatus  14  in seeking to disambiguate an ambiguous input, and since a language object  100  for “ÜBER” has been stored in the memory  20 . 
     If it is thereafter decided by the user to enter another new language object  100  comprising the character  48  “Ü”, such as if the user desired to enter the word “MÜNCHEN”, the user would begin the entry by actuating the keys  28 &lt;M&gt;, &lt;UI&gt;. The output in response to such an ambiguous input would be that depicted generally in  FIG. 15D , it being assumed that the alphabet  45  is in the condition depicted in  FIG. 16 . That is, the disambiguation routine will have employed the character  48  “M” that is active on the key  28 &lt;M&gt; and the characters  48  “I”, “U”, and “Ü” that are active on the key  28 &lt;UI&gt; to generate the prefix objects “MI”, “MU”, and “MÜ”. Since the character  48  “Ü” is active on the key  28 &lt;UI&gt;, the character “Ü” was automatically employed by the disambiguation routine in generating prefix objects in response to the ambiguous input represented in  FIG. 15D . 
     In generating the output depicted generally in  FIG. 15D , the disambiguation routine may have determined, for instance, that the prefix “MI” corresponded with the language object  100  for “MINE”, and that the prefix object “MU” corresponded with the language object  100  for the word “MUCH”. The various prefix objects are output in the variant component  72  in order of descending frequency value. If no language object  100  was identified as corresponding with the prefix object “MÜ”, the prefix object “MÜ” still may be output, as it is in  FIG. 15D , as an artificial variant at a position of relatively lower priority than the prefix objects for which corresponding language object  100  were identified. If it is assumed that the language object  100  for “ÜBER” is the only language object  100  in the new words database  92  that comprises the character “Ü”, the variant “MÜ” will have been output in  FIG. 15D  as being an artificial variant. 
     It thus can be seen that the handheld electronic device  4  is configured to allow dynamic expansion of the set of characters  48  that are active thereon to enable the entry of new language objects  100  having characters  48  that are not already active on the handheld electronic device  4 . This allows enhanced utility and customizability to the needs of the user. 
     While specific embodiments of the invention have been described in detail, it will be appreciated by those skilled in the art that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. Accordingly, the particular arrangements disclosed are meant to be illustrative only and not limiting as to the scope of the invention which is to be given the full breadth of the claims appended and any and all equivalents thereof.