GB2443653A - A partial predictive text entry system for a mobile communication device - Google Patents

A partial predictive text entry system for a mobile communication device Download PDF

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GB2443653A
GB2443653A GB0625447A GB0625447A GB2443653A GB 2443653 A GB2443653 A GB 2443653A GB 0625447 A GB0625447 A GB 0625447A GB 0625447 A GB0625447 A GB 0625447A GB 2443653 A GB2443653 A GB 2443653A
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user
text
sequence
keys
string
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GB0625447D0 (en
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S Bastien Racani Re
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Samsung Electronics Co Ltd
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Samsung Electronics Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
    • G06F3/023—Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
    • G06F3/023—Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
    • G06F3/0233—Character input methods
    • G06F3/0236—Character input methods using selection techniques to select from displayed items
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
    • G06F3/023—Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
    • G06F3/0233—Character input methods
    • G06F3/0237—Character input methods using prediction or retrieval techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/274—Converting codes to words; Guess-ahead of partial word inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M11/00—Coding in connection with keyboards or like devices, i.e. coding of the position of operated keys

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Computer Interaction (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Telephone Function (AREA)
  • Document Processing Apparatus (AREA)

Abstract

When a sequence of keys entered by a user is not recognised by a predictive text entry system on a device the device switches to a spelling mode. In the spelling mode knowledge of the sequence of keys already entered by the user is used to enter the unrecognised sequence of keys. A first string of text entry keys from the entered sequence may be selected. The most likely combination of characters corresponding to this string is displayed. A user may confirm or change the display characters. A further string may be selected and the process repeated. Remaining characters may also be entered using a multi-tap method.

Description

I
MOBILE COMMUNICATIONS
This invention relates to entry of text to mobile communications devices.
Background
An efficient way of entering text on a telephone kevad is desirable for using text messaging services such as the Short Message Service (SMS) provided by GSM and UMTS networks.
Currently, common ways of text-entry arc the so-called multi-press method (also referred to as tap'-method) and predictive text entry methods, * TM such as T9 Iext Input ( T9 ) by Tegic Communications. Inc.. iTAP Intelligent Keypad Entry System by Motorola, Inc., and eZiTextTM by Zi Corporation.
On a typical telephone keypad, groups of letters in alphabetical order are associated with number keys. This is illustrated in Fig. 1C. For example, "a", "b", and "c', are typically associated with number 2. Thus, any single press of a key is ambiguous, as it may represent any of the associated sets of three or four letters.
The multi-press method uses multiple taps on the same key to resolve this ambiguity. The user taps the key the number of times corresponding to the position of the letter in standard ordering. For example on the 2 key, the user taps once for "a", twice for "b', etc..
The disadvantage of the multi-press system is that more than one keystroke per letter is required.
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An improved method is the predictive text entry. It allows the user to enter text by pressing only one key per letter. As the user enters a word letter by letter the system automatically compares all possible letter combinations that can result from the entered sequence of keys with a dictionary of words and thus "guess' the intended letters and words. However. oflen many dictionary words share the same numerical codes and in these cases the system presents the user with alternatives in a list. The user then selects the intended word from the list.
Some predictive text entry systems select and display the word which is most often used in a particular language as the most probable word and display the other words sharing the same numerical code (in order of decreasing probability of occurrence in that language) in a list. This reduces further the need of interrupting text entry for selecting one of the words in the list if the word selected as the most probable solution is the one desired by the user.
The use of predictive text entry systems makes text entry considerably niore efficient.
However, a disadvantage arises as users olien wish to enter words not included in the dictionary.
When the user wishes to enter a word that is not in this dictionary, the predictive text entry system usually switches to a multi-tap mode where quickly prcssing a key N times successively means that the N-th letter attached to this key is to be chosen.
However, the information contained in the numerical code entered by the user s not used when Switching to the multi-tap mode.
It is therefore an aim of the present invention to afleviate at least some of the disadvantages described above.
It is another aim of the present invention to provide an improved method and system for entering text into a mobile cornmunicatjois device in a spelling mode.
According to one aspect of the present invention, there is provided a method of entering text on a mobile communications device, comprising the stepsof: I) using predictive text entry system based on a dictionary or a training text, ii) switching to spelling mode if a sequence of keys entered by the user does not match a word or expression in the dictionary or training text; ii) in spelling mode, using the knowledge olthe sequence olkeys already entered by the user in order to enter the unknown word or expression.
According to another aspect of the present invention, there is provided a method of entering text on a mobile con1municatjois device, comprising the steps of: i) receiving text entry of a sequence of keys presses corresponding to ambiguous alphanumeric characters; ii) selecting a flrst string of text entry keys from the entered sequence of step i)
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iii) determining strings of words or expressions matching the text entry of step i); and iv) determining the string with the highest probability of matching the already entered text.
Embodiments of the present invention will now he described, by example only, with reference to the accompanying figures, whereby: Fig. IA is a schematic front view of a mobile terminal in which the present invention can be implemented; Fig. I B is a schematic illustration of sonic of the elements of the mobile terminal of Fig. IA; Fig IC is a schematic diagrani of the numeric keypad of the mobile terminal of Fig. IA; and Fig. 2 is a flowchart diagram illustrating the text entry method according to one embodiment of the present invention: and Fig. 3 is a flowchart diagram illustrating an example of the text entry method of Fig. 2.
Figure IA is a schematic illustration of a mobile communication terminal 10. The terminal 10 includes a display 26, microphone 16, speakers 18, a keypad 21, antenna 20 and navigation keys 23.
Relernng now to Figure I B, the terminal 10 comprises a processor 22, radio means 24 for communicating with other devices via a mobile comniunications network, an antenna means 26, a memory 28 and a user interface 30. A subscriber identity module 32 (SIM) for GSM terminals or a
C
universal SlIvi (USTM) for UMTS mobile temiinals can usually be inserted into the mobile terminal to enable the provision of services via a mobile telecommunications network. The SIM or USEM includes a memory clip and a microprocessor.
Fig. IC schematically illustrates the numerical keypad of the mobile terminal of Fig. I A in more detail.
Each number key 40 indicates its number 42, followed by three or four associated letters 44. For example, "A', "B" and "C" are typically associated with number 2, D", "E", "F" with number 3 etc..
In this way alphanumeric text can be entered using the numerical keypad 21.
Text entry systems are usually software applications running on the terminal's processor 22. The dictionary required for the predictive systems are typically stored in memory 28.
In order to provide an improved text entry system for keypads with a small number of keys like, for example, a mobile communications device with 12 keys, it is desirable to provide an improved method of spelling niode.
Below an example is given to suninlarise the idea of an embodiment of the present invention.
Assume a user wishes to enter the word "cohomology" on a mobile phone using a predictive text entry system. I-Ic or she presses "264666" and the sequence of letters "arninon" is displayed on the screen, but when he or she presses "5' as the next key, the text entry system switches to a spelling
I
system using multi-tap. In the following it is briefly described how the knowledge is used that the word the user wishes to enter starts with alphanumeric characters which corresponds to the entered key sequence "2646665", According to one embodinient the prohahilitv is determined for all strings that can be written with the keys 2646665", and the string having the highest probability is proposed to the user. Since for large sequences of keys this might represent far too many probabilities to compute, the probability can be computed for a smaller set of strings. The user can then confirm that the letters proposed are correct one by one, or change them, in which case probabilities are recomputed.
In the following an embodiment based on language modelling will be described in more detail.
The text entry system is provided as a software application, stored in the mobile device's memory and running on the processor.
In the following it will now be referred to Figure 2.
The following variables will be used below: * curreniKey is the current key, * keySeq denotes the key sequence typed by the user, * the variable knownStr denotes the strings of characters known by the application, as they have been either confirmed or unambiguously entered by the user, sizeKnownStr is the number of letters in known5tr,
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* propSir denotes the string proposed to the user, * SetPermStr denotes a set of strings as described in more detail below, * sizePermStr is the size of' the strings in SetPerrnStr, * the application has a predetermined length of strings (referred to as maxSizePei-mStr in the following) for which the application determines matching strings of words or expressions with the highest probability as will be described in more detail below. The predetermined length of strings is usually preset by the applicati.
1-lowever, the user might wish to change the length to adapt the application to his or her individual preferences, * SetCandStr is a set of strings as described in more detail below, * currentCandStr is a string from SetCandStr, and * currentPen-nStr is a string from SetPermStr The process starts in step 101 with an initialisation. The application reads the sequence typed by the user. In the example above the sequence typed by the user is "2646665".
The variable currentKey is set to the first key of the sequence typed by the user, which is "2" in our example.
This variable knownStr is set to 0, and sizcPemiStr is set to the length ofsinngs maxSizePemiStr in the initialisation.
In step 103 a first function is called. In this function a string of the key sequence entered by the user is considered, starling from the first unknown key, i.e. the (sizeKnownStr + I)th key of the key sequence entered by the user. The length of the string is the predetermined length maxSizePermStr.
In our example the first three keys "264" are considered afier initialisation.
The function now populates SetPerniStr. This means that the function deten-nines the complete set of characters matching the above string of keys entered by the user. In our example the SetPermStr now includes all character combinations that are derivable from the entered string of keys "264". i.e. "arng", "amh", "ami", "bmg". etc. In step 105 a second function is called. In this function the probability is calculated for each string to occur in the text entered by the user. The string considered here is a combination of the characters already known to the application (knownStr) and the strings determined in function 1 (i.e. permStr, whereby perrnStr runs through the set of Set PcrrnStr described above).
These probabilities are computed using Prediction by Partial Matching (PPM). PPM counts the number of times words and sequences of words are encountered in a "training text". This "training text" is a combination of "a prior training text", a "prior use text" and an "usage text". made of: * A "prior training text" is a text stored in the device prior to the text entry system or algorithm being used by the user.
* A "prior user text" is text chosen by the user to train the text entry system. This may include one or more different texts selected by the user.
I
A "usage text" is the text recorded as the user input. This may include all the texts entered so far using the text entry system, or a selection of these texts.
Any of the above three training texts or any combination of texts can be used. The "usage text" is particularly advantageous as it allows the text entry to dynamically adapt to the user, thus providing for a seif-iniproving text entry system.
The training texts used for the application are stored in the mobile devices memory, in the SIM/USIM memory, or other memory connected to the mobile device, such as a memory card or the like.
In step 107 now the string of all strings in SetPemiStr is selected with the maximum probability of occurrence as determined in step 105. In step 109 the currentCandStr is filled with the combination of characters already known to the user and the string of characters (permStr) detemiined to be the one with the highest probability of occurrelice (propStr) as determined in step 107.
The currentCandStr is the string of characters displayed and thus proposed to the user in step 111. The application highlights the yet unknown string of characters (i.e. the propStr).
The user now checks whether the displayed solution is the desired one.
In order to do this the user considers the first yet unknown character (i.e. the first character of propStr). (
If this character is the character desired by the user, the user confirms the selection in step 113 by pressing the "next" key of the navigation keys of the niobile device.
In response to this the application adds the first letter of propStr as a (further) known character to the end of knownStr (step 115). In step 11 7 the application now checks whether the whole sequence of keys entered initially by the user is now known. If this is the case the process continues in step 121 by entering multi-tap mode so that the user can complete the word or expression he or she likes to enter.
If, on the other hand, not all of the characters are known, the process Continues in step 119 and the application sets the current key to the next key in the sequence of keys entered by the user and continues the process by repeating the above described process from step 103 onwards with the next key entry.
Jf the user determines in step 123 that the first yet unknown character (i.e. the (irst character of propStr) is not the character desired by the user, the user selects the desired character by pressing the "down" navigation key (or the currentKey). The application then determines that the currently considered proposed key in not the character the user wished to enter, but another one of the characters associated with the key initially entered by the user.
If the user presses the "down" navigation key (or the currentKey) once.
the application selects the next character associated with the key entered initially by the user, and if the user presses the "down" navigation key (or the
S
II
currcntKey) twice or more, the application selects the character after the next one or following characters associated with the key entered initially by the user. The next key here may mean, for example, the following key in the order shown in the keys (in a cyclical order) or the next key in the probabilities determined as described above In step 125 the applicatioi then determines all the strings of the set of strings permStr which start with the character selected in step 123.
The process then continues with step 109 as described above.
After the application displays the current CandStr to the user in step 111 or at any time, the use may also choose, instead of pressing either the next or the down navigation keys as described above, to press a predetermined key that ends the spelling mode (step 127). In this case the process continues by using the predictive text entry mode used before the spelling mode has been entered (step 129).
Alternatively, the user can, after the application displays the current CandStr to the user in slep III, select to press any other key (step 131). In this case the application enters multi-tap mode to complete the entry of the desired word. The application uses the string of characters already known (i.e. knownStr) as the beginning of the word and continues then the text entry with the multi-tap mode (step 133).
In the following and referring to Figure 3 it is described how the example of entering the word cohonrnJogy" works with the spelling system
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described above. It is noted that the characters proposed by the application to the user of course depends on the text(s) used to deterrn inc the probabilities.
As described above, the user presses "264666" and the sequence of letters "aminon" is displayed on the screen, but when he or she presses "5" as the next key, the text entry system switches to the spelling application described above. The variable nlaxSizepeniiStr in the following example is set 1o3. The application performs steps 101 to Ill. The flow-chart diagram of Figure 3 illustrates the following steps.
After performing steps 101 to 111, the application displays the string "anii" to the user (step 200), where all three letters are highlighted as unknown characters. As the "character "a" is not the one desired by the user, the user presses the "down" key twice to indicate that the desired character is "c" (step 201). The application then performs steps 125, 109 and 111, and as a result displays the string "cog" to the user (step 202).
1 5 In step 203, the User confirms the first character of the displayed sequence by pressing the "next" key on his or her mobile device. In response thereto the application recognises "c" as the first known character and performs steps I 1 7 to I I I of Figure 2. The application then displays the expression "cogl" to the user (step 204), whereby the first letter is marked as a known character and the other letters are highlighted as unknown characters.
In step 205 the user confirms the selection of "o" as the second character by pressing the next" key. In step 206 the appIicatior displays the string coglo' to the user and marks the first two letters as known. (
As the character g" is not the one desired by the user as the third letter, the user presses the "down" key once to indicate that the desired character is h" (step 207). As a result, the application displays the known characters co" together with the string "horn" as the proposed string to the user (step 208).
The process continues in steps 209 to 213 in the same way as described above, whereby the user always confirms the character proposed by [he application. En each case the application responds by marking the confirmed selection as known letter and proposing a new string of three highlighted 1 0 characters to the user.
However, in step 214, the application responds to the user confirmation of step 213 by marking the confirmed selection as known letter and proposing a new string of only two highlighted characters to the user. The reason for this is that the maximum length of the combination of known and proposed characters is the length of the key sequence originally entered by the user. In our example this length is 7.
In step 215 the user confirms the proposed next character. Similarly to the above, in step 216, the application responds by marking the confirmed selection as known letter and proposing a new string of only one highlighted characers to the user. In step 2l7 the user confirms the proposed last character.
In step 21 8 displays the whole string of characters "cohomol" correspond ini. to [he entered sequence of "2646665' as known characters In ( step 219 the user presses the next" key and the application enters multi-tap mode in step 220 for allowing the user to enter the remaining characters.
The user then enters the string "6664999" in order to uinalise entry of the word "cohomology" (step 221) and the application displays in step 222 the full word.
In this example, the user had to type only 18 keys (DDNNDNNNNIN6664999PI where D' is the Down Key and N' is the Next key), as opposed to 25 ("22266644666666655 56664999') with a multi-tap system. Moreover, the level of repetition (the same key being used many times) is very high. which makes the described text entry method very fast.
Whilst in the above described embodiments the text entry system is described to be provided as a software application being stored in the mobile device's memory and running on the mobile device's processor, it is appreciated that the text entry system can be implemented in other ways. For 1 5 example, the application may be stored on a memory card connected to the mobile device or niay be stored on the SIM/USIM card memory and/or running on the SIM/USIM card's processor.
Whilst in the above described embodiments PPM has been described as a technique for determining the probabilities it is appreciated that alternatively other techniques may be used.
It is to be understood that the above describes embodiments are set out by way of example only, and that niany variations or modifications are possible within the scope of the appended claims. (

Claims (25)

  1. CLAIMS: I. A method of entering text on a mobile communications device,
    comprising the steps of: using predictive text entry system; ii) switching to spelling mode if a sequence of keys entered by the user is not recognised; iii) in spelling mode, using the knowledge of the sequence of keys already entered by the user in order to enter the unknown word or expression.
  2. 2. A method according to claim 1, wherein in step iii) the knowledge of the sequence of keys already entered by the user is used by considering a sub-string of the sequence of keys already entered by the user and determining possible character combinations corresponding to the sub-string of the sequence of keys already entered by the user.
  3. 3. A method according to claim I, wherein in step iii) [he knowledge of the sequence of keys already entered by the user is used by considering the sequence of keys already entered by the user and determining possible character combinations corresponding to the sequence of keys already entered by the user. (
  4. 4. A method according to claim 2 or 3, wherein the probability of occurrence of the possible character combinations is determined using a dictionary or training text.
  5. 5. A method according to claim 4, wherein the probability of occurrence is determined using language modelling.
  6. 6. A method according to claim 4 or 5, wherein the probability of occurrence is determined using prediction by partial matching.
  7. 7. A method according to any preceding claims, comprising the steps of: a) receiving text entry of a sequence of keys presses corresponding to ambiguous alphanumeric characters; b) selecting a first string of text entry keys from the entered sequence of step 1) c) deterniining strings of words or expressions matching the text entry of step I); and d) determining the string with the highest probability of matching the already entered text.
  8. 8. A method of entering text on a mobile con1muflicatiois device, comprising the steps of: ( v) receiving text entry of a sequence of keys presses corresponding to ambiguous alphanumeric characters; vi) selecting a first string of text entry keys from the entered sequence of step i) vii) determining strings of words or expressions matching the text entry of step i); and viii) determining the string with the highest probability of matching the already entered text.
  9. 9. A method according to claim 8, wherein the string determined in step iv) is displayed to the user.
  10. 10. A niethod according to claim 9, wherein, if the first alphanumeric character of the siring of characters displayed to the user is the desired character, the user confirms the character as a known character.
  11. 11. A method according to claim 10, wherein the user confirms the character by pressing a first key.
  12. 12. A method according to claim 9, wherein, if the first of the proposed sequence oIcharaciers is not the desired character, the user selects a different character corresponding to the pressed key of ambiguous characters as a known character. (
  13. 13. A method according to claim 12, wherein the user sciect a different character by pressing a second key
  14. 14. A method according to any of claims 8 to 13. further comprising the Steps of ix) selectina a further string of text cntry keys from the entered keys of step i), excluding the known characters; x) delerminmg stnngs of words or expressions matching the text entry of step v); and xi) determining the string with the highest probability of matching the already entered text.
  15. IS. A method according to clam 14, wherein known characters and the string determined in step vii) are displayed to the user.
  16. 16. A method according to claim 14 or 15, wherein the string detcmiiried in step vii) is highlighted.
  17. I 7. A method according to any of clainis S to 16, wherein the rs1 and/or further string of characters have a predetermined length. (
  18. 18. A method according to any of claims 8 to 1 7, wherein the predetermined length of the first and/or further string of characters is settable.
  19. 19. A method according to any of claims 8 to 18, wherein language modelling is used in steps iv) and/or vii).
  20. 20. A method according to any of claims 8 to 19, wherein prediction by partial matching is used in steps iv) and/or vii).
  21. 21. A method according to any of claims 8 1020, wherein the probability of the occurrence of a particular word or expression in step iii) or vi) is determined from one or more training texts.
  22. 22. A method according to claim 21, wherein the one or more training texts include one or more of the following: a text prior to entering text on the mobile device, a text selected by the user for training purposes, the text being entered by the user.
  23. 23. A method ol entering text on a mobile communications device, comprising the steps of: i) using predictive text entry system based on a dictionary or a training text; ( ii) switching to spelling mode if a sequence of keys entered by the user does not match a word or expression in the dictionary or training text; iii) in spelling mode, using the knowledge of the sequence of keys already entered by the user in order to enter the unknown word or expression.
    S
  24. 24. A program or application adapted to perform the method of any of claims I to 23 when running on a processor.
  25. 25. A mobile terminal for use in a mobile communications network l() adapted to perform the method of any of claims I to 23.
GB0625447A 2006-11-08 2006-12-20 A partial predictive text entry system for a mobile communication device Withdrawn GB2443653A (en)

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US9424246B2 (en) 2009-03-30 2016-08-23 Touchtype Ltd. System and method for inputting text into electronic devices
CN102193639B (en) * 2010-03-04 2014-03-12 阿里巴巴集团控股有限公司 Method and device of statement generation
US20170270092A1 (en) * 2014-11-25 2017-09-21 Nuance Communications, Inc. System and method for predictive text entry using n-gram language model
GB201610984D0 (en) 2016-06-23 2016-08-10 Microsoft Technology Licensing Llc Suppression of input images

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