Title of Invention

COMPUTING APPARATUS, METHOD FOR OPERATING THE SAME AND METHOD AND APPARATUS FOR PREDICTION OF ANY OF USER WORDS OR USER ACTIONS

Abstract The invention concerns user entry of information into a system with an input device (12). A scheme is provided in which an entire word that a user wants to enter is predicted, and shown a display (10), after the user enters a specific symbol, such as a space character. If the user presses an ambiguous key thereafter, rather than accept the prediction, the selection list is reordered. The invention can also make predictions on context, such as the person to whom the message is sent, the person writing the message, the day of the week, the time of the week, etc. Other embodiments of the invention contemplate anticipation of user actions, as well as words, such as a user action in connection with menu items, or a user action in connection with form filling.
Full Text Computing apparatus, method for operating the same and method and
apparatus for prediction of any of user words or user actions
BACKGROUND OF THE INVENTION
TECHNICAL FIELD
The invention relates to user entry of information into a system with an input device. More
particularly, the invention relates to contextual prediction of intended user inputs and
actions.
DESCRIPTION OF THE PRIOR ART
For many years, portable computers have been getting smaller and smaller. The principal
size-limiting component in the effort to produce a smaller portable computer has been the
keyboard. If standard typewriter-size keys are used, the portable computer must be at
least as large as the keyboard. Miniature keyboards have been used on portable
computers, but the miniature keyboard keys have been found to be too small to be easily
or quickly manipulated by a user. Incorporating a full-size keyboard in a portable computer
also hinders true portable use of the computer. Most portable computers cannot be
operated without placing the computer on a flat work surface to allow the user to type with
both hands. A user cannot easily use a portable computer while standing or moving.


In the latest generation of small portable computers, called Personal Digital
Assistants (PDAs), companies have attempted to address this problem by
incorporating handwriting recognition software in the PDA. A user may directly enter
text by writing on a touch-sensitive panel or screen. This handwritten text is then
converted by the recognition software into digital data. Unfortunately, in addition to
the fact that printing or writing with a pen is in general slower than typing, the
accuracy and speed of the handwriting recognition software has to date been less
than satisfactory. Also, there are memory constraints. Recognition software often
needs more memory than is available on the device. This is especially true with
such devices as mobile telephones.
Presently, a tremendous growth in the wireless industry has spawned reliable,
convenient, and very popular mobile communications devices available to the
average consumer, such as cell phones, two-way pagers, PDAs, etc. These
handheld wireless communications and computing devices requiring text input are
becoming smaller still. Recent advances in two-way paging, cellular telephones,
and other portable wireless technologies have led to a demand for small and
portable two-way messaging systems, and especially for systems which can both
send and receive electronic mail ("e-mail"). Some wireless communications device
manufacturers also desire to provide to consumers devices with which the consumer
can operate with the same hand that is holding the device.
Disambiguation Background

Prior development work has considered use of a keyboard that has a reduced
number of keys. As suggested by the keypad layout of a touch-tone telephone,
many of the reduced keyboards have used a 3-by-4 array of keys. Each key in the
array of keys contains multiple characters. There is therefore ambiguity as a user
enters a sequence of keys, since each keystroke may indicate one of several letters.
Several approaches have been suggested for resolving the ambiguity of the
keystroke sequence, referred to as disambiguation.
One suggested approach for unambiguously specifying characters entered on a
reduced keyboard requires the user to enter, on average, two or more keystrokes to
specify each letter. The keystrokes may be entered either simultaneously (chording)
or in sequence (multiple-stroke specification). Neither chording nor multiple-stroke
specification has produced a keyboard having adequate simplicity and efficiency of
use. Multiple-stroke specification is inefficient, and chording is complicated to learn
and use.
Other suggested approaches for determining the correct character sequence that
corresponds to an ambiguous keystroke sequence are summarized in the article
"Probabilistic Character Disambiguation for Reduced Keyboards Using Small Text
Samples," published in the Journal of the International Society for Augmentative and
Alternative Communication by John L. Arnott and Muhammad Y. Javad (hereinafter
the "Arnott article"). The Arnott article notes that the majority of disambiguation
approaches employ known statistics of character sequences in the relevant
language to resolve character ambiguity in a given context.

Another suggested approach based on word-level disambiguation is disclosed in a
textbook entitled Principles of Computer Speech, authored by I. H. Witten, and
published by Academic Press in 1982 (hereinafter the "Witten approach"). Witten
discusses a system for reducing ambiguity from text entered using a telephone
touch pad. Witten recognizes that for approximately 92% of the words in a 24,500
word dictionary, no ambiguity will arise when comparing the keystroke sequence
with the dictionary. When ambiguities do arise, however, Witten notes that they
must be resolved interactively by the system presenting the ambiguity to the user
and asking the user to make a selection between the number of ambiguous entries.
A user must therefore respond to the system's prediction at the end of each word.
Such a response slows the efficiency of the system and increases the number of
keystrokes required to enter a given segment of text.
H. A. Gutowitz, Touch-Typable Devices Based on Ambiguous Codes and Methods
to Design Such Devices, WO 00/35091 (June 15, 2000) discloses that the design of
typable devices, and, in particular, touch-type devices embodying ambiguous codes
presents numerous ergonomical problems and proposes some solutions for such
problems. Gutowitz teaches methods for the selection of ambiguous codes from the
classes of strongly-touch-typable ambiguous codes and substantially optimal
ambiguous codes for touch-typable devices such as computers, PDA's, and the like,
and other information appliances, given design constraints, such as the size, shape
and computational capacity of the device, the typical uses of the device, and
conventional constraints such as alphabetic ordering or Qwerty ordering.

Eatoni Ergonomics Inc. provides a system called WordWise, (Copyright 2001 Eatoni
Ergonomics Inc.), adapted from a regular keyboard, and where a capital letter is
typed on a regular keyboard, and an auxiliary key, such as the shift key, is held
down while the key with the intended letter is pressed. The key idea behind
WordWise is to choose one letter from each of the groups of letters on each of the
keys on the telephone keypad. Such chosen letters are typed by holding down an
auxiliary key while pressing the key with the intended letter. WordWise does not use
a vocabulary database/dictionary to search for words to resolve ambiguous,
unambiguous, or a combination thereof entries.
Zi Corporation advertises a next word prediction, eZiText(R) (2002 Zi Corporation),
but to our knowledge does not anywhere suggest the presentation of multiple
predictions, or the reorder of selection lists to give precedence to words matching
context.
Other next word production systems that are known include iTAP, which is offered
by Motorola's Lexicus division (http://www.motorola.com/lexicus/). and the adaptive
recognition technology offered by AIRTX (http://www.airtx.com/).
Disambiguating an ambiguous keystroke sequence continues to be a challenging
problem. For example, known approaches to disambiguation focus primarily upon
completion of a partially entered sequence, and not upon predicting an as yet
unentered sequence. Further, the user context is not typically taken into account
when disambiguating an entered sequence, nor does the disambiguation of an

entered sequence result in the taking of an action on behalf of a user, but rather
merely focuses on the completion and display to a user of an intended sequence.
It would be advantageous to provide an approach to processing user inputs that
results in predicting an as yet unentered sequence. Further, it would be
advantageous to provide an approach in which the user context is taken into
account when disambiguating an entered sequence. Additionally, it would be
advantageous to provide an approach in which the disambiguation of an entered
sequence results in the taking of an action on behalf of a user.
SUMMARY OF THE INVENTION
The invention concerns user entry of information into a system with an input device.
A scheme is provided in which an entire word that a user wants to enter is predicted
after the user enters a specific symbol, such as a space character. If the user
presses an ambiguous key thereafter, rather than accept the prediction, the
selection list is reordered. For example, a user enters the phrase "Lets run to
school. Better yet, lets drive to ".""" After the user presses the space, after first
entering the second occurrence of the word "to," the system predicts that the user is
going to enter the word "school" based on the context in which the user has entered
that word in the past. Other predictions may be available if the user had previously
entered text with the same context (for example, "to work", "to camp"). These
predictions are presented if the user presses the "next" key; the key specified for
scrolling through the list. Should the user enter an ambiguous key after the space,
then a word list is reordered to give precedence to the words that match context. For

example, if the user presses the ambiguous key that contains the letters 'a', 'b', and 'c', the
word "camp" is given precedence in the list.
The invention can also make predictions on other forms of context, such as the person to
whom the message is sent, the person writing the message, the day of the week, the time
of the week, etc.
Other embodiments of the invention contemplate anticipation of user actions, as well as
words, such as a user action in connection with menu items, or a user action in connection
with form filling.
User actions or inputs can affect the automatic changing of the device's state based on
context. For example, the system might use context to change a mobile telephone from
'ring' to 'vibrate', during the time that the calendar shows that the user is in a meeting.
Another embodiment uses location context to increase the mobile telephone volume when
the user is outside or when the telephone detects high levels of background noise.
In another embodiment, the system learns the user habits. For example, based on the
learned user action, the system is able to offer services to the user that the user may not
be aware of.
In another embodiment, word prediction is based on the previous word context (bigram
context), but might also use the previous 'n' words (trigram context, etc).

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
Fig. 1 is a schematic representation of a device having a display and user information
input mechanism, and that incorporates next word prediction technology according to the
invention;
Fig. 2 is a block diagram of a preferred embodiment of a reduced keyboard
disambiguating system for a T9 implementation of the invention;
Fig. 3 is a flow diagram showing a next word prediction method according to the invention;
and
Fig. 4 is a flow diagram showing the processing of words in a next word prediction method
according to the invention.
DETAILED DESCRIPTION OF THE INVENTION
The invention concerns user entry of information into a system with an input device. A
scheme is provided in which an entire word that a user wants to enter is predicted after the
user enters a specific symbol, such as a space character. If the user presses an
ambiguous key thereafter, rather than accept the prediction, the selection list is reordered.
For example, a user enters the phrase "Lets run to school. Better yet, lets drive to "."""
After the user presses the space, after first


entering the second occurrence of the word "to," the system predicts that the user is
going to enter the word "school" based on the context in which the user has entered
that word in the past. Other predictions may be available if the user had previously
entered text with the same context (for example, "to work", "to camp"). These
predictions are presented if the user presses the "next" key; the key specified for
scrolling through the list. Should the user enter an ambiguous key after the space,
then a word list is reordered to give precedence to the words that match context. For
example, if the user presses the ambiguous key that contains the letters 'a', 'b', and
'c', the word "camp" is given precedence in the list.
The invention can also make predictions on other forms of context, such as the
person to whom the message is sent, the person writing the message, the day of the
week, the time of the week, etc.
In another embodiment of the invention, rather than explicitly define the context
parameters, such as sender/recipient/email/SMS/reply/forward/new email etc, the
system is passed a series of parameters by the device which may or may not be
relevant and the system learns which of the parameters are relevant for prediction
and which ones are not.
In other embodiments of the invention, prediction may go beyond words and predict
phrases. Prediction also may depend on grammar, semantics etc.

Other embodiments of the invention contemplate anticipation of user actions, as well
as words and phrases, such as a user action in connection with menu items, or a
user action in connection with form filling.
In further embodiments, the knowledge gained from user patterns can be
uploaded/downloaded and/or served from a server allowing this information to be
shared between devices and applications.
Discussion
For purposes of the discussion herein, with regard to the contextual completion of
words, the term 'Next Word Prediction' (NWP) means, inter alia:
1) Predicting, after entering a space character, the entire next word that the user
wants to enter, and
2) If the user presses an ambiguous key, rather than accept the prediction, the
selection lists are reordered.
Fig. 1 is a schematic representation of a device 14 having a display 10 and user
information input mechanism 12, and that incorporates next word prediction
technology according to the invention. In Fig. 1, the user has entered the phrase
"Lets run to school. Better yet, lets drive to." The user presses space after entering
the word "to," and the system predicts that the user is next going to enter the word

"school," based on the context in which the user has entered the word "school" in the
past. In this case, only the previous word for the context is looked at. The last time
the user entered the word "to," he entered the word "school" directly after. In the
example of Fig.1, the user has entered the word "to" again, and the prediction word
"school" is present. If in the past the user had entered other words after the word
"to," those additional predictions are provided, as well, in a list, for example. In this
example, context information comes from previous text entered in this message
only. In a preferred embodiment, context information is compiled from text entered
in prior messages/sessions as well.
Predictions are made when the context in the current message matches the context
in text the user previously entered. The concept of context can be very general.
Context can mean the nature of the text entered. Context can also be combined
with other contexts, such as, for example:
a) The person to whom a message is sent;
b) The person writing the message;
c) The day of the week;
d) The time of day.
Finally, the prediction system might not know what the most important factors are for
context, e.g. are they:

• Text and message recipient?;
• Text and message writer?;
• All three?.
A further embodiment starts with a very broad set of possible factors and performs
on-the-fly factor analysis of the user behavior to determine the most effective factor
to include as context. This system does more than adapt to user behavior based on
a priori specified factors, such as text, recipient, author, day, that are recorded, but
is also intelligent enough to determine which factors are most important and
emphasize those. This allows for better prediction.
Another example of prediction contemplated by the invention is based upon time of
day. For example, when entering a message "let's meet for" at lunchtime, the word
"lunch" is automatically predicted as the next word in the phrase. Later in the day the
word "dinner" is predicted. The phrases stored also can have time of day associated
with them as one of their attributes. This can be used to decide which phrases are
relevant when the user is entering text.
Prediction of User Actions
Prediction can also be applied to other concepts as well, such as menus and user
actions. When a user clicks a menu, the context module is provided with a keyword

for that menu as the preceding context word. The context module then produces
the entries previously selected from that menu because they are in the context
database as entries preceded by that keyword, and those words can be re-ordered
to the top of the menu. When a menu entry is selected, the context module then
automatically notes it as having occurred with the menu tag as context for re-
ordering to the front next time.
For example, when the user clicks the "Edit" menu, the context module is provided
"Edit:" as context. If the last time a user clicked "Edit" the user chose "Find," then
"Find" is shown at the front of the menu. If the user moves past that to "Replace,"
then a use of "Replace" in the context of "Edit:" is marked, so that the next time the
user selects the "Edit" menu, "Replace" becomes the first entry, followed by "Find"
and the other less-frequently used entries.
Note that for cell phones with limited screen space, moving commonly used entries
to the front of a menu can make them immediately visible when they otherwise are
not visible without scrolling.
In one embodiment, learning is used, in simple case context and reorder, to predict
the next macro-level user interface (Ul) behavior the user is expected to perform.
Instead of reordering menus based on past usage, the normal menu format is
superceded entirely by reordering immediate options for the next state/application
the user is expected to go to, and the most likely option can be performed
automatically, if desired.

For example, consider the situation where the system knows that whenever a user is
in the settings mode on the phone, and they are choosing an input method or
language, they are very likely to move next to their favorite messaging application.
Then, instead of presenting the user with the normal menu tree to get to the
messaging application, the system:
a) Goes there automatically, or if that is found to not be feasible;
b) Presents that as a visible prominent option right there in the settings window,
along with the next most likely option.
The last option would be "go to standard menu tree." This way, the user is
presented with the most likely next end state, rather than the most likely behavior
directly from here, which in a normal phone would be going back to the menu tree.
The user does not have to navigate a menu tree at all, but rather has one click (or
no click) to go to the next task.
Additional embodiments of the invention apply to contexts that, for example pose
any of the following questions:
• What end state is the user most likely to be in immediately after a messaging
application?
• What end state is the user most likely to be in after entering something into a
phonebook?

• What end state is the user most likely to be given the last two places he
was?
• Given the time of day?
• Should a factor analysis be performed on the fly to isolate the most relevant
factor's involved in deciding what the next move should be?
Forms
Form filling is another useful function performed by the invention. Context sensitivity
by field attribute, e.g. date only predicts months, day switches to numeric mode etc.
This can similarly be applied to form input. The browser, or other form-input
software, can provide the prompt for the input cell as context for text entry of the
cell. Thus, for example, when a form prompts for "Name:" the user's name is
available with few to no keystrokes, and other names he might fill in on forms are
also made easier to enter.
Implementation of contextual word prediction in Tegic T9 Technology
The herein disclosed next word prediction invention has been applied to Tegic
Corporation's T9 technology (see www.tegic.com and www.t9.com). T9 technology
combines the groups of letters found on each key of an input device, e.g. each
phone key, with a fast-access dictionary of words, and recognizes what a user wants

to input as text as he types. T9 offers the most commonly-used word for every key
sequence entered by default and then lets the user access other choices with one or
more presses of the NEXT or space key.
A block diagram of a preferred embodiment of a reduced keyboard disambiguating
system hardware for a T9 implementation of the invention is provided in Fig. 2. The
keyboard 54 and the display 53 are coupled to a processor 100 through appropriate
interfacing circuitry. Optionally, a speaker 102 is also coupled to the processor. In
this embodiment, the processor 100 receives inputs from the keyboard, and
manages all output to the display and speaker. The processor 100 is coupled to a
memory 104. The memory includes a combination of temporary storage media,
such as random access memory (RAM), and permanent storage media, such as
read-only memory (ROM), floppy disks, hard disks, or CD-ROMs. The preferred
memory 104 contains all software routines necessary to govern system operation.
Preferably, the memory contains an operating system 106, disambiguating software
108, associated filtering of ambiguous text entry software and/or extending and
interpreting software 110, and a contextual database 116, the latter of which is
discussed in additional detail below. The memory also includes a vocabulary
database 30. Optionally, the memory may contain one or more application
programs 112, 114. Examples of application programs include word processors,
software dictionaries, and foreign language translators. Speech synthesis software
may also be provided as an application program, thereby allowing the reduced
keyboard disambiguating system to function as a communication aid. Therefore, the
output, in this case, might be vocally output from the speaker.

It should be appreciated and understood by one of ordinary skill in the art that the
discussion herein applies to symbols and sequences of symbols, which, when
combined, make an object or part of an object. A typical example of a symbol is a
character in any language, such as a letter, digit, punctuation mark, or any other
symbol from a language. A typical example of an object or part of an object is a
word or part of a word. However, the discussion herein equally applies to Japanese
kana and Korean jamos. Also, it should be noted that the objects do not have to be
linguistic, as the claimed disambiguating system herein can be used to predict icons,
phone numbers, or inventory records, as long as a type of symbolic string
representation is present. Therefore, it should be appreciated that use of the terms
such as letter, word, word stem, and the like are not limited to only those
applications, and are used to facilitate ease of reading and understanding the
discussion herein.
For purposes of the discussion herein, T9 systems comprise at least three
components:
• An integration layer. This component contains the user interface (Ul) and handles
communications between the device and the T9 core. Communications can occur
either through an event-based or a function-based API, discussed below.
• A core engine, for example the core engine known as the T9 core, which is
provided by Tegic.

• One or more language databases (LDBs). Each LDB contains information on a
particular language. T9 uses this information to generate lists of words for that
language. LDBs can include, for example, any of Alphabetic T9 LDBs, Chinese T9
LDBs, and Korean T9 LDBs.
Supplemental Databases
Alphabetic T9 and Chinese T9 implementations can include the following
supplemental databases:
• User Database (Alphabetic T9). An Alphabetic T9 UDB contains custom words
entered by the user. Typically, these are words that cannot be generated by the
LDB, such as names, e-mail addresses, and instant messaging IDs. The database
also contains information on how frequently a user selects words—both custom
words and words from the LDB.
• Context Database (Alphabetic T9). An Alphabetic T9 CDB contains information on
the words the user has previously entered. T9 requires this information for its next-
word prediction and CDB word completion features. The context database contains
recently entered words. Alphabetic T9 uses this information to provide predicted and
completed words in the selection list, and to reorder full and completed words in the
selection list.
• Manufacturer Database (Alphabetic T9). An Alphabetic T9 MDB contains words
one wants to make available to T9 users but which typically cannot be generated by

the LDB. MDB entries can include geographic locations, stock ticker symbols, and
URLs.
• Chinese User Database (Chinese T9). A Chinese T9 CUDB contains user-entered
character phrases, i.e. strings of Chinese characters that together form a phrase.
• Chinese Automatically Reordering User Database (Chinese T9). A Chinese T9
CAUDB contains recently entered characters from a Chinese T9 LDB.
Generating Selection-List Words
When the user enters an active key sequence, Alphabetic T9 checks its databases
(LDB, UDB, CDB, and MDB) for words that match the key sequence.
The Alphabetic T9 selection list is designed to provide the words a user most likely
desires, based on 1) how frequently the user enters the word, 2) how common the
word is in the language and 3) the previous context in which the keys were entered,
so that the words appear at the beginning of the selection list.
The relative order of selection-list items depends on which databases are enabled
and which features, such as selection list reordering and word completion and word
prediction, are enabled.
The first word in Alphabetic T9's selection list is active by default. The term active

word refers to the currently active selection-list word.
An example of the selection list order is given below. It is assumed that keys have
been entered and no T9 database or database features are disabled.
1) CDB words of length of key sequence.
2) Reordered (highly used) LDB and Custom user words of length of key
sequence.
3) Top LDB words of length of key sequence.
4) Less commonly used Custom words of length of key sequence.
5) Less commonly used Manufacturer (MDB) words of length of key sequence.
6) Remaining LDB words of length of key sequence.
7) CDB words that are longer than entered key sequence (these are completed
by T9).
8) Custom and manufacturer words that are longer than entered key sequence
(these are completed by T9).
9) Words that are result of multiple database lookups. These are attempts to
match URLs and other long sequences.
Processing an Accepted Word
When the user accepts the active word by moving the cursor off the word (pressing
keys that correspond to the T9 key values T9KEYRIGHT, or T9KEYLEFT)
Alphabetic T9:

• Adjusts the word's selection frequency value if it is in the UDB as a custom word.
• Adjusts the word's selection frequency value if it is in the LDB and Alphabetic T9's
selection list reordering feature has not been disabled.
When the user accepts the active word by entering a space (pressing keys that
correspond to the T9 key value T9KEYSPACE) Alphabetic T9 performs the actions
above, as well as the following actions:
• Adds to the UDB as a custom word all the characters between the newly entered
space and the one before it, if the UDB and LDB do not already contain the word.
• Adds to the CDB all the characters between the newly entered space and the one
before it.
Predicting the Next Word
Fig. 3 is a flow diagram showing a next word prediction method according to the
invention. As text is entered, the words are stored in the CDB in the order in which
they were entered by the user. When the user enters a word (300), Alphabetic T9
attempts to predict the next word desired (302) if the implementation includes a
CDB. Alphabetic T9 searches the CDB (304) for the first previous occurrence of the
most recently entered word. If Alphabetic T9 finds the word (306), whatever word
appears after it in the database is offered to the user as a predicted word (308). If
the word is not found (306), processing is complete and T9 waits for next key entry

(314). If the predicted word is accepted by the user (310) the word is processed; T9
records use of word (316). If the user does not accept the word (310), but presses
the 'next' key (312), the CDB is searched for the next most recent occurrence of the
word just entered (318). If found, the word following it in the database is presented
as a prediction (306 and 308). If the user does not accept the word (310), and does
not press the next key, no processing is complete, and T9 waits for next key entry
(314), as further described in connection with Fig. 4.
Alphabetic T9 creates a selection list of predicted words. The maximum number of
predicted words in the selection list depends on the literal value of the #define
constant T9MAXCDBMATCHES. Unless a different value is assigned, this constant
is set to 6.
The user selects and accepts a predicted word using the same process used in T9
for selecting and accepting a word. After the user accepts a predicted word (310),
Alphabetic T9 processes the word (312). It will be appreciated by those skilled in
the art that the invention may be applied to other disambiguation systems than T9,
as well as other forms of T9 than Alphabetic T9.
Processing Words
Fig. 4 is a flow diagram showing the processing of words in a next word prediction
method according to the invention. When the user presses the Space key (400), to
indicate the start of a new word, Alphabetic T9:

• Adds to the UDB as a custom word (404) all the characters between the newly
entered space and the one before it, if the UDB and LDB do not already contain the
word (402).
• Adds to the CDB all the characters between the newly entered space and the one
before it (406).
• Adjusts the word's selection frequency value (410) if it is in the UDB as a custom
word (408).
• Adjusts the word's selection frequency value (410) if it is in the UDB as a LDB
reordered word (414).
• Adds the word to UDB as LDB reordered word (416) if it is in the LDB and
Alphabetic T9's selection list reordering or LDB word completion features have not
been disabled (412).
Alphabetic T9 Context Database
The following discussion describes how to implement and operate an Alphabetic T9
Context Database (CDB). A CDB contains information on recently entered words.
Alphabetic T9 uses this information to include predicted and completed words in the
selection list. Whereas Alphabetic T9 checks its other databases only for words that
match the current active key sequence, Alphabetic T9 also checks the CDB for the
most recently accepted word, i.e. the most recently entered non-active word. CDB
words do not necessarily have to match the active word to be included in the

selection list. For predicted words, which appear in the preferred embodiment only
when there is no active key sequence, the CDB match depends on the word before
the active word. For completed CDB words, the match depends on both the word
before the active word and the key sequence of the active word.
If Alphabetic T9 finds in the CDB the word the user has entered, Alphabetic T9
suggests the word that immediately follows in the CDB as a predicted word. For
example, if the CDB contains the word pair "text message" and the user enters the
word "text" and then presses the Space key, Alphabetic T9 places "message" in the
selection list as a predicted word.
Also, if Alphabetic T9 finds in the CDB the word the user has entered, Alphabetic T9
suggests the word that immediately follows in the CDB as a completed word if the
word matches the active key sequence, although the completed word contains
additional characters. For example, if the CDB contains the word pair "text message"
and the user enters the word "text," adds a space, and then enters the key
sequence 6-3-7-7, which corresponds to the first four letters in the word "message",
Alphabetic T9 places "message" in the selection list as a completed word.
In the preferred embodiment, CDB word completion operates independently of UDB
custom-word completion, LDB word completion, and MDB word completion.
Implementing a CDB
To implement an Alphabetic T9 CDB, the integration layer should:

1. Allocate persistent memory for the database.
2. Call T9AWCdbActivate to activate the CDB.
3. Indicate the CDB's size.
4. Reset the database, if desired.
5. Indicate that the integration layer writes data to the database, if necessary.

6. Disable next-word prediction, if desired.
7. Disable CDB word completion, if desired.
8. Handle requests submitted by T9.
9. Copy the database to persistent memory after T9 termination.
The implementation process described above assumes the CDB is stored in non-
volatile memory and that CDB data are copied to RAM before activating CDB
operations. If a different storage model is used, some of the steps above may not
apply.
Allocating Persistent Memory

The integration layer must allocate persistent memory to store the CDB. When the
integration layer activates CDB operations by calling T9AWCdbActivate, it copies
the CDB from persistent memory to RAM. The database is referenced as an
instance of the CDB Data Structure (T9AWCdblnfo).
Activating CDB Operations
If there is no existing CDB, e.g. the first time CDB operations are activated on the
device, the integration layer must initialize all T9AWCdblnfo structure fields values to
0. If the integration layer has copied an existing CDB from persistent memory to
RAM, it should not modify any T9AWCdblnfo structure field values.
The integration layer activates CDB operations by calling T9AWCdbActivate. When
the integration layer calls this function, it provides a pointer to an instance of the
CDB Data Structure (T9AWCdblnfo) for which it has allocated memory.
After the integration layer has activated enabled CDB operations, Alphabetic T9
automatically searches the CDB. The type of information Alphabetic T9 searches the
CDB for depends on whether there is an active key sequence:
• If there is an active key sequence, Alphabetic T9 searches the CDB for words that
match the key sequence.
• If there is no active key sequence, Alphabetic T9 searches the CDB for the most

recently entered word. Alphabetic T9 requires this information for next-word
prediction.
Indicating a CDB's Size
A CDB's size is indicated by the value of T9AWCdblnfo.wDataSize. The wDataSize
field indicates the total size of T9AWCdblnfo. This includes the data area, where
CDB data are stored, several related variables used by T9, and any structure-
padding bytes added by the compiler environment.
If T9's Function API is used, it is not necessary to set the value of
T9AWCdblnfo.wDataSize directly. Instead, the size of the CDB data area is provided
as an argument to the function T9AWCdbActivate. While handling the function, T9
sets the value of T9AWCdblnfo.wDataSize.
One can make the CDB area as large wanted, but it must be at least
T9MINCDBDATABYTES bytes. It is recommended, however, that the CDB be 1800
* T9SYMBOLWIDTH bytes in size.
Resetting the CDB
When the integration layer activates CDB operations, Alphabetic T9 ensures the
integrity of the database by:
• Ensuring the CDB is the same size that T9 is expecting.

• Verifying that the CUDB is at least T9CCUDBMINSIZE bytes in size and is an even
number of bytes.
• Verifying that the CDB uses the same character encoding as the LDBs.
If Alphabetic T9 detects a problem, it resets the CDB, which deletes all CDB data.
This process occurs without any action by the integration layer, and Alphabetic T9
does not notify the integration layer that the CDB has been reset. The integration
layer can explicitly reset the CDB by calling T9AWCdbReset. Under most
circumstances, the integration layer does not need to call this function.
Indicating the Integration Layer Writes Data to the CDB
If the CDB is stored in a memory area that Alphabetic T9 cannot write to, the
integration layer must write data to the database. Also, one may wish to have the
integration layer write data to the CDB if it is desired to monitor what is written to the
database or maintain a shadow copy of the CDB in non-volatile storage.
The integration layer informs Alphabetic T9 that it writes data by calling
T9AWSetCdbWriteByOEM.
After the integration layer calls this event, Alphabetic T9 requests that the integration
layer write data by calling T9REQCDBWRITE. If it is no longer necessary for the
integration layer to write data to the CDB, the integration layer calls

T9AWCIrCdbWriteByOEM to indicate that Alphabetic T9 can write data directly.
Disabling Next-Word Prediction
When CDB operations are activated, T9 by default provides predicted words, i.e.
words the user may want to enter, based on the words the user has already entered.
Next-word prediction is available in both Ambiguous and Multitap text-entry modes.
Alphabetic T9 places predicted words in the selection list when the word the user
has just entered is found in the CDB as the first part of one or more word pairs.
Whatever word appears in the CDB after each instance of the word the user has just
entered is provided as a predicted word.
It is possible to disable this functionality if one wants to use only CDB word
completion, and not next-word prediction, in an Alphabetic T9 implementation. To
disable CDB word completion, the integration layer calls T9AWCIrCdbPrediction. To
re-enable next-word prediction, the integration layer calls T9AWSetCdbPrediction.
Disabling CDB Word Completion
When CDB operations are activated, Alphabetic T9 by default places in the selection
list completed CDB words that match the active sequence (and contain additional
characters) if the word immediately before the active word is in the CDB immediately
before the completed word(s). One can disable this functionality if one want to use
only next-word prediction, and not CDB word completion, in an Alphabetic T9

implementation. To disable CDB word completion, the integration layer calls
T9AWCIrCdbCompletion. To re-enable CDB word completion, the integration layer
calls T9AWSetCdbCompletion.
Note that CDB word completion operates independently of UDB custom word
completion, LDB word completion, and MDB word completion. Many of the words in
a CDB are also in other Alphabetic T9 databases. Alphabetic T9 suppresses these
duplicates from the selection list. However, the potential effect of this duplication on
other API events functions should be noted. For example, a UDB custom word that
is deleted from the database still appears in the selection list if that word is also in
the CDB. Likewise, if one were to disable LDB word completion, words in the LDB
still appear in the selection list as completed words if they are also in the CDB and
CDB word completion is enabled.
Handling T9 Requests
Depending on how the CDB is implemented, the integration layer may need to
handle the following T9 request:
• T9REQCDBWRITE—Requests that the integration layer write data to the CDB. T9
submits this request only if the integration layer informs T9 that it, and not T9, writes
data to the CDB.
Copy an Updated CDB to Persistent Memory

The integration layer should copy the CDB data to persistent memory when it
terminates Alphabetic T9 if the database has been modified during the T9 session.
T9 increments the value of T9AWCdblnfo.wUpdateCounter whenever it modifies the
database. The integration layer can determine whether the database has been
modified by comparing the value of wUpdateCounter after the session to its value
before the session. If the value is different, the integration layer must copy the
updated CDB data to persistent memory. Note that it is likely that T9 modifies the
CDB during every session.
Operating an Alphabetic T9 CDB
Alphabetic T9 CDB operations consist of the following tasks:
• Adding data to a CDB.
• Retrieving data from a CDB.
• Deleting data from a CDB.
Adding Data to a CDB
Alphabetic T9 automatically adds data to the CDB. Note that if the CDB is stored in
a memory area that T9 cannot write to, the integration layer must write data to the
CDB.

Retrieving Data from a CDB
Alphabetic T9 automatically retrieves data from the CDB.
Deleting Data from a CDB
Alphabetic T9 does not permit users or the integration layer to delete words from the
database. Instead, Alphabetic T9 automatically begins deleting the oldest words in
the database when it is nearly full. This removal process is referred to as garbage
collection, and it occurs without any action by the user or integration layer.
Operation
In the presently preferred embodiment of the invention, saved context data are used
to return a prediction of the next word upon pressing the space, and to filter the word
completions after entering key strokes. This, in principle, allows a user to reduce the
number of keystrokes by quickly retrieving words that are correctly predicted based
on the previous word or words. This completion feature is presently implemented by
saving user entered text in a Context DataBase (CDB), and returning those words
that match context and keystrokes.

NWP saves the recently entered user text and uses that text to predict the next word
that the user enters. For example, if the user has typed the phrases 'hello Leslie,'
hello Inger,' and 'Hello Helena' in the recent past, when the user types and accepts
the word 'hello' by hitting space, the invention suggests:
Leslie
Inger
Helena
as possible next words.
If the user does not accept one of these words, but rather continues typing, the
invention uses context to prioritize completions presented to the user. In an
embodiment employing a 12-key input device, if the above user types the 4 key after
hitting space, the selection list presented to the user is:
i
h
g
4
Inger
Helena
If the above user types the 43 key after hitting space, selection list presented to the
user is:

he
if
id
ie
ge
gf
Helena
After a space, the context database (CDB) objects make up the entire selection list.
After pressing ambiguous keys, CDB objects appears as follows:
• If CDB objects are of the length of the active key sequence, the objects appear
at the top of the selection list.
• If CDB objects are of a length greater that is than that of the active key
sequence, the objects appear at the top of the completion portion of the list.
System state tracks completions after space with:
pFieldlnfo->nWordLen = 0;
pFieldlnfo->nCompll_en = length of context word.

After a user selects ambiguous keys, system state tracks CDB completions in the
preexisting way:
pFieldlnfo->nWordl_en = length of active key sequence;
pFieldlnfo->nComplLen = length of completion.
API
The T9 API consists of a global structure which holds word, wordlist, and buffer
information that is used by the customer, and a set of events or functions for
building, accepting, and deleting words, scrolling through word lists, and more. In
alphabetic T9, the API structure is referred to as the T9AWFieldinfo structure (often
referred to as pAWFieldlnfo). The T9AWFieldlnfo contains data that is specific to
alphabetic T9. The T9AWFieldlnfo structure contains another structure, T9Fieldlnfo
(often referred to as pFieldlnfo), which contains general word data that is also used
in Japanese, Chinese, and Korean T9.
New API structure data and functions were added to T9 to implement NWP. The
NWP feature is active if the host has allocated space for the context database and
set the pFieldlnfo->pCdblnfo to a non-zero value.
The following function API event is added to activate the CDB:

T9AWCdbActivate(T9AWFieldlnfo*pAWFieldlnfo,
T9AWCdblnfo T9FARUDBP0INTER *pCdblnfo,
T9UINT nDataSize, T9U8 bSymbolClass);
To set writing configuration:
T9EVTCDB : T9CTRLSETCDBWRITEBYOEM
Function API - T9AWSetCdbWriteByOEM(T9AWFieldlnfo *pAWFieldlnfo)
To clear writing configuration:
T9CTRLCLRCDBWRITEBYOEM
Function API - T9AWCIrCdbWriteByOEM(T9AWFieldlnfo *pAWFieldlnfo)
To reset the CDB:
T9EVTCDB:T9CTRLCDBRESET
(Function API call: T9AWUdbReset(T9AWFieldlnfo *pAWFieldlnfo)
To break CDB context:
T9EVTCDB:T9CTRLCDBBREAKCONTEXT
Function API - T9AWBreakCdbContext (T9AWFieldlnfo *pAWFieldlnfo)

To fill context buffer:
T9EVTCDB : T9CTRLCDBFILLCONTEXTBUFFER
buffer: pEvent->data.sCDBData.psBuf
buffer length pEvent->data.sCDBData.nBufl_en
Function API - T9AWFillContextBuffer(T9AWFieldlnfo *pAWFieldlnfo, T9SYMB
*psBuf, T9UINT nBufLen)
To get word prediction:
T9EVTCDB : T9CTRLCDBGETWORDPREDICTION
Function API - T9AWGetWordPrediction (T9AWFieldlnfo *pAWFieldlnfo)
To clear buffer but retain context:
T9EVTCLEARBUFFE
Function API - T9AWCIearBuffer (T9AWFieldlnfo *pAWFieldlnfo)
To turn off CDB completion:
T9CTRLCLRCDBCOMPLETION
Function API - T9AWCIrCdbCompletion (T9AWFieldlnfo *pAWFieldlnfo)
To turn on CDB completion:

T9CTRLSETCDBC0MPLETI0N
Function API - T9AWSetCdbCompletion (T9AWFieldlnfo *pAWFieldlnfo)
To turn off CDB completion:
T9CTRLCLRCDBPREDICTION
Function API - T9AWCIrCdbPrediction (T9AWFieldlnfo *pAWFieldlnfo)
To turn on CDB completion:
T9CTRLSETCDBPREDICTION
Function API - T9AWSetCdbPrediction (T9AWFieldlnfo *pAWFieldlnfo)
The following request type is added:
T9REQCDBWRITE
This is used to request writes to CDB if external write is on.
There is no additional direct access to write to the CDB through the API.
Internal CDB interfaces
Two interfaces to the CDB exist in the T9 embodiment of the invention:

AddCdbText(pFieldlnfo, psWordBuf, nLen)
Where:
pfieldlnfo = T9 fieldinfo
psWordBuf = buffer holding text
nLen = word length
And:
GetCdbObject(pFieldlnfo, nUdbObjNum, nWordLen, nCursor,
psBuildTxtBuf, nBuildTxtBufSize, pnComplLen, pnUdbObjCnt)
Where:
pfieldlnfo = T9 fieldinfo
nUdbObjNum = CDB object number (1 for 1st match, 2 for
second match, etc)
nWordLen = word length (o after space, 1 after 1 key, 2
after 2 keys, etc)
nCursor = cursor position
psBuildTxtBuf = pointer to build buffer
nBuildTxtBufSize = build buffer size
pnComplLen = pointer to completion length holder
pnUdbObjCnt = pointer to object count holder.

Functions
T9STATUS T9FARCALL T9AW_SaveAndAddToCdb(T9AWFieldlnfo
*pAWFieldlnfo)
Adds Saves word to context buffer and add to context database. This function is called
only after a space has been entered.
T9UINT T9FARCALL T9AW_GetCdbObject (T9AWFieldlnfo
*pAWFieldlnfo, T9UINT nCdbObjNum, T9UINT nWordLen, T9UINT
nCursor, T9U8 bObjectType, T9UINT *pnTerminal, T9U8 bRightMost,
T9SYMB *psBuildTxtBuf, T9UINT nBuildTxtBufSize, T9UINT
*pnComplLen, T9UINT *pnCdbObjCnt)
This function retrieves context matches from the CDB.
T9STATUS T9FARCALL T9AWCdbReset(T9AWFieldlnfo *pAWFieldlnfo)
This function resets the CDB.
T9STATUS T9FARCALL T9AWCdbActivate(T9AWFieldlnfo
*pAWFieldlnfo, T9AWCdbInfo T9FARUDBPOINTER *pCdblnfo, T9U8
bSymbol Class)

This function activates the CDB.
Database
Present minimum database size requirements are 1800 * symbol width (300 words *
6 chars/word * symbolwidth bytes/char). This is 1800 for one-byte systems, and
3600 for two-byte systems.
The CDB saves recently entered text in the same form that the user enters it. The
text is stored in a circular buffer. New words overwrite the least recent word in the
CDB.
The CDB has global information in its header:
T9U16 wDataSize; /* Total size in bytes of this struct*/
T9U16 wUpdateCounter; /* Count incremented each time user database
modified */
T9U16 wSymbolClass; /* T9 enum value indicating symbol table mapping for
CDB*/
T9U16 wDataBeginOffset; /* Offset to beginning of data */
T9U16 wDataEndOffset; /* Offset to end of data */
T9U16 wSavedOffset; /* pointer to last accessed position in database */
T9U32 dwOffsetSaver; /* identifier for thread that last saved offset. */
T9U8 bDataArea[1]; /* Really a variable size data array */

Reads
When requesting a word from the CDB, the system word builder passes a context
buffer. Using the context buffer the CDB retrieves context matches in order of
recency.
Writes
When the space key is hit, or white space is entered explicitly, the built word is
written to the CDB. "This happens in both ambiguous and multitap (MT) modes.
The word also goes through its normal RUDB processing. There is no garbage
cleanup in the CDB.
Context Buffer
A context buffer is maintained. The context buffer is updated on the pressing of
space key and is cleared with any action that tends to lose context, such as
cursoring and clearing. In a word API this is attached to the flushword function of a
separate confirm function.
Functional Description

In the T9 embodiment, the NWP feature is active if:
a) the compile includes the code for this feature; and
b) the field info member pFieldlnfo->pCdbinfo points to valid memory.
The functional elements that apply when the next word prediction feature is active in
T9 are listed below:

FD100: T9 core saves in the CDB every recent word that was used. The number of
words saved depends on the size allocated by the OEM to the CDB.
FD200: T9 ambiguous and MT modes return next word predictions after a space if there
is an active word or the previous key hit is a T9 number key.
FD300: T9 ambiguous and MT modes return next word predictions after right arrow and
space if there is an active word before the right arrow is pressed.

FD301: The result of FD300 and FD200 mean:
• After cursoring off a word, and moving around the buffer, T9 does not present
a prediction after space is hit.
• "Cursoring around the buffer," means pressing either the left arrow or the right
arrow, and ending with the cursor to the right of a word. The only exception is
when the right arrow is hit to only flush (deactivate) a word.
• T9 presents a prediction if a prediction is active and the user hits space to
clear the prediction, hits clear again to clear the space, and then hits space
again.

FD400: T9 always produces context matches when starting a word if that word is
preceded by a space and another word. As an example, no prediction is delivered after
cursoring around the buffer to the right of a word and hitting key space. However, if the
user continues to type ambiguous number keys, context matches are delivered in the
selection list.
FD500: CDB predictions/completions are retrieved in order of recency.
FD600: CDB is language independent.

FD700: After pressing space, the limit of the number of CDB matches is determined by
the compile-time macro T9MAXCDBMATCHES. After the user presses number keys,
there is no limit on the number of CDB matches delivered from the CDB to the builder.
FD800: No CDB predictions/completions are delivered across sentence punctuation.
Sentence punctuation is defined as trailing punctuation on a non-emoticon. See
FD1200 for definition of emoticon.
FD900: CDB predictions/completions are removed after pressing clear with a word
active, but completions are delivered as the user begins typing again.
FD1000: There is no aging of the CDB; the least recent word is replaced by the most
recent word entered.

FD1100: Context bigrams are recorded in the CDB on pressing space if there is an
active word, or the previously hit key is a T9 number key. If the user cursors off a word,
context is broken in the CDB.
FD1200: Candidates for context predictions are subject to the following processing:
• If the word has no leading punctuation, trailing punctuation is stripped unless
this looks like an emoticon. T9 assumes a word with trailing or leading
punctuation is an emoticon if the word is more than one character and the
number of non-alpha characters (punctuation and numbers) is at least one-
half the total number of characters in the word. This is the same rule that is
used for user database (UDB) processing.
• If the word HAS leading punctuation, the word is rejected unless it appears to
be an emoticon.
FD1300: If the user has pressed a number of T9 keys, context selection list items of the
length of the key sequence are delivered at the beginning of the selection list. Context
selection list items with completions are delivered at the top of the completion portion of
the list, followed by MDB, UBD, and LDB in previously specified order.
FD1400: If caps-lock is on when space is hit, predicted words are entirely in upper
case.

FD1500: The leading word is agnostic to case, but the trailing word is case sensitive.
So if one types in "cab fee" and then turns on caps-lock and types in "CAB" and space,
the system predicts "FEE." If one types in "cab fee," then types in "CAB" using shift
rather than caps-lock, and then selects space, the system predicts "fee." Likewise, if
one types in "Cab fee" and then types in "cab" and space, the system predicts "fee."
FD1600: Switches are available to turn on/off context predictions, and to turn on/off
context completions.
Context predictions and completions in T9
Use Case:
1) User has recently entered the bigrams 'my money,' 'my time,' and 'my
marriage' in order written here.

2) User enters and accepts the word 'my.'
3) Hit space.

4) Expect selection list:
marriage
time
money
5) User enters 6 key.
6) Expect selection list:
o
m
n
6
marriage
money
7) User enters 6 key again.
8) Expect selection list:

on
no
mm
mo
oo
money
Use Case:

1) User has recently entered the bigram 'bow tie'.
2) User enters and accepts the word 'bow.'
3) Hit space.
4) Expect selection list:
tie
5) User enters 8 4 3 keys.
6) Expect selection list:
tie
the
vie
vid
tid
NOTE: Even though the word 'the' is the most common word in the English
language, in this context, 'tie' is presented first in the list. It is the most likely
candidate when preceeded by the word 'bow.'
Context predictions and completions in Multitap

Use Case:

1) User has recently entered the bigrams 'my money,' 'my time,' and 'my
marriage' in order written here.
2) User enters the word 'my.'
3) Hit space.
4) Expect selection list:
marriage
time
money
5) User enters an 'm.'
6) User presses next key.
7) Expect selection list:
m
marriage
money
8) User enters 'o.'

9) User presses next key.
10) Expect selection list:
mo
money
Context predictions and completions in T9 (flushing before space).
Use Case:
1) User and has recently entered the bigrams 'my money,' 'my time,' and 'my
marriage' in order written here.
2) User enters the word 'my.'
3) Hit right arrow.
4) Hit space.

5) Expect No context predictions.
6) User enters 6 key.
7) Expect selection list:
o
m
n
6
marriage
money
8) User enters 6.key again.
7) Expect selection list:
on
no
mm
mo
oo
money

Context predictions and completions with UDB completions in T9
CDB completions appear ahead of UDB completions.
Use Case:
1) User has recently entered the bigrams 'my money,' 'my time,' and 'my
marriage,' and the unigram 'mobetterblues' in order written here.
2) User enters and accepts the word 'my.'
3) Hit space.

4) Expect selection list:
marriage
time
money
5) User enters 6 key.
6) Expect selection list:
o
m
n
6
marriage
money
mobetterblues
7) User enters 6 key again.
8) Expect selection list:

on
no
mm
mo
oo
money
mobetterblues
Context predictions and completions in T9 (Case Sensitivity)
Leading word is agnostic to case, trailing word is case sensitive. If space is hit with
caps-lock on, the predicted word is entirely in upper case.
Use Case:
1) User has recently entered the bigrams 'my MONEY,' 'my time,' and 'MY
marriage' in order written here.
2) User enters and accepts the word 'my.'
3) Hit space.
4) Expect selection list:
marriage

time
MONEY
5) User enters clear key.
6) User enters and accepts the word 'MY' without caps-lock on.
7) Expect selection list:
marriage
time
MONEY

8) User enters clear key.
9) User enters and accepts the word 'MY' with caps-lock on.
10) Expect selection list:
MARRIAGE
TIME
MONEY
Context predictions and completions with UDB completions in Multitap
CDB completions appear ahead of UDB completions.
Use Case:
1) User and has recently entered the bigrams 'my money,' 'my time,' and 'my
marriage,', and the unigram 'mobetterblues' in order written here.
2) User enters the word 'my.'
3) Hit space.
4) Expect selection list:

marriage
time
money
5) User enters 'm.'
6) User presses next key.
7) Expect selection list:
m
marriage
money
mobetterblues
8) User enters 'o.'
9) User presses next key.
10) Expect selection list:
mo
money
mobetterblues

Context predictions and completions with UDB completions in T9 (broken
context)
CDB completions appear ahead of UDB completions.
Use Case:

1) User and has recently entered the bigrams 'my money,' 'my time,' and 'my
marriage,', and the unigram 'mobetterblues' in order written here.
2) User enters and accepts the word 'my.'
3) Hit space.
4) Hit clear.
5) Hit clear again, or any other cursoring to end up with cursor directly to the
right of "my."
6) Enter Space.
7) Expect no context predictions (functional description FD200).
8) User enters 6 key.
9) Expect selection lists with context (functional description FD400).
10) Expect selection list:

0
m
n
6
marriage
money
mobetterblues
11) User enters 6 key again.
12) Expect selection list:
on
no
mm
mo
oo_
money
mobetterblues
Context predictions and completions in T9 (Recency over frequency)
CDB completions appear ahead of UDB completions.

Use Case:
1) User and has recently entered the bigrams, 'my money,' 'my money,' 'my
marriage' in order written here.
2) User enters and accepts the word 'my.'
3) Hit space.
4) Expect selection list (more recent 'marriage' comes before more frequent
'money'):

marriage
money
5) User enters 6 key.
6) Expect selection list:
o
m
n
6
marriage
money
Languages
CDB is language independent.
Reorder of non-completed words
RUDB processes around reordering of non-completed words remain unchanged.
Clearing

Context predictions are not delivered after clearing a current word, but are delivered
as the user begins typing again.
Punctuation
No context predictions are delivered across sentence punctuation.
Aging
There is no aging of the CDB, the least recent word is replaced by the most recent
word entered.
Garbage collection
When space is needed to enter a new word into the CDB, the least recent word in
the CDB is removed to make room.
Entering words in MT
Data for CDB is collected while in MT, and context predictions/completions are
delivered in MT.
My Words

CDB processing occurs on the addition of space character, whether or not the
context word was entered in a user maintained MyWords database.
Although the invention is described herein with reference to the preferred
embodiment, one skilled in the art will readily appreciate that other applications may
be substituted for those set forth herein without departing from the spirit and scope
of the present invention. For example, user actions or inputs can effect the
automatic changing of the device's state based on context. For example, the
system might use context to change a mobile telephone from 'ring' to 'vibrate',
during the time that the calendar shows that the user is in a meeting. Another
embodiment uses location context to increase the mobile telephone volume when
the user is outside or when the telephone detects high levels of background noise.
In another embodiment, the system learns the user habits. For example, based on
the learned user action, the system is able to offer services to the user that the user
may not be aware of.
In another embodiment, word prediction is based on the previous word context
(bigram context), but might also use the previous 'n' words (trigram context, etc).
Accordingly, the invention should only be limited by the Claims included below.

We claim:
1. A method for prediction of any of user words or user actions, comprising
operations of:
responsive to a user entering an input sequence into a input device or
performing a specific action associated with said input device, predicting an entire next
word or words that said user wants to enter or an action a user wants to be taken by said
device;
said predicting operation comprising operations of:
said user entering a specific symbol or taking a specific action;
responsive thereto, providing a keyword for a menu based upon context of an
immediately preceding user input sequence or action;
depicting to said user entries previously selected from said menu which are
stored in a context database as entries preceded by said keyword; reordering said entries
in said menu;
when a menu entry is selected, automatically noting it as having been selected
with a menu tag for use as context when re-ordering in the future.
2. The method as claimed in claim 1, which involves:
responsive to said user entering a specific symbol or taking a specific action,
and based upon context of an immediately preceding user input sequence or action,
superseding normal menu format by reordering immediate options for a next
state/application a user is predicted to go to.
3. The method as claimed in claim 2, which involves the step of:
automatically performing a most likely option.
4. A computing apparatus, comprising:
a user input device;
a display;
a context database comprising a list of words entered by a user in an order of
entry;
a processor coupled to the input device and display and context database, the
processor programmed to perform operations to receive input characters and symbols
from the input device, manage output to the display, and perform actions within the
apparatus, the operations comprising:


responsive to the user completing entry of a word, searching the context
database for occurrences of the entered word and upon finding the entered word in the
context database offering to the user one or more words occurring after the entered word
in the context database as a predicted next entry;
responsive to context of the apparatus, predicting a user action other than entry
of text via the input device and automatically changing apparatus state to carry out the
predicted action;
where said context of the apparatus comrpises arrival of a start time for an
entry of an calendar application of the apparatus, and said predicted action comprises
invoking a silent mode of operation of the apparatus.
5. A computing apparatus, comprising:
a user input device;
a display; a context database comprising a list of words entered by a user in an
order of entry; a processor coupled to the input device and display and context database,
the processor programmed to perform operations to receive input characters and
symbols from the input device, manage output to the display, and perform actions
within the apparatus, the operations comprising:
responsive to the user completing entry of a word, searching the context
database for occurrences of the entered word and upon finding the entered word in the
context database offering to the user one or more words occurring after the entered word
in the context database as a predicted next entry; responsive to context of the
apparatus, predicting a user action other than entry of text via the input device and
automatically changing apparatus state to carry out the predicted action;
where said context of the apparatus comprises user selection of a prescribed
menu entry, and said predicted action includes configuring the menu to streamline an
expected user-invoked follow up action.
6. A computing apparatus, comprising:
a user input device;
a display; a context database comprising a list of words entered by a user in an
order of entry;
a processor coupled to the input device and display and context database, the
processor programmed to perform operations to receive input characters and symbols
from the input device, manage output to the display, and perform actions within the
apparatus, the operations comprising:
responsive to the user completing entry of a word, searching the context
database for occurrences of the entered word and upon finding the entered word in the


context database offering to the user one or more words occurring after the entered word
in the context database as a predicted next entry;
responsive to context of the apparatus, predicting a user action other than entry
of text via the input device and automatically changing apparatus state to carry out the
predicted action;
where said context of the apparatus comprises a change in application state of a
first application, and said predicted action comprises one of the following:
automatically opening a second application, streamlining opening of the second
application, prioritizing access to functions offered by the second application.

Documents:

00614-kolnp-2006-abstract.pdf

00614-kolnp-2006-claims.pdf

00614-kolnp-2006-description complete.pdf

00614-kolnp-2006-drawings.pdf

00614-kolnp-2006-form 1.pdf

00614-kolnp-2006-form 3.pdf

00614-kolnp-2006-form 5.pdf

00614-kolnp-2006-gpa.pdf

00614-kolnp-2006-international exm report.pdf

00614-kolnp-2006-international publication.pdf

00614-kolnp-2006-international search report.pdf

00614-kolnp-2006-pct others.pdf

00614-kolnp-2006-pct request.pdf

00614-kolnp-2006-priority document.pdf

614-KOLNP-2006-(26-08-2011)-CORRESPONDENCE.pdf

614-kolnp-2006-assignment.pdf

614-KOLNP-2006-CORRESPONDENCE-1.1.pdf

614-kolnp-2006-correspondence.pdf

614-kolnp-2006-examination report.pdf

614-kolnp-2006-form 1.pdf

614-kolnp-2006-form 18.pdf

614-kolnp-2006-form 3.pdf

614-kolnp-2006-form 5.pdf

614-kolnp-2006-gpa.pdf

614-kolnp-2006-granted-abstract.pdf

614-kolnp-2006-granted-claims.pdf

614-kolnp-2006-granted-description (complete).pdf

614-kolnp-2006-granted-drawings.pdf

614-kolnp-2006-granted-form 1.pdf

614-kolnp-2006-granted-form 2.pdf

614-kolnp-2006-granted-specification.pdf

614-kolnp-2006-petetion under rule 137.pdf

614-kolnp-2006-reply to examination report.pdf

abstract-00614-kolnp-2006.jpg


Patent Number 251047
Indian Patent Application Number 614/KOLNP/2006
PG Journal Number 08/2012
Publication Date 24-Feb-2012
Grant Date 17-Feb-2012
Date of Filing 16-Mar-2006
Name of Patentee AMERICA ONLINE, INC.
Applicant Address 22000 AOL WAY, DULLES, VA
Inventors:
# Inventor's Name Inventor's Address
1 KAY DAVID JON 3824 WOODLAWN AVENUE N, SEATTLE, WA 98103
2 VAN MEURS PIM 6212 NE 193RD STREET, KENMORE, WA 98028
3 PEDDIE, PETER C 24 ROY STREET, #421, SEATTLE, WA 98109
4 BRADFORD ETHAN R 1412 N, 35TH STREET, SEATTLE, WA 98103
PCT International Classification Number G06F 17/21
PCT International Application Number PCT/US2004/023363
PCT International Filing date 2004-07-21
PCT Conventions:
# PCT Application Number Date of Convention Priority Country
1 60/504,240 2003-09-19 U.S.A.
2 10/866,634 2004-06-10 U.S.A.