Note: Descriptions are shown in the official language in which they were submitted.
CA 02856973 2014-07-15
METHODS AND DEVICES FOR PROVIDING A TEXT PREDICTION
BY
WILLIAM ALEXANDER CHEUNG
GERALD BEARD
CONRAD DELBERT SEAMAN
ANDREW DOUGLAS BOCKING
CHRISTOPHER WORMALD
DESCRIPTION
FIELD
[0001] This application generally relates to methods and devices for providing
a
text prediction.
BACKGROUND
[0002] Advances in technology permit electronic devices, such as computers,
netbooks, cellular phones, smart phones, personal digital assistants, tablets,
etc., to
process text input increasingly quickly on increasingly smaller devices and
keyboards
and keyboard layouts. Examples include word prediction functions to suggest or
recommend during an input process and, in some instances before the process is
completed, words that users intend to input. These functions increase both
data entry
speed and accuracy. Many users now depend on these features. Nevertheless,
further
optimization of textual entry is desirable. For example, existing word
prediction
functions do not account for intent of the user when the user takes a specific
action to
enter a word.
1
CA 02856973 2014-07-15
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] Fig. 1 is a block diagram of an electronic device, according to an
example embodiment.
[0004] Fig. 2 shows a front view of a keyboard, according to an example
embodiment.
[0005] Fig. 3 is a flowchart illustrating a method for an electronic device to
provide one or more text predictions, according to an example embodiment.
[0006] Fig. 4 is a flowchart illustrating a method for an electronic device to
provide one or more text predictions, according to an example embodiment.
DETAILED DESCRIPTION
[0007] Reference will now be made in detail to the disclosed example
embodiments, which are illustrated in the accompanying drawings. Wherever
possible,
the same reference numbers will be used throughout the drawings to refer to
the same
or like parts.
[0008] The present disclosure relates to an electronic device, such as a wired
communication device, e.g., a laptop computer, or a wireless communication
device
such as a cellular phone, smartphone, wireless organizer, personal digital
assistant,
wirelessly enabled notebook computer, tablet, or a similar device. The
electronic device
may also be an electronic device without wireless communication capabilities,
such as a
handheld electronic game device, digital photograph album, digital camera, or
other
device.
[0009] Predictive text input solutions have been provided to assist with input
on
electronic devices during word entry or editing operations. These solutions
include
2
CA 02856973 2014-07-15
predicting which word a user is entering or intends to enter using a keyboard,
for
example, and offering a suggestion for completing the word based on a specific
action
that the user takes to input the word. In some embodiments, the electronic
device
provides a word prediction based on a determination that a received input
associated
with an input character reflects more than a single tap on a key of a keyboard
of the
device, such as when the user capitalizes the character or uses a secondary
keyboard
layout to input the character. In some embodiments, the electronic device
provides a
word prediction based on a detection that the user explicitly edits in the
middle of a
word. In addition, some predictive text input solutions work in coordination
with auto-
correction functions that, for example, correct misspellings or mistyped words
during the
word entry or editing operations.
[0010] Fig. 1 is a block diagram of an electronic device 100, according to an
example embodiment. Electronic device 100 includes multiple components, such
as a
processor 102 that controls the overall operation of electronic device 100.
Communication functions, including data and voice communications, are
performed
through an optional communication subsystem 104. Data received by electronic
device
100 is decompressed and decrypted by a decoder 106. Communication subsystem
104
receives messages from and sends messages to a network 150. Network 150 may be
any type of network, including, but not limited to, a wired network, a data
wireless
network, a voice wireless network, or a dual-mode wireless network that
supports both
voice and data communications. In some embodiments. electronic device 100 may
be
a battery-powered device, in which case it includes a battery interface 142
for receiving
one or more batteries 144.
3
CA 02856973 2014-07-15
[0011] Processor 102 is coupled to and can interact with additional subsystems
such as a Random Access Memory (RAM) 108; a memory 110, such as a hard drive,
CD, DVD, flash memory, or a similar storage device; one or more actuators 120;
one or
more capacitive sensors 122; an auxiliary input/output (I/O) subsystem 124; a
data port
126; a speaker 128; a microphone 130; a short-range communications subsystem
132;
other device subsystems 134; and a touchscreen 118.
[0012] Touchscreen 118 may include a display 112 with a touch-active overlay
114 (or other similar touch input interface) connected to a controller 116.
User-
interaction with a graphical user interface (GUI), such as a virtual keyboard
rendered on
display 112 as a GUI for input of characters, or a web browser, is performed
through
touch-active overlay 114. Processor 102 interacts with touch-active overlay
114 via
controller 116. Characters, such as text, symbols, images, and other items are
displayed on display 112 of touchscreen 118 via processor 102. Characters are
input
into electronic device 100 using a keyboard (not shown in Fig. 1), such as a
physical
keyboard having keys that are mechanically actuated, or a virtual keyboard
having keys
displayed on display 112.
[0013] Touchscreen 118 is connected to and controlled by processor 102.
Accordingly, detection of a touch event and determining the location of the
touch event
is performed by processor 102 of electronic device 100. A touch event
includes, for
example, a tap by a finger, a swipe by a finger, a swipe by a stylus, a long
press by
finger or stylus, or a press by a finger for a predetermined period of time,
and the like.
[0014] While specific embodiments of a touchscreen are described, any suitable
type of touchscreen for an electronic device can be used, including, but not
limited to, a
4
CA 02856973 2014-07-15
capacitive touchscreen, a resistive touchscreen, a surface acoustic wave (SAW)
touchscreen, an embedded photo cell touchscreen, an infrared (IR) touchscreen,
a
strain gauge-based touchscreen, an optical imaging touchscreen, a dispersive
signal
technology touchscreen, an acoustic pulse recognition touchscreen or a
frustrated total
internal reflection touchscreen. The type of touchscreen technology used in
any given
embodiment will depend on the electronic device and its particular application
and
demands.
[0015] Processor 102 also, in some embodiments, interacts with a positioning
system 136 for determining a location of electronic device 100. The location
may be
determined in any number of ways, such as by a computer, by a Global
Positioning
System (GPS), either included or not included in electric device 100, through
a Wi-Fi
network, or by having a location entered manually. The location may also be
determined based on calendar entries.
[0016] In some embodiments, to identify a subscriber for network access,
electronic device 100 uses a Subscriber Identity Module or a Removable User
Identity
Module (SIM/RUIM) card 138 inserted into a SIM/RUIM interface 140 for
communication
with a network, such as network 150. Alternatively, user identification
information may
be programmed into memory 110.
[0017] Electronic device 100 also includes an operating system 146 and
programs 148 that are executed by processor 102 and are typically stored in
memory
110. Additional applications may be loaded onto electronic device 100 through
network
150, auxiliary I/O subsystem 124, data port 126, short-range communications
subsystem 132, or any other suitable subsystem.
5
CA 02856973 2014-07-15
[0018] A received signal such as a text message, an e-mail message, an instant
message, or a web page download is processed by communication subsystem 104
and
this processed information is then provided to processor 102. Processor 102
processes
the received signal for output to display 112, to auxiliary I/O subsystem 124,
or a
combination of both. A user can compose data items, for example, e-mail
messages,
which may be transmitted over network 150 through communication subsystem 104.
For voice communications, the overall operation of electronic device 100 is
similar.
Speaker 128 outputs audible information converted from electrical signals, and
microphone 130 converts audible information into electrical signals for
processing.
[0019] In addition, electronic device 100 predicts received text input and
provides one or more strings of characters as text predictions, such as words
or
phrases, acronyms, names, slang, colloquialisms, abbreviations, or any
combination
thereof, based on the input.
[0020] Fig. 2 shows a front view of a keyboard 200 of electronic device 100
(Fig. 1), according to an example embodiment. Referring to Fig. 2, keyboard
200
includes a set of rows, and each row further including a plurality of keys,
each key
associated with one or more characters. The input resolution of keyboard 200
is at
least to the level of a single key; in other words, responsive to an input
received via
keyboard 200, processor 102 (Fig. 1) is capable of detecting which one of the
keys of
keyboard 200 is contacted. Further, keyboard 200 includes various keys that
may
provide different inputs, such as character keys 202 that may provide inputs
of letters,
numbers, punctuation marks, and function keys 204 that may provide inputs of
functions, e.g., a Shift key, a Return key, etc. In the illustrated
embodiment, keyboard
6
CA 02856973 2014-07-15
200 has a standard QWERTY keyboard layout. However, any keyboard layout may be
used by electronic device 100, such as AZERTY, QWERTZ, or a layout based on
the
International Telecommunication Union (ITU) standard (ITU E.161) having "ABC"
on key
2, "DEF" on key 3, and so on.
[0021] In example embodiments, keyboard 200 includes a first keyboard layout
and a second keyboard. For example, the first keyboard layout is configured to
input a
first plurality of characters, such as lower case letters, and the second
keyboard layout
is configured to input a first plurality of characters, such as capitalized
letters or
punctuation marks. As used herein, a character may be any alphanumeric
character,
such as a letter, a number, a symbol, a punctuation mark, and the like. To
input a
character from the second keyboard layout, a function key, such as a shift
key, is
generally selected at the same time as, or before, a key associated with that
character
is selected. When the function key is selected, the keyboard switches from the
first
keyboard layout to the second keyboard layout.
[0022] Keyboard 200 may be the physical keyboard of electronic device 100.
Physical keyboard 200 includes a series of key covers overlaid on top of
physical or
electronic dome switches, and also includes actuators 120 (Fig. 1) and
capacitive
sensors 122 (Fig. 1) that permit both tactile input via depression of the key
covers on
top of actuators 120 and gesture input via capacitive sensors 122. In
addition, an input
field may be displayed on display 112 that displays inputted characters.
[0023] Keyboard 200 may be the virtual keyboard of electronic device 100
rendered on touchscreen 118. A position of virtue keyboard 200 is variable
such that
virtual keyboard 200 may be placed at any location on touchscreen 118.
Touchscreen
7
CA 02856973 2014-07-15
118 may include first and second areas, the first area being virtual keyboard
200 that
receives input from a user and the second area being the input field that
displays
inputted characters.
[0024] Fig. 3 is a flowchart illustrating a method 300 for electronic device
100
(Fig. 1) to provide one or more text predictions, according to an example
embodiment.
Referring to Fig. 3, electronic device 100 receives an input of one or more
characters
through the physical or virtual keyboard, by receiving a selection of one or
more keys of
the keyboard (302). In other words, electronic device 100 receives input
reflecting
selection of one or more characters through the keyboard. The inputted
characters are
displayed in an input field on display 112. In some embodiments the input
field is
located adjacent to keyboard 302, for example, above, below or a combination
of both
above and below keyboard 302 on display 112.
[0025] Processor 102 of electronic device 100 generates one or more predicted
strings of characters, such as words or phrases, acronyms, names, slang,
colloquialisms, abbreviations, or any combination thereof, based on the
received input
(304). For example, processor 102 generates the predicted strings by executing
one or
more of programs 148 in memory 110. The predicted strings may include, for
example,
strings that are pre-stored in a dictionary in memory 110, strings that were
previously
inputted by the user, strings based on a hierarchy or tree structure, or any
strings
predicted based on a predefined arrangement.
[0026] In example embodiments, processor 102 uses contextual data for
generating the predicted strings. Contextual data may include, for example,
information
about strings previously inputted by the user, grammatical attributes of the
input (e.g.,
8
CA 02856973 2014-07-15
whether a noun or a verb is needed in a sentence), or any combination thereof.
Using
the contextual data, processor 102 may also determine whether a character in
the
received input is incorrect. For example, processor 102 may determine that an
inputted
character is supposed to be "w" instead of "a", given the proximity of these
characters
on the QWERTY keyboard.
[0027] In example embodiments, processor 102 generates a predicted string to
which the received input is a substring. For example, if the characters "pl"
are received
as the input through the keyboard, processor 102 generates a predicted string
"pl," and
may additionally generate predicted strings "please," "plot," "place," etc.
Similarly, if the
characters "child" are received as the input through the keyboard, processor
102
generates a predicted string "child," and may additionally generate predicted
strings
"children" and "childish."
[0028] In example embodiments, processor 102 generates a predicted string to
which the received input is not a substring. For example, if the characters
"id" are
received as the input through the keyboard, processor 102 generates a
predicted string
"I'd," even though "id" is not a substring of "I'd." Also for example, if the
characters "reci"
are received as the input through the keyboard, processor 102 generates a
predicted
string "receive" even though "reci" is not a substring of "receive."
[0029] In alternative embodiments, the predicted strings may be generated by
another component in electronic device 100, instead of being generated by
processor
102. In these embodiments, processor 102 may provide the inputted characters
to a
prediction processor (not shown), which generates the predicted strings based
on the
provided inputted characters and sends the predicted strings to processor 102.
The
9
CA 02856973 2014-07-15
prediction processor may be a software- or hardware-based module
communicatively
coupled to processor 102. The prediction processor may be either local or
remote to
electronic device 100, for example, located at a server in communication with
device
100.
[0030] Still Referring to Fig. 3, electronic device 100 further determines
confidence values for the predicted strings, respectively (306). A confidence
value
reflects the confidence with which the user inputs the inputted characters,
and confident
typing is generally correlated with a lower likelihood of mistake.
Accordingly, in some
embodiments, processor 102 ranks or adjusts rankings of the predicted strings
based
on their respective confidence values. For example, processor 102 may increase
the
ranking of a predicted string when the confidence value for the predicted
string is high,
and keep unchanged or lower the ranking of the predicted string when the
confidence
value for the predicted string is low. The ranking of the predicted string
reflects a
likelihood or probability that the user intends to input that predicted
string, that is, the
likelihood that the predicted string is an intended input.
[0031] In example embodiments, processor 102 determines the confidence
value for a predicted string based on one or more confidence values determined
for the
one or more inputted characters, respectively. In one example embodiment,
processor
102 determines the confidence value for the predicted string by calculating an
average
or median value of a predetermined number of recently inputted characters. In
one
example embodiment, processor 102 first determines a long-term confidence
baseline
by averaging confidence values over a large number of inputted characters, and
then
CA 02856973 2014-07-15
combines the baseline with short-term confidence information regarding
recently
inputted characters.
[0032] In some embodiments, processor 102 determines the confidence value
for an inputted character based on strength applied to the key selected to
input that
character. For example, the keyboard measures the strength applied to the
selected
key, and passes the measured strength to processor 102 for determining the
confidence
value for the inputted character.
[0033] In some embodiments, electronic device 100 receives an inputted
character from the virtue keyboard rendered on touch screen 118. Accordingly,
processor 102 determines the confidence value for the inputted character based
on an
area of a touch on touchscreen 118 for selecting the key to input the
character, since a
confident touch is generally correlated with a relatively large touch area.
The touch
area may be calculated by touchscreen 118 and passed to processor 102 as a
parameter, for example, along with the (X, Y) coordinates of a center of the
touch area.
Alternatively, touchscreen 118 passes to processor 102 parameters defining the
approximated shape (square, circle, ellipse, etc.) formed by the touch, and
processor
102 calculates a size of the shape, i.e., the touch area.
[0034] In some embodiments, touchscreen 118 passes to processor 102 raw
data corresponding to the touch, i.e., unprocessed signal data registered by
touchscreen 118 during the touch, and processor 102 processes the raw data to
calculate the touch area and determine the confidence value. In some
embodiments,
processor 102 determines how close the center of the touch is to the center of
the
selected key when determining the confidence value.
11
CA 02856973 2014-07-15
[0035] In some embodiments, processor 102 determines the confidence value
for a predicted string based on a determination whether any one of the
inputted
characters is inputted through more than a single tap on a key. In other
words,
processor 102 makes a determination whether the received input associated with
any
selected character reflects more than a single tap on a key of the keyboard.
For
example, a tap is a touch of one of the keys of the keyboard for a duration
less than or
equal to a predefined time period, such as 0.5 seconds. When the keyboard is a
physical keyboard, a tap on a key does not engage the physical or electronic
dome
switch associated with that key.
[0036] In some embodiments, processor 102 determines that a first character of
the inputted characters is inputted through more than a single tap, by
determining that
the first character is inputted through a press of a key on the keyboard
longer than a
predetermined time period, or through multiple presses of the same key. For
example,
a capital letter can be inputted though the long press of the key or multiple
presses of
the same key. In some embodiments, processor 102 determines that the first
character
is inputted through more than a single tap, by determining that a first key is
touched to
switch the keyboard from a first keyboard layout to a second keyboard layout,
and that a
second key is touched to input the first character on the second keyboard
layout. For
example, processor 102 may determine that a capitalized letter or a
punctuation mark is
inputted through more than a single tap, by determining that a function key,
e.g., a Shift
key, is selected, e.g., touched, to switch the keyboard from the first
keyboard layout to
the second keyboard layout, and that a character key is selected, e.g.,
touched, to input
the capitalized letter or the punctuation mark on the second keyboard layout.
The
12
CA 02856973 2014-07-15
function key may be selected before, or at the same time as, the character key
is
selected.
[0037] Processor 102 further determines whether the predicted string includes
the first character that is inputted through more than a single tap. If
processor 102
determines that the predicted string includes the first character, processor
102
determines a relatively high confidence value for the predicted string,
because inputting
the first character through more than a single tap increases the probability
that the first
character is an intended input character. For example, processor 102 assigns a
confidence value for the predicted string, the assigned confidence value being
higher
than a confidence value determined for any other predicted string that does
not include
the first character. If processor 102 determines that the predicted string
does not
include any character that is inputted through more than a single tap,
processor 102
determines a relatively low confidence value for the predicted string.
[0038] For example, the keyboard of electronic device 100 receives from a user
a selection of the Shift key and a selection the "R" key, sequentially or at
the same time,
and a further selection of the "0" key. Accordingly, processor 102 generates,
e.g., a
first predicted string "Rose" and a second predicted string "rose." In
addition, processor
102 determines that the predicted string "Rose" includes a capitalized letter
"R" that is
inputted through the selection of the Shift key and the selection of the "R"
key, which is
more than a single tap, processor 102 determines a relatively high confidence
value for
the predicted string "Rose." In addition, processor 102 determines that the
predicted
string "rose" does not include any character that is inputted through more
than a single
13
CA 02856973 2014-07-15
tap on a key, processor 102 determines a relatively low confidence value for
the
predicted string "rose."
[0039] In another example, processor 102 determines an initial confidence
value for the predicted string based on strength or force applied on each
selected key or
a touch area corresponding to the selected key. Processor 102 further
increases the
confidence value for the predicted string, if it is determined that the
predicted string
includes the first character inputted through more than a single tap.
Processor 102
further increases the initial confidence value for the predicted string by
multiplying the
initial confidence value by a factor greater than one. Additionally, processor
102
increases the initial confidence value for the predicted string to be higher
than the
confidence value for another predicted string that does not include the first
character.
[0040] Still referring to Fig. 3, in some embodiments, processor 102 ranks the
predicted strings based on their respective determined confidence values
(308). The
ranking of a predicted string relates to the confidence value for the
predicted string. For
example, the ranking of the predicted string increases when the confidence
value
increases, and the ranking of the predicted decreases when the confidence
value
decreases. In some embodiments, processor 102 ranks the predicted strings
based on
additional ranking factors, such as contextual data, N-gram data, geolocation
data, a
typing speed, etc. And in some embodiments, processor 102 uses a ranking
function to
combine all of the ranking factors to rank the predicted strings, where each
factor may
be differently weighed.
[0041] In the above illustrated example, "rose" may be ranked higher than
"Rose" when the ranking is based only on factors such as the contextual data,
the N-
14
CA 02856973 2014-07-15
gram data, the geolocation data, and the typing speed. However, when processor
102
determines a high confidence value for the predicted string "Rose," based on a
determination that the predicted string "Rose" includes the character "R" that
is inputted
through more than a single tap, the high confidence value may increase the
ranking of
the predicted string "Rose," and render "Rose" as the highest-ranked predicted
string.
[0042] After ranking the predicted strings, processor 102 displays one or more
of the ranked strings on display 112 (310). The displayed strings may be
displayed at
or near the input field, or at any other suitable display location. In example
embodiments, processor 102 limits a number of ranked strings to display. For
example,
processor 102 may choose to display only a predetermined number of the
highest-ranked predicted strings.
[0043] Fig. 4 is a flowchart illustrating a method 400 for electronic device
100
(Fig. 1) to provide one or more text predictions, according to an example
embodiment.
In the illustrated embodiment, it is assumed that the user has inputted a
string of
characters, which is displayed on display 112 of electronic device 100. For
example,
the user may input the string by inputting each of the characters of the
string. Also for
example, the user may input ones of the characters of the string and then
select the
string from a list of predicted strings generated by electronic device 100. It
is further
assumed that the user subsequently decides to make a correction on the
displayed
string and positions a cursor in the middle of the displayed string.
[0044] Referring to Fig. 4, processor 102 of electronic device 100 detects the
cursor being positioned in the middle of the displayed string (402). Processor
102
further receives an edit input through the keyboard (404). For example,
processor 102
CA 02856973 2014-07-15
may receive a deleting input to delete one or more characters preceding the
cursor in
the displayed string. Also for example, processor 102 may receive an inserting
input to
add one or more characters preceding the cursor. Accordingly, processor 102
generates one or more predicted strings based on the edit input (406).
[0045] For each of the predicted strings, a first number of characters in the
predicted string correspond to the characters preceding the cursor in the
displayed
string, and a second number of characters of the predicted string correspond
to the
characters following the cursor in the displayed string. In some embodiments,
the
second number of characters in a predicted string is the same as the number of
characters following the cursor in the displayed string. In some embodiments,
the
second number of characters in a predicted string differs from the number of
characters
following the cursor in the displayed string. Processor 102 may generate the
predicted
strings by executing one or more of programs 148 in memory 110, and the
predicted
strings may include strings that are pre-stored in a dictionary in memory 110,
strings
that were previously inputted by the user, strings based on a hierarchy or
tree structure,
or any strings predicted based on a predefined arrangement.
[0046] Processor 102 also determines confidence values for the predicted
strings, respectively (408). For example, for each of the predicted strings,
processor
102 determines a first confidence value for the first number of characters
that
correspond to the characters preceding the cursor in the displayed string, and
determines a second confidence value for the second number of characters that
correspond to the characters following the cursor in the displayed string.
Processor 102
16
CA 02856973 2014-07-15
further determines a confidence value for the predicted string based on the
determined
first confidence value and the determined second confidence value.
[0047] In example embodiments, processor 102 determines the first confidence
value for the first number of characters in the predicted string based on the
edit input.
In some embodiments, processor 102 determines the first confidence value based
on
strength applied to one or more keys selected to input the edit. In some
embodiments,
processor 102 determines the first confidence value based on an area of a
touch on
each key selected to input the edit. In some embodiments, processor 102
determines
the first confidence value based on a determination whether or not any edited
character
is inputted through more than a single tap on a key, as described above.
[0048] In example embodiments, processor 102 determines whether the second
number of characters in the predicted string matches the number of characters
following
the cursor in the displayed string. For example, if the second number of
characters in
the predicted string is the same as the number of characters following the
cursor in the
displayed string, processor 102 determines that they match. Accordingly,
processor
102 determines the second confidence value for the second number of characters
in the
predicted string by determining a probability that the characters following
the cursor in
the displayed string form a word. For example, if the characters following the
cursor in
the displayed string are a word, processor 102 determines that the probability
is 100%.
Also for example, if the characters following the cursor are a suffix that is
used in a
relatively small number of words, processor 102 determines that the
probability is a
relatively small value, such as 50%.
17
CA 02856973 2014-07-15
[0049] If processor 102 determines that the probability that the characters
following the cursor in the displayed string form a word is relatively high,
processor 102
determines a relatively high confidence value as the second confidence value
for the
second number of characters in the predicted string, because a relatively
probability that
the characters following the cursor form a word increases the probability that
those
characters are intended input characters. For example, the characters
following the
cursor may be a second word in a compound word which the user intends to
enter.
Accordingly, processor 102 determines a relatively high confidence value for
the
characters following the cursor. In some embodiments, processor 102 may assign
an
initial confidence value for the second number of characters in the predicted
string, and
then multiply the initial confidence value by a predetermined factor greater
than one. In
some embodiments, processor 102 may increase the initial confidence value for
the
second number of characters in the predicted string, and decrease the
confidence value
for the first number of characters in the predicted string.
[0050] Still referring to Fig. 4, processor 102 ranks the predicted strings
(410),
and displays one or more of the ranked strings on display 112 of electronic
device 100
(412). The ranked strings may be displayed at or near the input field, or at
any other
suitable display location. In some embodiments, processor 102 also limits a
number of
ranked strings to display. For example, processor 102 may choose to display
only a
predetermined number of the highest-ranked strings. In addition, processor 102
may
perform an automatic correction by replacing the inputted string with the
highest-ranked
predicted string.
18
CA 02856973 2014-07-15
[0051] Embodiments and all of the functional operations described in this
specification may be implemented in digital electronic circuitry, or in
computer software,
firmware, or hardware, including the structures disclosed in this
specification and their
structural equivalents, or in combinations of them. Embodiments may be
implemented
as one or more computer program products, i.e., one or more modules of
computer
program instructions encoded on a computer readable medium, e.g., a machine
readable storage device, a machine readable storage medium, a memory device,
or a
machine readable propagated signal, for execution by, or to control the
operation of,
data processing apparatus.
[0052] The processes and logic flows described in this specification (e.g.,
Figs. 3 and 4) may be performed by one or more programmable processors
executing
one or more computer programs to perform functions by operating on input data
and
generating output. The processes and logic flows may also be performed by, and
apparatuses can also be implemented as, special purpose logic circuitry, e.g.,
a field
programmable gate array (FPGA) or an application specific integrated circuit
(ASIC).
[0053] Certain features which, for clarity, are described in this
specification in
the context of separate embodiments, may also be provided in combination in a
single
embodiment. Conversely, various features which, for brevity, are described in
the
context of a single embodiment may also be provided in multiple embodiments
separately or in any suitable sub-combination. Moreover, although features may
be
described above as acting in certain combinations and even initially claimed
as such,
one or more features from a claimed combination can in some cases be excised
from
the combination, and the claimed combination may be directed to a
subcombination or
19
CA 02856973 2014-07-15
variation of a subcombination. Additionally, particular embodiments have been
described. Other embodiments are within the scope of the following claims.
