US20070094024A1 - System and method for improving text input in a shorthand-on-keyboard interface - Google Patents
System and method for improving text input in a shorthand-on-keyboard interface Download PDFInfo
- Publication number
- US20070094024A1 US20070094024A1 US11/256,713 US25671305A US2007094024A1 US 20070094024 A1 US20070094024 A1 US 20070094024A1 US 25671305 A US25671305 A US 25671305A US 2007094024 A1 US2007094024 A1 US 2007094024A1
- Authority
- US
- United States
- Prior art keywords
- word
- lexicon
- words
- input
- candidate
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/237—Lexical tools
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
- G06F3/023—Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
- G06F3/0233—Character input methods
- G06F3/0237—Character input methods using prediction or retrieval techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0487—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
- G06F3/0488—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
- G06F3/04883—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures for inputting data by handwriting, e.g. gesture or text
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
Definitions
- the present invention generally relates to lexicon-based text entry and text prediction systems. More specifically, the present invention relates to text entry using shorthand-on-keyboard, an efficient method of entering words by drawing geometric patterns on a graphical on-screen keyboard.
- Shorthand on graphical keyboards (hereafter “shorthand-on-keyboard”) or Shorthand on a Keyboard as Graph (sokgraph), represent an input method and system for efficiently entering text without a physical keyboard, typically using a stylus.
- Shorthand-on-keyboard enables the user to trace letter or functional keys on the graphical keyboard to enter words and commands into a computer.
- Experienced users partly or completely memorize the geometric patterns of frequently used words and commands on the keyboard layout and may draw these patterns based on memory recall using, for example, a digital pen.
- Word-level recognition-based text entry systems such as shorthand-on-keyboard and handwriting/speech recognition as well as text prediction systems all rely on some form of lexicon that defines the set of words that these systems recognize.
- the input of the user is matched against choices in the lexicon. Words not included in the lexicon are usually not automatically recognized. In such a case, a special mode has to be provided.
- the user may initially check a candidate list (N-best list). If no choice on the candidate list is the intended word, the user decides if the patterns drawn were incorrect. If the patterns drawn were correct, the user realizes the word intended is not in the lexicon. The user then enters the new word in the lexicon by tapping the individual letters.
- the lexicon comprises all words a particular user needs to write, no more no less. A lexicon that is either too large or too small can introduce problems to the user.
- a larger lexicon could present certain challenges, since it tends to reduce the recognition accuracy due to the likelihood of a greater number of distracters for each user input.
- vocabulary tends to be specialized for a particular individual. For instance, an engineer may compose emails comprising highly technical terms and abbreviations for a particular field or business area. For other users, these specialized terms can be irrelevant and can introduce noise in the recognition process, making the recognition process less robust.
- a smaller lexicon is typically a more robust lexicon in that user input is more likely to be correctly recognized, provided the intended word is in the lexicon.
- a smaller lexicon provides more flexibility and tolerance for the input of the user, allowing the input to be imprecise and inaccurate compared to the ideal form of the intended input choice.
- a further advantage of a small lexicon is that the search space is smaller. Consequently, a small lexicon allows reduction in the latency of a search. This is especially important in mobile devices where processing power is severely limited.
- a commonly used method is to use a large lexicon and then take advantage of higher order language regularities such as a word-level trigram-model to filter out highly unlikely candidates.
- the downside of a language model approach is generally the overhead of creating and making efficient use of a large language model.
- a language model can introduce errors and mistakenly filter out the intended words. This is especially true if the language model is generic rather than customized to a particular user. In practice, efficient customization of a language model is difficult.
- a language model is difficult to integrate with a recognition technique that already has a high precision, such as shorthand-on-keyboard.
- An alternative conventional approach creates a customized lexicon for a user by mining the written text generated by the user, for example, written emails and other documents.
- this approach does result in a lexicon more closely tailored to a specific user, a previously written corpus generated by a user may be to be too small to cover all of the desired words.
- it is difficult to write a computer program code that can open and read all and various email and document formats that the user may be using. This approach often requires the user to locate and select the previous written documents, which is inconvenient for the user.
- a customized lexicon may also be difficult to carry over across different devices.
- the present invention satisfies this need, and presents a system, a computer program product, and an associated method (collectively referred to herein as “the system” or “the present system”) for improving text input in a shorthand-on-keyboard interface.
- the present system comprises a core lexicon and an extended lexicon.
- the core lexicon comprises commonly used words in a language.
- the core lexicon typically comprises approximately 5,000 to 15,000 words, depending on an application of the present system.
- the extended lexicon comprises words not included in the core lexicon.
- the extended lexicon comprises approximately 30,000 to 100,000 words.
- the core lexicon allows the present system to target commonly used words in identifying a gesture as a highest-ranked candidate word, providing more robust recognition performance associated with a smaller lexicon. Only words from the core lexicon can be directly outputted in the present system. Additional candidate words are available from the extended lexicon, allowing a user to find lesser-known words on the candidate list, but only through menu selection. The present system enhances word recognition accuracy without sacrificing selection of words from a large lexicon.
- the core lexicon provides more flexibility and tolerance for the input of the user to be imprecise and inaccurate from the ideal form of the intended input choice.
- the present system further comprises a recognition module, a pre-ranking module, and a ranking module.
- the recognition module generates an N-best list of candidate words corresponding to an input pattern.
- the pre-ranking module ranks the N-best candidate words according to predetermined criteria.
- the ranking module adjusts ranking of the N-best list of candidate words to place words drawn from the core lexicon higher than words drawn from the extended lexicon, generating a ranked list of word candidates. Only words in the core lexicon are presented as output by the present system.
- the present system lists candidate words found in the extended lexicon only in the N-best list; these words require user selection to become output. Once selected by a user from the N-best list, a word from the extended lexicon is admitted to the core lexicon.
- Words in the core lexicon are outputted by the recognition system. Words in the extended lexicon can only be listed in the N-best list and need explicit user selection to be outputted. Once selected, the words in the extended lexicon also gets admitted to the core lexicon.
- the present system reduces the overhead inflicted upon the user in the case the word gestured by the user is not in the vocabulary of the core lexicon. Instead of being unsure whether the word is included in the lexicon or if the system misrecognized the input, the user can scan the N-best list and select the desired candidate word.
- the present system further comprises a concatenation module and a compound word module.
- the concatenation module enables a user to input parts of a long word separately; the present system automatically combines words and part-of-words that are partial “sokgraphs” into one word that is intended by the user. Word parts can be stems, such as “work” and affixes, such as “ing” or “pre”.
- the compound word module combines two or more common shorter words whose concatenation forms a long word, such as short+hand in English. The concatenation of several short words into one compound word is more common in some European languages such as Swedish or German.
- the present system allows user interaction to adjust concatenation of a word 1 and a word 2 and decoupling of a combined word.
- a concatenated word for example “smokefree”
- a menu option “split to “smoke free”” or an equivalent option is given to the user.
- a pen trace motion such as a downward motion crossing the word smokefree, can be defined as a split command.
- a menu option is embedded in word 1 and word 2 .
- the option “snap to right” or an equivalent option is selectable.
- a pen gesture such as a circle crossing both word 1 and word 2 , is defined as the command to join the two words as one concatenated long word.
- FIG. 1 is a schematic illustration of an exemplary operating environment in which a word pattern recognition system of the present invention can be used;
- FIG. 2 is a block diagram of a high-level architecture of the word pattern recognition system of FIG. 1 ;
- FIG. 3 is a process flow chart illustrating a method of operation of the word pattern recognition system of FIGS. 1 and 2 in ranking candidate words according to location in a core lexicon or an extended lexicon;
- FIG. 4 is a diagram illustrating an N-best list generated by the word pattern recognition system of FIGS. 1 and 2 in which words from the core lexicon and words from the extended lexicon are displayed differently;
- FIG. 5 is a diagram illustrating an N-best list generated by the word pattern recognition system of FIGS. 1 and 2 in which words from the core lexicon are grouped and ranked higher than words from the extended lexicon;
- FIG. 6 is a process flow chart illustrating a method of operation of the word pattern recognition system of FIGS. 1 and 2 in recognizing a word candidate as a suffix or a prefix and concatenating the recognized prefix or suffix to a recognized word in a language appropriate manner;
- FIG. 7 is a process flow chart illustrating a method of operation of the word pattern recognition system of FIGS. 1 and 2 in combining words into a compound word;
- FIG. 8 is comprised of FIGS. 8A, 8B , and 8 C and represents a diagram illustrating a menu of the word pattern recognition system of FIGS. 1 and 2 in which the menu enables a user to split a compound word into a stem and a suffix;
- FIG. 9 is a diagram illustrating a pen gesture formed by a user on a compound word presented by the word pattern recognition system of FIGS. 1 and 2 in which the pen gesture splits a compound word into a stem and a suffix;
- FIG. 10 is comprised of FIGS. 10A, 10B , and 10 C and represents a diagram illustrating a menu of the word pattern recognition system of FIGS. 1 and 2 in which the menu is applied to a stem to enable a user to combine a stem and a suffix into a compound word;
- FIG. 11 is a diagram illustrating a menu of the word pattern recognition system of FIGS. 1 and 2 in which the menu is applied to a suffix enabling a user to combine a stem and a suffix into a compound word;
- Lexicon a collection of elements defining the recognizable elements that can be matched against a user's input in a recognition system.
- PDA Personal Digital Assistant. A pocket-sized personal computer. PDAs typically store phone numbers, appointments, and to-do lists. Some PDAs have a small keyboard; others have only a special pen that is used for input and output on a virtual keyboard.
- Sokgrah Shorthand on a Keyboard as a Graph. A pattern representation of words on a virtual keyboard.
- Virtual Keyboard A computer simulated keyboard with touch-screen interactive capability that can be used to replace or supplement a keyboard using keyed entry.
- the virtual keys are typically tapped serially with a stylus. It is also called graphical keyboard, on-screen keyboard, or stylus keyboard.
- FIG. 1 portrays an exemplary overall environment in which a system, a computer program product, and an associated method for improving text input in a shorthand-on-keyboard interface (the word pattern recognition system 10 or the “system 10 ”) according to the present invention may be used.
- System 10 includes a software program code or a computer program product that is typically embedded within, or installed on a computer.
- the computer in which system 10 is installed can be a mobile device such as a PDA 15 or a cellular phone 20 .
- System 10 can also be installed in devices such as tablet computer 25 , touch screen monitor 30 , electronic white board 35 , and digital pen 40 .
- System 10 can be installed in any device using a virtual keyboard or similar interface for entry, represented by auxiliary device 45 .
- System 10 can be saved on a suitable storage medium such as a diskette, a CD, a hard drive, or like devices.
- System 10 determines a word from the shape and location of a pen stroke formed by a user on a graphical keyboard.
- System 10 sends the determined words to a software recipient such as, for example, an application, an operating system, etc.
- FIG. 2 illustrates a high-level hierarchy of system 10 .
- System 10 comprises a lexicon 205 .
- the lexicon 205 comprises a core lexicon 210 and an extended lexicon 215 .
- the core lexicon 210 comprises commonly used words in a language.
- the core lexicon 210 typically comprises approximately 5,000 to 15,000 words, depending on an application of system 10 .
- the extended lexicon 215 comprises words not included in the core lexicon 215 .
- the extended lexicon 215 comprises approximately 30,000 to 100,000 words.
- System 10 further comprises a recognition module 220 , a pre-ranking module 225 , and a selector/ranking module 230 .
- the recognition module 220 generates an N-best list of candidate words corresponding to an input pattern 235 .
- the pre-ranking module 225 ranks the N-best candidate words according to predetermined criteria.
- the ranking module 230 adjusts ranking of the N-best list of candidate words to place words drawn from the core lexicon 210 higher than words drawn from the extended lexicon 215 , generating a ranked list of word candidates 240 .
- words drawn from the extended lexicon are not outputted; only words from the core lexicon are outputted.
- System 10 further comprises a concatenation module 245 and a compound word module 250 .
- the concatenation module 245 concatenates words selected from the ranked list of word candidates 240 ; e.g., concatenating “ing” with “code” to form “coding”.
- the compound word module 250 combines words selected from the ranked list of word candidates 240 into larger words.
- An output word 255 is a word selected from the ranked list of word candidates 240 and processed by the concatenation module 245 and the compound word module 250 , as necessary. Only words in the core lexicon 210 are presented as the output word 255 by system 10 .
- System 10 lists candidate words found in the extended lexicon 215 only in the N-best list; these words require user selection to become the output word 255 . Once selected by a user, system 10 admits a word from the extended lexicon 215 to the core lexicon 210 .
- System 10 adapts recognition of the input pattern 235 by the recognition module 220 to the vocabulary of the user while maintaining maximum signal to noise ratio in the recognition system.
- System 10 allows the core lexicon 210 and the extended lexicon 215 to participate in the recognition process of the recognition module 220 . However, only words in the core lexicon 210 directly enter output of the recognition module 220 ; these words are a default output. Words in the extended lexicon 215 that match the input pattern 235 are only listed in an “N-best” list for selection by the user. When a user selects one of these candidate words from the N-best list to replace the default output, the selected word is admitted to core lexicon 210 . After a word is admitted to the core lexicon 210 , the admitted word can directly enter the output of the recognition module when the word matches the input pattern 235 .
- FIG. 3 illustrates a method 300 of system 10 in generating an N-best list of candidates that match the input pattern 235 .
- the user gestures a word on a shorthand-on-keyboard interface (step 305 ).
- the recognition module 220 generates an N-best list of word candidates (step 310 ); the pre-ranking module 225 ranks the N-best list of word candidates from the core lexicon 210 and the extended lexicon 215 according to a criterion such as a confidence value or a similarity measure (step 315 ).
- the ranking module 230 determines whether the highest ranked word in the N-best list of candidate words is drawn from the core lexicon 210 (decision step 320 ). If yes, the ranking module 230 outputs the ranked N-best list of word candidates as the ranked list of word candidates 240 (step 325 ). If the highest ranked candidate in the N-best list of candidate words is not present in the core lexicon 210 , the ranking module 230 searches the N-best list of candidate words to locate the highest ranking word candidate drawn from the core lexicon 210 (step 330 ).
- the ranking module 230 If a word candidate drawn from the core lexicon 210 is not found in the N-best list of candidate words (decision step 335 ), the ranking module 230 outputs the ranked N-best list of word candidates as the ranked list of word candidates 240 . Otherwise, the ranking module 230 moves the found word candidate to the highest-ranking position in the N-best list of word candidates (step 335 ). The ranking module outputs the ranked N-best list of word candidates as the ranked list of word candidates 240 (step 340 ).
- a user interface component displays the next best candidate list (N-best list) from which a user can see alternative candidate words that closely match the input pattern 235 .
- the position of a candidate word on the list is determined by a rank associated with the candidate word independent of whether the candidate word is found in the core lexicon 210 or the extended lexicon 215 , with the exception of the highest ranked word must always be found in the core lexicon with the exception when no word in the core lexicon matches the user's input.
- candidate words are grouped by lexicon origin; i.e., candidate words from the core lexicon 210 are grouped together and candidate words from the extended lexicon 215 are grouped together.
- the origin of the candidate words can optionally be indicated by emphasizing different perceptual features that are associated with the candidate words, to facilitate the recognition of the source of the candidate words, e.g., from the core or extended lexicon.
- Exemplary perceptual features include, for example: color, background shading, bold fond, italicized font, etc.
- Words drawn from the extended lexicon 215 are accessed from the N-best list of candidate words. Consequently, error tolerance of system 10 in generating the highest-ranked candidate is greatly enhanced, since the highest-ranked candidate that is displayed by the system is drawn from the smaller core lexicon 210 . In rare situations in which the desired word is not found in the core lexicon 210 , the user activates the N-best list and selects the desired candidate.
- FIG. 4 illustrates an exemplary N-best list of candidate words 400 generated by the ranking module 230 .
- Candidate words from the core lexicon 210 comprise candidate word 1 , 405 , candidate word 2 , 410 , and candidate word 3 , 415 , collectively referenced as core candidate words 420 .
- Candidate words from the extended lexicon 215 comprise candidate word 4 , 425 , candidate 5 , 430 , candidate word 6 , 435 , candidate word 7 , 440 , and candidate word 8 , 445 , collectively referenced as extended candidate words 450 .
- Core candidate words 420 and extended candidate words 450 are displayed with different emphasis.
- core candidate words 420 are shown in bold text and extended candidate words 450 are shown in italicized text. Any form of emphasis may be used to differentiate the core candidate words 420 and the extended candidate words 450 such as, for example, text color, color background, shading, etc.
- the candidate words in the exemplary N-best list of candidate words 400 are positioned according to rank given by the recognition module 220 , with the exception of the top word candidate position 455 that is reserved for a word drawn from the core lexicon 210 unless no word from the core lexicon matches the user's input, in which case top word candidate position 455 may be taken by a word from the extended lexicon.
- FIG. 5 illustrates one embodiment in which an exemplary N-best list 500 comprises candidate words ranked according to source and according to ranking criteria provided by the recognition module 220 .
- core candidate words 420 and extended candidate words 450 are displayed with different emphasis.
- core candidate words 420 are shown in bold text and extended candidate words 450 are shown in italicized text.
- System 10 greatly reduces the overhead inflicted upon the user in the case the word gestured by the user is not in the vocabulary of the core lexicon 210 . Instead of being unsure whether the word is included in the core lexicon 210 or if the system misrecognized the input, the user can scan the N-best list and select the desired candidate word.
- the lexicon 205 can be conceptualized as layers, a core lexicon layer and an extended lexicon layer, ranked by frequency or priori probability.
- the frequency or priori probability of the selected word is adjusted to a threshold or other criterion having the effect that the selected word is adjusted to belong to the core layer.
- System 10 further enables a user to input parts of a long word separately; system 10 automatically combines partial “sokgraphs” into one that is intended by the user.
- Word parts can be stems, such as “work” and affixes, such as “ming”, or two or more common shorter words whose concatenation forms a long word, such as short+hand in English.
- the concatenation of several short words into one compound word is more common in some European languages such as Swedish or German.
- Concatenations are based on individually recognizing parts involved in the concatenated word.
- stem+suffix the user initially gestures an input pattern 235 for a word that represents the stem, then gestures an input pattern 235 of the suffix.
- the recognition module 220 finds the optimum matches and outputs these matches to an N-best list with strings S (i) ,i ⁇ [1,N], where a rank i of a string signifies the confidence of the recognition module 220 in the selected string matching input pattern 235 .
- the recognition module 220 stores the last N-best list in a temporary buffer.
- the buffered N-best list for a regular word (stem) is denoted as S 0 .
- suffixes are stored in a list called concatenable suffixes whose sokgraphs, the geometric pattern on a graphical keyboard, are represented in the same way as a common word sokgraph.
- sokgraph is a continuous trace starting from the i key to the n key ending on the g key.
- the system recognizes an input pattern 235 for sokgraph “ing” in the same way as any other sokgraph, except the suffix “ing” is stored in the list of concatenable suffixes.
- both suffixes and regular words can be stored in the same lexicon, but with an identifier differentiating the suffix from the regular word.
- concatenable suffixes are stored in a lookup table in which each suffix entry, such as “ing”, is associated with a series of pointers that point to the entries in a lexicon that ends with that suffix
- FIG. 6 illustrates a method 600 of system 10 in combining concatenable suffixes with a stem word.
- a user gestures a word on a shorthand-on-keyboard interface (step 605 ).
- the concatenation module 245 obtains a highest ranked word for an output N-best list of word candidates 240 (step 610 ).
- the concatenation module 245 determines whether the obtained word is a concatenable suffix by, for example, comparing the obtained word with a list of concatenable suffixes (decision step 615 ). If the obtained word is not a concatenable suffix, the concatenation module 245 takes no action (step 620 ).
- the concatenation module 245 finds concatenation candidates that end with the determined concatenable suffix (step 625 ).
- the concatenation module 245 strips the concatenable suffix from each concatenation candidate (step 630 ).
- Words ending with a current suffix are denoted as S 1(i) (e.g. coding or working) and their remainders stripped of the suffix are denoted S 2(i) (e.g. “cod” or “work”).
- the concatenation module 245 sorts the concatenation candidates by the associated edit distance (step 640 ).
- the concatenation module 245 returns the concatenation candidate with the smallest edit distance (step 645 ).
- word frequencies or prior probabilities, or higher-order language regularities are used to rank concatenation candidates that share the same edit distance.
- a threshold can be set as the lowest acceptable edit-distance mismatch.
- suffixes are not linked to all words that end with the suffix. Instead, when a suffix is recognized, the system 10 scans the lexicon 205 , finds words that end with the recognized suffix, strips the ending from the found words, matches the stripped remainders with the preceding word, and selects the closest match for concatenation as previously described.
- the difference between these two embodiments lies in computational time and memory space tradeoff. Scanning the lexicon implies that a separate list of pointers is not needed, hence reducing the storage requirement of the lexicon in the medium the software code is accessing. On the other hand, scanning the lexicon requires more time than to locate a word than a system comprising a lexicon that is indexed with a separate list of pointers.
- the concatenation module 245 initially recognizes a prefix-based word from the output of the ranked list of word candidates 240 from either a separate list of prefixes or a common lexicon with a prefix identifier. The concatenation module 245 then recognizes the word that follows the prefix. The concatenation module 245 matches all words containing the prefix, strips the matched word of the prefix, and returns the closest match for concatenation.
- the concatenation of two shorter words into a long one is not deterministic. For example, in Swedish both “smoke free” and “smokefree” are permitted, but their meanings are opposite (smoking allowed as opposed to smoking not allowed).
- the compound word module 250 uses a statistical and interactive method to handle the concatenation of two words. To support this method, system 10 stores in the lexicon 205 the statistical information including the frequencies of all words (based on the total number of occurrence of each word in a corpus of text) and frequencies of all bigrams (based on the total number of occurrence of two ordered words).
- FIG. 7 illustrates a method 700 of system 10 in combining words into compound words.
- Method 700 examines pairs of consecutive words (word 1 , word 2 ) (step 705 ).
- the compound word module replaces word 1 and word 2 with word 3 (step 730 ). Otherwise, no action is taken (step 715 ).
- the comparison of the frequency of word 3 and the frequency of the bigram (word 1 , word 2 ) is a weighted comparison.
- FIG. 8 ( FIGS. 8A, 8B , 8 C) illustrates decoupling of a combined word into two individual words or parts of words.
- An exemplary screen 805 displays to a user an exemplary concatenated word “coding” 810 .
- the user selects the displayed concatenated word “coding” by, for example, clicking on the word “coding” 810 ( FIG. 8A ).
- Selecting the word “coding” 810 displays a menu option 815 comprising, for example, selectable instruction “Split to “Code” and “ing”” or an equivalent option ( FIG. 8B ). If the user selects the instruction shown in menu option 815 , system 10 splits the displayed concatenated word “coding” 810 into stem “code” 820 and suffix “ing” 825 ( FIG. 8C ).
- FIG. 9 illustrates an exemplary alternative pen trace motion 905 used to split the concatenated word “coding” 810 .
- the screen 805 displays to the user a concatenated word “coding” 810 .
- the user forms the pen trace motion 905 over the concatenated word “coding” 810 .
- System 10 splits the displayed concatenated word “coding” 810 into stem “code” 820 and suffix “ming” 825 as illustrated in FIG. 8C .
- a menu option is embedded in word 1 and word 2 as illustrated in FIG. 10 .
- the screen 805 displays to the user a word 1 “code” 1005 and a word 2 “ing” 1010 as shown in FIG. 10A .
- Selecting the word 1 “code” 1005 displays an option menu 1015 comprising a selectable instruction “snap to right” or an equivalent option ( FIG. 10B ). If the user selects the instruction “snap to right” shown in the option menu 1015 , system 10 concatenates the word 1 “code” 1005 and the word 2 “ming” 1010 , forming the concatenated word “coding” 1020 ( FIG. 10C ).
- FIG. 11 illustrates an exemplary option menu 1105 displayed when the user selects the word 2 “ming” 1010 . If the user selects the instruction “snap to left” shown in the option menu 1105 , system 10 concatenates the word 1 “code” 1005 and the word 2 “ming” 1010 , forming the concatenated word “coding” 1020 as shown in FIG. 10C .
- FIG. 12 illustrates an exemplary alternative pen trace motion 1205 used to concatenate the word 1 “code” 1005 and the word 2 “ing” 1010 .
- the pen trace motion 1205 comprises, for example, a circle crossing the word 1 “code” 1005 and the word 2 “ming” 1010 .
- System 10 recognizes the command represented by the pen trace motion 1205 and concatenates the word 1 “code” 1005 and the word 2 “ing” 1010 , forming the concatenated word “coding” 1020 as shown in FIG. 10C .
Abstract
Description
- This application relates to the following co-pending U.S. patent application Ser. No. 10/325,197, titled “System and Method for Recognizing Word Patterns Based on a Virtual Keyboard Layout,” Ser. No. 10/788,639, titled “System and Method for Recognizing Word Patterns in a Very Large Vocabulary Based on a Virtual Keyboard Layout,” and Ser. No. 11/121,637, titled “System and Method for Issuing Commands Based on Pen Motion on a Graphical Keyboard,” all of which are assigned to the same assignee as the present application, and are incorporated herein by reference.
- The present invention generally relates to lexicon-based text entry and text prediction systems. More specifically, the present invention relates to text entry using shorthand-on-keyboard, an efficient method of entering words by drawing geometric patterns on a graphical on-screen keyboard.
- Shorthand on graphical keyboards (hereafter “shorthand-on-keyboard”) or Shorthand on a Keyboard as Graph (sokgraph), represent an input method and system for efficiently entering text without a physical keyboard, typically using a stylus. Shorthand-on-keyboard enables the user to trace letter or functional keys on the graphical keyboard to enter words and commands into a computer. Experienced users partly or completely memorize the geometric patterns of frequently used words and commands on the keyboard layout and may draw these patterns based on memory recall using, for example, a digital pen.
- Word-level recognition-based text entry systems such as shorthand-on-keyboard and handwriting/speech recognition as well as text prediction systems all rely on some form of lexicon that defines the set of words that these systems recognize. The input of the user is matched against choices in the lexicon. Words not included in the lexicon are usually not automatically recognized. In such a case, a special mode has to be provided. For example, in shorthand-on-keyboard the user may initially check a candidate list (N-best list). If no choice on the candidate list is the intended word, the user decides if the patterns drawn were incorrect. If the patterns drawn were correct, the user realizes the word intended is not in the lexicon. The user then enters the new word in the lexicon by tapping the individual letters. Ideally, the lexicon comprises all words a particular user needs to write, no more no less. A lexicon that is either too large or too small can introduce problems to the user.
- A larger lexicon could present certain challenges, since it tends to reduce the recognition accuracy due to the likelihood of a greater number of distracters for each user input. In any language, there tends to be a core set of vocabulary that is common to all individuals. Beyond this core set, vocabulary tends to be specialized for a particular individual. For instance, an engineer may compose emails comprising highly technical terms and abbreviations for a particular field or business area. For other users, these specialized terms can be irrelevant and can introduce noise in the recognition process, making the recognition process less robust.
- A smaller lexicon is typically a more robust lexicon in that user input is more likely to be correctly recognized, provided the intended word is in the lexicon. A smaller lexicon provides more flexibility and tolerance for the input of the user, allowing the input to be imprecise and inaccurate compared to the ideal form of the intended input choice. A further advantage of a small lexicon is that the search space is smaller. Consequently, a small lexicon allows reduction in the latency of a search. This is especially important in mobile devices where processing power is severely limited.
- However, when a small lexicon does not contain the word the user needs, the user experience can be frustrating. A user does not know, prior to entry, whether a word is in the lexicon, causing uncertainty for the user. The lack of recognition of a word by a conventional system can occur either when the word is input incorrectly or when the word is not in the lexicon. Consequently, it can be difficult for the user to determine why a word is not recognized. In general, the user cannot know whether a word is in the lexicon except by repeatedly trying the word. When the user is certain that the word is not in the lexicon, the user adds that word to the lexicon via an interface provided by the recognition system by tapping as described earlier. A smaller lexicon requires a user to add words to the lexicon more often.
- There are several conventional solutions to the lexicon size issue. A commonly used method is to use a large lexicon and then take advantage of higher order language regularities such as a word-level trigram-model to filter out highly unlikely candidates. The downside of a language model approach is generally the overhead of creating and making efficient use of a large language model. Moreover, a language model can introduce errors and mistakenly filter out the intended words. This is especially true if the language model is generic rather than customized to a particular user. In practice, efficient customization of a language model is difficult. Furthermore, a language model is difficult to integrate with a recognition technique that already has a high precision, such as shorthand-on-keyboard.
- An alternative conventional approach creates a customized lexicon for a user by mining the written text generated by the user, for example, written emails and other documents. Although this approach does result in a lexicon more closely tailored to a specific user, a previously written corpus generated by a user may be to be too small to cover all of the desired words. Furthermore, in practice, it is difficult to write a computer program code that can open and read all and various email and document formats that the user may be using. This approach often requires the user to locate and select the previous written documents, which is inconvenient for the user. A customized lexicon may also be difficult to carry over across different devices.
- Although these conventional solutions are adequate for their intended purpose, it is desirable to find a solution that enables a lexicon to have a relatively small number of irrelevant distracters to the user's desired input and yet allows easy access to almost all words the user may need, including more specialized words that are infrequently used by most users. Overall, there is a desire to include all words possibly needed by the user in a very large lexicon. However a very large lexicon implies that more words match the pattern drawn on the keyboard given the same matching threshold, reducing the signal-to-noise ratio in the input system. Consequently, a larger lexicon corresponds to less flexibility and robustness to the user. Thus, there is a need for a lexicon configuration for a shorthand-on-keyboard system that balances ease of use with flexibility and robustness.
- Another challenge to a conventional shorthand-on-keyboard input method is a requirement of entering text exactly at the word level, one word at a time. Some words are long. For relatively new users, it can be cognitively difficult to draw a long word with shorthand-on-keyboard in one stroke. This difficulty is particularly acute in some European languages in which compound long words are more common than in English. Furthermore, a user can find entry more convenient if common affixes can be drawn as a separate stroke from the stem of the word. For example, to write the word “working” with shorthand-on-keyboard, the user may wish to draw the pattern of w-o-r-k on a graphical keyboard, then draw i-n-g and combine the two into one word. Thus, there is a need for an effective system and method to automatically combine partial words on the keyboard (“sokgraphs”) into one word as intended by the user.
- What is therefore needed is a system, a computer program product, and an associated method for a system and method for improving text input in a shorthand-on-keyboard interface. The need for such a solution has heretofore remained unsatisfied.
- The present invention satisfies this need, and presents a system, a computer program product, and an associated method (collectively referred to herein as “the system” or “the present system”) for improving text input in a shorthand-on-keyboard interface. The present system comprises a core lexicon and an extended lexicon. The core lexicon comprises commonly used words in a language. The core lexicon typically comprises approximately 5,000 to 15,000 words, depending on an application of the present system. The extended lexicon comprises words not included in the core lexicon. The extended lexicon comprises approximately 30,000 to 100,000 words.
- The core lexicon allows the present system to target commonly used words in identifying a gesture as a highest-ranked candidate word, providing more robust recognition performance associated with a smaller lexicon. Only words from the core lexicon can be directly outputted in the present system. Additional candidate words are available from the extended lexicon, allowing a user to find lesser-known words on the candidate list, but only through menu selection. The present system enhances word recognition accuracy without sacrificing selection of words from a large lexicon. The core lexicon provides more flexibility and tolerance for the input of the user to be imprecise and inaccurate from the ideal form of the intended input choice.
- The present system further comprises a recognition module, a pre-ranking module, and a ranking module. The recognition module generates an N-best list of candidate words corresponding to an input pattern. The pre-ranking module ranks the N-best candidate words according to predetermined criteria. The ranking module adjusts ranking of the N-best list of candidate words to place words drawn from the core lexicon higher than words drawn from the extended lexicon, generating a ranked list of word candidates. Only words in the core lexicon are presented as output by the present system. The present system lists candidate words found in the extended lexicon only in the N-best list; these words require user selection to become output. Once selected by a user from the N-best list, a word from the extended lexicon is admitted to the core lexicon.
- More specifically, in a preferred embodiment, only words in the core lexicon are outputted by the recognition system. Words in the extended lexicon can only be listed in the N-best list and need explicit user selection to be outputted. Once selected, the words in the extended lexicon also gets admitted to the core lexicon.
- The present system reduces the overhead inflicted upon the user in the case the word gestured by the user is not in the vocabulary of the core lexicon. Instead of being unsure whether the word is included in the lexicon or if the system misrecognized the input, the user can scan the N-best list and select the desired candidate word.
- The present system further comprises a concatenation module and a compound word module. The concatenation module enables a user to input parts of a long word separately; the present system automatically combines words and part-of-words that are partial “sokgraphs” into one word that is intended by the user. Word parts can be stems, such as “work” and affixes, such as “ing” or “pre”. The compound word module combines two or more common shorter words whose concatenation forms a long word, such as short+hand in English. The concatenation of several short words into one compound word is more common in some European languages such as Swedish or German.
- The present system allows user interaction to adjust concatenation of a
word 1 and aword 2 and decoupling of a combined word. When the user clicks on a concatenated word, for example “smokefree”, a menu option “split to “smoke free”” or an equivalent option is given to the user. Alternatively a pen trace motion, such as a downward motion crossing the word smokefree, can be defined as a split command. For concatenable words with no action due to low confidence, a menu option is embedded inword 1 andword 2. When the user clicks onword 1, the option “snap to right” or an equivalent option is selectable. When the user clicks onword 2, the option “snap to left” or an equivalent option is selectable. Alternatively a pen gesture, such as a circle crossing bothword 1 andword 2, is defined as the command to join the two words as one concatenated long word. - The various features of the present invention and the manner of attaining them will be described in greater detail with reference to the following description, claims, and drawings, wherein reference numerals are reused, where appropriate, to indicate a correspondence between the referenced items, and wherein:
-
FIG. 1 is a schematic illustration of an exemplary operating environment in which a word pattern recognition system of the present invention can be used; -
FIG. 2 is a block diagram of a high-level architecture of the word pattern recognition system ofFIG. 1 ; -
FIG. 3 is a process flow chart illustrating a method of operation of the word pattern recognition system ofFIGS. 1 and 2 in ranking candidate words according to location in a core lexicon or an extended lexicon; -
FIG. 4 is a diagram illustrating an N-best list generated by the word pattern recognition system ofFIGS. 1 and 2 in which words from the core lexicon and words from the extended lexicon are displayed differently; -
FIG. 5 is a diagram illustrating an N-best list generated by the word pattern recognition system ofFIGS. 1 and 2 in which words from the core lexicon are grouped and ranked higher than words from the extended lexicon; -
FIG. 6 is a process flow chart illustrating a method of operation of the word pattern recognition system ofFIGS. 1 and 2 in recognizing a word candidate as a suffix or a prefix and concatenating the recognized prefix or suffix to a recognized word in a language appropriate manner; -
FIG. 7 is a process flow chart illustrating a method of operation of the word pattern recognition system ofFIGS. 1 and 2 in combining words into a compound word; -
FIG. 8 is comprised ofFIGS. 8A, 8B , and 8C and represents a diagram illustrating a menu of the word pattern recognition system ofFIGS. 1 and 2 in which the menu enables a user to split a compound word into a stem and a suffix; -
FIG. 9 is a diagram illustrating a pen gesture formed by a user on a compound word presented by the word pattern recognition system ofFIGS. 1 and 2 in which the pen gesture splits a compound word into a stem and a suffix; -
FIG. 10 is comprised ofFIGS. 10A, 10B , and 10C and represents a diagram illustrating a menu of the word pattern recognition system ofFIGS. 1 and 2 in which the menu is applied to a stem to enable a user to combine a stem and a suffix into a compound word; -
FIG. 11 is a diagram illustrating a menu of the word pattern recognition system ofFIGS. 1 and 2 in which the menu is applied to a suffix enabling a user to combine a stem and a suffix into a compound word; and -
FIG. 12 is a diagram illustrating a pen gesture formed by a user on a stem and a suffix presented by the word pattern recognition system ofFIGS. 1 and 2 in which the pen gesture combines the stem and the suffix into a compound word. - The following definitions and explanations provide background information pertaining to the technical field of the present invention, and are intended to facilitate the understanding of the present invention without limiting its scope:
- Lexicon: a collection of elements defining the recognizable elements that can be matched against a user's input in a recognition system.
- PDA: Personal Digital Assistant. A pocket-sized personal computer. PDAs typically store phone numbers, appointments, and to-do lists. Some PDAs have a small keyboard; others have only a special pen that is used for input and output on a virtual keyboard.
- Sokgrah: Shorthand on a Keyboard as a Graph. A pattern representation of words on a virtual keyboard.
- Virtual Keyboard: A computer simulated keyboard with touch-screen interactive capability that can be used to replace or supplement a keyboard using keyed entry. The virtual keys are typically tapped serially with a stylus. It is also called graphical keyboard, on-screen keyboard, or stylus keyboard.
-
FIG. 1 portrays an exemplary overall environment in which a system, a computer program product, and an associated method for improving text input in a shorthand-on-keyboard interface (the wordpattern recognition system 10 or the “system 10”) according to the present invention may be used.System 10 includes a software program code or a computer program product that is typically embedded within, or installed on a computer. The computer in whichsystem 10 is installed can be a mobile device such as aPDA 15 or acellular phone 20.System 10 can also be installed in devices such astablet computer 25,touch screen monitor 30, electronicwhite board 35, anddigital pen 40. -
System 10 can be installed in any device using a virtual keyboard or similar interface for entry, represented byauxiliary device 45.System 10 can be saved on a suitable storage medium such as a diskette, a CD, a hard drive, or like devices. -
System 10 determines a word from the shape and location of a pen stroke formed by a user on a graphical keyboard.System 10 sends the determined words to a software recipient such as, for example, an application, an operating system, etc. -
FIG. 2 illustrates a high-level hierarchy ofsystem 10.System 10 comprises alexicon 205. Thelexicon 205 comprises acore lexicon 210 and anextended lexicon 215. Thecore lexicon 210 comprises commonly used words in a language. Thecore lexicon 210 typically comprises approximately 5,000 to 15,000 words, depending on an application ofsystem 10. Theextended lexicon 215 comprises words not included in thecore lexicon 215. Theextended lexicon 215 comprises approximately 30,000 to 100,000 words. -
System 10 further comprises arecognition module 220, apre-ranking module 225, and a selector/rankingmodule 230. Therecognition module 220 generates an N-best list of candidate words corresponding to aninput pattern 235. Thepre-ranking module 225 ranks the N-best candidate words according to predetermined criteria. Theranking module 230 adjusts ranking of the N-best list of candidate words to place words drawn from thecore lexicon 210 higher than words drawn from theextended lexicon 215, generating a ranked list ofword candidates 240. As explained earlier, words drawn from the extended lexicon are not outputted; only words from the core lexicon are outputted. -
System 10 further comprises aconcatenation module 245 and acompound word module 250. Theconcatenation module 245 concatenates words selected from the ranked list ofword candidates 240; e.g., concatenating “ing” with “code” to form “coding”. Thecompound word module 250 combines words selected from the ranked list ofword candidates 240 into larger words. Anoutput word 255 is a word selected from the ranked list ofword candidates 240 and processed by theconcatenation module 245 and thecompound word module 250, as necessary. Only words in thecore lexicon 210 are presented as theoutput word 255 bysystem 10.System 10 lists candidate words found in theextended lexicon 215 only in the N-best list; these words require user selection to become theoutput word 255. Once selected by a user,system 10 admits a word from theextended lexicon 215 to thecore lexicon 210. -
System 10 adapts recognition of theinput pattern 235 by therecognition module 220 to the vocabulary of the user while maintaining maximum signal to noise ratio in the recognition system.System 10 allows thecore lexicon 210 and theextended lexicon 215 to participate in the recognition process of therecognition module 220. However, only words in thecore lexicon 210 directly enter output of therecognition module 220; these words are a default output. Words in theextended lexicon 215 that match theinput pattern 235 are only listed in an “N-best” list for selection by the user. When a user selects one of these candidate words from the N-best list to replace the default output, the selected word is admitted tocore lexicon 210. After a word is admitted to thecore lexicon 210, the admitted word can directly enter the output of the recognition module when the word matches theinput pattern 235. -
FIG. 3 illustrates amethod 300 ofsystem 10 in generating an N-best list of candidates that match theinput pattern 235. The user gestures a word on a shorthand-on-keyboard interface (step 305). Therecognition module 220 generates an N-best list of word candidates (step 310); thepre-ranking module 225 ranks the N-best list of word candidates from thecore lexicon 210 and theextended lexicon 215 according to a criterion such as a confidence value or a similarity measure (step 315). - The
ranking module 230 determines whether the highest ranked word in the N-best list of candidate words is drawn from the core lexicon 210 (decision step 320). If yes, theranking module 230 outputs the ranked N-best list of word candidates as the ranked list of word candidates 240 (step 325). If the highest ranked candidate in the N-best list of candidate words is not present in thecore lexicon 210, theranking module 230 searches the N-best list of candidate words to locate the highest ranking word candidate drawn from the core lexicon 210 (step 330). - If a word candidate drawn from the
core lexicon 210 is not found in the N-best list of candidate words (decision step 335), theranking module 230 outputs the ranked N-best list of word candidates as the ranked list ofword candidates 240. Otherwise, theranking module 230 moves the found word candidate to the highest-ranking position in the N-best list of word candidates (step 335). The ranking module outputs the ranked N-best list of word candidates as the ranked list of word candidates 240 (step 340). - To allow users to select a candidate word that is not highest ranked, a user interface component displays the next best candidate list (N-best list) from which a user can see alternative candidate words that closely match the
input pattern 235. In one embodiment, the position of a candidate word on the list is determined by a rank associated with the candidate word independent of whether the candidate word is found in thecore lexicon 210 or theextended lexicon 215, with the exception of the highest ranked word must always be found in the core lexicon with the exception when no word in the core lexicon matches the user's input. In another embodiment, candidate words are grouped by lexicon origin; i.e., candidate words from thecore lexicon 210 are grouped together and candidate words from theextended lexicon 215 are grouped together. - The origin of the candidate words can optionally be indicated by emphasizing different perceptual features that are associated with the candidate words, to facilitate the recognition of the source of the candidate words, e.g., from the core or extended lexicon. Exemplary perceptual features include, for example: color, background shading, bold fond, italicized font, etc. If a user selects no word,
system 10 outputs the highest ranked word in the N-best list of candidate words from the core lexicon. If a user does not select a word,system 10 outputs the highest ranked word in the N-best list of candidate words from the core lexicon. - Words drawn from the
extended lexicon 215 are accessed from the N-best list of candidate words. Consequently, error tolerance ofsystem 10 in generating the highest-ranked candidate is greatly enhanced, since the highest-ranked candidate that is displayed by the system is drawn from thesmaller core lexicon 210. In rare situations in which the desired word is not found in thecore lexicon 210, the user activates the N-best list and selects the desired candidate. -
FIG. 4 illustrates an exemplary N-best list ofcandidate words 400 generated by theranking module 230. Candidate words from thecore lexicon 210comprise candidate word candidate word candidate word core candidate words 420. Candidate words from theextended lexicon 215comprise candidate word 4, 425,candidate 5, 430,candidate word 6, 435,candidate word 7, 440, andcandidate word 8, 445, collectively referenced asextended candidate words 450.Core candidate words 420 andextended candidate words 450 are displayed with different emphasis. - In this example,
core candidate words 420 are shown in bold text andextended candidate words 450 are shown in italicized text. Any form of emphasis may be used to differentiate thecore candidate words 420 and theextended candidate words 450 such as, for example, text color, color background, shading, etc. The candidate words in the exemplary N-best list ofcandidate words 400 are positioned according to rank given by therecognition module 220, with the exception of the topword candidate position 455 that is reserved for a word drawn from thecore lexicon 210 unless no word from the core lexicon matches the user's input, in which case topword candidate position 455 may be taken by a word from the extended lexicon. -
FIG. 5 illustrates one embodiment in which an exemplary N-best list 500 comprises candidate words ranked according to source and according to ranking criteria provided by therecognition module 220. As forFIG. 4 ,core candidate words 420 andextended candidate words 450 are displayed with different emphasis. In this example,core candidate words 420 are shown in bold text andextended candidate words 450 are shown in italicized text. -
System 10 greatly reduces the overhead inflicted upon the user in the case the word gestured by the user is not in the vocabulary of thecore lexicon 210. Instead of being unsure whether the word is included in thecore lexicon 210 or if the system misrecognized the input, the user can scan the N-best list and select the desired candidate word. - For those familiar with the state of the art, it should be apparent that the division of words into separate lexicons is one implementation that is also a simple conceptual model. Alternatively the
lexicon 205 can be conceptualized as layers, a core lexicon layer and an extended lexicon layer, ranked by frequency or priori probability. When a word from the extended lexicon layer is selected from the N-best candidate interface, the frequency or priori probability of the selected word is adjusted to a threshold or other criterion having the effect that the selected word is adjusted to belong to the core layer. -
System 10 further enables a user to input parts of a long word separately;system 10 automatically combines partial “sokgraphs” into one that is intended by the user. Word parts can be stems, such as “work” and affixes, such as “ming”, or two or more common shorter words whose concatenation forms a long word, such as short+hand in English. The concatenation of several short words into one compound word is more common in some European languages such as Swedish or German. - Concatenations are based on individually recognizing parts involved in the concatenated word. For the case of stem+suffix, the user initially gestures an
input pattern 235 for a word that represents the stem, then gestures aninput pattern 235 of the suffix. For example for the word “coding”, the user initially writes the gesture for “code”, then writes the gesture for “ing”. For an input trace on the keyboard, therecognition module 220 finds the optimum matches and outputs these matches to an N-best list with strings S(i),iε[1,N], where a rank i of a string signifies the confidence of therecognition module 220 in the selected string matchinginput pattern 235. The string with the rank i=1 is the top choice of therecognition module 220. Therecognition module 220 stores the last N-best list in a temporary buffer. The buffered N-best list for a regular word (stem) is denoted as S0. - In one embodiment, suffixes are stored in a list called concatenable suffixes whose sokgraphs, the geometric pattern on a graphical keyboard, are represented in the same way as a common word sokgraph. For example, for the suffix “ing”, its sokgraph is a continuous trace starting from the i key to the n key ending on the g key. The system recognizes an
input pattern 235 for sokgraph “ing” in the same way as any other sokgraph, except the suffix “ing” is stored in the list of concatenable suffixes. Alternatively both suffixes and regular words can be stored in the same lexicon, but with an identifier differentiating the suffix from the regular word. In one embodiment, concatenable suffixes are stored in a lookup table in which each suffix entry, such as “ing”, is associated with a series of pointers that point to the entries in a lexicon that ends with that suffix -
FIG. 6 illustrates amethod 600 ofsystem 10 in combining concatenable suffixes with a stem word. A user gestures a word on a shorthand-on-keyboard interface (step 605). Theconcatenation module 245 obtains a highest ranked word for an output N-best list of word candidates 240 (step 610). Theconcatenation module 245 determines whether the obtained word is a concatenable suffix by, for example, comparing the obtained word with a list of concatenable suffixes (decision step 615). If the obtained word is not a concatenable suffix, theconcatenation module 245 takes no action (step 620). - If the obtained word is a concatenable suffix, the
concatenation module 245 finds concatenation candidates that end with the determined concatenable suffix (step 625). Theconcatenation module 245 strips the concatenable suffix from each concatenation candidate (step 630). Words ending with a current suffix (e.g. “ing”) are denoted as S1(i) (e.g. coding or working) and their remainders stripped of the suffix are denoted S2(i) (e.g. “cod” or “work”). - The
concatenation module 245 computes the string edit distance (specifically: the Morgan editing error using the Wagner-Fisher algorithm) between the stripped concatenation candidates and the concatenable suffix (step 635). The remainders S2(i) are then matched against the top choice S0(1) in the buffered N-best list. Since S0 contains whole words, not fragments of words (for example S0(1)=code) the matching is inexact.System 10 uses edit-distance (the minimum number of edit operations chosen from insertion, deletion, or substitution of a single character) to match two strings) to find the string in s2(i) (i=1,N) that is closest to s0(1) and denote it as s2 min. Theconcatenation module 245 sorts the concatenation candidates by the associated edit distance (step 640). Theconcatenation module 245 returns the concatenation candidate with the smallest edit distance (step 645). - In an alternative embodiment word frequencies or prior probabilities, or higher-order language regularities are used to rank concatenation candidates that share the same edit distance.
- The word corresponding to s2 min in S1(i) is returned as the concatenation candidate of choice. For example “code” is closer by edit-distance to “cod” (the stripped part of “coding”) than “code” to “work” (the stripped part of “working”). In one embodiment, a threshold can be set as the lowest acceptable edit-distance mismatch.
- In another embodiment suffixes are not linked to all words that end with the suffix. Instead, when a suffix is recognized, the
system 10 scans thelexicon 205, finds words that end with the recognized suffix, strips the ending from the found words, matches the stripped remainders with the preceding word, and selects the closest match for concatenation as previously described. The difference between these two embodiments lies in computational time and memory space tradeoff. Scanning the lexicon implies that a separate list of pointers is not needed, hence reducing the storage requirement of the lexicon in the medium the software code is accessing. On the other hand, scanning the lexicon requires more time than to locate a word than a system comprising a lexicon that is indexed with a separate list of pointers. -
System 10 treats prefix+stem in a manner similar to stem+suffix. Theconcatenation module 245 initially recognizes a prefix-based word from the output of the ranked list ofword candidates 240 from either a separate list of prefixes or a common lexicon with a prefix identifier. Theconcatenation module 245 then recognizes the word that follows the prefix. Theconcatenation module 245 matches all words containing the prefix, strips the matched word of the prefix, and returns the closest match for concatenation. - The concatenation of two shorter words into a long one is not deterministic. For example, in Swedish both “smoke free” and “smokefree” are permitted, but their meanings are opposite (smoking allowed as opposed to smoking not allowed). The
compound word module 250 uses a statistical and interactive method to handle the concatenation of two words. To support this method,system 10 stores in thelexicon 205 the statistical information including the frequencies of all words (based on the total number of occurrence of each word in a corpus of text) and frequencies of all bigrams (based on the total number of occurrence of two ordered words). -
FIG. 7 illustrates amethod 700 ofsystem 10 in combining words into compound words.Method 700 examines pairs of consecutive words (word 1, word 2) (step 705). Thecompound word module 245 determines whether the combined consecutive words (word 1+word 2=word 3) are found in the lexicon 205 (decision step 710). If the combined word,word 3, is not found, thecompound word module 250 takes no action (step 715). If a match (word 3=word 1+word 2) is found, thecompound word module 250 compares the frequency ofword 3 with bigram (word1, word2) (step 720). If the frequency ofword 3 is greater than the frequency of bigram (word1, word2) compared to a predetermined threshold or the ratio of the frequency ofword 3 with respect to the frequency of bigram (word1, word2) is greater than a predetermined threshold (decision step 725), the compound word module replacesword 1 andword 2 with word 3 (step 730). Otherwise, no action is taken (step 715). Alternatively, the comparison of the frequency ofword 3 and the frequency of the bigram (word 1, word 2) is a weighted comparison. -
System 10 provides a user interface that enables user interaction for adjusting concatenation and decoupling.FIG. 8 (FIGS. 8A, 8B , 8C) illustrates decoupling of a combined word into two individual words or parts of words. Anexemplary screen 805 displays to a user an exemplary concatenated word “coding” 810. The user selects the displayed concatenated word “coding” by, for example, clicking on the word “coding” 810 (FIG. 8A ). Selecting the word “coding” 810 displays amenu option 815 comprising, for example, selectable instruction “Split to “Code” and “ing”” or an equivalent option (FIG. 8B ). If the user selects the instruction shown inmenu option 815,system 10 splits the displayed concatenated word “coding” 810 into stem “code” 820 and suffix “ing” 825 (FIG. 8C ). -
FIG. 9 illustrates an exemplary alternativepen trace motion 905 used to split the concatenated word “coding” 810. Thescreen 805 displays to the user a concatenated word “coding” 810. The user forms thepen trace motion 905 over the concatenated word “coding” 810.System 10 splits the displayed concatenated word “coding” 810 into stem “code” 820 and suffix “ming” 825 as illustrated inFIG. 8C . - For concatenable words with no action due to low confidence, a menu option is embedded in
word 1 andword 2 as illustrated inFIG. 10 . For example, thescreen 805 displays to the user aword 1 “code” 1005 and aword 2 “ing” 1010 as shown inFIG. 10A . Selecting theword 1 “code” 1005 displays anoption menu 1015 comprising a selectable instruction “snap to right” or an equivalent option (FIG. 10B ). If the user selects the instruction “snap to right” shown in theoption menu 1015,system 10 concatenates theword 1 “code” 1005 and theword 2 “ming” 1010, forming the concatenated word “coding” 1020 (FIG. 10C ). -
FIG. 11 illustrates anexemplary option menu 1105 displayed when the user selects theword 2 “ming” 1010. If the user selects the instruction “snap to left” shown in theoption menu 1105,system 10 concatenates theword 1 “code” 1005 and theword 2 “ming” 1010, forming the concatenated word “coding” 1020 as shown inFIG. 10C . -
FIG. 12 illustrates an exemplary alternativepen trace motion 1205 used to concatenate theword 1 “code” 1005 and theword 2 “ing” 1010. Thepen trace motion 1205 comprises, for example, a circle crossing theword 1 “code” 1005 and theword 2 “ming” 1010.System 10 recognizes the command represented by thepen trace motion 1205 and concatenates theword 1 “code” 1005 and theword 2 “ing” 1010, forming the concatenated word “coding” 1020 as shown inFIG. 10C . - It is to be understood that the specific embodiments of the invention that have been described are merely illustrative of certain applications of the principle of the present invention. Numerous modifications may be made to the system and method for improving text input in a shorthand-on-keyboard interface described herein without departing from the spirit and scope of the present invention.
Claims (22)
Priority Applications (10)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/256,713 US20070094024A1 (en) | 2005-10-22 | 2005-10-22 | System and method for improving text input in a shorthand-on-keyboard interface |
PCT/EP2006/067338 WO2007045597A1 (en) | 2005-10-22 | 2006-10-12 | Improved text input in a shorthand-on-keyboard interface |
JP2008536022A JP2009512923A (en) | 2005-10-22 | 2006-10-12 | System, computer program and method for improving text input in short hand on keyboard interface (improving text input in short hand on keyboard interface on keyboard) |
CN2006800392497A CN101292214B (en) | 2005-10-22 | 2006-10-12 | Improved text input in a shorthand-on-keyboard interface |
US12/906,827 US8311796B2 (en) | 2005-10-22 | 2010-10-18 | System and method for improving text input in a shorthand-on-keyboard interface |
JP2012178642A JP5400200B2 (en) | 2005-10-22 | 2012-08-10 | System, computer program and method for improving text input in a short hand on keyboard interface |
JP2012178643A JP5738245B2 (en) | 2005-10-22 | 2012-08-10 | System, computer program and method for improving text input in short hand on keyboard interface (improving text input in short hand on keyboard interface on keyboard) |
US13/616,311 US8543384B2 (en) | 2005-10-22 | 2012-09-14 | Input recognition using multiple lexicons |
US13/866,994 US8712755B2 (en) | 2005-10-22 | 2013-04-19 | System and method for improving text input in a shorthand-on-keyboard interface |
US14/206,920 US9256580B2 (en) | 2005-10-22 | 2014-03-12 | System and method for improving text input in a shorthand-on-keyboard interface |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/256,713 US20070094024A1 (en) | 2005-10-22 | 2005-10-22 | System and method for improving text input in a shorthand-on-keyboard interface |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/906,827 Continuation US8311796B2 (en) | 2005-10-22 | 2010-10-18 | System and method for improving text input in a shorthand-on-keyboard interface |
Publications (1)
Publication Number | Publication Date |
---|---|
US20070094024A1 true US20070094024A1 (en) | 2007-04-26 |
Family
ID=37847184
Family Applications (5)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/256,713 Abandoned US20070094024A1 (en) | 2005-10-22 | 2005-10-22 | System and method for improving text input in a shorthand-on-keyboard interface |
US12/906,827 Expired - Fee Related US8311796B2 (en) | 2005-10-22 | 2010-10-18 | System and method for improving text input in a shorthand-on-keyboard interface |
US13/616,311 Active US8543384B2 (en) | 2005-10-22 | 2012-09-14 | Input recognition using multiple lexicons |
US13/866,994 Active US8712755B2 (en) | 2005-10-22 | 2013-04-19 | System and method for improving text input in a shorthand-on-keyboard interface |
US14/206,920 Expired - Fee Related US9256580B2 (en) | 2005-10-22 | 2014-03-12 | System and method for improving text input in a shorthand-on-keyboard interface |
Family Applications After (4)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/906,827 Expired - Fee Related US8311796B2 (en) | 2005-10-22 | 2010-10-18 | System and method for improving text input in a shorthand-on-keyboard interface |
US13/616,311 Active US8543384B2 (en) | 2005-10-22 | 2012-09-14 | Input recognition using multiple lexicons |
US13/866,994 Active US8712755B2 (en) | 2005-10-22 | 2013-04-19 | System and method for improving text input in a shorthand-on-keyboard interface |
US14/206,920 Expired - Fee Related US9256580B2 (en) | 2005-10-22 | 2014-03-12 | System and method for improving text input in a shorthand-on-keyboard interface |
Country Status (4)
Country | Link |
---|---|
US (5) | US20070094024A1 (en) |
JP (3) | JP2009512923A (en) |
CN (1) | CN101292214B (en) |
WO (1) | WO2007045597A1 (en) |
Cited By (68)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060067252A1 (en) * | 2004-09-30 | 2006-03-30 | Ajita John | Method and apparatus for providing communication tasks in a workflow |
US20060101504A1 (en) * | 2004-11-09 | 2006-05-11 | Veveo.Tv, Inc. | Method and system for performing searches for television content and channels using a non-intrusive television interface and with reduced text input |
US20070156747A1 (en) * | 2005-12-12 | 2007-07-05 | Tegic Communications Llc | Mobile Device Retrieval and Navigation |
US20070255693A1 (en) * | 2006-03-30 | 2007-11-01 | Veveo, Inc. | User interface method and system for incrementally searching and selecting content items and for presenting advertising in response to search activities |
US20070266406A1 (en) * | 2004-11-09 | 2007-11-15 | Murali Aravamudan | Method and system for performing actions using a non-intrusive television with reduced text input |
US20080141125A1 (en) * | 2006-06-23 | 2008-06-12 | Firooz Ghassabian | Combined data entry systems |
US20080313564A1 (en) * | 2007-05-25 | 2008-12-18 | Veveo, Inc. | System and method for text disambiguation and context designation in incremental search |
US20080313174A1 (en) * | 2007-05-25 | 2008-12-18 | Veveo, Inc. | Method and system for unified searching across and within multiple documents |
US20080313574A1 (en) * | 2007-05-25 | 2008-12-18 | Veveo, Inc. | System and method for search with reduced physical interaction requirements |
US20080313128A1 (en) * | 2007-06-12 | 2008-12-18 | Microsoft Corporation | Disk-Based Probabilistic Set-Similarity Indexes |
US20090063135A1 (en) * | 2007-08-31 | 2009-03-05 | Vadim Fux | Handheld Electronic Device and Method Employing Logical Proximity of Characters in Spell Checking |
US20090217203A1 (en) * | 2006-03-06 | 2009-08-27 | Veveo, Inc. | Methods and systems for segmeting relative user preferences into fine-grain and course-grain collections |
US20100073329A1 (en) * | 2008-09-19 | 2010-03-25 | Tiruvilwamalai Venkatram Raman | Quick Gesture Input |
US20100153380A1 (en) * | 2005-11-23 | 2010-06-17 | Veveo, Inc. | System And Method For Finding Desired Results By Incremental Search Using An Ambiguous Keypad With The Input Containing Orthographic And/Or Typographic Errors |
US20100238125A1 (en) * | 2009-03-20 | 2010-09-23 | Nokia Corporation | Method, Apparatus, and Computer Program Product For Discontinuous Shapewriting |
US20100286979A1 (en) * | 2007-08-01 | 2010-11-11 | Ginger Software, Inc. | Automatic context sensitive language correction and enhancement using an internet corpus |
US20110071834A1 (en) * | 2005-10-22 | 2011-03-24 | Per-Ola Kristensson | System and method for improving text input in a shorthand-on-keyboard interface |
US20110208512A1 (en) * | 2008-11-07 | 2011-08-25 | Jinglian Gao | Method and system for generating derivative words |
US8078884B2 (en) | 2006-11-13 | 2011-12-13 | Veveo, Inc. | Method of and system for selecting and presenting content based on user identification |
US8086602B2 (en) | 2006-04-20 | 2011-12-27 | Veveo Inc. | User interface methods and systems for selecting and presenting content based on user navigation and selection actions associated with the content |
US8107401B2 (en) | 2004-09-30 | 2012-01-31 | Avaya Inc. | Method and apparatus for providing a virtual assistant to a communication participant |
US8180722B2 (en) | 2004-09-30 | 2012-05-15 | Avaya Inc. | Method and apparatus for data mining within communication session information using an entity relationship model |
US8270320B2 (en) | 2004-09-30 | 2012-09-18 | Avaya Inc. | Method and apparatus for launching a conference based on presence of invitees |
US8417717B2 (en) | 2006-03-30 | 2013-04-09 | Veveo Inc. | Method and system for incrementally selecting and providing relevant search engines in response to a user query |
US20130262452A1 (en) * | 2010-12-17 | 2013-10-03 | Telefonaktiebolaget L M Ericsson (Publ) | Server for Conveying a Set of Contact Identification Data to a User Equipment, Methods Therefor, User Equipment, Computer Programs and Computer Program Products |
US8667414B2 (en) | 2012-03-23 | 2014-03-04 | Google Inc. | Gestural input at a virtual keyboard |
US8677236B2 (en) | 2008-12-19 | 2014-03-18 | Microsoft Corporation | Contact-specific and location-aware lexicon prediction |
US8701032B1 (en) | 2012-10-16 | 2014-04-15 | Google Inc. | Incremental multi-word recognition |
US20140164996A1 (en) * | 2012-12-11 | 2014-06-12 | Canon Kabushiki Kaisha | Apparatus, method, and storage medium |
US8782549B2 (en) | 2012-10-05 | 2014-07-15 | Google Inc. | Incremental feature-based gesture-keyboard decoding |
US8799804B2 (en) | 2006-10-06 | 2014-08-05 | Veveo, Inc. | Methods and systems for a linear character selection display interface for ambiguous text input |
US8819574B2 (en) | 2012-10-22 | 2014-08-26 | Google Inc. | Space prediction for text input |
US20140278368A1 (en) * | 2013-03-14 | 2014-09-18 | Google Inc. | Morpheme-level predictive graphical keyboard |
US8843845B2 (en) | 2012-10-16 | 2014-09-23 | Google Inc. | Multi-gesture text input prediction |
US8850350B2 (en) | 2012-10-16 | 2014-09-30 | Google Inc. | Partial gesture text entry |
US20140350920A1 (en) | 2009-03-30 | 2014-11-27 | Touchtype Ltd | System and method for inputting text into electronic devices |
US20150066500A1 (en) * | 2013-08-30 | 2015-03-05 | Honda Motor Co., Ltd. | Speech processing device, speech processing method, and speech processing program |
US9015036B2 (en) | 2010-02-01 | 2015-04-21 | Ginger Software, Inc. | Automatic context sensitive language correction using an internet corpus particularly for small keyboard devices |
US9021380B2 (en) | 2012-10-05 | 2015-04-28 | Google Inc. | Incremental multi-touch gesture recognition |
US9046932B2 (en) | 2009-10-09 | 2015-06-02 | Touchtype Ltd | System and method for inputting text into electronic devices based on text and text category predictions |
US9052748B2 (en) | 2010-03-04 | 2015-06-09 | Touchtype Limited | System and method for inputting text into electronic devices |
US9081500B2 (en) | 2013-05-03 | 2015-07-14 | Google Inc. | Alternative hypothesis error correction for gesture typing |
US20150248882A1 (en) * | 2012-07-09 | 2015-09-03 | Nuance Communications, Inc. | Detecting potential significant errors in speech recognition results |
US9135544B2 (en) | 2007-11-14 | 2015-09-15 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US9166714B2 (en) | 2009-09-11 | 2015-10-20 | Veveo, Inc. | Method of and system for presenting enriched video viewing analytics |
US9177081B2 (en) | 2005-08-26 | 2015-11-03 | Veveo, Inc. | Method and system for processing ambiguous, multi-term search queries |
US9189472B2 (en) | 2009-03-30 | 2015-11-17 | Touchtype Limited | System and method for inputting text into small screen devices |
US9384185B2 (en) | 2010-09-29 | 2016-07-05 | Touchtype Ltd. | System and method for inputting text into electronic devices |
US9400952B2 (en) | 2012-10-22 | 2016-07-26 | Varcode Ltd. | Tamper-proof quality management barcode indicators |
US9424246B2 (en) | 2009-03-30 | 2016-08-23 | Touchtype Ltd. | System and method for inputting text into electronic devices |
US9547439B2 (en) | 2013-04-22 | 2017-01-17 | Google Inc. | Dynamically-positioned character string suggestions for gesture typing |
US20170025117A1 (en) * | 2015-07-23 | 2017-01-26 | Samsung Electronics Co., Ltd. | Speech recognition apparatus and method |
CN106484133A (en) * | 2016-08-24 | 2017-03-08 | 苏娜香 | The method for input in Chinese being carried out using handwriting stenograph symbol |
US9646277B2 (en) | 2006-05-07 | 2017-05-09 | Varcode Ltd. | System and method for improved quality management in a product logistic chain |
US9703779B2 (en) | 2010-02-04 | 2017-07-11 | Veveo, Inc. | Method of and system for enhanced local-device content discovery |
US9747272B2 (en) | 2012-10-16 | 2017-08-29 | Google Inc. | Feature-based autocorrection |
US9830311B2 (en) | 2013-01-15 | 2017-11-28 | Google Llc | Touch keyboard using language and spatial models |
US20180349349A1 (en) * | 2017-06-02 | 2018-12-06 | Apple Inc. | Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling |
US10176451B2 (en) | 2007-05-06 | 2019-01-08 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US10191654B2 (en) | 2009-03-30 | 2019-01-29 | Touchtype Limited | System and method for inputting text into electronic devices |
US10235363B2 (en) * | 2017-04-28 | 2019-03-19 | Sap Se | Instant translation of user interfaces of a web application |
US10372310B2 (en) | 2016-06-23 | 2019-08-06 | Microsoft Technology Licensing, Llc | Suppression of input images |
US10445678B2 (en) | 2006-05-07 | 2019-10-15 | Varcode Ltd. | System and method for improved quality management in a product logistic chain |
CN110826322A (en) * | 2019-10-22 | 2020-02-21 | 中电科大数据研究院有限公司 | Method for discovering new words, predicting parts of speech and marking |
US10613746B2 (en) | 2012-01-16 | 2020-04-07 | Touchtype Ltd. | System and method for inputting text |
US10697837B2 (en) | 2015-07-07 | 2020-06-30 | Varcode Ltd. | Electronic quality indicator |
US11060924B2 (en) | 2015-05-18 | 2021-07-13 | Varcode Ltd. | Thermochromic ink indicia for activatable quality labels |
US11704526B2 (en) | 2008-06-10 | 2023-07-18 | Varcode Ltd. | Barcoded indicators for quality management |
Families Citing this family (153)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090193334A1 (en) * | 2005-05-18 | 2009-07-30 | Exb Asset Management Gmbh | Predictive text input system and method involving two concurrent ranking means |
US8036878B2 (en) * | 2005-05-18 | 2011-10-11 | Never Wall Treuhand GmbH | Device incorporating improved text input mechanism |
US9606634B2 (en) * | 2005-05-18 | 2017-03-28 | Nokia Technologies Oy | Device incorporating improved text input mechanism |
US8374846B2 (en) * | 2005-05-18 | 2013-02-12 | Neuer Wall Treuhand Gmbh | Text input device and method |
US8677377B2 (en) | 2005-09-08 | 2014-03-18 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US9318108B2 (en) | 2010-01-18 | 2016-04-19 | Apple Inc. | Intelligent automated assistant |
US8977255B2 (en) | 2007-04-03 | 2015-03-10 | Apple Inc. | Method and system for operating a multi-function portable electronic device using voice-activation |
WO2009024194A1 (en) * | 2007-08-17 | 2009-02-26 | Nokia Corporation | Method and device for word input |
US10002189B2 (en) | 2007-12-20 | 2018-06-19 | Apple Inc. | Method and apparatus for searching using an active ontology |
US9330720B2 (en) | 2008-01-03 | 2016-05-03 | Apple Inc. | Methods and apparatus for altering audio output signals |
US8996376B2 (en) | 2008-04-05 | 2015-03-31 | Apple Inc. | Intelligent text-to-speech conversion |
US20100030549A1 (en) | 2008-07-31 | 2010-02-04 | Lee Michael M | Mobile device having human language translation capability with positional feedback |
US8676904B2 (en) | 2008-10-02 | 2014-03-18 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US10255566B2 (en) | 2011-06-03 | 2019-04-09 | Apple Inc. | Generating and processing task items that represent tasks to perform |
US10241752B2 (en) | 2011-09-30 | 2019-03-26 | Apple Inc. | Interface for a virtual digital assistant |
US10276170B2 (en) | 2010-01-18 | 2019-04-30 | Apple Inc. | Intelligent automated assistant |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
US10057736B2 (en) | 2011-06-03 | 2018-08-21 | Apple Inc. | Active transport based notifications |
US20130091166A1 (en) * | 2011-10-06 | 2013-04-11 | Discovery Engine Corporation | Method and apparatus for indexing information using an extended lexicon |
EP2812777A4 (en) | 2012-02-06 | 2015-11-25 | Michael K Colby | Character-string completion |
US9330083B2 (en) * | 2012-02-14 | 2016-05-03 | Facebook, Inc. | Creating customized user dictionary |
US9330082B2 (en) * | 2012-02-14 | 2016-05-03 | Facebook, Inc. | User experience with customized user dictionary |
US10134385B2 (en) | 2012-03-02 | 2018-11-20 | Apple Inc. | Systems and methods for name pronunciation |
US10417037B2 (en) | 2012-05-15 | 2019-09-17 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US9721563B2 (en) | 2012-06-08 | 2017-08-01 | Apple Inc. | Name recognition system |
US20140136210A1 (en) * | 2012-11-14 | 2014-05-15 | At&T Intellectual Property I, L.P. | System and method for robust personalization of speech recognition |
CN105027040B (en) * | 2013-01-21 | 2018-09-21 | 要点科技印度私人有限公司 | text input system and method |
IN2013CH00469A (en) | 2013-01-21 | 2015-07-31 | Keypoint Technologies India Pvt Ltd | |
US9047268B2 (en) * | 2013-01-31 | 2015-06-02 | Google Inc. | Character and word level language models for out-of-vocabulary text input |
US9454240B2 (en) | 2013-02-05 | 2016-09-27 | Google Inc. | Gesture keyboard input of non-dictionary character strings |
EP2954514B1 (en) | 2013-02-07 | 2021-03-31 | Apple Inc. | Voice trigger for a digital assistant |
US10652394B2 (en) | 2013-03-14 | 2020-05-12 | Apple Inc. | System and method for processing voicemail |
US10748529B1 (en) | 2013-03-15 | 2020-08-18 | Apple Inc. | Voice activated device for use with a voice-based digital assistant |
US9672818B2 (en) | 2013-04-18 | 2017-06-06 | Nuance Communications, Inc. | Updating population language models based on changes made by user clusters |
FR3005175B1 (en) * | 2013-04-24 | 2018-07-27 | Myscript | PERMANENT SYNCHRONIZATION SYSTEM FOR MANUSCRITE INPUT |
US8756499B1 (en) * | 2013-04-29 | 2014-06-17 | Google Inc. | Gesture keyboard input of non-dictionary character strings using substitute scoring |
WO2014197334A2 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
WO2014197335A1 (en) | 2013-06-08 | 2014-12-11 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
WO2014200728A1 (en) | 2013-06-09 | 2014-12-18 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
US9229543B2 (en) * | 2013-06-28 | 2016-01-05 | Lenovo (Singapore) Pte. Ltd. | Modifying stylus input or response using inferred emotion |
US9423890B2 (en) * | 2013-06-28 | 2016-08-23 | Lenovo (Singapore) Pte. Ltd. | Stylus lexicon sharing |
CN103531197A (en) * | 2013-10-11 | 2014-01-22 | 安徽科大讯飞信息科技股份有限公司 | Command word recognition self-adaptive optimization method for carrying out feedback on user speech recognition result |
US10296160B2 (en) | 2013-12-06 | 2019-05-21 | Apple Inc. | Method for extracting salient dialog usage from live data |
US9633004B2 (en) | 2014-05-30 | 2017-04-25 | Apple Inc. | Better resolution when referencing to concepts |
US9715875B2 (en) | 2014-05-30 | 2017-07-25 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US9966065B2 (en) | 2014-05-30 | 2018-05-08 | Apple Inc. | Multi-command single utterance input method |
US10170123B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Intelligent assistant for home automation |
US9430463B2 (en) | 2014-05-30 | 2016-08-30 | Apple Inc. | Exemplar-based natural language processing |
US9338493B2 (en) | 2014-06-30 | 2016-05-10 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10127911B2 (en) | 2014-09-30 | 2018-11-13 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
US10074360B2 (en) | 2014-09-30 | 2018-09-11 | Apple Inc. | Providing an indication of the suitability of speech recognition |
CN104538032B (en) * | 2014-12-19 | 2018-02-06 | 中国科学院计算技术研究所 | A kind of methods for mandarin speech recognition and system for merging user feedback |
US10152299B2 (en) | 2015-03-06 | 2018-12-11 | Apple Inc. | Reducing response latency of intelligent automated assistants |
US9721566B2 (en) | 2015-03-08 | 2017-08-01 | Apple Inc. | Competing devices responding to voice triggers |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US9886953B2 (en) | 2015-03-08 | 2018-02-06 | Apple Inc. | Virtual assistant activation |
US9703394B2 (en) * | 2015-03-24 | 2017-07-11 | Google Inc. | Unlearning techniques for adaptive language models in text entry |
US10460227B2 (en) | 2015-05-15 | 2019-10-29 | Apple Inc. | Virtual assistant in a communication session |
US10083688B2 (en) | 2015-05-27 | 2018-09-25 | Apple Inc. | Device voice control for selecting a displayed affordance |
US10200824B2 (en) | 2015-05-27 | 2019-02-05 | Apple Inc. | Systems and methods for proactively identifying and surfacing relevant content on a touch-sensitive device |
US9578173B2 (en) | 2015-06-05 | 2017-02-21 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US20160378747A1 (en) | 2015-06-29 | 2016-12-29 | Apple Inc. | Virtual assistant for media playback |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US10740384B2 (en) | 2015-09-08 | 2020-08-11 | Apple Inc. | Intelligent automated assistant for media search and playback |
US10331312B2 (en) | 2015-09-08 | 2019-06-25 | Apple Inc. | Intelligent automated assistant in a media environment |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US10956666B2 (en) | 2015-11-09 | 2021-03-23 | Apple Inc. | Unconventional virtual assistant interactions |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10416868B2 (en) | 2016-02-29 | 2019-09-17 | Myscript | Method and system for character insertion in a character string |
US10248635B2 (en) | 2016-02-29 | 2019-04-02 | Myscript | Method for inserting characters in a character string and the corresponding digital service |
DK201670539A1 (en) * | 2016-03-14 | 2017-10-02 | Apple Inc | Dictation that allows editing |
US11227589B2 (en) | 2016-06-06 | 2022-01-18 | Apple Inc. | Intelligent list reading |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
US10586535B2 (en) | 2016-06-10 | 2020-03-10 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
DK179415B1 (en) | 2016-06-11 | 2018-06-14 | Apple Inc | Intelligent device arbitration and control |
DK201670540A1 (en) | 2016-06-11 | 2018-01-08 | Apple Inc | Application integration with a digital assistant |
RU2718154C1 (en) * | 2016-06-22 | 2020-03-30 | Хуавэй Текнолоджиз Ко., Лтд. | Method and device for displaying possible word and graphical user interface |
US10474753B2 (en) | 2016-09-07 | 2019-11-12 | Apple Inc. | Language identification using recurrent neural networks |
US10043516B2 (en) | 2016-09-23 | 2018-08-07 | Apple Inc. | Intelligent automated assistant |
US10884610B2 (en) | 2016-11-04 | 2021-01-05 | Myscript | System and method for recognizing handwritten stroke input |
US11281993B2 (en) | 2016-12-05 | 2022-03-22 | Apple Inc. | Model and ensemble compression for metric learning |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
US11204787B2 (en) | 2017-01-09 | 2021-12-21 | Apple Inc. | Application integration with a digital assistant |
DK201770383A1 (en) | 2017-05-09 | 2018-12-14 | Apple Inc. | User interface for correcting recognition errors |
US10417266B2 (en) | 2017-05-09 | 2019-09-17 | Apple Inc. | Context-aware ranking of intelligent response suggestions |
US10726832B2 (en) | 2017-05-11 | 2020-07-28 | Apple Inc. | Maintaining privacy of personal information |
US10395654B2 (en) | 2017-05-11 | 2019-08-27 | Apple Inc. | Text normalization based on a data-driven learning network |
DK201770439A1 (en) | 2017-05-11 | 2018-12-13 | Apple Inc. | Offline personal assistant |
DK180048B1 (en) | 2017-05-11 | 2020-02-04 | Apple Inc. | MAINTAINING THE DATA PROTECTION OF PERSONAL INFORMATION |
DK201770428A1 (en) | 2017-05-12 | 2019-02-18 | Apple Inc. | Low-latency intelligent automated assistant |
DK179745B1 (en) | 2017-05-12 | 2019-05-01 | Apple Inc. | SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT |
DK179496B1 (en) | 2017-05-12 | 2019-01-15 | Apple Inc. | USER-SPECIFIC Acoustic Models |
US11301477B2 (en) | 2017-05-12 | 2022-04-12 | Apple Inc. | Feedback analysis of a digital assistant |
DK201770431A1 (en) | 2017-05-15 | 2018-12-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
DK201770432A1 (en) | 2017-05-15 | 2018-12-21 | Apple Inc. | Hierarchical belief states for digital assistants |
US10303715B2 (en) | 2017-05-16 | 2019-05-28 | Apple Inc. | Intelligent automated assistant for media exploration |
DK179560B1 (en) | 2017-05-16 | 2019-02-18 | Apple Inc. | Far-field extension for digital assistant services |
US10311144B2 (en) | 2017-05-16 | 2019-06-04 | Apple Inc. | Emoji word sense disambiguation |
US20180336892A1 (en) | 2017-05-16 | 2018-11-22 | Apple Inc. | Detecting a trigger of a digital assistant |
US10403278B2 (en) | 2017-05-16 | 2019-09-03 | Apple Inc. | Methods and systems for phonetic matching in digital assistant services |
US20190079668A1 (en) * | 2017-06-29 | 2019-03-14 | Ashwin P Rao | User interfaces for keyboards |
US10445429B2 (en) | 2017-09-21 | 2019-10-15 | Apple Inc. | Natural language understanding using vocabularies with compressed serialized tries |
US10755051B2 (en) | 2017-09-29 | 2020-08-25 | Apple Inc. | Rule-based natural language processing |
US10636424B2 (en) | 2017-11-30 | 2020-04-28 | Apple Inc. | Multi-turn canned dialog |
US10733982B2 (en) | 2018-01-08 | 2020-08-04 | Apple Inc. | Multi-directional dialog |
US10733375B2 (en) | 2018-01-31 | 2020-08-04 | Apple Inc. | Knowledge-based framework for improving natural language understanding |
US10789959B2 (en) | 2018-03-02 | 2020-09-29 | Apple Inc. | Training speaker recognition models for digital assistants |
US10592604B2 (en) | 2018-03-12 | 2020-03-17 | Apple Inc. | Inverse text normalization for automatic speech recognition |
US10818288B2 (en) | 2018-03-26 | 2020-10-27 | Apple Inc. | Natural assistant interaction |
US10909331B2 (en) | 2018-03-30 | 2021-02-02 | Apple Inc. | Implicit identification of translation payload with neural machine translation |
US11010553B2 (en) * | 2018-04-18 | 2021-05-18 | International Business Machines Corporation | Recommending authors to expand personal lexicon |
US10928918B2 (en) | 2018-05-07 | 2021-02-23 | Apple Inc. | Raise to speak |
US11145294B2 (en) | 2018-05-07 | 2021-10-12 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US10984780B2 (en) | 2018-05-21 | 2021-04-20 | Apple Inc. | Global semantic word embeddings using bi-directional recurrent neural networks |
US10892996B2 (en) | 2018-06-01 | 2021-01-12 | Apple Inc. | Variable latency device coordination |
DK180639B1 (en) | 2018-06-01 | 2021-11-04 | Apple Inc | DISABILITY OF ATTENTION-ATTENTIVE VIRTUAL ASSISTANT |
US11386266B2 (en) | 2018-06-01 | 2022-07-12 | Apple Inc. | Text correction |
DK201870355A1 (en) | 2018-06-01 | 2019-12-16 | Apple Inc. | Virtual assistant operation in multi-device environments |
DK179822B1 (en) | 2018-06-01 | 2019-07-12 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US10496705B1 (en) | 2018-06-03 | 2019-12-03 | Apple Inc. | Accelerated task performance |
US11010561B2 (en) | 2018-09-27 | 2021-05-18 | Apple Inc. | Sentiment prediction from textual data |
US11462215B2 (en) | 2018-09-28 | 2022-10-04 | Apple Inc. | Multi-modal inputs for voice commands |
US10839159B2 (en) | 2018-09-28 | 2020-11-17 | Apple Inc. | Named entity normalization in a spoken dialog system |
US11170166B2 (en) | 2018-09-28 | 2021-11-09 | Apple Inc. | Neural typographical error modeling via generative adversarial networks |
US11475898B2 (en) | 2018-10-26 | 2022-10-18 | Apple Inc. | Low-latency multi-speaker speech recognition |
CN109712613B (en) * | 2018-12-27 | 2021-04-20 | 北京百佑科技有限公司 | Semantic analysis library updating method and device and electronic equipment |
US11638059B2 (en) | 2019-01-04 | 2023-04-25 | Apple Inc. | Content playback on multiple devices |
US11348573B2 (en) | 2019-03-18 | 2022-05-31 | Apple Inc. | Multimodality in digital assistant systems |
US11423908B2 (en) | 2019-05-06 | 2022-08-23 | Apple Inc. | Interpreting spoken requests |
DK201970509A1 (en) | 2019-05-06 | 2021-01-15 | Apple Inc | Spoken notifications |
US11475884B2 (en) | 2019-05-06 | 2022-10-18 | Apple Inc. | Reducing digital assistant latency when a language is incorrectly determined |
US11307752B2 (en) | 2019-05-06 | 2022-04-19 | Apple Inc. | User configurable task triggers |
US11140099B2 (en) | 2019-05-21 | 2021-10-05 | Apple Inc. | Providing message response suggestions |
DK180129B1 (en) | 2019-05-31 | 2020-06-02 | Apple Inc. | User activity shortcut suggestions |
US11289073B2 (en) | 2019-05-31 | 2022-03-29 | Apple Inc. | Device text to speech |
US11496600B2 (en) | 2019-05-31 | 2022-11-08 | Apple Inc. | Remote execution of machine-learned models |
DK201970511A1 (en) | 2019-05-31 | 2021-02-15 | Apple Inc | Voice identification in digital assistant systems |
US11360641B2 (en) | 2019-06-01 | 2022-06-14 | Apple Inc. | Increasing the relevance of new available information |
US11468890B2 (en) | 2019-06-01 | 2022-10-11 | Apple Inc. | Methods and user interfaces for voice-based control of electronic devices |
US11488406B2 (en) | 2019-09-25 | 2022-11-01 | Apple Inc. | Text detection using global geometry estimators |
RU2741322C1 (en) * | 2020-03-13 | 2021-01-25 | Хуавэй Текнолоджиз Ко., Лтд. | Method and device for displaying possible word and graphical user interface |
US11043220B1 (en) | 2020-05-11 | 2021-06-22 | Apple Inc. | Digital assistant hardware abstraction |
US11061543B1 (en) | 2020-05-11 | 2021-07-13 | Apple Inc. | Providing relevant data items based on context |
US11755276B2 (en) | 2020-05-12 | 2023-09-12 | Apple Inc. | Reducing description length based on confidence |
US11490204B2 (en) | 2020-07-20 | 2022-11-01 | Apple Inc. | Multi-device audio adjustment coordination |
US11438683B2 (en) | 2020-07-21 | 2022-09-06 | Apple Inc. | User identification using headphones |
Citations (23)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4819271A (en) * | 1985-05-29 | 1989-04-04 | International Business Machines Corporation | Constructing Markov model word baseforms from multiple utterances by concatenating model sequences for word segments |
US5680628A (en) * | 1995-07-19 | 1997-10-21 | Inso Corporation | Method and apparatus for automated search and retrieval process |
US5896321A (en) * | 1997-11-14 | 1999-04-20 | Microsoft Corporation | Text completion system for a miniature computer |
US5953541A (en) * | 1997-01-24 | 1999-09-14 | Tegic Communications, Inc. | Disambiguating system for disambiguating ambiguous input sequences by displaying objects associated with the generated input sequences in the order of decreasing frequency of use |
US6018708A (en) * | 1997-08-26 | 2000-01-25 | Nortel Networks Corporation | Method and apparatus for performing speech recognition utilizing a supplementary lexicon of frequently used orthographies |
US6175834B1 (en) * | 1998-06-24 | 2001-01-16 | Microsoft Corporation | Consistency checker for documents containing japanese text |
US6223059B1 (en) * | 1999-02-22 | 2001-04-24 | Nokia Mobile Phones Limited | Communication terminal having a predictive editor application |
US6349282B1 (en) * | 1999-04-20 | 2002-02-19 | Larnout & Hauspie Speech Products N.V. | Compound words in speech recognition systems |
US6401060B1 (en) * | 1998-06-25 | 2002-06-04 | Microsoft Corporation | Method for typographical detection and replacement in Japanese text |
US6438545B1 (en) * | 1997-07-03 | 2002-08-20 | Value Capital Management | Semantic user interface |
US20030110031A1 (en) * | 2001-12-07 | 2003-06-12 | Sony Corporation | Methodology for implementing a vocabulary set for use in a speech recognition system |
US20040070571A1 (en) * | 2001-10-11 | 2004-04-15 | Woodard Scott E. | Speed writer program and device with speed writer program installed |
US20040086179A1 (en) * | 2002-11-04 | 2004-05-06 | Yue Ma | Post-processing system and method for correcting machine recognized text |
US20040120583A1 (en) * | 2002-12-20 | 2004-06-24 | International Business Machines Corporation | System and method for recognizing word patterns based on a virtual keyboard layout |
US20040155869A1 (en) * | 1999-05-27 | 2004-08-12 | Robinson B. Alex | Keyboard system with automatic correction |
US6801893B1 (en) * | 1999-06-30 | 2004-10-05 | International Business Machines Corporation | Method and apparatus for expanding the vocabulary of a speech system |
US6956968B1 (en) * | 1999-01-04 | 2005-10-18 | Zi Technology Corporation, Ltd. | Database engines for processing ideographic characters and methods therefor |
US20050283364A1 (en) * | 1998-12-04 | 2005-12-22 | Michael Longe | Multimodal disambiguation of speech recognition |
US7120582B1 (en) * | 1999-09-07 | 2006-10-10 | Dragon Systems, Inc. | Expanding an effective vocabulary of a speech recognition system |
US7129932B1 (en) * | 2003-03-26 | 2006-10-31 | At&T Corp. | Keyboard for interacting on small devices |
US7158678B2 (en) * | 2001-07-19 | 2007-01-02 | Motorola, Inc. | Text input method for personal digital assistants and the like |
US7199786B2 (en) * | 2002-11-29 | 2007-04-03 | Daniel Suraqui | Reduced keyboards system using unistroke input and having automatic disambiguating and a recognition method using said system |
US7293231B1 (en) * | 1999-03-18 | 2007-11-06 | British Columbia Ltd. | Data entry for personal computing devices |
Family Cites Families (23)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB2193023B (en) * | 1986-07-25 | 1990-12-12 | Hoem Gideon Cullum | Display apparatus |
JPH04264668A (en) * | 1991-02-19 | 1992-09-21 | Nec Off Syst Ltd | Document preparing machine |
JPH06131328A (en) * | 1992-10-16 | 1994-05-13 | Just Syst Corp | Method and device for processing document |
JPH0744655A (en) * | 1993-08-03 | 1995-02-14 | Sony Corp | Handwritten input display device |
US5574482A (en) * | 1994-05-17 | 1996-11-12 | Niemeier; Charles J. | Method for data input on a touch-sensitive screen |
US6008799A (en) * | 1994-05-24 | 1999-12-28 | Microsoft Corporation | Method and system for entering data using an improved on-screen keyboard |
JPH0863468A (en) * | 1994-08-17 | 1996-03-08 | Sharp Corp | Japanese syllabary and chinese character conversion system |
JP3313978B2 (en) * | 1996-07-26 | 2002-08-12 | キヤノン株式会社 | Process cartridge and electrophotographic image forming apparatus |
JPH1185910A (en) * | 1997-07-16 | 1999-03-30 | Matsushita Electric Ind Co Ltd | Device for recognizing character and method therefor and recording medium for recording the same method |
JP2000200267A (en) * | 1998-12-28 | 2000-07-18 | Casio Comput Co Ltd | Input character converting device and its program recording medium |
GB2388938B (en) * | 1999-02-22 | 2004-03-17 | Nokia Corp | A communication terminal having a predictive editor application |
JP3539479B2 (en) * | 1999-03-11 | 2004-07-07 | シャープ株式会社 | Translation apparatus, translation method, and recording medium recording translation program |
JP3492981B2 (en) * | 1999-05-30 | 2004-02-03 | テジック・コミュニケーションズ・インコーポレーテッド | An input system for generating input sequence of phonetic kana characters |
JP2001034495A (en) * | 1999-07-27 | 2001-02-09 | Nec Corp | Dual system |
JP3935374B2 (en) * | 2002-02-28 | 2007-06-20 | 株式会社東芝 | Dictionary construction support method, apparatus and program |
US7380203B2 (en) * | 2002-05-14 | 2008-05-27 | Microsoft Corporation | Natural input recognition tool |
US7098896B2 (en) * | 2003-01-16 | 2006-08-29 | Forword Input Inc. | System and method for continuous stroke word-based text input |
JP4357240B2 (en) * | 2003-08-28 | 2009-11-04 | 三洋電機株式会社 | Character recognition device, character recognition method, program, and storage medium |
US7706616B2 (en) * | 2004-02-27 | 2010-04-27 | International Business Machines Corporation | System and method for recognizing word patterns in a very large vocabulary based on a virtual keyboard layout |
US7376938B1 (en) * | 2004-03-12 | 2008-05-20 | Steven Van der Hoeven | Method and system for disambiguation and predictive resolution |
US7487461B2 (en) * | 2005-05-04 | 2009-02-03 | International Business Machines Corporation | System and method for issuing commands based on pen motions on a graphical keyboard |
US7583205B2 (en) * | 2005-07-28 | 2009-09-01 | Research In Motion Limited | Handheld electronic device with disambiguation of compound word text input |
US20070094024A1 (en) * | 2005-10-22 | 2007-04-26 | International Business Machines Corporation | System and method for improving text input in a shorthand-on-keyboard interface |
-
2005
- 2005-10-22 US US11/256,713 patent/US20070094024A1/en not_active Abandoned
-
2006
- 2006-10-12 CN CN2006800392497A patent/CN101292214B/en active Active
- 2006-10-12 JP JP2008536022A patent/JP2009512923A/en not_active Withdrawn
- 2006-10-12 WO PCT/EP2006/067338 patent/WO2007045597A1/en active Application Filing
-
2010
- 2010-10-18 US US12/906,827 patent/US8311796B2/en not_active Expired - Fee Related
-
2012
- 2012-08-10 JP JP2012178643A patent/JP5738245B2/en active Active
- 2012-08-10 JP JP2012178642A patent/JP5400200B2/en not_active Expired - Fee Related
- 2012-09-14 US US13/616,311 patent/US8543384B2/en active Active
-
2013
- 2013-04-19 US US13/866,994 patent/US8712755B2/en active Active
-
2014
- 2014-03-12 US US14/206,920 patent/US9256580B2/en not_active Expired - Fee Related
Patent Citations (24)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4819271A (en) * | 1985-05-29 | 1989-04-04 | International Business Machines Corporation | Constructing Markov model word baseforms from multiple utterances by concatenating model sequences for word segments |
US5680628A (en) * | 1995-07-19 | 1997-10-21 | Inso Corporation | Method and apparatus for automated search and retrieval process |
US5953541A (en) * | 1997-01-24 | 1999-09-14 | Tegic Communications, Inc. | Disambiguating system for disambiguating ambiguous input sequences by displaying objects associated with the generated input sequences in the order of decreasing frequency of use |
US6286064B1 (en) * | 1997-01-24 | 2001-09-04 | Tegic Communications, Inc. | Reduced keyboard and method for simultaneous ambiguous and unambiguous text input |
US6438545B1 (en) * | 1997-07-03 | 2002-08-20 | Value Capital Management | Semantic user interface |
US6018708A (en) * | 1997-08-26 | 2000-01-25 | Nortel Networks Corporation | Method and apparatus for performing speech recognition utilizing a supplementary lexicon of frequently used orthographies |
US5896321A (en) * | 1997-11-14 | 1999-04-20 | Microsoft Corporation | Text completion system for a miniature computer |
US6175834B1 (en) * | 1998-06-24 | 2001-01-16 | Microsoft Corporation | Consistency checker for documents containing japanese text |
US6401060B1 (en) * | 1998-06-25 | 2002-06-04 | Microsoft Corporation | Method for typographical detection and replacement in Japanese text |
US20050283364A1 (en) * | 1998-12-04 | 2005-12-22 | Michael Longe | Multimodal disambiguation of speech recognition |
US6956968B1 (en) * | 1999-01-04 | 2005-10-18 | Zi Technology Corporation, Ltd. | Database engines for processing ideographic characters and methods therefor |
US6223059B1 (en) * | 1999-02-22 | 2001-04-24 | Nokia Mobile Phones Limited | Communication terminal having a predictive editor application |
US7293231B1 (en) * | 1999-03-18 | 2007-11-06 | British Columbia Ltd. | Data entry for personal computing devices |
US6349282B1 (en) * | 1999-04-20 | 2002-02-19 | Larnout & Hauspie Speech Products N.V. | Compound words in speech recognition systems |
US20040155869A1 (en) * | 1999-05-27 | 2004-08-12 | Robinson B. Alex | Keyboard system with automatic correction |
US6801893B1 (en) * | 1999-06-30 | 2004-10-05 | International Business Machines Corporation | Method and apparatus for expanding the vocabulary of a speech system |
US7120582B1 (en) * | 1999-09-07 | 2006-10-10 | Dragon Systems, Inc. | Expanding an effective vocabulary of a speech recognition system |
US7158678B2 (en) * | 2001-07-19 | 2007-01-02 | Motorola, Inc. | Text input method for personal digital assistants and the like |
US20040070571A1 (en) * | 2001-10-11 | 2004-04-15 | Woodard Scott E. | Speed writer program and device with speed writer program installed |
US20030110031A1 (en) * | 2001-12-07 | 2003-06-12 | Sony Corporation | Methodology for implementing a vocabulary set for use in a speech recognition system |
US20040086179A1 (en) * | 2002-11-04 | 2004-05-06 | Yue Ma | Post-processing system and method for correcting machine recognized text |
US7199786B2 (en) * | 2002-11-29 | 2007-04-03 | Daniel Suraqui | Reduced keyboards system using unistroke input and having automatic disambiguating and a recognition method using said system |
US20040120583A1 (en) * | 2002-12-20 | 2004-06-24 | International Business Machines Corporation | System and method for recognizing word patterns based on a virtual keyboard layout |
US7129932B1 (en) * | 2003-03-26 | 2006-10-31 | At&T Corp. | Keyboard for interacting on small devices |
Cited By (176)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8107401B2 (en) | 2004-09-30 | 2012-01-31 | Avaya Inc. | Method and apparatus for providing a virtual assistant to a communication participant |
US7936863B2 (en) * | 2004-09-30 | 2011-05-03 | Avaya Inc. | Method and apparatus for providing communication tasks in a workflow |
US20060067252A1 (en) * | 2004-09-30 | 2006-03-30 | Ajita John | Method and apparatus for providing communication tasks in a workflow |
US8270320B2 (en) | 2004-09-30 | 2012-09-18 | Avaya Inc. | Method and apparatus for launching a conference based on presence of invitees |
US8180722B2 (en) | 2004-09-30 | 2012-05-15 | Avaya Inc. | Method and apparatus for data mining within communication session information using an entity relationship model |
US20070266406A1 (en) * | 2004-11-09 | 2007-11-15 | Murali Aravamudan | Method and system for performing actions using a non-intrusive television with reduced text input |
US20060101504A1 (en) * | 2004-11-09 | 2006-05-11 | Veveo.Tv, Inc. | Method and system for performing searches for television content and channels using a non-intrusive television interface and with reduced text input |
US9177081B2 (en) | 2005-08-26 | 2015-11-03 | Veveo, Inc. | Method and system for processing ambiguous, multi-term search queries |
US9256580B2 (en) | 2005-10-22 | 2016-02-09 | Nuance Communications, Inc. | System and method for improving text input in a shorthand-on-keyboard interface |
US8543384B2 (en) | 2005-10-22 | 2013-09-24 | Nuance Communications, Inc. | Input recognition using multiple lexicons |
US20110071834A1 (en) * | 2005-10-22 | 2011-03-24 | Per-Ola Kristensson | System and method for improving text input in a shorthand-on-keyboard interface |
US8311796B2 (en) | 2005-10-22 | 2012-11-13 | Nuance Communications, Inc. | System and method for improving text input in a shorthand-on-keyboard interface |
US8370284B2 (en) | 2005-11-23 | 2013-02-05 | Veveo, Inc. | System and method for finding desired results by incremental search using an ambiguous keypad with the input containing orthographic and/or typographic errors |
US20100153380A1 (en) * | 2005-11-23 | 2010-06-17 | Veveo, Inc. | System And Method For Finding Desired Results By Incremental Search Using An Ambiguous Keypad With The Input Containing Orthographic And/Or Typographic Errors |
US8589324B2 (en) * | 2005-11-23 | 2013-11-19 | Veveo, Inc. | System and method for finding desired results by incremental search using an ambiguous keypad with the input containing typographic errors |
US20070156747A1 (en) * | 2005-12-12 | 2007-07-05 | Tegic Communications Llc | Mobile Device Retrieval and Navigation |
US8825694B2 (en) * | 2005-12-12 | 2014-09-02 | Nuance Communications, Inc. | Mobile device retrieval and navigation |
US20110126146A1 (en) * | 2005-12-12 | 2011-05-26 | Mark Samuelson | Mobile device retrieval and navigation |
US7840579B2 (en) * | 2005-12-12 | 2010-11-23 | Tegic Communications Inc. | Mobile device retrieval and navigation |
US8949231B2 (en) | 2006-03-06 | 2015-02-03 | Veveo, Inc. | Methods and systems for selecting and presenting content based on activity level spikes associated with the content |
US8380726B2 (en) | 2006-03-06 | 2013-02-19 | Veveo, Inc. | Methods and systems for selecting and presenting content based on a comparison of preference signatures from multiple users |
US9213755B2 (en) | 2006-03-06 | 2015-12-15 | Veveo, Inc. | Methods and systems for selecting and presenting content based on context sensitive user preferences |
US8583566B2 (en) | 2006-03-06 | 2013-11-12 | Veveo, Inc. | Methods and systems for selecting and presenting content based on learned periodicity of user content selection |
US9128987B2 (en) | 2006-03-06 | 2015-09-08 | Veveo, Inc. | Methods and systems for selecting and presenting content based on a comparison of preference signatures from multiple users |
US9092503B2 (en) | 2006-03-06 | 2015-07-28 | Veveo, Inc. | Methods and systems for selecting and presenting content based on dynamically identifying microgenres associated with the content |
US8825576B2 (en) | 2006-03-06 | 2014-09-02 | Veveo, Inc. | Methods and systems for selecting and presenting content on a first system based on user preferences learned on a second system |
US8543516B2 (en) | 2006-03-06 | 2013-09-24 | Veveo, Inc. | Methods and systems for selecting and presenting content on a first system based on user preferences learned on a second system |
US8478794B2 (en) | 2006-03-06 | 2013-07-02 | Veveo, Inc. | Methods and systems for segmenting relative user preferences into fine-grain and coarse-grain collections |
US8112454B2 (en) | 2006-03-06 | 2012-02-07 | Veveo, Inc. | Methods and systems for ordering content items according to learned user preferences |
US20090217203A1 (en) * | 2006-03-06 | 2009-08-27 | Veveo, Inc. | Methods and systems for segmeting relative user preferences into fine-grain and course-grain collections |
US8943083B2 (en) | 2006-03-06 | 2015-01-27 | Veveo, Inc. | Methods and systems for segmenting relative user preferences into fine-grain and coarse-grain collections |
US8438160B2 (en) | 2006-03-06 | 2013-05-07 | Veveo, Inc. | Methods and systems for selecting and presenting content based on dynamically identifying Microgenres Associated with the content |
US9075861B2 (en) | 2006-03-06 | 2015-07-07 | Veveo, Inc. | Methods and systems for segmenting relative user preferences into fine-grain and coarse-grain collections |
US8429188B2 (en) | 2006-03-06 | 2013-04-23 | Veveo, Inc. | Methods and systems for selecting and presenting content based on context sensitive user preferences |
US8429155B2 (en) | 2006-03-06 | 2013-04-23 | Veveo, Inc. | Methods and systems for selecting and presenting content based on activity level spikes associated with the content |
US20100325111A1 (en) * | 2006-03-06 | 2010-12-23 | Veveo, Inc. | Methods and Systems for Selecting and Presenting Content Based on Context Sensitive User Preferences |
US8417717B2 (en) | 2006-03-30 | 2013-04-09 | Veveo Inc. | Method and system for incrementally selecting and providing relevant search engines in response to a user query |
US20070255693A1 (en) * | 2006-03-30 | 2007-11-01 | Veveo, Inc. | User interface method and system for incrementally searching and selecting content items and for presenting advertising in response to search activities |
US9223873B2 (en) | 2006-03-30 | 2015-12-29 | Veveo, Inc. | Method and system for incrementally selecting and providing relevant search engines in response to a user query |
US8086602B2 (en) | 2006-04-20 | 2011-12-27 | Veveo Inc. | User interface methods and systems for selecting and presenting content based on user navigation and selection actions associated with the content |
US8423583B2 (en) | 2006-04-20 | 2013-04-16 | Veveo Inc. | User interface methods and systems for selecting and presenting content based on user relationships |
US8688746B2 (en) | 2006-04-20 | 2014-04-01 | Veveo, Inc. | User interface methods and systems for selecting and presenting content based on user relationships |
US10146840B2 (en) | 2006-04-20 | 2018-12-04 | Veveo, Inc. | User interface methods and systems for selecting and presenting content based on user relationships |
US9087109B2 (en) | 2006-04-20 | 2015-07-21 | Veveo, Inc. | User interface methods and systems for selecting and presenting content based on user relationships |
US8375069B2 (en) | 2006-04-20 | 2013-02-12 | Veveo Inc. | User interface methods and systems for selecting and presenting content based on user navigation and selection actions associated with the content |
US9646277B2 (en) | 2006-05-07 | 2017-05-09 | Varcode Ltd. | System and method for improved quality management in a product logistic chain |
US10726375B2 (en) | 2006-05-07 | 2020-07-28 | Varcode Ltd. | System and method for improved quality management in a product logistic chain |
US10037507B2 (en) | 2006-05-07 | 2018-07-31 | Varcode Ltd. | System and method for improved quality management in a product logistic chain |
US10445678B2 (en) | 2006-05-07 | 2019-10-15 | Varcode Ltd. | System and method for improved quality management in a product logistic chain |
US20080141125A1 (en) * | 2006-06-23 | 2008-06-12 | Firooz Ghassabian | Combined data entry systems |
US8799804B2 (en) | 2006-10-06 | 2014-08-05 | Veveo, Inc. | Methods and systems for a linear character selection display interface for ambiguous text input |
US8078884B2 (en) | 2006-11-13 | 2011-12-13 | Veveo, Inc. | Method of and system for selecting and presenting content based on user identification |
US10176451B2 (en) | 2007-05-06 | 2019-01-08 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US10776752B2 (en) | 2007-05-06 | 2020-09-15 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US10504060B2 (en) | 2007-05-06 | 2019-12-10 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US20080313564A1 (en) * | 2007-05-25 | 2008-12-18 | Veveo, Inc. | System and method for text disambiguation and context designation in incremental search |
US8296294B2 (en) | 2007-05-25 | 2012-10-23 | Veveo, Inc. | Method and system for unified searching across and within multiple documents |
US20080313174A1 (en) * | 2007-05-25 | 2008-12-18 | Veveo, Inc. | Method and system for unified searching across and within multiple documents |
US8429158B2 (en) | 2007-05-25 | 2013-04-23 | Veveo, Inc. | Method and system for unified searching and incremental searching across and within multiple documents |
US20080313574A1 (en) * | 2007-05-25 | 2008-12-18 | Veveo, Inc. | System and method for search with reduced physical interaction requirements |
US8549424B2 (en) * | 2007-05-25 | 2013-10-01 | Veveo, Inc. | System and method for text disambiguation and context designation in incremental search |
US8886642B2 (en) | 2007-05-25 | 2014-11-11 | Veveo, Inc. | Method and system for unified searching and incremental searching across and within multiple documents |
US7610283B2 (en) * | 2007-06-12 | 2009-10-27 | Microsoft Corporation | Disk-based probabilistic set-similarity indexes |
US20080313128A1 (en) * | 2007-06-12 | 2008-12-18 | Microsoft Corporation | Disk-Based Probabilistic Set-Similarity Indexes |
US20100286979A1 (en) * | 2007-08-01 | 2010-11-11 | Ginger Software, Inc. | Automatic context sensitive language correction and enhancement using an internet corpus |
US8914278B2 (en) * | 2007-08-01 | 2014-12-16 | Ginger Software, Inc. | Automatic context sensitive language correction and enhancement using an internet corpus |
US9026432B2 (en) | 2007-08-01 | 2015-05-05 | Ginger Software, Inc. | Automatic context sensitive language generation, correction and enhancement using an internet corpus |
US20090063135A1 (en) * | 2007-08-31 | 2009-03-05 | Vadim Fux | Handheld Electronic Device and Method Employing Logical Proximity of Characters in Spell Checking |
US8452584B2 (en) | 2007-08-31 | 2013-05-28 | Research In Motion Limited | Handheld electronic device and method employing logical proximity of characters in spell checking |
US8296128B2 (en) | 2007-08-31 | 2012-10-23 | Research In Motion Limited | Handheld electronic device and method employing logical proximity of characters in spell checking |
US7949516B2 (en) * | 2007-08-31 | 2011-05-24 | Research In Motion Limited | Handheld electronic device and method employing logical proximity of characters in spell checking |
US20110197127A1 (en) * | 2007-08-31 | 2011-08-11 | Research In Motion Limited | Handheld electronic device and method employing logical proximity of characters in spell checking |
US9836678B2 (en) | 2007-11-14 | 2017-12-05 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US9558439B2 (en) | 2007-11-14 | 2017-01-31 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US10262251B2 (en) | 2007-11-14 | 2019-04-16 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US9135544B2 (en) | 2007-11-14 | 2015-09-15 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US10719749B2 (en) | 2007-11-14 | 2020-07-21 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US10417543B2 (en) | 2008-06-10 | 2019-09-17 | Varcode Ltd. | Barcoded indicators for quality management |
US10049314B2 (en) | 2008-06-10 | 2018-08-14 | Varcode Ltd. | Barcoded indicators for quality management |
US10885414B2 (en) | 2008-06-10 | 2021-01-05 | Varcode Ltd. | Barcoded indicators for quality management |
US11238323B2 (en) | 2008-06-10 | 2022-02-01 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US10303992B2 (en) | 2008-06-10 | 2019-05-28 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US11341387B2 (en) | 2008-06-10 | 2022-05-24 | Varcode Ltd. | Barcoded indicators for quality management |
US9646237B2 (en) | 2008-06-10 | 2017-05-09 | Varcode Ltd. | Barcoded indicators for quality management |
US9626610B2 (en) | 2008-06-10 | 2017-04-18 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US10089566B2 (en) | 2008-06-10 | 2018-10-02 | Varcode Ltd. | Barcoded indicators for quality management |
US10776680B2 (en) | 2008-06-10 | 2020-09-15 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US10572785B2 (en) | 2008-06-10 | 2020-02-25 | Varcode Ltd. | Barcoded indicators for quality management |
US11704526B2 (en) | 2008-06-10 | 2023-07-18 | Varcode Ltd. | Barcoded indicators for quality management |
US9996783B2 (en) | 2008-06-10 | 2018-06-12 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US9710743B2 (en) | 2008-06-10 | 2017-07-18 | Varcode Ltd. | Barcoded indicators for quality management |
US10789520B2 (en) | 2008-06-10 | 2020-09-29 | Varcode Ltd. | Barcoded indicators for quality management |
US9317794B2 (en) | 2008-06-10 | 2016-04-19 | Varcode Ltd. | Barcoded indicators for quality management |
US11449724B2 (en) | 2008-06-10 | 2022-09-20 | Varcode Ltd. | System and method for quality management utilizing barcode indicators |
US9384435B2 (en) | 2008-06-10 | 2016-07-05 | Varcode Ltd. | Barcoded indicators for quality management |
US8769427B2 (en) | 2008-09-19 | 2014-07-01 | Google Inc. | Quick gesture input |
US20100073329A1 (en) * | 2008-09-19 | 2010-03-25 | Tiruvilwamalai Venkatram Raman | Quick Gesture Input |
US10466890B2 (en) | 2008-09-19 | 2019-11-05 | Google Llc | Quick gesture input |
US9639267B2 (en) | 2008-09-19 | 2017-05-02 | Google Inc. | Quick gesture input |
US8560302B2 (en) * | 2008-11-07 | 2013-10-15 | Guangdong Guobi Technology Co. Ltd | Method and system for generating derivative words |
US20110208512A1 (en) * | 2008-11-07 | 2011-08-25 | Jinglian Gao | Method and system for generating derivative words |
US8677236B2 (en) | 2008-12-19 | 2014-03-18 | Microsoft Corporation | Contact-specific and location-aware lexicon prediction |
US20100238125A1 (en) * | 2009-03-20 | 2010-09-23 | Nokia Corporation | Method, Apparatus, and Computer Program Product For Discontinuous Shapewriting |
US9659002B2 (en) | 2009-03-30 | 2017-05-23 | Touchtype Ltd | System and method for inputting text into electronic devices |
US9424246B2 (en) | 2009-03-30 | 2016-08-23 | Touchtype Ltd. | System and method for inputting text into electronic devices |
US20140350920A1 (en) | 2009-03-30 | 2014-11-27 | Touchtype Ltd | System and method for inputting text into electronic devices |
US9189472B2 (en) | 2009-03-30 | 2015-11-17 | Touchtype Limited | System and method for inputting text into small screen devices |
US10073829B2 (en) | 2009-03-30 | 2018-09-11 | Touchtype Limited | System and method for inputting text into electronic devices |
US10445424B2 (en) | 2009-03-30 | 2019-10-15 | Touchtype Limited | System and method for inputting text into electronic devices |
US10402493B2 (en) | 2009-03-30 | 2019-09-03 | Touchtype Ltd | System and method for inputting text into electronic devices |
US10191654B2 (en) | 2009-03-30 | 2019-01-29 | Touchtype Limited | System and method for inputting text into electronic devices |
US9166714B2 (en) | 2009-09-11 | 2015-10-20 | Veveo, Inc. | Method of and system for presenting enriched video viewing analytics |
US9046932B2 (en) | 2009-10-09 | 2015-06-02 | Touchtype Ltd | System and method for inputting text into electronic devices based on text and text category predictions |
US9015036B2 (en) | 2010-02-01 | 2015-04-21 | Ginger Software, Inc. | Automatic context sensitive language correction using an internet corpus particularly for small keyboard devices |
US9703779B2 (en) | 2010-02-04 | 2017-07-11 | Veveo, Inc. | Method of and system for enhanced local-device content discovery |
US9052748B2 (en) | 2010-03-04 | 2015-06-09 | Touchtype Limited | System and method for inputting text into electronic devices |
US10146765B2 (en) | 2010-09-29 | 2018-12-04 | Touchtype Ltd. | System and method for inputting text into electronic devices |
US9384185B2 (en) | 2010-09-29 | 2016-07-05 | Touchtype Ltd. | System and method for inputting text into electronic devices |
US20130262452A1 (en) * | 2010-12-17 | 2013-10-03 | Telefonaktiebolaget L M Ericsson (Publ) | Server for Conveying a Set of Contact Identification Data to a User Equipment, Methods Therefor, User Equipment, Computer Programs and Computer Program Products |
US10613746B2 (en) | 2012-01-16 | 2020-04-07 | Touchtype Ltd. | System and method for inputting text |
US8667414B2 (en) | 2012-03-23 | 2014-03-04 | Google Inc. | Gestural input at a virtual keyboard |
US20180158448A1 (en) * | 2012-07-09 | 2018-06-07 | Nuance Communications, Inc. | Detecting potential significant errors in speech recognition results |
US11495208B2 (en) * | 2012-07-09 | 2022-11-08 | Nuance Communications, Inc. | Detecting potential significant errors in speech recognition results |
US20150248882A1 (en) * | 2012-07-09 | 2015-09-03 | Nuance Communications, Inc. | Detecting potential significant errors in speech recognition results |
US9818398B2 (en) * | 2012-07-09 | 2017-11-14 | Nuance Communications, Inc. | Detecting potential significant errors in speech recognition results |
US9021380B2 (en) | 2012-10-05 | 2015-04-28 | Google Inc. | Incremental multi-touch gesture recognition |
US9552080B2 (en) | 2012-10-05 | 2017-01-24 | Google Inc. | Incremental feature-based gesture-keyboard decoding |
US8782549B2 (en) | 2012-10-05 | 2014-07-15 | Google Inc. | Incremental feature-based gesture-keyboard decoding |
US9747272B2 (en) | 2012-10-16 | 2017-08-29 | Google Inc. | Feature-based autocorrection |
US10140284B2 (en) | 2012-10-16 | 2018-11-27 | Google Llc | Partial gesture text entry |
US10977440B2 (en) | 2012-10-16 | 2021-04-13 | Google Llc | Multi-gesture text input prediction |
US9710453B2 (en) | 2012-10-16 | 2017-07-18 | Google Inc. | Multi-gesture text input prediction |
US9134906B2 (en) | 2012-10-16 | 2015-09-15 | Google Inc. | Incremental multi-word recognition |
US10489508B2 (en) | 2012-10-16 | 2019-11-26 | Google Llc | Incremental multi-word recognition |
US9678943B2 (en) | 2012-10-16 | 2017-06-13 | Google Inc. | Partial gesture text entry |
US9798718B2 (en) | 2012-10-16 | 2017-10-24 | Google Inc. | Incremental multi-word recognition |
US9542385B2 (en) | 2012-10-16 | 2017-01-10 | Google Inc. | Incremental multi-word recognition |
US11379663B2 (en) | 2012-10-16 | 2022-07-05 | Google Llc | Multi-gesture text input prediction |
US8850350B2 (en) | 2012-10-16 | 2014-09-30 | Google Inc. | Partial gesture text entry |
US8843845B2 (en) | 2012-10-16 | 2014-09-23 | Google Inc. | Multi-gesture text input prediction |
US8701032B1 (en) | 2012-10-16 | 2014-04-15 | Google Inc. | Incremental multi-word recognition |
US10552719B2 (en) | 2012-10-22 | 2020-02-04 | Varcode Ltd. | Tamper-proof quality management barcode indicators |
US10839276B2 (en) | 2012-10-22 | 2020-11-17 | Varcode Ltd. | Tamper-proof quality management barcode indicators |
US10242302B2 (en) | 2012-10-22 | 2019-03-26 | Varcode Ltd. | Tamper-proof quality management barcode indicators |
US8819574B2 (en) | 2012-10-22 | 2014-08-26 | Google Inc. | Space prediction for text input |
US9400952B2 (en) | 2012-10-22 | 2016-07-26 | Varcode Ltd. | Tamper-proof quality management barcode indicators |
US9965712B2 (en) | 2012-10-22 | 2018-05-08 | Varcode Ltd. | Tamper-proof quality management barcode indicators |
US10019435B2 (en) | 2012-10-22 | 2018-07-10 | Google Llc | Space prediction for text input |
US9633296B2 (en) | 2012-10-22 | 2017-04-25 | Varcode Ltd. | Tamper-proof quality management barcode indicators |
US20140164996A1 (en) * | 2012-12-11 | 2014-06-12 | Canon Kabushiki Kaisha | Apparatus, method, and storage medium |
US11727212B2 (en) | 2013-01-15 | 2023-08-15 | Google Llc | Touch keyboard using a trained model |
US9830311B2 (en) | 2013-01-15 | 2017-11-28 | Google Llc | Touch keyboard using language and spatial models |
US10528663B2 (en) | 2013-01-15 | 2020-01-07 | Google Llc | Touch keyboard using language and spatial models |
US11334717B2 (en) | 2013-01-15 | 2022-05-17 | Google Llc | Touch keyboard using a trained model |
US9199155B2 (en) * | 2013-03-14 | 2015-12-01 | Google Inc. | Morpheme-level predictive graphical keyboard |
US20140278368A1 (en) * | 2013-03-14 | 2014-09-18 | Google Inc. | Morpheme-level predictive graphical keyboard |
US9547439B2 (en) | 2013-04-22 | 2017-01-17 | Google Inc. | Dynamically-positioned character string suggestions for gesture typing |
US9841895B2 (en) | 2013-05-03 | 2017-12-12 | Google Llc | Alternative hypothesis error correction for gesture typing |
US9081500B2 (en) | 2013-05-03 | 2015-07-14 | Google Inc. | Alternative hypothesis error correction for gesture typing |
US10241673B2 (en) | 2013-05-03 | 2019-03-26 | Google Llc | Alternative hypothesis error correction for gesture typing |
US20150066500A1 (en) * | 2013-08-30 | 2015-03-05 | Honda Motor Co., Ltd. | Speech processing device, speech processing method, and speech processing program |
US9336777B2 (en) * | 2013-08-30 | 2016-05-10 | Honda Motor Co., Ltd. | Speech processing device, speech processing method, and speech processing program |
US11060924B2 (en) | 2015-05-18 | 2021-07-13 | Varcode Ltd. | Thermochromic ink indicia for activatable quality labels |
US11781922B2 (en) | 2015-05-18 | 2023-10-10 | Varcode Ltd. | Thermochromic ink indicia for activatable quality labels |
US11009406B2 (en) | 2015-07-07 | 2021-05-18 | Varcode Ltd. | Electronic quality indicator |
US11614370B2 (en) | 2015-07-07 | 2023-03-28 | Varcode Ltd. | Electronic quality indicator |
US11920985B2 (en) | 2015-07-07 | 2024-03-05 | Varcode Ltd. | Electronic quality indicator |
US10697837B2 (en) | 2015-07-07 | 2020-06-30 | Varcode Ltd. | Electronic quality indicator |
US20170025117A1 (en) * | 2015-07-23 | 2017-01-26 | Samsung Electronics Co., Ltd. | Speech recognition apparatus and method |
US9911409B2 (en) * | 2015-07-23 | 2018-03-06 | Samsung Electronics Co., Ltd. | Speech recognition apparatus and method |
US10372310B2 (en) | 2016-06-23 | 2019-08-06 | Microsoft Technology Licensing, Llc | Suppression of input images |
CN106484133A (en) * | 2016-08-24 | 2017-03-08 | 苏娜香 | The method for input in Chinese being carried out using handwriting stenograph symbol |
US10235363B2 (en) * | 2017-04-28 | 2019-03-19 | Sap Se | Instant translation of user interfaces of a web application |
US20180349349A1 (en) * | 2017-06-02 | 2018-12-06 | Apple Inc. | Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling |
US10657328B2 (en) * | 2017-06-02 | 2020-05-19 | Apple Inc. | Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling |
CN110826322A (en) * | 2019-10-22 | 2020-02-21 | 中电科大数据研究院有限公司 | Method for discovering new words, predicting parts of speech and marking |
Also Published As
Publication number | Publication date |
---|---|
CN101292214A (en) | 2008-10-22 |
JP2009512923A (en) | 2009-03-26 |
JP2012256354A (en) | 2012-12-27 |
JP2012256353A (en) | 2012-12-27 |
US20130006639A1 (en) | 2013-01-03 |
JP5738245B2 (en) | 2015-06-17 |
JP5400200B2 (en) | 2014-01-29 |
US8543384B2 (en) | 2013-09-24 |
WO2007045597A1 (en) | 2007-04-26 |
US20110071834A1 (en) | 2011-03-24 |
US20140278374A1 (en) | 2014-09-18 |
US9256580B2 (en) | 2016-02-09 |
US20130234947A1 (en) | 2013-09-12 |
US8712755B2 (en) | 2014-04-29 |
US8311796B2 (en) | 2012-11-13 |
CN101292214B (en) | 2010-07-21 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8543384B2 (en) | Input recognition using multiple lexicons | |
US11614862B2 (en) | System and method for inputting text into electronic devices | |
US10073829B2 (en) | System and method for inputting text into electronic devices | |
JP4829901B2 (en) | Method and apparatus for confirming manually entered indeterminate text input using speech input | |
US7319957B2 (en) | Handwriting and voice input with automatic correction | |
EP1018069B1 (en) | Reduced keyboard disambiguating system | |
CA2556065C (en) | Handwriting and voice input with automatic correction | |
US20050192802A1 (en) | Handwriting and voice input with automatic correction | |
US20140108004A1 (en) | Text/character input system, such as for use with touch screens on mobile phones | |
JP2005530272A (en) | Clear character filtering of ambiguous text input | |
JPH11312046A (en) | Input system for generating input sequence of voice kana character |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:KRISTENSSON, PER-OLA;ZHAI, SHUMIN;REEL/FRAME:017127/0040 Effective date: 20051020 |
|
AS | Assignment |
Owner name: NUANCE COMMUNICATIONS, INC., MASSACHUSETTS Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:INTERNATIONAL BUSINESS MACHINES CORPORATION;REEL/FRAME:025608/0301 Effective date: 20100813 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |