Abstract: Provided are a system and method of input to a computer in a vehicle. An electronic communication is formed between a first electronic device and a display of a second electronic device that is viewable by a user from a location in the vehicle, including providing by the first electronic device a first input mode for the second electronic device. A multimodal application is executed that permits the user to interact with the display of the second electronic device by a combination of the first input mode and at least one second input mode for the second electronic device. The combination of the first input mode and the at least one second input mode controls the display of the first electronic device.

Abstract: Systems and methods are described herein for performing actions based on a determined intent within messages received by a mobile device. In some embodiments, the systems and methods may access a message received by a mobile application (e.g., text messaging application, chat application, and so on) of the mobile device, analyze the message to determine an intent of the message (e.g., whether the message includes a request or a task for a recipient of the message), and perform an action based on the determined intent (e.g., set a reminder when the message includes a task for the recipient). Further details are described herein.

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: Systems and methods are described herein for performing actions based on a determined intent within messages received by a mobile device. In some embodiments, the systems and methods may access a message received by a mobile application (e.g., text messaging application, chat application, and so on) of the mobile device, analyze the message to determine an intent of the message (e.g., whether the message includes a request or a task for a recipient of the message), and perform an action based on the determined intent (e.g., set a reminder when the message includes a task for the recipient). Further details are described herein.

Abstract: Systems and methods are described herein for determining suggestions of emojis, and other pictorial or multimedia elements, to users based on the content (e.g., a derived intent, tone, sentiment, and so on) of their messages. In some embodiments, the systems and methods access a string of text input by a user of a messaging application of a computing device, assign a specific classification to the string of text, and identify one or more pictorial elements to present to the user for insertion into the string of text that are associated with the specific classification of the string of text.

Abstract: Disclosed herein is an adaptive redo technology for trace text input via a virtual keyboard, including systems and methods for adaptively modifying the top word suggestion after a user deletes a word and traces a pattern recognized as the same word. An adaptive redo system can recognize that the user has just rejected the default word it is about to present, and instead present an alternative candidate. As a result, the user can proceed without having to re-delete the same rejected word a second time. A text input system with adaptive redo can recognize when a redo trace matches a previous, deleted trace without needing to check the similarity of traces themselves or the similarity of any features of a trace path, reducing the computational requirements needed to improve text recognition after a user deletes and reenters a word.

Abstract: The disclosed system provides an efficient method of using a later word or words entered after a previous word along with one or more language models that show which words are likely to occur together to identify a better disambiguated choice for the previous word. To identify the better disambiguated choice for the previous word, the system can evaluate the conditional probability for the later word of various candidate previous words, and select the candidate previous word that has the highest conditional probability. If the conditional probability of the selected candidate previous word exceeds that of the previous word that was entered by at least a factor, then the system can include the selected candidate previous word in a selection list for user selection. The disclosed system also provides an efficient method for using one or more language models and a later word to correct errors in segmenting the word.

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: The disclosed system provides an efficient method of using a later word or words entered after a previous word along with one or more language models that show which words are likely to occur together to identify a better disambiguated choice for the previous word. To identify the better disambiguated choice for the previous word, the system can evaluate the conditional probability for the later word of various candidate previous words, and select the candidate previous word that has the highest conditional probability. If the conditional probability of the selected candidate previous word exceeds that of the previous word that was entered by at least a factor, then the system can include the selected candidate previous word in a selection list for user selection. The disclosed system also provides an efficient method for using one or more language models and a later word to correct errors in segmenting the word.

Abstract: A system and method that learns phrases from scratch based on capturing text entered on electronic devices by a user along with context for the captured text. The system constructs phrase resources based on analysis of the user's phrase usage in various contexts. By identifying similar or matching contexts for phrases employed by the user, the system dramatically improves the ability to predict phrases intended by the user. The disclosed system provides context-based text input that uses phrases previously entered by the user in similar contexts to provide meaningful phrase suggestions, as well as phrase completion suggestions taking into account previously entered text. In some implementations, the system utilizes linguistic models based on conditional probabilities to identify and/or rank suggested phrases for the relevant context.

Abstract: Technology is described for developing a language model for a language recognition system from scratch based on aggregating and analyzing text input from multiple users of the language. The technology allows a user to select a language, and if no existing language model is available for the selected language, provides a new language model for the selected language, monitors and collects information about the use of words in the selected language, combines information collected from multiple users of the selected language, and updates the user's language model based on the combined information from multiple users of the selected language.

Abstract: The present technology describes context based text input, which uses linguistic models based on conditional probabilities to provide meaningful word completion and modification suggestions, such as auto-capitalization, based on previously entered words. The technology may use previously entered left context words to modify a list of candidate words matching a current user input. The left context may include one or more previously input words followed by a space, hyphen, or another word. The technology may then modify the list of candidate words based on one or more conditional probabilities, where the conditional probabilities show a probability of a candidate list modification given a particular left context. The modifying may comprise reordering the list or modifying properties of words on the list such as capitalization. The technology may then display the modified list of candidate words to the user.

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).