Nuance Communications, Inc.

Cerence Operating Company

Donni McCray

David Jon Kay

Ethan Robert Bradford

Simon H Corston

Keith Trnka

Nils Lenke

Erland Unruh

Mohammad Mehdi Moniri

Aaron Sheedy

Christophe Couvreur

Lior Ben-Gigi

Claes-Fredrik Mannby

Stefan Hamerich

Quynh Nguyen

Brian Yee

Niels Does

Stefan Frank

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.

Multimodal input processing for vehicle computer

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.

Performing actions based on determined intent of messages

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 &#x201c;Waiheke&#x201d;), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Updating population language models based on changes made by user clusters

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.

Correction of previous words and other user text input errors

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Seattle, WA, United States of America

Donni McCray