Microsoft Technology Licensing, LLC

Jason Michael Bower

Daryn Elliot Robbins

Kenichi Morimoto

Eric Norman Badger

Peter Gregory Davis

Drew Elliott Linerud

Simon Cheng Liu

Chet Laughlin

Rui Li

Kenji Furuuchi

Honghui Sun

Word predictions in a message are selected or prioritized based on the recipient of the message and a previous location of use by a user. An input history is created based on messages sent to the recipient from the user at a particular location (e.g., global positioning system coordinates). As the user composes subsequent messages, a current location of the user is determined. Word predictions are performed based on a comparison of the current location to the previous locations, and based on the recipient(s). In further embodiments, location-aware spell-check functionality is provided for the messages.

Contact-specific and location-aware lexicon prediction

Embodiments are configured to provide one or more candidates based in part on an input identifier associated with an input interface. In an embodiment, a system includes an input method editor (IME) that can be configured to provide one or more candidates based in part on a prediction model and one or more input scope values associated with an input interface. The IME can also operate to store an entered string including one or more associated input scope values to a storage component. The stored information can be used when suggesting candidates.

Predicting candidates using input scopes

Embodiments are configured to provide one or more candidates based in part on an input. In an embodiment, a system includes a prediction engine which can be configured to provide one or more ranked candidates using one or more configurable data sources and/or candidate providers. Each data source can be configured to include a candidate provider to predict and return predicted candidates. The prediction engine can use a predicted candidate to rank and return a ranked candidate to a user interface, process, or other application. In one embodiment, a computing device can include and use a prediction engine which can use a customized ranking component to rank and return ranked candidates based in part on a query type. The customized ranking component can use predicted candidates provided by one or more data sources and/or candidate providers when making a ranking determination.

Predicting candidates using information sources

Context-based word prediction is provided. A software application utilizes words contained in an application document to provide context-based word prediction in the same or a related document. The software application creates an application defined data source and populates the data source with words occurring in a document. When the same or a related document is being edited via an input method, for example, typing, speech recognition, electronic handwriting, etc., a prediction engine presents candidate words from the application defined data source that match current text input, and the user may choose from the presented candidate words for automatic population into the document being edited. Information from the application defined data source may be transferred between computing devices, for example, between a mobile computing device and a desktop (non-mobile) computing device.

Context based word prediction

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Bothell, WA, United States of America

Jason Michael Bower