Touchtype Limited

Microsoft Technology Licensing, LLC

Benjamin William Medlock

Douglas Alexander Harper Orr

Jonathan Paul Reynolds

James Aley

Adam Clements

Richard Tunnicliffe

Stephen Thomas Spencer

Páidí Creed

Michael Bell

Joe Freeman

Emanuel George Hategan

Joseph Hayyim Benedict Osbourne

David Paul Williams

Joseph Hayyim Benedict Osborne

David Martinez Del Corral

There is provided a formatting module configured to format spaces in an electronic character sequence. The formatting module supports at least one language and comprises a language identifier configured to identify whether the electronic character sequence is written in a supported language, and a character identifier configured to identify a particular character or a particular sequence of characters in the electronic character sequence. The formatting module is configured to format spaces in the electronic character sequence on the basis of the language identified and the particular character identified or the particular sequence of characters identified, when a supported language is identified. A system and method for formatting text are also provided.

Formatting module, system and method for formatting an electronic character sequence

A system comprising: one or more processors; and program instructions for providing a user interface. Execution of the program instructions by the one or more processors causes the one or more processors to display a user interface comprising: an entry mechanism for entering an input comprising at least one character, symbol, numeral or punctuation mark; a typing pane configured to display the input entered via the entry mechanism; wherein, in response to selection of a prediction based on the input entered via the entry mechanism, the typing pane is configured to replace the input entered via the entry mechanism by the prediction; and an undo button, wherein in response to user selection of the undo button, the typing pane is configured to replace the selected prediction by the input entered via the entry mechanism.

System and method for inputting text into electronic devices

The present invention provides a system comprising a user interface configured to receive text input by a user, a text prediction engine comprising a plurality of language models and configured to receive the input text from the user interface and to generate concurrently text predictions using the plurality of language models, and wherein the text prediction engine is further configured to provide text predictions to the user interface for display and user selection. An analogous method and an interface for use with the system and method are also provided.

Disclosed are systems and methods that model a user's interaction with a user interface. There is provided a data input system, comprising a user interface having a plurality of targets and being configured to receive user input. The system comprises a plurality of models, each of which relates previous user input events corresponding to a target to that target. An input probability generator is configured to generate, in association with the plurality of models, a probability that a user input event corresponds to a particular target. There is also provided a method of modelling a target of a user interface having a plurality of targets and being configured to receive input, by modelling for each target previous user input events which correspond to that target. Furthermore, there is provided a method of inputting data into a system comprising a user interface having a plurality of targets and being configured to receive input. The method comprises generating using an input probability generator in association with a plurality of models, each model relating previous input events corresponding to a target to that target, a probability that a user input event corresponds to a particular target.

User input prediction

There is provided a search system comprising a statistical model trained on text associated with a piece of content. The text associated with the piece of content is drawn from a plurality of different data sources. The system is configured to receive text input and generate using the statistical model an estimate of the likelihood that the piece of content is relevant given the text input. A corresponding method is also provided.

Search system and corresponding method

A system comprising a feature identification means configured to generate one or more features from a plurality of samples, wherein each of the plurality of samples are sampled at a different time and correspond to a location of a single continuous gesture on a gesture-sensitive keyboard as the gesture is being performed and, wherein each of the one or more features relates to one of a plurality of targets of the gesture sensitive keyboard that a user may have intended to input when performing the gesture. The system comprises a prediction means configured to predict one or more terms from the one or more features, the prediction means comprising a prefix tree generating means configured to generate a prefix tree of terms which includes the one or more features, a path finding means configured to find one or more paths through the prefix tree of terms given the one or more features, and a predictor. A corresponding method is also provided.

System and method for inputting text

Systems comprising a user interface configured to receive text input by a user and a text prediction engine configured to receive the input text and generate text predictions. The text prediction engine may comprise a general language model and a context-specific language model. The text prediction engine is configured to generate text predictions from the general language model and the context-specific language model and combine the text predictions. The text prediction engine may comprise first and second language models and a first context-specific weighting factor associated with the first language model. The text prediction engine is configured to generate text predictions using the first and second language models, generate weighted probabilities of the text predictions from the first language model using the first context-specific weighting factor; and generate final text predictions from the weighted predictions generated from the first language model and the predictions generated by the second language model.

A text prediction engine, a system comprising a text prediction engine, and a method for generating sequence predictions. The text prediction engine, system and method generate multiple sequence predictions based on evidence sources and models, with each sequence prediction having a sequence and associated probability estimate.

There is provided a mechanism for synchronizing a plurality of dynamic language models residing in a plurality of devices associated with a single user, each device comprising a dynamic language model. The mechanism is configured to: receive text data representing text that has been input by a user into one or more of the plurality of devices; train at least one language model on the text data; and provide the at least one language model for synchronizing the devices. There is also provided a system comprising the mechanism and a plurality of devices, and a method for synchronizing a plurality of dynamic language models residing in a plurality of devices associated with a single user.

Mechanism for synchronising devices, system and method

A system for inputting text into an electronic device. The system comprises a candidate generator () configured to generate one or more candidates from an input sequence (). The input sequence () comprises a contiguous sequence of characters. Each candidate comprises two or more terms separated by one or more term boundaries. The candidate generator () is configured to assign a first probability estimate to each candidate by: searching for one or more terms of that candidate in a context language model, wherein the context language model comprises sequences of terms, each sequence of terms having a corresponding probability of occurring; and assigning the probability corresponding to the one or more terms of that candidate from the context language model to the candidate. The candidate generator () is configured to discard one or more candidates on the basis of the corresponding first probability estimates. There is provided a corresponding method of inferring term boundaries in a user input sequence.

Growing community of inventors

London, United Kingdom

Benjamin William Medlock