Abstract: A system comprising a user interface and a prediction engine is discussed. The user interface comprises a multi-input target associated with multiple input options available for input. In a first system, the user interface is configured to select the multi-input target and input of the multiple input options. The prediction engine is configured to determine the probability that the user intended to select an input of the multiple inputs other than the selected input. In a second system, the user interface is configured to identify a first input of the multiple input options and change the identified input from the first input to a second input. The prediction engine is configured to determine the time elapsed between first and second user selection events, the probability that the user intended to select the second input, and the probability that the user had intended to input the first input.

Abstract: 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.

Abstract: 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.

Abstract: There is provided a system and method for generating predictions. The predictions are generated using a model configured to associate text with at least one action associated with at least one of a plurality of applications.

Abstract: 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.

Abstract: The present invention provides systems and methods for integrating text prediction from a text prediction engine into a mechanism for searching/filtering/ordering a plurality of items. In one embodiment, the system comprises a text prediction engine configured to receive user text input relating to a sequence of two or more terms, generate a plurality of word predictions for a first term of the sequence of two or more terms and generate at least one word prediction for a second term of the sequence of two or more terms. The text prediction engine is configured to generate a distribution of candidate word predictions for the sequence of two or more terms using the plurality of word predictions for the first term and the at least one word prediction for the second term. The system further comprises a mechanism for filtering/searching/ordering a plurality of items.

Abstract: 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.

Abstract: 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.

Abstract: 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 a final set of sequence predictions, each with an associated probability value.

Abstract: The present invention relates generally to a system and method for text prediction and, in particular, a system and method for the adaptive weighting of text predictions. The present invention provides a modular language model-based text prediction system for the adaptive weighting of text prediction components and a method for doing so. The system uses category predictions, generated by a timed aggregated perceptron classifier, to weight, using a weighting module, text predictions generated by a text prediction engine comprising at least one predictor.

Abstract: 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 a final set of sequence predictions, each with an associated probability value.

Abstract: The present invention relates generally to a system and method for the reordering of text predictions. More particularly, the system and method reorders the text predictions based on modified probability values, wherein the probability values are modified according to the likelihood that a given text prediction will occur in the text inputted by a user.

Abstract: An embodiment is directed to an interface for a small screen device, such as a watch, that enables a user to enter text on the small screen device by touching in the vicinity of characters, rather than aiming for a particular button or the exact location of a character. Embodiments further enable the design of interfaces without the use of buttons for controlling the entry of text on the small screen device.

Abstract: The present invention relates generally to a system and method for text prediction and, in particular, a system and method for the adaptive weighting of text predictions. The present invention provides a modular language model-based text prediction system for the adaptive weighting of text prediction components and a method for doing so. The system uses category predictions, generated by a timed aggregated perceptron classifier, to weight, using a weighting module, text predictions generated by a text prediction engine comprising at least one predictor.

Abstract: 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.