Abstract: An electronic device is described which has a user interface which receives an input comprising a sequence of target indicators of data items. The data entry system has a search component which searches for candidate expanded sequences of indicators comprising the target indicators. The search component searches amongst indicators generated by a trained conditional language model, the conditional language model having been trained using pairs, each individual pair comprising a sequence of indicators and a corresponding expanded sequence of indicators.

Abstract: 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.

Abstract: An electronic device is described which has at least one input interface to receive at least one item of a sequence of items. The electronic device is able to communicate with a server, the server storing a neural network and a process which generates item embeddings of the neural network. The electronic device has a memory storing a copy of the neural network and a plurality of item embeddings of the neural network. In the case when there is unavailability at the electronic device of a corresponding item embedding corresponding to the received at least one item, the electronic device triggers transfer of the corresponding item embedding from the server to the electronic device. A processor at the electronic device predicts at least one candidate next item in the sequence by processing the corresponding item embedding with the copy of the neural network and the plurality of item embeddings.

Abstract: A data input system is described of the type which has a virtual keyboard which enables a user to type a text sequence into a computing device. The data input system has an input probability generator which is configured to compute keypress evidence. The keypress evidence comprises probabilities that user input events at the virtual keyboard correspond to characters or keyboard functions. The data input system has a trained keypress encoder, having been trained using keypress evidence and corresponding words. The trained keypress encoder encodes the keypress evidence into a numerical encoding. The data input system has a completion/correction predictor which is configured to take as input, the numerical encoding and an encoding of one or more text items of the text sequence already input to the computing device, in order to predict a text item in the text sequence.

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 data entry system is described which has a user interface which receives a sequence of one or more context text items input by a user. The data entry system has a predictor trained to predict a next item in the sequence. The predictor comprises a plurality of learnt text item embeddings each text item embedding representing a text item in a numerical form, the text item embeddings having a plurality of different lengths. A projection component obtains text item embeddings of the context text items and projects these to be of the same length. The predictor comprises a trained neural network which is fed the projected text item embeddings and which computes a numerical output associated with the predicted next item.

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: 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.

Abstract: A method of automatically imputing missing or erroneous data. The method comprises dividing a portion of input data (e.g. a sentence) into a sequence of smaller elements (e.g. words), and identifying points in the sequence at which missing or erroneous data is potentially to be imputed. For each point, a first search step generates a respective set of one or more paths for the respective point, each path comprising a candidate element to potentially replace the missing or erroneous data at the respective point, and an associated probability score generated by a neural network. In each subsequent search step for each point, a set of one or more of the preceding paths from one or more of the preceding search steps are selected to extend with an additional candidate element and score again. The paths are compared from across the different search steps to select at least one for output.

Abstract: 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.

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: A data input system has a processor which receives user input comprising a sequence of one or more items and a language model which computes candidate next items in the sequence using the user input. A training engine trains the language model using data about a plurality of true words which a user intended to input using the data input system, and for each true word, at least one alternative candidate, being a word computed assuming imperfect entry of the true word to the data input system.

Abstract: A data input system is described for inputting text items to an electronic device. The data input system has a store holding a vocabulary of embeddings of text items, each embedding being a numerical encoding of a text item. The data input system receives user input comprising a sequence of one or more context text items and a new text item, the new text item being a text item with an embedding to be computed and added to the vocabulary or with an embedding already in the vocabulary and to be updated. A neural network predictor predicts a next text item in the sequence given the context text items and the vocabulary. An online training module updates the vocabulary by using either a direction associated with the predicted next item, or, by comparing the new text item and the predicted next text item.

Abstract: A data entry system is described which has a user interface which receives a sequence of one or more context text items input by a user. The data entry system has a predictor trained to predict a next item in the sequence. The predictor comprises a plurality of learnt text item embeddings each text item embedding representing a text item in a numerical form, the text item embeddings having a plurality of different lengths. A projection component obtains text item embeddings of the context text items and projects these to be of the same length. The predictor comprises a trained neural network which is fed the projected text item embeddings and which computes a numerical output associated with the predicted next item.

Abstract: A data input system is described of the type which has a virtual keyboard which enables a user to type a text sequence into a computing device. The data input system has an input probability generator which is configured to compute keypress evidence. The keypress evidence comprises probabilities that user input events at the virtual keyboard correspond to characters or keyboard functions. The data input system has a trained keypress encoder, having been trained using keypress evidence and corresponding words. The trained keypress encoder encodes the keypress evidence into a numerical encoding. The data input system has a completion/correction predictor which is configured to take as input, the numerical encoding and an encoding of one or more text items of the text sequence already input to the computing device, in order to predict a text item in the text sequence.

Abstract: An electronic device is described which has a user interface which receives an input comprising a sequence of target indicators of data items. The data entry system has a search component which searches for candidate expanded sequences of indicators comprising the target indicators. The search component searches amongst indicators generated by a trained conditional language model, the conditional language model having been trained using pairs, each individual pair comprising a sequence of indicators and a corresponding expanded sequence of indicators.

Abstract: A data input system has a processor which receives user input comprising a sequence of one or more items and a language model which computes candidate next items in the sequence using the user input. A training engine trains the language model using data about a plurality of true words which a user intended to input using the data input system, and for each true word, at least one alternative candidate, being a word computed assuming imperfect entry of the true word to the data input system.

Abstract: An electronic device is described which has at least one input interface to receive at least one item of a sequence of items. The electronic device is able to communicate with a server, the server storing a neural network and a process which generates item embeddings of the neural network. The electronic device has a memory storing a copy of the neural network and a plurality of item embeddings of the neural network. In the case when there is unavailability at the electronic device of a corresponding item embedding corresponding to the received at least one item, the electronic device triggers transfer of the corresponding item embedding from the server to the electronic device. A processor at the electronic device predicts at least one candidate next item in the sequence by processing the corresponding item embedding with the copy of the neural network and the plurality of item embeddings.