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: 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: Method(s), apparatus and system(s) are provided for generating and using an ensemble model. The ensemble may be generated by training a plurality of models based on a plurality of datasets associated with compounds; calculating model performance statistics for each of the plurality of trained models; selecting and storing a set of optimal trained model(s) from the trained models based on the calculated model performance statistics; and forming one or more ensemble models, each ensemble model comprising multiple models from the set of optimal trained model(s). The ensemble model may be used by retrieving the ensemble model and inputting, to the ensemble model, data representative of one or more labelled dataset(s) used to generate and/or train the model(s) of the ensemble model; and receiving, from the ensemble model, output data associated with labels of the one or more labelled dataset(s).

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: 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 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 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 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 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: 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 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: 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.