Microsoft Technology Licensing, LLC

Matthew James Willson

Douglas Alexander Harper Orr

Juha Iso-Sipilä

Marco Fiscato

Joseph Osborne

Adam Clements

Douglas Orr

Marek Rei

James Peter John Withers

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.

Data input system with online learning

The present invention relates to an improved artificial neural network for predicting one or more next items in a sequence of items based on an input sequence item. The improved artificial neural network has greatly reduced memory requirements, making it suitable for use on electronic devices such as mobile phones and tablets. The invention includes an electronic device on which the improved artificial neural network operates, and methods of predicting the one or more next items in the sequence using the improved artificial neural network.

Neural network having input and hidden layers of equal units

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.

Sequence expander for data entry/information retrieval

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.

System and method for generating predictions

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.

Device/server deployment of neural network data entry system

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.

Data input system using trained keypress encoder

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.

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London, United Kingdom

Matthew James Willson