Apple Inc.

Douglas R Davidson

Brent D Ramerth

Jerome Rene Bellegarda

Jennifer Lauren Moore

Christopher S Linn

Ali T Ozer

Bishal Barman

Jason J Ivan

Joshua H Shaffer

Devang Kalidas Naik

Jannes G Dolfing

Jannes G A Dolfing

Vivek Kumar Rangarajan Sridhar

Jia Pu

John Harvey, Iii

Andreas Eminidis

Michael B Victor

Michael S Grady

In some implementations, a computing device can generate user input correction suggestions based on the user's context. For example, the user's context can include content that the user has open or has recently opened on the computing device or another computing device. For example, when the user opens an item of content, the computing device can generate a context dictionary that includes words, phrases, etc., that describe the opened content. When the user provides input (e.g., text, speech, etc.) the computing device can use the context dictionary to generate input correction suggestions. The computing device can synchronize the context dictionary with other computing devices that the user may be using so that the user's context on one device can be used by another device to generate input correction suggestions.

Correcting input based on user context

Systems and processes for operating an intelligent automated assistant are provided. In one example process a set of words including a grammatical error is received. The process can generate, using a neural network based on the set of words including the grammatical error and a reference set of words, a transformed set of words and further determine, based on the set of words including the grammatical error and the reference set of words, a reconstructed reference set of words. The process can also determine, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct and provide an indication of whether the transformed set of words is grammatically correct to the neural network.

Automatic grammar detection and correction

The present disclosure generally relates to text correction and generating text correction models. In an example process for text correction, text input is received. In response to receiving the text input, a text string corresponding to the text input is displayed. The text string is represented by a token sequence. The process determines whether an end of the token sequence corresponds to a text boundary. In accordance with a determination that the end of the token sequence corresponds to a text boundary, the process determines, based on a context state of the token sequence, one or more textual errors at one or more tokens of the token sequence. An error indication for a portion of the text string corresponding to the one or more tokens is displayed.

Text correction

Techniques to automatically correct or complete text are disclosed. An entered text and a context data indicating a context in which the entered text is used are received. Examples of context data include additional words and/or a phrase or sentence in which the entered text occurs. A replacement candidate to replace the entered text is determined based on the entered text and the context data.

Using parts-of-speech tagging and named entity recognition for spelling correction

Using a statistical language model to make information related to a word or other content of interest available is disclosed. An indication that a word or phrase is of interest is received. A contextual meta-information associated with the word or phrase of interest as used in context is used to make information related to the word or phrase of interest available, for example, by forming a more precise query and/or filtering search results based on the contextual meta-information.

Using statistical language models for contextual lookup

Systems and processes for multilingual word prediction are provided. In accordance with one example, a method includes, at an electronic device having one or more processors and memory, receiving context information associated with a current word; determining, for each of a plurality of languages, a set of monolingual probabilities based on the context information; determining a set of language weights based on the context information; determining a set of multilingual probabilities based on the respective sets of monolingual probabilities and the set of language weights; and providing a plurality of candidate words based on the set of multilingual probabilities.

Multilingual word prediction

Methods and systems which utilize, in one embodiment, automatic language identification, including automatic language identification for dynamic text processing. In at least certain embodiments, automatic language identification can be applied to spellchecking in real time as the user types.

Automatic language identification for dynamic text processing

Systems and processes for predictive text input are provided. In one example process, a text input can be received. The text input can be associated with an input context. A frequency of occurrence of an m-gram with respect to a subset of a corpus can be determined using a language model. The subset can be associated with a context. A weighting factor can be determined based on a degree of similarity between the input context and the context. A weighted probability of a predicted text given the text input can be determined based on the frequency of occurrence of the m-gram and the weighting factor. The m-gram can include at least one word in the text input and at least one word in the predicted text.

Predictive text input

A device displays a text entry area with an insertion point and detects activation of a first physical key in a physical keyboard. In response to a determination that the activation of the first physical key lasts more than a first predefined time period, the device displays a character selection area; while displaying the character selection area, the device detects activation of a second physical key in the physical keyboard; in response to a determination that the activated second physical key is an arrow key, the device moves a current character focus in accordance with a direction of the arrow key; and, in response to a determination that the activated second physical key is the first physical key, the device enters in the text entry area a single instance of a character that has the current character focus, and ceases to display the character selection area.

Device, method, and graphical user interface for entering alternate characters with a physical keyboard

Disambiguating multiple readings in language conversion is disclosed, including: receiving an input data to be converted into a set of characters comprising a symbolic representation of the input data in a target symbolic system; and using a language model that distinguishes between a first reading and a second reading of a character of the target symbolic system to determine a probability that the heteronymous character should be used to represent a corresponding portion of the input data.

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Palo Alto, CA, United States of America

Douglas R Davidson