Apple Inc.

Christopher P Willmore

Nicholas K Jong

Imran A Chaudhri

Chanaka G Karunamuni

Tiffany S Jon

Joshua H Shaffer

Jason Clay Beaver

Stephen W Swales

Justin S Hogg

A method includes displaying a suggestion region above an on-screen keyboard. The suggestion region includes multiple suggested character strings. The method further includes: detecting a gesture that begins within a predefined key of the on-screen keyboard; and responsive to detecting the gesture: in accordance with a determination that the gesture ends within the predefined key, inserting a first character string into a text field; and in accordance with a determination that the gesture ends outside of the predefined key, inserting a second character string into the text field, wherein the second character string is different from the first character string.

Device, method, and graphical user interface for a predictive keyboard

Methods and systems of multi-word automatic correction ('autocorrect') are provided. Autocorrect generally can select a corrected word based on a typed word and a dictionary of correctly-spelled words. Multi-word autocorrect can add to this functionality by revisiting the selection of an initial corrected word if a subsequently-typed word indicates that it would be more appropriate to instead select an additional corrected word. In some cases, an autocorrect system can make a multi-word correction based on a multi-word phrase in a dictionary, such as replacing 'new york' with &#x201c;New York&#x201d; as described above. In other cases, an autocorrect system can make a multi-word correction to correct a mistakenly-typed delimiter character. In other cases, an autocorrect system can use grammar rules to obtain additional context information with each subsequently-typed word and make multi-word corrections on that basis.

Multi-word autocorrection

A method includes displaying a set of one or more suggestions including one or more character strings that are suggested replacements for a first set of one or more entered characters. The method further includes: while displaying the set of suggestions, receiving one or more additional entered characters; and after receiving the additional entered characters, updating the set of suggestions based on an updated set of entered characters that includes the first set of entered characters and the additional entered characters. The updating comprises changing a first suggestion in the set of suggestions from a first character string that is a suggested replacement for the first set of entered characters to a second character string that is a suggested replacement for the updated set of entered characters.

Systems and processes are disclosed for predicting words in a text entry environment. Candidate words and probabilities associated therewith can be determined by combining a word n-gram language model and a unigram language model. Using the word n-gram language model, based on previously entered words, candidate words can be identified and a probability can be calculated for each candidate word. Using the unigram language model, based on a character entered for a new word, candidate words beginning with the character can be identified along with a probability for each candidate word. In some examples, a geometry score can be included in the unigram probability related to typing geometry on a virtual keyboard. The probabilities of the n-gram language model and unigram model can be combined, and the candidate word or words having the highest probability can be displayed for a user.

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Santa Clara, CA, United States of America

Christopher P Willmore