Apple Inc.

Nicholas K Jong

Imran A Chaudhri

Tiffany S Jon

Marcos Alonso Ruiz

Morgan H Winer

Christopher P Willmore

Stephen Olivier Lemay

Patrick L Coffman

Nicholas V King

Chanaka G Karunamuni

Craig Michael Federighi

Joshua H Shaffer

Jason Clay Beaver

Alice Emily Neels

Alastair Kenneth Fettes

Stephen W Swales

Justin S Hogg

An electronic device with a touch screen display displays content of an electronic document, including text, and a cursor is displayed at a line of text within the electronic document. A multi-finger swipe gesture is detected, and in accordance with a determination that the multi-finger swipe gesture is in a direction at least partially parallel to the line of text and towards an edge of the touch screen display, the cursor is moved to a distal point of the text.

Touch input cursor manipulation

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

An electronic device selects content and moves selected content displayed on a touch screen display. Content of an electronic document is displayed, and a selection input corresponding to selection of a respective portion of the content (selected content) is detected. In response, the device concurrently displays the selected content with a changed appearance, to indicate the selection, and a set of options related to the selected content. In response to detecting a single touch input at a location over the selected content, if the single touch input remains, during a first portion of the single touch input, at the location for a predetermined amount of time, followed, during a second portion of the single touch input, by a continuous touch gesture away from the location, the representation of the selected content is moved to a different location in a direction of the continuous touch gesture.

The method for manipulating a cursor is performed at a portable multifunction device with one or more processors, memory, and a touch screen display. Initially, content of an electronic document is displayed on the display, where a cursor is displayed within the electronic document. Two substantially simultaneous touch inputs are then detected on the touch screen display, and preferably anywhere on the touch screen display. In response to detecting the two substantially simultaneous touch inputs, a portion of the content in the document closest to the cursor is selected, and the portion of the content is displayed as selected content.

Utilization of error vector data representative of errors between the location of actual keystrokes and the location of determined intended keystrokes to compute 'bias' data indicative of the magnitude and direction of error vectors for a given location on the virtual keyboard is disclosed. This bias data can then be used to perform a number of operations such as keyboard re-spotting.

Modifying an on-screen keyboard based on asymmetric touch drift

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.

Text correction processing is disclosed. An initial score is assigned to each of a plurality of candidate sequences of one or more characters, based at least in part on a keyboard geometry-based value associated with the received user input with respect to the candidate key. Further processing is performed with respect to a subset of the candidate sequences having the highest initial score(s) to determine for each candidate sequence in the subset a refined score. A candidate sequence is selected for inclusion in a result set based at least in part on a determination that a refined score of the selected candidate is higher than an initial score of one or more candidate sequences that are not included in the subset and with respect to which the further processing has not been performed.

Text correction processing

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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Seattle, WA, United States of America

Nicholas K Jong