Abstract: The subject disclosure provides systems and methods for application-specific network data filtering. Application-specific network data filtering may be performed by a sandboxed process prior to providing the network data to an application to which the network data is directed. Any malicious or otherwise potentially harmful data that is included in the network data may be removed by the application-specific network data filter or may be allowed to corrupt the application specific network data filtering operations within the sandbox, thereby preventing the malicious or harmful data from affecting the application or other portions of an electronic device. In one or more implementations, a first process such as an application-specific network data filtering process may request allocation of memory for the first process from second process, such as an application, that is separate from a memory manager of the electronic device.

Abstract: Systems and methods for responding to continuous-path gestures are disclosed. An example method includes: displaying a plurality of keys on a touch-sensitive keyboard; and in response to a continuous-path gesture on the touch-sensitive keyboard in which a contact associated with the continuous-path gesture travels over two or more of the plurality of keys: begin displaying a punctuation symbol key on the touch-sensitive keyboard; and displaying one or more characters based on keys contacted by the contact during the continuous-path gesture.

Abstract: 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, identifying context information of the electronic device and generating, with the one or more processors, a plurality of candidate words based on the context information, wherein a first candidate word of the plurality of candidate words corresponds to a first language of a plurality of languages and a second candidate word of the plurality of candidate words corresponds to a second language of the plurality of languages different than the first language.

Abstract: 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, identifying context information of the electronic device and generating, with the one or more processors, a plurality of candidate words based on the context information, wherein a first candidate word of the plurality of candidate words corresponds to a first language of a plurality of languages and a second candidate word of the plurality of candidate words corresponds to a second language of the plurality of languages different than the first language.

Abstract: 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.

Abstract: 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.