Abstract: An input method editor (IME) provides a distributed platform architecture that enables associating multiple applications with the IME to provide extended functionalities. The presentations of the applications, such as skins, may be different from each other and that of the IME. The applications may be represented in a manifest file that is human-readable and editable. The IME collects multiple parameters relating to a user input into a host application including a query input by the user and a scenario of the host application, and selects one or more applications to provide candidates based on a score or ranking of the applications under the collected multiple parameters. Machine-learning may be used to improve the score or ranking. The candidates may include text candidates, rich candidates, and informative candidates.

Abstract: An input method editor (IME) described herein couples scenarios of the input of the user with specific network services to offer more relevant and richer candidates for higher input productivity. Data relating to a computer application in which the input candidates are to be input and/or context relating to a user-submitted query is collected and analyzed to determine a scenario. The input candidates may include text candidates and rich candidates. The IME may select a scenario-tuned and type specific engine to identify the text candidates and/or rich candidates. The scenario-tuned text candidate engines leverage scenario-tuned language models and lexicons, and the scenario-tuned rich candidate engines leverage scenario-relevant web services, such as image, mapping, and video search, when available and appropriate.

Abstract: An input method editor (IME) provides a distributed platform architecture that enables associating multiple applications with the IME to provide extended functionalities. The presentations of the applications, such as skins, may be different from each other and that of the IME. The applications may be represented in a manifest file that is human-readable and editable. The IME collects multiple parameters relating to a user input into a host application including a query input by the user and a scenario of the host application, and selects one or more applications to provide candidates based on a score or ranking of the applications under the collected multiple parameters. Machine-learning may be used to improve the score or ranking. The candidates may include text candidates, rich candidates, and informative candidates.

Abstract: An input method editor (IME) provides a distributed platform architecture that enables associating multiple applications with the IME to provide extended functionalities. The presentations of the applications, such as skins, may be different from each other and that of the IME. The applications may be represented in a manifest file that is human-readable and editable. The IME collects multiple parameters relating to a user input into a host application including a query input by the user and a scenario of the host application, and selects one or more applications to provide candidates based on a score or ranking of the applications under the collected multiple parameters. Machine-learning may be used to improve the score or ranking. The candidates may include text candidates, rich candidates, and informative candidates.

Abstract: An input method editor (IME) described herein couples scenarios of the input of the user with specific network services to offer more relevant and richer candidates for higher input productivity. Data relating to a computer application in which the input candidates are to be input and/or context relating to a user-submitted query is collected and analyzed to determine a scenario. The input candidates may include text candidates and rich candidates. The IME may select a scenario-tuned and type specific engine to identify the text candidates and/or rich candidates. The scenario-tuned text candidate engines leverage scenario-tuned language models and lexicons, and the scenario-tuned rich candidate engines leverage scenario-relevant web services, such as image, mapping, and video search, when available and appropriate.

Abstract: An input method editor (IME) described herein couples scenarios of the input of the user with specific network services to offer more relevant and richer candidates for higher input productivity. Data relating to a computer application in which the input candidates are to be input and/or context relating to a user-submitted query is collected and analyzed to determine a scenario. The input candidates may include text candidates and rich candidates. The IME may select a scenario-tuned and type specific engine to identify the text candidates and/or rich candidates. The scenario-tuned text candidate engines leverage scenario-tuned language models and lexicons, and the scenario-tuned rich candidate engines leverage scenario-relevant web services, such as image, mapping, and video search, when available and appropriate.

Abstract: The customization of an application user interface with a skin package based on context data that includes the emotional states of a user may strengthen the emotional attachment for the application by the user. The customization includes determining an emotional state of a user that is inputting content into an application. A skin package for the user interface of the application is selected based on the emotional state of the user. The selected skin package is further applied to the user interface of the application.

Abstract: An input method editor (IME) may include a normal mode and a rich candidate mode. The IME may present text candidates in response to receiving user inputs in the normal mode, and rich text candidates in addition to the text candidates in response to receiving user inputs in the rich candidate mode.

Abstract: Some implementations provide techniques and arrangements for cross lingual candidate suggestion. For instance, some display a user interface of a host application including a text entry area. An input method editor (IME) receives one or more characters as input. In response, one or more completion candidates are displayed, at least one of the completion candidates being a cross lingual completion candidate in a language different from the one or more characters.

Abstract: Swipe-stroke input and continuous handwriting are provided. A stroke sequence or a portion of a stroke sequence of a Chinese character may be input via selecting one or more stroke buttons via a swipe gesture. Candidate characters may be determined and provided when an indication is received that a stroke sequence input has ended. A candidate may be selected, or a next stroke sequence may be input. As additional input is received, phrase candidates may be predicted and dynamically provided. An end-of-input (EOI) panel may be provided, which when selected, provides an indication of an end of a current handwriting input and allowing a next handwriting input to be entered. By providing a selectable functionality to indicate an end of a current handwriting input, a continuous and more efficient handwriting experience is provided. Past handwriting input may be stored and accessed, allowing a user to edit the past handwriting input.

Abstract: An input method editor (IME) provides a distributed platform architecture that enables associating multiple applications with the IME to provide extended functionalities. The presentations of the applications, such as skins, may be different from each other and that of the IME. The applications may be represented in a manifest file that is human-readable and editable. The IME collects multiple parameters relating to a user input into a host application including a query input by the user and a scenario of the host application, and selects one or more applications to provide candidates based on a score or ranking of the applications under the collected multiple parameters. Machine-learning may be used to improve the score or ranking. The candidates may include text candidates, rich candidates, and informative candidates.

Abstract: An input method editor (IME) described herein couples scenarios of the input of the user with specific network services to offer more relevant and richer candidates for higher input productivity. Data relating to a computer application in which the input candidates are to be input and/or context relating to a user-submitted query is collected and analyzed to determine a scenario. The input candidates may include text candidates and rich candidates. The IME may select a scenario-tuned and type specific engine to identify the text candidates and/or rich candidates. The scenario-tuned text candidate engines leverage scenario-tuned language models and lexicons, and the scenario-tuned rich candidate engines leverage scenario-relevant web services, such as image, mapping, and video search, when available and appropriate.

Abstract: The customization of an application user interface with a skin package based on context data that includes the emotional states of a user may strengthen the emotional attachment for the application by the user. The customization includes determining an emotional state of a user that is inputting content into an application. A skin package for the user interface of the application is selected based on the emotional state of the user. The selected skin package is further applied to the user interface of the application.