User-centric soft keyboard predictive technologies
First Claim
1. A method, comprising:
- receiving first input data associated with a mobile device, wherein the first input data comprises one or more words;
automatically learning user tendencies using the first input data to generate user-specific input history data indicating that at least one input word of the first input data was previously auto-corrected and that the auto-correction was undone;
receiving second input data from a touch screen keyboard;
analyzing the second input data and at least a portion of the user-specific input history data; and
based on the analyzing, providing one or more user-specific auto-corrections or suggestion candidates for the second input data to the touch screen keyboard.
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Abstract
An apparatus and method are disclosed for providing feedback and guidance to touch screen device users to improve text entry user experience and performance by generating input history data including character probabilities, word probabilities, and touch models. According to one embodiment, a method comprises receiving first input data, automatically learning user tendencies based on the first input data to generate input history data, receiving second input data, and generating auto-corrections or suggestion candidates for one or more words of the second input data based on the input history data. The user can then select one of the suggestion candidates to replace a selected word with the selected suggestion candidate.
130 Citations
17 Claims
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1. A method, comprising:
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receiving first input data associated with a mobile device, wherein the first input data comprises one or more words; automatically learning user tendencies using the first input data to generate user-specific input history data indicating that at least one input word of the first input data was previously auto-corrected and that the auto-correction was undone; receiving second input data from a touch screen keyboard; analyzing the second input data and at least a portion of the user-specific input history data; and based on the analyzing, providing one or more user-specific auto-corrections or suggestion candidates for the second input data to the touch screen keyboard. - View Dependent Claims (2, 3, 4, 5, 6)
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7. One or more computer-readable storage device or memory storing computer-readable instructions that when executed cause a computer to perform a method, the method comprising:
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receiving first input data associated with a mobile device, wherein the first input data comprises one or more words, the first input data indicating that at least one of the words was previously auto-corrected and that the auto-correction was undone; automatically learning user tendencies using the first input data to generate user-specific input history data; receiving second input data from a touch screen keyboard, the receiving second data comprising resizing one or more target areas associated with one or more associated keys on the touch screen keyboard, the resizing being based on one or more characters previously received in the second input data; analyzing the second input data and at least a portion of the user-specific input history data; and based on the analyzing, providing one or more user-specific auto-corrections or suggestion candidates for the second input data to the touch screen keyboard. - View Dependent Claims (9, 10, 11, 12)
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8. One or more computer-readable storage device or memory storing computer-readable instructions that when executed cause a computer to perform a method, the method comprising:
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using a touch screen keyboard coupled with a mobile device, receiving first user input data, the first user input data comprising one or more characters and data indicating an input scope of the mobile device; using the text entry device touch screen keyboard, receiving a completion event; after the receiving, automatically analyzing the first user input data and storing user-specific input history data based on the analyzing, wherein the stored user-specific input history data includes an indication; using the touch screen keyboard, receiving second user input data; determining a weighted probability determined from one or more candidate source data selected from a group including at least one or more of the following;
the user-specific input history data, the second user input data, dictionary probability data, user-specific touch model data, or generic touch model data;determining a weighted edit distance determined by calculating one or more edit distances selected from a group of penalty categories including at least one or more of the following;
insertion, wildcard, deletion, transposition, non-fuzzy substitution, fuzzy substitution, case change, or accent;automatically determining one or more suggestion candidates based on a score determined by combining the weighted probability and the weighted edit distance; and displaying the suggestion candidates on the touch screen keyboard. - View Dependent Claims (13, 14, 15, 16, 17)
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Specification