SYSTEM AND METHOD FOR INPUTTING TEXT INTO ELECTRONIC DEVICES

• US 20160283464A1
• Filed: 06/03/2016
• Published: 09/29/2016
• Est. Priority Date: 09/29/2010
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Touchtype Limited (Microsoft Corporation)
• Inventors: ORR, Douglas Alexander Harper, MEDLOCK, Benjamin
• Granted Patent: US 10,146,765 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 3/0236 using selection techniques to select from displayed items
• CPC Class Codes: G06F 3/0237 using prediction or retrieval techniques
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/274 Converting codes to words; Guess-ahead of partial word inputs

Abstract

A text prediction engine, a system comprising a text prediction engine, and a method for generating sequence predictions. The text prediction engine, system and method generate multiple sequence predictions based on evidence sources and models, with each sequence prediction having a sequence and associated probability estimate.

Claims

1. A text prediction system, comprising:

one or more processors;

memory storing instructions that, when executed by the one or more processors, configure the one or more processors to;

generate at least one first sequence prediction based on a first evidence source and a first model, wherein each of the at least one first sequence prediction comprises a first sequence and a first associated probability estimate;

generate at least one second sequence prediction based on a second evidence source and a second model, wherein each of the at least one second sequence prediction comprises a second sequence and a second associated probability estimate; and

a display coupled to at least one of the one or more processors or the memory, wherein the display is configured to;

output at least one of the at least one first sequence prediction and the at least one second sequence prediction within a text entry graphical user interface.

2. The system according to claim 1, wherein one of the first evidence source or the second evidence source is based upon input representing observed evidence about a word that the user is currently entering and the other of the first evidence source or the second evidence source is not based upon the input representing the observed evidence about the word the user is currently entering.

3. The system according to claim 2, wherein the first evidence source is modelled independently of the second evidence source.

4. The system according to claim 3, wherein the first model is different from the second model.

5. The system according to claim 4, wherein the first model comprises a context model and the second model comprises an input model.

6. The system according to claim 4, wherein the first evidence source is modelled independently of the second evidence source.

7. The system according to claim 4, wherein the first evidence source is modelled by the first model to generate the at least one first sequence prediction and the second evidence source is modelled by the second model to generate the at least one second sequence prediction.

8. The system according to claim 5, wherein the input model comprises a candidate model and a language model.

9. The system according to claim 5, wherein the context model comprises a candidate model and a prefix match model.

10. The system according to claim 1, wherein the first model comprises a context model and the second model comprises an input model.

11. The system according to claim 10, wherein the input model comprises a candidate model and a language model.

12. The system according to claim 10, wherein the context model comprises a candidate model and a prefix match model.

13. The system according to claim 10, further comprising a prior model configured to generate a third set of sequences with associated probability estimates.

14. The system according to claim 1, further comprising a prior model configured to generate a third set of sequences with associated probability estimates.

15. The system according to claim 14, wherein the prior model comprises a unigram model.

16. The system according to claim 14, wherein the prior model comprises a character model.

17. The system according to claim 1, wherein the first evidence source is modelled independently of the second evidence source.

18. The system according to claim 17, wherein the first evidence source is modelled by the first model to generate the at least one first sequence prediction and the second evidence source is modelled by the second model to generate the at least one second sequence prediction.

19. A computing device comprising one or more processors and memory storing instructions that, when executed by the one or more processors, configure the computing device to:

generate at least one first sequence prediction based on a first evidence source and a first model, wherein each of the at least one first sequence prediction comprises a first sequence and a first associated probability estimate; and

generate at least one second sequence prediction based on a second evidence source and a second model, wherein each of the at least one second sequence prediction comprises a second sequence and a second associated probability estimate;

the computing device further comprising a display coupled to at least one of the one or more processors or the memory, wherein the display is configured to output at least one of the at least one first sequence prediction and the at least one second sequence prediction within a text entry graphical user interface.

20. A method for predicting text by a computing device, the method comprising:

generating, by the computing device, at least one first sequence prediction based on a first evidence source and a first model, wherein each of the at least one first sequence prediction comprises a first sequence and a first associated probability estimate;

generating, by the computing device, at least one second sequence prediction based on a second evidence source and a second model, wherein each of the at least one second sequence prediction comprises a second sequence and a second associated probability estimate; and

outputting, on a display device communicatively coupled to the computing device, at least one of the at least one first sequence prediction and the at least one second sequence prediction within a text entry graphical user interface.