SYSTEM AND METHOD FOR INPUTTING TEXT INTO ELECTRONIC DEVICES

• US 20140297267A1
• Filed: 06/17/2014
• Published: 10/02/2014
• Est. Priority Date: 03/30/2009
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Touchtype Limited (Microsoft Corporation)
• Inventors: Medlock, Benjamin William, Orr, Douglas Alexander Harper, Spencer, Stephen Thomas, Creed, Pid
• Granted Patent: US 9,424,246 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 3/0237 using prediction or retrieval techniques
• CPC Class Codes: G06F 3/04886 by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus
• CPC Class Codes: G06F 40/274 Converting codes to words; Guess-ahead of partial word inputs
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models

Abstract

Systems comprising a user interface configured to receive text input by a user and a text prediction engine configured to receive the input text and generate text predictions. The text prediction engine may comprise a general language model and a context-specific language model. The text prediction engine is configured to generate text predictions from the general language model and the context-specific language model and combine the text predictions. The text prediction engine may comprise first and second language models and a first context-specific weighting factor associated with the first language model. The text prediction engine is configured to generate text predictions using the first and second language models, generate weighted probabilities of the text predictions from the first language model using the first context-specific weighting factor; and generate final text predictions from the weighted predictions generated from the first language model and the predictions generated by the second language model.

Claims

1. A text prediction system comprising:

a user interface configured to receive text input by a user;

a text prediction engine, comprising a general language model and a context-specific language model, configured to receive the input text from the user interface and to generate text predictions from the general language model and the context-specific language model;

wherein the text prediction engine is further configured to combine the text predictions generated from the general language model and the context-specific language model.

2. The text prediction system of claim 1, wherein the general language model is a dynamic language model.

3. The text prediction system of claim 1, wherein the context-specific language model is a dynamic language model.

4. The text prediction system of claim 1, wherein the context-specific language model is a MessageThread-specific language model.

5. The text prediction system of claim 1, wherein the context-specific language model is a recipient-specific language model.

6. The text prediction system of claim 5, wherein the text prediction engine comprises one or more additional recipient-specific language models.

7. The text prediction system of claim 6, wherein the text prediction engine is configured to generate text predictions from the recipient-specific language model that is associated with a recipient to whom a message is being composed.

8. The text prediction system of claim 1, wherein a text prediction comprises a word candidate and an associated probability, and wherein when a word candidate generated from the general language model matches a word candidate generated by the context-specific language model, the text prediction engine is configured to apply a weighting factor to the probability of the word candidate generated from the general language model.

9. The text prediction system of claim 8, wherein the weighting factor is based on the probability of that word generated by the context-specific language model.

10. The text prediction system of claim 9, wherein the prediction engine comprises an additional, static, language model and the prediction engine is further configured to generate text predictions from the static language model, and wherein the text prediction engine is configured to combine the text predictions from the general language model, the context-specific language model and the static language model to generate a final set of text predictions.

11. The text prediction system of claim 10, wherein if a word candidate is generated by all language models the prediction engine outputs the word candidate with an associated probability, wherein the associated probability is the greatest of the probability from the static language model and the product of the probabilities from the general language model and the context-specific language model.

12. The text prediction system of claim 1, wherein a text prediction comprises a word candidate and an associated probability, and wherein when a word candidate generated from the general language model matches a word candidate generated by the context-specific language model, the prediction engine is configured to calculate a linear combination of the probability estimates generated by the general language model and the context-specific language model for that matching word.

13. The text prediction system of claim 12, wherein the prediction engine is configured to calculate a weighted linear combination, wherein the weighting factor is based on a ratio of the probability of that word in the context-specific language model to the probability of that word in the general language model.

14. The text prediction system of claim 13, wherein the prediction engine comprises an additional, static, language model and the prediction engine is further configured to generate text predictions from the static language model, and wherein the text prediction engine is configured to combine the text predictions from the general language model, the context-specific language model and the static language model to generate a final set of text predictions.

15. The text prediction system of claim 14, wherein if a word candidate is generated by all language models the prediction engine outputs the word candidate with an associated probability, wherein the associated probability is the greatest of the probability from the static language model and the weighted linear combination of probability estimates generated by the general language model and the context-specific language model.

16. A text prediction system comprising;

a user interface configured to receive text input by a user;

a text prediction engine comprising;

a first language model and a first context-specific weighting factor associated with the first language model; and

a second language model;

wherein the text prediction engine is configured to;

receive the input text from the user interface and to generate text predictions using the first language model and the second language model;

generate weighted probabilities of the text predictions from the first language model using the first context-specific weighting factor; and

generate final text predictions from the weighted predictions generated from the first language models and the text predictions generated from the second language model.

17. The text prediction system of claim 16, wherein the text prediction engine comprises a second context-specific weighting factor associated with the second language model, and the text prediction engine is further configured to:

generate weighted probabilities of the text predictions from the second language model using the second context-specific weighting factor; and

generate final text predictions from the weighted predictions generated from the first and second language models.

18. The text prediction system of claim 17, wherein the text prediction engine comprises additional language models, and each additional language model has an additional context-specific weighting factor associated with it.

19. The text prediction system of claim 18, wherein the text prediction engine comprises a data store having stored within it a set of context-specific parameters, the set of context-specific parameters comprising, for each context, a weighting factor for each language model.

20. The text prediction system of claim 19, wherein the context-specific parameters are recipient-specific parameters.

21. The text prediction system of claim 20, wherein the text prediction engine is configured to retrieve from the data store the recipient-specific weighting factors associated with a recipient to whom a message is being composed, and apply the respective recipient-specific weighting factor to the probabilities of the text predictions from each language model.

22. The text prediction system of claim 19, wherein the context-specific weighting factors are dynamic and are updated by the text prediction engine with use of the system.

23. A text prediction system comprising:

a user interface configured to receive text input by a user;

a text prediction engine, comprising a general dynamic language model and a plurality of recipient-specific dynamic language models, configured to receive the input text from the user interface and to generate text predictions from the general dynamic language model and one of the plurality of recipient-specific dynamic language models;

wherein the text prediction engine is further configured to combine the text predictions generated from the general dynamic language model and the one of the plurality of recipient-specific dynamic language models.