USER INPUT PREDICTION

• US 20140108994A1
• Filed: 05/08/2012
• Published: 04/17/2014
• Est. Priority Date: 05/16/2011
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Touchtype Limited (Microsoft Corporation)
• Inventors: Medlock, Benjamin, Orr, Douglas Alexander Harper
• Granted Patent: US 9,639,266 B2
• Time in Patent Office: Days
• US Class Current: 715/773
• 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

Abstract

The invention relates to a system and method that models a user'"'"'s interaction with a user interface. There is provided a data input system, comprising a user interface having a plurality of targets and being configured to receive user input. The system comprises a plurality of models, each of which relates previous user input events corresponding to a target to that target. An input probability generator is configured to generate, in association with the plurality of models, a probability that a user input event corresponds to a particular target. There is also provided a method of modelling a target of a user interface having a plurality of targets and being configured to receive input, by modelling for each target previous user input events which correspond to that target. Furthermore, there is provided a method of inputting data into a system comprising a user interface having a plurality of targets and being configured to receive input. The method comprises generating using an input probability generator in association with a plurality of models, each model relating previous input, events corresponding to a target to that target, a probability that a user input event corresponds to a particular target.

Claims

1. A data input system, comprising:

a user interface comprising a plurality of targets and being configured to receive user input;

a plurality of models, each of which relates previous user input events corresponding to a target to that target; and

an input probability generator configured to generate, in association with the plurality of models, a probability that a user input event corresponds to a particular target.

2. The system according to claim 1, wherein the user interface is a virtual keyboard.

3. The system according to claim 2, wherein an input event corresponds to a location on the virtual keyboard and each model is a distribution which models locations of the user'"'"'s previous input events corresponding to a particular target.

4. The system according to claim 3, wherein the distribution is a 2D Gaussian.

5. The system according to claim 3 or 4, wherein a prior is a conjugate to the distribution.

6. The system according to claim 4 or 5, wherein the parameters of the 2D Gaussian are learnt using maximum a-posteriori estimation based on the input events corresponding to the target.

7. The system according to claim 6, wherein the targets are keys on the virtual keyboard and a prior for each target is set empirically based on a keyboard layout.

8. The system according to any one of claims 2-7, wherein the virtual keyboard comprises multiple sets of target models, each set of target models being tailored to a different user, or usage scenario.

9. The system according to any preceding claim, wherein the input probability generator in association with the plurality of models outputs for a user input event the n most probable targets and their associated probabilities.

10. The system according to claim 9, further comprising:

a map mapping each of the n targets to one or more word fragments with associated probability values.

11. The system according to claim 10, wherein each word fragment is tagged in the map with the target to which it corresponds.

12. The system according to claim 9, further comprising a predictor configured to receive the n most probable targets and their associated probabilities and to generate text predictions having probability values.

13. The system according to claim 10 or 11, further comprising a predictor configured to receive the word fragments and their associated probabilities and to generate text predictions having probability values.

14. The system according to claim 12 or 13, wherein, upon selection of a prediction, the target(s) of the prediction are matched to the corresponding input event(s).

15. The system according to claim 14, wherein the targets of the prediction are matched by the predictor to the corresponding input events by the tagging of the word fragments of the prediction to their corresponding targets.

16. The system according to claim 14, Wherein the targets of the prediction are matched to the corresponding input events by the reverse mapping of the word fragments to their corresponding targets and the pairing of the targets to the input events.

17. The system according to any one of claims 14-16, wherein the selection of a prediction comprises the user'"'"'s selection of the prediction or the automatic selection of the most probable prediction.

18. The system according to any one of claims 14 to 17, further comprising a model updater configured to update a model to reflect mapping of an input event to the target corresponding to that model.

19. The system of claims 14 and 17, wherein the selected prediction is input into the data input system as text.

20. An electronic device comprising a data input system according to any preceding claim.

21. The electronic device of claim 20, wherein the electronic device is a computer, a PDA or a mobile telephone.

22. A method of modelling a target of a user interface having a plurality of targets and being configured to receive input, the method comprising:

modelling for each target previous user input events which correspond to that target.

23. The method according to claim 22, wherein the user interface is a virtual keyboard and an input event corresponds to a location on the virtual keyboard, the method further comprising modelling each target as a distribution which describes the locations of the user'"'"'s previous input events corresponding to the target.

24. The method according to claim 23, wherein the distribution is a 2D Gaussian

25. The system according to claim 23 or 24, wherein a prior is a conjugate to the distribution.

26. The method according to claim 24 or 25, wherein the parameters of the 2D Gaussian are learnt using maximum a-posteriori estimation based on the input events corresponding to the target.

27. The method according to claim 26, wherein the targets are keys on the virtual keyboard and a prior for each target is set empirically based on a keyboard layout.

37. The method of any one of claims 22-36, further comprising:

updating a model using a model updater when an input event has been matched to a target.

28. A method of inputting data into a system comprising a user interface having a plurality of targets and being configured to receive input, the method comprising:

generating using an input probability generator in association with a plurality of models, each model relating previous input events corresponding to a target to that target, a probability that a user input event corresponds to a particular target.

29. The method of claim 28, further comprising:

outputting from the input probability generator for a user input event the n most probable targets and their associated probabilities.

30. The method of claim 29, further comprising:

mapping with a map each of the n targets to one or more word fragments with associated probability values.

31. The method of claim 30, further comprising:

tagging with the map each of the one or more word fragments with the target from which it was mapped.

32. The method of claim 30, further comprising:

generating using a predictor configured to receive the n most probable targets and their associated probabilities text predictions having probability values.

33. The method of claim 30 or 31, further comprising:

generating using a predictor configured to receive the word fragments and their associated probabilities text predictions having probability values.

34. The method of claim 32 or 33, further comprising:

selecting a text prediction; and

matching the target(s) of the text prediction to the corresponding input event(s).

35. The method of claim 34, wherein the matching of the target(s) of the text prediction to the corresponding input event(s) comprises pairing using the predictor the targets which are tagged to the word fragments with the input events.

36. The method of claim 34, wherein the matching of the target(s) of the text prediction to the corresponding input event(s) comprises reverse mapping the word fragments to their targets and pairing the targets with the input events.

38. A system substantially as hereinbefore described with reference to and as shown in the accompanying drawing.

39. A method substantially as hereinbefore described with reference to and as shown in the accompanying drawing.