Voice-dialing system using model of calling behavior

• US 6,167,117 A
• Filed: 10/01/1997
• Issued: 12/26/2000
• Est. Priority Date: 10/07/1996
• Status: Expired due to Term

• Current Assignee: RPX Clearinghouse LLC (RPX Corporation)
• Original Assignee: Nortel Networks Limited (Nortel Networks Corporation)
• Inventors: Will, Craig A.
• Primary Examiner(s): Hunter, Daniel S.
• Assistant Examiner(s): Foster, Roland G.
• Application Number:
  US08/942,201
• Time in Patent Office: 1,182 Days
• Field of Search: 704/251, 704/252, 704/255, 704/256, 704/257, 704/275, 379/67.1, 379/88.01, 379/88.02, 379/88.03, 379/142, 379/201, 379/355, 379/356, 379/357
• US Class Current: 379/88.03
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: H04M 1/271 controlled by voice recognition
• CPC Class Codes: H04M 3/2218 Call detail recording
• CPC Class Codes: H04M 3/42204 Arrangements at the exchange for service or number selection by voice at the terminal H04M1/27
• CPC Class Codes: H04M 3/42229 Personal communication services, i.e. services related to one subscriber independent of his terminal and/or location diverting calls from one subscriber to another subscriber, i.e. two different subscriptions H04M3/54; selecting arrangements in intelligent networks H04Q3/005
• CPC Class Codes: H04M 3/46 Arrangements for calling a number of substations in a predetermined sequence until an answer is obtained
• CPC Class Codes: H04M 3/4931 Directory assistance systems
• CPC Class Codes: H04M 3/54 Arrangements for diverting calls for one subscriber to another predetermined subscriber based on the calling party profile H04M3/42059; based on location H04M3/42348; based on presence H04M3/42365; to voice mail H04M3/53308

Abstract

A method and apparatus for assisting voice-dialing using a model of an individual'"'"'s calling behavior to improve recognition of an input name corresponding a desired telephone number. When the individual picks up a telephone, activity is initiated in a neural network model of the individual'"'"'s calling behavior that predicts the likelihood that different numbers will be called, given such predictors as the day of the week and the time of day. The model is constructed by training the neural network with data from the user'"'"'s history of making and receiving telephone calls. The auditory output from an automatic speech recognition system and the output from the user model are integrated together so as to select the number that is most likely to be the number desired by the speaker. The system can also provide automatic directory assistance, by speaking the number aloud rather than dialing it. In one version, the system is a personal directory for an individual maintained on that individual'"'"'s personal computer. In another version, the system serves as a directory for a given physical or virtual site, with information about the institutional organization at the site in addition to individual calling histories used to track calling patterns and make predictions about the likelihood of calls within the site.

Claims

1. A method for assisting voice-dialing comprising the steps of:

receiving voice input from a user representing a name corresponding to a desired telephone number;

predicting a likelihood of the user calling telephone numbers based on a model of the user'"'"'s calling behavior over time period by applying weights such that recent calling behavior is favored over previous calling behavior; and

determining the desired telephone number according to the predicted likelihood of the user calling the telephone number corresponding to stored names that most closely match the voice input.

2. The method of claim 1, wherein the model of the user'"'"'s calling behavior includes weights determined from previous calls by the user to at least one of the telephone numbers, and wherein the determining step includes the substep of:

applying the weights to order telephone numbers corresponding to the selected names.

3. The method of claim 1, wherein the model of the user'"'"'s calling behavior includes weights determined from previous calls that the user received from at least one of the telephone numbers, and wherein the determining step includes the substep of:

applying the weighting factors to order telephone numbers corresponding to the selected names.

4. The method of claim 1, wherein the determining step includes the substep of:

generating a set of the telephone numbers that are most likely the desired telephone number.

5. The method of claim 1, wherein the determining step includes the substep of:

ordering a set of the telephone numbers according to the predicted likelihood that each telephone number in the set is the desired telephone number.

6. The method of claim 2, wherein the determining step includes the substep of:

ordering the telephone numbers associated with the selected names according to the predicted likelihood that each telephone number in the set is the desired telephone number.

7. The method of claim 3, wherein the determining step includes the substep of:

ordering the telephone numbers associated with the selected names according to the predicted likelihood that each telephone number in the set is the desired telephone number.

8. The method of claim 4, wherein the generating step includes the substeps of:

prompting the user to select one of the telephone numbers from the set; and

initiating a telephone call to the selected telephone number.

9. The method of claim 1 further comprising the step of:

dialing the desired telephone number.

10. The method of claim 1 further comprising the step of:

outputting the desired telephone number in a manner perceptible to the user.

11. The method of claim 1, wherein the model of the user'"'"'s calling behavior comprises an abstract representation based on the user'"'"'s environment and actions with respect to initiating telephone calls, and wherein the predicting step includes the substep of:

examining the abstract representation for indications that the user intends to call each of the telephone numbers.

12. The method of claim 1, wherein the model of the user'"'"'s calling behavior comprises an adaptive model that is alterable based on the user'"'"'s environment and actions with respect to initiating telephone calls, and wherein the predicting step includes the substep of:

examining the adapative model for indications that the user intends to call each of the telephone numbers.

13. The method of claim 1, wherein the model of the user'"'"'s calling behavior comprises a neural network and wherein the predicting step includes the substep of:

examining the neural network for indications that the user intends to call each of the telephone numbers.

14. The method of claim 1, wherein a speech recognition system is used to receive the voice input, and wherein the determining step includes the substep of:

integrating the calling behavior model with accuracy measures for the speech recognition system.

15. The method of claim 1, further comprising the step of:

training the model of the user'"'"'s calling behavior with previous calls from the user to each of the telephone numbers.

16. The method of claim 1, further comprising the step of:

training the model of the user'"'"'s calling behavior with previous calls received by the user from each of the telephone numbers.

17. The method of claim 4, wherein the generating step includes the substeps of:

selecting a name associated with one of the telephone numbers in the set;

presenting the selected name to the user; and

waiting for a response from the user indicating whether the selected name corresponds to the desired telephone number.

18. The method of claim 17, wherein the waiting step includes the substep of:

determining whether a predetermined period of time has passed since the user was presented with the selected name.

19. The method of claim 17, wherein the waiting step includes the substep of:

interpreting a lack of response as meaning that the selected name corresponds to the desired telephone number.

20. The method of claim 1, further comprising the step of:

building a training set including information related to at least one previous call.

21. The method of claim 20, further comprising the step of:

at a predetermined time, modifying the model of the user'"'"'s calling behavior in accordance with the training set.

22. The method of claim 21, wherein the model of the user'"'"'s calling behavior includes weights determined from previous calls by the user to at least one of the telephone numbers, and wherein the modifying step includes the substep of:

altering the weights of the user'"'"'s calling beavior model to reflect the information related to the previous call.

23. Voice-dialing apparatus comprising:

a receiver configured to receive voice input from a user representing a name corresponding to a desired telephone number;

a predicting component configured to predict a likelihood of the user calling telephone numbers based on a model of the user'"'"'s calling behavior over time period by applying weights such that recent calling behavior is favored over previous calling behavior; and

a determining component configured to determine the desired telephone number according to the predicted likelihood of the user calling the telephone number corresponding to each selected name.

24. The apparatus of claim 23, wherein the model of the user'"'"'s calling behavior includes weights determined from previous calls by the user to at least one of the telephone numbers, and wherein the determining component includes:

means for applying the weights to order telephone numbers corresponding to the selected names.

25. The apparatus of claim 23, wherein the model of the user'"'"'s calling behavior includes weights determined from previous calls that the user received from at least one of the telephone numbers, and wherein the determining component includes:

means for applying the weighting factors to order telephone numbers corresponding to the selected names.

26. The apparatus of claim 23, wherein the determining component includes:

means for generating a set of the telephone numbers that are most likely the desired telephone number.

27. The apparatus of claim 23, wherein the determining component includes:

means for ordering a set of the telephone numbers according to the predicted likelihood that each telephone number in the set is the desired telephone number.

28. The apparatus of claim 24, wherein the determining component includes:

means for ordering the telephone numbers associated with the selected names according to the predicted likelihood that each telephone number in the set is the desired telephone number.

29. The apparatus of claim 25, wherein the determining component includes:

means for ordering the telephone numbers associated with the selected names according to the predicted likelihood that each telephone number in the set is the desired telephone number.

30. The method of claim 26, wherein the generating means includes:

means for prompting the user to select one of the telephone numbers from the set; and

means for initiating a telephone call to the selected telephone number.

31. The apparatus of claim 23 further comprising:

means for dialing the desired telephone number.

32. The apparatus of claim 23 further comprising:

means for outputting the desired telephone number in a manner perceptible to the user.

33. The apparatus of claim 23, wherein the model of the user'"'"'s calling behavior comprises an abstract representation based on the user'"'"'s environment and actions with respect to initiating telephone calls, and wherein the predicting component includes:

means for examining the abstract representation for indications that the user intends to call each of the telephone numbers.

34. The apparatus of claim 23, wherein the model of the user'"'"'s calling behavior comprises an adaptive model that is alterable based on the user'"'"'s environment and actions with respect to initiating telephone calls, and wherein the predicting component includes:

means for examining the adapative model for indications that the user intends to call each of the telephone numbers.

35. The apparatus of claim 23, wherein the model of the user'"'"'s calling behavior comprises a neural network and wherein the predicting component includes:

means for examining the neural network for indications that the user intends to call each of the telephone numbers.

36. The apparatus of claim 23, wherein a speech recognition system is used to receive the voice input, and wherein the determining component includes:

means for integrating the calling behavior model with accuracy measures for the speech recognition system.

37. The apparatus of claim 23, further comprising:

means for training the model of the user'"'"'s calling behavior with previous calls from the user to each of the telephone numbers.

38. The apparatus of claim 23, further comprising:

means for training the model of the user'"'"'s calling behavior with previous calls received by the user from each of the telephone numbers.

39. The apparatus of claim 26, wherein the generating means includes:

means for selecting a name associated with one of the telephone numbers in the set;

means for presenting the selected name to the user; and

means for waiting for a response from the user indicating whether the selected name corresponds to the desired telephone number.

40. The apparatus of claim 39, wherein the waiting means includes:

means for determining whether a predetermined period of time has passed since the user was presented with the selected name.

41. The apparatus of claim 39, wherein the waiting step includes:

means for interpreting a lack of response as meaning that the selected name corresponds to the desired telephone number.

42. The apparatus of claim 23, further comprising:

means for building a training set including information related to at least one previous call.

43. The apparatus of claim 23, further comprising:

means for modifying the model of the user'"'"'s calling behavior in accordance with the training set at a predetermined time.

44. The apparatus of claim 43, wherein the model of the user'"'"'s calling behavior includes weights determined from previous calls by the user to at least one of the telephone numbers, and wherein the modifying means includes:

means for altering the weights of the user'"'"'s calling beavior model to reflect the information related to the previous call.