Call-forwarding system using adaptive model of user behavior

• US 5,905,789 A
• Filed: 02/26/1997
• Issued: 05/18/1999
• Est. Priority Date: 10/07/1996
• Status: Expired due to Term

• Current Assignee: RPX Clearinghouse LLC (RPX Corporation)
• Original Assignee: Northern Telecom Limited (Nortel Networks Corporation)
• Inventors: Will, Craig Alexander
• Primary Examiner(s): Zele, Krista
• Assistant Examiner(s): Wolinsky, Scott
• Application Number:
  US08/806,861
• Time in Patent Office: 811 Days
• Field of Search: 379/211, 379/355, 379/356, 379/265, 379/266, 379/309, 379/201, 379/219, 379/220, 379/221
• US Class Current: 379/211.03
• 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

Subscribers to a personal telephone number service can receive calls placed to telephone numbers associated with an individual rather than a physical location or telephone line. A subscriber predefines a set of telephone numbers for telephones at locations frequented by the subscriber. When a call to a subscriber'"'"'s personal telephone number is received, a model of the subscriber'"'"'s behavior predicts the likelihood of the subscriber being at different locations, and the call is forwarded to a telephone at the most likely location, given the current day of the week and time of the day. The model is trained using data obtained by cases in which a caller calling the personal telephone number is successful in locating the subscriber.

Claims

1. A method for forwarding a telephone call comprising the steps of:

receiving a call to a telephone number associated with a subscriber;

predicting, based on a model of the subscriber'"'"'s calling behavior, likelihoods of the subscriber being at locations corresponding to stored telephone numbers by applying weights associated with the telephone numbers such that recent calling behavior is favored over previous calling behavior; and

determining a sequence of the stored telephone numbers according to the predicted likelihoods.

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

forwarding the received call to at least one of the stored telephone numbers according to the sequence.

3. The method of claim 1, wherein the model of the subscriber'"'"'s behavior includes weights determined from data representing previous calls connecting the subscriber when located at a location corresponding to at least one of the stored telephone numbers, and wherein the determining step includes the substep of:

applying the weights to order the stored telephone numbers according to the predicted likelihoods.

4. The method of claim 1, wherein the model of the subscriber'"'"'s behavior comprises a representation based on the subscriber'"'"'s environment and actions with respect to receiving telephone calls, and wherein the predicting step includes the substep of:

examining an abstract representation for indications that the subscriber is located at the location corresponding to a particular telephone number.

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

activating the neural network to determine indications that the subscriber is located at the location corresponding to a particular telephone number.

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

training the model of the subscriber'"'"'s behavior with data indicating previous calls connecting a caller successfully with each of the stored telephone numbers.

7. The method of claim 1, further comprising the steps of:

creating a training set including information related to at least one previous received call; and

modifying the model of the subscriber'"'"'s behavior in accordance with the training set.

8. The method of claim 7, wherein the model of the subscriber'"'"'s behavior includes weights determined from data representing previous calls connecting the subscriber when located at a location corresponding to at least one of the stored telephone numbers, and wherein the modifying step includes the substep of:

altering the weights of the subscriber'"'"'s behavior model to reflect the information related to the previous call.

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

forwarding the received call to each of the stored telephone numbers according to the sequence.

10. The method of claim 9, wherein the forwarding step includes the substep of:

determining whether the subscriber is located at a location associated with the telephone number to which the received call was forwarded.

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

deleting telephone numbers from the sequence that are below a threshold predicted likelihood.

12. The method of claim 11 further comprising the step of:

forwarding the received call to each of the stored telephone numbers according to the sequence.

13. The method of claim 12, wherein the forwarding step includes the substep of:

determining whether the subscriber is located at a location associated with the telephone number to which the received call was forwarded.

14. A system for forwarding a telephone call comprising:

means for receiving a call to a telephone number associated with a subscriber;

means for predicting, based on a model of the subscriber'"'"'s calling behavior, likelihoods of the subscriber being at locations corresponding to stored telephone numbers by applying weights associated with the telephone numbers such that recent calling behavior is favored over previous calling behavior; and

means for determining a sequence of the stored telephone numbers according to the predicted likelihoods.

15. The system of claim 14 further comprising:

means for forwarding the received call to at least one of the stored telephone numbers according to the sequence.

16. The system of claim 14, wherein the model of the subscriber'"'"'s behavior includes weights determined from data representing previous calls connecting the subscriber when located at a location corresponding to at least one of the stored telephone numbers, and wherein the determining means includes:

means for applying the weights to order the stored telephone numbers according to the predicted likelihoods.

17. The system of claim 14, wherein the model of the subscriber'"'"'s behavior comprises a representation based on the subscriber'"'"'s environment and actions with respect to receiving telephone calls, and wherein the predicting means includes:

means for examining an abstract representation for indications that the subscriber is located at the location corresponding to a particular telephone number.

18. The system of claim 14, wherein the model of the subscriber'"'"'s behavior comprises a neural network and wherein the predicting means includes:

means for activating the neural network to determine indications that the subscriber is located at the location corresponding to a particular telephone number.

19. The system of claim 14, further comprising:

means for training the model of the subscriber'"'"'s behavior with data indicating previous calls connecting a caller successfully with each of the stored telephone numbers.

20. The system of claim 14, further comprising:

means for creating a training set including information related to at least one previous received call; and

means for modifying the model of the subscriber'"'"'s behavior in accordance with the training set.

21. The system of claim 20, wherein the model of the subscriber'"'"'s behavior includes weights determined from data representing previous calls connecting the subscriber when located at a location corresponding to at least one of the stored telephone numbers, and wherein the modifying means includes:

means for altering the weights of the subscriber'"'"'s behavior model to reflect the information related to the previous call.

22. The system of claim 14 further comprising:

means for forwarding the received call to each of the stored telephone numbers according to the sequence.

23. The system of claim 22, wherein the forwarding means includes:

means for determining whether the subscriber is located at a location associated with the telephone number to which the received call was forwarded.

24. The system of claim 14, wherein the determining means includes:

means for deleting telephone numbers from the sequence that are below a threshold predicted likelihood.

25. The system of claim 24 further comprising:

means for forwarding the received call to each of the stored telephone numbers according to the sequence.

26. The system of claim 25, wherein the forwarding means includes:

means for determining whether the subscriber is located at a location associated with the telephone number to which the received call was forwarded.

27. A computer program product comprising:

a computer usable medium having computer readable code embodied therein for forwarding a telephone call, the computer usable medium comprising;

a receiving module configured to receive a call to a telephone number associated with a subscriber;

a predicting module configured to predict, based on a model of the subscriber'"'"'s behavior, likelihoods of the subscriber being at locations corresponding to stored telephone numbers by applying weights associated with the telephone numbers such that recent subscriber behavior is favored over previous subscriber behavior; and

a determining module configured to determine a sequence of the stored telephone numbers according to the predicted likelihoods.

28. The computer program product of claim 27 further comprising:

a forwarding module configured to forward the received call to at least one of the stored telephone numbers according to the sequence.

29. The computer program product of claim 27, wherein the model of the subscriber'"'"'s behavior includes weights determined from data representing previous calls connecting the subscriber when located at a location corresponding to at least one of the stored telephone numbers, and wherein the determining module includes:

an applying module configured to apply the weights to order the stored telephone numbers according to the predicted likelihoods.

30. The computer program product of claim 27, wherein the model of the subscriber'"'"'s behavior comprises a representation based on the subscriber'"'"'s environment and actions with respect to receiving telephone calls, and wherein the predicting module includes:

an examining module configured to examine an abstract representation for indications that the subscriber is located at the location corresponding to a particular telephone number.

31. The computer program product of claim 27, wherein the model of the subscriber'"'"'s behavior comprises a neural network and wherein the predicting module includes:

an activating module configured to activate the neural network to determine indications that the subscriber is located at the location corresponding to a particular telephone number.

32. The computer program product of claim 27, further comprising:

a training module configured to train the model of the subscriber'"'"'s behavior with data indicating previous calls connecting a caller successfully with each of the stored telephone numbers.

33. The computer program product of claim 27, further comprising:

a training set building module configured to build a training set including information related to at least one previous call; and

a modifying module configured to modify the model of the subscriber'"'"'s behavior in accordance with the training set.

34. The computer program product of claim 33, wherein the model of the subscriber'"'"'s behavior includes weights determined from data representing previous calls connecting the subscriber when located at a location corresponding to at least one of the stored telephone numbers, and wherein the modifying module includes:

an altering module configured to alter the weights of the subscriber'"'"'s behavior model to reflect the information related to the previous call.

35. The computer program product of claim 27 further comprising:

a forwarding module configured to forward the received call to each of the stored telephone numbers according to the sequence.

36. The computer program product of claim 27, wherein the forwarding module includes:

a determining module configured to determine whether the subscriber is located at a location associated with the telephone number to which the received call was forwarded.

37. The computer program product of claim 27, wherein the determining module includes:

a deleting module configured to delete telephone numbers from the sequence that are below a threshold predicted likelihood.

38. The computer program product of claim 37 further comprising:

a forwarding module configured to forward the received call to each of the stored telephone numbers according to the sequence.

39. The computer program product of claim 38, wherein the forwarding module includes:

a determining module configured to determine whether the subscriber is located at a location associated with the telephone number to which the received call was forwarded.

40. A computer program product comprising a computer usable medium having computer readable code embodied therein for forwarding telephone calls, comprising a predictive model of a subscriber'"'"'s behavior including a representation based on the subscriber'"'"'s environment and actions with respect to receiving telephone calls, wherein the predictive model is used to predict likelihoods of the subscriber being at locations corresponding to schedule-independent telephone numbers by applying weights associated with the telephone numbers such that recent subscriber behavior is favored over previous subscriber behavior and to determine a sequence of the numbers according to the predicted likelihoods.

41. A method for forwarding a telephone call comprising the steps of:

receiving a telephone call to a subscriber;

predicting, likelihoods of the subscriber being at locations corresponding to schedule-independent telephone numbers based on a predictive model of the subscriber'"'"'s behavior based on the subscriber'"'"'s environment and actions by applying weights associated with the telephone numbers such that recent subscriber behavior is favored over previous subscriber behavior; and

forwarding the telephone call to at least one of the schedule-independent telephone numbers according to the predicted likelihoods.

42. A method for forwarding a telephone call comprising the steps of:

receiving a call to a telephone number associated with a subscriber;

predicting, based on a model of the subscriber'"'"'s calling behavior, likelihoods of the subscriber being at locations corresponding to stored telephone numbers, wherein the model of the subscriber'"'"'s behavior comprises a neural network activated to determine whether the subscriber is located at the location corresponding to a particular telephone number; and

determining a sequence of the stored telephone numbers according to the predicted likelihoods.

43. A system for forwarding a telephone call comprising:

means for receiving a call to a telephone number associated with a subscriber;

means for predicting, based on a model of the subscriber'"'"'s calling behavior, likelihoods of the subscriber being at locations corresponding to stored telephone numbers, wherein the model of the subscriber'"'"'s behavior comprises a neural network activated to determine whether the subscriber is located at the location corresponding to a particular telephone number; and

means for determining a sequence of the stored telephone numbers according to the predicted likelihoods.