BUILDING AND USING PREDICTIVE MODELS OF CURRENT AND FUTURE SURPRISES

• US 20140304211A1
• Filed: 04/17/2014
• Published: 10/09/2014
• Est. Priority Date: 11/16/2004
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Horvitz, Eric J.
• Application Number:
  US14/255,825
• Publication Number:
  US 20140304211A1
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions

Abstract

Methods are described for identifying events that would be considered surprising by people and identifying how and when to transmit information to a user about situations that they would likely find surprising. Additionally, the methods of identifying surprising situations can be used to build a case library of surprising events, joined with a set of observations before the surprising events occurred. Statistical machine learning methods can be applied with data from the case library to build models that can predict when a user will likely be surprised at future times. One or more models of context-sensitive expectations of people, a view of the current world, and methods for recording streams or events before surprises occur, and for building predictive models from a case library of surprises and such historical observations can be employed. The models of current and future surprises can be coupled with display and alerting machinery.

Claims

1-20. -20. (canceled)

21. A method of providing user notifications regarding events, the method comprising:

receiving a first predictive model, wherein the first predictive model is based on a user expectancy model designed to reflect whether a user would predict the event;

receiving contextual data that is at least potentially associated with an event;

with at least one processor, applying the first predictive model to at least a portion of the contextual data to generate a first prediction for the event;

determining that the first prediction differs from a second prediction, the second prediction being based on a second predictive model that is different than the first predictive model; and

in response to the determination, selectively outputting an indication regarding the event.

22. The method of claim 21, wherein:

the first prediction is also based at least on a current time and/or day;

the first prediction and the second prediction are different; and

the indication represents that the event is atypical for the current time and/or day.

23. The method of claim 21, wherein:

the first prediction is a prediction of a roadway traffic congestion condition and is also based at least on typical traffic congestion conditions for a current time of a day;

the first prediction and the second prediction are different; and

the indication represents that the event is atypical for the current time of the day.

24. The method of claim 23, wherein the indication also represents a prediction of atypically less than typical traffic congestion along a route.

25. The method of claim 21, wherein the second predictive model includes one or more statistical models that employ Bayesian networks, dynamic Bayesian networks, continuous time Bayesian networks, support vector machines, neural network models, Hidden Markov Models, Markov decision processes, and/or particle filtering.

26. The method of claim 21, wherein the first prediction is a prediction of roadway traffic conditions at a future time.

27. The method of claim 21, further comprising:

calculating a measure of utility associated with outputting an indication for the predicted event; and

outputting the indication if the measure of utility associated therewith is above a threshold.

28. The method of claim 21, wherein:

the first predictive model also corresponds to human modes of predicting events;

the second predictive model is expected to be more accurate than the first predictive model; and

the indication is representative of a likelihood that the user would be surprised by the event.

29. A computer-readable storage device having instructions stored therein, the instructions, when executed on a computing device, cause the computing device to perform operations, the operations comprising:

receiving contextual data that is at least potentially associated with an event;

generating a first prediction for the event based on the received contextual data and on a user expectancy model, wherein the user expectancy model emulates predictive capabilities of a user;

determining that the first prediction is different than a second prediction, wherein the second prediction is based on a second model that is different from the user expectancy model; and

in response to the determination, selectively outputting an indication regarding the event.

30. The computer-readable storage device of claim 29, wherein:

the second model is expected to be more accurate than the user expectancy model.

31. The computer-readable storage device of claim 29, wherein:

the event is a traffic congestion event on a commute route of the user.

32. The computer-readable storage device of claim 29, wherein:

the first prediction is a prediction of traffic congestion along a route and is based at least on typical traffic congestion along the route at a time of day.

33. The computer-readable storage device of claim 29, wherein:

the indication represents a prediction of atypically less than typical traffic congestion along a route for a time of day.

34. The computer-readable storage device of claim 29, wherein:

the user expectancy model corresponds to human modes of predicting events;

the second model is expected to be more accurate than the user expectancy model; and

the indication is representative of a likelihood that the user would be surprised by the event.

35. A computing device for providing notifications of events, the computing device comprising:

a memory and a processor that respectively store and execute computer-executable instructions, including instructions that enable the computing device to;

receive contextual data that is at least potentially associated with an event;

generate a first prediction for the event based on the contextual data and on a user expectancy model, wherein the user expectancy model reflects human predictive capabilities;

determine that the first prediction differs from a second prediction, the second prediction being based on a second predictive model that is different than the first predictive model; and

in response to the determination, provide an indication regarding the event.

36. The computing device of claim 35, wherein the instructions further enable the computing device to selectively provide the indication based on an expectation that the indication would surprise a user of the computing device.

37. The computing device of claim 35, wherein the user expectancy model is based on historical data for other events similar to the event.

38. The computing device of claim 35, wherein:

the first prediction is a prediction of a roadway traffic congestion condition and is based at least on typical traffic congestion conditions for a current time of day;

the first prediction and the second prediction are different; and

the indication represents that the event is atypical for the current time of day.

39. The computing device of claim 35, wherein the indication represents a prediction that a traffic congestion condition will happen at a future time on a commute route of a user of the computing device.

40. The computing device of claim 35, wherein:

the second predictive model is expected to be more accurate than the user expectancy model.