OPEN-WORLD MODELING

• US 20110288839A1
• Filed: 07/29/2011
• Published: 11/24/2011
• Est. Priority Date: 06/28/2007
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Horvitz, Eric J., Krumm, John C., Subramani, Murugesan S.
• Granted Patent: US 8,244,660 B2
• Time in Patent Office: Days
• US Class Current: 703/6
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

The claimed subject matter provides systems and/or methods that facilitate generating an inference about events that may not have yet been observed. Open-world modeling can be used to take a history of observation so as to understand trends over time in the revelation of previously unseen events, and to make inferences with subsets of data that new unseen events will be seen. Thus, inaccuracies associated with predictions generated from incomplete data sets can be mitigated. To yield such predictions, open-world submodels and closed-world submodels that do not allow for previously unseen events can be combined via a model mixture methodology, which fuses inferences from the open- and close-world models.

Claims

1. A system that facilitates generating an inference, comprising:

a component that makes a log of previously observed events; and

a modeling component, comprising at least one processor, that generates predictions of an event based at least in part upon a combination, the combination being a combination of outputs of;

an open-world submodeling component that provides probabilities that events that are not in the log are likely to occur, the open world submodeling component inferring the probabilities by distributing probabilities over unobserved events; and

a closed-world submodeling component that provides probabilities that events in the log are likely to recur, the closed-world submodeling component inferring the probabilities based on information in the log that indicates probabilities that previously observed events will recur,

wherein the modeling component generates a prediction of a future event by combining, for each of a plurality of events, a probability provided by the open-world submodeling component and a probability provided by the closed-world submodeling component.

2. The system of claim 1, wherein:

the open-world submodeling component reasons about a likelihood of seeing previously unobserved states given a specific observational context.

3. The system of claim 1, wherein:

the modeling component effectuates open-world decomposition by using the open-world model and the closed-world model to generate the predictions.

4. The system of claim 1, wherein:

the open-world submodeling component generates probabilities that events that are not in the log are likely to occur based on demographics of a user.

5. The system of claim 1, wherein:

the open-world submodeling component models the probabilities related to the events that are not in the log based upon a spatial distribution.

6. The system of claim 5, wherein:

the open-world submodeling component models the spatial distribution as a probability distribution over a distance from previously observed destination of a trip.

7. The system of claim 1, wherein:

the modeling component further comprising a weighting component that provides a weight for combining the probability provided by the open-world submodeling component and the probability provided by the closed-world submodeling component.

8. The system of claim 7, wherein:

the weighting component fuses a weighted combination of probabilities provided by the open-world submodeling component and the closed-world submodeling component as a function of duration of data collection.

9. The system of claim 8, wherein:

the weighting component employs curve fitting to model a change in a likelihood that allocation event will occur over time.

10. The system of claim 7, further comprising:

a profile component that generates a weight utilized by the weighting component based upon a consideration of user related characteristics.

11. The system of claim 1, further comprising a content component that provides information associated with an event that is predicted to occur.

12. A method that facilitates handling incompleteness of data sets to enable generating a prediction, comprising:

with at least one processor;

receiving observed data;

computing, based on the observed data, probabilities of previously observed events recurring;

extrapolating the observed data to yield probabilities related to non-observed events; and

generating a prediction of an event based at least in part upon a combination, the combination being a combination of the probabilities related to the non-observed events and the previously observed events, wherein the probabilities are weighted in forming the combination by a dynamic factor to place greater emphasis on probabilities related to the previously observed events when the amount of received data increases.

13. The method of claim 12, wherein:

extrapolating the observed data further comprises performing trend analysis to characterize how observational data changes given ongoing data collection.

14. The method of claim 12, the method further comprises:

generating a closed-world submodel based upon the observed data, the closed-world submodel being used to generate the probabilities of previously observed events recurring; and

generating an open-world submodel by evaluating a likelihood of observing the non-observed data over a future period of time, the open-world submodel being used to generate the probabilities of previously non-observed events occurring

15. The method of claim 14, wherein:

combining the closed-world submodel and the open-world submodel further comprises;

determining characteristics of a user;

identifying a subgroup to which the user belongs based upon the characteristics;

forecasting a trajectory of a submodel weight that varies as a function of observation time based upon the identified subgroup; and

combining the closed-world submodel and the open-world submodel by utilizing the forecasted submodel weight.

16. The method of claim 14, wherein:

generating the open-world submodel further comprises distributing probabilities associated with non-observed data based upon an analysis of spatial trends.

17. The method of claim 12, wherein:

the weight varies dynamically with a length of observation.

18. The method of claim 12, further comprising presenting relevant content based upon the prediction.

19. A computer readable storage device comprising computer-executable instructions that, when executed by at least one process perform a method that facilitates generating a prediction, the method comprising:

generating a closed-world inference based upon observed data, the closed world inference indicating a probability of recurrence of at least one event that has previously been observed;

generating an open-world inference by evaluating a probability of observing previously non-observed data over a future period of time; and

combining the closed-world inference and the open-world inference based upon a weight to yield the prediction, the weight being based on an amount of observed data.

20. The computer readable storage device of claim 19, wherein:

the method further comprises computing the weight based on data collected for a group of people deemed to have a common profile.