Methods for and applications of learning and inferring the periods of time until people are available or unavailable for different forms of communication, collaboration, and information access

• US 20050132005A1
• Filed: 01/31/2005
• Published: 06/16/2005
• Est. Priority Date: 06/28/2001
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Horvitz, Eric J., Jacobs, Andrew W., Kadie, Carl M.
• Granted Patent: US 7,519,676 B2
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06Q 10/109 Time management, e.g. calendars, reminders, meetings or time accounting

Abstract

A system and method are provided to learn and infer the time until a user will be available for communications, collaboration, or information access, given evidence about such observations as time of day, calendar, location, presence, and activity. The methods can be harnessed to coordinate communications between parties via particular modalities of interaction. The system includes a user state identifier that determines a user'"'"'s state from background knowledge, the flow of time, or one or more context information sources. A data log can be employed to store information about user state changes and observational evidence to accumulate statistics and build inferential models of the availability and unavailability of users for different kinds of communication, collaboration, and information access. A forecaster is constructed from the accumulated statistics and/or learned models to enable a determination of a user'"'"'s likely return, or, more generally, the probability distribution over a user'"'"'s likely return to particular states of availability. The forecaster can be employed to cache information for offline access, drive displays of availability and unavailability, to send messages that include availability forecasts, and to automatically perform scheduling or rescheduling of communications.

Claims

1. An information transfer system, comprising:

a predictive component to estimate when a user will likely be in a setting to review one or more messages; and

an answering component to provide an adaptive message in association with the predictive component relating to when the user is likely to review the one or more messages.

2. The system of claim 1, the estimate further comprising at least one of when the messages will likely be unseen for some amount of time.

3. The system of claim 2, wherein the messages are at least of some urgency, and the messages are from one or more people of particular importance to the user.

4. The system of claim 1, the one or more messages populated with dynamically computed availability status.

5. The system of claim 4, the dynamically computed availability status, including a forecast of at least one of how long it will be until the user will likely review the messages, be available to review the messages, and be in a particular situation.

6. The system of claim 1, further comprising at least one of a determination of the time until a user reviews different kinds of information based on review histories, and the time until the user will be in one or more types of settings.

7. The system of claim 4, each of the settings associated with one or more types of communications.

8. A hand-assessed communications system to provide information, comprising:

an availability profile configurable by a user indicating the user'"'"'s availability for different types of activities; and

a communications component to provide the availability profile to one or more other users to facilitate at least one of communications, collaboration, and coordination between the user and the one or more other users.

9. The system of claim 8, the availability profile indicated on at least one of a form and a calendar-like representation.

10. The system of claim 8, the availability profile is conditioned on one or more variables.

11. The system of claim 10, the one or more variables including at least one of a time of day, an appointment status, a meeting type, and a sensed activity.

12. The system of claim 8, further comprising one or more hand-assessed models that are refined through data collection and learning.

13. A method to retrieve information, comprising;

determining an expected time a user will be away from a system;

determining information access patterns of the user; and

performing an approximation to extract a maximum value associated with caching information.

14. The method of claim 13, performing an approximation to extract a maximum value associated with caching information is based on the information access patterns and the expected time away of the user.

15. The method of claim 13, the information access patterns comprising a determination of how long until various items are needed.

16. The method of claim 13, further comprising determining a cost associated with not having an item when needed given that the item is needed at some time t in the future.

17. The method of claim 16, the cost determination is optimized by considering how long until the user has network availability.

18. The method of claim 13, further comprising determining the size of the items.

19. The method of claim 14, further comprising utilizing a knapsack solution to fit the items into available memory space.

20. A computer readable medium having thereon computer executable instruction to perform the system of claim 13.