Computational architecture for managing the transmittal and rendering of information, alerts, and notifications

• US 6,618,716 B1
• Filed: 07/30/1999
• Issued: 09/09/2003
• Est. Priority Date: 07/30/1999
• Status: Expired due to Term

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Horvitz, Eric
• Primary Examiner(s): STARKS, WILBERT L
• Application Number:
  US09/365,287
• Time in Patent Office: 1,502 Days
• Field of Search: 706/55, 706/20, 351/224, 600/587, 709/206
• US Class Current: 706/55
• CPC Class Codes: G06F 9/451 Execution arrangements for user interfaces
• CPC Class Codes: G06F 9/453 Help systems
• CPC Class Codes: Y10S 706/902 Application using ai with detail of the ai system

Abstract

An architecture for managing alerts, notifications, and peripheral information is disclosed. In one embodiment, a computerized system includes three modules, an attentional status module, a peripheral information notification and alerts module, and an notifications decision-making module. The attentional-status module generates a probability distribution over the user'"'"'s focus of attention. In one embodiment the module also provides a probability distribution over the period of time until the user will become available to review alerts zero or minimal cost, or will review them without prompting. In one embodiment, this module utilizes a decision-theoretic analysis to identify ideal actions about the timing and rendering of notifications or alerts. The peripheral information notification and alerts module receives alerts from different sources, including the operating system, standard applications, special plug-ins designed to work with the architecture, and, in particular embodiments, from special sensory apparatus including ambient acoustical information. The notification decision-making module is to alert the user of the alerts received by the peripheral information notification and alerts module, based on the probability generated by the attentional status module.

Claims

1. A computerized system comprising:

“

an attentional status module to generate one or more probabilities about a user'"'"'s focus of attention;

“

a peripheral information notification and alert module to receive alerts; and

, “

a notification decision-making module to alert the user of the alerts based on the probabilities inferred about a user'"'"'s focus of attention.”

2. The system of claim 1, wherein the one or more probabilities comprises a probability distribution.

3. The system of claim 1, wherein the one or more probabilities comprises a probability distribution over a set of states considered to be a mutually exclusive and exhaustive set of foci of attention of the user.

4. The system of claim 1, wherein the attentional status module provides a single availability probability representing a likelihood that the user can receive at least one of alerts, notifications, and peripheral information at no greater than minimal cost.

5. The system of claim 1, wherein the attentional status module provides one or more probabilities about the focus of attention of the user and an estimation of a period of time until attentional status will become available to receive notifications or alerts at no greater than minimal cost.

6. The system of claim 1, wherein the attentional status module provides one or more probabilities about the focus of attention of the user and an estimation of the period of time until the user will seek to actively review stored information, notifications, or alerts without prompting.

7. The system of claim 1, wherein the attentional status module is to generate the one or more probabilities based on at least one of:

one or more contextual events, and a user profile.

8. The system of claim 7, wherein the user profile comprises a user profile of knowledge of responses of the user to previous alerts.

9. The system of claim 7, wherein the one or more contextual events comprise at least one of operating system events and application events.

10. The system of claim 7, wherein the one or more contextual events comprise information from a schedule of the user.

11. The system of claim 7, wherein the one or more contextual events comprise at least one of audio information and visual information.

12. The system of claim 1, wherein the attentional status module is to generate the one or more probabilities by utilizing a probabilistic model.

13. The system of claim 1, wherein the attentional status module is to generate the one or more probabilities based on a Bayesian network.

14. The system of claim 1, wherein the peripheral information notification and alerts module is to receive alerts from at least one of:

one or more applications, one or more networks, one or more operating systems.

15. The system of claim 1, wherein the peripheral information notification and alerts module is to receive alerts from one or more external devices.

16. The system of claim 1, wherein the notification decision-making module is to alert the user of the alerts also based on a history of previous alerts.

17. The system of claim 1, wherein the notification decision-making module is to alert the user of the alerts also based on available system resources.

18. The system of claim 1, wherein the notification decision-making module is to alert the user in a visual manner of the alerts.

19. The system of claim 1, wherein the notification decision-making module is to alert the user in an audio manner of the alerts.

20. The system of claim 1, wherein the notification decision-making module is to alert the user on a mobile device.

21. The system of claim 1, wherein the notification decision-making module is to alert the user on the mobile device based on NEVA=EVTA−

ECA−

TC, where NEVA is a net expected value of alert, EVTA is an expected value of alert, ECA is an expected cost of alert, and TC is a transmission cost.

22. A computerized system comprising:

“

an attentional status module to generate one or more probabilities via a probabilistic model and based on at least one of;

one or more contextual events, and a user profile;

“

a peripheral information notification and alert module to receive alerts from at least one of;

one or more applications, one or more networks, one or more operating systems, and one or more external devices; and

,

“

a notification decision-making module to alert the user of the alerts based on the one or more probabilities in at least one of;

an audio manner and a visual manner.”

23. The system of claim 22, wherein the user profile comprises a user profile of knowledge of responses of the user to previous alerts.

24. The system of claim 22, wherein the one or more contextual events comprise at least one of operating system events and application events.

25. The system of claim 22, wherein the one or more contextual events comprise information from a schedule of the user.

26. The system of claim 22, wherein the one or more contextual events comprise at least one of audio information and visual information.

27. The system of claim 22, wherein the attentional status module is to generate the one or more probabilities based on a Bayesian network.

28. The system of claim 22, wherein the notification decision-making module is to alert the user of the alerts also based on a history of previous alerts.

29. The system of claim 22, wherein the notification decision-making module is to alert the user of the alerts also based on available system resources.

30. A computer-implemented method comprising:

receiving an alert from at least one of;

one or more applications, one or more networks, one or more operating systems, and one or more external devices;

generating one or more probabilities about a user'"'"'s focus of attention via a probabilistic model and based on at least one of;

one or more contextual events, and a user profile; and

,

alerting the user based on the one or more probabilities.

31. The method of claim 30, wherein alerting the user comprises alerting the user in at least one of:

an audio manner and a visual manner.

32. The method of claim 30, wherein generating one or more probabilities comprises generating the ideal alerting action based on a maximum-utility decision-theoretic model.

33. The method of claim 30, wherein generating one or more probabilities comprises generating the probability based on a Bayesian network.

34. A machine-readable medium having instructions stored thereon for execution by a processor to perform a method comprising:

receiving an alert from at least one of;

one or more applications, one or more networks, one or more operating systems, and one or more external devices;

generating one or more probabilities via a probabilistic model and based on at least one of;

one or more contextual events, and a user profile; and

,

alerting the user based on the one or more probabilities.

35. The medium of claim 34, wherein alerting the user comprises alerting the user in at least one of:

an audio manner and a visual manner.

36. The medium of claim 34, wherein generating one or more probabilities comprises generating the one or more probabilities based on a maximum-utility decision-theoretic model.

37. The medium of claim 34, wherein generating one or more probabilities comprises generating the one or more probabilities based on a Bayesian network.