MULTI-LEVEL EVENT COMPUTING MODEL

• US 20110071971A1
• Filed: 09/22/2009
• Published: 03/24/2011
• Est. Priority Date: 09/22/2009
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Mitchell, William H., Copeland, Robert D., Parks, Gregory H.
• Application Number:
  US12/564,913
• Publication Number:
  US 20110071971A1
• Time in Patent Office: Days
• US Class Current: 706/45
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

High-order events may be generated and consumed in a cascading computing model. Low level information, such as changes in physical sensor readings, may be communicated to an application in the form of event messages that are generated by an operating system service. In one example, models that implement high level abstractions may also use events to communicate facts that have been inferred from lower level facts. For example, a program might generate events indicating that a particular type of motion (e.g., walking) has started or stopped, where the program infers the walking motion from sensor data about acceleration and position. Another program could consume those events and other data to draw higher level conclusions, such as “Joe is walking to a meeting”. Thus, events may be used in a cascading model in which events are generated and consumed at increasingly high levels of abstraction.

Claims

1. One or more computer-readable storage media that store executable instructions to use events, wherein the executable instructions, when executed by a computer, cause the computer to perform acts comprising:

receiving a first event at a first component;

applying a model to said first event to generate a second event, said second event comprising notification of a fact that said model infers from first information that comprises said first event, said second event further comprising data that characterizes the meaning or significance of said second event;

receiving, at a second component, said second event; and

making use of said second event to perform an action.

2. The one or more computer-readable storage media of claim 1, wherein said data that characterizes the meaning or significance of said second event comprises an indication of said first component'"'"'s assessment of a level of certainty with which said second event characterizes said fact.

3. The one or more computer-readable storage media of claim 1, wherein said first component generates said second event for consumption by said second component, and wherein said data that characterizes the meaning or significance of said second event comprises an indication said of said first component'"'"'s assessment of the salience that said second component ascribes to said second event.

4. The one or more computer-readable storage media of claim 1, wherein said data that characterizes the meaning or significance of said second event comprises an indication of the rate at which accuracy of said second event decays.

5. The one or more computer-readable storage media of claim 1, wherein said first event comprises an indication of a reading taken by a sensor that detects a physical aspect of an environment surrounding a machine to which said sensor is attached.

6. The one or more computer-readable storage media of claim 1, wherein said applying of said model comprises:

receiving second information from a database, and using said second information to generate said second event.

7. The one or more computer-readable storage media of claim 1, wherein said first event is generated at a first machine, and wherein said second event is generated at a second machine that is distinct from said first machine.

8. A method of using events, the method comprising:

using a processor to perform acts comprising;

receiving a first event;

applying a first model to first information that comprises said first event to generate a second event, said second event comprising notification of a fact that said first model infers from said first information, said second event further comprising an indication of said first model'"'"'s assessment of a level of certainty that said second event describes said fact; and

sending said second event to a component that implements a second model that is distinct from said first model, wherein said second model takes an action based on said second event.

9. The method of claim 8, wherein said second event further comprises an indication of said second model'"'"'s assessment of the rate at which accuracy of said second event decays.

10. The method of claim 8, further comprising:

including, in said first event, an assessment of the salience that said component will ascribe to said second event.

11. The method of claim 8, wherein said first event comprises data read from a physical sensor that is attached to a machine at which said first event is generated.

12. The method of claim 8, wherein said first information further comprises data read from a database, and wherein said first model generates said second event based on said first event and on said data.

13. The method of claim 8, further comprising:

using said second model to perform a tangible action that is based on said second event.

14. The method of claim 8, wherein said first event is generated on a first machine, and wherein said first model is applied to said first event on a second machine that is distinct from said first machine.

15. A system for using events, the system comprising:

a first machine that comprises a sensor that detects a physical condition present at said first machine, said first machine comprising software that generates a first event that comprises an indication of a value of said sensor, said first machine comprising a mechanism that allows components to subscribe to events relating to said sensor; and

a second machine that is distinct from said first machine, said second machine comprising a first program that subscribes to said first event, said first program implementing a first model that derives a first fact from information that comprises said first event, said first program generating a second event that comprises an indication of said first fact and an indication of a level of certainty that said second event accurately describes said first fact.

16. The system of claim 15, wherein said second event further comprises:

an assessment, by said first program, of a rate at which accuracy of said second event decays.

17. The system of claim 15, further comprising:

a third machine that is distinct from said first machine and from said second machine, said third machine comprising a second program that subscribes to said second event, said second program implementing a second model, said second model using said second event and said level of certainty to derive a second fact that is based on said second event and that is further based on said level of certainty.

18. The system of claim 15, wherein first program generates said second event for a second program, and wherein said second event comprises an assessment, by said first program, of the salience that said second program will ascribe to said first program.

19. The system of claim 15, wherein said first program retrieves data from a database, and wherein said information further comprises said data.

20. The system of claim 15, further comprising:

a third program that consumes said second event and that produces a tangible result based on said second event.