Fusing contextual inferences semantically

Patent metadata

Field	Value
Publication number	US-9336295-B2
Application number	US-201313784229-A
Country	US
Kind code	B2
Filing date	Mar 4, 2013
Priority date	Dec 3, 2012
Publication date	May 10, 2016
Grant date	May 10, 2016

Abstract

System and methods for performing context inference in a computing device are disclosed. In one embodiment, a method of performing context inference includes: determining, at a computing device, a first context class using context-related data from at least one data source associated with a mobile device; and determining, at the mobile device, a fusion class based on the first context class, the fusion class being associated with at least one characteristic that is common to the first context class and a second context class that is different from the first context class.

First claim

What is claimed is:

1. A method for performing context inference in a mobile device, the method comprising:
receiving, at a classifier implemented in one or more integrated circuits of the mobile device, sensor data from at least one data source associated with the mobile device, wherein the at least one data source comprises one or more sensors of the mobile device;
determining, by the classifier implemented in the one or more integrated circuits, a first context class for the received sensor data, the first context class corresponding to a context state indicated by the received sensor data;
determining, by the classifier implemented in the one or more integrated circuits, that a confidence value associated with the determination of the first context class is below a threshold value;
creating, by the classifier implemented in the one or more integrated circuits, a fusion class for the received sensor data at least in part by fusing the first context class for the received sensor data and at least a second context class for the received sensor data, the at least the second context class being different from the first context class, wherein the fusion class semantically encompasses the first context class and the at least the second context class and further wherein the fusion class is broader than each of the first context class and the at least the second context class;
substituting, by the classifier implemented in the one or more integrated circuits, the fusion class for the first context class; and
outputting, by the classifier implemented in the one or more integrated circuits, the fusion class as the inferred context of the mobile device.

2. The method of claim 1 wherein the at least one data source comprises one or more of an audio sensor, a transceiver, a motion sensor, a clock, a device usage sensor, a light sensor, a camera, or a calendar.

3. The method of claim 1 wherein determining the first context class further comprises determining the confidence value associated with the determination of the first context class.

4. The method of claim 1 wherein the determining the first context class is an initial classification of the received sensor data.

5. The method of claim 1 wherein the substituting the fusion class is performed in response to the confidence value associated with the determination of the first context class being below the threshold value.

6. The method of claim 4 further comprising identifying an operational property of the initial classification, the operational property including at least one of a motion, a location, an event, or an environment associated with the initial classification.

7. The method of claim 6 wherein the creating the fusion class is based on the operational property of the initial classification.

8. The method of claim 1 comprising:
receiving the sensor data over consecutive time periods;
determining the first context class for the received sensor data associated with a first time period; and
determining the at least the second context class for the received sensor data associated with at least a second time period that is after the first time period.

9. The method of claim 1 wherein determining the first context class comprises selecting the first context class from a plurality of predetermined available context classes.

10. The method of claim 1 wherein the creating the fusion class comprises selecting the fusion class from a plurality of predetermined available fusion classes.

11. A mobile device comprising:
means for receiving sensor data from at least one data source associated with the mobile device, wherein the at least one data source comprises one or more sensors of the mobile device;
means for determining a first context class for the received sensor data, the first context class corresponding to a context state indicated by the received sensor data;
means for determining that a confidence value associated with the determination of the first context class is below a threshold value;
means for creating a fusion class for the received sensor data at least in part by fusing the first context class for the received sensor data and at least a second context class for the received sensor data, the at least the second context class being different from the first context class, wherein the fusion class semantically encompasses the first context class and the at least the second context class and further wherein the fusion class is broader than each of the first context class and the at least the second context class;
means for substituting the fusion class for the first context class; and
means for outputting the fusion class as an inferred context for the mobile device.

12. The mobile device of claim 11 wherein the at least one data source comprises one or more of an audio sensor, a transceiver, a motion sensor, a clock, a device usage sensor, a light sensor, a camera, or a calendar.

13. The mobile device of claim 11 comprising means for determining the confidence value associated with the determination of the first context class.

14. The mobile device of claim 11 wherein the determination of the first context class is an initial classification of the received sensor data.

15. The mobile device of claim 11 wherein the means for substituting the fusion class is configured to substitute the fusion class in response to the confidence value associated with the determination of the first context class being below the threshold value.

16. The mobile device of claim 14 comprising means for identifying an operational property of the initial classification, the operational property including at least one of a motion, a location, an event, or an environment associated with the initial classification.

17. The mobile device of claim 16 comprising means for creating the fusion class based on the operational property of the initial classification.

18. The mobile device of claim 11 comprising:
means for receiving the sensor data over consecutive time periods;
means for determining the first context class for the received sensor data associated with a first time period; and
means for determining the at least the second context class for the received sensor data associated with at least a second time period that is after the first time period.

19. The mobile device of claim 11 wherein the means for determining the first context class is configured to determine the first context class by selecting the first context class from a plurality of predetermined available context classes.

20. The mobile device of claim 11 wherein the means for creating the fusion class is configured to create the fusion class by selecting the fusion class from a plurality of predetermined available fusion classes.

21. A mobile device comprising:
at least one data source comprising one or more sensors of the mobile device;
a classifier built into one or more hardware modules of the mobile device, the one or more hardware modules comprising one or more integrated circuits, and communicatively coupled to the at least one data source, wherein the classifier is configured to:
receive sensor data from the at least one data source; determine a first context class for the received sensor data, the first context class corresponding to a context state indicated by the received sensor data;
determine that a confidence value associated with the determination of the first context class is below a threshold value;
create a fusion class for the received sensor data a

Assignees

• Qualcomm Inc

Inventors

• Grokop Leonard Henry
• Sadasivam Shankar
• Teague Edward Harrison

Classifications

• H04M1/72569

  Electricity · mapped topic

• G06F17/30598 Primary

  Physics · mapped topic

• H04M1/72563

  Electricity · mapped topic

• H04M2250/12

  including a sensor for measuring a physical value, e.g. temperature or motion · CPC title

• H04M1/72448 Primary

  with means for adapting the functionality of the device according to specific conditions · CPC title