Creating Context Slices of a Storyline from Mobile Device Data

• US 20140031060A1
• Filed: 07/24/2013
• Published: 01/30/2014
• Est. Priority Date: 07/25/2012
• Status: Abandoned Application

• Current Assignee: ARO, Inc. (Dibona Holding)
• Original Assignee: ARO, Inc. (Dibona Holding)
• Inventors: Eustice, Kevin Francis, Bensley, Jeremy, Liu, Alan Linchuan
• Application Number:
  US13/950,150
• Publication Number:
  US 20140031060A1
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

Embodiments create and label context slices from observation data that together define a storyline of a user'"'"'s movements. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. Contexts can vary in their specificity, their semantic content, and their likelihood. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time. A storyline is created through a process of data collection, slicing and labeling. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the chaotic collection of contexts produced by data collection into a single consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the storyline produced by slicing.

Claims

1. A method of creating context slices of a storyline of a user'"'"'s movements, the method comprising:

collecting context data from a plurality of observation sources in a span of time;

grooming the collected context data to reduce errors; and

segmenting the span of time into a sequence of distinct context slices based on the groomed context data, each distinct context slice associated with one selected from a group consisting of a stay in a location, travel from one location to another, and a gap between contexts.

2. The method of claim 1, wherein the plurality of observation sources include a location module, an activity module, and a social module.

3. The method of claim 1, wherein grooming further comprises filtering collected context data to eliminate context data that are deemed more inaccurate than a desirable threshold.

4. The method of claim 3, wherein the desirable threshold is sensor-specific and depends on expected error characteristics of the respective observation source.

5. The method of claim 1, wherein grooming further comprises smoothing sequences of context data from an observation source to limit noise.

6. The method of claim 1, wherein grooming further comprises smoothing a sequence of context data from an observation source responsive to conflicting context data from another observation source.

7. The method of claim 1, wherein grooming further comprises interpolating context data between sensed context data.

8. The method of claim 1, wherein segmenting comprises performing clustering to distinguish a stay in a location from travel from one location to another.

9. The method of claim 1, wherein segmenting comprises performing time-series analysis to detect inflection points in a distribution of context data to determine how to segment a span of time into separate context slices.

10. The method of claim 1, further comprising removing a short context slice by merging it with an adjacent context slice that is likely to correspond to a same activity as the short context slice.

11. The method of claim 1, further comprising reconciling the sequence of distinct context slices with preexisting context slices by comparing type and length.

12. The method of claim 1, further comprising reconciling location data from a plurality of sensors into a single sequence of context slices.

13. The method of claim 12, further comprising varying a sampling frequency of the plurality of sensors in order to lower energy requirements of collecting context data.

14. A non-transitory computer-readable storage medium having computer program instructions embodied therein for creating context slices of a storyline of a user'"'"'s movements, the computer program instructions comprising instructions for:

collecting context data from a plurality of observation sources in a span of time;

grooming the collected context data to reduce errors; and

segmenting the span of time into a sequence of distinct context slices based on the groomed context data, each distinct context slice associated with one selected from a group consisting of a stay in a location, travel from one location to another, and a gap between contexts.

15. The computer-readable storage medium of claim 14, wherein the plurality of observation sources include a location module, an activity module, and a social module.

16. The computer-readable storage medium of claim 14, wherein the instructions for grooming further comprise instructions for filtering collected context data to eliminate context data that are deemed more inaccurate than a desirable threshold.

17. The computer-readable storage medium of claim 16, wherein the desirable threshold is sensor-specific and depends on expected error characteristics of the respective observation source.

18. The computer-readable storage medium of claim 14, wherein the instructions for grooming further comprise instructions for smoothing sequences of context data from an observation source to limit noise.

19. The computer-readable storage medium of claim 14, wherein the instructions for grooming further comprise instructions for smoothing a sequence of context data from an observation source responsive to conflicting context data from another observation source.

20. The computer-readable storage medium of claim 14, wherein the instructions for grooming further comprise instructions for interpolating context data between sensed context data.

21. The computer-readable storage medium of claim 14, wherein the instructions for segmenting comprise instructions for performing clustering to distinguish a stay in a location from travel from one location to another.

22. The computer-readable storage medium of claim 14, wherein the instructions for segmenting comprise instructions for performing time-series analysis to detect inflection points in a distribution of context data to determine how to segment a span of time into separate context slices.

23. The computer-readable storage medium of claim 14, the instructions further comprising instructions for removing a short context slice by merging it with an adjacent context slice that is likely to correspond to a same activity as the short context slice.

24. The computer-readable storage medium of claim 14, the instructions further comprising instructions for reconciling the sequence of distinct context slices with preexisting context slices by comparing type and length.

25. The computer-readable storage medium of claim 14, the instructions further comprising instructions for reconciling location data from a plurality of sensors into a single sequence of context slices.

26. The computer-readable storage medium of claim 25, the instructions further comprising instructions for varying a sampling frequency of the plurality of sensors in order to lower energy requirements of collecting context data.